• No results found

Employee participation in Continuous Improvement

N/A
N/A
Protected

Academic year: 2021

Share "Employee participation in Continuous Improvement"

Copied!
82
0
0

Bezig met laden.... (Bekijk nu de volledige tekst)

Hele tekst

(1)
(2)

Abstract:

The complexity of today’s business environments results in competition through the ability of continuously improving processes. Therefore, the aim of this paper is to identify and assess barriers and facilitators of employee participation in Continuous Improvement (CI). CI is a culture of sustained improvement involving all organisational participants. Employee participation is seen through the Motivation, Opportunity and Ability framework to facilitate prioritization of management interventions towards increasing participation. An embedded single case study was conducted at a large manufacturing organization. Interviews and questionnaires were used to capture in depth information regarding the influencing factors together with a statistical analysis for prioritization. The results show that factors influencing participation in CI vary throughout the organization and the most influential management interventions depend on the organizational area as well as the extent to which CI is implemented. The contribution of this paper is that employee participation is assessed in the context of the MOA-framework, which reflects the actual mechanism through which employee participation is influenced. Thereby, a practical approach has identified recommendations for management actions in order to enhance employee participation.

Keywords: Continuous Improvement, MOA-framework, Employee participation

(3)
(4)
(5)

1. Introduction

Due to an increasing pace and complexity of business environments, organizations no longer compete on processes but the ability to continually improve processes (Anand et al., 2009). Organisations try to continuously improve through the concept of Continuous Improvement (CI), which can be seen as a culture of sustained improvement aimed at eliminating waste and involving all organisational participants (Singh and Singh, 2015).

In CI, employees are key drivers in generating and implementing ideas (Swartling & Poksinska, 2013; Bessant & Francis, 1999; Lam et al., 2015), which emphasizes the importance of employee participation in CI practices. However, difficulties to obtain employee participation has been the foremost reason of failure in CI implementation (Bessant et al., 2001; Singh and Singh, 2015; Swartling and Poksinska, 2013)

Despite these findings, little research in operations management exists that investigates which management actions and behaviours lead to commitment and employee participation (Wu & Chen, 2006; Lam et al., 2015). In this paper we examine employee participation through the Motivation, Opportunity and Ability (MOA) framework, which is commonly used in the assessment of work performance. With the use of this framework, all factors influencing employee participation can be captured and categorized. Based on the outcome of the research of Siemsen et al. (2008), the complementarity of the MOA-variables is assessed in order to provide prioritization of the variables. Hereby, complementarity is defined as the degree to which the effect of one variable depends on the presence of other variables. Moderate complementarity implies that the effect of one variable depends on another variable, whereas extreme complementarity implies that one variable has no effect unless the other variable is present. This approach captures employee participation in the actual context through which participation is influenced and therefore provides better inside in the importance of different variables influencing employee participation.

(6)

complementarity. The qualitative part will consist out of a case study to obtain in depth knowledge of the factors influencing employee participation.

This research will contribute to prior literature by assessing and categorizing influencing factors that drive participation through the MOA-framework and identify possible complementarity. The managerial contribution will consist out of recommendations for management interventions in order to amplify facilitators of employee participation. Since the research includes identification of influencing factors it has an explorative character, and therefore adds to theory through comparison of the outcome of the research with existing literature.

The remainder of this research proposal is organised as follows. In the theoretical background I discuss prior literature regarding CI, employee participation and an outline of the MOA-variables and their relation. The theoretical background will be concluded with three research questions and a corresponding theoretical model. Thereafter, the methodology outlines the content of the research followed by the findings of both types of research. Finally, the findings are compared with literature in the discussion, which is followed by a conclusion and recommendations.

(7)

2. Theoretical background

2.1 Continuous improvement

CI was introduced by Imai (1986) under the name of ‘Kaizen’ and is defined as on-going improvements that apply new ways of working and evolve everyone in the organisation. Over the years, the importance of establishing certain CI systems and CI frameworks have become important (Bessant et al. 1994) as well as the understanding of the behavioural dimension (Bessant et al. 2001). CI can be seen as a ‘dynamic capability’, because it offers mechanisms whereby a high proportion of the organisation can become involved in its innovation and learning processes (Bessant et al., 1999). This provides the ability to continue adapt to an organisations’ environment, which can be seen as great competitive advantage. In addition, the cluster of behavioural routines of CI in the form of patterns that are learned and institutionalised, provides considerable competitive potential since they are hard to copy and transfer (Bessant et al., 1999).

Throughout the years the term Continuous Improvement has been widely used and has been adopted as part of improvement programs. With the increasing popularity of Lean and Six Sigma, organisations aim to achieve CI capability through the deployment of these improvement initiatives, which is shown in figure 1 (Anand et al., 2009).

Therefore, we consider CI processes as sequences of tasks aimed at creating value-adding transformations of inputs (material and information) to achieve the intended outputs (Anand et al., 2009). These intended outputs are aimed at added value towards the customer and the

(8)

techniques commonly used to execute these projects (Anand et al., 2009). Moreover, CI is an organization wide improvement process making use of the deployment of improvement initiatives in order to pursue the following goals (Wu and Chen, 2006): • A organization -wide focus to improve performance • Organisational activities with the involvement of all people in the organization from top managers to workers • A gradual improvement through step-by-step innovation • Creating a learning and growing environment Pursuing these goals, the behavioural dimension of employees across the whole organization is of great importance (Bessant et al., 1994; Bessant et al., 2001; Anand et al., 2009; Wu and Chen, 2006; Swartling and Poksinska, 2013; Lam et al., 2015). A lack of understanding the behavioural dimension is suggested as one of the main reasons of failure at the implementation of CI by Bessant et al. (2001). In addition, the vision of seeing CI as a binary split between having and not having CI rather than an evolutionary process, has been suggested as another reason for failure. Therefore, several authors proposed an evolutionary model to guide the employee behaviours through the implementation of CI.

