The role of human resource management in lean management: A system view

(1)

The role of human resource management in lean

management: A system view

Master’s thesis

MSc Supply Chain Management University of Groningen Faculty of Business and Economics

June 24th_{, 2019} LOTTE BOSMA Student number: S2696363

c.e.bosma@student.rug.nl Supervisor: Dr. Ir. Thomas Bortolotti

Co-assessor: Dr. X. Bruce Tong

Acknowledgements: I would like to thank Dr. Ir. Thomas Bortolotti for his guidance and

(2)

ABSTRACT

(3)

TABLE OF CONTENTS

1. Introduction ... 4

2. Theoretical background ... 6

2.1. Lean management ... 6

2.2. Lean as a socio-technical system ... 6

2.3. Human resource management ... 8

2.4. The input-process-output model ... 9

3. Research hypotheses ... 10

4. Methodology ... 14

4.1. Research method ... 14

4.2. Data collection and sample ... 14

4.3. Questionnaires ... 15

4.4. Ethical issues ... 15

4.5. Variables and scales ... 16

4.6. Data preparation ... 18

4.7. Construct unidimensionality, reliability, validity and model fit ... 18

4.8. Model fit ... 21

4.9. Analysis ... 21

4.10. Expert interviews ... 21

5. Results ... 22

5.1. Structural model fit ... 22

(4)

1. INTRODUCTION

In the past years, the economic environment has been changing rapidly. Especially, fast technological developments go together with globalization, leading to an increase in the flow of goods, services and information across the whole world (Gallagher, 2009). These changes lead to growing competition, uncertainty and market fragmentation, which stresses the necessity for companies to become more flexible, innovative and efficient in order to survive (Carlsson, 1996; Carree, Van Stel, Thurik, & Wennekers, 2002; De Menezes, Wood, & Gelade, 2010). Moreover, firms constantly have to evaluate and improve their processes. Lean management is a commonly used instrument to achieve this. Lean basically entails identifying value, eliminating waste and generating flow of value to the customer (Womack & Jones, 1996). Some typical benefits of lean are shorter lead times, smaller inventories and better knowledge management (Melton, 2005). It is a universally used and accepted approach that is associated with increased operational performance and enhanced competitiveness (Arnheiter & Maleyeff, 2005; Shah & Ward, 2003). In the past, many companies have proven the effectiveness of lean management implementation in order to increase performance (Cua, Mckone, & Schroeder, 2001; Fullerton, Kennedy, & Widener, 2014; Shah & Ward, 2003).

Lean management is considered as a socio-technical system consisting of social and technical aspects (Shah & Ward, 2007). Despite, in the past, many studies merely focused on the technical aspects of lean, while not taking into account the human and organizational aspects of lean (Marodin & Saurin, 2013). This lack of awareness is surprising when regarding that it is widely accepted that human resources possess the ability to positively influence

organizational performance (Ahmad & Schroeder, 2003).

Hence, in recent years, researchers are becoming more aware of the social side when researching lean manufacturing. Its importance is stressed by Shah and Ward (2003), who define human resource management (HRM) as a lean bundle and prove its individual relationship with operational performance. Moreover, it is clear that merely advanced

technologies and manufacturing applications are not able to increase operational performance, except if they are accompanied by the required human resource practices to build a cohesive socio-technical system (Ahmad & Schroeder, 2003). More recent literature on lean production shows that HRM forms the foundation for successful lean implementation (Bortolotti,

(5)

individual, team and organizational level (Tortorella, Marodin, Fogliatto, & Miorando, 2015). Furthermore, Furlan, Vinelli and Dal Pont (2011) show that HRM positively influences lean implementation by increasing complementarity between lean manufacturing bundles. Additionally, HRM positively influences lean production practices (Wickramasinghe & Wickramasinghe, 2017).

Although those researches show the acknowledgement of the importance of HRM practices for lean implementation, there is still a lack of understanding of the underlying mechanisms linking HRM with lean bundles (Wickramasinghe & Wickramasinghe, 2017). In this sense, this research focuses on mechanisms linking HRM and lean bundles. This is done by using the input-process-output model as a framework to divide both concepts into inputs, processes and outputs, in order to find meaningful results. Therefore, this paper addresses the following research question: How do lean and HRM interact?

This study will add to current literature on lean management by taking a system view of both lean and HRM to reveal underlying mechanisms and to gain deeper understanding of those linkages. Furthermore, it contributes to practice by providing more detailed information about the use of human resources in order to increase successful usage of lean management and with that operational performance. The hypotheses are tested by using structural equation modelling (SEM). Data of the high performance manufacturing (HPM) project will be used, containing data of 317 plants operating in ten different countries all over the world in machinery, electronics and transportation component sectors.

(6)

2. THEORETICAL BACKGROUND

In this section, firstly the concept of lean management is reviewed. After that, lean is divided into two parts using the socio-technical system perspective. Then, the concept of human resource management is discussed. Thereafter, the missing parts of the human resource management bundle are added by taking a system view on that concept. Moreover, the same is done for the technical side of lean. Lastly, the research hypotheses are developed, and the conceptual model is presented.

2.1. Lean management

Lean management is globally seen as a leading concept to implement in order to enhance competitiveness (Womack, Jones, & Roos, 1990). Its connection with exceptional

achievements and competitive advantage is widely acknowledged among researchers in that area (Krafcik, 1988; Shah & Ward, 2003; Wood, Stride, Wall, & Clegg, 2004). Lean is originated from the Toyota Production System (TPS) manufacturing philosophy and just-in-time (Ohno, 1988; Krafcik, 1988; Schonberger, 2007). It focuses on increasing process efficiency and eliminating waste (Liker, 1996). A recent and commonly used definition of lean is: “lean production is an integrated socio-technical system whose main objective is to

eliminate waste by concurrently reducing or minimizing supplier, customer, and internal variability.” (Shah & Ward, 2007, p. 791).

Shah and Ward (2003) posit four ‘lean bundles’ consisting of lean practices. The bundles individually and jointly contribute to the main aims of lean implementation. These bundles are: just-in-time (JIT), total quality management (TQM), total preventive maintenance (TPM) and human resource management (HRM) (Shah & Ward, 2003).

2.2. Lean as a socio-technical system

(7)

performance (Dabhilkar & Åhlström, 2013). With this in mind, the STS approach is an important topic with regard to system performance improvement (Hadid, Mansouri, & Gallear, 2016). This also applies to the lean concept, which Shah and Ward (2007) refer to as a socio-technical system consisting of technical and social practices. The four lean bundles proposed by Shah and Ward (2003) can be divided into technical and social practices (Hadid & Mansouri, 2014; Cua et al., 2001). The technical side of lean consists of JIT, TQM, TPM (Shah & Ward, 2003), whereas HRM shapes the social side of lean (Shah & Ward, 2003) (figure 2.1). In this research, social and technical lean practices are referred to as soft lean and hard lean. Soft lean practices refer to humans and relations, whereas hard lean practices concern technical and analytical mechanisms (Bortolotti, Boscari, et al., 2015).

