Knowledge intensive tasks and enabling use of control: the influence on the degree of autonomous motivation of employees

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Knowledge intensive tasks and enabling use of control: the influence

on the degree of autonomous motivation of employees

University of Groningen Faculty of Economics and Business

By: Joep Rooijers Student number: S2974649 Email: j.rooijers@student.rug.nl Supervisor: W.G. de Munnik Date of submission: 18-01-2021 Word count: 12,189

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Abstract

This study determines the relationship between Management Control Systems (MCS) and autonomous motivation, utilizing a moderator effect of complex tasks contributing to the task environment; combining both behavioral and management accounting literature. A dataset of 250 respondents, operating within a university of applied science, is used to test the proposed hypotheses according multiple linear regressions, with the intention to determine whether there is a significant impact on employees’ motivation in the proximity of a Management Control System (MCS), emphasizing enabling use of controls. An examination of results, concludes that there are differences regarding both components of autonomous motivation, as the degree of intrinsically motivated employees increased, whereas no significant effect on integrated motivation was found. Furthermore, this study shows no significant moderating effect of knowledge intensity degree on this relationship. Nonetheless, it seems that complex tasks are directly related to enhance intrinsic motivation of employees. These results compute new perspectives to the consideration how organizations emerge autonomously motivated employees, contributed to practitioners and literature.

Keywords: Autonomous motivation, Integrated motivation, Intrinsic motivation,

Management Control Systems (MCS), Enabling use of control, Belief systems, Interactive control systems, Complex tasks, Knowledge characteristics.

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Acknowledgment

First of all, I want to thank my supervisor Wilmar de Munnik for his guidance, enthusiastic discussions and valuable online sessions. Although, obtaining feedback was sometimes difficult to cope with, but his encouragement helped me to remain motivated. Furthermore, a special acknowledgement to my twin-brother Sjors Rooijers for his patience and linguistics help and my brother-in-law Stefan Koppers for his endless support and contribution, which ultimately increased the quality of this study. And of course, in the end I would thank my family for their infinite support in the whole process.

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1. Introduction

The Volkskrant stated on October 31st, 2019 that “one gets less motivated by a (monetary) bonus, than by a compliment”.

Also, in the literature is stated that fully integrated fully integrated and autonomously motivated employees result in a higher work engagement, involvement, and proactiveness (Gagné & Deci, 2005; Coats & Max, 2005). A study of Bevan et al. in 1997 found that employees who are not motivated by their employer, are more likely to be absent and resign. Therefore, one can argue that organizations have an interest in their employees to be motivated, as this is associated with positive outcomes and benefitting the organization.

Problem statement

Organizations are disappearing faster than ever, as markets become more diverse, more dynamic, more connected and less predictable (Reeves et al., 2016). It is therefore increasingly difficult to stay competitive. So, there is an increasing need for motivated employees for organizations to remain competitive in the long term. Research of Coats and Max (2005) has also concluded that the employers’ management, effective teamwork, and communication, leads to a higher employee wellbeing. In addition, Baard et al. (2004) stated, that a higher management support for autonomy resulted in a better performance and wellbeing of employees, as well as a higher work satisfaction among employees.The employees’ wellbeing is also becoming significantly more important, as the workforce will age more in the upcoming decades (Vaughan-Jones & Barham, 2009).Organizations tend to emphasize job enlargement to encourage interesting work for employees by increasing the number and variety of activities and tasks, because interesting work is found to be a primary motivational factor of employees (Lindner, 1998).

Literature gap

Research of Van der Kolk et al. (2019) argued that personnel control and cultural control are positively associated with employees’ intrinsic motivation, and results control is positively associated with employees’ extrinsic motivation. The research of Van der Kolk et al. (2019) focuses on employees with similar tasks, for example issuing passports and driver licenses, which have a low task complexity and low degree of knowledge intensity. So, these similar tasks are seen as routine-based tasks, where the outcome is predictable and known, and the employees know what is expected from them in specific situations (Fisher, 1995)

The Management Control System (MCS) of an organization consists of the objectives, rules, and values to ensure that all activities of employees contribute to the organizational objectives (Malmi & Brown, 2008). The MCS could be divided into enabling use of control and constraining use of control (Henri, 2006). Empowerment of employees is considered as the degree of autonomy and control of employees within the organization (Baird et al., 2018).

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Therefore, this empowerment of employees could have interfaces with autonomous motivation, since both concepts require autonomy and engagement.

Complex tasks mainly exist in an uncertain environment (Schwarzwald et al., 2004). Employees who have to deal with an uncertain environment, should consist of characteristics as creativity, and adaptability to anticipate on this unforeseen situation and be creative to carry out complex processes (Autor et al., 2003).

Employees should experience a high level of complexity within their job, to ensure their experience involvement within the organization and emerge themselves among the organizational goals and values. Besides that, complex jobs could be accompanied with interesting and enjoyment of the tasks, which results in an increased level of self-determination. Employees experience that they do not have to operate because of external pressure, but from their own willingness, in other words, they experience autonomous motivation (Gagné & Deci, 2005). Which is related to features as creativity and adaptability. Therefore, it could be argued that employees who execute tasks with a high degree of knowledge intensity are desiring a high level of autonomous motivation.

Within their conclusions, Van der Kolk et al. (2019) suggest future research among complex or non-complex tasks. Therefore, this could be an opportunity for providing new insights regarding relation between task environment, management control systems (MCS) and employee motivation. This indicates opportunities for a study regarding complex tasks with a high degree of knowledge intensity in relation with MCS and motivation. So, this study extends the research of Van der Kolk et al. (2019) by broadening their view regarding the task environment from simple tasks with complex tasks.

Research question/sub-questions

Combined with the future research suggestion of van der Kolk et al. (2019), what provides an opportunity for research on the effect of MCS on employee motivation, that lays emphasis regarding different types of tasks within the task environment. Which leads to the following research question:

How does a management control system emphasizing enabling use of controls affects forms of autonomous motivation and how do knowledge intense tasks affect this relationship?

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In order to answer the research question, this study distinguishes three different sub-questions.

1.What is a management control system?

2.What is (autonomous) motivation and how does a management control system affect (autonomous) motivation?

3. What are knowledge intensive tasks and how does these knowledge intensive tasks affect the relationship between MCS and employee motivation?