Continuous Improvement implementation

Over the years several models regarding the evolution of CI implementation have been established. First, Bessant and Caffyn (1997) propose a Continuous Improvement capability model in which they describe the key behaviours (or behavioural routines) that are essential for long-term success of CI. Thereafter, Bessant et al. (2001) developed a maturity model where a firm’s level of CI can be determined based on organisation wide behaviours. Wu and Chen (2006) adapted the maturity model of Bessant et al. (2001) and developed a super system, which placed a pyramid composed by problem, models and tools, and promotion, at its core. With this system they suggested that a firm could analyse its improvement ability from the presentation of cases in their system. Moreover, what these models have in common is that organisations can measure their current CI capability and identify how they could enhance it. In this research we assess the capabilities and compare them to literature in order to discover suitable improvements. The capabilities of the organisations are measured through the extent to which employees actively participate in problem initiation and solving in order to improve processes. This emphasizes the importance of active employee participation in CI practices, which is explained in the next section.

(9)

2.2 Employee participation

The importance of employee behaviour towards participation in CI practices is well established in prior literature (Bessant and Caffyn, 1997; Bessant et al., 2001; Anand et al., 2009; Wu and Chen, 2006; Lam et al., 2015). In order to determine and assess factors influencing this behaviour it should be clarified how participation in CI is defined in this research. The studies of Lam et al. (2015) and Bortolotti et al. (2015) base their research concerning organizational culture and influences of management on the organizational commitment of employees. This commitment is in line with research of Liker et al. (2008) who describes employees as the most critical part of the organization and emphasize the importance of their willingness to identify and solve problems. Bessant et al. (2001) stress the importance of behavioural routines as a competitive advantage and identify participation in CI as learning and practising these behavioural routines. Earlier research of Noe and Wilk (1993) define employee participation in improvement approaches as the active pursue of development activities. Whereas the authors of the maturity models mentioned above measure organizational capabilities through the extent to which employees actively participate in problem initiation and -solving in order to improve processes.

As can be seen, an exact definition of employee participation in CI practices seems to differentiate across literature and the impact of factors influencing participation seems to be unclear. Based on earlier literature regarding evolutionary models of CI I define participation in CI practices in this research as: the extent to which employees actively search for opportunities to improve current processes in day-to-day business, with use of the improvement tools (methods or procedures) that are provided.

(10)

2.3 The MOA-framework

The foundation of this research is on the well-known motivation-opportunity-ability (MOA) framework, which has been applied in many management disciplines and can be seen as a basis for the explanation of work performance (Blumberg and Pringle, 1982; Boudreau et al., 2003). This model is derived from other models of Vroom (1964) and of Campbell et al. (1993), who argue that performance is an interactive function of ability and motivation. Later on, opportunity was added to the model by Blumberg and Pringle (1982), who demonstrated that employees need resources such as information and technology and that their potential is limited by the extent to which others are supportive. In addition, Mathieu et al. (1992) describe opportunity as situational or operational constraints.

In the article of Siemsen et al. (2008), employee behaviour in the context of knowledge sharing has been determined as a function of the MOA variables. Participation in CI embraces knowledge sharing as one of its characteristics, which implicates that the applicability of the MOA framework to employee participation in CI can be highly expected. In addition, research in such an environment is recommended by Siemsen et al. (2008) for the reason of generalizability testing of the model. Hence, the developed research model of this study is based on the MOA-framework in order to assess and categorize factors influencing employee participation.

The factors influencing employee participation are described below, categorized among the MOA-variables. The impact of the factors cannot easily be determined but depends on the complementarity of the MOA-variables, which will be explained later on.

Motivation

Motivation has been widely described in literature and many definitions and descriptions have been given to it. In this research, motivation reflects the dynamic, personal energy with which an action is performed (Cummings and Schwab, 1973) or can be seen as the willingness to act (Siemsen et al., 2008).

(11)

improve processes and commitment must come from the highest level of management (Jaca et al., 2012).

Furthermore, autonomy and feedback are suggested by Hackman and Oldham (1980) to be influencing motivation in their job characteristic model. Hereby autonomy is to what extent an individual can design its own work methods and feedback is about providing knowledge about the work, preferably from work itself (Hackman and Oldham, 1980). From the motivational theory of Deci and Ryan (2012) a distinguish can be made between intrinsic and extrinsic motivation, wherein intrinsic motivation is the energizing basis for proactive initiation of engagement with the environment, and extrinsic motivation is acting in the pursuit of rewards or the avoidance of punishments. These types of motivation are related to empowerment and rewards that occur in intrinsic and extrinsic matters respectively. Empowerment, in the notion of a psychological construct, exists when people feel that they exercise some control over their work and can be seen as a significant predictor of employee participation in CI, according to the results of Tang et al. (2010). Rewards, that could take the form of financial bonuses, promotions or public recognition, are another way how organizations can create motivational incentives (Fadel and Durcikova, 2014).

These rewards are in line with the perceived meaningfulness of participation by employees, which can be explained by the expectancy value theory of Eccles and Wigfield (2002). According to this theory, an achievement and achievement-related choices are determined by expectancies for success and subjective task values. Derived from this theory, meaningfulness of participation in CI is seen as another factor that influences motivation.

(12)

Opportunity

Opportunity is generally used to capture elements that are posited to either inhibit or enable a person to act (Rothschild, 1999). These elements can be considered as all contextual factors that influence employees’ performance while lying beyond their immediate control (Blumberg & Pringle, 1982). Hence, in the context of participation, opportunity refers to the contextual mechanisms provided by management that enables employees to participate in CI.

The research of Sterling and Boxall underlines the findings of Appelbaum et al. (2000) and Felstead et al. (2010) that is it of major importance to analyse the impact of changes in the organization on workers’ opportunity to learn. A key variable herein is the extent to which employees have control over how they solve problems and organise their tasks, whereby greater control in problem solving and task organisation foster greater learnings. Furthermore, CI programs need the necessary resources and support across every stage of implementation, which can consist out of financial assignments to the program as well as more practical aspects like time and space dedicated to CI activities (Jaca et al. 2012). Thereby, although a wide variety of tools and procedures is provided by organisations, the use of it is of great importance for behaviour development (Bessant et al., 2001). In order to gain overview of the right tools and resources an important key element of CI systems is the establishment of a CI facilitator, who has good understanding of the process improvement approach and has the skills to manage conflicts (Jaca et al., 2012). This gives the opportunity of efficiently performing CI activities as well as an important supervising role providing assistance throughout the process. Partly, this assistance is also provided by management and fellow colleagues, which can be considered as enabling employee behaviour (Bessant et al., 2001; Anand et al., 2009). An overview of the influencing factors of opportunity can be seen in table 2.

Jaca et al.