JIT is a production method with the goal to reduce, and preferable completely eliminate, waste (Ohno, 1988; Sugimori, Kusunoki, Cho, & Uchikawa, 1977). The starting point of JIT is that all processes produce the necessary parts at the necessary time using minimum stock (Sugimori et al., 1977). When JIT is used in the right way, it supports a decrease in work-in-process inventory and unnecessary delays in lead time (Brown & Mitchell, 1991).

TQM is a manufacturing program that puts emphasis on continuous improvement and conservation of quality products and processes. It focuses on involving management,

workforce, suppliers and customers with the main objective of meeting customer expectations (Powell, 1995).

TPM is a production-driven improvement methodology designed to maximize equipment reliability and effectiveness by ensuring participation and motivation of all employees within the organization (Nakajima, 1988; Robinson & Ginder, 1995). The long-term focus is on eliminating sources of lost equipment time and on the short-term it addresses implementing maintenance programs in the production department (McKone, Schroeder, & Cua, 2001).

(8)

2.3. Human resource management

Human resources are considered extremely important in increasing firm performance (Alagaraja, 2013). Human resource management (HRM) can be defined as “the design and

management of human resource systems based on employment policy, comprising a set of policies designed to maximize organizational integration, employee commitment, flexibility and quality of work” (Alagaraja, 2013, p.119). HRM is regarded as a secondary activity

within the value chain of an organization. Those activities support the execution of an organization’s primary value activities, such as materials handling and creating products (Porter, 1985). Literature shows that there are three main perspectives on HRM (Chew & Chan, 2008). Firstly, the universalistic approach argues that there is one noticeable best set of HRM practices, that on themselves in each situation will lead to an increase in organizational performance (Pfeffer, 1994). This approach states that all organizations should apply these practices in order to be successful (Delery & Doty, 1996). Secondly, the contingency approach states that HRM practices should be adapted according to differences in

organizational environments (Arthur, 1994). This entails aligning HRM practices with other characteristics of the organization (Delery & Doty, 1996). Lastly, the configurational

approach claims that organizational aspects and HRM practices should be adjusted to fit each other in order to increase firm performance (Becker & Gerhart, 1996).

HRM practices. An HRM system can be subdivided into HRM practices (Bowen & Ostroff, 2004). Pfeffer (1994) divides HRM practices into a set of 16 practices. More recently, those 16 practices are summarized into seven practices by Pfeffer (1998): employment security, selective hiring, self-managed teams, provision of comparatively high payment compensation contingent on organizational performance, extensive training, reduced status differentiation and barriers (including dress, language, office arrangements, and wage differences across levels) and extensive sharing of financial and performance information throughout the

(9)

2.4. The input-process-output model

System view on HRM. However, Shah and Ward (2003) only take into consideration HRM processes in the HRM bundle, whereas inputs and outputs of these processes are not covered. This paper covers these inputs and outputs by regarding HRM as a system consisting of HRM practices (Pfeffer, 1998). The input-process-output (IPO) model is a commonly used tool to describe a system (Chan & Ngai, 2011; Hackman, 1987; McGrath, 1984; Steiner, 1972). The IPO framework is a classic systems model, defined as: “a framework consisting of inputs,

leading to processes that in turn lead to outputs” (Ilgen, Hollenbeck, Johnson, & Jundt, 2005,

p.519). These three steps function as ‘building blocks’ in the framework (Bushnell, 1990; Chan & Ngai, 2011). In this research, recruitment and selection are considered to be the most important inputs for HRM processes (Pfeffer, 1998), whereas high commitment is a desired HRM process output (Wright, Gardner, & Moynihan, 2003).

System view on the technical side of lean. The absence of inputs and outputs in the HRM bundle also applies to the technical side of lean. Therefore, the technical part of lean is also viewed as a system using an IPO model. When looking at the technical part of lean, technical resources within an organization can be regarded as important inputs for technical lean practices (Bhasin & Burcher, 2006). Being innovative, by having the ability to anticipate to new technologies, is considered an essential technological resource to enable improvement system implementations (Finger, Flynn, & Paiva, 2014; Sanders, Elangeswaran, & Wulfsberg, 2016). A preferable output of implementing and using technical lean practices is increased operational performance (Shah & Ward, 2003). Operational performance relates to

(10)

3. RESEARCH HYPOTHESES

By combining the system views on hard and soft lean, a framework can be developed. The framework of this research is depicted in the conceptual model in figure 3.1. It shows the relationships between the inputs, processes and outputs of the technical and social lean system.

Figure 3.1: Conceptual model. Social inputs – social processes

The first step of HRM practices is selection and staffing (Wright et al., 2003). Selective hiring is the starting point of all HRM practices (Pfeffer, 1998). An important aspect in selection processes is finding a good fit between an employee and the organization in order to ensure that potential employees are more likely to succeed in the organization (Boon, Den Hartog, Boselie, & Paauwe, 2011; Cable & Judge, 1997; O’Reilly III, Chatman, & Caldwell, 1991). Establishing this so-called ‘person-environment fit’ strengthens team work (Ahmad & Schroeder, 2003). Furthermore, research shows that selecting employees with a good person-environment fit positively influences the extent to which employees perform well in trainings and transfer their trainings to the job (Awoniyi, Griego, & Morgan, 2002; Behling, 1998; Huselid, 1995). Next to that, selecting the right people is important when it comes to the organization’s ability to solve problems (Behling, 1998). Therefore, the following hypothesis is suggested:

H1: Recruiting and selection positively influence soft lean practices. Technological

innovation

Recruiting and selection

Hard lean Operational

(11)

Social inputs – technical processes

Furthermore, the recruitment and selection process plays a crucial role in the sustainability of TQM. It is considered of vital importance to check prospective employees’ behavioral traits during the selection process in order to increase effectiveness of quality management

practices (Ahmad & Schroeder, 2002; Simmons, Shadur, & Preston, 1995). TQM emphasizes the role of employees in the manufacturing program (Powell, 1995) and TPM strongly relies on the entire workforce to increase effectiveness (Nakajima, 1998). Moreover, Partlow (1996) states that selecting employees should be focused on their motivation and ability to operate effectively in a TQM environment. This stresses the importance of selecting the right people with the required workforce characteristics. In addition, Madanhire and Mbohwa (2015) propose that the effective use of recruitment and selection positively influences the

performance of TPM initiatives. Lastly, the success and effectiveness of JIT implementation depends on the proper use of selection and recruitment techniques (Ahmad, Schroeder, & Sinha, 2003; Deshpande & Golhar, 1996). Hence, this research states the following hypothesis:

H2: Recruiting and selection positively influence hard lean practices. Social processes – social outputs

When employees are dissatisfied with a company and their way of working, they might leave voluntarily (Ulrich, 1997). However, if an organization provides their employees with training and development opportunities, learning is stimulated and employees will work in an

(12)

practices are positively related to commitment. Accordingly, the third hypothesis is formulated:

H3: Soft lean practices positively influence commitment. Social processes – technical processes