Contribution

This research aims to contribute to the existing body of research in management accounting literature, by researching the relationship of the MCS and employee motivation, using enabling use of control with the presence and degree of knowledge characteristics in the task and work environment. Therefore, it could contribute to and affect the theories within management control literature.

Additionally, this study aims to contribute to organization’s knowledge on how to motivate employees effectively. Work is an important part of employees’ lives, in current society. As, motivated employees are more creative and involved (Adler & Chen, 2011). Ultimately, this study may contribute to the organizations’ knowledge on how to manage complex tasks correctly, as well as finding the most suitable candidate for the execution of these complex tasks.

Structure

The remainder of this paper is structured as follows. In the following chapter, the relevant studies will be reviewed and used to develop the theory which leads to a set of hypotheses for empirical testing. Continuing with chapter three, the methodology of the research will be presented. Chapter four presents the results and analysis of the performed research. In chapter five the discussion of the results will be elaborated. In this discussion the implications of these results as well as the limitations and directions for future research will be outlined. Finally, chapter six presents the conclusion and answers the introduced research question.

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2. Literature review

In this chapter, the existing and relevant literature will be discussed. This review of the literature will lead to a set of hypotheses for empirical testing, to provide answers on the sub-questions.

2.1. Management Control System (MCS)

According to Malmi and Brown (2008), the MCS of an organization consists of the objectives, rules, and values to ensure that all activities of employees contribute to the organizational objectives. The ultimate purpose of this organizational wide integrated system is to enhance the performance of the organization. This can be achieved to ensure that all employees’ tasks, activities, and work are in line with the overall objectives of the organization (Malmi & Brown, 2008). Study of Chenhall (2003), stated that the presence of the MCS within organizations has developed expeditiously. In the beginning phases of introducing the MCS, the emphasis lies on provision of qualitative information to support the managerial decision-making process. Nonetheless, the advanced opportunities seemed endless to get insights in a much broader scope of information. So, a MCS is ultimately designed to influence employee’s behavior to ensure that employee execute activities in such a way that these are in line with the objectives of the organization.

This study uses the lever of control (LoC) framework of Simons (1995), as a MCS of the organization, because this LoC framework is developed to influence behavior of employees while get insights in maintain or change patterns in organization behavior, while ensure employees activities contribute to the overall objectives of the organization.

2.2. Levers of control framework

Simons (1995) levers of control (LoC) framework intends to arrange a balance between these different levers of control. The levers of control influences behavior of employees. For instance, encourage enterprising and innovative employees, while at the same time monitoring control about these employees based on the main objectives of the organization. This ultimately provides a tool to enhance control and corresponding performance (Simons, 1995). The LoC framework could be used by managers to combine innovation and control (Adler & Chen, 2011). Moreover, it is developed to shine light on implementing strategic changes (Simons, 2000).However, the development of the lever of control framework is used to regulate inherent organizational tension between goal prediction and innovation (Henri, 2006). So, the four combined levers of the LoC framework face all decision related factors from the manager’s perspective; decisions that affect employees’ behavior and could involve autonomous motivation (Widener, 2007). The four levers of control are belief systems, boundaries systems, diagnostic control systems and interactive control systems

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According to Henri (2006), these four different levers of control could be divided into two forces, namely enabling and constraining use of control.

2.3. Forces of control

The enabling use of control focuses on open communication and engagement of employees (Free, 2007). Where employees will be facilitated in enlarging and defining the scope for new initiatives of the organization and for example encourage creativity of employees through thinking out of the box regarding organizational issues (Free, 2007). In contrast, the constraining use of control set up barriers and boundaries for employees. These boundaries steer employees within their work and activities, with the use of monitoring mechanisms to control performance (Henri, 2006). So, organizations encourage employees to be creative and innovative, and so there must be controls that inspire autonomous motivation and empower employees to perform creative and innovative behavior (Baird et al., 2018). However, this behavior could not interminable be executed through employees when the organizations desire keep control. So, the organization could use constraining use of control, to obstructs employees’ freedom (Henri, 2006).

Therefore, the focus on this paper will merely be on the enabling use of control rather than the constraining use of control, to ultimately implement desired behavior in the forms of autonomous motivation.

2.4. Enabling use of control

The term ‘enabling use of control’ consists of a MCS emphasizing belief systems and interactive control systems (Baird et al., 2018). Enabling use of control focuses on providing opportunities for empowering organizational learning and encourages information sharing and enhancing employees’ performance (Free, 2007; Wouters & Wilderom, 2008). However, the design and implementation process of the MCS determines whether the MCS of an organization is enabling (Jordan & Messner, 2012). Simons (2000) states that belief systems and interactive control system could be defined as enabling levers. These enabling levers enhance information sharing, as well as learning processes and therefore could establish an informational environment for employees.

In the next paragraphs, both enabling levers of control will be explained in order to gain insights in how these levers affect behavior of employees.

2.4.1. Belief system

The main objective of the belief system is the communication and distribution of the core values of an organization to affect employee behavior and create willingness and commitment (Widener, 2007). These core values are, for example, the mission statement and the codes of ethics of an organization. The belief system focuses on motivating employees by using

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inspiration (Simon, 1995). Organizations normally communicate the beliefs by exploring the mission or vision. Where the mission is a short notice of the main purpose of the organization, identifying stakeholders and why it exists, the vision states the current and future objectives and discusses the opportunities and threats (Ozdem, 2011).

Belief systems influence employee’s behavior according these core set of values. It is important that everyone within the organization is willing to express the vision and mission of the organization. Because, for example when employees feeling responsible concerning the core set of values, the employees mutually could address each other on the relevance, while sharing the vision throughout the organization (Kruis et al., 2016). So, it is important for organizations to set up clear core values that are easy to understand, this results in a homogeneous expression. For instance, employees could be informed about specific organizational principles, and unconsciously implement and execute these as part of the company’s identity. Besides, an essential part of the belief system is the created culture within the organization. An evident culture, what explores norms, values and social behavior, could be seen as social control of individuals. It strengthens employees to adapt their behavior according the existing values within the organization. So, the belief systems could establish cohesiveness to positively influence the level of employee engagement (Herath, 2007).

