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WORK ENVIRONMENT, BURNOUT, ORGANIZATIONAL COMMITMENT, AND ROLE OF PERSONAL VARIABLES AS MODERATORS
BY
ANEELA MAQSOOD
Dr. Muhammad Ajmal NATIONAL INSTITUTE OF PSYCHOLOGY
Center of Excellence Quaid-i-Azam University, Islamabad
2011
WORK ENVIRONMENT, BURNOUT, ORGANIZATIONAL COMMITMENT, AND ROLE OF PERSONAL VARIABLES AS MODERATORS
BY
ANEELA MAQSOOD
A dissertation submitted to the
Dr. Muhammad Ajmal NATIONAL INSTITUTE OF PSYCHOLOGY
Center of Excellence Quaid-i-Azam University, Islamabad
In partial fulfillment of the requirements for the
DEGREE OF PHILOSOPHY OF DOCTORATE
IN
PSYCHOLOGY
2011
CCEERRTTIIFFIICCAATTEE
Certified that Ph.D. Dissertation “Work Environment, Burnout,
Organizational Commitment, and Role of Personal Variables as Moderators”,
prepared by Ms. Aneela Maqsood has been approved for submission to Quaid-e-
Azam University, Islamabad.
Dr. Ghazala Rehman Supervisor
Dr. Rubina Hanif Co-Supervisor
WORK ENVIRONMENT, BURNOUT,
ORGANIZATIONAL COMMITMENT, AND ROLE OF PERSONAL VARIABLES AS MODERATORS
BBYY
AANNEEEELLAA MMAAQQSSOOOODD
Approved by
_________________ Supervisor
_________________ Co-Supervisor
_________________ External Examiner
_________________ External Examiner
_________________ Director NIP
WORK ENVIRONMENT, BURNOUT, ORGANIZATIONAL COMMITMENT, AND ROLE OF PERSONAL VARIABLES AS MODERATORS
Dedicated to
My Parents for ever strengthening support, impetus, and prayers behind being I am
CONTENTS
List of Tables iList of Figures viiList of Appendices viiiAcknowledgements ixAbstract x CHAPTER 1: INTRODUCTION
Work Environment: Nature and Dimensionality 4Theoretical Foundations of Work Environment 8 Work Environment of Academic Settings 14Measurement of Work Environment 18Burnout 20Theoretical Models of Burnout 24Measurement of Burnout 27Relationship of Work Environment and Burnout 29Organizational Commitment 33Theoretical Models of Organizational Commitment 35Measurement of Organizational Commitment 40Relationship of Work Environment and Organizational Commitment 42Role of Personal Variables in Relationship of Work Environment and its Outcomes
46
Ratioanle of the Study 58
CHAPTER II: OBJECTIVES, HYPOTHESES, OPERATIONAL
DEFINITIONS, AND RESEARCH DESIGN
68
Objectives of the Study 68Hypotheses 69Operational Definitions of Variables 69Research Design
76
CHAPTER III: PHASE I: PILOT STUDY
Pilot Testing of Study Measures and Preliminary Testing of the Model of Work Environment and Outcomes
77
Method 77 Participants 77 Meaures 79 Procedure 85
Results 87Discussion 106
CHAPTER IV: PHASE II: MAIN STUDY
123
Step I: Examining the Measurement Models of Constructs 123Objectives of Step I of the Main Study 124Method 124 Participants 124
Instruments 125 Procedure 126
Results Testing the Factor Structue of Work Environment, Burnout,
Organizational Commitmnet, and Personality Measures
127 127
Factor Structure of Work Environment Scale 130 Factor Structure of Maslach Burnout Inventory-Educators Survey 136 Factor Structure of Organizational Commitment Questionnaire 145 Factor Structure of Mini Markers Set 151Discussion Conclusion Step II: The Role of Work Environment in Predicting Burnout and Organizational Commitment and the Moderating Role of Personal Variables
155 169 171
Objectives of Step II of the Main Study 171Instruments 171Results 170 Descriptive Analysis 173 Predictive Relationship between Work Environment and Burnout 180 Predictive Relationship between Work Environment and Organizational Commitment
187
The Moderating Role of Personal Variables 193Discussion 235 Psychometric Issues 237 Predictive Impact of Work Environment on Burnout and Organizational
Commitment 242
Moderating Effects of Personality 254 Moderating Effects of Organizational and Demographic related Personal
variables 258
Implications of the Study 266Limitations and Future Research 266Conclusion
267
REFERENCES
269
APPENDICES 319
i
LIST OF TABLES
Table 1 Mean & SD on scores representing Levels of Work
Environment, Burnout, Organizational Commitment, and
Personality Dimensions (N = 102)
89
Table 2 Cronbach’s Alpha (on the diagonal) and Pearson Product
Moment Correlation Coefficients on Scores of Study
Variables (N = 102)
90
Table 3 Multiple Regression Analysis on scores of Emotional
Exhaustion by Work Environment (N = 102) 92
Table 4 Multiple Regression Analysis on scores of Depersonalization
by Work Environment (N = 102) 93
Table 5 Multiple Regression Analysis on scores of Personal
Accomplishment by Work Environment (N = 102) 94
Table 6 Multiple Regression Analysis on total scores of Burnout by
Work Environment (N = 102) 95
Table 7 Regression Analysis on Burnout and its components by total
scores of Work Environment (N = 102) 96
Table 8 Multiple Regression Analysis on Affective Commitment by
Work Environment (N = 102) 97
Table 9 Multiple Regression Analysis on Continuance Commitment
by Work Environment (N = 102) 98
Table 10 Multiple Regression Analysis on Normative Commitment by
Work Environment (N = 102) 99
Table 11 Multiple Regression Analysis on total scores of
Organizational Commitment by Work Environment (N = 102) 100
Table 12 Regression Analysis on Organizational Commitment and its
components by total scores on Work Environment (N = 102) 101
Table 13 Goodness-of-fit statistics for ten-factor model of Work
Environment (N = 426) 127
ii
Table 14 Factor loadings and Standard Errors for ten factor model of
Work Environment (N = 426) 129
Table 15 Goodness-of-fit statistics for single, three and five-factor
models of MBI (N = 426) 135
Table 16 Factor loadings and Standard Errors for one factor model of
Maslach Burnout Inventory (N = 426) 137
Table 17 Factor loadings and Standard Errors for three factor model of
Maslach Burnout Inventory (N = 426) 139
Table 18 Factor loadings and Standard Errors for five factor model of
Maslach Burnout Inventory (N = 426) 140
Table 19 Goodness-of-fit statistics for a one-factor and three-factor
model of OCQ (N = 426) 142
Table 20 Factor loadings and Standard Errors for one factor model of
Organizational Commitment Questionnaire (N = 426) 144
Table 21 Factor loadings and Standard Errors for three factor model of
Organizational Commitment Questionnaire (N = 426) 146
Table 22 Goodness-of-fit statistics for five-factor models of MM (N =
426) 148
Table 23 Factor loadings and Standard Errors for five factor model of
Mini Markers Set (N = 426) 150
Table 24 Mean & SD on scores representing Levels of Work
Environment, Burnout, Organizational Commitment, and
Personality Variables (N = 426)
172
Table 25 Cronbach’s Alpha (on the diagonal), Pearson Product
Moment Correlations for Predictive, Criterion, and
Moderator Variables (N = 426)
173
Table 26 Multiple Regression Analysis on scores of Emotional
Exhaustion and its components by Work Environment (N =
426)
177
iii
Table 27 Multiple Regression Analysis on scores of Depersonalization
by Work Environment (N = 426) 179
Table 28 Multiple Regression Analysis on scores of Personal
Accomplishment and its components by Work Environment
(N = 426)
180
Table 29 Multiple Regression Analysis on scores of Burnout by Work
Environment (N = 426) 181
Table 30 Regression Analysis on Burnout and its components by total
scores of Work Environment (N = 426) 183
Table 31 Multiple Regression Analysis on Affective Commitment by
Work Environment (N = 426) 185
Table 32 Multiple Regression Analysis on Continuance Commitment
by Work Environment (N = 426) 186
Table 33 Multiple Regression Analysis on Normative Commitment by
Work Environment (N = 426) 187
Table 34 Multiple Regression Analysis on Organizational
Commitment by Work Environment (N = 426) 189
Table 35 Regression Analysis on Organizational Commitment and its
components by total scores on Work Environment (N = 426)
190
Table 36 Moderating Effects of Personality in predicting Burnout-
three-factor model (N = 426) 193
Table 37 Interaction Effects of Extraversion in predicting Work
Environment and Burnout Relationship 195
Table 38 Interaction Effects of Agreeableness in predicting Work
Environment and Burnout Relationship 196
Table 39 Interaction Effects of Openness in predicting Work
Environment and Burnout Relationship 197
Table 40 Moderating Effects of Personality in predicting Burnout-
five-factor model (N = 426) 200
iv
Table 41 Interaction Effects of Extraversion in predicting Work
Environment and Burnout (five- factor model) Relationship 203
Table 42 Interaction Effects of Agreeableness in predicting Work
Environment and Burnout (five- factor model) Relationship 204
Table 43 Interaction Effects of Openness in predicting Work
Environment and Burnout (five- factor model) Relationship 205
Table 44 Moderating Effects of Personality in predicting
Organizational Commitment (N = 425) 207
Table 45 Interaction Effects of Extraversion in predicting Work
Environment and Organizational Commitment Relationship
(N = 426)
209
Table 46 Interaction Effects of Agreeableness in predicting Work
Environment and Organizational Commitment Relationship
(N = 426)
210
Table 47 Interaction Effects of Conscientiousness in predicting Work
Environment and Organizational Commitment Relationship
(N = 426)
211
Table 48 Interaction Effects of Openness in predicting Work
Environment and Organizational Commitment Relationship
(N = 426)
212
Table 49 Moderating Effects of Sector in predicting Burnout (N =
426) 216
Table 50 Interaction Effects of Sector in predicting Work Environment
and Burnout (three-factor model) Relationship 217
Table 51 Interaction Effects of Sector in predicting Work Environment
Burnout (five-factor model) 218
Table 52 Moderating Effects of Sector in predicting Organizational
Commitment (N = 426) 219
v
Table 53 Interaction Effects of Sector in predicting Work
Environment and Organizational Commitment (N = 426)
220
Table 54 Moderating Effects of Rank in predicting Burnout (N = 426) 221
Table 55 Moderating Effects of Rank in predicting Organizational
Commitment (N = 426) 222
Table 56 Moderating Effects of Employment Duration in predicting
Burnout (N = 426) 222
Table 57 Moderating Effects of Employment Duration in predicting
Organizational Commitment (N = 426) 223
Table 58 Moderating Effects of Faculties in predicting Burnout (N =
426) 223
Table 59 Moderating Effects of Faculties in predicting Organizational
Commitment (N = 426)
224
Table 60 Moderating Effects of Side Jobs in predicting Burnout (N =
426) 224
Table 61 Moderating Effects of Side Jobs in predicting Organizational
Commitment (N = 426) 225
Table 62 Moderating Effects of Age in predicting Burnout (N = 426) 225
Table 63 Interaction Effects of Age in predicting Work Environment
and Burnout (three factor model) (N = 426) 226
Table 64 Interaction Effects of Age in predicting Work Environment
and Burnout (five-factor model) (N = 426) 227
Table 65 Moderating Effects of Age in predicting Organizational
Commitment (N = 426) 228
Table 66 Moderating Effects of Gender in predicting Burnout (N =
426) 228
Table 67 Moderating Effects of Gender- Men (N = 268) vs. Women
(N = 158) in predicting Organizational Commitment 229
vi
Table 68 Moderating Effects of Education in predicting Burnout (N =
426) 229
Table 69 Moderating Effects of Education in predicting Organizational
Commitment (N = 426) 230
Table 70 Moderating Effects of Marital Status in predicting Burnout
(N = 426) 230
Table 71 Moderating Effects of Marital Status in predicting
Organizational Commitment (N = 426) 231
vii
LIST OF FIGURES
Figure 1 Conceptual Model of Organizational and Personal Factors and Outcomes
12
Figure 2 Theoretical conceptualization of the present study 67
Figure 3 Moderating Effects of Extraversion in predicting Burnout (three factor model)
195
Figure 4 Moderating Effects of Agreeableness in predicting Burnout (three factor model)
196
Figure 5 Moderating Effects of Openness in predicting Burnout (three factor model)
197
Figure 6 Moderating Effect of Extraversion in predicting Organizational Commitment
203
Figure 7 Moderating Effects of Agreeableness in predicting Organizational Commitment
204
Figure 8 Moderating Effects of Consciousness in predicting Organizational Commitment
205
Figure 9 Moderating Effects of Openness in predicting Organizational Commitment
209
Figure 10 Moderating Effects of Extraversion in predicting Burnout (five-factor model)
210
Figure 11 Moderating Effects of Agreeableness in predicting Burnout (five-factor model)
211
Figure 12 Moderating Effects of Openness in predicting Organizational Commitment
212
Figure 13 Moderating Effects of Sector in predicting Burnout (three factor model)
217
Figure 14 Moderating Effects of Sector in predicting Burnout (five-factor model)
218
Figure 15 Moderating Effects of Sector in predicting Organizational Commitment
220
Figure 16 Moderating Effects of Age in predicting Burnout (three factor model)
226
Figure 17 Moderating Effects of Age in predicting Burnout (five-factor model)
227
viii
LIST OF APPENDICES
Appendix A Consent Letter to Participate in the Research Study 320
Appendix B General Instructions & Demographic Information Sheet 321
Appendix C Work Environment Scale 322
Appendix D Work Environment Scale- Scoring Key 326
Appendix E MBI-Educators Survey 328
Appendix F Organizational Commitment Questionnaire 330
Appendix G MINI-MARKERS 332
Appendix H MINI-MARKERS- Scoring Key 334
Appendix I Descriptive Profile of Pilot Sample 335
Appendix J Descriptive Profile of Sample of Main Study 336
ix
ACKNOWLEDGEMENTS
The time, when I am finalized with the completion and reporting of the
dissertation, I am thankful to God Almighty for His Blessings- leading me towards
the way in my life up to the stage today. Though it was a long journey enchained
with different flavours of life; however, the “persistence” remains rewarding at the
end.
I feel immense gratitude to all those people who made valuable
contribution in completing this task. I would like to express my deepest gratitude
to my supervisors, Dr. Ghazala Rehman and Dr. Rubina Hanif, for their excellent
guidance, patience, and valuable critical evaluation in this learning process. They
remained a source of inspiration for me as well. I would never have been able to
complete my task without their critical evaluation.
I am thankful to the management of Nottingham Trent University, UK., for
giving me the opportunity of Ph.D fellowship. It was a great learning experience to
work with Dr. Glenn A. Williams, Senior Lecturer in Psychology at School of
Social Sciences, Nottingham Trent University. I speak very highly about him for
his professional commitment and expert guidance in data analysis, write up, and
interpretation of the results.
I would like to appreciate the management of universities of Pakistan for
provision of data collection and to all those teachers who participated in this study.
I am thankful to Mr. Abdul Qayoom for providing assistance in formatting issues
and to the library personnel of Nottingham Trent University, Uk., Fatima Jinnah
Women University, Rawalpindi, Quaid-i-Azam University, Islamabad, and
PASTIC Islamabad, for providing access to digital library resources.
My heartily acknowledgment to my parents for providing a supporting
environment for me and to whom I dedicate this thesis. I would never have been
x
able to complete my task without their moral support. Sweet cheers to my daughter
Malaika for the time and laughter she brought in my life.
Aneela Maqsood
xi
ABSTRACT
Theoretical orientation of psychosocial context of work place based on
Moos’ model of work environment (1986, 1994) explaining the interplay of work
environment and its outcomes was investigated in the context of academic settings
in Pakistan. In explaining the relative effect of work environment in predicting
burnout and organization commitment within academic settings, present study also
addressed the question of moderating role of personal variables which so far was
remained open in this process. Universities teachers (N = 420) employed in public
and private sector Universities located in Rawalpindi, Islamabad, and Lahore
cities were approached using opportunity sampling. The work environment was
assessed (Work Environment Scale: Moos, 1994) on basis of ten indicators
namely: involvement; co-worker cohesion; supervisor support; autonomy; task
orientation; work pressure; clarity; managerial control; innovation and physical
comfort. Teachers’ burnout was assessed using three-facet approach defining
burnout as emotional exhaustion, depersonalization and reduced sense of personal
accomplishment (Maslach Burnout Inventory–Educators Survey: Maslach,
Jackson, & Leiter, 1996). A three facet measure of organizational commitment
namely affective, continuance, and normative commitment (Organizational
Commitment Questionnaire: Meyer & Allen, 1990) was used. Personality
dimensions oriented to Big-Five theory of personality were assessed using Mini-
Markers Set (Saucier, 1994). The study was carried out in different phases. Phase I
of the study aimed to conduct pilot study (n = 102) for evaluating preliminary
psychometric issues and trend in data regarding the results of hypothesized
xii
relations of study variables. The phase II as main study (N = 426) was further
subject to two parts. The part I focusing to test the factor structure of study
measures using Confirmatory Factor Analysis demonstrated support for existing
theoretical structure of study measures. Results of fit indices, factor loadings,
consideration of reliability indices, and understanding of items in perspective of
our culture were used as decision criteria to retain or exclude items of respective
factors. The exclusion of items was discussed in perspective of use of these
measures in our culture. Findings of multiple regression analyses highlighted that
involvement as a negative predictor and work pressure as a positive predictor are
explaining variance in emotional exhaustion. Additionally, managerial control and
task orientation were negative predictors explaining the elaborative structure of
emotional exhaustion. For depersonalization, involvement is a negative predictor.
Co-worker cohesion and work pressure as positive predictors and physical comfort
as a negative predictor are explaining variance in personal accomplishment.
Additionally, task orientation explained variance in self related personal
accomplishment. In predicting affective commitment, autonomy is a positive
predictor. Co-worker cohesion and supervisor support as negative predictors and
clarity as positive predictor explains variance in continuance commitment. Results
of multiple moderated regression analyses provided evidences of moderation
effects of certain personality dimensions, age, and public and private sector for
relationship of work environment with burnout and organizational commitment.
Findings of the study were discussed in light of deducing implications for
improving the quality of work life of teachers.
1
Chapter I
INTRODUCTION
The research drift in occupational psychology from last two decades has
focused the construct of work environment as a mean for assessing employees’
perceptions about organizational processes influencing the employee and
organizational related outcomes (Carr, Schmidt, Ford, & DeShon, 2003: Kopelman,
Brief, & Guzzo, 1990; Moos, 1994; Ostroff, 1993; Ostroff, Kinicki, & Tamkins,
2003; Parker et al., 2003). In organizational settings, experiencing work environment
is considered as a dominating and central characteristic of many people lives
(Muchinsky, 2007). This recognizes the fact that employees do report contrasting
work experiences and impending influences in form of morale, commitment,
satisfaction, despair, feeling of underutilization of individual abilities, work pressure,
stress, burnout, alienation, etc. (Moos & Billings, 1991). As regard quality of work
life, it’s important to consider ‘contextual factors’ of work settings such as policies,
operational procedures, management style, and lot many other factors of working
conditions (Wadsworth, Chaplin, Allen, & Smith, 2010). Blum and Nayler (2004)
concluded that people prefer pleasant environment to work in. In other words,
workplace as a social setting exerts profound influence by means of physiological and
psychological processes and thereby influences employees’ reactions (Quick,
Simmons, & Nelson, 2000). Organizational management has emphasized increasing
concerns of employees with work related issues particularly their expectation and
demand of the better wok environment (Sverke, 2008).
Within this context, the construct of work environment as multifaceted nature
is further described under “molar” and “particular referent” approaches. The molar
2
approach focuses on multiple or diverse psychosocial dimensions of the work
environment oriented towards processes for organizational goals attainment (Carr,
Schmidt, Ford, & DeShon, 2003). Most theoretical models explain the work
environment within this molar domain (James & James, 1989; James, James, & Ashe,
1990; Moos, 1994; Newman, 1977; Ostroff’s, 1993). Focusing particular referent of
work environment defines a specific aspect e.g., climate for creativity (Amabile &
Gryskiewicz, as cited in Taylor & Gryskiewicz, 1993), and then looks for sub-
dimensions contributing in creativity environment.
The perceptions of work environment as a critical determinant of individual
behavior imply that issues of employees have direct relationship with the work
environment (Moos & Billings, 1991). For instance, employees’ health or well-being
has been associated with positive impact of work environment (Cooper & Cartwright,
1994; Kompier, 2005). Certain aspects of the work environment might be perceived
as demanding (Sears, Urizar, & Evans, 2000), or may be stressful (Sulsky & Smith,
2005) which may have negatively impact upon employees’ attitudes, e.g., burnout.
Furthermore, impact of work environment has also been associated with positive
emotional states and organizational productivity level variables e.g., satisfaction,
organizational commitment, turnover, absenteeism, and job performance (Leka &
Houdmont, 2010). This emphasizes the importance of evaluating the dynamics of
work environment; more specifically this aims to appreciate, diagnose and prioritize
improvements in managing human resource system e.g., in academic settings (Wilk,
& Redmon, 1998) and in health settings (Kotzer, Koepping, & LeDuc, 2006).
Therefore, for the past two decades focus of research attention has been
devoted to understand the role of psychosocial work environment in determining the
behavior and attitudes of employees concerning their work and the organization
3
(Moos, 1994; O’Driscoll & Evans, 1988). There has been increased attention on
exploring certain outcome variables particularly related to occupational health and
well-being (Hyvones, Feldt, Tolvanen, & Kinnunen, 2010; Parkes & Von Rabenau,
1993; Stansfeld & Candy, 2006). More specifically, these include both positive and
negative outcome measures, such as research attention has been given to outcome
variables including job stress (Haines, Williams, & Carson, 2004; Portello & Long,
2001); negative attitudinal outcomes including burnout (Adali et al., 2003; Boyas &
Wind, 2010; Kumar, Hatcher, Dutu, Fischer, & Ma’u, 2011; Turnispeed, 1998),
turnover (Hayhurst, Saylor, & Stuenkel, 2005; Hemingway & Smith, 1999); positive
attitudinal outcomes including job satisfaction (Blegan, 1993; Tumulty, Jernigan, &
Kohut, 1994; Westerman & Yamamura, 2007), organizational commitment (Grau,
Chandler, Burton, & Kolditz, 1991; Karsh, Booske, & Sainfort, 2005; Stewart, Bing,
Gruys, & Helford, 2007), job morale (Day, Minichiello, & Madison, 2007; Gaynor,
Verdin, & Bucko, 1995; Schaefer & Moos, 1996); and the ultimate concern of
organizational dynamics reflecting in performance (Cotton, Dollard, & De Jonge,
2002; Evans & Dion, 1991; Westerman & Simmons, 2007); etc.
With the recognition that psychosocial factors of work environment may affect
employee and organizational related outcomes, it became an important research
question to identify what aspects of a work environment contribute in determining
employees’ attitudes. This seems an important issue of work place management when
examined in context of a particular work setting, e.g., the academic workplaces.
Based on well established theoretical premises of Moos (1994), the present study
examined the model explaining the complex interplay of work environment and its
outcomes. The current study speculated that work environment factors may contribute
in affecting burnout and organizational commitment in context of academic work
4
place culture in Pakistan. This may investigate more thoroughly through examining
the possible moderating effects of personality and demographic variables.
Work Environment: Nature and Dimensionality
From the perspective of organizational behavior, concept of work environment
can be traced back in Lewin’s field observations of work environment in
organizational settings (Lewin, 1951), which suggest that …. ‘Behavior is a function
of environment or some part of the environment’, B = f (E). This conceptualization
explains work environment as a behavior setting or a small-scale social system
comprises of people and physical objects, governed by behavioral rules. In other
words this refers to a set of ‘routine’ activities shaping the behavior of people who
inhibit them (Barker, 1965). The pioneering research on work environment defines
work environment as an interaction between observable set of organizational
conditions and the perceptual interpretation of organizational characteristic features
by its participants (Guion, 1973; Hellreigel & Slocum, 1974; James & Jones, 1974;
Litwin & Stringer, 1968). There is emerging consensus that work environment can be
defined through employees’ perceptions about characteristic features of the
organization events, and processes (James & Jones, 1974; Schneider, 1990).
Hellriegel and Slocum (1974) elaborated that perceived characteristics of work
environment distinguish one organization from another, which may influence the
behavior of members of the organization. By the end of the 1970s, in literature of
work environment, researchers identified various dimensions or indicators to define or
describe the work environment. A meta-analytic review conducted by Parker et al.
(2003) mentioned that different terminologies are being used in literature when
5
referring to the work environment, e.g., psychological climate, organizational climate,
working conditions, or organizational culture. However, work environment has been
treated distinct from the organizational culture (Flarey, 1991). Owens (1998) research
attempted to provide further clarification: this defined “culture as the behavioral
norms, assumptions and beliefs of an organization, whereas environment refers to
perceptions of persons in the organization that reflect those norms, assumptions and
beliefs” (p. 165).
In organizational perspective, the concept of work environment is defined in
varied ways. Robbins and Coulter (1999) referred this as ‘a force that affects
organization’s performance’ and he tried to differentiate employees’ general and
specific environment. This definition explains that the general environment includes
factors outside the organization that affects the organization, e.g., economic factors,
political conditions, socio-cultural influences, globalization issues, and technological
factors; whereas, the specific environment has taken as an organizational part directly
relevant to the achievement of organizational goals.
Deer (1980) defined work environment as average of the individuals’
perceptions which they have about their daily work environments.
Moos and Billings (1991) defined work environment as the social-
psychological characteristics of work settings i.e. attitudes of employees toward their
job tasks and interpersonal communication.
James, James, and Ashe (1990) attempted to defined psychological
environment evaluated through individual’s cognitive appraisal of his or her
organizational environment, which helps assessing individual’s significance and
meaning of work environments.
6
Literature treated the construct of work environment as of multidimensional
nature (e.g., see James & James, 1989; James & Sells, 1981; Litwin & Stringer, 1968;
Moos, 1986; Ostroff, 1993). The construct has also treated as specific referent i.e.,
competitive work environment (Brown, Cron, & Slocum, 1998; Fletcher, Major, &
Davis, 2008; Fletcher & Nusbaum, 2010), and safety climate (Schneider, 2000).
Earlier, Litwin and Stringer (1968) attempted to explain the dimensionality of
work environment namely: structure, responsibility, reward, risk, warmth, support,
standards, conflict, and identity. James and James (1989) explained four dimensions
of work environment including: “(1) perceptual indicators of job attributes like job
challenge, job autonomy, (2) characteristics of leader and leadership processes, e.g.,
leader consideration and support, leader work facilitation, (3) workgroup
characteristics and processes, e.g., work group cooperation, workgroup esprit and (4),
interfaces between individuals and subsystems or organizations, e.g., role ambiguity,
fairness and equity of reward system” (p. 739). James and Sells (1981) proposed eight
factor model of psychosocial work environment including; work group cooperation
and friendliness, leadership facilitation and support, organizational concern and
identification, job challenge, job importance, job variety, role ambiguity, and role
conflict.
Furthermore, Ostroff’s (1993) taxonomy of work environment facets explained
three higher order facets of work environment namely affective, cognitive, and
instrumental climate perceptions including underlying overall 12 climate dimensions.
This taxonomy explains affective facet as interpersonal and social relations among
workers characterized with four underlying dimensions of participation, cooperation,
warmth, and social rewards. Whereas, cognitive facet represents those dimensions,
which are primarily related with involvement in work activities or with himself. This
7
comprised of four dimensions including growth, innovation, autonomy, and intrinsic
rewards. The third facet i.e. instrumental relates with involvement or getting things
done in the organization. Dimensions that fall under the instrumental facet include
achievement, hierarchy, structure, and extrinsic rewards. Brown and Leigh (1996)
explained six factor model of work environment factors, this include, management
support, clarity, self-expression, contribution, recognition, and challenge.
Moos (1986), one of the most influential contributors in research on work
environment, mentioned that each work setting develops a “style” or a work climate,
which influences the overall behavioral aspects of the management and the employee.
Work environment is the outgrowth of generalized attributions that stem from
judgments of particular environmental characteristics or events. Some pioneering
studies include research on physical features, organizational structure (Damanpour,
1991), policies and procedures, suprapersonal or collective attributes of its members
(Moos, 1986), varying tasks and demands (Wilkes, Stammerjohn, & Lalich, 1981),
values of organizations (Ashforth, 1985). These studies have identified potential
determinants and characteristics features of work environment of an organization.
The empirical framework proposed by Newman (1977) suggests that person's
own characteristics form the frames of reference for perceptual processes that in turn
determines persons’ evaluations of (attitudes toward) the work environment. This
further proposes that the evaluations of attitudes in notion of person-environment fit
are related to work motivation, behavioral intentions, absenteeism, performance, and
turnover. Whilst explaining the role of perception in conceptualizing the work
environment, Moos (1986) further explained his conceptualization of work
environment from Gestalt’s perspective:
8
Individuals try to create order by selecting and integrating specific perceptions
into meaningful patterns. These cognitive schemas or maps are the product of
a constructive process in which new information is interpreted in the light of
prior experience. In turn, these schemas guide subsequent information
processing and shape the way in which organizational factors alter an
individual’s mood and behavior. By comparing with functional perspective,
individuals need to learn about the environment so that they can behave
appropriately and attain homeostasis. Cognitive appraisal thus must be based
on reasonably accurate perceptions of environmental characteristics rather
than simply on idiosyncratic personal factors. But individuals are predisposed
to construct reality in terms that are compatible with their current needs and
beliefs. These needs may cause people to attend selectively to particular
aspects of their work environment (p. 12).
The definitional issues discussed above refer to the most dominant stance of
treating work environment as comprising multiple facets. The theoretical groundings
of the construct and the research question inquiring environment-outcome
relationships can be explained by different theoretical models explained below.
Theoretical Foundations of Work Environment
Theoretical perspectives explaining work environment and its implications
particularly in the form of environment-to-outcome relationships can be grouped
under broader categorization namely: the person-environment interactional
perspective and the environmental perspective. The person-environment interactional
models (Holland, 1985; Pervin, 1968; Stern, 1970) examine environment as the
9
product of interactions between individuals and environment. Holland’s (1985) model
assumes that environment can be defined by describing its participants. Furthermore,
personality orientation of individuals leads to develop characteristic work
environments namely realistic, investigative, artistic, social, enterprising, and
conventional environments. This is further clarified by Strange and Banning (2001);
they elaborated that individual performance is optimized when one’s needs and
abilities are congruent with the demands of the environment. Stern’s person-
environment model (1970) describes work environment in terms of characteristic
demands or features of the setting as perceived by its participants. This explains that
individual’s responses to activities are associated with a particular personal need
orientation e.g., achievement, adaptability, dominance, etc. Pervin (1968) proposes
that an environment which is stimulating for congruency between individual’s
perceived and ideal self serves an important determinant of individuals’ satisfaction
and productivity. Walsh and Betz (1994) criticized as interactional models do not
effectively describe developmental processes or work environment related outcomes.
The environmental perspective as proposed by researchers such as Karasek’s
(1979), Moos (1986), and Siegrist (1996) have taken a different position in this
regard, i.e. individual’s behavior is mainly a function of environmental or situational
factors. These models provide a focus on assessing perceptual attributes of
environments with an elaborated stance for linking these perceptions to behavioral
and attitudinal outcome variables.
The Demand-Control-Support Model. The demand-control-support model
proposed by Karasek (1979) describes psychosocial work environment as a
combination of the demands of the work situation and the amount of control
10
employees have to cope with these demands. It further explains that incompatibility
between job demands (the perceived psychological stressors) and job control act as
stressful situation leading to characteristic high and low strain jobs. The demand-
control-support model is well documented especially predicting health related
outcomes, such as risk of poor health and stress related problems (Vermeulen &
Mustard, 2000); and to lesser extent certain organizational related outcomes, e.g.,
productivity, motivation, and engagement at work (Demerouti, Bakker, deJonge,
Janssen, & Schaufeli, 2001).
The Effort-Reward Imbalance Model. Another model namely the effort-
reward imbalance model (Siegrist, 1996) maintains its position explaining the
outcomes of psychosocial wok place facets mainly in health domain. The model
explains that imbalance between efforts spent and rewards received in work settings
leads to a state of distress. Researches have shown that model predicts adverse health
effects, e.g., myocardial infarction (Peter et al., 2002); morbidity and mortality
(Oxenstierna, Widmark, Finnholm, & Elofsson, 2008); lifestyle risk factors, such as
smoking, unhealthy dietary habits, and sedentary behavior (Kouvonen et al., 2006).
Further, the model was extended to explain the role of personal characteristic namely
overcommitment as a third variable which includes attitudes, behaviors, and emotions
reflecting excessive striving in combination with a strong desire of being approved
and esteemed (Peter et al., 2002).
Oxenstierna, Widmark, Finnholm, and Elofsson (2008) proposed a new
expanded model, which is based on Karasek’s (1979) demand-control-support model
and Siegrist’s (1996) effort-reward imbalance model. This model explains the the
impact of work environment on employee’s health. This has further propose that
11
workplace factors (goals, structure, leadership, workplace freedom, democracy and
justice, conflict and handling of conflicts, and humanity and social support) and work
factors (skill discretion, work decision authority, demands, and resources) lead to
outcomes involving stress symptoms (exhaustion, burnout, cognitive disruption,
physical symptoms, insomnia and restlessness) and health (sick leave, self-rated
health, and self-rated work capacity). Study highlighted that among various variables
only humanity and social support (workplace factors) and demands (work factors) had
a direct connection. This model has apparently stimulated further research into
investigating new dimensions particularly conflict and its management, work-leisure
relationships, and employment security, in explaining the impact of work on health
outcomes.
The Social Ecological Model. Moos’s social ecological model (1986)
proposes that the way one perceives the environment tends to influence the way one
will behave in that environment. The model holds view that perceived environment in
which individuals live and work tends to have a significant impact on attitudes,
behavior, and physical and psychological well being. In order to explain the
development and outcomes of work environment, the model explains the interplay
between five systems namely: the organizational system; personal system; work
stressors; coping responses; and the individual adaptation or outcomes (see Figure 1).
The organizational system comprises of physical features, organizational structure and
policies, suprapersonal and work task factors, and work climate. Personal factors
include characteristics including employee’s job position and level of experience,
socio demographic background, personal resources such as self confidence, their
expectations and preferences about the work place, etc.
12
The model explains that the association between the organizational system and
contextual factors e.g., work environment (Panel I) leads to certain outcomes (Panel
V). The understanding of environment-to-outcome relationship is a complex one i.e.
routed through the complex interplay of employees’ personal system involving
demographic or personal variables (Panel II). Furthermore, the model extended to the
understanding of certain important dimensions, e.g., work stresses (Panel III), and
employees’ coping skills (Panel IV), which may exert impact on association between
environment (Panel I), personal system (Panel II), and its outcomes (Panel IV).
(Source: Moos, 1994, p. 29)
Figure 1. Conceptual Model of Organizational and Personal Factors and Outcomes
Major theories of organizational management are providing support to the
propositions of Moos’s model. For example, scientific management approach
contributes to see the environment as a set of task-relevant reinforces that can be used
PANEL I
ENVIRONMENTAL SYSTEM
ORGANIZATIONAL AND WORK CONTEXT
PANEL II
PERSONAL SYSTEM
TYPE OF JOB
AND WORK ROLE
DEMOGRAPHIC AND
PERSONAL FACTORS
PANEL V
INDIVIDUAL ADAPTATION
WORK MORALE AND
PERFORMANCE
OVERALL
WELL-BEING
AND HEALTH
PANEL IV
COPING
RESPONSES
PANEL III
WORK
STRESSORS
13
to regulate employees’ behavior. The human relation approach takes into account the
personal and social context of work. The socio-technical approach provides an
inclusive conception of the organizational environment as composed of the interplay
of task and social factors (Moos, 1994). This supports strong arguments in favor of
superiority of Moos’ model.
The environmental perspective posited by Moos (1986) provides a more
detailed description of development of work environment and its influence on
employee and organization related outcomes. Moos’ model is extended to explain the
possible moderating influence of employees’ personal variables (demographic and
certain other personal factors) while explaining the environment-to-outcome
relationships. Other approaches e.g., Karasek’s (1979) demand-control-support model
also occupies a dominant position in classical psychosocial work environment
research; however, the critique of this approach maintain that the concepts are too
general to be used to examine work environment issues (Oxenstierna, Widmark,
Finnholm, & Elofsson, 2008). Another critique of Karasek’s model (Sulsky & Smith,
2005) stated that model is conceptually very narrow as it considers few constructs
whilst this tend to ignore the complexity of operating dynamics of work environment
and role of confounding effects of socio-demographic factors. One of the advantages
of using Moos’s model highlights its usefulness in identifying strengths of the
environment (Flarey, 1991). Moos’s socio-ecological approach occupies a prominent
position partly because that model was extended to operationalized the construct of
work environment and has offered a multi-facet measure which so far has remained
widely used in literature (Belicki & Woolcott, 1996; Straker, 1989).
In reviewing empirical studies on work environment, an important
consideration or distinction is based on the broader classification of the setting itself,
14
e.g., academic settings, health settings, etc. Generally, academic settings within the
context of work environment is less explored area. In following, a comprehensive
review of studies particularly in academic settings is discussed.
Work Environment of Academic Settings
Research on work environment related issues is mainly focused on human
service related organizations particularly the health sector (see Chan & Huak, 2004;
Day, Minichiello, & Madison, 2007; Dickens, Sugarman, & Rogers, 2005; Kotzer,
Koepping, & LeDuc, 2006). Moos’s model of work environment is applied for
assessing the work environment of academic settings in varied directions. Some
contemporary empirical researches have explored the dynamics of academic work
environment on samples of teachers at post graduate teaching institutions/universities
(Goddard, O’Brien, & Goddard, 2006; Rehman & Maqsood, 2008). Studies have
focused on samples of teachers of secondary schools (Wu, 1998); Australian science
teachers of secondary schools (Fisher & Fraser, 1983); nursing faculty (Thompson, as
cited in Moos, 2008); university students (Cotton, Dollard, & de Jonge, 2002); nurse
students of medical university (Margall & Duquette, 2000); students at teaching
hospitals (Waryszak, 1999); university admission staff (Wilk & Redmon, 1998);
employees of language institutes (Walker, 2007); and academic centre of a service
oriented company (Miranda, as cited in Moos, 2008).
There are concerns that in the educational settings, monitoring of work
environment is recommended. This was revealed in a longitudinal study (Goddard et
al., 2006) on Australian university graduate teachers reporting their work environment
more negative over time. Differences in workplace may be attributed to teachers’
15
profile. For example, Shechtman, Levy, and Leichtentritt (2005) reported that teachers
who had high duration of training perceived their work environment as more positive
as being high on involvement, co-worker cohesion, supervisor support, task
orientation, clarity, managerial control, and innovation, and lower in work pressure
compared to their counterparts. The above mentioned studies and similar have
supported that management of academic settings should monitor workplace
periodically.
Studies recruiting involving teachers as participants have reported dominant
characteristics of the academic work environments. Wu (1998) mentioned that
teachers of secondary schools in England and Wales reported above average emphasis
on the dimensions of work environment including: involvement, coworker cohesion,
task orientation, and clarity, but also on work pressure and management control. The
findings of the study further elaborated that teachers reported below average emphasis
on supervisor support, autonomy, and physical comfort. Moos (1994) mentioned that
various comparisons of work environment assessments can be made: i.e., (i)
expectations of work environment (employees’ before entering the organization), (ii)
employees’ perceptions of their current operating work environment, and (iii)
employees’ aspirations or perceptions of ideal work environment. Thompson (as cited
in Moos, 2008) has assessed perceptions of expected work environment of a newly
established wing of nursing faculty and compared it with the existing perceptions of
work environment. The study reported that faculty reported overly positive
expectations with new work setting by reporting high emphasis for involvement,
autonomy, task orientation, clarity, and innovation, and less managerial control but
with high work demands compared to present in the workplace involving existing
wings.
16
Fraser, Docker, and Fisher (1989) comparing the real and ideal work
environmental characteristics of elementary and high school teachers. Findings
highlighted that teachers in different types of schools did not have significant
differences in their perception of real and ideal work environment. Further, study
revealed that teachers’ perceptions of the actual school setting varied markedly with
the type of school. Schools that had better staff development practices also had more
positive work environment. Similar findings are obtained in another study (Docker,
Fisher, & Fraser, 1989).
Academic research on work environment has recruited participants including
studies on university supporting staff (Okoh, 2007), university clerical employees
(Wilk & Redmon, 1998), comparing teachers, principals, and parents as members of
school advisory council (McClure & DePiano, 1983). Wilk and Redmon (1998)
reported that various aspects of the work environment involving increased interaction
between supervisors and employees, and greater clarity and specificity of work goals
were found to be linked with effectiveness of a behavior management intervention
program which was especially designed to improve the employees’ work productivity
and job satisfaction. McClure and DePiano (1983) reported that among members of
school advisory council, principals reported more negative experience of work
environment.
Margall and Duquette (2000) mentioned that student nurses in a university
hospital reported high levels of involvement and coworker cohesion and moderate
levels of supervisor support and autonomy. They also emphasized the importance of
task orientation and clarity in the work environment. Cotton, Dollard, and de Jonge
(2002) reported that within academic settings, work demands and less support are
linked with performance. Waryszak’s (1997) reported significant overall differences
17
between students’ expected, actual, and ideal work environments among Australian
business students. Perceived involvement, task orientation, and physical comfort
approximated students’ expectations and preferences, but students preferred more
supervisor support and innovation and less work pressure and control than they
actually experienced in the workplace. Some other studies on academic work
environment have focused on sample of students, e.g., graduate counseling students
(MacGuffie & Henderson, 1977); dental students (Lusk, Diserens, Cormier,
Geranmayeh, & Neves, 1983). Norton’s study conducted in 1989 focused on
assessing the work environment dimensions of medical institutes experienced by
students and their faculty. The study highlighted the contrasting differences among
both groups due to nature of their role requirements (as cited in Moos, 1994).
Pioneering research in Pakistan conducted by Rehman and Maqsood (2008)
explored that work environment was found to be linked with employees and
organizational outcomes among university teachers. The research further showed that
work environment exerts positive influence on employees’ job satisfaction and
showed link with work stress and employees turnover. The study highlighted
significant differences in perceptions of work environment of teachers of public and
private sector universities and post graduate institutions of Pakistan. This has further
suggested the need of enhancing the job morale of employees in teachers of public
sector. However, this study was limited in terms of sample size and locale. Another
pioneering research (Imam, 1993) examined the perception of men and women
college teachers using the Work Environment Scale (Moos, 1994). The study reported
that teachers perceived college environment as a dominant factor in controlling and
task oriented characteristics of work environment. The study highlighted the need to
deduce implications for improvements of academic work settings. Imam’s study was
18
based on college setting only and the intended environment of college may
significantly vary from university setting primarily due to difference in level of
education (e.g., higher education) to offer and the level of research output expected
from teachers. Keeping in view that college and university based institutions may
differ with respect to operating environment; the present study counts in very few
reported in the extensive literature on work environment particularly within academic
(university) settings.
Furthermore, Khan (1999) examined differences in the perceptions of work
environment of government and private school teachers using the Urdu version of the
Moos’ measure of work environment. The findings reported that teachers employed at
private schools perceived their work setting as dominant on the dimensions of work
pressure, control, innovation, and physical comfort. On other hand, environment of
government schools was reported to be high on involvement, peer cohesion,
supervisor support, autonomy, task orientation, and clarity. However, public and
private sectors revealed non-significant differences. The observation emerged out of
research direction in Pakistan highlighted the paucity of empirical research on work
environment issues. Evaluating aforesaid studies indicate the need to further extend
the research on work environment and outcome relationships using more
comprehensive approach i.e., employing larger sample and the strong methodological
approach.
Measurement of Work Environment
Several measures have been developed to evaluate the assessment of
institutional attributes of environments which adheres to perceived group consensus
19
as formal measurement of psychological properties of the environments (Betz &
Walsh, 1994). For instance, Newman’s (1977) measure namely Perceived Work
Environment (PWE) assesses the perceived work environment in terms of eleven
dimensions namely: supervisory style, task characteristics, performance-reward
relationships, co-worker relations, employee work motivation, equipment and
arrangement of people and equipment, employee competence, decision making
policy, work space, pressure to produce, and job responsibility/importance. Among
other multifacets measures, Psychological Climate Inventory by Gavin and Howe
(1975) measures six factors: spirit, managerial trust and consideration, rewards,
challenge and risk, clarity of structure and hindrance structure. The Michigan
Organizational Assessment Questionnaire (Camman, Fichman, Jenkins, & Klesh,
1983) assesses dimensions of work group cohesion, openness of communication,
internal fragmentation, supervisor-subordinate communication and consideration,
participation in decision making, production, orientation, role overload, role
conflict, role clarity, work group clarity, supervisor control, supervisor goal setting
and problem solving, and decision centralization. Koys and DeCotiis' (1991)
measure of work environment comprising thirty-five Likert scale items is based on
the eight global dimensions namely autonomy, cohesion, trust, pressure, support,
recognition, fairness, and innovation.
The Work Environment Scale (Insel & Moos, 1974; Moos, 1994) is
extensively utilized in studies conducted within health sector, academic settings,
service oriented organizations, and industrial settings. The author mentioned that the
measure discriminates among environments about as well as personality tests
discriminate among people. The measure highlights that employees are participant
observer in work milieu and are uniquely qualified to appraise it. The measure
20
assesses ten dimensions of work environment including clarity, managerial control,
innovation, and physical comfort. The internal consistencies of the subscales for
sample of teachers range from .60 to .84 (Fisher & Fraser, 1983, 1991), which were
quite similar to those of the original WES normative sample (Moos, 1994). Work
Environment Scale (WES) is used to collect data on the sample of present study. This
has been used extensively in researches conducted in social service settings including
studies that have linked characteristics of the work environment to burnout (the
outcome variable of the present study) particularly using Maslach Burnout Inventory
(Koran, Moos, Moos, & Zasslow, 1983; Wilber & Specht, 1994). In explaining
environment-to-outcome relationship, one of the areas of organizational research
relates to explain how employees experience and respond to their work environment
particularly in the form of job burnout (Swider & Zimmerman, 2010). The theoretical
review of job burnout as one of the outcome variables of the present study is
presented below.
Burnout
Burnout is considered as a serious mental health hazard in the workplace
(Pretty, McCarthy, & Catano, 1992). In the context of work of human service
professionals, burnout often develops as a result of emotionally charged contacts with
recipients of their services (Van Dierendonck, Schaufeli, & Buunk, 2001) and is
conceptualized as the result of interaction with clients, organizational demands,
inadequate support, and personal vulnerabilities (Wilber & Specht, 1994). Job burnout
effects both the organization and the employee, for instance, in terms of effecting
organizational commitment (Lee & Ashforth, 1996; Jones, Flynn, & Kelloway, 1995),
21
job satisfaction, turnover intentions (Lee & Ashforth, 1996), and performance
outcomes (Halbesleben & Bowler, 2007).
The term ‘Burnout’ appeared in the literature first time in 1969 when Bradley
published a paper on probation officers (as cited in Cooper, Dewe, & O’Driscoll,
2001) and further elaborated in 1974 as a description of the emotional and physical
depletion among human service employees, that resulted from the conditions of the
work environment (Freudenberger, 1974a). Burnout is distinct from the normal
experience of stress (Sulsky & Smith, 2005) and has treated differently from related
concepts e.g., depression, dissatisfaction, tension, conflict, pressure, and particularly
stress (Densten, 2001). It is conceptualized as a specific manifestation of job related
strain, which is considered as a “psychological process caused by unrelieved work
stress” (Posig & Kickul, 2003, p. 3). The experience of burnout is characterized with
cynicism, negativism, inflexibility, a know-it-all attitude, absenteeism, psychosomatic
complaints, and physical illnesses (Freudenberger, 1974a, 1974b).
There is consensus among researchers that burnout has taken as a negative
attitude or behavior resulting from excessive occupational demands or stressors
(Maslach & Jackson, 1984. The most cited definition as pointed out by Lee and
Ashforth (1990) and Maslach (1993) has taken the construct as a psychological
syndrome of emotional exhaustion, depersonalization of others, and a feeling of
reduced personal accomplishment.
The literature on environmental assessment has focused on exploring the
dynamic interplay of burnout in context of caregiver-client relationship as the
outcome of extensive contact with individuals having many complex needs (Adali et
al., 2003; Chan & Huak, 2004; Eastburg, Williamson, Gorsuch, & Ridley, 1994;
Miller, Birkholt, Scott, & Stage, 1995; Salyers & Bond, 2001). Human service
22
professionals are considered as more vulnerable to risk of burnout (Schaufeli, 2003),
including teachers (Fejgin, Ephraty, & Ben-Sira, 1995; Greenglass, Fiksenbaum, &
Burke, 1994; Peeters & Rutte, 2005), nurses (Haque & Khan, 2001; Hochwater et al.,
2004; Koniarek & Dudek, 1996); physicians (Barnett, Gareis, & Brennan, 1999; Van
Dierendonck, Schaufeli, & Buunk, 2001); salespersonnel (Sand & Miyazaki, 2000),
and school psychologists (Mills & Huebner, 1998; Sandoval, 1993).
