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Master Thesis

The influence of employees in lean service

companies

January 26, 2018 Course code: EBM720B20 MSc Supply Chain Management

Student number: 2479419

Supervisor: Dr. Ir. T. Bortolotti Co-assessor: Prof. Dr. H. Broekhuis

University of Groningen Faculty of Economics and Business

Nettelbosje 2 Groningen, 9747 AE

The Netherlands Tel: +31 50 363 911

Louke (Wilhelmina Maria) Mom Oostersingel 32

Groningen, 9711 XD The Netherlands Tel: +31650615774

Acknowledgement

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Abstract

Nowadays, service organizations are in high competition to remain profitable and competitive. Lean Management has been integrated to facilitate this. Although, many companies have implemented the approach inappropriately, which causes ineffective results. In literature, much attention is paid to lean manufacturing companies, while in these days lean services are becoming even more important. The aim of this research is to indicate the influence of employee commitment in lean service companies and why this can explain current deficiencies. An online survey is performed to obtain information from pure service organizations performing in the Germanic Europe and Confucian Asia service industry. It was found that employees who have pride towards the organization and are affiliated with the organization its goals generate more effective lean programs. This study is the first to show the role of employee commitment in lean services accompanied with of a report for lean service companies including implications for this sector. Therefore, this thesis assists service companies in handling employee commitment and improving lean performance.

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Content

Preface ... 5

1. Introduction ... 6

2. Theoretical background ... 8

2.1 Lean Management ... 8

2.2 Lean Management in services ... 10

2.2.1 Prior research ... 10

2.2.2 Lean in services ... 10

2.2.3 Pure services ... 12

2.3 Employee commitment ... 12

2.3.1 Prior research ... 12

2.3.2 Definition of employee commitment ... 13

2.3.3 Self-determination theory ... 14

2.4 Research Hypotheses ... 15

2.4.1 Lean Management and performance ... 15

2.4.2 Employee commitment and performance ... 17

2.4.3 The moderating effect of employee commitment ... 19

2.5 The research model ... 20

3. Method ... 21 3.1 Research design ... 21 3.2 Sample selection ... 21 3.3 Data collection ... 22 3.4 Measurement elaboration ... 23 3.5 Data Analysis ... 24 3.5.1 Pilot testing ... 24 3.5.2 Hypotheses testing ... 24 4. Findings ... 25 4.1 Background statistics ... 25 4.2 Data reduction ... 25 4.2.1 Unidimensionality ... 25

4.2.2 Reliability and validity ... 26

4.3 Hypotheses results ... 27

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6. Conclusion ... 33

6.1 Managerial implications ... 34

6.2 Limitations and further research ... 35

References ... 37

Appendices ... 47

Appendix A: Lean practices described in literature ... 47

Appendix B: Lean in manufacturing sector generated into a volume/variety matrix ... 48

Appendix C: Constructs, items and labels ... 48

Appendix D: Performance pyramid ... 51

Appendix E: Results correlations for pilot test ... 52

Appendix F: Results correlations for hypothesis testing... 52

Appendix G: Discriminant validity (with latent variables) ... 55

Appendix H: Report for companies (management summary) ... 56

Tables ... Table 1: Sources of soft and hard practices ... 9

Table 2: Sources of employee commitment ... 14

Table 3: Descriptive statistics ... 25

Table 4: Component matrix with correlations ... 26

Table 5: Pearson correlations and squared root of AVE ... 27

Table 6: Hierarchical regression model ... 28

Table 7: Results of H1 ... 28

Table 8: Results of H2a, H2b and H2c ... 29

Table 9: Results of H3 ... 30

Figures ... Figure 1: Lean in service sector generated into a volume/variety matrix ... 11

Figure 2: Conceptual model ... 21

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Preface

The curiosity for making processes more effective again started during my Bachelor in International Business. In these years, I became familiar with several courses that included the concepts of lean, Six Sigma and other process improvement programs. Since I really enjoyed performing the group assignments of these courses, I immediately knew that I wanted to do my Masters in Supply Chain Management. Even at in the beginning of this Master, I decided to do an internship to get more familiar with processes within a company. During this internship, I got the opportunity to execute process innovations in which I became extremely involved. After 5 months, I had learned so many new things and how to implicate theories in practice and therefore I was eager to finish my Master SCM. I really hoped that there was a subject related to process innovation. That is the reason that I wrote this thesis. Since this subject is in my field of interests, it was easy to schedule my weeks to do lots of research. Even though I liked completing the research part, the period for data collection was a more stressful time for me. I was afraid that not enough people would participate and that it would take lots of time to get a sufficient amount of respondents. Luckily, there were enough people who were able to fill in the questionnaire. I would therefore like to thank all participants who took their time for helping me out. Secondly, and not less importantly, I would like to thank my supervisor T. Bortolotti. He helped and supported me through the process by providing valuable feedback and sharing his ideas. Overall, writing this thesis was not an easy period for me due to the loss of my father during my Masters. I really missed my father’s advice along this process as we had such similarities in fields of interest. However, I really would like to thank my mom, who supported me over and over again to go on and suggested that I make my father proud. Moreover, I would like to thank my friends, family and boyfriend, who were always there for me.

Louke Mom

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1. Introduction

Nowadays, high pressure and fierce competition force companies to find more effective processes to deliver excellent customer service (Vignesh, Suresh & Aramvalarthan, 2016). For decades, Lean Management (LM) has been considered as the appropriate managerial approach for eliminating waste and making processes more effective (Shah & Ward, 2007). It is an interrelated system of social and technical bundles of practices: soft practices to focus on human aspects and hard practices to address technical or analytical aspects. Lean has been released in the pursuit to increase the competitiveness of the American auto industry (Womack, Jones & Roos, 1990), but recently services have adopted the approach as well. Yet many companies worldwide, either manufacturing or service, are struggling to achieve expected advantages. Where Cisco’s implementation of lean realized positive results (Cohen, 2009), 62% of companies in healthcare failed (Albliwi, Lim, Antony & van der Wiele, 2014). Beyer showed that in 2017 still less than 10% of the companies who implemented a lean program attained the results they visualized. Consequently, these mixed results need to be studied.

