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http://www.iaeme.com/IJM/index.asp 675 [email protected]
International Journal of Management (IJM) Volume 11, Issue 7, July 2020, pp. 675-694, Article ID: IJM_11_07_060
Available online at http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=11&IType=7
ISSN Print: 0976-6502 and ISSN Online: 0976-6510
DOI: 10.34218/IJM.11.7.2020.060
© IAEME Publication Scopus Indexed
HIGH PERFORMANCE WORK SYSTEMS
(HPWSs) AND HR OUTCOMES: A
SUSTAINABLE SOLUTION FOR HIGHER
EDUCATION
Shakeel Sarwar
Assistant Professor, School of Business, Management and Administrative Sciences,
The Islamia University of Bahawalpur, Pakistan
Dr. Owais Shafique
Assistant Professor, School of Business, Management and Administrative Sciences,
The Islamia University of Bahawalpur, Pakistan
Dr. Mazhar Abbas*
Assistant Professor, Department of Management and MIS,
College of Business Administration,
University of Hail, Hail Kingdom of Saudi Arabia.
Basheer M. Al-Ghazali
Assistant Professor, Department of Business Administration,
Dammam Community College,
King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
*Corresponding Author Email: [email protected] & [email protected]
ABSTRACT
Purpose of this study was to investigate the impact of high performance work
systems (HPWSs) on human resource (HR) outcomes in higher education of Pakistan.
Cross sectional design is used to collect the data from 178 regular faculty members
working at COMSATS University Islamabad. The results of partial least square
structural equation modelling analysis revealed that ability enhancing, motivation
enhancing and opportunity enhancing HPWSs has a direct and significant effect on
HR outcomes. Study also proved the moderating role of resource based view (RBV)
between ability enhancing HPWSs, motivation enhancing HPWSs and HR outcomes.
However, current study rejected the moderating role of RBV between opportunity
enhancing HPWSs and HR outcomes. The study concluded the sustainable solution of
long term positive HR outcomes by implementing unique bundles of HPWSs. In line
with suggestions, the study contributes to the literature in designing unique bundle of
High Performance Work Systems (HPWSs) and HR Outcomes: A Sustainable Solution for Higher
Education
http://www.iaeme.com/IJM/index.asp 676 [email protected]
HPWSs. Finally, current study discusses the implications and limitations and suggests
areas for future research.
Key words: High performance work systems, airline industry, AMO framework
Cite this Article: Shakeel Sarwar, Dr. Owais Shafique, Dr. Mazhar Abbas and
Basheer M. Al-Ghazali, High Performance Work Systems (HPWSs) and HR
Outcomes: A Sustainable Solution for Higher Education, International Journal of
Management, 11(7), 2020, pp. 675-694.
http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=11&IType=7
1. INTRODUCTION
Employees are believed to be an essential strategic asset for any organization (Roslender,
Monk & Murray, 2020). Therefore, in order to achieve the strategic advantage, organizations
need to continuously invest in employees‟ skills and pay attention to performance driven
human resource practices (Bindhya & Harikumar, 2020; Zaharie and Osoian, 2013).
Likewise, higher education institutions (HEIs) strive to attract and retain the talent, train and
motivate the faculty and provide compatible research environment in order to develop the new
generations (Lew, 2009). Previous researches agree that successful implementation of
performance driven human resource practices empower the faculty and generates positive HR
outcomes e.g. lower absenteeism and turnover (Saeed, Tasmin, Mehmood & Hafeez, 2020).
Researchers believe that faculty members play the vital role in enhancing university
performance through many ways such as academic reputation, research quality and
community services (Alazmi and Alazmi, 2020) and positive HR outcomes are prerequisite to
achieve this. Thus, high performance human resource (HR) practices generate positive
behavioral HR outcomes that ultimately promote the university performance. Concisely,
universities need to implement the high performance HR practices in order to achieve better
individual HR outcomes and university performance.
1.1. Problem Background
Higher education in Pakistan starts after twelve years of schooling that engages the students in
advanced training and research activities to make them compatible in all walks of national
life. Higher education is considered as the highly prioritized level of education because it
provides the skilled workforce to the industry hence leads the nation through resources,
productivity and solutions (HamidUllah, 2005). Projection of higher education in a country
ensures availability of qualified professionals in the fields of agriculture, medicine,
engineering and applied sciences. A country's social and economic development depends
upon the quality of its higher education (Kayani, Akbar, faisal, Kayani & Ghumman, 2017).
The question of quality in higher education is directly related to the outcomes of its
faculty members (HamidUllah, Ajmal & Rehman, 2011). As university teachers are main
pillars in ensuring the quality of higher education system so progress in higher education is
wholly based upon the positive behaviors of the faculty members.