During early 1980s, educational researchers became more interested in
examining the causes, intensity, and prevalence of burnout among teachers
(Golembiewski, Scherb, & Munzenrider, 1994; Iwanicki, 1982; Kottkamp &
Mansfield, 1985; Sahu & Misra, 1996; Winnubst, 1993). During current decade,
research impetus is more focus towards the work environment issues in academic
settings, i.e. studies comparing teachers of different work settings (i.e. elementary and
secondary school institutions). High levels of emotional exhaustion and
depersonalization is reported among elementary school teachers which seems to
negative impact on their involvement and innovative approach in classroom
management (Yavuz, 2009). Khan (2000) reported high indicators of burnout amongst
teachers of deaf and dumb schools. Studies on burnout have focused on school
counselors (Wilkerson, 2009); Chinese school teachers (Luk, Chan, Cheong, & Ko,
2010); academic teaching librarians (Sheesley, 2001); Pakistani teachers involved in
teaching at higher education (Basir, 2006) in Pakistan; university teachers in China
(Zhong et al., 2009); university professors (Otero-López, Mariño, & Bolaño, 2008),
etc. Other studies involving primary and high school teachers (Moghadam &
Tabatabaei, 2006) and Turkish pre-service and in-service preschool teachers
(Kabadayi, 2010) are highlighting the common denominator that significant
differences in burnout are found in contrast groups. Furthermore, students’ disruptive
23
classroom behavior and the teachers’ competence to cope with that behavior predicted
depersonalization and personal accomplishment among teachers (Ever, Tomic, &
Brouwers, 2004).
Maslach and her associates (Maslach, Jackson, & Leiter, 1996) conceptualized
burnout having three components: emotional exhaustion, depersonalization, and lack
of personal accomplishment. Cordes and Dougherty (1993) also supported this three
component nature of the construct. The first component exhaustion refers to the
depletion or draining of emotional resources (Van Dierendonck, Garssen, & Visser,
2005) caused by excessive psychological and emotional demands (Lee & Ashforth,
1993), and compassion fatigue because the employee is unable to give support and
care to his clients (McShane & Glinow, 2003). There is consensus amongst researches
that burnout results in physical, emotional, and mental exhaustion (Edelwich &
Brodsky, 1980; Maslach, 1982a, 1982b; Paine, 1981; Pines, Aronson, & Kafry, 1981).
Individual’s physical exhaustion may manifest in the forms of low energy, chronic
fatigue, weakness, accident-proneness, increased susceptibility to illness, weariness,
frequent headaches, nausea, muscle tension, alterations in eating habits and weight,
somatic complaints, and increased frequency of illnesses (Golembiewski,
Minzenrider, & Stevenson, 1986; Pines, Aronson, & Kafry, 1981). Emotional
exhaustion may involve feelings of depression, entrapment, hopelessness,
helplessness, and distress and may be demonstrated by decreased coping ability,
marital problems, substance abuse, and incessant crying (Jackson & Maslach, 1982;
Ratliff, 1988). Mental exhaustion is evidenced by negative attitudes towards work and
life in general. These attitudes may be demonstrated by tardiness, leaving work early,
taking long breaks, clock watching, a rigid by-the-book stance toward problematic
situations and clients, avoiding client contact, stereotyping clients, discussing clients
24
only in a detached manner, absenteeism, employee turnover, and the intention to leave
one’s job (Cherniss, 1980; Maslach, 1982a, 1982b; Pines et al., 1981).
Depersonalization, the second component of burnout viewed as a coping
mechanism (Cordes & Dougherty, 1993) characterized with distant attitude toward
work and the people on the job (Maslach & Leiter, 1997). Fox and Leif (as cited in
Akram, 2003) found that moderate levels of “detached concern” toward clients is
appropriate, necessary and effective performance in some occupation but excessive
detachment with too little concern is assumed to exist when a staff member reports
feelings of callousness and cynicism. The third component of reduced personal
accomplishment happens when individual experiences decline in feelings of
competence and success, as well as feelings of diminished competency (McShane &
Glinow, 2003). Individual view their contribution as unworthy letting to develop lack
of self-esteem and depression which further prevents individual from performing up
to his/her full potential (Hamann & Gordon, 2000). Substantial empirical evidence
signifies the importance of these components (emotional exhaustion,
depersonalization, and low personal accomplishment) of burnout (Bakker, Schaufeli,
Sixma, Bosveld, & Van Dierendonck, 2000).
Theoretical Models of Burnout
Various theoretical models (Cherniss, 1980; Golembiewski, Minzenrider, &
Stevenson, 1986; Hobfoll, 1989; Maslach, Jackson, & Leiter, 1996) of burnout are
proposed which explain the development of burnout within work settings
Cherniss’s Model. Cherniss’s model evolved in 1980 out of research focus on
human service employees suggested that aspects of the work environment
25
(orientation, workload, stimulation, scope of client contact, autonomy, individual
goals, leadership/supervision, social isolation), available resources, and characteristics
of the individual (career orientation, support/ demands outside work) can function as
sources of strain by creating doubts in the person’s mind about his or her competence,
bureaucratic interference with task completion or goal achievement, and lack of
collegial coworker relationships. Individuals endeavor to cope with work stressors in
a variety of ways, some of which may entail negative attitude changes, including
reducing work goals, taking less responsibility for work outcomes, becoming less
idealistic in one’s approach to the job, and becoming detached from clients or the job
itself. While critically analyzing, Cooper, Dewe, and O’Driscoll (2001) commented
that the distinctiveness of the construct of burnout is missing in this theory as it
includes variety of variables as explanation of burnout and make burnout
indistinguishable from job strain.
Hobfoll’s (1989) Conservation of Resources Theory. Conservation of
resources theory propounded by Hobfoll (1989) imply that burnout as an outcome of
depletion of resources leads to potential inadequateness to resolve any impending
demands when confronted with stressful situations at workplace. These resources
includes: material resources (e.g., house, car), conditions (e.g., status, social support),
personal characteristics (e.g., self-esteem and optimize), and various forms of energy
(e.g., money, favors owed by other persons). In a work setting, the major resources
include social support, personal control over jobs, involvement, and appropriate
reward system. The major demands that tend to relates to resources are role
ambiguity, role conflict, overload, inadequate resources to perform the job, and
excessive demands. While critically evaluating, Cooper, Dewe, and O’Driscoll (2001)
26
commented that the conservation of resource theory is a more general approach
compared to other models.
Maslach, Jackson, and Leiter’s (1996) Model. Originally Maslach and
Jackson (1981), and later Maslach, Jackson, and Leiter (1996) theorized a conceptual
model conceptualizing burnout as a response syndrome of emotional exhaustion
(feelings of being emotionally over extended and exhausted by one’s work),
depersonalization (impersonal responses towards the recipients of one’s work), and
reduced personal accomplishment (low feelings of competency and achievement in
one’s work). The model highlights the role of work demands and lack or resources as
predictors of burnout. It explains the impact of burnout in terms of organizational
costs mainly the organizational commitment. The model supports the current stance of
the study by highlighting that various aspects of work environment, e.g., excessive
work demands, personal conflict, and diminished social support, autonomy, or
involvement develops emotional exhaustion leading consequently to coping by
depersonalization, which then results in reduced personal accomplishment.
Maslach, Jackson, and Leiter’s (1996) model is important in systematically
measuring the three components of burnout using the most widely used measure of
occupational burnout (Densten, 2001; Worley, Vassar, Wheeler, & Barnes, 2008)
namely the Maslach Burnout Inventory developed earlier by Maslach and Jackson
(1981). Despite evidences of factorial validity of Maslach’s three component model of
burnout (Byrne, 1993; Evans & Fischer, 1993); Densten (2001) further proposed an
extension of the model. The author cited that emotional exhaustion being felt at
physical and psychological level has demonstrated relationship with psychological
strain and somatic complains, emerging an extended factor structure of the emotional
27
exhaustion namely psychological and somatic strain. Moreover, personal
accomplishment as being linked with self inefficacy as related to job success and
failure and learned helplessness in terms of job expectations and the work
environment may emerge sub-factors in form of self and others related components of
personal accomplishment (for complete detail, see Densten, 2001).
Measurement of Burnout
Different measures of burnout have focused primarily on individual’s own
reporting of their level of burnout. Among widely used measures, Maslach Burnout
Inventory (MBI: Maslach, Jackson, & Leiter, 1996) and the Burnout Index (BI; Pines,
Aronson, & Kafry, 1981) have dominated the burnout researches. Maslach Burnout
Inventory, by far, remains the most widely employed measure of burnout in almost
90% of all studies assessing occupational burnout (Schaufeli & Enzman, 1998). It
established three factors of burnout that measures emotional exhaustion,
depersonalization, and feelings of reduced personal accomplishment.
MBI as most widely acceptable measure (Posig & Kickul, 2003) is being
credited as psychometrically sound (Lindblom, Linton, Fedeli, & Bryngelsson, 2006).
Despite its wide applicability, its construct validity is not beyond question.
Exploratory factor analysis of MBI have tended to support the construct validity of
the measure, as well as it’s convergent and discriminant validity (Cordes &
Dougherty, 1993). Studies using confirmatory factor analyses (Byrne, 1993; Kim &
Ji, 2009; Lee & Ashforth, 1990; Schaufeli & Van Dierendonck, 1993) have identified
the original three factor model as superior to other alternative models. Substantial
studies involving sample of teachers have supported the three factor structure of the
28
inventory (Aluja, Blanch, & Garcia, 2005; Boles, Dean, Ricks, Short, & Wang, 2000;
Byrne, 1991, 1993, 1994; Evans & Fischer, 1993; Gold, 1984; Gold, Roth, Wright, &
Michael, 1991; Holland, Mishael, & Kim, 1994; Iwanicki & Schwab, 1981;
Kokkinos, 2006; Richardson & Martinussen, 2004; Schaufeli, Daamen, & Van
Mierlo, 1994). Studies (Iwanicki & Schwab, 1981; Powers & Gose, 1986) have
reported that MBI measures four factors, while others (Brookings et al., 1985;
Dignam, Barrera, & West, 1986; Green, Walkey, & Taylor, 1991; Kalliath,
O’Driscoll, Gillespie, & Bluedorn, 2000; (Walkey, & Green, 1992) maintained that
MBI measures only two factors. Recently, Densten (2001) supported the five factor
structure of MBI measure.
In a subsequent effort, Friedman’s (1995) proposed a self-report questionnaire
based on Cherniss’s conceptualization of burnout which measures four components of
burnout: exhaustion, aloofness, self-dissatisfaction, and deprecation. The first two of
elements represent internal (exhaustion) and external (aloofness) weariness, whereas
the remaining two reflect internal (self-dissatisfaction) and external (deprecation)
discontent. These experiences are quite similar to MBI measure as well.
Burnout Index (BI; Pines et al., 1981) as a second most widely used measure
of burnout initially labeled as tedium that was considered to apply to wide range of
situations, instead of burnout that was primarily associated with emotionally
demanding settings. However, author contended that both burnout and tedium are
identical constructs. The measure reflects the core dimension of exhaustion by
differentiating physical, mental, and emotional kinds of exhaustion. Henceforth, is
regarded as a uni-dimensional measure.
29
Relationship of Work Environment and Burnout
Aspects of the work environment have been found to be important in
maintaining the concept of healthy work among employees (Karasek & Theorell,
1990). Insel and Moos (1974) argued that environment and individual’s perception
about this are critically important in order to understand the development of
employees’ attitudes. Cherniss, (1980) endorsed that the extent of negative changes in
mood and behavior are strongly influenced by the nature of the work setting. Leiter
and Maslach (1988) stressed the relationship aspects particularly the interpersonal
contact with supervisors and coworkers as contributory factor in development of
burnout. Among early stimulating investigations, Savicki and Cooley (1987)
concluded that those work environments that are associated with low levels of burnout
are the ones in which workers are committed to their work, relationships with
coworkers are encouraged, and the supervisory relationship is supportive. Further,
high levels of burnout are found in those work environments where worker freedom
and flexibility are restricted, and there is a de-emphasis on planning and efficiency for
completion of work tasks. They also found high levels of burnout to be associated
with vague or ambiguous job expectations, and a lack of support or encouragement
for new ideas.
Furthermore, antecedents of job burnout grouped at organizational level
(Maslach, Schaufeli, & Leiter, 2001) have been associated with psychosocial work
environment (Pretty, McCarthy, & Catano, 1992), access to organizational resources
(Shirom, 2003), social support (Dick, 1992; Fong, 1993), etc. Literature review
reveals that various studies had supported the predictive relationship between work
environment facets and the burnout (Dorman, 2003; Escribà-Agüir, Martín-Beena,
30
Pérez-Hoyos, 2006; Hochwälder, 2007; Jaffe, 1995; Langballe, Innstrand, Aasland, &
Falkum, 2011; Turnipseed, 1994). Researches on burnout within academic settings
have focused on sample of elementary teachers (Peeters & Rutte, 2005); teachers of
public schools (Russell, Altmaier, & Van Velzen, 1987); elementary, intermediate,
and secondary teachers (Byrne, 1994); teachers of nursing faculty (Dick, 1986);
university teachers (Goddard et al., 2006); school psychologists (Huberty & Huebner,
1988); library and computing staff of universities of Pakistan (Munir, 2005); etc.
The work environment related factors associated with burnout within human
service professions include supervisory and peer support among Korean elementary
school teachers (Kim, Lee, & Kim, 2009); role ambiguity and group pressure among
Indian engineering college male teachers (Pandey & Tripathi, 2001) and among
nursing faculty (Goldenberg & Waddel, 1990); role overload due to time limitations
among nursing educators (Fong, 1993); etc. Different work environment facets, e.g.,
high work pressure (Brown & Pranger, 1992; Huberty & Huebner, 1988; Constable &
Russell, 1986; Turnipseed, 1994); low work involvement (Brown & Pranger, 1992);
the extent of social support (Hochwälder, 2007; Russell, Altmaier, & Van Velzen,
1987) particularly from supervisors (Boyas & Wind, 2010; Lee & Ashforth, 1996)
and with coworkers (Savicki & Cooley, 1987; Turnipseed, 1994); and factors related
to job enhancement, e.g., low levels of autonomy, clarity (Constable & Russell, 1986;
Turnipseed, 1994), task orientation, innovation, and physical comfort (Constable &
Russell, 1986) have found to be the strong predictors of burnout. Byrne (1994)
suggested that role conflict, work load, and co-workers support serves as stronger
predictors of burnout. Contrary to dominant trend in literature, Dick (1986) found that
development of burnout may be irrespective to workload.
31
The pattern of interpersonal relationships at work appears a dominating
contributor in developing feelings of emotional exhaustion and depersonalization
(Leiter & Maslach, 1988). The indicators of relationship dynamics, e.g., supervisors’
and co-workers’ support is found to has negatively impact upon emotional exhaustion
(Escribà-Agüir, Martín-Beena, & Pérez-Hoyos, 2006). Employees’ emotional health
is linked with high emphasis on task orientation and work pressure and less emphasis
on innovation (Chan & Huak, 2004). Adali et al. (2003) found that work places high
on involvement and clarity were leading towards better personal accomplishment and
less emotional exhaustion. Moreover, supervisor support, clarity, and managerial
control, and fewer work demands were linked with less emotional exhaustion. In a
study (Robinson et al., 1991), high work pressure and low work involvement were
diagnosed as problematic aspects which predict emotional exhaustion.
Different work environment facets including work load, coworkers support,
and role clarity were found to explain significant variance in emotional exhaustion
and depersonalization (Levert, Lucas, & Ortlepp, 2000). The extent of physical
comfort was found to be linked with depersonalization (Salyers & Bond, 2001).
Depersonalization was reported as negatively linked with managerial control,
involvement, and coworker cohesion (Adali et al., 2003).
A longitudinal study conducted by Goddard et al. (2006) reported that high
work pressure and less emphasis on innovation tended to predict increases in
emotional exhaustion and depersonalization and a decline in the sense of personal
accomplishment over two year period. Role clarity explains significant variance in
personal accomplishment (Levert et al., 2000). In another study of Savicki (2002) on
burnout involving treatment providers, educators, and managers of child and youth
care across thirteen cultures included Australia, Austria, Canada, Denmark, England,
32
Germany, Israel, Poland, Scotland, the Slovak Republic, and the United States. The
findings of the study suggested though somewhat variations with respect to different
cultures, that emotional exhaustion and depersonalization was linked with high work
pressure and less supervisor support, task orientation, and innovation. High coworker
cohesion and innovation was associated with a greater sense of personal
accomplishment at work. Robinson et al. (1991) also noted that depersonalization and
personal accomplishment were predicted by task orientation, work pressure, and
involvement. Literature review has suggested some contradictory findings in reporting
the relationship between work environment and burnout. For instance, study
conducted by Salgado, Remeseiro, and Iglesias (1996) have mentioned non-
significant relationship between work environment and burnout.
In context of Pakistan, a noticeable unpublished study (Munir, 2005) was an
attempt to examine Moos’s work environment model within academic settings.
Munir’s study (2005) on librarians reported that supervisor support is related with
depersonalization; task orientation is linked to each component of burnout; clarity as
linked with depersonalization and personal accomplishment; and managerial control
with emotional exhaustion. Evaluating the study implies that the study was limited in
scope due to sample size and the study design which included only few colleges and
universities. For examining burnout among teachers, unpublished study of Basir
(2006) also faces critic of using limited sample and locale.
Examining the direction of research on environment and burnout relationship
along with the demonstrated importance of examining burnout among teachers has
found to be lacking in our cultural context. This draws attention that organizational
research in Pakistan generally has done in varied directions that adds to inconsistency
in empirical support. This further supports the observation that a consistent pattern of
33
research in a particular area of investigation with objective to address gaps in
literature is generally missing. This provided strong impetus to extend the empirical
studies in environment and burnout relationship particularly within academic settings.
In explaining environment-to-outcome relationship, present study specified
“organizational commitment” as another dimension of employees’ attitudes.
Organizational Commitment
Organizational commitment as a critical employee attitude is regarded as key
component in human resource management (McKenna, 2000). This is related with
several key aspects of work behavior, e.g., employees’ performance (Herscovitch &
Meyer, 2002), job satisfaction and turnover (Cooper-Hakim & Viswesvaran, 2005),
organizational citizenship behaviors (Riketta, 2002), counterproductive behavior
(Dalal, 2005), organization’s well being (Guion, 1973), etc. The foundation work on
commitment (Becker, 1960) revealed that the term has been used in analyses of
variety of phenomena including occupation, power, religion, political behavior and so
on. The commitment of employees to their work may takes many forms including
career, occupation, organization, union, work ethic, job involvement, and other
conceptually related variables (Cooper-Hakim & Viswesvaran, 2005), which added in
conceptualizing commitment as a domain specific construct (Meyer, Allen, & Smith,
1993; Ellemers, Gilder, & Heuevl, 1998).
Furthermore, commitment is defined and measured differently (Meyer &
Allen, 1991; Morrow, 1993; Mowday, Porter, & Steers, 1982). Generally, it has taken
as the nature of the relationship of the member to the system as a whole (Grusky,
1966). An earlier definition grows out of Becker’s (1960) work viewed commitment
34
in terms of side bet or investments. This suggests that employees make side bets as
investments in form of tenure, status, organization specific skills, pensions, etc.,
which pull them to continue their affiliation with the organization. Some authors
(Salanick, 1977; Scholl, 1981) have viewed organizational commitment as a form of
attitude results from behavioral acts that the individual engages in. Porter and his
associates (1974) explain commitment as the result of three factors: 1) acceptance of
organizational goals and values; 2) willingness to help the organization to achieve its
goals; and 3) the desire to remain within the organization. This definition reflecting
the element of individual vs. organizational goal congruence was also supported by
Buchanan (1974), who defined organizational commitment in terms of effective
attachment to one’s role in relation to goals and values of the organization, apart from
its purely instrumental worth.
Definitional issues discussed above suggest that generally commitment is
viewed as employees’ psychological attachment or a bond (Armstrong, 1996). Mainly
these definitions differ in terms of how psychological bond to the organization
develops (Mitchell, 1979). The concept of organizational commitment explains the
core essence of the concept so that organizational commitment is distinguished from
existing conceptualizations of motivation, morale, and general attitude. Meyer and
Herscovitch’s (2001) clearly differentiate commitment from motivation and general
attitude. They viewed commitment as distinguishable from exchange-based forms of
motivation and suggested that commitment influences behavior without even in
absence of extrinsic motivation.
Commitment seems to develop gradually and change over the course of an
employee’s career (Reilly & Orsak, 1991). Miner (1992) suggested that initially
personal factors (values, beliefs, and personality) and organizational characteristics
35
interplay to develop the commitment which soon after passing sometime in the
organization became linked to employees’ work experiences (job, supervision, work
group, pay, and organization). Later on investments with passage of time fosters long
term commitment with the organization. Morrow (1993) commented that concept
redundancy has been noted as a major problem in commitment literature. In a meta-
analytic study (Cooper-Hakim & Viswesvaran, 2005) examining 997 studies
associated with organizational commitment. They have found the presence of a
common psychological construct underlying different commitment forms, with the
exception of calculative, continuance, and union commitment.
In the context of educational settings, organizational commitment is associated
with teachers’ empowerment (Bogler & Somech, 2004; Finegan, 2000); personal
factors, e.g., teachers' understandings of their perceived failures (Joffres & Haughey,
2001); normative value orientation among elementary and high school teachers (Shaw
& Reyes, 1992); teachers’ satisfaction and retention in contributing effective schools
(Singh & Billinsgley, 1998); students’ level of achievement and teachers’ job
satisfaction (Kushman, 1992); and studies in field of library management (Hovekamp,
1994; Karim & Noor, 2006; Rubin & Butllar, 1992).
Theoretical Models of the Organizational Commitment
Commitment appears as a complex and multifaceted construct (Meyer, Allen,
& Smith, 1993). It has been treated as a unidimensional construct (e.g., Becker, 1960;
Mowday, Steers, & Porter, 1979; Wiener, 1982) as well as multidimensional construct
(e.g., Allen & Meyer, 1990; O’Reilly & Chatman, 1986). Different efforts in
explaining the multidimeniusoality of the commitemt concept revealed somewhat
36
similarities among existing multidimensional models. Earlier researchers (e.g.,
McGee & Ford, 1987; Meyer & Allen, 1984) have emphasized that organizational
commitment has two and, possibly three components (Allen & Meyer, 1990)
including affective, continuance and normative. Later, several other multideminsionl
frameworks seems to have extended the existing conceptualization of the construct
(e.g., O’Reilly & Chatman, 1986; Angle & Perry, 1981; Jaros, Jermier, Koehler, &
Sincich, 1993; Mayer & Schooman, 1998).
Mowday, Porter, and Steers’ Model. Mowday, Porter, and Steers’ model.
The attitudinal and behavioral diemnsions of the commitement were distinguished by
Mowday, Porter, and Steers’ (1982) model. Attitudinal commitment refelects the
individual’s identification with organiztaional goals and his/her willingness to work
towards them. Whereas, behavioral commitment represents from the binding of
individuals to behvaioral acts. Mowday and his assocaites mentioned that reciprocal
relationship exists between both aspects of the commitment. Based on Organiztaioanl
Commitment Questionnaire (Mowday et al., 1979), Angle and Perry (1981) supported
two underlying factors of comitment namely, acceptance of organiztaional goals and
the willingness to exert effort (value commitment) and desire to maintain membership
(continuance commitment). In similar lines, subsequently, Mayer and Schoorman
(1992) model suggested that organizational commitment comprises two dimenisons
referred as continuance commitment (desire to remain) and value commitment
(willingess to exert extra effort).
O’Reilly and Chatman’s Model. This multidimensional framework (1986)
focuses on commitment as an attitude towards the organization that developes
37
through various mechanisms. The model argued that commitment could take three
distinct forms: compliance, identification, and internatlization. Compliance occurs
when attitudes, and corresponding behaviors, are adopted in order to gain specific
rewards. Identification occurs when an individual accepts influence to establish or
maintain a satisfying relationship. Finally, internalization occurs when influence is
accepted because the attitudes and behaviors one is being encouraged to adopt are
congruent with existing values. Vanderberg, Self, and Seo (1994) examined O'Reilly
and Chatman's compliance, identification, and internalization scales, and compared
the latter measures to the OCQ. Findings indicated that although reliable, the
identification measure was redundant with the OCQ. The internalization measure was
reliable and valid in that most items strongly loaded upon a different factor than did
items of all other measures and the compliance measure obtained some validity only
after the removal of two of its items, but possessed weak reliability throughout the
analysis.
Despite of psychometric support provided by O’Reilly and colleagues,
subsequent researches reported difficulty in distinguishing identification and
internalization (Vanderberg, Self, & Seo, 1994). As a result, O’Reilly and colleagues
combined the identification and internaliztion items to form the dimension which they
labelled as normative commitment; whereas, compliance is what referred to as
instrumental commitment.
Meyer and Allen’s Model. Meyer and Allen’s (1991) model of commitrment
integratres numerous definitions of commitment that had prolifereated in the literature
and can be conceptualized under three mainstreams namely affective, continunace,
and normative basis of commitment. Affective commitment is conceptualized that
38
nature and quality of work experiences affect employees’ positive emotional
attachment characterized by strong links, an identification with, and involvement in
the organizationan. Identification in this case, means the employees sense of unity
with the organization. In an organizational setting loyalty and feelings of attachment
develop as individuals share values in common with other members of the group. This
identification, expressed through the adoption of organizational goals, occurs when
individuals take pride in the organization, participate with intense interest in its
activities, and speak positively about their connection with the organization (Etzioni,
1975; Mowday et al., 1982). Tracing back, earlier work by Kanter (1968) provided
foundation for the conceptualization of this form of commitment. The concept was
further posited by Mowday, Porter, and Steers (1982) which was the main basis for
conceptualization of affective component of the Meyer and Allen’s model. Further,
Buchanan (1974) aslo viewed commitment as orietned to the goals and values of an
organization apart from its purely instrumental worth.
Continuance commitment is related to side bets approach of Beckers’theory
(1960) and Herbiniak and Alutto (1972) conceptualization of commitment as a cost
induced desire to remain in the organization. Continuance commitment refers to
strength of a person’s tendency as a need to continue working for an organization (as
cited in Greenberg & Baron, 1993) and as feeling “struck” in one’s present position
(Angle & Lawson,1993). Continuance commitment include perception of high
sacrifice and few alternatives (Reilly & Orsak, 1991).
Normative commitment reflects an employee’s feelings of obligation toward
the organization. Individual committed to the organization on a normative basis
engage in activities on the basis of a sense of duty. Wiener (1982) suggested that
employees behave in accordance with organizational goals because “they believe it is
39
the right and moral things to do (p. 421). Normative commitment describes a process
whereby organizational actions (e.g., selection, socialization, procedure) as well as
individual predispositions such as personal organizational value congruence and
generalized loyalty or duty attitude leads to the development of organizational
commitment (Mathieu & Zajac, 1990).
These three types of commitment reflect a link between an organization and an
employee and distinguish between commitment based on a desire to stay, need to
stay, and obligation to stay in an organization. Allen and Meyer (1990) provided
empirical support that each component represents a somewhat distinct link between
employees and an organization that develops as the result of different work
experiences. Therefore, the link between commitment and on-the-job behavior may
vary as a function of the strength of the three components. Further, these components
of commitment are not mutually exclusive: an employee can simultaneously be
committed to an organiztaion in an affective, continunace, and normative sense, at
varying level of intensity (as cited in Popper & Lipshitz, 1992).
Based on Allen and Meyer’s framework, Jaros et al. (1993) suggested a
multidimensional conceptualization namely, affective, continuance, and moral
commitment. The differences were in terms of defining the moral commitment which
corresponds more closely to Allen and Meyers’ definition of affective commitment
than to their definition of normative commitment.
In conclusion, all theoretical models presented above have their own strengths
and weaknesses; but Meyer and Allen’s model is the most widely used theoretical
framework in studies. WeiBo, Kaur, and Jun (2010) provided a critical review of
different models of orgnaiztaional commitment. They argued that measurment
approach offered by Porter et al. (1974) is not having good content and discriminant
40
validity. They further mentioned that O’Reilly and Chatman’s model (1986) is
relatively unclear in its porpositions; hencforth Meyer and Allen’s modle is more
empirically validated and its measurment approach has been crediated as of wide use
in most of studies.
Measurement of Organizational Commitment
Reichers (1985) pointed out that commitment literature had dominantly
focused on assessing intra-personal approaches of attitude and attribution formation to
measure commitment. Morrow (1983) identified over 25 commitment related
constructs and measures which highlighted the need to clarify the measurement of the
construct. In this regard, Whitener and Walz (1993) highlighted that effort of Meyer
and Allen (1991) is an important milestone in measurements of the construct. In
subsequent research, Allen and Meyer (1996) reviewed results from over 40 samples
and claimed that construct validity of the measure was strong enough to support the
continued use of the Organizational Commitment Questionnaire. The measure of
Organizational Commitment Questionnaire is of widespread usage in organizational
commitment research (Jaros, 2007). Allen and Meyer (1990) provided the preliminary
evidence that affective and continuance components of attitudinal commitment are
empirically distinguishable constructs. However, the affective and normative
components, although distinguishable, appear to be somewhat related as antecedents
of affective commitment have found to be highly correlated with both affective and
normative commitment. Recent study by Karim and Noor (2006) provided theoretical
support for measure including items of distinguishable components of affective and
continuance commitment on sample of academic librarians. A concern about
41
continuance commitment (Dunham, Grube, & Castaneda, 1994; Meyer, Stanley,
Herscovitch, & Topolnytsky, 2002; McGee & Ford, 1987) observed that continuance
commitment scale underlies two sub dimensions, (a) a low job alternatives and (b)
high personal sacrifice. However, Wasti (2002) supported that continuance
commitment is reflecting perceived cost reference to their specific source. Studies
have provided empirical support to demonstrate that the components of the measure
are distinguishable from one another (Dunham, Grube, & Castaneda, 1994; Karim &
Noor, 2006; McGee & Ford, 1987; Reilly & Orsak, 1991).
Porter and his colleagues developed the Organizational Commitment
Questionnaire (OCQ) to measure the affective approach to commitment (Mowday et
al., 1979). This 15-item measure has been used extensively in research and has
acceptable psychometric properties. A parallel measure developed by Cook and Wall
(1980) has also showed adequate and stable psychometric properties. Allen and
Meyer (1990) mentioned that other measures of affective commitment have not been
subjected to rigorous psychometric evaluation.
The measures based on perceived cost view of commitment by Ritzer and
Trice (1969), which was further modified by Hrebiniak and Alutto (1972), that
requires respondents to indicate the likelihood that they will leave the organization
given various inducements to do so e.g., increase in pay, status, freedom, promotional
opportunity. Meyer and Allen (1984) criticized that high scores on these scales reflect
an unwillingness to leave the organization, suggests that it may measure affective
commitment rather than, or in addition to, cost-induced commitment.
Wiener and Vardi (1980) developed a measure based on normative view of
commitment. The scale measures the extent to which employees feel a person should
be loyal to his/her organization, should make sacrifices on its behalf, and should not
42
criticize it. However, Allen and Meyer (1990) mentioned that other than internal
consistency, the psychometric properties of the measure have not reported
Relationship of Work Environment and Organizational Commitment
There is empirical evidence suggesting that organizational commitment can
develop by fostering a positive climate in the organization (Grau et al., 1991; see also
Witt, 1989). Organizational commitment as an outcome variables has been linked to
work environment variables (Brierley, 2000; Painter & Akroyd, 1998; Richards,
O’Brien & Akroyd, 1994); dimensions of organizational structure along with several
personal variables and organizational factors (Littler, 1985); supervision practices and
job control factors as influencing employees’ affective responses (Mobley, Griffith,
Hand, & Meglino, 1979); organizational support (Casper, Martin, Buffardi, &
Erdwins, 2002; Johnson & Chang, 2008), etc.
Holmergen, Hensing, & Dellve (2010) reported that work environment
particularly within public sector organizations influences employees’ attitudes namely
the organizational commitment. The study also highlighted that considering subgroup
of employees with respect to job ranks provide meaningful understanding of this
relationship. Karsh, Booske, and Sainfort (2005) suggested that in work setting,
quality improvement efforts to enhance employees’ intrinsic and extrinsic job
satisfaction and managing their intention to leave the job was supported by findings
that management of service oriented organization need to consider how high task
orientation, clarity, and innovation, and less work pressure have potential relation in
building employees’ strong identification with their organization. Mauser (as cited in
Moos, 2008) added that workplaces characterized with high involvement, cohesion,
43
clarity, and openness to change and less work pressure contribute in developing high
level of organizational commitment. For organizational improvement, Westerman and
Cyr (2004) concluded that the discrepancy between employees’ actual and preferred
work environment is needed to consider in order to manage their commitment with
the organization as found to be linked with their commitment especially in case of
those work settings where extensive contacts with one’s recipients of services are
involved.
In educational organization, work environment aspects which enhance
employees’ satisfaction contributes most to the development of affective commitment
and variation in psychosocial characteristics of environment effects emotional
attachment with the organization (Dramstad, 2004). More recently, Stewart, Bing,
Gruys, and Helford (2007) attempted to link the perceptions of work environment
(cohesion, trust, pressure, support, autonomy, recognition, fairness, and innovation)
with affective and continuance commitment among 553 employees. Results showed
that task-oriented dimension of organizational support was a significant predictor of
affective commitment whereas the relationship-oriented dimension of workplace
recognition was a significant predictor of continuance commitment.
While investigating a model of work environment and its outcomes, Clarke
and Iles (2000) attempted to investigated a model of work environment for diversity
(as assessed by perceptions of policy support, organizational justice, support for
diversity and recognition of the need for diversity) as showing strong predictors of the
presence of positive organizational, job and career attitudes. The findings further
suggested that the perception of positive organizational justice strongly predicts
organizational commitment, career commitment, career planning, job satisfaction and
satisfaction with supervisors, careers, and career future satisfaction. Among other
44
studies, Ervin and Langkamer (2008) assessed leadership as one of the indicators of
psychosocial work environment exerts impact on affective commitment. Brooks and
Seers (1991) suggested the relationship between facets of work environment as
predictor of organizational commitment in five different career stages within a sample
of 1536 employees. Within stage analysis showed that, relative to task challenge and
supervisory behavior, team cohesion had a larger effect.
Studies in context of Pakistan (Chughtai & Zafar, 2006; Hayat, 2004)
explained the dynamics of relationship between work environment facets and
organizational commitment. Chughtai and Zafar (2006) examined the variables of
satisfaction with promotion opportunities, pay, coworkers, actual work undertaken,
job security, supervision, working conditions, and training opportunities as related to
commitment among full time Pakistani university teachers taken from Lahore,
Islamabad/Rawalpindi, and Peshawar cities of Pakistan. Results revealed that
satisfaction with job security, supervision, training opportunities, and the actual work
undertaken are positively related with organizational commitment. However, working
conditions have showed the non-significant link. Overall, predictive variables were
responsible to explain 39% variance in organizational commitment. Contradictory
findings are reported in a study (Hayat, 2004) conducted on a sample of bank
employees. The findings indicated non-significant relationship between work
environment and organizational commitment. In this study, organizational
commitment was conceptualized as a single domain assessing the overall commitment
of employees.
In addition, studies on organizational commitment carried out in Pakistan are
in varied directions using varied samples. For example, an important milestone in
literature of organizational commitment was an effort to develop an indigenous
45
multidimensional organizational commitment questionnaire following theoretical
conceptualization of Porter et al. (1974). The measure comprises three dimensions
namely identification, involvement, and loyalty with adequate reliability and construct
validity indices (Tayyab & Tariq, 2001). The study further reported that executive
employees of public sector organizations are higher on organizational commitment
compared to the public sector executives. Nasir and Haque (1996) reported that job
stress is negatively related with organizational commitment among 50 federal
government officials. Shah, Kaur, and Haque (1992) reported a significant correlation
between intrinsic work values and commitment for the employees of public sector
industry. The differences on work values and commitment among public and private
sector employees were not significant.
In context of Pakistan, research evidence in establishing the relationship
between work environment and organizational commitment is lacking particularly
within academic settings. A study by Chughtai and Zafar (2006) reported that
research focus on organizational commitment in educational settings is not sufficient.
The trend of literature on environment and outcome relationship reinforces the
impression that consistent line of empirical evidence in a particular area of research is
generally lacking in organizational researches in Pakistan. This eventually provided
the theoretical stance for conducting the present study as an effort to explore work
environment and outcome relationships among working group of universities
teachers.
In establishing the environment and outcome relationship, proceeding section
of literature review will explain how personality, organizational, and demographic
related factors influence the relationship of work environment with burnout and
commitment.
46
Role of Personal Variables in Relationship of Work Environment and its
Outcomes
Personality variables. In organizational research, understanding the
dispositional basis provide an explanation of how employees respond to their work
experiences (Swider & Zimmerman, 2010). In conceptualization of this process,
Moos’s (1994) theoretical approach adheres to consider the role of personal variables
in explaining the interplay of environment-to-outcome relationship. Personality plays
an important role in determining how individuals encode and evaluate information
from their environment (as cited in Swider & Zimmerman, 2010); whereby,
individual differences have taken to influence job attitudes which are involved in
favorable or unfavorable evaluation of workplace facets (Meyer & Allen, 2006). In
understanding personality, trait approach has taken the position for consensus on basic
structure of personality converging on five basic traits referring to the Five Factor
Model (Mooradian & Nezlek, 1995). The Big Five personality factor model
represents the dominant conceptualization of personality structure which has received
immense empirical attention in organizational research (Barrick & Mount, 1991). The
Big Five factor model of personality has demonstrated cross culturally equivalence
(Digman, 1997; Nye, Roberts, Saucier, & Zhou, 2008). The leading theoretical
approaches to explain Big Five Model were proposed by Costa and McCrae (1995)
and Goldberg (1992). Costa and McCrae developed NEO-PI-R inventory with 240
items assessing 30 sub-facets to assess five factors of personality. Later on, a shorter
version (NEO-FFI) with 60-items version was developed. Goldberg (1992) proposed
a 100-item inventory, which later on was revised by Saucier (1994) in form of shorter
version.
47
The Five Factor Model comprises five relatively independent dimensions:
Extraversion, Agreeableness, Conscientiousness, Emotional Stability, and Openness
to Experience. Extroversion reflects the nature of relationship with ones social
environment enriched with energy, enthusiasm and confidence (Rolland, 2002).
Extraversion dimension reflects typical behavior tendencies including assertive,
talkative, sociable, gregarious, and active (Barrick & Mount, 1991). Agreeableness
refers to the nature of ones relationship with others (Rolland, 2002). Agreeableness
describing the human aspect of compliance associates with traits including
cooperative, courteous, flexible, trusting, good-natured, and tolerant (Barrick &
Mount, 1991). The dimension of Conscientiousness relates to dependability reflected
in traits including hardworking, achievement oriented, persevering, careful, and
responsible (Barrick & Mount, 1991). Emotional stability or neuroticism represents
the general tendency to experience negative affects associated with traits including
being anxious, depressed, angry, embarrassed, emotional, worried and insecure
(Barrick & Mount, 1991). Openness to experience is manifested in a wide range of
interest and eagerness to seek out and live new and unusual experiences without
anxiety. The acceptance of new experiences relate to different sphere of behavior
(Rolland, 2002). The behavioral tendencies typically associated with openness to
experience include being imaginative, cultured, curious, broad-minded, and intelligent
(Digman, 1990).
In perspective of understanding how personality or people influence
workplace, researches have conducted in varied directions. Generally, it is considered
that employees showed congruence to different facets of work settings in relation to
their personality traits (Chesney & Rosenman, 1980) and has taken as a source of
attribution in perceiving differences in workplace environment (McManus, Keeling,
48
& Paice, 2004). In this context, most of researches have devoted to study the impact
of disposition towards independence (Wetzel, 1978; Wetzel & Redmond, 1980),
achievement and competition (Chesney & Rosenman, 1980; Chesney, Sevelius,
Black, Ward, Swan, & Rosenman, 1981; hardiness (Thomson & Wendt, 1995); the
sense of mastery (Phelan, Bromet, Schwartz, Dew, Parkinson, & Curtis, 1993), etc.
Chesney and Rosenman (1980) reported that employees having extroverted assertive
Type A personality traits feel more comfortable in a cohesive and independent setting.
Whereas, Type B persons feel more satisfaction in a structures work setting
characterized with lack of autonomy and less interaction with coworker cohesion.
The role of personality has appreciated contextual variables of an
organizational environment while predicting burnout among teachers (Cano-Garcia,
Padillo, & Carrasco-Ortiz, 2004). Personality characteristics may potentially relate to
the experience of burnout particularly among teachers (Otero-López, Mariño, &
Bolaño, 2008). Researches had revealed that association of personality characteristics
and perception of work environment results in employees’ stress related outcomes
(Kobasa & Puccetti, 1983). Many researches (Buhler & Land, 2004; Downey,
Hemenover, & Rappoport, 2000; Harris & Lee, 2004) have been conducted to find the
relationship of personality disposition in experiencing burnout. Burnout is linked with
personality hardiness (Sahu & Misra, 2004) among teachers. Dodd and Jacobs (2003)
suggested that personality along with social support and workload predicts burnout. A
high level of burnout is caused by negative temperament and subjective workload, but
actual workload (academic and vocational) had no effect on burnout. In examining the
role of Big Five personality traits in explaining link between work environment and
burnout, Eastburg et al. (1994) reported that extraverted personality dimension in
association with peer support predicted less emotional exhaustion. The study
49
suggested that high on extrovert may require more coworker support to avoid work
related emotional exhaustion. Independently, emotional exhaustion was found to be
predicted by emotional stability; depersonalization was predicted by extraversion; and
personal accomplishment was predicted by extraversion and emotional stability
(Bakker, Van Der Zee, Lewig, & Dollard, 2006). Decrease in emotional exhaustion
and depersonalization and increase in personal accomplishment was linked with
emotional stability, extraversion, openness to experience, agreeableness and
conscientiousness (Rothman & Storm, 2003). Another study (Hochwarter, Zellars,
Perrewé, Hoffman, & Ford, 2004) highlighted that exhaustion and depersonalization
components of burnout was predicted by neuroticism; whereas, diminished personal
accomplishment was predicted by extraversion. A recent study (Swider &
Zimmerman, 2010) documented that each one of the traits of Five-Factor model of
personality have demonstrated relationship with burnout components.
Teaching as a highly stressful occupation remained the focus of various
studies while investigating the role of various personality characteristics.
Organizational factors including social support, workload (Dodd & Jacobs, 2003),
role expectations (Huebner & Mills, 1994), peer support (Eastburg et al., 1994) etc.,
have been studied in explaining the link between burnout and personality. Study of
Kim-Wan (1991) noted that personality had a significant as well as mediating role in
burnout among teachers. In another research, Kokkins (2005) reported that teachers
who were unable to manage misbehaved students, and were high on neuroticism were
found to be burned out. Study of Huebner and Mills (1994) on school psychologists
reported that higher levels of burnout were associated with high competitiveness and
egocentricity and low levels of extraversion and conscientiousness. The study
reported that psychologists effecting with burnout reported greater dissatisfaction
50
towards their professional roles. The study highlighted that personality variables
relate significantly to burnout more than demographic and work condition variables.
Literature review indicates that some preliminary exploratory researches have
been carried out in Southeast Asia. For instance, Sahu and Misra (2004) assessed
personality hardiness of 240 teachers from Lucknow District (India) as negatively
related to burnout. In Pakistan, Basir (2006) attempted to find out the association of
Big Five personality traits with burnout among male college and university teachers.
The study found significant relationship with personality trait of extraversion with
emotional exhaustion, depersonalization, and personal accomplishment. The study
further reported that agreeableness and conscientiousness showed significant
relationship with composite score of burnout.
Several personal characteristics, including various personality traits have been
consistently related to organizational commitment. For instance, while exploring the
role of Big Five personality dimensions in explaining relationship between work
environment and organizational commitment, Armstrong (1996) noted that it is
reasonable to believe that strong commitment to work is likely to result in
conscientious individuals who may exhibit self directed effort to do the job, showing
regular attendance, prefer nominal supervision and demonstrate a high level of effort.
Extraversion was significantly related to affective, continuance, and normative
commitment; while neuroticism, conscientiousness, and openness to experience were
all significantly related to continuance commitment along with showing agreeableness
as significantly linked to normative commitment (see Erdheim, Wang, & Zickar,
2006; see also Meyer & Allen, 2006). Meyer and Allen (2006) pointed out the
scarcity of empirical evidence and stressed upon need to further explore the dynamics
of Big Five personality traits and commitment.
51
The aforesaid review of literature for examining the moderating role of
personality variables, particularly the Big Five model of personality for relationship
between work environment and outcome variables, revealed the need to expand
research in this direction. This provided a direction for specifying the objectives of
present study.
Demographic variables. Investigating the impact of organizational and
demographic related personal factors, studies have reported contrasting and diverse
findings when examined in context of particular social environments. For instance,
employees with high job status do report experiencing more positive work
environment (McCrae, Prior, Silverman, & Banerjee, 2007). Whereas, Margall and
Duquette (2000) reported that more positive perceptions of the work environment is
associated with less professional experience. Similarly, Farid (2001) reported that
variation in employees’ work experience explains differences in perception of
workplace autonomy. Studies have reported that differences in work environment
perceptions may attribute to departmental differences (Avallone & Gibbon, 1998;
Maloney, Anderson, Gladd, Brown, & Hardy, 1996; Straker, 1989). Among
demographic factors, employees’ age, education, and gender were found to be
significantly linked with certain dimensions of work environment (Maqsood &
Rehman, 2004). However, Imam (1993) did not reported any differences in perception
of overall work environment among different age groups and also with respect to
gender when he studied college teachers in Pakistan. Okoh (2007) reported that highly
educated and professional staff tended to have a more negative view of their work
environment. Maloney et al. (1996) reported that employees’ with high education
level reported a more positive work environment, probably because of more
52
supervisory responsibilities. Studies addressing gender differences in environmental
dimension highlighted that men and women perceive, evaluate, and react to their work
environment differently (Kirschenbaum, 1991; Weisberg & Kirschenbaum, 1993)
partly because of differences in their work experiences (Repetti, Matthews, &
Waldron, 1989). However, Phelan et al. (1993) found that professional women and
men perceived the work place similarly.
Interaction effects of work environment factors particularly work pressure and
supervisor support, and demographic and job related variables influence burnout
(Constable & Russell, 1986). However, Fejgin, Ephraty, and Ben-Sira (1995)
suggested that burnout is independent of personal variables and job related variables.
Similarly, Patterniti, Niedhammer, Lang, and Consoli (2002) suggested that
association between psychosocial factors and employees’ health is independent of
moderating effects of occupational grade, stressful occupational events, working
hours, physical workload factors, age, education, income, marital status, and stressful
personal events. Kim, Lee, and Kim (2009) reported that teachers in upper-grade
experience high burnout compared to their counterparts. Long (1993) suggested that
time or job duration play a role in employee well being. In assessing work
environment and burnout relationship, comparisons on basis of public and private
sector organizations is an important consideration (Kim, 2011). A systematic
literature review on burnout among university teaching staff (Watts & Robertson,
2011) emphasized that research evidence is lacking for comparative studies across
different sectors. This review paper shared the comparable nature of burnout studies
on university teaching staff while generalizing findinlthcare sector and with teachers
of school level.
53
Examining the relationship between demographic factors and burnout
experienced by teachers, younger professionals tend to have higher burnout scores
than do older professionals (Russell, Atmaier, & Van Zelen, 1987; Watts &
Robertson, 2011). Ever, Tomic, and Brouwers (2004) suggested that teachers' age
may significantly link with their experience of personal accomplishment. Interaction
effects of age and experience of workplace situation with co-workers cohesion and
supervisors support has found to exert impact on burnout (Turnipseed, 1998).
Examining the direct effects of demographic factors, emotional exhaustion is
associated with employees’ age (Hochwälder, 2007). However, Armelius and
Jeanneau (2000) found that age and gender had no effects on burnout. Recently, it was
reported that age, marital status, job experience, education background and
satisfaction with income among teachers are significant factors explaining variation in
burnout (Luk, Chan, Cheong, & Ko, 2010). Haque and Khan (2001) reported that
within human service professionals in Pakistan, employees’ age and job experience
had found negatively linked with personal accomplishment and showed positive
relationship with depersonalization. Wilber and Specht (1994) reported that
employees’ education is able to produce a considerable variance in personal
accomplishment. Moghadam and Tabatabaei (2006) reported that teachers’ level of
education showed positive relationship with burnout scores.
