strategic human resource management practices and competitive priorities of the manufacturing...
TRANSCRIPT
ORIGINAL ARTICLE
Strategic Human Resource Management Practicesand Competitive Priorities of the Manufacturing Performancein Karachi
Muhammad Shahnawaz Adil
Received: 25 February 2014 / Accepted: 25 September 2014
� Global Institute of Flexible Systems Management 2014
Abstract Pakistan is a developing country with scarcity
of HRM research. An ineffective and maladaptation of
strategic HRM practices potentially reduce the manufac-
turing performance in Karachi. This study investigates the
impact of eight strategic human resource management
practices on the four competitive priorities (i.e., cost,
quality, delivery and flexibility) of the manufacturing per-
formance when controlled for information sharing and
relationship with employees. A sample of 182 usable survey
questionnaires is collected from 90 organizations of 15
manufacturing sectors based in Karachi. The method of
exploratory and confirmatory factor analyses were used to
assess the reliability and validity of the measurement
model. The structural equation modeling method was then
applied to examine the theoretical framework. The results
of the structural model show that all of the eight strategic
HRM practices collectively demonstrate a very good model
fit between the theory and the sample drawn however, both
performance appraisal and employment security have been
found statistically significant to predict these four priorities
in isolation too. The study validates the theory of the
integration between HRM practices and manufacturing
operations, and investment perspective of strategic HRM,
in particular. The contribution of this study is the con-
struction of structural and measurement models of strate-
gic HRM practices and the four chosen competitive
priorities that could facilitate future research on human
resource management. It also highlights important impli-
cations for HR managers in developing countries such as
Pakistan.
Keywords Cost � Competitive priorities � Delivery �Flexibility � Manufacturing � Pakistan � Performance �Strategic human resource management �Structural equation modeling � Quality
Introduction
Human Resource Management (henceforth, HRM) and
strategic HRM are two separate concepts. HRM recognizes
employees as one of the ‘organization resources’ whereas
Strategic HRM acknowledges employees as a ‘strategic
resource’ who could help the organization achieve a
sustainable competitive advantage. However, different
researchers (e.g. Barney 1991; Grant 1991; Wright and
McMahan 1992; Collis and Montgomery 1995,2008; Mad-
hok and Priem 2010; Brahma and Chakraborty 2011) have
established that resource-based view of the firm (Barney
1991; Wernerfelt 1984, 1995) determines a base for strategic
asset in an organization. Strategic HRM principally con-
centrates on organizational performance instead of individ-
ual performance and its major role is to provide solutions to
different businesses and management problems rather than
merely performing individual HRM functions in isolation
(Becker and Huselid 2006). Furthermore, there are two main
goals of Strategic HRM, i.e. to provide maximum business
and economic value to all concerned business units by cur-
tailing costs and realigning HR role as a ‘Strategic Partner’
with all non-HR business managers (Groysberg et al. 2006).
Indeed, human resource is one of the most difficult and
complex types of firm’s resources to manage. D’Aveni
et al. (1995) have argued that it is becoming substantially
very difficult to first create and then sustain competitive
advantage over adversaries in a hypercompetitive world. In
addition to a distinctive knowledge and relationship base,
M. Shahnawaz Adil (&)
Department of Management Sciences, IQRA University,
Karachi 75300, Pakistan
e-mail: [email protected]
123
Global Journal of Flexible Systems Management
DOI 10.1007/s40171-014-0084-7
superior market position, a superior resource base mainly
serves as the third source of competitive advantage (Wilson
and Gilligan 2005). In order to sustain competitive edge
through distinctive human resource base, employees need
to be motivated in such a manner that they could develop
themselves through different but useful career opportuni-
ties. For this, it is also equally imperative that the talented
workforce should also be managed in accordance with the
needs of the time. Historically, the main theme of the
underlined discussion has been to empirically identify
whether there is any significant relationship between HRM
practices and business strategies (Tyson and York 2000).
The central idea is to ensure strategic fit between these two
entities. This is not only necessary to avoid demise of the
business but it also facilitates the top management in
achieving organizational effectiveness, which may only be
possible if organizations establish a constructive bond
between the strategic objectives and the way the HRM
policies and practices are implemented.
Initially, Porter (1985) revealed HRM as one of the four
supporting activities to his world-renowned ‘generic’ value
chain model for a manufacturing business. However, orga-
nizations increasingly need to develop ‘value enhancement
activities’ besides Porter’s nine value activity areas in order
to create and deliver more collective value to customers from
a number of resources. These ‘value enhancement activities’
include organizational entrepreneurship; organizational
learning; cross-functional synergy; core competence build-
ing; and organizational creativity, innovation and imagina-
tion (Normann and Ramırez 1993). Nevertheless, these
values should be shared across all of the functional areas of
organizations and also reflect in their corporate values,
administrative policies and operating procedures (Porter and
Kramer 2011). These ‘shared values’ will help the organi-
zations improve the degree of competitiveness while
improving their economic and social conditions in the
environment in which they operate. But, value enhancement
activities can only produce competitive results if senior
executives of ‘for-profit’ firms could effectively establish
and sustain a culture of a ‘Learning Organization’. This is
reasonably important for getting a sustainable competitive
advantage by spotting and eliminating what Argyris (1986)
calls ‘Skilled Incompetence’ in teams with agility (Williams
1997). Failure to do so will result in financial distress for the
organization largely because of one of its seven ‘learning
disabilities’ (Senge 2006). Earlier, Walton (1985) has sug-
gested that satisfaction with respect to human resources leads
to higher firm performance. In addition, positive behavior of
organizational members is usually driven by their implicit
and explicit motivational factors which they also express in
the form of their affective commitment towards employer
(Guerrero and Barraud-Didier 2004; Guest 1997) which
leads to higher job satisfaction (Tsui and Pearce 1997).
In fact, organizational performance is an analysis of a firm
performance against the goals and objectives set by the senior
business managers usually in the beginning of a financial
year. However, four primary performances (or outcomes) are
analyzed within corporate businesses i.e., financial, market,
shareholder value and manufacturing performances.
With a population of approximately 180 million, Pakistan
is the sixth most populous country in the world. Pakistan is a
strategically-important country in South East Asia with
scarcity of HRM research. An ineffective and maladaptation
of strategic HRM practices are potentially reducing the
performance of the manufacturing organizations in Pakistan.
In the context of today’s hypercompetitive environment, the
problem is that a number of family businesses as well as
small-and-medium-sized enterprises (SMEs) in Pakistan
hardly realize the contribution of HR functions towards the
success of manufacturing companies. With few exceptions,
majority of the management of these ‘self-centered’ orga-
nizations merely concentrate on their administration func-
tions, thus utilize their administrative capabilities in hiring
and managing their employees. Since they do not have or
intend to establish their separate dedicated HR function, their
administrative staff (who are not usually professionally-
trained HR people) tend to incline towards hiring those
individuals who may not be the ‘right’ candidate for the
position. As a result, common HR functions inter alia, career
planning, health, safety and environment (HS&E) compli-
ance issues, fair treatment of employees, employee relations,
organization learning and development, performance man-
agement, etc., could not be over-emphasized in Pakistan.
Noticeably, Khan (2010) was perhaps the last relevant
contribution on HRM–firm performance relationship in
Pakistan however, the study was conducted only in the oil
and gas sector of the country. It is therefore, imperative to
demonstrate the said relationship in other manufacturing
and industrial sectors of Pakistan. In addition, previous
studies have endorsed the HRM–firm performance rela-
tionship by taking a few traditional HRM practices with
only one dependent variable (in the form of either financial
or non-financial performance measures). On the contrary,
instead of taking financial measures which has been a
conventional practice in several previous studies (e.g.,
Huselid 1995; Koch and McGrath 1996; Wright et al. 2003;
Akhtar et al. 2008), this study takes four different com-
petitive priorities (namely, quality, cost-efficiency savings,
flexibility, and delivery) as performance measures of
manufacturing organizations. Then the study attempts to
construct a (multivariate) canonical correlation in order to
ascertain the significant impact of strategic HRM practices
on these four competitive priorities in sixteen different
manufacturing sectors of Sindh Industrial Trading Estates
(SITE), Karachi—the largest business hub of Pakistan. The
goal of this study is to discover the strategic HRM
Global Journal of Flexible Systems Management
123
practices, which significantly contribute towards achieving
the competitive priorities of the manufacturing companies
in Pakistan. It will surely help its reader in further refining
the closed bond of the relationship between strategic HRM
practices and organizational performance in the context of
Pakistan, in particular.
Review of the Related Literature
Measuring Organizational Performance
Before measuring the organizational performance, it is
necessary to identify the items with which it is composed of,
called a ‘Black Box’. Three noticeable black boxes have
been emerged so far i.e., Becker et al. (1997), Guest et al.
(2000) and Delery and Shaw (2001). In addition, Wright and
Snell (1998) have also presented a holistic linkage between
mission of the organization and firm performance through
HR behaviors and practices. In essence, human resource
strategy is derived from business strategy and then HRM
practices are formulated and aligned with both business and
HR strategies. A clear and effective fit between the HRM
policies as well as the business strategies is required to be
observed (Baird and Meshoulam 1988; Lengnick-Hall and
Lengnick-Hall 1988). HR effectiveness is ensured to
observe HR outcomes in the form of high commitment and
engagement, development of competence, behavior and skill
flexibility (Beltran-Martın and Roca-Puig 2013). Sharma
et al. (2010) presented a comprehensive review of various
types of flexibilities in organizations exploring its impact on
performance. These HR outcomes could lead towards higher
rate of productivity by maintaining a superior quality of
goods and services thus financial performance is achieved
(Guest et al. 2000). In addition, modest relationship has been
observed between HRM and firm performance (Stavrou and
Brewster 2005; Gooderham et al. 2008).
The measurement of organizational performance has
been a contradictory issue since long. Initially Snell (1992),
followed by Snell and Dean (1992) and later on, Chanda
and Shen (2009) have argued that both financial and non-
financial measures are required to measure firm perfor-
mance. Numerous researchers (e.g., Becker and Huselid
2010) have been found in favor of using financial measures
to assess firm performance. But, a study (which involves a
strategic focus) does not necessarily require financial
measures though, the dependent variable should possess a
strategic significance (Skaggs and Youndt 2004).
Noticeably, the competitive priorities of a manufactur-
ing function (for instance, quality, delivery performance,
flexibility and cost) were used by different researchers
(e.g., Jayaram et al. 1999; Santos 2000; Nauhria et al.
2011; Awwad et al. 2013) to examine the relationship
between HRM practices and organizational performance
and thus argued that these performance facets should be
taken into consideration in order to attain business success.
The aforesaid theme has been to analyze the coherence of
strategy and programs in both manufacturing and human
resources. Initially, strategic HRM was an HR-focused
paradigm but in the existing scenario, it is rapidly moving
into the hands of senior executives and line managers. This
paradigm shift also reflects a ‘black box’ between firm
performance and human resource architecture (Becker and
Huselid 2006). It is due to the considerable variations
in HR managers’ skills, competencies, and the dynamic
capability (Teece et al. 1997; Eisenhardt and Martin 2000)
of the competing firms. In contrast, we are still unsure
whether the HRM discipline has observed a new ‘paradigm
shift’ (Warner 2012). Despite, HRM could continue to play
a role towards integrating HRM functions with overall firm
performance it has been moderately involved in strategic
decision-making process (Andersen et al. 2007).
HRM and Organizational Performance
Theoretically, Hayes and Wheelwright (1984) and Slack
et al. (1995) have established a relationship between differ-
ent competitive dimensions of HR strategy and competitive
priorities of manufacturing strategy. Manufacturing organi-
zations produce better organizational performance when
they implement consistent innovative HR practices (e.g.,
Ichniowski et al. 1997). They explained that the steel man-
ufacturers, which ensure a set of innovative employment
practices, obtain substantially higher productivity gains than
those which largely concentrate on traditional approach. In a
similar vein, Som (2008) examined the impact of innovative
strategic HRM practices on the performance of sixty nine
(69) Indian firms. This study revealed that innovative com-
pensation and recruitment functions have been found
significant and largely influenced by the economic liberal-
ization in India. Nevertheless, firm performance could not be
predicted by the consonance or fit among innovative HRM
practices. HRM fosters innovation which ultimately enhan-
ces business performance (Jimenez-Jimenez and Sanz-Valle
2008). On the other hand, majority of the American manu-
facturing companies demonstrated their tendency to achieve
technical HRM effectiveness (Huselid et al. 1997). Opera-
tional performance may be measured by different predictors,
e.g. customer satisfaction, customer retention, sales revenue,
defects because of not maintaining quality standards, scrape
rates, order backlog, productivity, production downtime,
labor costs, etc. Moreover, different measures of employee
and customer satisfaction are still in the development phase
(Wright and Gardner 2000).
The relationship and the influence of strategic HRM
practices on organizational performance have been studied
Global Journal of Flexible Systems Management
123
by different researchers (e.g., Dimba and K’Obonyo 2009;
Vlachos 2009; Zhou-ling 2009). Similarly, the quality-
focused HR practices have strong impact on organizational
performance (Akdere 2009). Wattanasupachoke (2009)
concluded the impact of human resource strategies on Thai
enterprises in two facets. Firstly, extra financial incentives
and profit sharing plans develop employees’ sense of asso-
ciation with their organization which enable them to put
more ‘meaningful’ emphasis on workplace commitment,
involvement and higher productivity. Secondly, employees’
positive and healthy attitude, their politeness and emotional
stability bring a long-lasting image, reputation of their
organization and customer satisfaction. Another study of
flexible leadership and managing talent employees in Thai
securities industry is reported by Piansoongnern (2013). De
Menezes et al. (2010) studied the integration between
Operations Management and HRM practices and firm per-
formance in the UK. They argued that it is the integration of
practices that would have the ability to achieve multiple
goals thereby observes superior firm performance. Their
study used ‘productivity’ as only one measure of perfor-
mance hence believed that this measure may be more reliable
than financial performance indices including several eco-
nomic factors that cannot be controlled by one organization
alone. Islam and Siengthai (2010) also revealed a significant
relationship between HRM functions and the overall per-
formance fifty three (53) Dhaka Export Processing Zone
(DEPZ) enterprises. Collings et al. (2010) concluded that
‘HRM-strategy fit’ remained the only predictor which
strongly influenced employee skills and abilities, employee
motivation, and organizational performance. Surprisingly,
‘competence-based performance appraisal’ has no relation-
ship with organizational financial outcomes.
