義守大學管理學院管理博士班

Postgraduate Program in Management

I-Shou University

博 士 論 文

高績效工作系統自相矛盾特質之解密：

工作要求資源模式觀點

Disentangling the Paradoxical Nature of High

Performance Work Systems: A Perspective of the

Job Demands-Resources Model

指導教授： 高月慈 博士 林鉦棽 博士 研 究 生： 陳媛玲 中 華 民 國 104 年 7 月

i

In memory of my dearest father

Ching-Huei Chen

who lived his life as a hero and a role model for the principles of

kindness, integrity, and persistence.

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Acknowledgements

Six Years’ Worth of Thanks

So many people deserve a note of thanks for making this dissertation possible,

rewarding, and (I hope) successful. First and foremost, I give incalculable thanks to

Professor Julia Lin, Professor Cheng-Chen Timothy Lin, and Professor Yueh-Tzu Kao,

incalculable thanks. Not only are they talented scholars and dedicated advisors, but

they have also become a family to me. Our time together was simply the best. I thank

them for saying yes, for caring as deeply about the research as I do, for their giving,

and for being such a super league of mentors and friends. I also thank Karen Liu and

S. J. Chuang for proofreading the survey items as well as my committee members

especially Professor Shyh-Jer Chen and Professor Hsi-An Shih, whose innovative and

inspirational expertise and spirit lend sophistication to this research.

Finally, I am enormously appreciative of the eager willingness of Sean Huang,

Joanne Chen, Helen Lien, and my beloved family to devote some of their valuable

time to accompany me on my great and grand PhD adventure, and for all they have

done—and still do—for me.

While the task is challenging, the potential rewards are significant.

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Abstract

In the 21st century industrial environment, human resource management (HRM) has

been widely recognized as the key determinant of organizational competitive

advantage. Recently, high performance work systems (HPWS) have received

considerable attention from scholars and practitioners who value a bundled system of

HR practices that will benefit organizations in promoting employee value and

performance. However, there exist both positive and negative effects of

HPWS-employee outcomes linkages. Thus, this study aims to explore the possible

mechanisms and boundary conditions of HPWS implementation. First, relying on the

job demands and job resources perspectives, this study proposes a cross-level

moderated-mediation framework to disentangle the paradoxical nature of HPWS.

Second, drawing on the job demands-resources (JD-R) theory, this study elucidates

how work engagement and burnout, two opposing mechanisms, mediate the

relationships between HPWS and employee outcomes. Simultaneously, employee

proactive behaviors matter as organizations enact HPWS. Third, through seeking

resources, seeking challenges, and reducing demands, employees are able to reshape

HPWS. Job crafting theory provides the theoretical underpinning for depicting HPWS

and job crafting moderated-mediation effects on employee well-being, eventually

leading to both positive and negative employee working attitudes and behaviors.

Overall, based on an integrated comprehensive perspective taken from the JD-R

theory and job crafting theory, this study brings insights in answering above three

research issues. Data were obtained from 240 employees and 45 supervisors in

Taiwan. The HPWS data was aggregated to the group level, testing (1) the mediating

effects of work engagement and burnout on the relationships between HPWS and

employee attitudinal and behavioral outcomes, respectively; and (2) the

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moderated-mediation effects between HPWS and employee job crafting behaviors on

HPWS-employee outcomes relationships via work engagement and burnout,

respectively. No supports has been found for the mediating hypotheses. Yet the

empirical results lend strong support to the moderated-mediating hypotheses, showing

that (1) job crafting moderates the mediating effect of the cross-level relationship

between HPWS and employee job satisfaction, affective commitment, and person-job

fit through work engagement; and (2) the mediating effect of the cross-level

relationship between HPWS and employee intention to leave, work-family conflict,

and self-handicapping through burnout, such that the mediating effect is stronger

when the level of job crafting is high rather than low. Theoretical, practical

implications as well as limitations and future research directions are discussed.

KEYWORDS: high performance work systems (HPWS), job demands-resources

(JD-R) theory, work engagement, burnout, job crafting

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摘 要

在二十一世紀的產業環境中，人力資源管理已成為企業創造競爭優勢的關鍵。近

年來，高績效工作系統受到學者與實務工作者的高度重視，認為透過一套高績效

人力資源管理系統有助於組織提高員工的價值與績效。然而，高績效工作系統與

員工績效間之關聯存在著正反兩面的結果。爲此，本研究旨在剖析高績效工作系

統運作中可能的中介機制與影響因素。本研究目的有三：首先，以工作要求-資

源的觀點，提出一個跨層次調節式中介的研究架構與方法，從而深入探討高績效

工作系統自相矛盾的特質。其次，基於工作要求-資源理論，本研究提出兩個對

立的員工健康幸福中介機制，討論工作投入及精疲力竭對於高績效工作系統與員

工的工作態度與行為之間的關係。在組織推動高績效工作系統時，員工積極主動

的行為也自有其重要性，員工透過尋找資源、追求挑戰及減輕要求來重新評估聚

積於個人身上之高績效工作系統。藉由工作形塑之立論，本研究也同時討論工作

形塑與高績效工作系統交互作用後，透過工作投入及精疲力竭的中介歷程，進一

步影響員工的工作態度與行為。因此，本研究以一個獨特且完整性的角度，以工

作要求-資源及工作形塑的立論，對上述的三個研究課題做出回答。本研究對象

來自於台灣 240 位員工與 45 位主管。高績效工作系統資料彙整到團隊層次來檢

驗：當工作投入及精疲力竭為中介變數，檢視高績效工作系統是否分別透過工作

投入及精疲力竭而影響員工的工作態度與行為；工作形塑與高績效工作系統調節

式中介的交互作用是否分別透過工作投入及精疲力竭而影響高績效工作系統與

員工工作態度與行為間之關聯性。研究結果顯示，工作投入及精疲力竭中介之假

說沒有得到支持。工作形塑調節假說達顯著水準，說明在員工展現較高的工作形

塑行為情形下，工作形塑調節了高績效工作系統透過工作投入的中介歷程而影響

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員工的情感性認同、工作滿意度及個人工作適配度;又，工作形塑調節了高績效

工作系統透過精疲力竭的中介歷程而影響員工的離職傾向、職家衝突及自我設限。

最後，本研究說明理論面及管理實務的意涵，並依據研究結果加以討論本研究之

限制與未來的研究方向。

關鍵詞：高績效工作系統、工作要求資源理論、工作投入、精疲力竭、工作形塑

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Table of Contents

List of Tables ............................................................................................................. ix

List of Figures ............................................................................................................. x

Chapter 1 INTRODUCTION ................................................................................... 1

Chapter 2 LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT ..................................................................................... 6

2.1 High Performance Work Systems (HPWS): A Glance at the Past and a New

Light for the Future ................................................................................................. 6

2.1.1 Attributes of HPWS ........................................................................................ 7

2.1.2 Theoretical Perspectives Related to HPWS Literature .................................... 9

2.1.3 The Value of HPWS on Employees: Enrichment vs. Exploitation ................ 10

2.2 Job Demands-Resources Theory ............................................................................ 13

2.2.1 The Impacts of HPWS on Job Demands and Job Resources ......................... 15

2.2.2 Mediation: The Mechanisms of HPWS’s Influence as Reflected in Work

Engagement ............................................................................................... 17

2.2.3 Mediation: The Mechanisms of HPWS’s Influence as Reflected in

Burnout ...................................................................................................... 19

2.3 Job Crafting Theory ............................................................................................... 21

2.3.1 Job Crafting Framed in the Job Demands-Resources Model ........................ 24

2.3.2 Moderation: The Boundary Condition of HPWS’s Influence as

Reflected in Job Crafting ........................................................................... 26

2.4 Research Model ..................................................................................................... 30

Chapter 3 METHODS .............................................................................................. 32

3.1 Sample and Procedures .......................................................................................... 32

3.2 Measures ............................................................................................................... 33

3.3 Analytical Techniques............................................................................................ 36

Chapter 4 RESULTS ................................................................................................ 38

4.1 Confirmatory Factor Analyses ............................................................................... 38

4.2 Descriptive Analyses .............................................................................................. 41

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4.3 Hypothesized Structural Model ............................................................................... 41

4.4 The Mediating Effects and an Integrated Moderated-Mediation Model of Work

Engagement .......................................................................................................... 44

4.5 The Mediating Effects and an Integrated Moderated-Mediation Model of

Burnout ................................................................................................................ 53

Chapter 5 DISCUSSION ......................................................................................... 63

5.1 Theoretical Contributions and Implications ............................................................. 64

5.2 Practical Implications ............................................................................................. 67

5.3 Limitations and Directions for Future Research ....................................................... 69

5.4 Conclusions ............................................................................................................ 71

REFERENCES ........................................................................................................... 73

Appendix A Survey .................................................................................................. 96

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List of Tables

Table 1 Confirmatory Factor Analysis (Level 2: HPWS) ............................................. 39

Table 2 Confirmatory Factor Analysis (Level 1) .......................................................... 40

Table 3 Means, Standard Deviations, and Correlations of the Study Variables ............. 42

Table 4 Results of Hierarchical Linear Modeling Models (Affective Commitment) .... 47

Table 5 Bootstrap Analysis on the Moderated-Mediation of HPWS on Affective

Commitment ................................................................................................. 48 Table 6 Results of Hierarchical Linear Modeling Models (Job Satisfaction) ................ 49

Table 7 Bootstrap Analysis on the Moderated-Mediation of HPWS on Job

Satisfaction ................................................................................................... 50

Table 8 Results of Hierarchical Linear Modeling Models (P-J fit) ............................... 51

Table 9 Bootstrap Analysis on the Moderated-Mediation of HPWS on P-J Fit............. 52

Table 10 Results of Hierarchical Linear Modeling Models (Intention to Leave) .......... 56 Table 11 Bootstrap Analysis on the Moderated-Mediation of HPWS on Intention to

Leave ............................................................................................................ 57

Table 12 Results of Hierarchical Linear Modeling Models (Work-Family Conflict) .... 58

Table 13 Bootstrap Analysis on the Moderated-Mediation of HPWS on

Work-Family Conflict ................................................................................... 59

Table 14 Results of Hierarchical Linear Modeling Models (Self-Handicapping) ......... 60

Table 15 Bootstrap Analysis on the Moderated-Mediation of HPWS on

Self-Handicapping ........................................................................................ 61

x

List of Figures

Figure 1 Proposed Model: High Performance Work Systems with Job Crafting .......... 31

Figure 2 The Interactive Effect of HPWS and Job Crafting on Work Engagement ....... 53

Figure 3 The Interactive Effect of HPWS and Job Crafting on Burnout ....................... 62

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CHAPTER 1 INTRODUCTION

Organizations today are increasingly utilizing systems of human resource (HR) practices

to develop a workforce in improving organizational performance via enhancing employee

competences, knowledge, motivation, skills, and opportunities (Appelbaum, Bailey, Berg, &

Kalleberg, 2000; Huselid, 1995; Jiang, Lepak, Hu, & Baer, 2012; Patel, Messersmith, &

Lepak, 2013). Numerous studies (e.g., Arthur, 1994; Bae & Lawler, 2000; Datta, Guthrie, &

Wright, 2005; Guest, 2001; Huselid, 1995; Patel & Conklin, 2012; Ramsay, Scholarios, &

Harley, 2000; Sun, Aryee, & Law, 2007; Way, 2002) have exemplified that organizations

adopting high performance work systems (HPWS) are able to exert employees to contribute

to superior organizational outcomes, including increased employee productivity, lower

turnover rates, and improved financial performance. Such work systems are also labeled as

'high involvement work systems' (Bae & Lawler, 2000; Vandenberg, Richardson, & Eastman,

1999), or 'high commitment work systems' (Arthur, 1994; Whitener, 2001). Rather than

emphasizing HR systems as a competitive advantage and an inimitable resource in achieving

organizational operational goals (Becker & Huselid, 1998), HPWS stress a series of separate

yet mutually reinforcing HR practices (Takeuchi, Lepak, Wang, & Takeuchi, 2007) and work

in a way that gives employees the latitude to participate in decision making, to improve skills

and motivation, and to seize opportunities to contribute effectively (Appelbaum, 2002; Harley,

Sargent, & Allen, 2010).

Within this perspective, theoretically, by granting more opportunities, latitude, and

discretion vis-à-vis enhancing skills to improve employee performance (Snape & Redman,

2010), HPWS have emphasized the effects of influencing employee attitudes and behaviors

(Kehoe & Wright, 2013; Takeuchi, Chen, & Lepak, 2009). Empirically speaking, HPWS have

been shown to improve employee attitudes, such as commitment (e.g., Ang, Bartram, McNeil,

Leggat, & Stanton, 2013; Macky & Boxall, 2007; Takeuchi et al., 2009), engagement (e.g.,

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Bal, Kooij, & De Jong, 2013), job satisfaction (e.g., Harley et al., 2010; Zhang, Zhu, Dowling,

& Bartram, 2013; Wu & Chaturvedi, 2009), and to decrease turnover intentions (e.g., Alfes,

Shantz, & Truss, 2012; Boon, Den Hartog, Boselie, & Paauwe, 2011; Jensen, Patel, &

Messersmith, 2013). They have also been shown to increase employee helping behaviors (e.g.,

Chuang & Liao, 2010), job performance (e.g., Butts, Vandenberg, DeJoy, Schaffer, & Wilson,

2009; Chang & Chen, 2011; Ehrnrooth & Björkman, 2012; Kuvaas, 2008; Sun & Pan, 2008),

service performance (e.g., Aryee, Walumbwa, Seidu, & Otaye, 2012; Liao, Toya, Lepak, &

Hong, 2009), and organizational citizen behaviors (OCB) (e.g., Alfes et al., 2012; Kehoe &

Wright, 2013; Snape & Redman, 2010; Uen, Chien, & Yen, 2009).

Despite these benefits, begging the question of whether there are two sides to HPWS,

that is, if HPWS have both bright- and dark-side effects on employee outcomes. As evidenced,

conventional accounts of HPWS show the connection between HPWS and positive employee

attitudes and behaviors. However, the existing HPWS literature remains incomplete as little is

known about how HPWS might negatively affect employees' attitudes and behaviors—the

so-called 'dark side' effects of HPWS. Drawing on labor process theory (Braverman, 1974),

Ramsay et al. (2000) have suggested that HPWS may provoke stress or work intensification,

thus endangering the psychological health of employees despite improving organizational

performance. Furthermore, looking at things from an 'exploitation' perspective, Kroon, van de

Voorde, and Van Veldhoven (2009) found a positive association between HPWS and

emotional exhaustion via job demands. Closely related to the essence of above perspectives,

Godard (2001, 2004) has depicted the skepticism of HPWS effects on employees, observing

an increased stressfulness associated with HPWS in which employees capture the subjective

feeling of being languished. Empirically, White, Hill, McGovern, Mills, and Smeaton (2003)

found that employees do not always benefit from HPWS; long working hours and certain

practices are more strongly related to negative job-to-home spillover. In addition, several

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studies have also lent supports for these and related concerns, finding that employees tend to

respond more negatively than positively to HPWS, as they perceive them to consist of a set of

manipulative job demands or as a form of work intensification resulting in anxiety, role

overload (Jensen et al., 2013), stress, and dissatisfaction (Wood, Van Veldhoven, Croon, & de

Menezes, 2012), and workload (Ehrnrooth & Björkman, 2012).

As can be seen, there is disparate empirical evidence about how HPWS affect employees.

While a plethora of research has found that HPWS are beneficial in terms of positive

employee working attitudes and behaviors as a result of positive mediating mechanisms (e.g.,

Boxall, Ang, & Bartram, 2011; Kehoe & Wright, 2013; Liao et al., 2009; see review article,

Jiang, Takeuchi, & Lepak, 2013), few studies have examined negative employee work

experiences, attitudes, and behavior related to HPWS in detail (e.g., Kroon et al., 2009).