Studies examining the impact of gender on burnout have revealed contrasting
findings. Recent study by Yavuz (2009) reported higher level of depersonalization
among male teachers compared to female teachers. Sahu and Misra (1996) reported
that female teachers are more vulnerable to emotional exhaustion and
depersonalization, but not to personal accomplishment as compared to males. Later,
on, Sahu and Misra (2004) reported non-significant impact of gender on three
54
dimensions of burnout. Moghadam and Tabatabaei (2006) reported high burnout
among male teachers and employees of the education organization compared to
females. Watts and Robertson (2011) suggested that generally male university
teachers endorse high on depersonalization, whereas female teachers reported high
emotional exhaustion. Seltzer and Numerof (1986) reported that married individuals
reported lower levels of burnout than those who were single. Unmarried teachers with
graduate level education, less teaching experience and lacking in social support were
more burned out (Kim-Wan, 1991). Haque and Sohail (1997) found that age and
marital status was significantly correlated with personal accomplishment dimension
of burnout among younger employees.
Among potential influential demographic factors influencing the
organizational commitment, job duration has often been used as a surrogate measure
of continuance commitment (Meyer & Allen, 1984). The study of Fresko, Kfir, and
Nasser (1997) reported that job experience of teachers has found to be negatively
associated with commitment. Job status was found positively related to affective and
normative forms of commitment and negatively related to continuance commitement
(Meyer et al., 1993). Clarke and Iles (2000) treated gender, age, ethnicity, marital
status, domestic care responsibilities, disability, management level, and work hours as
moderating variables in predicting the outcomes of the work environment. The study
indicated that gender and management level influenced both the diversity as indicator
of work environment and commitment related outcome variables. The study done by
Shirbagi (2007) provided insight to assess university teachers’ organizational
commitment while considering comparison across different universities on some
meaningful distinction. In examining work attitudes particularly organizational
55
commitment, Boardman, Bozeman, and Ponomariov (2010) suggested that
comparison of public and private sector is an important consideration.
In examining the moderating role of demographic variables, researches have
shown consistent as well as varied pattern of findings. In most of researches age
(Angle & Perry, 1981; Brimeyer, Perrucci, & Wadsworth, 2010) and job duration has
been consistently shown as positively linked with organizational commitment
probably because of having better autonomy and control on job with passage of time
(Brimeye et al., 2010). Based on two dimensional view of organizational
commitment, Mayer and Schoorman (1998) suggested that organizational tenure,
retirement benefits, education, and age were more strongly associated with
continuance commitment compared to value commitment. However, contradictory
findings have also been observed. For example, Chughtai and Zafar (2006) reported
that age, tenure, marital status, and the level of education were not significantly
contributing in explaining variance in organizational commitment. Findings of a
meta-analytic study (Mathieu & Zajac, 1990) highlighted negative relationship
between education and commitment with stronger relationship found for attitudinal
commitment compared to calculative commitment. Whereas, finidngs of a study
(Grau, Chandler, Burton, & Kolditz, 1991) suggested that less educated and older
epmloyees reorted more institutional loyality. Findings of a study (Mishra &
Srivastava, 2001) reported that age and education level have accounted for 53% of the
variance in employees’ commitment.
Interestingly, gender differentiation in organizational commitment have come
up with findings that women tend to be less committed to their jobs than men
(Karrasch, 2003) may be with this conceotion that women place greater emphasis on
family roles than men (Dodd-McCue & Wright, 1996). Researchers who appear to be
56
focused on the continuance component of commitment have often argued that women
are more committed to organizations than men (Stewart, Bing, Gruys, & Helford,
2007; Wahn, 1998). The finding of met-analytical research (Aven, Parker, &
McEvoy, 1993) has reported no gender differentiation in terms of affective
commitment. Researches (e.g., Meyer et al., 2002; Riketta, 2005; Thorsteinson, 2003)
have found that there were no gender differences in organizational
commitment. Further, some studies found that even when there was a mean difference
in organizational commitment between men and women, there was no gender effect
when predicting organizational commitment using control variables such as age, job
level, educational, job and organizational tenure (Abdulla & Shaw, 1999; Vander,
Bossink, & Jansen, 2003; Ngo & Tsang, 1998). In explaining modertaing role of
gender moderates, Witt (1989) found that relationship between psychological climate
and organizational commitment does change across gender as stronger for men than
for women, with the relationship being positive in both cases. Stewart, Bing, Gruys,
and Helford (2007) explored moderating effect of gender and reported that task-
oriented dimension which reflects in form of organizational support was a significant
predictor of affective commitment for men employees; whereas, the relationship-
oriented dimension which reflects in form of workplace recognition was a significant
predictor of affective commitment for women. Marital status has emerged as a
consistent predictor of organizational commitment. Findings reported by John and
Taylor (1999) and Tsui, Leung, Cheung, Mok, and Ho (1994) reported that married
people were more committed to their organization than unmarried people because of
having more family responsibilities which requires more stability and security in their
jobs; and therefore, they are likely to be more committed to their current organization.
57
Aforesaid description highlights that a consistent pattern of findings with
respect to impact of demographic variables in generally missing. The nature of
findings varies in different studies. Therefore, investigating the influence of possible
demographics adds in thorough understanding of the dynamics of work environment
and outcome relationships.
The literature review presented in this chapter highlights that work
environment may serves as potential causal factor in developing and determining
employees’ attitudes. The review highlights that burnout and organizational
commitment are important attitudinal variables that influences employees’ as well as
organizational well being particularly within working group of teachers. This
eventually serves as the basis for specifying the research question of present research
to explore which dimensions of workplace environments are contributive factors in
experience of burnout and for organizational commitment among teachers. Given that
gaps in existing knowledge in line with research question have identified in literature
review; it’s well considering to explore and build empirical evidence in establishing
work environment and outcome relationships through explaining the moderator
influences as well. More specifically, research evidence in case of work environment
and burnout has not well established among teachers in Pakistan. Focusing on
organizational commitment as an outcome variables with respect to multidimensional
approach of the construct is lacking empirical evidence in both West and Pakistan.
Literature highlights that in explaining the work environment and outcome
relationship, exploring the moderating role of dispositional factors is needed to
establish. The research question is a timely topic especially for our country where
strong academic research in occupational psychology is needed as a base for leading
to policy level decisions.
58
Rationale of the Study
Research has provided substantial empirical support to explore the impact of
work environment facets on employees’ behavior and attitudes. Moos (1988), one of
the dominant proponent in literature of pioneering research on environment, explains
that employees are receptive and reactive to influence of the social settings in which
they are involved. In past three decades research on work environment has tried to
conceptualize and explore work environments, its various facets, moderating
variables, and more specifically impact of these variables upon employee and
organizational related outcomes (Parker et al., 2003). The theoretical
conceptualization of work environment has underpinning in major perspectives of
organizational psychology including human relation, sociotechnical, and the social
information processing approaches (Moos, 1986). The model (Moos, 1994)
explaining the interplay of work environment, personal system, and organizational
and personal outcomes serves the theoretical basis of the present study.
Among various models explaining psychosocial work factors and outcomes,
Karasek’s (1979) job-demand-control support model and Siegrist’s (1996) effort-
reward imbalance model more specifically are oriented towards limited aspects, e.g.,
looking into outcomes as a result of the process of expectations of positive outcomes
and an imbalance of reward and effort (Lindblom, Linton, Fedeli, & Bryngelsson,
2006). Vanroelen, Levecque, Moors, Gadeyne, and Louckx (2009) criticized that that
demand control model is restricted in assessing range of occupational stressors. In
comparison, Moos’ approach carries much broader view of constructs explaining
work environment (Moos, 1990) providing relatively a big range of psychosocial
factors of work environment. Moos’s model stimulated a massive body of research
59
and most of studies investigating the impact of outcomes of work environment have
utilized Moos’ model (Chan & Huak, 2004; Goddard et al., 2006; Hemingway &
Smith, 1999; Karsh, Booske, & Sainfort, 2005; Long, 1993; Salyers & Bond, 2001;
Westerman & Cyr, 2004; Westerman & Yamamura, 2007; Wilber & Specht, 1994;
Wu, 1998). One of the advantageous of testing propositions of Moos’s model in our
cultural context is based on the assumption that the model extends to assess somewhat
a wide range of psychosocial factors assuming to offer a more comprehensive view of
the intended work environment. Keeping in view the significant role, the present
research has postulated its assumption on Moos’ theoretical model.
Within academic settings, research trend in West seems to have focus on
varied samples, i.e. the perceptions of teachers’ and principals’ of their workplace
(Docker, Fisher, & Fraser, 1989); schools advisory councils (McClure & De Piano,
1983); school psychologists in urban public schools (Lusk et al., 1983); students in
university settings (Cotton, Dollard, & de Jonge, 2002); faculty in medical and dental
schools (Berry, 1994; Lubbert, 1995); and university faculty members (Corley, 2005).
Substantial researches on work environment have focused on assessment, monitoring,
and improving of academic settings; however, the situation is quite different in
context of Pakistan. A pioneering research conducted by Imam (1993) proposed the
need of modifications in existing work environment of school teachers. Another study
involving private and public sector universities had focused on assessment of the
work environment and its relationship with job satisfaction, job morale, turnover, and
job stress (Rehman & Maqsood, 2008). Other studies had focused on assessment of
work environment involving banking sector (Farid, 2001; Hayat, 2004),
telecommunication sector (Maqsood & Rehman, 2004), industrial (Haq & Sheikh,
1992) and health care organizations (Khan, 1999). In context of Pakistan, the scarcity
60
of researches particularly on psychosocial work environment and its outcomes within
academic settings involving teachers of higher education provided rationale to
conduct the present study.
In understanding the interplay between work environment and its outcomes
among working group of teachers, burnout as a negative attitude is considered as an
important aspect of psychological health when examining work environment factors
as causal factors (Lindblom, Linton, Fedeli, & Bryngelsson, 2006). According to a
national study of United States done in 1983 by Gmelch, Wilke, and Lovrich (as cited
in Benjamin, 1987), stress faced by faculty in university settings seems to be a
discipline-specific problem. Faculty members involved in higher education in
university settings may be considered at high risk for burnout because the demanding
job functions of education, teaching, and research (Benjamin, 1987). Their daily work
life routine demands heavy interaction (Wood & McCarthy, 2002). Enhancing work
environment aspects, e.g., coworker cohesion, supervisor support, autonomy, and
clarity of job procedures help to decrease the prevalence and intensity of burnout
(Turnipseed, 1994). In Pakistan, studies have supported burnout as an important
concern reported by working group of teachers (Bashir, 2006; Khurshid, Butt, &
Malik, 2011; Qureshi & Hijazi, 2006). Research evidence highlighted that work
environment factors explain variation in burnout (Adali et al., 2003; Escribà-Agüir,
Martín-Beena, & Pérez-Hoyos, 2006; Goddard et al., 2006; Hochwälder, 2007;
Langballe, Innstrand,; Levert, Lucas, & Ortlepp, 2000; Munir, 2005; Salyers & Bond,
2001), and has also suggested by recent researches (Aasland, & Falkum, 2011; Boyas
& Wind, 2010).
While assessing outcome variables of work environment, it seems logical to
assess employees’ attitude namely organizational commitment. According to current
61
perspectives of work environment, commitment as a critical employee attitude is
needed to be managed for effective functioning of the organization (Gummer, 2001).
Studies have suggested association between work environment facets and
organizational commitment (Clarke & Iles, 2000; Grau, Chandler, Burton, & Kolditz,
1991; Stewart, Bing, Gruys, & Helford, 2007). In Pakistani context, studies on work
environment and its outcomes have found to be limited. One such study on bank
employees (Hayat, 2004) reported non-significant relationship between work
environment and organizational commitment. Moreover, this study was limited in
scope because it focused on the unidimenional model of organizational commitment.
Literature review identified gaps in existing literature when it comes to explicit the
relational dynamics between work environment variables and multidimensional view
of the organizational commitment. This provided the rationale in direction of need of
research exploring the predictive impact of work environment variables with different
domains of commitment particularly considering the widely used model of Allen and
Meyer (1990). Therefore, it seems meaningful to explore relatively less explored
outcome variable of organizational commitment particularly the facet based approach
in context of academic work environment. In work environment literature, research
evidence has treated organizational commitment as one of the outcome variables.
Therefore, present study specified organizational commitment as an outcome variable
rather than as a predictor or consequence of the burnout. Moreover, specification of
organizational commitment as outcome variable was built on the argument to address
the literature gap in environment and commitment relationship.
The aforesaid researches provide substantial ground to hypothesize that work
environment effects employee and organizational related outcomes e.g., burnout and
organizational commitment. According to Moos’s model of work environment
62
(1986), such general effects may be moderated by personal factors. Teachers’
experience of burnout and their commitment to the organization may be affected if
their personal disposition is not supported by the environment of the workplace.
Literature review carried out for present study pointed out that further investigation is
required to explore the dispositional basis of employees’ attitudes. Very few
researchers have taken into account the role of Big Five Factor model of personality
(extraversion, agreeableness, emotional stability, conscientiousness, and openness)
while explaining the link between work environment and burnout (Dodd & Jacobs,
2003: Eastburg et al., 1994). An important concern pointed out by Toppinen-Tanner,
Kalimo, & Mutanen (2002) highlighted the importance of considering dispositional
factors in models of organizational factors predicting burnout due to scarcity of
researches in this line. Present study is noteworthy because it addresses the
researchers’ concern based on meta-analytical studies as pointed out in a recent article
by Johnson and Chang (2008), that studies should move towards exploring interactive
relationship by considering moderator variables instead of only focusing the bivariate
relationships. It was also reported by mainstream contributors (Meyer & Allen, 2006),
about the scarcity of literature in linking Big Five model of personality with
organizational commitment.
A meta-analytic review (Parker et. al., 2003) emphasized the need to explore
the moderating effects considering organizational related personal factors in
explaining the association between work environment and its outcomes. Among
demographic variables, research evidence is available to investigate the moderating
link of organizational related and demographic variables while establishing the link
between work environment with burnout (see e.g., Turnipseed, 1994; Wilber &
Specht, 1994) and with organizational commitment (see Clarke & Iles, 2000). Leka
63
and Houdmont (2010) pointed out that individual differences and demographic factors
has received less attention by researchers say around 5% of total in published research
themes in area of occupational health psychology. Based on observation, present
study intends to explore the possible moderating impact of teachers’ involvement in
other paid jobs. However, no study was found for this particular variable. Literature
review reveals variation in specifying personal variables. For present study,
demographic, organizational related, and personality factors are treated as moderating
variables. Though, personal variables are too many, but primarily the reason was to
assess them as a mean to comprehensively evaluate the possible influential factors.
Instead of omitting personal variables, researcher stance is to carry and elaborate this
work in form of article publications.
It is to be noted that the model used for predictor variable is of
multidimensional nature. The review has mentioned in detail about the contribution of
sub-facets of work environment model. Though the number of predictor variables to
be examined are large; however, limiting the predictors may not be appreciated as the
study is focusing on the multidimensional model of work environment. This seems
particularly more relevant to explore interrelationships among each sub-dimension in
cases where empirical evidence is needed to generate. Even, in pilot results, if only
few predictors emerge; still decision to limit predictors might not be appreciative
primarily due to small sample size involved in the pilot study. Since, constructs of the
study are of multidimensional in nature; therefore, evaluating sub-dimensions
provides in-depth understanding. Moreover, the study will focus on formulating non-
directional hypotheses. This relates to the rationale that study objectives should open
for exploration rather than strict directional one, especially where research evidence is
lacking in particular cultural context. Further, literature highlights that the directions
64
of research studies on work environment assessments are in varied directions;
therefore, the present study is based on stance to formulate non-directional
hypotheses.
Another important aspect in conducting any research is to find out that
measures should be best fitted for intended sample as a way to evaluate the cross-
cultural applicability. Testing the factorial validity of study measures seems to have
an association with evaluating the psychometric feasibility of using scales in English
when their first language may not be English. This is especially important when
respondents may be operating under different cultural norms from some Western
countries. For present study, it is well understood that the sample of university
teachers at higher education level can understand English. As their own medium of
studies during higher education (Masters, M.Phil or Ph.D) is English and their
required mode of teaching instructions to students and syllabus is in English. It is of
more importance in case of adopting western models, even though having empirical
evidences of cross-cultural soundness, it would be logical to test the factorial structure
of these theoretical models using confirmatory factor analysis. Examining the
dominant factorial structure of study measures on sample of university teachers of
Pakistan is helpful to establish the meaningfulness of measures to be used in the
study.
The propositions of present study are also based on the established differences
found in work values of individualistic and collectivistic cultures. Based on
Hofstede’s work (2001), it may be assumed that within our collectivistic culture, the
work environment and outcomes relationship might yield different interpretation. This
is also supported by a meta-analytic review study which highlighted that majority of
researches in area of work environment perceptions and outcomes had employed
western cultures particularly the individualistic cultures (Parker et al., 2003), which in
65
turn may lead to assumption that collectivistic cultures could carry more stronger
effects. Henceforth, present study is of preliminary nature in Pakistan on basis of
paucity of academic researches particularly in industrial psychology as of the fact that
literature indicates majority of researches involving western samples. This especially
is more evident, when it comes to examine the construct validity of work areas related
constructs in particular cultural context (Tayyeb & Riaz, 2004). Another strong
impetus for doing present study was based on the fact that in Pakistan, there is strong
need to strengthen the academic research particularly within field of occupational
psychology partly due to realization of growing concern of its implications for human
resource management system. Moreover, in developing country like us where the job
market of industrial psychologists is not much explicit with defined job titles, there is
strong need to link the academic research with industry. For instance, conducting in
depth studies on psychosocial characteristics of workplace considering the host of
organizational and individual based factors will be helpful in deducing implications
for the human resource management systems of the higher education providing
institutes and universities. Within educational organizations, the need to utilize the
research based objective information is imperative to cater the work conditions
conducive to employees’ preferences. The present study is an effort to put the
academic management or policy makers in a position to realize and at least think
about their working culture in terms of psychosocial factors carrying power to
influence behavioral outcomes. The study may contribute positively to promote the
concern that research based objective information is conducive for the effective
management of employees.
The rationale of present study highlights that based on a well-established
theoretical framework (Moos, 1994), the present study aimed to address gaps in
existing literature evident through limited empirical support. This is especially more
66
evident in case of exploring the three dimensional model of organizational
commitment with work environment facets both in West as well as in Pakistan.
Moreover, investigating work environment and burnout relationship is lacking
empirical support in context of Pakistan particularly on working group of teachers.
Evaluating the workplaces and investigating its outcomes particularly within
academic settings is an important topic in terms of its scope in promoting research
within the field of Organizational/ Occupational Psychology, which as reported by a
recent study (Zadeh & Ghani, 2012), the field comparatively is of not well flourished
status in Pakistan. It should be noted that investigating the three facet model of
organizational commitment with work environment is of exploratory nature.
Furthermore, investigating the moderating interplay of dispositional factors which so
far was remained open for investigation strongly adds in qualifying the study as
indigenous one. Moreover, demographic and organizational related variables will be
examined for thorough assessment of work environment and outcome relationship.
The aforesaid aspects of study rationale credited the study as noteworthy contribution
in academic knowledge.
The theoretical conceptualization of the study is depicted below as Figure 2.
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Demographic/ Personal Factors Sector, hierarchical status, job duration, departmental differences, involvement in side jobs, age, education,
gender, marital status
Criterion/ Outcomes Burnout Emotional Exhaustion Depersonalization Personal Accomplishment Organizational Commitment Affective Commitment Continuance Commitment Normative Commitment
Predictors Work Environment
Involvement Peer Cohesion Supervisor Support Autonomy Task Orientation Work Pressure Clarity Managerial Control Innovation
Physical Comfort
Figure 2: Theoretical conceptualization of the present study
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Chapter II
OBJECTIVES, HYPOTHESES, OPERATIONAL DEFINITIONS,
AND RESEARCH DESIGN
Objectives of the Study
The study intends to pursue following objectives of the study.
1. To establish the psychometric properties of the measures (including reliability
and validity indices) on sample of University teachers in context of Pakistan.
2. To examine the factor structure of measurement models of study constructs to
see how well data supports the existing factor structure of the constructs.
3. To investigate the predictive relationship of perceived work environment
facets with employee and organizational related outcomes including burnout
(emotional exhaustion, depersonalization, and personal accomplishment) and
organizational commitment (affective, continuance, and normative
commitment).
4. To explore the moderating role of personality factors (extraversion,
agreeableness, emotional stability, conscientiousness, and openness) in
explaining predictive relationship of work environment with burnout and
organizational commitment.
5. To explore the moderating role of job related and demographic factors (public
vs. private sector, hierarchical status, job duration, departmental differences-
natural vs. social sciences, involvement in side jobs, age, education, gender
and marital status) while assessing the predictive relationship of work
environment with burnout and organizational commitment.
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Hypotheses
The stance of non-directional hypotheses testing in present study looks into
exploring how data guides about the conceptual relationships of variables instead of
relying too heavily on directional ones within the consideration that research
methodology happens to be in a state of flux (see Davis & Smith, 2005). Below
mentioned are the non-directional declarative statements tested to investigate the
predictive relationship between work environment and its outcomes.
1. There exists predictive relationship in relative effects of perceived facets of
work environment on burnout (emotional exhaustion, depersonalization, and
personal accomplishment).
2. There exists predictive relationship in relative effects of perceived facets of
work environment on organizational commitment (affective, continuance, and
normative commitment).
3. Personal variables (e.g, dispositional, job related and demographic factors)
interact with work environment perceptions influencing the burnout and
organizational commitment.
Operational Definitions of Variables
Work Environment. Work environment is considered as the immediate
operating social work environment and refers to the psychosocial characteristics of a
work setting characterized with the way in which individuals in a setting relate to each
other (the relationship domain), the personal growth goals towards which a setting is
oriented (personal growth or goal oriented domain), and the amount of structure and
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openness to change that characterize it (system maintenance and change domain
(Moos, 1994).
Work Environment was assessed using Work Environment Scale (WES:
Moos, 1994), which measures ten subscales which primarily fall into three broader
dimensions namely: (1) Relationship Dimensions, (2) Personal Growth Dimensions
and (3) System Maintenance and Change Dimensions. An above average score on the
overall sum of the subscales could be considered “positive”, whereas, a below average
score could be considered “negative”. Furthermore, high score on each of the
dimension will indicate high endorsement of employees on respective dimension of
the work environment.
The relationship dimensions. The relationship dimension is defined as the
nature and intensity of personal relationship in the environment and further taps the
concepts like involvement, peer cohesion, and supervisor support.
Involvement: The extent to which employees are concerned about and
committed to their job.
Peer Cohesion: How much employees are friendly and supportive of one
another.
Supervisor Support: The extent to which management is supportive of
employees and encourages employees to be supportive of one another.
Personal growth dimensions. The personal growth dimension is being defined
as the opportunities in the environment for personal growth and development and
explained by concepts like autonomy and task orientation.
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Autonomy: How much employees are encouraged to be self-sufficient and to
make their own decisions.
Task Orientation: The degree of emphasis on good planning, efficiency, and
getting the job done.
Work Pressure: The degree to which high work demands and time pressure
dominate the job milieu.
System maintenance and system change dimensions. The system
maintenance and system change dimensions is defined as the extent to which the
environment is orderly and clear in its expectations, maintain control, and is
responsive to change. This dimension tapes concepts of clarity, managerial control,
innovation, and physical comfort.
Clarity: The extent to which employees know what to expect in their daily
routine and how explicitly rules and policies are communicated.
Managerial Control: How much management uses rules and procedures to
keep employees under control.
Innovation: The degree of emphasis on variety, change, and new approaches.
Physical Comfort: The extent to which the physical surroundings contribute to
a pleasant work environment.
Burnout. Educators’ burnout is defined as the subjects’ responses to the three
subscales (Emotional Exhaustion, Depersonalization, and Personal Accomplishment)
of the Maslach Burnout Inventory- Educator Survey (Maslach, Jackson, & Leiter,
1996). Emotional Exhaustion includes feelings of being emotionally overextended
and exhausted by one's work. Depersonalization refers to an unfeeling and impersonal
response toward recipients of one's service, care treatment, or instruction. Personal
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Accomplishment means feelings of competence and successful achievement in one's
work and a reduced sense of personal accomplishment from the job is an indicator of
educators’ burnout.
The high, moderate, and low levels of burnout were computed using median as
cut-off score.
Organizational commitment. Meyer and Allen (1991) defined organizational
commitment as a three component construct namely affective commitment,
continuance, and normative commitment. The affective component represents
attachment to the organization because of a sense of unity and shared values and
employees’ willingness to remain in relationship. The continuance Component
represents a perceived cost of leaving an organization. The normative Commitment
develops as a result of socialization experiences and emphasizes the obligations of
remaining in the organization.
The assessment of affective, continuance, and normative commitment was
based on measure of Organizational Commitment Questionnaire (Allen & Meyer,
1990). Median was taken as the cut off score; where, the scores falling above the
median were considered as high score, showing high organizational commitment and
vice-versa.
Personal variables. Educators’ personal variables include dispositional, job
and demographic related factors.
In present study, the typological approach- Big Five personality model of
Saucier (1994) based on Goldberg’s approach (1992) provided theoretical base for
assessment of personality. The model comprises five relatively independent
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dimensions: Extraversion, Agreeableness, Emotional Stability, Conscientiousness,
and Openness.
Extraversion. It is the quantity and intensity of interpersonal interaction,
activity level, and capacity of joy. A person scoring low on extraversion subscale of
the Mini-Marker Personality Inventory would be an Introvert who is less friendly,
prefers to be alone, and is more reserved.
Agreeableness. Agreeableness is the quality of interpersonal orientation along
a continuum from compassion to antagonism in thoughts, feelings, and actions. A
person scoring high on the agreeableness subscale would be altruistic, sympathetic,
and cooperative.
Conscientiousness. It describes the individual’s degree of organization,
persistence, dependability, and motivation in goal-directed behavior in a
conscientious person. The contrasting qualities of the trait marked by low scores on
the subscale give lackadaisical, impractical, and sloppy people.
Emotional Stability. It describes individual prone to psychological distress,
unrealistic ideas, and maladaptive coping responses. A high score on this trait would
be relaxed, secure, and self-satisfied.
Openness. Proactive seeking and appreciation of experience for its own sake,
tolerance for and exploration of the unfamiliar is openness. This is also considered as
a measure of intelligence. High scorers reporting high openness have the qualities of
being imaginative, creative, and introspective.
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Organizational and demographic related personal variables. These
continuous and nominal variables included information related to employees’
organizational and demographic related personal factors. Below is the detail of each
variable.
Public vs. Private Sector. In education system of Pakistan, public sector
universities providing higher education are fully governed under regulations of the
Government. The private sector universities are playing a dominant role in providing
education and usually operate under private sector regulations. Therefore, present
study focused on examining the sector wise differences.
Hierarchical Status. In both public and private sector universities, the hiring
of teachers used to done against a standard hierarchical system with four different job
levels namely lecturers, assistant professors, associate professors, and professors. The
study divided the sample into two groups. The first group (entry level rank) on basis
of entry level status of teachers includes lecturers. The second group (high rank)
includes teachers of next promotional cadres, e.g., assistant professors, associate
professors, and professors.
Job Duration. The length of the service of employees was considered in terms
of job duration in the present organization.
Faculties. Mainly universities are involved to provide education in natural and
social sciences. However, certain universities are specialized in certain discipline as
well. To examine the impact of belongingness to natural vs. social disciplines, the
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sample information related to affiliation with different departments was divided into
two major groups of faculties namely natural and social sciences.
Side Jobs. It was also in observation that teachers used to involve in certain
job assignments outside of their regular job. For instance, hiring teachers as Visiting
Faculty is being widely used both in public and private sector universities. Other than
paid teaching assignments, certain individuals may be involved in running their own
personal private type education or tuition systems or some else kind of paid or income
generating nature of job. Therefore, based on this observation, it was assumed that
involvement or non-involvement in paid side jobs could have an impact. Therefore,
based on this information, sample was divided in two groups; those who reported
involvement in paid side jobs and those who reported non-involvement in paid jobs.
Age. Information related to this continuous demographic variable was taken.
For analysis purpose in main study, this variable was also converted into nominal
variable by splitting sample into two groups. The first group represents younger group
of participants and the second group represents older participants.
Gender. Sample was divided into groups of men and women participants to
examine the impact of gender.
Education. For entry level teachers under the hierarchical status as lectures,
the 16 years of education (masters degree) is a basic requirement. However, certain
universities prefer to hire lectures having research degree (M.Phil or MS with 18
years of education). For hiring assistant professors, research degree and certain level
of experience is the basic criterion. For associate professors and professors, doctorate
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degree with certain level of experience and publications is a basic criterion. Therefore,
education serves as an important indicator in teachers’ hiring and promotional system.
To examine the impact of education, the sample was divided into two groups. The
first group includes participants having masters degree and the second group
comprises participants with research degree or doctorate level education.
Marital Status. This demographic information categorized respondents in
groups of married vs. single participants.
Research Design
Present study was conducted in two phases. The phase I was conducted as
pilot study by involving a sample of university teachers (N = 102), aimed to determine
preliminary psychometric issues involving the reliability and validity indices of the
measures used in current investigation for our indigenous population. Further, the
pilot study step aimed to see the pattern of results for basic hypothesized predictive
and outcome relationship of study variables. The phase II of present research was
conducted as main study involving sample of university teachers (N = 426). The step I
of the main study aimed to examine the factorial validity of study measures to see
how well the sample of present study supports the existing structure of the constructs.
After scrutiny of study measures merged through confirmatory factor analyses, the
data was then subject to further analyses under step II of the study. In step II,
specifically, the hypothesized predictive and moderated relationships between study
variables were examined.
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Chapter III
PHASE I: PILOT STUDY
Pilot Testing of Study Measures and Preliminary Testing of the Model of
Work Environment and Outcomes
The phase I of the study (i.e., pilot study) was conducted following two steps.
Step I of study aimed to achieve the following objective.
1. To examine the psychometric characteristics of the measures of the study i.e.,
estimates of reliability (Cronbach’s alpha reliability) and validity (inter-scale
correlations) of study measures for the sample of current study.
Step II of the study aimed at achieving the following objectives:
2. To investigate the predictive relationship of work environment with burnout.
3. To investigate the predictive relationship of work environment with
organizational commitment.
Method
Participants
The participants of pilot study comprised of full time permanent teachers of
public and private sector Universities of Pakistan. They belonged to the universities
and post-graduate colleges of Punjab Province of Pakistan; located in the cities of
Rawalpindi, Islamabad, and Lahore. In order to select comparable public and private
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sector universities, the ranking of the Universities by Higher Education Commission
(HEC) of Pakistan was followed. This ranking system is based on assigned scores to
different universities based on the performance in different functional areas of output
(e.g., number of pass out graduates, publications, research output, etc). Since, allotted
scores were varied in magnitude; therefore, as an inclusion criteria only those
universities were selected which were showing above average scores (as per based on
the range of allotted scores to the universities). (i) full time permanent of universities
employed on the position of; lecturers, assistant professors, associate professors, and
professors; (ii) pay scale associated with rank, e.g., system of basic pay scale (BPS)
applicable in government sector. As part of initial try out study for examining the
feasibility of random sampling, return rate of the questionnaire by participants was
very low up to 10%. A very important factor of low response rate remained the lack
of cooperation of selected participants. Therefore, it was decided to adopt the non-
random convenience/ opportunity sampling procedure as a mean to enhance the
response rate.
In total, a sample of 150 university teachers was approached, who belonged to
six universities of Punjab province (from Rawalpindi, Islamabad and Lahore
provinces). Researcher contacted the teachers individually and briefed about the
objectives of the study. Only those participants were selected who showed their
consent to participate in the study. The participants were handed over with a
questionnaire pack (including informed consent form, demographic information sheet,
Work Environment Questionnaire, Maslach Burnout Inventory- Educators Survey,
Organizational Commitment Questionnaire, and Mini Marker Set). With response rate
of 70%, 105 university teachers returned the completed questionnaires; among these
three incomplete questionnaires were dropped. The sample of this phase of study
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comprised of 102 teachers of universities of Punjab, who showed their willingness to
participate in to study and completed the written consent form. For detailed
descriptive profile of the sample, see Appendix I.
Measures
For nominal data related to respondents’ personal job related and demographic
information, a demographic sheet was used. This includes information related to
organizational variables (sector, hierarchical status, job duration in the present
organization, Natural vs. Social Sciences department, and teachers’ Involvement in
other paid jobs) and demographic variables (age, gender, education and marital
status). The respondents were asked to report the required information.
Following is the detailed description of the measures used in the study.
The Work Environment Scale (WES). Employee’s perception of work
environment was measured by using English version of the Work Environment Scale
(WES; Moos, 1994), a very popular well-known measure used for the assessment of
employees’ perception of their work environment and operating institutional attributes
or psychosocial properties of environments. Reason for using English version was that
that English is a medium of instructions at university level and all communication and
reading material is in English, therefore the researcher’s decided to use the original
version of WES, which is already being used in original form in earlier studies
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(Maqsood & Rehman, 2004; Rehman & Maqsood, 2008) having quite satisfactory
validity indices.
WES measures how people perceive the environment and how these
perceptions influence their behavior. WES comprises ten subscales comprises nine
items in each (see Appendix D for details of corresponding items in each
subscale/dimensions). Sample items for each subscales are as follows: “The work is
really challenging” (for Involvement), “People go out of their way to help a new
employee feel comfortable” (for Coworker Cohesion), “Supervisors usually
compliment an employee who does something well” (for Supervisor Support),
“Employees have a great deal of freedom to do as they like” (for Autonomy),
“Getting a lot of work done is important to people” (for Task Orientation), “There
always seems to be an urgency about everything” (for Work Pressure), “things are
sometimes pretty disorganized” (for Clarity), “People are expected to follow set rules
in doing their work” (for Managerial Control), “Doing things in a different way is
valued” (for Innovation), “Work place is awfully crowded” (for Physical Comfort).
The participants of pilot study were administered original WES, this has
dichotomous (True/ False) response format; score ‘I’ is assigned to true items and ‘0’
to false items. The score of WES ranges from 0-9 on each nine subscales and total
score ranges between 0-90 on the aggregate. Items numbers are reverse scored (see
Appendix D). The total score for each subscale was obtained by summing the scores
on each item; the total score on subscales except for work pressure and managerial
control were collectively summed up. For work pressure and managerial control,
scores for each subscale were reversed by subtracting each of the subscale scores
from 9. Finally, adding the total of reverse scores for these two subscales to the total
scores of other subscales yielded the overall score. As regards interpretation of the
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total sum of scores on WES, Moss’s recommendation was followed which explains a
“positive” work environment score can be designated to an above average score on
the overall sum of the subscales; whereas, a below average score could be considered
as “negative” (R. H. Moos, personal communication, March 10, 2009).
Moos (1994) has reported internal consistency reliabilities for 10 subscales of
WES ranged from .69 to .86 (N = 1045), and for subsequent sample (N = 742) range
from .68 to .82 Moos (1994) reports WES as a cross-culturally valid instrument. The
use of WES in present study is based on the evidence of demonstrated use in studies
in context of Pakistan. However, for present study, no committee based assessment
procedure was used to evaluate the face validity of the instrument to be used for
sample of teachers. Studies conducted in Pakistan using English version of WES in
various organizational settings yielded satisfactory evidence. For instance, Rehman
and Maqsood (2008) reported satisfactory estimates of reliability (Cronbach’s Alpha
coefficients for WES’s total scores (.78) and subscales of relationship dimension (.71)
personal growth (.52) and system maintenance change (.75) on a sample of 500
Pakistani university teachers. The study also reported the satisfactory ‘construct
validity’ of WES. Maqsood and Rehman, (2004) reported the satisfactory internal
consistency reliability estimates of WES on a sample (N = 130) of service provider
telecommunication company. Munir’s study (2005) has also used the WES in source
language within academic settings.
The Maslach Burnout Inventory-Educators Survey. Assessment of
Burnout was performed using 22 items Maslach Burnout Inventory (MBI-ES)–
Educators Survey (MBI: Maslach, Jackson, & Leiter, 1996). This is a popular
measure frequently used in academic settings. The MBI-ES comprises of three
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subscales namely: Emotional Exhaustion (EE); Depersonalization (DP) and reduced
sense of Personal Accomplishment (PA). The emotional exhaustion subscale
(measured by item nos. 1, 2, 3, 6, 8, 13, 14, 16, 20) assesses feelings of being
emotionally over-extended and exhausted by one’s work. One example item for
emotional exhaustion is: “I feel fatigued when I get up in the morning and have to
face another day on the job”. The depersonalization subscale (measured by item # 5,
10, 11, 15, 22), measures an unfeeling and impersonal response toward recipients of
one’s service, care, treatment, or instruction assessed by items e.g., “I feel I treat some
students as if they were impersonal objects”. The Personal accomplishment (measured
by item # 4, 7, 9, 12, 17, 18, 19, 21) assesses feelings of competence and successful
achievement in one’s work with people assessed by items e.g., “I feel I’m positively
influencing other people’s lives through my work”. The inventory measures the
frequency of experiences of feelings related to each subscale using anchors ranged
from 0 (never) to 6 (always).
The possible score range on total MBI-ES is 0-88 and scores falling on and
above the median scores are considered as the cut-off scores. Some studies conducted
in Pakistan have reported yielded satisfactory internal consistency estimates of MBI.
Basir (2006) reported satisfactory estimates of reliability (Cronbach alpha coefficient)
for emotional exhaustion (.64) depersonalization (.34) personal accomplishment (.66)
and .61 for the total scores on MBI. Munir (2005) reported satisfactory estimates of
Cronbach’s alpha coefficient for emotional exhaustion (α = .79), depersonalization (α
= .62), and personal accomplishment (α = .71) on sample of university academic staff
of Library Sciences and Computer Sciences from a provincial university of Punjab
province of Pakistan. Substantial empirical evidence is available for satisfactory
validity estimates of the measure (Maslach, Jackson, & Leiter, 1996).
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The Organizational Commitment Questionnaire. For the assessment of
organizational commitment the measure developed by Meyer and Allen (1993) was
used. The OCQ aims to measure employee’s experience of organizational
commitment as three simultaneous mindsets encompassing; affective, continuance,
and normative commitment. Originally, the questionnaire comprises of 24 items
(Meyer & Allen, 1991) with eight items in each sub domain. Meyer, Allen, and Smith
(1993) later revised a six -item measure of normative commitment. Studies conducted
in Pakistan, have mainly used 22-item OCQ (see e.g., Hussain, 2004; Hussan, 2008;
Rashid, 2000). Meyer and Allen (2004) reported variation in number of items in using
the OCQ questionnaire as a way of modification for reducing scale length which
thereof is important to test through pilot test.
The affective commitment is related with emotion-based view of commitment;
this includes items like “I feel a strong sense of belonging to (name of organization)”.
The continuance component represents a perceived cost of leaving an organization
and represents calculative and exchanged-based view of commitment to the
organization. This includes item like “Too much in my life would be disrupted if I
decided I wanted to leave (name of organization) now”. The normative component is
based on feelings of moral obligations or responsibilities and this is considered to
developed as a result of employee’s socialization experiences and emphasizes
employees obligations of ‘remaining’ in the organization. For instance, one item
states that “It would be wrong to leave (name of organization) right now because of
my obligation to the people in it”.
In the present study we have used 22-item OCQ, it’s a five point Likert type
scale, items ranging from Strongly agree to strongly disagree, with scoring of strongly
agree (5), agree (4), neutral (3), disagree (2), and strongly disagree (1). Some items
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are reverse scored (i.e., no’s 1, 5, 10, 13, and 17). Items 1-9 correspond to Affective
component, 10-16 to Continuance Component, whereas items 17-22 correspond to
Normative Component. Rashid (2000) reported alpha coefficient equivalent to .57 for
total scores on OCQ, .73 for affective commitment, .58 for continuance commitment,
and .70 for normative commitment on sample of teachers in Pakistan.
The Mini-Markers Set. The Mini-Marker Set developed by Saucier (1994) is
based on Goldberg’s Big Five theory of personality (1992). The Mini-Markers subset
as an abbreviated version of Goldberg’s Big-Five Personality Inventory comprises 5
subscales with 8 items for each factor. Each subscale may have a score range of 8 to
40. The shorter bipolar inventory measures the five factors of personality by
adjectives like talkative, energetic (for extraversion), cooperative, cold (for
agreeableness), organized, practical (for conscientiousness), jealous, moody (for
emotional stability), and deep, imaginative (for openness). The sub-scale of
extroversion measures the extent to which an individual is sociable, active, optimistic
and fun loving. Agreeableness covers individual traits like helpful, trusting, kind and
cooperative. Conscientiousness primarily describes one’s task orientation, hard work,
reliability and socially required impulse control. Emotional Stability indicates one’s
capacity to remain calm and composed and being free from traits, which carry
negative emotional tone. Intellect or Openness scale represents creativity, originality,
imagination and complexity. The Mini Marker Set was used in this study with 9-point
Liket type scale. These include positive (efficient, kind) and negative (inefficient,
unsympathetic) items; which assess respondents’ response in varying degrees from
accurateness (extremely accurate = 9, very accurate = 8, moderately accurate = 7,
and slightly accurate = 6) to inaccurateness (extremely inaccurate = 1, very
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inaccurate = 2, moderately inaccurate = 3, and slightly inaccurate = 4) along with a
middle value (neither inaccurate nor accurate = 5).
Saucier (1994) described that abbreviated MMS with 40 items has impressive
features. The subscales of abbreviated MMS are reported having significantly high
inter-item consistency reliability estimates than that of the 100 MMS, however indices
of alpha reliability coefficient are still constantly low (typically by 0.05 to 0.10).
Subscale scores of abbreviated MMS correspond closely with the scores of full set of
100 markers. Saucier (1994) reported that abbreviated factors scores of MMS
correlated at 0.92-0.96 in raw data and 0.91-0.96 on scored data, with the
corresponding factors of full MMS. Some advantages of the MMS are this has
reduced items and the time taken to complete MMS is reduced.
Based on an empirical procedure of face validity adopted in a study (Shahid,
2006), the author came with synonymous explanation for certain items of MM as
reported difficult in terms of understandability. The present study used the same
version keeping in view its demonstrated face validity to be used in context of
workplaces. Basir (2006) reported Cronbach’s alpha reliability coefficients of five
subscales of MM as follows: extraversion (α = .59), agreeableness (α = .47),
conscientiousness (α = .66), emotional stability (α = .36), and intellect or openness (α
= .65) on a sample of Pakistani university academics.
Procedure
For the purpose of data collection, researcher contacted the relevant
administrative staff of the universities of Punjab personally. These included Incharge
student affairs, respective Deans of the Faculties, Directors/ Head of the Departments,
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Registrar, or Incharge of Faculty Affairs. The researcher debriefed them in writing
about the objectives and outcome of the study. Researcher also explained to them
about the estimated period for the data collection and she shared with them about the
study protocols and synopsis. After obtaining the written informed consent by the
respective authorities of each university, in agreement with respective
Deans/Registrars/In-charge Faulty to the respective HODs would then send formal
letters/Memos to the teachers, which introduces the researcher, explains the purpose
of study and the outcome of research. This has further facilitated the researcher for
contacting the participants of study.
The criterion of selection of participants of the study was that only those
University teachers were included who showed their willingness to participate in the
study. These were approached individually, they were briefed about the purpose and
objectives of the study. They were also assured about the confidentiality of the data
and that the information obtained would purely be used for research purpose. Those
who consented to take part in study, were requested to complete the written ‘informed
consent form’ and to confirm their availability (day/time) to participate in the study.
The participants were also given general instructions to complete the
questionnaires, were also requested to provide their comments regarding their
experience while responding to the study questionnaires. Majority of the participants
agreed to return the completed questionnaires to the researcher within a week time.
The participants were contacted after 3-4 days to enquire about the completion of the
questionnaires. Follow up telephonic reminders were helpful in getting the completed
questionnaires from the respondents on time. The researcher personally collected
these completed questionnaires from the participants of the study, whereas some
respondents preferred posting the completed questionnaires to the researcher.
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Results
The data of pilot study (N = 102) collected from the university faculties of
public and private sector universities was analyzed using SPSS 15.0.
Descriptive Analysis
At initial level, descriptive analysis of the data included mean, standard
deviations, and skewness of scores distribution was computed. Furthermore, to
estimate the relationship between variables, Pearson Product Moment correlations
were computes. Cronbach’s Alpha coefficients were computed to see the internal
consistency of study measures.
Findings reported in Table 1 explains the descriptive trends in data set through
computing levels of variables (see high, medium, & low scores) with corresponding
mean and standard deviation values on measures of study i.e., work environment,
burnout, organizational commitment, and personality dimensions. This is particularly
meaningful to relate the general understanding of study variables how generally the
sample is showing orientation to what level of the variables. Median of scores were
considered as cut-off scores; i.e., scores falling above median scores were considered
as high and below median as low and falling on median as average scores. For
burnout, values within parentheses are based on previously established cut-off scores
computed on normative sample of teachers mentioned in MBI scoring sheet (Maslach,
Jackson, & Leiter, 1996). These cut-off scores comprising of the following: emotional
exhaustion (high 27 or above, moderate 17-26, low 0-16), depersonalization (high 14
or above, moderate 9-13, low 0-8), and personal accomplishment (for reversed
scoring, high 0-30, moderate 31-36, low 37 or over). The Table 1 displays general
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trends of data of university teachers of Pakistani academic settings. The findings
indicate positive psychosocial facets of perceived work environment; as reflected in
higher mean values on positive dimension of work environment. Respondents were
high on personal accomplishment. Comparison based on cut-off scores derived from
normative sample of teachers, highlighted that present sample highly endorsed the
dimension of personal accomplishment. It is interesting that levels of
depersonalization have found to be similar when computed against median based cut-
off scores and cut-off scores representing normative sample. They endorsed higher
level of affective commitment compared to continuance and normative commitment.
Findings further highlighted that respondents strongly endorsed high level of
agreeableness.
Table 2 presents the pattern of relationship of predictive variables with
criterion and moderator variables along with obtained Cronbach’s alpha coefficients
explaining the internal consistency of study measures.
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90
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The above table indicates a significantly high magnitude of Cronbach’s alpha
reliability coefficient for the total scores of WES (α = .89) and high alpha coefficients
for primary subscales of Relationship Dimension (α = .79), Personal Growth
Dimension (α = .77), and System Maintenance and Change Dimension (α = .80). For
secondary subscales of WES, the magnitude of alpha coefficients ranged from .73 to
.53. Table 2 further indicates high magnitude of alpha reliability coefficient on total
scores of MBI (α = .81), it yields moderate magnitude of alpha reliability coefficients
for its subscales, which ranges from .60 to .67. High Alpha Reliability Coefficients is
yielded for Organizational Commitment Questionnaire (α = .76), for subscales of
OCQ (i.e.) it ranged from.54 to .71. The findings in Table 2 further indicates, that for
Mini Markers Set (MMS), the Cronbach’s alpha coefficients was high for
extraversion (α = .76), agreeableness (α = .80), conscientiousness (α = .83), openness
(α = .78), to low on emotional stability (α = .27).
The findings further indicated that certain dimensions of WES including
coworker cohesion (r = -.29, p < .01), clarity (r = -.35, p < .05), and physical comfort
(r = -.29, p < .01) have inverse relationship with emotional exhaustion. Whereas,
work pressure has positive association with emotional exhaustion (r = .40, p < .01)
depersonalization (r = .20, p < .05), involvement (r = -.24, p < .05), coworker
cohesion (r = -.25, p < .05), supervisor support (r = -.23, p < .05), task orientation (r =
-.21, p < .05), clarity (r = -.22, p < .05), and physical comfort (r = -.22, p < .05) have
found to be inversely linked with depersonalization. Task orientation (r = .24), clarity
(r = .22) and innovation (r = .23) have significant positive relationship with personal
accomplishment.
Burnout as a unitary factor is showing association with most of dimensions of
WES. Significant positive relationship is observed between each subscale of work
environment with affective commitment except for coworker cohesion and autonomy.
92
Supervisor support (r = .26, p < .01) and innovation (r = .20, p < .05) are significantly
related with normative commitment.
Organizational commitment is significantly related with involvement (r = .25,
p < .05), supervisor support (r = .26, p < .01), work pressure (r = .20, p < .05), clarity
(r = .27, p < .01), managerial control (r = .25, p < .05), innovation (r = .30. p < .01)
and physical comfort (r = .22, p < .05). Except extraversion and openness in most of
cases, other personality dimensions are showing significant associated with certain
dimensions of WES. For example, agreeableness (r = .32, p < .01) and
conscientiousness (r = .47, p < .01) are showing stronger relationship with totals
scores on WES. The interpretations of relationship between study variables discussed
above, may help interpreting the discriminant validity of the constructs (for
interpretation Pl see discussion).