Despite the substantial growth in higher education institutions (HEIs) of Pakistan in the
recent years, higher education system of Pakistan is considered underdeveloped in
comparison to the world. Hoodbhoy (2009) pointed that none of Pakistan's HEIs comes even
close to being a university in the real sense of the word. As unfortunately, in 2019 no
Pakistani university could make its place among the top 600 universities of the world ranked
by The Times Higher Education World University Rankings
(www.timeshighereducation.com, 2019). Whereas, (25) neighboring Chinese and (9) Indian
Shakeel Sarwar, Dr. Owais Shafique, Dr. Mazhar Abbas and Basheer M. Al-Ghazali
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universities have made their place in the same list. This indicates a big room for improvement
in Pakistan‟s higher education system. According to the same ranking that is majorly based
upon faculty output dimensions, Pakistan has the world‟s weakest higher education system
while United States and United Kingdom have the strongest (https://tribune.com.pk/, 2016).
Researchers have identified various problems causing negative HR outcomes of faculty
members of higher education institutions. According to Iqbal (2004), poor recruitment and
selection practices, inadequate development of faculty, insufficient support for research,
ineffective management practices, inefficient use of available resources and inadequate
funding are the few reasons causing lower productivity in Pakistan‟s higher education
institutions. Likewise Arshad (2003) pointed that there is no faculty development system for
university teachers in Pakistan. He also mentioned that university teachers are not ready to
accept challenges or take extra workload because they are not offered any extra financial
rewards.
Concisely, we need to generate positive HR outcomes of faculty members in order to
boost the quality of higher education sector of Pakistan. For this, major reforms in human
resource (HR) practices are required. One system that may rectify our problems is the high
performance work systems (HPWSs).
Researchers define high performance work systems through various ways. Way (2002),
for example, define it as function of interrelated human resource practices such as selection,
motivation and development of the workforce to gain the sustainable competitive advantage.
Another author i.e. Datta et al. (2005) elaborate the HPWSs as system that promotes faculty
productivity and make them the source of strategic advantage. Similarly, Guthrie et al. (2009)
commented that HPWSs enhances the employees‟ skills, motivation and commitment to
perform. Number of researchers has explored the relationship between HPWSs and HR
outcomes (Patel et al., 2013; Fu et al., 2015). United States Department of Labor (1993)
concluded that the firms using performance generated HR practices gain better shareholder
return.
1.2. Research Questions
The general purpose of this study is to investigate the impact of unique bundle of HPWSs on
faculty HR outcomes and understanding the reasons and process of above mentioned impact.
So based upon the purpose, following research questions arise;
Do ability enhancing, motivation enhancing and opportunity enhancing unique set of
high performance work systems (HPWSs) have a positive impact on faculty HR
outcomes of higher education sector?
Does Resource Based View (RBV) have a significant positive impact on HR
outcomes?
Does Resource based view (RBV) plays a moderating role between ability enhancing,
motivation enhancing and opportunity enhancing HPWSs and HR outcomes i.e.
employee‟s attitude and behavior?
2. LITERATURE REVIEW
2.1 High Performance Work Systems (HPWSs)
HPWSs have the common interdependent and comprehensive objective of attracting,
selecting, managing, training, retaining and motivating Human Resources, eliciting desired
attitudes and behaviors, in order to achieve organizational goals (Kellner et al., 2017). This
objective may be accomplished by creating a fit between the KSA´s of an employee and the
tasks, duties and responsibilities required by a job (Úbeda-García et al., 2018). HPWSs can
High Performance Work Systems (HPWSs) and HR Outcomes: A Sustainable Solution for Higher
Education
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facilitate employee involvement, skills enhancement and stronger motivation (Özçelik, Aybas
& Uyargil, 2016). Generally speaking, HPWSs is believed to be a combination of HR
functions aimed to enhance skills, abilities and productivity for sustainable competitive
advantage (Zhu, Liu & Chen, 2018).
To explore the HR function involved in building HPWSs, Posthuma et al. (2013)
conducted an extensive literature review from 1992 to 2011 and highlighted 61 HR practices
discussed 2042 times in various researches as potentially viable for becoming the part of
HPWSs. Among these 61, 9 practices are more frequently used either individually or in
interrelated groups are selection and recruitment; training and development; performance
management and appraisal; promotions, compensation and benefits; job design; employee
relations; effective communications; retention, turnover and exit management.
According to researchers, HPWSs are the „best fit‟ group of HR practices aimed to
achieve synergistic impact on organizational outcomes (Rabl et al., 2014). Investigations
prove that outcomes achieved through individual HR practices are far less than achieved
through the selected best fit of related practices due to the synergistic impact (Demortier et al.,
2014). E.g. launching self-managed teams in organization without training reduces the
expected results from teamwork (Kroon, Van & Timmers, 2013). So a smartly chosen
combination of HPWSs makes organizations‟ more flexible and participative by transforming
their structure to achieve greater strategic advantage (Kalleberg, 2006).
The AMO approach was initially suggested by Appelbaum et al., (2000) derived from
Bailey (1993) work where he concluded that employees‟ efforts in organizations may be
ensured through the necessary skills, appropriate motivation and opportunity to participate.
This approach was later verified by Bailey, Berg & Sandy (2001). So the AMO framework
consists of three elements of “Best fit” that enhance together employee performance i.e.
individual ability (A), motivation (M), and the opportunity to participate (O) (Claudia, 2015).