In order to maintain equipment effectiveness, it is important that operators perform a daily maintenance. Training is crucial to improve operators’ skills in this process (Cua et al., 2001). When considering JIT practices, employees should be trained to be able to execute multiple tasks and they need the right information to stay involved in the improvement of processes (Cua et al., 2001; Furlan et al., 2011). Furthermore, working in small groups and employee participation are required to promote productive maintenance (Furlan et al., 2011; Tsuchiya, 1992). Lastly, the TQM approach emphasizes team-based problem solving and employee participation to be able to continuously improve and meet customer expectations (Ross, 1993). Overall, it is clear that the human part plays an essential role in performance of the different technical lean practices. Thus, the following hypothesis is proposed:

H4: Soft lean practices positively influence hard lean practices. Social outputs – technical processes

Organizational commitment is characterized by a belief in the organization’s goals, a

willingness to make efforts for the organization and a will to be a member of the organization (Porter, Steers, Mowday, & Boulian, 1974). Lack of commitment may lead to various issues, for example limited access to resources, lengthy decision-making processes and lack of communication (Scherrer-Rathje, Boyle, & Deflorin, 2009). When management is committed to an approach from the beginning, there will be more transparency in communication to the employees (Crute, Ward, Brown, & Graves, 2003). It makes the employees see the necessity and priority of a project (Scherrer-Rathje et al., 2009). Without commitment to throughout the whole organization, it is likely that managers avoid their responsibilities and important

decisions for leading change (Nash & Poling, 2007). Briefly, in order to support JIT, TQM and TPM, it is important that all employees are participating and committed to the programs (Cua, et al., 2001; Tsuchiya, 1992). This leads to the fifth hypothesis:

(13)

Technical inputs – technical processes

It is generally accepted that successful organizations distinguish themselves by their

willingness and competences to adopt new technologies and take technological risks (Olesen, 1990). Anticipation of new technologies stimulates the development of proprietary equipment (Hayes & Wheelwright, 1984), that in turn provides a basis for implementation of TPM (Cua et al., 2001). Furthermore, staying innovative in terms of advanced technologies, leads to higher process and product quality (Finger et al., 2014). This subsequently improves the ability to control processes (Sanders et al., 2016) and therefore positively affects the implementation of TQM (Powell, 1995). Besides, innovation stimulates continuous improvement, which is part of the TQM program (Singh & Singh, 2015). Future-oriented technologies and innovations can positively influence production processes (Wan, Cai, & Zhou, 2015). Lastly, new technologies can improve productivity and eliminate wastes, and with that increase overall technical lean performance (Sanders et al., 2016). Hence, many attributes of being technologically innovative are supporting and facilitating the main aims of lean management. This leads to the following hypothesis:

H6: Technological innovation positively influences hard lean practices. Technical processes – technical outputs

JIT focuses on streaming production flow (Shah & Ward, 2003). This is done by reducing lot sizes, reducing cycle times, using rapid changeover techniques and decreasing inventory levels (Im & Lee, 1989). This leads to a reduction in total production costs (Dal Pont, Furlan, & Vinelli, 2008). In addition, TQM aims on reducing rework, increasing teamwork and involving the entire workforce, suppliers and customers in order to continuously improve their processes and deliver quality products (Cua et al., 2001; Ross, 1993; Samson & Terziovski, 1999). Besides, combining JIT and TQM even produces synergies that enable organizations to further improve (Flynn, Sakakibara, & Schroeder, 1995). Moreover, TPM supports decreasing lead times and maximizes equipment effectiveness by eliminating sources of lost equipment time (McKone et al., 2001) and by participation of the entire work floor (Nakajima, 1988). This leads to lower production costs and higher quality. All these efforts aim on increasing operational performance. Therefore, the following hypothesis is suggested:

(14)

4. METHODOLOGY 4.1. Research method

In order to analyze the conceptual framework, I performed a confirmatory survey research. Confirmatory survey research aims to test relationships between concepts and the validity boundaries of the framework (Karlsson, 2016). This methodology fits the aim of this research best, because the theoretical field of lean manufacturing has already been researched

extensively and the existing knowledge consists of well-defined concepts, models and propositions (Karlsson, 2016), which allows researchers to further investigate linkages between concepts and to reveal the underlying structures and mechanisms (Soni & Kodali, 2012).

4.2. Data collection and sample

To test the hypotheses, survey data from the dataset of the third round of the high

performance manufacturing (HPM) project will be used. This is an international project that investigates links between a plant’s practices and its performance. The dataset includes more than 300 plants operating all over the world. The plants involved in the project are located in ten different countries: Finland, the USA, Japan, Germany, Sweden, South Korea, Italy, Austria, Spain and P.R. China. All participating plants operate in machinery, electronics and transportation component sectors (SIC codes 35, 36 and 37 respectively). In each country, a local research team was responsible for contacting the corresponding plant manager and collection of the data. The sample is stratified: plants were randomly chosen from a country-specific list of manufacturing plants. Each research team had to include an equal number of plants per industry and country. Furthermore, the teams had to incorporate an equal number of high-performing and traditional manufacturing plants. Lastly, all plants should have more than 100 employees, in order to provide an acceptable number of managers and employees that could complete the survey (Naor, Linderman, & Schroeder, 2010). Eventually, the plants’ response rate for the mail survey was around 65%, which led to data from 317 plants.

(15)

Table 4.1: Sample structure and characteristics. 4.3. Questionnaires

Data was gathered using survey questionnaires. Content validity of the project was guaranteed by letting experts and managers review the questionnaire beforehand (Bortolotti, Boscari, et al., 2015). Furthermore, common method bias was diminished by composing the

questionnaire in such a way that it partly consists of reverse coded elements and a

composition of subjective and objective items (Bortolotti, Danese, Flynn, & Romano, 2015), and by targeting each questionnaire at a specific set of respondents that possesses the required knowledge to fill in that particular questionnaire (Bortolotti, Danese, et al., 2015). The latter was also used to ensure a diminished key informant bias (Liu, Shah, & Schroeder, 2006). The scales in the questionnaire were developed based on the literature and previously used scales, they were tested using a pilot test and eventually revised (Flynn, Schroeder, & Sakakibara, 1994). Questionnaires were distributed by the research teams, who sent a batch of 23 questionnaires to each participating plant (Bortolotti, Boscari et al., 2015). Table 4.2 shows the way questionnaires were distributed within each plant. For each country, the

questionnaires were translated into the corresponding language by a native speaker of the research team, and again translated back to English to assure a punctual translation (Bortolotti, Boscari et al., 2015).

4.4. Ethical issues

Before the respondents filled in the questionnaires, the research team informed them about the expectations of the research and a letter with background information on the research was attached to all questionnaires. The team confirmed the confidentiality of participants’

responses and the data. To ensure anonymity and confidentiality (Karlsson, 2016), all filled in

Country Total

Electronics Machinery Transportation components

(16)

questionnaires were put in a sealed envelope (Huo, Ye, Zhao, & Shou, 2016) and the organizational names were removed in the eventual dataset.

Table 4.2: Questionnaire distribution.