Since, the obtained values of skewness are not greater than 1.0 and are less
than -1.0; therefore, it may be interpreted that the skewness is substantial and
distribution of scores is somewhat less symmetrical.
93
Impact of Work Environment on Burnout
The predictive relationship of work environment with outcome variables was
tested on pilot sample using multiple linear regression analysis. Since, the rationale of
research involves exploratory case as a base of paucity of research evidence in our
cultural context; therefore, methodology of regression analysis- Entry method was
preferred. This is especially more preferable in this research case to see the
contribution of each one of the predictor of WES model. Pallant (2007) suggested that
standard multiple regression analysis is preferable as it treats set of independent
variables as a group and evaluates the predictive power of each of the independent
variable.
The exact reflection of population data might not be a case with small pilot
sample; therefore, for pilot study, values of Adjusted R2 will be interpreted instead of
R square. Nicola, Richard, and Rosemerg (2006) suggested that Adjusted R2 provides
useful estimate of the success of the models as it takes into account the number of
predictor variables in the model and the number of participants.
Following tables (Table 3-to-7) presents the results of regression analyses on
scores of burnout components as well as on overall burnout scores regressed against
scores on work environment scale (subscales and total score).
94
Table 3
Multiple Regression Analysis on scores of Emotional Exhaustion by Work
Environment (N = 102)
Emotional Exhaustion
Work Environment Variables B SE B Β 95% CI
LL UL
Involvement -.76 .70 -.15 -2.15 .63
Coworker Cohesion -.71 .52 -.15 -1.74 .34
Supervisor Support .78 .53 .17 -.32 1.79
Autonomy .43 .48 .10 -.53 1.38
Task Orientation .27 .57 .06 -.87 1.41
Work Pressure 1.7 .43 .40** .82 2.51
Clarity -1.76 .54 -.44** -2.82 -.70
Managerial Control .15 .50 .03 -.84 1.14
Innovation .57 .43 .14 -.29 1.42
Physical Comfort -.18 .52 -.04 -1.21 .85
R = .60, R2= .36, ∆R2= .29 (F = 5.02**)
**p ≤ .00
Results in Table 3 indicated moderate association (Multiple R = .60) between
emotional exhaustion and work environment variables showing overall significant
model F(10, 91) = 5.02, p < .00. Magnitude of Adjusted R2 (∆R2= .26) implies that
together subscales of work environment (work pressure and clarity) accounts for 26%
change in emotional exhaustion. Results highlighted work pressure as significant
positive predictor of emotional exhaustion (β = .40, t = 3.91, p < .01). To elaborate
further, this indicates that one unit increase in work pressure will result in a 1.66
increase in emotional exhaustion (B = 1.66). The dimension of Clarity as negative
predictor of emotional exhaustion implies that introducing one unit increase in clarity
will decrease emotional exhaustion by 1.76 units (β = -.44, B = -1.76, t = 3.29, p <
95
.01). Evaluating the strength of individual predictors, clarity (β = -.44) is better
predictor compared to work pressure (β = .40). To ensure that multicollinearity was
not a problem in regression analysis, variance inflation factor (VIF) and tolerance
statistics was calculated for each regression coefficient. The potential values of
variance inflation factor should be below 10. Tolerance greater than .20 is considered
as satisfactory estimate (as cited in Field, 2005). The obtained values of VIF are in
acceptable range ranged from .38 up to .73 suggested that multicollinearity is not
likely a threat to the substantive conclusions drawn from the data.
Table 4
Multiple Regression Analysis on scores of Depersonalization by Work Environment
(N = 102)
Depersonalization
Work Environment Variables B SE B β 95% CI
LL UL
Involvement -.44 .40 -.17 -1.23 .35
Coworker Cohesion -.22 .30 -.09 -.81 .38
Supervisor Support -.19 .30 -.06 -.80 .41
Autonomy .48 .28 .21 -.07 1.03
Task Orientation -.49 .33 -.21 -1.14 .17
Work Pressure .62 .24 .29* .13 1.10
Clarity .01 .31 .01 -.59 .62
Managerial Control -.03 .29 -.01 -.60 .54
Innovation -.04 .25 -.02 -.52 .45
Physical Comfort -.10 .30 -.04 -.69 .49
R = .44, R2= .19, ∆R2= .10 (F = 2.16*)
*p < .05
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Results in Table 4 indicated that association between depersonalization and
work environment variables (F(10, 91) = 2.16, p < .05) is a moderate fit, indicating
that the model explains 19% of the variance (∆R2= .29) in depersonalization. Work
pressure indicated significant positive association (β = .29, t = 2.54, p < .05) which
implies that increase in work pressure by one unit accounts for increase in
depersonalization by .62 units (B = .62). The values of tolerance fall within acceptable
range that is below 10.
Table 5
Multiple Regression Analysis on scores of Personal Accomplishment by Work
Environment (N = 102)
Personal Accomplishment
Work Environment Variables B SE B
Β
95% CI
LL UL
Involvement -.32 .56 -.09 -1.44 .79
Coworker Cohesion .29 .42 .09 -.54 1.12
Supervisor Support -.29 .43 -.10 -1.14 .56
Autonomy -.20 .39 -.07 -.97 .57
Task Orientation .78 .46 .25 -.13 1.70
Work Pressure .19 .34 .07 -.49 .87
Clarity .24 .43 .09 -.61 1.10
Managerial Control -.23 .40 -.07 -1.03 .56
Innovation .54 .35 .20 -.14 1.23
Physical Comfort .10 .42 .03 -.72 .93
R = .34, R2= .12, ∆R2= .02 (F = 1.22)
p = n.s
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Findings in Table 6 indicated that work environment dimensions are not
significantly contributing in predicting personal accomplishment (∆R2= .02, F(10, 91)
= 1.22, p = n.s).
Table 6
Multiple Regression Analysis on total scores of Burnout by Work Environment (N =
102)
Burnout
Work Environment Variables B SE B Β 95% CI
LL UL
Involvement -.88 1.23 -.10 -3.33 1.57
Coworker Cohesion -1.21 .92 -.15 -3.05 .62
Supervisor Support .83 .94 .11 -1.03 2.70
Autonomy 1.11 .85 .15 -.58 2.79
Task Orientation -1.00 1.01 -.13 -3.01 1.01
Work Pressure 2.09 .75 .30* .60 3.58
Clarity -1.99 .95 -.30* -3.87 -.11
Managerial Control .35 .88 .04 -1.40 2.09
Innovation -.01 .76 -.00 -1.52 1.49
Physical Comfort -.38 .92 -.05 -2.20 1.43
R = .52, R2= .27, ∆R2= .19 (F = 3.38**)
*p < .05, **p = .00
Results in Table 6 indicate a moderate fit of association between burnout (total
score) and work environment variables (R = .52, F(10, 91) = 3.38, p < .01). The
model accounts for producing 19% variability in burnout scores (∆R2 = .19). Among
subscales of work environment, work pressure and clarity are significantly
contributing in producing variance in burnout scores. Work pressure as positive
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predictor (β = .30*) explains increase in burnout scores by 2.09 units. Clarity as
negative predictor (β = -.30*) explains decrease in burnout by 1.99 units.
This seems interesting to examine the effect of work environment (as total
scores) on total scores of burnout and its individual components. Following analysis
shows pattern of findings for total scores on burnout, and its subscales namely
emotional exhaustion, depersonalization, and personal accomplishment regressed
against total scores on work environment.
Table 7
Regression Analysis on Burnout and its components by total scores of Work
Environment (N = 102)
Work Environment B SE Β 95% CI
LL UL
Burnout
-.32 .12 -.26 -.56 -.08
R = .26, R2= .07, ∆R2= .06 (F = 6.93*)
Emotional Exhaustion
-.11 .08 -.14 -.25 .04
R = .14, R2= .02, ∆R2= .01 (F = 2.01)
Depersonalization
-.08 .04 -.21* -.16 -.01
R = .21, R2= .05, ∆R2= .05 (F = 4.74*)
Personal Accomplishment
.13 .05 .25* .03 .23
R = .25, R2= .06, ∆R2 = .054 (F = 6.82*)
*p < .05
Results in Table 7 indicate significant role of work environment (total scores)
to accounts for variation in overall burnout (∆R2 = .06, F(1, 100) = 6.93, p = .01).
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Findings indicated non-significant predictive role of work environment in predicting
emotional exhaustion (∆R2 = .01, F(1, 100) = 2.01, p = n.s). For depersonalization,
the value of Adjusted R2 (∆R2 = .045) explains 4.5 percentage of variance in
depersonalization with significant model fit (F(1,100) = 4.74, p < .05). Evaluation of
unstandardized Beta weights (B = -.08) suggests that one unit increase in work
environment will result in .08 decrease in depersonalization. The significant t-value
revealed that work environment is a significant negative predictor of
depersonalization (β = -.21, t = 2.18, p < .05). Work environment as positive predictor
of personal accomplishment (β = .25) account for 5.4% (∆R2 = .054) variance with
significant model fit (F(1,100) = 6.82, p < .05) is significant. Beta weight indicated
that one unit increase in work environment will result in .13 increase in personal
accomplishment (B = .13, t = 2.61, p < .05).
Impact of Work Environment on Organizational Commitment
To assess the pattern of predictive relationship of work environment with
organizational commitment, following tables (8-12) present the findings of regression
analyses performed on scores of affective, continuance, normative, and overall
commitment regressed against the subscales of work environment.
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Table 8
Multiple Regression Analysis on Affective Commitment by Work Environment (N =
102)
Affective Commitment
95% CI
LL UL Work Environment Variables B SE B Β
Involvement .55 .39 .20 -.22 1.32
Coworker Cohesion -.36 .29 -.14 -.94 .21
Supervisor Support .56 .29 .23 -.03 1.14
Autonomy -.09 .27 -.04 -.62 .44
Task Orientation -.45 .32 -.19 -1.08 .18
Work Pressure .24 .24 .11 -.22 .71
Clarity .25 .30 .11 -.34 .83
Managerial Control .72 .28 .26** .17 1.26
Innovation .47 .24 .22* -.00 .94
Physical Comfort .10 .29 .04 -.47 .67
R = .56, R2= .31, ∆ R2= .24 (F = 4.15**)
*p ≤ .05, **p < .00
Findings in Table 8 indicate that work environment facets including
managerial control and innovation are significant positive predictors of affective
commitment. Both dimensions account for a moderate fit of the model (∆R2= .24,
F(10, 91) = 4.15, p < .00). Managerial control and innovation predicted 24% variance
in affective commitment. The unstanderized beta-value, scoring high on managerial
control scale by one unit implies that affective commitment will increase by .72 units
(t = 2.60, p < .05). This suggest high scores on innovation by one unit will result
increase in affective commitment by .47 units (B = .47, t = 1.98, p < .05). Comparing
the strength of significant predictor revealed that managerial control (β = .26) is
101
stronger predictor compared to innovation (β = .22). The obtained values of VIF are in
acceptable range.
Table 9
Multiple Regression Analysis on Continuance Commitment by Work Environment (N
= 102)
Continuance Commitment
95% CI
Work Environment Variables B SE B Β LL UL
Involvement -.28 .34 -.13 -.96 .39
Coworker Cohesion -.43 .25 -.21 -.93 .08
Supervisor Support .21 .26 .12 -.30 .73
Autonomy .13 .23 .07 -.33 .60
Task Orientation -.13 .28 -.07 -.69 .42
Work Pressure .04 .21 .02 -.37 .45
Clarity .02 .26 .01 -.50 .53
Managerial Control .44 .24 .21 -.04 .92
Innovation .37 .21 .22 -.04 .79
Physical Comfort .10 .25 .05 -.40 .60
R = .33, R2= .11, ∆ R2= .01 (F = 1.08)
p = n.s
Results of correlation matrix revealed that continuance commitment is not
significantly related with any of predictor variables (see Table 2). Consistent with
this, the results as shown in Table 9 indicated that work environment facets do not
significantly account for predicting variance in employees’ continuance commitment
(∆R2= .01, F(10, 91) = 1.08, p > .05).
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Table 10
Multiple Regression Analysis on Normative Commitment by Work Environment (N =
102)
Normative Commitment
95% CI
LL UL Work Environment Variables B SE B Β
Involvement -.19 .32 -.09 -.83 .46
Coworker Cohesion -.06 .24 -.03 -.54 .43
Supervisor Support .12 .25 .07 -.37 .61
Autonomy .38 .22 .22 -.06 .82
Task Orientation .12 .27 .07 -.41 .64
Work Pressure .12 .20 .07 -.27 .51
Clarity -.09 .25 -.06 -.58 .41
Managerial Control .09 .23 .05 -.37 .55
Innovation .26 .20 .16 -.14 .65
Physical Comfort .02 .24 .01 -.46 .50
R = .31, R2= .10, ∆ R2= -.00 (F = .98)
p = n.s
Results as shown in Table 10 indicate that work environment variables do not
significantly account for predicting variance in employees’ normative commitment (∆
R2= -.00, F(10, 91) = .98, p > .05).
Following table represents the extent of predictive relationship between work
environment variables and taking organizational commitment as a composite factor.
103
Table 11
Multiple Regression Analysis on total scores of Organizational Commitment by Work
Environment (N = 102)
Organizational Commitment
95% CI
LL UL Work Environment Variables B SE B Β
Involvement .09 .75 .02 -1.41 1.58
Coworker Cohesion -.85 .56 -.18 -1.96 .27
Supervisor Support .89 .57 .20 -.25 2.02
Autonomy .42 .52 .10 -.61 1.45
Task Orientation -.47 .62 -.11 -1.69 .76
Work Pressure .40 .46 .10 -.51 1.31
Clarity .18 .58 .04 -.97 1.32
Managerial Control 1.24 .54 .25* .18 2.30
Innovation 1.10 .46 .27* .18 2.02
Physical Comfort .23 .56 .05 -.88 1.34
R = .49, R2= .24, ∆ R2= .15 (F = 2.80*)
*p < .05
Regression analysis on total scores of organizational commitment by work
environment variables (Table 11) indicated that overall relationship is significant
(∆R2= .15, F(10, 91) = 2.80, p < .05). The magnitude of model fit is low indicating
that managerial control and innovation together accounts for producing 15.1%
variability in organizational commitment. Increase in managerial control by one unit
accounts for an increase of 1.24 units in organizational commitment (B = 1.24, t =
2.32, p < .05). Similarly, increase in innovation by one unit results an increase in
organizational commitment by 1.10 units (B = 1.10, t = 2.38, p < .05). The
unstanderized beta-values indicated that innovation (β = .27) is comparatively
104
stronger predictor than managerial control (β = .25). The values of tolerance fall
within acceptable range.
To estimate the pattern of findings for total scores on organizational
commitment and its subscales regressed against total scores on work environment,
bivariate regression analysis was performed.
Table 12
Regression Analysis on Organizational Commitment and its components by total
scores on Work Environment (N = 102)
Work Environment B SE B β 95% CI
LL UL Organizational Commitment
.27 .07 .36** .13 .41
R =.36, R2= .13, ∆R2= .12 (F = 15.27**) Affective Commitment .18 .04 .44** .11 .25 R = .44, R2= .19, ∆R2 = .19 (F = 24.61**) Continuance Commitment .03 .03 .08 -.04 .09 R = .08, R2= .01, ∆R2= -.00 (F = .65) Normative Commitment .07 .03 .23* .01 .12 R = .23, R2= .05, ∆R2 = .04 (F = 5.33*) *p < .05, **p ≤ .00
Findings as shown in Table 12 indicated that by taking work environment as a
composite factor to predict unitary organizational commitment, the magnitude of
model fit (∆R2 = .12) although not fairly high revealed the significant overall
relationship (F(1, 100) = 15.27, p < .00) by contributing 12 % of variability in the
organizational commitment. This implies that one unit increase in work environment
will result .27 increase in organizational commitment (B = .27). The associated t-
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value (t(101) = 3.91, p = .00) reflects that work environment is a significant predictor
of organizational commitment. The value of Adjusted R2 (∆R2 = .19) with significant
F ratio (F(1, 100) = 24.61, p < .00) reflects that work environment significantly
accounts for 19% variance in affective commitment. Assessing beta weights reflect
that as work environment increases by one unit, affective commitment increase by .18
units (B = .18, β = .44, t = 4.96, p = .00). For normative commitment, work
environment accounts for marginal variance up to 4 % (∆R2= .04, F(1, 100) = 2.31, p
< .05). Assessing beta weights reflect that an increase of one unit in work
environment results in increase of normative commitment by .07 unit (B = .07, β =
.23, t = 2.31, p = .02). Further, results revealed work environment does not
significantly (B = .07, β = .23, t = .81, p > .05) accounts for any increase or decrease
unit changes in continuance commitment.
106
Discussion
The pilot study intended to explore the psychometric properties of the
measures used in study on the participants of University teachers. This also focused to
find out the pattern of results/findings in testing the model of work environment and
its outcomes along with personal variables as moderating variables.
Psychometric Issues
Investigating the psychometric properties of the measures in pilot testing is an
important preliminary step, which allows evaluating the quality and suitability of the
measures for the sample of the study. This further helps confident use of measures in
the main study on a large sample. Reliability indices of the measures calculated using
Cronbach’s Alpha Reliability coefficient. Moreover, the discriminant validity of the
measures would reflect the psychometric soundness of the measures. The internal
consistency reliability of the measures of study for the participants of study
(university teachers i.e., lecturers, assistant professor and professors) was reported.
Indices of Work Environment Scale (WES) indicate significant alpha reliability
indices for primary scales of Relationship Dimension (α = .79), Personal Growth
Dimension (α = .77) with 27 number of items each, and System Maintenance and
Change Dimension with 36 items (α = .80). The reliability coefficients of secondary
sub scales with nine items each ranged from .73 to .53. Here, this is to be mentioned
that magnitude of alpha coefficients in secondary subscales is low compared to
primary dimensions; this may be attributed to lesser number of items in secondary
107
sub-scales. Results indicate that the alpha reliability coefficient obtained for total
scores on English version of WES (α = .89) is high. English form of WES has been
used in Pakistan in various studies using sample of academicians (Imam, 1993;
Rehman & Maqsood, 2008). The results of pilot study indicate that scores on WES
present a high magnitude of alpha reliability coefficient on subscale of involvement (α
= .89) and relatively moderate on managerial control (α = .53). More recently, a study
(Rehman & Maqsood, 2008) reported somewhat similar trend of internal consistency
estimate for subscale of managerial control (α = .49) on sample of university teachers.
Evaluation of qualitative responses obtained during pilot study indicates that
the study questionnaires generally received a very positive response by the
participants of study. This further indicate that none of them had difficulty in
understanding the items or and reading the instructions. However, in case of WES,
72% of the respondents showed their concern that responding to items in simple true/
false option seems forced-choice response pattern the respondents tend to have
consensus on that it would have been better if these items were arranged in a Likert
Type scale allowing them a choice to explain their degree of agreement to
disagreement. However, none of them reported that they are not satisfied with tier
responses on WES. This issue was further discussed with judges following
‘Committee Approach’, Judges included five teachers teaching course of research
methods and psychometrics.
Based upon the recommendations of expert committee and consultation with
the author (Moos) and supervisors, we decided to use five-point Likert type response
format for WES. This revised format of WES (i.e., response categories true, mostly
108
true, false, and mostly false) was further administered on small pilot sample of
university teachers of public and private sector University (N = 40).
The inclusion criterion for sample of this stage of study was similar as applied
for sample of the main pilot testing. The data obtained by this pilot was analyzed by
using SPSS 15.0. Cronbach’s Alpha Reliability coefficients were calculated on the
scores of WES. In our study, obtained magnitude of Cronbach’s alpha reliability
coefficient is .68 for secondary subscale of involvement, .49 for coworker cohesion,
.59 for supervisor support, .46 for autonomy, .61 for task orientation, .64 for work
pressure, .67 for clarity, and a low of .38 for managerial control, .74 for innovation,
.52 for physical comfort, and .68 for total scores on WES. Subscales of clarity,
managerial control, innovation, and physical comfort comprise the primary dimension
of system maintenance and change dimension and as overall yielded high magnitude
of alpha coefficient equivalent to .82. Therefore, in case of managerial control, an
increase in magnitude of alpha coefficient may observe using larger sample in main
study. An earlier study of Abraham and Foley (1984) pointed out an important
concern that that reliability estimate of Likert-type format of WES may possibly be
low in magnitude compared to dichotomous options.
The present researcher fully acknowledge the concerns of WES’s author
(Moos) regarding changing the response format and its possible impact on reliability
and validity indices.’ In this context Moos was consulted; he communicated that
...’reliability and validity are joint function of scale items and response formats and of
the characteristics and diversity of specific samples (Moos, 1990). Straker (1989)
emphasized that in using WES, researchers should use some procedure to enhance the
response rate of the respondents because it has shown as the problematic aspect.
109
Following these comments, perhaps one of the reasons for preferring Likert response
format may serve as a way to enhance the response rate.
Mehwish (2006) used WES on a sample of university teachers. She
highlighted possible limitations in using WES is its dichotomous response format,
which restricts the subjects’ responses, she suggested using Likert-type format in
future researches. Moreover, qualitative responses of out participants of study
highlighted that majority of the respondents in pilot study showed preference for
variation in response format. Committee approach and recommendation of original
author R. H. Moos (personal communication, March 10, 2009), we decided to use
Likert response format with dichotomous scoring in main study.
The results of pilot study indicated satisfactory estimate of internal
consistency of the Maslach Burnout Inventory (MBI). Reliability analysis on scores of
MBI yielded high magnitude of alpha coefficient for total score (α = .81). The
moderate magnitude of alpha coefficient has obtained for the subscales of emotional
exhaustion (.60), depersonalization (.65), and for personal accomplishment (.67).
Here, comparatively the magnitude of alpha coefficient for emotional exhaustion is
low. Since, Cronbach’s alpha estimates of internal consistency are essentially an
average of inter-item correlations for a scale; therefore, examining the effect of
individual items on reliability coefficient would also be helpful in this regard. The
alpha coefficient was compute using SPSS function of ‘scale reliability if item
deleted’. It showed that excluding item 14 of emotional exhaustion “I feel I’m
working hard on my job”, yielded an increase in alpha coefficient up to .22.
Subsequently, confirmatory factor analysis will provide a more detailed analysis of
this item.
110
Moreover, studies using English version of MBI in Pakistan has reported the
magnitude of Cronbach’s alpha coefficients in somewhat similar manner compared to
present study. Basir (2006) reported Cronbach’s alpha coefficient of .64 for emotional
exhaustion, .34 for depersonalization, .66 for personal accomplishment, and .61 for
total scores on MBI for sample of university teachers on sample of university
teachers. On varied sample, Munir (2005) reports Cronbach alpha coefficient of .79
for emotional exhaustion, .62 for depersonalization, and .71 for personal
accomplishment.
The scores on Organizational Commitment Questionnaire (OCQ) yielded that
total scores produces an alpha coefficient of .76 along with its subscales off affective
commitment (α = .68), continuance commitment (α = .55), and normative
commitment (α = .71). However, comparatively low magnitude has obtained for
continuance commitment (α = .55). Examining the reliability analysis by estimating
the effect of individual item on scale reliability demonstrated that deletion of item 13
“It wouldn’t be too costly for me to leave the organization now” could change the
magnitude up to .58. Since, this increase is marginal; therefore, thorough picture will
be clear in main analysis. Consistent with this finding, a study using sample of
Pakistani school teachers (Rashid, 2000) reported relatively low magnitude of alpha
coefficient for continuance commitment (α = .58).
The five scales of Mini Markers Set demonstrated satisfactory internal
consistency except for the factor of emotional stability. The magnitude of alpha
reliability coefficient for extroversion (α = .76), agreeableness (α = .80),
conscientiousness (α = .83), and openness (α = .78) are high. The low magnitude of
alpha coefficient has obtained for subscale of emotional stability (α = .28). The
111
reliability analysis by estimating the effect of individual item on scale reliability
showed that deletion of adjective no. 31 “Touchy” related to the factor of emotional
stability would add to increase the alpha reliability magnitude up to .53, which is
worth considering. We will examine this aspect further while we analyze the data of
main study using larger sample size. In comparison, previous study (Basir, 2006)
done in context of Pakistan also reported low alpha coefficient for factor of emotional
stability (α = .36) with sample (N = 40) of graduate and post graduate level teachers.
One possible reason for low alpha coefficient for subscale of emotional
stability might be that alpha is very dependent on the variability of the item and
subscale scores in the particular sample employed. This may be the fact that we may
not find much variations in our sample of (N = 102) University teachers. In the next
chapters of the study, the detailed confirmatory factor analysis would probably help
further clarify issues related with reliability indices of emotional stability.
Findings shown in Table 2 explain scales-total correlation- that is correlation
of subscales of a measure with total score is one of the procedures to estimate the
construct validity of the measures (see, Anastasi & Urbina, 1997). For example, each
of the subscale of the WES is showing highly significant correlation with total score
on WES. For burnout, emotional exhaustion (r = .84, p < .01), depersonalization (r =
.75, p < .01), and personal accomplishment (r = .24, p < .05) are showing significant
relationship with the total score of MBI. This indicates that subcomponent of burnout
are closer in examining the construct of burnout. For organizational commitment,
affective commitment (r = .82, p < .01), continuance commitment (r = .62, p < .01),
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and normative commitment (r = .74, p < .01) are significant related with total score of
OCQ.
Moreover, bivariate correlations between subscales of measures (Table 2) may
also lead to estimation of distinctiveness of the constructs. For example, theoretically
the constructs of Supervisor Support and Managerial Control seems to be different.
The results in Table 2 indicated very weak correlation (r = .00) between both
constructs. Similarly, Managerial Control as part of System Maintenance dimension is
showing lowest magnitude of correlation with Relationship (r = -.01) and Personal
Growth (r = .15) dimensions, and with subscales of Involvement (r = .07) and
Coworker Cohesion (r = -.09). Similarly, weakest relationship of Work Pressure with
Relationship dimension (r = .02), Coworker Cohesion (r = -.20), and Supervisor
Support (r = .03) and Autonomy (r = .05) provides satisfactory estimate regarding the
distinctiveness of the construct. Innovation is showing weak relationship with work
pressure (r = .06) and managerial control (r = -.08). Moreover, apparently different
constructs including Task Orientation and Innovation (r = .15) are showing weakest
correlation. The desirable discriminant validity of the Work Environment Scale may
be inferred from this estimate.
Moreover, the measure of Mini Markers comprises a set of five independent
trait factor structures. Theoretically, for a satisfactory estimate of discriminant
validity, there should be low or weak correlation between trait structures. Results
revealed that personality trait of extroverted is showing low correlation with
agreeableness (r = .18), emotional stability (r = .10) and openness (r = .14).
Extroverted is showing significant but relatively low magnitude of correlation
coefficient with conscientiousness (r = .24).
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Descriptive Trends of Data
Data obtained on the pilot sample of study was descriptively analyzed to
understand the patterns of scores obtained from the participants (university teachers).
While evaluating the mean scores of different indices of the work environment; it
seems quite interesting to observe that the participants of our study have endorsed
positive psychosocial facets of their work environment. High mean values for positive
work environment seems encouraging, especially from employee and employer’s
perspective, and is useful for the management. Majority of the teachers have endorsed
positive aspects of their work environment. The participants of our study highly
endorsed the dimension of task orientation, which indicates a high emphasis on
planning, efficiency and satisfactory accomplishment of the job targets. Similarly, the
dimensions of involvement in job and clarity of work procedures were categorized as
‘dominant’ aspects of their work environment. The participants of our study endorsed
average level of managerial control and physical comfort. Whereas, coworker
cohesion and the innovation aspect of the job, seems to appear relatively less
emphasized indicators of work environment in universities.
The trend of mean scores reflected that our participants reported significantly
high on personal accomplishment (M = 42.52), and emotional exhaustion (M =
25.96); whereas, teachers reported low experience of depersonalization (M = 17.00).
Since, participants are reporting high sense of competence and achievement and
comparatively low on emotional exhaustion and depersonalization; therefore, it
provides an estimation that they are not experiencing high level of burnout. If we see,
participants have dominantly endorsed the high levels compared to moderate and
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average scores on emotional exhaustion and depersonalization. This indicates that
perhaps a moderate level of burnout is being experienced by the respondents.
Descriptive trends of data highlighted that participants of our study reported strong
sense of affective commitment towards their organizations. This further highlights
high level of endorsement of our participants for emotional attachment and
willingness to continue/remain with the organization. It may be inferred that being
socialized in a collectivist culture, perhaps we in a social unit are probably more
oriented for group affiliations. Being high on emotional attachment may possibly be
related to the product of the cognitive schemas of individuals living in a collectivist
culture. Mean scores obtained on the measure of personality indicated that
agreeableness is highly endorsed characteristic by the participants of our study,
compared to other dimensions of personality. This finding reflects that the participants
of our study are dominant on trait of agreeableness. However, computing high,
medium, and low levels on each of the personality dimension indicated that
participants have dominantly endorsed for high levels. This provides somewhat a
general idea about the personality profile of university teachers. In other words, its
encouraging that dispositional characteristics of teachers may positively contribute in
their performance. Future research may also need to look into exploring this aspect as
well.
The pattern of relationship of work environment with burnout using Pearson
Product Moment correlations, suggests further exploring of the relationships through
complex methodology e.g., regression analyses. The pattern of correlation coefficients
indicates that burnout is showing negative associations with most of dimensions of
work environment except with work pressure. The positive relationship between
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burnout and work pressure is logical; as increase in work pressure may lead to an
increase in burnout experience. Workplace dimensions of ‘autonomy’ and ‘task
orientation’ are not showing significant relationship with burnout. If we see,
respondents have reported that academic settings are high on ‘task orientation’,
dimension. This helps to infer that workplaces with high emphasis on work planning
and emphasis on efficiency to complete the job assignments may logically explains
that their employees will possibly have less experience of burnout. Work pressure is
also showing positive association with emotional exhaustion and depersonalization;
whereas, non-significant relationship has shown with personal accomplishment. This
seems satisfying that work pressure does not seem having any considerable role in
effecting teachers’ sense of personal accomplishment. In order to prevent or mange
the burnout among employees, management of academic settings need to plan
strategies to enhance and monitor the relationship dimensions of their workplace. For
example, management needs to prioritize the involvement of teachers in different
requirements of their job. The cohesiveness among coworkers should be more
strengthen to promote the positive work spirit. Moreover, the system maintenance and
system change dimension has also shown important considerations in this regard. For
example, clarity of organizational rules and procedures, along with improving the
physical aspects of the work environment may contribute very effectively in
managing the burnout.
Work environment facets including ‘clarity’ and ‘physical comfort’ are
showing inverse relationship with emotional exhaustion; whereas, involvement,
coworker cohesion, supervisors’ support, task orientation, clarity, and physical
comfort are inversely related with feelings of depersonalization in our participants of
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study. Furthermore, the dimensions of task orientation, clarity, innovation, physical
comfort, and overall work environment showed positive association with teachers’
personal accomplishment. Since teachers have dominantly endorsed for the positive
dimensions of their work environment along with high on personal accomplishment;
therefore, personal accomplishment is neutral to the negative aspects of the work
environment e.g., work pressure and managerial control.
The relationship of work environment with organizational commitment
revealed that organizational commitment of our participants of study is linked with
high emphasis on workplace organizational facets; which include involvement of
employees and supervisors’ support, work pressure, clarity, managerial control,
innovation and physical comfort. Affective component of the commitment is a
powerful factor, which is associated with most of psychosocial factors of our
participants of study. However, being emotionally attached to one’s organization
seems neutral of the extent of coworker cohesion and amount of autonomy to perform
one’s job. This is interesting to observe that the continuance based view of
employees’ commitment is neutral of any facets of the work environment. For
normative dimension of commitment, supervisor support and innovation might prove
potential variables during regression analyses. The pattern of results demonstrated
that affective commitment might be a potential dimension in later regression analyses.
To qualify personality traits as moderators, there should not be strong
correlation with work environment components. Findings highlighted that
extroversion is showing weaker association with work environment as a whole and
with each of its dimension. Agreeableness is showing weak relationship with
dimensions of coworker cohesion, autonomy, work pressure, managerial control and
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innovation. Conscientiousness is showing significant relationship with most of work
environment components except for work pressure and managerial control. Emotional
stability is showing non-significant relationship with total of personal growth
dimension, autonomy, work pressure, clarity, managerial control, innovation, and
physical comfort. Openness is showing weak relationship with all work environment
components except for total of personal growth dimension and subscales of task
orientation and work pressure. This description indicates that dispositional dimensions
have shown variation in findings; which in turn further strengthened the need to
explore the role of personality dimensions in work environment and outcome
relationships.
Impact of Work Environment on Burnout and Organizational Commitment
According to objectives of the pilot study, the pattern of results in
investigating the role of work environment in predicting burnout and organizational
commitment was determined on the findings of pilot study. For regression analysis,
individual work environment variable were regressed against each dimension of the
constructs as well as on total scores. In predicting burnout, the work environment
facets of work pressure (personal growth dimension) and clarity (system maintenance
and change dimension) produced a significant equation when regressed against the
emotional exhaustion dimension of burnout. Both dimensions of work environment
accounts for 26% change in emotional exhaustion. Emotional exhaustion is found
inversely associated with clarity of work procedures in the workplace. This further
explains that keeping the work procedures explicit and well communicated to
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employees in turn help to reduce role ambiguity among employees. Previously,
inverse relationship between clarity and emotional exhaustion has been reported
(Adali et al., 2003).
It seems fair to assume that our participants of study (i.e., university teachers)
in case feel overburdened or are required to work under pressure; this may lead to
emotional exhaustion. The positive association between work pressure and emotional
exhaustion obtained in pilot study carries existing empirical support (Goddard,
O’Brien, & Goddard, 2006; Robinson et al., 1991). Furthermore, work pressure is
also found as a contributory factor influencing our participants’ feelings of
depersonalization. Empirically supported, it’s logical to link that being high on work
pressure may leads to increase in depersonalization (Levert, Lucas, & Ortlepp, 2000;
Savicki, 2002). However, findings highlighted that effect of work pressure on
depersonalization accounts for relatively low level of variation up to 10%. The
remaining amount of variance responsible to produce depersonalization accounts for
reasons other than the work pressure.
Previous findings have provided support that positive aspects of the work
environment especially those aspects which are influencing emotional exhaustion, and
depersonalization may also effect the sense of personal accomplishment (Savicki,
2002). However, findings of present study demonstrated that teachers’ reported sense
of personal accomplishment is independent of the influence of different facets of the
work environment. Examining the mean scores showed that teachers have reported
high level of reduced sense of personal accomplishment; however, complex analysis
highlighted its negligible effects. Maslach, Jackson and Leiter (1996) recommends
reporting ‘Personal Accomplishment’ by directly computing sum of item scores rather
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than as Diminished Personal Accomplishment based on reversed items. However, for
composite score, scores of personal accomplishment are first reversed to add up in
total scores of emotional exhaustion and depersonalization.
It is also noteworthy to comment on positive dimension of teachers’ attitude
that despite of demonstrated effects of workplace environment (in form of clarity and
work pressure) in developing emotional exhaustion, teachers are reporting positive
sense of personal accomplishment.
Taking burnout as a composite factor, work pressure (personal growth
dimension) and clarity of work procedures (system maintenance and change
dimension) produced significant equation. These factors of work environment were
contributing their role to produce 19% variation in overall reported burnout. Analysis
on total scores of work environment showed marginal variations in scores of burnout
and depersonalization in negative direction and showed positive association with
personal accomplishment. In conclusion, findings of pilot study highlighted that work
environment factors including work pressure and clarity are powerful predictors in
producing variation in burnout and its components. For management system, these
work environment factors stands out as important concerns for effectively preventing
the burnout among teachers. In academic settings, keeping employees involved in
work activities might directly help them to reduce their sense of depersonalization and
detachment from work activities. An optimum level of work pressure is always
required in work setting to monitor the output of teachers. However, it is very
important that management should realistically understand that how employees
perceive the extent of work pressure as it might link to alleviate the burnout among
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teachers. This further suggests that the clarity of work procedures requires being well-
managed to prevent the symptoms of burnout.
In order to assess the pattern of relationship between work environment and
organizational commitment, non-directional hypothesis was preferred partly because
of scarcity of literature explaining the dynamics of relationship. Predicting teachers’
organizational commitment revealed that work environment facets including the
extent of managerial control and opportunities of innovation (system maintenance
and change dimension) in work produced a significant equation when regressed
against the affective commitment dimension of organizational commitment. The
dimensions of managerial control and innovation emerged as positive predictors and
accounted for 24 % variations in affective commitment among participants of our
study. Both factors showed positive association when regressed against the total score
of organizational commitment and accounted for 15% variation.
Previously, supervision control (Mobley, Griffith, Hand, & Meglino, 1979) or
management styles of influence (Ervin & Langkamer, 2008) and innovation (Stewart,
Bing, Gruys, & Helford, 2007) found to be associated with employees’ affective
responses. From the results of pilot study, we may infer that characteristics of work
conditions lead to affective attachment among employees. Furthermore, work places
providing stimulating environment to use innovative approaches to perform work
tasks seem to play contributory role to foster employees’ emotional attachment. This
emphasis on innovative approaches to get the job done seems very practical keeping
in view the requirements of the teaching profession itself. For example, planning the
courses and assignments, preparing and delivering lectures, stimulating students’
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creativity and involvement in classrooms demands innovative attitude toward one’s
job.
Analysis on total scores of work environment highlighted positive association
with total scores on organizational commitment, with a comparatively stronger effect
on affective commitment, and with a marginal variation in normative commitment.
Despite the empirical support especially for relating continuance commitment with
work environment facets (Stewart, Bing, Gruys, & Helford, 2007); its very interesting
to observe that the findings of pilot study highlighted that Continuance and Normative
dimensions of the Organizational Commitment showed non-significant relation with
any of the facets of the work environment. Evaluating the mean scores revealed that
majority of teachers reported moderate to high concern for continuance and normative
dimensions of commitment to their organizations. However, this may be inferred that
their reported levels of commitment based on perceived cost of leaving an
organization is irrespective of psychosocial factors of their work settings. Similarly,
commitment to organization based on moral obligations of remaining in the
organization seems indifference to any of operating characteristics of the work
environment.
The previously mentioned discussion highlights that initial scrutiny of
psychometric issues provided considerable support regarding suitability of study
measures. Taking pilot study as preliminary quality control test provides logic to
further extensively analyzed psychometric indices using larger sample of the main
study. Extending this to further validate measures for indigenous sample of university
teachers in the context of Pakistan; it will be useful to examine how well sample
confirms the factor structure of measures. The findings of pilot study pointed out that
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among different facets of work environment only few indices have yielded significant
predictive power; which thereof relates to in support of the arguments (Proctor &
Capaldi, as cited in Davis & Smith, 2005) to be cautious about the high probability of
disconfirming the true hypotheses in cases where empirical evidence is lacking and
thereby supports the formulation of non-directional hypotheses.
In conclusion, the findings of pilot study demosntarted that emotional
exhaustion has found to be negatively linked to clarity; whereas, work pressure had
showed positive association with emotional exhaustion and depersonalization. Work
pressure does account for explaining variance in depersonalization. Managerial
control and innovation are positive predictors and accounted for relatively stronger
variance in affective commitment. Above discussion highlights that whilst assessing
work environment and outcome relationships, some subscales of WES were able to
produce significant variance in the criterion variables. This may be assumed that a
more detailed understanding of hypothesized relations may emerge when this
relationship is analyzed on a larger sample. However, initial understanding of the
factor structure of the study measures seems important to examine as a way to
confirm the existing structure of the constructs on sample of university teachers. This
validation of constructs would help further to have more in-depth understanding of the
hypothesized relationships of study variables.
The proceeding chapter of main study thoroughly explains the processes and
outcomes of the next phases of the study.
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Chapter IV
PHASE II: MAIN STUDY
The main study was carried out in two steps: the first step aimed to examine
the factor structure of study measures on sample of university teachers; and at second
step after scrutiny of factorial validity, data was subject to main analyses related to
hypotheses testing.
Step I: Examining the Measurement Models of Constructs
The first step of the main study focused to examine the factorial validity of
study measures. Since, the measures used in the study were developed in other
cultures and were validated on varied sample; therefore, it was meaningful to see how
well the existing stricture of study measure may confirm with sample of university
teachers of Pakistan. Additionally, the measures used in the present study are in
English language; therefore, it will be meaningful to evaluate their psychometric
issues in terms of examining the cross-cultural transferability of tools through testing
their factor structure with working group in Pakistani cultural context. Moreover, key
arguments for testing theoretical structure of study measures on larger sample of main
study and then subjecting data to further analysis instead of using data of pilot sample
is to control error variance associated with sample characteristics. Moreover,
generally it is expected that estimation of model fit involving Maximum Likelihood
(ML) estimation should reasonably be about 200 observations (Hox & Bechger,
1998). Another support for relevant sample size was derived from a meta-analytic
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study mentioned that studies using confirmatory factor analysis have used sample size
ranged from 133 to 1,590 (Worley, Vassar, Wheeler, & Barnes, 2008).
Following are the specific objectives targeted in this step.
Objectives of Step I of the Main Study
1. Testing the ten factor model of work environment.
2. Testing the factor structure of competing theories of burnout to see how well a
one factor, a three factor, and a five factor model of burnout may confirms
with the sample of University teachers in Pakistan.
3. Testing the factor structure of unitary and a three factor model of the
organizational commitment.
4. Testing the factor structure of Big Five personality model to see how well it
may support by the data of present study.
Method
Participants
The participants of the main study comprises University teachers (N = 426)
belonging to public (n = 212) and private (n = 214) sector universities of three cities
i.e., Islamabad, Rawalpindi and Lahore (Pakistan) during the year 2008. The 575 test
booklets (containing measures of the study) were handed over personally to the
sample of study after explaining the purpose of study and obtaining their informed
consent. In total, twelve comparable public and private universities were selected on
basis of performance ranking criteria provided by Higher Education Commission of
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Pakistan. Out of 575 participants, 445 respondents returned the completed
questionnaires, and 19 incomplete questionnaires were discarded. To deal with
missing data, the scoring equivalent to middle or neutral level was assigned in case of
burnout, organizational commitment, and personality measures. For measure of work
environment, in case of few missing responses, the missing value was replaced with
the Mean value. There were only two missing responses in terms of demographic
information which were kept as intact. The response rate in non-random -
opportunity/convenient sampling procedure was 77.4%. For the purpose of analysis,
data was sub classified as following: The information related to employees’
organizational related personal factors e.g., hierarchical status i.e., lecturers (i.e., entry
level rank: n = 185) and assistant professors, associate professors, and professors (i.e.,
high rank: n = 241), duration of job in current organization (M = 5.09, SD = 3.00),
comparison between social sciences (n = 198) vs. natural sciences (n = 228), and
involvement in any paid side jobs other than their regular job (involvement: n = 21,
non-involvement: n = 405). The mean age of participants of the study was M = 36.57
(SD = 8.96). These included 268 men and 158 women. They were classified having
master degree (n = 112) vs. participants having post graduate research (M.Phil) or
doctorate degree (Ph.D: n = 314), and marital status (married: n = 280; unmarried: n =
143). These informations were obtained through demographic information sheet.
Instruments
Participants of the main study using self-administration procedure responded
to a questionnaire pack including following questionnaires with Demographic
Information Sheet attcahed on top;
1) Work Environment Scale (WES; Moos, 1994) measuring perceptions of work
environment;
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2) Maslach Burnout Inventory– Educators Survey (Maslach, Jackson, & Leiter,
1996) measuring burnout;
3) Organizational Commitment Questionnaire (Meyer & Allen, 1991) measuring
organizational commitment;
4) Mini-Markers Set (Saucier, 1994) measuring Big-Five dimensions of
personality
(see complete detail of the instruments under pilot study).
Procedure
After seeking formal written consent from the management of the selected
universities, the teachers of respective universities were approached individually by
the researcher. In some universities, Deans, departmental heads and administrative in
charge of the faculty were contacted initially to obtain their informed consent. The
respondents were given an average time ranges from 2-3 days. Only those included in
the study who consented formally to participate in the study. The respondents were
briefed about the objectives of the study and were provided with written instructions
to complete the questionnaires of the study. Follow-up procedure was adopted via
telephonic contact. The questionnaires were returned back personally by the
researcher, and in some cases these were sent to the researcher via post. At the time of
return of these questionnaires, it was ensured to check that the forms are completed
duly.
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Testing the Factor Structure of Work Environment, Burnout, Organizational
Commitment, and Personality Measures
The measurement models of study variables were examined through
Confirmatory Factor Analysis with Maximum Likelihood estimation procedure using
LISREL 8.80.
Confirmatory Factor Analyses (CFA). Confirmatory Factor Analysis (CFA)
as an example of the measurement model of the Structural Equation Modeling
assuming Maximum Likelihood estimation was done using program of Linear
Structural Relations abbreviated as LISREL. CFA was conducted to test how well
data supports the factor structure of the measures on individual item scores available
for 426 participants. The purpose of assessing a model’s overall fit is to determine the
degree to which the hypothesized model as a whole is consistent with the empirical
data at hand. Statistical tests of the model for all tests are tests of differences between
the variance/covariance matrix predicted by the model and the sample
variance/covariance matrix from the observed data. Those differences are referred to
as “fit” or “goodness of fit”, namely how similar the hypothesized model is to the
observed data (Maruyama, 1998). A wide range of goodness-of-fit indices can be used
as summary measures of a model’s overall fit. Its difficult to rely only on any of the
indices due to the fact that we can’t say that any one is superior to other. As they
operate somewhat differently based on given sample size, estimation procedure,
model complexity, violation of the underlying assumptions of multivariate normality
and variable independence, or any combination thereof (Diamantopoulos & Siguaw,
2000). The fit indices used in study were; chi-square statistic denoted as Minimum Fit
Function Chi-Square, the Root Mean Square Residual (RMR), Root Mean Square
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Error of Approximation (RMSEA), Goodness of Fit Index (GFI), the Adjusted
Goodness of Fit Index (AGFI), Incremental Fit Index (IFI), Normed Fit Index (NFI),
Comparative Fit Index (CFI), Akaike’s Information Criterion (AIC), and Consistent
Version of AIC (CAIC).
Chi-square likelihood ratio statistic is highly sensitive to small differences
and, hence, misleading in large samples. It is suggested that instead of reading chi-
square as a test statistic, one should regard it as a goodness (or badness)-of-fit
measure in a sense that large chi-square values correspond to bad fit and small chi-
square values to good fit (Diamantopoulos & Siguaw, 2000). One of the goodness-of-
fit indices based on residuals is root mean square residual (RMR), which is suitable
for judging between the fit of different models to the same data; the smaller the value,
the better the fit. There are no cut-off points as it happens to be in case of some of
other indices (Kline, 1993). Standardised RMR is used to overcome the problem
which can be raised in case of RMR due to effect on its value as a result of unit of
measurement. The values of Standardised RMR below .05 are indicative of acceptable
fit (Maruyama, 1998). For root mean square error of approximation (RMSEA)
coefficient values less than 0.05 are indicative of good fit, between 0.05 and under
0.08 of reasonable fit, between 0.08 and 0.10 of mediocre, and values fall greater than
0.10 indicates poor fit. The RMSEA is generally considered as one of the most
informative fit indices (Diamantopoulos & Siguaw, 2000). Cabrera-Nguyen (2010)
cited recommended cut-off values for RMSEA (≤ .06) and CFI (≥ .95).
The goodness of fit (GFI) and adjusted goodness of fit (AGFI) are other
widely used indices of goodness-of-fit indices based on residuals. The GFI should be
between 0 and 1. The data probably do not fit the model if the GFI is negative or
much larger than 1. The AGFI is the GFI adjusted for the degrees of freedom of the
model. The AGFI should be between 0 and 1. The data probably do not fit the model
if the AGFI is negative or much larger than 1 (Diamantopoulos & Siguaw, 2000). Hu
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and Bentler (1995) mentioned that values greater than .90 denote acceptable fit for
GFI and AGFI.
Relative fit indices also known as “incremental” or “comparative” fit indices
which assess how much better the model fits compared to a baseline model, usually
the independence model. Incremental fit index (IFI), normed fit index (NFI), non-
normed fit index (NNFI), parsimonious normed fit index (PNFI), and the comparative
fit index (CFI) comes under this category of relative fit indices. All the indices in this
group have value range from 0 to 1, with values closer to 1 interpreted as good fit.
However, NNFI can take value greater than 1. The lower value of PNFI is desirable
compare do its non-parsimonious counterpart- NFI. Literature recommends that NNFI
and CFI are dominantly relied upon compared to other indices of this group
(Diamantopoulos & Siguaw, 2000). Hu and Bentler (1995) mentioned that values
greater than .95 are set as acceptable fit for CFI and greater than .90 for NFI.
The next set of fit indices based on information criteria incorporate the
parsimony of the hypothesized model in assessing its fit in comparison with other
models including independence model (baseline model) and the saturated model.