Above all, the paradoxical research findings regarding the effects of HPWS on employee

outcomes may be due to the 'black box' of underlying processes embedded in HPWS that has

not been fully explored (Takeuchi et al., 2007). Clearly, there are theoretical and empirical

oversights to consider in terms of how and why HPWS engender bright- and dark-side effects

on employees. To the best of my knowledge, no research has simultaneously explored the

positive and negative employee working experiences, attitudes, and behaviors relating to

HPWS. From this vantage point, within an overarching theoretical perspective, it would be

helpful to have a synthesis of what we know about two opposing intermediary routes to map

the contours of HPWS for both organizations and employees.

Having introduced the juxtaposition of two opposing mechanisms that mediate the

effects of HPWS on employee outcomes, going forward, this study focuses on employees in

HPWS research, thus echoing the research call of bringing more employees center-stage

HPWS studies (Boselie, Dietz, & Boon, 2005). Rather than assuming that all employees in

organizations using HPWS are managed in the same way, however, do HPWS work in a way

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that affects all employees in a similar manner? When do HPWS lead to positive employee

outcomes? When do HPWS lead to the other way? For good or for evil? The potential of

'bright side' and 'dark side' of HPWS effects may depend upon how individual factors

manifest vis-à-vis work. By integrating employee factors into HPWS research, this study

aims to provide theoretical and empirical explanations for the paradoxes of HPWS and the

boundary conditions of HPWS studies.

To sum up, the intent of this study is to answer the following major questions in respect

of the managerial effects of HPWS on employees: Why might HPWS simultaneously involve

negative effects on employees in addition to positive ones? How do HPWS work differently

on employees? When do HPWS characterize a higher possibility of evoking positive or

negative performance effects on employees? To answer these questions, this study seeks to

disentangle the paradoxical nature of HPWS. Consistent with past theories about HPWS

effects on positive employee outcomes, I suggest that both positive and negative effects of

HPWS as manifested by employees' psychological work experiences (i.e., well-being) affect

employee outcomes. Based on the overarching perspective of the JD-R model (Bakker &

Demerouti, 2007, 2008; Demerouti, Bakker, Nachreiner, & Schaufeli, 2001), I suggest HPWS

are associated with two types of job characteristics—demands and resources (Bakker &

Demerouti, 2007, 2008; Brenninkmeijer, Demerouti, Le Blanc, & Van Emmerik, 2010). In

particular, this study explores the moderating effect of job crafting (JC; Wrzesniewski &

Dutton, 2001), which refers to self-initiated behaviors whereby employees seek to alter the

parameters of their jobs in terms of resources, challenges, and demands to better suit their

interests and abilities in order to make their work more meaningful and with preferred work

identities (Petrou, Demerouti, Peeters, Schaufeli, & Hetland, 2012). Although the moderating

effect of job crafting has not been tested, this study intends to situate job crafting in relation

to existing HPWS research as it can have an impact on how employees help to redesign

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HPWS. Accordingly, this study examines two opposite employee psychological experiences

as the underlying mechanisms to illustrate the dynamics of HPWS, while also taking into

account the role of employee job crafting in the effects of HPWS. The purpose of this is to

build a JD-R model of HPWS.

Speculating on possible explanations for paradoxical HPWS effects, this study develops

a new theoretical framework with a provision of three contributions. First, in considering

bright- and dark-side HPWS effects, this study extends and refines the HPWS literature by

incorporating novel perspectives (Guest, 2013)—the JD-R theory (Bakker & Demerouti,

2014) and job crafting theory (Wrzesniewski & Dutton, 2001)—in relation to the effects of

HPWS to improve the understanding of the consequences of HPWS. Second, this study

examines how HPWS impact employees via JD-R theoretical mechanisms, particularly

employee well-being, work engagement, and burnout (Langelaan, Bakker, van Doornen, &

Schaufeli, 2006; Schaufeli & Bakker, 2004). The main focus is on incorporating JD-R

mediating processes as they unfold over time. Third, in considering the moderating impact of

employee job crafting, this study presents a more integrated model of the effects of HPWS by

considering how employee job crafting interacts with HPWS to influence employee outcomes.

This study presents a cross-level moderated-mediation model of the linkages between HPWS

and employee outcomes (Guest, 2013), in which HPWS influence employee working

attitudes and behaviors via a reflection of employee well-being and as moderated by

employee job crafting. Overall, in doing so, this study extends the HPWS literature by

providing a more refined examination of the HPWS black box issue and boundary conditions

by better testing various theories and providing stronger theoretical and practical

implications.

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CHAPTER 2 LITERATURE REVIEW AND

HYPOTHESES DEVELOPMENT

2.1 High Performance Work Systems (HPWS): A Glance

at the Past and a New Light for the Future

For more than two decades, a vast amount of transcendent HPWS research has casted in

demonstrating the positive impacts via the improvements of employee competence, attitudes,

and motivation relating to organizational, departmental, and individual performance

outcomes. The literature is replete with different HR best practices, HPWS, developmental

HR systems, high commitment HR systems, high involvement work systems (HIWS),

human capital enhancing HR systems, etc. These studies have suggested that 'bundles' of HR

practices are designed and used to reinforce additive and synergistic effects on several

performance outcomes (Subramony, 2009). For example, HR practices are mainly designed

to manage and empower an organization's human resources and to motivate, satisfy, and

inspire employees (Way, 2002). In this way, organizations are not only able to experience

superior performance (e.g., Bae & Lawler, 2000; Guthrie, 2001; Sun et al., 2007; Way, 2002)

but employees benefit via meaningful performance, attitudes, and behaviors (cf. Boxall et al.,

2011; Jensen et al., 2013; Kehoe & Wright, 2013; Macky & Boxall, 2007; Wu & Chaturvedi,

2009). However, the outcomes of HPWS have been inconsistent and contingent (Paauwe,

2009). While HPWS researchers have utilized different theoretical lenses, to date, the

empirical research has not sufficiently addressed the duality of HPWS. Indeed, HPWS from

an employee perspective differs from HPWS from a managerial one (e.g., Boselie, Brewster,

& Paauwe, 2009). Thus, there remain paradoxes and deficiencies in this research area.

A new direction in terms of understanding the effectiveness of HPWS can be explored

by advancing theoretical and practical research on HPWS that relies on a more complete

7

model to elucidate the critical processes revealed by employees. In line with the HRM

research call to study employee well-being (Guest, 2002) and the active work orientation and

proactive role of employees within the context of HPWS (Evans & Davis, 2005), this study,

in building on employee perceptions of HPWS, argues that a set of opposite mechanisms of

employee well-being, together with an acknowledgement of employee proactive behaviors,

influences employee outcomes. Thus, a JD-R model of HPWS is presented as an alternative

way of understanding the effectiveness of HPWS, proposing that employees correspond with

job demands and job resources to reinforce the structures, processes, and functions of

organizations. In other words, this study attempts to take a closer look at how employees

utilize intended HPWS along with job crafting, and how these two different instruments

work together to reveal either positive or negative employee psychological processes leading

to attitudinal and behavioral outcomes. By elucidating the 'why', 'what', and 'how' issues in

HPWS, this study seeks to enhance the methods and literature of HPWS (Guest, 2013) by

analyzing HPWS paradoxes. Uncovering mechanisms and contextual factors will help HRM

researchers to reconsider the core values of HPWS, which has been an important yet

inadequately theorized research stream in the literature.

2.1.1 Attributes of HPWS

It is vital that any research on HRM includes an analysis of the HR practices and

attributes that constitute HPWS. HPWS are based on specific coordinated HR practices that

aim to maximize employee commitment, knowledge, motivation, skills, and satisfaction and

to combat burnout and turnover (Bayo-Moriones & Galdón-Sánchez, 2010). They build a

particular human capital of aggregate knowledge, skills, and abilities (Lepak, Liao, Chung,

& Harden, 2006) to encourage personal effort to increase organizational effectiveness and

efficiency (e.g., Huselid, 1995; Wright, Gardner, Moynihan, & Allen, 2005; Zacharatos,

8

Barling, & Iverson, 2005). Building on existing HPWS research streams and logic, I target a

broader scope of separate but interconnected practices, namely, 'staffing', 'training',

'involvement and participation', 'performance appraisals', 'compensation/rewards', and

'caring' (Chuang & Liao, 2010).

Staffing encompasses the HR activities designed to secure high-quality employees at

the right time (Delaney & Huselid, 1996). Training programs are designed to help

employees meet organizational skill requirements and to actualize their knowledge, skills,

and abilities to the maximum (Takeuchi et al., 2007; Youndt & Snell, 2004). Involvement and

participation programs empower employees to make decisions, share information, and

strengthen employee relatedness to organizations (Meyer & Herscovitch, 2001).

Organizations implement HR practices directly and indirectly by employing performance

appraisals to assess how well employees perform their jobs in relation to organizational

strategies (Cabello-Medina, Lopez-Cabrales, & Valle-Cabrera, 2011; Yang & Lin, 2009).

Performance appraisals are important to align individual performance with established

organizational standards (Zhang & Li, 2009). Compensation and rewards encourage

employees to devote energy to certain productive behaviors in order to receive payment and

rewards from organizations (Sheppeck & Militello, 2000). Caring involves the areas of

work–home balance, occupational health and safety, coping with stress, and grievance

procedures (Chuang & Liao, 2010).

Bundling various HR practices into an HR system represents a composite score

approach (Wall & Wood, 2005). Most HRM studies have grouped various HR practices into

an overall HR system in which individual practices reinforce each other to drive employee

performance (Van De Voorde, Paauwe, & Van Veldhoven, 2012). Indeed, research has shown

that when high performance HR practices are in alignment, the effectiveness of HPWS is

9

greater (Combs, Liu, Hall, & Ketchen, 2006). Therefore, this study recognizes HPWS as

whole HR systems instead of differentiating individual components.

2.1.2 Theoretical Perspectives Related to HPWS Literature

The positive side of HRM has been examined from different perspectives. Based on the

resource-based view (RBV) of firms (Barney, 1991; Huselid, 1995), some HRM researchers

have postulated that HPWS link a firm's strategies with its valuable, rare human capital,

imperfectly imitable employee outputs, and non-substitutable internal resource pool to create

a sustained firm-specific competitive advantage (Huselid, 1995; Delery & Shaw, 2001).

Others use arguments derived from contingency theory (Delery & Doty, 1996; Schuler &

Jackson, 1987; Snell & Youndt, 1995), suggesting that by aligning strategies and HR

practices, organizations are able to achieve superior performance (Youndt, Snell, Dean, &

Lepak, 1996). Another theoretical perspective, the ability-motivation-opportunity (AMO)

model (Appelbaum et al., 2000), states that employees are able to adequately delineate

bundles of HR practices and highlight abilities, motivation, and opportunity in linking

employee activities to organizational performance (Gardner, Moynihan, Park, & Wright,

2001).

Employee-focused behavioral theory (Jackson, Schuler, & Rivero, 1989) focuses on

employee behaviors in linking strategy with firm performance, based on the assumption of

eliciting and controlling employee attitudes and behaviors that serve the competitive needs of

the business (Wright & McMahan, 1992). Other HR researchers have incorporated human

capital theory (Becker, 1964; Coff, 1997; Flamholtz & Lacey, 1981; Schultz, 1971) in

explaining that investing in human capital will improve employee skills, knowledge,

experiences, and abilities that will be actualized in the form of economic returns via higher

individual productivity and better firm performance (Lepak & Snell, 1999). Building on

10

social exchange theory (Blau, 1964), HPWS stimulate employee trust in HRM and induce

commitment, involvement, and satisfaction, serving as a driving force to reciprocate efforts to

improve organizational performance (Allen, Ericksen, & Collins, 2013; Gong, Chang, &

Cheung, 2010; Gong, Law, Chang, & Xin, 2009; Messersmith, Patel, Lepak, &

Gould-Williams, 2011; Takeuchi et al., 2007). The effects of HR practices have been

suggested by social exchange theory and norm of reciprocity in explaining the HRM black

box issue by which employee perceptions, attitudes, and behaviors are shaped (e.g., Purcell &

Hutchinson, 2007).

However, the value of HRM may provoke negative effects for employees. In terms of

the labor process theory (Braverman, 1974; Ramsay et al., 2000), HPWS can be seen as a

form of work intensification, bringing higher stress levels to employees. From an HRM

process perspective (Ehrnrooth & Björkman, 2012), psychological empowerment

mechanisms stimulate the generic process qualities of HR practices both in terms of

employee performance and work intensification. The focus on burnout research (Schaufeli,

2006) in combination with social exchange theory shows that HPWS, aiming at creating a

competitive advantage for the organization at the costs of employee work intensification, can

be viewed as exploitation in terms of job demands (Godard, 2001; Kroon et al., 2009).

2.1.3 The Value of HPWS on Employees: Enrichment vs.

Exploitation

In the following, this study explains the paradoxical nature of HPWS vis-à-vis the

influences of HR practices on employee enrichment and exploitation. HPWS are the intended

HR practices assigned by organizations, making sense at business-unit, divisional, group

levels, leading to actual HR practices. The goals of these actual HR practices are to develop

valuable, rare, hard-to-imitate, and non-substitutable human resources that support

11

organizational competitiveness (e.g., Delery & Shaw, 2001; Kepes & Delery, 2007). However,

not all employees in an organization may accept the HR practices aligned with organizational

goals. Reasonably, individual employees in different occupational contexts may recognize or

perceive HR practices differently and respond in reactive or proactive ways, thus reflecting

their working attitudes and behaviors. However, from an employee's perspective, do HPWS

enrich or exploit employees? Associating with advocacy of positive impacts of HRM, HPWS

aim to exert employee motivations, and endow discretions along with positive management

relations to fully foster two types of employee-level outcomes. The first is working attitudes

such as commitment, engagement, job satisfaction, lower turnover intentions (Ang et al.,

2013; Bal et al., 2013; Boon & Kalshoven, 2014; Boon et al., 2011; Butts et al., 2009; Harley

et al., 2010; Jensen et al., 2013; Kooij, Jansen, Dikkers, & de Lange, 2010; Kuvaas, 2008;

Macky & Boxall, 2007, 2008; Takeuchi et al., 2009; Wu & Chaturvedi, 2009; Zhang et al.,

2013). These employee working attitudes are encouraged by the fact that HPWS utilize

human capital in accordance with workable HR practices to encourage employee ability,

motivation and opportunity (AMO model; e.g., Jensen et al., 2013), and in exchange

employees make individual contributions to establish long-term reciprocity with

organizations (social exchange theory; e.g., Wu & Chaturvedi, 2009). Through empowerment

(e.g., Bonias, Bartram, Leggat, & Stanton, 2010; Butts et al., 2009), perception of fit (e.g.,

Boon et al., 2011), and trust in management (e.g., Macky & Boxall, 2007), high performance

HR practices not only advance employee skills, knowledge, and competence but also positive

organizational commitment, job satisfaction, engagement, and lower intentions to leave.

The second employee-level outcome propelled by HPWS are the proactive or productive

behaviors that boosts OCB, creativity and enriches in-role, job, service, task, and work

performance (Alfes et al., 2012; Aryee et al., 2012; Boxall et al., 2011; Butts et al., 2009;

Ehrnrooth & Björkman, 2012; Kehoe & Wright, 2013; Kuvaas, 2008; Liao et al., 2009; Snape

12

& Redman, 2010; Sun & Pan, 2008; Uen et al., 2009). Drawing on the notion of an AMO

model, HPWS trigger employee motivation, which encourages employees in exchanging

skills, knowledge, information or other resources and leads to enhanced employee OCB (e.g.,

Alfes et al., 2012; Kehoe & Wright, 2013) and core performance (e.g., Snape & Redman,

2010). HPWS' investment in employees helps foster employee OCB and in-role, job, service,

task, and work performance. This includes higher commitment, empowerment (e.g., Boxall et

al., 2011), individual human capital (e.g., Liao et al., 2009), and psychological contract (e.g.,

Uen et al., 2009), which all certainly contribute to nurturing social exchange relationships.

