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Predictors of Employee Turnover in the Dutch Fashion Retail:
The Role of Work Overload, Emotional Labour, Emotional
Exhaustion and Multi-faceted Job Satisfaction
Author: Lonneke Schaap
Student ID: 10380965
Supervisor: Daphne Dekker
Submission Date: 15-08-2014
Paper Type: Master Thesis – Final version
University: University of Amsterdam
Course: Business Studies
Track : Leadership & Management
3
ABSTRACT
Purpose – The purpose of this study is to examine the predictors of employee turnover in the
retail fashion industry. The research model analyzes the role of work overload, emotional
labour, emotional exhaustion and multi-faceted job satisfaction in predicting employee
turnover.
Design/methodology/approach – Data was collected by the use of a self-administered
questionnaire. 103 employees working in the fashion retail industry participated in the study.
Data was analyzed with IBM SPSS statistics 22.
Findings – Both work overload and emotional labour positively influence emotional
exhaustion. In turn, emotional exhaustion is negatively related to six dimensions of multi-
faceted job satisfaction; job satisfaction with overall job, co-workers, supervision, policy, pay
and customers. Only two dimensions are negatively related to employee turnover; job
satisfaction with overall job and job satisfaction with promotion. Only job satisfaction with
overall job mediates the relation between emotional exhaustion and employee turnover.
Practical implications – Both job satisfaction with overall job and job satisfaction with
promotion are negatively related to employee turnover. In order to decrease employee
turnover, management of fashion retail organizations should consider ways to increase job
satisfaction with overall job and job satisfaction with promotion. The model shows that an
increase of job satisfaction with overall job can be achieved by lowering emotional
exhaustion, work overload and emotional labour.
Originality/value – This study fills the research gap of employee turnover in the fashion
retail industry. A comprehensive model is used including industry-specific characteristics
such as work overload, emotional labour and emotional exhaustion. The overall model has
never been studied before. This is also the first time that the construct of multi-faceted job
satisfaction is examined in a fashion retail context specifically.
Keywords - Work Overload, Emotional Labour, Emotional Exhaustion, Multi-faceted Job
Satisfaction, Turnover Intentions, Fashion, Retail
4
TABLE OF CONTENTS
1. Introduction 7
1.1. Introduction 7
1.2. The Dutch Fashion Retail Industry 10
1.3. Theoretical and Practical Relevance 10
2. Literature Review 12
2.1. Work Overload 12
2.2. Emotional Labour 13
2.3. Antecedents of Emotional Exhaustion 15
2.4. Antecedents of Multi-faceted Job Satisfaction 17
2.5. Antecedents of Employee Turnover 21
2.6. Research Model and Hypotheses 23
3. Methodology 24
3.1. Research Design 24
3.2. Questionnaire 24
3.3. Research Sample 25
3.4. Data Collection 25
3.5. Measures 26
3.6. Control Variables 27
3.7. Data Analysis 28
4. Results 31
4.1. Data Cleaning 31
4.1.1. Coding Variables and Recoding Counter-indicative Items 31
4.1.2. Missing Values 31
4.1.3. Detecting Outliers 32
4.1.4. Confirmatory Factor Analysis (CFA) 32
4.1.5. Computing Reliability 32
4.1.6. Computing Scale Means 33
4.1.7. Checking Normality and Distribution of Data 33
4.2. Testing Hypotheses 35
4.2.1. The Influence of Work Overload and Emotional Labour on Emotional 35
Exhaustion
5
4.2.2. The Relation between Emotional Exhaustion and Multi-faceted Job 36
Satisfaction
4.2.3. Emotional exhaustion as Mediator between Work Overload and 39
Multi-faceted Job Satisfaction
4.2.4. Emotional Exhaustion as Mediator between Emotional Labour and 42
Multi-faceted Job Satisfaction
4.2.5. The Relation between Multi-faceted Job Satisfaction and 45
Turnover Intentions
4.2.6. Multi-faceted Job Satisfaction as Mediator between 46
Emotional Exhaustion and Turnover Intentions
4.3. Research Model with Results 47
5. Discussion 48
5.1. Conclusion 48
5.2. Discussion 49
5.3. Theoretical Implications 51
5.4. Practical Implications 51
5.5. Limitations and Future Research 52
6. References 54
7. Appendices 61
6
LIST OF FIGURES AND TABLES
Figure 1: Statistics on Employees in the Dutch Retail Industry in 2010 11
Figure 2: Research Model 11
Figure 3: Mediation Model 30
Figure 4: Research Model with Results 47
Table 1: Means, Standard Deviations, Correlations and Reliability Coefficients 34
Table 2: Kurtosis, Skewness and the Kolmogorov-Smirnov Test 35
Table 3: Summary of Multiple Regression Analysis for Emotional Exhaustion 36
Table 4: Summary of Linear Regression Analyses for Multi-faceted Job Satisfaction 38
Table 5: Path Coefficients and Confidence Intervals; Emotional Exhaustion 41
as a Mediator between Work Overload and Multi-faceted Job Satisfaction
Table 6: Path Coefficients and Confidence Intervals; Emotional Exhaustion 44
as a Mediator between Emotional Labour and Multi-faceted Job Satisfaction
Table 7: Summary of Multiple Regression Analyses for Turnover Intentions 45
Table 8: Path Coefficients and Confidence Intervals; Multi-faceted Job Satisfaction 46
as a Mediator between Emotional Exhaustion and Turnover Intentions
7
1. INTRODUCTION
1.1. Introduction
Imagine that you are in charge of a large fashion retail company which is in need for many
front-line employees on a regular basis. However, employee turnover rate appears to be high
and each time a new employee is hired, this person or another quits within a short period of
time. As one might picture, this situation is rather problematic and leads to numerous
organizational disadvantages. The overall subject of employee turnover is not a novel one but
has been of great interest over the last decades and has proven to be detrimental for
companies (Regts & Molleman, 2013). In general, prior literature supports this viewpoint
(Argote & Epple, 1990; Shaw, Gupta & Delery, 2005; Siebert & Zubanov, 2009) and
numerous organizational disadvantages of employee turnover are found such as an incline in
productivity (Batt, 2002; Huselid, 1995), future revenue growth (Baron, Hannan & Burton,
2001) and profitability (Glebbeek & Bax, 2004; Ton & Huckman, 2008). Additional
organizational disadvantages of high employee turnover are a decrease in customer service
(Kacmar, Andrews, Van Rooy, Steilberg & Cerrone, 2006) and customer satisfaction
(Morrow & McElroy, 2007).
Most research explains the negative relation between employee turnover and
organizational performance on the basis of the social and human capital theory, as these two
theories have received most attention in prior literature (Park & Shaw, 2013). The human
capital theory assumes that employees who have experience are of more value to a company
because they possess the needed skills and knowledge. When these employees decide quit, it
is harmful to the organization because it loses employees with experience who contribute to
the human capital of the firm. It takes time to substitute these employees and to reach similar
levels of knowledge. The replacement of personnel also adds costs in terms of recruiting,
selecting and training new employees. The social capital theory argues that turnover has a
negative effect on organizational performance because when an employee decides to leave, he
or she disturbs the organization’s social capital which has to do with social relations between
employees such as trust and collective goal orientation. Moreover, socialization costs are
involved when new employees enter an organization.
Originally, labour turnover can be divided into either voluntary or involuntary
turnover (Morrell, Loan-Clarke & Wilkinson, 2001; Price, 1977). “An instance of voluntary
turnover, or a quit, reflects an employee's decision to leave an organization, whereas an
instance of involuntary turnover, or a discharge, reflects an employer's decision to terminate
8
the employment relationship” (Shaw, Delery, Jenkins & Gupta, 1998, p. 511). Because this
study aims to predict the antecedents of why employees decide to quit, this paper refers to the
concept of voluntary employee turnover when using the definition of employee turnover.
Voluntary turnover is often unexpected and hard to manage for organizations (Shaw et al.
1998).
Much attention has been paid in prior literature to the subject of employee turnover in
general but less is known about employee turnover in the retail industry. Rhoads, Swinyard,
Geurts and Price (2002) argue that having and retaining a good workforce is a major
requirement for being successful as a retail company. By maintaining competent employees,
recruitment and training costs can be reduced and more value can be offered to customers.
This results in benefits while gaining competitive advantage over other market players. It is
therefore important to decrease employee turnover in the retail industry (Kim, Knight &
Crutsinger, 2009).
Although a number of studies investigated employee turnover in the retail industry,
there is still much to discover (Booth & Hamer, 2007). Even more absent is a comprehensive
model on the most important predictors of employee turnover in the fashion retail industry
specifically. Research on this subject is lacking in this specific branch. This study tries to gain
more insight into the predictors of employee turnover in the fashion retail industry and will
thereby contribute to the lack of research in this specific area. Due to this lack of knowledge,
this study is mainly based on arguments from research conducted in the overall retail industry
since the fashion retail is presented as part of the overall retail industry (Peek & Veghel,
2011). It is therefore assumed that there are many similarities between the fashion retail
industry and the overall retail industry. Figure 1 illustrates this in terms of statistics.
Multiple factors influence the decision of employees to leave an organization in the
fashion retail industry. Prior literature claims that job satisfaction is one of the most important
drivers behind employee turnover in general (Jaramillo, Mulki & Solomon, 2006). This also
accounts for the retail industry (Booth & Hamer, 2007; Henrie, 2004). In order to draw an as
complete model as possible, this study focuses on the concept of multi-faceted job satisfaction
instead of single-faceted job satisfaction as key antecedent of employee turnover. Multi-
faceted job satisfaction has mainly been used in business-to-business context and so far, only
Chung, Rutherford and Park (2012) explored the concept of multi-faceted job satisfaction in
the retail industry. They argue that future research is needed to test the effects of multi-faceted
job satisfaction on outcomes such as employee turnover. Therefore this study includes multi-
faceted job satisfaction as major predictor of employee turnover.
9
Multi-faceted job satisfaction in turn, is found to be predicted by emotional exhaustion
in both a sales and retail environment (Chung et al., 2012; Rutherford, Boles, Hamwi,
Madupalli & Rutherford, 2009). Broadbridge (1999) argues that jobs in retail have become
more stressful over time due to organizational changes which are forced by the multiplicity
and complexity of retail organizations. These changes can be assigned to technological,
environmental and market issues, such as online shopping, customer needs and
internationalization. Since jobs in the retail industry are related to long, unsocial hours;
psychical endeavor and routine work, one can imagine that emotional exhaustion plays
especially in this particular context an important role (Broadbridge, 1999). Therefore
emotional exhaustion is assumed to be a major predictor of multi-faceted job satisfaction.
In turn, this study argues that work overload and emotional labour are again major
stressors of emotional exhaustion and are specifically present in the retail sector. Broadbridge
(2002) found that jobs in retail are considered to be stressful among the majority and that this
is partly caused by work overload. Another industry specific characteristic is emotional
labour. The display of emotions is particularly important in the service industry in which
emotional labour is part of front-line employees’ jobs (Ashforth & Humphrey, 1993). In this
study, comparison between front-line employees from the service industry and the retail
industry is often made since both can be considered as service jobs which require ‘person to
person’ interaction and ‘soft skills’ (Warhurst & Nickson, 2007). According to Warhurst and
Nickson (2007), most research on front-line employees from both the retail and service
industry is concerned with emotional labour. However, the impact of emotions in the service
industry is not a well-understood area yet (Hennig-Thurau, Groth, Paul & Gremler, 2006).
Limited research on emotional labour in the workplace has been conducted (Schaubroeck &
Jones, 2000).
In sum, this study examines the antecedents of employee turnover in the fashion retail
industry. Specific industry characteristics are included in the research model and the role of
work overload, emotional labour, emotional exhaustion and multi-faceted job satisfaction in
predicting employee turnover is analyzed. A questionnaire will be distributed among a sample
of Dutch front-line employees in the fashion retail branch. The following research question
will be addressed: “What is the role of work overload, emotional labour, emotional
exhaustion and multi-faceted job satisfaction in predicting employee turnover?” See figure 2
for the conceptual model of this research.
10
1.2. The Dutch Fashion Retail Industry
The Dutch retail industry is strongly diversified since there are 45 subsectors including
fashion (Peek & Veghel, 2011). According to het Hoofdbedrijfschap Detailhandel (hbd, 2011)
in 2011, a total number of 680.200 people were employed in the Dutch retail sector. The retail
sector is one of the largest commercial sectors and 10% of all jobs in the Netherlands can be
found in the retail industry (hbd, 2011). Both absenteeism and labour turnover are found to be
high in the retail industry (Broadbridge, 1999; Rhoades & Eisenberger, 2002). According to
Eurostat data, in 2006, just over 60% of the Dutch retail workforce stayed in their current job
for less than five years and 27% of annual leavers have been counted in the Netherlands.
In 2009, consumers spend 14.6 billion Euros on fashion in the Netherlands
(Fashionunited, 2009). A total of 76.800 paid jobs in fashion stores have been counted in the
Netherlands in 2013 (hbd, 2013). This number does not only include sales assistants, but
covers all positions in a store. Most of the jobs in fashion stores are part-time. 35% contains
of jobs less than 16 hours whereas approximately 19% of the fashion stores contains full-time
jobs (38 hours or more). The remaining part works somewhere in between part-time and full-
time. 86% of all employees in fashion stores are female; this is slightly more than the overall
retail industry (hbd, 2010). See figure 1.