Furthermore, Ayanda and Sani (2011) analyzed the
impact of eight strategic HRM practices on the financial
performance of twenty-one (21) Nigerian manufacturing
companies listed in Nigerian Stock Exchange. The study
shows that the sampled firms moderately practice strategic
HRM practices. Sun and Pan (2011) argued that high per-
formance HR practices would only be of a significant rela-
tionship with firm performance if they are associated with
employee commitment in the Chinese hotel industry.
Therefore, they urged on developing employee’s affective
commitment if business managers intend to achieve opti-
mum organizational performance through high performance
HR practices. Moreover, there is an increasing need to
implement new and better organizational practices in order
to understand HRM with respect to realities and politics
(Lucio and Stuart 2011). In addition, the collectivism-ori-
ented HRM has a significant and positive effect on firm
performance, however a moderating effect of the firm
strategy of product diversification on the relationship
between HRM practice and firm performance was also
observed. The firms involving in a low level of product
diversification strategy usually observe better positive effect
of collectivism-oriented HRM on the organizational per-
formance (Ji et al. 2012). But, there are very few researches
which have identified specific dimensions based on which
the participation of HR function in formulating and imple-
menting corporate strategies could be elucidated. Never-
theless, the capability of HR professionals in creating
organizational value set for customers (Uen et al. 2012).
More recently, four strategic HRM practices (namely,
strategic HRM alignment, line management training, career
planning system and job definition) have shown a significant
influence on organizational performance of eighteen (18)
Nigerian insurance companies with a moderate influence of
organizational climate (Sani 2012). HRM, as a strategic dis-
cipline, can play its role towards the environment-friendly
(‘green’) performance of airline industry in the form of both
indirect and direct contributions. HRM policies and practices
usually serve as indirect effects on employees’ attitudes,
which are reflected in the form of their job satisfaction,
involvement and commitment. HR policies and practices can
also ensure freedom of speech in the organizations. As a result,
employees remain confident in sharing and suggest organi-
zation-wide developments/improvements. The more the
employees share their ideas, the more chances will be there for
organization development. In contrast, effective performance
management systems, employees training, and management
development programs can serve as direct effects on achiev-
ing optimum business performance. They also highlighted
that HRM role becomes more challenging when there is a
requirement to meet both financial and environmental objec-
tives to satisfy different stakeholders of the aviation industry
(Harvey et al. 2013). However, there is a strong causal rela-
tionship among HR practices and the process, marketing and
organizational innovation activities (Ceylan 2013).
Hypothesis
Employee’s Training and Development,
and Competitive Priorities
Bartel (1994) studied the impact of training programs on
employee’s productivity and concluded that firms which
implemented new employee training programs after 1983,
observed significantly higher productivity growth between
1983 and 1986. Similarly, Dreyfus and Vineyard (1996)
collected a random sample of 747 managers working in
manufacturing organizations and identified that employee
training and education programs were significantly related
with quality performance of manufacturing products.
Moreover, Magnan et al. (1995) reported that employee’s
training programs are significantly related with flexibility
Global Journal of Flexible Systems Management
123
performance in the furniture industry. It has been argued that
just-in-time (JIT) program in operations may be succeeded if
employees are trained in the form of JIT training (Im et al.
1994). Besides, Kinnie and Staughton (1991) studied the
impact of human resource management practices in imple-
menting in manufacturing strategies in seven batch manu-
facturing firms. They reported that employee’s training
programs (e.g., technical or educational trainings) are one of
the three HRM practices, which significantly contributed
towards the successful implementation of manufacturing
strategy. Snell and Dean (1992) have also emphasized on the
importance of employee’s training in total quality manage-
ment. These quality training sessions will enable employees
to effectively use state-of-the-art manufacturing technolo-
gies. Hence, the following hypothesis is posited:
Hypothesis 1 Training and development have significant
impact on the four competitive priorities of manufacturing
performance.
Hypothesis 1a Training and development have signifi-
cant impact on cost.
Hypothesis 1b Training and development have signifi-
cant impact on flexibility.
Hypothesis 1c Training and development have signifi-
cant impact on delivery.
Hypothesis 1d Training and development have signifi-
cant impact on quality.
Decentralization and Empowerment, and Competitive
Priorities
Employee’s autonomy is now reflected in the form of
decentralization and empowerment (Spreitzer 1995), which
has been studied in the existing literature. For instance,
Dreyfus and Vineyard (1996) identified that empowerment
has significantly related with product quality performance.
Similarly, Magnan et al. (1995) examined that empower-
ment was significantly related with flexibility performance
in the furniture manufacturing industry. Moreover, Powell
(1995) has also revealed that empowerment has signifi-
cantly related with both total quality management and
overall organizational performance. Similarly, MacDuffie
(1995) also ascertained that a culture of participative work
including employee empowerment and suggestions and
collaboration were significantly related with both produc-
tivity and quality performance. On the same lines, Powell
(1995) studied the structural variables and found that the
organizations having an open culture were significantly
related with quality performance. Moreover, while study-
ing the plant performance, Keefe and Katz (1990) analyzed
that broad jobs were significantly related with quality
performance. Finally, Cooper and Kleinschmidt (1995)
concluded that the organizations having an open entrepre-
neurial culture were significantly related with new product
performance. Hence, based on the related literature, the
following hypothesis is suggested:
Hypothesis 2 Decentralization and empowerment have
significant impact on the four competitive priorities of
manufacturing performance.
Hypothesis 2a Decentralization and empowerment have
significant impact on cost.
Hypothesis 2b Decentralization and empowerment have
significant impact on flexibility.
Hypothesis 2c Decentralization and empowerment have
significant impact on delivery.
Hypothesis 2d Decentralization and empowerment have
significant impact on quality.
The linkage between HRM and organizational perfor-
mance will remain tenuous in the absence of intermediate
elements, e.g. intellectual capital (Yang and Lin 2009), or
strategic and financial controls (Govindarajan and Fisher
1990; Hoskisson and Hitt 1994; Simons 1995; Liao 2006).
However, Paauwe and Richardson (1997) have provided
with a list of contingency and/or control variables. Based
on the reviews of the related literature presented above, it
can be easily observed that almost all of the previous
studies examined a linear relationship of HRM practices on
organizational performance with one outcome variable. In
contrast, this study used a multivariate statistical approach
to investigate the said relationship in the presence of two
control variables, i.e. relationship with employees and
information sharing. Unlike financial measures indicators,
productivity related performance measures can better pre-
dict the manufacturing performance because financial
measures take account of several economic factors which
may not be controlled by any one organization alone
(Chadwick 2010). Therefore, the main objective of this
study is to present a holistic framework of the strategic
HRM–performance relationship by examining the associ-
ations between the sets of strategic HRM practices and four
‘competitive priorities’ of the manufacturing organizations.
The main idea of this study is not to test specific hypoth-
eses. Rather, to see how differences in strategic HRM
practices (called Set 1) related to the differences in the
competitive priorities (called Set 2) of manufacturing
organizations. Thus, the conceptual framework of the study
attempts to determine which subset of ‘strategic HRM
practices’ variables best relate to which subset of
the ‘competitive priorities’ variables (Leech et al. 2005).
Figure 1 depicts the research model of the present study.
Global Journal of Flexible Systems Management
123
Methodology
Sample and Data Collection
In this study, the unit of analysis is manufacturing orga-
nizations. The target population is all of the manufacturing
and industrial organizations largely operating in Sindh
Industrial Trading Estates (SITE) Karachi. On a total area
of 4,700 acres with approximately 3,000 plots, 59 go-
downs, and approximately 60.5 miles length of roads, there
were 1,020 personal goods oriented industries in the SITE
area of Karachi (including textiles = 550, allied tex-
tile = 250, silk = 140 and garments = 80), In addition,
there are 35 foodstuffs oriented industries, 65 chemicals,
300 engineering, 75 plastics, 40 pharmaceuticals industries.
However, for detailed insights of the manufacturing sec-
tors, the study adapts the classification of aforementioned
industries from the Karachi Stock Exchange (henceforth,
KSE) Website. There is no restriction on the sampled
organizations to be enlisted in KSE however, they may be
registered with Karachi Chamber of Commerce and
Industry (KCCI). This target population has been found
consistent with previous studies (e.g., Wattanasupachoke
2009). The study caters the three following classifications
of manufacturing industry in Karachi:
(a) Heavy industries (e.g., steel, oil refinery, chemicals,
engineering and ship building);
(b) Light-consumer industries (e.g., electrical goods,
toys, clothing, food-processing); and
(c) High-Tech industries (e.g., computer, business sys-
tems, microprocessors, communications equipment).
To achieve the research objectives, a sample of 218
responses was drawn from ninety organizations of sixteen
different manufacturing sectors by using simple random
sampling method. Bryman (2008), Gray (2004), Newman
(2007) and later on, Tharenou et al. (2007) explained that
in simple random sampling method ‘‘…the selection of any
given participant has no effect on the inclusion or exclusion
from the sample of other members of the population’’ (p.
54). Moreover, the primary concern in quantitative survey
studies is to make generalizations from findings (DeMar-
rais and Lapan 2004). Thus, there was a need to draw
sample in such a manner that it should be a representative
of a defined population in question of interest. Therefore,
simple random sampling method was used to collect pri-
mary data during March and April 2012 on a self-com-
pletion questionnaire. Chanda and Shen (2009)
commented, ‘‘They are the most used survey instrument for
HRM measurement’’ (p. 122).
Measures
The performance of manufacturing organizations was
assessed by the extent to which they remain succeed in
their product quality, cost efficiency savings, delivery of
finished products, and flexibility in their manufacturing
operations. Moreover, these four dependent variables have
been found consistent with previous studies (e.g., Jayaram
et al. 1999; Santos 2000; Nauhria et al. 2011; Awwad et al.
2013). All of these studies have taken these four variables
as ‘competitive priorities’ of the manufacturing strategy.
Based on a comprehensive literature review written on the
competitive priorities, Leong et al. (1990) contended that
these four competitive priorities (such as flexibility,
delivery, cost, and quality) are critical to manufacturing
performance. On the same lines, there are numerous studies
which have used these dimensions (e.g., Jayaram et al.
1. Recruitment and Selection 2. Performance Appraisal 3. Training and Development 1. Quality4. Compensations and Rewards 2. Cost Savings
Delivery.3Employment Security.5Flexibility.4Job Descriptions.6
7. Career Opportunities 8. Decentralization and Empowerment
Control Variables1. Relationship with employees 2. Information sharing
PerformanceHRM Activities
Adapted from: Jayaram, Droge and Vickery (1999)
(may be predicted after achieving four competitive priorities)
(Shortlisted from: Boselie, Dietz and Boon, 2005)
Fig. 1 The theoretical model
Global Journal of Flexible Systems Management
123
1999; Santos 2000; Nauhria et al. 2011; Awwad et al.
2013). However, some other studies have also used these
dimensions in addition to other competitive priorities too
(e.g., Ferdows and DeMeyer 1990; Ward et al. 1995, 1998;
Vickery et al. 1996; Vokurka et al. 1998;Ward and Duray
2000).
A total of 133 items representing all constructs
(including four dependent variables, eight predictors and
two control variables) were rated on a five-point Likert
scale from 1 (strongly disagree) to 5 (strongly agree).
However, almost all adapted items were reworded or
rephrased not only for the better understanding of respon-
dents but also to reflect a contextualized picture of
domestic business environment. For instance, Hoque’s
(1999, p. 425) item ‘‘Deliberate use of realistic job pre-
views during recruitment and selection’’ was rephrased
‘‘Selected candidates are explained about the challenges
and potential problems associated with the job position
‘before’ appointment’’.
Dependent Variables
Quality
Quality scale was measured through 11 items adapted from
Challis et al. (2005) and Hoque (1999). Sample items
include ‘‘We have much less lost time due to industrial
accidents than our competitors,’’ ‘‘A majority of workers
currently involved in quality circles or quality improve-
ment teams,’’ and ‘‘Finished products conform to given
specifications’’. The internal consistency coefficient
(Cronbach Alpha) for this scale was 0.84 (11 items).
Cost
Cost scale was measured through six items adapted from
Challis et al. (2005). Sample items include ‘‘We are able to
reduce costs of product inspection,’’ ‘‘Our total cost per
unit of product is much lower than our competitors,’’ and
‘‘We have extremely positive cash flow (preinvestment)’’.
The internal consistency coefficient (Cronbach Alpha) for
this scale was 0.86 (6 items).
Delivery
Delivery scale was measured through six items adapted
from Challis et al. (2005). Sample items include ‘‘We
maintain short lead time from order to delivery,’’ ‘‘We are
able to serve specific geographical markets efficiently,’’
and ‘‘We deliver in full on time (DIFOT) to our clients’’.
The internal consistency coefficient (Cronbach Alpha) for
this scale was 0.79 (6 items).
Flexibility
Flexibility scale was measured through six items adapted
from Ngo et al. (1998). Sample items include ‘‘We are able
to handle difficult/non-standard orders,’’ ‘‘Capacity can be
quickly adjusted,’’ and ‘‘We often transfer employees who
have the skills needed in other areas within the company’’.
The internal consistency coefficient (Cronbach Alpha) for
this scale was 0.81 (6 items).
Predictors
The following eight frequently-researched HRM practices
were shortlisted from a list of twenty-six (26) practices
provided by Boselie et al. (2005) (Fig. 1).
Recruitment and selection (RNS)
Recruitment and selection scale was measured through 12
items adapted from Kundu and Malhan (2007) and Hoque
(1999). Sample items include ‘‘We select personnel that fits
our culture,’’ ‘‘The candidate’s ability to be trained is one
of the major selection criteria,’’ and ‘‘Selected candidates
are explained about the challenges and potential problems
associated with the job position ‘before’ appointment’’.
The internal consistency coefficient (Cronbach Alpha) for
recruitment and selection scale was 0.78 (12 items).
Performance Appraisal (PA)
Performance appraisal scale was measured through 12
items adapted from Kundu and Malhan (2007) and Ngo
et al. (1998). Sample items include ‘‘To prevent future
unpleasant feedback, the immediate boss provides an
employee with the necessary performance feedback during
the year (if needed),’’ ‘‘Performance appraisal is done
regularly in the organization,’’ and ‘‘Employees feedback is
collected through a 360 degree feedback system’’. The
internal consistency coefficient (Cronbach Alpha) for
recruitment and selection scale was 0.81 (12 items).
