Understanding the Impact of Management Control Systems on the Work Environment from an Employee’s Perspective

Understanding the Impact of Management

Control Systems on the Work Environment

from an Employee’s Perspective

Effects of the Levers of Control on employees’ Basic Needs satisfaction

Name: A.B. Wildeboer Student number: 2838931

Study: MSc Business Administration & MSc Accountancy and Controlling BA Track: Management Accounting and Control

A&C Track: Controlling Supervisor: W.G. de Munnik

Abstract

Prior accounting literature has primarily focused on the organization-level outcomes of management control systems. However, the goal of management control systems is to align the behaviour of employees with the organization’s objectives and strategy. The behaviour of employees acts as a mediator between management control systems and organization-level outcomes. So, the focus should be on these employees, because they are the driving forces in an organization. Self-Determination theory proposes that the motivational mechanism that energizes and directs people’s behaviour, is the satisfaction of three basic psychological needs. Since the various types of management control may sustain or hinder the fulfilment of the three basic psychological needs, they may either contribute to or get in the way of the satisfaction of the basic needs. Recent literature researched effects of management control systems on motivation, but the effects of management control systems on the drivers (i.e. the three basic psychological needs) of autonomous motivation is still unknown. The study uses a survey from employees of two higher educational organization in the Netherlands (N = 249). The study finds that management control systems have a clear effect on the autonomy of employees, no effect on the perceived competence and a small effect on relatedness. Although significant relationships where found, the practical influence of the management control systems is low. Culture and structure are also found to have clear impact on the work environment of employees and thus their autonomous motivation.

Inhoud

I. Introduction ... 1

II. Literature review ... 3

Management Control Systems ... 3

Lever of Control ... 3 Self-Determination theory ... 5 Hypothesis Development ... 6 III. Methodology ... 10 Method ... 10 Bias... 11

Measures and variables ... 11

IV. Data Analysis ... 13

Descriptives ... 13 Analysis ... 15 Autonomy ... 17 Competence ... 19 Relatedness ... 21 Cross-sections ... 23 Further comments ... 27

V. Discussion and Conclusion ... 28

Conclusion ... 28

Limitations & Suggestions for further research ... 29

References ... 31

Appendix 1 ... 36

List of Constructs and Variables ... 36

Appendix 2 ... 37

Table A1: Age and Tenure brackets ... 37

Table A2: Cross-sections of the dataset... 38

Table A3: Correlation matrix ... 40

Table A4: Autonomy – Support staff ... 42

Table A5: Autonomy – Educational staff ... 43

Table A6: Competence – Support staff ... 44

Table A7: Competence – Educational staff ... 45

Table A8: Relatedness – Support staff ... 46

Table A9: Relatedness – Educational staff ... 47

Table A10: Hypotheses results ... 48

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I. Introduction

Prior accounting literature (e.g. Abernethy & Brownell, 1999; Bisbe & Otley, 2004; Chapman & Kihn, 2009; Davila, 2000; Henri, 2006; Widener, 2007; Bedford, 2015) has primarily focused on the organization-level outcomes of management control systems. However, the goal of management control systems is to align the behaviour of employees with the organization’s objectives and strategy (Malmi & Brown, 2008). The behaviour of employees acts as a mediator between management control systems and organization-level outcomes. So, the focus should be on these employees, because they are the driving forces in an organization. The focal point of management control is to influence and motivate the employee to perform well (Van der Kolk, van Veen-Dirks & Ter Bogt, 2019).

Self-Determination theory proposes that the motivational mechanism that energizes and directs people’s behaviour, is the satisfaction of three basic psychological needs. These are the need for competence, autonomy and relatedness (Deci & Ryan, 2000). The designers of management control systems should be aware of the effects of these systems on the satisfaction of the three basic psychological needs, because it will impact the behaviour and motivation of the employees. The theory is also used in the field of management control to theorize about roles of motivation (e.g. de Baerdemaeker & Bruggeman, 2015, Groen et al., 2017). Gagné & Deci (2005) argued that the satisfaction of the needs is different for all individuals. So, focus should be on how individuals are able to satisfy their needs within social or work environments, instead of the general strength of that need for different individuals. Recent literature (Chen, Hill & Vance, 2020; van der Kolk et al., 2019) researched effects of management control systems on motivation, but the effects of management control systems on the drivers (i.e. the psychological needs) of autonomous motivation is still unknown. Van den Broeck, Ferris, Chang & Rosen (2016) have reported the relationships between the satisfaction of the basic needs and various antecedents & consequences. But, their meta-analysis does not include the impact of management control systems on the basic needs. This leads to the research question of this study. The main research question will be: How do management control systems affect the satisfaction of the three basic psychological needs? The main research question can be divided into three sub-questions:

1. What compromises a management control system? 2. What is included in the three basic psychological needs?

3. What does satisfaction of the three basic psychological needs mean?

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motivation and behaviour of employees. According to Van den Broeck et al. (2016), Self-Determination theory suggests that each of three needs are essential, which means that thwarting one need can cause disruptions in developing motivation. For example, a possible effect of promotion might be that the employee is more satisfied with the increase in autonomy, but might see a decrease in their satisfaction of competence because of the new tasks. The result could be that there is not an overall net increase in motivation. So, the Self-Determination theory enables studying how management control systems increase or decrease the satisfaction of the individual basic needs. This allows studying the effects of the different components in a management control system. The study complements prior studies such as Kunz & Linder (2012) and Kunz (2015) to provide additional empirical evidence that management control systems can play an important role in promoting employees’ autonomous motivation. But, this study focuses on the process behind promoting employees’ autonomous motivation. For managers, paying attention to employees’ need satisfaction and the influence of the management control system might furthermore enhance employees’ functioning. This could help to reduce costs associated with stress or turnover, and increase productivity. On the other side, employees might be more informed about how the work environment influences their need satisfaction. This could result in employees to be able to assess and regulate the need supportive character of their jobs, and seek for environments which nourish their motivational energy and stimulate optimal functioning (van den Broeck, Vansteenkiste, De Witte, Soenens & Lens, 2010).