4.5. Variables and scales

Likert-scale questions were used to measure the scales. All scales are derived from the HPM project and are generated by building on formerly validated measures and existing literature (Bortolotti, Danese et al., 2015). In this study, the parts of the conceptual framework (figure 3.1) are measured using elements of the HPM survey questionnaire in order to ensure content validity (Karlsson, 2016). All final items per construct used in the analysis are shown in the Appendix.

Technological innovation. Technological innovation can be measured by using scales that measure the degree of anticipation to new technologies. This entails foresight, technology assessment and horizon scanning (Stilgoe, Owen, & Macnaghten, 2013). The scale considers acquiring manufacturing capabilities, anticipating potential of new manufacturing

technologies, technological position within the industry and constantly thinking of future technologies.

Recruiting and selection. In order to measure recruiting and selection, scales are derived from Ahmad and Schroeder (2002) and slightly expanded (Pfeffer, 1998). Ahmad and Schroeder (2002) used items that measure the extent to which a plant emphasizes the

assessment of future employees’ characteristics during the recruitment and selection process.

Respondent Number of respondents per plant

Plant accounting manager 1

Direct labor 10

Human resources manager 1

Information systems manager 1 Production control manager 1

Inventory manager 1

Member of product development team 1

Process engineer 1

Plant manager 1

Quality manager 1

Supervisor 3

Plant superintendent 1

(17)

Those entail desire to work in a team, values and attitudes, providing improvement ideas, job skills and cultural fit. Abovementioned items are expanded by items that measure the

interview instrument for hiring employees (Pfeffer, 1998).

Hard lean practices. In this study, the technical side of lean was defined as being a

multidimensional concept, consisting of three practices: JIT, TQM and TPM. Those practices are all represented in the hard lean practices, by combining the following scales retrieved from previous researches: equipment layout (Cua et al., 2001; Mackelprang & Nair, 2010; Swink, Narasimhan, & Kim, 2005), setup time reduction (Flynn et al., 1995; Shah & Ward, 2003), process control (Bortolotti, Danese et al., 2015) and preventive maintenance

(Nakajima, 1988).

Soft lean practices. Measurements for HRM practices are based on the seven practices

formulated by Pfeffer (1998). Scales are slightly adapted and combined, using the scales from the questionnaire of the HPM project. Drawing upon previous research of Ahmad and

Schroeder (2003), this leads to the following scales: small group problem solving, multi-functional employees, supervisory interaction facilitation, communication of strategy and autonomous maintenance. Many studies use autonomous maintenance to define hard lean practices, but since it refers to operator involvement, this study considers it a human resource dimension (Mostafa, Lee, Dumrak, Chileshe, & Soltan, 2015).

Commitment. Commitment is measured by a multi-scale item validated by and based on Mowday, Steers and Porter (1979). The scale considers willingness to work at the organization, alignment of values and degree of pride to work for the organization.

(18)

industries and smooth measurement problems arising from having various industry properties (Bortolotti, Danese et al., 2015).

4.6. Data preparation

In order to decrease the negative effects of missing data, all missing values for each item were substituted by the mean for all respondents answering the question on that item (Tsikriktsis, 2005). This method ensures sample retention (Little & Rubin, 1987; Quinten & Raaijmakers, 1999).

4.7. Construct unidimensionality, reliability, validity and model fit

In order to test the unidimensionality, reliability, validity and model fit of the measurement constructs, confirmatory factor analysis (CFA) was performed using IBM SPSS Amos 25. This was an iterative modification process in which items were deleted until reliability, construct validity, and unidimensionality were reached.

Construct unidimensionality. Unidimensionality was ensured by deleting items that did not have a significant factor loading on the corresponding construct (Karlsson, 2016). After this process, all items loaded onto the same underlying dimension with a factor loading above 0,5. This indicates that unidimensionality is reached (McDonald, 1981). Furthermore, CFA shows that all indicators load significantly on the corresponding first-order constructs. Besides, first order constructs of soft lean and hard lean load significantly on the corresponding second-order construct. All factor loadings are shown in tables 4.3, 4.4 and 4.5.

Table 4.3: Measurement scales assessment of hard lean construct.

First-order construct Indicator Factor loading

Equipment layout 0,570*

JIT_EL1 0,790

JIT_EL5 0,580

JIT_EL6 0,810

Setup time reduction 0,630*

JIT_STR1 0,770 JIT_STR5 0,760 Process control 0,560* TQM_PC2 0,870 TQM_PC3 0,900 TQM_PC5 0,920 Preventive maintenance 0,970* TPM_PM1 0,580 TPM_PM2 0,540

(19)

Table 4.4: Measurement scales assessment of soft lean construct.

Table 4.5: Measurement scales assessment of commitment, recruiting and selection, and technological innovation.

Reliability. During this iterative process, the Cronbach’s coefficient alpha was assessed (Cronbach, 1951). All measures exceeded the acceptable minimum value for Cronbach’s

First-order construct Indicator Factor loading Supervisory interaction facilitation 0,760*

HRM_SIF1 0,860

HRM_SIF2 0,810

HRM_SIF3 0,730

Small group problem solving 0,740*

HRM_SGPS1 0,630

HRM_SGPS2 0,820

HRM_SGPS3 0,810

HRM_SGPS4 0,850

Multi functional employees 0,710*

HRM_MFE1 0,770 HRM_MFE2 0,840 HRM_MFE4 0,790 Autonomous maintenance 0,650* TPM_AM2 0,660 TPM_AM5 0,710

Communication of management strategy 0,760*

HRM_CMS1 0,780

HRM_CMS3 0,810

*Factor loading on second-order construct

Construct Indicator Factor loading

Commitment COMMIT1 0,860 COMMIT4 0,730 COMMIT5 0,880 COMMIT6 0,770 COMMIT7 0,810

Recruiting and selection

(20)

alpha of 0,60 (Hair, Black, Babin, & Anderson, 2010), implying that they were internally consistent (table 4.6). Besides, the composite reliability of each construct was checked and turned out to be greater than 0,7 for each construct, implying high reliability (table 4.6).

Table 4.6: Cronbach’s alpha, composite validity and AVE values for each construct.

Construct validity. Construct validity consists of two aspects: convergent validity and discriminant validity (Campbell & Fiske, 1959). Convergent validity entails the degree to which multiple items measure the same concept. In order to ensure convergent validity, items were deleted until all constructs had an average variance extracted (AVE) value greater than 0,5 (Hair et al., 2010). Final AVE values are visible in table 4.6. Discriminant validity refers to the degree to which items measure different concepts (Karlsson, 2016). In order to evaluate discriminant validity, a chi-squares difference test was performed for each pair of constructs (Bagozzi, Yi, & Phillips, 1991). All differences in chi-squares between the pairs of constructs were significant, providing evidence of discriminant validity. Table 4.7 contains the chi-square values for each pair of constructs.

Table 4.7: Chi-square difference tests for all construct pairs.