This approach believes that employees‟ productivity in an organization is based upon his/her
ability, motivation and opportunity to involve in organizational functions. Organizations may
generate long term advantage by improving the employee‟s ability, enhancing their
motivation and providing sufficient opportunities for growth, this ultimately leads toward
improved productivity and higher organizational performance (Bailey, Berg & Sandy, 2001).
As previous discussions show, due to the variety of high performance HR practices, it is
difficult to select a common bundle to achieve HR Practices-employee productivity linkage.
This phenomenon opposes the universalistic perspective which states as “One practice that
effectively contributing to an organization may be applied to another organization in the same
manner” (Schimansky, 2014).
By believing role of AMO framework in “best fit” selection of HR, several authors have
developed three dimensional criteria of HR practices selection i.e. skill-enhancing HR bundle
(Ability), motivation-enhancing HR bundle and opportunity-enhancing HR bundle
(Appelbaum et al., 2000). Each bundle represents a combination of HR practices that share
the same purpose. Ability enhancing practices include comprehensive recruitment, rigorous
selection, and extensive training (Jiang et al., 2012). Typical HR practices classified within
the motivation enhancing domain are related to incentives and rewards, extensive benefits,
and career development (Subramony, 2009). Finally, the opportunity-enhancing HR bundle
includes initiatives to empower employees to contribute to organizational goals, such as
employee participation in firm decision making, and the use of communication channels from
the firm to employees (Subramony, 2009).
Shakeel Sarwar, Dr. Owais Shafique, Dr. Mazhar Abbas and Basheer M. Al-Ghazali
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1.2. HR Outcomes
High Performance Work Systems (HPWSs) improve employees‟ abilities, skills, and
motivation and gives them opportunities to contribute (Jiang et al., 2012). Human capital
theory (Crook et al., 2011) and the behavioral perspective (Schuler & Jackson, 1987) serve as
the primary theoretical bases for deriving the causal impact of HPWSs on employee ability,
skill and motivation. Thus, desirable HR outcomes, such as lower voluntary turnover and
absenteeism positively affect the productivity of employees.
HR outcomes like employee‟s intention to leave and absenteeism can be influenced
through performance oriented HR practices (Huang, 2000). While the early models of Beer et
al. (1984) and Guest (1989) provided conceptual foundation of the link between HR practices
and outcomes, the empirical research to explore this link emerged in 1990‟s in the work of
Huselid (1995) and many others. Later this link became a key part of literature and research
(Huang, 2000).
Assessing that committed employees are valuable asset, the conceptual framework of this
study highlights that there is a direct impact of unique bundles of HPWSs on HR outcomes to
enhance the HEIs‟ performance. It is being assumed that if faculty is motivated and
committed than they will be more productive and effective; the satisfaction with their job i.e.
duties and responsibilities make the faculty more productive as compared to dissatisfied and
confused members; and intention to stay affects their performance positively as compared to
intention to leave the institution. It means HPWSs contribution depends upon the above
mentioned HR outcomes (Guest, 2011).
Thus, in this study, it is assumed that the employee‟s behaviors are directly affected by
HPWSs. The Figure 2.1 explains the relationship between HPWSs and faculty HR outcomes.
Thus, based on the literature review and the above discussion following relationships are
hypothesized. These hypotheses focus on the direct relationships between HPWSs and
faculty‟s behavior.
Hypothesis 1: Ability Enhancing HPWSs (AEH) are positively related to HR Outcomes
Hypothesis 2: Motivation Enhancing HPWSs (MEH) are positively related to HR Outcomes
Hypothesis 3: Opportunity Enhancing HPWSs (OEH) are positively related to HR Outcomes.
2.3. Resource Based View- Sustainable Solution
The resource based view (RBV) theory was introduced by Wernerfelt (1984) in his article
“Resource based view of the firm”. The central focus of RBV is the exploitation and
implementation of firm resources to gain a competitive advantage (Wernerfelt, 1984). The
resource based view (RBV) is an economic tool that helps organization to identify and
implement key available resources. By implementing the strategic resources in key processes,
organizations can achieve competitive advantage (Wernerfelt, 1984). If the organization‟s
vital resources are heterogeneous and immoveable, then short term competitive advantage can
be transformed into sustainable competitive advantage (Barney, 1991).
In the continuation of the previous work Barney (1995) provided the VRIO framework
and concluded that a firm´s potential to achieve a competitive advantage depends on the
value, rarity, inimitability and organizational focus of its resources and capabilities.
High Performance Work Systems (HPWSs) and HR Outcomes: A Sustainable Solution for Higher
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Table 1 The VRIO framework and competitive advantage
Valuable? Rare? Costly to
imitate?
Exploited by
the
organization?
Competitive
implications
Strengths or
weaknesses
No – – No Competitive
disadvantage
Weakness
Yes No – Competitive
parity
Strength
Yes Yes No Temporary
competitive
Advantage
Strength and
distinctive
competence
Yes Yes Yes Yes Sustained
competitive
advantage
Strength and
sustainable
distinctive
competence
Source: Adapted from Barney and Hesterly (2010)
As this literature developed, the Resource base view (RBV) theory provided base to the
above arguments that HPWSs directly affect employee HR outcomes (Paauwe and Boselie,
2003). Perhaps, the resource based theory helped to understand the conversion of HPWSs into
high employee performance. Hence, motivation, job satisfaction and organizational
commitment made an employee a valuable asset and become more productive, rare, and
irreplaceable (MacDuffie, 1995).