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II. Literature review

Management Control Systems

Malmi & Brown (2008) propose that the goal of management control systems is about ensuring that the behaviour of employees is consistent with the organization’s objectives and strategy. Merchant & Van der Stede (2007) argued that employees are who make things happen in organizations and that management controls are necessary to prevent them from doing something the organization does not want them to do, or fail to do something they should do. The management control system is comprised of multiple control systems that work together (Otley, 1980), and are able to influence the behaviour of the employees in an organization and also organizational capabilities. For example, Van der Kolk et al. (2019) proved that management control systems influence the motivation of employees, and in turn their performance. They also proved that certain controls (which specify and monitor the actions that need to be executed) can directly influence employee performance. Henri (2006) proved that the use of management control systems influences organizational capabilities, such as innovativeness and organizational learning. Chen et al. (2020) proved that the design of management control systems also influences the (autonomous) motivation of employees. This is important, because the design of management control systems is dependent on the context in which they operate (Chenhall, 2003). Based on the framework of Chenhall (2003), contingency variables such as organizational structure, size and strategy can impact what design of a management control system is needed to be effective.

Lever of Control

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operational controls, and performance versus boundary controls. These control systems can be used diagnostically or interactively and can lead to either reward or punishment. The goal of revised framework of Tessier & Otley (2012) was to address issues regarding the vague and ambiguous definitions in Simons’ Lever of Control framework. But, Tessier & Otley (2012) also stated that their revised framework is mostly conceptual, and quantitative analysis should be used first to verify construct validity for the framework’s concepts. Therefore, the levers of control framework of Simons (1995) will be used and each of the levers will now be described.

The beliefs system is “the explicit set of organizational definitions that senior managers communicate formally and reinforce systematically to provide basic values, purpose, and direction for the organization” (Simons, 1995, p. 34). A beliefs system communicates core values in order to inspire and motivate employees to search, explore, create, and expend effort engaging in appropriate actions. Often, firms communicate beliefs through a mission or vision statement (Widener, 2007). Baird, Su & Munir (2018) reported that in order for beliefs systems to be an effective lever of control, managerial and executives must uphold the key values and ethics that are being communicated. While the beliefs system motivates employees to explore, create and engage in appropriate actions, it is important for managers to educate the employees on what the limits and boundaries are in which they should operate. This is to stop employees from engaging in high-risk behaviours (Widener, 2007). These boundaries and code of conduct are called the boundary system. The purpose of boundary systems is to allow employees freedom to innovate and achieve within certain pre-defined areas. The boundary and beliefs systems are similar in that they both are intended to motivate employees to search for new opportunities; however, the boundary system does so in a controlling way through the constraint of behaviour while the beliefs system does so in an enabling way through inspiration (Widener, 2007; Simons, 1995). A boundary system “delineates the acceptable domain of strategic activity for organizational participants” (Simons, 1995, p. 39). So, the boundary system communicates the actions that employees should avoid. Tessier & Otley (2012) argue that boundary controls can be both technical (e.g. procedures and rules) and social (e.g. values and norms).

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While the diagnostic system allows managers to manage results on an exception basis, an interactive system is forward-looking and characterized by active and frequent dialogue among top managers (Widener, 2007). Interactive control systems, which focus on strategic uncertainties, are “formal information systems that managers use to involve themselves regularly and personally in the decision activities of subordinates” (Simons, 1995, p. 95). Not only do interactive controls have an attention focusing role, but they also stimulate search and learning which can result in new emergent strategies (Simons, 1995; Tessier & Otley, 2012). Control systems such as performance measurement or brand management can be used interactively by top managers (Widener, 2007).

In conclusion, management control systems are about ensuring that the behaviour of employees is consistent with the organization’s objectives and strategy. These management control systems also influence the motivation of employees and performance. The levers of control framework of Simons (1995) give managers the tools (called ‘levers’) to steer and guide the behaviour of employees and to harness the creativity of the employees.

Self-Determination theory

Self-Determination theory starts from the premise that humans focus on increasing their psychological growth, internalization, and well-being. The aim of psychological growth is to find inherently interesting and enjoyable activities, that are carried out without external reinforcement (Deci & Ryan, 2000). Internalization refers to the process where external reasons are transformed into more internal reasons to carry out those activities. For example, when those activities are seen as important or in line with one’s closely held values (Deci & Ryan, 2000). The results of psychological growth, internalization and well-being are related to many positive outcomes (Van den Broeck et al., 2016). From an organizational perspective, it is important to help employees achieve growth and internalization. The Self-Determination theory argues that the satisfaction of three basic psychological needs is essential for individuals to achieve the psychological growth, internalization and well-being

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that work climates that promote satisfaction of the basic psychological needs will enhance intrinsic motivation of employees and this yields work outcomes as job satisfaction, effective performance and maintained behaviour changes. Each need will now be described.

The need for autonomy is defined as people’s desire to experience ownership of their behaviour and to act with a sense of volition (Deci & Ryan, 2000). This sense of volition can be achieved through having the opportunity to make personal choices, but also through the full endorsement of an externally induced request. The latter is stimulated if one is provided with a meaningful rationale for executing the request and one’s feelings are acknowledged. The need for autonomy does not imply a need to act independently from the desires of others; rather, it implies the need to act with a sense of choice and volition, even if doing so means complying with the wishes of others (Van den Broeck et al., 2016). The need for relatedness is defined as the human striving for close and intimate relationships and the desire to achieve a sense of communion and belongingness (Deci & Ryan, 2000). Employees who feel part of a team and feel free to express their work-related and personal troubles are more likely to have their need for belongingness fulfilled than employees who feel lonely and lack confidants at work. The need for competence represents individuals’ desire to feel capable of mastering the environment, to bring about desired outcomes, and to manage various challenges (Deci & Ryan, 2000). Because the Self-Determination theory enables us to study the extent to which organizational contexts facilitate or hinder the enhancement of employees’ autonomous motivation, the hypotheses will be on possible relationships between the levers of control and the three basic psychological needs. Self-Determination theory proposes that the satisfaction of three basic human needs is of particular importance for facilitating autonomous motivation (Ryan & Deci, 2000). Self-Determination theory enables studying the extent to which organizational contexts facilitate or hinder the enhancement of employees’ autonomous motivation, and the theory is also used in the field of management control to theorize about roles of motivation (e.g. de Baerdemaeker & Bruggeman, 2015, Groen et al., 2017). Self-Determination theory claims that autonomous motivation can be fostered through a need-supportive environment. Since the various types of management control may sustain or hinder the fulfilment of the three basic psychological needs, they may either contribute to, or get in the way of a need-supportive environment.

