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University of Groningen Faculty of Business and Economics

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Due to acute global competition, the service organizations are searching for management methods, such as Lean, to improve internal efficiency and service quality. However, service operations management is still under-researched and lag far behind manufacturing. And the benefits of Lean Management on service organizations remain unclear. Besides, organizational culture could play an important role in LM application by either driving or hindering the success of LM. In this light, this study empirically analyzes the influence of lean management on organizational performance for pure service organizations, as well as the interaction role that organizational culture plays with LM on organizational performance. Data of this study was gathered by self-administrated questionnaire survey towards service practitioners in China and UK. The results confirmed the positive association with Lean Service and organizational performance. And by comparing two alternative models, OC was confirmed to have a direct impact on the extent of LM implementation in service. And certain OC dimensions were found to be positively associated with LM extent, including future orientation and uncertainty avoidance for both lean soft and hard practices; in-group collectivism and institutional collectivism for lean soft practices; and performance orientation for lean hard practices. This research underscored the importance of implementing lean management as a socio-technical system. Service organizations are recommended to simultaneously focus on lean soft and hard practices in order to achieve more favorable results from LM application. And service organizations should better foster a conducive OC prior to the LM implementation.



With increasing competition in the ever-changing global marketplace, service organizations are confronted with challenges, like market turbulence, increasing customer demands and declining profit margins (Alsmadi et al. 2012; Carlborg et al. 2013). In this light, service companies are seeking potential changes and operations management approaches as levers to improve service productivity and quality (Schmenner, 2004; Machuca et al., 2007; Staudacher & Tantardini 2012). One common approach is to apply proven manufacturing improvement techniques or tools in service sector, such as Lean Management (Alsmadi et al., 2012). During the last two decades, lean principles have been applied in many service industries, including insurance (Hammer, 2004), education (Emiliani, 2004), consulting (Ball and Maleyeff, 2003), public services (Radnor et al., 2006) and healthcare (Brandao de Souza, 2009).

As an improvement method originated from manufacturing, Lean management has been proven to be capable of improving operational performance in manufacturing context (Moyano-Fuentes & Sacristán-Díaz, 2012; Bortolotti et al. 2015). While, considering the unique characteristics of service, some researchers are doubtful about whether Lean Management could also improve the performance in service organizations (Hadid & Mansouri 2014). The unique service characteristics, like intangibility, heterogeneity, perishability, etc (Alsmadi et al. 2012; Seddon et al. 2011), could set constrains for the use of certain lean techniques and tools. In turn, these constrains might lead to suboptimal results compared with other industries (Andrés-López et al. 2015). Previous studies had mainly discussed the feasibility and adaptivity of lean practices to different service industries through conceptual and case-based studies. (Suárez-Barraza et al., 2012; Hadid et al. 2016). There is a gap in empirical research regarding LM effectiveness in service sector.


leaving service manager doubting whether it is enough to only implement lean hard practices.

Another potential issue for LM implementation lies in organizational culture (Atkinson, 2010; Bhasin 2012). Misfit between culture and practices would possibly decrease either the fidelity or the extensiveness of lean practice, thus resulting in “inefficient adaptations and superficial adoptions and lead to suboptimal performance results” ( Bortolotti et al. 2015). On the contrary, a synergy between OC and LM could result in superior organization performance. The study of Bortolotti et al. (2015) indicated that previous discussions on OC and LM were fragmented and limited to narrow sets of OC and LM. They suggested a holist approach to examine the relationship between OC and LM based on GLOBE model (House et al., 2004). And they found successful lean adopters are characterized by specific OC profile (e.g. high level of institutional collectivism, future orientation, and humane orientation) in manufacturing industries. However, it is questionable whether such OC profile could also hold in a pure service setting. Besides, it is still not clear about the role of these OC dimensions in the LM-performance relationship. As a contingency factor, OC could possibly play a mediater or moderator role in LM-performance relationship (Venkatraman, 1989). Depending on what kind of the role is valid for OC, the managerial implications are different (Narasimhan et al. 2012).

The aforementioned literature gaps compromise our understanding of whether lean management could bring identical performance improvement for pure service industries as for manufacturing sector (Hadid et al. 2016); and how to ensure a more favorable results from LM through a suitable implementing approach, i.e. different level of hard and soft practices, in different cultural environments. It is important to have a clearer view on these issues from the perspectives of both scholars and practitioners, considering the major contribution of service to global economy and the challenges that service managers are currently facing. The service industries is reported to accounts for over half of the gross domestic product (GDP) and employment of most modern economies (Leite & Vieira 2015), yet in contrast, current service operations management is still under-researched and lag quite behind manufacturing industry.

Thus, our study is aiming at answering the following question:

Does an extensive implementation approach of lean management lead to better organizational performance in a pure service setting, and what is the role of organizational culture in LM-performance relationship?




2.1 Lean Management

Lean Management is a management system derived from the Toyota Production System (TPS) (Clark et al. 2013). With its popularization owing to “The Machine that Changed the World” by Womack et al. (1991), lean management has been studied and applied in various industries during the past two decades.

The primary philosophy of LM is to enhances the value perceived by customers, through adding product and/or service features and continuously removing waste (Muda in Japanese) (Hines et al. 2004). Muda, defined as “any human activity which absorbs resources but creates no value”, is the key concept proposed by Taiichi Ohno for quality improvement activities in Toyota during 1950s (Dahlgaard-Park, 2000; Dahlgaard & Dahlgaard-Park 2006). Seven sources of waste were identified by Ohno (1988): overproduction, defects, transportation, waiting, inventory, motion and processing.

Although LM has been the focus of operations management all these years, there is still not a unique and shared definition for it. LM could be considered as a set of high-level principles (Womack & Jones, 1996) or operational bundles of practices (e.g. Swank, 2003; Wei, 2009;Shah & Ward, 2007). When considering LM as principles, the five lean principles (Value, Value Stream, Pull, Flow and perfection) set by Womack & Jones (1997) provide a framework for organizations interested in transforming from traditional mass production to lean organization.

While, from the operational perspective, LM is characterized by specific techniques/tools and mechanism needed to achieve the objective of waste elimination (Shah & Ward, 2007; Alsmadi et al. 2012). These techniques or tools could evolve with time or be adapted in different contexts. (Shah & Ward 2007). Further to our study, the main intention is focused on empirically examining the influence of LM on organizational performance. Hence, the operational perspective is adopted in this study, as it provides concrete measurable constructs of LM.


definition of LM: “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: p791).