The above-mentioned empirical evidence consistently shows that HPWS directly or

indirectly enrich employee social exchange, engagement, ability-motivation-opportunity

endowment, and performance enactment via positive working attitudes and behaviors. In

contrast, it is increasingly being argued that HPWS may simultaneously have negative

consequences for employees (Boxall & Macky, 2009; Grant & Shields, 2002; Janssens &

Steyaert, 2009). By integrating contingency theory, labor-process theory, the demand-control

model, and the psychological empowerment perspective, the research findings have revealed

a major problem concerning the influence of HPWS on the intensification of work (e.g.,

Ramsay et al., 2000), authoritative control (e.g., Barker, 1993), and manipulative job

demands that increase employee work stress (e.g., Guerrero & Barraud-Didier, 2004; Kroon

et al., 2009).

According to the studies by Guest (2002) and Conway (2004), the greater experience of

HR practices, the more job-induced stress. Macky and Boxall (2008) indicated that high

involvement work processes reflect a form of work intensification, resulting in employee

fatigue, work-related stress, and work-life imbalance. Kroon et al. (2009) pointed out that

HPWS disguised as job demands have significant negative impacts on employee emotional

exhaustion. Ehrnrooth and Björkman (2012) found a significant linkage between experience

13

of HRM processes and workload via psychological empowerment. In Jensen et al.'s study

(2013), HPWS were to some extent related to individual-level anxiety and employee role

overload. Using social exchange perspective, Zhang et al. (2013) reported that HPWS leads

to lower job satisfaction due to employees' emotional exhaustion. In other words, these

studies have associated HPWS with employee exploitation in the form of work intensification,

workload, and stress.

2.2 Job Demands-Resources Theory

Theoretically speaking, the JD-R model ( Bakker & Demerouti, 2007, 2008; Demerouti

et al., 2001) considers work motivation and job strain simultaneously. First, the JD-R model

captures the notion that there are two main types of work characteristics, 'job demands' and

'job resources', inherent in every occupation through dual psychological processes—'a health

impairment process' and 'a motivational process' (Bakker & Demerouti, 2007, 2008;

Brenninkmeijer et al., 2010). Second, the JD-R model states that these dual psychological

processes are elicited by either job demands or job resources. Through the health impairment

process, high job demands deplete employees' energy reservoir, triggering negative outcomes.

On the other hand, the motivational process allows employees to strive to accomplish goals

in line with job resources, eventually leading to positive outcomes (Schaufeli & Bakker,

2004).

Many different job demands and job resources (see Schaufeli & Taris, 2014) may

influence employee well-being (Bakker, Demerouti, De Boer, & Schaufeli, 2003a)—burnout

(Demerouti et al., 2001) and work engagement (Petrou et al., 2012; Schaufeli & Bakker,

2004). Job demands, primarily related to burnout (Demerouti et al., 2001), necessitate

individual effort that is associated with certain aspects of physiological and/or psychological

14

costs (Bakker & Demerouti, 2014; Demerouti et al., 2001; Petrou et al., 2012). Examples are

interpersonal conflict, work-home conflict, role conflict, role ambiguity, workload, work

overload, high work pressure, time pressure, emotional demands, emotional dissonance,

organizational changes, and poor environmental conditions. Nevertheless, these demands are

not all job stressors unless they produce negative effects, such as depression, anxiety, or

burnout (Schaufeli & Bakker, 2004).

On the contrary, job resources are primarily related to work engagement (Schaufeli &

Bakker, 2004; Xanthopoulou, Bakker, Demerouti, & Schaufeli, 2007), including 'vigor',

'dedication', and 'absorption' (Schaufeli, Salanova, González-Romá, & Bakker, 2002), and

help individuals achieve job goals and stimulate personal growth by reducing the demands

and costs related to social, psychological, physical, and organizational aspects of jobs

(Bakker & Demerouti, 2014; Demerouti et al., 2001). Resources may be at the organization

level (e.g., financial rewards, job security, opportunities for professional development); in the

form of interpersonal and social connections (e.g., coaching, social support from supervisors

and coworkers, team cohesion); assigned work (e.g., open communication, participation in

decision making, trust in management); or at the task level (e.g., job control, performance

feedback, task variety). When employees utilize resources adequately, not only are they able

to deal with high job demands (Schaufeli & Bakker, 2004), but they can also fulfill basic

psychological needs (Deci & Ryan, 2000). In addition, they tend to craft their jobs based on

personal initiatives (Wrzesniewski & Dutton, 2001).

Empirical evidence supports the proposition of dual pathways in promoting employee

well-being and outcomes. Demerouti et al. (2001) applied the JD-R model using a series of

structural equation analyses and showed job demands and exhaustion are positively related,

whereas job resources and work disengagement are negatively related. The study by Bakker,

Demerouti, and Schaufeli (2003b) examined call center employees with self-reported

15

absenteeism and turnover intentions and found that job demands led to health problems,

which in turn predicted absenteeism, and dedication and organizational commitment evoked

by job resources predicted turnover intentions. Bakker et al. (2003a) used the JD-R model to

investigate future absenteeism among employees using self-reported data. Consistent with the

dual processes hypotheses, burnout partially mediates the effect of job demands on absence

duration, whereas organizational commitment mediates the effect of job resources on absence

frequency. Further, Schaufeli and Bakker (2004) tested the JD-R model with a multi-sample

cross-sectional design. Job demands are the antecedents of burnout, which in turn lead to

health impairment issues; job resources are the primary predictors of engagement, which in

turn reduces turnover intention. Hakanen, Bakker, and Schaufeli (2006) found support for the

dual processes being involved in teachers' work related well-being. Results have confirmed

that the health impairment process reveals relationships between job demands and burnout

and ill health, and the motivational process reveals relationships between job resources and

work engagement and organizational commitment. In addition, burnout also mediates the

relationship between lacking resources and poor engagement. Finally, Hakanen, Schaufeli,

and Ahola (2008) used a cross-lagged panel analysis to examine the relationships among job

demands, job resources, work engagement, burnout, organizational commitment, and

depression, lending supports to both health impairment process and motivational process.

Taken together, these findings are supportive of the dual processes proposed in the JD-R

model that eventually affect individual and organizational outcomes.

2.2.1 The Impacts of HPWS on Job Demands and Job

Resources

In terms of the work environments where HPWS are implemented, job resources can be

motivators that stimulate employee engagement, while job demands can be stressors that

16

induce employee burnout (Peters, Poutsma, Van der Heijden, Bakker, & De Bruijn, 2014).

Drawing upon the JD-R theory (Bakker & Demerouti, 2014), the central idea here is that

HPWS exemplify organizational goals and assign employees the active jobs to facilitate

motivation, to improve skills, and to provide opportunities for learning and promotion. Such

notion, in turn, integrates with AMO model and empowerment-focused HRM to the extent

that HPWS are seen as valuable 'job resources'. Job resources implications embedded in

HPWS help employees not only to learn and develop but also to cope with job demands in

terms of developing positive attitudes and behaviors both on and off the job. Particularly

important in this respect are employee perceptions about HRM goals in implementing HPWS,

which shape employee outcomes. Previous HRM research has also demonstrated the

positivity of HR practices via which organizations offer resources and empowerment to

improve employee motivation, skills, attitudes, and behaviors (Collins & Smith, 2006),

including organizational commitment, job satisfaction (Allen, Shore, & Griffeth, 2003;

Takeuchi et al., 2009), employee turnover (Allen et al., 2003), and employee service

performance (Liao et al., 2009).

However, it is increasingly being disputed that HPWS also represent job demands to

employees and are closely linked to the work intensification perspective (Ramsay et al.,

2000), which focuses on the negative influence of HPWS on employees. In line with the

labor process theory (Braverman, 1974; Ramsay et al., 2000), HPWS are interpreted as

highly demanding for employees and associated with challenges or hindrances imposed by

high work pressure. Therefore, highly exacting HPWS become stressful and impose greater

levels of intensification and more demands on workers. This is the case if employees

experience HPWS practices as being implemented primarily to increase organizational

competitiveness rather than to promote employee benefits (Kroon et al., 2009; Jensen et al.,

2013). Research has shown that HRM practices tend to be a disguised form of coercion and

17

exploitation, resulting in decreased employee well-being and increased stress (White et al.,

2003).

Whether HPWS symbolized as the embodiments of either job demands or job resources

depends on how employees interpret these HR practices. Apparently, job resources connote

enhancement and inspiration. As long as inspired employees believe in their ability to

perform, are motivated to perform, and have opportunities to perform, they are able to cope

with stress successfully using the job resources in place. In contrast, employees' perceptions

of HPWS as job demands rely on how they recognize HPWS in terms of work intensification,

frustration, and extra burdens placed on them. Drawing upon theoretical perspectives such as

the AMO model (Appelbaum et al., 2000), the behavioral approach, and the process model

(Nishii & Wright, 2008), HPWS embody as job resources or job demands can result in

employee well-being—work engagement or burnout—the representation of the overall

quality of employees’ perceptions, experiences, and functioning in the HRM-employee

outcomes linkages (Appelbaum, 2002; Guest, 2002; Van De Voorde et al., 2012). To sum up,

such notions fit JD-R dual pathways (Hakanen et al., 2006), with which HPWS encompass

three stages, namely high performance HR practices perceived as either job demands or job

resources, employee well-being, and employee outcomes.

2.2.2 Mediation: The Mechanisms of HPWS's Influence as

Reflected in Work Engagement

Employees' 'perceptions' of HPWS determine whether they view HPWS as being

pertinent to organizational goals and as being compatible with their own goals. These

perceptions affect the extent to which they are likely to meet performance expectations in

accordance with inspired motivation, deployed ability, and identified opportunities (Bowen &

Ostroff, 2004). Regarding the link between HPWS and positive outcomes, the AMO

18

perspective (Appelbaum et al., 2000; Moynihan, Gardner, & Wright, 2002) suggests that

employees may perceive a more direct effect of HPWS on their motivation, ability, or

opportunities for improvement. This, in turn, should strengthen employees' energy and

commitment to perform well.

According to the JD-R theory (Bakker & Demerouti, 2014), employees view HPWS as

providing the important job resources (Peters et al., 2014) that stimulate their development

and learning; consequently, a critical psychological state of 'work engagement' is likely to

occur (Bakker & Demerouti, 2007; Christian, Garza, & Slaughter, 2011; Schaufeli &

Salanova, 2007). Work engagement is a motivational work-related state of mind constituted

by 'vigor', 'dedication' and 'absorption' (Schaufeli et al., 2002). Vigor excites motivation and

entails feelings of being proactively energized, having mental endurance, and having

perseverance to perform at work. Dedication invokes high levels of willingness, experienced

significance, enthusiasm, and challenge. Absorption indicates a sense of complete

concentration and being happily immersed in one's work. Consistent with the fulfilling,

motivational state of mind relating to job resources, the research has revealed that when the

implementation of HPWS aligns with employees' espoused HR practices, HPWS encourage

greater engagement (Bal et al., 2013; Zhang et al., 2013), indicating a positive relationship

between HPWS and work engagement.

Regarding the link between work engagement and positive outcomes, the JD-R theory

(Bakker & Demerouti, 2014) predicts that employees who engage actively in work will

demonstrate positive attitudes and behaviors based on their work engagement (Bakker,

Demerouti, & Sanz-Vergel, 2014). This engenders employees' willingness to dedicate their

efforts and abilities to these assigned HR practices and tasks. In alignment with work

engagement, employees who are psychologically attached to their roles, who are willing to

invest effort in their work, and who are highly committed to fulfill performance expectations

19

should engage proactively with their work (Bakker & Leiter, 2010).

Adhering to the motivational process of the JD-R theory (Bakker & Demerouti, 2014),

this study therefore argues that HPWS represent an attempt to drive employees into a state of

engagement, increasing vigor, dedication, and absorption based on motivation, ability, and

opportunity to achieve positive outcomes. HPWS can have significant effects, such as

enhancing human capital by increasing employee engagement, which results in increased job

satisfaction (Macky & Boxall, 2008) and greater organizational commitment (Kehoe &

Wright, 2013). Empirically, work engagement clearly elucidates the theorized mechanisms of

HPWS effectiveness, particularly as a result of how employees perceive HPWS (e.g., Ang et

al., 2013; Wood et al., 2012; Zhang et al., 2013). In essence, work engagement links

increased ability, motivation, and opportunities to perform empowered by HPWS and

positive employee outcomes, such as employee job satisfaction, affective commitment, and

person-job fit. Following the above lines of reasoning, this study posits that:

Hypothesis 1. Work engagement mediates the cross-level relationships between HPWS

and employee affective commitment, job satisfaction, and person-job fit.

2.2.3 Mediation: The Mechanisms of HPWS's Influence as

Reflected in Burnout

Notwithstanding the above, HPWS should not be characterized as only having positive

effects on employees; sometimes, the effectiveness of HPWS is achieved at the expense of

employees (Boxall & Macky, 2009). As noted in the introduction, Guest (2002) has called for

more research dealing with employee well-being specifically related to stress, work

intensification, and workload. Regarding negative outcomes of HPWS, I suggest that HPWS

enacted as the manipulative management of job demands (Kroon et al., 2009; Ramsay et al.,

20

2000) that increases control, stress, and employee effort (Pil & MacDuffie, 1996; Guerrero &

Barraud-Didier, 2004) leads to turnover intentions (Jensen et al., 2013), work-family conflicts

(White et al., 2003), and self-handicapping (Bakker, 2014).

To be more specific, the labor process theory (Braverman, 1974; Ramsay et al., 2000)

states that HPWS sometimes build up organizational performance at the expense of employee

well-being, especially when employees interpret HPWS in terms of stress or as work

intensification (Boxall & Macky, 2009). In terms of the link between HPWS and burnout, the

logic of this study is that individual employees cognitively experience HR practices as

antecedents of burnout, leading them to behave in ways that reflect their burnout. Burnout is

defined as "a state of mental and physical exhaustion caused by one's professional life"

(Freudenberger, 1974) and is characterized by 'exhaustion' and 'cynicism', especially with "a

low level of energy and poor identification with one's work" (see Demerouti et al. 2001).

Empirically, HPWS provoke a critical exploitation perspective of employees, resulting in

negative effects on well-being, such as burnout (Godard, 2001). Furthermore, Van Veldhoven

(2005) suggests that compensation is associated with strain. Rewards for increased effort and

job involvement are related to work pressures, based on the notion of management by stress

(Wood et al., 2012).

Regarding the link between burnout and negative outcomes, the conservation of resource

(COR) theory (Hobfoll, 1989; Hobfoll & Shirom, 2000) predicts that those individuals who

are more likely to experience increased resource loss and job stress attempt to limit additional

resource expenditure through energy conservation and reciprocate with negative attitudes and

behaviors to limit further depletion. Podsakoff, LePine, and LePine (2007) found that

hindrance stressors had positive relationships with turnover intentions, turnover, and

withdrawal behavior. Further, JD-R evidence has suggested that burnout is positively related

to absenteeism and negatively related to organizational commitment and job satisfaction

21

(Schaufeli, Bakker, & Van Rhenen, 2009).

Central in the health impairment process of the JD-R theory (Bakker & Demerouti,

2014), organizations' continuous efforts in utilizing employee potentials

excessively describes in a way that is compatible with such burnout process. For instance, the

stress-retention model (Schaubroeck, Cotton, & Jennings, 1989) in part, elucidates that

particular stressors (i.e., job demands) account for turnover intention, and withdrawal

behavior through job strain. Overall, the theorized mechanisms through which HPWS

influence employee working attitudes and behaviors are also likely to explain why employees

suffer heavier work-related burnout. Summing up the above arguments, the proposed model

is based on the JD-R theory (Bakker & Demerouti, 2014) assumptions that HPWS, as

work-intensified forms of job demands, indirectly affect employees' intention to leave,

work-family conflicts, and self-handicapping via the effects on burnout. Thus, the following

hypothesis is formulated:

Hypothesis 2. Burnout mediates the cross-level relationships between HPWS and

employee intention to leave, work-family conflicts, and self-handicapping.

2.3 Job Crafting Theory

Job crafting theory (Wrzesniewski & Dutton, 2001) is based on job descriptions and job

designs and emphasizes how employees actively shape boundaries in tasks, social

relationships, and cognitive aspects to build up the meaning of work and work identities. Job

crafting involves both 'physical changes' in the form, scope, or number of job tasks and

'cognitive changes' in how an individual sees his or her job (Wrzesniewski & Dutton, 2001).