(2012)

Anand et al. (2009)

(13)

Ability

The ability of the employees to participate in CI practices refers to an individual’s skills or knowledge base, related to the action (Rothschild, 1999) or its general capacity to perform in specific types of situations (Cummings and Schwab, 1973). In the context of CI, Bortolotti et al. (2015) emphasize that people can learn from their experience but active training and personal development are important factors for successful CI. In addition, they stress the importance of organisational culture containing intellectual collectivism, future orientation and human orientation. In other words, successful CI derives from employees’ ability to personally develop and actively participate in training and CI practices. In this context, Sterling and Boxall (2013) emphasize the difference between passive participation and learning. Training of employees often provokes passive participation, such as attendance in workshops, whereas learning requires active participation, which is of major importance in CI, since employees have to endure the new learned behaviour. Thereby, the content of the information given is of great importance in order to achieve understanding of CI and lean (Bessant et al., 2001). In order to facilitate meaningful content, there should be an alignment between the goals of CI and overall goals of the organization. Herein, communication is of great importance, not only for managing the change, but also to continually get people involved in daily improvement activities (Jaca et al., 2012). An overview of the influencing factors of ability can be seen in table 3. Sterling & Boxall (2013)

Jaca et al. (2012) Bortolotti et al. (2015)

(14)

2.4 Relation between motivation, opportunity and ability

To summarize, motivation is an individual’s willingness to act, ability represents the individual’s skills and knowledge base related to the action, and opportunity represents environmental or contextual mechanisms that enable action (Siemsen et al., 2008). The three factors motivation, opportunity and ability are related constructs (Blumberg and Pringle, 1982) in a way that one factor could influence the others and eventually strengthen or weaken performance behaviour. Therefore it is of great importance to study the effect of the variables in the right context and encompass their interrelation. However, the precise causal relationships between the MOA-variables are difficult to justify theoretically and many studies have been conducted trying to reveal the existence of complementarity between the variables, but failed. Therefore, Siemsen et al. (2008) have developed three competing models: the linear model, multiplicative model and constraining-factor model (CFM). These models are used in order to reflect different levels of complementarity and interactions among motivation, opportunity and ability and their link to performance behaviours. In this exploratory study we consider these models as alternatives and will search for the suitable model for the context of employee participation.

Linear model

Cummings and Schwab (1973) question the assumption of moderate complementarity and suggest that performance should be predicted equally well by a model that does not capture complementarity, which leads to the linear model. Consequently, the model implies that interventions on one variable does not have an influence on the other variables and thus the strongest effect should be focused on in order to have the greatest overall effect.

Multiplicative model

In the multiplicative model, the perspective on the variables is that all variables must be present to some degree for an action to occur, whereby the lower value of any one of the variables strongly reduces action (Blumberg and Pringle, 1982). The common understanding is that moderate complementarity among motivation, opportunity and ability ought to exist and that practices reinforce each other. This implies that management interventions should focus on simultaneous implementation of practices in order to gain the greatest effect.

(15)

Constraining-factor model

Existing empirical evidence suggest that little explanatory power is gained by interaction terms that support theories that found complementarity (Siemsen et al. 2008). Therefore, Siemsen et al. (2008) suggest that a different model is called for to support the existence of this complementarity. To address this issue they propose the Constraining-factor model (CFM), which imposes a bottleneck perspective in which the minimum of the three variables ultimately determines behaviour. In other words, the model suggests that there are interactions, but only for the weakest link, therefore, the focus should be on the weakest link to see the greatest improvement. In their results they found superiority of the CFM over the multiplicative- and linear model and confirm no significant improvement of the multiplicative model over the linear model. Moreover, based on their outcomes they suggested to apply the CFM in a continuous improvement setting where complementarity of the variables could theoretically be expected to exist. This research investigates the complementarity of the variables in addition to the qualitative research. By investigating these models in the context of CI, this research contributes to the existing literature in the way of generalization.

The context of this research is similar to the research of Siemsen et al. (2008) in a way that knowledge sharing can be seen as a major part of participation in CI as also referred to in section 2.3. In the context of CI, different models try to facilitate guidance on the focus of organizations in order to increase participation (Bessant et al., 2001; Wu and Chen, 2006). Translated to the MOA-framework it can be concluded that one should focus on the least developed variable in order to gain the greatest increase in participation. Therefore, in this research we assume the confirmed hypothesis of Siemsen et al. (2008) that the constraining-factor model is a better predictor of the effects on employee participation than the linear- or multiplicative model.

2.5 Research questions

(16)

Siemsen et al. (2008) to assess the MOA-framework variables in CI environment, results in three research questions concerning the factors influencing employee participation in CI. The first question embraces the influence of the MOA-variables on employee participation in CI by assessing how the variables influence participation: 1. How do motivation, opportunity and ability influence participation of employees in CI practices? The second question addresses the impact and importance of factors influencing participation in the form of barriers and facilitators, resulting in the following question: 2. What are the most important barriers and facilitators of participation in CI?

(17)

3. Methodology

This research constitutes an exploratory single case study with an integrated questionnaire to generate direction for the research. Both the qualitative and quantitative approaches are conducted to comprehend the factors influencing participation in CI. The explorative nature of this study requires a qualitative approach that enables the detection of influencing factors in the organization together with the correlated barriers and facilitators. This approach in the form of semi-structured interviews is chosen in order to gain in depth information of factors influencing employee participation. Thereby, the quantitative approach is used to test the relationship of the MOA-variables and participation, and identify possible complementarity in order to give direction to the outcome of the qualitative research.

The research consists out of an embedded single case study containing more than one sub-unit of analysis. The researched organization can be divided an office environment and factory environment, which are compared in the analysis. This embedded case study provides a means of integrating quantitative and qualitative methods into a single research study and is an empirical form of inquiry, with the goal to describe the features, context and process of a phenomenon. The case study relies on multiple sources of evidence to add depth to data collection through triangulation and to contribute to the validity of the research (Yin, 2003). The comparison of the different areas within one business group and the use of both qualitative and quantitative research approaches enhance validity and triangulation of the data resulting in more reliable results.

3.1 Case selection

The unit of analysis for this research is the employee. An embedded single case study is chosen in order to explore the factors influencing employee participation in depth (Yin, 2003), meaning that all possible influencing factors in the different departments are tried to be revealed. In addition, a distinction will be made between barriers and facilitators, which later will be compared between the different areas within the business group.

