The Impact of Management Control on Autonomous Motivation and Performance: The Use of Control and the Role of Job Types Niels Löbach S4149491 [email protected]Supervisor: Prof. dr. ir. P.M.G. van Veen-Dirks Word count: 12.888 22-06-2020 Master Thesis MSc Business Administration Management Accounting and Control Faculty of Economics and Business University of Groningen
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Autonomous motivation has long been promoted as superior for an individual’s performance
and overall wellbeing. Although the feeling of pressure and control has shown to undermine
this type of motivation, psychology suggests that it can also be facilitated. However, how
management control can facilitate autonomous motivation and in turn drive organizational
success is still an open question. Moreover, the importance of the role of individuals’ job types
for their autonomous motivation is still under discussion. This thesis draws on Self-
Determination Theory and Job Characteristics Theory to investigate these relations and to
better understand the complex phenomenon of human motivation. In particular, the effects of
Simons’ levers-of-control (i.e. beliefs systems, boundary systems, diagnostic control systems
and interactive control systems) for two different job types (i.e. educational job type,
educational support job type) are examined using 215 employee surveys that were collected in
two Higher Educational Institutions in the Dutch public sector. The findings indicate that
beliefs systems have a positive impact on employees’ autonomous motivation. Further, the
examination of the Management Control System as a package revealed a positive effect of
positive controls (i.e. beliefs systems and interactive control systems) on autonomous
motivation. Furthermore, the study provides strong evidence that autonomous motivation
enhances performance. Lastly, findings do not show an effect of the job type on autonomous
motivation.
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CONTENT
I. INTRODUCTION ...................................................................................................................... 5
II. LITERATURE REVIEW ........................................................................................................... 7
2.1 Self-Determination Theory .................................................................................................................................. 7
2.1.1 Autonomous and controlled motivation .......................................................................................................... 7
2.1.2 Enhancing and undermining autonomous motivation .............................................................................. 8
2.2.1 Management Control System ...................................................................................................................... 9
2.2.2 MCS as a package ........................................................................................................................................ 11
2.2.3 MC and motivation hypotheses .................................................................................................................. 11
2.3 The moderating effect of job types ..................................................................................................................... 13
2.3.1 Job Characteristic Theory ........................................................................................................................... 13
2.3.2 Hypothesis development ............................................................................................................................ 14
2.4 Motivation and performance ............................................................................................................................. 17
2.5 Conceptual model ............................................................................................................................................... 17
III. METHODS ........................................................................................................................... 18
3.1 Research method and sample.............................................................................................................................18
3.2.1 Independent and dependent variables .......................................................................................................18
3.2.2 Control variables ......................................................................................................................................... 19
3.3 Data analysis ....................................................................................................................................................... 21
3.3.1 Exploratory Factor Analysis and Reliability Analysis ................................................................................ 21
4.2 Early and late respondents................................................................................................................................ 24
Figure 2. The job characteristic model ......................................................................................................................... 13
Figure 3. Conceptual model .......................................................................................................................................... 17
Table 3. Early and late respondents ............................................................................................................................ 24
**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).
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boundaries (BOUND), diagnostic controls (DIAGN) and interactive controls (INTER). A
significant regression equation was found (F(9,205)=2.034, p<.05), with an R² of .082 (Adj.
R²=.042). BELIEFS had a positive effect on autonomous motivation that was significant
(p=.002). All other levers of control were not significant predictors of autonomous motivation.