2.4.2. Interactive control system

Interactive control systems are used to communicate the organizational strategy in an informal way where employees are involved in organizational activities and concerned within the implementation process (Henri, 2006). Interactive control system accentuates the reciprocal and continued dialogue with employees about the input and output regarding business strategy and strategic choices of the organizations accompanied (Baird et al., 2018). For instance, be part of this process concerning organizational objectives could enhance responsibility and emerge engagement and commitment, while contribute to the overall goals and strategy of the organization (Henri, 2006; Kruis et al., 2016). Besides, Rieckhof et al. (2015) suggest that the interactive control system of an organization should be designed in such a way that it stimulates innovation and corresponds to enhance organizational development. However, Rieckhof et al. (2015) argue that an interactive control system is a formal information system to support managers. In contrast with Kruis et al. (2016) who argue that interactive control systems are informal, because of the employee engagement and discussion. So, emphasizing the interactive control system as enabling lever affects the employee behavior through increasing commitment and responsibility which finally have a significant impact on the job environment (Widener, 2007).

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Summary

Summarized, it could be stated that a MCS consist of objectives, rules, and values to affect the behavior of employees and ensure that all activities contribute to the overall goals of the organization (Malmi & Brown, 2008). Simons LoC framework shed lights, according four levers of control, on two ways of behavioral influence, stated as enabling use of control and constraining use of control (Simons, 2000). These different forces of control aim for employees to work according the organizational objectives of the organization, which corresponds to the main objective of the MCS (Bisbe & Otley, 2004).

An ideal workplace should be supportive of the required motivation applied to the specific function integrated with the organization’s objectives, rules, and values (Stone et al., 2009). This study focuses on complex tasks, which indicates that this study especially focusses on the enabling use of control as part of the MCS, because it specifies relatedness to high degree of knowledge intensity task, to ultimately implement desired behavior in the forms of autonomous motivation. Organizations encourages employees to be creative and innovative, which have interfaces with autonomous motivation, and so there must be controls that inspire autonomous motivation and empower employees to perform creative and innovative behavior (Baird et al., 2018). So, the implementation and execution of the MCS depends on the type of job, and the required employee motivation. In the next paragraph employee motivation as a concept will be further explored.

2.5. Employee motivation

As mentioned before, an organization’s way of controlling affects the employees’ autonomous motivation. Organizations value motivated employees, because motivated employees have a positive effect on organizational performance and operations (Adler & Chen, 2011).There are different concepts and corresponding definitions of employee motivation, although they all have in common that motivation is not measured according to one single measure, as it depends on several different factors and mechanisms. Van der Kolk et al. (2019) suggest that organizations use different types of management controls to gain desired behavior of employees. Desired behavior of employees in this context can be seen as behavior according activities of employees to enhance or contribute to the overall objectives of the organization. The Self-Determination Theory (SDT) is a motivational based theory that contains a continuum of motivation to indicate behavior of employees and illustrate autonomous motivation (Groen et al., 2017). As shown in figure 1, the SDT builds on the controlled-to-autonomous motivation continuum of Gagné and Deci (2005) and diverts the two main concepts of motivation, which are extrinsic motivation and intrinsic motivation. Decomposing these concepts shed lights in the underlying mechanisms of motivation.

According to Gagné and Deci (2005) motivation concerns a person’s relation to tasks or activities. The SDT can be applied in the workforce, regardless of the specific position and form of motivation. The focus in this study concerns autonomous motivation, because complex

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tasks implement desired behavior in the forms of autonomous motivation. Stone et al. (2009), state that autonomous motivation entails that employees do not experience external pressure and operate according their own willingness. Employees tend to be autonomously motivated when they experience autonomy, create engagement, and employ in interesting activities (Gagné & Deci, 2005).

Autonomous motivation consists of two different regulations. The two divided components of autonomous motivation are integrated regulation and intrinsic motivation. As Figure 1 visualizes, the difference between these two regulations is caused by the underlying mechanisms of extrinsic motivation versus intrinsic motivation. However, both are part of autonomous motivation and therefore this study divides the concept autonomous motivation into integrated motivation and intrinsic motivation. Besides, integrated motivation appears when employees experience importance and coherence of the goals and values of the organization (Gagné & Deci, 2005). The second mechanism of autonomous motivation is a regulation of intrinsic motivation. People who are intrinsically motivated are inherently autonomously motivated (Gagné & Deci, 2005). So, intrinsic motivation could be seen as the ultimate form of motivation.

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2.5.1. Summary

The discussed literature stated that autonomous motivation is divided into two different components. The two mechanisms integrated motivation and intrinsic motivation are seen as the underlying mechanisms to indicate autonomous motivation. Integrated motivation is seen as regulation of extrinsic motivation, while intrinsic motivation emerged according intrinsic motivation (Gagné & Deci, 2005).

After exploring the concept autonomous motivation, both levers of control are outlined in relation to autonomous motivation. Ultimately, this results in several hypotheses, which will be tested, to gain insights in this direct positive relation between enabling levers of control and both components of autonomous motivation.

2.5.2. Belief systems in relation to autonomous motivation

Autonomously motivated employees do not experience external pressure and operate from their own willingness (Stone et al., 2009). For example, an organization could establish norms and values that attracts employees who are engaged to those norms and values, and are congruent with their personal goals and values. So, this results in enhanced personal interest of the employees. Moreover, if the required employee activities are in line with their personal interest, it could lead to a diminishing of the degree of aversion or reluctance and at the same time enhance engagement (Gagné & Deci, 2005).

Besides, employees emerge engagement regarding the core set of values, for instance feeling responsibility to distribute these vision and mission through the organization, where employees jointly create commitment according the organizational objectives and for example are able to reprimand each other (Kruis et al., 2016). The presence of a strong culture could impact the behavior of employees. When for instance, the prevailing culture is hard to get involved, and employees cannot adapt to it, it could diminish job satisfaction. However, become part of this culture could be accompanied with special awareness of solidarity towards the organization and reciprocal adherence with employees (Herath, 2007).

Therefore, this study suggests that a MCS emphasizing belief system has a positive effect on autonomous motivation. As mentioned in the previous paragraph, autonomous motivation is investigated based on the two underlying components. This results in the following hypotheses concerning autonomous motivation.