Training and Development (TND)
Training and development scale was measured through 12
items adapted from Akhtar et al. (2008), Hoque (1999),
Kundu and Malhan (2007), and Ngo et al. (1998). Sample
items include ‘‘Employees normally go through extensive
training programs after every few years,’’ ‘‘There are for-
mal training programs to teach new hires the skills they
need to perform their jobs,’’ and ‘‘On-the-job training is
more important than formal education or experience with
other organizations.’’ The internal consistency coefficient
(Cronbach Alpha) for this scale was 0.90 (12 items).
Global Journal of Flexible Systems Management
123
Compensations and Rewards (CR)
Compensations and rewards scale was measured through
12 items adapted from Kundu and Malhan (2007) and Ngo
et al. (1998). Sample items include ‘‘The organization pays
competitive salaries to the employees,’’ ‘‘In determining
compensation, we emphasize the individual’s contributions
more than his/her job title,’’ and ‘‘We closely tie com-
pensation (including salary and bonuses) to seniority’’. The
internal consistency coefficient (Cronbach Alpha) for this
scale was 0.86 (12 items).
Employment Security (ES)
Employment security scale was measured through 12 items
adapted from Akhtar et al. (2008) and Ngo et al. (1998).
Sample items include ‘‘A permanent employment is guar-
anteed after completing probation,’’ ‘‘Employees can
expect to stay in the company as long as they wish,’’ and
‘‘If the company were facing economic problems,
employees would be the last to get downsized’’. The
internal consistency coefficient (Cronbach Alpha) for this
scale was 0.63 (12 items).
Job Descriptions (JD)
Job descriptions scale was measured through 12 items
adapted from Akhtar et al. (2008) and Hoque (1999).
Sample items include ‘‘A job description (JD) contains all
of the duties to be performed by an employee,’’ ‘‘Jobs are
designed to make a full use of employees’ knowledge,
skills and abilities,’’ and ‘‘I believe that my job responsi-
bilities lead me towards a positive contribution for the
organization’’. The internal consistency coefficient (Cron-
bach Alpha) for this scale was 0.87 (12 items).
Career Opportunities (CO)
Career opportunities scale was measured through six items
adapted from Akhtar et al. (2008) and Ngo et al. (1998).
Sample items include ‘‘Management provides career paths
to all concerned within the organization,’’ ‘‘Employees
know their career paths within the organization,’’ and ‘‘We
prefer to promote senior people from within rather than
hiring from outside the organization’’. The internal con-
sistency coefficient (Cronbach Alpha) for this scale was
0.67 (6 items).
Decentralization and Empowerment (DE)
Seven items were developed to measure decentralization
and empowerment. Sample items include ‘‘We use teams to
decide about production related problems,’’ ‘‘All team
members contribute to decision making,’’ and ‘‘We
encourage decentralized decision making’’. The internal
consistency coefficient (Cronbach Alpha) for this scale was
0.71 (7 items).
Control Variables
In order to improve the robustness of the hypothesized
relationships, the study controlled for the two new vari-
ables namely, relationship with employees and information
sharing. Instead of using organization age and size as
control variables, it is believed that the top management
relationship (with their employees) and sharing ‘right’
piece of information in time to all of its concerned stake-
holders are equally important to achieve organizational
objectives and manufacturing performance. The role of HR
function is very important however, the organization will
earn no significant value if the HRM are merely imitated
(Khilji and Wang 2006). Instead, it is essential for all of the
departmental managers and HR officials to remain com-
mitted as well as supportive in developing effective HRM
systems in their business enterprise. This can be achieved
by implementing the ‘right’ but ‘flexible’ strategic HRM
systems which could facilitate the business in creating
‘meaningful’ value to all of its stakeholders. All of this
could take place when employees observe an established
culture of information sharing and ‘useful’ professional
relationships with each other. Therefore, the statistical
analysis used these two control variables.
Relationship with Employees (RE)
Good relationship with employees can affect manufactur-
ing performance because it entails effective and faster
communication. As a result, they will be highly receptive
and adaptive towards any technological change. Cost-
effective technological change would introduce better ways
of manufacturing operations, which would certainly
improve product quality, more flexibility in switching
machine jobs quickly, reduce operational costs due to low
scrape rates or product wastages, and finally, on-time
delivery of finished products. All of these four competitive
priorities may be achieved if employees own their orga-
nization. They should have a firm belief that their efforts
would be appropriately and timely rewarded. This phe-
nomenon leads to meaningful relationship among
employees which could ultimately enhance manufacturing
performance.
Relationship with employees scale was measured
through 12 items adapted from Akhtar et al. (2008), Kundu
and Malhan (2007), and Ngo et al. (1998). Sample items
include ‘‘We try with great effort to build up a harmonious
employee relationship,’’ ‘‘Employees are provided with the
Global Journal of Flexible Systems Management
123
opportunity to suggest improvements in the way things are
done,’’ and ‘‘The organization regularly conducts employee
attitude surveys’’. The internal consistency coefficient
(Cronbach Alpha) for this scale was 0.90 (12 items).
Information Sharing (IS)
Information (or knowledge) sharing has a strong relation-
ship with both operational and strategic levels of HRM
practices. Fong et al. (2011) emphasized that employees
observe a successful career path if they gain access to the
required information on time together with their skills and
abilities. They, in turn, not only generate new knowledge
but also disseminate it with co-workers, thus a learning
organization may evolve. If the knowledgeable employees
leave the organization due to unsatisfactory HRM prac-
tices, they are often attracted by the rival firms with better
employment and career opportunities. The depth and
variety of knowledge, which could significantly benefit the
organization would now benefit its rival organizations
mainly because of the brain drain. Since, the knowledge-
able employees carry and share the competitive knowledge
with their new employer (potentially a rival firm), it would
enable them to contribute in achieving a sustainable com-
petitive advantage without wasting scarce resources (Lin
2007).
Information sharing scale was measured through seven
items adapted from Akhtar et al. (2008), Hoque (1999), and
Kundu and Malhan (2007). Sample items include ‘‘The
organization has a proper human resource information
system,’’ ‘‘All staff are informed about the market position,
competitive pressures and company performance,’’ and
‘‘Superiors keep open communications with employees’’.
The internal consistency coefficient (Cronbach Alpha) for
this scale was 0.84 (7 items).
Ethical Considerations
There was no tangible or intangible harm coming to any
participants of the study. Necessary steps were taken to
ensure that the identification of the respondents should not
be discernible through any means. All participants of the
study understood the aims and objectives of the research,
there was no sponsor to this research, the nature of
involvement of each participant and how long their par-
ticipation would take. Each participant also knew that their
participation was voluntary however, deeply requested but
they could withdraw from participation at any time. They
were also intimated that their privacy shall not be violated.
Moreover, it was also mentioned how the collected data
was going to be retained and any audio or video aids would
not be used for data collection. These ethical measures
were taken in conjunction with the guidelines of Dillman
(1978, 1991).
Data Analysis and Results
Data were analyzed through the 22nd version of both
Statistical Package for Social Sciences (henceforth, SPSS)
and Analysis of Moment Structure (AMOS).
Composition of the Data
The sample included 96 % male and 4 % female respon-
dents. Out of them 37.2 % were within the age of 25 and
30, between 31 and 35 (22 %) and 36 and 40 (16.5 %)
however, the remaining respondents were above 40 years
of age. The highest qualification of 40.4 % of the sample
was Master of Business Administration (MBA), 30.7 %
held first university degree (including BE/BA/BSc/BCom/
BS/MBBS/BPharm), 12.8 % possessed other masters
qualification (including MA/MSc/MCom), 6.4 % held
postgraduate research degrees (including MPhil/MS/MRes)
and 4.6 % were diploma holders. In addition, the sample
also revealed 1.8 % ACCAs and CA/CFA each and 0.5 %
with doctoral qualification (incl. PhD/DBA/DPharm).
Moreover, approximately 19 % respondents studied
mechanical and manufacturing engineering as their major
concentration in their highest qualification, supply chain
(14.2 %), marketing (12.8 %), and finance (11.5 %). Rest
of the sample revealed that they studied other different
engineering qualification including textile designing, civil
engineering and architecture, electrical and computer
engineering, chemical and process engineering, bio-medi-
cal engineering, etc. They were professionals serving at
any one of the four different levels of responsibility in their
respective organizations namely, at supervisory level
(24 %), in middle-management (59 %), in the senior
management (14 %) and 3 % of the sample were board
members too. There were 55 % respondents who were
associated in the same organization for the last 1–5 years,
for the last 6–10 years (17 %), for the last 11–15 years
(7.3 %), for the last 21–25 years (5.5 %) in the same
organization. Moreover, approximately 30 % of the total
sample ranged in between 6 and 10 years, 25.2 %
(1–5 years), and 17.4 % (10–15 years). Over 28 % of the
total sample had more than 16 years of total work
experience.
Furthermore, the study collected approximately 23 and
77 % responses from the public and private sector orga-
nizations, respectively out of which 18.3 % respondents
revealed that their organization was also enlisted at KSE.
Approximately 7 % of the total respondents were found
unaware whether the shares of their organizations are
Global Journal of Flexible Systems Management
123
traded at KSE. However, almost 75 % of the respondents
said that their organizations were not enlisted at KSE.
In addition, responses were collected from 90 different
organizations of 16 different manufacturing sectors. They
include personal goods including textiles (19.3 %), phar-
maceuticals and bio-tech (17 %), industrial engineering
(11.5 %), food producers (8.3 %), industrial metals and
mining (7.8 %), automobiles and parts (6.9 %), and bev-
erages (6 %). Rest of the sectors including chemicals,
household goods, forestry and paper, general industrials,
etc., accounted for less than 5 % each. No responses were
received from the tobacco manufacturing sector. There
were nine respondents (4.1 %) who did not highlight their
organization name. Almost 82 % of the sampled organi-
zations were operating their manufacturing facilities in
Karachi.
Removal of Univariate and Multivariate Outliers
from the Dataset
A total of 36 univariate and multivariate outliers were
detected and removed from the dataset by using standard-
ized (Z) score (cutoff value |3.00|) and CDF.CHISQ
function (Mahalanobis D2, p \ .001), respectively. Ta-
bachnick and Fidell (2007) stated ‘‘Cases with standardized
scores in excess of 3.29 (p \ .001, two-tailed test) are
potential outliers’’ (p. 73) however, the study used a more
conservative absolute value for univariate outlier detection.
It resulted in a sample of 182 useable responses for data
analysis.
Using SPSS, exploratory factor analysis was performed
to group 133 questionnaire items into the required 14
factors (i.e., 4 for dependent variables, 8 for predictors and
2 factors for the control variables). After that reliability
(Cronbach coefficient Alpha) of the measuring scale was
computed for each factor. Then AMOS was used to per-
form confirmatory factor analysis which developed a
measurement model highlighting the construct validity of
each items loaded on its respective factor. Based on the
measurement model, AMOS was further used to develop
SEM structural model to test the hypotheses.
Exploratory Factor Analysis (N = 182)
The study used ‘Principal components’ as the type of
factoring to reduce all of the questionnaire Likert-based
items into the required 14 factors based on the idea that
these ten components theoretically serve as separate HRM
practices which have an impact on the competitive priori-
ties of the manufacturing performance. The value of Kai-
ser–Meyer–Olkin Measure of Sampling Adequacy was
0.86 which clearly reflects that there are sufficient items for
each component. A minimum value of 0.70 was suggested
by Leech et al. (2005). The Bartlett’s Test of Sphericity
(Approx. v2 = 7,583.142, df = 1,891, p \ .000) depicts
that ‘‘the correlation matrix is significantly different from
an identity matrix, in which correlations between variables
are all zero’’ (Leech et al. 2005, p. 80). Tabachnick and
Fidell (2007) stated ‘‘In the Bartlett method, factor scores
correlate only with their own factors and the factor scores
are unbiased (that is, neither systematically too close nor
too far away from ‘‘true’’ factor scores)’’ (p. 651). These
fourteen components explained over 70.30 % of the total
variance.
For improving readability, the initial solution was then
rotated through varimax orthogonal rotation with Kaiser
Normalization method. In this nexus, Tabachnick and Fi-
dell (2007, p. 620) argued that ‘‘Varimax is a variance
maximizing procedure. The goal of varimax rotation is to
maximize the variance of factor loadings by making high
loadings higher and low ones lower for each factor’’. Factor
loadings less than |0.40| were omitted thus, a total of 62
items were loaded onto their respective variables having a
very strong convergent validity (Tharenou et al. 2007).
Discriminant validity was also achieved since there was
no cross-loadings in the rotated components matrix as well
as values are less than 0.70 threshold (Tharenou et al.
2007) in ‘Component Transformation Matrix’.
Reliability Analysis (N = 182)
After exploratory factor analysis, the reliability (Cronbach
Coefficient Alpha) of each of the factor was computed.
Table 1 highlights the Cronbach Alpha, eigenvalues, per-
centage of variance, cumulative percentage of variance
explained and total number of items loaded after factor
analysis under each factor. The overall reliability of 62
items loaded after exploratory factor analysis was 0.95.
Why Structural Equation Modeling is a Better
Statistical Technique than a Series of Simultaneous
Multiple Regression Models for This Study?
With a few exceptions (e.g., Ward and Duray 2000),
almost all of the previous studies have analyzed a linear
relationship between HRM practices and organizational
performance with one outcome variable. The purpose of
this study is to investigate the linkages between strategic
HRM practices and the four competitive priorities of
manufacturing performance in Karachi. These priorities
served as four separate dependent variables. However, if a
study requires to predict a set of dependent variables with
separate regression analyses, it will increase Type-I error
(Sherry and Henson 2005). As a result, ‘‘The researcher
cannot identify which of the significant results are errors
and which reflect true relationships between the variables,
Global Journal of Flexible Systems Management
123
thereby potentially invalidating the entire study’’ (Sherry
and Henson 2005, p. 38). However, Pallant (2001) argued
that Type-I error can be controlled by Bonferroni adjust-
ments where the standard Alpha level (0.05) is divided by
the total number of dependent variables (e.g., 0.05/4 gives
0.01 for this study). This new Alpha level (p \ 0.01) might
be used to test the hypotheses in case of separate regression
analysis but this will then cause an increase in Type-II error
(i.e., accepting the false null hypothesis) because as Pallant
(2001) also identified that both of these errors are inversely
proportional to each other. It means that if the study goes to
minimize the Type-I error (through Bonferroni adjust-
ments), the Type-II error will automatically increase.