Hypothesis Development

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When individuals first decide what goals to pursue, they will take into consideration the desirability of the goal and, as individuals work toward achieving the goal, they will consider the feasibility of achieving the goal (Liberman & Trope, 1998). Goals that are viewed as desirable and feasible to achieve are more likely to be internalized by employees (Gagné, 2018). The organization should communicate what is important to them. This can be done through both mission statements and statements of purpose. These statements should outline how feasible it is to achieve these goals. This way, the statements should increase an employee’s internalization of the organization’s goals. Ryan & Deci (2000) argued that this is called regulation through identification. The employee values the organizations’ goals, such that the goal is accepted as personally important. Another possibility is that the identification leads to integration. Integration occurs when identified regulations are brought into congruence with one’s other values and needs (Ryan & Deci, 2000). As individuals internalize goals, they feel less controlled and more self-regulated in their work (Ryan, 1995). The feeling of autonomy can be satisfied when people experience ownership of their behaviour. When the organization’s goals are fully internalized, people have a full sense that the behaviour is an integral part of who they are, that it emanates from their sense of self and is thus self-determined (Gagné & Deci, 2005). The feeling of relatedness can be satisfied when a person relates to others or to the organization (Merchant & van der Stede, 2007). When the organizations’ goals are fully internalized, the employees feel more connected to their organization and group if they share the same values and goals. It is therefore likely that that, by clearly articulating and communicating the organizational goals, beliefs control systems can foster employees’ internalization of these goals, which could lead to a higher satisfaction of the need for autonomy and relatedness. Therefore:

H1a: The use of beliefs systems is positively related to the satisfaction of the need for autonomy

H1b: The use of beliefs systems is positively related to the satisfaction of the need for relatedness

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and social (e.g. values and norms). It is therefore likely that that having an emphasis on the boundary system decreases the need for autonomy through limiting the number of choices an employee could make in their own work. The need for competence represents individuals’ desire to feel capable of mastering and manipulating the environment, to bring about desired outcomes, and to search and manage various challenges (Deci & Ryan, 2000).

By limiting the freedom employees have to manipulate the environment, and by limiting the area employees are able to search and manage challenges, it is likely that boundary control systems have a negative impact on the satisfaction of the need for competence. It is also likely that boundary systems decrease the need for relatedness through social boundaries, e.g. work culture that gets in the way of bonding with colleagues. Therefore:

H2a: The use of boundary systems is negatively related to the satisfaction of the need for autonomy

H2b: The use of boundary systems is negatively related to the satisfaction of the need for competence

H2c: The use of boundary systems is negatively related to the satisfaction of the need for relatedness

Diagnostic control systems act in a manner similar to boundary control systems in that they can put constraints on employee attention and behaviour (Simons, 2000). That is, diagnostic controls provide motivation by focusing attention on positive and negative variances to particular performance standards, such as mistakes (Henri, 2006). But, Henri (2006) also reported that the comparison of goal and outputs results in a feedback signal that can be used to adjust the process. The result of diagnostic control systems is that employees are forced to adhere to the (performance) measures, and that they should align their behaviour with organizational objectives. Employees are now less able to choose their own behaviour and goals, which can reduce an individual’s sense of autonomy (Ryan & Deci, 2017). Vallerand & Reid (1984) proved that performance information (i.e. feedback), influences the perceived competence of individuals. In line with Henri (2006) & Chen et al. (2020), it is expected that because diagnostic control systems focus attention on positive and negative variances and put constraints on employee behaviour to prevent future negative variances, decrease the satisfaction of the need for autonomy and the need for competence. Therefore:

H3a: The use of diagnostic control systems is negatively related to the satisfaction of the need for autonomy

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Interactive control systems facilitate cross-hierarchy communication in the firm (Simons, 1995) and involve frequent dialogue between employees and managers to help the firm position itself within the market (Widener, 2007). As described by Simons (1995), interactive controls should be carried out in a way that creates a “positive informational environment that encourages information sharing” (Simons, 1995, p. 158) and that “does not usurp the decision rights of subordinates” (Simons, 1987, p. 353). According to Self-Determination theory, designing interactive control systems that empower employees to share information, and allows them the freedom to make decisions, will result in a greater sense of volition (Millette & Gagné, 2008). A well-made interactive control system allows employees the freedom to make decisions, increasing the satisfaction of their need for autonomy. When employees are encouraged to participate in interactive control systems, they will feel more involved in the company, increasing the satisfaction of their need for relatedness.

H4a: The use of interactive control systems is positively related to the satisfaction of the need for autonomy

H4b: The use of interactive control systems is positively related to the satisfaction of the need for relatedness

As the Self-Determination theory proposes that the satisfaction of three basic human needs is of particular importance for facilitating autonomous motivation (Ryan & Deci, 2000), these hypotheses are consistent with prior accounting research that suggests employee participation can increase intrinsic motivation (Groen et al., 2017). Additional evidence was found for positive relations between a composite score of need satisfaction (i.e., aggregated across the three needs) and employees’ work-related well-being (i.e., job satisfaction, work engagement, and lower burnout), favourable attitudes (i.e., decreased turnover intentions, increased readiness to change), and higher performance (see Gagné & Deci, 2005; Van den Broeck, Vansteenkiste, & De Witte, 2008, for overviews). Work-related need satisfaction has furthermore been related to increased general well-being and to less ill-being (Baard, Deci, & Ryan, 2004). When employees feel controlled, in contrast, their need for autonomy is clearly forestalled (Deci & Ryan, 2000). Employees, who are, for instance, forced to meet a deadline, will experience little volition in executing the task. Despite this pressure, they might, however, manage to satisfy their needs for competence and relatedness by accomplishing the assigned task or by receiving social support from others. Such satisfaction is, however, not guaranteed as feeling pressured to engage in a work activity is not necessarily accompanied by feelings of effectiveness and interpersonal connection (Markland & Tobin, 2010).

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satisfaction of the basic needs depends on the individual perception, instead of the general strength. Figure 1 on the next page displays the conceptual model including hypothesis H1a to H4b. Positive hypotheses are have a continued line, negative hypotheses have dotted lines.

Figure 1. The conceptual model

As can be seen in the conceptual model above, the levers of control and the basic needs are related between each other. Widener (2007) proves that the use of beliefs systems is associated with the use of the other three levers, and that the use of interactive control systems is associated with the use of diagnostic controls and less so to the use of boundary systems. Widener (2007) argues that the results provide support that the control systems are inter-related and are complementary. Van den Broeck et al (2016) reported that the individual needs are also positively and significantly correlated with each other.

III. Methodology

Method

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the performance measurement system meant for the individuals and that the performance measures could be quite subjective. With explicit permission of the executive board of the organizations, data was collected. The data consists of 250 self-reported completed surveys of employees working at 2 higher professional educational organizations.

