كتاب Risk Profile Contingent Analysis of Management Control Systems
منتدى هندسة الإنتاج والتصميم الميكانيكى
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 كتاب Risk Profile Contingent Analysis of Management Control Systems

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عدد المساهمات : 16333
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تاريخ التسجيل : 01/07/2009
العمر : 32
الدولة : مصر
العمل : مدير منتدى هندسة الإنتاج والتصميم الميكانيكى
الجامعة : المنوفية

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مُساهمةموضوع: كتاب Risk Profile Contingent Analysis of Management Control Systems    كتاب Risk Profile Contingent Analysis of Management Control Systems  Emptyالسبت 01 أغسطس 2020, 1:42 am

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أحضرت لكم كتاب
Risk Profile Contingent Analysis of Management Control Systems
Evidence from the Mechanical Engineering Industry
Peter Gostl
Reihe herausgegeben von
Wolfgang Becker, Bamberg, Deutschland
Patrick Ulrich, Aalen, Deutschland  

كتاب Risk Profile Contingent Analysis of Management Control Systems  R_p_c_10
و المحتوى كما يلي :


Table of contents
1 Introduction 1
1.1 Research questions and design 3
1.2 Structure and outline . 7
2 Literature Review . 11
2.1 Introduction to management control 11
2.1.1 Control problem in management . 12
2.1.2 Definitions and evolution of management control . 14
2.1.3 Uncertainty, risk and management control 17
2.1.3.1 Risk management and management control . 19
2.1.3.2 Kaplan & Mikes’ conceptualization of risk types 24
2.1.4 Overlaps with other fields in management literature . 27
2.1.4.1 Cybernetics and management control . 28
2.1.4.2 Agency theory and management control . 30
2.1.4.3 Organizational theory and management control . 34
2.1.4.4 Contingency theory and management control . 36
2.2 Design of management control systems . 41
2.2.1 Control systems and package view in
management control 41
2.2.2 Conceptualizations and evolution of MCS frameworks . 46
2.2.3 Simons’ levers of control framework . 54
2.2.3.1 Beliefs systems 58
2.2.3.2 Boundary systems . 59
2.2.3.3 Diagnostic control systems 61
2.2.3.4 Interactive control systems 63
2.2.3.5 Interrelationship of the levers of control 65
2.2.3.6 Criticism of the LOC framework 68X Table of contents
2.3 Contingency-based studies in management control . 70
2.3.1 The concept of fit in contingent control theory . 72
2.3.2 Drivers of the emergence of MCS . 81
2.3.2.1 MCS and uncertainty . 82
2.3.2.2 MCS and strategy 85
2.3.2.3 MCS and size 88
2.3.2.4 MCS and age . 89
2.3.2.5 MCS and ownership 90
2.3.3 Contingency-based performance analysis
of MCS – state-of-the-art . 90
2.3.4 Interim conclusion on contingency-based studies
in MC . 98
3 Theory Development and Hypotheses 101
3.1 Development of a risk-based MCS framework
by extension of the LOC framework 102
3.2 Development of propositions . 112
3.2.1 Risk profile and (risk-based) MCS design and use . 113
3.2.1.1 Association between preventable risks
and (risk-based) controls . 113
3.2.1.2 Association between strategy execution risks
and (risk-based) controls . 116
3.2.1.3 Association between external risks
and (risk-based) controls . 119
3.2.2 Risk profile and packages of (risk-based) MCS 121
3.2.3 Superior performance through matching risk profile
and (risk-based) MCS 123
3.2.4 Overview of hypotheses 125
3.3 Theoretical model of this study 126Table of contents XI
4 Methods . 129
4.1 Data set . 129
4.2 Data collection . 131
4.2.1 Internal and external validity 132
4.2.2 Survey 134
4.2.3 Database . 139
4.3 Variable measurement 140
4.3.1 Conceptual specification and epistemic
relationships in constructs . 141
4.3.2 Construct validity and reliability . 146
4.3.3 Risk profile . 149
4.3.4 MCS design and use . 154
4.3.5 Strategy . 163
4.3.6 Performance 167
4.3.7 Control variables 170
4.3.8 Summary of constructs 171
4.3.9 Descriptive statistics 173
4.4 Data analysis . 175
4.4.1 Multiple regression analysis 175