1.3. Theoretical and Practical Relevance
As described earlier, there is a lack of research and theories in the area of employee turnover
in the fashion retail industry. However, the contribution of this study is not only relevant for
theoretical purposes but also for practical ones. The size of the retail industry in relation with
the commercial and economic interests, the height of the employee turnover rate and the
aforementioned disadvantages of employee turnover indicate the importance of the topic. It is
therefore crucial to understand the main drivers behind employee turnover in the fashion retail
sector. The results of this study can be helpful for organizations in order to reduce their
employee turnover rates and increase performance. For example, if employee turnover
appears to be a result of low job satisfaction, management of an organization can make
decisions on how to improve job satisfaction among front-line employees. Improved job
satisfaction can for example be achieved by giving employees more responsibility and
autonomy, stimulating variety in skills and enhancing interpersonal relationships (Harter,
Schmidt & Hayes, 2002).
11
Figure 1: Statistics on Employees in the Dutch Retail Industry in 2010
Fashion Stores Total Retail
Gender
Male 14% 37%
Female 86% 63%
Age
till 17 years 5% 11%
17 to 20 years 19% 24%
21 to 25 years 16% 14%
26 to 35 years 17% 16%
36 to 45 years 19% 17%
46 to 55 years 15% 12%
56 years and older 9% 6%
Ethnicity
Natives 80% 83%
Western Immigrants 9% 7%
Non-Western Immigrants 11% 10%
HBD en ITS, 2010
Figure 2: Research Model
Work Overload
Emotional
Labour
Emotional
Exhaustion
Multi-faceted Job
Satisfaction
- Overall-job
- Co-workers
- Supervision
- Policy
- Pay
- Promotion
- Customers
Turnover
Intentions
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2. LITERATURE REVIEW
This chapter will provide an overview of the relevant literature in the research area of this
study. Furthermore, hypotheses will be formed. First, the theoretical background of work
overload and emotional labour will be discussed. Second, the literature on emotional
exhaustion will be explained in relation to work overload and emotional labour. Third, the
concept of multi-faceted job satisfaction will be elaborated in relation to emotional
exhaustion. Last, the theoretical background on employee turnover and its predictors will be
defined.
2.1. Work Overload
Based on Rizzo, House and Lirtzman (1970), Bolino and Tumely (2005) state that “Role
overload describes situations in which employees feel that there are too many responsibilities
or activities expected of them in light of the time available, their abilities, and other
constraints”, p.741. This definition is used throughout this paper to describe the definition of
“work overload”. It should be noticed that “role overload” is replaced by the term “work
overload”. Much research on work overload has been conducted in general; especially the
impact of work overload on the outcomes of sales people (Jones, Chonko, Rangarajan &
Roberts, 2007). However, there is a lack of research on work overload in the (fashion) retail
industry specifically.
In general, work (over)load has increased over the past decades. This is partly due to
organizational restructuring and a larger focus on productivity improvements, resulting in
more responsibilities for employees. Besides, companies focus more and more on maximizing
their profits and revenues which means that cost-cutting appears wherever possible, including
lay-offs. This in turn leads to fewer employees per organization who remain stuck with too
much work (Mulki, Lassk & Jaramillo, 2008). This increase in work overload is found to be
harmful to the well-being of employees. Physical and mental health decrease when employees
experience more stress (Jones et al., 2007). Also found is that work overload leads to a
decrease in organizational commitment and higher levels of absenteeism because of sickness.
This leads again to a decline in the overall profitability of an organization (Duxbury &
Higgins, 2001). A study by Broadbridge (1999) in the retail sector supports this and claims
that outcomes of pressures on the work floor can have a negative impact on an employee’s
well-being. Therefore work overload is assumed to be a major job stressor in this study.
13
2.2. Emotional Labour
Morris and Feldman (1996) state that organizations progressively want to influence the kind
of behavior and image their employees transcend to clients. According to Cho, Rutherford and
Park (2013) it is important that employees successfully control their emotions, not only for
themselves but also for customers. As a result, organizations have increasingly been
developing specific guidelines which prescribe desired emotions to be expressed by
employees, called display rules. Ekman (1973) specified display rules as standards of
behavior which prescribe the appropriate emotions in specific situations and how these
emotions should be displayed.
The display of emotions is particularly important in the service industry in which
emotional labour is part of front-line employees’ jobs. There are a few reasons for this
(Ashforth & Humphrey, 1993). First, front-line employees carry out and promote the
organization to customers directly because they operate in boundary spanning roles. Second,
front-line employees are often involved in face-to-face interactions with customers in which
front-line employees have direct contact with them. Third, the presence of customers can
cause uncertain situations in which the quality of the service encounter might fluctuate. Last,
it is difficult for customers to judge the level of service quality, because the provided services
are often hard to ‘grasp’ and are intangible.
These aspects emphasize the importance of service front-line employees because the
behavior heavily influences how customers perceive the overall quality of the organization.
Front-line employees have to welcome their customers in a positive manner and during the
interaction, positive emotions will be transferred to customers (Cho et al., 2013). Since the
behavior of front-line employees plays an important role in the organization’s performance,
they are expected to behave according to an organization’s display rules. According to
Grandey, Fisk, Mattila, Jansen and Sideman (2005), a widespread display rule is the “service-
with-a-smile” rule which means that employees should always express indefectible positive
emotions when interacting with customers. Display rules thus emphasize the “superficial”
side of emotions; these emotions which are direct observable. Deeper emotions or actual
feelings are not taken into account (Ashforth & Humphrey, 1993).
Researchers all agree with the underlying assumption that emotional labour is about
the regulation of emotions to ensure that these are in line with the organizational display rules
(Goffman, 1959). However, there are different theoretical approaches concerning emotional
labour and there is no clear consensus about its specific construct (Glomb & Tews, 2004).
According to Morris and Feldman (1996) emotional labour is defined as “…the effort,
14
planning, and control needed to express organizationally desired emotion during interpersonal
transactions”, p. 987. They further argue that as long as the expressed emotions are in line
with the organizational display rules, it creates emotional labour. This is supported by
Brotheridge and Lee (2003). According to Ashforth and Humphrey (1993), emotional labour
is the “act of expressing socially desirable emotions”, p. 88-89. No matter if internal feelings
are in line with these desirable emotions. These researchers thus argue that emotional labour
involves both genuine and in-genuine feelings.
However, Mann (1999a) describes emotional labour as: “The state that exists when
there is a discrepancy between the emotional demeanor that an individual displays because it
is considered appropriate, and the emotions that are genuinely felt but that would be
inappropriate to display”, p. 353. This definition is closely related to emotional dissonance.
Although researchers agree that dissonance is included in the concept of emotional labour,
there is no agreement on whether emotional dissonance is a necessary condition for emotional
labour (Glomb & Tews, 2004). Nevertheless, this paper follows the reasoning of Mann
(1999a). Thereby arguing that emotional dissonance is a requirement for emotional labour to
exist and that emotional labour is solely present when employees fake or oppress certain
feelings whereby genuinely felt emotions are not taken into account. This definition is chosen,
because employees have to make more effort when organizational display rules are
incongruent with their genuine feelings (Ashforth & Humphrey, 1993; Morris & Feldman,
1996). This has proven to lead to negative outcomes such as job-related stress (Adelmann,
1995) and emotional exhaustion (Morris & Feldman, 1997) and fits the conceptual model of
this study best.
It is thus argued that emotional labour is concerned with the discrepancy between an
employee’s true feelings and the expressed feelings which are desired by the organization.
“The process of emotional labour itself typically involves two common processes:
suppressing the negative emotions that one is feeling and faking positive emotions that one is
not feeling”, p. 470 (Glomb & Tews, 2004 in Sliter, Jex, Wolford & McInnerney, 2010). Prior
literature has argued that employees in the service industry are often involved in both
processes. This means that they welcome customers in a friendly manner when actually not
feeling well and they hide their irritation when customers behave impolite (Grandey, Fisk &
Steiner, 2005). Engaging in the two processes of emotional labour; suppressing negative
emotions and faking positive emotions, has direct implications for an employee’s well-being
(Grandey, 2003).
15
2.3. Antecedents of Emotional Exhaustion
Emotional exhaustion is the most prominent dimension of job burnout, among
depersonalization and personal accomplishment (Maslach & Jackson, 1981). Based on these
authors, Rutherford et al. (2009) define burnout as “… a psychological syndrome or condition
that manifests in reactions to chronic stress experienced by people who provide services”,
p.1147). Also based on Maslach and Jackson (1981), Chung et al. (2012) define emotional
exhaustion as “…the feeling of emotional overextension and exhaustion attributable to one’s
work, p.703. Emotional exhaustion is known to be as a type of stress that is caused by
stressors at the work floor (Cropanzano, Rupp & Byrne, 2003) and is the most researched
dimension of job burnout in a sales environment (Rutherford et al., 2009). According to
Singh, Goolsby and Rhoads (1994), emotional exhaustion is more likely to arise at employees
who are involved in boundary spanning roles. Front-line employees engage in boundary-
spanning roles because they are always in between the customer and the employer. There are
three reasons for this.
First, they are responsible for representing the store and the image of the store. Second,
their task is to guarantee service quality and communicate improvements internally in order to
meet the demands of the customer better. Last, the level of quality and customer satisfaction
relies for a big part on the behavior of front-line employees (Bettencourt & Brown, 2003).
Service employees are in a position which requires them to engage in boundary-spanning
activities and it is therefore presumed that they are more likely to experience role stress
(Kahn, Wolfe, Quinn & Snoek, 1964; Singh, 1993). Cooper and Baglioni (1988) tried to
distinguish the level of stress among different occupations and found that jobs in the retail are
exposed to an above average level of stress. Broadbridge (1999) found that positions in the
retail industry are considered to be stressful. As such, emotional exhaustion is particularly
present within the service and retail industry, since employees are often in direct contact with
their customers, who expect them to live up to high demands. This makes them more
vulnerable to emotional exhaustion (Cordes & Dougherty, 1993; Rutherford et al., 2009).
It is important to understand the antecedents of emotional exhaustion since emotional
exhaustion leads to important organizational outcomes, such as job satisfaction,
organizational commitment and turnover intentions (Babakus, Cravens, Johnston & Moncrief,
1996). According to Cho et al. (2013) the emotional understanding of employees also affects
the way customers perceive levels of service quality, satisfaction and loyalty. Robinson and
Griffiths (2005) found that work overload is most often mentioned as the main source of job
related stress. This is supported by other research. For example, Janssen, De Jonge and
16
Bakker (1999) conducted a study among nurses and found that burnout is mainly caused by
both workload and limited social support. Furthermore they state that stress is a result of
resources which are threatened by demands (e.g. workload or role stress). Another meta-
analysis conducted by Lee and Ashforth (1996) claims that there is a relationship between
work overload and limited support on one side and emotional exhaustion on the other.
However, these outcomes concern organizations in general and are not specifically focused on
the retail industry.
Broadbridge (2002) however, did focus on the retail industry and found that work
overload is one of the key stressors for employees. Also applicable to the retail environment,
Firth, Mellor, Moore and Loquet (2004) found that certain stressors, among which work
overload, have a direct influence on stressful feelings and they further argue that work
overload has a direct relation with stress feelings and job satisfaction. Since work overload is
a potential job stressor, it is argued that there exists a positive relation between stress related
feelings and emotional exhaustion. So when an employee experiences more work overload, it
is likely that an employee’s emotional exhaustion increases. Therefore the following is
hypothesized:
H1. Work overload is positively related to an employee’s emotional exhaustion.
Further argued is that emotional labour is an important antecedent of emotional exhaustion.
As stated, front-line employees are more prone to emotional exhaustion due to their
participation in boundary spanning roles. Sales persons need to express positive emotions and
hide negative emotions in order to accomplish customer satisfaction and customer loyalty
(Lam, Kraus & Ahearne, 2010). This refers to the concept of emotional labour: a discrepancy
between real feelings and feelings which need to be expressed by the organization. Emotional
dissonance, which is in this case closely related to emotional labour, is even larger in
organizations where employees have face-to-face contact with customers all the time because
they always have to live up to the organizational display rule, even though this is not in line
with their genuine feelings (Morris & Feldman, 1996).
Hochschild (1979, 1983) argues that complying with organizational display rules
might cause harmful psychological effects among front-line employees. This is supported by
other researchers. The engagement in organizational display rules has been found to be linked
to stress-related physiological arousal (Gross & Levenson, 1997) and job strain which
concerns bad work attitudes and burnout (Brotheridge & Grandey, 2002).
17
In general, employees have to make more effort when organizational display rules are
incongruent with their genuine feelings (Ashforth & Humphrey, 1993; Morris & Feldman,
1996). The negative effects of emotional labour can be explained by the Conservation Of
Resources (COR) theory by Hobfoll and Freedy’s (1993). This theory argues that people,
when possible, try to preserve their resources. When they engage in fake emotions or when
they suppress their emotions, their resources cannot be guarded which leads to emotional
exhaustion (Sliter et al., 2010). Grandey, Fisk and Steiner (2005) also argue that faking and
oppressing feelings drain personal resources and causes job stress for front-line employees.