This research proposes that unique bundle of HPWSs affects the HR outcomes (employee
behavior), ultimately induces the positive HR outcomes of faculty members. Simultaneously,
HPWSs become a capability when they fulfill the requirement of RBV i.e. value they add,
rare, inimitable and organization focused.
Therefore following hypotheses are developed.
Hypothesis 4: Resource Based View (RBV) is positively related to HR Outcomes
Hypothesis 5: RBV Moderates the Relationship between ability enhancing HPWSs and HR
Outcomes
Hypothesis 6: RBV Moderates the Relationship between motivation enhancing HPWSs and
HR Outcomes
Hypothesis 7: RBV Moderates the Relationship between opportunity enhancing HPWSs and
HR Outcomes
Figure 1 Research Framework
Ability Enhancing HPWSs
Resource Based View
HR Outcomes
Opportunity Enhancing HPWSs
Motivation Enhancing HPWSs
Shakeel Sarwar, Dr. Owais Shafique, Dr. Mazhar Abbas and Basheer M. Al-Ghazali
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3. RESEARCH METHODOLOGY
3.1. Research Design
The main goal of this causal study is to investigate the impact of HPWSs on HR outcomes so
positivist approach is used as it supports the questionnaire for data collection through survey
design and statistical analysis for hypotheses testing that the relation may be explained and
generalized (Malhotra & Birks, 2000).
3.2. Research Strategy
The target population of this study is the regular faculty members of COMSATS University
Islamabad. Through convenience sampling data was collected from 178 faculty members
using a questionnaire (Instrument is attached in annex). The instrument of this study was
adopted from the work of Guerci et al. (2015) for measuring ability, motivation and
opportunity enhancing HPWSs, Jiang et al. (2012) for measuring HR outcomes (i.e. turnover
intentions and absenteeism) and Barney (1997) for measuring sustainable solution i.e. RBV).
For face validity, the instrument was sent to following experts;
Table 2 Experts for Validity
Expert title and
Designation
Institution Field Mean of
communication
Response
Dr. Inmaculada Beltran-
Martin
Associate Professor
Business management
and Marketing
Department, Universitat
Jaume I., Spain
Strategic Human
Resource Management
Email Suggested on
AMO
measurement
Dr. Tay Lee Chin
Assistant Professor
Department of
management, Tunku
Abdul Rehman
University, Malaysia
Management Email Suggested on
AMO and HR
outcomes
measurement
Dr. S. Sridhar
Adjunct Professor
Department of Business
Administration, AIMIT,
India
Human Resource
Management
Email Suggested on RBV
and Overall look of
the tool
Suggestions received from above mentioned experts (Emails attached in Appendix) were
incorporated in the instrument and measurement scale was sent back to the same experts again
to validate the instrument.
4. DATA ANALYSIS AND FINDINGS
Before proceeding for advanced data analysis, raw data collected during the survey was
examined to ensure the accuracy, consistency, eligibility and completeness of the respondents
as recommended by various researchers (Zikmund, 2013).
4.1. Data Coding
After the careful screening, the data was coded and entered into SPSS V20. The coding of the
data was performed on the variable view page of the SPSS and each variable was coded
according to the combination of alphabets of what makes the name of the variable. Again, a
number was assigned to each item based on its position in the questionnaire. For instance,
ability enhancing HPWSs (AEH) construct that has 5 items were coded as AEH1, AEH2,
AEH3, AEH4 and AEH5. Each of the coding was made against the respective statements. The
procedure applied in coding ability enhancing HPWSs (AEH) was applied to code all other
independent variables.
High Performance Work Systems (HPWSs) and HR Outcomes: A Sustainable Solution for Higher
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Table 3 Variable Coding
Variables Code
Ability Enhancing HPWSs AEH
Motivation Enhancing HPWSs MEH
Opportunity Enhancing HPWSs OEH
Resource Based View RBV
HR Outcome HRO
4.2. Fundamental Statistical Assumption
Initially, data was tested for some basic statistical assumptions such as multicollinearity,
regarding the study variables to be able to confirm the results and to deal with some basic
errors such as Type I and Type II error.
4.2.1. Multicollinearity Test
With respect to Hair et al., (2010), multicollinearity test infers the association between two or
more exogenous variables where the independent variable demonstrates little correlation with
other independent variables. If multicollinearity among the independent variables is high then
the most reliable statistical test for multicollinearity is identification of variance inflation
factor (VIF) and tolerance with standard threshold of VIF less than 5 and tolerance more than
0.2 (Tabachnick & Fidel, 2013).
Table 4 Multicollinearity Test
Model Collinearity Statistics
Tolerance VIF
AEH .903 1.107
MEH .777 1.286
OEH .959 1.042
RBV .735 1.360
a. Dependent Variable: HRO
Note: AEH = Ability Enhancing HPWSs; MEH = Motivation Enhancing HPWSs; OEH = Opportunity
Enhancing HPWSs; RBV = Resource Based View; HRO = HR Outcomes.