(18)

meaningful conclusions. In addition, the organisation should contain a systematic implementation approach of CI in order to attribute the findings to specific CI characteristics. The chosen organization is a large international manufacturing organization of consumer products with its headquarters based in the Netherlands. The studied business group within the organization employs around 800 employees across the globe. For several years the organization has built its strategy around CI, which is operationalized by the lean principles. Currently, extensive knowledge throughout the organization has been established with regards to CI and lean. However, practical deployment is less developed in certain areas, which makes it an interesting organization for this research.

3.2 Data collection

The collected data at the organisation consisted out of in depth information regarding barriers and facilitators of employee participation together with management interventions aimed at increasing participation. In addition, the relation between the MOA-variables and employee participation is identified by analysis of the questionnaire. The data for the case study is collected via multiple semi-structured interviews with a variety of employees within different departments of the organisation. The interviews are conducted in mostly face-to-face conditions in the period from September until November at the organisation. At the beginning of September, interviews were held to gain information regarding employee participation at the organisation (execution of the research) and as instrument development for the questionnaire. The interviewees are selected based on the department they are working in and the function they fulfil. At first I have interviewed several employees of the management team with different functions in order to gain a broad perspective of how they assess the current situation at the business group. Thereby, the first insights were gained regarding the influencing factors together with their view on possible improvements. Thereafter I conducted several interviews with employees in the factory environment where I gained an overview of important influencing factors in that environment, which enabled comparison with the office environment. Furthermore, I interviewed several employees in the office in order to assess the obtained opinions of management and the most important influencing factors. An overview of all interviewees can be seen in table 4.

Interviewee Department Function

A Office employee Lean master BG

B Management HR Business partner BG

C Management Business leader

D Management Operations leader

(19)

Due to the semi-structured structure of the interviews, the time needed fluctuated depending on the position of the interviewee in the organization, however, the planned time has been around one hour. The interview started with a number of predetermined questions of general perspective regarding CI in the organisation. The interview protocols for both office employees and factory employees can be seen in Appendix A. Since a single researcher examined the interviews, the interviews were recorded in order to assure complete transcripts of the interviews. The transcripts, which were composed soon after the interview, have been made available to the interviewees for review and possible revise of certain provided responses. In the same two months of data gathering, analyses of the outcomes have been performed in order to obtain the possibility for secondary interviews to clarify results from previous interviews and from the questionnaire.

(20)

3.3 Data analysis

The gathered data from the interviews was analysed by open coding based on the three steps of Strauss and Corbin (1990) in order to establish a chain of evidence. First, open inductive coding have identified the different concepts, second the observations and sentences have been organised into categories, and third they have been formed in main themes. Coding of the transcripts has been conducted through a coding tree, which can be seen in Appendix B. For the analysis of the data gathered by interviews and observations, the program Atlas.ti is used to gather and organise the coding of the responses in order to get a good overview of the usable data. After gathering and organising the data, a within case analysis is performed in order to look for key variables and patterns in the answers of the interviewees. Second, the data of the different departments are compared in the between case analysis in order to identify patterns that explain possible different case characteristics. Since the aim of the study is to identify barriers and facilitators for employee participation in CI practices, possible preliminary findings were discussed with a small group of experts to verify the results.

(21)

3.4 Quality criteria

Reliability and validity of qualitative research

(22)

Reliability and validity of quantitative research

The used items and corresponding constructs can be seen in appendix C. The used constructs are all based on existing scales and are adapted to a CI context. Furthermore, the constructs are measured on a fully labelled 7-point Likert scale, which provides the greatest benefit to the researcher (Eutsler and Lang., 2015). As an extension to the research of Siemsen et al. (2008) I have divided the construct opportunity in four different constructs in order to gain more specific results and enable comparison. The different constructs encompass opportunity in the sense of information availability, time availability, support of colleagues and support of management as can be seen in figure 3.

Prior to the pilot study, the items have been pretested by six item-sorting exercises. Respondents in the item-sorting exercises were experts in the field of research of the University of Groningen, whom were given a list of all the items that they had to sort into the different constructs. After the analysis, confusing and vague items were adapted or deleted. Thereafter, a pilot study has been conducted among 54 employees in order to gain insight into initial results and a last possibility for adaption. After the pilot study, a not applicable (N/A) box was added to the scale to increase the reliability of the answers of the respondents. Each of the scales were designed unidimensional, because the theoretical constructs of the MOA variables are distinct singular theoretical concepts, rather than separate dimensions treated as a single theoretical concept (Edwards, 2003). Therefore we determined the questions within each scale as homogeneous in terms of their content, but they substantially vary the language used to test this content (DeVellis, 1991). In order to assess construct validity, a principal component analysis with Varimax rotation was performed on the multi-item scales. Hereby the following criteria had to be retained for each construct: (1) each measure must have a loading greater than 0.4; (2) no measure must have a loading greater than 0.4 to more than one factor; (3) each measure must load into the correct factor (Song et al., 2011). Thereby, the Eigenvalues should be 1 or more.

(23)

same phenomenon. However, since the constructs are based on established scales and a big part of the research entails comparison between motivation, opportunity and ability, I have decided to use the different constructs as intended.

Thereafter the Cronbach’s alpha was examined in order to determine scale reliability. An alpha estimate greater than .70 constitutes evidence of adequate reliability (Nunally, 1978). All of the scales satisfy this criterion as can be seen in appendix F, indicating a high level of internal consistency for the variables.

After obtaining evidence for the reliability of the measurement scales, the control variables were added to the scales to control the organization fixed effects. Dummy variables were created in order to transform the control variables to categorical variables as can be seen in Appendix G. The global region that employees work in, is split up in North America and the rest of the world, because I predict that America could have another attitude towards employee participation relative to the rest of the world. In addition, regarding the extent of training, the basic training together with no training is compared with employees who have had more training in order to gain a clear picture whether the amount of training influences the results. Finally, the time with the organization is divided into less than 5 years of experience and more than 5 years of experience to gain good overview if the tenure influences participation.

(24)

4. Results

4.1 Qualitative results

The following section will present the findings of the case study, which entails an assessment of factors influencing the MOA-variables. These factors are translated into barriers and facilitators of participation and the correlated recommendations for action. The information of the interviews has been corroborated by observations and informal conversations with employees throughout the organization. Also an analysis of organizational documents and information derived from meetings with the transformation team amplify the results. First a case related description is given of participation in CI together with the MOA-variables in order to gain insight into the use of these concepts in this research.