Canonical correlation analysis could be an alternative for
this study (e.g., see Ahmad and Schroeder 2003) but Ta-
bachnick and Fidell (2007) argued that ‘‘Perhaps, the most
critical [theoretical] limitation [of canonical correlation
analysis] is interpretability; procedures that maximize
correlation do not necessarily maximize interpretation of
pairs of canonical variates. Therefore, canonical solutions
are often mathematically elegant but uninterpretable’’ (pp.
569–570). Therefore, structural equation modeling (SEM)
was used.
According to Hoyle (1995, p. 1), ‘‘Structural equation
modeling (SEM) is a comprehensive statistical approach to
testing hypotheses about relations among observed and
latent variables’’. Since ‘‘… traditional multivariate pro-
cedures are incapable of either assessing or correcting for
measurement error, SEM provides explicit estimates of
these error variance parameters’’ (Byrne 2010, p. 3). SEM,
also known as covariance structure analysis (Skrondal and
Rabe-Hesketh 2004) has become a standard approach for
hypotheses testing because of two prime reasons: an
increasing level of complexities and specificity of research
questions in behavioral and social sciences (e.g., Hoyle
1994; Reis and Stiller 1992) and the availability of flexible
and user-friendly computer software programs (e.g., Ben-
tler 2004; Joreskog and Sorbom 1993; Muthen 1987).
However, an in-depth understanding of both theoretical and
empirical literature in the area of study is the most
important pre-requisite for using SEM (Foster et al. 2006;
Tabachnick and Fidell 2007; Kline 2011).
Confirmatory Factor Analysis (Measurement Model)
At this stage the measurement model is tested, where the
confirmatory factor analysis is applied on the strategic
HRM practices to evaluate the construct validity. In the
present study, the measurement model consists of 27 items
that explains ten factors, namely recruitment and selection,
performance appraisal, training and development, com-
pensations and rewards, employment security, job
descriptions, career opportunities, decentralization and
empowerment, relationship with employees, and informa-
tion sharing. In contrast with Cronbach coefficient Alpha,
the composite reliability has been found to be a more
suitable indicator of construct validity which measures the
overall reliability of a collection of heterogeneous but
similar items (Fornell and Larcker 1981; Lin and Lee 2004;
Molina et al. 2007). Table 5 in Appendix I shows the
results of convergent validity and internal reliability of
constructs in addition to Cronbach Alpha, composite reli-
ability (CR), and average variance explained (AVE) sep-
arately for each of the 14 latent constructs. Overall, the CR
and AVE of both constructs are as under:
Strategic HRM practices ðCR ¼ 0:97; AVE ¼ 0:55Þ;Dimensions of competitive priorities
ðCR ¼ 0:91; AVE ¼ 0:53Þ:
Overall, the measurement is good hence with few excep-
tions, almost all of the constructs have CR and AVE well
over 0.70 and 0.50, respectively (Hair et al. 2010; Molina
et al. 2007).
Furthermore, to check for the multicollinearity between
predictors, Hair et al. (2010) argued that multicollinearity
problem will be assumed if r-value exceeds 0.90. As
indicated in the Table 2, the highest coefficient value,
namely the job descriptions and compensations and
rewards, is 0.796, which is still less than 0.90. Hence, it
Table 1 Summary of Reliability and Validity Testing
Component (N = 182) Overall
Cronbach Alpha 0.911 0.861 0.851 0.855 0.866 0.822 0.776 0.794 0.823 0.756 0.749 0.716 0.844 0.551 0.953
Eigenvalues 18.250 3.764 2.804 2.688 2.580 1.977 1.766 1.720 1.677 1.503 1.351 1.231 1.165 1.111
% of variance 9.426 6.737 6.716 5.976 5.550 4.704 4.635 4.533 4.294 4.268 3.736 3.475 3.375 2.878
Cumulative %
of variance
explained
9.426 16.163 22.879 28.855 34.404 39.109 43.744 48.276 52.570 56.838 60.574 64.049 67.424 70.302
Number of
items retained
after EFA
9 4 6 5 5 5 4 4 3 4 3 3 4 3 62
Name of
factor
TND DE RE Quality JD Cost Flexibility CO PA Delivery CR RNS IS ES
Global Journal of Flexible Systems Management
123
confirms that no multicollinearity problem exist among the
constructs in the measurement model (Hair et al. 2010; Lin
and Lee 2004).
Furthermore, Anderson and Gerbing (1982, 1988),
Fornell and Larcker (1981), and Hair et al. (2010) recom-
mended that we should construct the CFA measurement
model before the structural model is tested. Byrne (2010)
notified that the measurement model depicts the links
between the observed and unobserved variables. Five
common measures were used to measure the goodness of
fit of the measurement model. According to Byrne (2010),
Kline (2011), Loehlin (2004), Marcoulides and Schu-
macker (2001) and Segars and Grover (1998), the widely-
used measures are the ratio of x2 statistics to the degree of
freedom (CMIN/DF), goodness-of-fit index (GFI), adjusted
goodness-of-fit index (AGFI), non-normed fit index
(NNFI), comparative fit index (CFI) and root mean square
error of approximation (RMSEA). As indicated in Table 3,
the ratio of the minimum discrepancy (CMIN) to the
degree of freedom (DF) for this model was 1.55
(p \ 0.000) which is smaller than 5 as recommended by
Byrne (2010) however, Hair et al. (2010) identified that the
CFA model may have a ‘‘significant p value [of CMIN/DF]
even with good fit’’ (p. 647) if the sample size is less than
250 with 12–30 observed variables. Other model fit indices
include GFI = 0.82; CFI = 0.91; NNFI (also called
TLI) = 0.88; and RMSEA = 0.05 all exceeded the sug-
gested cut-off level as described by different authors
(shown in the Table 3). On the other hand, AGFI = 0.75
appears to be below the cut-off level of 0.80 as recom-
mended by Bagozzi and Yi (1988). For a complex model
with a number of observed variables, it is unrealistic to
have the values of GOF measures above 0.90 (Hair et al.
2010). The combination of these results suggests that the
CFA (measurement model) appears to show a good fit
between the observed and unobserved variables (Byrne
2010).
Structural Relationship Between Specific HRM
Practices and Competitive Priorities of Manufacturing
Performance
The structural model highlights relations among the
unobserved variables (Byrne 2010). Table 3 also shows the
overall results of the structural model analysis. The struc-
tural model (shown in Fig. 2) has a good fit, determined by
the Chi square index (CMIN/DF) (1.23) and other indices
(GFI = 0.85; AGFI = 0.80; NNFI = 0.95; CFI = 0.96;
RMSEA = 0.03). All the model-fit indices reasonably
exceeded their recommended value, suggesting that the
structural model portrays an acceptable fit to the sample
drawn (Bagozzi and Yi 1988; Browne and Cudeck 1993;
Lin and Lee 2005; Sit et al. 2009). Both measurement and
structural models are recursive in nature. In fact, the
recursive model is a kind of structural models with two
rudimentary features: (a) it stipulates the direction of cause
Table 2 SEM correlations between strategic HRM practices
CO CR DE ES IS JD PA RE RNS TND
CO 1
CR 0.703 1
DE 0.542 0.403 1
ES 0.377 0.448 0.159 1
IS 0.403 0.282 0.665 0.106 1
JD 0.673 0.796 0.550 0.410 0.386 1
PA -0.082 -0.047 -0.166 -0.174 -0.116 -0.085 1
RE 0.626 0.691 0.366 0.525 0.302 0.677 -0.060 1
RNS 0.473 0.755 0.324 0.156 0.432 0.660 0.141 0.617 1
TND 0.526 0.625 0.508 0.322 0.598 0.659 0.081 0.718 0.636 1
Table 3 Model fit indices
Goodness-of-fit measures CMIN/DF GFI AGFI NNFI CFI RMSEA (PCLOSE)
Recommended value \3.00a C0.80a C0.80b Close to 1c C0.90a B0.05d ([0.05)
CFA modele 1.55 0.82 0.76 0.88 0.91 0.05 (0.133)
Structural modele 1.23 0.85 0.80 0.95 0.96 0.03 (0.998)
a Byrne (2010), b Bagozzi and Yi (1988), c Bentler and Bonett (1980), d Browne and Cudeck (1993), e the model is recursive
Global Journal of Flexible Systems Management
123
from one direction only (Byrne 2010) i.e., unidirectional
(Kline 2011); and (b) ‘‘their disturbances are uncorrelated’’
(Kline 2011, p. 106).
Hypothesis Testing
Because of a traditional way of hypothesis testing, the
statistical significance of all the structural parameters val-
ues was estimated in order to determine the validity of the
hypothesized regression paths. The results show that five
hypotheses have been found statistically significant to
predict the competitive priorities of the manufacturing
performance. However, rests of the 35 hypotheses were not
supported. Table 6 in Appendix II provides details of SEM
regression paths, their standardized regression weights (in
isolation), standard error, critical ratio, p-value, remarks
(whether the particular hypothesis is supported).
The critical ratio was calculated by dividing the
unstandardized regression weights by its standard error.
Byrne (2010) explained that the critical ratio ‘‘operates as a
z-statistic in testing that the estimate is statistically dif-
ferent from zero’’ (p. 68). She added that the value of
critical ratio should be [±1.96 to make the hypothesis
supported. Therefore, for the sake of a deeper under-
standing, the last column in Table 6 in Appendix II cal-
culates by subtracting the critical ratio from ±1.96. In fact,
it shows ‘the value’ which is further needed to make the
hypothesis supported (in other words, rejection of its null
hypothesis). For example, for H1 (cost / TND),
1.96–1.226 = 0.734. It means that very slight increase in
the critical ratio will eventually make this hypothesis
supported too. Similarly, there are 12 hypotheses shown in
Table 6 in Appendix II which could be supported at
p \ 0.05 if the sample size is further increased. As dis-
cussed earlier, the sample size is 182 after removing both
univariate and multivariate outliers from the dataset. This
could be a reason of having a list of insignificant paths in
Table 6 in Appendix II (Byrne 2010). Moreover, Lei and
Wu (2007) highlighted that SEM is a large sample tech-
nique thus the sample size should be greater than 200 for
SEM purpose. In contrast, Hair et al. (2010) recommended
the sample size between 50 and 400 will be reasonably
appropriate for SEM.
Testing Statistical Significance of Hypothesis in SEM:
A Contradictory Issue
I was advised by Mahfooz A. Ansari, a professor of
International Management and HRM and Organizational
Studies at University of Lethbridge, Canada (personal
communication, July 15, 2014) that in SEM, we don’t need
to bother about a number of unsupported hypotheses as
long as we have plausible explanations for those unsub-
stantiated hypotheses (as discussed below). Noticeably,
Byrne (2010) argued that testing statistical significance of
hypothesis in SEM has been a contradictory issue at least
for the last four decades. Both researchers and reviewers
merely emphasize on testing the individual hypothesis (in
isolation) for its statistical significance and often condone
its collective significance (i.e., the goodness of fit of the
structural model) which reflects practical significance with
respect to its baseline theory. Recently, Lam and Maguire
(2012, p. 6) explained ‘‘In general, statistical tests for the
overall model fit and P values of parameter estimates are
less important in SEM than in univariate regression mod-
els’’ because of three prime reasons. First, all parameters
are simultaneously entered in SEM therefore, the signifi-
cance of each parameter estimates should be interpreted in
the context of the entire model instead of testing statistical
significance of each hypothesis in isolation. Even in this
study, out of 40 hypotheses, only five hypotheses have been
found statistically significant at p \ 0.10 but from the
context of the overall SEM model, one can easily observe
that the squared multiple correlations (SMC) for all of the
four dependent variables (flexibility, cost, quality, and
delivery) are very strong i.e., 100, 85, 78 and 64 % of
variance respectively are explained by its predictors (see
Table 4). Second, the confirmatory facet of the model will
be weak if the significance of individual parameter is
considered instead of the theory behind the model. Finally,
as Lei and Wu (2007) have also contended, SEM is a large
sample technique thus it is generally affected by sample
size.
In response to the issue of statistical significance, dif-
ferent methodologists (e.g., Cohen 1994; Kirk 1996;
Schmidt 1996; Thompson 1996) have been found in favor
of phasing out the dubious practice of testing null
hypothesis. Even Carver (1978) has argued up to this extent
that that statistical significance testing is useless thus
should be eliminated. In addition, Byrne (2010) have also
identified that a task force of American Psychological
Association (APA) was working on the same issue. As a
result, she contended that. ‘‘…the end of statistical signif-
icance testing relative to traditional statistical methods may
soon be a reality’’ (p. 72). In addition, there are a number of
authors (e.g.,Harlow et al. 1997; Johnson 1999; Thompson
Table 4 Squared multiple correlations (structural model)
Dependent variables Estimate
Flexibility 1.03
Cost 0.85
Quality 0.78
Delivery 0.64
Global Journal of Flexible Systems Management
123
1999; Krueger 2001; Gliner et al. 2002; Schmidt and
Hunter 2002; Cumming and Finch 2005; Levine et al.
2008) who have further detailed the problems with null
hypothesis significance testing (henceforth, NHST). The
above references are only provided to serve as a strong
support for the current study where the structural model
reflects good model fit indices with only five supported
hypotheses. Moreover, discussing NHST further is beyond
the scope of the current study.
Discussion
The purpose of this study was to examine the impact of
strategic human resource management practices on the four
competitive priorities of the manufacturing performance in
Karachi. The structural model supports the theory which
holds that HRM practices have practically significant
impact on flexibility, cost, quality, and delivery. All of the
eight HRM practices demonstrated a very strong squared
multiple correlations i.e., 100, 85, 78 and 64 % when
controlled for information sharing and relationship with
employees. The goodness-of-fit (GOF) of the structural
model demonstrated very low discrepancy over degree of
freedom for the structural model (CMIN/DF = 1.55,
p \ .000) showing a good reflection of the theory too.
After satisfying multivariate assumptions, 133 Likert-
based self-completion questionnaire items were reduced to
the required 14 dimensions including 8 factors for the
strategic HRM practices, 2 factors for the control variables,
and 4 factors for the four competitive priorities of manu-
facturing performance. Only 62 items (Alpha = 0.953)
were loaded after exploratory factor analysis. No missing
values were identified during missing values analysis
(MVA) however, 36 items were loaded onto their respec-
tive constructs in the measurement model after confirma-
tory factor analysis (CFA). Construct, convergent, and
discriminant validity were also verified with the help of
composite reliabilities and average variance explained by
each latent construct. Based on the measurement model,
the structural model was developed showing a good model
fit indices with the sample drawn. The impact of the five
statistically significant hypotheses on the four competitive
priorities when controlled for information sharing and
relationship with employees in the structural model is
discussed below.