Job crafting differs from job design/job re-design concepts in which personal initiative and

proactive behavior (Grant & Ashford, 2008) are used to change one's job with or without

22

negotiating with supervisors (Wrzesniewski & Dutton, 2001). In addition, job crafting is not

always aligned with organizational goals. In the end, proactive behaviors are necessary to

achieve personal goals and benefits. Thus, in the relatively new concept of job crafting,

employees may actively choose tasks, alter job descriptions, and assign personal meaning to

their jobs in order to achieve individual well-being and positive work outcomes.

Job crafting involves an action, a behavior, and a means by which individual employees

customize their jobs and interactions with others in the workplace. Those who engage in job

crafting typically employ three different practices. First, 'changing task boundaries' occurs as

employees take more or fewer tasks or create new jobs. Employees actively enlarge or

narrow their job scope, changing how they complete projects or tasks. Second, 'changing

relational boundaries' entails changing the quality of interactions with others by altering the

essence of those interactions and by integrating others into the workflow. Job crafters can

decide the beneficiaries, the frequency, and the quality of interactions. Third, 'changing

cognitive task boundaries' involves taking different views of jobs by changing the way of

seeing the work as a whole or the individual aspects.

Job crafting is a specific scheme of proactive behavior to change levels of job demands

and job resources (Tims & Bakker, 2010). The job crafting in this stream of research is the

driver of engagement in resourcefulness, active jobs, and psychological capital (Bakker,

2010). Considering job crafting an extended model of work engagement (Bakker, 2010),

scholars have tested a hypothesis that states proactive personality can be a factor in predicting

work engagement, which further affects job performance (Bakker, Demerouti, & Ten

Brummelhuis, 2012; Bakker, Tims, & Derks, 2012). Petrou et al. (2012) have explored the

contextual impacts of job crafting on work engagement and confirmed a three-factor structure

of job crafting, involving 'seeking resources', 'seeking challenges', and 'reducing demands' at

both the general and day levels, with a moderate fit for general-level job crafting and an

23

excellent fit for day-level job crafting.

Tims, Bakker, and Derks (2012) developed and validated a 21-item job crafting scale

that includes 'increasing social job resources', 'increasing structural job resources',

'increasing challenging job demands', and 'decreasing hindering job demands'. Further, Tims,

Bakker, and Derks (2013a) discussed the impacts of job crafting on employee well-being by

shaping job resources and job demands. In addition, Tims, Bakker, Derks, and Van Rhenen

(2013b) tested a cross-over relationship among job crafting, work engagement, and

performance, revealing an isomorphism of the job crafting construct at both the individual

and team levels.

Based on job redesign theory, Demerouti and Bakker (2014) conceptualized job crafting

as a process in which employees proactively engage in balancing job demands, job resources,

personal abilities, and needs (cf. Tims & Bakker, 2010) and developed a nomological

network of job crafting with antecedents (i.e., decision latitude, job autonomy, proactive

personality, job control, task interdependence, discretion to craft a job, job demands, task

complexity, and job challenges) and outcomes (i.e., job satisfaction, organizational

commitment, self-image, perceived control, and readiness to change). They have also stated

that job crafting, one of the proactive behaviors, consists of 'seeking resources', 'seeking

challenges', and 'reducing demands', which is in line with Petrou et al. (2012) and Petrou

(2013).

Job crafting basically emphasizes core values in setting job boundaries, the meaning of

work, and work identities based on which individual employees shape their own tasks to

achieve improved job performance, work engagement (Bakker et al., 2012; Tims et al., 2013a,

2013b), resilience (Barker Caza, 2007), and wellbeing (Tims et al., 2013a). This means that

the conceptualizations of job crafting as 'seeking resources', 'seeking challenges', and

'reducing demands' (Petrou et al., 2012) that have been merged in the companion discipline

24

of job demands and job resources are able to be integrated into HRM process models (Peters

et al., 2014; Tims & Bakker, 2013).

2.3.1 Job Crafting Framed in the Job Demands-Resources

Model

In addition to answering calls for more 'black box' HPWS research, research focusing on

the dynamics of boundary conditions in the HPWS-performance relationships fits with recent

trends in practice (Allen & Wright, 2006). The main emphasis of the contingency theory is on

how HR practices fit organizational strategies and on how HRM will improve organizational

performance (i.e., labor productivity). Underlying this assertion, some studies have identified

a variety of strategic postures or moderators (i.e., market segmentation, industry capital

intensity, growth, product differentiation, dynamism, and group culture) of the relationships

between HR practices/systems and organizational performance (Batt, 2002; Datta et al., 2005;

Patel & Conklin, 2012). In addition, researchers adopting the RBV have suggested that the

proper industry type (Chi & Lin, 2011), organizational strategy (Delery & Doty, 1996), and

manufacturing strategy (Youndt et al., 1996) can strengthen the relationships between HPWS

and organizational, financial, and operational performance, respectively. In addition to the

moderating roles of business strategies, researchers have also explored how an institutional

environment enables a firm to build dynamic capabilities by having HPWS fit external

conditions to achieve better financial performance (Wei & Lau, 2010).

Apart from environmental factors, individual factors are also at work. There is growing

research interest in acknowledging individual proactive approaches in line with

environmental factors. In essence, employees process the experiences resulting from the

implementation of HPWS and form some individual discretionary behaviors to cope. This

highlights the variation across employees that exists (Wright & Nishii, 2007). However,

25

actively defining work roles or going beyond job assignments (Evans & Davis, 2005) should

be aimed at benefiting both employees and organizations within task, relational, and

cognitive task boundaries (i.e., job crafting; Wrzesniewski & Dutton, 2001).

COR theory (Hobfoll, 1989) suggests that employees reinvest task proficiency or

reengage in work after experiencing organizational resources (i.e., high-commitment HR

practices) or coping with work inadequacy (i.e., low-commitment HR practices) to ensure

that resource depletion is not threatened, leading to organizational commitment (Boon &

Kalshoven, 2014). When examining employee behaviors, researchers have incorporated

social exchange theory to explicate the perceptions of economic and social exchanges that are

expected to shape the degree of reciprocations in the HPWS-job satisfaction relationship

(Zhang et al., 2013). Drawing upon the AMO framework, researchers have implied that

employees trust in employers modifies the impact of HRM practices on employees' ability,

motivation, and opportunity to perform in terms of their wellbeing, task performance, OCB,

and turnover intentions (Alfes et al., 2012).

Another challenge for HPWS research is to put more efforts in testing contextual factors

situated in individual proactive behaviors. To gain a deeper understanding of relevant

contextual factors that promote the effectiveness of HPWS, this study suggests understanding

the influence of 'job crafting' in HPWS domain. As such, framing job crafting into a HPWS

JD-R model is worthwhile to explore how job crafting affects individual outcomes. Job

crafting theory (Wrzesniewski & Dutton, 2001) puts much emphasis on how employees

actively shape boundaries in tasks, social relationships, and cognitive aspects to build up the

meaning of work and work identities. Indeed, the JD-R theory (Bakker & Demerouti, 2014)

refers to a theoretical perspective for measuring the motivation potential of jobs and for

guiding job crafting. Tims and Bakker (2010) framed JD-R theoretical perspectives in

relation to job crafting which is conceptualized as 'increasing social job resources',

26

'increasing structural job resources', 'increasing challenging job demands', and 'decreasing

hindering job demands'. Based on proactive initiatives, employees may adapt to the demands

and resources associated with a job (Tims et al., 2012).

Petrou et al. (2012) conceptualized job crafting, aligned with the JD-R theory (Bakker &

Demerouti, 2014), with dimensions of 'seeking resources', 'seeking challenges', and 'reducing

demands' as personal tactics to benefit personal needs, to meet personal goals, to cope with

stress, and to remain healthy. Individual employees undertake job crafting in fulfilling jobs

for different reasons. The motives of individual employees determine which tasks to complete,

how jobs are carried out, and how relational dynamics evolve. Individuals strive to do their

jobs by changing certain aspects to suit a preferred work identity. They craft their jobs to

fulfill their own needs, to benefit personal goals, to cope with stress, and to remain healthy

(Tims & Bakker, 2010). Empirical evidence (Tims et al., 2013a) has shown that job crafting

predicts future job demands and job resources, leading indirectly on employee well-being like

work engagement, job satisfaction, and burnout.

Hence, using the JD-R theory (Bakker & Demerouti, 2014) as a foundation, HPWS may

provide opportunities for employees to undertake job crafting as a success tactic in embracing

HPWS as "redesigning". Putting differently, how employees reshaping, reinventing the job

boundaries to suit one’s needs, growth, strength, and passions (Wrzesniewski, Berg, & Dutton,

2010) parallels to job crafting. Therefore, employees are able to cope with HPWS by seeking

resources, seeking challenges, and reducing demands on one hand and by redesigning their

tasks, altering their work meaning, and positioning their work identities on the other.

2.3.2 Moderation: The Boundary Condition of HPWS's

Influence as Reflected in Job Crafting

Essentially, HR managers and researchers do expect HPWS to have positive impacts and

27

controlling effects on employee attitudes, behaviors, and performance (Combs et al., 2006).

Indeed, the role of individual employee accounts (Wright & Nishii, 2007), and how

individual employee manages HPWS matters (Evans & Davis, 2005). Experiencing HPWS as

job resources or job demands may activate proactive coping strategies, such as redesigning

HPWS, which is similar to the core tenets of job crafting (Aryee et al., 2012; Grant &

Ashford, 2008; Lovelace, Manz, & Alves, 2007). A job crafting perspective helps explain

why these various HR practices affect employees' performance and discretionary in terms of

working attitudes and behaviors, and also how to renovate the effects of various HR practices.

Thereby, this study proposes that job crafting has a role in examining HPWS-performance

relationships when both assigned HR practices and individual employee personal initiatives

are in flux.

At heart, there are two major reasons why this study suggests the effect of HPWS are

varied when employees perceive the opportunities for crafting assigned HR practices

(Wrzesniewski & Dutton, 2001). First, given that job crafting deals with types of proactive

behaviors, employees should be able to align HR practices with personal abilities,

motivations, knowledge, preferences and needs (Tims & Bakker, 2010). Similarly, Ghitulescu

(2006) explored how employees use job crafting to handle complex and multiple task

demands. Based on the JD-R theory (Bakker & Demerouti, 2014), job crafting has a more

theoretically direct impact on employees to interplay the complexity of HR practices,

allowing intervention to regulate employee work engagement or burnout (Tims et al., 2013a).

A second reason, in line with the contingent HRM perspective (Delery & Doty, 1996; Schuler

& Jackson, 1987; Snell & Youndt, 1995), is that employees realize job crafting in the

utilization of job resources embedded in organization-assigned HR practices to cope with job

demands. Job crafting takes into account the values of an individual worker's need to control

their job, have a positive self-image, and positive interactions with others. By crafting task,

28

cognitive, and relational boundaries (Wrzesniewski & Dutton, 2001), employees are able to

redesign HPWS, to change the social environment at work, to redefine the meaning of work,

to create work identities, and to customize person-job fit. In addition, drawing upon

organizational change literature (Verhaeghe, Vlerick, De Backer, Van Maele, & Gemmel,

2008), HR practices can be considered as changes in tasks and flexwork.

Indeed, HPWS require employee participations. Grounded in the JD-R theory (Bakker &

Demerouti, 2014), employees with high levels of job crafting view HPWS as important job

resources that cannot be fully realized unless these HR practices are aligned with how

employees perceive the intentions behind them. In line with this argument, employees with

high levels of job crafting will have the internal motivations to shape HPWS to fulfill their

own needs, to benefit personal goals, to cope with stress, and to remain healthy (Tims &

Bakker, 2010) by challenging themselves to review how their work can be modified or

rethink how their jobs can be performed to meet their needs, growth, strengths, and interests.

Yet, for those who are with low levels of job crafting, they are possibly not able to

reexamine HPWS to remold their work or think how their jobs can be recrafted. If the aim of

HPWS is to make employees participate more or to base compensation, rewards, and

performance appraisals based on individual employee effort and contributions, employees

may view these HR practices as job demands, which is in line with the JD-R theory (Bakker

& Demerouti, 2014). Accordingly, those who are not able to craft job demands and cope with

stressful working conditions often experience cognitive and emotional exhaustion—a state of

burnout. It is a sound argument that workplace demands tend to be much more threatening to

those who do not believe themselves of being able to reshape, remold, and redesign their

tasks. When experiencing job demands associated with HPWS, those who are with high

levels of job crafting are less likely to suffer increased psychological stress compared to those

who are with low levels of job crafting when taking advantage of opportunities for learning

29

and growth. It is also likely that employees who are better at job crafting will react better to

HPWS in the form of greater work engagement. If employees craft their jobs well, they are

able to engage proactively in seeking job resources and job challenges while reducing job

demands.

Thus far, the moderating impact of job crafting on HPWS effects has not been

empirically examined at the individual level; still, there is indirect evidence to support the

theoretical expectations. Jensen et al. (2013) found that the relationships between HPWS and

anxiety/role overload are strengthened when employees are given less rather than more job

control. For those who demonstrate higher, rather than lower, levels of job crafting, the

relationship between HPWS and work engagement will be strengthened while for those who

show lower, rather than higher, levels of job crafting, the relationship between HPWS and

burnout will be strengthened. Extending the arguments to the paradoxical effects of HPWS, I

propose that the effect of HPWS on employee well-being should be considered in light of

employee job crafting. Accordingly, this study examines job crafting as a moderator of the

effects of HPWS on employee wellbeing, with the aim of further elucidating possibly

HPWS-performance paradoxes. Therefore, this study predicts:

Hypothesis 3a. Job crafting moderates the mediating effect of the cross-level

relationship between HPWS and employee affective commitment, job satisfaction, and

person-job fit through work engagement, such that the mediating effect is stronger when

the level of job crafting is high rather than low.

Hypothesis 3b. Job crafting moderates the mediating effect of the cross-level

relationship between HPWS and employee intention to leave, work-family conflicts, and

self-handicapping through burnout, such that the mediating effect is weaker when the

level of job crafting is high rather than low.

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2.4 The Research Model

HRM practices can take three different forms—intended HR practices designed and

launched by organizations, actual HR practices enacted by line managers, and perceived HR

practices perceived by employees (Boxall & Macky, 2007; Wright & Nishii, 2007).

Regarding the level of analysis, a growing sophistication implies the use of individual

perceptions with respect to HPWS into the unit level with a more holistic orientation as well

(Van Iddekinge, Ferris, Perrewé, Perryman, Blass, & Heetderks, 2009).

In HRM, although HR practices are indeed designed by organizations and enacted by

units, these practices are directly perceived by employees who actually transmit these HR

practices effects back into units, articulating as a unit-level phenomenon which is supported

by prior research (Aryee et al., 2012; Gardner et al., 2001; Messersmith et al, 2011; Takeuchi

et al., 2009). Allied to this, to measure the validity of HPWS, this study uses aggregated

employee perceptions of HPWS data at the unit level, which not only allows the advantage of

reducing random error (Guest, 2001) but enables observations of both within-unit variability

and between-unit differences. Overall, this study aims to provide a more holistic

understanding of the link between HPWS and performance by investigating the effects of

HPWS on employees' psychological state and on employee working attitudes, behaviors, and

performance at the unit level. Figure 1 shows the research model. This moderated mediation

model describes how the implementation of HPWS at the unit level influences the ways

employees perceive these HR practices in line with the dual JD-R processes and how it

indirectly impacts employee outcomes. Based on the dual JD-R processes, the boundary

condition of how employee job crafting amplifies or attenuates the relationships between

HPWS and employees' psychological state is also presented.