4.1.1 CI as an organisational context

CI can be seen as an environment of continually improving current processes by rethinking the way employees currently go about. Improvements that are continuously made are based on the values of lean that entail delivering value to the customer and eliminate waste. Thereby, Lean provides ways of improving processes in the form of lean tools, which could be used in many situations. Overall one can see lean as the guidance of CI:

“Lean is the full package that drives CI. So CI can be driven by through many different initiatives and you can actually apply the lean principles to any area I would say. Lean is the means to CI”. (D)

(25)

degree of participation are the same in all situations. How the MOA-variables and participation in CI are defined in the organization is outlined below. In the table 6, the definitions of the constructs are outlined in the context of the case study in order to gain a case specific insight of the concepts. This overview contributes to the research, because it clarifies possible indistinctness concerning the different concepts. Several quotes are assigned to the different constructs in order to give a good overview of different definitions.

Construct In context of case Data from interviews

(26)

Now that the constructs are placed in the context of the case study, the factors influencing the different constructs are outlined below, categorized in barriers and facilitators. Thereby the importance and impact of the different constructs are discussed together with recommendations for actions.

(27)

4.1.2 Barriers, facilitators and recommendations for action of the

MOA-variables

4.1.2.1 Motivation

Based on the number of interviewees that were asked to prioritize the MOA-variables, it appeared that motivation, or engagement towards CI, has been seen as the most important variable influencing participation. It has to be developed through mechanisms that convince employees of the meaningfulness of CI. Currently the employees of the leadership team are convinced that CI is the only way to improve processes:

“So for me it is really a way of working to deal with a lot of changes in the organization as well as pointing out in a factual way where we have waste, and think differently and rethink what we are doing” (C)

However, office interviewee K did not see the benefit in using CI instead of his current way of working. Confirmed by three other office employees in several informal conversations, it became clear that there is very limited perceived value from the use of CI. As a result, office employees do not put effort in learning the CI method and only see it as a barrier. Therefore, it is of great importance to include clear incentives in the information given. This could be in the forms of achieved results, but mostly by practical information about how employees can enhance their job.

Secondly, both office employees and employees in the factory valued the involvement of leadership as an important motivator for the use of CI. This was also acknowledged by leadership that want to present themselves as role models and try to adopt the use of CI themselves:

“So for me in CI it is a very critical thing to be a role model in order for employees to feel very connected to their daily work” (B)

(28)

A fourth factor influencing motivation is the experienced empowerment when suggesting and/or implementing improvements. Derived from the interviews with G, F and H is that in the factory the perceived empowerment to have an influence on the nature of their jobs is the foremost driver of motivation. This is due to the nature of their tasks, which is repetitive and naturally do not contain much empowerment or autonomy. Contrary, in the office, employees do not perceive empowerment as their foremost driver since they already experience that in their jobs. However, the accessibility of management towards suggestions for improvement can be seen as a facilitator in both business areas.

In table 7 you can see an overview of the factors influencing motivation and their corresponding barriers and facilitators. In the fourth column the recommendations are described that can be undertaken in order to enhance facilitators. The colours of the boxes determine in which department the factors are of influence. The green colour refers to both areas, the blue colour refers to the office environment and the yellow colour to the factory environment.

Influencing factors of motivation

Barrier Facilitator Recommendations for action

Behaviour of leadership Difference between desired behaviour and behaviour of management Involvement and role modelling of leadership Management has to live CI, also in times of high pressure (they tend to fall back in old behaviour). Management always has to be open for suggestions and take them into account (encourage CI behaviour). Rewards No rewards in terms of financial bonuses, promotion or recognition are given to the employees Incentivize the use of CI in terms of financial bonuses, promotion or recognition that are given to the employees Have awards for good use of CI, visualize use of CI in work environment. Provide recognition from management and colleagues Meaningfulness Employees do not see added value in the use of lean Employees get incentivized by the right information given Provide information that incentivizes employees and clearly shows the added value of participating in CI. Attention on ‘what is in it for them’. Empowerment Suggested improvements are not taken seriously Openness for improvements and suggestions of employees Management has to react on suggestions for improvement. Give employees the possibility to improve.

(29)

4.1.2.2 Opportunity

Interviewees in the office mentioned that there was a lack of time to participate in CI and is therefore seen as the foremost barrier:

“It is about a set of time management”. (B) “The bar is high so that is a barrier”. (E)

However, in the factory, employees cleared up time or even worked extra time in order to participate in CI, because they knew that eventually it would improve their jobs. This insinuates that the lack of time can be seen as a matter of prioritization in which employees in the office do not want to make time free for CI participation because they do not see the benefit. This opinion is supported by several interviewees of leadership: “For me it is a matter of prioritization, and I think if you do that well it can save time in your daily job” (B) Therefore the use of CI should gain prioritization by providing the employees with the incentive of using CI in their jobs or make it part of their KPI’s (Key Performance Indicators). Since it is experienced to be hard to incentivize office employees, it would be a facilitator to include the use of CI in employee’s KPI’s. Besides the time to participate, another opportunity that employees are given is the availability of training. Currently there is more demand than can actually be offered in terms of training, which can be seen as a barrier: “So we know that there is more demand by our employees than the actual offering currently of the CI and lean trainings. Actually that is also something that the whole learning community is trying to step up in the capacity so that we can have enough training”. (B)

(30)

The difficulty with application of the training is that employees sometimes go back to their work environment where they are not able or at least not encouraged to apply what they have learned:

“People sometimes go to lean training and come back in an environment where lean deployment has not started or where their management doesn’t know what lean is. And that gives a difficulty for this people to do something with the ideas about improving the work”. (A)

Therefore is it important that everybody in the organisation gets trained and well educated in CI and lean. This is already greatly implemented in the factories where for example in England all employees will have finished the lean basic course by April 2016. Furthermore, in order to ensure the use of the learnings, management could decide to align employees based on CI use or create interdepartmental groups in order to transfer CI use between departments. The interdepartmental approach already is adapted in the factory environment in terms of training, where employees of different departments within the factory get trained together. In this way, employees get in contact with employees from other departments and gain insight in their jobs and how they could apply lean. This is by all factory interviewees seen as a great facilitator for the collective participation and cohesion in the factory.