Figure 2 shows the SEM structural model showing
significant and insignificant paths between eight strategic
human resource management practices and the four com-
petitive priorities of the manufacturing performance when
controlled for ‘information sharing’ and ‘relationship with
employees’. Out of eight strategic HRM practices, the
performance appraisal (composite reliability = 0.71) has
been found a statistically significant predictor of the
delivery component (standardized regression weight =
0.526, p = 0.093) as well as of the quality component
Bold line = Significant path at p<0.10Do�ed lines = Insignificant pathControl variables: Informa�on Sharing and Rela�onship with Employees.SMC denotes Squared Mul�ple Correla�ons (Structural Model).
Performance Appraisal
Career Opportuni�es
Decentraliza�on and Empowerment
Recruitment and Selec�on
Employment Security
Compensa�ons and Rewards
Job Descrip�ons
Training and Development
Flexibility (SMC=1.03)
Cost (SMC=0.85)
Delivery (SMC=0.64)
Quality (SMC=0.78)
0.526
0.552
1.285
1.897
1.126
0.604
0.568
-0.479
0.371
0.439
0.263
0.082
0.254
-0.014
-0.418
0.367-1.078
-1.831
1.681
0.322
1.24
1.835
-0.721
0.178
0.506
0.636
-0.61
-0.429
-0.213
-0.221
-0.612
-0.173
Fig. 2 Structural model of strategic HRM practices and the four competitive priorities of manufacturing performance
Global Journal of Flexible Systems Management
123
(0.552, p = 0.058) of the competitive priorities of the
manufacturing performance in Karachi. In a developing
country like Pakistan, domestically-created, predomi-
nantly, family business do not share the details of the
performance assessment criteria with employees rendering
them unaware about the key performance standards duly
acceptable in the eyes of the management of the time. This
situation gets more complicated under new management.
People at the senior positions bring their own performance
standards, which are usually not shared with all concerned
employees. Particularly, in the context of the manufactur-
ing organizations, the study revealed that performance
appraisal is a significant predictor of both delivery and
quality aspects of the manufacturing performance. In fact,
if employees are made aware of the performance assess-
ment criteria in detail well in advance and if their related
queries or confusions are also answered on time then they
would be in a position to embark on understanding the
importance of error-free working practices as much as
possible. It would further lead them to play their active role
in on-time manufacturing and delivering of the finished
products to clients. It is therefore, equally important for
both managers and employees to internalize the signifi-
cance of performance appraisal in delivering products on
the ‘right’ time.
Moreover, the manufacturing organizations should
develop a regular annual but meaningful practice of per-
formance management system coupled with constructive
feedback which should be free from personal biases and
predisposition of evaluators. It will possibly inculcate a
culture among employees in which they would not only
ensure better handling of manufacturing parts or compo-
nents during production cycle but also help the entire
operations in reducing the scrape rates (wastages). Col-
lectively, the percentage output of each production
employee may increase if it is further reinforced by
appropriate financial incentives for non-managerial staff
and both financial and non-financial incentives for
employees having managerial or administrative responsi-
bilities. An appraisal report may highlight that an employee
needs immediate training of handling sensitive and costly
spare parts (e.g., airbag sensors, integrated circuit chips,
etc.) in the ‘right’ way. To ensure quality of an air-bag
sensor to be used in an automotive vehicle, all concerned
employees must be explained with the help of disaster
summary videos that an appropriate handling of these very
sensitive components (according to an international stan-
dards) will minimize the rate of wastages and serious road
accidents. Even if the finished equipment (e.g., an air-bag
sensor) is rejected at a final functional test (FFT) stage
during six sigma, it will increase the production cost with
no financial gains. With the help of different audio and
video evidences captured through an established
surveillance system throughout the manufacturing facili-
ties, the quality of manufacturing products could be
ensured in the light of other contextual variables (e.g.,
employees motivation, knowledge, skills and abilities they
hold, job satisfaction, list of responsibilities they hold,
occupational stress, etc.). Therefore, both delivery and
quality dimensions serve as competitive priorities of the
manufacturing concerns which are largely affected by a
meaningful performance management or appraisal system.
In addition, sharing the required piece of information to
all concerned stakeholders of the organization will make
sure that instead of taking organization as a whole, the
management agenda should now be concerned with
developing core competencies, identifying critical resour-
ces as well as key success factors. For this, the frequencies
between both line and staff managers should be aligned in
such a manner that they could not only achieve strategic fit
among functional policies but also the strategic needs of
the underlined business in Pakistan. While criticizing the
role of HRM professionals, Tzafrir et al. (2007) urged that
it is also very important for them to understand the core
business of their organizations. Sharing the ‘right’ piece of
information to HRM professionals will also equip them in
aligning and devising HR policies and practices. This is
however, a challenging task in the manufacturing sector of
Pakistan. It requires an immediate attention of the top
management because once the ‘right’ candidate has been
introduced in the business s/he should be capable enough to
achieve ‘strategic fit’ between related functions and busi-
ness strategies. In fact, line managers often remain skep-
tical about HRM role in firm performance. This is one of
the major reasons why a number of HR positions are
occupied and enjoyed by non-HR professionals (Long and
Ismail 2008; Huselid and Becker 2011).
Additionally, employment security (composite reliabil-
ity = 0.51) is another HRM practices which has been
found statistically significant to predict cost (1.285,
p = 0.072), flexibility (1.897, p = 0.084), and quality
(1.126, p = 0.061). The human resource department
should offer a probationary period for all new incumbents
however, a fulltime permanent employment may be offered
after a satisfactory performance during this period. There
are a number of crucial employee’s considerations with
respect to their ‘employment security’. These beliefs
include less rate of employee’s turnover, employees with
moderate performance should not be laid off, reduced rate
of mental distress, eager to switch to another job in the
same industry due to unmanageable or unwanted workload,
their wish to stay in the organization as long as they wish,
dismissal of an individual because of a high political
influence through trade unions, employees should receive
formal warnings and show-cause notices before termina-
tion, and dismissals of employees without notice who have
Global Journal of Flexible Systems Management
123
been found guilty in involving gross-misconduct at
workplace.
Besides, there is a widespread hope in the manufactur-
ing employees at operational level that the firm will retain
them as long as it is possible even in the economic
downturn and the immediate superiors should maintain an
annual confidential or performance appraisal reports with
an impartial, objective, and quantitative analysis. In fact,
when an employee has a satisfactory answer to all of these
beliefs, then a sense of employment security is emerged
which leads to their flexibility in not only accepting addi-
tional workload willfully but also be an active team player
in different manufacturing units. They welcome frequent
job rotations thereby remained motivated to perform on
consistent basis however, they would also need to earn
hands-on training in managing a number of new respon-
sibilities in the manufacturing operations. Based on their
motivation and active participation, one could float con-
siderable amount of innovative ideas to reduce operational
costs. By the passage of time, these employees would
reflect on the prevailing situation in the business unit and
explore better learning and development opportunities.
These positive attitude towards organization development
would ultimately help them design and manufacture quality
products with the help of state-of-the-art technology and
top management financial and emotional support. In short,
the higher the level of employment security, the better the
chances to have skills-wise flexible staff who could work
together to marginally reduce operations cost and improved
quality.
Furthermore, the involvement of HR management in
strategic decision making process has been a controversial
issue (Hubben 1983). Instead of making themselves dis-
tinct or separate in their organization, the HR management
should work in closed collaboration with the corporate
management if HR managers intend to gain a professional
image in the manufacturing sector of Pakistan. Andersen
et al. (2007) have also revealed that HRM function was
moderately involved in strategic decision-making process
of both manufacturing and service firms. Therefore, the
senior management should include HR professionals in the
boardroom decision making process alongside with senior
line managers (Buyens and De Vos 2001) if they intend
their HR function to play an effective role as a ‘strategic
partner’ (Pritchard 2010). Moreover, the efforts of HR
managers should however, be aligned with the operational
needs of line managers too.
Indeed, the philosophy of attracting and employing the
‘right’ person for the ‘right’ job at the ‘right’ time leads
towards developing the dynamic capability (e.g., Ambro-
sini et al. 2009; McKelvie and Davidsson 2009; Chien and
Tsai 2012) of organizational members in the form of
double-loop learning (Argyris 1977, 1991, 2002).
However, it is a challenging task for the senior manage-
ment to institutionalize a healthy atmosphere of corporate-
wide learning across all levels of management. It usually
serves the purpose of organization development through
human process, strategic change, techno-structural as well
as HR management interventions. The cogent idea is to
realize the indispensable involvement of HR officials in
crafting and implementing corporate strategies in the
manufacturing organizations in Karachi.
In addition, the business managers should take other six
HRM practices into their consideration while managing
HRM affairs to better attain the four competitive priorities.
The first level of concentration should be on introducing
the ‘right’ candidates at the ‘right’ time thereby they need
to be equipped with all necessary information to manage
the designated tasks. Only then, at the second level, the
‘investment perspective’ of strategic HRM should be
emphasized to a large extent. In a for-profit organization
operating in Karachi, having more investment on those
individuals who are indeed, not the ‘best fit’ people for the
official duties, may not reveal the desired competitive
results on time. Therefore, it is essential thus beneficial to
provide the ‘right’ individuals with a series of learning and
development opportunities, empowerment (within capac-
ity) as well as meaningful relationship with other
employees.
Noticeably, at one side the organization will be making
serious attempts to develop future leaders (called succes-
sion planning) and on the other side, in addition to other
important contextual variables, these ‘right and best-fitted’
individuals will demonstrate their affective organizational
commitment leads to optimum organizational performance.
Thus, a portfolio of effective and successful management
people may institutionalize SMART (specific, measurable,
attainable, realistic, time-bound) and innovative ideas into
practice which could in turn, help the organization in
sustaining their competitive advantage. For this, the per-
ception a management holds regarding its employees is
very essential for the effective implementation of HRM
practice (Jackson 2002). In Karachi, the line managers
should also particularly urge upon internalizing this belief
that the competitive priorities of the manufacturing strat-
egies (or even organizations) have a direct relationship
with strategic HRM variables used in the study.
Notwithstanding, the ‘right’ individuals should be
assigned those tasks in which they have developed com-
petencies. It is imperative to note that other dimensions
(e.g., compensations and rewards, career opportunities, and
information sharing) are equally important towards com-
petitive priorities if the senior management intends to
observe performance from these individuals too. The
relationship between aforementioned two orthogonal sets
of variables has been found consistent with previous
Global Journal of Flexible Systems Management
123
studies (e.g., Dyer and Reeves 1995; Becker and Gerhart
1996; Paauwe and Richardson 1997; Rogers and Wright
1998).
Apart from discussing NHST for each of the forty
hypothesized relationships in isolation, one needs to mainly
concentrate on overall goodness of fit indices to predict
whether the sampled data fit with the theory. Without
exaggeration, it can be summarized that all of the eight
strategic HRM practices have a good model fit to predict
the four competitive priorities of the manufacturing per-
formance when controlled for information sharing and
relationship with employees in the SEM structural model.
Limitations
The contributions of this empirical research should be
viewed in the light of four limitations. Firstly, this research
took only major manufacturing sector by ignoring their
sub-sectors. As a result, the results may not be generalized
to its sub-sectors. Secondly, the data could be inflated by
single-source bias as only one employee filled in both parts
of dependent and independent variables. Over 76 %
responses were collected from the middle and top man-
agement of the Karachi organizations. This was an attempt
to reduce the presence of response bias in the dataset
(Podsakoff et al. 2003). Thirdly, the last column in Table 6
in Appendix II reveals that there are 12 individual
hypotheses which may turn out to be a significant relationship
(at p \ 0.05) if the sample size is further increased. Finally,
this study incorporated only four commonly-used competitive
priorities to determine manufacturing performance.
Conclusion
The main purpose of this study is to investigate the impact
of strategic HRM practices on the four competitive prior-
ities (such as ‘quality’, ‘cost’, ‘flexibility’ and ‘delivery’)
of manufacturing performance in Karachi. The results of
the structural model show that eight strategic HRM prac-
tices have a strong impact on the four competitive priorities
of the manufacturing performance in Karachi when con-
trolled for relationship with employees and information
sharing. Based on these results, it can be concluded that the
senior management should particularly emphasize on the
‘investment perspective’ of strategic HRM by introducing
‘right’ individuals in their business and then facilitate them
with all necessary information to help them take well-
informed and rational decisions. In Karachi, the human
resource practitioners should primarily concentrate on
developing their prowess in gaining in-depth business
acumen as well as an improved capacity to effectively
communicate with other functional areas of the
organization.
Practical Implications
Findings from this study should be beneficial for both
human resource and operations managers in developing
countries such as Pakistan, who intend to capitalize on the
competitive priorities of the manufacturing performance
through the adoption of strategic HRM practices.
Almost all of the previous studies investigated a bivar-
iate relationship between HRM practices and firm perfor-
mance. On the contrary, this study is one of the first to
examine the ‘multivariate’ relationship between the sets of
strategic HRM practices and the competitive priorities of
sixteen manufacturing sectors in Karachi. The main idea of
this study was to empirically ascertain whether HRM and
operations functions can be linked together to optimum
manufacturing performance in Karachi.
Moreover, it is important to note that almost all of the
business schools in Karachi currently offer taught and
research-based higher education in HRM discipline having
only one taught course on strategic HRM. This study will
be very useful for young recent graduates and human
resource professionals in identifying the major HRM
functions and practices which significantly impact on the
competitive priorities of the manufacturing performance in
Karachi.
Since this study involves responses from a number of
officials having supervisory, middle management, senior
management and board level responsibilities, it is believed
that HR professionals will earn adequate insights how to
manage their functions and their practices. It will also
assist them in making well-informed decisions in making
appropriate investments in their under-developed functions
as well as help them avoid unnecessary investments in
insignificant domains associated with HR. For example, the
SEM results revealed that ‘performance appraisal’ and
‘employment security’ are the most influencing predictors
towards predicting the competitive priorities of the manu-
facturing performance in Karachi. Therefore, the manage-
ment of these manufacturing companies should pay a close
heed on crafting and institutionalizing cost-effective and
efficient strategic HRM practices. Thus, this approach will
surely contribute towards the effective implementation of
the ‘Investment Perspective’ of strategic HRM in the
context of Karachi—the largest business hub of Pakistan.