LM Practices LM literature Hard/Soft LM

Lean Management

Supplier related

JIT delivery Shah & Ward (2007) Hard LM Supplier development Shah & Ward (2007) Soft LM Customer

related Customer involvement Shah & Ward (2007) Soft LM

Internally related

Pull Shah & Ward (2007) Hard LM Continuous flow Shah & Ward (2007) Hard LM Set-up time reduction Shah & Ward (2007) Hard LM Autonomous Maintenance Shah & Ward (2007) Hard LM Statistical process control Shah & Ward (2007) Hard LM Continuous Improvement Shah & Ward (2003);

Shah & Ward (2007); Soft LM Employee Involvement Shah & Ward (2007) Soft LM Human- &

Strategic Oriented

Management leadership for quality Flynn et al. (1995);

Cua et al. (2001) Soft LM Small group problem solving Flynn et al. (1995); Cua et al. (2001) Soft LM Table 1. Lean Practices Constructs - Adapted from Shah & Ward (2007), Flynn et al. (1995), Cua et al. (2001), (Alsmadi et al. 2012)

Other literatures, e.g. (Flynn et al. 1995) and (Cua et al. 2001) were consistent with Shah & Ward (2003, 2007) in characterizing LM with lean practices bundle. But they additionally highlighted the important supporting role of Human- and Strategic Oriented lean practices in LM, including top management leadership for quality, small group problem solving, and employee training (Bortolotti et al. 2015).

Therefore, organizations could be considered to apply LM if they more or less implement these practices (Shah & Ward 2007). An overview of lean practices characterizing LM based on the studies of Shah & Ward (2007), Flynn et al. (1995) and Cua et al. (2001) is presented in Table 1.

2.2 Lean Management in Service


also been applied in a wide range of service operations (Maleyeff 2006). However, studies in regard to Lean Management in service are still scarce (Piercy & Rich, 2008b). The extant literatures for lean service are mainly concerned with the applicability and validity of LM and its possible outcome through conceptual building or case studies (Suarez-Barraza et al., 2012; Hadid & Mansouri 2014). Scant research exists in empirically examining different aspects of lean management in service through large sample surveys, according to Holm and Ahlstrom (2010b) and Suarez-Barraza et al. (2012).

The general argument for the relevance of LM in non-manufacturing industries is that lean management is an improvement method designed from a process perspective rather than product (Hadid et al. 2016). And all types of organizations are a compilation of processes aiming at providing products and/or services, therefore, service organizations could also use LM in service delivery processes to reduce cost and improve quality, and in turn satisfy customers’ needs (Allway & Corbett, 2002; Alsmadi et al. 2012; Hadid & Mansouri 2014).

Although case studies offer implications of some specific lean practices used in various service firms, there still does not exist a comprehensive understanding of lean practices and its relationship with organizational performance under service context (Hadid & Mansouri, 2014). A lot of scholars argue that many lean practices could not be immediately and obviously applicable to service organizations due to the unique characteristics of pure services, such as intangibility; perishability; inseparability; variability and lack of ownership (Gronroos, 1978; Parasuraman et. al, 1988; Vargo & Lusch, 2004).

As reported by Alsmadi et al. (2012), which is the only relevant survey study in this field (Hadid & Mansouri 2014), service companies are more interested in soft lean practices (people and relationship related) such as employee and customer involvement, while they use less manufacturing-related practices such as TPM, set-up time reduction and supplier feedback compared to manufacturing companies. An explanation for this finding is probably related to the significant role that human factor, including both employees and customers, play in the service delivery process (Leite & Vieira 2015).

On one hand, customers play an active role in service process, as customer presence and customer inputs, in terms of time, knowledge, and skills are normally required for delivering service (Parasuraman 2002; Grönroos & Ojasalo 2004; Geum et al. 2011; Carlborg et al. 2013). Hence, customer inputs are highly associated with service productivity. Thus, customer involvement, as a potential tool to address variations of customer inputs, should receive more attentions for LM in service.


(Bonaccorsi et al. 2011), because their rapidity, efficiency, willingness and cordiality, and their reactions to atypical circumstances will positively/ negatively denote the way in which a service is supplied, and ultimately impact customer satisfaction (Bonaccorsi et al. 2011; Maleyeff, 2006). Therefore, employee involvement practices, like employee training is highly relevant and recommended in service organizations.

2.3 LM and Organizational Performance from a Socio-technical System Perspective

According to Shah & Ward (2007), LM could also be treated as an integrated socio-technical system (STS) as companies are required to effectively manage their social and technical systems simultaneously when pursuing LM. As coined by Trist (1981), the STS theory argued that organizations consist of two components, technical (e.g. techniques, structure, process) and social (people and relationships). The social and technical elements are separate but interdependent (Trist, 1981), and only joint optimization of social and technical systems in an organization could lead to successful organizational performance (Emery, 1990; Cua et al. 2001). However, emphasizing one side alone in the organization while neglecting the other side, will lead to unfavorable results to the organizations (Fox, 1995; Hadid et al. 2016). In line with this theory, previous studies, like Cua et al. (2001) had found that common (soft) practices of LM provide supporting mechanism for hard (unique) practices of LM and implementation of both social and hard LM practices could achieve higher organizational performance.

2.3.1 Lean Hard Practices

The aforementioned mentioned lean practices could also be group into soft (socio-orientated) and hard (technical-related) practices accordingly. Table 1 also presents a soft or hard classification for each of the LM practices.

For the hard practices in our research, JIT delivery, Pull, Continuous Flow, Set-up reduction are main techniques/tools under JIT bundle. These practices could address two major forms of waste, i.e. work-in-progress inventory and unnecessary delays in flow time (Shah & Ward 2003); in turn, improve utilization efficiency and realize on-time-delivery for customer. And there are many literatures, such as Koufteros et al. (1998), Sakakibara et al., (1997), and White et al. (1999) had proven that the use of these JIT tools could lead to improved operational performance in manufacturing organizations.


customers to pull the service through service request, service catalogue, etc. And Kanban method could also be used in service to monitor the process after adaption within IT environment (Andrés-López et al. 2015).

As for other hard practices, for instance, Statistical Process Control, it is an indispensable practice for TQM, which is about “using statistical process control charts to provide operators with feedback, allowing them to base their actions on the variability of the process” (Flynn et al., 1995: p1327). The main intention of SPC is to reduce process variation and improve quality of goods and services, and further lead to more satisfied customer (Flynn et al. 1995). Alsmadi et al. (2012) has also found positive association with SPC application and organizational performance in service companies through a survey study using a sample of 278 UK manufacturing and service firms.

Based on the above discussion, it leads to the following hypotheses:

H1a: The implementation of lean hard (technical) practices is positively associated with organizational performance in pure service organizations.