31

Group Level

----------------------------------------------------------------------------------------------------------------

Individual Level

Figure 1. Research Model: High Performance Work Systems with Job Crafting

Job Crafting (H3a & H3b)

Work Engagement

(H1)

Burnout

(H2)

High Performance Work Systems

Positive outcomes • job satisfaction • affective commitment • person-jot fit

Negative outcomes • intention to leave • work-family conflicts • self-handicapping

32

CHAPTER 3 METHODS

3.1 Sample and Procedures

The participants in this study were employees and direct supervisors working in

service-based industries in Taiwan. These employees were encouraged to meet job

assignments creatively and to resolve work-related problems skillfully. To ensure sufficient

representation when aggregating employee perceptions of HPWS to the group level, this

study included in the analyses only groups having at least five employees reporting.

Concerning common method variance (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003), this

study applied a three-wave data collection strategy and a 6-week interval design. At time 1,

employees were surveyed to evaluate their perceptions of HPWS, job crafting, and other

control variables, and they provided demographic information. Supervisors were asked to

report their transformational leadership and provide demographic information. The time 2

survey measured the engagement and burnout of those employees who completed the time 1

questionnaires. At time 3, employees (only those whose supervisors and subordinates had

completed the time 1 and time 2 questionnaires) reported their affective commitment, job

satisfaction, person-job fit, intention to leave, work-family conflict, and self-handicapping.

Data collection was conducted by supervisors. The supervisors of service-based firms

received letters explaining the survey procedures, together with questionnaires and return

envelopes, and they helped the author organize each round of surveying. The employees were

guaranteed confidentiality and returned the completed questionnaires in sealed envelopes

directly to the team leader. A researcher-assigned identification number was given to each

questionnaire to match the three sets of employee responses. In the first wave, 255 employees

in 48 groups from 31 organizations completed questionnaires (97.7% response rate). Six

weeks later, 48 supervisors delivered the time 2 (T2) survey to these 255 employees, and 46

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groups totaling 248 employees returned the T2 questionnaires (97.2% response rate). Finally,

six weeks after the T2 survey, the time 3 (T3) survey was distributed to the 46 supervisors of

the 248 employees who had completed both the T1 and T2 surveys. A total of 45 groups

totaling 240 employees (96.7% response rate) returned the completed T3 questionnaires.

Thus, the final sample consisted of 45 groups totaling 240 employees.

A heterogeneous sample of professional jobs allowed for generalization across jobs and

industries. Participants worked in a wide range of occupational fields, including information

technology (69.6%), banking (12.5%), health services (7.5%), and others (10.4%). Of the 240

employees, 51.7% were male, and the average age was 34.78 (SD = 6.57). Referring to their

education, 3.3% had a high school diploma or below, 8.3% had a junior college education,

52.9% had a bachelor's degree, and 35.4% had a postgraduate degree.

3.2 Measures

All items were taken from existing measures to maximize the content validity of the

questionnaire. Prior to adopting a multipronged approach to test the hypotheses, the

questionnaires were translated from the English survey items via translations procedures

(Brislin, 1970). To validate the survey translations, two HR managers proofread the Chinese

surveys for readability and ease of comprehension. Any concerns about or discrepancies

between the English and Chinese versions were addressed to ensure they were the same in

terms of meaning. Each scale is rated from 1, indicating "strongly disagree", to 5, indicating

"strongly agree". All the items can be found in Appendix A.

HPWS. The study adopted a 35-item scale to measure bundled high-performance human

resource practices (Chuang & Liao, 2010). The high-performance HR practices scale

included eight dimensions: 'staffing', 'training', 'involvement and participation', 'performance

34

appraisals', 'compensation/rewards', and 'caring' (Chuang & Liao, 2010). Example items are

"Employees are often asked to participate in work-related decisions" and "Employee salaries

and rewards are determined by their performance." All responses from the employees for a

unit were then aggregated to create the unit-level variables of HPWS. The reliability of this

scale was .94.

Individual employee perceptions of HPWS indeed exemplifies as a shared group

property, thus, the appropriateness of within-group agreement and of between-group

variability need to be justified. Empirically speaking, aggregating data to the mean level in

the group can be justified by assessing the Rwg and interclass correlations. HPWS were with

Rwg mean value of .98, ranging from .88 to 1.00. The ICC(1) estimate was .19 and the ICC(2)

estimate was .56. Though ICC(2) value was lower than ideal owing to smaller group sizes

(Klein & Kozlowski, 2000), the ICC(1) value exceeded the levels of reliability and agreement.

Overall, aggregating HPWS responses to the group level met statistical justification.

Job crafting. Job crafting was measured based on a scale developed by Petrou et al.

(2012) and included 13 items relating to general-level crafting from among the three

dimensions of 'seeking resources', 'seeking challenges', and 'reducing demands'. Example

items are "I ask my supervisor for advice.", "I ask for more responsibilities.", and "I try to

simplify the complexity of my tasks at work." The Cronbach's alpha for this scale was .83.

Work engagement. Work engagement was assessed using nine items based on Bakker

(2014). These items measure aspects of individuals' engagement. Example items are "At my

work, I feel bursting with energy." and "I feel happy when I am working intensely." The

Cronbach's alpha for this scale was .91.

Burnout. Burnout was assessed using nine items based on Bakker (2014). Example items

are "There are days when I feel tired before I arrive at work." and "During my work, I often

feel emotionally drained." The Cronbach's alpha for this scale was .85.

35

Affective commitment. Affective commitment was measured by five items from Meyer

and Allen's Affective Commitment Scale (Meyer & Allen, 1997). Example items are "I feel a

strong sense of belonging to my organization.", and "I would be happy to work at my

organization until I retire." The Cronbach's alpha for this scale was .80.

Job satisfaction. Job satisfaction was measured by three items from Hackman and

Oldham (1976). An example item is "All in all, I am satisfied with my job here at this

organization." The Cronbach's alpha for this scale was .95.

Person-job fit. Person-job fit was measured by three items from Saks and Ashforth

(1997). An example item is "To what extent does the job fulfill your needs?" The Cronbach's

alpha for this scale was .92.

Intention to quit. Intention to quit was measured with the 3-item scale from Camman,

Fichman, Jenkins, and Klesh (1979). An example item is "I often think of leaving the

organization." The Cronbach's alpha for this scale was .80.

Work-family conflict. Work-family conflict was measured with the 4-item scale from

Gutek, Searle, and Klepa (1991). An example item is "After work, I come home too tired to

do some of the things I'd like to do." The Cronbach's alpha for this scale was .80.

Self-handicapping. Self-handicapping was measured with three items from Bakker

(2014). An example item is "I tend to put things off until the last moment." The Cronbach's

alpha for this scale was .84.

Control variables. Following other researchers (e.g., Jensen et al., 2013), age, gender,

and education were included as control variabless. Gender is usually measured as a

dichotomous variable and is coded such that 1 is female and 2 is male. In addition, this study

controlled goal orientation because previous research has indicated that goal orientation can

explain individuals' well-being, fatigue, job satisfaction (e.g., Van Yperen & Janssen, 2002),

and job performance (e.g., Porath & Bateman, 2006). Goal orientations also reveal

36

work-related reinforcement associated with individuals' well-being and job outcomes in the

work environment (e.g. Vansteenkiste, Duriez, Simons, & Soenens, 2006; Vansteenkiste,

Neyrinck, Niemiec, Soenens, De Witte, & Van den Broeck, 2007). Following this, this study

suggests that HPWS-espoused goals for employees may be interfered by employees' own

goal orientations; thus, the inclusion of goal orientation in the analyses may decrease the

likelihood that any observed relationships between HPWS and employee work outcomes are

confounded by individual goal orientations. Goal orientations are composed of the following

dimensions: learning (five items), performance (four items), and avoiding (four items)

(VandeWalle, 1997). The sample items are "I am willing to select a challenging work

assignment that I can learn a lot from.", "I try to figure out what it takes to prove my ability

to others at work.", and "I prefer to avoid situations at work where I might perform poorly."

The Cronbach's alpha for this scale was .78. Recent research suggests that transformational

leadership acts as a valuable leadership model in enhancing occupational safety and is worth

being integrated within HPWS implementations (e.g., Zacharatos et al., 2005). In addition,

transformational leadership nurtures employees with feelings of trust and commitment, thus

enabling the implementation of HR practices (Ang et al., 2013). Therefore, this study also

included transformational leadership as the control variable, which was measured with four

items from McCollKennedy and Anderson (2002). A sample item is "Gives personal

attention to each team member." The Cronbach's alpha for this scale was .72.

3.3 Analytical Techniques

The employees in this sample were nested within groups of employees/supervisors. This,

this study used multilevel data modeling (Raudenbush & Bryk, 2002) to test the hypotheses,

specifically hierarchical linear modeling (HLM) version 6.0 for the analysis, allowing for the

37

correct parameter estimates and significance tests for multilevel and non-independent data

(Raudenbush & Bryk, 2002). HPWS were the group-level predictor, and transformational

leadership was the group-level control variable. Job crafting, work engagement, burnout,

affective commitment, job satisfaction, person-job fit, intention to leave, work-family conflict,

and self-handicapping were individual-level variables, together with gender, age, education,

and goal orientations as the control variables at the individual level in the analyses. Following

the conventional HLM approach, centering is able to remove high correlations between

group- and individual-level variables and cross-level interactions as well as between the

random intercept and slopes. In this study, HPWS, the group level predictor, were grand mean

centered (Hofmann & Gavin, 1998) to reduce multicollinearity (Aiken & West, 1991), and

the individual variables were group mean centered in an attempt to explain the variability

among employees (Hofmann & Gavin, 1998). Finally, by using R programming, this study

bootstrapped the 95% confidence interval not containing 0 for significance to examine the

significance of mediation and moderated mediation effects.

38

CHAPTER 4 RESULTS

4.1 Confirmatory Factor Analyses

Before testing the hypotheses, two sets of confirmatory factor analyses (CFAs) were

performed to verify the distinctiveness of the constructs. First, the first group of tests focused

on the six dimensions of HPWS, which were derived from aggregating employees' ratings.

Five models are compared: (1) a null model, a one-factor model where HPWS were the

expression of a single latent factor; (2) a theoretical model, a six-factor model in which all six

dimensions were independent; (3) model 1, a second order six-factor model in which all the

dimensions were independent; (4) model 2, a three-factor model in which "staffing and

training", "involvement & participation and performance appraisals" were combined into

one factor, and "compensation & rewards and caring" were combined into one factor; and (5)

model 3, a two-factor model in which "staffing, training, and involvement & participation"

were combined into one factor while "performance appraisals, compensation & rewards, and

caring" were combined into another factor. Table 1 presents the HPWS CFA results. As

shown, the six-factor theoretical model fitted the data well (χ2 = 791.41; df = 521; RMSEA

= .047; SRMR = .070; CFI = .935; TLI = .926).

The second group tested evaluates the distinctiveness of the nine constructs obtained

from level 1 (i.e., job crafting, work engagement, burnout, affective commitment, job

satisfaction, person–job fit, intention to leave, work–family conflict, and self-handicapping).

Regarding their conceptual overlap, three alternative models were compared with the

theoretical nine-factor model: (1) model 1 was a four-factor model where the moderator of

job crafting was a single factor; mediators of work engagement and burnout were combined

into one factor; positive outcomes of affective commitment, job satisfaction, and person-job

fit were combined into one factor; and negative outcomes of intention to leave, work-family

39

Table 1 Confirmatory Factor Analysis (Level 2: HPWS)

Model Factor Structures χ2 df Δχ2 RMSEA CFI TLI SRMR Null model 4765.68 595 .171 .000 .000 .335

Theoretical model

Six factors: All the dimensions are independent 791.41 521 .047 .935 .926 .070

Model 1

Second order with six factors: All the dimensions are independent

818.06 530 26.65** .048 .931 .922 .077

Model 2

Three factors: Two HPWS dimensions were combined into one factor. - staffing, training - involvement & participation, performance appraisals - compensation & rewards, caring

1274.55 533 483.14*** .076 .822 .802 .081

Model 3

Two factors: Three HPWS dimensions were combined into one factor. - staffing, training, involvement & participation - performance appraisals, compensation & rewards, caring

1445.44 535 654.03*** .084 .782 .757 .106

Model 4

One factor: All six dimensions were combined into one factor. 1739.17 536 947.76*** .116 .712 .680 .091

* p < .05; ** p < .01; *** p < .001

40

Table 2 Confirmatory Factor Analysis (Level 1)

Model Factor Structures χ2 df Δχ2 RMSEA CFI TLI SRMR Null model 8864.56 1738 .150 .000 .000 .243

Theoretical model

Nine factors: All the factors are independent 1849.06 1229 .046 .917 .907 .073

Model 1 Four factors: Job crafting was a single factor; work engagement and burnout were combined into one factor; affective commitment, job satisfaction and person-job fit were combined into one factor; and intention to leave, work-family conflict and self-handicapping were combined into one factor.

2274.15 1259 425.09*** .058 .864 .852 .099

Model 2 Three factors: Job crafting was a single factor; work engagement and burnout were combined into one factor; and affective commitment, job satisfaction, person-job fit, intention to leave, work-family conflict and self-handicapping were combined into one factor.

2325.31 1262 476.25*** .059 .858 .845 .099

Model 3 Two factors: Job crafting, work engagement and burnout were combined into one factor; affective commitment, job satisfaction and person-job fit were combined into one factor; and intention to leave, work-family conflict and self-handicapping were combined into one factor.

2448.76 1264 599.70*** .062 .842 .827 .105

Level 1: Job Crafting; Work Engagement; Burnout; Affective Commitment; Job Satisfaction; Person-Job Fit; Intention to Leave; Work-Family Conflict;

Self-handicapping

* p < .05; ** p < .01; *** p < .001

41

conflict, and self-handicapping were combined into one factor; (2) model 2 was a three-factor

model where the moderator of job crafting was a single factor; and work engagement merged

with burnout formed a single factor; all outcomes of affective commitment, job satisfaction,

person-job fit, intention to leave, work-family conflict and self-handicapping were combined

into one factor; and (3) model 3 was a two-factor model where job crafting, work engagement,

and burnout were combined into one factor, and all outcome measures were combined into

one factor. As Table 2 shows, the fit indices indicate that the theoretical nine-factor model

fitted the data well (χ2 = 1849.06; df = 1229; RMSEA = .046; SRMR = .073; CFI = .917; TLI

= .907), whereas models 1–3 exhibited poorer fit, supporting the construct distinctiveness of

job crafting, work engagement, burnout, affective commitment, job satisfaction, person-job

fit, intention to leave, work-family conflict, and self-handicapping. Thus, the CFA results

support the discriminant validity for the measures in the study.

4.2 Descriptive Analyses

Table 3 displays the results of the descriptive analyses, showing means, standard

deviations, and correlations among the study variables, with coefficient alphas presented on

the diagonal. This study assigned the means of transformational leadership to each member of

the same group to calculate the individual-level correlations. The reliabilities of the study

variables were above .70, and their correlations were as expected.

4.3 Hypothesized Structural Model

To partition the variance at individual and group levels, this study adopted multilevel

modeling approach in hypotheses testing. These HLM models could allow the estimations of

the individual level effects as well as the group level predictor on both intercepts and slopes.