Moreover, in the context of Karachi, even the line
managers should now start to realize that they are also
equally responsible for their human resource (Thornhill
and Saunders 1998) not only in safeguarding the social
and official interests of subordinates but also their learning
and development needs throughout their stay in the
Global Journal of Flexible Systems Management
123
manufacturing organization. Because there is no doubt that
this is the human resource of an organization that could
ensure whether they would like to serve either as a con-
ventional employee or human capital. The replacement of a
conventional employee is frequently made available when
need arises. However, the replacement of a human capital
has been found very difficult. In fact, this is the type of
employees who not only contribute towards the growth of
organization but also ensure a long-term survival of busi-
ness and continued success. Therefore, the business man-
agers in Karachi manufacturing organizations should not
overlook the notion of how the logic of ‘black box’
explains the HR contribution in sustaining the competitive
advantage. More recently, Ceylan (2013) made an initial
attempt to unveil the black-box of the relationship between
Strategic HRM practices and organizational performance.
Finally, it is also believed that the senior management of
the operations function in manufacturing organizations
under major industrial sectors of Karachi [e.g., automobiles
and parts, personal goods (including textiles), pharmaceu-
ticals and bio-tech, food producers, etc.] will get benefit
from this study in the sense that they could analyze the
‘realistic’ variance in between the expected and actual
implementation of HRM practices. Thus, the chief oper-
ating officers may generate a number of innovative ideas
mainly with respect to the culture of the domestic work-
force in order to accelerate the organizational performance
while being within the constraints posed by the business
community as well as uncontrollable factors in the city.
Directions for Future Research
Future studies should concentrate on how these four
competitive priorities (cost, quality, delivery and flexi-
bility) can be managed to gain and sustain competitive
advantage. Based on the findings of Awwad et al.
(2013), some more variables (e.g., sustainability, product
technology, customer perspective, and innovation) may
be included—as used by Nauhria et al. (2011). In
addition, the future studies may also concentrate on the
cause-and-effect analysis between the relationship of
competitive priorities and the competitive advantage
particularly in other major industrial cities of Pakistan,
e.g. Faisalabad (a hub for textile manufacturing), Lahore,
Sialkot (world-famous city known for its top quality
sport goods), etc.
Acknowledgments The author wishes to thank Prof. Stephen
Procter (Alcan Chair of Management, Head of HRM, Work and
Employment Subject Group, Newcastle University, UK) and Prof.
Mahfooz A. Ansari (University of Lethbridge, Canada) for their
expert opinions. Moreover, the author also wishes to thank Dr. Imran
Khan (Assistant Professor, Research and Graduate Studies, Faculty of
Education and Learning Sciences, IQRA University, Karachi), Dr.
M. Azam (Associate Dean, Department of Management Sciences,
IQRA University, Karachi), and Mr. Muhammad Muzammil Ghayas
(PhD Scholar) for their useful comments and assistance in producing
the initial draft of the manuscript.
Appendix I
Table 5 Construct measurement summary: CFA and composite reliability
Constructs Indicator Items Standardized
loading
Alpha, CR,
and AVE
Strategic HRM practices (CR = 0.97; AVE = 0.55)
Training and development TND_3.1 We conduct Training Needs Assessment (TNA)
before designing a training program
0.783 Alpha = 0.911
CR = 0.80
TND_3.11 The senior management believes in developing a
‘learning organization’
0.689 AVE = 0.49
TND_3.3 Employees normally go through extensive training
programs after every few years
0.676
TND_3.2 We systematically train and develop our personnel 0.659
Decentralization and empowerment DE_9.2 We use teams to decide about production related
problems
0.879 Alpha = 0.861
DE_9.3 We regularly use teams to perform various tasks 0.860 CR = 0.88
DE_9.5 All team members contribute to decision making 0.807 AVE = 0.65
DE_9.7 Generally our employees own what they are made
responsible
0.659
Global Journal of Flexible Systems Management
123
Table 5 continued
Constructs Indicator Items Standardized
loading
Alpha, CR,
and AVE
Relationship with employees RE_5.3 Employees enjoy taking fair benefits of knowledge,
skills and abilities from one another
0.802 Alpha = 0.851
CR = 0.76
RE_5.4 Employees maintain a high level of trust among
each other
0.705 AVE = 0.52
RE_5.2 Employees maintain a high level of understanding
among each other
0.653
Job descriptions JD_7.11 My JD provides an appropriate level of freedom
(autonomy) of managing my tasks
0.773 Alpha = 0.866
CR = 0.78
JD_7.9 With the help of JD, I am fully aware of what I am
responsible and accountable for
0.762 AVE = 0.55
JD_7.12 According to my JD, my manager provides
constructive feedback to improve my performance
0.679
Career opportunities CO_8.4 Employees’ career aspirations within the company
are known by their immediate supervisors
0.864 Alpha = 0.794
CR = 0.72
CO_8.6 Employees who desire promotion have more than
one potential position they could be promoted to
0.707 AVE = 0.48
CO_8.5 We prefer to promote senior people from within
rather than hiring from outside the organization
0.436
Performance appraisal PA_2.4 Performance evaluation has a lot to do with one’s
salary
0.806 Alpha = 0.823
PA_2.3 The assessment criteria of performance evaluation
are shared among employees well in advance
0.668 CR = 0.71
AVE = 0.55
Recruitment and selection RNS_1.7 Selected candidates are explained about the
challenges and potential problems associated with
the job position ‘before’ appointment
0.808 Alpha = 0.716
CR = 0.68
AVE = 0.51RNS_1.5 We select personnel that fit our culture 0.613
Compensations and rewards CR_4.8 We intend to keep a large salary difference between
high and low performers in the same position
0.749 Alpha = 0.749
CR = 0.66
CR_4.10 The salary package is intended to promote employee
retention
0.657 AVE = 0.50
Information sharing IS_10.3 The organization has a proper human resource
information system
0.931 Alpha = 0.844
CR = 0.91
IS_10.4 Employees can use HRIS to obtain related updates
(e.g., earned/casual/sick leaves status)
0.888 AVE = 0.83
Employment security ES_6.4 Employees that perform modestly do not get fired 0.745 Alpha = 0.551
ES_6.1 There is a probationary period for all newly-
appointed employees (regardless of their position)
0.414 CR = 0.51
AVE = 0.36
Dimensions of competitive priorities (CR = 0.91; AVE = 0.53)
Quality Quality_11.10 We have very high customer satisfaction 0.932 Alpha = 0.855
Quality_11.4 We maintain the level of high performance products 0.711 CR = 0.81
AVE = 0.69
Cost Cost_12.4 We are able to reduce overhead costs 0.807 Alpha = 0.822
Cost_12.1 We are able to reduce costs of product inspection 0.765 CR = 0.80
Cost_12.6 We have extremely positive cash flow
(preinvestment)
0.694 AVE = 0.57
Flexibility Flexibility_14.3 We ensure the level to make products to orders 0.657 Alpha = 0.776
Flexibility_14.5 We are able to scale production up and down
quickly
0.635 CR = 0.59
AVE = 0.42
Global Journal of Flexible Systems Management
123
Appendix II
Table 5 continued
Constructs Indicator Items Standardized
loading
Alpha, CR,
and AVE
Delivery Delivery_13.6 We deliver in full on time (DIFOT) to our clients 0.822 Alpha = 0.756
Delivery_13.3 We maintain short lead time from order to delivery 0.442 CR = 0.59
AVE = 0.44
Composite reliability (CR) of scale = (P
standardized loading)2/(P
standardized loading)2 ?P
indicator measurement error) where, indicator
measurement error = 1 - standardized loading
Average variance explained (AVE) = (P
squared standardized loading)/(P
squared standardized loading ?P
indicator measurement error)
Table 6 Construct measurement summary: CFA and composite reliability
Hypothesis Regression path SRW Std. error Critical ratio p-value Remarks More value required to
make the path significant
H1a Cost / TND 0.604 0.453 1.226 0.22 Not supported 0.734
H1b Flexibility / TND 0.568 0.545 0.775 0.439 Not supported 1.185
H1c Delivery / TND -0.479 0.159 -1.135 0.256 Not supported 3.095
H1d Quality / TND 0.371 0.244 0.926 0.354 Not supported 1.034
H2a Cost / DE 0.439 0.587 1.083 0.279 Not supported 0.877
H2b Flexibility / DE 0.263 0.707 0.434 0.664 Not supported 1.526
H2c Delivery / DE 0.082 0.173 0.281 0.779 Not supported 1.679
H2d Quality / DE 0.254 0.318 0.769 0.442 Not supported 1.191
H3a Cost / RE -1.082 0.988 -1.405 0.16 Not supported 3.365
H3b Flexibility / RE -1.418 1.188 -1.238 0.216 Not supported 3.198
H3c Delivery / RE 0.384 0.29 0.696 0.486 Not supported 1.264
H3d Quality / RE -0.742 0.534 -1.185 0.236 Not supported 3.145
H4a Cost / PA -0.014 0.319 -0.044 0.965 Not supported 2.004
H4b Flexibility / PA -0.418 0.391 -0.838 0.402 Not supported 2.798
H4c Delivery / PA 0.526 0.124 1.678 0.093* Supported Not applicable
H4d Quality / PA 0.552 0.188 1.894 0.058* Supported Not applicable
H5a Cost / CR -1.078 1.134 -0.998 0.318 Not supported 2.958
H5b Flexibility / CR -1.831 1.414 -1.099 0.272 Not supported 3.059
H5c Delivery / CR 1.681 0.458 1.578 0.115 Not supported 0.382
H5d Quality / CR 0.322 0.608 0.369 0.712 Not supported 1.591
H6a Cost / ES 1.285 1.515 1.799 0.072* Supported Not applicable
H6b Flexibility / ES 1.897 1.881 1.729 0.084* Supported Not applicable
H6c Delivery / ES 0.367 0.439 0.727 0.467 Not supported 1.233
H6d Quality / ES 1.126 0.847 1.874 0.061* Supported Not applicable
H7a Cost / IS -0.395 0.373 -0.788 0.431 Not supported 2.748
H7b Flexibility / IS -0.472 0.454 -0.626 0.531 Not supported 2.586
H7c Delivery / IS 0.54 0.131 1.259 0.208 Not supported 0.701
H7d Quality / IS 0.156 0.201 0.384 0.701 Not supported 1.576
H8a Cost / RNS 1.24 1.435 1.572 0.116 Not supported 0.388
H8b Flexibility / RNS 1.835 1.773 1.521 0.128 Not supported 0.439
H8c Delivery / RNS -0.721 0.468 -1.148 0.251 Not supported 3.108
H8d Quality / RNS 0.178 0.739 0.291 0.771 Not supported 1.669
Global Journal of Flexible Systems Management
123
References
Ahmad, S., & Schroeder, R. G. (2003). The impact of human resource
management practices on operational performance: Recognizing
country and industry differences. Journal of Operations Man-
agement, 21, 19–43.
Akdere, M. (2009). A multi-level examination of quality-focused
human resource practices and firm performance: Evidence from
the US healthcare industry. The International Journal of Human
Resource Management, 20(9), 1945–1964.
Akhtar, S., Ding, D. Z., & Ge, G. L. (2008). Strategic human resource
management practices and their impact on company perfor-
mance in Chinese enterprises. Human Resource Management,
47(1), 15–32.
Ambrosini, V., Bowman, C., & Collier, N. (2009). Dynamic
capabilities: An exploration of how firms renew their resource
base. British Journal of Management, 20, 9–24.
Andersen, K. K., Cooper, B. K., & Zhu, C. J. (2007). The effect of
SHRM practices on perceived firm financial performance: Some
initial evidence from Australia. Asia Pacific Journal of Human
Resources, 46(2), 168–179.
Anderson, J. C., & Gerbing, D. W. (1982). Some methods for respecifying
measurement models to obtain unidimensional construct measure-
ment. Journal of Marketing Research, 19(4), 453–460.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation
modeling in practice: A review and recommended two-step
approach. Psychological Bulletin, 103(3), 411–423.
Argyris, C. (1977). Double loop learning in organizations. Harvard
Business Review, 55(5), 115–125.
Argyris, C. (1986). Skilled incompetence. Harvard Business Review,
64(5), 74–79.
Argyris, C. (1991). Teaching smart people how to learn. Harvard
Business Review, 69(3), 99–109.
Argyris, C. (2002). Teaching smart people how to learn. Harvard
Business Review, 4(2), 4–15.
Awwad, A. S., Al Khattab, A. A., & Anchor, J. R. (2013). Competitive
priorities and competitive advantage in Jordanian manufacturing.
Journal of Service Science and Management, 6, 69–79.
Ayanda, O. J., & Sani, A. D. (2011). Strategic human resource
management and organizational performance in the Nigerian
manufacturing sector: An empirical investigation. International
Journal of Business and Management, 6(9), 46–56.
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural
equation model. Journal of Academy of Marketing Science,
16(1), 74–94.
Baird, L., & Meshoulam, I. (1988). Managing two fits of strategic
human resource management. Academy of Management Review,
13(1), 116–128.
Barney, J. B. (1991). Firm resources and sustained competitive
advantage. Journal of Management, 17(1), 99–120.
Bartel, A. P. (1994). Productivity gains from the implementation of
employee training programs. Industrial Relations, 33(4),
411–425.
Becker, B., & Gerhart, B. (1996). The impact of human resource
management on organizational performance: Progress and
prospects. Academy of Management Journal, 39(4), 779–801.
Becker, B. E., & Huselid, M. A. (2006). Strategic human resources
management: Where do we go from here? Journal of Manage-
ment, 32(6), 898–925.
Becker, B. E., & Huselid, M. A. (2010). SHRM and job design:
Narrowing the divide. Journal of Organizational Behavior,
31(2–3), 379–388.
Becker, B. E., Huselid, M. A., & Pickus, P. S. (1997). HR as a source
of shareholder value: Research and recommendations. Human
Resource Management, 36(1), 39–47.
Beltran-Martın, I., & Roca-Puig, V. (2013). Promoting employee
flexibility through HR practices. Human Resource Management,
52(5), 645–674.
Bentler, P. M. (2004). EQS 6 structural equations program manual.
Encino, CA: Multivariate Software, Inc.
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and
goodness of fit in the analysis of covariance structures.