H1a: A MCS emphasizing Belief system has a positive effect on integrated motivation H1b: A MCS emphasizing Belief system has a positive effect on intrinsic motivation

2.5.3. Interactive control systems in relation to autonomous motivation

Interactive control system stimulates discussions and involvement of employees. When participate in face-to-face meetings concerning specific organizational goals, they could for example experience a high level of appreciation from the top management, which could have a positive impact on employees’ commitment and corresponding autonomous motivation

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(Kruis et al., 2016). Besides, employees who for example becoming part of the continual dialogue concerning organization strategy, enhance their coherence of the organizational values that will be discussed, which could result in an increased level of engagement. Additionally, it could provide opportunities in enhancing skills, abilities, and responsibility (Widener, 2007). So, emphasizing interactive control system influences the behavior of employees and simultaneously triggers the underlying mechanisms of autonomous motivation. Therefore, it could be argued that a MCS emphasizing interactive control systems will have a positive effect on both components of autonomous motivation. This results in the following hypotheses.

H2a: A MCS emphasizing interactive control system has a positive effect on integrated motivation.

H2b: A MCS emphasizing interactive control system has a positive effect on intrinsic motivation.

2.5.4. Enabling use of control in relation to autonomous motivation.

The single hypotheses are proposed in the previous part built upon prior literature and suggest a positive relationship between these constructs. As a result, every single relationship will be tested with the impact of knowledge intensity degree. Ultimately, the effect on enabling use of control will be tested. So, all single relationships provide in-depth insights, while the whole construct is tested in order to provide an answer on the main question of this study. This results in the following hypotheses.

H3a: MCS emphasizing enabling use of control has a positive effect on integrated motivation H3b: MCS emphasizing enabling use of control has a positive effect on intrinsic motivation Existing literature has mainly focused on the relation between management control and motivation, which provides useful insights in the design of management control systems for organizations to optimize employees’ motivation. However, van der Kolk et al. (2019) focus their research on similar tasks. This study expands their research to investigate the effect of tasks with a high level of complexity, mainly seen as complex tasks, which are ultimately measured according the knowledge intensity degree.

2.6. Knowledge intensity degree

Organizations tend to emphasize job enlargement to encourage interesting work for employees by increasing the number and variety of activities because interesting work is a primary motivational factor for employees (Lindner, 1998). Individual workers contribute a higher productivity and performance when their employer assigns more specific tasks (Bevan, 2010). Jobs accompanied with a low level of complexity and task variety have, according to Fisher (1995), a high extent of routine. The opposite of routine based jobs are complex jobs,

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accompanied with a low extent of routine (Schwarzwald et al., 2004). Autor et al. (2003), stated that non-routine jobs are activities which require creativity, flexibility and complex communication, and are featured with high variability, which requires adaptability. These characteristics are positively related to autonomous motivation (Gerhart & Fang, 2015). These non-routine jobs need to fulfill a high number of unfamiliar tasks, without a pre-determined sequence (Pentland, 2003), which is in this study stated as complex jobs. So, the opposite of routine based jobs are complex based jobs. Meanwhile, a high degree of task variety will provide more opportunities for employees to gain necessary knowledge, experience, and resources and are less monitored and controlled (Zaniboni et al., 2013).

Morgeson and Humprey (2006) define knowledge characteristics as “the kinds of knowledge, skill and ability demands that are placed on an individual function of what is done on the job” and are parts of the tasks and work environment of employees. So, according to Morgeson and Humprey (2006) knowledge characteristics indicates the degree of knowledge intensity. Hereby knowledge characteristics consists of five different characteristics: job complexity, information processing, problem solving, skill variety, and specialization. All five characteristics will be comprehensively discussed and featured, to gain insights into the whole concept.

2.6.1. Job complexity

The first knowledge characteristic defined by Morgeson and Humprey (2006) is job complexity. This is the degree to which jobs are complex and tough to execute or perform, and is based on the number of tasks or activities the job requires (Morgeson & Humphrey, 2006). A high degree of job complexity could be seen as challenging to fulfill, while jobs with a low level of job complexity are stated as simple (Morgeson & Humphrey, 2006; Campion, 1988). Edwards et al. (2000), stated that if a job requires complex tasks, the job is more demanding and challenging. Meanwhile, Humprey et al. (2007) stated that increasing activities to perform, could result in job overload. This may result in an increasing level of stress and fear of failing. This could affect the self-worthiness contingent of employees. However, the same research contradicts this by stating that increasing activities are positively related to job satisfaction and perceived performance (Humprey et al., 2007). So, a high job complexity is challenging for employees and require flexibility and adaptability. Free (2007) stated that the enabling use of control levers could focus on open communication and engagement, which is needed to deal with for example, the thin line between job overload and job satisfaction and accompanied autonomous motivation. This implies that it could be important for organizations to align the job complexity and it could enhance the relationship between enabling use of control and autonomous motivation.

2.6.2. Information processing

The second knowledge characteristic defined by Morgeson and Humprey (2006) is information processing. Information processing refers to the degree to which a job requires attending to and processing data or other information. This includes monitoring, analyzing and thinking

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(Morgeson & Humprey, 2006). An important factor underlying the main relationship is thinking along the process. Adler and Borys (1996) suggest that employees who prefer the enabling process could mainly be seen as thinking employees. They are expected to provide contribution during the process to think along the process of the organization (Adler & Borys, 1996). So, processing data or other information is accompanied with gaining knowledge of the organization processes and strategic objectives. This could increase for example the importance of organizational values and involvement within the process, which is established on the direct positive relationship between enabling use of control and autonomous motivation. So, this relationship is heightened for organizations who provide jobs with a high degree of information processing.

2.6.3. Problem solving

The third knowledge characteristic defined by Morgeson and Humprey (2006) is problem solving, which reflects the generating process of diagnosing and solving unique and new problems or preventing them from occurrence, accompanied with a certain level of creativity and innovative thinking process (Morgeson & Humprey, 2006). So, problem solving is involved with high cognitive demands, that are related to the requirements for employees performing complex jobs (Autor et al., 2003). Besides, the level of creativity could be related to research of Verbeeten (2008), who argues that the user of the MCS influence innovative behavior of the organization. These creative employees prefer operating from their own willingness instead of being primarily motivated by external pressure (Stone et al., 2009), which suggest that these creative employees, working from their own willingness, are related to autonomous motivation.

2.6.4. Skill variety

The fourth knowledge characteristic defined by Morgeson and Humprey (2006) is skill variety and should be dignified from task variety. Skill variety indicates the use of numerous skills, while task variety focuses on the performance of different tasks. Task variety can be seen as the degree of these multiple tasks are used during activities of employees (Morgeson & Humprey, 2006). Kanfer and Ackerman (1989) mentioned that managing a high degree of skill variety, increases self-regulation and dedication of cognitive skills. This is in line with the effect of belief systems on autonomous motivation, where organizations establish required activities employees have to perform and the recommended employees are in possession of high degree of skill variety. And therefore, fits within that organization and increases autonomous motivation.