All variables in the table 4 have their value of tolerance greater than 0.2 and VIF value
less than 5 which means there is no multicollinearity exist between the variables.
Also, to detect the existence of multicollinearity among the independent latent construct
correlation matrix was generated from the SPSS as can be seen in Table 5 The values
indicates that none of the independent latent construct is highly correlated with one another
i.e. to say all the values on the correlation matrix are below the critical value (r=.9). This
indicates the non-existence of correlation among the variables (Hair, et al., 2010).
Table 5 Correlations
AEH MEH OEH RBV HRO
AEH Pearson Correlation 1
MEH Pearson Correlation .064 1
.229
OEH Pearson Correlation -.027 .098 1
.604 .064
RBV Pearson Correlation .246 -.323 -.191 1
.000 .000 .000
HRO
Pearson Correlation .210 .253 .017 .213 1
.000 .000 .748 .000
.047 .002 .036 .002 .000
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4.3. Descriptive Statistics of the Study
After the screening process of data the description of statistical analysis for the study
variables is determined by using descriptive analysis where the statistical value of all
variables such as dependent variables, independent variable and moderating variables are
analyzed. The descriptive statistics for study variables as shown in the table 6 presents the
minimum and maximum scores, the values of standard deviation and mean of the study
variables as employed in this study, as previously mentioned in chapter three the
questionnaire used in this study was designed on seven point likert scale ranging from 1 to 7.
Table 6 shows that the mean scores of the study variables are within the range of 3.17 to
4.77, the value of standard deviation for the study variables ranges from 0.91 to 1.2.
Table 6 Results of Descriptive Statistics of the Study Variables
Variable N Minimum Maximum Mean Std. Deviation
AEH 178 1.00 7 3.7648 1.11762
MEH 178 1.29 5.86 3.1732 .91031
OEH 178 1.25 7 4.7703 1.00790
RBV 178 1.20 6.30 3.7992 1.26035
HRO 178 1.00 6.29 3.5072 1.23003
Note: AEH = Ability Enhancing HPWSs; MEH = Motivation Enhancing HPWSs; OEH = Opportunity
Enhancing HPWSs; RBV = Resource Based View; HRO = HR Outcomes.
4.4. Assessment of PLS-SEM Path Model
To evaluate the PLS-SEM path, analysis of data is carried out in two basic steps i.e.
measurement model and structural model as suggested by Henseler et al. (2009) and Chin
(1998).
4.4.1. Measurement Model
In outer model evaluation, the measurement model is carried out to ensure the constructs
validity and reliability. In line with arguments of Vinzi et al. (2010) outer loading should be
0.5 and above. Based upon the following argument all the items in outer loading that are
below than 0.5 should be deleted one by one with lowest value, this technique is also
validated by (Hair et al., 2012) as it improves the quality of data. According to Hair et al.
(2011), while performing the outer model evaluation individual items reliability, internal
consistency reliability, content validity, discriminant validity and convergent validity must be
ascertained.
4.4.1.1. Individual Item Reliability
To determine the convergent validity, loading and cross-loadings of the variables are
examined first, as pre-requisite for assessing the outer model. In line with the criteria
suggested by the Hair et al. (2014), convergent validity is attained by meeting the criteria that
factor loading of each item is above 0.6 and no single loading of an item from other construct
is higher than the construct being measured. The result in table 4.5 reveals that no individual
item has value of outer loading less than 0.6 so all 50 items are included in the instrument.
4.4.1.2. Internal Consistency Reliability
Internal consistency reliability is the extent to which all items on a particular subscale
measure the same concept (McCrae et al., 2011). The acceptable value for composite
reliability defined in the literature (Hair et al., 2011) should not be lower than the threshold
value of 0.7, and the average variance extracted (AVE) acceptable value should be at least
High Performance Work Systems (HPWSs) and HR Outcomes: A Sustainable Solution for Higher
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0.5. The table 7 shows that all the variables are highly reliable, and the AVE value of each
variable is above than the cutoff point of 0.50, which shows that the measurement model is
reliable for further analyses. The Cronbach‟s alpha is also calculated to validate the internal
consistency of the constructs. As per the rule of thumb given by George and Mallery (2003),
the value of greater than 0.9, 0.8 and 0.7 are classified as excellent, good and acceptable
respectively. Table 7 below shows the AVE, Cronbach‟s alpha and composite reliability
scores of all variables.
Table 7 Construct Reliability, Cronbachs Alpha, Composite Reliability and AVE of all the Latent
Variables
Construct Items Loading Cronbach's
Alpha
Composite
Reliability
Average Variance Extracted
(AVE)
Ability
Enhancing
HPWS
0.890 0.919 0.694
AEH1 0.824
AEH2 0.829
AEH3 0.845
AEH4 0.889
AEH5 0.775
Motivation
Enhancing
HPWSs
0.886 0.913 0.636
MEH1 0.788
MEH2 0.807
MEH3 0.834
MEH4 0.731
MEH5 0.8
MEH6 0.82
Opportunity
Enhancing
HPWSs
0.842 0.891 0.671
OEH1 0.796
OEH2 0.865
OEH3 0.749
OEH4 0.861
Resource
Based View
0.924 0.937 0.623
RBV1 0.884
RBV2 0.824
RBV3 0.767
RBV4 0.765
RBV5 0.81
RBV6 0.717
RBV7 0.763
RBV8 0.756
RBV9 0.804
HR Outcomes 0.931 0.944 0.707
HRO1 0.789
HRO2 0.823
HRO3 0.844
HRO4 0.85
HRO5 0.861
HRO6 0.863
HRO7 0.853
Note: AEH = Ability Enhancing HPWSs; MEH = Motivation Enhancing HPWSs; OEH = Opportunity
Enhancing HPWSs; RBV = Resource Based View; HRO = HR Outcomes.