Construct ⍺ Composite validity AVE

Soft lean 0,736 0,847 0,67

Hard lean 0,646 0,789 0,50

Commitment 0,919 0,924 0,67

Technological innovation 0,806 0,831 0,50 Recruiting and selection 0,828 0,833 0,50

Construct pairs c2 _df _c2

df c2_difference

Soft lean

Hard lean 960,457 424 806,759 423 153,698*

Technological innovation 480,831 184 297,644 183 183,187*

Recruiting and selection 478,143 184 340,811 183 137,332*

Commitment 539,432 204 400,878 203 138,554*

Hard lean

Technological innovation 470,034 165 308,758 164 161,276*

Commitment 536,112 184 381,664 183 154,448*

Technological innovation

Commitment 250,715 44 69,497 43 181,218*

Recruiting and selection

Commitment 231,022 44 87,446 43 143,576*

Note: *p < 0,000

(21)

4.8. Model fit

Then, the model fit of the whole measurement model was evaluated. Modification indices and model fit measures were assessed, and items were deleted one by one until all values were acceptable. This means that the χ2/df should be between 1 and 3, CFI value should be greater than 0,90, PCLOSE should be greater than 0,05, RMSEA should be lower than 0,08 (Hair, Black, Babin, Anderson, & Tatham, 2006). Eventually, the process revealed acceptable model fit indices:

χ2/df = 1,769; CFI = 0,910; PCLOSE = 0,599; RMSEA = 0,049

4.9. Analysis

To investigate the research hypotheses, structural equation modelling (SEM) is used in IBM SPSS Amos 25. SEM allows for testing relationships between one or more independent variables and one or more dependent variables (Ullman & Bentler, 2003). It has the ability to analyze more complex theoretical models (Schumacker & Lomax, 2010) and to evaluate multiple relations simultaneously (Karlsson, 2016). Furthermore, SEM depicts the interrelations between latent constructs and observable variables (Schreiber, Nora, Stage, Barlow, & King, 2006). Based on these characteristics, SEM is considered most suitable to perform the analysis.

4.10. Expert interviews

(22)

5. RESULTS 5.1. Structural model fit

First, structural model fit must be assessed again in order to test fit indices of the structural model. After deleting a few items, fit statistics of the final structural model show good fit indices:

χ2/df = 1,977; CFI = 0,934; PCLOSE = 0,101; RMSEA = 0,056

5.2. Findings

Table 5.1 contains the results of the analysis on the hypothesized relationships. The results show that the proposed relationships H1, H3, H4, H6 and H7 are supported. In other words, recruiting and selection positively influence soft lean practices. Furthermore, soft lean practices have a positive effect on both commitment and hard lean practices. Besides, technological innovation positively influences hard lean practices. Eventually, hard lean practices have a positive impact on operational performance.

Nevertheless, H2 and H5 are not supported. This indicates that recruiting and selection do not show a significant effect on hard lean practices, and commitment does not influence hard lean practices significantly.

Table 5.1: Outcomes hypotheses testing.

Thereafter, some controls were added in order to check for other non-hypothesized relations within the structural model (table 5.2). These analyses show one additional significant relationship, which reveals that technological innovation not only influences hard lean

practices, but it also positively influences soft lean practices. SEM results, including controls, are depicted in figure 5.1. All SEM results in this section are reported for the situation in which both hypothesized relationships and controls are included in the model.

Hypothesis Proposed relationship Coefficient p

1 Recruiting and selection → soft lean 0,555 **

2 Recruiting and selection → hard lean -0,014 0,794

3 Soft lean → commitment 0,615 **

4 Soft lean → hard lean 0,440 **

5 Commitment → hard lean -0,031 0,532

6 Technological innovation → hard lean 0,424 **

7 Hard lean → operational performance 0,237 **

(23)

Table 5.2: Control tests.

Figure 5.1: SEM results.

Subsequently, in order to get a more accurate overview of the relationships within the

structural model, all indirect, direct and total effects within the model were tested (table 5.3). Lastly, to provide a more detailed insight into indirect effects, specific indirect effects within the structural model were revealed (Gaskin & Lim, 2018). This analysis specifies a number of significant indirect pathways (table 5.4).

Testing for those effects shows that recruiting and selection, despite that it is not directly related to hard lean practices, is indirectly positively related to hard lean practices. This is due to its direct effect on soft lean practices. Furthermore, all parts of the structural model, except for commitment, seem to have a significant indirect and positive effect on operational

performance. However, hard lean practices are the only aspect in the model that have a significant direct and positive effect on operational performance.

Controls Effect on Coefficient p

Technological innovation Soft lean 0,294 ** Operational performance 0,003 0,189

Commitment 0,039 0,453

Commitment Operational performance 0,118 0,075 Soft lean Operational performance 0,111 0,225 Recruiting and selection Commitment -0,010 0,862 Operational performance 0,013 0,848 Note: ** p < 0,001; * p < 0,05 Technological innovation Recruiting and selection

Hard lean Operational

(24)

Table 5.3: Testing for indirect, direct and total effects.

Table 5.4: Testing for indirect paths.

Indirect paths Coefficient p

Technological innovation → soft lean → commitment 0,181 *

Technological innovation → soft lean → hard lean 0,129 **

Technological innovation → soft lean → operational performance 0,033 0,189 Technological innovation → commitment → hard lean -0,001 0,364 Technological innovation → commitment → operational performance 0,005 0,284 Technological innovation → hard lean → operational performance 0,100 **

Recruiting and selection → soft lean → commitment 0,341 **

Recruiting and selection → soft lean → hard lean 0,245 **

Recruiting and selection → soft lean → operational performance 0,062 0,214 Recruiting and selection → soft lean → hard lean → operational performance 0,058 ** Recruiting and selection → commitment → hard lean 0,000 0,749 Recruiting and selection → commitment → operational performance -0,001 0,782 Recruiting and selection → hard lean → operational performance -0,003 0,725

Soft lean → commitment → hard lean -0,019 0,533

Soft lean → commitment → operational performance 0,072 0,054

Soft lean → hard lean → operational performance 0,104 **

Commitment → hard lean → operational performance -0,007 0,448

Note: ** p < 0,001; * p < 0,05

Indirect Direct Total

Effect of recruiting and selection on:

Soft lean - 0,555** 0,555**

Hard lean 0,234** -0,014 0,221**

Commitment 0,341** -0,010 0,331**

Operational performance 0,153** 0,013 0,166**

Effect of technological innovation on:

Soft lean - 0,294** 0,294**

Hard lean 0,123** 0,424** 0,546**

Commitment 0,181** 0,039 0,219**

Effect of soft lean on:

Hard lean -0,019 0,440** 0,421**

Commitment - 0,615** 0,615**

Effect of hard lean on:

Operational performance - 0,237** 0,237**

Effect of commitment on:

Hard lean - -0,031 -0,031

Operational performance -0,007 0,118 0,111

(25)

6. DISCUSSION

This research provides more detailed insights into interactions between lean and HRM. The findings of this study come with some theoretical contributions to lean literature as well as some implications for managerial purposes.