In literature, there are still contradictory perspectives about lean since there is a lack of empirical articles that emphasize the significant impact of employee commitment (EC) on the effectiveness of lean. Employee commitment is essential for companies as it is the psychological state “that binds the individual to the organization” (Allen & Meyer, 1990; p.14). In general management literature, EC is seen as crucial for improved managerial practices, which means that different levels of EC can lead to the success or failure of lean (Meyer, Stanley, Herscovitch & Topolnytsky, 2002). However, lack of studies in this area is surprising and researching EC in a lean setting can help in answering why, when comparing divergent studies, there are still mixed results. Moreover, performing this research in the service sector is even more crucial. Services are people-oriented (Oliva & Sterman, 2001), create globally the most jobs and generate more than 65% of the global GDP nowadays (OECD, 2017). Therefore, the following research question will be analyzed: What is the role of employee commitment in Lean Management in the service sector?

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affective (desire to stay), cognitive (need to stay) and behavioral commitment (ought to stay). These profiles generate different levels of EC, which apparently influence the effectiveness of lean practices. In literature, only a few studies took the role of EC in lean companies into account. Parker (2003) reported that lean can be a harmful approach for employees. Her study suggests that companies need to pay attention to lean initiatives and the severity of consequences they have on the committed workforce because different practices of lean possess different outcomes. Lee and Peccei (2007) analyzed employee commitment to quality in high and low lean companies. They emphasized that high lean organizations have highly committed employees when the employees receive intrinsic rewards. They proposed that in order to receive EC to quality companies need to adapt the rewards according to the progress level of lean implementation, but this is difficult to measure. Eventually, both papers are limited. Parker focused only on the influence of affective commitment in UK companies whereas Lee and Peccei narrowed their scope to only the outcomes of employee commitment to quality in Korean organizations. Not the least important, this evidence is from manufacturing companies only.

The scarcity of literature about lean services increases concerns. While services have a principal role in our economy nowadays (OECD, 2017), only limited articles analyzed the role of EC in lean service companies. One of the exceptions is the article of Lam, O’Donnell, and Robertson (2015) who researched the motivation of employees to participate in lean activities in a service-related context. Due to sparse evidence, empirical research needs to be continued in order to fill the gap about the role of EC on the relationship between LM and performance in the service sector. This is done by first clarifying which practices of lean lead to the success of a lean program in services. Afterwards, the importance of EC will be emphasized and why it is important that this concept will be investigated. Finally, this research will show how critical EC will be for obtaining positive organizational outcomes from a lean strategy and how this could be achieved. This research will, therefore, provide service companies the opportunity to achieve the results they visualize by creating knowledge on how to approach employees in such a way that they can strengthen the efficiency of the company. As a result, this study will contribute to service literature in Operations Management.

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2. Theoretical background

The lean paradigm has a long history which dates back to the early 1950’s of Japan. From the onset of lean until today, its popularity has grown tremendously in many companies worldwide. Womack et al. (1990) revealed the Toyota lean production system in his book to be ‘the machine that changed the world’. In literature, lean production and lean manufacturing are seen as the predecessors of the Lean Management of today because its roots lie in Production and Operations Management (Ohno, 1988; Shah & Ward, 2003). As the lean principles can be applied to any organization, the approved management description in this thesis will continue with the term Lean Management (LM).

2.1 Lean Management

From one point of view, lean (i.e. continuous improvement or TPS) embodies a multifaceted perspective, which together enables definite bundles of organizational practices (Yang, Hong & Modi, 2011). Examples of these practices can be found in appendix A. In literature, the practices are categorized into four big bundles that cover the concept of lean: Just-In-Time (JIT) (reducing flow times), Total Quality Management (TQM) (continuously improving quality), Human Resource Management (HRM) (maximizing employee performance) and Total Productive Maintenance (TPM) (maintaining/improving integrity of a product/service) (Cua, McKone & Schroeder, 2001; Shah & Ward, 2003; Shah & Ward, 2007). The study of Cua et al. (2001) is a predecessor of both studies conducted by Shah and Ward (2003 & 2007). Cua et al. (2001) studied the overlapping practices of the lean bundles, which was not researched before. Due to this, the importance of the human and strategic practices (e.g. employee involvement and cross-functional training) became emphasized. Shah and Ward (2003) also focused on the four bundles, although they included the internal and interdependent consistent practices with its synergistic effects on performance. A few years later, they expanded their research to embody “the integrated nature of lean systems” (2007; p. 801). The first article assessed mainly the aspects of processes and people in a company; the latter incorporated the internal (firm-related) and external (supplier/customer-related) aspects of a company (Shah & Ward, 2007). Appendix A (p. 45) displays an overview of lean practices and shows the overlap in the literature based on Cua et al. (2001), Shah and Ward (2007) and Bortolotti, Boscari and Danese (2015).

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organizations to perform better than competitors due to their challenging quantifications (Powell, 1995; Samson & Terziovski, 1999). They are main contributors to the performance of firms (Samson & Terziovski, 1999). According to Phan, Abdallah and Matsui (2011), the relevance of them is increased because the implementation of soft practices enable companies to be more competitive. The second bundle contains the hard practices which pursue the analytical and technical mechanisms of lean (e.g. continuous flow, statistical process control, autonomous maintenance, cleanliness & organization and design for quality). These factors are system-related and function as support for the implementation of the soft ones. Where soft practices are recognized as intangible, hard practices are tangible and therefore easier to quantify (Samson & Terziovski, 1999). For companies, it is important to keep in mind that soft and hard practices are interrelated and that they are important indicators for quality management. As a result, the soft ones facilitate the evolvement of the hard ones (Fotopoulos & Psomas, 2009). Table 1 summarizes for both bundles the studies that researched them. This table is a composition of the table figured in Bortolotti et al. (2015) and self-studied articles. Even though appendix A shows some other practices of lean, this thesis will focus on the practices mentioned below.