4.4.2 Cluster analysis . 179
4.4.3 Logistic regression analysis . 182
4.5 Research framework of this study . 186
5 Results . 189
5.1 Risk profile contingent design and use of MCS 189
5.1.1 Theoretical model and quantitative techniques . 189
5.1.2 Analysis . 195
5.1.2.1 LOC framework . 195
5.1.2.2 Risk-based MCS framework 200
5.1.2.3 Additional results on MCS design and use 203
5.1.3 Discussion of hypotheses 207XII Table of contents
5.2 Risk profile contingent packages of MCS 213
5.2.1 Theoretical model and quantitative techniques . 214
5.2.2 Analysis . 218
5.2.2.1 LOC framework . 218
5.2.2.2 Risk-based MCS framework 224
5.2.2.3 Additional results on predictability of MCS
cluster membership . 230
5.2.3 Discussion of hypotheses 233
5.3 Risk profile contingent performance analysis of MCS . 235
5.3.1 Theoretical model and quantitative techniques . 235
5.3.2 Analysis . 243
5.3.2.1 LOC framework . 243
5.3.2.2 Risk-based MCS framework 249
5.3.2.3 Additional results on superior performance
through matching MCS and risk profile . 253
5.3.3 Discussion of hypotheses 254
6 Conclusions . 257
6.1 Findings and contributions 257
6.2 Limitations and implications for future research 260
Appendix 263
References 271List of figures
Figure 1: Research design 7
Figure 2: Outline of dissertation 8
Figure 3: Enterprise risk management – Integrated framework 23
Figure 4: Kaplan & Mikes’ conceptualization of risk types 27
Figure 5: Cybernetic feedback model . 29
Figure 6: Control strategy in agency theory 33
Figure 7: Control strategy in organizational theory . 36
Figure 8: The minimum necessary contingency framework 39
Figure 9: Organic and mechanistic forms of MCS 43
Figure 10: Social and informational prerequisites of control . 47
Figure 11: Control types and control problems . 50
Figure 12: Levers of control 57
Figure 13: Relationship between levers of control and
realized strategies . 67
Figure 14: Levels of contingent control analysis . 70
Figure 15: Interaction fit 74
Figure 16: Systems fit . 77
Figure 17: Gerdin & Greve’s classificatory framework for
different forms of contingency fit . 78
Figure 18: Theoretical model of Widener’s (2007) study 96
Figure 19: Theoretical model of Sandino’s (2007) study 97
Figure 20: Extending Simons’ LOC framework to develop a
risk-based MCS framework (Source: own illustration) 107
Figure 21: Theoretical model of this study 128
Figure 22: Predictive validity framework . 133
Figure 23: Reflective and latent models . 144
Figure 24: Formative and emergent models . 145XIV List of figures
Figure 25: Conceptual specification of risk profile 151
Figure 26: Conceptual specification of Simons’ MCS –
design attributes 155
Figure 27: Conceptual specification of Simons’ MCS –
attention patterns . 157
Figure 28: Conceptual specification of risk-based MCS . 161
Figure 29: Conceptual specification of strategy 165
Figure 30: Conceptual specification of perceived firm performance 168
Figure 31: Conceptual specification of perceived usefulness
of MCS . 169
Figure 32: Research framework of this study based on the PVF . 187
Figure 33: Theoretical model for analyzing risk profile
contingent design and use of MCS . 190
Figure 34: Graphical depiction of significant results on risk profile
MCS design and use . 208
Figure 35: Theoretical model for risk profile contingent
performance analysis of MCS . 237
Figure 36: Graphical depiction of significant results on risk profile
contingent performance . 255List of tables
Table 1: Overview of hypotheses . 126
Table 2: Non-response bias . 138
Table 3: Descriptive statistics for financial measures
from database . 140
Table 4: Factor analysis of survey constructs – risk profile 153
Table 5: Factor analysis of survey constructs – MCS 159
Table 6: Factor analysis of survey constructs – risk-based
dimensions of MCS 163
Table 7: Factor analysis of survey constructs – strategy . 166
Table 8: Factor analysis of survey constructs – performance 170
Table 9: Multitrait matrix . 172