This research method allows us to examine the complex phenomena of management control and motivation ‘as they occur in their natural setting, while at the same time maintaining the degree of standardization that is necessary for quantitative analysis and theory testing.’ (Speklé & Widener, 2018, p. 3). Surveys produce (empirical) data on real-world observations, and to more easily obtain a representative sample that could be generalizable to a (larger) population (Kelley, Clark, Brown & Sitzia, 2003).

Bias

With the survey, perceptions are measured at a certain point in time. Therefore, the study is faced with several forms of bias and noise. A larger sample size can help reduce noise. The survey has to deal with sampling and (non-)response bias. Sampling bias is associated with the result of a non-random sample. This is counteracted by including a multitude of different job types and departments, and by sending it to large group of employees at the educational organizations. Response bias includes tendencies of participants responding inaccurate to questions. For example, by anonymizing the survey and doing it online, the goal is reducing the impact of the social acceptability bias. Social acceptability bias means that employees will give socially desirable responses instead of responses that reflect their true feelings. By incorporating positive and negative worded questions, acquiescence bias is reduced, which is associated with agreeing with all the questions in a measure. There will be several checks on the data to validate it, and exclude other constructs that might influence the main constructs. The early and late respondents will be investigated to determine if there is a risk of non-response bias. If the means and standard deviations of the measured constructs between early and late respondents do not have a significant difference, then the non-response bias will not be a concern for this study. To enhance the quality of the dataset and possible results, special attention should be paid to the validity and reliability of the survey (Heale & Twycross, 2015). Therefore, to measure the variables, the survey consists of validated survey items that have been proven to correctly measure the concepts. To determine the reliability of the survey items, when able, Cronbach alphas of the data set and data of the original author will be compared.

Measures and variables

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IV. Data Analysis

Descriptives

As mentioned, the dataset consists of 250 completed surveys, filled in by employees working at 2 higher professional educational organizations in the Netherlands. The data was gathered in months October, November and December of 2017. One survey had a missing value and therefore is taken out of the dataset. Please see appendix 1 for all constructs and variables included in the dataset. Please see Table 1 and 2 for the descriptive statistics. For the scaled variables, the mean, standard deviation and the range is given. For the nominal variables, the frequencies of each option are given. All data is imported and analysed with IBM SPSS Statistics 26.

Variable Minimum Maximum Mean Std. Deviation

Age 23 66 48,8 10,3 Organizational tenure 1 42 11,7 9,3 Departmental tenure 0 42 9,0 8,0 Group culture 1 7 4,8 1,1 Developmental culture 1 7 4,0 1,3 Hierarchical culture 1 7 4,1 1,3 Rational culture 1 7 4,1 1,1 Low cost 1 7 4,3 1,2 Innovation 1 7 4,2 1,0 Student focus 1 7 4,6 1,1 Structure 1 7 4,4 1,2 Beliefs systems 1 7 3,8 1,2 Boundary systems 1 7 3,8 1,5

Diagnostic control systems 1 7 3,9 1,3

Interactive control systems 1 6,25 3,4 1,2

Autonomy 1 5 3,7 0,8

Competence 1,67 5 4,2 0,5

Relatedness 1,5 5 3,7 0,7

Table 1: Descriptives of scaled variables

Age ranges from 23 to 66 years (M = 48.8, SD = 10.3), organizational tenure from 1 to 42 years (M = 11.7, SD = 9.3) and departmental tenure 0 to 42 years (M = 9, SD = 8). Group culture has a higher mean than the other sorts of culture (M = 4.8 versus Mavg = 4.1). This could be explained with the sample,

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but different standard deviations. For these levers, certain employees have given these levers the absolute minimum value (1) and the maximum value (7). The outlier is interactive control systems (M = 3.4, SD = 1.2), which indicates lesser use of these systems. The maximum is also 6.25, which indicates that not one of the employees in the dataset fully perceives that performance measures are used in this way. The need for autonomy (M = 3.7, SD = 0.8) has a similar mean and standard deviation as the need for relatedness (M = 3.7, SD = 0.7). The range for the need for autonomy also spans from no perceived autonomy at all (1) to fully autonomous (5). The range of relatedness spans from very low (1.5) to maximum (5). The outlier is the need for competence (M = 4.2, SD = 0.5), which is significantly higher than the other basic needs. This could be due to several reasons that will be discussed later. The range for the need for competence is 1.67 (very low) to 5, the maximum value.

Organization N % B 91 37% C 158 63% Gender Male 121 49% Female 128 51% Education level Secondary education 4 2%

Secondary vocational education 21 8%

Bachelor’s degree 71 29%

Master’s degree of higher 153 61%

Job type

Educational staff 152 61%

Educational support staff 97 39%

Job type

I do not have a job as supervisor/manager 214 86%

I have a part-time job as supervisor/manager 13 5%

I have a full-time job as supervisor/manager 22 9%

Type of contract Temporary/Self-employed 28 11% Fixed 221 89% Type of agreement Part-time 130 52% Full-time 119 48%

Table 2: Descriptives of ordinal and nominal variables

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Our large dataset enables us to split the population according to personal characteristics, such as gender, age and tenure. This helps us comparing effects and hypotheses in different populations. These cross sections will be covered after the general analysis. Independent-samples T tests show several significant differences in the means of the independent and dependent variables, if the population is split based on gender, tenure, educational level, job type and type of agreement. Table A2 in Appendix 2 shows the preliminary results of the means in these cross sections. The Independent-samples T tests show that especially educational staff and support staff significantly differ from each other.

Analysis

In the sample, 2 organizations are used (organization B and C). Before combining these 2 populations, the organizations should be checked if they are statistically different from each other. Independent-samples T Test are used to compare the means and standard deviation. In the test, the constructs levers of control and basic needs are used. These are the combined constructs of individual levers and needs. The analysis shows that there are no significant differences for the construct’s levers of control (t(247) = 0.822, p = .412) and basic needs (t(247) = -0.656, p = .513)

To determine if there is a risk of non-response bias, the sample will be checked for differences between early and late responders. In organization B, the survey was available for 14 days. Around equal responses were recorded in the first and last 7 days. Therefore, there should be a test for any statistical differences. In organization C, the survey was available for 21 days, and approximately 75% of the responses were recorded in the first few days. Therefore, there will not be a test for non-response bias in organization C. For organization B, Independent-samples T Test are used. In the test the constructs levers of control and basic needs are used again. The analysis shows that there are no significant differences for the construct’s levers of control (t(89) = 0.996, p = .322) and basic needs (t(80.2) = 1.705, p = .092).