However, the impact of emotions in the service industry is not a well-understood area yet
(Hennig-Thurau et al., 2006). Nevertheless, based on the COR theory, the following is
hypothesized:
H2. Emotional labour is positively related to an employee’s emotional exhaustion.
2.4. Antecedents of Multi-faceted Job Satisfaction
Job satisfaction can be described as “a pleasurable or positive emotional state resulting from
the appraisal of one’s job or job experiences” (Locke, 1976, p. 1304). This overall
understanding of job satisfaction using global measurements has helped in the exploration of
the antecedents of job satisfaction (Arnold, Flaherty, Voss & Mowen, 2009; Babin & Boles,
1996). However, there has been critique on global measures of job satisfaction because they
leave out measures of individual aspects (Churchill, Ford & Walker, 1974). Widely held
literature views job satisfaction as a universal and single-layered dimension, while it is often
approached as both a precursor and consequence (Rutherford et al., 2009), as a respond to
this, several multi-faceted job satisfaction scales have been designed.
Wood, Chonko and Hunt (1986) use a four dimension scale, including satisfaction on
pay, closure, information and variety. Smith, Kendall and Hulin (1969) designed a five
dimension scale which covers satisfaction with pay, opportunities for promotion, supervision,
type of work and coworkers on the job. Churchill et al. (1974) developed a seven dimension
scale with 95 items named INDSALES, covering satisfaction with overall job, co-workers,
supervision, policy, pay, promotion and customers. Similar to Chung et al. (2012), this study
uses the reduced 28-item INDSALES scale (Comer, Machleit & Lagace, 1989). This scale is
chosen because it explains more facets than the other scales and is therefore more likely to
obtain richer information. Moreover, the scale has been ultimately developed to be used in a
sales environment and is therefore applicable to this study. Besides, the scale simultaneously
18
measures individual facets of job satisfaction as well as global job satisfaction (satisfaction
with overall job), thereby taking advantages from both measures.
The study of Rutherford et al. (2009) is one of the few which focuses on the link
between emotional exhaustion and multi-faceted job satisfaction. They found that emotional
exhaustion is related to five of the seven dimension of multi-faceted job satisfaction, namely
satisfaction with: overall job, supervision, policy, pay and promotion. No significant
relationship was found between emotional exhaustion and satisfaction with co-workers and
customers. However, they conducted their study in a sales environment and not specifically in
a retail context. Another interesting study explored the link between emotional exhaustion and
multi-faceted job satisfaction particularly in the retail context (Chung et al., 2012). Results
indicate that emotional exhaustion is one of the most prominent predictors of multi-faceted
job satisfaction. They found that emotional exhaustion is significantly and negatively related
to job satisfaction with: overall job, co-workers, supervision, pay and promotion. They did not
find any relation between emotional exhaustion and job satisfaction with policy and
customers.
These studies on multi-faceted job satisfaction show different outcomes and in general,
not much research has been conducted yet on the effect of emotional exhaustion in relation to
multi-faceted job satisfaction, but rather in relation to global job satisfaction (Rutherford et
al., 2009). The rest of this paragraph will build theoretical support for the relations between
emotional exhaustion and each of the seven dimensions of job satisfaction.
Contrasting results have been discussed in prior research about the relation between
emotional exhaustion and global job satisfaction. A number of studies did not find any
relationship, assuming that emotional exhaustion has no effect on job satisfaction (Boles,
Johnston & Hair, 1997). However, in general, it is widely acknowledged that burnout is
negatively related to job satisfaction and as described earlier, emotional exhaustion is a part of
burnout (Maslach, 1981; Singh et al., 1994). Singh et al. (1994) argue that in contrast to role
stressors, burnout is always destructive and has a negative, linear curve with various job
outcomes such as job satisfaction. The negative relation is based on two theoretical
arguments. ‘First, because psychological burnout is the outcome of an appraisal process by
which an individual evaluates the demands vis-a-vis his or her re-sources, it is posited that the
outcome of this appraisal should affect an individual's psychological well-being on the job,
including job satisfaction. Second, because both are affective responses, it is hypothesized
that burnout feelings should be related to job satisfaction’ (Singh et al., 1994, p.561). The
negative link has found support in prior literature in financial firms and service organizations
19
(Jaramillo et al., 2006), the retail banks sector (Karatepe & Tetinkus, 2006) and in a sales
environment (Babakus et al., 1996). A recent study in the retail industry of Cho et al. (2013)
also found support for the negative link between emotional exhaustion and job satisfaction.
However, their study is conducted in Asia and might therefore not be generalized.
Furthermore, it is expected that emotional exhaustion is negatively related to job
satisfaction with co-workers and customers, although this latter construct has not found full
support in any of the aforementioned studies on multi-faceted job satisfaction. However,
Leiter and Maslach (1988) argue that emotional exhaustion is closely related to interpersonal
relationships and that it correlates high with being in contact with other people. When
someone is emotionally exhausted, a person’s resources are sooner depleted. It is therefore
expected that emotional exhaustion sooner leads to negative experiences with co-workers and
customers and in turn, reduces job satisfaction on these dimensions.
Also, a negative link between emotional exhaustion and job satisfaction with
supervision and policy is assumed. Employees prefer to work for an organization in which
managers respond to their needs (Booth & Hamer, 2007). Following this reasoning, a possible
explanation for the negative relation between emotional exhaustion and job satisfaction with
supervision and policy could be that an employee, who feels emotionally exhausted, has not
many resources left and has more difficulties coping with job-related tasks or activities. This
person might be sooner depleted and irritated, requesting for more flexibility. When the policy
or supervision of a company is not able to respond to this, an employee might develop a more
negative view of the organization and thereby reducing his or her job satisfaction concerning
these two facets.
Moreover, emotional exhaustion is expected to have a negative relation with job
satisfaction with pay and promotion. This can be explained by the social exchange theory
(Hatfield & Sprecher, 1984), assuming that people always strive for equity in an employee-
organization ratio. Emotionally exhausted employees have probably put much energy and
effort in the organization. This in turn results in a demand for equivalent rewards from the
organization in order to maintain a feeling of equity. These rewards might for example be
promotion or pay (Van Dierendonck, Schaufeli & Buunk, 1996). When the organization is not
able to provide higher rewards, an employee’s job satisfaction with pay and promotion will
decrease.
Based on the aforementioned theories, this study argues that emotional exhaustion has
a negative impact on all dimensions of multi-faceted job satisfaction. This leads to the
following hypothesis:
20
H3. Emotional exhaustion is negatively related to an employee’s multi-faceted job
satisfaction (including 1.Overall job, 2. Co-workers, 3. Supervision, 4. Policy,
5. Pay, 6. Promotion and 7. Customers).
Some researchers have found a direct effect of certain job stressors on global job satisfaction
(Wunder & Dougherty, 1982; Currivan, 2000). In a retail context, Firth & al. (2004) found
that workload directly relates to feelings of stress and overall job satisfaction. However, this
study expects that emotional exhaustion acts as a mediator in the negative relation between
work overload on multi-faceted job satisfaction and emotional labour on multi-faceted job
satisfaction. Both work overload and emotional labour are expected to result in higher levels
of emotional exhaustion. Experiencing emotional exhaustion makes it more likely that the
perception of an employee on the different dimensions of job satisfaction changes, rather than
work overload or emotional labour on their own. This is supported by Singh et al. (1994),
arguing that burnout (among which is emotional exhaustion) is not a job stressor in itself, but
rather a result of multiple role stressors. The study recognizes that there is in general a
significant and direct effect of role stressors (e.g. conflict, overload) on important
organizational outcomes, such as job satisfaction. However, these outcomes do not reflect
enough strength and consistency to be fully supported. Further argued is that burnout is a far
more prominent predictor of organizational outcomes than role stressor(s). Based on this
theory and the overall lack of understanding about the antecedents of multi-faceted job
satisfaction in a retail environment (Chung et al., 2012), the following hypotheses are
assumed:
H4. The negative relation between work overload and multi-faceted job satisfaction
(including 1.Overall job, 2. Co-workers, 3. Supervision, 4. Policy, 5. Pay, 6.
Promotion and 7. Customers) is mediated by an employee’s emotional
exhaustion.
H5. The negative relation between emotional labour and multi-faceted job
satisfaction (including 1.Overall job, 2. Co-workers, 3. Supervision, 4. Policy,
5. Pay, 6. Promotion and 7. Customers) is mediated by an employee’s
emotional exhaustion.
21
2.5. Antecedents of Employee Turnover
Prior research has shown that turnover intention is the best predictor of actual turnover (Regts
& Molleman, 2012). “Turnover intentions can be defined as an employees’ state of mind to
leave an organization” (Singh, Verbeke & Rhoads, 1996 in Alexandrov, Babakus and Yavas,
2007, p.357). In this study, voluntary employee turnover is measured in terms of turnover
intentions. Sager’s (1991) has shown that turnover intentions provide enough validity and
makes an effective distinction between those who leave and those who stay.
Multiple factors influence employee turnover in general. For example, a meta-analysis
of Hom and Griffeth (1995) found that employees leave an organization when they become
dissatisfied and lose their organizational commitment. Hom and Kinicki (2001) build further
on this and state that there are three main reasons for employee turnover: the external business
environment, the personal element and the job satisfaction element. According to Chang et al.
(2013) the main antecedents of turnover intention in previous studies are: job autonomy, fair
reward, job satisfaction, organizational commitment, tenure, social support, and demographic
variables. Other studies have investigated employee turnover in a retail context specifically.
One interesting study is conducted in by Henrie (2004) who found that key arguments for
employee turnover are: too little working hours, bad payments, no career opportunities,
overwork, unsocial work hours, bad training, poor staff facilities, being afraid of redundancy
and staff views were not heard. However, this study took place in the UK and might not be
generalized. Alexandrov et al. (2007) explored the effects of psychological climate on
turnover in a retail environment and argue that this results in affective responses as job
satisfaction and affective organizational commitment and in turn influences employees’
turnover intentions.
As illustrated, there are numerous predictors of employee turnover in the retail context
which poses a limitation for identifying a comprehensive model which covers all its
antecedents. However, almost all of the aforementioned studies include job satisfaction and as
an important predictor of employee turnover. This is supported by other authors, stating that
dominant models in literature focus on job satisfaction as one of the main drivers of labour
turnover in organizations (Henrie, 2004; Jaramillo et al., 2006; March & Simon, 1958). Based
on this, job satisfaction is considered as the major predictor of employee turnover. In order to
capture as much information as possible, the concept of multi-faceted job satisfaction is used
instead of global job satisfaction.
The relation between job satisfaction and turnover intentions of salespersons has been
widely examined (Ladik, Marshall, Lassk & Moncrief, 2002) and several studies (Boles,
22
Johnston & Hair, 1997; Jaramillo et al., 2006) found a negative relation between general job
satisfaction and turnover intentions. However, there is only limited research on the relation
between multiple facets of job satisfaction and the intention to leave (Rutherford et al., 2009).
Rutherford et al. (2009) only found a significant link between job satisfaction with overall job
and job satisfaction with promotion on the propensity to leave, whereas the other dimensions
did not strongly enough relate to turnover intentions. However, this study has been conducted
in a sales environment whereas findings in a retail context might differ. Based on previous
results in the literature and the relative lack of knowledge on the relationship between multi-
faceted job satisfaction and turnover intentions, this paper argues that all dimensions of job
satisfaction are negatively related to employee turnover intentions. Therefore, the following is
proposed:
H6. Multi-faceted job satisfaction (including 1.Overall job, 2. Co-workers, 3.
Supervision, 4. Policy, 5. Pay, 6. Promotion and 7. Customers) is negatively
related to an employee’s turnover intentions.
Given that emotional exhaustion is expected to predict multi-faceted job satisfaction and
multi-faceted job satisfaction predicts in turn employee turnover intentions, it is assumed that
multi-faceted job satisfaction mediates the relationship between emotional exhaustion and
employee turnover intentions. However, there are mixed findings on the mediating role of job
satisfaction in relation to emotional exhaustion and turnover intentions (Rutherford et al.,
2009). Boles et al. (1997) argue that emotional exhaustion is directly related to turnover
intentions; they did not find support for the mediating role of job satisfaction. O’Driscoll and
Beehr (1994) found the opposite; job satisfaction mediates the effects of uncertainty and role
stressors on turnover intentions and strain. However, both studies address global job
satisfaction instead of multi-faceted job satisfaction. Rutherford et al. (2009) found that multi-
faceted job satisfaction mediates the relation between emotional exhaustion and the
propensity to leave. Given these mixed findings and the lack of previous research on multi-
faceted job satisfaction as a mediator, the following hypotheses will be tested:
H7. The positive relation between emotional exhaustion and employee turnover
intentions is mediated by an employee’s multi-faceted job satisfaction
(including 1.Overall job, 2. Co-workers, 3. Supervision, 4. Policy, 5. Pay, 6.
Promotion and 7. Customers).
23
2.6. Research Model and Hypotheses
Work Overload
Emotional
Labour
Emotional
Exhaustion
Multi-faceted Job
Satisfaction
- Overall-job
- Co-workers
- Supervision
- Policy
- Pay
- Promotion
- Customers
Turnover
Intentions
H2
H1
H3 H6
H4 + H5
H7
H1. Work overload is positively related to an employee’s emotional exhaustion.