2.3.2 Lean Soft Practices

As per socio-technical system, soft (socio) practices are practices related to human and relationship. Within STS, worker/employee plays a central role (Dibia & Onuh, 2010). Meanwhile, employees are also essential to Lean Management, as they are involved in identification and elimination process of waste, and act as the main driver for continuous improvement (Wickramasinghe & Wickramasinghe, 2011). Employee involvement is one of the most common soft lean practices, which is previously studied under different terminologies, e.g. HRM (Shah & Ward 2003) or workforce management (Flynn et al. 1995) and people management (Samson & Terziovski 1999). Both Shah & Ward (2003) and Samson & Terziovski (1999) had documented positive influence of employee involvement on organizaitoan performance. As argued by Wickramasinghe & Wickramasinghe (2011), employee involvement increases flexibility and reduces the vulnerability of the production system; and meanwhile, employee involvement improve product/service quality with their efforts in solving production problems and devising process improvements. Employee flexibility is more important for service companies, because the demand variation is normally higher in service production than in manufacturingp production (Maleyeff 2006). And employees’ skills, knowledge, as well as their rapidity, efficiency, willingness and cordiality, and their reactions to atypical circumstances will influence the experiences of customer as discussed in subsection 2.2.


services. This quality is not only decided by adhering to material specifications but also by on-time-in-full delivery at the right place and at the right price (Jadhav et al. 2014). And many researchers had argued that the success of LM in Toyota cannot be achieved without its long-standing relationship with highly competent suppliers and their engineering expertise (Jadhav et al. 2014; Moyano Fuentes & Sacrist n D az 2012); Supplier is even more critical for service when considering triadic service relationship in which supplier is in direct contact with customer for delivering part of the services, for instance, call centers (Wynstra et al. 2015). The lack of communications between focal company and its supplier could lead to “failure demand”, which is recognized as the major and unique waste in service organizations (Seddon et al. 2011). Supplier involvement could be of great help to address such waste.

Finally, customer involvement could also contribute to organization performance by improving product/service quality through customer contact and feedback (Cua et al. 2001; Flynn et al. 1995). Moreover, as explained in section 2.2, customer inputs are required in service delivery and highly associated with service productivity (Carlborg et al. 2013). Customer involvement could possibly address the variance of these inputs and lead to higher service productivity and customer satisfaction.

Accordingly, the following hypotheses is formulated:

H1b: The implementation of lean soft (socio) practices is positively associated with organizational performance in pure service organizations.

2.4 Organizational Culture and Lean Management

Despite the potential great improvement brought by LM to service companies, in reality many companies cannot achieve expected benefits. And among all the barriers towards lean, ignorance of cultural factors is recognized by many scholars and practitioners as a critical issue (Bhasin 2012; Prajogo & McDermott 2005; Jadhav et al. 2014; Achanga et al. 2006).

Organizational culture is defined as “a pattern of basic assumptions - invented, discovered, or developed by a given group as it learns to cope with its problems of external adaption and internal integration - that has worked well enough to be considered valid and, therefore, to be taught to new members as the correct way to perceive, think, and feel in relation to those problems” (Schein,1985: p.9).


Recent literature, e.g. Abdul Rashid et al. (2004), Hernández-Mogollon et al. (2010) and Baird et al. (2011), have underscored the role of OC in the implementing new managerial/operational initiatives (Haffar et al. 2014). A conducive organizational culture could function as a driver, or “fertile soil” to generate more benefits; conversely, a non-conducive OC will set barriers for companies to achieve expected results.

Accordingly, it is essential to create a supportive organizational culture for LM application. The implementation of LM systems requires organizations to adapt to new processes, new routines, new methods of work performance etc. (Wickramasinghe & Wickramasinghe 2011). The inherent organizational culture would determine whether an idea or process required by LM could be accepted within the organization and to what extent (Crandall & Crandall, 2011; Pakdil & Leonard 2015). This is in line with the argument for congruence between organizational culture and managerial practices. In case of incongruence or misfit between OC and LM practices, organizations tend to “corrupt” practices applied instead of transforming the OC, resulting in low fidelity (i.e. using an unintended form of the practice) or low extensiveness (i.e. superficial implementation of the practice); and in turn deter the improvement (Ansari et al., 2010; Lozeau et al., 2002; Kull et al. 2014; Bortolotti et al. 2015).

However, despite the widespread recognition of the influence of OC on LM, there is a gap in the empirical literature examining the OC-LM relationship (Narasimhan et al. 2012; Bortolotti et al. 2015). Prior studies mainly analyzed the relationship between OC and a specific or a narrow set of LM practices (Bortolotti et al. 2015), mostly for TQM (Prajogo & McDermott, 2005; Naor et al., 2008; Zu et al., 2010). Some of these studies treated OC as an antecedent of TQM practices, such as Prajogo & McDermott (2005) and Naor et al. (2008). The results of Prajogo & McDermott (2005) revealed that different TQM practices are determined by different types of OC. And the research of Naor et al. (2008) found that OC has a stronger influence on infrastructure practices (social and behavioral aspects) than on core practices (technical aspects) of TQM, regardless of the country where the plants were located. Moreover, they also postulated that OC could play a moderator role between relationship of TQM and performance. Although, this proposed moderation effect was not supported by the results, it provided an alternative perspective for depicting the relationship between OC and OM practices. As suggested by Brett et al. (1997), culture research could be designed with different perspectives, including “culture as a main effect”, i.e. antecedent to dependent variable; and “culture as a moderator” of main effects (Narasimhan et al. 2012).


future orientation and high uncertainty avoidance. These empirical findings indicated that OC really matters to LM, but the dynamics of the interplay between OC and LM remain unclear.

The present study would like to address this gap of empirical study for OC-LM relationship, by exploring how OC contribute to LM and performance. As such, we extend the study of Bortolotti et al. (2015) by comparing two alternative model capturing the relationships among OC, hard & soft LM, and organizational performance. The first model is to treat OC as an antecedent of LM, which could either drive or impede LM implementation, thus determine the LM extent/level. The second model is to approach OC as a moderator of relationship between LM and organizational performance, which affect the effectiveness of the LM on organizational performance.

As was the case of Bortolotti et al. (2015), GLOBE model of OC is used in the present study for building and testing LM-OC model. The GLOBE model is a culture framework developed to assess both national and organizational culture and analyze its impact on leadership, organizational process and performance (House et al., 2004). The GLOBE team identified nine dimensions on which national/organizational cultures differ: power distance, institutional collectivism, in-group collectivism, future orientation, performance orientation, gender egalitarianism, assertiveness, uncertainty avoidance, and humane orientation. Detailed definitions of each dimension are presented below as Table 2. And each dimension of GLOBE model, except for gender egalitarianism dimension, is examined in regard to its effect on LM.