In Model 2 a multiple regression was performed to examine whether the use of positive
controls, relative to negative controls represented by the PNR of the MCS, and the job type
(EDUC_JOB) could significantly predict employees’ autonomous motivation (AUTON_MOT)
as proposed in H1 and H2. A significant regression equation was found (F(7,207)=1.961,
p<.100), with an R² of .062 (Adj. R²=.030). The use of positive controls relative to negative
controls had a positive significant effect on autonomous motivation (p=.017). This combined
effect of the positive controls was higher than the direct effect of beliefs systems that was found
Table 5. Results of multiple linear regression analysis and moderation analysis
Model 1 Coefficient
estimate (Standard error)
Model 2 Coefficient
estimate (Standard error)
Model 3 Coefficient
estimate (Standard error)
Model 4 Coefficient
estimate (Standard error)
Hypothesis
Intercept 4.431***
(.469) 5.176***
(.401) 4.505 (.486)
3.189*** (.031)
BELIEFS .201** (.064)
BOUND -.038 (.026)
DIAGN .026
(.068)
INTER -.039 (.076)
PNR .427** (.178)
.579* (.339)
H1: supported
EDUC_JOB .192
(.145) .401
(.421) H2: not supported
PNR_x_EDUC -.210 (.398)
H3: not supported
AUTON_MOT .134*** (.031)
H4: supported
Control variables
Age .010
(.007) .010
(.007) .009
(.007) -.004 (.003)
Tenure -.014
(.009) -.010
(.009) -.010
(.009) .001
(.004)
Contract_dummy -.061
(.209) -.150
(.208) -.138 (.210)
-.098 (.096)
Agreement_dummy .141
(.125) .151
(.125) .150
(.125) .201*** (.057)
Education_dummy .208
(.198) .065
(.215) .048
(.218) -.213** (.090)
R² .082 .062 .063 .162
Adjusted R² .042 .030 .027 .138
F value 2.034** 1.961* 1.745* 6.690***
***, ** and * indicate that coefficients are statistically significant at the 1%, 5% and 10% level, respectively. Significance is based on two-sided testing.
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in Model 1. The job type was not a significant predictor of autonomous motivation.
Consequentially, H1 is supported and H2 must be rejected.
Model 3 examines the moderating effect of the job type as proposed in H3 in a moderation
analysis. The dependent variable for the analysis is autonomous motivation (AUTON_MOT).
Predictor variable for the analysis is the PNR of the MCS and the job type (EDUC_JOB). The
moderator variable evaluated for the analysis is the educational job type (EDUC_JOB). The
results show that there is no significant interaction effect between the PNR of the MCS and the
job type. Hypothesis H3 must therefore be rejected.
Model 4
A multiple linear regression was performed to predict performance based on the degree of
autonomous motivation as postulated in H4. A significant regression equation was found
(F(6,208)=6.690, p<.001), with an R² of .162 (Adj. R²=.138). Autonomous motivation had a
positive effect on performance that was significant (p<.0001). This strongly supports
hypothesis H4. Further, results show a significant effect of the type of agreement on
performance. Accordingly, fulltime employees perform better than part-time employees
(p=.001). Finally, higher educated employees show a lower level of performance than lower
educated employees. (p=.019). In sum, performance was predicted by autonomous
motivation, type of agreement and educational level.
V. DISCUSSION AND CONCLUSION
Management accounting research has long acknowledged the importance of autonomous
motivation for employee performance and overall well-being in knowledge-intensive
organizations such as HEIs. However, it still remains an unsolved puzzle how management
control can support autonomous motivation and make use of the positive effects that
accompany this type of motivation. This thesis seeks to shed more light on these complex
relations by investigating theoretically and empirically how the opposing forces that are
reflected by positive and negative controls in the MCS are associated with autonomous
motivation, and how this type of motivation relates to performance. For this, I draw on Self-
Determination Theory to hypothesize these relations and thus, continue a current stream in
management accounting literature (e.g. Sutton & Brown, 2016; Ter Bogt & Scapens, 2012; Van
der Kolk et al., 2019). Moreover, I seek to expand the scope of studying employee motivation
by examining the role of the job type as both predictor of autonomous motivation and
moderator of the impact of management control on autonomous motivation using Job
Characteristic Theory. Although founders of SDT Gagné and Deci agree that job characteristics
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impact individual’s motivation they point out three major differences. First, SDT expands the
narrow focus of job characteristics as predictor for employee motivation by considering
management style as major influential factor on autonomous motivation. Second, Job
Characteristic Theory does not contemplate the compromising role of controlled motivation
for autonomous motivation. Third, whereas the need strength that enhances motivation is
central to Job Characteristic Theory, SDT is more concerned with the satisfaction of different
needs that enable a specific type of motivation (Gagné & Deci, 2005). Since this study is only
concerned with autonomous motivation both theories do not conflict with each other and can
both be used to make hypotheses and interpretations. This thesis provides several significant
findings both expected, based on theory and past studies, as well as surprising or somewhat
unexpected.