6.1. Theoretical contributions

First of all, results support H1 and H3 by respectively confirming that recruiting and selection positively influences soft lean practices, and that soft lean practices positively influence commitment. This is consistent with literature supporting that recruiting and selection is the basis of performing well in soft lean practices, such as small group problem solving and training capabilities (Awoniyi et al., 2002; Behling, 1998; Huselid, 1995). Furthermore, it confirms findings of earlier researches stating that an organization that adopts soft lean practices, such as focusing on training and development opportunities, open communication, self-maintenance and team work, creates higher commitment among employees (Allen & Shanock, 2013; Angelis et al., 2011; Armstrong, 2006). Additionally, a direct relationship between recruiting and selection and commitment turned out not to be significant, whereas testing for an indirect effect showed a significant indirect positive effect.

Those findings display that only an input, in this case selecting the right people, is not enough to directly create an output, in this case commitment among employees.

In addition to this, interviewee 2 states that “it takes human processes, such as working together in teams, clear communication, stimulating employees and training them, to make people feel committed to their organization. They really appreciate the efforts the

organization puts into them and they can perform optimally in their jobs, which satisfies them”. This confirms the proposed IPO division of the soft side of lean (Pfeffer, 1998; Wright et al., 2003). Organizations can select the right people, but they need to invest in them by using soft lean practices. Those processes eventually create the desired social output: committed employees.

(26)

(Finger et al., 2014; Sanders et al., 2016; Singh & Singh, 2015; Wan et al., 2015). Besides, these findings confirm previous studies on the positive effect of using hard lean practices on operational performance (Cua et al., 2001; Dal Pont et al., 2008; Flynn et al., 1995; Samson & Terziovski, 1999). Furthermore, there is a significant indirect and positive effect of

technological innovation on operational performance, but there is no significant direct effect. Those findings reveal that new technological resources need to be ‘processed’ first, by means of creating a suitable equipment layout, controlling processes and preventive maintenance. Those processes support and enable technological innovations to eventually lead to an increase in operational performance. Without those processes, these resources will not be optimally used. This is supported by interviewee 1: “When we have just adopted a completely new and advanced manufacturing technology, but we are not able to use this technology because our machines and processes are not arranged properly, operational performance will not increase”. Thus, new technologies on itself will not directly lead to an increase in

performance, but it requires hard lean practices to turn technological inputs into the desired outputs: operational performance. Those results confirm the proposed IPO division of the hard side of lean (Finger et al., 2014; Sanders et al., 2016; Shah & Ward, 2003).

Nevertheless, the findings do not support H2, because recruiting and selection does not

significantly influence hard lean practices directly. This result does not confirm earlier studies about a positive effect of recruiting and selection on hard lean practices (Ahmad & Schroeder, 2002; Ahmad et al., 2003; Simmons et al., 1995). However, results do show an indirect, positive and significant relationship of recruiting and selection on hard lean practices, through its significant, positive and direct effect on soft lean practices. This is confirmed by

interviewee 2: “Recruiting the right people is a really good starting point, but only with that you will not perform better in the technical aspects of lean. You really need to train your employees, communicate your plans to them and let them work together to be successful in the technical part of lean implementation”. Therefore, this finding reveals the underlying mechanism of this relationship by showing that recruiting and selection on itself does not influence hard lean practices. After having selected the right employees, training, clear communication and team work are necessary in order to improve the performance of hard lean practices.

Besides, based on the results H5 is rejected, since commitment does not significantly

(27)

a positive effect of commitment on hard lean practices (Cua et al., 2001; Nash & Poling, 2007; Tsuchiya, 1992). Nevertheless, interviewee 2 confirms that commitment on itself does not always influence performance of hard lean practices. The interviewee refers to a previous experience with a lean project: “In that lean project, the involved employees were really committed to the project and the company. However, they did not possess the skills and knowledge to execute the required technical tasks in order to make the project a success. The project failed”. This case shows that, although commitment is a good thing, hard lean

practices demand for more than only committed employees in order to become a success. Based on the experiences of interviewee 2, it could be argued that this lack of positive

relationship is caused by the fact that commitment on itself is not enough to directly influence hard lean practices. It is plausible that an underlying mechanism forms the basis of this relationship, such as a combination of soft lean practices and commitment that leads to a stronger effect on hard lean practices. Moreover, it could be that commitment positively moderates the relationship between soft lean practices and hard lean practices (Crute et al., 2003; Scherrer-Rathje et al., 2009). Further research is necessary in order to validate this hypothesis.

Further, results support H4, since they demonstrate a significant positive relationship between soft lean practices and hard lean practices. Therefore, this confirms literature stating that the human part of lean plays an essential role in performing hard lean practices (Bortolotti, Boscari et al., 2015; Cua et al., 2001; Ross, 1993; Shah & Ward, 2003; Tsuchiya, 1992). Additional tests show that soft lean practices do not directly affect operational performance, but they do have a significant indirect positive influence on operational performance through their direct relationship with hard lean practices. This means that soft lean practices should be adapted according to differences in hard lean practices in order to increase operational

performance. This is in line with the contingency approach on HRM, stating that HRM practices on themselves do not lead to an increase of organizational performance in each situation, but in order to achieve this, they should be aligned with other characteristics of the organization (Arthur, 1994; Delery & Doty, 1996). Furthermore, this finding is in agreement with Porter’s value chain model (Porter, 1985), by showing that soft lean practices support primary organizational activities. Moreover, it shows that only the process part of the soft side of lean directly influences hard lean practices.

(28)

but did not involve and engage them in the implementation process. We accomplished a small increase in operational performance, but not to the satisfaction of our management. After that, we did a second implementation attempt, but this time we wanted to do it better and started from a human perspective. This was done by creating an interactive course in which

employees were involved in the implementation and they had the opportunity to consult, think along with the management and to provide their own ideas and inputs. After this, we

implemented technical changes. This approach led to a much greater increase in operational performance”. Those experiences confirm the positive relationship between soft and hard lean practices, and also highlight the vital importance of the indirect effect of soft lean practices on operational performance. Additionally, interviewee 2 emphasizes that “this approach only works when you have the right people”. The interviewee stresses the importance of a good and matching recruiting and selection process for a successful lean implementation. In line with interviewee 2, interviewee 1 refers to a lesson he learned from a past Kaizen implementation. “This implementation was performed by an external team that took one week to make some technical changes and shortly involve the employees. Improvements were visible immediately. However, after this week, the external team left the company and employees put all changes back to the old situation. They were overwhelmed and did not get enough time to handle all changes”. This experience also stresses the importance of soft lean practices, but additionally demonstrates the time aspect. Soft lean practices need time and effort to lead to desired effects.

Furthermore, adding controls in order to check for non-hypothesized relationships within the structural model, reveals that technological innovation not only directly influences hard lean practices, as expected. It also positively influences soft lean practices. Accessory,

(29)

It seems that anticipating to new technologies enables organizations to better communicate to, train and encourage their employees. Further research on this relationship can for example be done in the context of Industry 4.0, by researching firms that are highly digitalized, have a high automatization level or use advanced information systems, in order to explore in more detail how new technologies influence the social side of lean.

Additionally, checking for indirect effects shows that technological innovation indirectly positively influences commitment through soft lean practices. This adds to the IPO division of the soft side of lean, since next to recruiting and selection, technological innovation as well seems to be an input to increase soft lean practices and therefore commitment (Pfeffer, 1998; Wright et al., 2003).