Psychological Bulletin, 88(3), 588–606.
Boselie, P., Dietz, G., & Boon, C. (2005). Commonalities and
contradictions in HRM and performance research. Human
Resource Management Journal, 15(3), 67–94.
Brahma, S. S., & Chakraborty, H. (2011). From industry to firm
resources: Resource-based view of competitive advantage. IUP
Journal of Business Strategy, 8(2), 7–21.
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing
model fit. Newbury Park, CA: Sage Publications.
Bryman, A. (2008). Social research methods. New York: Oxford
University Press.
Buyens, D., & De Vos, A. (2001). Perceptions of the value of the HR
function. Human Resource Management Journal, 11(3), 70–89.
Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic
concepts, applications, and programming. New York:
Routledge.
Carver, R. P. (1978). The case against statistical significance testing.
Harvard Educational Review, 48, 378–399.
Table 6 continued
Hypothesis Regression path SRW Std. error Critical ratio p-value Remarks More value required to
make the path significant
H9a Cost / CO 0.506 0.474 1.044 0.296 Not supported 0.916
H9b Flexibility / CO 0.636 0.574 0.874 0.382 Not supported 1.086
H9c Delivery / CO -0.61 0.174 -1.405 0.16 Not supported 3.365
H9d Quality / CO -0.429 0.265 -1.05 0.294 Not supported 3.010
H10a Cost / JD -0.213 0.597 -0.43 0.667 Not supported 2.390
H10b Flexibility / JD -0.221 0.721 -0.299 0.765 Not supported 2.259
H10c Delivery / JD -0.612 0.229 -1.322 0.186 Not supported 3.282
H10d Quality / JD -0.173 0.321 -0.431 0.666 Not supported 2.391
SRW standardized regression weights
* p \ 0.10
Global Journal of Flexible Systems Management
123
Ceylan, C. (2013). Commitment-based HR practices, different types
of innovation activities and firm innovation performance. The
International Journal of Human Resource Management, 24(1),
208–226.
Chadwick, C. (2010). Theoretic insights on the nature of performance
synergies in human resource systems: Toward greater precision.
Human Resource Management Review, 20(2), 85–101.
Challis, D., Samson, D., & Lawson, B. (2005). Impact of technolog-
ical, organizational and human resource investments on
employee and manufacturing performance: Australian and New
Zealand evidence. International Journal of Production
Research, 43(1), 81–107.
Chanda, A., & Shen, J. (2009). HRM strategic integration and
organizational performance. New Delhi: Sage Publications.
Chien, S. Y., & Tsai, C.-H. (2012). Dynamic capability, knowledge,
learning, and firm performance. Journal of Organizational
Change Management, 25(3), 434–444.
Cohen, J. (1994). The earth is round (p \ .05). American psychol-
ogist, 49(12), 997–1003.
Collings, D. G., Demirbag, M., Mellahi, K., & Tatoglu, E. (2010).
Strategic orientation, human resource management practices and
organizational outcomes: Evidence from Turkey. The Interna-
tional Journal of Human Resource Management, 21(14),
2589–2613.
Collis, D. J., & Montgomery, C. A. (1995). Competing on resources:
Strategy in the 1990s. Harvard Business Review, 73(4),
118–128.
Collis, D. J., & Montgomery, C. A. (2008). Competing on resources.
Harvard Business Review, 86(7–8), 140–150.
Cooper, R. G., & Kleinschmidt, E. J. (1995). Benchmarking the firm’s
critical success factors in new product development. Journal of
Product Innovation Management, 12(5), 374–391.
Cumming, G., & Finch, S. (2005). Confidence intervals and how to
read pictures of data. American Psychological Association,
60(2), 170–180.
D’Aveni, R. A., Canger, J. M., & Doyle, J. J. (1995). Coping with
hypercompetition: Utilizing the new 7S’s framework [and
executive commentary]. The Academy of Management Execu-
tive, 9(3), 45–60.
De Menezes, L. M., Wood, S., & Gelade, G. (2010). The integration
of human resource and operation management practices and its
link with performance: A longitudinal latent class study. Journal
of Operations Management, 28(6), 455–471.
Delery, J. E., & Shaw, J. D. (2001). The strategic management of
people in work organizations: Review, synthesis, and extension.
In A. Chanda & J. Shen (Eds.), HRM strategic integration and
organizational performance (p. 75). London: Sage Publications.
DeMarrais, K., & Lapan, S. D. (2004). Foundations for research:
Methods of inquiry in education and the social sciences.
Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
Dillman, D. A. (1978). Mail and telephone surveys. New York:
Wiley.
Dillman, D. A. (1991). The design and administration of mail surveys.
Annual Review of Sociology, 17(1), 225–249.
Dimba, B., & K’Obonyo, P. (2009). The effect of strategic human
resource management practices on performance of manufactur-
ing multinational companies in Kenya: Moderating role of
employee cultural orientations and mediating role of employee
motivation. Proceedings of the International Conference on
Human Capital Management in University of Nairobi, 2009,
403–408.
Dreyfus, P. L., & Vineyard, M. L. (1996). Impact of employee
relations on quality of products in a manufacturing environment.
In Proceedings of the 1996 Annual Decision Sciences Institute
Conference (pp. 1365–1366).
Dyer, L., & Reeves, T. (1995), Human resource strategies and firm
performance: What do we know and where do we need to go? In
Paper Presented at the 10th World Congress, May. Washington,
DC: International Industrial Relations Association.Eisenhardt, K., & Martin, J. (2000). Dynamic capabilities: What are
they? Strategic Management Journal, 21(10–11), 1105–1121.
Ferdows, K., & DeMeyer, A. (1990). Lasting improvements in
manufacturing performance: In search of new theory. Journal of
Operations Management, 9(2), 168–184.
Fong, C.-Y., Ooi, K.-B., Tan, B. I., Lee, V.-H., & Chong, A. Y.-L.
(2011). HRM practices and knowledge sharing: An empirical
study. International Journal of Manpower, 32(5/6), 704–723.
Fornell, C., & Larcker, D. F. (1981). Evaluating structuring equation
models with unobservable variables and measurement error.
Journal of Marketing Research, 18(1), 39–50.
Foster, J. J., Barkus, E., & Yavorsky, C. (2006). Understanding and
using advanced statistics. London: Sage Publications.
Gliner, J. A., Leech, N. L., & Morgan, G. A. (2002). Problems with
null hypothesis significance testing (NHST): What do the
textbooks say? The Journal of Experimental Education, 71(1),
83–92.
Gooderham, P., Perry, E., & Ringdal, K. (2008). The impact of
bundles of strategic human resource management practices on
the performance of European firms. International Journal of
Human Resource Management, 19(11), 2041–2056.
Govindarajan, V., & Fisher, J. (1990). Strategy, control systems, and
resource sharing: Effects on business-unit performance. Acad-
emy of Management Journal, 33(2), 259–285.
Grant, R. M. (1991). The resource-based theory of competitive
advantage: Implications for strategy formulation. California
Management Review, 33(3), 114–135.
Gray, D. E. (2004). Doing research in the real world. London: Sage
Publications.
Groysberg, B., McLean, A. N., & Reavis, C. (2006). Delivering
strategic human resource management (industry and back-
ground note). Boston: Harvard Business School Press.
Guerrero, S., & Barraud-Didier, V. (2004). High-involvement prac-
tices and performance of French firms. The International Journal
of Human Resource Management, 15(8), 1408–1423.
Guest, D. E. (1997). HRM and performance: A review and agenda.
The International Journal of Human Resource Management,
8(3), 263–276.
Guest, D. E., Michie, J., Sheehan, M., Conway, N., & Metochi, M.
(2000). Effective people management. London: Chartered Insti-
tute and Personnel and Development (CIPD).
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010).
Multivariate data analysis. Prentice Hall.
Harlow, L. L., Mulaik, S. A., & Steiger, J. H. (Eds.). (1997). What if
there were no Significance tests?. Mahwah, NJ: Erlbaum.
Harvey, G., Williams, K., & Probert, J. (2013). Greening the airline
pilot: HRM and the green performance of airlines in the UK. The
International Journal of Human Resource Management, 24(1),
152–166.
Hayes, R. H., & Wheelwright, S. C. (1984). Restoring our competitive
edge: Competing through manufacturing. New York: Wiley.
Hoque, K. (1999). Human resource management and performance in
the UK hotel industry. British Journal of Industrial Relations,
37(3), 419–443.
Hoskisson, R. E., & Hitt, M. A. (1994). Downscoping: Taming the
diversified firm. New York: Oxford University Press.
Hoyle, R. H. (1994). Introduction to the special section: Structural
equation modeling in clinical research. Journal of Consulting
and Clinical Psychology, 62(3), 427.
Hoyle, R. H. (1995). Structural equation modeling: Concepts, issues,
and applications. London: Sage Publications.
Global Journal of Flexible Systems Management
123
Hubben, H. (1983). What line management expect of HR managers.
Human Resource Planning, 6(3), 153–157.
Huselid, M. A. (1995). The impact of human resource management
practices on turnover, productivity, and corporate financial
performance. Academy of Management Journal, 38(3),
635–872.
Huselid, M. A., & Becker, B. E. (2011). Bridging micro and macro
domains: Workforce differentiation and strategic human
resource management. Journal of Management, 37(2),
421–428.
Huselid, M. A., Jackson, S. E., & Schuler, R. S. (1997). Technical and
strategic human resource management effectiveness as determi-
nants of firm performance. Academy of Management Journal,
40(1), 171–188.
Ichniowski, C., Shaw, K., & Prennushi, G. (1997). The effects of
human resource management practices on productivity: A study
of steel finishing lines. The American Economic Review, 87(3),
291–313.
Im, J. H., Hartman, S. J., & Bondi, P. J. (1994). How do JIT systems
affect human resource management? Production and Inventory
Management Journal, 35(1), 1–4.
Islam, M. Z., & Siengthai, S. (2010). Human resource management
practices and firm performance improvement in Dhaka Export
Processing Zone (DEPZ). Research and Practice in Human
Resource Management, 18(1), 60–77.
Jackson, T. (2002). The management of people across cultures:
Valuing people differently. Human Resource Management,
41(4), 455–475.
Jamestown, ND: Northern Prairie Wildlife Research Center Home
Page (Version 16SEP99). http://www.npwrc.usgs.gov/resource/
1999/statsig/statsig.htm.
Jayaram, J., Droge, C., & Vickery, S. K. (1999). The impact of human
resource management practices on manufacturing performance.
Journal of Operations Management, 18(1), 1–20.
Ji, L., Tang, G., Wang, X., Yan, M., & Liu, Z. (2012). Collectivistic-
HRM, firm strategy and firm performance: An empirical test. The
International Journal of Human Resource Management, 23(1),
190–203.
Jimenez-Jimenez, D., & Sanz-Valle, R. (2008). Could HRM support
organizational innovation? The International Journal of Human
Resource Management, 19(7), 1208–1221.
Johnson, D. H. (1999). The insignificance of statistical significance
testing. Journal of Wildlife Management, 63(3), 763–772.
Joreskog, K. G., & Sorbom, D. (1993). LISREL 8: Structural equation
modeling with the SIMPLIS command language. Hillsdale, NJ:
Erlbaum.
Keefe, J. H., & Katz, H. C. (1990). Job classifications and plant
performance in the auto industry. Industrial Relations, 29(1),
111–118.
Khan, M. A. (2010). Effects of human resource management practices
on organizational performance—An empirical study of oil and
gas industry in Pakistan. European Journal of Economics,
Finance and Administrative Sciences, 24, 157–175.
Khilji, S. E., & Wang, X. (2006). ‘Intended’ and ‘implemented’
HRM: The missing linchpin in strategic human resource
management research. The International Journal of Human
Resource Management, 17(7), 1171–1189.
Kinnie, N. J., & Staughton, R. V. W. (1991). Implementing
manufacturing strategy: The human resource contribution.
International Journal of Operations and Production Manage-
ment, 11(9), 24–40.
Kirk, R. E. (1996). Practical significance: A concept whose time has
come. Educational and Psychological Measurement, 56(5),
746–759.
Kline, R. B. (2011). Principles and practice of structural equation
modeling. New York: The Guilford Press.
Koch, M. J., & McGrath, R. G. (1996). Improving labor productivity:
Human resource management policies do matter. Strategic
Management Journal, 17(5), 335–354.
Krueger, J. (2001). Null hypothesis significance testing—On the
survival of a flawed method. American Psychological Associa-
tion, 56(1), 16–26.
Kundu, S. C., & Malhan, D. (2007). HRM practices in insurance
companies: A study of Indian and multinational companies. In
Proceedings of the 13th Asia Pacific Management Conference,
2007, Melbourne, Australia (pp. 472–488).
Lam, T. Y., & Maguire, D. A. (2012). Structural equation modeling:
Theory and applications in forest management. International
Journal of Forestry Research, 2012, 1–16. doi:
10.1155/2012/263953.
Lei, P. W., & Wu, Q. (2007). Introduction to structural equation
modeling: Issues and practical considerations. Educational
Measurement: Issues and Practice, 26(3), 33–43.
Leech, N. L., Barrett, K. C., & Morgan, G. A. (2005). SPSS for
intermediate statistics: Use and interpretation. Mahwah, NJ:
Lawrence Erlbaum Associates, Inc.
Lengnick-Hall, C. A., & Lengnick-Hall, M. L. (1988). Strategic
human resources management: A review of the literature and a
proposed typology. Academy of Management Review, 13(3),
454–470.
Leong, G. K., Snyder, D. L., & Ward, P. T. (1990). Research in the
process and content of manufacturing strategy. OMEGA, 18(2),
109–122.
Levine, T. R., Weber, R., Hullett, C., Park, H. S., & Lindsey, L. L. M.
(2008). A critical assessment of null hypothesis significance
testing in quantitative communication research. Human Com-
munication Research, 34(2), 171–187.
Liao, Y. (2006). Human resource management control system and
firm performance: A contingency model of corporate control.
The International Journal of Human Resource Management,
17(4), 716–733.
Lin, H. F. (2007). Knowledge sharing and firm innovation capability:
An empirical study. International Journal of Manpower, 28(3/4),
315–332.
Lin, H. F., & Lee, G. G. (2004). Perceptions of senior managers
toward knowledge sharing behaviour. Management Decision,
42(1), 108–125.
Lin, H.-F., & Lee, G.-G. (2005). Impact of organizational learning
and knowledge management factors on e-business adoption.