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and remaining an academic is the work that goes to the heart of what being an academic is. This means that employees have become academics because they like the tasks academics perform and are intrinsically motivated to do so. This might explain why this item did not score consistently with the other autonomy items.

The Cronbach’s alpha of the beliefs and boundary systems is very close to the alpha’s found by the Kruis et al. (2016), the original authors of the survey items used. The alpha’s are respectively ⍺ = .855 and ⍺ = .871. For diagnostic control systems, the Cronbach’s alpha is higher compared to that of the original author, which is ⍺ = .89 (Bedford & Malmi, 2015). The internal consistency of the basic needs is already validated (Van den Broeck et al., 2010).

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Assumption six indicates that the data should not show multicollinearity. The analysis shows that VIF-values range between 1.2 and 3.7. Generally, a VIF-value above 4 is problematic (Miles & Shevlin, 2001), which means that is not a problem in the dataset. Assumption seven requires that there a no significant outliers that can skew the regression results. Because Likert scales are used, the Cook’s Distance test was used. The maximum value found was 0.135, where any values above 1 are likely to be significant outliers. The final assumption requires that residual errors are approximately normally distributed. This is checked with Normal Q-Q plots of the studentized residuals, and the residuals do have an approximately normal distribution. In the multiple regression, all control and contingency variables are entered first into a base model. Next, each lever is entered stepwise. This results in 5 models for each dependent variable. Although not every lever is hypothesized to have an effect on each need, all the levers are entered. This is because Widener (2007) has proved that the levers of control are related to each other. The basic needs are not entered into the models, because the needs are not antecedents of each other, as they are consequences of the work environment. The next paragraphs will address each dependent variable (the basic needs).

Autonomy

Please see table 3 on the next page for the results of the basic need autonomy.

18 Table 3: Regression results Autonomy

Variables Model 1 (base) Model 2 Model 3 Model 4 Model 5

Control variables Control variables Control variables Control variables Control variables

Beliefs systems Beliefs systems Beliefs systems Beliefs systems

Boundary systems Boundary systems Boundary systems

DCS DCS

ICS

Independent variables

Beliefs systems (H1a) 0.135 (0.045)*** 0.134 (0.045)*** 0.137 (0.045)*** 0.115 (0.046)**

Boundary systems (H2a) 0.006 (0.03) 0.012 (0.031) 0.002 (0.031)

DCS (H3a) -0.028 (0.036) -0.099 (0.049)**

ICS (H4a) 0.125 (0.058)**

Control & Contingency variables

Gender 0.03 (0.081) -0.014 (0.081) -0.013 (0.082) -0.005 (0.082) -0.023 (0.082) Age 0 (0.005) -0.001 (0.005) -0.001 (0.005) -0.001 (0.005) -0.001 (0.005) Organizational_tenure 0.008 (0.008) 0.007 (0.008) 0.007 (0.008) 0.007 (0.008) 0.008 (0.008) Departmental_tenure -0.017 (0.008)** -0.015 (0.008)* -0.015 (0.008)* -0.016 (0.008)* -0.016 (0.008)** Education_level 0.014 (0.026) 0.022 (0.026) 0.022 (0.026) 0.022 (0.026) 0.020 (0.026) Education_or_support -0.163 (0.088)* -0.19 (0.087)** -0.191 (0.087)** -0.190 (0.087)** -0.189 (0.087)** Manager_or_not 0.054 (0.112) 0.063 (0.11) 0.063 (0.11) 0.060 (0.11) 0.068 (0.110) Temp_or_fixed_contract 0.025 (0.131) 0.066 (0.129) 0.066 (0.129) 0.057 (0.13) 0.06 (0.129) Parttime_fulltime 0 (0.081) -0.017 (0.079) -0.018 (0.08) -0.016 (0.08) 0.005 (0.080) Department_size 0.002 (0.001)* 0.002 (0.001) 0.002 (0.001) 0.002 (0.001) 0.002 (0.001) Structure 0.266 (0.047)*** 0.248 (0.047)*** 0.248 (0.047)*** 0.251 (0.047)*** 0.238 (0.047)*** Lowcost -0.066 (0.035)* -0.054 (0.034) -0.054 (0.034) -0.051 (0.035) -0.047 (0.034) Innovation -0.059 (0.054) -0.096 (0.054)* -0.097 (0.055)* -0.096 (0.055)* -0.102 (0.055)* Studentfocus 0.070 (0.049) 0.065 (0.049) 0.064 (0.049) 0.067 (0.049) 0.071 (0.049) Groupculture 0.106 (0.05)** 0.085 (0.049)* 0.086 (0.049)* 0.084 (0.049)* 0.079 (0.049) Developmentalculture 0.054 (0.047) 0.057 (0.046) 0.057 (0.046) 0.056 (0.046) 0.052 (0.046) Hierarchicalculture -0.016 (0.039) -0.019 (0.038) -0.019 (0.038) -0.018 (0.038) -0.018 (0.038) Rationalculture 0.099 (0.047)** 0.043 (0.05) 0.040 (0.051) 0.046 (0.052) 0.043 (0.052) Constant 1.645 (0.641)** 1.758 (0.631)*** 1.752 (0.633)*** 1.766 (0.634)*** 1.779 (0.629)*** R^2 0.458 0.479 0.479 0.480 0.490 R^2 change 0.021 0.000 0.001 0.010 F-value 10.792*** 11.063*** 10.467*** 9.980*** 9.885***

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Educational staff are negatively related with autonomy, which might be due to more standardization for these employees. The goal of educational organizations is to give quality education (and research output). To meet these goals, standardization might be important to guarantee a consistent quality of both. The variable structure is positively associated with the satisfaction of the need for autonomy. A higher score on structure means that the organization is more organic, instead of mechanistic. In an organic organization, employees need less direct supervision, because they are well trained and get support from their peers. They also have more responsibility. The need for autonomy is defined as people’s desire to experience ownership of their behaviour (Deci & Ryan, 2000), thus a more organic organization lets their employees have more autonomy. The innovation strategy is negatively related to autonomy. The strategy is associated with a focus on improving, experimenting and being the first to do so. The emphasis is on differentiation through new product development. A possible explanation might be that the employees who are focusing on delivering high quality work are forced to work on innovation, which might reduce their autonomy to choose what their focus in work is.