Interviewee 2 concludes by stating that “all lean implementations should start from a human perspective, but you always need the technical side of lean to be successful in the end. This takes more time but will eventually lead to lasting and better results”. According to

interviewee 2, best results can be achieved when taking into account links between all aspects within both the soft and hard part of lean. Recruiting and selection is especially important to find the right people that are able to perform soft lean practices, with that hard lean practices, and eventually operational performance. Besides, technological innovation is important to facilitate execution of hard lean practices.

6.2. Managerial implications

The findings of this research have some managerial implications. First of all, hard lean practices are the only ones that directly influence operational performance. This displays the importance of ensuring among others a well equipment layout, reducing setup times,

statistically controlling processes and prevention of equipment problems.

However, results of this research confirm the vital importance of people in being a successful lean plant. The human side should be used as a means to increase performance of hard lean practices and, therefore, overall operational performance. This entails for example taking into account the following aspects: employees’ self-reliance with regard to equipment

(30)

transparent communication towards employees. It is advisable to align those aspects with hard lean practices within the company.

In addition, this research shows that being technologically innovative leads to positive

outcomes for both soft and hard lean practices. It even indirectly positively affects operational performance through hard lean practices. This means that it is recommendable to keep in mind that constantly anticipating to new technologies and investing in those kinds of technological resources, stimulates performance of both soft and hard lean practices. Lastly, recruiting and selection turned out to be an important factor in successful soft lean practices and indirectly increases commitment and hard lean practices. Being critical during the selection process and with that, recruiting the right people that fit into the company’s culture, will lead to better outcomes in terms of trained employees, teamwork and autonomous maintenance.

Altogether, the most important message of those findings is to look at hard lean practices within a company and build other organizational aspects around it, by improving soft lean practices in order to increase execution of hard lean practices. This goes together with taking into account and developing the direct antecedents of soft lean practices, which are the

(31)

7. CONCLUSION

This research contributes to lean literature by providing more detailed insights into

mechanisms between lean and HRM. Shah and Ward (2003, 2007) considered lean as a socio-technical system consisting of social and socio-technical aspects and defined HRM practices as the soft side of lean. Previous researches examined the relationship between hard lean practices and soft lean practices. This research adds to lean literature by examining the underlying mechanisms linking HRM with lean bundles. This is done by viewing the hard and soft side of lean as a system, dividing both sides of lean into inputs, processes and outputs, and examining direct and indirect relationships between those aspects. Findings show that soft lean practices are important in increasing operational performance, although they do not directly influence operational performance. Instead, they do influence operational performance indirectly via their strong positive direct influence on hard lean practices. Besides, hard lean practices directly influence operational performance. Furthermore, results show that recruiting and selection are an important antecedent of soft lean practices and they indirectly influence hard lean practices. In addition, technological innovations directly

influence both hard lean practices and soft lean practices. Those discoveries reveal underlying mechanisms that confirm and describe in more detail the relationship between soft and hard lean practices with regard to operational performance.

In general, this confirms that when considering lean practices, managers should always take into account the human aspect in order to influence technical aspects and eventually create a successful lean plant. This can be done by improving social lean practices to increase

performance of hard lean practices, and by taking into account the recruiting and selection process and technological innovations as direct antecedents of soft lean practices.

Limitations and suggestions for future research

(32)

Furthermore, this research only used data based on subjective measures. This limitation especially applies to the concept of operational performance. A suggestion for future research would be to use only objective data, or a mix of objective and subjective data, with the aim of minimizing disadvantages of subjective measures.

With regard to further opportunities for future research, a number of suggestions could be given. Firstly, both the hard and soft side of lean could be shaped in another way, for example by using different inputs. This could give more insights into antecedents and outcomes of hard and soft lean practices and therefore operational performance. Secondly, future studies could search for differences in interactions of HRM and lean by making comparisons between countries.

(33)

APPENDIX: MEASUREMENT SCALES Technological innovation

“Please indicate to what extent you agree/disagree with the following:” (1 = strongly disagree; 7 = strongly agree).

Item Item code

We pursue long-range programs, in order to acquire manufacturing capabilities in advance of our needs.

ANTINT1 We make an effort to anticipate the potential of new manufacturing practices

and technologies.

ANTINT2 Our plant stays on the leading edge of new technology in our industry. ANTINT3 We are constantly thinking of the next generation of manufacturing

technology. ANTINT4

Recruiting and selection

We use attitude/desire to work in a team as a criterion in employee selection. RS1 We use work values and attitudes as a criterion in employee selection. RS3 In hiring, we select employees who can provide ideas to improve the

manufacturing process. RS4

We have developed an effective interview instrument for hiring employees. RS7 In addition to job skills, we look closely at how well prospective employees

will fit in our culture.

RS8

Hard lean practices

Construct Item Item code

Equipment layout

We have laid out the shop floor so that processes and machines are in close proximity to each other.

JIT_EL1 Our processes are located close together, so that material

handling and part storage are minimized. JIT_EL5 We have located our machines to support JIT production

flow.

JIT_EL6 Setup time

(34)

Process control A large percent of the processes on the shop floor are currently under statistical quality control.

TQM_PC2 We make extensive use of statistical techniques to reduce

variance in processes.

TQM_PC3 We monitor our processes using statistical process control. TQM_PC5 Preventive

maintenance

We upgrade inferior equipment, in order to prevent equipment problems.

TPM_PM1 In order to improve equipment performance, we sometimes

redesign equipment.

TPM_PM2

Soft lean practices

Autonomous

maintenance Cleaning of equipment by operators is critical to its performance. TPM_AM1 Operators understand the cause and effect of

equipment deterioration.

TPM_AM2 Basic cleaning and lubrication of equipment is done by

operators.

TPM_AM3 Production leaders, rather than operators, inspect and

monitor equipment performance. (reverse coded)

TPM_AM4 Operators inspect and monitor the performance of their

own equipment.

TPM_AM5 Operators are able to detect and treat abnormal

operating conditions of their equipment. TPM_AM6 Small group

problem solving

During problem solving sessions, we make an effort to get all team members’ opinions and ideas before making a decision.

HRM_SGPS1

Our plant forms teams to solve problems. HRM_SGPS2 In the past three years, many problems have been

solved through small group sessions.

HRM_SGPS3 Problem solving teams have helped improve

manufacturing processes at this plant.

HRM_SGPS4 Supervisory

interaction facilitation

Our supervisors encourage the people who work for them to work as a team.

HRM_SIF1 Our supervisors encourage the people who work for

them to exchange opinions and ideas. HRM_SIF2 Our supervisors frequently hold group meetings where

the people who work for them can really discuss things together.

HRM_SIF3

Multi-functional employees

Our employees receive training to perform multiple tasks.

(35)

Employees at this plant learn how to perform a variety of tasks.

HRM_MFE2

Employees are cross-trained at this plant, so that they can fill in for others, if necessary.