H2. Emotional labour is positively related to an employee’s emotional exhaustion.
H3. Emotional exhaustion is negatively related to an employee’s multi-faceted job
satisfaction (including 1.Overall job, 2. Co-workers, 3. Supervision, 4. Policy, 5. Pay,
6. Promotion and 7. Customers).
H4. The negative relation between work overload and multi-faceted job satisfaction
(including 1.Overall job, 2. Co-workers, 3. Supervision, 4. Policy, 5. Pay, 6.
Promotion and 7. Customers) is mediated by an employee’s emotional exhaustion.
H5. The negative relation between emotional labour and multi-faceted job satisfaction
(including 1.Overall job, 2. Co-workers, 3. Supervision, 4. Policy, 5. Pay, 6.
Promotion and 7. Customers) is mediated by an employee’s emotional exhaustion.
H6. Multi-faceted job satisfaction (including 1.Overall job, 2. Co-workers, 3. Supervision,
4. Policy, 5. Pay, 6. Promotion and 7. Customers) is negatively related to an
employee’s turnover intentions.
H7. The positive relation between emotional exhaustion and employee turnover intentions
is mediated by an employee’s multi-faceted job satisfaction (including 1.Overall job,
2. Co-workers, 3. Supervision, 4. Policy, 5. Pay, 6. Promotion and 7. Customers).
24
3. METHODOLOGY
3.1. Research Design
This study enhances a deductive and explanatory approach, because preconceived hypotheses
based on existing theory are used while explaining relations between variables (Lewis &
Saunders, 2012). In order to collect the data, a quantitative approach was chosen which
allows for reaching a high number of responses. This was done through the use of a self-
administered, internet-mediated questionnaire. This approach is efficient and simultaneously
keeps costs low (Lewis & Saunders, 2012). Moreover, data was collected through the
distribution of this self-administered questionnaire by delivery and collection. An advantage
of this latter approach is that it is more personal and a decent response is often given (Lewis
& Saunders, 2012). The study is cross-sectional which means that data is collected at one
point in time instead of a longitudinal study, measuring multiple points in time. Even though
results of the latter design appear to be more reliable, a cross-sectional design was chosen in
this study due to restrictions of costs and time.
3.2. Questionnaire
The developed questionnaire consists of closed questions in which a likert scale of 1 (e.g.
strongly disagree) to 5 (e.g. strongly agree) is used. Closed questions are often recommended
in this type of study since this increases reliability (Lewis & Saunders, 2012). Besides, control
variables such as age, gender and level of education are adopted in the questionnaire. The
questionnaire is based on existing measures in the literature of which the language of
instruction is English. Since the questionnaire was distributed in the Netherlands, most
respondents were expected to be Dutch speaking natives. Probably, a Dutch questionnaire will
therefore be easier accessible to people and increase the response rate. Moreover, a Dutch
questionnaire will increase the validity of the answers since familiarity with the language
increases understanding (Lewis & Saunders, 2012). Therefore, the questionnaire was
translated into Dutch with the “back and forward method”, recommended by Field (2005).
Following, the questionnaire was evaluated on clarity and possible errors by two different
people, after which improvements were implemented. Last, an introduction was written with
clear instructions. The introduction explained the purpose of the research and the time it takes
to fill out the survey. It also stated that anonymity was guaranteed; results would be treated in
a strictly confidential manner and participation was voluntary. Furthermore, contact
information was provided in case respondents had any questions or remarks. Also, a word of
25
thanks was given. In order to maximize response rates, a gift card of €50, - was allotted
among all participants. These last steps all contribute to conducting research in an ethical and
responsible manner.
3.3. Research Sample
The sample consists of 103 front-line employees working in fashion retail stores in the
Netherlands. 76.7% is female and 23.3% is male. This is congruent with statistics on gender
of hbd (figure 2) showing that more females than males are working in this industry. The age
of the participants is mostly between 17 and 35 years old (92.4%), representing relative
young employees. 31.1% works full-time whereas 68.9% works part-time. This is also in line
with statistics from hbd, showing that more employees work part-time than full-time. The
tenure in job ranges from 5 days to 15 years, and the average tenure is 3 years. The level of
education is respectively 19.4% for secondary or high school, 34% for middle level applied
education (MBO), 43.7% higher education or bachelor (HBO) and 1.9% a university or
master’s degree (WO). The nationality is predominantly Dutch (87.4%) which is also
congruent with statistics of hbd. Since this study is specifically focused on the fashion retail
industry, only front-line employees working in fashion stores are included in this research.
Data was collected by using non-probability sampling methods. This forms a limitation,
because it lowers theoretical value and generalizability (Lewis & Saunders, 2012). However,
this method is chosen because there was no sampling frame on front-line employees in the
fashion retail industry available.
3.4. Data Collection
The questionnaire was developed and distributed with Qualtrics (www.qualtrics.com). In
order to reach the right focus group, a digital link was posted and distributed through social
media channels such as Facebook. Everyone in the researchers’ network was requested to
further distribute or share the link within their own network. Furthermore, possible
respondents working in a fashion store were approached via a private message or email in
which the link of the survey was pasted. Besides, an email with the link was distributed
among students of the Amsterdam Fashion Institute in order to increase the response rate.
According to Lewis and Saunders (2012) it is beneficial to distribute digital questionnaires
because one is able to reach a high number of participants in a time and cost efficient
manner. Besides, this strategy enhances feelings of being ‘anonymous’ which results in less
social desirable answers and interviewer bias. Furthermore, questionnaires were printed and
26
personally distributed among fashion stores. After, these results were uploaded by hand into
the online program of Qualtrics.
3.5. Measures
The independent variable is work overload which is measured according to the modified role
overload scale, using 3 items (“I never seem to have enough time to get everything done at
work”; α = .82) of Bolino and Turnley (2005). This is measured on a five-point Likert-type
scale with anchors 1 = Strongly Disagree and 5 = Strongly Agree. Based on Rizzo et al.
(1970), Bolino & Tumely (2005) state that “Role overload describes situations in which
employees feel that there are too many responsibilities or activities expected of them in light
of the time available, their abilities, and other constraints”, p.741.
Emotional labour is defined as “the state that exists when there is a discrepancy
between the emotional demeanor that an individual displays because it is considered
appropriate, and the emotions that are genuinely felt but that would be inappropriate to
display” (Mann, 1999a, p. 353). This variable is measured by the Response-Focused
Emotion Regulation Scale from Grandey, Fisk and Steiner (2005) (“I fake a good
mood”). The measurement scale from 1 = Never to 5 = Always. A principal-components
analysis showed that it met traditional critera (eigenvalues 1.0). α = .89 for the U.S.
sample and α = .83 for the French sample. Items 1, 2, 3, 5 and 7 are from the Surface
Acting scale used in Grandey (2003) and items 4 and 6 are taken from the 3-item
Surface Acting scale created by Brotheridge and Lee (2003).
Emotional exhaustion is measured in this study by 4 items from Kreitner and Kinicki
(1992) (“I feel emotionally drained from my work”; α = .80). Items are measured on a five-
point scale, anchored with 1 = never and 5 = always and measured is how often one feels
emotionally overextended and exhausted by one’s work. This construct is an adaptation of
the Maslach Burnout Inventory (MBI) which originally consists of 9 items.
Multi-faceted job satisfaction consists of seven dimensions (including 1.Overall job,
2. Co-workers, 3. Supervision, 4. Policy, 5. Pay, 6. Promotion and 7. Customers). It is
measured with the reduced 28-item INDSALES scale by Comer, Machleit and Lagace
(1989). It measures the level of agreement for each statement (“My job is satisfying”; α =
.89) with anchors of 1 = strongly disagree and 5 = strongly agree. In measuring
satisfaction with supervision, “supervisor” is replaced by “manager” since this definition is
practically more relevant.
27
Intention to leave is the dependent variable and is based on the 4-item scale of
Bluedorn (1982). The scale assesses the chance of quitting the job during the next 3 months,
6 months, next year and next 2 years (“How would you rate your chances of quitting this job
in the next 3 months”; α = .84). It is anchored by 1 = very low and 5 = very high.
3.6. Control Variables
Control variables are included in the study to control the relations between predictor and
outcome variables, thereby reducing unintended effects and improving results (Field, 2005).
In this study, the following control variables are included: age, gender, tenure in job, number
of hours working, level of education, nationality and organizational service climate.
Age appears to have influence on the research model of this study. According to
Bedeian, Ferris and Kacmar (1992), older employees possess more self-confidence than
younger employees and are therefore more likely to indicate a higher degree of job
satisfaction. Besides, younger employees generally switch more often between jobs because
they are still full of ambition and are less committed to one specific organization (Bedeian,
Pizzolatto, Long & Griffeth, 1991). Moreover, one can imagine that older employees in the
fashion retail have more difficulty with switching from one organization to another.
Therefore, older employees are more likely to stay for a longer period at the same company
than younger employees.
Gender is incorporated in the analysis since prior literature has proven that there are
fundamental differences between male and female in the way they behave and respond to
work-related matters. In retail positions, women might experience more intervention between
work and private life than men (Babin & Boles, 1998). It therefore appears that men and
women respond differently to stress related issues and job satisfaction (Chung et al., 2012).
Tenure in job is also controlled for. The longer an employee works in an
organization, the less likely this person will leave the company because organizational
benefits are more likely to arise (e.g. promotion, status) (Hellman, 1997). Also, employees
who are new to the company go through a process of socialization first. This might affect
their thoughts about their job (Hofstede, 1980).
The number of hours also influences the research model. It appears that employees
who work part-time experience lower levels of stress than employees who have a full-time
job. Besides, part-time workers are more satisfied with their job and are less prone to quit
(Wotruba, 1990). Number of hours is measured by asking respondents if they work part-time
or full-time.
28
Level of education is also included as control variable. It appears that the level of education
has an influence on the working experience of employees (Bedeian et al., 1992), which again
influences important variables in the research model of this study. One might also imagine
that someone who is highly educated might have different ambitions than working in a
fashion retail store for which an educational background is not required. This again
influences an employees’ motivation and also variables such as job satisfaction and turnover
intentions.
Nationality is another control variable. Each nationality has a different cultural
background and this background influences perceptions and behaviors (Hofstede, 1980). As
one can imagine, these perceptions and behaviors might in turn influence job related stress
factors, job satisfaction and turnover intentions.
The last variable which is controlled for is service climate. “Service climate refers to
employees’ shared perceptions of the practices, procedures, and behaviors that are rewarded,
supported and expected by the organization with regard to customer service and customer
service quality” (Schneider, White & Paul, 1998 in Salanova, Agut & Peiro, 2005, p. 1217).
Service climate appears to influence employee attitudes, perceptions and organizational
citizenship behavior (Walumbwa, Hartnell & Oke, 2010) and thus influences the research
model of this study. Service climate is measured with a reduced version (4 items) of the 7-
item Global Service Climate Scale (Schneider et al., 1998) (“Employees in our organization
have knowledge of the job and the skills to deliver superior quality work and service”; α=.84)
Answers range from 1 = strongly disagree to 5 = strongly agree.
3.7. Data Analysis
Data analysis of this study can roughly be divided into two steps. First, data was explored and
basic assumptions were checked, also referred to as ‘data cleaning’. These steps are needed to
prepare the data for the second step of the data analysis; testing hypotheses. In order to
perform the data analysis, IBM SPSS statistics 22 was used. In order to perform the first step,
all data was coded and variables were created. Next, data was checked on counter-indicative
items and missing values. Furthermore, the overall distribution and normality of the data was
explored. After, a Confirmatory Factor Analysis (CFA) was performed. CFA focuses on the
latent structure of a measurement instrument. Since this is not possible in SPSS, SmartPLS
(Ringle, Wende, & Will 2005) is used as software to conduct CFA. “CFA verifies the number
of underlying dimensions of the instrument (factors) and the pattern of item-factor
relationships (factor loadings). CFA also assists in the determination of how a test should be
29
scored” (Brown, 2012, p.3). Last, scale reliability was assessed and scale means were
computed.
After this first step, hypotheses were tested. Throughout all tests, a significance level
of p = .05 was maintained. Hypotheses 1 and 2 were simultaneously tested with a multiple
regression analysis. Multiple regression analysis tests the effect of multiple predictor variables
on one outcome variable (Field, 2005). This also accounts for hypothesis 6, which tests the
relation of multi-faceted job satisfaction on turnover intentions. Multi-faceted job satisfaction
contains of multiple variables and thus multiple predictors. There is one outcome variable
only, turnover intentions, and therefore multiple regression analysis was also used to test this
hypothesis.
Hypothesis 3 predicts the relation between emotional exhaustion and the different
dimensions of multi-faceted job satisfaction. In this case, it is not possible to conduct a
multiple regression analysis, because there is only one predictor variable and multiple
outcome variables. Therefore simple linear regression analysis was used seven times to test
the relations between emotional exhaustion and the seven dimensions of multi-faceted job
satisfaction. Although better programs exist instead of SPSS to perform this analysis with
(e.g. LISREL 8 by Joreskog & Sorbom, 1993), this study chooses for regression analyses in
SPSS over other methods or programs due to the time and scope of this research. Besides, the
model is very complex and there is a lack of research-based knowledge on how to perform
the analysis from and to the different dimensions of multi-faceted job satisfaction
(Rutherford et al., 2009).
