2007
DOI: 10.1506/car.24.3.10
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Relations among Measures, Climate of Control, and Performance Measurement Models*

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Cited by 135 publications
(131 citation statements)
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References 48 publications
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“…In contrast to these developments, however, research shows that accounting information does not need to be elegant, complete or accurate to be useful for developing knowledge. Malina, Norreklit and Selto (2007) show that the ability of a performance measurement system to communicate a common sense and credible story of business operations is far more important than the creation of a statistically-valid predictive business model. Rowe, Birnberg and Shields (2008a) report that technical jargon and complexity of accounting information limited managers' ability to identify the economic effects of competing initiatives.…”
Section: The Role Of Accounting Information In Developing Knowledge Omentioning
confidence: 99%
“…In contrast to these developments, however, research shows that accounting information does not need to be elegant, complete or accurate to be useful for developing knowledge. Malina, Norreklit and Selto (2007) show that the ability of a performance measurement system to communicate a common sense and credible story of business operations is far more important than the creation of a statistically-valid predictive business model. Rowe, Birnberg and Shields (2008a) report that technical jargon and complexity of accounting information limited managers' ability to identify the economic effects of competing initiatives.…”
Section: The Role Of Accounting Information In Developing Knowledge Omentioning
confidence: 99%
“…The performance measurement literature distinguishes between logical, empirical, and hierarchical interdependencies among measures [47,53,65]. Logical interdependencies result from definitions (e.g., profit = revenue -expenses) or mathematical transformation (e.g., return on investment = capital turnover / profit margin).…”
Section: Foundations Of Performance Measurement Systemsmentioning
confidence: 99%
“…If for all variables of the causal map manifest time series data are available, the validation of causal relationships can be done by using a multivariate time series model. When the variables of the causal map are not directly observable, but can be operationalised as latent variables with appropriate factors, structural equation modelling can be used to validate the cause-and-effect relationships among the SFs [29]. The statistical validation of the causal relationship network objectifies the previous ordinal data in metric forms to achieve relative comparability and clear predictability.…”
Section: Resultsmentioning
confidence: 99%
“…(Intersections of subjective maps would deliver intersubjectively based ones.) But, only a statistical validation of the causal map generates an objective understanding of the causal relationships, which thus are directly empirically verifiable [5,29]. As a consequence, valid predication of the performance generation can be given.…”
Section: Knowledge Management: Explicit and Implicit Knowledgementioning
confidence: 99%