Culture dimensions Definitions

Power Distance The degree to which members of an organization or society expect and agree that power should be stratified and concentrated at higher levels of an organization or government

Institutional collectivism The degree to which organizational and societal institutional practices encourage and reward collective distribution of resources and collective action.

In-group collectivism The degree to which individuals express pride, loyalty, and cohesiveness in their organizations or families.

Future orientation The degree to which individuals in organizations or societies engage in such behavior as planning, investing in the future, and delaying individual or collective gratification.

Performance orientation The degree to which an organization or society encourages and rewards group members for performance improvement and excellence.

Gender egalitarianism The degree to which an organization or society minimizes gender role differences while promoting gender equality.

Assertiveness The degree to which individual in organizations or societies are assertive, confrontational, and aggressive in social relationships (House et al., 2004).

Uncertainty avoidance The extent to which members of an organization or society strive to avoid uncertainty by relying on established social norms, rituals, and bureaucratic practices.

(14) Power Distance (PD)

Low PD is characterized by a decentralization of power in the organization. Empowerment of employees is fundamental for Lean Management, as necessary authorities and capabilities, like decision making shall be delegated to ordinary employees (Dahlgaard & Dahlgaard-Park, 2006) so that they could be actively involved in activities, such as identification and elimination of waste without having to follow the normal decision-making procedures (Jadhav et al. 2014). As such, employees are more motivated to participate in LM practices and an environment for continuous improvement could be sustained within the organization. And this is more important for service companies, as great involvement of employees is required in delivering services.

In addition, low PD increases the accessibility of middle management to employees (Naor et al. 2010), and their role switches from a traditional supervisor to a facilitator to assist the employees in putting the improvement thoughts into practice (Wong 2007). Thus, higher performance improvement could be expected in a low PD service organization. Accordingly:

For Model 1

H2a: Power distance is negatively associated with the extent of lean hard (technical) practices in pure service organizations.

H3a: Power distance is negatively associated with the extent of lean soft (socio) practices in pure service organizations.

For Model 2

H4a: Power distance negatively moderates the relationship between lean hard (technical) practices and organization performance.

H5a: Power distance negatively moderates the relationship between lean soft (socio) practices and organization performance.

2.4.2 Institutional collectivism (IC)


customers (Staudacher & Tantardini 2012). In this manner, it is easier to identify the waste along the process flow and avoid sub-optimization within single stage/function. Hence, we posit:

For Model 1

H2b: Institutional collectivism is positively associated with the extent of lean hard (technical) practices in pure service organizations.

H3b: Institutional collectivism is positively associated with the extent of lean soft (socio) practices in pure service organizations.

For Model 2

H4b: Institutional collectivism positively moderates the relationship between lean hard (technical) practices and organization performance.

H5b: Institutional collectivism positively moderates the relationship between lean soft (socio) practices and organization performance.

2.4.3 In-group collectivism (IGC)

People in high IGC organizations are expected to show loyalty and relatedness with groups (Deming, 1986; Kull et al., 2014). This loyalty and relatedness increase employees’ responsibilities and make them feel obligated to be involved in organizational improvement. On the other hand, the loyalty and relatedness also increase individuals’ need of belonging, as well as their commitments to the organization. Consequently, they are willing to interchange or apply their knowledge and experience without restrictions (Recht & Wilderom 1998) and spend more energy and efforts to fulfill organizational tasks and provide quality products/services to customers (Anderson et al. 1994; Naor et al. 2010). Hence, in-group collectivism could potential facilitate the LM implementation. Accordingly,

For Model 1

H2c: In-group collectivism is positively associated with the extent of lean hard (technical) practices in pure service organizations.

H3c: In-group collectivism is positively associated with the extent of lean soft (socio) practices in pure service organizations.

For Model 2

H4c: Institutional collectivism positively moderates the relationship between lean hard (technical) practices and organization performance.

H5c: Institutional collectivism positively moderates the relationship between lean soft (socio) practices and organization performance.

2.4.4 Future orientation (FO)


achieve long-term performance. This is in line with the “challenge” view described in the Toyota Way (TMC, 2001), which reflects long-term oriented visionary thinking rather than short-term focused results-oriented thinking in Lean Management (Jayamaha et al. 2014). In addition, employees in a high FO organization would also look forward to continuous growth and development with the organization, and thereby provide high quality services for the betterment of the firm (Gnanlet & Yayla-Kullu 2014). Based on this, we posit:

For Model 1

H2d: Future orientation is positively associated with the extent of lean hard (technical) practices in pure service organizations.

H3d: Future orientation is positively associated with the extent of lean soft (socio) practices in pure service organizations.

For Model 2

H4d: Future orientation positively moderates the relationship between lean hard (technical) practices and organization performance.

H5d: Future orientation positively moderates the relationship between lean soft (socio) practices and organization performance.

2.4.5 Performance orientation (PO)

According to the definition of House et. al (2004), a high PO organization would have fair incentive system to reward employees who contribute to performance improvement. As for LM, recognition and rewards from the top management should be in place to boost employee participation and continuous improvement (Wong et al., 2009). Lack of rewards will ultimately undermine the employee’s commitment and future efforts as they don’t feel appreciated and respected by the management.

Moreover, managers in high PO organizations are likely to set ambitious goals, communicate high expectations for their subordinates, and intellectually challenge them (House et al., 2004; Kull et al. 2014). As a result, employees and working teams would be motivated by the LM goals and challenges set by managers, thus make more efforts (Kull et al. 2014). Hence, organization with high PO could potentially achieve higher performance improvements due to increased motivations and commitments from employees. Accordingly,

For Model 1

H2e: Performance orientation is positively associated with the extent of lean hard (technical) practices in pure service organizations.

H3e: Performance orientation is positively associated with the extent of lean soft (socio) practices in pure service organizations.


H4e: Performance orientation positively moderates the relationship between lean hard (technical) practices and organization performance.

H5e: Performance orientation positively moderates the relationship between lean soft (socio) practices and organization performance.

2.4.6 Assertiveness (AS)

Low assertiveness organizations value equality, solidarity, social relationships, use of intuition, and seeking consensus; conversely, high assertiveness organizations encourage competition, material values (Hofstede, 2001; Çakar & Ertürk 2010). Seeking consensus through communications is important to solve possible conflicts in cross-functional team (CFT) which is an essential element for LM implementation (Jadhav et al. 2014). Destructive conflicts between departments or functions would derail LM implementation. Besides, managers in high assertiveness organizations tend to impose their decisions on others and to control their behaviors (Calza et al. 2010). However, use of coercive means to control others have a detrimental effect on team morale and performance (Tjosvold, 1986; Bortolotti et al. 2015). Therefore, we would expect higher performance improvement in low assertiveness organizations. Accordingly,

For Model 1

H2f: Assertiveness is negatively associated with the extent of lean hard (technical) practices in pure service organizations.