Table 1. Sources of soft and hard practices

Practice Soft or hard Literature

Multi-function employees Soft Flynn, Schroeder & Sakakibara (1994), Forza & Filippini (1998), Cua, McKone, & Schroeder (2001), Shah & Ward (2003), Rahman & Bullock (2005), Shah & Ward (2007), Bortolotti et al. (2015) b

Small group problem solving Soft Samson & Terziovski (1999), Flynn et al. (1995), Cua et al. (2001), Phan et al. (2011), Bortolotti et al. (2015) a,b

Continuous improvement Soft Forza & Filippini (1998), Samson & Terziovski (1999), Shah & Ward (2003), Rahman & Bullock (2005), Lagrosen & Lagrosen (2005), Fotopoulos & Psomas (2009), Bortolotti et al. (2015) a,b

Top management leadership for quality

Soft Forza & Filippini (1998), Samson & Terziovski (1999), Flynn et al. (1995), Cua et al. (2001), Phan et al. (2011), Bortolotti et al. (2015) a,b

Employee suggestions Soft Forza & Filippini (1998), Phan et al. (2011), Bortolotti et al. (2015) b

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Statistical process control Hard Forza & Filippini (1998), Samson & Terziovski (1999), Phan et al. (2011), Rahman & Bullock (2005), Bortolotti et al. (2015) a,b Autonomous maintenance Hard Cua et al. (2001), Shah & Ward (2003), Shah & Ward (2007),

Bortolotti et al. (2015) a,b

Cleanliness and organization Hard Flynn et al. (1994), Flynn et al. (1995), Cua et al. (2001), Bortolotti et al. (2015) b

Design for quality Hard Flynn et al. (1994), Flynn et al. (1995) aBortolotti, Boscari & Danese (2015)

bBortolotti, Danese, Flynn & Romano (2015)

2.2 Lean Management in services

2.2.1 Prior research

Lean was initially integrated into the manufacturing industry to make the American auto industry more effective (Womack et al., 1990). Although, lean has been converted into a suitable model in order to be appropriate for the service industry as well. This enabled service companies to think in more efficient ways (Bowen & Youngdahl, 1998). The conversion of the approach is a result of the shift from physical work (manufacturing) towards knowledge-based (service) work in which more than 60% of companies operate these days (Bureau of Labor Statistics, 2017). This percentage can be explained by the increased level of mechanization in the production of goods; manual work became obsolete (Verma, 2006). As a result, service functions became more important and more service jobs were created. Bowen and Youngdahl (1998) did research on how service companies could achieve as much as efficient and positive outcomes as manufacturing companies achieved with lean. They found the following statement of the economist Levitt: “if customer service is consciously treated as manufacturing in the field, it will get the same kind of detailed attention that manufacturing gets […] More importantly, the same kinds of technological, labor saving, and systems approaches that now thrive in manufacturing operations will begin to get a chance to thrive in customer service and service industries” (p. 30). Based on Levitt’s research, rather than a shift a convergence was visible from the manufacturing production line into the service production line. As a result, characteristics of the service industry are often compared with the characteristics of the manufacturing industry and are rarely configured alone. This can be seen in figure 1 and appendix B (p. 46) where both industries are still categorized along the same criteria.

2.2.2 Lean in services

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from the manufacturing-related approach; the more efficient it seemed for the service (Bowen & Youngdahl, 1998). The main difference between the two sectors is that in services the customer is the ‘intangible product’ on which to work whereas in manufacturing it is the physical product (product-service strategy vs. product-centric strategy) (He, Sun, Lai & Chen, 2015). As (product-services became more important over the years, the use of lean generated new and different complexities for companies (Ngai & Petrongolo, 2017). Especially at the time when the demand for pure services increased; the variety of services needed to evolve accordingly. This can be seen in figure 1, which shows a compiled framework of the articles by Schmenner (1986), Arlbjørn, Freytag and de Haas (2011) and the book of Slack, Chamber, and Johnston (2010). This matrix represents the convergence of lean manufacturing characteristics into lean service characteristics with the aim to show the differences. Former frameworks did not incorporate the lean principles of cost and service efficiency for services. Therefore, implementing them can cause service companies to strive for reduced variability without incorporating the manufacturing point of view. As a result, the efficiency of the service should increase. From a theoretical point of view, it makes sense. Although, practically it should be researched since lean can have different faces while implementing in diverse settings.

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2.2.3 Pure services

This thesis will focus on companies that deal with a pure service. A pure service is considered to be “the highest form of service given to consumers” and differs from mass service (see figure 1) in that it does not consider pricing and promotion to be the most important indicators (Celine, 2011). Examples of pure services are consultancy agencies, banks, the government and airline companies. Even though they operate in the same industry, they can follow different lean programs. Nevertheless, the abovementioned matrix is standardized for every lean program in the service industry. It is useful to keep in mind when implementing lean in different service organizations. This is mainly due to the fact that companies who implement lean think that lean is all about eliminating waste and improving performance in their specific branch. However, companies should also consider quality assessment of the given service and the people (external aspect) (Shah & Ward, 2007). Customers are the ‘intangible product’ for service companies who expect a service with the highest labor intensity needed for their best-customized and heterogeneous service. The demand for a specific service is due to fierce market competition; customers demand faster, better and a more varied service. This increases the demand of pure servicing as well. This results in that companies need to distinguish themselves from others and will search for new service designs (Allway & Corbett, 2002). Service companies that use a traditional service design have shown to produce poor customer service with high costs (Piercy & Rich, 2009). These companies focus too much on speed instead of quality. Besides, they see both goals as conflicting and not as complementary. However, as Piercy and Rich (2009) researched in call service centers, implementing a lean service design into these pure service companies can generate the opposite. It seemed really successful to implement this approach by mapping out and redesigning the internal problems. A lean system recognizes cost reduction as a task of quality improvement instead of a conflict due to its decline in system failures (Piercy & Rich, 2009). As a result, the companies are able to excel their cost and service-efficiency to a greater extent (e.g. by measuring success with the number of calls resolved the first time with no callbacks instead of using the total number of calls processed) (Allway & Corbett, 2002). Since this thesis is especially interested in pure services and the relation with lean, only companies operating in this industry will be researched that fit the upper-left box of the matrix.