First, the results show that the hypothesized relationship between management control and
autonomous motivation indeed exist. In particular, findings indicate that an increased use of
positive controls relative to negative controls in the MC package leads to more autonomous
motivation. This outcome confirms Simons’ (1994) proposition that positive controls serve as
a force to motivate, guide and provide freedom and are overall of supportive nature to the
individual. STD explains this positive effect on autonomous motivation with the satisfaction
of the three basic needs: autonomy, competence and relatedness. Another interesting finding
was that solely a formal control system had a direct impact on autonomous motivation. As
illustrated in the Model 1 (please see Table 5.) only beliefs systems had a positive effect on
autonomous motivation that was significant, whereas all other control levers did not correlate
with autonomous motivation directly. This complements a study in the Dutch public sector by
Van der Kolk et al. (2019), who found that the communication of core norms and values (i.e.
cultural controls) enhances intrinsic motivation. Different from a case study by Sutton &
Brown (2016), who report positive effects of diagnostic controls such as performance
evaluations on autonomous motivation of researchers in a university, I did not find a direct
effect of the diagnostic or interactive use of control on autonomous motivation. Furthermore,
there was no significant impact of boundaries, which indicates that boundaries were perceived
as neutral rather than autonomy restricting. The examination of the MCS as a package showed
an increase of autonomous motivation when more positive controls relative to negative
controls were used, that was higher than the direct effect of beliefs systems. This could be
explained by existing complementary effects of other MC elements within the system.
However, more analysis is needed to make assumptions about what had caused this increased
effect as I did not find a direct effect of interactive controls.
Second, I found no evidence that supports the in H2 postulated relationship between
the job type and autonomous motivation. A possible explanation could be that the separation
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into two job types was chosen too broadly. For instance, one can imagine that teaching a first-
year bachelor’s course with many participants involves different levels of task uncertainty and
interdependence compared to teaching a small master’s course with only a few participants or
supervising the writing of a master thesis. A similar spectrum of those two characteristics can
be assumed in the variety of jobs that support the educational process and were examined as
one job type in this study. Adler and Chen (2011) propose a third job type, that reflects both
high levels of interdependence and task uncertainty. Although the hypothesis must be rejected
in this study, I suggest more research on different job types.
Third, other than expected I could not find a significant interaction effect of
management control and job type. The assumption that the MCS would have a stronger effect
on the autonomous motivation of the educational staff can therefore not be declared correct.
Instead, all employees perceived the MCS as equally need-supportive. Sheldon et al. (2003)
state correctly that the control-oriented mindset of individuals that demands more structure
and direction does not lead to individuals wanting to be more controlled and that all
individuals benefit equally from more autonomy. Main argument for the assumption that the
MCS affects the educational job type stronger than the educational support job type was the
increased need of behavioural freedom due to higher task uncertainty and independence,
which would be relatively easier constrained by an extensive use of negative controls and
would at the same time benefit more from positive controls. Two factors could explain why
this assumption was not supported. First, negative controls were not perceived as constraining
at all. I did not find direct effects of any control elements on autonomous motivation other
than beliefs systems which indicates that they were perceived as neutral. Second, there were
very little outliers of strictly positive and strictly negative MCSs in the data sample. Overall,
the MCS was perceived as very balanced by the employees indicating that both organizations
made use of all four control levers to the same extent (please see Table 3.). This limited the
effects of extreme uses of either of the opposing forces or single MC elements which could have
constrained an individual. In sum, I have to reject the H3 with the remark that in other
samples and settings results could have been different. Therefore, I suggest further research
with a larger data sample.
Fourth, this thesis provides strong evidence that autonomous motivation is positively
associated with performance and thus, substantiates past research on this topic (e.g. Van der
Kolk et al., 2019; Sutton & Brown, 2016). This finding does not only underline the importance
of having overall motivated employees in HEIs, but also confirms the significance of
autonomous motivation as one specific type of motivation that drives performance.
It is worth mentioning that additional findings emerged from this thesis, some of which
were unexpected. First, I found that employees who had a fulltime agreement showed higher
levels of performance compared to employees that worked part-time. A possible explanation
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for this effect could be that fulltime employees are overall more involved with their jobs
compared to part-time employees as a meta-analysis by Thorsteinson (2003) confirms.
Second, results show that employees with a lower educational level (secondary degree) rated
their performance higher than those with a higher educational level (bachelor’s degree or
higher), which supports a study by Kahya (2007), who reports a negative effect of the level of
education on task performance. Finally, in my sample both performance and autonomous
motivation were not influenced by age, tenure, or type of contract. These findings stand in
contrast to the reviewed literature (Inceoglu et al., 2012; Ng and Feldman, 2010; Kinman et
al. 1998).
