strategic human resource management practices and competitive priorities of the manufacturing...

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


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


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


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.


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


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’’.



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’’.



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’’.


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



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


123

opportunity to suggest improvements in the way things are

done,’’ and ‘‘The organization regularly conducts employee

attitude surveys’’. The internal consistency coefficient


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’’.



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


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,


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


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


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


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


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


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


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


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


123

Table 5 continued


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


123

Appendix II

Table 5 continued


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


123

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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

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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


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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].


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