H3f: Assertiveness is negatively associated with the extent of lean soft (socio) practices in pure service organizations.

For Model 2

H4f: Assertiveness negatively moderates the relationship between lean hard (technical) practices and organization performance.

H5f: Assertiveness negatively moderates the relationship between lean soft (socio) practices and organization performance.

2.4.7 Uncertainty avoidance (UA)

Employees in high UA organization prefer to sticking to rules and procedures with meticulousness (Recht & Wilderom 1998). This resonates well with LM as LM aims for reducing process and outcome variations through standardization to improve product/service quality (Clark et al. 2013). Additionally, high UA orientation also implies a strong reliance on expert knowledge (Recht & Wilderom 1998). Therefore, suggestions from every employee, which could be assumed as an expert in his/her particular working environment, is valued by high UA organizations. This could help to sustain a continuous improvement environment. Hence, we posit:


H2g: Uncertainty avoidance is positively associated with the extent of lean hard (technical) practices in pure service organizations.

H3g: Uncertainty avoidance is positively associated with the extent of lean soft (socio) practices in pure service organizations.

For Model 2

H4g: Uncertainty avoidance positively moderates the relationship between lean hard (technical) practices and organization performance.

H5g: Uncertainty avoidance positively moderates the relationship between lean soft (socio) practices and organization performance.

2.4.8 Humane orientation (HO)

Humane orientation manifest the way people treat one another (Gnanlet & Yayla-Kullu 2014). A high HO organization cherishes fairness and inter-personal care, thus foster mutual respect and trust between employees and management. Correspondingly, continuous improvement in LM make extensive use of intimate relationships among employees (Recht & Wilderom 1998). It requires an environment in which frankness and trust are appreciated and scheming is condemned.

In addition, individuals in HO culture are motivated with the need for belonging and affiliation (Kull et al. 2014). They tend to show great concerns for the interest and well-being of others; and further prioritize the organization’s goals above personal benefits (Naor et al. 2010). As such, employees would strive to help organization reach its goal. Hence, we posit:

For Model 1

H2h: Humane orientation is positively associated with the extent of lean hard (technical) practices in pure service organizations.

H3h: Humane orientation is positively associated with the extent of lean soft (socio) practices in pure service organizations.

For Model 2

H4h: Humane orientation positively moderates the relationship between lean hard (technical) practices and organization performance.


Model 1 Organizational Culture Dimensions Power Distance - Assertiveness – Institutional Collectivism + In-group Collectivism + Future Orientation+ Performance Orientation+ Uncertainty Avoidance+ Humane Orientation+

Lean Hard Practices

Lean Soft Practices

Organizational Performance H1a + H1b+ H3a-h H4a-h


Model 2

Organizational Culture Dimensions

Power Distance - Assertiveness – Institutional Collectivism + In-group Collectivism + Future Orientation+ Performance Orientation+ Uncertainty Avoidance+ Humane Orientation+ Organizational Performance Lean Hard Practices

Lean Soft Practices

H1a +

H1b +

Figure 2. Conceptual Model - Model 2




3.1 Data Collection and Sample

Survey method is adopted in this study to empirically test hypotheses regarding the relationship between lean management and organizational performance in service organizations, as well as the potential moderating effect of organizational culture. Data was gathered by means of a self-administrated questionnaire towards practitioners in pure service industries, such as financial, consulting industries etc. The questionnaire was initially drafted in English and underwent a pilot test with 22 respondents from 15 service organizations. And the questionnaire was further modified with the feedbacks from the pilot test and translated into Chinese for distribution. Websites, like professional social media and financial database were used to targeting potential respondents working in service organizations. And the questionnaires were sent to potential respondents by emails. Within 8 six weeks, a total of 158 valid response were received, and the summary of the sample distribution is presented below as Table 3.

Table 3. Sample Distribution

Industry No. Consulting 50 Financial 108 Country No. China 99 UK 59

Company Size No.

Small 28 Medium 66 Large 64 Total 158 3.2 Variables 3.2.1 Lean Management


Shah & Ward (2007) is a well validated measurement and widely recognized and cited by academics. We also include constructs from the other two literatures to make it more comprehensive. In addition, adaptions were made to the constructs and items used in the scale in order to better fit the service context. As such, the final scale consists of 12 constructs which are also grouped in to soft and hard bundles: six multi-item constructs under soft practices, including top management leadership for quality (TML), supplier development (SD), small group problem-solving (SGP), continuous improvement (CI), employee involvement (EI) and customer involvement (CUS); and six under hard practices, consisting of continuous flow (CF), JIT delivery (JD), Pull (PU), set-up time reduction (STR), Statistical Process Control (SPC) and autonomous maintenance (AM). Each item under these constructs is evaluated on a seven-point Likert scale (1 = strongly disagree and 7 = strongly agree).

3.2.2 Organizational Culture

Organizational culture is studied as a moderating variable, and measured based on the GLOBE cultural model using items developed by Naor et. a (2010). In line with GLOBE framework, eight multi-item constructs, except for gender egalitarianism, were included: power distance (PD), institutional collectivism (IC), in-group collectivism (IGC), future orientation (FO), performance orientation (PO), assertiveness (AS), uncertainty avoidance (UA), and humane orientation (HO). Necessary adaptions were also made to fit the research goal of the study. The final measurement scale is presented in Appendix A.

3.2.3 Organizational Performance

Organizational performance as the dependent variable in this study, is measured via the five general accepted performance dimensions in operations management based on Slack et al. (2007), including speed, dependability, flexibility, quality, and cost. There is no consensus in academia on performance measurement for service organizations, due to the unique characteristics of service (Franco-Santos et. al, 2007). Respondents were asked to assess the organizational performance compared to their competitors on a five-point Likert scale (5 for “superior”, 1 for “poor or low”).

3.2.4 Control Variables

Firm size and age are considered as control variables in this study as these contextual factors may also affect companies’ performance (Capon et al., 1990; Hadid & Mansouri 2014). Additionally, as the survey data was collected from companies located in two different countries, country is also considered as a control variable.


Cronbach’s alpha is used to assess the construct reliability, i.e. the consistency of respondent’s answers across items in multi-item constructs. As the constructs in this research are mainly adapted from existing scales, its reliability score (alpha value) should exceed a threshold of 0.70 to ensure the construct reliability (Hair et al., 1995). Table 4 presents an overview of the Cronbach’s alpha for all construct under study. Since all the constructs have a a value higher than 0.7, the internal consistency and the reliability of the survey are considered to be highly maintained.