HRM_MFE4

Communication

of strategy In our plant, goals, objectives and strategies are communicated to me. HRM_CMS1 I understand the long-run competitive strategy of this

plant.

HRM_CMS3

Commitment

I talk up this organization to my friends as a great organization to work for. COMMIT1 I find that my values and this organization’s values are very similar. COMMIT3 I am proud to tell others that I am part of this organization. COMMIT4 This organization really inspires the best in me in the way of job

performance.

COMMIT5 I am extremely glad that I chose this organization to work for, over others I

was considering at the time I joined.

COMMIT6

For me, this is the best of all organizations for which to work. COMMIT7

Operational performance

Please circle the number that indicates your opinion about how your plant performs on each dimension, compared to global competition in your industry.

(Rated on a 5-point Likert-scale: 1 = poor or low; 5 = superior).

Delivery On-time delivery performance OP_D1

Fast delivery OP_D2

Cost Unit cost of manufacturing OP_C1

Quality Conformance to product specifications OP_Q1

Flexibility Flexibility to change product mix OP_F1

Flexibility to change volume OP_F2

(36)

REFERENCES

Ahmad, S., & Schroeder, R. G. 2002. The importance of recruitment and selection process for sustainability of total quality management. International Journal of Quality and Reliability Management, 19(5): 540-550.

Ahmad, S., & Schroeder, R. G. 2003. The impact of human resource management practices on operational performance: recognizing country and industry differences. Journal of Operations Management, 21(1): 19–43.

Ahmad, S., Schroeder, R. G., & Sinha, K. K. 2003. The role of infrastructure practices in the effectiveness of JIT practices: Implications for plant competitiveness. Journal of

Engineering and Technology Management, 20(3): 161-191.

Alagaraja, M. 2013. HRD and HRM perspectives on organizational performance: A review of literature. Human Resource Development Review, 12(2): 117-143.

Allen, D. G., & Shanock, L. R. 2013. Perceived organizational support and embeddedness as key mechanisms connecting socialization tactics to commitment and turnover among new employees. Journal of Organizational Behavior, 34(3): 350-369.

Angelis, J., Conti, R., Cooper, C., & Gill, C. 2011. Building a high-commitment lean culture. Journal of Manufacturing Technology Management, 22(5): 569-586.

Armstrong, M. 2006. Strategic human resource management - A guide to action (3rd_ed.). London: Kogan Page.

Arnheiter, E.D., & Maleyeff, J. 2005. The integration of lean management and Six Sigma. The TQM Magazine, 17(1): 5-18.

(37)

Awoniyi, E. A., Griego, O. V., & Morgan, G. A. 2002. Person‐environment fit and transfer of training. International Journal of Training and Development, 6(1): 25-35.

Bagozzi, R. P., Yi, Y., & Phillips, L. W. 1991. Assessing construct validity in organizational research. Administrative Science Quarterly, 36(3): 421-458.

Becker, B., & Gerhart, B. 1996. The impact of human resource management on

organizational performance: Progress and prospects. Academy of Management Journal, 39: 779-801.

Behling, O. 1998. Employee selection: Will intelligence and conscientiousness do the job? Academy of Management Perspectives, 12(1): 77-86.

Bhasin, S., & Burcher, P. 2006. Lean viewed as a philosophy. Journal of Manufacturing Technology Management, 17(1): 56-72.

Boon, C., Den Hartog, D. N., Boselie, P., & Paauwe, J. 2011. The relationship between perceptions of HR practices and employee outcomes: Examining the role of person– organisation and person–job fit. The International Journal of Human Resource Management, 22(1): 138-162.

Bortolotti, T., Boscari, S., & Danese, P. 2015. Successful lean implementation: Organizational culture and soft lean practices. International Journal of Production Economics, 160: 182-201.

Bortolotti, T., Danese, P., Flynn, B. B., & Romano, P. 2015. Leveraging fitness and lean bundles to build the cumulative performance sand cone model. International Journal of Production Economics, 162: 227-241.

Bostrom, R. P., & Heinen, J. S. 1977. MIS problems and failures: A socio-technical perspective, part II: The application of socio-technical theory. MIS Quarterly, 11-28.

(38)

Bozarth, C. C., Warsing, D. P., Flynn, B. B., & Flynn, E. J. 2009. The impact of supply chain complexity on manufacturing plant performance. Journal of Operations Management, 27(1): 78–93.

Brown, K.A., & Mitchell, T.R. 1991. A comparison of Just-in-Time and batch manufacturing: The role of performance obstacles. Academy of Management Journal, 34(4): 906–917.

Bushnell, D. S. 1990. Input, process, output: A model for evaluating training. Training & Development Journal, 44(3): 41-44.

Cable, D. M., & Judge, T. A. 1997. Interviewers' perceptions of person–organization fit and organizational selection decisions. Journal of Applied Psychology, 82(4): 546.

Campbell, D. T., & Fiske, D. W. 1959. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2): 81.

Carlsson, B. 1996. Small business, flexible technology, and industrial dynamics. Oxford: Blackwell Publishers.

Carree, M.A., Van Stel, A., Thurik, A.R., & Wennekers, A.R.M. 2002. Economic

development and business ownership: An analysis using data of 23 OECD countries in the period 1976-1996. Small Business Economics, 19(3): 271-290.

Chan, Y. Y., & Ngai, E. W. 2011. Conceptualising electronic word of mouth activity: An input-process-output perspective. Marketing Intelligence & Planning, 29(5): 488-516.

Chen, C. J., & Huang, J. W. 2009. Strategic human resource practices and innovation performance - The mediating role of knowledge management capacity. Journal of Business Research, 62(1): 104-114.

(39)

Collins, C. J., & Clark, K. D. 2003. Strategic human resource practices, top management team social networks, and firm performance: The role of human resource practices in creating organizational competitive advantage. Academy of Management Journal, 46(6): 740-751.

Cronbach, L. J. 1951. Coefficient alpha and the internal structure of tests. Psychometrika, 16(3): 297-334.

Crute, V., Ward, Y., Brown, S., & Graves, A. 2003. Implementing lean in aerospace: Challenging the assumptions and understanding the challenges. Technovation, 23(12): 917-928.

Cua, K. O., McKone, K. E., & Schroeder, R. G. 2001. Relationships between implementation of TQM, JIT, and TPM and manufacturing performance. Journal of Operations

Management, 19(6): 675-694.

Dabhilkar, M., & Åhlström, P. 2013. Converging production models: The STS versus lean production debate revisited. International Journal of Operations and Production

Management, 33(8): 1019-1039.

Dal Pont, G., Furlan, A., & Vinelli, A. 2008. Interrelationships among lean bundles and their effects on operational performance. Operations Management Research, 1(2): 150-158.

Delery, J. E., & Doty, D. H. 1996. Modes of theorizing in strategic human resource management: Tests of universalistic, contingency, and configurational performance predictions. Academy of Management Journal, 39(4): 802-835.

De Menezes, L. M., Wood, S., & Gelade, G. 2010. The integration of human resource and operation management practices and its link with performance: A longitudinal latent class study. Journal of Operations Management, 28(6): 455-471.