2.3 Employee commitment

2.3.1 Prior research

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career opportunities increase the level of EC. As a result, different sets of policies create certain levels of commitment. Sanders, Shipton, and Gomes (2014), on the other hand, concentrated more on the organizational outcomes of the different EC levels. Subsequently, many cause and effect relations are known for EC in HRM. However, in the field of Operations Management only a few researchers have discussed this. Ahmad and Schroeder (2003) discovered that EC provides a high potential for organizational efficiency. Jurburg, Viles, Tanco, and Mateo (2017) complemented this by providing an in-depth analysis of increased organizational efficiency. They illustrated how employees need to be motivated to become committed to certain activities. Even though this information is already available, more research is needed about EC’s interaction effect with lean. As globalization made the competition in services stronger than ever, the pressure to perform better increases at all times (Duarte, Cabrita & Machado, 2011). Consequently, bad functioning organizations reorganized their service design, which created an increase in job cuts. This, taken together with the lasting growth in human’s individualism, decreased the overall level of EC (Varnum & Grossman, 2017). This made it even more crucial that employees become obligated to the organization and to research how this can be done. As a result, committed employees produce high productivity and are aware of the quality specifications, which brings value to the organization (Ahmad & Schroeder, 2003). If not, the organization’s success will be on hold or will even decline. To sum up, previous mentioned literature shows how important it is to do more research about EC supposing that companies will become more effective in servicing.

2.3.2 Definition of employee commitment

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respectively. Defining EC in this way relates to the view of many practitioners who suggest that employees are committed in different ways to organizational goals and values. In Table 2 the articles and authors are summarized who studied the components of the TCM.

Table 2. Sources of employee commitment

Components Definition Found in literature

Affective commitment

(AC)

Indicators include pride in affiliation to the company’s goals and feelings of satisfaction derived from involvement with the

company’s goals.

Meyer & Allen (1990), Meyer & Allen (1991), Shore & Wayne (1993), Becker, Billings, Eveleth & Gilbert, (1996), Meyer et al. (2002), Jackson, (2004), Markovits, Ullrich, van Dick & Davis (2008), Marique et al. (2002), Garcia-Cruz, Real & Roldan (2017)

Cognitive commitment

(CC)

Indicators include identification with the organization’s goals and value, and a shared sense of the importance of the company’s goals.

Meyer & Allen (1990), Meyer & Allen (1991), Meyer et al. (2002), Jackson, (2004), Markovits et al. (2008)

Behavioral commitment

(BC)

Indicators include active participation in the goals of the organization and willingness to exert effort towards goal accomplishment.

Meyer & Allen (1990), Meyer & Allen (1991), Shore & Wayne (1993), Jackson (2004), Markovits et al. (2008)

2.3.3 Self-determination theory

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of commitment will facilitate the level of motivation. For example, autonomous motivation regulates the level of satisfaction derived from the involvement with the organization’s goals (Meyer & Gagne, 2008). Meyer, Becker, and Vandenberghe (2004) suggested that there is a significant relationship between the two theories: the level of motivation stimulates the level of commitment in an employee’s daily activities to actually perform the activities as effective as possible. This increases, therefore, the importance of researching employee commitment even more because it is recognized as a facilitator for desired outcomes.

2.4 Research Hypotheses

2.4.1 Lean Management and performance

As mentioned earlier, the original four bundles of lean are significant predictors of performance. However, they are interpreted from a historical perspective. To be more accurate, lean should be researched from a more recently developed view. This view determines Lean Management as a whole by reviewing the soft and hard practices.

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In order to wrap lean as a whole, the hard practices should be integrated too because these practices also contribute to performance. As a result, companies who implement both soft and hard practices are likely to have a better functioning of the company. Researchers show positive results due to the implementation of hard practices as they generate high value (Forza & Filippini, 1998). Hard practices increase the quality of a firm’s service that, in return, enhances the effectiveness and efficiency. In service organizations, the most important aspect in this is related to the flow of customers (Sheu, McHaney & Babbar, 2003). This seems logical because customers are the intangible product on which service designs need to be developed. Therefore, Allway and Corbett (2002) integrated a lean five-phase plan for service companies to develop such a design. The implementation of this well-developed service design is critical as it could enhance positive results. The procedure explains how companies can generate excellent results by implementing both bundles of practices (Allway & Corbett, 2002). Some finance and insurance companies already increased their performance by redesigning, for example, the office layout. This, in return, optimized the flow of information and of its people (Allway & Corbett, 2002). Another example is the use of statistical process control. This helped organizations to implement flexibility into the processes and to integrate the option to switch between designs (Sheu et al., 2003). Switching is important in order to achieve optimality of a service (Sheu et al., 2003). Moreover, those technical innovations are certainly inevitable for service companies. It reduces customer-waiting time and ensures flexibility in the service design. The availability and implementation of these analytical and technical tools in services increase the efficiency and reliability of the service (as the service delivery is customized). Therefore, due to the socio-technical design, service companies can become ‘the Toyota of their industry’ (Allway & Corbet, 2002; p. 54).

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a decision to emphasize one of the two sets of practices; service firms should implement both practices equally and should recognize them as interchangeable. Therefore, the following can be said:

Hypothesis 1: Lean Management practices lead to high performance at service companies

2.4.2 Employee commitment and performance

Employee commitment is based on the three-component model that can take various forms, each defined by a certain state of mind. Each component is supposed to be experienced by employees and is supposed to have different influences on performance (Meyer et al., 2012). The different influences of AC, CC and BC will be explained below.