4.4.1.3. Discriminant Validity
According to Farrell and Rudd (2009), discriminant validity is the extent to which a particular
latent variable is different from other latent variables. In this study, discriminant validity is
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predicted by the AVE values, the criteria proposed by (Fornell & Larcker, 1981).
Discriminant validity is obtained by evaluating the correlation between the latent variables
along with the square root of AVE (Fornell & Larcker, 1981). Fornell and Larcker (1981)
recommended the use of AVE of 0.50 or greater to assess discriminant validity. They also
recommended that the square root of AVE should be above the value of the latent variables.
To examine discriminant validity, this study analyzed the model‟s external consistency and
compared the value of AVE of all latent variables. The table 8 shows that the square root of
AVE is greater than the correlation among the latent variables, indicating adequate
discriminant validity (Fornell & Larcker, 1981). After performing CFA, none of the variables
are dropped even the deletion of some items. However, Hair et al. (2013) argued that a
variable with two items should not be subject to removal.
Table 8 Discriminant Validity Matrix (Fornell-Larcker Criterion)
AEH FP HRO MEH OEH RBV
AEH 0.833
FP 0.107 0.784
HRO 0.214 0.509 0.841
MEH 0.065 0.172 0.284 0.797
OEH 0.011 0.209 0.169 0.168 0.819
RBV 0.247 0.179 0.229 -0.295 -0.056 0.789
Note: AEH = Ability Enhancing HPWSs; MEH = Motivation Enhancing HPWSs; OEH = Opportunity
Enhancing HPWSs; RBV = Resource Based View; HRO = HR Outcomes.
Figure 2 Measurement Model (PLS Algorithim)
4.4.2. Structural Model
In this study there are two structural models i.e. direct relationship structural model and
structural model that includes moderating variables.
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4.4.2.1. Direct Relationships
According to Hair et al. (2013), the paths with non-significant or signs of opposite direction to
the hypothesized relationships do not accept the hypotheses, while significant paths
empirically provide statistical evidence of causality of the relationships in the model. Before
the mediating effect was tested, bootstrapping with a resample of 500 was run to get the t-
value to assess if direct relationships exist.
A total of 05 direct relationships with faculty productivity and HR outcomes are tested in
this study and all are significantly supported as shown in Table 9
Figure 2 displays the path coefficients, t-values, p-values along with the standard
deviation values. Based on these results, decision is made to support or reject a hypothesis.
The t-values are obtained from bootstrapped procedure (with 500 sampling iterations for 178
cases observations). Hair et al. (2013) argued that bootstrapping serves as a proxy of
parameters for standard error. As Hair et al. (2013) explained, the paths that are non-
significant or showing signs the opposite direction to the hypothesized do not support prior
hypotheses while significant paths empirically support the proposed causal relationship.
Before the mediating effect is tested, bootstrapping with a resample of 500 was run to get the
t-value to assess if the direct relationships are significant. Figure 3 and Table 9 show the
bootstrapping results as discussed below;
Hypothesis 1: Ability Enhancing HPWSs (AEH) are positively related to HR Outcomes
The result from the output of the PLS algorithm and bootstrapping shows a positive and
significant association between AEH and HR Outcomes (β = 0.114, t = 2.255). Therefore,
Hypothesis 1 is supported.
Hypothesis 2: Motivation Enhancing HPWSs (MEH) are positively related to HR Outcomes
A significant and positive relationship between MEH and HR Outcomes is found (β = 0.3477,
t = 7.281). Hence, Hypothesis 2 is supported.
Hypothesis 3: Opportunity Enhancing HPWSs (OEH) are positively related to HR Outcomes.
A positive and significant association between OEH and HR Outcomes (β = 0.127, t = 3.115)
is found. Hence, hypothesis 3 is supported.
Hypothesis 4: Resource Based View (RBV) is positively related to HR Outcomes
A positive and significant association between RBV and HR Outcomes (β = 0.311, t =
6.970) is found. Hence, hypothesis 4 is supported.
Table 9 Results of hypothesis testing (Direct effects)
Hypotheses Relationship Beta SE t-value P-value Decision
H1 AEH -> HRO 0.114 0.050 2.255 0.025 Supported
H2 MEH -> HRO 0.347 0.048 7.281 0 Supported
H3 OEH -> HRO 0.127 0.041 3.115 0.002 Supported
H4 RBV -> HRO 0.311 0.045 6.970 0 Supported
Note: AEH = Ability Enhancing HPWSs; MEH = Motivation Enhancing HPWSs; OEH = Opportunity
Enhancing HPWSs; RBV = Resource Based View; HRO = HR Outcomes.