For a big manufacturing organization it is hard to facilitate a change towards CI, which is the reason that a transformation team has been formed that is responsible for the implementation of CI and Lean. This team is seen as a great facilitator in the sense of a central contact point and the drive behind employee participation in CI. The transformation team should be up to date about the progress of employee participation in the business group in order to continually stimulate the use of it and incentivize employees.

(31)

the organization should roll out an improvement plan for every department containing clear information regarding the goals, targets and relationship of the employees. To summarize, the different influencing factors of opportunity are summarized in table 8 below together with the barriers, facilitators and recommendations for action. Influencing factors of opportunity

Barrier Facilitator Recommendations for action

(32)

4.1.2.3 Ability

From higher management, the message is that the whole organisation should encompass lean in their daily processes, whereby an overview of all the available tools and meanings of CI and lean is available online. However, this awareness alone does not convince the employees enough for them to include CI in their daily processes, therefore more focus on embedding the information should increase the level of participation. From the informal conversations with several office employees came forward that the passive existence of general information about CI and lean (such as the online tools) is seen as a barrier, because it does not give them insight in how to use it in their jobs. Whereas more specific practical information in the form of training with real life examples is seen as an enabling activity, because employees then get the knowledge of how they can use it in their jobs: “Well awareness is one thing but you also need practice. It is not powerful when you say here is your new tool right. It is a new behaviour, it needs practice, it needs rehearsal, it needs feedback, it needs learning by doing, and it needs coaching. That’s embedding, so I think the embedding of it is not there.”. (E)

Therefore, enabling actions should consist out of the provision of clear and concise information towards how employees can use CI via internet, training or visibility in the office. Thereby it is important that the information is function specific, because information that is provided too generally will only hamper participation because of its lack of applicability: “So on the one hand you need to train them, because without training you can’t get the skills and you don’t know what it is about. And second is that you have to show it in your work, show it in your projects and have line managers and other leaders think and understand how you can use it and apply it”. (B)

(33)

To summarize, the different aspects of ability are summarized in table 10 on the next page together with the barriers, facilitators and recommendations for management for action.

Note: The colours of the boxes determine in which department the factors and correlated actions are of interest. Yellow: factory, blue: office, green: both.

TABLE 9 INFLUENCING FACTORS, BARRIERS AND ENABLERS AND RECOMMENDATIONS FOR ACTION

Influencing factors of ability

Barrier Facilitator Recommendations for action

(34)

4.2 Quantitative results

4.2.1 Descriptive statistics and correlation

This research took participation in CI as the dependent variable and included motivation, ability and opportunity (four types) as independent variables. We included several control variables in order to capture possible influencing factors of the organisation. The descriptive statistics of the control variables can be seen in Appendix H, whereas the descriptive statistics of the latent variables together with their correlations can be seen in table 10. The correlation of the independent variables with the dependent variable is relatively high, indicating that an increase in any of the independent variables is a rise of participation. In addition, it generally insinuates that all the constructs are dependent on each other. Furthermore, the high correlation of motivation and ability indicates potential multicollinearity, which will be discussed later on. Table 10 Descriptive statistics and correlations of the latent variables Mean S.D. 1 2 3 4 5 6 7 1 Motivation 5.77 0.86 - 2 Ability 5.74 0.85 .74* - 3 Opportunity (OIA) 4.76 1.21 .32* .41* - 4 Opportunity (OTA) 3.84 1.54 .44* .35* .47* - 5 Opportunity (OSC) 4.91 1.15 .43* .41* .39* .43* - 6 Opportunity (OSM) 5.47 1.07 .36* .44* .39* .18 .47* - 7 Participation in CI 5.38 0.97 .64* .66* .42* .38* .46* .53* - *p<.01 Note: OIA = Information Availability, OTA = Time Availability, OSC = Support of Colleagues, OSM = Support of Management. Number in red: only insignificant relation.

4.2.2 Regression

In the regression analysis I have tested the linear model, constraining-factor model and multiplicative model. Model 1 corresponds to the linear model and allows only for the basic linear effects of the MOA variables. Model 2 corresponds to the constraining-factor model and allows for all linear terms to change, depending upon whether motivation, opportunity or ability is the minimum. Model 3 corresponds to the multiplicative model and includes the interaction effects in addition to the linear terms (Siemsen et al., 2008).

(35)

For every model (of the opportunity OIA regression), the data was tested on the assumptions for multiple regression of which the outcome can be seen in Appendix I (Scatter plot, Q-Q-plot and VIF score). A linear relationship has been established by a scatterplot for every model. In the scatter plot it can also be seen that the data suits the assumption of homoscedasticity. In addition, the data was tested on normality by Q-Q-plots where can be seen that the data has a fitted distribution for the analysis. Furthermore, I scanned for outliers with the use of box plots, whereby the boxplot of the residuals show that there are no outliers. However, in earlier analysis of separate boxplots of the constructs, minor outliers have been identified, which insinuate that the estimates were influenced by influential cases. Based on these boxplots, respondent 103 was deleted because of certain patterns in the data.

Another assumption that was confirmed for the linear- and multiplicative model is low multicollinearity, whereby the values of the Variance Inflation Factor (VIF) all were under 4. However, for the constraining-factor model, most of the VIF outcomes were far above 4, which insinuates a high interrelation of the constructs and needs careful interpretation. A result of high multicollinearity is that the estimates are very sensitive to minor changes in the model and therefore difficult to interpret. This will be taken into account in the analysis of the results. My assumption of the hypothesis of Siemsen et al. (2008) leads to comparison of the three models, whereby comparison results in the following assumptions: (1) the CFM must explain more variance than the linear model and (2) the multiplicative model must not explain more variance than the linear model. In addition, fit statistics like the adjusted R2 must indicate that

the CFM provides a better fit.

In table 11 a summary of the results of the different models is shown together with the fit statistics. The CFM delivers an increase in the adjusted R2 of 1% compared to the linear model and the multiplicative model an increase in 2%, which are minor increases that can be the result of several reasons. On the one hand it could be that the CFM and the multiplicative model are not that much different from the linear model, on the other hand, it could be that the linear effect of the variables in the CFM is conditional on the identification of the constraining factor. Thereby, the qualitative insights are very different across models.

(36)

Table 11

Opportunity type: Information Availability

Parameter estimates (dependent variable: Participation in CI)

Model Model 1 Model 2 Model 3

Linear Constraining factor Multiplicative

Variable Est. S.E. Est. S.E. Est. S.E.