2.6.5. Specialization

The last knowledge characteristic defined by Morgeson and Humprey (2006) is specialization. Narayanan et al. (2009) stated that specialization differs from common jobs, due to the need for optimization. Specialization is an exception, because certain jobs require full engrossment on specific tasks, which could ultimately result in a low degree of task variety, to gain expertise

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and optimize the performance of the employee. This is referred to as specialization, which is accompanied with a low level of task variety (Narayanan et al., 2009). Besides, Morgeson and Humprey (2006) emphasize the depth of knowledge and skill in a specific area or field to determine specialization. Specialization emphasizes organizational learning, what provides in-depth knowledge of all relevant processes.

2.6.6. Knowledge intensity degree

The construct knowledge intensity degree consists of the above-mentioned knowledge characteristics. All single characteristics combined, forming the variable knowledge intensity degree (Morgeson & Humprey, 2006). This study sheds light onto the effect of these knowledge characteristics as knowledge intensity degree on the positive relationship between the levers of enabling use of control and both components of autonomous motivation. Therefore, it could be argued that factors underlying the main relationship, such as engagement, experiencing responsibility and identifying among the values are heightened for jobs which require a high degree of knowledge intensity of employees. This results in the following hypotheses.

H4a: Knowledge intensity degree affects the relationship between belief systems and integrated motivation.

H4b: Knowledge intensity degree affects the relationship between interactive control system and integrated motivation

H4c: Knowledge intensity degree affects the relationship between enabling use of control and integrated motivation

H4d: Knowledge intensity degree affects the relationship between belief systems and intrinsic motivation

H4E: Knowledge intensity degree affects the relationship between interactive control system and intrinsic motivation

H4F: Knowledge intensity degree affects the relationship between enabling use of control and intrinsic motivation

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2.7. Conceptual model

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3. Methodology

In this chapter, the research design used in this study is introduced. All variables discussed in the literature review are translated into constructs to provide insights what is actually measured and how they are measured and used in this study. The first paragraph discusses the research method and data set. Following, the measures of all variables are elaborated in the second paragraph. The third paragraph explains the control variables used in this study, explaining their underlying relation with the dependent variables. Furthermore, the reliability of these constructs is elaborated and compared with the literature. Ultimately, the empirical strategy is described in the last paragraph to get insights in the tested assumptions and the following regressions used.

3.1. Research method & data set

For this research, a deductive approach is taken, which means that the development of hypotheses is based on existing literature. This approach is the most appropriate in order to explain the cause and effect of relationships between variables (Adams et al., 2014). The data set contains of 250 completed surveys conducted at two different universities of applied sciences, in this study referred to as higher professional educational organizations.

3.2. Descriptive of data

This data set used in this research contains of 250 respondents, divided over 91(36.4%) respondents from organization B and 159(63.6%) respondents from organization C. All responses are fully completed, resulting in 250 useful responses. This population consist of 122 male and 128 female useful respondents. The average age of the respondents is 49 years old, where the oldest is 66 years old and the youngest respondent is 23 years old. The average time the respondents work for their organization are 11.65 year and 9 years for their current department. Furthermore, 97(38.9%) of all respondents performs educational support staff tasks, while 153 (61.1%) of the respondents operating educational staff tasks. The longest working respondent at their organization and at their department is 42 years. Additionally, 71 (28.4%) out of the 250 respondents obtain a bachelor degree, while 154 (61.6%) out of the 250 respondents earned a master degree or higher. Only 4(1.6%) out of 250 respondents accomplished secondary education as their highest educational degree, while 21(8.4%) out of 250 respondents accomplished secondary vocational education as their highest educational degree.

3.3. Variables

This research focuses on three main constructs, which are divided into five different variables. The three main constructs are the enabling use of control, autonomous motivation and knowledge intensity degree. Enabling use of control consist of belief system and interactive

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control system, while autonomous motivation exists of two underlying mechanisms, stated as integrated motivation and intrinsic motivation.

All variables are measured according the data set used in this research. All questions are validated questions which indicates that these questions built upon measuring certain constructs according to the literature. All dependent, independent and control variables will be explained separately, with the variable explanations including the symbol, description and value, visualized in appendix A. The Cronbach alpha of all variables is outlined in paragraph 3.5, to check whether the questions of the questionnaire measure the variables these questions are proposed to.

3.3.1. Autonomous motivation

Based on the literature framework in chapter two, this study provides the construct autonomous motivation as the dependent variable. The whole concept employee motivation is measured according to the research which introduced the multidimensional work motivational scale. These six types of motivation exist on the controlled-to-autonomous continuum of motivation and is visualized in Figure 1. On the right spectrum of this continuum, autonomous motivation is subdivided into integrated motivation and intrinsic motivation. Six of these questions are measuring autonomous motivation. Both integrated motivation and intrinsic motivation consist of three different questions.

3.3.2. Enabling use of control

According to the research of Baird et al. (2018), enabling use of control is measured with the combination of belief system and interactive control system. It ensures that enabling use of control is merged, sum the average of the two levers belief system, and interactive control system. So, both belief system and interactive control system should be measured as single independent variables before merging both singles variables into one combined variable.

3.3.3. Belief system

One of the single levers of enabling use of control is belief system. According to Kruis et al. (2016) the belief system is measured by letting participants answer on four different statements about the communication and inspiring level of organizational core values and mission statements.

3.3.4. Interactive control system

One of the single levers of enabling use of control is interactive control systems. Interactive control systems are measured according to the research of Bedford and Malmi (2015). In their research, Bedford and Malmi (2015) used the formative measurement model of Bisbe et al. (2007). This formative measurement model identifies five different constitutive properties. Besides, the interactive control system is measured by the question “to what extent does the top management team use budgets and performance measures for the following...” which have four different statements (Bedford & Malmi, 2015).