Affective commitment (AC), a commitment that involves a sense of belonging and sentimental attachment to the organization, is expected to be superior on performance. When employees are satisfied and actively involved in their daily job, the likelihood that they enjoy their job increases (Meyer et al., 2002). This likelihood enhances even more when employees obtain organizational support for what they actually perform (Meyer et al., 2002). Organizations that provide a supportive work environment ensure their employees to act accordingly (Meyer et al., 2002). When an employee receives higher levels of rewards for the work he or she performs (rated by supervisors), its level of satisfaction will increase and will, therefore, be higher committed (Van Scotter, 2000; Meyer et al., 2002). In the study of Meyer and Maltin (2010), the TCM and SDT were tested together and were estimated to be significantly compatible. Results showed that AC positively correlates with the needs of SDT in which the needs are considered to serve as a universal basis to understand the outcomes implicated by AC. Individuals who possess high affective commitment show their connection and attraction to the organization. This is the reason that AC negatively correlates with absenteeism (Meyer et al., 2002). Even when an employee is absent, it is more related to involuntary than voluntary reasons. Employees are willing to take responsibility for their day-to-day job requirements as well as for extra duties to show their affection with the organization (Mathieu & Zajac, 1990). This increases job satisfaction and results in a value for the organizational (Becker at al., 1996; Meyer et al., 2002; Meyer & Maltin, 2010). Since literature claims the positive influence of AC, the following can be stated:

Hypothesis 2a: Affective commitment (AC) has a positive influence on performance at service companies

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obtained benefits from a strong AC in combination with strong CC. The combination is exclusively related and demonstrated to be superior to AC alone (Meyer et al., 2012). High CC in combination with high AC is positioned as the ‘moral imperative’ (first side of the ‘two faces’ theory) in which employees have the strong motivation to stay in the organization and to exercise their job requirements. As a result, employees are more committed and more willing to remain within the organization. Employees recognized this as the ‘moral’ thing to do (Meyer et al., 2012). Moreover, literature showed that the two profiles created the highest value outcomes of the SDT needs. This is due to the fact that context effects are created in high AC, which needs to be exposed from the employees themselves in which their desires and values are manifested (Meyer et al., 2012). This means that AC designs situations in which employees with high CC determine their values and goals to be consistent with the organizations’ values and goals. As a result, turnover rates are low (Somers, 2010). In this way, organizations believe that CC creates great organizational advantages. Jaros (2017) confirmed this by summarizing the powerful effect of a high AC/CC commitment profile. He evaluated the effect by arguing “if AC is strong, this causes CC to be experienced as a ‘want to’ moral duty face” (p. 529). Due to these findings, the following can be stated:

Hypothesis 2b: Cognitive commitment (CC) has a positive influence on performance at service companies

Even though the former has a positive influence, behavioral commitment is negatively related (Meyer & Herscovitch, 2001). Meyer and Herscovitch (2001) argue that BC is considered as a problem for commitment. This component is analyzed as the negative factor of the three commitment profiles, in which the other two are positive. Regarding the ‘two faces’ theory of Meyer and Parfyonova (2010), the relationship of high BC with high CC creates negative context effects, which result in the creation of duties. It is reflected as the ‘indebted obligation’ whereby performance outcomes are experienced to decrease. For example, the level of job tension is conceived to increase. This is due to the fact that when the level of expectations increases, employees perform activities better over time (Meyer & Maltin, 2010). Employees feel required to escape from the anxious situations because of this pressure and consequently, they want to resign(Meyer & Maltin, 2010). Therefore, it can be claimed that BC negatively influences employees’ commitment. Compared to the former two components, behavioral commitment does not have a positive impact on organizational outcomes. This can be formulated as:

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2.4.3 The moderating effect of employee commitment

Lean Management involves both benefits and costs. As a result, failure of the lean program still occurs nowadays (Bhasin & Burcher, 2006; Albliwi et al., 2014). Organizations expect quick improvements and hold on to expectations that cannot be met (McLean, Antony & Dahlgaar, 2017). Although, companies do not fail because of deficiencies in the improvement program but due to lack of attention to employee commitment. This thesis, therefore, focuses on the moderation effect of employee commitment. The following analysis explains why companies still fail to achieve LM effectiveness.

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results are higher when it is implemented through a human resource strategy that promotes the commitment and involvement of all individuals in the organization with quality objectives” (p. 82).

However, the aforementioned is only considering the human aspects in the interaction. One way to cover the whole theory is to discuss also the analytical and technical aspects. When LM is used “in conjunction with a high-commitment (HC) strategy, employees direct their efforts to the achievement of quality goals, rather than the pursuit of their own interests” (Bou & Beltrán, 2005; p.78). This HC strategy goes along with a high-performance work system. This system, which is the technical side, helps companies to achieve greater and more long-term oriented benefits of lean (Bou & Beltrán, 2005). As mentioned above, training the employees is very important because this highlights the evolving social matters. It emphasizes technical matters that constantly apply the day-to-day tasks and new dilemmas (Bou & Beltrán, 2005). Training does not only involve training the social part but also training the technical tools in, for example, statistical process control. Lam et al. (2015) confirmed that training both helps in attaining visualized results. They suggested that proactive influence tactics stimulate employee commitment in a lean service context and therefore generate higher effectiveness of the lean program. Bou and Beltrán (2005) and Lam et al. (2015) both did research in the service sector from which the following can be assumed:

Hypothesis 3: Employee commitment positively moderates the relationship between Lean Management practices and performance in the service industry

2.5 The research model

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Figure 2. Conceptual model

3. Method

3.1 Research design

For this research, the appropriate method to answer the research question is a survey. A survey is chosen since it gains specific knowledge on ‘what’ questions. Besides, “it allows for testing whether the hypothesized relationships or differences hold in a different context” (Karlsson, 2016; p.85). This is in line with the aim of the research, however, this research is focused on one specific context only, namely services. Another important element is that a survey can emphasize generalization of results (Krosnick, 1999). It will analyze and test for how, when and to what degree a relation is evident (Karlsson, 2016). This research will use a web-based survey since this ensures response anonymity, higher response rates (due to a user-friendly device) but above all, better data accuracy (Krosnick, 1999). Overall, this research can be seen as a theory-testing research. This paper examines the existing theory of the influence of employee commitment on the relationship between LM and performance by using well-defined approaches and frameworks. First, a pilot is conducted in order to discover any problems regarding the survey (Kelley, Cark, Brown & Sitzia, 2003). Afterwards, the hypotheses can be tested. The setting is Germanic Europe and Confucian Asia service industry. These clusters are chosen because of both the service market shares surpass the shares of other clusters (OECD, 2017).