42

Table 3 Means, Standard Deviations, and Correlations of the Study Variablesa

Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1. HPWS 3.23 .50 (.94)

2. Job Crafting 3.59 .42 .22** (.83)

3. Work Engagement 3.47 .56 .16* .32** (.91)

4. Burnout 3.27 .67 .03 -.13* -.29** (.85)

5. Affective Commitment 3.31 .67 .42** .27** .36** -.17** (.80)

6. Job Satisfaction 3.48 .69 .41** .33** .36** -.20** .74** (.95)

7. Person-Job Fit 3.57 .64 .33** .30** .37** -.18** .65** .83** (.92)

8. Intention to Leave 2.54 .75 -.26** -.19** -.27** .26** -.50** -.63** -.62** (.80)

9. Work-Family Conflict 2.89 .70 -.07 -.01 -.09 .28** -.01 -.19** -.18** .32** (.80)

10. Self-handicapping 2.64 .54 .02 -.03 -.10 .31** .04 -.06 -.12 .34** .42** (.84)

11. Gender .52 .50 .24** .06 .00 -.05 .16* .16* .10 -.03 .14* .13* (--)

12. Age 34.78 6.57 .04 -.02 .06 -.04 .09 -.03 .02 -.11 .02 -.16* -.02 (--)

13. Education 3.20 .73 .12 .04 -.04 .03 .04 .13 .10 .03 .07 .14* .34** -.37** (--)

13. Goal Orientation 3.60 .44 .16* .36* .25* .20* .11 .18** .16* .02 .06 .11 .02 -.11 .11 (.78) 14. Transformational

Leadership 4.13 .45 .11 -.02 -.06 .04 .05 .05 .11 -.01 -.09 .12 .00 -.15* -.19** .06 (.72) a n = 240. Coefficient alphas are presented along the diagonal in parentheses. Data on variables 1-13 were reported by individual group members;

variable 14 was evaluated by group leaders. b * p < .05, ** p < .01, *** p < .001

43

The group mean centering technique was adopted when testing the cross-level interactive

effects of HPWS and job crafting on work engagement and burnout (the first stage

moderation effects) and the level 1 interactive effects of work engagement, burnout, and job

crafting on outcome variables, respectively (the second stage moderation effects). In addition,

grand mean centering was applied to level 2 intercept and slope terms in order to reduce

potential collinearity, modeling the potential influences of within- and between-group

variances (Hofmann & Gavin, 1998). Overall, such an analytical approach not only integrated

moderated regression procedures for testing both mediation and moderated mediation effects

but also clearly depicted the mediated and moderated nature of the relationships among

variables.

Hypothesis 1 proposes that work engagement (level 1) mediates the cross-level

relationship between HPWS (level 2) and affective commitment, job satisfaction, person-job

fit, intention to leave, work-family conflict, and self-handicapping, respectively. Hypothesis 2

proposes that burnout (level 1) mediates the cross-level relationship between HPWS (level 2)

and affective commitment, job satisfaction, person-job fit, intention to leave, work-family

conflict, and self-handicapping, respectively. Hypotheses 3a predicts that job crafting (level 1)

moderates the mediating effect of the cross-level relationship between HPWS (level 2) and

positive employee outcomes, such as job satisfaction, affective commitment, and person-job

fit, at the high and low levels of job crafting. Hypotheses 3b predicts that job crafting (level 1)

moderates the mediating effect of the cross-level relationship between HPWS (level 2) and

negative employee outcomes, such as intention to leave, work-family conflicts, and

self-handicapping, at the high and low levels of job crafting.

For Hypothesis 1 and Hypothesis 2, this study followed Baron and Kenny (1986) to test

for mediation effects. Three steps were performed when entering the variables into the model.

The level 1 control variables, gender, age, education, and goal orientation, and the level 2

44

control variable, transformational leadership, were entered first, followed by the independent

variable of HPWS (Model 1, Model 3, and Model 5), and finally the mediators of work

engagement and burnout were entered to test the mediation effect (Model 6, Model 7, and

Model 8). To test moderated mediation Hypotheses 3a and 3b, this study applied Edwards

and Lambert's (2007) moderated path analysis to estimate a first-stage effect (i.e., Model 2

and Model 4, the interactive effect of HPWS and job crafting on work engagement and

burnout, respectively); a second-stage effect (i.e., Model 9 and Model 10, the interactive

effects of work engagement and job crafting as well as burnout and job crafting on each

outcome variable, respectively); and an overall direct effect (i.e., Model 12, controlling the

interactive effects of work engagement and job crafting and burnout and job crafting and the

interactive effect of HPWS and job crafting on each outcome variable through work

engagement and burnout, respectively). The rationale for testing the second-stage and overall

indirect effects is to check if HPWS × job crafting moderation effects exist in those stages.

Next, bootstrapping was employed to conduct a product of coefficients test for a series

of mediation hypotheses; these results indicate the indirect effect of HPWS on each outcome

variable via work engagement and burnout. Further, a series of moderated mediation

hypotheses were tested to estimate the interactive effect of HPWS and job crafting on each

outcome variable via work engagement and burnout. The results of all hypotheses testing are

presented in Table 4-15 for each outcome variable.

4.4 The Mediating Effects and an Integrated Moderated-

Mediation Model of Work Engagement

Affective Commitment. As shown in Table 4, Model 1 specifies the path from HPWS to

work engagement (γ = .18), and Model 6 specifies the path from work engagement to

45

affective commitment (γ = .36, p < .01), providing the results for paths modeled respectively.

To test the mediation effects, a traditional indirect effect analysis (a × b) was used by

applying bootstrapping analysis. Additionally, Table 4 shows that job crafting moderated the

indirect effect of HPWS on employee affective commitment via work engagement due to its

moderating effect on the relationship between HPWS and employee work engagement (i.e.,

the first-stage effect; Model 2, γ = .81, p < .05). Job crafting does not moderate the

relationship between employee work engagement and employee affective commitment (i.e.,

the second-stage effect; Model 9, γ = -.13). The interactive effect of HPWS and job crafting

does not have a direct impact on employee affective commitment (i.e., the direct-stage effect;

Model 12, γ = .64). Table 5 summarizes the indirect mediating effects and the

moderated-mediating effects. Thus, given that affective commitment is the target outcome,

Hypothesis 1 is not supported; yet, Hypothesis 3a is fully verified, indicating that a high level

of job crafting moderates the mediating effect of the cross-level relationship between HPWS

and affective commitment via work engagement.

Job Satisfaction. As shown in Table 6, Model 1 specifies the path from HPWS to work

engagement (γ = .18), and Model 6 specifies the path from work engagement to job

satisfaction (γ = .37, p < .01), providing the results for paths modeled respectively. To test the

mediation effects, a traditional indirect effect analysis (a × b) was used by applying

bootstrapping analysis. Additionally, Table 6 shows that job crafting moderated the indirect

effect of HPWS on employee job satisfaction via work engagement due to its moderating

effect on the relationship between HPWS and employee job satisfaction (i.e., the first-stage

effect; Model 2, γ = .81, p < .05). Job crafting does not moderate the relationship between

employee work engagement and employee job satisfaction (i.e., the second-stage effect;

Model 9, γ = -.08). The interactive effect of HPWS and job crafting shows a direct impact on

employee job satisfaction (i.e., the direct-stage effect; Model 12, γ = .61, p < .05). Table 7

46

summarizes the indirect mediating effects and the moderated-mediating effects. Thus, given

that job satisfaction is the target outcome, Hypothesis 1 is not supported; yet, Hypothesis 3a

is fully verified, indicating that a high level of job crafting moderates the mediating effect of

the cross-level relationship between HPWS and job satisfaction via work engagement.

Person-Job Fit. As shown in Table 8, Model 1 specifies the path from HPWS to work

engagement (γ = .18), and Model 6 specifies the path from work engagement to person-job fit

(γ = .35, p < .01), providing the results for paths modeled respectively. To test the mediation

effects, a traditional indirect effect analysis (a × b) was used by applying bootstrapping

analysis. Additionally, Table 8 shows that job crafting moderated the indirect effect of HPWS

on employee person-job fit via work engagement due to its moderating effect on the

relationship between HPWS and employee work engagement (i.e., the first-stage effect;

Model 2, γ = .81, p < .05). Job crafting does not moderate the relationship between employee

work engagement and employee person-job fit (i.e., the second-stage effect; Model 9, γ =

-.25). The interactive effect of HPWS and job crafting shows a direct impact on employee

person-job fit (i.e., the direct-stage effect; Model 12, γ = .74, p < .05). Table 9 summarizes

the indirect mediating effects and the moderated mediating effects. Thus, given that

person-job fit is the target outcome, Hypothesis 1 is not supported; yet, Hypothesis 3a is fully

verified, indicating that a high level of job crafting moderates the mediating effect of the

cross-level relationship between HPWS and person-job fit via work engagement.

Regarding the interactive effect of HPWS and job crafting on work engagement, using a

cut value of one standard deviation above and below the mean of job crafting, additional

simple slope tests showed that HPWS are positively related with work engagement under

conditions of higher job crafting. Figure 2 indicates the interactive effect of HPWS and job

crafting on work engagement when job crafting is higher (β = .55, t = 2.40, p < .05) rather

than lower (β = -.14, t = -.93, ns).

47

Table 4 Results of Hierarchical Linear Modeling Models

Variables

Work Engagement Burnout Affective Commitment

M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12

Control Variable

Gender (L1) -.16 -.16 .07 .08 .19 .24* .20 .24* .24* .21* .24* .22*

Age (L1) .00 .00 .00 .00 .01 .01 .01 .01 .01 .01 .01 .01 Education (L1) -.07 -.06 .06 .04 -.07 -.05 -.05 -.04 -.05 -.05 -.04 -.04 GO (L1) .46*** .31** .23 .39** .26* .12 .31** .16 .02 .16 .06 .05 TFL (L2) -.14 -.12 .06 .04 .00 .00 .00 .00 .00 .00 .00 .00

Independent Variable HPWS (L2) .18 .23 -.11 -.13 .61*** .61*** .61*** .61*** .62*** .62*** .63*** .63***

Mediator WE (L1) .36** .31** .31** .27* .25* Burnout (L1) -.19* -.10 -.17* -.09 -.08

Moderator

JC (L1) .33*** -.33** .32* .36** .30* .31** Interaction

HPWS*JC (L1*L2) .81* -.94* .64 WE*JC (L1*L1) -.13 -.13 -.13

Burnout*JC (L1*L1) .11 .06 .07 GO = Goal Orientations; TFL = Transformational Leadership; WE = Work Engagement; JC = Job Crafting / L1 = Level 1; L2 = Level 2

Unstandardized coefficients reported. / * p < .05, ** p < .01, *** p < .001

48

Table 5 Bootstrap Analysis on the Moderated-Mediation of HPWS on Affective Commitment

Path Mediation Effect 95% Bootstrap

Lower Upper

HPWS Work Engagement Affective Commitment .065 -.02259 .17970

High Levels of Job Crafting (+1SD) .028 .05162 .41380 Low Levels of Job Crafting (-1SD) -.028 -.18650 .08987

Unstandardized coefficients reported.

* p < .05, ** p < .01, *** p < .001

49

Table 6 Results of Hierarchical Linear Modeling Models

Variables

Work Engagement Burnout Job Satisfaction

M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12

Control Variable

Gender (L1) -.16 -.16 .07 .08 .17 .22* .18 .22* .22* .20 .23* .21*

Age (L1) .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 Education (L1) -.07 -.06 .06 .04 .00 .02 .02 .03 .01 .02 .02 .03 GO (L1) .46*** .31** .23 .39** .42** .27** .47*** .33* .12 .27* .18 .17 TFL (L2) -.14 -.12 .06 .04 .03 .03 .03 .03 .03 .03 .04 .04

Independent Variable HPWS (L2) .18 .23 -.11 -.13 .44** .44** .44** .44** .45** .44** .45** .45**

Mediator WE (L1) .37** .30* .30* .25* .24

Burnout (L1) -.23** -.15 -.19* -.12 -.11 Moderator

JC (L1) .33*** -.33** .43*** .46*** .41** .42** Interaction

HPWS*JC (L1*L2) .81* -.94* .61* WE*JC (L1*L1) -.08 -.11 -.11 Burnout*JC (L1*L1) -.03 -.07 -.07

GO = Goal Orientations; TFL = Transformational Leadership; WE = Work Engagement; JC = Job Crafting / L1 = Level 1; L2 = Level 2

Unstandardized coefficients reported. / * p < .05, ** p < .01, *** p < .001

50

Table 7 Bootstrap Analysis on the Moderated-Mediation of HPWS on Job Satisfaction

Path Mediation Effect 95% Bootstrap

Lower Upper

HPWS Work Engagement Job Satisfaction .067 -.02098 .18650

High Levels of Job Crafting (+1SD) .029 .04840 .43000 Low Levels of Job Crafting (-1SD) -.029 -.19680 .09842

Unstandardized coefficients reported.

* p < .05, ** p < .01, *** p < .001

51

Table 8 Results of Hierarchical Linear Modeling Models

Variables

Work Engagement Burnout P-J Fit

M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12

Control Variable

Gender (L1) -.16 -.16 .07 .08 .04 .09 .00 .08 .08 .06 .09 .07

Age (L1) .00 .00 .00 .00 .01 .01 .01 .01 .00 .00 .01 .01 Education (L1) -.07 -.06 .06 .04 .02 .03 .05 .04 .04 .03 .04 .05 GO (L1) .46*** .31** .23 .39** .38** .23* .44*** .26** .14 .25* .15 .15 TFL (L2) -.14 -.12 .06 .04 .13 .13 .18* .13 .13 .13 .14 .14

Independent Variable HPWS (L2) .18 .23 -.11 -.13 .31* .31* .26 .31* .34* .31 .33* .33*

Mediator WE (L1) .35** .31** .30* .29** .27*

Burnout (L1) -.19* -.08 -.13 -.05 -.04 Moderator

JC (L1) .33*** -.33** .31* .36** .29* .29**

Interaction

HPWS*JC (L1*L2) .81* -.94* .74* WE*JC (L1*L1) -.25 -.29 -.31 Burnout*JC (L1*L1) -.09 -.16 -.16

GO = Goal Orientations; TFL = Transformational Leadership; WE = Work Engagement; JC = Job Crafting / L1 = Level 1; L2 = Level 2

Unstandardized coefficients reported. / * p < .05, ** p < .01, *** p < .001

52

Table 9 Bootstrap Analysis on the Moderated-Mediation of HPWS on P-J Fit

Path Mediation Effect 95% Bootstrap

Lower Upper

HPWS Work Engagement P-J Fit .063 -.01889 .17620

High Levels of Job Crafting (+1SD) .027 .04638 .41030 Low Levels of Job Crafting (-1SD) -.027 -.18340 .09441

Unstandardized coefficients reported.

* p < .05, ** p < .01, *** p < .001

53

FIGURE 2. The Interactive Effect of HPWS and Job Crafting on Work Engagement

4.5 The Mediating Effects and an Integrated Moderated-

Mediation Model of Burnout

Intention to Leave. As shown in Table 10, Model 3 specifies the path from HPWS to

burnout (γ = -.11), and Model 7 specifies the path from burnout to intention to leave (γ = .21,

p < .01), providing the results for the paths modeled respectively. To test the mediation effects,

a traditional indirect effect analysis (a × b) was used by applying bootstrapping analysis.

Additionally, Table 10 shows that job crafting moderated the indirect effect of HPWS on

employee intention to leave via burnout due to its moderating effect on the relationship

between HPWS and employee burnout (i.e., the first-stage effect; Model 4, γ = -.94, p < .05).

Job crafting does not moderate the relationship between employee burnout and employee

intention to leave (i.e., the second-stage effect; Model 10, γ = .04). The interactive effect of

HPWS and job crafting does not have a direct impact on employee intention to leave (i.e., the

3

3.1

3.2

3.3

3.4

3.5

3.6

3.7

3.8

3.9

4

Low HPWS High HPWS

Wor

k E

ngag

emen

t

Low Job Crafting

High Job Crafting

54

direct-stage effect; Model 12, γ = -.12). Table 11 shows the indirect mediating effects and the

moderated mediating effects. Therefore, given that intention to leave is the target outcome,

Hypothesis 2 is not supported; yet, Hypothesis 3b is fully supported, indicating the a high

level of job crafting moderates the mediating effect of the cross-level relationship between

HPWS and intention to leave via burnout.

Work-Family Conflict. As shown in Table 12, Model 3 specifies the path from HPWS to

burnout (γ = -.11), and Model 7 specifies the path from burnout to work–family conflict

(γ = .28, p < .01), providing the results for the paths modeled respectively. To rest the

mediation effects, a traditional indirect effect analysis (a × b) was used by applying

bootstrapping analysis. Additionally, Table 12 shows that job crafting moderated the indirect

effect of HPWS on employee work–family conflict via burnout due to its moderating effect

on the relationship between HPWS and employee burnout (i.e., the first-stage effect; Model 4,

γ = -.94, p < .05). Job crafting does not moderate the relationship between employee burnout

and employee work–family conflict (i.e., the second-stage effect; Model 10, γ = .18). The

interactive effect of HPWS and job crafting does not have a direct impact on employee

work-family conflict (i.e., the direct-stage effect; Model 12, γ = .44). Table 13 summarizes

the indirect mediating effects and the moderated mediating effects. Therefore, given that

work-family conflict is the target outcome, Hypothesis 2 is not supported; yet, Hypothesis 3b

is fully supported, indicating that a high level of job crafting moderates the mediating effect

of the cross-level relationship between HPWS and work–family conflict via burnout.