Independence model hypothesized that all variables are uncorrelated; while, saturated
model on another extreme proposes that number of parameters to be estimated is
equal to the number of variances and covariances among observed variables. Akaike's
Information Criterion (AIC) used in this perspective is interpreted for selecting the
best model among a number of candidate models. It’s desirable that model value of
AIC should be lower than independence and saturated model. The model that yields
the smallest value of AIC is considered the best. Another index namely consistent
version of AIC (CAIC) adjusts the AIC for sample size effects. Smaller value of
CAIC denoted as Model CAIC compared to Independence CAIC and Saturated CAIC
is preferable.
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Factor Structure of Work Environment Scale
A dominant and mostly used measure of psychosocial work environment
namely Work Environment Scale (Moos, 1994) was tested for its ten factor structure
using scores on WES to test how well the data supports the existing structure of the
measure.
Goodness of Fit Indices. Following table displays goodness-of-fit indices for
10 factor model of WES.
Table 13
Goodness-of-fit statistics for ten-factor model of Work Environment (N = 426)
Note. χ 2 = chi-square; df = degree of freedom; RMR = root mean square residual; RMSEA = root mean square error of approximation; GFI = goodness-of-fit index; AGFI = adjusted goodness-of-fit index; IFI = incremental fit index; NFI = normed fit index; CFI = comparative fit index; AIC = Akaike’s information criterion; and CAIC = Consistent Version of AIC.
Fit statistic Ten-factor Model
χ 2 8755.36 (p = 0.0)
df 3870
RMR .08
RMSEA .07
GFI .62
AGFI .60
IFI .78
NFI .66
CFI .78
AIC 12101.61
CAIC 13238.85
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Findings as sown in Table 13 indicated that significant value of chi-square is
undesirable. To have better estimate of model fit, other indices will be evaluated. The
value of RMR should be below .05, which thereof id not good. The value of RMSEA
falls under reasonable fit. GFI and AGFI should closer to 1. Therefore, it somewhat
comes under mediocre level fit. In comparison of obtained values of GFI, AGFI, NFI,
the obtained values of IFI (.78) and CFI (.78) are better. The obtained value of Model
AIC (12101.61) is smaller than Independence AIC (26077.62) which is desirable.
However, the value of model AIC is greater than Saturated AIC (8190.00), which is
undesirable. For CAIC, as desirable, the obtained value of Model CAIC (13238.85) is
smaller than Independence CAIC (26532.52) and also with Saturated CAIC
(28887.93) as well. In comparison with AIC, the results of CAIC are representing
better model fit. The value of CN should be greater than 200; which in this case
(198.93) is of not up to the standard.
Overall, the values of CFI, IFI, RMSEA, CAIC are in good support of model
fit along with considering AIC as partial fit and GFI and AGFI as of mediocre fit.
Factor loadings of items with corresponding factors. Below is the detail of
factor loadings for ten factor model of WES.
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Table 14
Factor loadings and Standard Errors for ten factor model of Work Environment (N =
426)
Item Nos.
Measure and Variable
Standardized Factor
Loading
SE
Involvement
1 The work is really challenging. .16 .97
11 There’s not much group spirit. .40 .84
21 A lot of people seem to be just putting in time. .15 .98
31 People seem to take pride in the organization. .36 .87
41 People put quite a lot of effort into what they do. .54 .71
51 Few people ever volunteer. .17 .97
61 It is quite a lively place. .48 .77
71 It’s hard to get people to do any extra work. .38 .85
81 The work is usually very interesting. .41 .83
Co-worker Cohesion
2 People go out of their way to help a new employee feel
comfortable. .14 .98
12 The atmosphere is somewhat impersonal. .33 .89
22 People take a personal interest in each other. .03 1.00
32 Employees rarely do things together after work. .21 .96
42 People are generally frank about how they feel. .48 .77
52 Employees often eat lunch together. .14 .98
62 Employees who differ greatly form the others in the
organization don’t get on well. .43 .82
72 Employees often talk to each other about their personal
problems. .07 .99
82 Often people make trouble by talking behind other’s back. .28 .92
Supervisor Support
3 Supervisors tend to talk down to employees. .08 .99
13 Supervisors usually compliment an employee who does
something well. .29 .92
Continued…
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Item Nos.
Measure and Variable
Standardized Factor
Loading
SE
23 Supervisors tend to discourage criticism from employees. .32 .90
32 Supervisors usually give full credit to ideas contributed
by employees. .36 .87
42 Supervisors often criticize employees over minor things. .37 .86
52 Employees generally feel free to ask for a raise. .36 .87
62 Supervisors expects far too much from employees. .31 .91
72 Employees discuss their personal problems with
supervisors. .01 1.00
82 Supervisors really stand up for their people. .35 .88
Autonomy
4 Few employees have any important responsibilities. .08 .99
14 Employees have a great deal of freedom to do as they like. .48 .77
24 Employees are encouraged to make their own decisions. .48 .77
34 People can use their own initiative to do things. .53 .72
44 Supervisors encourage employees to rely on themselves
when a problem arises. .27 .93
54 Employees generally do not try to be unique and different. .13 .98
64 Employees are encouraged to learn things even if they
are not directly related to the job. .32 .90
74 Employees function fairly independently of supervisors. .37 .86
84 Supervisors meet with employees regularly to discuss
their future work goals. .25 .94
Task Orientation
5 People pay a lot of attention to getting work done. .45 .80
15 There’s lot of time wasted because of inefficiencies. .43 .82
25 Things rarely get “put off till tomorrow.” .05 1.00
35 This is a highly efficient, work-oriented place. .64 .59
45 Getting a lot of work done is important to people. .27 .93
55 There’s an emphasis on “work before play.” .27 .93
65 Employees work very hard. .55 .70
Continued…
134
Item Nos.
Measure and Variable
Standardized Factor
Loading
SE
75 People seem to be quite inefficient. .35 .88
85 There’s a tendency for people to come to work late. .36 .87
Work Pressure
6 There is constant pressure to keep working. .50 .75
16 There always seems to be an urgency about everything. .24 .94
26 People cannot afford to relax. .30 .91
36 Nobody works too hard. .26 .93
46 There is no time pressure. .37 .86
56 It is very hard to keep up with your workload. .27 .93
66 You can take it easy and still get your work done. .27 .93
76 There are always deadlines to be met. .32 .90
86 People often have to work overtime to get their work done. .07 .99
Clarity
7 Things are sometimes pretty disorganized. .34 .88
17 Activities are well-planned. .58 .67
27 Rules and regulations are somewhat vague and
ambiguous. .53 .72
37 The responsibilities of supervisors are clearly defined. .48 .77
47 The details of assigned jobs are generally explained to
employees. .54 .71
57 Employees are often confused about exactly what they
are supposed to do. .51 .74
67 Fringe benefits are fully explained to the employees. .40 .84
77 Rules and polices are constantly changing. .14 .98
87 Supervisors encourage employees to be neat and orderly. .38 .85
Managerial Control
8 There’s a strict emphasis on following policies and
regulations. .51 .74
18 People can wear wild looking clothing while on the job if
they want. .18 .97
Continued…
135
Item Nos.
Measure and Variable
Standardized Factor
Loading
SE
28 People are expected to follow set rules in doing their work. .34 .89
38 Supervisors keep a rather close watch on employees. .31 .91
48 Rules and regulations are pretty well enforced. .53 .72
58 Supervisors are always checking on employees and
supervise them very closely. .26 .93
68 Supervisors do not often give in to employee pressure. .09 .99
78 Employees are expected to conform rather strictly to the
rules and customs. .26 .93
88 If employee comes in late, he or she can make it up by
staying late. .04 1.00
Innovation
9 Doing things in a different way is valued. .42 .83
19 New and different ideas are always being tried out. .53 .72
29 This place would be one of the first to try out a new idea. .50 .75
39 Variety and change are not particularly important. .45 .80
49 The same methods have been used for quite a long time. .23 .95
59 New approaches to things are rarely tried. .31 .90
69 Things tend to stay just about the same. .31 .90
79 There is a fresh, novel atmosphere about the place. .56 .69
89 Things always seem to be changing. .08 .99
Physical Comfort
10 It sometimes gets too hot (room conditions). .12 .99
20 The lighting is extremely good (room conditions). .35 .88
30 Work place is awfully crowded. .08 .99
40 This place has a stylish and modern appearance. .41 .84
50 The place could stand some new interior decorations. .06 1.00
60 The colors and decorations make the place warm and
cheerful to work in. .30 .91
70 It is rather drafty (disorganized) at times. .33 .89
80 The furniture is usually well arranged. .54 .71
90 The rooms are well ventilated. .21 .96
Note. Factor loadings > .30 are in boldface.
136
Results of confirmatory factor analysis shown in Table 14 present the
standardised factor loadings along with residuals. For each factor, item with bold font
represent strong association of the item with its respective sub-scale. The initial
criterion to evaluate the factor loadings was set that values should be above .30.
According to the strength of factor loadings following items were considered for
further investigation to either to be retained or deleted form the measure: Involvement
(1, 21, & 51); Co-worker Cohesion (2, 22, 32, 52, 72, & 82); Supervisor Support (3,
13, & 73); Autonomy (4, 44, 54, & 84); Task Orientation (25, 45, & 55); Work
Pressure (16, 36, 56, 66, & 86); Clarity (77); Managerial Control (18, 58, 68, 78, &
88); Innovation (49 & 89); and physical Comfort (10, 30, 50, & 90).
Factor structure of Maslach Burnout Inventory-Educators Survey. The
present study has focused on examining the factorial structure of burnout measure
(Maslach, Jackson, & Leiter, 1996), to see whether it can be feasibly analyzed as a
unitary (one-factor) construct or whether it's a lot more complex than that with this
specific sample in this particular country. The complexity of multidimensional nature
of burnout measure was aimed to examine by testing three and five factor models
supported by the existing literature. CFA was computed using scores on burnout
measure to establish the most appropriate factor structure of burnout measure. Three
models of Maslach Burnout Inventory (MBI), as suggested by previous exploratory
factor analysis were tested; a one-factor, a three-factor, and a five-factor model. The
one-factor model tested to see the grouping of all items as one factor. Densten’s
research (2001) on factor structure of MBI reported to test original one factor model
compared with another one factor model with reduced items. However, Densten
reported that no previous test of burnout model as a unitary construct has been
137
reported in the literature. In comparison of models, his study also supported
multidimensional aspect of burnout compared to burnout as a unitary construct.
The second model to be tested contained three factors, as originally specified
by Maslach, Jackson, and Leiter (1996), namely emotional exhaustion (9 items),
depersonalization (five items), and personal accomplishment (eight items). Studies
using sample of teachers (Byrne, 1993; Evans & Fischer, 1993), elementary and high
school teachers of California (Gold, 1984), and cross validation on sample of 469
Massachusetts teachers (Iwanicki & Schwab, 1981), have demonstrated support for
three factor model. Using confirmatory factor analyses, studies have identified the
original three factor model ‘superior’ to other alternative models (Lee & Ashforth,
1990; Schaufeli & Van Dierendonck, 1993).
The third model to be tested was a five-factor model as suggested by previous
confirmatory factor analysis (Densten, 2001). This elaborated factor structure of
burnout with reduced items (19 items) was established using a sample of 480
Australian law enforcement managers. It suggested emotional exhaustion loaded on
two factors namely psychological (included items 6, 16, and 20) and somatic strain
(items 1, 2, 3, 8), depersonalization items loaded on a single factor (5, 10, 11, 15, and
22), personal accomplishment loaded on two factors namely self (4, 9, 18, and 19) and
others (7, 17, and 21).
Following is the description of findings obtained by LISREL output in form of
goodness of fit indices for each measurement model followed by factor loadings of
items along with values of residuals. The values arranged in descending order of the
strengths of the factor loadings so it’s clear as to which items are the most strongly
associated with each factor.
138
Goodness of fit indices obtained for testing measurement models of MBI.
Following table displays goodness-of-fit indices for each of the MBI models obtained
for unitary, three and five factor models of MBI
Table 15
Goodness-of-fit statistics for single, three and five-factor models of MBI (N = 426)
Fit statistic Single-factor Model Three-factor Model Five-factor Model
χ 2 718.24* 572.91* 347.84*
df 209 206 142
RMR 0.24 0.22 0.19
RMSEA 0.09 0.07 0.06
GFI 0.85 0.89 0.91
AGFI 0.81 0.86 0.89
IFI 0.88 0.93 0.95
NFI 0.85 0.90 0.91
CFI 0.88 0.93 0.94
AIC 937.22 680.31 474.84
CAIC 1159.62 917.87 717.45
*p = 0.0,
Note. χ 2 = chi-square; df = degree of freedom; RMR = root mean square residual; RMSEA =
root mean square error of approximation; GFI = goodness-of-fit index; AGFI = adjusted
goodness-of-fit index; IFI = incremental fit index; NFI = normed fit index; CFI = comparative
fit index; AIC = Akaike’s information criterion; and CAIC = Consistent Version of AIC.
Findings shown in Table 15 display ten goodness-of-fit indices for each of the
models. Among absolute fit indices, the values of chi-square statistic (χ 2) denoted as
Minimum Fit Function Chi-Square for five-factor model is comparatively better in
terms of magnitude of obtained values. However, for each model, the value of chi-
square is significant which is undesirable to conclude about better fit of the models.
Kline (1993) mentioned that interpreting χ 2 test of significance especially in large
139
samples is more likely to fall under unacceptable range, even though the residual
matrix is small. Therefore, author mentioned that in such cases it’s recommended that
it’s best to ignore the test and focus on other indices. Comparing the magnitude of chi-
square values, the five factor model yielded a smaller value which is better.
For RMR, the five-factor model is more suitable. The RMSEA for one factor
model represents mediocre fit. The values for three-factor and five-factor model fall
under reasonable fit. The goodness-of-fit index (GFI) for five factor model is at
acceptable fit; whereas, its value for three factor model is also closer to 1. For AGFI,
five factor model is at better position. Among incremental fit indices, the value of IFI
and CFI comes under acceptable fit for three factor and five factor solutions.
However, the five-factor model is comparatively on edge due to slightly high values
of fit statistics closer to 1. Among parsimonious fit indices, the normed fit index NFI
is indicative of acceptable fit for three and five factor solutions. Since this value
should be closer to 1 and more desirably greater than .90; therefore, its value for one
factor model (.85) is also somewhat closer to 1. However, three and five factor
models are at better position. Evaluating PNFI for one factor (.77), three factors (.80),
and five factors (.76), values are lower than corresponding value of NFI which is
desirable for model fit.
Further, the values for Akaike’s information criterion (AIC) for one factor
model revealed value of hypothesized value (937.22) is acceptable as it’s less than
independence model (5735.29). However, it’s undesirable that model value is greater
than saturated model (506.00). Interpreting CAIC, value of model CAIC (1159.62) is
less than Independence CAIC (5846.49) and also less than Saturated CAIC (1784.77).
For three factor model, model value (680.31) is smaller than independence model
(5735.29) which is desirable. However, conflicting picture is portrayed due to greater
value in comparison with saturated model (506.00). Evaluating CAIC, similar picture
has been observed. For five factor model, value of Model AIC (474.84) is less than
140
Independence AIC (4491.63) but greater than Saturated AIC (380.00). In case of
CAIC, value of Model CAIC (717.45) is less compared to Independence CAIC
(4587.67) and Saturated CAIC (1340.34).
The statistics for three and five factor models are very closer in case of
RMSEA, IFI, NFI, CFI, and AIC with obtained values of fit-statistics slightly high or
comparatively at better position for five-factor model. The cutting edge for five factor
model has observed for CAIC and slightly on RMR, IFI, NFI, CFI, GFI and AGFI. In
conclusion, both three and five factor seems better solution as compared to unitary
model. However, five-factor solution comparatively is at better position.
Following is the detail of factor loadings for each model along with values of
standard errors.
Detail of factor loadings with corresponding factors. Below is the detail of
factor loadings for one factor, three factor, and five factor model solutions.
Table 16
Factor loadings and Standard Errors for one factor model of Maslach Burnout
Inventory (N = 426)
Item Nos.
Variables & Statements
Standardised Factor
Loadings SE
10 I have become more callous towards people since I took
this job. .68 .53
15 I don’t really care what happens to some recipients. .62 .62
Continued…
141
Item Nos.
Variables & Statements
Standardized Factor
Loadings SE
7 I deal very efficiently with the problems of my recipients. .58 .67
13 I feel frustrated by my job. .57 .67
8 I feel burned out from my work. .57 .68
17 I can easily create a relaxed atmosphere with my
recipients. .56 .69
5 I feel I treat some recipients as if they were impersonal
“objects”. .55 .70
11 I worry that this job is hardening me emotionally. .54 .71
12 I feel very energetic. .50 .75
20 I feel like I am at the end of my rope. .50 .75
18 I feel exhilarated after working closely with my
recipients. .49 .76
3 I feel fatigued when I get up in the morning and have to
face another day on the job. .48
.77
4 I can easily understand how my recipients feel about
things. .44 .81
16 Working directly with people puts too much stress on me. .43 .81
6 Working with people all day is really a strain for me. .41 .83
9 I feel I am positively influencing other people’s lives
through my work. .41 .83
21 In my work I deal with emotional problems very calmly. .41 .83
1 I feel emotionally drained. .38 .86
19 I have accomplished many worthwhile things in this job. .34 .88
22 I feel recipients blame me for some of their problems. .34 .88
2 I feel used up at the end of the day. .29 .91
14 I feel I am working too hard on my job. .00 1.00
Note: Factor loadings > .30 are in boldface.
Findings as shown in Table 16 revealed that item 14 is showing very weak
factor loading (.00) with total score of MBI. For item 2, factor loading is less than the
desirable criteria, e.g., .30.
142
Following table presents factor loadings and standard errors for five factor
model.
Table 17
Factor loadings and Standard Errors for three factor model of Maslach Burnout
Inventory (N = 426)
Item Nos.
Variables & Statements Standardised
Factor Loadings SE
Emotional Exhaustion
8 I feel burned out from my work. .62 .62
3 I feel fatigued when I get up in the morning and have to
face another day on the job. .57 .67
13 I feel frustrated by my job. .57 .67
20 I feel like I am at the end of my rope. .52 .72
16 Working directly with people puts too much stress on me. .48 .77
6 Working with people all day is really a strain for me. .46 .79
1 I feel emotionally drained. .45 .80
2 I feel used up at the end of the day. .42 .83
14 I feel I am working too hard on my job. .10 .99
Depersonalization
10 I have become more callous towards people since I took
this job. .71 .50
15 I don’t really care what happens to some recipients. .62 .61
11 I worry that this job is hardening me emotionally. .57 .68
5 I feel I treat some recipients as if they were impersonal
“objects”. .56 .69
22 I feel recipients blame me for some of their problems .35 .88
Personal Accomplishment
4 I can easily understand how my recipients feel about things .67 .55
17 I can easily create a relaxed atmosphere with my recipients .61 .62
Continued…
143
Item Nos.
Variables & Statements Standardised
Factor Loadings SE
18 I feel exhilarated after working closely with my
recipients. .55 .70
12 I feel very energetic. .52 .73
7 I deal very efficiently with the problems of my recipients. .51 .74
9 I feel I am positively influencing other people’s lives
through my work. .51 .74
21 In my work I deal with emotional problems very calmly. .47 .78
19 I have accomplished many worthwhile things in this job. .40 .84
Note: Factor loadings > .30 are in boldface.
Findings in Table 17 indicated that item 14 is showing weak factor loading
(.10) against component of emotional exhaustion.
Following table presents factor loadings and standard errors for five factor
model with 19 items.
Table 18
Factor loadings and Standard Errors for five factor model of Maslach Burnout
Inventory (N = 426)
Item Nos. Variables & Statements
Standardised Factor Loadings
SE
Emotional Exhaustion (psychological strain)
6 Working with people all day is really a strain for me. .45 .80
16 Working directly with people puts too much stress on me. .47 .77
20 I feel like I am at the end of my rope. .54 .71
Continued…
144
Item Nos.
Variables & Statements Standardised
Factor Loadings SE
Emotional Exhaustion (somatic strain)
1 I feel emotionally drained. .45 .80
2 I feel used up at the end of the day. .46 .79
3 I feel fatigued when I get up in the morning and have to
face another day on the job. .64 .59
8 I feel burned out from my work. .67 .55
Depersonalization
5 I feel I treat some recipients as if they were impersonal
“objects”. .57 .68
10 I have become more callous towards people since I took
this job. .69 .52
11 I worry that this job is hardening me emotionally. .56 .69
15 I don’t really care what happens to some recipients. .63 .60
22 I feel recipients blame me for some of their problems .36 .87
Personal Accomplishment (Self)
9 I feel I am positively influencing other people’s lives
through my work. .52 .73
4 I can easily understand how my recipients feel about things. .52 .73
18 I feel exhilarated after working closely with my recipients. .57 .68
19 I have accomplished many worthwhile things in this job. .41 .83
Personal Accomplishment (Others)
7 I deal very efficiently with the problems of my recipients. .66 .56
17 I can easily create a relaxed atmosphere with my recipients. .60 .64
21 In my work I deal with emotional problems very calmly. .45 .80
Note: Factor loadings > .30 are in boldface.
Findings as shown in Table 18 indicated that factor loadings for each item is
acceptable in explaining five factor model of burnout.
145
Factor Structure of Organizational Commitment Questionnaire (OCQ)
CFA was computed using scores on OCQ measure to test how well the
hypothesized factor structure (Meyer & Allen, 1984) supported by data obtained on
sample of teachers. The unitary model of organizational commitment was also tested
as a comparison model to see support for multidimensional nature of the construct.
Goodness of fit indices obtained for testing measurement model of OCQ.
Following table displays goodness-of-fit indices for three factor model of OCQ.
Table 19
Goodness-of-fit statistics for a one-factor and three-factor model of OCQ (N = 426)
Note. χ 2 = chi-square; df = degree of freedom; RMR = root mean square residual; RMSEA =
root mean square error of approximation; GFI = goodness-of-fit index; AGFI = adjusted
goodness-of-fit index; IFI = incremental fit index; NFI = normed fit index; CFI = comparative
fit index; AIC = Akaike’s information criterion; and CAIC = Consistent Version of AIC.
*p = 0.0
Fit statistic One-factor Model Three-factor Model
Χ 2 852.68* 636.76*
df 209 206
RMR .10 .10
RMSEA .09 .07
GFI .82 .87
AGFI .79 .84
IFI .83 .89
NFI .79 .85
CFI .82 .89
AIC 1079.87 783.97
CAIC 1302.26 1021.53
146
Findings shown in Table 19 highlighted goodness of fit indices which
indicated that significant value of chi-square has obtained for one factor and three
factor models, which is undesirable. RMR value is not below .05 for both models. The
value of RMSEA indicates reasonable fit for three factor model and represents
mediocre fit for one factor model. The values of GFI and AGFI are closer to 1; though
not greater than .90, but indicate reasonable fit. Both indices are comparatively
stronger for three factor model. The values of IFI, NFI, and CFI are reasonable as
ranged closer to 1 and are stronger for three factor model. To further compliment the
value of NFI (.85), the obtained value of Parsimony Normed Fit Index (PNFI) for
three factor model is smaller (.76) than NFI, which is desirable. Similarly, for three
factor model, the PNFI (.71) is smaller than obtained value of NFI (.79). For three
factor model, value of AIC is smaller than Independence model (4740.56), which is
desirable. However, conflicting picture is reflected as model AIC is greater than
Saturated AIC (506.00). In similar patter, one factor model showed desirable AIC
(1079.87) in comparison with independence model (4740.56) but conflicted when
compared against saturated model (506). To further evaluate AIC, value of CAIC for
one factor model (1302.26) and for three factor model (1021.53) is desirable as in
both cases it is smaller than Independence (4851.76) and Saturated (1784.77) models.
The CAIC is reflecting an adequate fit of the model with observed data for both
factors with somewhat more strong for three factor model. Overall, consistent with
theory, data found prominent support for three factor structure model of
organizational commitment.
147
Factor loadings of items with corresponding factors. Following tables
represents the factor loadings of each item along with values of residuals obtained on
scores of OCQ using one factor and three factor structure models.
Table 20
Factor loadings and Standard Errors for one factor model of Organizational
Commitment Questionnaire (N = 426)
Item Nos.
Variables & Item Statements Standardised Factor Loading
SE
4 I feel a strong sense of belonging to (name of
organization). .71 .50
2 I feel emotionally attached to (name of organization). .66 .56
7 I would be happy to work at (name of organization)
until I retire. .64 .59
3 Working at (name of organization) is a great deal of
personal interest to me. .61 .63
9 I enjoy discussing (name of organization) with people
outside of it. .57 .67
6 I am proud to tell others that I work at (name of
organization). .55 .70
8 I really feel that many problems faced by (name of
organization) are also my problems. .54 .71
1 I do not feel like part of family (name of organization). .52 .73
21 It would be wrong to leave (name of organization) right
now because of my obligation to the people in it. .52 .73
5 (Name of organization) does not deserve my loyalty. .50 .75
20 (Name of organization) deserves my loyalty. .47 .78
12 Too much in my life would be disrupted if I decided I
wanted to leave (name of organization) now. .38 .86
Continued…
148
Item Nos.
Variables & Item Statements Standardised Factor Loading
SE
11 It would be very hard for me to leave (name of
organization) right now even if I wanted to. .36 .87
18 Even if it were to my advantage, I do not feel like it
would be right to leave (name of organization) now. .34 .88
19 I would feel guilty if I left (name of organization) now. .34 .89
22 I owe a great deal to (name of organization). .33 .89
17 I do not feel any obligation to remain with (name of
organization). .30 .91
10 I am not concerned about what might happen if I left
(name of organization) without having another position
lined up.
.22 .95
14 Right now, staying with (name of organization) is a
matter of necessity as much as desire. .21 .96
16 One of the reasons I continue to work for (name of
organization) is that leaving would require considerable
sacrifices i.e., another organization may not match the
overall benefits I have here.
.16 .97
13 It wouldn’t be too costly for me to leave (name of
organization) now. .13 .98
15 One of the serious consequences of leaving (name of
organization) would be the scarcity of available
alternatives
.07 1.00
Note: Factor loadings ≥ .30 are in boldface.
Findings as shown in Table 20 pointed out items with factor loadings below
.30 including items 10, 14, 16, 13, and 15. These items will be examined further under
three factor model.
149
Table 21
Factor loadings and Standard Errors for three factor model of Organizational
Commitment Questionnaire (N = 426)
Item Nos.
Variables & Item Statements Standardised
Factor Loading
SE
Affective Commitment
4 I feel a strong sense of belonging to (name of
organization). .73 .47
2 I feel emotionally attached to (name of organization). .69 .52
7 I would be happy to work at (name of organization)
until I retire. .64 .59
3 Working at (name of organization) is a great deal of
personal interest to me. .63 .60
6 I am proud to tell others that I work at (name of
organization). .57 .67
9 I enjoy discussing (name of organization) with people
outside of it. .57 .67
8 I really feel that many problems faced by (name of
organization) are also my problems. .54 .71
1 I do not feel like part of family (name of organization). .53 .72
5 (Name of organization) does not deserve my loyalty. .49 .76
Continuance Commitment
12 Too much in my life would be disrupted if I decided I
wanted to leave (name of organization) now. .67 .54
11 It would be very hard for me to leave (name of
organization) right now even if I wanted to. .51 .74
Continued…
150
Item Nos.
Variables & Item Statements Standardised
Factor Loading
SE
14 Right now, staying with (name of organization) is a
matter of necessity as much as desire. .50 .75
16 One of the reasons I continue to work for (name of
organization) is that leaving would require considerable
sacrifices i.e., another organization may not match the
overall benefits I have here.
.48 .77
15 One of the serious consequences of leaving (name of
organization) would be the scarcity of available
alternatives
.24 .94
13 It wouldn’t be too costly for me to leave (name of
organization) now. .15 .98
10 I am not concerned about what might happen if I left (name
of organization) without having another position lined up. .04 1.00
Normative Commitment
21 It would be wrong to leave (name of organization) right
now because of my obligation to the people in it. .70 .76
20 (Name of organization) deserves my loyalty. .53 .86
19 I would feel guilty if I left (name of organization) now. .49 .86
22 I owe a great deal to (name of organization). .45 .71
17 I do not feel any obligation to remain with (name of
organization). .38 .94
18 Even if it were to my advantage, I do not feel like it
would be right to leave (name of organization) now. .38 .77
Note: Factor loadings > .30 are in boldface.
The findings as shown in Table 21 showed that items including 10, 13, and 15
are showing weak loadings for both one factor and three factor models. In
comparison, item nos. 14 and 16 showed stronger factor loadings when assessed
under three factor model.
151
Factor Structure of Mini-Markers Set (MM)
A recently emerged well-accepted personality structure comprising of Big
Five factors (Barrick & Mount, 1993) carries supporting evidence of its factorial
validity. John and Srivastava (1999) mentioned adequate model fit of Big Five
dimensions. Using data of university teachers on scores of MM measure, CFA was
computed to test how well the hypothesized factor structure fits the data obtained on
sample of teachers.
Goodness of fit indices obtained for CFA on scores of MM. Following table
displays goodness-of-fit indices for five factor model of MM.
Table 22
Goodness-of-fit statistics for five-factor models of MM (N = 426)
Note. χ 2 = chi-square; df = degree of freedom; RMR = root mean square residual; RMSEA = root mean
square error of approximation; GFI = goodness-of-fit index; AGFI = adjusted goodness-of-fit index;
IFI = incremental fit index; NFI = normed fit index; CFI = comparative fit index; AIC = Akaike’s
information criterion; and CAIC = Consistent Version of AIC.
*p = 0.0,
Fit statistic Five-factor Model
χ 2 2896.77*
df 730
RMR .45
RMSEA .09
GFI .72
AGFI
IFI
.68
.83
NFI .79
CFI .83
AIC 3516.70
CAIC 3971.60
152
The findings as shown in Table 22 highlighted that obtained value of chi-
square is significant which is not desirable. The value of RMR value is not below .05.
The value of RMSEA indicates mediocre fit. The value of GFI is somewhat closer to
1 and represents mediocre fit. The value of AGFI is less compared to GFI. IFI and
CFI are somewhat closer to 1 representing the reasonable fit.
NFI should also be closer to 1; although, its value is somewhat of mediocre fit.
Further evaluating the NFI on the basis of parsimony of the model, Parsimony
Normed Fit Index (PNFI) is lower than (.74) than NFI representing the better fit of the
model. The value of Model AIC (3516.70) is smaller than Independence AIC
(15949.32) which is desirable. However, it’s greater than Saturated AIC (1640.00)
which is undesirable. Further, complementing the value of AIC, the values of CAIC
are representing better fit of the model. The value of CAIC is smaller (3971.60) than
Independence CAIC (16151.49) and also smaller than Saturated CAIC (5784.64).
Factor loadings of items with corresponding sub-scales. Following table
presents the factor loadings along with residuals on scores of Mini Markers Set.
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Table 23
Factor loadings and Standard Errors for five factor model of Mini Markers Set (N =
426)
Item
Nos.
Variables & Item
Statements Standardised Factor Loading SE
Extroversion
3. Bold .76 .41
4. Energetic .71 .50
2. Extroverted .60 .64
1. Talkative .57 .68
6. Quiet .41 .83
7. Bashful .40 .84
5. Shy .32 .90
8. Withdrawn .24 .94
Agreeableness
11. Kind .74 .46
9. Sympathetic .70 .51
12. Cooperative .68 .54
10. Warm .63 .60
15. Rude .57 .67
14. Unsympathetic .56 .68
16. Harsh .53 .72
13. Cold .19 .97
Conscientiousness
18. Efficient .76 .42
19. Systematic .74 .45
17. Organized .71 .50
23. Inefficient .62 .61
20. Practical .58 .66
24. Careless .56 .68
Continued…
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Item
Nos.
Variables & Item
Statements Standardised Factor Loading SE
21. Disorganized .51 .74
22. Sloppy .43 .81
Emotional Stability
31. Touchy .63 .60
29. Temperamental .61 .63
30. Envious .61 .63
32. Fretful .48 .77
28. Jealous .47 .78
27. Moody .44 .81
26. Relaxed .07 .99
25. Un-envious .00 1.00
Openness
34. Imaginative .76 .43
33. Creative .74 .46
35. Philosophical .70 .50
36. Intellectual .62 .62
39. Uncreative .43 .82
38. Deep .36 .87
40. Un-intellectual .34 .88
37. Complex .16 .97
Note: Factor loadings > .30 are in boldface.
Findings as shown in Table 23 indicated that items including 8 (for
extroversion), 13 (for agreeableness), 25 and 26 (for emotional stability), and 37 (for
openness) are showing weak loadings with corresponding factors.
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Discussion
The present study aimed to evaluate the psychometric issues of study
measures in terms of examining the cross-cultural transferability of tools developed in
English and testing their factor structure with working group in Pakistani cultural
context.
Factor Structure of Work Environment Model
Work Environment Scale (Moos, 1994) has been used in English language
with different samples of working groups in Pakistan (Imam, 1993; Maqsood &
Rehman, 2004; Rehman & Maqsood, 2008). However, no attempt was made to
examine the factor structure of the scale with respect to use with sample in cultural
context. Studies investigating the exploratory factor structure of the Work
Environment Scale have identified variations in findings. For example, studies have
identified seven factors (Booth, Norton, Webster, & Berry, 1976), and two factors
(Brookings, Chacos, Hightower, Howard, & Weiss, 1985). Moos (1994), however,
suggested that information about each three of underlying dimensions is desirable in
studies as it provides a comprehensive understanding of different facets of the work
environment. Further, he elaborated that the factor analytic dimensions identified in
different studies depend on conceptual considerations, characteristics of the specific
sample, decisions about statistical procedures, goodness of fit indices, and so on. The
scarcity of researches on confirmatory factor analysis of WES encouraged the focus
of present study to examine the items that may not well supported by the sample
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responses. In that way, a more objective interpretation of further analysis may come
out.
For evaluating factor structure of WES, multiple solutions of CFA were tested,
e,g, testing with random sample as identified through SPSS procedure, examining
sub-sample, e.g., teachers of public sector employees, and also assessing
unstandardised solutions as well. The items were cross checked in obtained outputs of
multiple solutions before deleting from the measure.
For involvement, items including 1, 21, & 51 are showing weak factor
loadings; therefore, these items will be excluded from the inventory. For coworker
cohesion, items including 2, 22, 32, 52, & 72 will be deleted due to weak factor
loadings. For item 82 “often people make trouble by talking behind other’s back”, the
obtained factor loading (.28) is closer to the criteria. If we reset the criteria to retain
items as equivalent to factor loading of .25; there is a rationale to do so particular to
retain maximum items in WES. If we see the content of the item, it seems meaningful
to retain as it measures an important aspect which in turn may play very important
role to define how teachers see their relationships with colleagues. For supervisor
support items including 3 and 73 will be deleted. Item 13 “supervisors usually
compliment an employee who does something well” is showing factor loading of .29
which is considerable. Keeping in view the face validity of the item for assessing
supervisor support, the item will be retained.
For autonomy, item 4 “few employees have any important responsibilities”
will be deleted. There is possibility that interpretation of item may be done in
different context when responded by teachers of different hierarchical status. Item 54
“employees generally do not try to be unique and different” will be deleted due to
weak factor loadings. Item 44 “supervisors encourage employees to rely on
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themselves when a problem arises” is showing factor loading of .27. However,
unstandardised solutions yielded factor loading of .33, which provided rational to
retain this item. Similarly, item 84 “supervisors meet with employees regularly to
discuss their future work goals” showed .25 factor loading and with unstandardised
solution, it was .27. Keeping in view the meaningfulness of the item to assess
autonomy of employees; there is rationale to retain the item by setting the criteria of
acceptable factor loading up to .25. For task orientation, item 25 “things rarely get put
off till tomorrow”, is showing very weak factor loading perhaps due to perception of
working culture of educational sector. Therefore, this item will be deleted. For item
45, the obtained factor loading is .27 which gets enhanced up to .30 for
unstandardised solution and .39 when examine don on sub sample of public sector
employees. Therefore, this item will be retained. For item 55, the factor loading .27 is
considerable in order to retain maximum items in the measure. For work pressure,
items including 16 and 86 will be deleted due to weak factor loadings. Whereas, item
36 (.26) yielded better factor loading equivalent to .34 with unstandardised solution.
Items including 56 and 66 with factor loadings equivalent to .27 will be considered to
retain due to importance of work pressure dimension as reflected through significant
findings obtained in pilot study results. Therefore it seems wise to decide to retain
maximum items in this dimension.
For clarity, item 77 “rules and polices are constantly changing” is showing
weak factor loading (.14). The content of the item seems less applicable as in
educational sector, particularly the Government sector, the rules and regulations once
decided are not frequently subject to change. Therefore, it seems logical that this item
is not sufficiently contributing in assessing clarity of work procedures. Henceforth,
this item will be deleted. For managerial control, item 18, 68, and 88 are showing
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weak factor loadings; therefore, will be deleted. Item 58 “supervisors are always
checking on employees and supervise them very closely” with obtained factor loading
.26 will be retained to keep maximum items in inventory. Similarly, item 78
“employees are expected to conform rather strictly to the rules and customs” with
factor loading .26 will also be retained. Moreover, the content of items seems very
meaningful to assess managerial control. For innovation, item 49 “the same methods
have been used for quite a long time”, is showing weak factor loading and therefore,
will be deleted. Item 89 “things always seem to be changing” is showing very weak
factor loading perhaps due to working culture of educational sector. Therefore, this
item will be deleted. For physical comfort, items including 10, 30, 50 will be deleted
due to weak factor loadings. Item 90 “the rooms are well ventilated”, is showing
factor loading equivalent to .29. However, when examined on subsample of public
sector, it yielded factor loading equivalent to .29 with error variance .92. Therefore,
to keep maximum items in scale, item 90 will be retained.
In conclusion, following is the detail of retained items leaving the scale with
total of 66 items: Involvement (11, 31, 41, 61, 71, & 81); Co-worker Cohesion (12,
42, 62, & 82); Supervisor Support (13, 23, 33, 43, 53, 63, & 83); Autonomy (14, 24,
34, 44, 64, 74, & 84); Task Orientation (5, 15, 35, 45, 55, 65, 75, & 85); Work
Pressure (6, 26, 36, 46, 56, 66, & 76); Clarity (7, 17, 27, 37, 47, 57, 67, & 87);
Managerial Control (8, 28, 38, 48, 58, & 78); Innovation (9, 19, 29, 39, 59, 69, & 79);
and physical Comfort (20, 40, 60, 70, 80, & 90).
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Factor Structure of Burnout Models
Examining the factorial structure of burnout measure, one factor model
(Densten, 2001), three factor model (Maslach, Jackson, & Leiter, 1996), and an
extended five factor model was tested to see how well data may support the
competing models of burnout. Testing the unitary model of burnout indicated low
factor loadings for certain items including item 2 (.29) and 14 (.00) showing high
values of residuals. The factor loadings for item 2 which phrased “I feel used up at the
end of the day”, will be observed in three and five factor solutions as well. One of the
possible factors might be related to understandability of this item. However, swapping
back to unstandardised solution for this particular item exceeds factor loading up to
.57. Item 14 “I feel I’m working too hard on my job” is showing no association with
the construct. This item might be effected by element of social desirability. In work
settings, and especially in our collectivist culture (see Hofstede, 2001), sometimes
people are not much expressive in reporting about what exactly they are feeling.
Testing the three facet model of burnout, findings indicated that for item 2
corresponding to emotional exhaustion, there is considerable increase in magnitude of
factor loading compared to one factor solution. However, residual is still of high
values. Item 14 “I feel I’m working too hard on my job” which corresponds to
emotional exhaustion is showing poor association also in three factor model. Here, the
pressure of social desirability or difficulty in understandability of this particular item
seems very relevant. The standardised solution provided factor loadings of each item
pertaining to the relevant factors do not fall below 0.40.
The extent of variance that items of one factor can explain for another factor
can be examined to see the inter-correlations between subscales. This overlap of
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explaining variance might be taken as one of the possible explanations while
examining the strength of factor loadings. The subscale of emotional exhaustion (by
retaining and excluding item no 14) with strength of factor loadings from .62 to .42
has shown same magnitude of correlation coefficients with subscales of
depersonalization (r = .60, p = .01) and personal accomplishment (r = -.40, p = .01).
The subscale of depersonalization with range of factor loadings from .71 to .35 has
showed association with subscale of personal accomplishment (r = -.59, p = .01).
Testing the five factor model of burnout indicated that in comparison with one
and three factor models, item 2 is showing high strength of factor loading in five
factor solution along with a slight decrease in corresponding residual value as well.
For three factor solution, the magnitude of standardised factor loadings obtained for
each item do not fall below 0.40. For five factor solution, the factor loadings do not
fall below .41. In fact the factor loadings for each item are very similar across three
and five factor models. This indicates that both the three and five factor models
account for a similar amount of variance in each item.
The reliability estimate indicated that Cronbach’s alpha reliability coefficient
for original three factor model with 22 items (α = .50) got affected due to deletion of
one item from the inventory and reached at .47. For five factor solution, the alpha
coefficient for total inventory with 19 items demonstrated an alpha coefficient
equivalent to .47. In three factor solution, an increase in alpha coefficient has
observed for subscale of emotional exhaustion from .72 (for 9 items) to .74 (for 8
items). In five factor solution, emotional exhaustion with dimension of psychological
strain obtained an alpha coefficient of .50. For emotional exhaustion- somatic strain,
an alpha coefficient of .64 has obtained. The subscale of depersonalization
maintaining its original item nos. maintained an alpha coefficient of .69 both in three
and five factor solutions. The subscale of personal accomplishment with 8 items
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indicated an alpha coefficient of .76. In five factor solution, personal accomplishment
with dimension of self received an alpha coefficient of .59 and for personal
accomplishment-others an alpha coefficient of .60 has obtained.
In conclusion, findings supported that five factor model of burnout tested with
19 items found prominent support without excluding any item. For three factor model
of MBI and also in unitary model, item 14 of emotional exhaustion was identified to
be deleted from the inventory. In comparison of three and five factor solution, the
strength of factor loadings is also somewhat similar. There is not much variation in
strength of factor loadings. However, the comparison of fit indices put five-factor
solution at better position. A recent meta-analytic study (Worley et al., 2008) based on
45 factor analytic studies reported support for three factor structure of MBI measure.
The findings of present study may also be considered as supporting evidence for three
factor structure of MBI measure. However, present study contributed in establishing
the support for Densten’s (2001) extending model of MBI measure. Since, present
study found support for both original three-factor model and for its elaborated
structure; therefore, examining both three and five factor solutions for subsequent
analyses might yield a detailed picture of the role of burnout dimensions.
Factor Structure of Organizational Commitment Models
The present study focused to examine how well the dominant three factor
model of organizational commitment (Allen & Meyers, 1990) seems feasible to apply
on sample of university teachers in our cultural context. For comparison, a unitary
model was also tested. The values of fit indices found prominent support for three
factor structure model of organizational commitment compared to unitary model.
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Result of confirmatory factor analysis revealed that sub dimension of
continuance commitment is showing weak association with items including 10, 13,
and 15 for both unitary and three factor models. Considering the dominant three factor
model, item 15 “one of the serious consequences of leaving (name of organization)
would be the scarcity of available alternatives”, is comparatively showing better
association, though beyond the acceptable criteria, with the dimension of continuance
commitment with factor loading .24 and marginal contribution in producing variance
in responses (R² = 0.06). According to definition given by scale’ authors, the
dimension of ‘Continuance Commitment’ represents perceived cost of leaving an
organization. Evaluating the content of item 15, it reflects non-availability of
alternatives. If responses related to the concern with lack of available alternatives are
no more contributing in explaining commitment based on perceived cost of leaving an
organization, it probably indicates the strong sense of loyalty among teachers in many
of universities.
Responses given to item 13 i.e., “It wouldn’t be too costly for me to leave
(name of organization) now”, does not seem to produce variance (R² = .02). The
content of item reflects probably the perceived cost of changing a job. This indicates
that perhaps fear of losing one’s job due to lack of alternatives is a strong underlying
motive behind teachers’ attitude. The item does not seem to produce variance in the
responses of teachers commitment based on perceived cost of leaving an organization.
One of the possibilities behind this fear of losing job might be the lack of available
job alternatives.
Similarly, the content of item 10 “I am not concerned about what might
happen if I left (name of organization) without having another position lined up” is
not contributing in producing any variance (R² = 0.00) in responses towards
continuance commitment dimension. Linking the content of item 10 with the main
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concept of continuance commitment which represents the perceived cost of leaving an
organization, the item is representing the aspect of Job Security. This finding also
draws attention to comment on the current employment situation and limited
opportunities in job market in Pakistan, which is applicable for our sample of
university teachers. The possible fear of losing one’s job is perhaps a potential factor
due to which this particular item does not seem to contribute to the factor of
Continuance Commitment amongst the sample of Pakistani university teachers.
Furthermore, the authors of the Organizational Commitment Questionnaire
defined that ‘Continuance commitment’ represents a perceived cost of leaving an
organization. The items in this dimension mainly validated on Western sample are
showing contrasting differences in sample of Pakistani university teachers. Gelade,
Dobson, and Auer (2008) mentioned that potential sources of organizational
commitment may depend on cultural characteristics. In present findings, cross-
cultural variations with reference to costs of changing jobs and the job security are
clearly visible. The responses of our participants on this factor partly reflect on the
problematic job situation/job market, which seems to be directly linked with weak
Pakistani economy and political instability. Findings further reveal that the
participants of our sample are showing least concern with lack of available
alternatives in case of leaving the job. This indicates a sense of continuance
commitment irrespective of the concern which identifies the lack of available job
alternatives. This situation perhaps is carrying double loaded information. We may
interpret this as presence of strong sense of loyalty with their organizations. While; on
other hand, teachers are also showing concern with cost of leaving job especially with
reference to job security, indicating that it might be a case of tolerating problems at
work and keeping quiet for the sake of holding one’s job and associated benefits from
it.
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While critical evaluation of contents of rest of the retained items of
Continuance Commitment revealed that if we decide to delete the items, it wouldn’t
cause any effect in assessing the indicators intended to be measured by the respective
dimension. Shifting LISREL output options to unstandardised solutions, item 10 (.05),
13 (.17), and 15 (.28) are showing weak factor loadings with dimension of
continuance commitment.
Based on these observations, it may be concluded that confirmatory factor
analyses on scores of Organizational Commitment Questionnaire (22 items) is overall
well supported by the data except for the dimension of Continuance Commitment.
Findings suggested reducing certain items (10, 13, and 15), which were not
contributing a considerable variance in assessing the ‘Continuance Commitment’.
Therefore, for present sample leaving the subscale with remaining four items and total
of 19 items will help to refine the measure. In this way, the use of measure for
drawing inferences about study hypotheses may lead to strong effect size of the
findings. The estimate of Cronbach’s alpha coefficient also supported the deletion of
items from OCQ. For instance, the original form of Continuance Commitment has
shown alpha reliability coefficient of .52. By deleting items (10, 13, & 15) after the
results of confirmatory factor analysis, alpha coefficient rises up to .61. There is slight
increase in overall reliability of the scale as well. The alpha reliability coefficient of
scale with 22 items was .82 which rises up to .84 for scale with total of 19 items.
Keeping in view the retained items in the scale, the strength of factor loadings
ranged from .73 to .38. The relatively moderate nature of strength of factor loadings
might be associated with the overlap between subscales up to a substantial extent.
This would help to know how some items naturally associate with one factor and
could also be explaining a sizeable amount of the variance in another factor as well.
For example, the subscale of affective commitment is strongly related with normative
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commitment (r = .50, p = .01) and also with subscale of continuance commitment (r =
.29, p = .01). Continuance commitment is also showing association with normative
commitment (r = .33, p = .01).
In conclusion, findings of CFA recommended excluding items 10, 13, & 15 of
continuance commitment leaving the inventory with 19 items compared to original 22
items.
Factor Structure of Big- Five factor Model of Personality
The widely used Big Five factor structure of personality (Saucier, 1994) in
organizational behavior research was examined to see how well the data of the study
supports its applicability. Results of confirmatory factor analysis revealed that for
scale of Extroversion, i.e., adjective 8 “Withdrawn” is showing weak association with
factor loading of .24. Here, an interpretation may be directed towards understanding
of the concept itself. This might be relevant to sensitivities in admitting to certain
psychological experiences within the Pakistani culture. Even, if people do understand
it, they might not want to admit to it in a collectivist culture. Further, it might be an
aspect of some social desirability pressures at work with this item. However, looking
at LISREL output options, the unstandardised solution for this particular item yielded
better factor loading equivalent to .51. The item is capable of producing 6% variance
in responses towards extroversion tendencies. For an estimate of effect on internal
consistency of the scale, omitting item 8, Cronbach’s alpha coefficient slightly rises
from .76 to .77. The result of unstandardised solution provides a safer mean to retain
this item in the scale.