Constant -.28** .11 .14 .42 -.40 .11 Regional area .19 .12 .19* .12 .23** .12 Training .09 .12 .10 .12 .05 .11 Tenure .25** .11 .22* .12 .25* .11 Motivation (M) .38*** .09 .17 .26 .37*** .11 Opportunity (OIA) .12** .05 -.27 .35 .07 .06 Ability (A) .41*** .10 .81* .42 .46*** .11 M * OIA .01 .08 M * A .10 .07 OIA * A .12 .08 M * OIA * A .04 .08 ΘOIA -.46 .41 ΘOIA* M .28 .29 ΘOIA* OIA .36 .36 ΘOIA* A -.36 .44 ΘA -.36 .95 ΘA* M 1.61** .66 ΘA * OIA -.22 .76 ΘA* A -1.00* .65 N 155 155 155 F 29.546 13.75 19.65 R2 (adj. R2) .55*** (.53) .58 (.54) .58** (.55) *p<.10 **p<.05 ***p<.01 Then concerning the interpretation of the values in the CFM, the effects are conditional on the factor that is the bottleneck. If motivation is the bottleneck, the effect of motivation is .17, which is lower than the effect in the linear model. If opportunity is the bottleneck, the effect of opportunity is .36-.27=.09 and when ability is the bottleneck, the effect of ability is -1.00+.83=-.17. Moreover, in addition to the above findings that the model fit in terms of R2 of the CFM is not

(37)

An overview of the calculated effects in the CFM are given in table 12. Here can be seen that the effect of ability majorly increases when motivation is the constraining factor, which insinuates that employees are triggered by the ability to participate even when their motivation is low. Or in other words, in order to increase participation, the organization should focus on training and learning of employees even if their motivation seems to be the biggest bottleneck. Furthermore, when opportunity is the constraining factor, the effect of both motivation and ability increase, which insinuates that when little information is available, the organization should focus on motivating and training employees. A third surprising observation is that the effect of motivation majorly increases when ability is the constraining factor, which would insinuate that provision of motivational incentives would largely increase participation. However, the above described results have to be interpreted with great care, because of the high multicollinearity in the CFM. Constraining factor Effect of variable Motivation Motivation: .17 Opportunity: -.27 Ability: .81

Opportunity Motivation: .45 Opportunity: .09 Ability: .45

Ability Motivation: 1.78 Opportunity: -.49 Ability: -.17

TABLE 12

Relying on the statistically significant results of the linear model, it can be concluded that the focus of the organization should be mostly on ability, thereafter on motivation and little on the availability of information. Thereby it should be noticed that employees that work longer than 5 years at the organization have an effect of .25 (p<.05) compared to employees who work there less than 5 years. This result insinuates that the time an employee works at the organization has a positive effect on participation in CI. The other control variables do have significant results. Analysis of other opportunity constructs

(38)

Many similarities can be identified when looking at the outcome of the different analyses. The adjusted R2 in every analysis shows little variance between the models. Therefore, we also

adhere to the linear models at the other opportunity constructs. In these linear models, similar effects can be seen, except in the opportunity OSM analysis. There, the effect of motivation is larger than the effect of ability, and the effect of opportunity (in the sense of management support) is relatively large and significant. This stresses the importance of management support at participation in CI. Another surprising finding in the opportunity OTA regression, is that the effect of opportunity in the sense of time availability has almost zero effect, while participants score the lowest on this construct (mean=3.84). When looking at the CFM of opportunity OTA, it can be seen that the standard error of all the outcomes are very high, which results in little reliability of the effects. In the CFM of opportunity OSC, it can be seen that the effect of motivation is large at every constraining factor. A possible explanation could be that the importance of motivation is larger when interpreting opportunity as an interpersonal factor. Thereby, looking at the CFM of opportunity SCM, it can be seen that the effect of motivation is also relatively high. In that analysis also the opportunity effects are relatively high, which stresses again the importance of management support.

Concerning the multiplicative models it can be seen that in every analysis the impact of interaction effects are very limited. Only in the opportunity OSC analysis, there is some significant effect. This can be explained by the notice that support of colleagues consists out of motivating and advising fellow colleagues, which is strongly in line with the motivation and ability constructs. Hence, the interrelation.

(39)

5. Discussion

The aim of the study was to identify barriers and facilitators of participation in CI, in combination with an impact assessment of the MOA variables to identify the most influential management actions. First the results from the case study will be discussed and compared to literature, followed by a discussion of the statistical analysis. Thereafter the outcomes of both studies will be combined and translated into conclusions and implications.

5.1 Case study

First the factors influencing the MOA variables have been assessed and translated into barriers and facilitators. Thereafter, management interventions have been derived in order to amplify the facilitators and thus enhance employee participation. In this section, the assessment of the influencing factors will be compared to literature as described in section 2 and the derived implications will be discussed.

The meaningfulness and rewards that employees perceive from participating in CI are influenced by the expectancy value theory (Eccles and Wigfield, 2002). Office employees currently do not see the added value of participating in CI, whereas employees in the factory are convinced that CI participation improves their jobs. Subsequently, the opportunity given to participate is partially determined by their motivation, which is derived from employees’ expectancies for success and subjective task values. This can also be derived from the availability of time that can be seen as a prioritization matter rather than a factual time constraint. This indicates an interrelation between motivation and opportunity, which is an interesting addition to the MOA-framework in the context of participation in CI. The involvement of management was seen as a major influence on motivation, which is in line with findings of Naor et al. (2008) and Bortolotti et al. (2015). However, despite the extensive efforts of management to lead by example it remains hard to convince employees to follow their behaviour. Therefore, management involvement needs further specification in terms of how to be involved. In the context of CI, management should embrace a process oriented mind-set and approach employees with questions towards this view. Thereby, in addition to literature, the behaviour of management tend to fall back to old behaviour under pressure, which is an important result considering the amount of work pressure these days.

(40)

implicated that job type moderates the impact of empowerment, which results in the need for other incentives in autonomous jobs. In this context, empowerment is not seen as the ‘what’ of work targets, which remain under management control, but to the ‘how’ of problem solving and organising.

In addition, the feedback incentive in the job characteristic model of Hackman and Oldham (1980) appears to require a more rewarding content in this research. It requires to contain forms of recognition (Fadel and Durcikova, 2014) in order to be perceived as an incentive. Further, incentivizing of any kind is considered difficult when employees do not see the added value of CI, which relates back to the expectancy theory of Eccles and Wigfield (2002).