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3.3.5. Knowledge intensity

Degree of knowledge intensity is measured according to the knowledge characteristics introduced by the research of Morgeson and Humprey (2006). In order to measure the degree of knowledge intensity of respondents, four different questions are designed per knowledge characteristic (Morgeson & Humphrey, 2006). These five characteristics are job complexity, information processing, problem solving, skill variety, and specialization. In total, this construct will exist of 20 statements. Combined, these five different scores of the single knowledge characteristics provide the total level of the whole construct knowledge intensity degree. The first four questions related to job complexity are asked reversed. To prevent that a low score will ultimately result in an incorrect score of knowledge intensity degree, the corresponding four questions are recoded. In other words, low scores on this measure now indicate a low job complexity, while a higher score indicates a high job complexity.

3.4. Control variables

In order to increase the reliability of this research, control variables are used which also influence the dependent variable but are not proposed to test. This increases the reliability of the research. Both integrated motivation and intrinsic motivation are concepts that are influenced by many factors. Therefore, several control variables have been introduced to the model. All control variables will be tested if there is a significant relationship with the dependent variable, visualized in appendix B. As a result, all control variables could be used in this study, except for the degree of education of the respondents. There is a significant relationship with integrated motivation, which implies that educational background influences the outcomes of the regression. Therefore, educational background is excluded as control variable.

3.5. Reliability test

To check if the question related to the different variables actually measure the validated constructs, the reliability test is executed. All questions and corresponding constructs are tested with the Cronbach’s alpha to measure the internal consistency, which indicates how closely an item is related to a group and in this case the corresponding variable, and if certain questions have to be neglected. A Cronbach’s alpha above the threshold of 0.6 ensures that the different questions are valid for measuring the particular variable. Table 2 provides all relevant Cronbach’s alphas concerning the variables of this study. Remarkable is that the reliability test of both single levers of control are higher than the Cronbach’s alpha of the derivative research. The study of Kruis et al. (2016), measured a Cronbach’s alpha of 0.885 concerning belief systems, while this research measured a Cronbach’s alpha of 0.873. Moreover, the corresponding Cronbach’s alpha of interactive control system is also higher in this study compared to initial research of Bedford and Malmi (2015).

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In fact, all different Cronbach’s alphas are above the threshold of 0.6. This implies that all questions related to the variables provide a well indication of the particular construct. This could be expected, because the questions are based on validated construct in the literature. So, it is already tested and accepted as a validated questionnaire. Remarkable is the Cronbach’s alpha of enabling use of control. It is a combination of both belief system and interactive control system, while the ultimate Cronbach’s alpha is higher than both single variables. So, the 8 questions related to the construct enabling use of control are higher/more validated, than the single variables. However, this conspicuity can be a result of the lever of control framework of Simons (1995). Which stated that the mutual levers of control are interrelated.

Table 1 Cronbach’s Alpha

3.6. Empirical strategy

To test the hypotheses, which are built upon existing literature, the linear regression is used to test if the independent variable will affect the dependent variable using the IBM SPSS 27 Statistics software. Before executing this linear regression, there are assumptions which should be tested and achieved. These assumptions are essential before drawing inferences or using a regression model to make predictions. The comprehensive assumptions including tables and graphs are described and shown in appendix C.

The assumption starts with executing the linearity of the data in the regression. A scatterplot visualized the standardized residuals across the predicted values. Besides, the Mahalanobis values are extracted to find evidence of any outliers in the data. It is looking for unusual combinations of the scores as outliers. Next, the Durbin-Watson score is tested to approve if it is between the 0 and 4 to indicate if all residuals are uncorrelated and so independent. The following assumption is testing for homoscedasticity to check if all random records within the

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dataset have the same finite variance. The Breusch-Pagan test is used for testing the homoscedasticity.

The final assumption concerning the presence of multicollinearity is tested according two different tests. Firstly, the Pearson correlation matrix, to check mutually significant correlation between the variables. Secondly, the presence of multicollinearity is tested according the Variance Inflation Factor (VIF)-values. When the VIF is below 10, there is no evidence of multicollinearity. This correlation will be discussed in the following chapter, where the results of the linear regression will be elaborated.

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4. Results

In this fourth chapter, the results of the linear regression will be elaborated. First, the descriptive statistics are presented. Besides, the Pearson correlation matrix outlines mutual possible relations between the independent variables. Ultimately, the regression results are presented which could be seen as precursor for answering the main research question in the next chapter.

4.1. Descriptive statistics

The descriptive statistics in this study are presented in Table 3. As a result of the outcomes of the Mahalanobis value, the data set used in this study contains not outliers, meaning that all 250 respondents are included in these descriptive statistics.

As presented in Table 3, the descriptive statistics are rounded towards two numbers behind the comma. However, as a result of merging the single questions related to the variable into one construct, the outcomes are based on rounded numbers. So, for instance, integrated motivation is the average of three related questions regarding integrated motivation. Important to notice is that different scales are used to measure different questions.

The average of integration motivation is found to be 5.62, while the average of intrinsic motivation is 5.48. This implies that the average degree of integrated motivation is 0.14 higher than intrinsic motivation, meaning that the respondents experience higher integrated motivation compared to intrinsic motivation. However, the difference between these two variables is relatively small. Moreover, both components are considered as autonomous motivation (Gagné & Deci, 2005). Thus, it could be concluded that no major differences between these average scores are found.

The average of belief systems is found to be 3.80, while the average of interactive control systems is 3.44. So, comparing both levers imply an emphasize on belief systems relative to interactive control system. Besides, the maximum score of belief system is 7 although interactive control system has 6.25 as maximum score. This indicates that there is at least one respondent who filled in the maximum score at all four related questions concerning belief system, which is not the case for interactive control system. The variables concerning knowledge characteristics consists of two parts, namely the single knowledge characteristics and the combined construct of knowledge intensity degree of respondents. The average of these five characteristics records is measuring the construct knowledge intensity degree of the respondents. The mean of 4.02 with a maximum score of 5 points out a high degree of knowledge intensity among the respondents. This could be related to the level of function, because 215 respondents have a manager function and 35 are supervisor.