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The scale of Agreeableness, i.e., adjective 13 ”Cold” is showing weak
association with factor loading of .19. This is another item that possibly could cause
confusion in terms of understandability of the concept and relating it to the concept of
agreeableness. It’s also important to understand here that how often this happens to be
used by participants to reflect how they usually feel. However, swapping back to
unstandardised solution yielded better factor loading equivalent to .45. The item is
capable of producing 3.4% variance in responses towards the trait of agreeableness.
Cronbach’s alpha coefficient of the scale rises from .79 to .82 in case of deleting this
item. Due to satisfactory estimate yielded by unstandardised solution, it was decided
to retain this item in the scale.
On scale of conscientiousness, an adequate range of factor loadings (.42 to
.81) have obtained which fully supports the factor structure of this scale. The high
magnitude of alpha coefficient (.83) on scores of the scale is a satisfactory estimate of
internal consistency of the scale items.
For the scale of emotional stability, adjectives 25 “un-envious” and 26
“relaxed” showed poor association with the scale in both standardised and
unstandardised solutions. By deleting both items, there is considerable increase in
alpha coefficient from .18 to .28. ‘Un-envious’ might be a confusing concept for
respondents in terms of what it means. But, interestingly, a high loading for adjective
of ‘envious’ (.61) has obtained. So this seems to overlap with people’s ups-and-downs
emotionally, but this item (un-envious) is probably not associated with participants’
levels of emotional stability. The item 26 ‘Relaxed’ is also not much surprising to
show low factor loading due to sensitivities in admitting to certain psychological
experiences within the Pakistani culture.
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Looking at items with higher factor loadings including “Touchy” (item 31),
“Temperamental” (item 29), and “Envious” (Item 30), points out our attention to
cross-cultural differences. These experiences might explain well to reflect the
emotional experiences of those within a Pakistani collectivist culture, where generally
the expressions of extreme emotions such as being temperamental or touchy would be
acceptable. Whereas, for example in British culture, generally expressing emotions,
especially negative emotions would emphasize to avoid at all costs This reflects that
this would be even more pronounced in the workplace too.
For openness, i.e., adjective 37 “Complex” is showing weak association with
the scale with factor loading .16. Possibly, this may relate to somehow confusion
related to linking this concept with personality trait of openness to experience.
However, generally this concept seems simple to relate with the concept of openness.
Therefore, in case of swapping back to unstandardised solution, factor loading of the
item with scale rises up to .40, which is quiet satisfactory. Further, the item is
showing 2.5% variance in responses for openness tendencies. Evaluating the effect on
internal consistency of the scale, in case of deleting this item, there’s increase in
Cronbach’s alpha coefficient from .73 to .76. The situation reveled by unstandardised
solution provides a safer way to retain this item in the scale.
Data of the study well supports the factor structure of the Mini Marker scales
on sample of Pakistani teachers. However, it suggests that certain items including
item 25 (un-envious) and item 26 (relaxed) are showing weak association along with
high residuals with factor structure of emotional stability. Therefore, deleting these
items from the subscale will yield better representation of the trait structure in
subsequent analysis.
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It is also useful to examine the correlation between personality traits to assess
the overlap between the factors. It is due to the reason that in case of using many
models of personality, there might be some overlap between personality traits. Some
behaviors that might be indicative of a particular trait could also overlap to some
extent with behaviors related to another trait. If they overlap to a substantial extent,
this would help to know how some items naturally associate with one factor and could
also be explaining a sizeable amount of the variance in another factor as well.
Evaluating the subscale of emotional stability, it does correlate with agreeableness (r
= .34, p = .01) and conscientiousness (r = .31, p = .01). Specifically the item
measuring the emotional stability including “un-envious” is showing considerable
correlation with subscale of conscientiousness (r = -.13, p = .01) other than the total
score on emotional stability (r = .43, p = .01). The item “relaxed” measuring the
emotional stability is showing correlation with subscales of agreeableness (r = .21, p
= .01) other than the emotional stability (r = .35, p = .01). This might be explaining
why weak factor loadings have obtained on both items (un-envious and relaxed) of
emotional stability. For subscale of extroversion, the range of strength of factor
loadings has observed from .76 to .32. This moderate range of strength of factor
loadings might be linked with overlap of extroversion items with subscale of
conscientiousness (r = .24, p = .05). Agreeableness with range of factor loadings from
.74 to .53 is showing strong association with conscientiousness (r = .51, p = .01).
Openness with range of factor loadings from .76 to .34 has showed association with
agreeableness (r = .41, p = .01) and conscientiousness (r = .37, p = .01) as well. In
conclusion, findings recommended excluding item 25 and 26 from subscale of
emotional stability leaving the 38 item inventory.
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The study contributed to validate instruments with working group of teachers
of higher education level. This in turn helps to establish the validity of study measures
as a preliminary quality control test before performing the analysis related to
hypotheses testing.
Conclusion
The findings suggest that the factor loadings for most of the work
environment, burnout, commitment, and personality measures are within an
acceptable range and this seems to indicate that the concepts are still translating to the
Pakistani culture and within their working culture in Universities too. In Pakistan, no
noticeable earlier studies have tried to examine the factor structure of the measures
especially using sample of university teachers. For work environment scale, findings
suggested moderate support with recommendation of excluding certain items. For
burnout measure, there is prominent support for both three and five-factor model of
burnout with five factor model at better position. Incorporating original three and an
elaborative five-factor model in subsequent analyses will help to comprehensively
examine the role of burnout. However, in three factor model, only one item seems to
be might not translate that well to the Pakistani culture. The study is adding in
establishing the construct validity of five-factor model of the burnout, which
previously is relatively a less explored dimension in burnout research. The study also
added by demonstrating support for existing three factor structure of organizational
commitment. However, there are some discrepancies with reference to the
Commitment subscales (i.e., Continuance), and personality measures as well.
However, less supported items are culturally-bounded as especially in case of
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commitment where job circumstances in our culture are very much important for
respondents. Hence, overall, it is satisfying to note that there still seems to be a clear
factor structure, in the most part, for all of the assessments tools of the study and the
findings have got a decent level of fit for these proposed models. The findings of the
study also support the cross-cultural validity of the instruments used in source
language i.e., in the English versions. This further supports to use these measures in
source language (English) for prospective research using the sample of university
teachers.
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STEP II: MAIN STUDY
The Role of Work Environment in Predicting Burnout and Organizational
Commitment and the Moderating Role of Personal Variables
The step II of the main study aimed to test the following objectives.
Objectives of the Main Study
The phase II of the main study aimed to test the following objectives of the
study.
1. To establish the psychometric properties (i.e. reliability and validity) of
modified measures of study on the sample of main study.
2. To test the hypothesized predictive relationship of work environment with
burnout and organizational commitment.
3. To test the moderating role of personal variables in assessing the predictive
relationship with criterion variables.
Instruments
The modified versions of instruments; i.e. work environment, burnout,
organizational commitment, and personality variables are used, these are modified
based on the results of confirmatory factor analyses. These modifications were
utilized for the analyses of the main study- step II. Below are relevant details of each
instrument to be used in this phase of the study.
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Work Environment Scale (WES). Perceived group consensus about the
psychosocial characteristics of the academic settings was measured using modified
63-item Work Environment Scale with ten subscales (Moos, 1994). The format of the
scale responses were modified in Likert format in which a score of “1” was given to
response options of Mostly True and True and a score of “0” was given to options of
Mostly False and False.
The Maslach Burnout Inventory-Educators Survey (MBI-ES). Maslach
Burnout Inventory – Educators Survey (Maslach, Jackson, & Leiter, 1996) was
modified leaving a 21-item measure to assess the burnout reported by teachers in
terms of frequency of experiences ranged from never (0) to always (6). The 3-factor
inventory measures emotional exhaustion, depersonalization, and reduced sense of
personal accomplishment aspects of the burnout. This modifies version incorporated
the change in emotional exhaustion component by excluding item 14 ‘I feel I am
working too hard on my job’ from this subscale. Burnout is conceptualized as ranging
from low to moderate to high degree of experienced feelings.
Organizational Commitment Questionnaire (OCQ). Three facets
Organizational Commitment Questionnaire (Allen & Meyer, 1990) was modified
leaving the 19-item measure anchored with 5-point Likert format. The component of
affective commitment as the emotion-based view of organizational commitment was
retained in terms of its individual items. Continuance component as a perceived cost
of leaving an organization was modified by omitting item nos. 10 “I am not concerned
about what might happen if I left (name of organization) without having another
position lined up”, 13 “It wouldn’t be too costly for me to leave (name of
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173
organization) now”, and 15 “one of the serious consequences of leaving (name of
organization) would be the scarcity of available alternatives”. Normative component
as feelings of moral obligations or responsibilities was also retained in terms of its
indicators.
Mini-Marker Set (MM). The Mini-Marker Set (Saucier, 1994) as an
abbreviated version of Goldberg’s Big Five Personality Inventory was modified
leaving 38 items. Each item of subscales was retained in modified form except for the
subscale of emotional stability leaving 6 items by omitting adjectives 25 “un-envious”
and 26 “relaxed”.
Results
The data of main study (N = 426) collected from university teachers was
analyzed using SPSS 15.0.
Descriptive Analysis
Mean and standard deviation are computed (see Table 24) on the participants’
variations in (high, moderate, and low) the levels of work environment, burnout,
organizational commitment, and personality dimensions. The comparison of mean
values indicated that our sample of university teachers dominantly endorsed the work
environment on positive dimension. Teachers reported their academic settings as high
on clarity and task orientation and low on co-worker cohesion. Computing the levels
of burnout, teachers reported high on personal accomplishment and comparatively
low on depersonalization. In comparison with cut-off scores of normative sample of
177
174
teachers, the present sample showed high on personal accomplishment and low on
depersonalization. Teachers reported high on affective commitment and low on
continuance commitment. Moreover, scores indicated that teachers are high on
conscientiousness dimension of personality.
Following table 24 represents the levels (high, moderate, and low) computed
on scores of variables. The table 25 presents the Cronbach’s Alpha coefficients, inter-
correlations of variables using Pearson product moment correlation coefficients, and
the values of skewnees representing the distribution of scores.
177
Table 24 Mean & SD on scores representing Levels of Work Environment, Burnout, Organizational Commitment, and Personality Variables (N = 426) Levels of WES
Positive WE (n = 231)
Negative WE (n =
184)
Average WE (n =
11)
Overall WE (N = 426)
Levels of Burnout Levels of Organizational Commitment
Levels of Personality Factors
Mean S.D Mean S.D Mean S.D Mean S.D Mean S.D N Mean S.D N
Mean S.D
N
IN 5.43(.50) 2.25(.87) 4.00(.00) 4.18(1.46) High EE 27.99(31.47) 5.55(4.22) 199 High AC 38.51(3.36) 211 High EX 54.30(6.38) 208 CC 3.30(.46) .72(.45) 2.00(.00) 2.02(1.16) Mod. EE 10.28(21.35) 4.56(2.97) 206 Mod. AC 27.05(3.87) 185 Low EX 35.67(5.67) 200 SS 5.69(.76) 2.28(.94) 4.00(.00) 4.33(1.60) Low EE 17.99(9.74) .03(4.36) 21 Low AC 33.00(.00) 30 Mod. EX 44.02(.10) 18 AT 6.45(.50) 2.93(1.13) 5.00(.00) 4.71(1.69) Total EE 18.21 9.38 426 Total AC 33.15(6.52) 426 Total EX 45.12(10.87) 426 TO 6.86(.79) 2.97(1.18) 5.00(.00) 5.43(1.90) High DP 15.73(19.05) 4.30(2.77) 193 High CC 16.48(1.35) 184 High AG 61.83(4.45) 196 WP 5.72(.77) 2.27(.86) 4.00(.00) 3.99(1.64) Mod. DP 10.44(10.56) .04(1.47) 30 Mod. CC 10.82(2.03) 194 Mod. AG 43.01(7.91) 206 CL 7.01(.77) 2.80(1.25) 5.00(.00) 5.20(2.10) Low. DP 2.72(7.05) 2.12(2.65) 203 High CC 14.00(.00) 48 Low AG 53.97(.07) 24 MC 5.47(.50) 2.29(.81) 4.00(.00) 4.09(1.49) Total DP 8.07 6.32 426 Total CC 13.62(3.13) 426 Total AG 52.29(11.10) 426 INN 5.70(.75) 2.05(1.04) 4.00(.00) 4.18(1.82) High PA 41.50(41.87) 5.43(3.50) 204 High NC 24.91(1.81) 158 High CT 62.49(4.70) 204 PC 5.40(.49) 2.40(.79) 4.00(.00) 4.32(1.35) Mod. PA 35.00(33.86) .00(1.66) 19 Mod. NC 18.07(2.78) 204 Mod. CT 42.20(7.26) 212 WES 32.11(4.90) 14.85(7.04) 24.00(.00) 24.45(10.30) Low PA 25.92(24.15) 3.68(4.66) 203 Low NC 22.00(.00) 64 Low CT 54.0(6.15) 10 Total PA 33.78 8.87 426 Total NC 21.20(3.85) 426 Total CT 52.20(11.73) 426 Burnout 60.06 12.08 426 OCQ 67.96(10.56) 426 HighES 39.32(3.70) 199 Low ES 28.63(3.55) 194 Mod. ES 34.00(.00) 33 Total ES 34.04(6.20) 426 High OP 59.53(5.22) 203 Low OP 41.83(6.22) 204 Mod. OP 52.0(3.11) 19 Total OP 50.72(10.32) 426 Note. WES = work environment scale, RtD = relationship dimension, IN = involvement, CC = co-worker cohesion, SS = Supervisor Support, PGD = personal growth dimension, AT = autonomy, TO = task orientation, WP = work pressure, SMD = system maintenance and change dimension, CL = clarity, MC = managerial control, INN = innovation, PC = physical comfort, MBI = burnout, EE = emotional exhaustion, Dp = depersonalization, PA = personal accomplishment, OCQ = organizational commitment, AC = affective commitment, CC = continuance commitment, NC = normative commitment, EX = extroverted, AG = agreeableness, CT = conscientiousness, ES = emotional stability, and OP = openness.
Table 25
177
Cronbach’s Alpha (on the diagonal), Pearson Product Moment Correlations for Predictive, Criterion, and Moderator Variables (N = 426) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Skew 1 WES .88 -.57 2 RtD .82** .74 -.12 3 IN .74** .80** .55 -.67 4 CC .57** .77** .45** .42 .01 5 SS .67** .85** .49** .53** .46 -.40 6 PGD .84** .55** .51** .40** .44** .68 -.65 7 AT .63** .48** .33** .31** .51** .67** .54 -.62 8 TO .78** .59** .59** .42** .43** .80** .34** .63 -.67 9 WP .33** .06 .12* -.08 -.04 .67** .09 .27** .47 -.09 10 SMD .90** .61** .61** .35** .49** .63** .49** .63** .18** .79 -.63 11 CL .77** .57** .53** .36** .49** .50** .41** .58** .02 .85** .69 -.52 12 MC .35** .02 .14** -.11* -.01 .31** .17** .22** .26** .50** .29** .53 -.48 13 INN .73** .61** .55** .43** .49** .52** .44** .48** .17** .73** .47** -.04 .61 -.46 14 PC .68** .46** .49** .26** .35** .47** .36** .50** .11* .76** .54** .21** .50** .44 -.70 15 MBI -.29** -.29** -.26** -.23** -.21** -.24** -.14** -.26** .09* -.23** -.21** -.11* -.19** -.13** .86 .20 16 MBI (five) -.28** -.28** -.25** -.23** -.20** -.23** -.12* -.26** .10* -.21** -.19** -.12 -.20** -.11* .99** .86 .22 17 EE -.30** -.31** -.29** -.21** -.24** -.21** -.20** -.27** .05 -.27** -.27** -.08 -.19** -.20** .83** .79** .74 .56 18 Dp -.23** -.23** -.22** -.18** -.16** -.19** -.08 -.22** -.11* -.17** -.16** -.11* -.12* -.08 .85** .86** .60** .69 .94 19 PA .17** .17** .13** .17** .12* .19** .04 .17** .18** .11* .08 .10* .11* .01 .81*** .81** -.40** -.59** .76 -.69 1 2 3 4 5 6 7 8 9 10 11 12 13 14 20 21 22 23 20 OCQ .40** .29** .30** .17** .22** .34** .29** .30** .13** .38** .34** .20** .27** .26** .84 -.66 21 AC .44** .36** .33** .27** .28** .39** .31** .34** .16** .39** .33** .18** .29** .29** .88** .83 -.49 22 CC .12* -.01 .11* -.07 -.04 .12* .07 .12* .05 .16** .18** .12* .06 .10* .59** .29** .61 .06 23 NC .24** .16** .17** .07 .15** .18** .20** .15** .03 .25** .22** .15** .20** .14** .77** .50** .33** .64 -.60
Continued…
177
1 2 3 4 5 6 7 8 9 10 11 12 13 14 24 25 26 27 28 Skew 24 EX .02 .01 .04 .01 .00 .03 .01 -.01 .09 .01 .01 .01 .06 .02 .76 .15 25 AG .08 .05 .05 .07 .02 .13** .01 .10* .17** .04 .00 .07 .04 .01 .41** .79 -.91 26 CT .17** .11* .16** .11* .01 .14* -.01 .19** .11* .17** .15** .13** .10* .11* .42** .55** .83 -.55 27 ES .12* .11* .17** .05 .04 .10* .10* .10* -.00 .10* .07 .09 .07 .07 .09 .18** .25** .28 .09 28 OP .05 .06 .04 .08 .04 .06 .00 -.05 .07 .01 -.04 .03 .05 .01 .35** .53** .52** .09 .73 -.39 Note. WES = work environment scale, RtD = relationship dimension, IN = involvement, CC = co-worker cohesion, SS = Supervisor Support, PGD = personal growth dimension, AT = autonomy, TO = task orientation, WP = work pressure, SMD = system maintenance and change dimension, CL = clarity, MC = managerial control, INN = innovation, PC = physical comfort, MBI = burnout, EE = emotional exhaustion, Dp = depersonalization, PA = personal accomplishment, OCQ = organizational commitment, AC = affective commitment, CC = continuance commitment, NC = normative commitment, EX = extroverted, AG = agreeableness, CT = conscientiousness, ES = emotional stability, and OP = openness. *p = .05, **p = .01 .
177
Findings as shown in Table 25 highlighted the estimate about internal
consistency of the Work Environment Scale with reduced items. High magnitude of
alpha coefficient has obtained on total scores of WES (α = .88); whereas, the primary
scales of Relationship Dimension (α = .74), Personal Growth Dimension (α = .68),
and System Maintenance and Change Dimension (α = .79) have demonstrated
substantial evidence for concluding satisfactory estimate of internal consistency of the
measure. The reliability coefficients of secondary sub scales ranged from moderate (α
= .69) for the subscale of clarity to relatively low (α = .42) for co-worker cohesion.
For MBI, the refined 21-item measure yielded high magnitude of alpha
coefficient for total scores (α = .86). Examining the subscales’ internal consistency,
emotional exhaustion (α =.74) and personal accomplishment (α =.76) demonstrated
good internal consistency. The subscale of depersonalization also yielded satisfactory
estimate (α = .69) of the internal consistency. The five factor model of burnout with
19 items also yielded high magnitude of alpha coefficient (α = .86).
The magnitude of alpha coefficients for total scores on Organizational
Commitment Questionnaire (α = .84) and for subscales of affective commitment (α =
.83) is high. The moderate level of reliability indices have obtained for subscales of
continuance commitment (α = .61), and normative commitment (α = .64). Results
indicated that the scales of Mini Markers set including Extraversion (α = .76),
Agreeableness (α = .79), Conscientiousness (α = .83), and Openness (α = .73)
demonstrated high internal consistency. Whereas, the subscale of emotional stability
(α = .28) yielded low magnitude of alpha coefficient.
Results shown in Table 25 indicated bivariate correlations between study
variables. The inter-scale correlations of study constructs (see discussion section)
provides an estimate of construct validity. Examining the link between work
environment and burnout, work environment dimensions except work pressure are
177
showing significant relationship. For five factor model of burnout (with reduced 19
items), each of work environment dimensions are significantly associated. Emotional
exhaustion is found inversely linked to most of indicators of WES. Emotional
exhaustion has showed positive but non-significant relationship with work pressure.
Depersonalization has also found to be inversely associated with most of the
indicators of work environment. However, the subscale of autonomy has shown non-
significant relationship with depersonalization. Personal accomplishment has showed
positive relationship with most of the indicators of WES. However, the subscales of
autonomy, clarity, and physical comfort showed non-significant relationship with
personal accomplishment.
The pattern of relationship between work environment indicators and
organizational commitment revealed that each of the facets of work environment is
significantly linked in positive direction with organizational commitment and with
affective commitment. For subcomponent of continuance commitment, dimensions of
task orientation (r = .12, p < .05), clarity (r = .18, p < .01), managerial control (r =
.12, p < .05), and physical comfort (r = .10, p < .05) have found to be significantly
linked. For normative commitment, most of the dimensions are associated with work
environment factors except the dimensions of coworker cohesion and work pressure.
For moderator variables, extroversion is showing non-significant relationship
with work environment and each of its dimensions. Agreeableness is showing
significant positive relationship with task orientation (r = .10, p < .05) and with work
pressure (r = .17, p < .01). Relationship between conscientiousness and most of work
environment components have observed. Moreover, emotional stability and openness
have found significant association only with few indicators of work environment. The
values of skewness reported in t 25 are within acceptable range.
177
Predictive Relationship between Work Environment and Burnout
The predictive relationship of work environment with burnout was
investigated among University teachers using multiple regression analysis- enter
method. The tables below (26-30) presents the results for burnout and its components
including components of elaborated factor structure regressed against work
environment and its variables.
Table 26
Multiple Regression Analysis on scores of Emotional Exhaustion and its components
by Work Environment (N = 426)
Note. IN = involvement, CC = co-worker cohesion, SS = Supervisor Support, AT = autonomy, TO =
task orientation, WP = work pressure, CL = clarity, MC = managerial control, INN = innovation, and
PC = physical comfort.
*p < .05, **p = .00
WES
EE Psy. Strain Somatic Strain
B
SE Β
Β
95% CI
LL UL
B
SE Β
Β
95% CI
LL UL
B
SE Β
β
95% CI
LL UL
IN -.97 .42 -.15* -1.79 -.16 -.50 .17 -.19** -.84 -.16 -.33 .24 -.09 -.79 .14
CC -.52 .47 -.07 -1.45 .41 -.35 .20 -.11 -.74 .04 -.39 .27 -.09 -.92 .14
SS -.15 .38 -.03 -.90 .61 -.06 .16 -.02 -.37 .25 .03 .22 .01 -.40 .46
A -.39 .32 -.07 -1.01 .23 .09 .13 .04 -.17 .35 -.32 .18 -.10 -.67 .04
TO -.56 .33 -.11
-1.20 .09-.21
.14-.11 -.49 .04 -.38
.19 -.14*
-.75 -
.02
WP .64 .29 .11* .07 1.22 .18 .12 .08 -.06 .42 .45 .17 .14** .12 .78
CT -.28 .30 -.06 -.87 .31 .10 .12 .06 -.14 .35 -.04 .17 -.02 -.38 .30
MC -.25 .33 -.04 -.90 .41 -.40 .14 -.15** -.67 -.13 -.05 .19 -.02 -.43 .32
INV .17 .33 .03 -.47 .81 -.13 .14 -.06 -.40 .13 .22 .19 .08 -.14 .58
PC -.03 .42 -.01 -.86 .79 .14 .17 .05 -.20 .49 -.23 .24 -.06 -.69 .24
R = .36, R2= .13,
F = 6.09**
R = .35, R2= .12,
F = 5.65**
R = .32, R2= .10,
F = 4.59**
177
Findings as shown in Table 26 revealed that work environment variables
including involvement and work pressure produced significant equation (F = 6.09, p =
.00) when regressed against scores of emotional exhaustion and accounts for 13%
variance (R2 = .13). Involvement as a negative predictor (B = -.97, t = 2.34, p < .02)
implies that one unit increase in employees’ involvement will result in a .97 decrease
in emotional exhaustion. Interpreting the beta value (β = -.15) implies that a change of
one standard deviation in involvement will result in a change of .15 standard deviation
in emotional exhaustion. Work pressure as a positive predictor accounts for .64 unit
increase in emotional exhaustion (B = .64, t = 2.19, p < .05). Beta value (β = .11)
implies that a change of one standard deviation in work pressure will result in .11
standard deviation change in emotional exhaustion The values of standardised betas
indicated that involvement is relatively a stronger predictor (β = -.15) compared to
work pressure (β = .11). Evaluating the elaborated structure of emotional exhaustion
namely psychological strain indicated that involvement (B = -.50, β = -.19, t = 2.91, p
= .00) and managerial control are negative predictors (B = -.40, β = -.15, t = 2.91, p =
.00). For somatic strain, task orientation is a negative predictor (B = -.38, β = -.14, t =
2.05, p < .05) and work pressure is a positive predictor (B = .45, β = .14, t = 2.68, p <
.05).
The obtained values of VIF (1.25 to 2.14) and tolerance (.47 to .80) are in
acceptable range, which ensures that multicollinearity is not likely a threat to the
substantive conclusions drawn from the parameter estimates.
177
Table 27
Multiple Regression Analysis on scores of Depersonalization by Work Environment
(N = 426)
Depersonalization
Work Environment Variables B SE Β Β 95% CI
LL UL
Involvement -.56 .29 -.13* -1.12 .01
Coworker Cohesion -.50 .33 -.09 -1.14 .15
Supervisor Support -.27 .27 -.07 -.79 .25
Autonomy .18 .22 .05 -.25 .61
Task Orientation -.31 .23 -.09 -.76 .13
Work Pressure -.22 .20 -.06 -.62 .18
Clarity -.08 .21 -.03 -.49 .32
Managerial Control -.36 .23 -.08 -.81 .10
Innovation .11 .23 .03 -.33 .55
Physical Comfort .38 .29 .08 -.19 .95
R = .29, R2= .08, F = 3.67**
*p = .05, **p = .00
Findings as shown in Table 27 revealed involvement as a negative predictor (B
= -.45, β = -.13, t = 1.93, p = .05) explaining 8% variance in depersonalization (R2=
.08, F = 3.67, p = .00). Beta values indicated that one unit change in involvement
leads to .45 unit decrease in depersonalization.
177
Table 28
Multiple Regression Analysis on scores of Personal Accomplishment and its
components by Work Environment (N = 426)
Note. IN = involvement, CC = co-worker cohesion, SS = Supervisor Support, AT = autonomy, TO =
task orientation, WP = work pressure, CL = clarity, MC = managerial control, INN = innovation, and
PC = physical comfort.
*p ≤ .05, **p = .00
Findings as shown in table 28 indicated that together coworker cohesion, work
pressure, and physical comfort accounts for 8% variance in personal accomplishment
(R2= .08, F = 3.65, p = .00). Co-worker cohesion (B = .93, β = .12, t = 2.03, p < .05)
and work pressure (B = .76, β = .14, t = 2.67, p < .05) are positive predictors; whereas,
physical comfort is a negative predictor (B = -.79, β = -.12, t = 1.96, p = .05).
Standardised beta values indicated that comparatively work pressure is a stronger
predictor of personal accomplishment. Evaluating the elaborated structure namely the
sub-component of self related personal accomplishment, task orientation (B = .41, β =
WES
PA PA (Self) PA (others)
B
SE Β
Β
95% CI
LL UL
B
SE Β
Β
95% CI
LL UL
B
SE Β
β
95% CI
LL UL
IN .16 .40 .03 -.64 .95 .05 .22 .02 -.38 .48 .08 .19 .03 -.30 .45
CC .93 .46 .12* .03 1.84 .24 .25 .06 -.25 .73 .60 .22 .17* .17 1.03
SS .44 .37 .08 -.29 1.17 .34 .20 .11 -.05 .74 .13 .18 .05 -.22 .48
AT -.39 .31 -.08 -1.00 .21 -.24 .17 -.08 -.56 .09 -.09 .15 -.04 -.38 .20
TO .38 .32 .08 -.25 1.00 .41 .17 .16* .07 .75 -.02 .15 -.01 -.32 .28
WP .76 .28 .14* .20 1.32 .46 .15 .16** .16 .77 .24 .14 .09 -.03 .51
CT -.06 .29 -.02 -.64 .51 -.19 .16 -.08 -.50 .12 .10 .14 .05 -.17 .38
MC .55 .32 .09 -.08 1.19 .20 .18 .06 -.14 .54 .27 .15 .09 -.04 .57
INV .21 .32 .04 -.41 .83 -.00 .17 -.00 -.34 .34 .07 .15 .03 -.23 .36
PC -.79 .41 -.12* -1.59 .00 -.42 .22 -.12 -.85 .02 -.38 .19 -.12* -.76 .00
R = .28, R2= .08,
F = 3.65**
R = .29, R2 = .08,
F = 3.67**
R = .25, R2 = .06,
F = 2.81**
177
.16, t = 2.39, p < .05) and work pressure (B = .46, β = .16, t = 3.01, p = .00) are
positive predictors. For sub-component of personal accomplishment by others, co-
worker cohesion (B = .60, β = .17, t = 2.73, p < .05) is a positive predictor; whereas,
physical comfort (B = -.38, β = -.12, t = 1.96, p = .05) is a negative predictor.
Taking the burnout as a single composite construct, following analysis
highlighted the findings obtained for total scores on burnout (for three and five factor
models) regressed against the subscales of work environment.
Table 29
Multiple Regression Analysis on scores of Burnout by Work Environment (N = 426)
Note. IN = involvement, CC = co-worker cohesion, SS = Supervisor Support, AT = autonomy, TO =
task orientation, WP = work pressure, CL = clarity, MC = managerial control, INN = innovation, and
PC = physical comfort.
*p < .05, **p = .00
WES
Burnout (three
factor model)
Burnout (five factor
model)
B SE Β β 95 % CI
LL UL B SE Β β
95% CI
LL UL
IN -1.69 .91 -.12 -3.48 .09 -1.52 .82 -.12 -3.13 .10
CC -1.94 1.03 -.11 -3.97 .09 -2.08 .94 -.13* -3.92 -.24
SS -.88 .84 -.07 -2.52 .76 -.79 .76 -.07 -2.27 .70
AT .20 .69 .02 -1.15 1.55 .29 .62 .03 -.93 1.51
TO -1.22 .72 -.11 -2.63 .189 -1.29 .65 -.13* -2.56 -.02
WP -.33 .64 -.03 -1.58 .92 -.29 .58 -.03 -1.43 .85
CT -.31 .65 -.03 -1.60 .97 .06 .59 .01 -1.11 1.22
MC -1.17 .72 -.09 -2.59 .25 -1.29 .66 -.11* -2.57 .00
INV .06 .71 .01 -1.33 1.45 .12 .64 .01 -1.14 1.38
PC 1.11 .91 .07 -.68 2.91 1.07 .83 .08 -.55 2.70
R = .33, R2 = .11, F = 5.06** R = .33, R2 = .11, F = 5.11**
177
Results in Table 29 indicated that model predicting burnout (three factor
model) as composite factor indicated overall moderate fit (R = .33, F = 5.06, p = .00).
However, none of the variables of WES reaches at statistical significance. Evaluating
the five-factor model of burnout revealed that co-worker cohesion (B = -2.08, β = -
.13, t = 2.22, p = .03), task orientation (B = -1.29, β = -.13, t = 1.99, p = .05), and
managerial control (B = -1.29, β = -.11, t = 1.96, p = .05) are negative predictors.
Together, these values account for11% variability in five-factor structure of burnout
(R2 =.11 F = 5.11, p = .00).
Following bivariate regression analysis highlighted the extent of relationship
between burnout (total scores and subscales) and the work environment as a single
composite construct.
177
Table 30
Regression Analysis on Burnout and its components by total scores of Work
Environment (N = 426)
Work Environment B SE B Β 95% CI
LL UL Burnout (three-factor model)
-.57 .09 -.29** -.75 -.39
R = .28, R2 = .08, F = 38.49** Burnout (five-factor model)
-.49 .08 -.28** -.65 -.33
R = .28, R2 = .08, F = 34.65** Emotional Exhaustion
-.28 .04 -.30** -.36 -.19
R = .30, R2= .09, F = 42.71** Emotional Exhaustion-Psy. Strain
-.10 .02 -.28** -.14 -.07
R = .28, R2= .08, F = 34.77** Emotional Exhaustion- Somatic Strain
-.12 .02 -.24** -.17 -.07
R = .24, R2= .06, F = 25.17** Depersonalization
-.14 .03 -.23** -.20 -.08
R = .23, R2= .05, F = 22.93** Personal Accomplishment
.15 .04 .17** .07 .23
R = .17, R2 = .03, F = 13.23** Personal Accomplishment- Self
.06 .02 .13* .02 .10
R = .13, R2 = .02, F = 6.89* Personal Accomplishment- Others .06 .02 .16** .03 .10
R = .16, R2 = .03, F = 10.89** *p < .05, **p = .00
177
Findings in Table 30 indicate that work environment is a negative predictor
when regressed against the total scores of burnout three-factor and five-factor model
by accounting for small variance in burnout (R2 = .05). Work environment as a
negative predictor (B = -.28, β = -.30, t = 6.54, p = .00) accounts for 8% variance in
emotional exhaustion (R2 = .08, F = 34.77, p = .00). However, emotional exhaustion
as a whole is a stronger predictor (β = -.30) compared to its sub-components, e.g.,
psychological strain (β = -.28), and somatic strain (β = -.24).
In explaining depersonalization, work environment accounts for marginal
variation (R2 = .05) with a significant model fit (F = 22.93, p = .00). Model
parameters indicated work environment as a negative predictor of depersonalization
(B = -.14, β = -.23, t = 4.79, p = .00).
Work environment accounts for a very marginal (R2 = .03) variance in reduced
sense of personal accomplishment with significant model fit (F = 13.29, p = .00).
Model parameters indicated work environment as a positive predictor of personal
accomplishment (B = .15, β = .17, t = 3.64, p = .00). However, personal
accomplishment as a whole is a stronger predictor (β = .17) compared to its sub-
components, e..g, personal accomplishment related to self (β = .13), and others (β =
.16).
Predictive Relationship between Work Environment and Organizational
Commitment
The hypothesized predictive relationship of work environment with
organizational commitment was investigated using multiple regression analysis- enter
177
method. Below is the detail of analyses using scores of affective, continuance,
normative, and overall commitment regressed against the subscales of work
environment as well as the total scores.
Table 31
Multiple Regression Analysis on Affective Commitment by Work Environment (N =
426)
Affective Commitment
Work Environment Variables B SE BΒ
95% CI
LL UL
Involvement .43 .28 .10 -.11 .97
Coworker Cohesion .56 .32 .10 -.06 1.18
Supervisor Support .12 .25 .03 -.38 .62
Autonomy .51 .21 .13* .10 .92
Task Orientation .22 .22 .06 -.21 .64
Work Pressure .33 .19 .08 -.05 .71
Clarity .23 .20 .08 -.16 .62
Managerial Control .40 .22 .09 -.03 .83
Innovation .06 .22 .02 -.36 .48
Physical Comfort .24 .28 .05 -.31 .79
R = .46, R2 = .20, F = 10.64**
*p < .05, **p = .00
Results as shown in Table 31 indicated that autonomy as a positive predictor
produced significant equation when regressed against the scores of affective
commitment (F = 10.64, p = .00) and explains 20% variance (R2 = .20) in affective
commitment. The value of unstanderdized beta for autonomy (B = .51) indicated that
one unit increase in autonomy will lead to increase in affective commitment by .51
177
units. Whereas, standardised beta (β = .13) explains .13 standard deviation change in
affective commitment with significant t-statistics (t = 2.43, p = .02).
To check the issue of multicollinearity, the values of VIF are below 10 ranged
from 1.25 to 2.14, which is desirable. The values of tolerance should be greater than
.20 and the obtained values of tolerance ranged from .47 to .80.
Table 32
Multiple Regression Analysis on Continuance Commitment by Work Environment (N
= 426)
Continuance Commitment
Work Environment Variables B SE Β
β 95% CI
LL UL
Involvement .20 .14 .09 -.08 .48
Coworker Cohesion -.37 .16 -.14* -.69 -.05
Supervisor Support -.32 .13 -.17* -.58 -.07
Autonomy .13 .11 .07 -.08 .34
Task Orientation .07 .11 .04 -.15 .29
Work Pressure .04 .10 .02 -.16 .24
Clarity .32 .10 .22** .12 .52
Managerial Control .00 .11 .00 -.22 .23
Innovation .01 .11 .01 -.21 .23
Physical Comfort -.06 .14 -.03 -.35 .22
R = .27, R2= .08, F = 3.35**
*p < .05, **p = .00
Results as shown in Table 32 indicated that coworker-cohesion, supervisor
support, and clarity explains 8% variance (R2= .08) in continuance commitment (F =
3.35, p = .00). Standardised beta values indicated that clarity is the strongest predictor
177
compared to others. Findings revealed coworker-cohesion (B = -.37, β = -.14, t = 2.27,
p < .05) and supervisor support (B = -.32, β = -.17, t = 2.46, p < .05) as negative
predictors; whereas, the dimension of clarity is a positive predictor (B = .32, β = .22, t
= 3.13, p = .00).
Table 33
Multiple Regression Analysis on Normative Commitment by Work Environment (N =
426)
Normative Commitment
Work Environment Variables B SE Β β
95% CI
LL UL
Involvement .10 .18 .04 -.24 .45
Coworker Cohesion -.15 .20 -.05 -.55 .24
Supervisor Support .01 .16 .00 -.31 .32
Autonomy .25 .13 .11 -.01 .51
Task Orientation -.03 .14 -.01 -.30 .24
Work Pressure -.06 .12 -.02 -.30 .19
Clarity .22 .13 .12 -.03 .47
Managerial Control .24 .14 .10 -.03 .52
Innovation .25 .14 .12 -.02 .52
Physical Comfort -.13 .18 -.04 -.47 .22
R = .29, R2 = .08, F = 3.69*
*p = .00
Results as shown in Table 33 highlighted overall model predicting normative
commitment by work environment variables is significant (F = 3.69, p = .00).
However, individual variables were not able to reach at significance level to interpret
as significant predictors. For example, autonomy and innovation showed p level
equivalent to .06.
177
Table 34
Multiple Regression Analysis on Organizational Commitment by Work Environment
(N = 426)
Organizational Commitment
Work Environment Variables B SE Bβ
95% CI
LL UL
Involvement .74 .46 .10 -.16 1.63
Coworker Cohesion .04 .52 .00 -.99 1.06
Supervisor Support -.19 .42 -.03 -1.02 .63
Autonomy .89 .35 .14* .21 1.56
Task Orientation .26 .36 .05 -.45 .97
Work Pressure .32 .32 .05 -.32 .95
Clarity .78 .33 .16* .13 1.42
Managerial Control .65 .36 .09 -.07 1.36
Innovation .32 .36 .06 -.38 1.02
Physical Comfort .05 .46 .01 -.85 .95
R = .42, R2= .17, F = 8.68**
*p < .05, **p = .00
Results as shown in Table 34 indicated that autonomy and clarity as positive
predictors produced significant equation when regressed against the scores of
affective commitment (F = 8.68, p = .00). Together, both variables are able to explain
17% variance (R2 = .17) in scores of affective commitment. Comparing the strength of
predictors using standardized Beta values, clarity is slightly on edge (β = .16)
compared to autonomy (β = .14). The value of unstanderdized beta for autonomy (B =
.89) indicated that one unit increase in autonomy will lead to increase in affective
commitment by .89 units. Whereas, standardised beta (β = .14) accounts for .14
standard deviation change with significant t-statistics (t = 2.56, p = .01). Similarly,
clarity accounts for .78 unit change, and .16 standard deviation change in affective
177
commitment (B = .78, β = .16, t = 2.37, p < .05). The values of VIF and tolerance are
desirable ensuring that multicollinearity is not likely a threat to the findings drawn
from the parameter estimates.
Work environment as a composite factor was regressed against total scores of
organizational commitment and its subscales. For this, following table explains the
bivariate regression analysis.
Table 35
Regression Analysis on Organizational Commitment and its components by total
scores on Work Environment (N = 426)
Work Environment B SE Β Β 95% CI
LL UL
Organizational Commitment (total scores)
.41 .05 .40** .32 .50
R =.40, R2 = .16, F = 79.29**
Affective Commitment
.28 .03 .44** .23 .34
R = .45, R2 = .20, F = 104.20**
Continuance Commitment
.04 .02 .12* .01 .07
R = .12, R2 = .02, F = 6.24*
Normative Commitment
.09 .02 .24** .06 .12
R = .24, R2= .06, F = 25.74**
*p < .05, ** p = .00
Results as shown in Table 35 indicated that total scores of work environment
regressed against the total scores of organizational commitment accounts for a
177
significance model fit (F = 79.29, p = .00) and explains 16 % variation in
organizational commitment (R2 = .16). The model parameters (B = .41, β = .40, t =
8.90, p = .00) revealed that one unit increase in work environment results in increase
of organizational commitment by .41 units along with .40 standard deviation change
in organizational commitment. For affective commitment, work environment explains
20% variance in affective commitment (R2 = .20, F = 104.20, p = .00). Results
revealed that work environment predicts small variance in continuance commitment
(R2= .02 F = 6.24, p = .01) and in normative commitment (R2= .06, F = 25.74, p =
.00).
The Moderating Role of Personal Variable
For explaining predicting relationship of work environment with burnout and
organizational commitment, the moderating role of personal variables was
investigated by using Multiple Moderated Regression Analysis (MMR). The
methodology to test hypotheses regarding moderator variables with MMR involving
both dichotomous and continuous moderators relies upon the statistical test of the
unstandardized regression coefficient carrying information about the moderating (i.e.,
interaction) effect as mentioned by Aguinis and Stone-Romero (1997). In this way, a
regression model formed including predictor variables X, Z, and the X • Z product
term, which carries information regarding their interaction. The statistical significance
of the unstandardized regression coefficient of the product term (i.e., bx.z) indicates
the presence of the interaction.
In MMR process, the approach of hierarchical regression analysis was used in
which work environment (predictor) and each of the dimensions of personality (as
177
second predictor variables) were entered separately in first step (model 1) followed by
entering these variables again in next step (model 2) along with the interaction term
(predictor variable multiplied by the moderator variable). Hair, Anderson, Tatham and
Black (1998) suggested that due to multicollinearity among the variables, it is
recommended to first estimate the original (unmoderated) equation (model 1) and
then estimates the moderated relationships (model 2). Therefore, the incremental
effect assessed instead of individual values. In this case, a statistically significant
change in R2 indicates a significant moderator effect. For estimating moderator effect,
Aiken & West (1991) recommends using centering procedure, which involves
removing mean from raw score of variables leaving deviation scores. This may also
act as an advantage to reduce multicollinearity among predictor variables. Therefore,
for performing MMR, scores of independent and continuous moderator variables are
centered before deriving interaction term. Moreover, following the procedure
recommended by Jose (2008), the interaction plot along with significance test of
slopes was used for interpretation. The graphical display of interaction make use of
computed scores reflecting high, moderate, and low levels of the main effect of
independent variable (continuous variable) and of the moderator variable. Following
Aiken and West (1991), the levels are computed by using the mean as the medium
value, one standard deviation above the mean as high value, and one standard
deviation below the mean as the low value. In case of categorical variables, the
interaction plot will depict moderation through two lines representing the concerned
groups.
Following is the detail of moderator analysis computed on scores of
personality dimensions and considering composite scores of predictor and criterion
variables.
177
Table 36
Moderating Effects of Personality in predicting Burnout- three-factor model (N =
426)
Predictors B SE Β Β 95% CI LL UL
Step 1
Work Environment -.55 .08 -.28** -.72 -.39Extraversion -.70 .08 -.38** -.86 -.54R = .47, R2 = .22, F = 60.93**
Step 2
Work Environment -.60 .09 -.31** -.77 -.43Extraversion -.67 .08 -.36** -.83 -.51Work Environment × Extraversion .02 .01 .11* .00 .03R = .48, R2 = .24, F = 43.11**
Step 1
Work Environment -.48 .08 -.24** -.62 -.33Agreeableness -1.01 .07 -.56** -1.15 -.88R = .63, R2 = .39, F = 135.26**
Step 2
Work Environment -.62 .08 -.31** -.77 -.46Agreeableness -.99 .07 -.54** -1.13 -.86Work Environment × Agreeableness .04 .01 .19** .02 .05R = .65, R2 = .42, F = 101.57**
Step 1
Work Environment -.53 .09 -.27** -.71 -.35Emotional Stability -.48 .15 -.15** -.78 -.18
R = .32, R2 = .10, F = 24.63** Step 2
Work Environment -.54 .09 -.27** -.72 -.36Emotional Stability -.48 .15 -.15** -.77 -.18Work Environment × Emotional
Stability .01 .02 .04 -.02 .04
R = .33, R2 = .11, F = 16.64** Continued…
177
Predictors B SE Β Β 95% CI
LL UL
Step 1
Work Environment -.39 .08 -.20** -.55 -.24
Conscientiousness -.90 .07 -.52** -1.03 -.76
R = .59, R2 = .34, F = 109.98**
Step 2
Work Environment -.43 .08 -.22** -.59 -.27
Conscientiousness -.89 .07 -.51** -1.02 -.75
Work Environment ×
Conscientiousness .01 .01 .07 -.00 .03
R = .59, R2 = .35, F = 74.76**
Step 1
Work Environment -.45 .07 -.25** -.59 -.31
Openness -.84 .07 -.47** -.98 -.69
R = .55, R2 = .30, F = 89.24**
Step 2
Work Environment -.57 .08 -.32** -.72 -.42
Openness -.80 .07 -.45** -.94 -.66
Work Environment × Openness .03 .01 .19** .02 .05
R = .57, R2 = .33, F = 68.14**
*p < .05, **p = .00
For testing the statistical significance of moderating effects of extraversion,
agreeableness, and openness; the graphical display (Figure 3-5) and slope
computation (Table 37-39) using Jose’s procedure will help to see the statistical
significance of slopes.
177
Figure 3. Moderating Effects of Extraversion in predicting Burnout (three factor
model)
Table 37
Interaction Effects of Extraversion in predicting Work Environment and Burnout
Relationship
Moderator Slope SE t
High Agreeableness -.39 26.18 .02
Medium Agreeableness -.60 1.94 .31
Low Agreeableness -.81 26.18 .03
p = n.s
177
Figure 4. Moderating Effects of Agreeableness in predicting Burnout (three factor
model)
Table 38
Interaction Effects of Agreeableness in predicting Work Environment and Burnout
Relationship
Moderator Slope SE t
High Agreeableness -.23 26.48 .01
Medium Agreeableness -.62 2.40 .26
Low Agreeableness -1.00 26.48 .04
p = n.s
177
Figure 5. Moderating Effects of Openness in predicting Burnout (three factor model)
Table 39
Interaction Effects of Openness in predicting Work Environment and Burnout
Relationship
Moderator Slope SE t
High Agreeableness -.31 27.21 .01
Medium Agreeableness -.66 2.66 .25
Low Agreeableness -1.01 27.21 .04
p = n.s
Results as shown in Table 36 indicated the moderating role of personality for
relationship between work environment and burnout (three-factor model). Model 1
demonstrated significant predicting power of extraversion in model 1 (B = -.64, β = -
.38, t = 8.80, p = .00). After adding the interaction term, the value of R2 change was
.01, F(1, 422) = 5.88, p = .02. The significant change statistics further leads to
significant interaction term in model 2 indicated ‘extraversion’ as a significant
predictor (B = .02, β = .11, t = 2.43, p = .02). Further testing this statistically
significant interaction through Jose’ procedure (2008), the interaction plot (Figure 3)
177
and analysis of interaction (Table 37) indicated that slopes representing high,
medium, and low levels of agreeableness are not significantly different from zero.
Thus, the results show that when variations in agreeableness (i.e. high, medium, and
low levels) are observed, perceptions of work environment are not found having
potential influence on variations in burnout. This indicates that extraversion does not
account for moderation of the relationship between work environment and burnout.
For agreeableness, after adding the interaction term, the value of R2 change
was .03, F(1, 422) = 21.25, p = .00. The interaction term revealed significant
moderation effect of agreeableness (B = .04, β = .19, t = 4.61, p = .00). Interaction
plot (Figure 4) and analysis of interaction (Table 38) indicated that slopes
representing high, medium, and low levels of agreeableness are non-significant
showing that variations in agreeableness have not found potential influence to account
for variations in burnout. This indicates that agreeableness does not account for
moderation of the relationship between work environment and burnout.