3.2 Sample selection

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pure service companies. With regard to the sample size, there is no rigorous answer how large it must be because this depends per research (Kelley et al., 2003). Although, a large sample size will be more powerful since the data will be more accurate (Kelley et al., 2003). Due to time limits, this study will be therefore sufficient with a sample size greater than 50 (Karlsson, 2016). Per company, a random sampling is used. Random sampling ensures that the results can be performed statistically and can be generalizable to a larger population (Kelley et al., 2003). In order to know sure the respondents are acknowledged they were required to be responsible or have at least some knowledge about lean. In some companies, multiple employees completed the questionnaire. However, the majority only supplied one employee and therefore the reliability of the results is partly sufficient.

3.3 Data collection

For this research, data is collected from October until December. A new database is created that only includes respondents of pure service companies. The survey is sent to 700 people working for service companies worldwide. The respondents were approached via Email, LinkedIn or WhatsApp and received a hyperlink that was one click away from answering. This study included an English and a Chinese version of the questionnaire. The survey started with a short introduction about the topic in order to inform the respondents properly about the research (Kelley et al., 2003). Thereafter, a set of 19questions (each containing 5 to 7 sub-questions) was distributed. Besides this, 10 general questions were formulated in order to know, e.g., the nationality and company size of the respondents. The survey took on average twenty-five minutes per respondent. In order to test attention, some questions were posted in the opposite way. In case they did not answer this question consistently, their data was deleted so response bias was reduced. An overview of the topic questions can be found in appendix C.

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of documents and questionnaires to increase triangulation. As a result, the belief that the results will be will be increased (Jick, 1979).

3.4 Measurement elaboration

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3.5 Data Analysis

Data analysis and the interpretation of results are divided into a pilot test and hypothesis test. In both analyses, the statistical program SPSS will be used. It facilitates preliminary data-analysis actions by showing visual representations. As this research is related with human sciences, a cumulative explained variance between 50% and 60% per construct will be accepted as good (Williams, Onsman & Brown, 2010).

3.5.1 Pilot testing

The pilot test is important to perform since this demonstrates any problems related with the research design (Karlsson, 2016). Once there are problems in the research, the mean is biased and this can lead to either rejecting the right hypothesis or accepting the wrong hypothesis (Karlsson, 2016). These errors lead to less generalizable results, which lower the external validity of the research. In the pilot test, the reliability of every latent variable is tested. The reason of estimating the reliability is to search how much of the variability in the scores is caused by measurement error and how much is caused by true scores. The analysis is performed for constructs of LM, EC, and performance (results can be seen in appendix E). The values of the Cronbach Alpha (CA) should be sufficient with a value higher than 0.7 (Yang & Green, 2011). In this study, a value lower than 0.7 is considered as unacceptable. Moreover, unidimensionality of every latent variable is analyzed; this ensured that the formulated questions are only related to one construct. It can be said that items belong to only one construct when the Eigenvalue provided 1 extraction (EV = 1). For the constructs to have the abovementioned requirements (CA > 0.7; EV = 1), some questions were deleted. Afterwards, the quality of the questions could be assessed. The Kaiser-Meyer-Olkin should maintain a value above the 0.6 and the Barlett’s Test of Sphericity should be significant (ρ < 0.05) (Malhotra, 2010). In the pilot study, some value problems appeared, which are marked in capital letters in appendix E. However, the pilot contained not enough respondents to draw conclusions on these results yet.

3.5.2 Hypotheses testing

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4. Findings

In this chapter, the results of the quantitative analysis are presented. This analysis tested the theoretical framework depicted in figure 2.

4.1 Background statistics

In total, 118 respondents started the questionnaire but only 61 respondents completed. This means that around 50% of the respondents stopped the survey, which is relatively high. On the one hand, this may be due to the sensitivity of the topic, a wrong approach of the research design or the researcher delivered the survey to the wrong target population (Baruch & Holtom, 2008). On the other hand, it could be the reluctance of the respondents in that it took too much time to complete as experience showed a time of twenty-five minutes (Baruch & Holtom, 2008). This is an important note as it limits this research. According to Sapnas and Zeller (2002), however, a sample size of 61 respondents is still sufficient to run a regression analysis. The participants who completed the survey can be categorized into three different sectors, two different cultural clusters and 3 different sizes of company. In total, 45 different companies are represented. An overview can be seen in table 3.

Table 3. Descriptive statistics

4.2 Data reduction

To ensure validity and reliability of the items and constructs, the data is tested for unidimensionality, reliability, and validity.

4.2.1 Unidimensionality

The first requirement to comply with is the unidimensionality of the variables. Unidimensionality specifies whether items belong to one measure or multiple measures. In order to evaluate the data, the factor analysis is performed. Although the measurement instruments are based on previous studies, this study made some alterations in order to guarantee that instruments explain only one dimension. In this way, the correlations and measures of the constructs are provided. To interpret the results, Varimax Rotation and Principal Component Analysis (PCA) are used. It is necessary to use PCA in order to show (on average) the same results as previous studies and it is helpful in reducing the data (Costello & Osborne, 2005). While analyzing the results, the questionnaire included some items that should have been deleted because of cross-loadings. Also, correlation values below the 0.5 should have been deleted since lower values are practically not significant and explain less than 50% of the relationship

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within the data (Williams, Onsman & Brown, 2010). In appendix F can be seen from which constructs items are removed and what the correlations are between the items. As a result, the table demonstrates no further significant problems of the constructs. One important finding during the test of unidimensionality is that the constructs of soft and hard practices belong to only one variable. As can be seen in table 4 they all highly correlate on one variable, which means that there is no distinct difference between them. Former studies interpret the two bundles as two separate variables. Therefore, no significant distinction exists between soft and hard practices in services. Further interpretations on this finding will be discussed in section 5.