Competence

Please see table 4 on the next page for the results of the basic need competence:

In the base model, only control and contingency variables are entered. The model explains 11.0% of the variance of the need for competence. In each successive model, a lever is added. Beliefs systems explain an additional 0.1%, boundary systems zero, diagnostic control systems 0.3% and interactive systems 0.1%. In the final model, parttime versus fulltime employees is a weak significant predictor (β = .135 (0.075), p < .10) and developmental culture (β = 0.074 (0.043), p < .10) too. The levers of control do not hold any significant predictors, which means that hypothesis H2b and H3b are not supported. Unfortunately, the regression model was not significant (F(22, 226) = 1.330, p = 0.153), which means that the model cannot be supported.

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impact of these variables on the R^2-value and the model is unknown. Based on their analysis, Van den Broeck et al. (2010) showed that the relationship between skill utilization and competence satisfaction was not consistently positive. Van den Broeck et al. (2010) mentioned that the opportunity to use one’s skills may not guarantee that one masters each of the tasks, but the satisfaction of the need for competence is largely dependent upon feelings of mastery.

Table 4: Regression results Competence

Variables Model 1 Model 2 Model 3 Model 4 Model 5

Control variables Control variables Control variables Control variables Control variables

Beliefs systems Beliefs systems Beliefs systems Beliefs systems

Boundary systems Boundary systems Boundary systems

DCS DCS ICS Independent variables Beliefs systems 0.021 (0.042) 0.022 (0.042) 0.020 (0.042) 0.022 (0.043) Boundary systems (H2b) -0.008 (0.028) -0.015 (0.029) -0.014 (0.029) DCS (H3b) 0.029 (0.033) 0.036 (0.046) ICS -0.012 (0.055)

Control & Contingency variables

Gender 0.126 (0.074)* 0.119 (0.076) 0.118 (0.076) 0.109 (0.077) 0.111 (0.077) Age 0.002 (0.004) 0.002 (0.004) 0.002 (0.005) 0.002 (0.005) 0.002 (0.005) Organizational_tenure 0.003 (0.007) 0.003 (0.007) 0.003 (0.007) 0.003 (0.007) 0.002 (0.007) Departmental_tenure 0.006 (0.007) 0.006 (0.007) 0.006 (0.007) 0.006 (0.008) 0.007 (0.008) Education_level 0.019 (0.024) 0.020 (0.024) 0.021 (0.024) 0.021 (0.024) 0.022 (0.024) Education_or_support 0.033 (0.080) 0.028 (0.081) 0.030 (0.081) 0.028 (0.081) 0.028 (0.081) Manager_or_not -0.119 (0.102) -0.118 (0.102) -0.118 (0.103) -0.115 (0.103) -0.116 (0.103) Temp_or_fixed_contract -0.181 (0.119) -0.174 (0.120) -0.174 (0.121) -0.165 (0.121) -0.165 (0.121) Parttime_fulltime 0.141 (0.074)* 0.138 (0.074)* 0.139 (0.074)* 0.137 (0.074)* 0.135 (0.075)* Department_size 0 (0.001) 0 (0.001) 0 (0.001) 0 (0.001) 0 (0.001) Structure 0.032 (0.043) 0.029 (0.043) 0.029 (0.044) 0.026 (0.044) 0.027 (0.044) Lowcost -0.020 (0.032) -0.018 (0.032) -0.018 (0.032) -0.022 (0.032) -0.023 (0.032) Innovation -0.028 (0.049) -0.034 (0.051) -0.032 (0.051) -0.033 (0.051) -0.032 (0.051) Studentfocus -0.017 (0.045) -0.018 (0.045) -0.016 (0.046) -0.019 (0.046) -0.020 (0.046) Groupculture 0.047 (0.045) 0.044 (0.046) 0.044 (0.046) 0.045 (0.046) 0.046 (0.046) Developmentalculture 0.072 (0.043)* 0.073 (0.043)* 0.072 (0.043)* 0.073 (0.043)* 0.074 (0.043)* Hierarchicalculture 0.014 (0.035) 0.014 (0.035) 0.014 (0.035) 0.012 (0.036) 0.012 (0.036) Rationalculture 0.004 (0.043) -0.005 (0.046) -0.002 (0.048) -0.008 (0.049) -0.008 (0.049) Constant 3.633 (0.586)*** 3.651 (0.588)*** 3.66 (0.590)*** 3.645 (0.591)*** 3.644 (0.592)*** R^2 0.110 0.111 0.111 0.114 0.115 R^2 change 0.001 0.000 0.003 0.001 F-value 1.581* 1.506* 1.429 1.397 1.330

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In the analysis, employees with a fulltime contract report a higher competence. This could be explained by the fact that employees that work fulltime have had more time to learn and master the tasks of their job because they work more hours. This is in line with Miller & Terborg (1979), who also reported that parttime contracts is associated with less advancement and job satisfaction. Reported competence is increased in work environments with a high score on developmental culture. Developmental culture is associated with entrepreneurship, innovation and desire to meet new challenges. This could be explained through the idea that in order to innovate and create new ideas, employees should be competent in their work. This is in line with Rosen, Ferris, Brown, Chen & Yan (2014), which reported that need satisfaction is associated with creativity.

Relatedness

Please see table 5 on the next page for the results of the basic need relatedness:

In the base model, only control and contingency variables are entered. The model explains 40.3% of the variance of the need for relatedness. In each successive model, a lever is added. Beliefs systems explain an additional 0.3%, boundary systems 0.4%, diagnostic control systems 0.2% and interactive systems 0.5%. In the final model, boundary systems are a weak significant predictor (β = 0.052 (0.031), p < .10). Other significant predictors are gender (β = 0.214 (0.082), p < .10), parttime versus fulltime (β = 0.236 (0.080), p < .01) and group culture (β = 0.296 (0.049), p < .01). Developmental (β = 0.083 (0.045), p < .10) and hierarchical culture (β = -0.074 (0.043), p < .10) are weaker significant predictors. The levers of control do not have any significant predictors at the p < 0.05 level, which means that hypothesis H1b, H2c and H4b are not supported.

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Hierarchical culture is associated with a formal and bureaucratic environment, and negatively influences the satisfaction of the need for relatedness. Lund (2003) reported that employees in a hierarchical culture have significantly less job satisfaction compared to other cultures, and Van den Broeck et al. (2016) reported that job satisfaction is positively related to the satisfaction of the need for relatedness.