For openness, addition of interaction term in model 2 leads to significant R2
change .03, F(1, 422) = 17.10, p = .00. Interaction effect revealed openness as a
significant moderator (B = .03, β = .18, t = 4.14, p = .00). Interaction plot (Figure 5)
and analysis of interaction (Table 39) indicated that slopes representing high,
medium, and low levels of openness are non-significant showing that variations in
openness have not found potential influence to account for variations in burnout. This
indicates that openness does not account for moderation of the relationship between
work environment and burnout. Interaction term indicates emotional stability (B =
.01, β = .02, t = .83, p = n.s) and conscientiousness (B = .01, β = .07, t = 1.67, p =
n.s).
The elaborated structure of burnout (five-factor model) is examined to have a
comprehensive understanding of the moderating role of the construct. Below table
represents findings obtained for total score of burnout five-factor model.
177
Table 40
Moderating Effects of Personality in predicting Burnout- five-factor model (N = 426)
Predictors B SE Β Β 95% CI LL UL
Step 1
Work Environment -.47 .08 -.27** -.63 -.32
Extraversion -.64 .07 -.38** -.78 -.50
R = .47, R2 = .22, F = 59.20**
Step 2
Work Environment -.52 .08 -.29** -.67 -.36
Extraversion -.61 .07 -.36** -.76 -.47
Work Environment × Extraversion .02 .01 .11* .00 .03
R = .48, R2 = .23, F = 41.89**
Step 1
Work Environment -.41 .07 -.23** -.54 -.27
Agreeableness -.92 .06 -.56** -1.05 -.80
R = .62, R2 = .39, F = 132.30**
Step 2
Work Environment -.53 .07 -.30** -.67 -.39
Agreeableness -.90 .06 -.55** -1.02 -.78
Work Environment × Agreeableness .03 .01 .18** .02 .05
R = .64, R2 = .41, F = 99.28**
Step 1
Work Environment -.45 .08 -.26** -.62 -.29
Emotional Stability -.48 .14 -.16** -.75 -.22
R = .32, R2 = .10, F = 24.03**
Step 2
Work Environment -.46 .08 -.26** -.62 -.30
Emotional Stability -.48 .14 -.16** -.75 -.21
Work Environment × Emotional Stability
.01 .01 .04 -.01 .04
R = .32, R2 = .10, F = 16.30**
Continued…
177
Predictors B SE Β Β 95% CI
LL UL
Step 1
Work Environment -.33 .07 -.19** -.47 -.19
Conscientiousness -.82 .06 -.52** -.94 -.70
R = .59, R2 = .34, F = 109.98**
Step 2
Work Environment -.37 .07 -.21** -.51 -.22
Conscientiousness -.81 .06 -.52** -.93 -.68
Work Environment ×
Conscientiousness .01 .01 .07 -.00 .03
R = .59, R2 = .35, F = 74.76**
Step 1
Work Environment -.45 .07 -.25** -.59 -.31
Openness -.84 .07 -.47** -.98 -.69
R = .55, R2 = .30, F = 89.24**
Step 2
Work Environment -.57 .08 -.32** -.72 -.42
Openness -.80 .07 -.45** -.94 -.66
Work Environment × Openness .03 .01 .19** .02 .05
R = .57, R2 = .33, F = 68.14**
*p < .05, **p = .00
Following is the graphical display of interactions (Figure 6-8) and slope
computation analysis (Table 41-43) for interpretation of Moderating Effects of slopes.
177
Figure 6. Moderating Effects of Extraversion in predicting Organizational
Commitment
Table 41
Interaction Effects of Extraversion in predicting Work Environment and Burnout
(five- factor model) Relationship
Moderator Slope SE t
High Agreeableness -.33 23.77 .01
Medium Agreeableness -.52 1.54 .34
Low Agreeableness -.70 23.77 .03
p = n.s
177
Figure 7. Moderating Effects of Agreeableness in predicting Organizational
Commitment
Table 42
Interaction Effects of Agreeableness in predicting Work Environment and Burnout
(five- factor model) Relationship
Moderator Slope SE t
High Agreeableness -.19 24.07 .01
Medium Agreeableness -.53 1.90 .28
Low Agreeableness -.88 24.06 .04
p = n.s
177
Figure 8. Moderating Effects of Conscientiousness in predicting Organizational
Commitment
Table 43
Interaction Effects of Openness in predicting Work Environment and Burnout (five-
factor model) Relationship
Moderator Slope SE t
High Agreeableness -0.24 24.51 .01
Medium Agreeableness -0.57 2.15 .27
Low Agreeableness -0.90 24.51 .04
p = n.s
Results shown in Table 40 revealed that addition of interaction term in model
1 indicated significant R2 change i.e. 0.01, F(1, 422) = 5.88, p = .02. Interaction term
indicated the significant moderating effects of extraversion (B = .02, β = .11, t = 2.43,
177
p = .02) in predicting burnout (five-factor model). However, significant statistical
effects were not further supported when slopes representing high, medium, and low
levels of extraversion were observed (Table 41).
For agreeableness addition of interaction term (model 2) produced R square
change .03, F(1, 422) = 20.83, p = 00. Significant moderation effects were obtained
(B = .03, β = .18, t = 4.56, p = .00). However, these moderation effects were further
not supported through test of significance of slopes (Figure 7). Results presented in
Table 42 indicates that slopes representing high, medium, and low levels of
agreeableness do not significantly differ from zero. Thus, the results show that
relationship between work environment and burnout (five-factor structure) is
independent of moderating influence of agreeableness.
Addition of interaction term in model 2 showed significant R2 change .03, F(1,
422) = 18.53, p = .00. Openness demonstrated significant moderation effects (B = .03,
β = .19, t = 4.31, p = .00). However, moderation effects were not further supported
when high, medium, and low levels of openness were considered for slope test
(Figure 8, Table 43).
Results demonstrated non-significant moderation effects of conscientiousness
(B = .01, β = .07, t = 1.79, p = n.s) and emotional stability (B = .01, β = .04, t = .92, p
= n.s).
177
Table 44 Moderating Effects of Personality in predicting Organizational Commitment (N =
425)
Predictors B SE B Β 95% CI LL UL
Step 1
Work Environment .40 .05 .39** .32 .49
Extraversion .20 .04 .20** .11 .28
R = .45, R2 = .20, F = 50.09** Step 2
Work Environment .43 .05 .42** .34 .52
Extraversion .18 .04 .18** .10 .26
Work Environment × Extraversion -.01 .00 -.13** -.02 -.00
R = .46, R2 = .21, F = 38.11** Step 1
Work Environment .38 .04 .37** .30 .47
Agreeableness .29 .04 .31** .21 .37
R = .50, R2 = .25, F = 70.75** Step 2
Work Environment .44 .05 .43** .35 .53
Agreeableness .28 .04 .30** .20 .36
Work Environment × Agreeableness -.02 .00 -.15** -.02 -.01
R = .52, R2 = .27, F = 52.23** Step 1
Work Environment .36 .04 .35** .27 .44
Conscientiousness .25 .04 .28** .18 .33
R = .49, R2 = .24, F = 64.99** Step 2
Work Environment .39 .05 .38** .30 .48
Conscientiousness .24 .04 .27** .17 .32
Work Environment ×
Conscientiousness -.01 .00 -.12* -.02 -.00
R = .50, R2 = .25, F = 46.31** Continued…
177
Predictors B SE B Β 95% CI
LL UL Step 1
Work Environment .40 .05 .39** .31 .49
Emotional Stability .16 .08 .10* .01 .31
R = .41, R2 = .17, F = 42.26** Step 2
Work Environment .40 .05 .39** .31 .49
Emotional Stability .16 .08 .10* .01 .31
Work Environment × Emotional
Stability -.00 .01 -.02 -.02 .01
R = .41, R2 = .17, F = 28.22** Step 1
Work Environment .40 .05 .39** .31 .49
Openness .19 .05 .18** .10 .28
R = .44, R2 = .19, F = 49.97** Step 2
Work Environment .45 .05 .44** .36 .55
Openness .17 .05 .17** .08 .26
Work Environment × Openness -.01 .01 -.14** -.02 -.01
R = .46, R2 = .21, F = 36.90**
*p < .05, **p = .00
Following is the graphical display of obtained statistically significant
moderation effects (see Figures 9 to 12) followed by analysis of slopes (Tables 45 to
48) used for interpretation of moderating effects.
177
Figure 9. Moderating Effects of Openness in predicting Organizational Commitment
Table 45
Interaction Effects of Extraversion in predicting Work Environment and
Organizational Commitment Relationship (N = 426)
Moderator Slope SE t
High Extraversion .30 16.75 0.02
Medium Extraversion .43 2.26 .19
Low Extraversion .56 10.11 0.06
p = n.s
177
Figure 10. Moderating Effects of Extraversion in predicting Burnout (five factor
model)
Table 46
Interaction Effects of Agreeableness in predicting Work Environment and
Organizational Commitment Relationship (N = 426)
Moderator Slope SE t
High Agreeableness .29 19.14 .02
Medium Agreeableness .44 3.16 .14
Low Agreeableness .60 6.73 .09
p = .00
177
Figure 11. Moderating Effects of Agreeableness in predicting Burnout (five-factor
model)
Table 47
Interaction Effects of Conscientiousness in predicting Work Environment and
Organizational Commitment Relationship (N = 426)
Moderator Slope SE t
High Conscientiousness .28 17.10 .02
Medium Conscientiousness .39 2.36 .17
Low Conscientiousness .50 9.12 .06
p = n.s
177
Figure 12. Moderating Effects of Openness in predicting Organizational Commitment
Table 48
Interaction Effects of Openness in predicting Work Environment and Organizational
Commitment Relationship (N = 426)
Moderator Slope SE t
High Openness .31 20.18 .02
Medium Openness .45 3.27 .14
Low Openness .60 7.98 .08
p = n.s
Results in Table 44 present the moderation analysis on personality dimensions
in predicting work environment and organizational commitment relationship. For
extraversion, interaction term with significant overall model fit (R2 = .21, F(3, 422) =
177
38.11, p = .00) explains 21% variance in organizational commitment. This leads to
significant value of R2 change (.02, F(I, 422) = 8.34). Interaction effect indicated
extraversion as a significant moderator (B = -.01, β = -.13, t = 2.89, p = .00). Further
extending the analysis, the interaction plot (Figure 9) indicated that slope representing
low level of extraversion seems more strongly associated to predict organizational
commitment. However, associated t-value indicated that slopes representing high,
medium, and low levels of moderator do not significantly differ from zero. This
indicated that when levels of extraversion are considered, interaction terms did not
significantly predicted organizational commitment over and above the statistical main
effects of the work environment (see Table 45).
For agreeableness, the interaction term yielded the overall significance of the
model (R2 = .27, F(3, 422) = 52.23, p = .00) and explains 27% variance in the
organizational commitment. The addition of interaction term leads to significant value
of R2 change (.02, F(I, 422) = 11.63). Interaction effect indicated agreeableness as a
significant moderator (B = -.02, β = -.15, t = 3.41, p = .00). The interaction plot
(Figure 10) indicated that slope representing low level of agreeableness seems more
strongly associated to predict organizational commitment. However, analysis
indicated that slopes representing high, medium, and low levels of moderator do not
significantly differ from zero. This indicated the non-significance of interaction terms
in predicting organizational commitment over and above the statistical main effects of
the work environment (see Table 46).
For conscientiousness, addition of interaction term in model 2 leads to R2
change (.01, F(1, 422) = 7.09, p = .01). Interaction effect revealed conscientiousness
as a significant moderator (B = .01, β = -.12, t = 2.66, p < .05). Further analysis
177
indicated that high level of conscientiousness is comparatively better in predicting
organizational commitment. However, analysis indicated the non-significance of
interaction terms in predicting organizational commitment over and above the
statistical main effects of the work environment (Figure 11, Table 47).
For emotional stability, interaction effect revealed emotional stability as a
non-significant moderator (B = -.00, β = -.02, t = .52, p = n.s).
For openness, the interaction term indicated significance of the model (R2 =
.21, F(3, 422) = 36.90, p = .00) with significant value of R2 change (.02, F(1, 422) =
8.91, p = .00). The interaction term revealed openness as a significant moderator (B =
-.01, β = -.14, t = 2.98, p = .00). The interaction plot (Figure 12) indicated that low
level of openness is more explanatory. However, analysis indicated that slopes do not
significantly differ from zero. This indicates the non-significance of interaction terms
in predicting organizational commitment over and above the statistical main effects of
the work environment (see Table 48).
Moderating Role of Organizational and Demographic Related Personal Variables
The Multiple Moderator Regression analysis (MMR) was performed to
investigate the impact of demographic variables including organization related
(sector, rank, job duration, faculties, and side jobs) and demographic information
(age, gender, education and marital status). This analysis was done taking work
environment as a composite dimension.
Following tables present the details of findings in investigating the moderating
impact of employees’ organization related personal variables.
177
Table 49
Moderating Effects of Sector in predicting Burnout (N = 426)
Predictors B SE β β 95% CI LL UL
Burnout (three factor model)
Step 1
Work Environment -.58 .09 -.30* -.77 -.40
Sector -1.51 1.92 -.04 -5.29 2.26
R = .29, R2 = .09, F = 19.54*
Step 2
Work Environment -.95 .14 -.48* -1.23 -.67
Sector -1.73 1.90 -.04 -5.46 2.00
Work Environment × Sector .63 .19 .24* .26 .99
R = .33, R2 = .11, F = 17.05*
Burnout (five factor model)
Step 1
Work Environment -.50 .09 -.28* -.67 -.33
Sector -1.13 1.74 -.03 -4.56 2.30
R = .28, R2 = .08, F = 17.51*
Step 2
Work Environment -.83 .13 -.47* -1.08 -.57
Sector -1.33 1.73 -.04 -4.72 2.07
Work Environment × Sector .56 .17 .24* .22 .89
R = .32, R2 = .10, F = 15.48*
*p = .00
177
Figure 13. Moderating Effects of Sector in predicting Burnout (three factor model)
Table 50
Interaction Effects of Sector in predicting Work Environment and Burnout (three-
factor model) Relationship
Moderator Slope SE t
Public Sector -.32 .12 2.74*
Private Sector -.95 .15 6.55**
*p < .01, **p = .00
177
Figure 14. Moderating Effects of Sector in predicting Burnout (five-factor model)
Table 51
Interaction Effects of Sector in predicting Work Environment Burnout (five-factor
model)
Moderator Slope SE t
Public Sector -.53 .18 3.03**
Private Sector -.45 .10 4.75**
*p = .00
177
Table 52
Moderating Effects of Sector in predicting Organizational Commitment (N = 426)
Predictors B SE B Β 95% CI LL UL
Organizational Commitment
Step 1
Work Environment .43 .05 .42** .34 .52
Sector 2.64 .95 .13* .77 4.51
R = .42, R2 = .17, F = 44.11**
Step 2
Work Environment .68 .07 .67** .54 .82
Sector 2.79 .93 .13* .96 4.62
Work Environment × Sector -.42 .09 -.31** -.60 -.24
R = .46, R2 = .21, F = 37.92**, R2 change = .04, F change (1, 422) = 21.29**
*p < .05, **p = .00
177
Figure 15. Moderating Effects of Sector in predicting Organizational Commitment
Table 53
Interaction Effects of Sector in predicting Work Environment and Organizational
Commitment (N = 426)
Moderator Slope SE t
Public Sector .258 .05 4.71*
Private Sector .682 .07 9.65*
p = .00
177
Table 54 Moderating Effects of Rank in predicting Burnout (N = 426)
Predictors B SE B Β 95% CI LL UL
Burnout (three factor model)
Step 1
Work Environment -.57 .09 -.29* -.75 -.39Rank -.62 1.90 -.02 -4.36 3.11R = .29, R2 = .08, F = 19.26*
Step 2
Work Environment -.63 .19 -.32* -1.01 -.25Rank -.62 1.90 -.02 -4.35 3.12Work Environment × Rank .03 .09 .04 -.14 .20R = .29, R2 = .08, F = 12.86*
Burnout (five factor model)
Step 1
Work Environment -.49 .08 -.27* -.65 -.32Rank -1.20 1.73 -.03 -4.60 2.19R = .28, R2 = .08, F = 17.55*
Step 2
Work Environment -.56 .18 -.31* -.90 -.21Rank -1.20 1.73 -.03 -4.59 2.20Work Environment × Rank .04 .08 .05 -.12 .19R = .28, R2 = .08, F = 11.75*
*p = .00
177
Table 55
Moderating Effects of Rank in predicting Organizational Commitment (N = 426) Predictors B SE B β 95% CI
LL UL Organizational Commitment
Step 1 Work Environment .41 .05 .40* .32 .50Rank .22 .43 .02 -.63 1.07R = .40, R2 = .16, F = 39.70**
Step 2 Work Environment .40 .10 .39* .21 .59Rank .22 .43 .02 -.63 1.07Work Environment × Rank .01 .04 .01 -.08 .09R = .40, R2 = .16, F = 26.41**
*p = .00, p = n.s
Table 56
Moderating Effects of Employment Duration in predicting Burnout (N = 426) Predictors B SE B Β 95% CI
LL UL Burnout (three factor model)
Step 1 Work Environment -.60 .09 -.31* -.78 -.42Employment Duration -.70 .17 -.19* -1.04 -.36R = .34, R2 = .12, F = 28.10*
Step 2 Work Environment -.63 .09 -.32* -.81 -.45Employment Duration -.67 .17 -.18* -1.01 -.32Work Environment × Duration .02 .01 .08 -.00 .05R = .35, R2 = .12, F = 19.82*
Burnout (five factor model) Step 1
Work Environment -.52 .08 -.29* -.68 -.36Employment Duration -.60 .16 -.18* -.91 -.29R = .33, R2 = .11, F = 25.15*
Step 2 Work Environment -.55 .08 -.31* -.71 -.38Employment Duration -.57 .16 -.17* -.88 -.26Work Environment × Duration .02 .01 .09 -.00 .05R = .34, R2 = .11, F = 17.98*
*p = .00,
177
Table 57
Moderating Effects of Employment Duration in predicting Organizational Commitment (N = 426)
Predictors B SE B β 95% CI LL UL
Organizational Commitment Step 1
Work Environment .42 .05 .41* .33 .51Employment Duration .29 .09 .15* .12 .46R = .42, R2 = .18, F = 46.28*
Step 2 Work Environment .43 .05 .42* .34 .52Employment Duration .28 .09 .14* .11 .45Work Environment × Emp. Duration -.01 .01 -.05 -.02 .01R = .43, R2 = .18, F = 31.23*, R2 change = .00, F change (1, 422) = 1.11
*p = .00, p = n.s
Table 58
Moderating Effects of Faculties in predicting Burnout (N = 426) Predictors B SE B β 95% CI
LL UL Burnout (three factor model)
Step 1 Work Environment -.57 .09 -.29** -.74 -.39Faculties -4.83 1.87 -.12* -8.51 -1.15R = .31, R2 = .10, F = 22.83**
Step 2 Work Environment -.66 .125 -.34** -.91 -.42Faculties -4.83 1.87 -.12* -8.51 -1.15Work Environment × Faculties .20 .18 .07 -.16 .56R = .32, R2 = .10, F = 15.64**
Burnout (five factor model) Step 1
Work Environment -.49 .08 -.27** -.65 -.33 Faculties -4.58 1.70 -.13* -7.92 -1.24R = .30, R2 = .09, F = 21.21**
Step 2 Work Environment -.57 .11 -.32** -.79 -.34Faculties -4.58 1.70 -.13* -7.92 -1.24Work Environment × Faculties .17 .17 .07 -.16 .49R = .31, R2 = .09, F = 14.49**
*p < .05, **p = .00
177
Table 59
Moderating Effects of Faculties in predicting Organizational Commitment (N = 426) Predictors B SE B β 95% CI
LL UL Organizational Commitment
Step 1 Work Environment .41 .05 .38** .32 .51Faculties 2.62 1.00 .12* .66 4.58R = .40, R2 = .16, F = 39.76**
Step 2 Work Environment .42 .07 .39** .29 .55Faculties 2.62 1.00 .12* .66 4.58Work Environment × Faculties -.02 .10 -.01 -.21 .17R = .40, R2 = .16, F = 26.46**
*p < .05, **p = .00, p = n.s Table 60 Moderating Effects of Side Jobs in predicting Burnout (N = 426)
Predictors B SE B Β 95% CI LL UL
Burnout (three factor model) Step 1
Work Environment -.61 .09 -.31* -.79 -.43
Side jobs -18.62 4.28 -.20* -27.03
-10.21
R = .35, R2 = .12, F = 29.53* Step 2
Work Environment -.66 .09 -.34* -.84 -.47
Side jobs -16.34 4.48 -.18* -25.14 -7.54
Work Environment × Side jobs .52 .31 .09 -.08 1.12 R = .36, R2 = .13, F = 20.73*
Burnout (five factor model) Step 1
Work Environment -.53 .08 -.30* -.69 -.37 Side jobs -16.54 3.89 -.20* -24.19 -8.89R = .34, R2 = .11, F = 27.06*
Step 2 Work Environment -.57 .09 -.32* -.74 -.40Side jobs -14.64 4.08 -.17* -22.65 -6.63Work Environment × Side jobs .43 .28 .08 -.12 .98R = .34, R2 = .12, F = 18.90*
*p = .00
177
Table 61
Moderating Effects of Side Jobs in predicting Organizational Commitment (N = 426) Predictors B SE B β 95% CI
LL UL Organizational Commitment
Step 1
Work Environment .42 .05 .41* .33 .51
Side jobs 3.62 2.18 .07 -.67 7.91
R = .40, R2 = .16, F = 41.19* Step 2
Work Environment .43 .05 .42* .34 .53
Side jobs 2.84 2.29 .06 -1.66 7.33
Work Environment × Side jobs -.18 .16 -.06 -.49 .13
R = .41, R2 = .17, F = 27.91* *p = .00
Table 62
Moderating Effects of Age in predicting Burnout (N = 426) Predictors B SE Β β 95% CI
LL UL Burnout (three factor model)
Step 1 Work Environment -.57 .09 -.29* -.75 -.39Age -.18 .11 -.08 -.34 .02R = .30, R2 = .09, F = 20.85*
Step 2 Work Environment -.74 .10 -.38* -.93 -.55Age -.17 .10 -.08 -.37 .03Work Environment × Age .04 .01 .22* .02 .06R = .36, R2 = .13, F = 21.51*
Burnout (five factor model) Step 1
Work Environment -.49 .08 -.28* -.66 -.33Age -.12 .10 -.06 -.30 .07 R = .28, R2 = .08, F = 18.09*
Step 2 Work Environment -.64 .09 -.36* -.82 -.47 Age -.11 .09 -.05 -.29 .08 Work Environment × Age .04 .01 .22 .02 .05 R = .35, R2 = .12, F = 19.48**
*p = .00
177
Figure 16. Moderating Effects of Age in predicting Burnout (three factor model)
Table 63
Interaction Effects of Age in predicting Work Environment and Burnout (three factor
model) (N = 426)
Moderator Slope SE t
High Age -.36 26.06 .01
Medium Age -.74 .10 7.79**
Low Age -1.12 26.06 .04
p = .00
177
Figure 17. Moderating Effects of Age in predicting Burnout (five-factor model)
Table 64
Interaction Effects of Age in predicting Work Environment and Burnout (five-factor
model) (N = 426)
Moderator Slope SE t
High Age -.29 23.73 .01
Medium Age -.63 .09 7.09**
Low Age -.98 23.73 .04
p = .00
177
Table 65
Moderating Effects of Age in predicting Organizational Commitment (N = 426) Predictors B SE B β 95% CI
LL UL Organizational Commitment
Step 1
Work Environment .41 .05 .40** .32 .50
Age .14 .05 .12* .04 .24
R = .42, R2 = .17, F = 43.93** Step 2
Work Environment .42 .05 .41** .33 .51
Age .14 .05 .12* .04 .24
Work Environment × Age -.01 .01 -.06 -.02 .00
R = .42, R2 = .18, F = 29.91**
*p < .05, **p = .00
Table 66 Moderating Effects of Gender in predicting Burnout (N = 426)
Predictors B SE B β 95% CI LL UL
Burnout (three factor model) Step 1
Work Environment -.57 .09 -.29* -.75 -.39Gender -.68 1.95 -.02 -4.52 3.15R = .29, R2 = .08, F = 19.27*
Step 2 Work Environment -.72 .16 -.37* -1.04 -.40
Gender -.74 1.95 -.02 -4.58 3.09Work Environment × Gender .23 .20 .10 -.16 .61R = .29, R2 = .09, F = 13.30*
Burnout (five factor model) Step 1
Work Environment -.49 .08 -.28* -.65 -.33 Gender -.13 1.77 -.00 -3.62 3.35R = .28, R2 = .08, F = 17.29*
Step 2 Work Environment -.62 .15 -.35* -.91 -.33Gender -.18 1.77 -.01 -3.67 3.30Work Environment × Gender .20 .18 .09 -.16 .55R = .28, R2 = .08, F = 11.93*
*p = .00
177
Table 67
Moderating Effects of Gender- Men (n = 268) vs. Women (n = 158) in predicting Organizational Commitment
Predictors B SE B β 95% CI LL UL
Organizational Commitment Step 1
Work Environment .41 .05 .40* .32 .50Gender .06 .98 .00 -1.86 1.97R = .40, R2 = .16, F = 39.55*
Step 2 Work Environment .48 .08 .47* .32 .64Men .09 .98 .00 -1.83 2.00Work Environment × Gender -.11 .10 -.09 -.30 .08R = .40, R2 = .16, F = 26.80*
*p = .00
Table 68
Moderating Effects of Education in predicting Burnout (N = 426) Predictors B SE B β 95% CI
LL UL Burnout (three factor model)
Step 1 Work Environment -.55 .09 -.28* -.73 -.37Education -2.68 2.16 -.06 -6.92 1.56R = .29, R2 = .09, F = 20.04*
Step 2 Work Environment -.53 .11 -.27* -.73 -.32Education -2.50 2.19 -.05 -6.80 1.80Work Environment × Education -.11 .22 -.03 -.54 .33R = .30, R2 = .09, F = 13.41*
Burnout (five factor model) Step 1
Work Environment -.47 .08 -.26* -.63 -.31 Education -3.15 1.96 -.08 -6.99 .70R = .29, R2 = .08, F = 18.69*
Step 2 Work Environment -.45 .10 -.25* -.64 -.26Education -3.01 1.99 -.07 -6.91 .90Work Environment × Education -.08 .20 -.02 -.47 .31R = .29, R2 = .08, F = 12.49*
*p = .00
177
Table 69
Moderating Effects of Education in predicting Organizational Commitment (N = 426) Predictors B SE B Β 95% CI
LL UL Organizational Commitment
Step 1
Work Environment .41 .05 .40* .32 .50
Education -.49 1.08 -.02 -2.61 1.63
R = .40, R2 = .16, F = 39.67* Step 2
Work Environment .45 .05 .44* .35 .56
Education -.18 1.09 -.01 -2.33 1.96
Work Environment × Education -.18 .11 -.09 -.40 .03
R = .40, R2 = .16, F = 27.50* *p = .00
Table 70
Moderating Effects of Marital Status in predicting Burnout (N = 426) Predictors B SE B Β 95% CI
LL UL Burnout (three factor model)
Step 1 Work Environment -.55 .09 -.28** -.73 -.37Marital Status 3.02 2.01 .07 -.92 6.97R = .30, R2 = .09, F = 20.23**
Step 2 Work Environment -.54 .15 -.27** -.84 -.24Marital Status 3.04 2.01 .07 -.91 6.99Work Environment × Marital Status -.03 .19 -.01 -.40 .35R = .30, R2 = .09, F = 13.46**
Burnout (five factor model) Step 1
Work Environment -.47 .08 -.27** -.64 -.31 Marital Status 3.43 1.82 .09 -.15 7.00R = .29, R2 = .08, F = 19.04**
Step 2 Work Environment -.45 .14 -.25** -.72 -.17Marital Status 3.45 1.83 .09 -.13 7.04Work Environment × Marital Status -.04 .17 -.02 -.39 .30R = .29, R2 = .08, F = 12.69**
*p < .05, **p = .00
177
Table 71
Moderating Effects of Marital Status in predicting Organizational Commitment (N = 426)
Predictors B SE B Β 95% CI LL UL
Organizational Commitment
Step 1
Work Environment .41 .05 .41* .32 .50
Marital Status .91 1.00 .04 -1.05 2.87
R = .40, R2 = .16, F = 40.96* Step 2
Work Environment .46 .08 .45* .31 .61
Marital Status .94 1.00 .04 -1.02 2.91
Work Environment × Marital Status -.06 .10 -.05 -.25 .12
R = .41, R2 = .16, F = 27.42* *p = .00
Results in Table 49 present the moderation effects of sector (public and
private) using hierarchical multiple regression analysis. Adding interaction effect in
model 2 produced significant model fit (R2 = .11, F(3, 422) = 17.05, p = .00) and
gives rise to a significant change statistic (R2 change = .02, F(1, 422) = 11.14, p =
.00). Interaction term highlighted that in predicting burnout (three factor), sector does
serve as a significant moderator (B = .63, β = .24, t = 3.34, p = .00). The slope
computation when examined through analysis of interactions (see Table 50) indicated
that slopes representing public and private sector significantly differ from zero.
However, considering the moderating influence of sector as negative predictor (Figure
13), work environment more strongly relates to explain burnout within private sector
universities.
For elaborated structure of burnout- five factor model (Table 49), addition of
interaction term in model 2 leads to significant R2 change (.02, F(1, 422) = 10.61, p =
.00) and demonstrated significant moderation effect (B = .56, β = .24, t = 3.26, p =
177
.00). The interaction plot (Figure 14) when examined through analysis of interactions
(Table 51) indicated that slopes representing public and private sectors significantly
differ from zero. However, magnitude of slope representing public sector is stronger.
This indicated that for burnout five-factor structure, work environment more strongly
relates to predict burnout within private sector universities.
Results presented in Table 52 indicated that sector demonstrated significant
predictive power in model 1 (B = 2.64, β = .13, t = 2.77, p < .05) and in model 2 (B =
2.79, β = .13, t = 3.00, p = .00). Interaction term produced significant change
statistics, R2 change = .04, F(1, 422) = 21.29, p = .00. Interaction effect indicated
significant moderation effect (B = -.42, β = -.31, t = 4.61, p = .00) of sector in
predicting organizational commitment. Analysis of interactions (see Table 53)
indicated that slopes (see Figure 15) representing public and private sector
significantly differ from zero. However, slope representing private sector seems more
strongly relates to organizational commitment. This indicated the significance of
interaction terms for relationship between work environment and organizational
commitment.
Results presented in Table 54 indicated that with R2 change of .00, non-
significant interaction effect (B = .03, β = .04, t = .37, p = n.s) of rank in predicting
burnout (three factor model) was obtained. Similarly, for five factor model of burnout,
non-significant interaction effect was obtained (B = .04, β = .05, t = .46, p = n.s).
Results shown in Table 55 indicated that due to addition of interaction term in model
2, R2 change was .00, F(1, 422) = .01, p = n.s leading to non-significant interaction
effect (B = .01, β = .01, t = .12, p = n.s) of rank in predicting organizational
commitment.
177
Results presented in Table 56 indicated that with R2 change = .01, non-
significant interaction effect (B = .02, β = .08, t = 1.75, p = n.s) of employment
duration in predicting burnout- three factor model. Similarly, with R2 change = .01,
non-significant interaction effect was obtained for burnout-five factor model (B = .02,
β = .09, t = 1.83, p = n.s). Results presented in Table 57 indicated that with R2 change
= .00, non-significant moderation effect (B = -.01, β = -.05, t = 1.05, p = n.s) of
employment duration in predicting organizational commitment was obtained
Results presented in Table 58 indicate that after addition of interaction term in
model 2, R2 change was .00 (F(1, 422) = 1.23, p = n.s). Interaction term highlighted
the non-significant moderation effects (B = .20, β = .07, t = 1.11, p = n.s). Results
presented in Table 59 indicated that variable of faculties and work environment is
showing independent predictive power in model 1 with significant model fit F(2, 423)
= 39.76, p = .00 explaining 16% variance in organizational commitment. With
inclusion of interaction term in model 2, R2 change was .00, F(1, 422) = .04, p = n.s
leading to non-significant interaction effect (B = -.02, β = -.01, t = .20, p = n.s) of
faculties in predicting organizational commitment.
Results presented in Table 60 indicated that interaction term in model 2 with
non-significant change statistic (R2 change = .01, F(1, 422) = 2.87, p = n.s) leads to
non-significant interaction effect (B = .52, β = .09, t = 1.69, p = n.s) of side jobs in
predicting burnout (three factor model). For burnout five factor model, with R2 change
= .01, F(1, 422) = 2.40, p = n.s is showing non-significant moderation effect (B = .43,
β = .08, t = 1.55, p = n.s).
Results presented in Table 61 indicated that variable of side jobs is not
showing statistically significant predictive power in model 1 and in model 2. The
177
addition of interaction term in model 2 gives rise to non-significant R2 change
equivalent to .00, F(1, 422) = 1.30, p = n.s. leading to non-significant interaction
effect of side jobs in predicting organizational commitment (B = -.18, β = -.06, t =
1.14, p = n.s).
Results presented in Table 62 indicated that addition of interaction term leads
to non-significant R2 change (.04), F(1, 422) = 20.88. Interaction effect revealed age
as a significant moderator in predicting burnout (B = .04, β = .22, t = 4.57, p = .00).
Extending this analysis to Jose’s procedure indicated that age as positive moderator
(Figure 16) significantly explains moderation effects. Analysis of slope test (Table 63)
indicated that when medium level of age is considered, work environment influences
burnout (three factor model). Similarly, for elaborated structure of burnout, R2 change
was significant (.04, F(1, 422) = 20.58, p = .00) with significant interaction effects (B
= .04, β = .22, t = 4.53, p = .00). Age as positive moderator (Figure 17) indicated that
medium level of age seems more associated with predicting burnout five-factor model
(Table 64).
Results presented in Table 65 indicated age as a significant predictor in model
1 (B = .14, β = .12, t = 2.72, p = .01) and in model 2 (B = .14, β = .12, t = 2.67, p =
.01). With non-significant R2 change (.00), F(1, 422) = 1.71, age showed its non-
significant moderating effect (B = -.01, β = -.06, t = 1.31, p = n.s) in predicting the
organizational commitment.
Results in Table 66 indicates that with non-significant R2 change (.00), F(1,
422) = 1.33, interaction effect revealed gender as a non-significant moderator (B =
.23, β = .10, t = 1.16, p = n.s) in predicting burnout. Similar findings have obtained
for elaborated structure of burnout. Results presented in Table 67 indicated non-
significant predictive power of gender in model 1 (B = .06, β = .00, t = .06, p = n.s)
177
and in model 2 (B = .09, β = .00, t = .09, p = n.s). With non-significant R2 change
(.00), F(1, 422) = 2.22, interaction effect revealed gender as a non-significant
moderator (B = -.11, β = -.09, t = 1.11, p = n.s) in predicting organizational
commitment.
Results presented in Table 68 indicated that education with addition of
interaction term in model 2, R2 change was .00, F(1, 422) = .23 was non-significant.
Interaction effect highlighted education as a non-significant moderator in predicting
burnout (B = -.11, β = -.03, t = .48, p = n.s). Findings in Similar pattern of findings
have obtained for elaborated structure of the burnout. Results presented in Table 69
indicated that addition of interaction term produced non-significant R2 change (.01),
F(1, 422) = 2.81. Interaction effect highlighted education as a non-significant
moderator in predicting organizational commitment (B = -.18, β = -.09, t = 1.68).
Results presented in Table 70 indicated that marital status carries non-
significant independent predictive power both in model 1 and in model 2. Addition of
interaction term produced non-significant R2 change (.00), F(1, 422) = .02. Examining
interaction effect of marital status revealed marital status as a non-significant
moderator in predicting burnout (B = -.03, β = -.01, t = .14). Similar pattern of
findings have obtained for elaborated structure of the burnout. Results presented in
Table 71 indicated that with non-significant R2 change (.00), F(1, 422) = .45, the
interaction effect of marital status in predicting organizational commitment is also
non-significant (B = -.06, β = -.05, t = .67).
The proceeding section will thoroughly discuss the findings of the study.
177
Discussion
The propositions deduced from Moos’ model (1994) of the psychosocial work
environment concerning with impact of the work environment on employee and
organizational related outcomes are examined in the context of academic settings (i.e.
university level) of Pakistan. The burnout and organizational commitment of
University teachers as predicted by different facets of work environment were
investigated. Further, in explaining the work environment and outcome relationships,
the moderating role of employees’ personal system including personality,
organizational, and demographic variables were also examined. Considering a host of
organizational and demographic variables helped to control the possible influential
factors contributed in generalizing the study results across different groups of
teachers. One of the main objectives of present study was to establish the
psychometric properties of the measures used in the study. Testing the factor structure
of study measures highlighted few discrepancies related to cultural aspects. The
scrutiny of measures through confirmatory factor analyses and exclusion of certain
items contributed to qualify the measures i.e. Work Environment Scale, Maslach
Burnout Inventory, Organizational Commitment Questionnaire, and Min Markers to
be more useful for teachers (lecturers to professors) involved in offering services at
higher education level in Pakistan.
For comparison of scores of work environment, burnout, and organizational
commitment, cut-off scores, (i.e. high, moderate, & low) and estimated mean and
standard deviation of scores were used. Findings highlights that academic work
settings are highly oriented towards positive psychosocial work environment.
Teachers reported that overall universities’ work environmnet characterized with
177
clarity of work procedures along with high emphasis on getting the job done. Its also
encouraging that autonomy is being valued at universities. However, cohesion among
colleagues has found to be lacking. Greater variation of scores have obtained for
dimension of managerial control, work pressure, and clarity. Teachers also reported
high on reduced personal accomplishment and on emotional exhaustion, and
comparatively low on depersonalization. Dispersions in scores are evident in high
levels of emotional exhaustion and depersonalization. Variations in responses on low
personal accomplishment, is probably due to social desirability aspect of the
construct. For burnout, cut-off scores based on standard sample of teachers as
reported in MBI scoring sheet showed that present sample of teachers are
experiencing high level of emotional exhaustion and depersonalization and low level
of reduced sense of personal accomplishment. In this case, diverse responses have
obtained for low level of emotional exhaustion and high levels of depersonalization
and reduced sense of personal accomplishment.
Teachers reported high level of endorsement for affective commitment;
whereas mean scores are showing low on continuance commitment. Greater
dispersion in responses has observed in reporting low levels on affective, continuance,
and normative commitment. For measure of personality factors, teachers dominantly
endorsed for high levels of each trait factor. Teachers gave diverse responses in case
of high levels of extraversion and emotional stability and low levels of agreeableness,
conscientiousness, and openness. For emotional stability, having diverse responses
due to element of social desirability, seems a good explanation. After this initial level
of analysis, scores were subject to examine reliability and validity indices to assess
the psychometric soundness of the study measures.
177
Psychometric Issues
The psychometric properties of the measures of the study were established by
using estimates of the internal consistency and the construct validity and inter-scale
correlations. The findings of study have demonstrated that total score on Work
Environment Scale with reduced items yielded high magnitude of Cronbach’s alpha
coefficient (α = .88). For primary subscales of Relationship (α = .70), Personal
Growth (α = .66), and System Maintenance and Change Dimension (α = .75),
magnitude of alpha coefficients are providing substantial support for internal
consistency of the measure. Evaluating the secondary subscales yielded variation in
magnitude of alpha coefficients. For example, the subscale of clarity (α = .69) has
demonstrated comparatively high alpha coefficient compared to others. However,
subscale of coworker cohesion (α = .42) has shown relatively low alpha coefficient. In
comparison with results of pilot study (N = 102), coworker cohesion yielded better
estimate equivalent to .54. Moreover, in second pilot study (N = 40), the obtained
alpha coefficient was .49. The value of alpha got affected when computed on main
sample (N = 426) perhaps due to more restricted responses given by sample. This will
be supported if we compare the mean and standard deviation values on scores of work
environment obtained in pilot and main study (see Table 1 & 24).
Moreover, coworker cohesion comes under the primary dimension- the
relationship dimension (comprising involvement, coworker cohesion, and supervisor
support) which overall yields a satisfactory estimate of internal consistency equivalent
to .74 alpha coefficients. For subscale of managerial control, the value of alpha
coefficient in second pilot study was .38, which now in main study was enhanced up
177
to .53. However, collectively, managerial control as part of primary dimension of
system maintenance and change dimension (along with other subscales, e.g., clarity,
innovation, and physical comfort) yielded alpha coefficient of .79. Studies using
English version of WES also reported somewhat similar trends in magnitude of alpha
coefficients when examined on sample in context of Pakistan.
For example, in an earlier study (Rehman & Maqsood, 2008) using sample of
university teachers (N = 500) reported alpha coefficients of secondary sub scales
ranged from a lower for work pressure (α = .29) to substantial for task orientation (α
= .63). Study by Maqsood and Rehman (2004) using sample of service oriented
organization reported alpha coefficient ranged from .77 to .88 for primary subscales
and for secondary subscales alpha coefficients ranged from a lower for managerial
control to (α = .45) to moderate for innovation (α = .68). For measures of environment
assessment based on relatively broader concept (e.g., cohesion, areas of personal
growth etc.),
Moos (1990) commented that such measures may be less internally consistent
depending on length of a subscale or the type of response options used, or the
variability of responses. Moos further elaborated that relying too much on less diverse
items in a measure may end leaving the measure having a narrower construct.
Whereas, a measure with diverse items tends to assess the construct in a more real
context, which in turn may grade the measure high on validity.
For Maslach Burnout Inventory (21-item measure) with high obtained alpha
coefficient (α = .86) provided satisfactory evidence of psychometric soundness of the
revised measure to be used for sample of university teachers. Satisfactory estimate of
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internal consistency has obtained for subscale of emotional exhaustion (α = .74),
depersonalization (α = .69), and personal accomplishment (α = .76). Pilot study
reported moderate magnitude of alpha coefficient for subscales of emotional
exhaustion (α = .60), depersonalization (α = .65), and for personal accomplishment (α
= .67). In comparison, findings of main study yielded increase in magnitude of
respective subscales. Due to exclusion of item 14 from subcomponent of emotional
exhaustion, the magnitude of alpha coefficient is increased in comparison with the
results of pilot study. Moreover, in main study, elaborated structure of burnout was
also examined. For psychological strain (α = .50) and somatic strain (α = .64)
components of emotional exhaustion, and self (α = .59) and others (α = .60) related
components of personal accomplishment yielded relatively moderate to low
magnitude of coefficients partly because of reduced number of items in respective
scales.
Organizational Commitment Questionnaire yielded high magnitude of alpha
coefficients on total scores (α = .84) and on dimension of affective commitment (α =
.83). The moderate level of reliability indices have obtained for subscales of
continuance commitment (α = .61), and normative commitment (α = .64). In
comparison with pilot results, there is an increase in magnitude of alpha coefficient
for subscale of continuance commitment from .55 to .61.
Mini Markers set has shown Cronbach’s alpha coefficients for subscales
ranges from high magnitude for Extroversion (α = .76), Agreeableness (α = .79),
Conscientiousness (α = .83), Intellect or openness (α = .73), to low on Emotional
Stability (α = .28). There is not much increase in magnitude of alpha coefficient in
comparison with pilot study. The sub-scale of emotional stability with excluded items
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25 and 26 yielded slight increase in alpha coefficient from .27 to .28. The value of
Cronbach’s Alpha based on standardized items with reduced items is .30. Pallant
(2007) suggested that magnitude of alpha coefficient may not approach to an
appropriate value in case of small number of items e.g., less than 10. Saucier (1994)
noted consistently lower alpha coefficients typically by .05 to .10 for abbreviated
version of 40-item Mini-Marker subset in comparison to 100-item Markers.
Moreover, the decision to retain the subscale also relates to the estimate of
confirmatory factor analysis which leads to deletion of two items of the subscale.
Evaluation of reliability estimates may also be made in comparison of mean
and variance of subscales in comparison of rest of subscales in a particular sample
(see Moos, 1990). For example, if we refer back to Table 24 in result section, mean
scores on low, moderate, and high levels of emotional stability are less in comparison
of other dimensions. In connection with this, comparatively low values of variance are
showing restricted range of responses for dimension of emotional stability.
Inter-scale correlations presented in Table 25 are showing that each subscale
of WES is significantly related with total score on WES. This indicates the
satisfactory estimate of construct validity of the measure. The primary dimensions of
WES, relationship dimension (r = .82, p < .01), personal growth dimension (r = .84, p
< .01), and system maintenance and change dimension (r = .90, p < .01) are showing
highly significant correlation with total scores of WES. Rehman and Maqsood (2008)
reported construct validity of WES by correlating the scores of secondary subscales
with score of its primary dimension. For instance, involvement (r = .85, p < .01),
coworker cohesion (r = .73, p < .01), and supervisor support (r = .75, p < .01) showed
high correlation with relationship dimension. Autonomy (r = .63, p < .01), task
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Orientation (r = .76, p < .01), and work pressure (r = .44, p < .01) showed substantial
correlation with personal growth dimension. Clarity (r = .78, p < .01), managerial
control (r = .55, p < .01), innovation (r = .84, p < .01), and physical comfort (r = .71,
p < .01) showed significant correlation with system maintenance and change
dimension. With regard to psychometric issues of WES, Flarey (1991) claims that
repeated use of the Work Environment Scale has accounted in establishing its content
reliability and validity. Moos and Billings (1991) suggested that items of WES are
easy to understand and have high face validity.
For burnout, emotional exhaustion (r = .80, p < .01), depersonalization (r =
.56, p < .01), and personal accomplishment (r = -.81, p < .01) are showing significant
relationship with the total score of MBI. The similar patter of findings has obtained
for elaborated structure of burnout as well. This suggests the satisfactory estimate of
construct validity of MBI. Moreover, affective commitment (r = .44, p < .01),
continuance commitment (r = .12, p < .05), and normative commitment (r = .24, p <
.01) are significant related with total score of OCQ.
The aforesaid discussion provided satisfactory estimates to conclude about
substantial support for psychometric soundness of the measures of the present study.
The possible explanation of low Cronbach’s alpha coefficients for subscales (in case
of Work Environment Scale) seems to be at modest in context of evaluating the
overall alpha for total score as discussed above. Low Cronbach’s alpha in case of
personality measure obtained for subscale of emotional stability was justified by a
referring note by original author of the measure along with the possible explanation
attributed to cultural differences in responses for subscale items emerged out of
examining the theoretical factor structure. In conclusion, the detailed psychometric
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examination through confirmatory factor analysis provides a safer mode to retain the
respective subscales with low alpha coefficients.
Following is the description of findings related to hypothesized relationships
of study variables.
Predictive Impact of Work Environment on Burnout and Organizational
Commitment
Teachers reported that they were experiencing burnout particularly high on
personal accomplishment and emotional exhaustion. Findings of regression analysis
found that work environment variables are non-significant predictors of burnout.
Findings suggested support in favor of five factor model of burnout. Findings
revealed that coworker cohesion, task orientation, and managerial control are negative
predictors of burnout five-factor structure. Overall, together these dimensions account
for 11% variability in burnout. Coworker cohesion has thought to influence burnout
(Kim, Lee, & Kim, 2009; Savicki & Cooley, 1987; Turnipseed, 1994). The findings
of our study suggested the negative relationship. This is an important dimension to
consider the importance of relationship dimensions in influencing burnout. In
predicting burnout, task orientation was found as a negative predictor. Overall, task
orientation is linked with burnout and its components (Constable & Russell, 1986;
Savicki, 2002). Our findings suggested negative relationship between task orientation
and burnout. Therefore, it may be expected that too much emphasis on planning and
efficiency to get the job is helpful in getting the job done, which in turn lessens the
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feeling of burnout. It may be interpreted that high emphasis on task orientation may
lead to a characteristic managerial style, which may influences teachers’ sense of
personal accomplishment. Findings highlighted managerial control as a negative
predictor of burnout. This may important to relate that managerial control is expected
to reflect in workplace dynamics, e.g. maintaining equity, feedback and evaluation of
employees, etc., which in turn is expected to influence the experience of burnout.
Emotional Exhaustion as feelings of emotionally overextended and exhausted
by one's work has found to be prevalent high among university teachers compared to
depersonalization. The work environment facets including involvement and work
pressure is found linked with explaining variance in emotional exhaustion. Findings
highlighted that together these facets accounts for 13% change in emotional
exhaustion. With empirical support (Adali et al., 2003; Robinson et al., 1991), current
findings revealed involvement as a negative predictor of emotional exhaustion.
Teachers reporting high emotional exhaustion are considered exerting an impact on
teachers’ involvement in job tasks.