Management Decision, 43(2), 171–188.
Loehlin, J. C. (2004). Latent variable models: An introduction to
factor, path, and structural equation analysis. Mahwah, NJ:
Lawrence Erlbaum Associates, Inc.
Long, C. S., & Ismail, W. K. B. W. (2008). Human resource
competencies: A study of the HR professionals in manufacturing
firms in Malaysia. International Management Review, 4(2),
65–76.
Lucio, M. M., & Stuart, M. (2011). The state, public policy and the
renewal of HRM. The International Journal of Human Resource
Management, 22(18), 3661–3671.
MacDuffie, J. P. (1995). Human resource bundles and manufacturing
performance: Organizational logic and flexible production
systems in the world auto industry. Industrial and Labor
Relations Review, 48(2), 197–221.
Madhok, A., Li, S., & Priem, R. L. (2010). The resource-based view
revisited: Comparative firm advantage, willingness-based isolat-
ing mechanisms and competitive heterogeneity. European Man-
agement Review, 7(2), 91–100.
Magnan, G. M., Vickery, S. K., & Droge, C. (1995). The use of
human resource strategies to support manufacturing flexibility.
In Proceedings of the 1995 Annual Decision Sciences Institute
Conference (pp. 1332–1334).
Global Journal of Flexible Systems Management
123
Marcoulides, G. A., & Schumacker, R. E. (2001). New developments
and techniques in structural equation modeling. Mahwah, NJ:
Lawrence Erlbaum Associates, Inc.
McKelvie, A., & Davidsson, P. (2009). From resource base to
dynamic capabilities: An investigation of new firms. British
Journal of Management, 20, 63–80.
Molina, L. M., Montes, J. L., & Ruiz-Moreno, A. (2007). Relation-
ship between quality management practices and knowledge
transfer. Journal of Operations Management, 25(3), 682–701.
Muthen, B. (1987). LISCOMP: Analysis of linear statistical equation
with a comprehensive measurement model. Mooresville, IN:
Scientific Software, Inc.
Nauhria, Y., Pandey, S., & Kulkarni, M. S. (2011). Competitive
priorities for Indian car manufacturing industry (2011–2020) for
global competitiveness. Global Journal of Flexible Systems
Management, 12(3 and 4), 9–20.
Newman, W. L. (2007). Basics of social research: Qualitative and
quantitative approaches. Boston: Pearson Education.
Ngo, H.-Y., Turban, D., Lau, C.-M., & Lui, S.-Y. (1998). Human
resource practices and firm performance of multinational corpo-
rations: Influences of country origin. The International Journal
of Human Resource Management, 9(4), 632–652.
Normann, R., & Ramırez, R. (1993). From value chain to value
constellation: Designing interactive strategy. Harvard Business
Review, 71(4), 65–77.
Paauwe, J., & Richardson, R. (1997). Strategic human resource
management and performance. International Journal of Human
Resource Management, 8(3), 257–262.
Pallant, J. (2001). SPSS survival manual. Philadelphia: Open
University Press.
Piansoongnern, O. (2013). Flexible Leadership for Managing Tal-
ented Employees in the Securities Industry: A Case Study of
Thailand, Global Journal of Flexible Systems Management,
14(2), 107–113.
Podsakoff, P. M., MacKenzie, S. B., Jeong-Yeon, L., & Podsakoff, N.
P. (2003). Common method biases in behavioral research: A
critical review of the literature and recommended remedies.
Journal of Applied Psychology, 88, 879–903.
Porter, M. E. (1985). Competitive advantage: Creating and sustaining
superior performance. New York: Free Press.
Porter, M. E., & Kramer, M. R. (2011). Creating shared value.
Harvard Business Review, 89(1/2), 62–77.
Powell, T. C. (1995). Total quality management as competitive
advantage: A review and empirical study. Strategic Management
Journal, 16(1), 15–37.
Pritchard, K. (2010). Becoming an HR strategic partner: Tales of
transition. Human Resource Management Journal, 20(2), 175–188.
Reis, H. T., & Stiller, J. (1992). Publication trends in JPSP: A three-
decade review. Personality and Social Psychology Bulletin,
18(4), 465–472.
Rogers, E. W., & Wright, P. M. (1998). Measuring organizational
performance in strategic human resource management: Prob-
lems, prospects, and performance information markets. Human
Resource Management Review, 8(3), 311–331.
Sani, A. D. (2012). Strategic human resource management and
organizational performance in the Nigerian insurance industry:
The impact of organizational climate. Business Intelligence
Journal, 5(1), 8–20.
Segars, A. H., & Grover, V. (1998). Strategic information systems
planning success: An investigation of the construct and its
measurement. MIS Quarterly, 22(2), 139–163.
Santos, F. C. (2000). Integration of human resource management and
competitive priorities of manufacturing strategy. International
Journal of Operations and Production Management, 20(5),
610–628.
Schmidt, F. L. (1996). Statistical significance testing and cumulative
knowledge in psychology: Implications for training of research-
ers. Psychological Methods, 1(2), 115–129.
Schmidt, F., & Hunter, J. (2002). Are there benefits from NHST?
American Psychological Association, 57(1), 65–71.
Senge, P. M. (2006). The fifth discipline: The art and practice of the
learning organization. New York: Currency, Doubleday.
Sharma M. K., Sushil and Jain P. K. (2010). Revisiting Flexibility in
Organizations: Exploring its Impact on Performance, Global
Journal of Flexible Systems Management, 11(3), 51–68.
Sherry, A., & Henson, R. K. (2005). Conducting and interpreting
canonical correlation analysis in personality research: A user-
friendly primer. Journal of Personality Assessment, 84(1),
37–48.
Simons, R. (1995). Levers of control: How managers use innovative
control systems to drive strategic renewal. Boston: Harvard
Business School Press.
Sit, W. Y., Ooi, K. B., Lin, B., & Chong, A. Y. L. (2009). TQM and
customer satisfaction in Malaysia’s service sector. Industrial
Management and Data Systems, 109(7), 957–975.
Skaggs, B. C., & Youndt, M. (2004). Strategic positioning, human
capital and performance in service organizations: A customer
interaction approach. Strategic Management Journal, 25(1),
85–99.
Skrondal, A., & Rabe-Hesketh, S. (2004). Interdisciplinary Statistics,
generalized latent variable modeling: Multilevel, longitudinal,
and structural equation models. New York: Chapman and Hall/
CRC.
Slack, N., Chambers, S., Harland, C., Harrison, A., & Johnston, R.
(1995). Operations management. London: Pitman.
Snell, S. (1992). Control theory in strategic human resource
management: The mediating effect of administrative informa-
tion. Academy of Management Journal, 35(2), 292–327.
Snell, S., & Dean, J. (1992). Integrated manufacturing and human
resource management: A human capital perspective. Academy of
Management Journal, 35(3), 467–504.
Som, A. (2008). Innovative human resource management and
corporate performance in the context of economic liberalization
in India. The International Journal of Human Resource
Management, 19(7), 1278–1297.
Spreitzer, G. M. (1995). Psychological empowerment in the work-
place: Dimensions, measurement, and validation. Academy of
Management Journal, 38(5), 1442–1465.
Stavrou, E. T., & Brewster, C. (2005). The configurational approach
to linking strategic human resource management bundles with
business performance: Myth or reality? Management Review,
16(2), 186–201.
Sun, L., & Pan, W. (2011). Differentiation strategy, high-performance
human resource practices, and firm performance: Moderation by
employee commitment. The International Journal of Human
Resource Management, 22(15), 3068–3079.
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate
statistics. New York: Pearson.
Teece, D., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and
strategic management. Strategic Management Journal, 18(7),
509–533.
Tharenou, P., Donohue, R., & Cooper, B. (2007). Management
research methods. Cambridge, MA: Cambridge University
Press.
Thompson, B. (1996). Research news and comment: AERA editorial
policies regarding statistical significance testing: three suggested
reforms. Educational Researcher, 25(2), 26–30.
Thompson, B. (1999). If statistical significance tests are broken/
misused, what practices should supplement or replace them?
Theory and Psychology, 9(2), 165–181.
Global Journal of Flexible Systems Management
123
Thornhill, A., & Saunders, M. N. K. (1998). What if line managers
don’t realize they’re responsible for HR? Lessons from an
organization experiencing rapid change. Personnel Review,
27(6), 460–476.
Tsui, A., & Pearce, J. (1997). Alternative approaches to employee–
organization relationship: Does investment in employees pay
off? Academy of Management Journal, 40(5), 1089–1121.
Tyson, S., & York, A. (2000). Essentials of HRM. New Delhi:
Butterworth-Heinemann.
Tzafrir, S. S., Meshoulam, I., & Baruch, Y. (2007). HRM in Israel:
New challenges. The International Journal of Human Resource
Management, 18(1), 114–131.
Uen, J. F., Ahlstrom, D., Chen, S., & Tseng, P. (2012). Increasing
HR’s strategic participation: The effect of HR service quality
and contribution expectations. Human Resource Management,
51(1), 3–23.
Vickery, S. K., Droge, C., & Markland, R. E. (1996). Dimensions of
competitive strength in manufacturing: An analysis of compet-
itive priorities in the furniture industry. Journal of Operations
Management, 15, 317–330.
Vlachos, I. P. (2009). The effects of human resource practices on firm
growth. International Journal of Business Science and Applied
Management, 4(2), 17–34.
Vokurka, R. J., O’Leary-Kelly, S., & Flores, B. (1998). Approaches to
manufacturing improvement: Use and performance implications.
Production and Inventory Management Journal, 39(2), 42–48.
Walton, R. E. (1985). Towards a strategy of eliciting employee
commitment based on policies of mutuality. In R. E. Walton &
P. R. Lawrence (Eds.), HRM: Trends and challenges (pp.
35–65). Boston: Harvard University Press.
Ward, P. T., & Duray, R. (2000). Manufacturing strategy in context:
Environment, competitive strategy and manufacturing strategy.
Journal of Operations Management, 18, 123–138.
Ward, P. T., Duray, R., Leong, G. K., & Sum, C. C. (1995). Business
environment, operations strategy, and performance: An empir-
ical study of Singapore manufacturers. Journal of Operations
Management, 13(2), 99–116.
Ward, P. T., McCreery, J. K., Ritzman, L. P., & Sharma, D. (1998).
Competitive priorities in operations management. Decision
Sciences, 29(4), 1037–1048.
Warner, M. (2012). Whither Chinese HRM? Paradigms, models and
theories. The International Journal of Human Resource Man-
agement, 23(19), 3943–3963.
Wattanasupachoke, T. (2009). Strategic human resource management
and organizational performance: A study of Thai enterprises. The
Journal of Global Business Issues, 3(2), 139–148.
Wernerfelt, B. (1984). A resource-based view of the firm. Strategic
Management Journal, 5(2), 171–180.
Wernerfelt, B. (1995). A resource-based view of the firm: Ten years
after. Strategic Management Journal, 16(3), 171–174.
Williams, M. J. (1997). Agility in learning: An essential for evolving
organizations-and people. Harvard Management Update, 2(5),
3–5.
Wilson, R. M. S., & Gilligan, C. (2005). Strategic marketing
management, planning, implementation and control. Oxford:
Elsevier Butterworth Heinemann.
Wright, P. M., & Gardner, T. M. (2000). Theoretical and empirical
challenges in studying: The HR practice-firm performance
relationship. Working Paper. European Institute for Advanced
Studies in Management. Fontainebleau: INSEAD.
Wright, P. M., Gardner, T. M., & Moynihan, L. M. (2003). The
impact of HR practices on the performance of business units.
Human Resource Management Journal, 13(3), 21–36.
Wright, P. M., & McMahan, G. (1992). Theoretical perspectives for
strategic human resources management. Journal of Management,
18(2), 295–320.
Wright, P. M., & Snell, S. A. (1998). Toward a unifying framework
for exploring fit and flexibility in strategic human resource
management. The Academy of Management Review, 23(4),
756–772.
Yang, C., & Lin, C. Y. (2009). Does intellectual capital mediate the
relationship between HRM and organizational performance?
Perspective of a healthcare industry in Taiwan. The International
Journal of Human Resource Management, 20(9), 1965–1984.
Zhou-ling, X. (2009). Impact of university’s optimal human resource
management practices on organizational performance. Systems
Engineering-Theory and Practice, 29(11), 112–122.
Key Questions
i. What is the holistic (multivariate) impact of strategic human
resource management practices on the competitive priorities
(quality, flexibility, delivery, and cost) of the manufacturing
performance in Karachi?
ii. Which specific strategic HRM practices predict well the
manufacturing performance in Karachi?
iii. Is it possible to validate the investment perspective of
strategic HRM in the context of the manufacturing
organizations in Karachi?
Muhammad Shahnawaz Adil In the United
Kingdom, Shahnawaz Adil has served different
SMEs, MNCs, as well as British government on
interim projects. He also worked on different
management consultancy projects with UK-based
multinational organizations and a few academic
projects with world’s top-ranked universities e.g.
London Business School (UK). He had the expo-
sure of working in diverse multicultural environment therefore, his
life has been influenced and shaped by a number of successful busi-
ness leaders, entrepreneurs and renowned British and French uni-
versity professors. Besides corporate experience, he has worked as a
fulltime lecturer at Mohammad Ali Jinnah University, Karachi and an
invited visiting professor at Shaheed Zulfikar Ali Bhutto Institute of
Science and Technology (SZABIST), Karachi. Currently, he has been
serving as a fulltime assistant professor of management and strategies
at IQRA University Karachi since August 2008. Mr Shahnawaz Adil
is currently a Ph.D scholar and holds an M.Phil degree from Pakistan,
an AMBA, EQUIS, and AACSB-accredited MBA degree from
Newcastle University (UK), a postgraduate diploma (PGD) in Com-
puter Studies from London, B.Sc (Honors) in Computing and Infor-
mation Systems from London Guildhall University (UK) and a three-
year Diploma of Associate Engineer in Mechanical Technology from
Pakistan. He holds seven distinctions in his academic career and has
clinched first position at Sindh Board of Technical Education Karachi.
In addition, he has been serving as an official alumni ambassador of
Newcastle University (UK) in Pakistan since April 2007 and has been
featured in well-reputed British academic magazines twice. He has
been enjoying membership with Harvard Business Publishing (USA)
as a premier educator for higher education in Pakistan since February
2012. He can be best reached at [email protected].
Global Journal of Flexible Systems Management
123