The findings of Sterling and Boxall (2013) indicate that the impact of improvements depends on the extent to which employees have control over how they solve problems and organise their tasks. This implies that employees in the office environment would foster greater improvements than employees in the factory, because they experience greater control over their problem solving. However, the results in this research show that employees in the factory currently foster greater improvements than office employees. Therefore, it appears that employee control of tasks and problem solving above a certain extent, mitigates the use of prescribes improvement approaches. On the management side, this might suggest alternative ways of incentivizing employees compared to factory employees. This is in line with the findings that office employees are more incentivized by meaningfulness of the information as opposed to factory employees who value incentives in the form of empowerment.

(41)

Moreover, the influencing factors of the MOA-variables seem to interrelate in the sense that employees in the office need to learn the meaningfulness of CI (motivation) through participation in training (ability). Thereby, understanding of CI goals, also mentioned by Bessant et al. (2001), should be equivalent to meaningful information. Sterling and Boxall (2013) identify learning as active participation that bring about changed attitudes or behaviour. Hence, the outcome of this research fully agrees with the importance of this phenomenon, because employees need to change their attitude towards CI and see the added value from participating in it.

The results also indicate the importance of focussing on the least developed MOA-variable in order to gain the greatest results, which is in line with the constraining-factor model of Siemsen et al. (2008). In the less developed CI context of the office environment, employees currently need insight of the meaningfulness of CI through the facilitation of training, which refers to the focus on ability. In the more developed CI context of the factory, employees already experience CI as meaningful. Here, participation in CI can be enlarged by provision of a suitable work environment and the availability of training, which refers to the focus on opportunity. In both areas, an increase in participation is only established when focussed on these constructs.

5.2 Statistical analysis

In the quantitative part of this paper we have empirically tested a theoretical model of the way that motivation, opportunity and ability drive participation in CI based on the outcome of the paper of Siemsen et al. (2008). Drawing from that paper and other theory regarding the MOA variables, we tested the superiority of the constraining-factor model (CFM) opposed to other models showing a certain extent of complementarity between the variables. Because of the similarity of the research context compared to Siemsen et al. (2008) we have assumed the same hypothesis containing the superiority of the CFM, which we had to reject. In our explorative research the CFM was found not to be superior compared to the other models, but many other useful insights have been captured.

(42)

the results. The paper of Siemsen et al. (2008) provided a solution for this unknown complementarity in the context of knowledge sharing, whereas they have confirmed superiority of the CFM. However, based on our results also this form of complementarity is not confirmed, whereby the calculations of our CFM are strongly influenced by high multicollinearity. The variance of the adjusted R2 in the different models is negligible, which leads to adherence to the

linear model. The outcome of my results that the linear model should be adhered to in the context of participation in CI is in line with findings of Cummings and Schwab (1973), who suggest that performance could be predicted equally well by a model that does not include potential complementarity between the MOA variables.

Furthermore, the results show that the constructs motivation and ability in this research appear to be very similar. First, this was identified by the exploratory factor analysis in which the constructs loaded to the same component. Thereafter, it was found that the correlation between the constructs was very high (.74) and there was high multicollinearity in the CFM of every analysis. This could be explained by the interpretation of statements in the survey, where participants apparently see willingness to participate equal to the ability to participate. Or in other words, participants that were motivated to participate, were automatically also able to participate and vice versa. This finding suggests that the ability of an employee automatically goes together with the motivation of an employee to participate. This is confirmed by the outcome of the linear models where motivation and ability more or less have the same effect.

Another surprising finding is that the effect of opportunity in all analyses is relatively low as opposed to the descriptive statistics that show that participants on average score the lowest on opportunity. Thereby, time availability is seen as the largest barrier, whereas the effect of opportunity OIA in the regression analysis is .05 (n.s.), which can be considered negligible. In addition, the availability of information is scored the second lowest, but the effect in OIA is .12 (p<.05). Only the effect of management support is relatively large with .25 (p<.01), which emphasizes the importance of the involvement of management.

Moreover, the influence of ability is the largest in the linear model, which stresses the importance of training and especially learning of employees. The relations of the statistical outcomes with the findings of the case study are discussed below.

5.3 Comparison of studies

(43)

office environment. This is confirmed by the descriptive statistics of the survey where time availability and information availability score the lowest. However, the effect of time availability in the regression analysis is negligible as well as the effect of information availability. This can be explained by the expectancy value theory (Eccles and Wigfield, 2002), in which office employees do not perceive participation in CI to be valuable or expect to gain positive outcomes of it. Therefore, management should focus in the meaningfulness of information provided. In the survey analysis can be seen that the constructs motivation and ability seem to relate to a similar component. This interrelation can also be derived from the interviews, where employees that have greater practical knowledge concerning CI, show higher motivation towards CI. In addition, it also appears from the interviews that the least developed construct should be focussed in order to increase participation. This is in line with the finding of Siemsen et al. (2008) in his proposed constraining-factor model, but opposed to the statistical results of this research. An explanation of these results could be the high multicollinearity that influenced the results and thus the interpretation of the outcome. Another explanation could be that the survey is conducted throughout the organization where data of both areas, office and factory, is combined in one analysis. This could have resulted in an analysis of divergent data, which negatively influenced the computation of the models.

Referenties

GERELATEERDE DOCUMENTEN

For instance, it is assumed that the residuals are normally distributed, they have a mean of zero and a constant variance across levels of independent variables,

Coal Mining and Processing, Petroleum and Natural Gas Extraction, Ferrous Metals Mining and Processing, Nonferrous Metals Mining and Processing, Nonmetal Minerals Mining

a wide variance in post-deal performance 71 large deals from 1989 to 1993 compared to peer group market value change from one year before to two years after the deals..

The average rating given externally to quality and selection of the products and services from COS is

Variation in quality Collaboration; Relationship management; (Vertical) Integration; Information sharing and systems; Multiple suppliers; Human cognitive

in P e rc e n ta g e rt,t+1 Stock Return (t, t+1) TDNIt,t+1 Net Debt Issuing ENIt,t+1 Net Equity Issuing ADRt+1 Ending Actual Debt Ratio ADRt Starting Actual Debt Ratio

b. Did they support the project financially?.. If yes, what influence did they want to practice on aspects of the Children Museum like location, exhibitions, educational

Appendix B: Country list Austria Belgium Czech Republic Cyprus Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg