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Determinants of firm performance control

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Abstract

This thesis develops and tests a theory of the firm's performance control system and its determinants. Firms have unique combinations of production, investment, environmental, and competitive attributes, and therefore have unique sets of work processes. As a result, performance control demands are also firm-specific. Previous Contingency Theory research has provided insights into the appropriate use of accounting information for performance control, and normative accounting system research has promoted the need for non-financial performance measurement under some circumstances. However, neither provides clear guidelines for the application of their findings to practical control system design. More recently, implicit contracting researchers have described an alternative governance style, implicit contracting, which has advantages in circumstances where empirical information is costly and ambiguous. The transaction cost economics framework presents an opportunity to develop and test an holistic and parsimonious theory of performance control which addresses issues of both measurable and non-measurable performance control. Its application to the issue of internal firm governance in this research extends costly contracting research into an area where little has previously been reported. A model was developed incorporating uncertainty and asset-specificity as the determinants of performance control choices. Six sets of hypotheses were developed describing choices from different control system components in consideration of levels of the two determinants. The hypothesised choices reflect the impact of different levels of uncertainty and asset-specificity on availability of performance information. Data to test the hypotheses were collected from diverse work units because the two determinants in the model, uncertainty and asset-specificity, are present in all work situations. Membership lists from three professional bodies provided the basis of a non-probability sample; the three were the Australian Society of Certified Practicing Accountants, the Australian Computer Society, and the Australian Institute of Management. Using a mail survey, data were collected from three hundred and ten work units representing a range of different industries and functions, sizes of organisation, technologies, and ownership groups. A limited sub-sample of these respondents subsequently took part in structured interviews about their performance control practices. Four major findings emerged from the study. First, uncertainty within a work unit significantly affects the mix of explicit and implicit controls, with an observed shift in mix towards greater reliance on implicit contracts. At the same time that the mix shifts towards implicit contracting, reliance on both implicit and explicit contracts increases. This finding indicates that, even though the information content of explicit data is imperfect, it has net value in assisting interpretation of performance. Second, greater levels of asset-specificity are also associated with increases in reliance on both implicit and explicit contracting. However, the mix between the two components remains essentially unaltered. Third, reliance on measures of both input and output increase at higher levels of uncertainty and asset-specificity. There was no change in the mix between input and output measures, indicating that they supplement each other. Imperfect input measures assist in the interpretation of imperfect output measures. Fourth, both determinants are associated with greater use of financial and non-financial measurement units. The mix between measurement unit remains approximately unaltered when considering level of uncertainty, but greater asset specificity is associated with a slight shift towards greater use of non-financial measurement. The findings of this research have implications when firms make choices about what performance to measure, and how to control performance in circumstances where empirical performance indicators are imperfect. Firms choosing their optimum mix of controls can refer to guidelines developed from this research.

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