Table 10: Descriptive statistics for survey items and constructs 174
Table 11: Multiple regressions on design attributes of MCS 196
Table 12: Multiple regressions on attention patterns of MCS 198
Table 13: Multiple regressions on risk-based dimensions of MCS 201
Table 14: Multiple regressions – additional results
on design attributes of MCS . 204
Table 15: Multiple regressions – additional results
on attention patterns of MCS 205
Table 16: Multiple regressions – additional results
on risk-based dimensions of MCS 206
Table 17: Cluster analysis of the LOC framework 219
Table 18: Discriminant analysis for the cluster solution
of the LOC framework 220
Table 19: Logistic regression for prediction
of MCS cluster membership . 222
Table 20: Cluster analysis of the risk-based MCS framework . 225XVI List of tables
Table 21: Discriminant analysis for the cluster solution
of the risk-based MCS framework 226
Table 22: Logistic regression for prediction
of risk-based MCS cluster membership . 228
Table 23: Logistic regression – additional results on predictability
of MCS cluster membership . 231
Table 24: Logistic regression – additional results on predictability
of risk-based MCS cluster membership . 232
Table 25: Logistic regression for predicting MCS
cluster membership via risk profile . 244
Table 26: Univariate analyses on performance – LOC framework 245
Table 27: Multiple regressions on performance – LOC framework 247
Table 28: Logistic regression for predicting risk-based MCS
cluster membership via risk profile . 249
Table 29: Univariate analyses on performance – risk-based
MCS framework 250
Table 30: Multiple regressions on performance – risk-based
MCS framework 252List of equations
Equation 1: t-statistic . 136
Equation 2: Cronbach’s ? 148
Equation 3: Final score of construct measures . 148
Equation 4: Linear regression model 175
Equation 5: Goodness of fit measure R2 . 176
Equation 6: F-ratio . 176
Equation 7: F-statistic for significance testing of R2 177
Equation 8: Fchange-statistic 177
Equation 9: Variance inflation factor (VIF) 179
Equation 10: Discriminant function 181
Equation 11: Logarithmic regression model 183
Equation 12: Measure of log-likelihood . 183
Equation 13: Deviance 184
Equation 14: Likelihood-ratio . 184
Equation 15: Goodness of fit measure Nagelkerke’s RN2 . 184
Equation 16: Risk profile contingent design and use of MCS . 191
Equation 17: Calculation of dummy variable for STRATRISK 203
Equation 18: Calculation of dummy variable for FIT . 236
Equation 19: Predicted MCS cluster membership 238
Equation 20: Risk profile contingent performance analysis of MCS . 240List of abbreviations
AGE measure of company age
AIC Akaike information criterion
BELIEF measure of beliefs systems
BOUND measure of boundary systems
CEO chief executive officer
COSO Committee of Sponsoring Organizations of
the Treadway Commission
COSTSTRAT measure of cost leadership strategy
DIFFSTRAT measure of differentiation strategy
DIAGNOST measure of diagnostic control systems
e.g. exempli gratia
ERM Enterprise Risk Management
EXTRISK measure of external risks
i.e. id est
INTERACT measure of interactive control systems
ISO International Organization for Standardization
LOC levers of control
MC management control
MCS management control systems or
management control systems’
OC organizational control
OLS ordinary least squares
OWN dummy variable of ownership structure
PERCPERF measure of perceived firm performance
PMS Performance measurement systems
PREVRISK measure of preventable risks
PVF predictive validity frameworkXX List of abbreviations
rbFORMALMCS measure of risk-based formal controls
rbUSEMCS measure of risk-based use of controls
RQ research question
SE standard error
SIZE measure of organizational size
STRATRISK measure of strategy execution risks
USEFULMCS measure of usefulness of MCS
VDMA Verband Deutscher Maschinen- und Anlagenbau
VIF variance inflation factor


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