Self-Handicapping. As shown in Table 14, Model 3 specifies the path from HPWS to

burnout (γ = -.11), and Model 7 specifies the path from burnout to self-handicapping (γ = .21,

p < .001), providing the results for the paths modeled respectively. To test the mediation

effects, a traditional indirect effect analysis (a × b) was used by applying bootstrapping

analysis. Additionally, Table 14 shows that job crafting moderated the indirect effect of

55

HPWS on employee self-handicapping via burnout due to its moderating effect on the

relationship between HPWS and employee burnout (i.e., the first-stage effect; Model 4, γ =

-.94, p < .05). Job crafting does not moderate the relationship between employee burnout and

employee self-handicapping (i.e., the second-stage effect; Model 10, γ = .10). The interactive

effect of HPWS and job crafting does not have a direct impact on employee

self-handicapping (i.e., the direct-stage effect; Model 12, γ = -.74, p < .05). Table 15

summarizes the indirect mediating effects and the moderated mediating effects. Therefore,

given that self-handicapping is the target outcome, Hypothesis 2 is not supported; yet,

Hypothesis 3b is fully supported, indicating that a high level of job crafting moderates the

mediating effect of the cross-level relationship between HPWS and self-handicapping via

burnout.

With respect to the interactive effect of HPWS and job crafting on burnout, using a cut

value of one standard deviation above and below the mean of job crafting, additional simple

slope tests showed that HPWS are positively related with burnout under conditions of higher

job crafting. Figure 3 demonstrates the interactive effect of HPWS and job crafting on

burnout when job crafting is higher (β = -.52, t = -2.01, p < .05) rather than lower (β = .28, t =

1.72, ns).

56

Table 10 Results of Hierarchical Linear Modeling Models

Variables

Work Engagement Burnout Intention to Leave

M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12

Control Variable

Gender (L1) -.16 -.16 .07 .08 .04 .00 .03 .00 .00 .02 .00 -.01

Age (L1) .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 Education (L1) -.07 -.06 .06 .04 .12 .10 .09 .09 .10 .10 .09 .09 GO (L1) .46*** .31** .23 .39** -.22 -.09 -.26 -.15 -.02 -.15 -.07 -.08 TFL (L2) -.14 -.12 .06 .04 .03 .03 .03 .03 .03 .03 .02 .02

Independent Variable HPWS (L2) .18 .23 -.11 -.13 -.44** -.44** -.44** -.44** -.44** -.44** -.44** -.44**

Mediator WE (L1) -.31** -.24* -.27* -.22* -.22*

Burnout (L1) .21** .14 .19* .13 .13 Moderator

JC (L1) .33*** -.33** -.22 -.24 -.19 -.17

Interaction

HPWS*JC (L1*L2) .81* -.94* -.12 WE*JC (L1*L1) .06 .10 .09 Burnout*JC (L1*L1) .04 .08 .05

GO = Goal Orientations; TFL = Transformational Leadership; WE = Work Engagement; JC = Job Crafting / L1 = Level 1; L2 = Level 2

Unstandardized coefficients reported. / * p < .05, ** p < .01, *** p < .001

57

Table 11 Bootstrap Analysis on the Moderated-Mediation of HPWS on Intention to Leave

Path Mediation Effect 95% Bootstrap

Lower Upper

HPWS Burnout Intention to Leave .023 -.08186 .02123

High Levels of Job Crafting (+1SD) .011 -.25830 -.01213 Low Levels of Job Crafting (-1SD) -.011 -.03072 .17720

Unstandardized coefficients reported.

* p < .05, ** p < .01, *** p < .001

58

Table 12 Results of Hierarchical Linear Modeling Models

Variables

Work Engagement Burnout Work-Family Conflict

M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12

Control Variable

Gender (L1) -.16 -.16 .07 .08 .18 .17 .16 .17 .16 .16 .16 .15

Age (L1) .00 .00 .00 .00 .01 .01 .01 .01 .01 .01 .01 .01 Education (L1) -.07 -.06 .06 .04 .09 .08 .06 .06 .09 .06 .07 .03 GO (L1) .46*** .31** .23 .39** -.06 -.02 -.12 -.14 .01 -.11 -.11 -.11 TFL (L2) -.14 -.12 .06 .04 -.11 -.11 .11 -.11 -.11 -.12 -.11 -.11

Independent Variable HPWS (L2) .18 .23 -.11 -.13 -.20 -.20 -.20 -.20 -.17 -.18 -.17 -.18

Mediator WE (L1) -.09 .04 -.07 .04 .02

Burnout (L1) .28** .29** .27** .27** .32** Moderator

JC (L1) .33*** -.33** -.06 .01 .00 .08

Interaction

HPWS*JC (L1*L2) .81* -.94* .44 WE*JC (L1*L1) -.31 -.23 -.26 Burnout*JC (L1*L1) .18 .14 -.05

GO = Goal Orientations; TFL = Transformational Leadership; WE = Work Engagement; JC = Job Crafting / L1 = Level 1; L2 = Level 2

Unstandardized coefficients reported. / * p < .05, ** p < .01, *** p < .001

59

Table 13 Bootstrap Analysis on the Moderated-Mediation of HPWS on Work-Family Conflict

Path Mediation Effect 95% Bootstrap

Lower Upper

HPWS Burnout Work-Family Conflict .031 -.10210 .02650 High Levels of Job Crafting (+1SD) .014 -.32060 -.02009

Low Levels of Job Crafting (-1SD) -.014 -.03963 .22280 Unstandardized coefficients reported. * p < .05, ** p < .01, *** p < .001

60

Table 14 Results of Hierarchical Linear Modeling Models

Variables

Work Engagement Burnout Self-Handicapping

M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12

Control Variable

Gender (L1) -.16 -.16 .07 .08 .20* .20* .19* .20* .19* .19* .19* .20*

Age (L1) .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 Education (L1) -.07 -.06 .06 .04 .03 .03 .01 .01 .03 .01 .01 .01 GO (L1) .46*** .31** .23 .39** -.01 .01 -.05 -.08 .03 -.05 -.07 -.07 TFL (L2) -.14 -.12 .06 .04 .15* .15* .15* .15* .15* .14* .15* .14*

Independent Variable HPWS (L2) .18 .23 -.11 -.13 -.10 -.10 -.10 -.10 -.09 -.09 -.08 -.09

Mediator WE (L1) -.04 .06 -.03 .06 .08

Burnout (L1) .21*** .23** .21** .22** .22**

Moderator JC (L1) .33*** -.33** .02 .36** .00 -.02

Interaction

HPWS*JC (L1*L2) .81* -.94* -.74* WE*JC (L1*L1) -.11 -.05 -.06 Burnout*JC (L1*L1) .10 .08 .04

GO = Goal Orientations; TFL = Transformational Leadership; WE = Work Engagement; JC = Job Crafting / L1 = Level 1; L2 = Level 2

Unstandardized coefficients reported. / * p < .05, ** p < .01, *** p < .001

61

Table 15 Bootstrap Analysis on the Moderated-Mediation of HPWS on Self-Handicapping

Path Mediation Effect 95% Bootstrap

Lower Upper HPWS Burnout Self-Handicapping .023 -.07567 .02031

High Levels of Job Crafting (+1SD) .011 -.23690 -.01994 Low Levels of Job Crafting (-1SD) -.011 -.03178 .16460

Unstandardized coefficients reported.

* p < .05, ** p < .01, *** p < .001

62

FIGURE 3. The Interactive Effect of HPWS and Job Crafting on Burnout

3

3.1

3.2

3.3

3.4

3.5

3.6

3.7

3.8

3.9

4

Low HPWS High HPWS

Bur

nout

Low Job Crafting

High Job Crafting

63

CHAPTER 5 DISCUSSION

This study examines why and how HPWS may cascade down through different levels

and impact employee outcomes. Using the JD-R theory (Bakker & Demerouti, 2014) and job

crafting theory (Wrzesniewski & Dutton, 2001), this study suggests that the effects of HPWS,

either perceived as job demands or job resources, may carve employee well-being, and that

the associated employee job crafting behavior regulates employees' experiences of work

engagement and burnout, ultimately driving employee work attitudes and behaviors. The key

findings of this study are twofold. First, although in line with two JD-R psychological

processes, the empirical evidence reveals that work engagement does not mediate the

relationships between HPWS and employee affective commitment, job satisfaction, and

person-job fit, and nor does burnout mediate the relationships between HPWS and employee

intention to leave, work-family conflict, and self-handicapping. In fact, this study did not

discover significant relationships between HPWS and employee well-being. Possibly,

relationships among subsystems of HPWS and employee well-being exist, or there are some

missing links between such relationships. Another explanation could be that there is an

offsetting effect whereby the work engagement mechanism of HPWS influences offsets

burnout mechanism of HPWS influences and vice versa.

The second and main finding of this study is that there is support for the

moderated-mediation hypotheses. This study finds that only when these cascading mediating

effects are moderated by employee job crafting behavior, then, HPWS are able to have

indirect effects on employee affective commitment, job satisfaction, and person-job fit

through work engagement and indirect effects on employee intention to leave, work-family

conflict, and self-handicapping through burnout. These findings imply that individual factors

are more highly valued in employees' conceptions of HPWS than are enacted HPWS that

flow outward through employee well-being to activate the mediating links between the

64

independent variable and the outcomes. Specifically, this study finds that job crafting plays

an important role in shaping employee working attitudes and behaviors, leading to two key

observations. First, employees with high levels of job crafting are able to shape HPWS into

job resources, which, in turn, facilitate work engagement, and eventually augment his or her

own affective commitment, job satisfaction, and person-job fit. Second, employees with high

levels of job crafting are able to interact with HPWS in seeking resources, seeking challenges,

and reducing demands. Thus, for those employees with high levels of job crafting

significantly renovate HPWS, even shifting from work engagement to burnout as they take on

different tasks, they are able to find silver lining in making the best of things and to avoid

indulging oneself with the intention to leave, floating oneself ups and downs in work-family

conflict, and crushing oneself with self-handicapping.

Overall, although mediations are not hypothesized, the findings of this study indicate

that, with employee proactive job crafting behaviors, HPWS effectively facilitate employee

attitudinal and behavioral outcomes. As individuals craft jobs, work engagement and burnout

emerge as important links between HPWS and employee working attitudes and behaviors. In

sum, I develop a JD-R model of HPWS, test employee well-being in the relationships

between HPWS and employee outcomes, and explore job crafting as the boundary condition

of HPWS-employee outcomes linkages. As well, this study sheds light on the impact and

influence process of HPWS in line with the JD-R theory (Bakker & Demerouti, 2014) and job

crafting theory, building bridges across the various streams to disentangle the paradoxical

nature of HPWS.

5.1 Theoretical Contributions and Implications

The current findings reveal several theoretical contributions and implications. Building

65

on the JD-R theory (Bakker & Demerouti, 2014), the first major contribution of` this study is

the simultaneous investigations of bright- and dark-sides of the HPWS effects. Existing

HPWS research has investigated positive HPWS outcomes and negative HPWS outcomes

separately. This study links the outcomes of these two opposite spectrums by proposing a

JD-R model to elucidate the dual processes, a health impairment process and a motivational

process, in disentangling why HPWS simultaneously involve negative effects on employees

in addition to positive ones. Thus, this study measured employees' perceptions of HPWS and

assessed their attitudinal and behavioral responses to HPWS. Employees are the key

informants regarding the extent to which HPWS intentions are enacted. These analyses were

able to illustrate how HPWS work differently on employees. Based on the findings, it is clear

that the implementation of HPWS does not rest on employee perceptions of job resources or

job demands that can result in work engagement or burnout. The results differ from

Hypotheses 1 and Hypotheses 2 in that the weight falls much more on the side of individual

proactive stances against HPWS than on the side of individual reactions toward HPWS.

The second major contribution is examining the potentially important role of individual

proactive behavior, namely job crafting, when encountering HPWS in the workplace. Per

JD-R theory (Bakker & Demerouti, 2014), HPWS may trigger either a health impairment

process or a motivational process. The inclusion of two opposing mechanisms is necessary to

explain the paradoxical nature of HPWS. However, individual proactive behaviors matter.

Integrating job crafting theory (Wrzesniewski & Dutton, 2001), this study proposed that

employee job crafting in line with HPWS may trigger either a health impairment process or a

motivational process in predicting employee outcomes. The results of this study show that

employee with a high level of job crafting amplifies the mediating effect of the cross-level

relationships between HPWS and positive employee affective commitment, job satisfaction,

and person-job fit. Also, employee with a high level of job crafting attenuates the mediating

66

effect of the cross-level relationships between HPWS and negative employee intention to

leave, work-family conflicts, and self-handicapping. Although this study draws on the JD-R

theory (Bakker & Demerouti, 2014) to explain how HPWS work differently on employees,

job crafting theory (Wrzesniewski & Dutton, 2001) invokes and constructs a multilevel,

employee-focused moderated-mediation JD-R model together with the JD-R theory (Bakker

& Demerouti, 2014), framing a more comprehensive overview of the process of HPWS.

Extending previous research, the findings delineate that employee job crafting plays a critical

role in HPWS, strengthening the contingency perspective.

A third major contribution is to take job crafting a step further; it is job crafting that

characterizes HPWS a higher possibility of evoking positive or negative effects on employees.

HPWS offer opportunities for employees with high levels of job crafting to seek resources

and challenges to fulfill their needs, growth, strength, and interests (Wrzesniewski et al., 2010)

and empower employees with high levels of job crafting to reduce demands to cope with

stress and to remain healthy, reflecting individual achievement as regards affective

commitment, job satisfaction, and person-job fit. Following this, it is not surprising that job

crafting plays useful role in attaining affective commitment, job satisfaction, and person-job

fit, and in weakening intention to leave through work engagement even with a state of

burnout. These findings shed a new light that future research need to pay greater attention to

employee proactive behaviors in HPWS in order to develop a better understanding of HPWS

effectiveness.

My results address recent calls made in the HPWS literature for making employees the

focus in HPWS (Boselie et al., 2005; Guest, 2013) by looking into individual differences

(Wright & Nishii, 2007) in terms of proactive job crafting (e.g., Evans & Davis, 2005).

My research theorizes HPWS literature by integrating the JD-R theory (Bakker &

Demerouti, 2014) and job crafting theory (Wrzesniewski & Dutton, 2001) for a more

67

theoretically composite study of HPWS. Taking a step forward, this study frames an

integrative moderated-mediation JD-R model to reveal the contingent role of employee job

crafting behavior in stimulating or preventing how HPWS cascade down to employee

outcomes.

This study also provides solid evidence that the extent to which work engagement or

burnout mediating the cross-level relationships between HPWS and employee positive or

negative outcomes depends on employees' levels of job crafting. As such, this study provides

valuable insights into how to simultaneously disentangle paradoxical effects of HPWS as

well as the boundary conditions by conceptualizing and testing a moderated-mediation JD-R

model.

5.2 Practical Implications

Although HPWS are important to improve organizational human capital using unique

goals associated with HR practices, this study highlights the importance of the role of

employee job crafting in redesigning HPWS and in simultaneously strengthening employee

work engagement and attenuating employee burnout. This study brings a number of

significant practical implications. First, employees are essential in the execution of HPWS.

Therefore, organizations should be more vigilant regarding how employees play their roles in

the process of HPWS, especially when managing employees focused on developing their

well-being. One promising way is for organizations to evaluate employees' perceptions of

HPWS, which should help them assess the positive effects of HPWS on employee work

engagement and the negative effects of HPWS on employee burnout. Thus, this study

recommends organizations apply practical exercises to elaborate HR goals in line with

employee needs. By doing so, employees will be encouraged to adopt a mutual gain strategy

68

when organizations introduce HPWS (Zhang, Fan, & Zhu, 2014), especially for those

organizations seeking to increase employees' ability, motivation, and competence.