Table 5: Regression results Relatedness

Variables Model 1 Model 2 Model 3 Model 4 Model 5

Control variables Control variables Control variables Control variables Control variables

Beliefs systems Beliefs systems Beliefs systems Beliefs systems

Boundary systems Boundary systems Boundary systems

DCS DCS ICS Independent variables Beliefs systems (H1b) 0.041 (0.045) 0.037 (0.045) 0.039 (0.045) 0.054 (0.046) Boundary systems (H2c) 0.039 (0.030) 0.046 (0.031) 0.053 (0.031)* DCS -0.028 (0.036) 0.020 (0.049) ICS (H4b) -0.084 (0.058)

Control & Contingency variables

Gender 0.200 (0.080)** 0.187 (0.081)** 0.194 (0.081)** 0.202 (0.082)** 0.214 (0.082)** Age -0.005 (0.005) -0.006 (0.005) -0.005 (0.005) -0.005 (0.005) -0.005 (0.005) Organizational_tenure 0.009 (0.007) 0.009 (0.008) 0.008 (0.008) 0.009 (0.008) 0.008 (0.008) Departmental_tenure 0.004 (0.008) 0.005 (0.008) 0.004 (0.008) 0.004 (0.008) 0.005 (0.008) Education_level -0.026 (0.026) -0.023 (0.026) -0.025 (0.026) -0.026 (0.026) -0.025 (0.026) Education_or_support 0.017 (0.086) 0.008 (0.087) -0.001 (0.087) 0.001 (0.087) 0 (0.087) Manager_or_not 0.004 (0.110) 0.007 (0.110) 0.008 (0.101) 0.005 (0.110) -0.001 (0.110) Temp_or_fixed_contract -0.017 (0.128) -0.005 (0.129) -0.003 (0.129) -0.013 (0.13) -0.014 (0.129) Parttime_fulltime 0.256 (0.079)*** 0.251 (0.079)*** 0.249 (0.079)*** 0.25 (0.079)*** 0.236 (0.08)*** Department_size 0 (0.001) 0 (0.001) 0 (0.001) 0 (0.001) 0 (0.001) Structure -0.002 (0.046) -0.008 (0.047) -0.005 (0.047) -0.003 (0.047) 0.006 (0.047) Lowcost 0.016 (0.034) 0.019 (0.034) 0.019 (0.034) 0.023 (0.035) 0.020 (0.034) Innovation 0.035 (0.053) 0.024 (0.054) 0.014 (0.055) 0.015 (0.055) 0.019 (0.055) Studentfocus -0.003 (0.049) -0.004 (0.049) -0.011 (0.049) -0.008 (0.049) -0.011 (0.049) Groupculture 0.299 (0.049)*** 0.293 (0.049)*** 0.295 (0.049)*** 0.293 (0.049)*** 0.296 (0.049)*** Developmentalculture 0.076 (0.046)* 0.077 (0.046)* 0.081 (0.046)* 0.080 (0.046)* 0.083 (0.046)* Hierarchicalculture -0.075 (0.038)** -0.076 (0.038)** -0.077 (0.038)** -0.075 (0.038)* -0.075 (0.038)* Rationalculture -0.053 (0.046) -0.070 (0.050) -0.087 (0.051)* -0.081 (0.052) -0.078 (0.052) Constant 1.788 (0.630)*** 1.823 (0.632)*** 1.781 (0.632)*** 1.795 (0.632)*** 1.786 (0.631)*** R^2 0.403 0.406 0.41 0.412 0.417 R^2 change 0.003 0.004 0.002 0.005 F-value 8.637*** 8.222*** 7.920*** 7.560*** 7.345***

23 Cross-sections

The Independent-samples T tests showed that between employees working as support staff or as educational staff, differences in mean values were significant. Other cross-sections also revealed significant differences between groups (e.g., male – female, parttime – fulltime), but the support versus educational staff is the most promising to analyse. The differences might lie in the reason why the employees have become academics or support staff. Bellamy et al. (2003) reported that academics primarily choose for education because of the intrinsic values of being an academic, and less so for extrinsic conditions. This is probably not the case for support staff. For the levers of control, and the need for competence and relatedness, educational staff reported a lower mean. The hypotheses are analysed again in the respective groups and compared to the final model of the whole population, which will be called the overall model. The models for each basic need in the respective groups can be found in tables A4-A9 in Appendix 2. For each model, changes compared to the overall model are discussed. Please see the next table for an overview of the difference in means:

Variable Educational staff Support staff p-value

Mean Sdt. Dev. Mean Sdt. Dev.

Beliefs systems 3.59 1.09 4.15 1.25 < .001

Boundary systems 3.61 1.38 4.13 1.54 < .01

Diagnostic control systems 3.71 1.28 4.07 1.22 < .05

Interactive control systems 3.28 1.17 3.71 1.15 < .01

Competence 4.14 0.58 4.27 0.49 < .10

Relatedness 3.63 0.72 3.81 0.72 < .10

Table 6. Results Independent-samples T Tests

Autonomy – Support staff (Table A4)

The base model including the control and contingency variables explained 52.6% of the variance in the need for autonomy. This is an increase of 5.6% compared to the base model of the whole population. Beliefs systems explain an additional 1.8%, boundary systems zero, diagnostic control systems 2.1% and interactive systems 1.2%. The final model in this group explains an additional 8.7% compared to the overall model. The following significant predictors were found:

Variable β

Beliefs systems 0.111 (0.062)*

Diagnostic control systems -0.184 (0.058)*** Interactive control systems 0.132 (0.069)*

Temp or fixed contract -0.319 (0.161)** Developmental culture 0.138 (0.062)**

Innovation -0.123 (0.067)*

Student focus 0.140 (0.058)**

Structure 0.135 (0.065)**

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Compared to the overall model, the effect of the levers of control remained relatively the same. The negative effect of the diagnostic control systems has been nearly doubled, and significance levels of the levers are higher or lower compared to the overall model. In this new model, new significant predictors are introduced. For example, employees with a fixed contract are found to report less autonomy compared to those with a temporary contract or self-employed. The student focus strategy is associated with meeting expectations, offering support and providing high quality support services. The strategy is also associated with a higher score on autonomy. It is possible that this strategy allows employees to make choices that they seem fit to provide the best quality for the students, other than standard and rigid actions.