Management of universities needs to plan strategies to enhance employees’
involvement. Moreover, the careful monitoring and maintaining an optimal level of
workload is desirable to manage emotional exhaustion among employees. Since, work
pressure has received empirical support as a potential contributor in experiencing
emotional exhaustion (Chan & Huak, 2004; De Croone, Sluiter, & Blonk, 2004;
Goddard, O’Brien, & Goddard, 2006; Levert, Lucas, & Ortlepp, 2000; Robinson et
al., 1991); therefore, this is essential to recognize the nature of teaching job itself and
the potential harm that work pressure may exert of teachers. For instance, teachers’
contribution in outside work assignments and contribution in community related
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issues are considered as important criteria of their performance evaluation. If teachers
are reporting work pressure in performing their routine office job, then concerning
question about their expected outstanding contribution in the field of education may
arise. Turnipseed (1994) suggested that work pressure might affect the cognitive
abilities of the individual. Teachers’ attempt to reduce work stressors, consciously and
unconsciously, may results in influencing the level of emotional exhaustion. This
might be of the reason that work pressure may directly affect the emotional
exhaustion or may act indirectly by influencing the individual’s ability to cope with
stress. This is also encouraging that teachers’ feeling of depersonalization is not being
influenced by work pressure.
Evaluating the elaborated structure of emotional exhaustion in terms of its
underline constructs namely psychological strain and somatic strain demonstrated
some significant differences. This in turn added in empirical support for five-factor
model of burnout. It may be concluded that future researches need to examine the
emotional exhaustion more extensively. Current findings highlighted that involvement
and managerial control were explaining 12% variance in psychological strain. In line
with expected direction, involvement and managerial control are found as negative
predictors, with involvement as more powerful predictor.
While testing the elaborated structure, managerial control is found as an
important predictor explaining the psychological strain. Previous studies have
supported that managerial control is associated to explain variance in burnout (Adali
et al., 2003). Current findings highlighted the need for careful monitoring and further
in depth investigation using group interviews or focus groups of teachers. This will
help to probe their prevailing perceptions and knowing their preference for a desirable
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shape of managerial control in educational settings. Perhaps, the diagnostic efforts
need to explore the management style in various directions, e.g., subjective views
about the negative aspects of working conditions, equity in work place, feedback
mechanisms, control procedures, involvement of teachers, etc., if implemented at
management level, may contribute well in understanding the role of managerial
control in developing impersonal and negative attitude among employees.
For somatic strain component of emotional exhaustion, work pressure as
positive predictor and task orientation as negative predictor is explaining 10%
variance. Task orientation may be observed related with high efficiency and emphasis
on proper planning to complete the job. Since, employees have reported that work
settings are dominantly high on task orientation; therefore, it seems logical that high
emphasis on getting the work done in desirable manner may put teachers in a
condition to effect through emotional exhaustion. Overall, studies have found that
task orientation has association with burnout (Constable & Russell, 1986; Munir,
2005). Specifically, emotional exhaustion has found to be linked with task orientation
(Chan & Huak, 2004; Savicki, 2002).
Findings highlighted that among different psychosocial factors, involvement-
an important variable of relationship dimension has found to be a negative predictor
explaining low variation up to 8% in depersonalization. Since, teachers are reporting
relatively low on depersonalization; therefore, a negative relationship with
involvement was expected. The influence of involvement on depersonalization as well
as on emotional exhaustion can be explained to effect due to mental preoccupation
due to demanding nature of the profession as the causal stressor. The pattern of
interpersonal relationships at work has taken as a potential contributor to exert impact
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on depersonalization (Leiter & Maslach, 1988). Previous studies also reported that
depersonalization is inversely linked with involvement (Adali et al., 2003; Robinson
et al., 1991).
In our study, teachers have reported high on personal accomplishment.
Extending its causal factors with workplace characteristics revealed that certain
aspects of relationship, personal growth, and system maintenance and change
dimensions are explaining considerable variance in personal accomplishment.
Workplace characteristics including coworker cohesion and work pressure as positive
predictors and physical comfort as negative predictor are explaining marginal
variance up to 8% in personal accomplishment. Generally, cohesiveness with
coworkers (Savicki & Cooley, 1987; Turnipseed, 1994) is linked to explain burnout
experience and particularly the sense of personal accomplishment (Savicki, 2002).
The enhanced feelings of personal accomplishment might be a logical result of
working in such a work environment where element of support from coworkers is
present. However, in present study, coworker cohesion is adding in expalining the
reduced sense of persoanl accomplishmnet. The positive association between
coworker cohesion and reduced perosnal accomplishment implies that propbably
teachers are self sufficient and an increase in cohesion may not likely to enhance their
sense of personal accomplihsment. Moreover, personal accomplishment related to
self, is the one independent of the level of cohesion.
Work pressure stands out as a stronger predictor compared to dimensions of
co-worker cohesiveness and physical comfort. Previous studies have pointed out work
pressure as an important predictor of personal accomplishment (Goddard, O’Brien, &
Goddard, 2006; Robinson et al., 1991). Similarly, work pressure is explaining
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variance in personal accomplishment related to self. Whereas, in predicting personal
accomplishment related to others, work pressure has found to be a non-significant
predictor. Findings of the study revealed physical comfort as negative predictor for
personal accomplishment and for its sub-component related to others. Previous
studies have shown that physical comfort is associated with experience of burnout
(Constable & Russell, 1986) and depersonalization (Salyers & Bond, 2001). Since,
teachers of our study have reported high level of personal accomplishment; therefore,
it may interpret that physical conditions of work environment including comfort
associated with physical surroundings and its pleasantness, the proper ventilation and
lighting system, privacy and proper office space, etc., may not lead to decrease in
personal accomplishment. In predicting personal accomplishment related to self, task
orientation found as a positive predictor. In line with previous finding, Robinson et
al., (1991) noted that personal accomplishment was predicted by task orientation.
Overall, task orientation is linked with burnout and its components (Constable &
Russell, 1986; Savicki, 2002). Since, teachers are reporting their work settings high
on task orientation and also reporting high on personal accomplishment; therefore, it
may be concluded that emphasis on structured job will positively influence the
feelings of personal accomplishment.
In support of effective role of work environment in predicting burnout, current
findings highlights that work environment as a composite factor (both three and five
factor models) is explaining significant variance in burnout and each of its
components. Inverse relationship is demonstrated with burnout and its components,
except for reduced sense of personal accomplishment. Work environment as a
composite factor did account for marginal variance in burnout components.
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Comparing the direction and magnitude of difference in pilot and main study,
different findings are obtained. For instance, the findings of our pilot study (N = 102)
showed stronger influence of work pressure as positive predictor and clarity as
negative predictor for explaining 26% variance in emotional exhaustion which was
highly significant. Whereas, findings of our main study highlights involvement as
negative and work pressure as positive predictor contributing 13% variance in
emotional exhaustion. For elaborated structure, task orientation and managerial
control also provided significant explanations. In the pilot study, work pressure as
positive predictor accounts for 10% variance in depersonalization; whereas, in main
study, involvement accounts for 8% variance. Pilot study did not demonstrate the
significant contribution of work environment variables in predicting personal
accomplishment. In comparison, main study highlights that majority of factors of
work environment are explaining variance in personal accomplishment and for its
elaborated structure. Based on findings of present study, the present study contributes
well in establishing empirical support for Densten’s (2001) elaborated model of
burnout.
While reporting organizational commitment, teachers reported high on
affective commitment; whereas, normative commitment was on second level priority.
Teachers reported low on continuance commitment. This is somewhat consistent with
findings of a validity study (Cheng & Stockdale, 2003) of Meyer and Allen’s model
of organizational commitment; the findings mentioned Chinese sample as being the
collectivistic culture reported low on continuance commitment. However, authors also
mentioned that reporting high on affective commitment is considered to be linked
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with individualistic cultures and being low on continuance commitment seems
contradictory particularly in context of masculine cultures.
In the context of Pakistani culture, the participants of study endorsed highly
for affective based commitment and lowest on continuance based commitment may
lead towards understanding the similarities and difference in broader national and
particular organizational culture, e.g., academic settings. At this point, the study is
points towards a new direction for future research that cross-cultural studies may need
to consider that organization environment as being a unique culture may be in
contradiction with broader national culture and thus leading to characteristic
differences in employees’ attitudes. Future researches may need to consider the
differences in national and particular organizational cultures while interpreting
findings in context of broader national cultural classifications or cross-cultural
comparisons.
Various researchers have identified existing gaps in research on work
environment and organizational commitment. Present study provided empirical
evidence suggesting significant relationship of two, particularly in academic settings.
Teachers’ organizational commitment is found to enhance by work environment
characteristics including autonomy and clarity. Findings revealed that autonomy and
clarity are positive predictors and contribute 17% variance in organizational
commitment. Some researchers have although mentioned that workplaces with clarity
of rules and procedures may exert profound influence on employees’ commitment
(Mauser, as cited in Moos, 2008). Clarke and Iles (2000) suggested that workplace-
promoting support for diversity could enhance employees’ commitment.
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With respect to present findings, this may be inferred that diversity in
academic settings may foster through certain strategies i.e. by letting employees be
autonomous in certain job related decisions. For maintaining the performance of
teachers affiliated with higher education; this is important to acknowledge that
teachers require having strong attachment with the organization. Maintaining a sense
of shared values and unity among teachers should be of utmost importance for the
management system of universities. In this regard, the findings of present study help
to suggest that management needs to explore for suitable/adequate mechanism, which
help promote personal growth of teachers. For example, academic settings should
design strategies to promote the autonomous environment. In academic settings,
preferred environment should be the autonomous culture. This particular is logical to
understand especially within group of those teachers who are involved in teaching at
higher education level. Since, effective teaching is a demanding task and requires one
to be innovative while managing it. This is important to understand that an innovative
environment should be the autonomous one. The autonomous environment may
contribute well in performing the job responsibilities. Moreover, if teachers are
autonomous to contribute in decision making, especially those which are related to
them, it will positively add in maintain healthy work environment by satisfying
personal growth needs. Along with this, an important consideration pointed out by
present study is that management requires explicit rules and procedures of the
organization. This focus on clarity of rules and procedures particularly comes under
system maintenance of the academic settings, which in turn positively contributes in
developing emotional bonding of employees with their organization.
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Affective commitment is linked with work environment characteristics
(Dramstad, 2004). The findings of present study suggest that autonomy (personal
growth dimension) accounts for 21% variance in affective commitment. This is in line
with previous findings of Stewart et al. (2007), the researchers supported that
autonomy and fairness in workplace environment is linked with affective
commitment. This is reasonable to believe that autonomy should be valued in
workplace because of the nature of teaching profession, which demands continuous
input for effective performance. The current findings point out towards implications
for the respective management staff of these academic institutions for access weather
teachers are fully involved in decision-making process, especially those decisions
which can directly affect them, or exert positive influence in maintaining their
affective based commitment to the organization.
Continuance commitment, which reflects in the form of investments and
associated perceived cost, has shown association with relationship dimension and
system maintenance of the workplace. Stewart et al. (2007) supported that work
places characterized with cohesion, trust, support, and fairness contributes in
strengthening the continuance-based commitment. Certain other studies also provided
support for association of supervision and cohesion with organizational commitment
(Brooks & Seers, 1991; Chughtai & Zafar, 2006). Findings of present study
highlighted that supervisor support and coworker cohesion as negative predictors
explain variability in continuance commitment. Moreover, the dimension of clarity
(system maintenance and change dimension) as positive predictor has found to be
contributing in explaining continuance-based commitment. Together, coworker-
cohesion, supervisor support and clarity accounts for relatively low variance (8%) in
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continuance commitment. The present study suggested that clarity of workplace rules
and procedures is the strongest predictor of continuance commitment. Work settings
characterized with clear job procedures positively add in predicting teachers’
investments in their job, this may results in their tenure or continuation to work in the
organization. This seems imperative for the management of these institutions to
carefully manage the relationship dynamics of work settings as well as the aspects of
system maintenance and change dimension of the work setting.
Consistent with findings of pilot study, results of main study highlight that
normative based commitment to the organization has non-significant influence on
work environment factors. The participants of our study have reported good emphasis
on normative based experience of commitment. However, none of workplace
dimension reached at statistical significance to infer association with normative
commitment. Study of Cheng and Stockdale (2003) mentioned that individual within
collectivistic culture report better on normative commitment, as being high in group
emphasis.
This further may lead to understand that workplace characteristics may not
influence the normative commitment if considered that within collectivist cultures, the
development of normative commitment seems a product of individuals’ internalized
values within cognitive schema of group preference. Our Findings highlighted that
work environment as a composite factor explains 15 % variance in organizational
commitment, 20% in affective commitment, and 6% in normative commitment. This
also explains that affective commitment stands out as the most important dimension
of work environment, which may exert significantly effect in combination with work
environment characteristics. Comparing the direction and magnitude of difference in
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results of pilot and main study, considerable differences are observed for continuance
commitment.
In the pilot study, non-significant findings were obtained for continuance
commitment. However, the findings of main study point towards small variance in
continuance commitment produced by cohesiveness, supervisor support, and clarity of
work procedures. For affective commitment, pilot study pointed out considerable
variance (24%) produced by managerial control and innovation; whereas, main study
highlighted that only autonomy has found to explain considerable variance in
affective commitment.
This is worth mentioning that the extent of variance which work environment
characteristics are significant but it’s not large in effect size especially when we see
that co-worker cohesion, supervisor support, and clarity contributes in explaining 8%
variance in continuance commitment. This aspect leads towards various possible
explanatory factors. This indicates the influential role of certain other variables as
well in predicting work environment and commitment relationship. It further adds in
complexity of understanding employees’ workplace behavior with its sensitivity
towards getting influence from host of certain other organizational, situational,
contextual, and personal factors.
This may be associated with degree of strength of relationship between work
environment and organizational commitment. For instance, meta-analytic review have
reported 0.24 average absolute value correlation between perceptions of work
environment and job attitudes including involvement and commitment (see Parker et
al., 2003). Moreover, since employees are showing dominant endorsement for
positive aspects for work environment (involvement, coworker cohesion, supervisor
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support, autonomy, task orientation, clarity, innovation, and physical comfort) as
compared to negative aspects (work pressure and managerial control); this may be
attributed as one of the reasons explaining low magnitude of variance in burnout and
organizational commitment produced by work environment.
Moderating Effects of Personality
Based on Moos’s (1986) model of psychosocial environment, it may be
assumed that personal factors moderate the link between work environment and its
outcomes. Present study focused to explore the moderating role of Big Five factor
model to see how does personality influences perceptions regarding work
environment in predicting burnout and organizational commitment. The moderator
analysis was performed using composite scores of criterion variables regressed
against total scores of work environment. The subscales were excluded keeping in
view the page limit of report writing. A recent meta-analysis (Alarcon, Eschleman, &
Bowling, 2009) supports that burnout is associated with Big-Five dimensions of
personality. The current findings highlighted that extraversion, agreeableness, and
openness are predictors of burnout. Extraversion is producing 24% variance in
burnout. Similar pattern of finding has obtained for burnout five factor model; with
extraversion explaining 23%varaince. Agreeableness, demonstrates as the strongest
moderator explains 42% variance in burnout; whereas, accounts for 41% variance in
five factor model of burnout. Openness is explaining 32% variance in burnout three
factor model and 33% variance in burnout five factor model. Findings suggested
emotional stability and conscientiousness as non-significant moderators of burnout.
177
These findings are in line with previous studies. For example, extroverts are reported
more prone for maintaining interpersonal relationships and are considered
demonstrating relationship with burnout (Maslach, Schaufeli, & Leiter, 2001).
Similarly, individuals’ disposition to openness reflects through flexibility of thoughts
and readiness to new ideas (Barrick & Mount, 1991) is related with the experience of
burnout (Swider & Zimmerman, 2010). Findings of studies are in direction to indicate
that burnout is associated with extraversion and openness to experience (Bakker, Van
Der Zee, Lewig, & Dollard, 2006; Rothman & Storm, 2003).
The significant moderation effect of personality dimensions (extraversion,
agreeableness, and openness) suggested through multiple moderation analysis (MMR)
was further tested at an advance level, to see the significance of slopes reflecting
levels (high, medium, & low) of predictor and moderator variables. Based on Aiken
and West (1991) methodology, Jose (2008) recommended computing significance of
slopes for interpreting moderating effects. This analysis further revealed that
evaluating extraversion as moderator revealed it as a negative moderator of the
relationship between work environment and burnout (three factor model).
Comparatively slope representing low level of extraversion seems to exert stronger
influence compared to medium and low levels. However, slopes representing level
(i.e. high, medium, and low) do not reach at statistical significance (Table 37). This
helps to interpret that interaction term does not significantly predicted burnout over
and above the statistical main effects of work environment. Similar pattern of findings
have obtained for agreeableness and openness. In evaluating moderating effects of
personality dimensions for relationship between work environment and burnout (five
factor model), similar pattern of findings have obtained. The statistically significant
177
moderation effects of extraversion, agreeableness, and openness were not further
supported through Jose’s procedure.
Our findings highlighted non-significant moderating influence of
conscientiousness and emotional stability. Findings suggested that conscientiousness
and emotional stability have shown their independent predictive power.
Conscientiousness characteristics reflect as one being careful, thorough, and
responsible includes typical behavior like hard working, achievement oriented, and
consistent (Barrick & Mount, 1991). Since, our participants (university teachers) are
dominantly scoring high on conscientiousness; therefore being over emphasis on
conscientiousness thought to influence their experiences of burnout. Present findings
suggest that conscientiousness as a negative predictor. Emotional stability, which
reflects individual differences in explaining tendency towards distress (McCrae &
John, 1992), has found to be a negative independent predictor.
Findings revealed that teachers’ workplace perceptions and their personality
factors or dispositional factors contribute to predict their organizational commitment.
Findings revealed that extraversion as moderator explains 21% variance in overall
organizational commitment; agreeableness as strongest moderator explains 26%
variance; conscientiousness explains 24% variance; and openness explains 20%
variance in the organizational commitment. Emotional stability stands as carrying
predictor power but is regarded as a non-significant moderator. Each of personality
factors demonstrated significant direct predictive effects as well.
Meyer and Allen (2006) cited rationale in explaining the reason for relating
commitment with extraversion tendencies by considering emotionality as core
extraversion, which may suggest that these individuals would show more emotional
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attachments with their organization. This is supported, if we see that in moderator
analysis, extraversion is showing independent positive predictive power to predict
organizational commitment. Interaction effect revealed extraversion as negative
predictor which implies that the work environment of universities when interacts with
extraversion personalities, it might influence their commitment. Individuals high on
agreeableness are viewed as more trustworthy, which can aid them to maintain good
interpersonal relationships at work setting (Costa & McCrae, 1995) helping them to
build strong emotional affiliation with the organization. However, current findings
suggest that workplace perceptions through agreeableness have inverse relationship
with organizational commitment. Moderator analysis revealed agreeableness as
positive predictor, whereas, interaction effect revealed its negative moderating power.
This implies that workplace characteristics are carrying powerful effects; therefore,
individuals with certain personality traits interact with workplace characteristics to
influence work outcomes.
Similar pattern of findings obtained for conscientiousness and openness.
Conscientious individuals are highly oriented towards good managing ability in work
behavior; for instance to be more orderly and good emotionally; controls the positive
work related outcomes (Thoreson et al., 2004). Meta-analytical study of Salgado
(2003) provides empirical support in building rationale to test conscientiousness as
strongest predictor of job outcomes. Employees’ predisposition to openness to
experience may help them to adapt and change considering requirements of their work
environments, which may enhance positive outcomes (Westerman, Simmons, & Bret,
2007). Current findings supported that conscientiousness influences workplace
perceptions to explain variance in organizational commitment. Openness as negative
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moderator is explaining variance in organizational commitment. The pattern of
current findings have supported the theoretical assumptions of work environment
model of Moos (1994) which implies that workplace environment is unique to each
organization requiring its employees to sense the environment of the organization to
adapt or behave accordingly.
It is important to note that although moderation effect of certain personality
dimensions reached at statistical significance level. However, extending moderation
effect to complex testing, e.g., examining the significance of slope tests highlighted
that high, medium, and low levels of personality dimensions were not significantly
explaining moderation effects over and above statistical main effects of the work
environment. The pattern of findings in initial moderation analysis is significant.
However, the extended analysis is presenting a different picture. At this stage, this is
to acknowledge that findings provide us direction for future research.
The findings of the study suggested that five-factor model appeared as an
informative framework in examining the dispositional sources of burnout and
organizational commitment.
Moderating Effects of Organizational and Demographic related Personal
Variables
The study focused to explore how personal variables e.g., organizational
(public or private sector universities, hierarchical status, job duration, faculties
differences, involvement in other paid jobs) and demographic variables (age, gender,
education, marital status) influence work environment perceptions to predict burnout
177
and organizational commitment. Moderator analysis on sector based differences
highlighted that belongingness to public and private sector universities, moderates the
work environment and burnout relationship. This moderating influence accounts for
11% variance in predicting burnout. Similar pattern of findings have obtained for
five-factor model of burnout and 10% variance in burnout is being explained by
interaction term.
In Pakistan, studies very few studies are conducted which aimed at assessment
of work environment and its outcomes within public and private sector universities
(Rehman & Maqsood, 2008). It also has been reported in a recent systematic literature
review that burnout studies on university teachers are needed to explain by means of
comparison across sectors (Watts & Robertson, 2011). The findings of present study
contributed in establishing empirical support for comparative studies explaining
differences in work environment for university academics of public and private sector.
Present study extended the moderation analysis using Jose’s (2008) procedure,
which helped to understand the interpretation of graphical display of moderation
effects. The findings of significance of slope indicated that slopes representing public
and private sector significantly differ from zero. This indicated that sector is a
significant moderator for relationship between work environment and burnout.
However, findings of the study suggested that private sector is more strongly
associated to predict burnout (three factor model); whereas, for burnout (five factor
model), slope representing public sector is more strongly associated. This finding
helps to deduce that public and private sector is an important variable of comparison
to manage the relationship between work environment and burnout.
177
As regards relationship of work environment and commitment findings
suggests significant moderation effect of sector. The model explaining moderation
effect highlights that belongingness to public and private sector contributes 21%
variance in organizational commitment. Previous studies have provided support that
affiliation with private vs. public sector serves as an important variable to influence
organizational commitment (Boardman, Bozeman, & Ponomariov, 2010; Shirbagi,
2007). Further extending moderation analysis through Jose’s procedure, the statistical
significance of moderation impact appeared strengthened. The findings demonstrated
that both public and private sector are contributing significant moderating influence.
However, magnitude of slopes highlighted that private sector more strongly relates to
explain work environment and commitment relationship compared to public sector.
This helps us to infer that perhaps characteristics of work environment operating in
public sector universities might have been a potential reason, to explain differences in
level of commitment of our participants. The findings of the study highlights the need
of ‘intervention planning’ that the management of public sector universities need to
design suitable strategies, based upon the feedback of their perceptions of work
environment and commitment with their organizations. In this respect, this is to
consider important for the personnel management of public sector universities to
improve the work environment facets; i.e. making work environment conducive to
their professional growth and preferences.
Investigating the moderating role of hierarchical status /job position, revealed
that teachers’ hierarchical status served as a non-significant moderator in predicting
burnout and for organizational commitment. However, teachers’ belongingness to
basic and high rank is showing direct predictive relationship with burnout variable. If
177
we see, hierarchical status as independent predictor in model 1 and in model 2 is
showing negative relationship in predicting burnout (for both three and five factor
models). This is in line with previous finding where academic rank was negatively
associated with burnout (Haque & Khan, 2001). Previously, job status was found
positively related to aspects of organiztaional commitment (Meyer et al., 1993).
Researches have shown that the duration of job appears as an independent
predictive power. For example, organizational tenure is a potential factor to enhance
employees’ well being (Long, 1993) and particularly their organizational commitment
(Kushman, 1992; Meyer & Allen, 1997). However, the findings of present study
indicated that in predicting organizational commitment, employment position/rank is
not showing independent predictive power. However, current findings suggest that
employment duration is not significantly moderating work place perceptions to
predict burnout (three and five factor models) and organizational commitment. This
helps to interpret that tecahers percpetons and outocme variables are indepndnet of
their job status. This refelcts the explicit importance of workplace charcateristics as
mor important influential factors comapred to tecahers’ perosnal variables.
Some researchers have reported that faculty/departmental affiliation does
relate to employees’ perception of work environment (Avallone & Gibbon, 1998;
Maloney et. al., 1996; Straker, 1989). Affiliation with natural and social sciences
disciplines might be an influential variable to explain differences in burnout and
commitment; mainly due to the nature of their job and work environment. Current
findings suggests that affiliation with natural or social sciences departments does not
serve as a moderating factor in predicting teachers’ burnout and their commitment.
177
However, its direct relationship was evident for burnout and also for organizational
commitment.
Present study reported that only a small portion of teachers have reported
about their involvement in side jobs. Moderator analysis revealed that teachers’
involvement and non-involvement in side jobs does not serve as a moderate to
influence workplace perceptions in predicting burnout and organizational
commitment. Although, the direct predictive power of this variable was evident; the
variable accounts for 13% variance in burnout (three factor model) and 12% in
burnout five factor model.
Moderator analysis performed for age highlighted that association between
work environment and its outcomes is being moderated by teachers’ age. In
explaining work environment and burnout (three factor model), age as moderator
explains 13% variance; whereas for predicting burnout five factor model, age as
moderator accounts for 12% variance. A recent research (Luk, Chan, Cheong & Ko ,
2010) suggests that age is associated with burnout. This finding is an important
consideration within work settings that management should consider the possible
impact of employees’ age in designing and implementing strategies. Further,
extending this statistically significant moderation analysis to test the significance of
slope tests, results revealed that slope representing moderate level of age is more
stronger to predict burnout (both three and four factor models). This implies that when
moderate level of age is considered, work environment is significantly associated with
burnout.
Some earlier empirical studies (Grau et al., 1991; Mathieu & Zajac, 1990)
have suggested positive and stronger relationship of age with organizational
177
commitment. The findings of our study are in line with these studies, suggesting that
age is positively related with organizational commitment explaining 17% variance.
However, these findings highlight the non-significant moderating influence of age in
predicting organizational commitment.
Moderating impact of gender is revealed as a non-significant moderator in
predicting burnout. Phelan et al. (1993) also suggested that non-significant gender
difference in the perceptions of work environment. However, substantial body of
research suggests that gender does influence work environment and work subsequent
work related attitudes, e.g., organizational commitment (Clarke & Iles, 2000; Stewart
et al., 2007; Witt, 1989). Findings of present study added in arguments in favor of non-
significant impact of gender, which otherwise is also well supported by previous
studies (Phelan et al., 1993; Sahu & Misra, 2004). However, we do acknowledge that
evaluating gender as independent predictor may add to some valuable information.
Findings highlighted that gender is not a significant independent predictor for burnout.
The findings of present study indicated that in predicting organizational
commitment, gender does not serve as a significant moderator. Current findings also
aid in supporting (see Meyer et al., 2002; Riketta, 2005; Thorsteinson, 2003) that
teachers’ commitment to their organizations is independent of their gender perhaps
because of the dominant influence of workplace practices and management on work
outcomes instead of gender. Substantial body of research suggests that gender does
influence work environment and work subsequent work related attitudes, e.g.,
organizational commitment (Clarke & Iles, 2000; Stewart et al., 2007; Witt, 1989).
The findings of present study indicated that in predicting organizational commitment,
gender does not serve as a significant moderator. Current findings also aid in
177
supporting (see Meyer et al., 2002; Riketta, 2005; Thorsteinson, 2003) that teachers’
commitment to their organizations is independent of their gender perhaps because of
the dominant influence of workplace practices and management on work outcomes
instead of gender.
Previously, Maloney et al. (1996) reported that employees’ education level
relates to the aspect of work environment. Investigating the impact of teachers’
education highlighted that educational level (masters degree vs. higher research
degrees i.e., Master of Philosophy or Doctorate of Philosophy: MPhil or PhD) does
not act as moderators in predicting teachers’ burnout and organizational commitment.
Bivariate relationship between education and burnout has been reported in earlier
studies (Kabadayi, 2010; Moghadam & Tabatabaei, 2006; Wilber & Specht, 1994).
However, our study does not support the direct predictive relationship between
education and burnout. Literature has provided support for stronger bivariate
relationship between level of education and organizational commitment (Grau et al.,
1991; Mishra & Srivastava, 2001). However, education showed no direct relationship
with organizational commitment.
Findings highlight that teachers’ marital status does not moderates the
relationship of work environment with burnout and organizational commitment.
Earlier some studies have found out that marital status is linked to explain burnout
(Kim-Wan, 1991). However, present study did not suggest the direct as well as
moderating influence of marital status in predicting burnout. Moreover, previous
findings (John & Taylor, 1999; Tsui et al., 1994) have demonstrated that married
people were more committed to their organization than unmarried people were.
177
However, in present study, marital status did not showed independent power to
predict organizational commitment.
In support of present findings, Patterniti, Niedhammer, Lang, and Consoli
(2002) had pointed out the non-significant moderating influence demographic and
organizational variables, e.g. age, education, marital status, and hierarchical status in
examining the link between work environment and health outcomes. Similarly,
Fejgin, Ephraty, and Ben-Sira (1995) suggested that burnout experience is
independent of influence of personal and job related variable. For variables of
hierarchical status, further analysis (ANOVA) was also computed to see how each
level of rank (e.g., lecturers, assistant professors, associate professors, and
professors), separately tend to influence the reporting about burnout and commitment.
However, non-significant findings were obtained for each case. Keeping in view the
possible interaction of age and hierarchical status, One-Way Between Group analyses
showed non- significant associations. These further added to conclude that overall, the
non-significant impact of most of personal variables compliment the point raised by
Moos (1994), who posited that actual characteristics of the work settings are the major
determinants of employees’ perceptions of workplace rather than the demographic
and organizational related personal variables.
177
Implications of the Study
The present study contributed to evaluate the psychometric feasibility of using
instruments of study variables for university teachers in Pakistan. This aided in
making this study as indigenized one to further use the validated instruments (i.e.
Work Environment Scale, Maslach Burnout Inventory, Organizational Commitment
Questionnaire, & Mini Markers) for teachers’ population. Moreover, this research
provided cross cultural empirical evidence for establishing the relationship between
work environment and outcomes. The study has pointed out that management need to
consider teachers’ personality and age while improving or managing the workplace
environment. The study provided a comparison for future researches for the
evaluation of work environment of academic settings for designing possible
interventions in context of the organizational development. The findings of the study
are important to realize and encourage the management to understand and manage
their work settings on basis of desirable psychosocial characteristics which in turn
influences teachers’ burnout and commitment to their organizations.
Limitations and Future Research
To understand the applicability of research findings within the framework of
research design, it’s important to acknowledge the limitations of the study. Keeping in
view the response rate of the participants, implementation of random sampling was
not feasible in present study. The present study selected the instruments on basis of
demonstrated use in researches in context of Pakistan. However, the procedure of
177
examining the face validity in perspective of intended use within academic settings
was not done. This in turn might be one of the potential reasons for obtaining a
moderate model fit especially in case of Work Environment Scale. It was also
observed that for studies reporting perceptions about work environment should also
focus on qualitative methodology as well. This will help to understand the unique
characteristic features of the environment, which otherwise may missed out while
using pure quantitative approaches.
The present research raises certain questions for direction of the future
research. Keeping in view the gaps in literature, it is needed further exploration of
moderating as well as mediating role of personal variables. Future studies need to
focus on nationwide data, so that generalizability of the findings may be enhanced.
Since, quite low reliability coefficient has obtained in case of emotional stability;
which thereof, got unaffected even using large sample during main study phase.
Future research need to explore the possible reasons explaining how individuals
respond to the dimension of emotional stability in our culture. For future research,
intervention based studies are needed to plan; this will help in systematic diagnose of
workplace concerns leading to design appropriate planning for its effective
management. Future researches may focus ‘case study’ research design for thorough
description, diagnosis, and interventions of work environment related concerns. This
will add in expanding the field of occupational psychology in Pakistan.
Conclusion
The theoretical contribution of present study tested Moos’s (1994) model of
work environment in the context of work related outcomes (for the teaching faculty
177
members of Universities of Punjab, Pakistan) and elaborated the moderating role of
personal variables in this process. The findings provided empirical support for
existing measurement models of study variables. Given that work environment
influences burnout and organizational commitment, requires attention of policy maker
i.e. management of the universities, requires re evaluating the existing dynamics of
their work environment, especially from the feedback of findings of the present study.
The study emphasizes that interventions and organizational development programs of
universities’ faculty require emphasis on aspects such as; enhancing teachers’
involvement and coworker cohesion. An optimal emphasis on work pressure,
managerial control and task orientation is needed. This in turn will lead to healthy
environment potentially carrying less tendencies of burnout experience among
teachers. Managing high commitment among teachers found to have association with
autonomous environment where clarity of job procedures was highly considered for
effective performance of university teachers. The study suggests that extraversion,
agreeableness, and openness may combine to interact with operating working
conditions leading to experience of burnout. Personality has also found to be
contributing in effecting employees’ commitment except for the dimension of
emotional stability. The potential moderating influence of public and private sector
suggests the evaluation of existing employees’ policies for planning the training and
interventions for the effective management of teachers’ burnout and organizational
commitment. Moreover, across different subgroup of teachers, age is an important
consideration in managing teachers’ burnout. The study highlights that situational
context of working conditions is needed to consider for understanding the impact of
personality and other personal variables on environment and outcome relationship.
269
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Appendix A
Consent Letter to Participate in the Research Study
National Institute of Psychology (NIP), Quaid-i-Azam University, Islamabad, is an
educational/research institute. NIP conducts researches on different educational,
organizational, and social issues. Present research/study is a part of study program.
This research is related with employees’ views about their work place, experiences at
work place, and their personal characteristics.
This letter concerns to seek out your consent to participate in the study. Your true
responses are very much important and serve as facilitation in this academic activity.
It is assured that your responses will be kept confidential and will used only for the
research purpose.
Please clearly your consent to participate in this study by responding to the
undertaking below.
I hereby agree to participate in this study. I also have right to decline to participate in
this study as well.
Signature of Participant Signature of Researcher
Thank you for participation in this study and for your time.
320
Appendix B
General Instructions & Demographic Information Sheet
To participate in this study, you are required to fill out this attached Performa. This
Performa is composed of 04 questionnaires along with instructions. Kindly read
instructions carefully mentioned on each questionnaire and respond to all questions.
Please do not leave any question unattempted.
You are required to respond to all questions keeping in mind the views related to your
current organization (university), where you are employed in the capacity of
permanent employee. It is desirable to fully ignore your views related to your
involvement to any side jobs or work settings.
Demographic Information Sheet
Before proceeding, kindly provide the detail of your demographic information cited
below.
Gender --------------------------
Age --------------------------
Education --------------------------
Marital Status --------------------------
Duration of Employment in the present organization -------------------------------
Rank/ Job Title/ Grade ---------------------------
Department -----------------------------
Private/ Public Sector (tick any one option) ------------------------------
University Name (optional): --------------------------
Are you involved in any paid side-jobs? (tick relevant one) YES NO
If “yes”, please specify in what capacity you are working. ---------------------------------
-------------------------------------------------------------------------------------------------------
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Appendix C
Work Environment Scale Instructions: These are 90 statements about the place in which you work. The
statements are intended to apply to all work environments. However, some words may
not be quite suitable for your work environment. For example, the term supervisor is
meant to refer to the boss, manager, department head, or the person or persons to
whom all employees report. You are to decide which statements are true of your work
environment and which are false.
1. The work is really challenging. True False
2. People go out of their way to help a new employee feel comfortable. True False
3. Supervisors tend to talk down to employees. True False
4. Few employees have any important responsibilities True False
5. People pay a lot of attention to getting work done. True False
6. There is constant pressure to keep working. True False
7. Things are sometimes pretty disorganized. True False
8. There’s a strict emphasis on following policies and regulations. True False
9. Doing things in a different way is valued. True False
10. It sometimes gets too hot (room conditions). True False
11. There’s not much group spirit. True False
12. The atmosphere is somewhat impersonal. True False
13. Supervisors usually compliment an employee who does something well. True False
14. Employees have a great deal of freedom to do as they like. True False
15. There’s lot of time wasted because of inefficiencies. True False
16. There always seems to be an urgency about everything. True False
17. Activities are well-planned. True False
18. People can wear wild looking clothing while on the job if they want. True False
19. New and different ideas are always being tried out. True False
20. The lighting is extremely good (room conditions). True False
21. A lot of people seem to be just putting in time. True False
22. People take a personal interest in each other. True False
23. Supervisors tend to discourage criticism from employees. True False
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24. Employees are encouraged to make their own decisions. True False
25. Things rarely get “put off till tomorrow.” True False
26. People cannot afford to relax. True False
27. Rules and regulations are somewhat vague and ambiguous. True False
28. People are expected to follow set rules in doing their work. True False
29. This place would be one of the first to try out a new idea. True False
30. Work place is awfully crowded. True False
31. People seem to take pride in the organization. True False
32. Employees rarely do things together after work. True False
33. Supervisors usually give full credit to ideas contributed by employees. True False
34. People can use their own initiative to do things. True False
35. This is a highly efficient, work-oriented place. True False
36. Nobody works too hard. True False
37. The responsibilities of supervisors are clearly defined. True False
38. Supervisors keep a rather close watch on employees. True False
39. Variety and change are not particularly important. True False
40. This place has a stylish and modern appearance. True False
41. People put quite a lot of effort into what they do. True False
42. People are generally frank about how they feel. True False
43. Supervisors often criticize employees over minor things. True False
44. Supervisors encourage employees to rely on themselves when a
problem arises.
True False
45. Getting a lot of work done is important to people. True False
46. There is no time pressure. True False
47. The details of assigned jobs are generally explained to employees. True False
48. Rules and regulations are pretty well enforced. True False
49. The same methods have been used for quite a long time. True False
50. The place could stand some new interior decorations. True False
51. Few people ever volunteer. True False
52. Employees often eat lunch together. True False
53. Employees generally feel free to ask for a raise. True False
54. Employees generally do not try to be unique and different. True False
55. There’s an emphasis on “work before play.” True False
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56. It is very hard to keep up with your workload. True False
57. Employees are often confused about exactly what they are supposed to
do.
True False
58. Supervisors are always checking on employees and supervise them very
closely.
True False
59. New approaches to things are rarely tried. True False
60. The colors and decorations make the place warm and cheerful to work
in.
True False
61. It is quite a lively place. True False
62. Employees who differ greatly form the others in the organization don’t
get on well.
True False
63. Supervisors expects far too much from employees. True False
64. Employees are encouraged to learn things even if they are not directly
related to the job.
True False
65. Employees work very hard. True False
66. You can take it easy and still get your work done. True False
67. Fringe benefits are fully explained to the employees. True False
68. Supervisors do not often give in to employee pressure. True False
69. Things tend to stay just about the same. True False
70. It is rather drafty (disorganized) at times. True False
71. It’s hard to get people to do any extra work. True False
72. Employees often talk to each other about their personal problems. True False
73. Employees discuss their personal problems with supervisors. True False
74. Employees function fairly independently of supervisors. True False
75. People seem to be quite inefficient. True False
76. There are always deadlines to be met. True False
77. Rules and polices are constantly changing. True False
78. Employees are expected to conform rather strictly to the rules and
customs.
True False
79. There is a fresh, novel atmosphere about the place. True False
80. The furniture is usually well arranged. True False
81. The work is usually very interesting. True False
82. Often people make trouble by talking behind other’s back. True False
324
83. Supervisors really stand up for their people. True False
84. Supervisors meet with employees regularly to discuss their future work
goals.
True False
85. There’s a tendency for people to come to work late. True False
86. People often have to work overtime to get their work done. True False
87. Supervisors encourage employees to be neat and orderly. True False
88. If employee comes in late, he or she can make it up by staying late. True False
89. Things always seem to be changing. True False
90. The rooms are well ventilated. True False
325
Appendix D
Work Environment Scale Scoring Key The following list contains the scoring key for the Work Environment Scale (WES) - Form (Real). The scale has 90 items listed below in form of item numbers. An item number listed as true (T) is scored 1 point if marked “true” and an item listed as false (F) is scored 1 point if marked “false”. The total subscale score is the number of items answered in the scored direction. Involvement Item # 1 11 21 31 41 51 61 71 81
Scoring T F F T T F T F T
Coworker Cohesion
Item # 2 12 22 32 42 52 62 72 82
Scoring T F T F T T F T F
Supervisor Support Item # 3 13 23 33 43 53 63 73 83
Scoring F T F T F T F T T
Autonomy Item # 4 14 24 34 44 54 64 74 84
Scoring F T T T T F T T T
Task Orientation Item # 5 15 25 35 45 55 65 75 85
Scoring T F T T T T T F F
326
Work Pressure Item # 6 16 26 36 46 56 66 76 86
Scoring T T T F F T F T T
Clarity
Item # 7 17 27 37 47 57 67 77 87
Scoring F T F T T F T F T
Managerial Control Item # 8 18 28 38 48 58 68 78 88
Scoring T F T T T T T T F
Innovation Item # 9 19 29 39 49 59 69 79 89
Scoring T T T F F F F T T
Physical Comfort Item # 10 20 30 40 50 60 70 80 90
Scoring F T F T F T F T T
327
Appendix E
MBI-Educators Survey The purpose of this survey is to discover how educators view their job and the people
with whom they work closely.
Instructions: There are 22 statements of job-related feelings. Please read each
statement carefully and decide if you ever feel this way about your job. If you have
never had this feeling, write a “0” (zero) in the space provided for options right after
the statement. If you have had this feeling, indicate how often you feel it by writing
the number (from 1-6) that best describes how frequently you feel that way.
S# Statements 0 1 2 3 4 5 61. I feel emotionally drained.
2. I feel used up at the end of the day.
3. I feel fatigued when I get up in the morning and have to face another
day on the job.
4. I can easily understand how my recipients feel about things.
5. I feel I treat some recipients as if they were impersonal “objects”.
6. Working with people all day is really a strain for me.
7. I deal very efficiently with the problems of my recipients.
8. I feel burned out from my work.
9. I feel I am positively influencing other people’s lives through my
work.
How often:
0 1 2 3 4 5 6
Never A few times a year or less
Once a month or less
A few times a month
Once a week
A few times a week
Every day
328
10. I have become more callous towards people since I took this job.
11. I worry that this job is hardening me emotionally.
12. I feel very energetic.
13. I feel frustrated by my job.
14. I feel I am working too hard on my job.
15. I don’t really care what happens to some recipients.
16. Working directly with people puts too much stress on me.
17. I can easily create a relaxed atmosphere with my recipients.
18. I feel exhilarated after working closely with my recipients.
19. I have accomplished many worthwhile things in this job.
20. I feel like I am at the end of my rope.
21. In my work I deal with emotional problems very calmly.
22. I feel recipients blame me for some of their problems.
329
Appendix F
Organizational Commitment Questionnaire Instructions: These are the statements about how you feel towards your organization.
Read each statement carefully and indicate the extent (strongly agree, agree, neutral,
disagree, and strongly disagree) to which you agree or disagree with the statement.
1. I do not feel like part of family (name of
organization). SA Ag N DA SD
2. I feel emotionally attached to (name of organization).
SA Ag N DA SD
3. Working at (name of organization) is a great deal of personal interest to me.
SA Ag N DA SD
4. I feel a strong sense of belonging to (name of organization).
SA Ag N DA SD
5. (Name of organization) does not deserve my loyalty.
SA Ag N DA SD
6. I am proud to tell others that I work at (name of organization).
SA Ag N DA SD
7. I would be happy to work at (name of organization) until I retire.
SA Ag N DA SD
8. I really feel that many problems faced by (name of organization) are also my problems.
SA Ag N DA SD
9. I enjoy discussing (name of organization) with people outside of it.
SA Ag N DA SD
10. I am not concerned about what might happen if I left (name of organization) without having another position lined up.
SA Ag N DA SD
11. It would be very hard for me to leave (name of organization) right now even if I wanted to.
SA Ag N DA SD
12. Too much in my life would be disrupted if I decided I wanted to leave (name of organization) now.
SA Ag N DA SD
13. It wouldn’t be too costly for me to leave (name of organization) now.
SA Ag N DA SD
14. Right now, staying with (name of organization) is a matter of necessity as much as desire.
SA Ag N DA SD
15. One of the serious consequences of leaving (name of organization) would be the scarcity of available alternatives.
SA Ag N DA SD
16. One of the reasons I continue to work for (name of organization) is that leaving would require considerable sacrifices i.e., another organization may not match the overall benefits I have here.
SA Ag N DA SD
330
17. I do not feel any obligation to remain with (name of organization).
SA Ag N DA SD
18. Even if it were to my advantage, I do not feel like it would be right to leave (name of organization) now.
SA Ag N DA SD
19. I would feel guilty if I left (name of organization) now.
SA Ag N DA SD
20. (Name of organization) deserves my loyalty. SA Ag N DA SD 21. It would be wrong to leave (name of
organization) right now because of my obligation to the people in it.
SA Ag N DA SD
22. I owe a great deal to (name of organization). SA Ag N DA SD
331
Appendix G MINI-MARKERS
How Accurately Can You Describe Yourself?
Instructions: Please use this list of common human traits to describe yourself as
accurately as possible. Describe yourself as you see yourself at the present time, not
as you wish to be in the future. Describe yourself as you are generally or typically, as
compared with other persons you know of the same sex and of roughly your same
age.
Before each trait, please write a number indicating how accurately that trait describes
you, using the following rating scale:
______________________________________________________________
INACCURATE.......................................................................................... ACCURATE
------------------------------------------------------------------------------------------------------------ Extremely...Very...Moderately...Slightly........Slightly...Moderately...Very...Extremely
_______ _______ ________ ______ ? ________ ________ ______ _______
......1..............2.............3...............4..............5........6...............7...............8..............9
332
Personality traits INACCURATE ACCURATE
Extre
mel
y
Ver
y
Mod
erat
ely
Slig
htly
Nei
ther
in
accu
rate
no
r t
Slig
htly
Mod
erat
ely
Ver
y
Extre
mel
y
1 2 3 4 5 6 7 8 9 1.Talkative 2. Extroverted (expressive) 3. Bold 4. Energetic 5. Shy 6. Quiet 7. Bashful (reserved) 8. Withdrawn 9. Sympathetic 10. Warm 11. Kind 12.Cooperative 13. Cold 14.Unsympathetic 15. Rude 16. Harsh 17. Organized 18. Efficient 19. Systematic 20. Practical (realistic) 21. Disorganized 22. Sloppy (casual) 23. Inefficient 24. Careless 25. Un-envious 26. Relaxed 27. Moody 28. Jealous 29. Temperamental 30. Envious (resentful) 31. Touchy 32. Fretful (anxious) 33. Creative 34. Imaginative 35. Philosophical 36. Intellectual 37. Complex 38. Deep 39. Uncreative 40. Un-intellectual
333
Appendix H
MINI-MARKERS- Scoring Key Extraversion: It consists of 1-8 items in the inventory (see annexure F) with
negatively phrased items including 5, 6, 7 and 8.
Agreeableness: It consists of 9-16 items with negative item nos. 13, 14, 15 and 16.
Conscientiousness: It consists of 17-24 items and negative items are 21, 22, 23 and
24.
Emotional Stability: It consists of 25-32 items with negative item nos. 27, 28, 29, 30
and 32.
Intellect or Openness: It consists of 33-40 items with item nos. 39 and 40 as negative
items.
The inventory is scored by adding up the numbers that have been circled for
each of these traits, so that it ends up with 5 scores, one for each of the traits.
334
Appendix I
Descriptive Profile of Pilot Sample
Mean, SD, Percent, Range, & Frequency of Personal Variables of University
Teachers (N = 102)
Organizational & Demographic
Variables M SD Percent Range N
Age 31.08 7.6 43 102
Duration of Employment 2.48 3.17 21.90 102
Sector
Public Sector Universities 60.8 62
Private Sector Universities 39.2 40
Departments
Natural Sciences 27.5 28
Social Science 71.6 73
Hierarchical Status
Basic Rank 63.7 65
High Rank 36.3 37
Side Paid Jobs
Involvement in side jobs 21.6 22
Non-Involvement in side jobs 77.5 80
Gender
Men 49 50
Women 51 52
Education
Master Level 46.1 47
Research Degree or Doctorate 53.9 55
Marital Status
Married 48 49
Single 52 53
335
Appendix J
Descriptive Profile of Sample of Main Study
Mean, SD, Percent, Range, & Frequency of Personal Variables of University
Teachers (N = 426)
Organizational & Demographic
Variables Mean S.D Percent Range Frequency
Age 36.57 8.96 52 426
Duration of Employment 5.06 5.20 31.98 426
Sector
Public Sector Universities 49.8 212
Private Sector Universities 50.2 214
Departments
Natural Sciences 53.5 228
Social Science 46.5 198
Hierarchical Status
Basic Rank 43.4 185
High Rank 56.6 241
Side Paid Jobs
Involvement in side jobs 4.9 21
Non-Involvement in side jobs 95.1 405
Gender
Men 62.9 268
Women 37.1 158
Education
Master Level 26.3 112
Research Degree or Doctorate 53.9 314
Marital Status
Married 65.7 280
Single 33.6 143