Second, employees who perceive HPWS positively are willing to engage in

organizationally desirable working attitudes and behaviors. This study identifies that HPWS,

to some extents, help employees attain their growth goals by providing them the opportunities

in crafting jobs. Employees with high levels of job crafting report higher affective

commitment, greater job satisfaction, better person-job fit, reduced intention to leave, lesser

work-family conflict, and weaker self-handicapping. This means that organizations should

design and implement HPWS in a way that leads to the accomplishment of the intended goals

of HPWS aligned with the pursued goals of employees. It takes the active cooperation of two

parties to leverage HR practices. These issues should be of interest to HR practitioners in

focusing on how HPWS elicit employees' proactive behavior in remodeling work and induce

positive employee responses instead of reckoning employees' perceptions of HPWS into

work intensification, leading employees to feel confident in developing valuable proactive

behaviors, such as job crafting, within HPWS. As well, organizations should include

proactive behaviors in the selection criteria when recruiting employees. By doing so,

organizations might benefit from employees' proactive behaviors.

Third, for those who view HPWS as work intensification or for those who are not able to

undertake job crafting appropriately, organizations can provide support in combating

exhaustion, stress, and fatigue. For example, organizations can encourage supervisors,

usually considered the embodiments of organizations, to have regular interactions with

subordinates to show employees care, ensure employees' security, to try to mitigate the

notions that HPWS comes at the expense of employees.

At the same time, job crafting training (Demerouti & Bakker, 2014; i.e., seeking

resources, seeking challenges, and reducing demands) and give-and-take exercises (Grant,

69

2014) are encouraged to enable HPWS to catapult employees and organizations to success.

Organizations must be aware of not making employees craft HPWS at their expense. Overall,

the findings of this study suggest that organizations can enhance employee well-being and

proactive behaviors by framing HPWS in ways that work to fulfill the needs of employees,

ensure employees' psychological well-being, and stimulate desirable employee proactive

behaviors.

5.3 Limitations and Directions for Future Research

As with any research, this study has limitations. First, with regard to well-being, this

study did not examine particular categories of job demands and job resources within the

domain of HPWS. Rather, it built on the insights of the JD-R theory (Bakker & Demerouti,

2014), arguing that it is how employees interpret HPWS that affects their perceptions of

HPWS in terms of job demands and job resources. This study tested the effects of perceived

high performance work practices on employee work engagement and burnout. Although the

arguments about the relationships between HPWS and work engagement and burnout are

important in themselves, future research can investigate job demands and job resources by

elucidating their roles in HRM.

Second, there still remains a doubt with causality and the threat of common method

variance (Podsakoff et al., 2003). However, this study adopted a time-lagged design (i.e.,

three-wave data collection) and two sources (i.e., employees and supervisors) to test the

cross-lagged effects, which is more rigorous than these HPWS studies with cross-sectional

approaches. To be more rigorous, future research can adopt longitudinal data collection that

allows for the examination of reverse causal relationships and helps reduce common method

bias. Third, this study takes employee job crafting as the key moderator into consideration.

70

Nevertheless, additional possible boundary conditions deserve further discussion. For

instance, replicating extant HPWS literature to examine aspects of unit-level climate (i.e.,

Takeuchi et al., 2009) and organizational power distance (i.e., Wu & Chaturvedi, 2009)

acknowledge future research directions. Another interesting avenue for future research is to

investigate macro HPWS contingency factors with departmental, organizational or industrial

factors (e.g., concern for employee climate at the unit level, Takeuchi et al., 2009; business

strategy, Sun et al., 2007; industry types, Chin & Lin, 2010) as potential moderators in

HPWS implementation processes. Fourth, this study explores employees' perceptions of

HPWS only and does not focus on supervisors' or line managers' reports about HPWS.

Although this study does not distinguish the effectiveness of managerial aspects of HPWS

from the effectiveness of employee perceptions of HPWS, it is still valuable for future

research to investigate the consistencies or discrepancies in management-HPWS and

employee-HPWS (i.e., Liao et al., 2009), recognizing potential mechanisms or contextual

factors in HPWS literature.

On the other hand, managerial factors count. Supervisors may execute HR practices

using different types of leadership and exert the most impact on how employees behave at

work. For instance, transformational leadership or perceived supervisor supports may

accentuate the effectiveness of HPWS, engendering a potential synergistic effect (i.e.,

Zacharatos et al., 2005). This study acknowledges the ongoing research about leadership style,

playing the role of either a coordinator or a promoter in amplifying HPWS effectiveness. Still,

there are some possible other well-being mechanisms (i.e., depression-enthusiasm,

anxiety-comfort; Wood et al., 2012) accounted for the roles of HPWS in shaping employees'

outcomes beyond the mediating effect of work engagement and burnout derived from the

JD-R theory (Bakker & Demerouti, 2014). It will be valuable for future research to assess

understudied yet promising mediators of psychological capital, regulatory foci, or

71

approach-avoidance conflict to refine our understanding of how HPWS shape employees'

working outcomes.

This study discusses the fact that HPWS only reward employee working attitudes and

behaviors. However, future studies should certainly pay attention to actual turnover and or

employee productivity. Finally, in line with the existing literature (e.g., Lepak et al., 2006;

Liao et al., 2009), this study advocates that HR practices are aligned into an HR system; thus,

HPWS is best elucidated as an entire HR system rather than as different components of

HPWS. Consequently, HPWS may facilitate employees' work engagement due to their

interpretations of HPWS as job resources or may intensify their burnout in terms of

experiencing HPWS as job demands. However, some scholars (Melian-Gonzalez &

Verano-Tacorante, 2004) have argued that organizations should not apply the same HPWS to

all employees but should rather design unique configurations of HR practices in accordance

with the value and uniqueness of the jobs. Alternatively, dividing a bundle of HR practices

into subsystems is often seen in HRM research. For example, existing research has

categorized HR practices into maintenance-oriented practices and performance-oriented

practices (e.g., Gong et al., 2009). Looking at the AMO model of HPWS, some researchers

has conceptualized and validated HR practices into 'skill-enhancing', 'motivation-enhancing',

and 'opportunity-enhancing' HR practices (e.g., Jiang et al., 2012; Lepak et al., 2006).

Following this, different HR practice may have its own impact on employees' work

engagement or burnout. Future research that explores such rationale would be worthwhile.

5.4 Conclusions

Despite these limitations, this study takes a step further in HPWS multi-level research

by not only simultaneously discussing the bright- and dark-sides of HPWS on employee

72

attitudinal and behavioral outcomes but also by exploring employee proactive behaviors

leading to these outcomes. Bringing together the HPWS literature with JD-R and job crafting

theories, this study responds to the research call by Guest (2002) to examine employee

well-being and that by Evans and Davis (2005) to better understand the proactive role of

employees within HPWS contexts. This multi-level approach thus reveals how an

organizational factor (i.e., HPWS) intertwines with an individual factor (i.e., job crafting) in

shaping HPWS effects on employees and thereby provides organizations, supervisors, and

employees with actionable knowledge about how to utilize HPWS effectively.

73

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APPENDIX A

Survey

員工填答

High-Performance Work System

Source: Chuang, C. H., & Liao, H. (2010). Strategic human resource management in service

context: Taking care of business by taking care of employees and customers. Personnel

Psychology, 63(1), 153–196.

Staffing

1. 用人的時候，會挑選出最好的人選。

2. 用人的時候，以最有學習潛力的人為優先。

3. 用人的時候，以具備能提供最佳客服品質特質與能力的人為優先。

4. 用人的時候，內部員工會優先考量。

5. 有升遷的機會時，以具備足夠資格的員工為優先。

Training

1. 公司提供新進員工了解公司的教育訓練課程。

2. 公司提供長期的培訓/教育訓練的機會。

3. 公司投入大量的時間及預算於技能培訓/教育訓練。

4. 技能培訓/教育訓練的課程內容是相當地廣泛。

5. 技能培訓/教育訓練內容著重於高品質客戶服務為主的課程。

Involvement & Participation

1. 公司決策若會影響員工時，公司會事先詢問員工的意見。

2. 員工常有參與跟工作相關決策的機會。

3. 員工在處理客戶額外的要求時有充分的自主權。

4. 員工在處理客戶抱怨時有充分的自主權，不需要向主管報告。

5. 員工在執行業務時能作必要的彈性處理。

6. 公司全力支援員工在工作上有充分的設備及資源來提供高品質客戶服務。

7. 公司資訊不透明，例如營運狀況、銷售績效等。(R)

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Performance Appraisals

1. 工作績效考核包括生涯發展需求上的回饋。

2. 工作績效考核是以多元評核為基準，例如自我、同事、主管、顧客評鑑等。

3. 工作績效考核是以客觀的或量化的成果為基準。

4. 主管沒有跟員工站在同一陣線作共同目標設定的考量。(R)

5. 客戶滿意是最重要的工作準則。

6. 工作績效考核強調達成客戶需求。

Compensation/Rewards

1. 公司的平均薪資比競爭同業來得高。

2. 公司的薪資及獎酬制度會隨工作績效表現而有所調整。

3. 公司的獎酬制度中包含了提升客戶服務的新點子。

4. 公司提供了相當廣泛的福利制度。

5. 公司的薪酬結構及獎酬制度並不公平。(R)

6. 員工會因特別的努力及優良績效得到財務上或金錢以外的獎酬。

7. 公司會提供給服務客戶優良的員工特別的獎酬。

Caring

1. 公司排班時會考量員工私人的家庭生活或學習情況來調整班表。

2. 公司會關心員工的工安及健康狀況。

3. 公司會關心員工的工作與家庭平衡。

4. 公司有制定一套紓解員工壓力的方法或方案。

5. 公司有制定一套正式申訴的管道及程序。

Job Crafting

Source: Petrou, P., Demerouti, E., Peeters, M. C., Schaufeli, W. B., & Hetland, J. (2012).

Crafting a job on a daily basis: Contextual correlates and the link to work engagement. Journal

of Organizational Behavior, 33(8), 1120–1141.

General level of seeking resources

1. 我會向其他人詢問關於我工作上的表現。

2. 我會向同事詢問工作上的指點。

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3. 我會向我的主管詢問工作上的指點。

4. 在工作中，我試著學習新的事物。

5. 我會接觸工作中的其他人，如主管、同事，來獲得完成任務的必要資訊。

6. 當我在工作上遇到問題或困難時，我會跟工作環境中的同儕討論。

General level of seeking challenges

1. 如果自己的工作任務完成，我會自告奮勇多做一些工作事項。

2. 我會自告奮勇多擔負一些工作職責。

3. 我會自告奮勇做一些工作上額外的雜務。

General level of reducing demands

1. 我試著去相信我的工作不會讓我處於情緒上的緊繃狀態。

2. 我確信我的工作不太會讓我處於精神上的壓縮狀態。

3. 我試著去相信我的工作不會使我體力耗竭。

4. 我試著把工作化繁為簡。

Work engagement

Source: Bakker, A. B. (2014). The job demands-resources questionnaire. Rotterdam: Erasmus

University.

1. 在工作中，我充滿活力。

2. 在工作中，我是精力充沛的人。

3. 我對於我的工作充滿熱誠。

4. 我的工作會鼓舞我。

5. 每天早上起床時，我都會想要去工作。

6. 當我緊湊地工作時，我感到很開心。

7. 我以我的工作為傲。

8. 我沉浸在我的工作中。

9. 當我在工作時，我會太專注在一件事上，而忘了其他的事。

Burnout

Source: Bakker, A. B. (2014). The job demands-resources questionnaire. Rotterdam: Erasmus

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University.

1. 有好一陣子，在工作之前，我都會感到疲倦。

2. 相較於過去，在下班後，我需要更多的時間放鬆和調適自己。

3. 在工作中，我時常覺得精疲力盡。

4. 下班後，我通常會覺得精疲力盡，倦怠於工作。

Affect Commitment

Source: Meyer, J. P., & Allen, N. J. (1997). Commitment in the workplace: Theory, research,

and application. Thousand Oaks, CA: Sage Publications.

1. 我很樂意將我未來的工作生涯與目前這個公司共度。

2. 目前的公司對我而言，具有許多的個人意義。

3. 我覺得公司的問題就是我的問題。

Job Satisfaction

Source: Hackman, J. R., & Oldham, G. R. (1976). Motivation through the design of work: Test

of a theory. Organizational Behavior and Human Performance, 16(2), 250–279.

1. 整體而言，我很滿意目前這份工作。

2. 整體而言，我很喜歡目前這份工作。

3. 整體而言，我很喜歡在這家公司上班。

Person-Job fit

Source: Saks, A. M., & Ashforth, B. E. (1997). A longitudinal investigation of the relationships

between job information sources, applicant perceptions of fit, and work outcomes. Personnel

Psychology, 50(2), 395–426.

1. 我覺得我的知識、技能與能力符合現在這份工作的要求。

2. 目前這份工作符合我的需求。

3. 目前這份工作適合我。

4. 目前這份工作是一份讓我有動力做下去的工作。

Intention to Quit

Source: Cammann, C., Fichman, M., Jenkins, D., & Klesh, J. (1979). The Michigan

organizational assessment questionnaire. Unpublished manuscript, University of Michigan.

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1. 我想離開目前這家公司。

2. 我有可能在明年換工作。

3. 如果再來一次，我仍然會選擇目前的這份工作。(R)

Self-handicapping

Source: Bakker, A. B. (2014). The job demands-resources questionnaire. Rotterdam: Erasmus

University.

1. 我常常拖延事情到最後一刻。

2. 我會犯錯。

3. 在工作上，當我跟別人溝通時，我常會有困惑。

4. 工作時，我會積壓工作相關的事務。

5. 我承認我會給自己工作壓力。

6. 當我做錯事時，我會怪罪於周遭的人事物。

7. 在工作中，我常常闖禍。

8. 我承認我會引起工作上的紛爭。

9. 一到夜晚，我不能入睡。

10. 我承認當我沒有符合他人的期待時，我會忍不住合理化自己的錯誤。

Work-family Conflict

Source: Gutek, B. A., Searle, S., & Klepa L. (1991). Rational versus gender role explanations

for work-family conflict. Journal of Applied Psychology, 76(4), 560–568.

1. 我每天下班後總是累得提不起勁作想做的事。

2. 我工作量太多導致沒時間從事自己有興趣的事。

3. 我的家人或朋友常常抱怨我下班後還得在家處理公事。

4. 我的工作佔據了許多我與家人共處的時間。

Goal Orientations

Source: VandeWalle, D. (1997). Development and validation of a work domain goal orientation

instrument. Educational and Psychological Measurement, 57(6), 995–1015.

Learning Orientation

1. 我願意選擇一個能讓我學習及成長的工作。

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2. 我常常在尋找一個能讓我學習新技能與知識的工作機會。

3. 我很享受那些可讓我學習新技能的充滿挑戰與困難工作。

4. 對我來說，為了學習新技能，我願意承受風險。

5. 我比較喜歡從事一些需要高能力與天賦的工作。

Prove (Performance) Orientation

1. 我喜歡證明我比同事能有更佳的工作績效。

2. 我會想辦法讓別人知道我的工作能力不錯。

3. 當同事知道我的工作表現不錯時，我會樂在其中。

4. 我比較喜歡從事一些可證明我工作能力不錯的工作。

Avoid (Performance) Orientation

1. 我會避免一些讓我表現出很無能的新工作。

2. 對我而言，避免讓人覺得無能比學習新技能來的重要。

3. 我很在意那些讓我看起來很無能的工作項目。

4. 我會極力避免那些讓我看起來很無能的工作狀況。

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主管填答

Transformational Leadership

Source: McCollKennedy, R. R., & Anderson, R. D. (2002). Impact of leadership style and

emotions on subordinate performance. The Leadership Quarterly, 13(5), 545–560.

1. 我會關注我的每一位部屬。

2. 我會將組織的使命感灌輸給我的每一位部屬。

3. 我會激發我的每一位部屬的工作熱誠。

4. 我會鼓勵我的每一位部屬發揮所長。