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Variables Minimum Maximum Mean Std Dev Scale Dependent variable Integrated mot Intrinsic mot 1.00 1.67 7.00 7.00 5.62 5.48 1.04 1.15 1-7 1-7 Independent variable (Enabling) Belief system Interactive system 1.00 1.00 1.00 6.00 7.00 6.25 3.62 3.80 3.44 1.04 1.19 1.18 1-7 1-7 1-7 Moderators Knowledge characteristics Job complexity Information processing Problem Solving Skill variety Specialization 2.16 1.00 1.75 2.00 1.33 1.25 5.00 5.00 5.00 5.00 5.00 5.00 4.02 4.10 4.22 4.20 3.85 3.73 0.56 0.79 0.63 0.62 0.81 0.78 1-5 1-5 1-5 1-5 1-5 1-5 Table 2 Descriptive Statistics N=250

4.2. Correlations

The mutual correlation is indicated with the use of the Pearson correlation matrix, shown in Appendix D. It consists of all dependent, independent, and control variables. This is used to test the existence of multicollinearity and to get insights on whether the underlying variables are related. First of all, the relation between organization tenure and departmental tenure is highly correlated with a Beta of above the 0.7, while mutually strongly significant, r (0.793), **p≤0.01. This could be explained, because the questions are based on the tenure for the longest department of the respondents. Therefore, it directly indicates that the question for department tenure has similarities with the question of organizational tenure. As a result, the questions concerning the departmental tenure is deleted.

Besides, the autonomous motivation and both components of motivation as dependent variables have a strongly significant relation, which is the same for enabling use of control and both single levers as independent variables. However, this is caused by combining the single factors in these two variables. This also explains the mutual relationships between these single variables.

For the control variables, the type of agreement is correlated significantly with three different (in)dependent variables. Firstly, with gender, r (0.199), p≤0.01, the function, r (-0.187), p≤0.01 and finally type of contract, r (0.138), p≤0.05. Besides, tasks (activities) correlates significantly with other (in)dependent variables, belief system, r (0.235), p≤0.01 and

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interactive control system, r (0.185), p≤0.01. Ultimately, type of contract significantly correlates with age, r (0.188), p≤0.01, organizational tenure r (0.327), p≤0.01, the departmental tenure r (0.275), p≤0.01 and finally, significant correlation with function r (-0.171), p≤0.01. Despite the certain significant correlation among particular variables, all correlations except for the organizational tenure and department tenure, are below the threshold of 0.7

So, both the VIF value and the Pearson correlation matrix (except for departmental tenure) indicates that there is no evidence of multicollinearity. Where multicollinearity is used in order to test if there are no mutual significant relationship between all concerning variables.

4.3. Regression results

After testing all assumptions that are needed to accomplish linear regression with IBM SPSS 27 Statistics, linear regression is executed. The regressions are executed concerning both different underlying mechanisms of autonomous motivation, which are integrated motivation and intrinsic motivation. The results are provided in the regression tables, where the different models indicate different linear regressions, adding independent variables into a new model. The regression analysis is divided into four different models, because it provides insights in the impact of adding for example the independent variable into the regression, while there are two types of relations tested. The direct relationship between the levers of control and components of autonomous motivation and after this an eventual moderating effect of knowledge intensity degree on this direct relationship.

Model 1 consist of the control variables into the dependent variable and maintain basis of the regression. The control variables of this study are, gender, age, organization tenure, departmental tenure, tasks, function, type of contract and type of agreement.

Next, the independent variable is introduced in model 2. The regression coefficient of the independent variable shed light on the effect of this independent variable on the components of autonomous motivation. This varies between belief system, interactive control system and enabling use of control. Next, model 3 adds the moderator effect of knowledge intensity degree which test the direct effect of knowledge intensity degree on the dependent variable. Model 4 introduces the interaction effect, which is the product of knowledge intensity degree as moderator and the corresponding independent variable. The interaction effect determines a possible moderating effect on the direct relationship.

The threshold of R-square depends on the field of the study. This study is based on two different types of autonomous motivation as dependent variable, and motivation is seen as behavioral aspect and concerns the psychological perspective. In the field of arts, humanities and social sciences, the criteria and corresponding threshold of the R-square is 0,1, equals to 10% of the explanation variance, because for example behavior could be influenced by several different factors (Van Tonder & Petzer, 2018). The moderating effect of knowledge intensity degree is tested according the 3.5 SPSS process models of Hayes Macro, to indicate if there is

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a moderating effect on the relationship between enabling use of control and components of autonomous motivation.

4.3.1. Belief systems on integrated motivation

The first regression analysis is executed to test direct relationship H1a and moderating effect H4a, shown in regression Table 3.

H1a: A MCS emphasizing belief systems has a positive effect on integrated motivation

H4a: Knowledge intensity degree affects the relationship between belief systems and integrated motivation

The effect of model 1 is small, where the control variables only explain 5.5% of all variation regarding integrated motivation. Subsequently, adding belief systems as independent variable into the model does not have a significant impact. The explaining power increases towards 7% and the effect of belief systems is relatively small with B 0.108, p ≤ 0.1. Therefore, the combination of this significantly small Beta and the corresponding R-square, which is below the threshold of 10%, provides not enough evidence to conclude that belief systems are positive related with integrated motivation. Furthermore, model 3 seems to shed lights on the significant effect of knowledge intensity degree on integrated motivation with B 0.423, p ≤ 0.01. However, this direct effect is associated with a decrease of the regression coefficient of belief systems into B 0.070, p ≥ 0.1. Besides, the interaction effect does not have a significant regression coefficient. So, adding knowledge intensity into the linear regression results in a non-significant effect of belief systems, while the interaction effect does not have any impact within the regression.

Nonetheless, the combination of belief systems and knowledge intensity degree has a variation of 11.3%, where the threshold of 10% is based on one independent variable. So, this model is not allowed to draw further inferences regarding integrated motivation.

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Variables Model 1 Control variables Model 2 Control variables + independent variable Model 3 Control variables + independent variable + direct effect moderator Model 4 Control variables + independent variable + direct effect moderator + interaction effect Independent variables Belief system .108 (0.057)* 0.070 (0.057) 0.072 (0.058) Moderator

Knowledge intensity degree 0.423 (0.125)*** 0.413 (0.126)***

Interaction effect

K-intensity * Belief system -0.051 (0.092)

Control variables Gender Age Org. Tenure Tasks (activities) Function Type of Contract Type of Agreement -0.238 (0.139)* 0.007 (0.008) -0.011 (0.009) -0.279 (0.140)** -0.261 (0.186) -0.174 (0.225) 0.298 (0.137)** -0.212 (0.139) 0.005 (0.008) -0.011 (0.009) -0.309 (0.147)** -0.223 (0.186) -0.139 (0.224) 0.287 (0.137)** 0.212 (0.136) 0.006 (0.008) -0.011 (0.009) -0.248 (0.145)* -0.241 (0.182) -0.209 (0.221) 0.211 (0.136) -0.210 (0.136) 0.006 (0.008) -0.011 (0.009) -0.250(0.145)* -0.242 (0.182) -0.205 (0.221) 0.217 (0.136) Constant R Square F-value 6.527 (0.800)*** 0.055 1.994* 6.194 (0.832)*** 0.070 2.022** 4.867 (0.904)*** 0.113 3.049*** 6.785 (0.789)** 0.114 2.791*** Table 3 Regression analysis of enabling use of on control on integrated motivation

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4.3.2. Belief systems on intrinsic motivation

The second regression analysis is executed to test direct relationship H1b and moderating effect H4d, shown in regression Table 4.