Shakeel Sarwar, Dr. Owais Shafique, Dr. Mazhar Abbas and Basheer M. Al-Ghazali
http://www.iaeme.com/IJM/index.asp 687 [email protected]
Figure 3 Bootstrapping (Direct Relationships)
4.4.2.2. The Moderating Effects
A test of moderation as pointed out by Ramaya et al. (2011) is applied to evaluate how
moderator variable effects the direction or strength of the relationship between the
independent and dependent variable. Consistent with previous idea, moderator variable are
typically introduced when there is inconsistent relationship or weak relationship between the
independent variable and dependent variable.
There are number of techniques available to test the moderation effects such as hierarchal
regression procedure that has three steps. However, the drawback of this technique is that
researchers have to calculate the interaction terms manually by using functions,
transformation, computation, and taking the product of each pair. Another technique is the
cross products of the indicator of the independent variable and the moderator (Chin et al.,
2003; Dawson, 2014). In this study, the researcher applied the moderating variable as an
additional construct using the cross product of the indicator of the predictor variable and the
moderator (Chin et al., 2003). This method of testing is called a product indicator approach.
Subsequently, an interaction model was tested by creating interaction terms between ability
enhancing HPWSs (AEH), motivation enhancing HPWSs (MEH), opportunity enhancing
HPWSs (OEH) and HR Outcomes. This model included the moderating effect of Resource
Based View (strategic implementation of abilities, motivation and opportunities framework)
on the relationship between AEH, MEH, OEH and HR Outcomes. This product indicator
approach involved determining the path coefficients and t-values. Figure 4 illustrates the
moderating effect of the Resource Based View.
Based on Hair et al. (2013) analysis of the moderation effect, the results suggest that the
relationship between AEH, MEH and HR Outcomes would be strengthened by strategic
implementation of AMO i.e. RBV. This result signifies that positive nexuses between AEH,
MEH and HR Outcomes are stronger for firms with long term strategic implementation of
AMO through resource based view. Simultaneously, analysis also shows that relationship
between OEH and HR Outcomes are not affected by RBV in Pakistan‟s perspective. As
shown in table 10 out of three (3) moderating interaction hypothesis two (2) hypothesis are
significant at p<0.05 and remaining one (1) is insignificant at p<0.05.
High Performance Work Systems (HPWSs) and HR Outcomes: A Sustainable Solution for Higher
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Table 10 Moderator Hypothesis Testing
Hypotheses Relationship Beta SE t-value P-value Decision
H9 AEH_Mod -> HRO 0.137 0.050 2.737 0.006 supported
H10 MEH_Mod ->HRO 0.119 0.056 2.147 0.032 supported
H11 OEH_Mod -> HRO -0.030 0.054 0.555 0.579 Not Supported
Note. *p < 0.05 (t = 1.96)
Note: AEH = Ability Enhancing HPWSs; MEH = Motivation Enhancing HPWSs; OEH = Opportunity
Enhancing HPWSs; RBV = Resource Based View; HRO = HR Outcomes; Mod = Moderator.
Hypothesis 5: RBV Moderates the Relationship between AEH and HR Outcomes
As shown in Table 10 and Figure 4, resource based view (moderating variable) showed a t-
value of 2.737, which is more than the cutoff value of 1.96, indicating that the result is
statistically significant. Thus, the result showed credible evidence of the moderating effect of
RBV on the relationship between AEH and HR Outcomes (β = 0.137, t = 2.737, p = 0.006).
Hence, hypothesis 5 is supported.
Hypothesis 6: RBV Moderates the Relationship between MEH and HR Outcomes
As shown in Table 10 and Figure 4, resource based view (moderating variable) showed a t-
value of 2.147, which is more than the cutoff value of 1.96, indicating that the result is
statistically significant. Thus, the result showed credible evidence of the moderating effect of
RBV on the relationship between MEH and HR Outcomes (β = 0.119, t = 2.147, p = 0.032).
Hence, hypothesis 6 is supported.
Hypothesis 7: RBV Moderates the Relationship between OEH and HR Outcomes
As shown in Table 10 and Figure 4, resource based view (moderating variable) showed a t-
value of 0.555, which is less than the cutoff value of 1.96, indicating that the result is
statistically insignificant. Thus, the results prove no moderating effect of RBV on the
relationship between OEH and HR Outcomes (β = -0.030, t = 0.555, p = 0.579). Hence,
hypothesis 7 is not supported.
Figure 4 Bootstrapping Moderating Effect
Shakeel Sarwar, Dr. Owais Shafique, Dr. Mazhar Abbas and Basheer M. Al-Ghazali
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5. CONCLUSION AND RECOMMENDATIONS
5.1. Conclusion
This sub-section provides the highlights of the key findings and relates them with the research
objective by answering research questions. The main objective of the investigation was to
evaluate the impact of high performance work systems (HPWSs) on faculty HR outcomes in
higher education sector in the presence of resource based view (RBV) as moderator. In
essence, this study has made a significant breakthrough by evaluating a unique set of ability,
motivation and opportunity enhancing high performance work systems (HPWSs) in the light
of RBV and confirms its strategic impact on faculty outcomes. Hence, this study provides the
answers of the research questions mentioned previously.