Autonomy – Educational staff (Table A5)

The base model including the control and contingency variables explained 52.4% of the variance in the need for autonomy. This is an increase of 5.4% compared to the base model of the whole population. Beliefs systems explain an additional 3.6%, boundary systems zero, diagnostic control systems 0.4% and interactive systems zero. The final model in this group explains an additional 7.4% compared to the overall model. The following significant predictors were found:

Variable β

Beliefs systems 0.161 (0.074)**

Departmental tenure -0.025 (0.011)**

Temp or fixed contract 0.533 (0.220)**

Structure 0.354 (0.066)***

Table 8. Significant variables Autonomy – Educational staff, * p < 0.10, ** p < 0.05, *** p < 0.01

Compared to the overall model, of the levers of control, only Beliefs systems remain a significant predictor of the satisfaction of the need for autonomy. The effect of diagnostic and interactive control systems on autonomy is not found among educational staff. The reason might be that a focus on for example diagnostic control systems is problematic. An important condition for performance control is that results should be specified accurately in the area that is being controlled. But, the objectives of educational organizations can be ambiguous (Speklé & Verbeeten, 2014). Employees with a fixed contract are also associated with a higher satisfaction of the need for autonomy. This might be due to temporary or third-party employees being hired for certain jobs or tasks, which have been specified beforehand.

Competence – Support staff (Table A6)

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Beliefs systems explain an additional 0.4%, boundary systems 0.9%, diagnostic control systems 0.4% and interactive systems 0.1%. The final model in this group explains an additional 3.1% compared to the overall model. Only developmental culture (β = 0.108 (0.061), p < 0.10) was found the be a weak significant predictor. But, the regression model was not significant (F(21, 130) = 1.058, p = 0.402).

Competence – Educational staff (Table A7)

The base model including the control and contingency variables explained 24.7% of the variance in the need for competence. This is an increase of 13.7% compared to the base model of the whole population. Beliefs systems explain an additional 0.8%, boundary systems 0.2%, diagnostic control systems 0.4% and interactive systems 1.0%. The final model in this group explains an additional 15.6% compared to the overall model. The following significant predictors were found:

Variable β

Gender 0.228 (0.115)*

Parttime_fulltime 0.313 (0.122)**

Department_size -0.003 (0.001)**

Table 9. Significant variables Competence – Educational staff * p < 0.10, ** p < 0.05, *** p < 0.01

Females are associated with a higher perceived competence. The meta-analysis of Van den Broeck et al. (2016) reported there is not clear explanation. They showed that the effects of gender can be both positive or negative on perceived competence, and that both females and males can have a higher score. Department size is negatively associated with competence. This might be due to an error in the dataset, as most values for department size were rounded. Like the previous model, the regression model was not significant again (F(21, 75) = 1.326, p = 0.187).

Relatedness – Support staff (Table A8)

The base model including the control and contingency variables explained 41.1% of the variance in the need for competence. This is an increase of 0.8% compared to the base model of the whole population. Beliefs systems explain an additional zero percent, boundary systems 0.7%, diagnostic control systems 1.0% and interactive systems 0.2%. The final model in this group explains an additional 1.3% compared to the overall model. The following significant predictors were found:

Variable β Boundary systems 0.075 (0.043)* Gender 0.273 (0.111)** Parttime_fulltime 0.274 (0.106)** Groupculture 0.271 (0.065)*** Hierarchicalculture -0.084 (0.050)*

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The final model of this group is quite similar the overall model. The explained variance in the need for relatedness is also similar to the overall model. Only developmental culture was not significant in this group compared to the overall model. Interestingly, boundary systems are positively related to relatedness, while the expectation was a negative relationship. A possible explanation could be that due to the code of conduct, people will behave similarly, which could lead to being more easily accepted into a group of colleagues.

Relatedness – Educational staff (Table A9)

The base model including the control and contingency variables explained 44.8% of the variance in the need for competence. This is an increase of 4.5% compared to the base model of the whole population. Beliefs systems explain an additional 1.9%, boundary systems and diagnostic control systems zero, and interactive systems 0.2%. The final model in this group explains an additional 8.0% compared to the overall model. The following significant predictors were found:

Variable β

Beliefs systems 0.148 (0.078)*

Interactive control systems -0.246 (0.115)**

Age -0.023 (0.009)**

Organizational_tenure 0.018 (0.011)*

Parttime_fulltime 0.313 (0.148)**

Groupculture 0.348 (0.086)***

Table 11. Significant variables Relatedness – Educational staff * p < 0.10, ** p < 0.05, *** p < 0.01

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The negative influence of age can be explained through the fact that older employees might invest less time and effort into making connections at work, because they are married and have kids and connection with peers on the job might be less important. Whereas for younger employees, this might more important. The positive effect of organizational tenure might be caused by employees that have taken the time to really mix and connect with their colleagues, and becoming close friends. The results regarding age and organizational tenure are also consistent with the findings of the meta-analysis of Van den Broeck et al. (2016).

Further comments

For an overview of the results of the hypotheses and the final model, please see table A12 and figure 2 in Appendix 2. Analysis shows that job roles are an important factor to consider. Significant differences are found between educational staff and support staff, regarding the perceived importance of the management control systems in their work environment and the satisfaction of the need for competence and relatedness. Further analysis shows varying results in the two groups. For support staff, coercive controls play a bigger role, and for educational staff, enabling controls are more prevalent. In these groups, the final models were better able to explain the variance in the basic need then the overall model over the whole dataset. An explanation might be that the role of support staff can be more compared to jobs in the private sector with objective measures, while educational staff has to deal with more judgemental and subjective measures in the management control system.

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V. Discussion and Conclusion

Conclusion

29 Theoretical and practical insights

Several insights can be made from this study. First, management control systems are largely able to influence the satisfaction of the basic needs and thus motivation, through changes in the autonomy and relatedness of employees. As Gagné & Deci (2005) suggested, the strength of each need is different for all individuals, but it might be possible that there are similarities in need strength when looking at job characteristics. This could be the cause of the different effects of control systems on the basic needs. So, designers of management control systems should also investigate characteristics of the group that will be affected by implementing the management control system. For the need for autonomy, there is a trade-off. Simplistically, more autonomy leads to more motivation. But in this scenario, it might be harder to align the behaviour of the employees with the organization’s objectives and strategy. The takeaway is that for managers, investing in belief systems and the joint use of diagnostic and interactive control systems is the best way to increase the satisfaction of the need for autonomy. Other variables that cause an increase or decrease in the satisfaction of the need for autonomy are harder to influence for managers. Regarding the need for relatedness, boundary systems can be put in place without affecting the satisfaction of the need for relatedness negatively. The takeaway is that managers that want to increase the satisfaction, should invest in a group culture where teamwork and morale are important. This is something managers can accomplish with teambuilding activities or organizing social gatherings.

Limitations & Suggestions for further research

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