To check construct validity, exploratory factor analysis (EFA) was conducted on SPSS for all the independent variables used. For lean practices, 12 factors were extracted representing the 12 lean practices. And the factor loading of each item for the factors was obtained with varimax rotation and detailed loadings for lean hard and soft practices are presented respectively in Table 5 & 6.

Table 4. Reliability Statistics - Cronbach's a coefficient

Cronbach's Alpha N of Items Organizational Culture Power Distance 0.886 3 Institutional Collectivism 0.884 5 In-group Collectivism 0.916 4 Future Orientation 0.878 4 Performance Orientation 0.919 3 Assertiveness 0.886 4 Humane Orientation 0.816 4 Uncertainty Avoidance 0.792 3

Hard Lean Practices 0.765

Continuous Flow 0.790 4

JIT Delivery 0.806 3

Pull 0.773 3

Set-up Time Reduction 0.786 3

SPC 0.854 4

Autonomous Maintenance 0.818 4

Soft Lean Practices 0.890

Top Management Leadership for Quality 0.890 4

Supplier Development 0.871 5

Small Group Problem Solving 0.866 5

Continuous Improvement 0.846 4

Employee Involvement 0.891 5

Customer Involvement 0.856 4

Organizational Performance


Table 5 EFA for Lean Hard Practices

Practices Factor Loading

1 2 3 4 5 6 CF1 0.374 0.121 0.35 0.573 0.083 0.126 CF2 0.013 0.186 0.438 0.656 0.174 -0.03 CF3 0.262 0.269 -0.126 0.711 0.18 0.118 CF4 0.244 0.101 0.132 0.74 0.268 0.049 JD1 0.226 0.313 0.717 0.11 0.289 -0.021 JD2 0.203 0.199 0.737 0.3 0.1 0.033 JD3 0.28 0.129 0.743 0.005 0.152 0.156 PU1 -0.05 -0.082 0.078 0.038 0.002 0.848 PU2 -0.067 -0.034 -0.022 -0.009 0.074 0.897 PU3 0.268 0.156 0.089 0.144 0.067 0.712 STR1 0.084 0.138 0.182 0.076 0.792 0.017 STR2 0.169 0.146 0.042 0.196 0.757 0.104 STR3 0.192 0.171 0.26 0.308 0.735 0.04 SPC1 0.699 0.219 0.274 0.269 0.012 0.026 SPC2 0.708 0.151 0.253 0.287 0.041 0.027 SPC3 0.75 0.241 0.106 0.16 0.198 0.057 SPC4 0.863 -0.023 0.135 0.045 0.26 -0.008 AM1 0.217 0.592 0.143 0.139 0.104 0.091 AM2 0.148 0.795 0.164 0.056 0.164 -0.137 AM3 -0.031 0.82 0.205 0.158 0.144 -0.066 AM4 0.152 0.822 0.068 0.159 0.069 0.113

Extraction Method: Principal Component Analysis; Rotation Method: Varimax with Kaiser Normalization.

Table 6 EFA for Lean Soft Practices

Practices Factor Loading


SGP2 0.73 0.247 0.075 0.138 0.213 0.114 SGP3 0.78 0.196 0.083 0.167 0.166 0.154 SGP4 0.756 0.25 0.14 -0.005 0.097 0.199 SGP5 0.597 0.266 0.236 0.158 0.195 0.135 CI1 0.384 0.129 0.224 0.184 0.202 0.675 CI2 0.005 0.163 0.149 0.101 0.232 0.758 CI3 0.232 0.124 0.121 0.229 0.182 0.725 CI4 0.35 0.176 0.143 0.166 0.093 0.686 EI1 0.224 0.656 0.188 0.227 0.22 0.382 EI2 0.263 0.672 0.254 0.095 0.208 0.206 EI3 0.307 0.72 0.177 0.133 0.262 0.095

Extraction Method: Principal Component Analysis; Rotation Method: Varimax with Kaiser Normalization.

Table 7 presents the means, standard deviations, and correlations for all the variables used in this study. As it could be seen from this table that, most of the independent and dependent variables are significantly correlated. This indicates a possibility for multi-collinearity. To avoid such multi-collinearity, all data used for the moderation model testing were standardized before processing. And variance inflation factor (or VIF) was used to assess possible multi-collinearity, as it could measure how much the variance of a regression coefficient is inflated due to multicollinearity in the model. As a rule of thumb, a VIF value that exceeds 5 or 10 indicates a problematic amount of collinearity (James et al., 2014).

Table 7. Description Statistics and Correlation Matrix

Variable Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13 1. Size 2.23 0.73 2. Age 25.38 33.03 .323** 3. Cntry 1.63 .49 .008 -.440** 4. OC_PD 3.19 1.25 .072 .246*** -.044 5. OC_IC 5.57 0.94 -.020 -.323** .262** -.470** 6. OC_IGC 5.16 1.17 -.017 -.192* .183* -.524** .594** 7. OC_FO 5.20 1.06 .214** -.219** .303** -.375** .632** .573** 8. OC_PO 5.36 1.37 -.042 -.268** .372** -.434** .616** .616** .636** 9. OC_AS 3.02 1.06 .011 .226** -.229** .407** -.615** -.634** -.695** -.569** 10. OC_HO 4.91 0.98 -.031 -.144 .086 -.420** .514** .416** .417** .499** -.512** 11. OC_UA 5.43 1.00 .119 -.324** .491** -.378** .504** .337** .521** .420** -.447** .284** 12. HardLM 5.09 0.71 .132 -.217** .325** -.347** .587** .550** .698** .605** -.612** .405** .569** 13. SoftLM 5.49 0.74 .145 -.193* .204** -.521** .764** .663** .741** .590** -.621** .481** .540** .741** 14. PER 3.68 0.62 .004 -.160* .186* -.454** .529** .616** .613** .577** -.623** .475** .358** .572** .630**



4.1 Hypotheses Testing: Lean Management and Organizational performance

In order to test Hypotheses 1a, 1b, hierarchical linear regression test was conducted to examine the effect of lean social practices and lean hard practices on organizational performance. Control variables, i.e. company size, company history (age) and country were entered in the test as explained in section 3.2.4. Although country is a categorical variable, it was still entered directly in the test models as it has only two levels (dichotomous).