H1b: A MCS emphasizing interactive control system has a positive effect on integrated motivation H4d: Knowledge intensity degree affects the relationship between belief systems and intrinsic motivation

The overall R-square of the control variables together in model 1 is 2.2%. Moreover, adding belief systems as independent variable increasesboth R-square and the F-value. The R-square increases towards 13.4%, which is above the threshold of 0.1 concerning behavioral studies (Van Tonder & Petzer, 2018). It presents two significant variables with have a contradiction effect on intrinsic motivation. Firstly, the independent variable belief systems, what reflects the main relationship of this study, is positively related with B 0.339, p ≤ 0.01. This provide enough evidence to argue that there is a positive effect of belief systems on intrinsic motivation. The second significant variable is the control variable, tasks (activities) with have, in contrast to belief systems, a negative impact on intrinsic motivation with B -0,307 p ≤ 0.05. Subsequently, adding the moderator as direct variable has a significant impact on intrinsic motivation, because the R-square of model 3 increases compared to model 2 with 17.9%. So, the combination of belief systems and knowledge intensity degree explains 31.3% of the variation of intrinsic motivation. Meanwhile, introducing knowledge intensity degree results in a positive direct relationship with B 0.952, p ≤ 0.01, while it is accompanied with a reduction of the regression coefficient, which remains significant with B 0.254, p ≤ 0.01, of belief systems. Additionally, the interaction effect of the moderator is not significant. This indicates that there is not enough evidence to conclude that there is no moderator effect of knowledge intensity degree on the relation between belief systems and intrinsic motivation.

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Variables Model 1 Control variables Model 2 Control variables + independent variable Model 3 Control variables + independent variable + direct effect moderator Model 4 Control variables + independent variable + direct effect moderator + interaction effect Independent variables Belief system 0.339 (0.061) *** 0.254 (0.056) *** 0.255 (0.056) *** Moderator

Knowledge intensity degree 0.952(0.121) *** 0.947 (0.122) ***

K-intensity * Belief system -0.030 (0.089)

Control variables Gender Age Org. Tenure Tasks (activities) Function Type of Contract Type of Agreement 0.016 (0.156) 0.015 (0.009*) -0.008 (0.010) -0.127 (0.157) -0.088 (0.209) 0.093 (0.252) 0.104 (0.154) 0.103 (0.148) 0.011(0.009) -0.012 (0.010) -0.307 (0.156) ** 0.004 (0.197) 0.203 (0.238) 0.075 (0.145) 0.105 (0.132) 0.012 (0.008) -0.011 (0.009) -0.169 (0.140) * -0.037 (0.176) 0.047 (0.214) -0.097 (0.131) -0.106(0.132) 0.012 (0.008) -0.011 (0.009) -0.171 (0.140) * -0.038 (0.177) 0.049 (0.214) -0.093 (0.132) Constant R Square F-value 4.820 (0.895)*** 0.022 0.784 3.522 (0.884)*** 0.134 4.120*** 0.532(0.875) 0.314 10.877*** 5.235 (0.764)** 0.313 9.862*** Table 4 Regression analysis of belief systems on intrinsic motivation

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4.3.3. Interactive control system on integrated motivation

The third regression analysis is executed to test direct relationship H2a and moderating effect H4b, shown in regression Table 5.

H2a: A MCS emphasizing interactive control system has a positive effect on Intrinsic motivation

H4b: Knowledge intensity degree affects the relationship between interactive control system and integrated motivation

There are similarities between the first and third regression analysis, because both regressions are based on integrated motivation as independent variable. However, the first regression indicates a small significant effect of belief systems on integrated motivation, which is concerning interactive control system not the case. Besides, the R-square of the first two models are below the threshold of 0.1. Together with the non-significant regression coefficient, it concludes that interactive control system is not related with integrated motivation.

Again, introducing knowledge intensity into the linear regression provides a direct significant relationship on the dependent variable. However, the explanatory power of both interactive control systems and knowledge intensity degree together is small, where only knowledge intensity degree seems to have an impact with B 0.459, p ≤ 0.01. Nonetheless, the interaction effect is not significant, which concludes that there is no moderator effect.

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Variables Model 1 Control variables Model 2 Control variables + independent variable Model 3 Control variables + independent variable + direct effect moderator Model 4 Control variables + independent variable + direct effect moderator + interaction effect Independent variables

Interactive control system -0.01 (0.058) -0.025 (0.056) -0.019 (0.057)

Moderator

Knowledge intensity degree 0.459 (0.123)*** 0.445 (0.124)***

K-intensity * Interact. control -0.123 (0.104)

Control variables Gender Age Org. Tenure Tasks (activities) Function Type of Contract Type of Agreement -0.238 (0.139)* 0.007 (0.008) -0.011 (0.009) -0.279 (0.140)** -0.261 (0.186) -0.174 (0.225) 0.298 (0.137)** -0.240 (0.141)* 0.007 (0.008) -0.011 (0.009) -0.251 (0.141)* -0.252 (0.188) -0.175 (0.226) 0.296 (0.138)** -0.237 (0.137)* 0.006 (0.008) -0.010 (0.009) -0.199 (0.143) -0.269 (0.183) -0.244 (0.221) 0.206 (0.137) -0.239 (0.137)* 0.006 (0.008) -0.010 (0.009) -0.208 (0.143) -0.270 (0.183) -0.243 (0.221) 0.234 (0.138) Constant R Square F-value 6.527 (0.800)*** 0.055 1.994* 6.612 (0.856)*** 0.057 1.610 5.134 (0.925)*** 0.108 2.906*** 6.834 (0.794)*** 0.114 2.774*** Table 5 Regression analysis of interactive control system on integrated motivation

Knowledge intensive tasks and enabling use of control: the influence on the degree of autonomous motivation of employees