This thesis makes several contributions to management accounting research. Most
importantly is the positive effect of beliefs systems on autonomous motivation. Past research
was mostly concerned with the use of the MCS represented by interactive and diagnostic
controls but less with the role of beliefs and boundaries. The findings could stimulate more
future research on those two formal control systems. Further, the examination of the MCS as
a package contribute to past research on this topic. An important feature of this thesis is the
examination of employee responses that allowed the investigation of human perception of
management control. Scholars experimented with mixed surveys (e.g. Groen et al., 2017) to
capture different perceptions on different issues (i.e. management and employee perception).
For this thesis I followed Tessier and Otley’s (2012) recommendation to study employee
responses instead of management response due to different perceptions. Accordingly, this
thesis relied purely on employee data for management control, motivation and performance.
This also allowed to better compare these three variables with each other. Additionally, the
study of variables that were measured on an individual level such as motivation and
management control in combination with a variable that was measured on a unit level (i.e.
performance) add to these so-called ‘cross-level’ studies in prior research (e.g. Van der Kolk
et al., 2019).
Beyond the already mentioned limitations this thesis has several general limitations. First, the
relatively small sample size of 215 participants from only two organizations limits the
generalization of this study. Only two different MCSs were examined that were both very
balanced. In addition, the job types were not represented equally by the data set. Only 35% of
the participant worked in educational support jobs. Another possible limitation was non-
response bias. Significant differences in means of a main construct in the second organization
of early and late respondents could have affected the results. Lastly, although quantitative
research allows to investigate the complex phenomenon of human motivation and enhancing
generalization of the results, quantitative models still explain relatively little of the variance
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and give limited insight in human experience attached to this phenomenon. Future research
could therefore investigate the relationships also in qualitative studies.
The aim of this thesis was to answer the question what the impact of the use of control on
autonomous motivation for different job types is. In addition, this thesis aimed to substantiate
the findings by providing evidence that autonomous motivation enhances performance. In
regard to the research question I hypothesised a positive relationship between positive relative
to negative controls and autonomous motivation (H1). Further, I hypothesized a relationship
between job type and autonomous motivation so that the educational staff is more
autonomously motivated than the educational support staff (H2). Furthermore, I assumed
that an interaction effect between job type and management control exists (H2). In particular,
I postulated a stronger perceived effect of the use of control on the educational staff (H2) than
on the educational support staff. The findings support only H1. However, hypothesis H2 and
H3 must be rejected. Lastly, the examination of the relationship between autonomous
motivation and performance concluded that autonomous motivation and performance are
positively associated, which supports H4. Prior research has focussed much attention on those
control elements that determine the use of the MCS (i.e. interactive and diagnostic control)
(e.g. Henri, 2006; Bobe & Taylor, 2010; Bisbe & Otley, 2004). Future research could continue
this stream by focussing more on formal control systems (i.e. beliefs and boundaries) and on
the opposing forces reflected by positive and negative controls. In addition, the comparison of
different job types and their effect on different types of motivation could be studied. In
particular, it would be interesting to study the effects on intrinsic and extrinsic motivation
separately.
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APPENDIX
Appendix A. Results of exploratory factor analysis for Levers of Control
Component
Diagnostic control systems
Beliefs systems Boundary systems Interactive control
systems
BELIEFS_1 0,061 0,805 0,050 0,215
BELIEFS_2 0,154 0,687 0,170 0,353
BELIEFS_3 0,174 0,878 0,112 0,064
BELIEFS_4 0,139 0,858 0,109 0,102
BOUND_1 0,192 0,092 0,830 0,105
BOUND_2 0,121 0,007 0,864 0,141
BOUND_3 0,196 0,376 0,586 0,268
BOUND_4 0,183 0,142 0,828 0,065
DIAGN_1 0,912 0,104 0,180 0,178
DIAGN_2 0,887 0,129 0,154 0,230
DIAGN_3 0,889 0,151 0,194 0,247
DIAGN_4 0,884 0,178 0,176 0,194
DIAGN_5 0,835 0,125 0,159 0,274
INTER_2 0,418 0,241 0,231 0,775
INTER_3 0,396 0,307 0,179 0,758
INTER_4 0,435 0,299 0,189 0,748
Appendix B. Results of exploratory factor analysis for autonomous motivation
Component
Intrinsic motivation Identified motivation
IDENT_M_1 -0,034 0,872
IDENT_M_2 0,202 0,882
IDENT_M_2 0,470 0,745
INTR_M_1 0,827 0,107
INTR_M_2 0,944 0,136
INTR_M_3 0,895 0,191
Appendix C. Results of exploratory factor analysis for autonomous motivation