Hypotheses 4, 5 and 7 all focus on mediating effects. These mediating effects were
tested in SPSS with the process script created by Hayes (2012) in which a bootstrap
confidence level of 95% was maintained throughout the analyses. Simple mediation was used
by means of process 4, focusing only on one mediating variable. An example of a simple
mediation model is showed in figure 3. The relation between the independent (X) variable
and the mediator (M) is called a. The path between M and the dependent variable (Y),
controlled for X, is named b. The indirect effect is the product of a and b (ab). The direct
relation between X and Y is expressed by c’. The total effect is the sum of the direct and
indirect effect: c = c’ + ab (Preacher & Hayes, 2008).
31
4. RESULTS
This chapter explains the findings of the data analysis. First, data cleaning and the associated
results will be discussed. Second, hypotheses will be tested and the outcomes will be
described. The variables used in the data analysis are work overload, emotional labour,
emotional exhaustion, multi-faceted job satisfaction (including 1.Overall job, 2. Co-workers,
3. Supervision, 4. Policy, 5. Pay, 6. Promotion and 7. Customers) and turnover intentions. The
control variables included are age, gender, level of education, tenure in job, hours in job and
service climate. Nationality has not been included since this variable contained more missing
values than all other variables and this would decrease the reliability of the study (Field,
2005). IBM SPSS statistics 22 is used for all analyses, except for the CFA, which is analyzed
with SmartPLS (Ringle, Wende, and Will 2005).
4.1. Data Cleaning
4.1.1. Coding Variables and Recoding Counter-indicative Items
The first step in the analysis was to code all items. After, counter-indicative items were re-
coded. Coding scales were reversed by using the button ‘Recode into Same Variables’ in
SPSS.
4.1.2. Missing Values
Missing values were replaced by using a HotDeck imputation which is, according to Myers
(2011), a valid and simple method to perform. A frequency table was produced in which
missing values were shown with the frequencies option. In total, 10 items contained missing
values. First it was tested if the missing values were less than 10% compared to the total
sample, otherwise it is not possible to run a HotDeck imputation (Myers, 2011). This was the
case for all variables which contained missing values.
In order to check corresponding deck variables, a correlation test was done with the
correlate-bivariate option. Important is that deck variables have almost no missing data and
show discrete values. Also, deck variables should be related to the variable in which data are
missing but the relation between these should not be too important for the outcomes of the
research (Myers, 2011). Considering this, deck variables were chosen on basis of the highest
(Pearson) correlation. After running the HotDeck macro, frequencies options was used again
to check whether all missing values were corrected.
32
4.1.3. Detecting Outliers
Furthermore, univariate outliers were checked. In order to do this, variables were transformed
into Z-scores. Z-scores with a value >|3| indicate outliers. In total, there were 4 outliers which
surpassed the value of |3|. However, these outliers are considered as legitimate since the
related values are still within the measurement scale. The outliers in this case are probably not
representing an error or mistake, but truly deviate from the overall opinion given on those
four variables. In this case, it is better to keep the outliers because it better represents the
population as a whole (Orr, Sackett & DuBois, 1991).
4.1.4. Confirmatory Factor Analysis (CFA)
After, CFA was performed to measure the construct validity of the existing measurement
instruments. To ensure indicator reliability, the correlation between a latent variable and its
indicator should be higher than .70, because the latent variable should explain enough (>50%)
of the variance of its indicator (Henseler, Ringle & Sinkovics, 2009). This is supported by
Hair, Ringle and Sarstedt (2011) stating that the absolute standardized loading should be
higher than .70. Indicators with factor loadings lower than .40 should be removed from the
analysis and indicators with a factor loading between .40 and .70 are advised to be removed
from analysis only when scale reliability significantly increases to ensure indicator reliability
(from lower than .70 to .70 or higher). In order to guarantee convergent validity, the average
variance extracted (AVE) must be more than .50. Furthermore, the factor loading with the
latent construct is supposed to be higher than the associated cross loadings in order to ensure
discriminant validity. Following these guidelines, only two indicators were removed from the
analysis: one item of job satisfaction with promotion and one item of job satisfaction with
pay. The item of job satisfaction with promotion showed a factor loading of .440 and when
removing this item, overall scale reliability improved from α=.678 to α=.745. The item of job
satisfaction with pay was .373 and was therefore immediately removed from further analysis.
All other indicators complied with the guidelines described by Hair, Ringle and Sarstedt
(2011) and were therefore kept in further analyses.
4.1.5. Computing Reliability
The next step was to check the scale reliability for all variables. This comes down to
measuring the internal consistency. In general, Cronbach’s alpha (α) should be .70 or higher
(Field, 2005; Nunnally & Bernstein, 1994). This was the case for almost all variables (work
overload, α=.902; emotional labour, α=.845; emotional exhaustion, α=.817; satisfaction with
33
overall job, α=.823; satisfaction with co-workers, α =.755, satisfaction with supervision,
α=.865; satisfaction with policy α=.756; satisfaction with promotion α=.745, satisfaction with
customers, α=.818; turnover intentions, α=.915). Only one variables represented α<.70, which
is satisfaction with pay, α=.632. However, only values below .60 indicate a serious lack of
reliability (Hair, Ringle & Sarstedt, 2011) and therefore this variable is kept in the analysis.
Corrected item-total correlation was for all items higher than .30 which is sufficient.
4.1.6. Computing Scale Means
Based on the CFA and scale reliability, variable means were computed in order to create a
‘total variable’ under which all constructs of one variable were collected. This was necessary
to proceed with the following steps. Table 1 presents means, standard deviations, correlations
and reliability coefficients (cronbach’s alpha) for all variables used in this study.
4.1.7. Checking Normality and Distribution of Data
The data should have a normal distribution, because most sophisticated analyses (e.g.
regression analysis) are based on this (Field, 2005). Data was explored by using frequency
distributions, histograms, the Kolmogorov-Smirnov Test, and QQ plots. The histogram should
present a bell-shape when data is normally distributed (Field, 2005). Visually, data looked
close to normal.
However, the Kolmogorov-Smirnov test showed other results. Most test values were
significant (p<.005) which indicates values of kurtosis and skewness. Kurtosis is related to the
extent to which data is distributed in a more peaked shape or a more flattened shape. Positive
kurtosis means that data is distributed around the tails of a bell-shape (a flat shape) whereas
negative kurtosis means that data is closely centered around the mean value (a peak shape)
(Field, 2005). Skewness refers to the symmetry of the bell-shape. When data is positively
skewed it means that data is mainly centered on the left side of the mean with extreme values
on the right side. A negative skewness indicates the opposite; data is mainly centered around
the right side with extreme values on the left. Values of kurtosis and skewness > |1| indicate
extreme values (Field, 2005). Table 2 provides an overview of values for kurtosis, skewness
and the Kolmogorov-Smirnov test. As shown, data is not normally distributed. However,
correction was not needed in this case.
Table 1: Means, Standard Deviations, Correlations and Reliability Coefficients
Variable Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Gender 1.77 0.42 1.00
Age 25.42 8.00 .009 1.00
Tenure in job 2.95 3.07 -.037 .466** 1.00
Hours 1.69 0.47 .126 -.062 -.013 1.00
Education level 4.31 0.84 -.125 .064 .099 -.152 1.00
Service Climate 3.65 0.62 -.126 .098 -.018 -.151 -.170 1.00
Work overload 2.58 0.96 .076 .088 .073 -.254** .181 -.115 .902
Emotional labour 2.43 0.70 -.115 -.207* -.052 .037 .041 -.076 .143 .845
Emotional exhaustion 2.37 0.76 .085 -.165 .023 .055 .143 -.123 .516** .462** .817
Job satisfaction with overall job 3.24 0.72 .153 .212* -.047 -.060 -.117 .293** -.056 -.464** -.319** .823
Job satisfaction with co-workers 4.10 0.57 -.162 .072 .008 -.061 -.079 .241* -.208* -.247* -.289** .215* .755
Job satisfaction with supervision 3.60 0.91 .010 -.104 -.091 .062 -.157 .243* -.331** -.152 -.281** .210* .336** .865
Job satisfaction with policy 3.24 0.73 .002 .050 -.104 -.072 -.128 .323** -.269** -.348** -.373** .386** .314** .388** .756
Job satisfaction with pay 3.29 0.75 .018 -.149 -.110 .127 .131 .172 -.320** -.067 -.160 .094 .043 .221* .234* .625
Job satisfaction with promotion 2.85 0.88 .071 -.158 -.152 -.093 -.126 .242* .091 -.149 -.021 .285** .107 .231* .267** .131 .745
Job satisfaction with customers 3.35 0.64 -.231* -.047 -.078 -.054 .089 .190 -.254** -.103 -.247* .207* .090 -.112 .000 .224* .011 .818
Turnover intentions 2.72 1.24 .108 -.401** -.218* .141 .185 -.192 .117 .288** .305** -.455** -.266** -.189 -.271** .006 -.236* -.090 .915
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
35
Table 2: Kurtosis, Skewness and the Kolmogorov-Smirnov Test
Variables Skewness Kurtosis
Kolmogorov-
Smirnov
Work overload .689 -.051 .192**
Emotional labour -.085 -.957 .104
Emotional exhaustion .094 -.605 .084
Job satisfaction with overall job -.185 -.168 .146**
Job satisfaction with co-workers -.444 .431 .185**
Job satisfaction with supervision -.432 -.121 .119**
Job satisfaction with policy .205 -.417 .125**
Job satisfaction with pay -.072 -.223 .095
Job satisfaction with promotion .263 -.471 .129**
Job satisfaction with customers -.523 .808 .119**
Turnover intentions .223 -1.03 .109*
**. Significant at the .001 level (2-tailed).
*. Significant at the .005 level (2-tailed).
4.2. Testing Hypotheses
This subchapter will test the hypotheses. Throughout all statistical analyses, control variables
are included and a confidence level of 95% is maintained.
4.2.1. The Influence of Work Overload and Emotional Labour on Emotional Exhaustion
First, hypotheses 1 and 2 were tested. Hypothesis 1 predicts the relation between work
overload and emotional exhaustion. Hypothesis 2 focuses on the relation between emotional
labour and emotional exhaustion. Since work overload and emotional labour are both
predictor variables of emotional exhaustion, multiple regression analysis is used.
After conducting multiple regression analysis in SPSS, it appears that the control
variables on their own explain 7.9% of the variance in emotional exhaustion. Adding work
overload and emotional labour results in 47.5% of the explanation of the variance in
emotional labour. This means that both work overload and emotional labour explain (47.5% -
7.9%) 39.6% of the variance in emotional labour. Further tested was whether the model in
general presents a significant model. This was done by an ANOVA test which “tests whether
the model is significantly better at predicting the outcome than using the mean as a ‘best
guess’” (Field, 2005, p.189). It appeared that the model only representing control variables
36
was insignificant since (F=1.355; p=.241). However, the overall regression model was
significantly useful (F=10.533; p<.001). To test whether this distribution was also significant,
the value of the t-test was checked. “If the t-test associated with the b-value is significant then
the predictor is making a significant contribution to the model” (Field, 2005, p.193). In this
case, work overload has the strongest effect on emotional exhaustion (B=.496), compared to
emotional labour (B=.362). Both work overload and emotional labour have a significant effect
on emotional exhaustion. The effect of work overload on emotional exhaustion was positively
significant (β=.390; t=6.093; p<.001) and the effect of emotional labour on emotional
exhaustion was also positive significant (β=.389; t=4.589; p<.001). Both Hypothesis 1 and 2
are supported.
Table 3: Summary of Multiple Regression Analysis for Emotional Exhaustion
Variables B β t Sig.
Work overload 0.496** .390** 6.093 .000
Emotional labour 0.362** .389** 4.589 .000
**. Significant at the 0.01 level (2-tailed).
*. Significant at the 0.05 level (2-tailed).
4.2.2. The Relation between Emotional Exhaustion and Multi-faceted Job Satisfaction
Hypothesis 3 predicts the relation between emotional exhaustion and the different dimensions
of multi-faceted job satisfaction. First, the relation between emotional exhaustion and job
satisfaction with overall job was tested. The control variables explained 17.5% of the variance
in job satisfaction with overall job and this model appeared to be significant (F=3.366;
p=.005). The overall model explained 24.1% of the variance in job satisfaction with overall
job and was also significant (F=4.273; p<.001). Emotional exhaustion on its own thus
explained (24.1% - 17.5%) 6.6% of the variance in job satisfaction with overall job. The
contribution also showed to be significant (β=-.256; t=-2.861; p=.005). It can therefore be
stated that emotional exhaustion has a significant negative effect on job satisfaction with
overall job.
Second, the relation between emotional exhaustion and satisfaction with co-workers
was examined. The control variables on their own explained 7.9% of the variance in job
satisfaction with co-workers. When emotional exhaustion was added, the variance increased
to 13.2%. This means that emotional exhaustion on its own explains (13.2% - 7.9%) 5.3% of
the variance in job satisfaction with co-workers. The model with only control variables was
37
not significantly useful (F=1.350; p=.243) neither was the entire regression model, including
emotional exhaustion (F=2.038; p=.058). This is probably caused by the control variables
since the t-test of emotional exhaustion appeared to be significant (β=-.178; t=-2.401;
p=.018). It can therefore be stated that the relation between emotional exhaustion and job
satisfaction with co-workers was significantly negative.