Research questions RQ1 and RQ2 examine the direct effect while RQ3 evaluates the
moderating effect. The results of PLS-SEM path modeling disclosed that ability enhancing,
motivation enhancing and opportunity enhancing bundle of high performance work systems
have a significant impact on faculty HR outcomes. Results of findings revealed that in direct
effects all four hypotheses were supported as shown in table 11 below;
Table 11 RQ1 and RQ2 - Direct Effects
Hypotheses Relationship Beta SE t-value P-value Decision
H1 AEH -> HRO 0.114 0.050 2.255 0.025 Supported
H2 MEH -> HRO 0.347 0.048 7.281 0 Supported
H3 OEH -> HRO 0.127 0.041 3.115 0.002 Supported
H4 RBV -> HRO 0.311 0.045 6.970 0 Supported
Note: AEH = Ability Enhancing HPWSs; MEH = Motivation Enhancing HPWSs; OEH = Opportunity
Enhancing HPWSs; RBV = Resource Based View; HRO = HR Outcomes; FP = Faculty Productivity.
Above mentioned table revealed that effect of ability enhancing HPWSs on HR outcomes,
motivation enhancing HPWSs on HR outcomes, opportunity enhancing HPWSs on HR
outcomes HR outcomes on Faculty Productivity and Resource Based View on HR outcomes
are significant and positive. This implies that in the environment where careful HPWSs are
implemented carefully, employee HR outcomes are improved in a significant way. These
findings are in line with those of Úbeda-Garcíaet al. (2014), where ability, motivation and
opportunity HPWSs was found to positively influence the employee behaviors. Also, it
implies that organizations that need to improve their HR outcomes should capitalize on
HPWSs (Amin, Ismail, Rasid, & Selemani, 2014). Hence, the findings of this study are
consistent with previous findings that prioritize careful implementation of HPWSs as a
significant element in performance improvement (Georgiadis & Pitelis, 2012).
The PLS-SEM path modeling also revealed that strategic moderating role of Resource
Based View (RBV) on the effects of ability enhancing HPWSs and motivation enhancing
HPWSs on HR outcomes is statistically supported. Importantly, moderating role of RBV on
the effect of opportunity enhancing HPWSs on HR outcomes was not supported. Hence it
confirmed the long term strategic impact of ability and motivation enhancing bundles of
HPWSs on HR outcomes that ultimately nourishes the faculty productivity. Above mentioned
results provide the answer to RQ5 as shown in table 12 below;
Table 12 RQ3 - Moderation Effect
Hypotheses Relationship Beta SE t-value P-value Decision
H6 AEH_Mod -> HRO 0.137 0.050 2.737 0.006 supported
H7 MEH_Mod ->HRO 0.119 0.056 2.147 0.032 supported
H8 OEH_Mod -> HRO -0.030 0.054 0.555 0.579 Not Supported
Note: AEH = Ability Enhancing HPWSs; MEH = Motivation Enhancing HPWSs; OEH = Opportunity
Enhancing HPWSs; RBV = Resource Based View; HRO = HR Outcomes; Mod = Moderator.
High Performance Work Systems (HPWSs) and HR Outcomes: A Sustainable Solution for Higher
Education
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The findings highlighted the fact that resource based view can play a moderating role
between ability enhancing HPWSs (AEH), motivation enhancing HPWSs (MEH) and HR
outcomes i.e. employee‟s behavior, this is in line with (Barney, 1991) study, i.e. if the bundle
of HPWSs is valuable, rare, inimitable and organizational focused, long term HR outcomes
may be achieved. Although the moderation of RBV on the effect of opportunity enhancing
HPWSs on HR outcomes was not confirmed in this study, this does not invalidate the
propositions of AMO because previous studies have confirmed the effects. In particular, the
insignificant relationship could be a result of environmental and economic factors that directly
impacts the available opportunities compared to developed economies.
5.2. Recommendations and Implications
The findings of this study recommends and imply that, in order to achieve greater faculty HR
outcomes, there is need to implement unique bundles of HPWSs (Combs et al., 2006;
Appelbaum et al., 2000). That is, there is need for greater synergy between performance
oriented HR practices and human resource strategies to ensure the faculty members to
discharge positive behaviors. Also, the effect of ability enhancing HPWSs was positively
related with the HR outcomes of faculty members, implying that the more management
focuses on enhancing faculty abilities, the greater will be the positive effect on their HR
outcomes.
2nd
, Findings of this study indicate motivation enhancing HPWSs influence the faculty HR
outcomes. The implication is shows HEIs management needs to pay adequate attention to
performance based compensation system capable of eliciting positive HR outcomes and
faculty productivity. Furthermore, the findings of the study also revealed a significant effect
of opportunity enhancing HPWSs on faculty HR outcomes. This indicates that faculty
empowerment, autonomy of work and knowledge shearing positively influences the HR
outcomes. Specifically, management of HEIs needs to involve the faculty in job design and
important decision making.
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