Table 8. Regression Analysis (DV: Organizational Performance)

Model 1 Model 2 B p B p Intercept 3.378 .000*** .601 .070 Control Company size .033 .643 -.088 .124 Age -.002 .233 .001 .716 Country .174 .130 .033 .717 Main effect HardLean .204 .015* SoftLean .394 .000*** R2 .043 .431 ΔR2 .387 *. p < 0.05; **. p < 0.01.; ***. p<0.001

As shown above in Table 8, only control variables were entered in Model 1 of the regression test and the result indicated that there is no significant association between these variables and organizational performance, with R2 = 0.043. In Model 2, the two predictors, lean soft practices and lean hard practices, were added. It was found that lean hard practices significantly predicted organizational performance (B= 0.204, p<0.05), so did lean soft practices (B=0.394, p<0.001). Andlean hard and soft practices together explained a 38.7% of the variance in organizational performance. Hence, Hypotheses 1a and 1b are supported.

4.2 Hypotheses Testing: Model 1 – OC as an antecedent of the extent of LM Practices


lean soft practices as dependent variable respectively. The results of the tests are presented below as Table 9.

Table 9. Results of simultaneous multiple regression analysis: OC as antecedents of use of LM practices

Lean Hard Practices Lean Soft Practices

OC dimensions B p B p PD .039 .311 -.055 .096 IC .049 .428 .313 .000*** IGC .075 .123 .136 .001** FO .190 .002** .175 .001** PO .085 .047* -.007 .844 AS -.074 .193 .014 .773 UA .175 .001** .106 .017* HO .018 .715 .026 .521 F 20.021*** 37.149*** R2 .601 .737 *. p < 0.05; **. p < 0.01.; ***. p<0.001

The results reveal that five culture dimensions are significantly associated with the extent of the use of LM practices. Specifically, future orientation (FO) and uncertainty avoidance exhibited significant positive association with both lean hard and soft practices. Besides, performance orientation (PO) is only positively associated with lean hard practices; while, institutional collectivism (IC) and in-group collectivism (IGC) are only associated with lean soft practices instead. The findings provide support for Hypotheses 2d, 2f, 2g, 3b, 3c, 3d and 3g.

4.2 Hypotheses Testing: Model 2 – OC as a moderator of LM-performance relationship

The hierarchical regression analysis was adopted to test hypotheses 4a-h and 5a-h, which assume OC as a moderator in LM-performance relationship. Moderation effects are normally tested via an interaction term by multiplying the first-order variables (Cohen et al., 2003; Narasimhan et al. 2012). In this study, after controlling for company size, age and country, the regression equation (1) is as follows:

Performance = b0 + b1 * size + b2 * age + b3 * country + b4 * Lean hard practices (LHP) + b5 * Lean soft practices (LSP) + b6 * OC + b7 * LSP_x_OC + b8 * LHP_x_OC (1)


Table 10 Summary of Moderated Regression Analysis for OC Dimensions (N = 90)



This study is seeking to explore the influence of lean management on organizational performance in a pure service setting and explain the possible interaction role that organizational culture plays with LM on organizational performance.

First, since few empirical studies had been conducted in regard to LM-performance relationship in service sector, this study is aiming at providing empirical evidence for necessity of LM implementation in pure service organizations. By adopting a socio-technical system (STS) perspective, LM practices can be grouped into lean soft (social) and lean hard (technical). It is proposed in this study that both sides of the lean practices were associated with better organizational performance. The empirical results confirmed this proposition. This result seems to hold across different service organizations with different size, age and location. And it is consistent with some of the previous study on LM management practices on performance. For example, Alsmadi et al. (2012) confirmed in their study that different lean practices could predict better firm performance in both manufacturing and service organizations, although they didn’t differentiate soft and hard practices of LM.

And it is quite important to have such knowledge. First, many researchers were concerned that the unique characteristics of pure service could set constraints for LM to be effective in service organizations (Hadid & Mansouri, 2014). With the proof of the present study, the argument for LM effectiveness in service sector is reinforced. And LM could be treated as an antecedent of organizational performance in service organization, which could contribute to performance improvement. Secondly, previous studies indicated that lean soft practices were often neglected by many organizations as companies tend to focus on the tangible aspects, i.e. the hard practices when introducing LM (Dibia & Onuh, 2010; Hines et al., 2004). By viewing LM as an integrated socio-technical system (STS), our study indicated that both soft and hard lean practice were of great importance to service organizations. Thus, more attention shall be paid to soft LM from both scholars and practitioner. Future studies might delve deeper into specific lean tools (both soft and hard) which are most suitable for service organizations, and their interactions with OP.

Another focus of this study is related to the role of OC played in relationship between LM and performance. As suggested by many previous studies, such as Bortolotti et al. (2015), organizational culture might be a key influencer to the successful implementation of LM. Thus, this study proposed two alternative models to have a deeper understanding of OC-LM-performance relationship.


Lean hard and soft practices in a service organization. Among them, future orientation (FO) and uncertainty avoidance (UA) were found to be positively associated with both hard and soft practices. It is quite understandable to have such findings. FO is the basis for continuous improvement (Flynn et al. 1994; Liker, 2004; Achanga et al., 2006; Bortolotti et al. 2015). As high FO organizations strive for service/process innovation, they are more willing to collaborate with their customers and suppliers for demand prediction and product design. While, UA is expected to promote the use of standardized process to eliminate waste/variance (Nascimento & Francischini, 2004; Carlborg et al. 2013), as well as structured and scientific methods (e.g. PDCA) for continuous improvement (Staudacher & Tantardini 2012).

Apart from these two OC dimensions, the results also indicated that institutional collectivism (IC) and in-group collectivism (IGC) have positive association with lean soft practices; and performance orientation (PO) could positively influence the extent of lean hard practices in service organizations. High IC motives teamwork and intra- and inter-firm collaborations (Rother and Shook, 1998; Shah and Ward, 2007), while high IGC could increases self-motivation and responsibility from employees (Gnanlet & Yayla-Kullu 2014). These properties could reduce the resistance from employees and support practices, e.g. supplier development and employee involvement. PO is also important for lean transformation, as it could raise employee motivation and participation. Wong (2007) argued that short-term incentive system is needed, especially at the beginning stage of lean transformation. The reason why PO only significantly predicts hard practices is possibly related to the more tangible and short-term achievements by applying lean tools/techniques application. In contrast, achievements through soft practices application might be less tangible and often take longer time.

The findings of Model 1 is highly consistent with the study of Bortolotti et al. (2015). Although they didn’t investigate OC as antecedent of LM, they did find high lean adopters are characterized by high IGC, FO, and UA.

The second model proposed that OC has a moderating effect on the relationship between LM and performance. However, based on the results, no moderating effect was found for all the OC dimension on LM-performance relationship, regardless of lean hard or soft practices. Although this moderation effect was not evidenced by this study, it is still meaningful to compare alternative models as Drazin and Van de Ven (1985) recommended that studies should be designed to permit comparative evaluation of as many forms of fit as possible, especially in cases that no consensus could be drawn from the previous empirical studies. (Naor et al. 2008).