After, the relation between emotional exhaustion and job satisfaction with supervision
was analyzed. Regression analysis showed that control variables on their own explained 9.5%
of the variance in job satisfaction with supervision. When emotional exhaustion was added,
the model explained 16.6% of the variance in job satisfaction with supervision. Emotional
exhaustion on its own thus explained (16.6% - 9.5%) 7.1% of the variance in job satisfaction
with supervision. The model with only control variables was insignificant (F=1.660; p=.139),
whereas the total model was (F=2.675; p=.014). The contribution of emotional exhaustion
was also significant negative (β=-.334; t=-2.834; p=.006). It can therefore be stated that the
negative relation between emotional exhaustion and job satisfaction with supervision found
significant support.
Moreover, the relation between emotional exhaustion and job satisfaction with policy
was examined. Control variables explained 12.5% of the variance in job satisfaction and when
emotional exhaustion was added, the model explained 22.9% of het variance. In total,
emotional exhaustion explained (22.9%-12.5%) 10.4%. The model with only control variables
was significant (F=2.258; p=.044) as was the model when emotional exhaustion was added
(F=3.996; p=.001). Furthermore, the relation also appeared significant since (β=-.327; t=-
3.571; p=.001). This means that the relation between emotional exhaustion and job
satisfaction with policy is significant negative.
After, the relation of emotional exhaustion on job satisfaction with pay was tested. The
control variables explained 12.5% of the variance in job satisfaction with pay. The model with
emotional exhaustion included explained 16.8% of the variance which means that emotional
exhaustion on its own explain (16.8% - 12.5%) = 4.3% of the variance in job satisfaction with
pay. The model with only control variables appears to be significantly useful (F=2.265;
p=.044) as well as the model when emotional exhaustion was added (F=2.720; p=.013). The
contribution of emotional exhaustion was negatively significant (β=-.217; t=-2.213; p=.029) it
can therefore be stated that the negative relation between emotional exhaustion and job
satisfaction with pay found support.
This does not account for the relation between emotional exhaustion and job
satisfaction with promotion. The control variables on their own explained 11.8% of the
38
variance in job satisfaction with promotion. When emotional exhaustion was added to the
model, the same percentage of 11.8% explained the variance in job satisfaction with
promotion. Therefore it can be stated that there is no difference in variance explained when
emotional exhaustion is included. Both models are not significantly useful, the model with
only control variables (F=2.119; p=.058) and overall model (F=1.799; p=.096). The
contribution of emotional exhaustion to job satisfaction with promotion was also insignificant
(β=-.013; t=-.114; p=.909) and therefore the negative relation between emotional exhaustion
and job satisfaction with promotion is not supported.
In the last simple linear regression analysis, the effect of emotional exhaustion on job
satisfaction with customers was tested. Control variables on their own explained 9.5% of the
variance in job satisfaction with customers. When emotional exhaustion was added, the total
model explained 15.1% which means that emotional exhaustion on its own explained (15.1%
– 9.5%) 5.6% of the variance in job satisfaction with customers. The model of only control
variables was not significant (F=1.669; p=.137) whereas the total model was (F=2.394;
p=.027). The contribution was also significant (β=-.209; t=-2.489; p=.015) which means that
there is a significant negative relation between emotional exhaustion and job satisfaction with
customers.
In sum, emotional exhaustion is significant negatively related to an employee’s multi-
faceted job satisfaction (including, 1. Overall job, 2. Co-workers, 3. Supervision, 4. Policy, 5.
Pay, 7. Customers); partially supporting hypothesis 3.
Table 4: Summary of Linear Regression Analyses for Multi-faceted Job Satisfaction
Variables β t Sig.
Emotional exhaustion - Overall job -.256** -2.861 .005
Emotional exhaustion - Co-workers -.178* -2.401 .018
Emotional exhaustion - Supervision -.334** -2.834 .006
Emotional exhaustion - Policy -.327** -3.571 .001
Emotional exhaustion - Pay -.217* -2.213 .029
Emotional exhaustion - Promotion -.013 -0.114 .909
Emotional exhaustion - Customers -.209* -2.489 .015
**. Significant at the 0.01 level (2-tailed).
*. Significant at the 0.05 level (2-tailed).
39
4.2.3. Emotional Exhaustion as Mediator between Work Overload and Multi-faceted Job
Satisfaction
The mediating effect of emotional exhaustion between work overload and multi-faceted job
satisfaction was examined. According to Hayes (2009) all individual paths in a mediation
model should be tested to define if M acts as a mediator, regardless of their statistical
significance. The significance or non significance of the individual paths is not related to
whether the indirect effect is statistically significant, even if X and Y are not associated.
Therefore, all variables in the mediation model of hypothesis 4 were tested. Initially, there are
two independent variables (work overload and emotional labour) predicting multi-faceted job
satisfaction through emotional exhaustion. Therefore, emotional labour was controlled for and
was included in the analysis together with the control variables (Hayes, 2013). An effect is
significant when the 95% confidence interval does not include zero and an effect is non-
significant when the interval does include zero (Hayes, 2013).
First, the mediating effect of emotional exhaustion between work overload and job
satisfaction with overall job was tested. The total effect of the mediation was positive, but
non-significant (β=.014, LLCI: -.1232, ULCI: .1516). The direct effect of work overload on
job satisfaction with overall job was positive, but also non-significant (β=.076, LLCI=-.0859,
ULCI=.2374). The indirect effect of work overload on job satisfaction with overall job also
appeared to be negative and insignificant (β=-.062, LLCI=-.1733, ULCI=.0149).
Second, emotional exhaustion was tested as a mediator between work overload and job
satisfaction with co-workers. The total effect was insignificant (β=-.085, LLCI=-.2049,
ULCI=.0349) as well as the direct effect (β=-.052, LLCI=-.1944, ULCI=-.0897) and the
indirect effect (β=-.033, LLCI=-.1202, ULCI=.0589).
After, the mediating effect of emotional exhaustion between work overload and job
satisfaction with supervision was tested. The total effect was significant (β=-.251, LLCI=-
.4418, ULCI=-.0603). The direct effect of work overload on job satisfaction with supervision
appeared to be non significant (β=-.182; LLCI=-.4075, ULCI=.0430). The indirect effect of
work overload on job satisfaction with supervision also appeared to be non-significant (β=-
.069, LLCI=-.1900, ULCI=.0360). Therefore emotional exhaustion does not mediate the
relation between work overload and job satisfaction with supervision. It might be somewhat
remarkable that the total effect appeared to be significant while the indirect effect and direct
effect were not. However, this happens very often and a detectable total effect is not a
requirement for an indirect effect to exist. Many different ‘paths’ are involved in mediation
40
analysis and it might happen for example, that different paths point towards different direction
in which a positive sign cancel out a negative sign (Hayes, 2009).
Next, the mediating role of emotional exhaustion was tested between work overload
and job satisfaction with policy. The total effect was significant (β=-.160, LLCI: -.3059,
ULCI: -.0140). However, both direct and indirect effects were non-significant. The outcome
of the direct effect was (β=-.099, LLCI: -.2712, ULCI: .0728) and the outcome of the indirect
effect was (β=-.061, LLCI: -.1577, ULCI: .0293). In this case, emotional exhaustion does not
mediate the relation between work overload and job satisfaction with policy.
After, the mediating effect between work overload and job satisfaction with promotion
was examined. The total effect was insignificant (β=-.154, LLCI: -.0323, ULCI: .3405). Both
direct and indirect effects were also non-significant. The outcome of the direct effect was
(β=.167 LLCI: -.0546, ULCI: .3888) and the outcome of the indirect effect was (β=-.013,
LLCI: -.1349, ULCI: .1007).
Furthermore, the mediating effect between work overload and job satisfaction with
pay was examined. The total effect appeared significant (β=-.237, LLCI: -.3926, ULCI: -
.0808) as well as the direct effect (β=-.213, LLCI: -.3982, ULCI: -.0279). However, the
indirect effect was non-significant (β=-.024, LLCI: -.1346, ULCI: .0697). This means that
emotional exhaustion is not mediating the relation between work overload and job satisfaction
with pay. However, there is a direct relation between work overload and job satisfaction with
pay.
Last, the mediating role of emotional exhaustion in the relation between work
overload and job satisfaction with customers was tested. The total effect was significant (β=-
.159, LLCI: -.2957, ULCI: -.0231). The direct effect of work overload on job satisfaction with
customers was non-significant (β=-.116, LLCI: -.2771, ULCI: .0453). The indirect also
appeared to be non-significant (β=-.044, LLCI: -.1247, ULCI: .0343). Emotional exhaustion
does not mediate the relation between work overload and the different dimensions of job
satisfaction. Therefore, hypothesis 4 is not supported.
41
Table 5: Path Coefficients and Confidence Intervals; Emotional Exhaustion as a Mediator
between Work Overload and Multi-faceted Job Satisfaction
Variables β LLCI ULCI
Work overload to job satisfaction with overall job
Total effect (c1) .014 -.1232 .1516
Direct effect (c'1) .076 -.0859 .2374
Indirect effect (a1b1) -.062 -.1733 .0149
Work overload to job satisfaction with co-workers
Total effect (c1) -.085 -.2049 .0349
Direct effect (c'1) -.052 -.1944 .0897
Indirect effect (a1b1) -.033 -.1202 .0589
Work overload to job satisfaction with supervision
Total effect (c1) -.251* -.4418 -.0603
Direct effect (c'1) -.182 -.4075 .0430
Indirect effect (a1b1) -.069 -.1900 .0360
Work overload to job satisfaction with policy
Total effect (c1) -.160* -.3059 -.0140
Direct effect (c'1) -.099 -.2712 .0728
Indirect effect (a1b1) -.061 -.1577 .0293
Work overload to job satisfaction with pay
Total effect (c1) -.237** -.3926 -.0808
Direct effect (c'1) -.213* -.3982 -.0279
Indirect effect (a1b1) -.024 -.1346 .0697
Work overload to job satisfaction with promotion
Total effect (c1) .154 -.0323 .3405
Direct effect (c'1) .167 -.0546 .3888
Indirect effect (a1b1) -.013 -.1349 .1007
Work overload to job satisfaction with customers
Total effect (c1) -.159* -.2957 -.0231
Direct effect (c'1) -.116 -.2771 .0453
Indirect effect (a1b1) -.044 -.1247 .0343
**. Significant at the 0.01 level (2-tailed).
*. Significant at the 0.05 level (2-tailed).
42
4.2.4. Emotional Exhaustion as Mediator between Emotional Labour and Multi-faceted Job
Satisfaction
Hypothesis 5 predicts that emotional exhaustion acts as a mediator between emotional labour
and multi-faceted job satisfaction. The analysis has been controlled for the control variables
and work overload.
First the mediating role of emotional exhaustion was tested in the relation between
emotional labour and job satisfaction with overall job. The total effect was significant (β=-
.414, LLCI: -.5964, ULCI: -.2324). The direct effect also appeared to be significant (β=-.353,
LLCI: -.5535, ULCI: -.1525) but the indirect effect was not (β=-.061 LLCI: -.1630, ULCI:
.0211). Therefore, emotional exhaustion is no mediator between emotional labour and job
satisfaction with overall job. However, there appears to be a direct significant effect from
emotional labour to job satisfaction with overall job.
Second, the mediating effect of emotional exhaustion between emotional labour and
job satisfaction with co-workers was tested. The total effect was significant (β=-.172, LLCI: -
.3307, ULCI: -.0131). The direct effect was non-significant (β=-.139, LLCI: -.3155, ULCI:
.0368) as well as the indirect effect (β=-.033, LLCI: -.1427, ULCI: .0559) indicating that
emotional exhaustion does not mediate the relation between emotional labour and job
satisfaction with co-workers.
After, emotional exhaustion was tested as a mediator in the relation between emotional
labour and job satisfaction with supervision. The total effect was non-significant (β=-.146,
LLCI: -.3990, ULCI: .1062). The direct effect of emotional labour on job satisfaction with
supervision also appeared to be non-significant (β=-.078, LLCI: -.3571, ULCI: .2016). This
insignificance also accounts for the indirect effect (β=-.069, LLCI: -.2580, ULCI: .0260).
After, the mediating role of emotional exhaustion between emotional labour and job
satisfaction with policy was examined. The total effect was significant (β=-.304, LLCI: -
.4977, ULCI: -.1110). The direct effect of emotional exhaustion on job satisfaction with
policy also appeared to be significant (β=-.244, LLCI: -.4570, ULCI: -.0304). The indirect
relation however turned out to be non significant (β=-.061, LLCI: -.1660, ULCI: .0193). This
means that emotional exhaustion does not mediate the relation between emotional labour and
job satisfaction with policy, but emotional labour has a direct significant effect on job
satisfaction with policy.
Next, emotional exhaustion was examined as mediator in the link between emotional
labour and job satisfaction with pay. The total effect was non-significant (β=-.049, LLCI: -
43
.2559, ULCI: .1571), as well as the direct effect (β=-.026, LLCI: -.2554, ULCI: .2039) and the
indirect effect (β=-.024, LLCI: -.1433, ULCI: .0729).