Table 4. Component matrix with correlations

Construct Component

Multifunction employees 0.849

Small group problem solving 0.818

Continuous improvement 0.862

Top management leadership for quality 0.894

Employee suggestions 0.890

Continuous flow 0.750

Statistical process control 0.620

Autonomous maintenance 0.829

Cleanliness and organization 0.729

Design for quality 0.878

4.2.2 Reliability and validity

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discriminant validity is assessed and that the constructs are distinct (Hamid, Sami & Sidek, 2017). An important note is that discriminant validity proves (again) that all practices belong to Lean Management. As can be seen in appendix G, some problems appeared regarding the latent variables of soft and hard practices, which refers to the insignificant distinction between soft and hard practices in services. This problem is resolved by the fact that the lean practices are combined into one construct: LM (Henseler, Ringle & Sarstedt, 2015). Therefore, the reliability and validity are confirmed.

Table 5. Pearson correlations and squared root of AVE

Variable AC CC BC LM Performance AC 0.91 CC 0.747 0.65 BC 0.739 0.535 0.83 LM 0.817 0.570 0.674 0.84 Performance 0.609 0.409 0.471 0.766 0.81 4.3 Hypotheses results

In order to see whether there are significant relationships between the dependent variable and independent variables, the hierarchical regression analysis is used. The results of the analysis tell us to what degree certain variables are related to one another. In order to find out whether LM, EC, and performance are related with each other, the correlation analysis in SPSS is performed. The results are presented for the hypotheses developed during this research based on the sample of 61 respondents. The results are shown in table 6. In model 1 the control variables are introduced. Afterwards, the independent variables LM, AC, BC, and CC are introduced in model 2 to test direct effects. The final model, 3, incorporated the moderation effects of the different commitment forms between LM and performance. In order to interpret the results of the hypotheses, the results of the hierarchical regression analysis will show the coefficient of every independent variable and interaction variables. Also R-squared, T-statistics, F-statistics, and P-value are included to determine the meaning of the results.

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60% of the observations and model 3 more than 65%, which are statistically interpreted as sufficient (differs per research context, but for social sciences above the 60% is sufficient) (Hair, Sarstedt & Ringle, 2011). By taking previously mentioned into account, the results of the hypotheses can be interpreted below.

Table 6. Hierarchical regression model

Independent Variable Model 1 Sig. Model 2 Sig. Model 3 Sig.

(Constant) 3.690 0.000 3.763 0.000 3.660 0.000 Confucian Asia -0.186 0.281 -0.085 0.546 -0.079 0.567 Small company 0.148 0.533 0.001 0.993 0.070 0.663 Large company 0.041 0.838 -0.0145 0.310 -0.137 0.322 Lean Management 0.557 0.000** 0.600 0.000** Affective commitment -0.009 0.944 0.098 0.491 Cognitive commitment -0.062 0.530 -0.083 0.409 Behavioral commitment -0.035 0.689 -0.083 0.356 LM*AC 0.210 0.043* LM*CC -0.142 0.112 LM*BC -0.013 0.883 R2 0.035 0.606 0.653 Adjusted R2 -0.015 0.554 0.584 F 0.699 11.660 9.427 dF 3 7 10 Sig. 0.557 0.000** 0.000** * ρ < 0.05 ** ρ < 0.01

Hypothesis 1: Lean Management practices lead to high performance at service companies. Based on the outcome, the first hypothesis of this study is supported. This hypothesis claimed that different practices of Lean Management have a positive significant linear relationship with performance outcomes for companies operating in the service industry. As can be seen in table 7, there is a positive correlation (β = 0.557) and the relation is highly significant (ρ < 0.01). From this can be argued that a service company, which ensures at the same time social and process related improvements, will increase its operational performance by implementing a Lean Management program.

Table 7. Results of H1

Independent variable β Std. Deviation T Statistics Significance

Lean Management 0.557 0.105 5.287 0.001**

* ρ < 0.05 ** ρ < 0.01

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profiles, has a significant direct effect on the level of performance. As can be seen in table 8, the results show that this hypothesis cannot be supported. With almost a significance level of 1 (ρ > 0.05), the data is statistically not significant and therefore affective commitment (relative to CC and BC) does not directly generate operational performance for a company. This means that companies cannot generate positive results if the management is only making its employees affiliated with and proud of the organization’s goals and values.

Hypothesis 2b: Cognitive commitment (CC) has a positive influence on performance at service companies. Based on the results shown in table 8, hypothesis 2b will not be supported (β = -0.062, ρ > 0.05). Based on previous literature, it was claimed that common identifications with the goals and values of an organization (which create a common sense of goal commitment), relative to AC and BC, could increase the reliability and efficiency of a service company. However, this is not the case. Cognitive commitment, let alone, does not act as a predictor for organizational outcomes. Therefore, organizations that have integrated a shared sense of importance towards organizational goals and values do not directly generate higher operational performance.

Hypothesis 2c: Behavioral commitment (BC) has a negative influence on performance at service companies. The final commitment profile is somewhat different than AC and CC. Behavioral commitment expressed that, contrary to AC and CC, it would negatively influence the accomplishments of a service company. However, this hypothesis will be rejected since there is no significant relationship visible (β = -0.035, ρ > 0.05). This means that active participation due to pressure and expectations towards organizational goals will not affect the performance in a negative way. The significance level can be seen in table 8.

Table 8. Results of H2a, H2b, H2c

Independent variable β Std. Deviation T Statistics Significance

Affective commitment -0.009 0.135 -0.070 0.944

Cognitive commitment -0.062 0.097 -0.633 0.530

Behavioral commitment -0.035 0.088 -0.402 0.689

* ρ < 0.05 ** ρ < 0.01

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employee commitment interacted with the degree of effort of lean practices. However, as can be seen in table 9, only the interaction of affective commitment can be verified as a moderator for the relationship (β = 0.210; ρ < 0.05). Therefore, hypothesis three is partly accepted. From this can be confirmed that employees that are more affectively committed to the organization, generate higher LM effectiveness than the ones who are not affiliated with the company’s goals. This is a remarkable finding which will be discussed in section 5.