(2010), when organizations experience misfit between management practices and OC, late adopters will implement high-fidelity but less extensive version of the practice. Late adopters have fewer “degrees of freedom” to adapt practices to their local needs, as practices has already been defined and specified by early adopter. And in order to reduce to cost of misfits, later adopters will implement less extensive versions of the practice. Thus, they could choose part of the practices which are compatible with their culture. Specifically for LM, OC would decide the extent of LM implemented in different organizations; while organization will mostly stick to specified rules and requirements of the practices they choose, as these practices has already been defined and validated by early LM adopters.

This study contributes to the literature of OC-LM linkage by empirically identifying LM as an antecedent of LM. Although OC has been considered as a major issue in LM(Jadhav et al. 2014; Clark et al. 2013), few empirical studies has been conducted to investigate the role of OC in OC-LM relationship by using a well-established culture model (Bortolotti et al., 2015). By using the GLOBE cultural model, we found that certain OC dimensions, i.e. FO, UA, IGC, IC and PO could impact the extent of the LM implementation in service organizations. Future studies could compare this finding with situations in manufacturing context. In addition, as GLOBE cultural model provides an opportunity of investigating culture across different levels (i.e., national and organizational levels), future studies could also compare differences in effect of NC and OC on LM.


With such efforts, a conducive OC for LM could be approached, thus lead to extensive LM and in turn make more improvements.

5.1 Limitations and Future Research

There are several limitations in this study. First, the samples size of this study is relatively small, with only 158 single respondents. And the respondents were mainly from China and UK, therefore, generalization of the findings is limited. Future studies are suggested to have a bigger sample with diverse distribution of countries and industries. And it would be better to have multiple respondents from one company to ensure response consistency. Besides, single respondent survey could possibly result in misunderstanding of survey question (Alsmadi et al. 2012).

Second, the results of the study can only confirm the significant correlation between LM and organizational performance. However, no causality can be drawn from the results. Organizational performance improvement is associated with LM, but not necessarily caused by LM. It is suggested by many other researchers to consider longitudinal studies to address this issue. Longitudinal studies could also help with the exploration of lean duration on organizational performance, as it often takes time for LM to be effective on organizational performance (Wickramasinghe & Wickramasinghe, 2011).

Third, performance measures of the study are based a general OM scale, while neglecting the characteristics of pure service. Future studies are encouraged to focus on performance indicators which are more related to LM and valued in the service setting. And a combination of subjective and objective data could be used to increase validity of the studies.



a more extensive approach of LM application, including both soft and hard practices would be more beneficial for service organizations.



Measurement scales for organizational culture dimensions Organizational Culture

Please indicate on a scale from 1 to 7 (1 = strongly disagree and 7 = strongly agree) how much do you either agree or disagree with the statements:

Construct Items In-group

Collectivism (IGC)

I talk up this organization to my friends as a great organization to work for. 0.873 For me, this is the best of all organizations for which to work. 0.893 I am proud to tell others that I am part of this organization. 0.905 I am extremely glad that I chose this organization to work for, over others I

was considering at the time I joined.


Power Distance (PD)

Managers in this organization believe in using a lot of face-to-face contact with Employees. *

0.898 This organization is a good place for a person who likes to make his/her

own decisions. *

0.876 My suggestions are never taken seriously around here. 0.823 Institutional

Collectivism (IC)

Generally speaking, everyone in this organization works well together. 0.820 Our supervisors encourage the people who work for them to work as a


0.847 We work as a partner with our suppliers, rather than having an adversarial


0.859 We believe that cooperative relationships will lead to better performance

than adversarial relationships.

0.857 We believe that an organization should work as a partner with its

surrounding community.



Orientation (FO)

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

0.822 We make an effort to anticipate the potential of new service practices and


0.876 Our organization stays on the leading edge of new innovation/technology in

our industry.

0.802 We are constantly thinking of the next generation of services. 0.851

Performance Orientation (PO)

Our incentive system encourages us to vigorously pursue organization objectives.

0.933 The incentive system at this organization is fair at rewarding people who

accomplish organization objectives.

0.941 Our reward system really recognizes the people who contribute the most to

our organization.


Assertiveness (AS)

Our managers do a good job of solving inter-functional conflicts. * 0.854 Our managers communicate effectively with managers in other functions. * 0.853 The functions in our organization cooperate to solve conflicts between

them, when they arise. *

0.887 Our business strategy is implemented without conflicts between functions.




Orientation (HO)

In my view, most employees are more concerned with personal gain than with helping our organization accomplish its goals. *

0.842 Some of our employees are probably only out to get what they can from this

Organization. *

0.821 Although there may be a few “bad apples”, most of our employees try to

help our organization achieve its goals.


Uncertainty Avoidance (UA)

I believe that the scientific method provides a better input to decision making than intuition or opinion.

0.870 In my view, organizations should use objective data as the basis for making


0.859 In this organization, management is based on facts, not on intuition or



Hard Lean Practices

Please indicate on a scale from 1 to 7 (1 = strongly disagree and 7 = strongly agree) how much do you either agree or disagree with the statements:

Construct Items Continuous Flow


Service tasks/ customer demands are classified into groups with similar processing and routing requirements.

0.675 Pace of service provision is directly linked with the rate of customer


0.762 Resources (People, equipment and facilities) are grouped to produce a

continuous flow of service families.

0.818 We have laid out office so that processes are in close proximity to each



JIT Delivery (JD) Our key suppliers deliver to us on a just-in-time basis. 0.863 We can rely upon on-time delivery from our suppliers. 0.841 Our suppliers are linked with us by a pull system. 0.789

Pull (PU) Service provision is ‘pulled’ by the arrival of customer demands. 0.872 Service operation at processes is “pulled” by the current demand of the next


0.898 We use kanban squares, containers or signals for service operations control. 0.743

Set-up Time Reduction (STR)

We are working to lower setup times (equipment, systems, etc.) in our process.

0.769 We have low setup times of equipment in our processes. 0.870 Our crews practice setups, in order to reduce the time required. 0.875

SPC A large percent of the processes are currently under statistical process control.

0.872 We make extensive use of statistical techniques to reduce variance in


0.879 We use charts to determine whether our processes are in control. 0.853 We monitor our processes using statistical process control. 0.884

Autonomous Maintenance (AM)

Front-line employees/operators understand the cause and effect of equipment deterioration.

0.658 Basic cleaning and maintenance of equipment is done by front-line

employees/ operators.

0.831 Front-line employees/ operators inspect and monitor the performance of

their own equipment.





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