Moreover, the mediating effect of emotional exhaustion was examined between
emotional labour and job satisfaction with promotion. The total effect was non-significant
(β=-.235, LLCI: -.4824, ULCI: .0115), as well as the direct effect (β=-.222, LLCI: -.4974,
ULCI: .0525) and the indirect effect (β=-.013, LLCI: -.1365, ULCI: .1051).
Last, the mediating effect of emotional exhaustion was analyzed between emotional
labour and job satisfaction with customers. The total effect appeared to be insignificant (β=-
.087, LLCI: -.2675, ULCI: .0936). The direct effect was also insignificant (β=-.044, LLCI: -
.2435, ULCI: .1564) as well as the indirect effect (β=-.043, LLCI: -.1440, ULCI: .0284).
Thus, emotional exhaustion is not a mediator in the relation between emotional labour
and multi-faceted job satisfaction and therefore hypothesis 5 is not supported.
44
Table 6: Path Coefficients and Confidence Intervals; Emotional Exhaustion as a Mediator
between Emotional Labour and Multi-faceted Job Satisfaction
Variables β LLCI ULCI
Emotional labour to job satisfaction with overall job
Total effect (c1) -.414** -.5964 -.2324
Direct effect (c'1) -.353** -.5535 -.1525
Indirect effect (a1b1) -.061 -.1630 .0211
Emotional labour to job satisfaction with co-workers
Total effect (c1) -.172* -.3307 -.0131
Direct effect (c'1) -.139 -.3155 .0368
Indirect effect (a1b1) -.033 -.1427 .0559
Emotional labour to job satisfaction with supervision
Total effect (c1) -.146 -.3990 .1062
Direct effect (c'1) -.078 -.3571 .2016
Indirect effect (a1b1) -.067 -.2580 .0260
Emotional labour to job satisfaction with policy
Total effect (c1) -.304** -.4977 -.111
Direct effect (c'1) -.244* -.457 -.0304
Indirect effect (a1b1) -.061 -.166 .0193
Emotional labour to job satisfaction with pay
Total effect (c1) -.049 -.2559 .1571
Direct effect (c'1) -.026 -.2554 .2039
Indirect effect (a1b1) -.0236 -.1433 .0729
Emotional labour to job satisfaction with promotion
Total effect (c1) -.235 -.4824 .0115
Direct effect (c'1) -.222 -.4974 .0525
Indirect effect (a1b1) -.013 -.1365 .1051
Emotional labour to job satisfaction with customers
Total effect (c1) -.087 -.2675 .0936
Direct effect (c'1) -.044 -.2435 .1564
Indirect effect (a1b1) -.043 -.1440 .0284
**. Significant at the 0.01 level (2-tailed).
*. Significant at the 0.05 level (2-tailed).
45
4.2.5. The Relation between Multi-faceted Job Satisfaction and Turnover Intentions
The effect of multi-faceted job satisfaction on turnover intentions was analyzed by multiple
regression analysis. In this regression, all dimensions of multi-faceted job satisfaction were
included. The control variables on their own explained 25.1% of the variance in turnover
intentions. Adding all dimensions of multi-faceted job satisfaction resulted in 44.4%
explanation of the variance in turnover intentions, which means that all dimensions together
explain (44.4% - 25.1%) 19.3% of the variance in turnover intentions. The model with only
control variables was significantly useful (F=5.306, p<.001) as well as the total model
(F=5.407, p<.001). Job satisfaction with overall job had the highest standardized coefficient
(B=-.306) and second was job satisfaction with promotion (B=-.181). Only these two
dimensions of multi-faceted job satisfaction appeared to be significantly and negatively
related to turnover intentions; job satisfaction with overall job (t=-3,103 p=.003) and job
satisfaction with promotion (t=-2.038, p=.045).
The other five dimensions were negatively related to turnover intentions, but these
relations were not significant. Job satisfaction with co-workers (B=-.088, t=-.987, p=.326),
job satisfaction with supervision (B=-.088, t=-.927, p=.356), job satisfaction with policy (B=-
.036, t=-.371, p=.711), job satisfaction with pay (B=-.026, t=-.282, p=.779) and job
satisfaction with customers (B=-.040, t=-.438, p=.663). Hypothesis 6 predicts that multi-
faceted job satisfaction is negatively related to an employee’s turnover intentions. This
appears to be true for only two dimensions of multi-faceted job satisfaction: job satisfaction
with overall job and job satisfaction with promotion. Hypothesis 6 is therefore partially
supported.
Table 7: Summary of Multiple Regression Analyses for Turnover Intentions
Variables B β t Sig.
Overall job - turnover intentions -.306** -.529** -3,103 .003
Co-workers - turnover intentions -.088 -.196 -0.987 .326
Supervision - turnover intentions -.088 -.121 -0.927 .356
Policy - turnover intentions -.036 -.062 -0.371 .711
Pay - turnover intentions -.026 -.042 -0.282 .779
Promotion - turnover intentions -.181* -.257* -2,038 .045
Customers - turnover intentions -.040 -.078 -0.438 .663
**. Significant at the 0.01 level (2-tailed).
*. Significant at the 0.05 level (2-tailed).
46
4.2.6. Multi-faceted Job Satisfaction as Mediator between Emotional Exhaustion and
Turnover Intentions
The mediating effect of multi-faceted job satisfaction was tested on the relation between
emotional exhaustion and turnover intentions. According to Hayes (2009) all individual paths,
whether significant or not, should be included in the mediator model and thus in the analyses.
Therefore all dimensions of job satisfaction were included in the mediator analysis. The
process script created by Hayes (2012) allows multiple mediators whereby all dimensions of
multi-faceted job satisfaction can be simultaneously tested as mediator between emotional
exhaustion and turnover intentions. As the results showed, the total effect of the model was
positive and significant at the .05 level (β=.332, LLCI: .0347, ULCI: .6293). However, the
direct effect of emotional exhaustion on turnover intentions appeared non-significant (β=.010,
LLCI: -.2201, ULCI: .4190) whereas the indirect effect appears to be significant only for job
satisfaction with overall job (β=.132, LLCI: .0342, ULCI: .2879). The indirect effect of the
other dimensions appeared to be insignificant (see table 8 for results). Hypothesis 7 is
therefore partially supported. The positive relation between emotional exhaustion and
employee turnover intentions is mediated by an employee’s multi-faceted job satisfaction
(including 1.Overall job).
Table 8: Path Coefficients and Confidence Intervals; Multi-faceted Job Satisfaction as a
Mediator between Emotional Exhaustion and Turnover Intentions
Variables β LLCI ULCI
Job satisfaction as overall job as mediator
Indirect effect (a1b1) .132* .0342 .2879
Job satisfaction with co-workers as mediator
Indirect effect (a1b1) .033 -.0416 .1694
Job satisfaction with supervision as mediator
Indirect effect (a1b1) .035 -.0448 .1483
Job satisfaction with policy as mediator
Indirect effect (a1b1) .012 -.0851 .1142
Job satisfaction with pay as mediator
Indirect effect (a1b1) .007 -.0772 .0822
Job satisfaction with promotion as mediator
Indirect effect (a1b1) .004 -.0579 .0684
Job satisfaction with customers as mediator
Indirect effect (a1b1) .010 -.0946 .1160
**. Significant at the 0.01 level (2-tailed).
*. Significant at the 0.05 level (2-tailed).
47
4.3. Research Model with Results
Figure 4: Research Model with Results
**. Significant at the 0.01 level (2-tailed).
*. Significant at the 0.05 level (2-tailed).
48
5. DISCUSSION
5.1. Conclusion
This chapter discusses all findings and provides an answer to the research question of this
study: “What is the role of work overload, emotional labour, emotional exhaustion and multi-
faceted job satisfaction in predicting employee turnover?” It is found that when work
overload increases, an employee’s feeling of being emotional exhausted will also increase.
This is congruent with prior literature, arguing that an overload of work is related to stress and
thereby harming an employees’ (Firth et al., 2004). Emotional labour also appears to be
positively related to emotional exhaustion. In this paper, emotional labour is defined as the
discrepancy between an employee’s genuine feelings and expressed feelings. This discrepancy
has proven to result in job-related feelings of stress and negatively influences an employees’
well-being. This can be explained by the Conservation Of Resources (COR) theory by
Hobfoll and Freedy’s (1993). This theory argues that trying to overcome this discrepancy
depletes an employee’s resources which in turn lead to emotional exhaustion.
As expected, emotional exhaustion is negatively related to job satisfaction with overall
job, co-workers, supervision, policy, pay and customers. The relation between emotional
exhaustion and job satisfaction with promotion is negative, but not significant. Further,
emotional exhaustion appears not to be mediator in the relation between work overload and
multi-faceted job satisfaction and emotional labour and multi-faceted job satisfaction.
However, work overload appears to have a direct negative effect on job satisfaction with pay.
It also appears that emotional labour is directly and negatively related to job satisfaction with
overall job and job satisfaction with policy.
Furthermore, only two dimensions of multi-faceted job satisfaction are negatively
related to an employee’s turnover intentions; job satisfaction with overall job and job
satisfaction with promotion. The other five dimensions turned out to be negative but
insignificant related to turnover intentions. These findings are congruent with the study of
Rutherford et al. (2009). The negative link between job satisfaction with overall job and
turnover intentions has found broad support in existing literature and can be explained by the
idea that employees who are in general unsatisfied with their job, search for another job,
resulting in higher turnover. The negative link between job satisfaction with promotion and
turnover intentions can be explained by the idea that when employees are satisfied with their
career prospects, they are more willing to stay at an organization because there are many
49
challenges and opportunities, guaranteeing a long-term future at the organization (Booth &
Hamer, 2007). Moreover, the positive relation between emotional exhaustion and turnover
intentions is mediated by only one dimension of multi-faceted job satisfaction; job satisfaction
with overall job.
An answer to the research question can now be provided. Both work overload and
emotional labour are positively related to emotional exhaustion. Emotional exhaustion is in
turn negatively related to six dimensions of multi-faceted job satisfaction: overall job, co-
workers, supervision, policy, pay and customers. Emotional exhaustion does not mediate the
negative relation between work overload and the different dimensions of multi-faceted job
satisfaction and neither does it mediate the negative relation between emotional labour and
multi-faceted job satisfaction. However, work overload appears to have a direct negative
effect on job satisfaction with pay and emotional labour is directly negatively related to job
satisfaction with overall job and job satisfaction with policy. Job satisfaction with overall job
and promotion are in turn related to employee turnover intentions and job satisfaction with
overall job shows to be a mediator between emotional exhaustion and turnover intentions.
5.2. Discussion
Not all hypotheses are fully supported and therefore some unexpected findings will be
discussed throughout this paragraph. Hypothesis 3 predicts that emotional exhaustion is
negatively related to all seven dimensions of multi-faceted job satisfaction. However,
emotional exhaustion appeared to be negative, but insignificant to job satisfaction with
promotion. This is contrasting to findings of previous studies (Chung et al., 2012; Rutherford
et al., 2009) and to the social exchange theory (Hatfield & Sprecher, 1984), assuming that
emotional exhaustion is significant negatively related to job satisfaction with promotion.
However, not much research has been conducted yet on the relation between multi-faceted job
satisfaction and employee turnover. A possible alternative explanation for the insignificant
relation between emotional exhaustion and job satisfaction with promotion might be that
employees who are emotionally exhausted do not feel the need to grow further and take on
more challenges, because they simply lack the energy. Promotion opportunities might not be
of much interest to employees who feel emotionally exhausted and therefore the relation
between emotional exhaustion and job satisfaction with promotion turns out to be
insignificant. In addition, moderators might be involved such as an employee’s ambition to
grow. Employees who do not have the ambition to grow within the company might be less
50
concerned about future career opportunities and therefore the role of job satisfaction with
promotion is undermined, regardless their level of emotional exhaustion.
Surprisingly, emotional exhaustion does not mediate the relation between work
overload and multi-faceted job satisfaction as predicted by hypothesis 4. Neither does it
mediate the relation between emotional labour and multi-faceted job satisfaction, as assumed
by hypothesis 5. It is therefore argued that additional work-related characteristics and
emotions, beyond this research model, play a role in these mediating effects. For example,
Chung et al. (2012) examined more antecedents of multi-faceted job satisfaction in the retail
industry such as role ambiguity, role conflict, work-family conflict and family-work conflict.
These additional job stressors might explain the insignificant mediating role of emotional
exhaustion in this study.
Appearing from the mediation tests is that work overload is directly negative related to
job satisfaction with pay. This is contrasting to Singh (1994), arguing that emotional
exhaustion is a far more prominent predictor of organizational outcomes than role stressor(s).
This unexpected finding can also be explained by the social exchange theory (Hatfield &
Sprecher, 1984). When an employee experiences work overload, he or she is making effort to
cope with this work overload. According to the social exchange theory, he or she wants to get
compensated for this. One way of getting compensation is to receive higher rewards in the
form of pay. However, if the company does not increase pay rates, an employee might
experience less job satisfaction with pay.
Furthermore, emotional labour is directly negative related to job satisfaction with
overall job and job satisfaction with policy. Apparently, the negative outcomes of the
discrepancy between genuine and expressed feelings of an employee directly effects job
satisfaction on these two dimensions. This direct negative effect of emotional labour on
overall job satisfaction has found support in earlier literature (Hochschild, 1983; Morris &
Feldman, 1996).