Table 9. Results of H3

Independent variable β Std. Deviation T Statistics Significance

LM*AC 0.210 0.101 2.076 0.043*

LM*CC -0.142 0.087 -1.619 0.112

LM*BC -0.013 0.088 -0.148 0.883

* ρ < 0.05 ** ρ < 0.01

The moderation effect is plotted in figure 3 from which can be seen that a high (or low) level of Lean Management does indeed influence the level of performance when there is a high (or low) level of affective commitment. This can be noticed at the point where the two lines cross each other (i.e. antagonistic interaction effect). The plot tells us that there is a moderate interaction effect since the lines are not perpendicular. Although, still significant to claim the interference between LM and AC. Figure 3. Interaction effect of Lean Management and affective commitment

To conclude, the results have shown full support for hypothesis 1, rejections for hypothesis 2a, 2b and 2c and partial support for hypothesis 3. Furthermore, this analysis has shown that Lean Management practices are merged into one variable instead of being dependent upon soft and hard practices in services, which was actually expected by the researcher. Therefore, the results give enough insights for further discussion.

Low Lean Management High Lean Management

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5. Discussion

For many years, Lean Management is a well-known approach that is implemented in lots of companies. Nowadays, it has already been introduced in different industries. As lean found its way to the service industry, this sector is chosen to be the objective of this study. Less is known about the service industry and therefore new insights can be found. As mentioned earlier in this study, the realization of lean effectiveness failed for more than 60% of the lean companies while they actually expected quick improvements in performance. As a result, this creates the assumption that the effectiveness of lean in this sector has the opportunity to increase and has the opportunity to reconstruct the industry with its related practices. Therefore, this study offers two essential contributions to OM literature in services.

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but the composition differs per industry. Therefore, a people-centered approach combined with equal process-based tools can increase the performance of pure service companies.

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6. Conclusion

This research is conducted to determine the role of employee commitment in lean service companies. This research area is chosen because still few companies achieved visualized results of their lean program. Even though previous studies focused on the role of affective commitment in an organization, the impact of employee commitment on quality and the motivation to participate in lean activities (Parker, 2003; Lee & Peccei, 2007; Lam et al., 2015), literature is lacking research about the influence of employee commitment in a lean service context. This is surprising because most of the current jobs are fulfilled in this sector (OECD, 2017). As a result, this industry cannot be ignored during research. This stimulated the researcher even more to fill the gap in the service industry. Therefore, this research investigated the role of employee commitment in Lean Management in the service sector.

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To conclude, this research found new insights about employee commitment in a lean service context. The research question can be answered by arguing that employee commitment plays a very important role in the execution of Lean Management within service companies. As a result, organizations operating in the service industry should be able to create organizational advantages by implementing Lean Management accompanied with an affective commitment strategy. In this way, pride and satisfaction of employees will make it possible that lean programs will be carried out effectively in service organizations.

6.1 Managerial implications

The findings in this study show clearly that the practices of lean are perceived differently per industry. This means that companies operating in a service environment cannot just take the same perspective of a lean strategy as companies serving the manufacturing industry. The difference can be explained by the fact that pure service companies provide direct customer (contact) service with the support of analytical tools. For manufacturing companies, there is barely both customer contact and technical tool usage at the same time. This shows the clear distinction between the two-sided procedures per industry in a lean strategy. Furthermore, results suggest that affective commitment is the most relevant indicator for lean service companies that operate in a lean service environment. This indicates that lean effectiveness can grow when companies pay appropriate attention to their human proficiency. Due to these findings, some implications can be advised.

First, as for service companies, the lean effectiveness is still not what it is supposed to be. However, there are some implications that pure service companies can implement to generate benefits and to increase the probability that a lean strategy will be effective. When a lean strategy employs multi-functional employees, small group problem solving, continuous improvement, top management leadership for quality, employee suggestions and operates in a continuous flow, use statistical process control, includes autonomous maintenance, cleanliness and organization, and creates a design for quality, it is able to improve its performance. This is relevant since it suggests that managers should not only care about process improvements but also about the people who make the service possible. If managers do not consider all practices to be identical, the value of the lean strategy might decrease. As a result, this can harm the market position of companies whereas competitors will deliver a more sophisticated and integrated lean service to its customers.

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companies effective in their lean approach, they should implement a strategy that increases the affiliation in pride of its employees. When implementing a well-designed lean program without increasing the pride and satisfaction, it is less likely that the performance will elevate. One way to do this carefully is by, for example, implementing an affiliation strategy. This strategy is an approach to create a two-way relationship between the organization and the individual (Elsdon, 2003). Managers should consider maximizing the ‘bond of connection’ while minimizing the ‘tug of separation’ (Elsdon, 2003). This means that the (inter)relationships between and among employees need to be maximized and the number of disputes need to be eliminated. A manager’s challenge is to tighten the gap between the potential capabilities of an employee and the definite capabilities of an employee (Elsdon, 2003). In this way, by implementing a well-developed lean strategy accompanied with an affiliation strategy, the ability to create respect for each other and build organizational communities will motivate the employees. This result in superior organizational value (Elsdon, 2003; Angelis et al., 2011).

6.2 Limitations and further research

Even though this study provided relevant results, it is subjective to some limitations. The first restriction concerns the design of the survey because only a small percentage of the target population completed the survey (8.7%). On the one hand, this may be a result of the sensitivity of the topic, the wrong approach of the research design or the wrong-targeted population. On the other hand, it could be the reluctance of the respondents in that it took too much time to complete as experience showed a time of twenty-five minutes. Some participants admitted that time-span was the biggest struggle. Moreover, another reason why participants stopped or did not participate at all is related to the language options of the survey. The study contained only an English and Chinese version. Therefore, many Dutch operating companies did not want to participate. Further research should focus on enabling a shorter survey and should include a Dutch version. Next, the target population should be chosen more carefully in order to reach the right respondents that do not consider the topic to be sensitive.

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this study. Therefore, future research should resolve former problems by including companies that operate in other service industries and other cultural clusters (e.g. airline companies). This can provide the possibility to compare more service companies with different nationalities in order to see whether other significant differences exist in making employees committed in lean activities. Besides, future research should include at least two respondents per company. This increases the reliability and generalization of the results.

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