Five dimensions of multi-faceted job satisfaction were not significantly negative
related to turnover intentions (co-workers, supervision, policy, pay, customers). As Rutherford
(2009) argues, there are few studies on the relation between multi-faceted job satisfaction and
turnover intentions. Apparently, these dimensions are not strong enough related to turnover
intentions. Moderators might be involved which are not covered in the current study. These
dimensions could also be linked to other important organizational outcomes such as
51
organizational commitment of organizational citizenship behavior (OCB), thereby indirectly
influencing turnover intentions. This might explain the insignificant findings.
5.3. Theoretical Implications
This study contributes to the existing knowledge on predictors of employee turnover in the
fashion retail industry. As described, there is an overall lack of research on employee turnover
in this specific branch. This study fills this gap by providing a comprehensive research model,
covering industry-specific characteristics such as work overload, emotional labour and
emotional exhaustion. In general, prior studies have examined overall job satisfaction as main
predictor of employee turnover (Jaramillo et al., 2006), also in the retail industry (Booth &
Hamer, 2007; Henrie, 2004) and research has mainly focused on the link between emotional
exhaustion and overall job satisfaction (Babakus et al., 1996).
This is the first time the construct of multi-faceted job satisfaction has been conducted
in a fashion retail context, although this has been investigated before in the overall retail
industry (Chung et al., 2012). The multi-faceted job satisfaction construct can be
recommended over the global job satisfaction construct in future research. The multi-faceted
job satisfaction construct is able to explain relations between different variables better,
thereby providing more details and drawing a more complete picture. Although parts of this
model have been conducted in prior research (Chung et al., 2012; Rutherford et al., 2009), the
overall model has never been studied before. Therefore the study provides an interesting
addition to the existing literature.
5.4. Practical Implications
This study has some important practical implications. Reducing employee turnover is linked
to numerous organizational advantages, improves organizational performance and results in
competitive advantage (Kim et al., 2009). Therefore, the results of this study can be helpful
for fashion retail organizations. This study confirms that work overload, emotional labour,
emotional exhaustion and some dimensions of job satisfaction play an important role in the
prediction of employee turnover and shows both direct and indirect ways to decrease
employee turnover.
Both job satisfaction with overall job and job satisfaction with promotion appear to
have a direct negative relation with employee turnover. In order to decrease employee
52
turnover, management of fashion retail organizations should consider ways to increase job
satisfaction with overall job and job satisfaction with promotion.
Prior literature argues that improved job satisfaction with overall job can be achieved
by giving employees more responsibility and autonomy, stimulating variety in skills and
enhancing interpersonal relationships (Harter et al., 2002). This study shows that a decrease of
work overload and emotional labour results in less emotional exhaustion and more job
satisfaction on multiple dimensions, among which is job satisfaction with overall job. Job
satisfaction with overall job mediates the positive relation between emotional exhaustion and
employee turnover.
It can therefore be argued that both work overload and emotional labour are stepping
stones in the conceptual model. Management should therefore consider the amount of work
overload employees need to perform. For instance, work overload can be reduced by creating
a supportive climate in which employees help each other out and share the work load (House,
1981). A way to reduce the level of emotional labour is to provide social support and establish
rewarding relationships. This helps to replenish an employee’s resources (Brotheridge & Lee,
2002). The decrease of these two job stressors results in a decrease of emotional exhaustion.
In turn, a lower level of emotional exhaustion increases job satisfaction on multiple
dimensions, among job satisfaction with overall job.
An increase of job satisfaction with promotion cannot be explained by the research
model of this study. However, prior literature argues that maintaining a professional climate
has a positive impact on job satisfaction with promotion. Companies should therefore have
clear guidelines on ethics and maintain widespread company rules; this enhances perceptions
of fairness and increases job satisfaction (Deshpande, 1996).
5.5. Limitations and Future Research
Even though this study has important theoretical and practical implications, some limitations
and suggestions for future research need to be addressed. First of all, the data is not normally
distributed which means that some variables show high values of kurtosis or skewness. This
poses limitations concerning the reliability of the data since regression analyses initially
require a normal distribution. The-non parametric distribution might have implications for the
findings of this study (significance, correlations), resulting in slightly different outcomes.
Future research might focus on replicating this study using suitable programs, other than
53
SPSS, for analyzing non-parametric data with (e.g. “partial least squares structural equation
modeling” with SmartPLS by Ringle, Wende, & Will 2005).
Another limitation concerning the data analysis is the way in which the relation
between emotional exhaustion and the seven dimensions of multi-faceted job satisfaction is
tested. SPSS does not allow one to perform a regression analysis with multiple dependent
variables. Therefore, regression analysis is done by performing a simple linear regression
analysis seven times. However, the different dimensions of multi-faceted job satisfaction
might also correlate with each other but this has now been excluded from the analysis.
Therefore, results might be slightly different by using different analytical tools. Although this
choice is well-considered, see 3.7., future research could use a different program for analysis
which allows one to include multiple outcome variables in one regression analysis (e.g.
LISREL 8 by Joreskog & Sorbom, 1993).
Future research could also examine an employee’s ambition to grow as a possible
moderator between emotional exhaustion and job satisfaction with promotion. Besides,
additional work-related characteristics and emotions, such as role ambiguity, could be added
in future research as possible mediators.
Future research could also investigate the role of moderators and mediators (e.g.
commitment or OCB) in the relation between multi-faceted job satisfaction and employee
turnover. This might help in drawing an as complete model as possible and to obtain a better
understanding of the different facets of multi-faceted job satisfaction and employee turnover.
Overall, future research could help in getting a better understanding of the predictors of
employee turnover in the fashion retail industry.
54
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7. APPENDICES
7.1. Questionnaire
The questionnaire has been distributed in both Dutch and English. This appendix only shows
the English version.
Dear sales assistant,
I am working on my master graduation project at the University of Amsterdam (UvA) in which I conduct
research on the job satisfaction of sales assistants in the Dutch retail fashion industry. The goal of the research is
to get more insight in how sales assistants experience their job in this specific industry. Therefore I need many
participants and I would like to ask you if you can fill out the survey attached. Your help is very much
appreciated!
All participants will remain completely anonymous and the results are strictly confidential. The outcomes will
only be used for study purposes. You do not have to fill out your name or other sensitive data. Filling out this
survey will take you less than 10 minutes.
By participating you have a chance to win a 50 Euro gift card from H&M. At the end of the survey you can
chose to participate in the lottery by filling in your email address. Again: respondents will remain completely
anonymous and your potential email address cannot be linked to the survey you have filled out.
Please click on the link below to participate in the survey:
https://nlpsych.qualtrics.com/SE/?SID=SV_72srrjqoR5LtmSx
For any further questions you can send an email to: [email protected].
Thank you very much and goodluck!
Lonneke Schaap
When you click on the link:
Thank you for filling out this survey!
Please select right above in the screen the language you are most comfortable with.
By participating you have a chance to win a 50 Euro gift card from H&M.
At the end of the survey you can chose to participate in the lottery by filling in your email address. Again:
respondents will remain completely anonymous and your potential email address cannot be linked to the survey
you have filled out. During the survey, you will be provided with statements. Please click with each statement on
the answer which suits you best. Only your own opinion is important; not the opinion of others. It is important
that you fill out all the questions, even though they might be difficult and keep in mind that there are no wrong or
good answers.
For any further questions you can send an email to: [email protected].
Thank you very much and goodluck!
Lonneke Schaap
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The following statements are related to how satisfied you are with your job. We would like to remind you again
that all answers given will be completely anonymous. Please indicate how much you agree with the statements.
Satisfaction with overall job
Strongly
Disagree (1)
Disagree (2) Neutral (3) Agree (4) Strongly Agree
(5)
My work gives a
sense of
accomplishment.
(1)
My job is
exciting. (2)
My work is
satisfying. (3)
I’m really doing
something
worthwhile in
my job. (4)
Satisfaction with management
Strongly
Disagree (1)
Disagree (2) Neutral (3) Agree (4) Strongly Agree
(5)
My manager
really tries to
get our ideas
about things. (1)
My manager
has always been
fair in dealings
with me. (2)
My manager
gives us credit
and praise for
work well done.
(3)
My manager
lives up to
his/her
promises. (4)
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Satisfaction with company policy and support
Strongly
Disagree (1)
Disagree (2) Neutral (3) Agree (4) Strongly Agree
(5)
Management is
progressive. (1)
Top
management
really knows its
job. (2)
This company
operates
efficiently and
smoothly. (3)
I receive good
support from the
headquarters.
(4)
Satisfaction with promotion
Strongly
Disagree (1)
Disagree (2) Neutral (3) Agree (4) Strongly Agree
(5)
The company
has an unfair
promotion
policy. (1)
My
opportunities for
advancement
are limited. (2)
There are plenty
of good jobs
here at the
company for
those who want
to get ahead. (3)
I have a good
chance for
promotion. (4)
64
Satisfaction with pay
Strongly
Disagree (1)
Disagree (2) Neutral (3) Agree (4) Strongly Agree
(5)
My pay is low
in comparison
with what others
get for similar
work in other
companies. (1)
In my opinion,
the pay here is
lower than in
other
companies. (2)
I’m paid fairly
compared with
other employees
in this company.
(3)
My income is
adequate for
normal
expenses. (4)
Satisfaction with customers
Strongly
Disagree (1)
Disagree (2) Neutral (3) Agree (4) Strongly Agree
(5)
My customers
live up to their
promises. (1)
My customers
are trustworthy.
(2)
My customers
are loyal. (3)
My customers
are
understanding.
(4)
65
Satisfaction with co-workers
Strongly
Disagree (1)
Disagree (2) Neutral (3) Agree (4) Strongly Agree
(5)
My fellow
workers are
selfish. (1)
My fellow
workers are
pleasant. (2)
The people I
work with are
very friendly.
(3)
The people I
work with help
each other out
when someone
falls behind or
gets in a tight
spot. (4)
66
The following statements are statements about behaviors and feelings related to your job. Please fill out the
answer which suits you best.
How much do you agree with the following statements?
Strongly
Disagree (1)
Disagree (2) Neutral (3) Agree (4) Strongly Agree
(5)
The amount of
work I am
expected to do
is too big. (1)
I never seem to
have enough
time to get
everything done
at work. (2)
It often seems
like I have too
much work for
one person to
do. (3)
67
When interacting with the public (customers, clients), how often do you actually do the following behaviors
during a typical work day?
Never (1) Rarely (2) Sometimes (3) Most of the
Time (4)
Always (5)
I fake a good
mood. (1)
I put on a show
or performance.
(2)
I just pretend to
have the
emotions I need
to display for my
job. (3)
I hide my true
feelings about
situations. (4)
I put on an act in
order to deal
with
customers/clients
in an appropriate
way. (5)
I resist
expressing my
true feelings. (6)
I put on a mask
in order to
display the
emotions I
needed to for my
job. (7)
68
How often do you actually experience the following feelings?
Never (1) Rarely (2) Sometimes (3) Most of the
Time (4)
Always (5)
I feel
emotionally
drained from
my work. (1)
I feel used up at
the end of the
workday. (2)
I feel fatigued
when I get up in
the morning and
have to face
another day on
the job. (3)
I feel burned out
from my work.
(4)
How would you rate your chances of:
Very low (1) Low (2) Average (3) High (4) Very high (5)
Quitting this job
in the next 3
months. (1)
Quitting this job
in the next 6
months. (2)
Quitting in this
job in the next
year. (3)
Quitting this job
in the next 2
years. (4)
69
The following questions are related to the brand or organization you work for and are supposed to give insight
into the service climate. Again: answers will remain completely anonymous and strictly confidential; the
outcomes are only used for study purposes.
1. Which brand/organization are you working for?
……………………………………………………………………………………………………………………….
2. How much do you agree with the following statements?
Strongly
Disagree (1)
Disagree (2) Neither Agree
nor Disagree (3)
Agree (4) Strongly Agree
(5)
Sales assistants
in our
organization
have knowledge
of the job and
the skills to
deliver superior
quality work
and service. (1)
Sales assistants
receive
recognition and
rewards for the
delivery of
superior work
and service. (2)
The overall
quality of
service provided
by our
organization to
customers is
excellent. (3)
Sales assistants
are provided
with tools,
technology, and
other resources
to support the
delivery of
quality work
and service. (4)
70
Please fill out the following information.
3. What is your gender?
Male (1)
Female (2)
4. What is your age in years? (Fill out numbers)
……………………………………………………………………………………………………………………….
5. What is your nationality?(Multiple choice: 193 options)
6. How long have you been in this job?(Years)
……………………………………………………………………………………………………………………….
7. Do you work full-time or part-time?
Full-time (1)
Part-time (2)
8. What is the highest level of education you have received?
None (1)
Primary or elementary school (2)
Secondary or high school (3)
Middle level applied education (MBO) (4)
Higher education or bachelor (HBO) (5)
University or Master's degree (WO) (6)
Prefer not to say (7)
9. If you want to participate in the lottery and make a chance to win a 50 Euros gift card of H&M, please fill out
your email address below. Again, your email address will not be linked to this survey.
……………………………………………………………………………………………………………………….
Thank you very much for filling out this survey!