Taylor (2020) proposed a conceptual framework in which gaming of performance measurement (PM) systems by public officials — manipulating recorded performance rather than actual performance — results from the interplay of officials' motivations and the opportunities for manipulation presented by the context, and articulated specific propositions and a causal model. The framework has never been empirically tested as an integrated whole. The open problem is to establish empirically whether and how motive configurations and opportunity structures jointly and interactively produce gaming; evidence on whether these relationships generalize across public-sector settings and levels of analysis would strengthen such a test, and extension of the account to gaming in other stages of performance management such as the use of performance information in decision-making is at most an optional follow-on direction beyond the framework's scope. Later research has tested isolated links — single motives (prosocial impact, blame avoidance) or single opportunity structures (competition, performance feedback, multi-goal targets, patronage networks) — but no study measures motives and opportunities jointly and tests their interaction, and the extension beyond the measurement stage remains essentially untouched.

Background

Performance measurement and management (PM) systems require public organizations to record and report quantified indicators of their performance and to pursue explicit performance targets. A long-documented dysfunctional response is gaming: behaving so that the measured indicator improves while the underlying performance does not — for example, excluding difficult cases, shifting effort to measured dimensions at the expense of unmeasured ones, or outright manipulation of reported data. Campbell (1979) warned that 'the more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures', an effect now called Campbell's law, and Bevan and Hood (2006) documented gaming responses to targets in the English public health care system. Understanding why public officials game — which conditions turn a performance regime into an invitation to manipulate — is a central question of public-management research. Taylor (2020) offered a systematic answer: gaming is a product of motivation (why officials want to game — the framework distinguishes instrumental motives (gaining tangible rewards or avoiding sanctions), intrinsic motives (psychological benefits such as feeling competent), which in turn subsume self-interested and other-interested (prosocial) orientations, and conformity-based motives (going along with what peers or superiors expect)) and opportunity (whether the context makes gaming feasible — arising from the design of the performance measurement system itself, from the organization (e.g. supervision and resources), and from the broader operating environment). The article articulated specific propositions and a causal model linking motivation and opportunity to gaming, but it is a conceptual contribution: its propositions were not tested against data. The framework is also scoped by its title and abstract to gaming of performance measures and targets; how gaming operates in other stages of performance management, including the use of performance information (reported performance data) in decision-making, lies beyond that scope. In this field an 'empirical test' conventionally means field data, quasi-experimental designs exploiting policy reforms, or survey/vignette experiments, with attention to levels of analysis (individual officials, work units, organizations, whole systems) — gaming being a covert behavior, so direct observation is difficult and self-reports are biased.

Problem Statement

The motivation–opportunity framework for gaming of performance measures and targets has been articulated conceptually (Taylor 2020) but not validated. The problem is to establish empirically, and refine on that basis, the causal account of why public officials game performance measures and targets: (i) do officials' motivations and contextual opportunities interact in producing gaming, as the framework's propositions and causal model predict — for instance, does the same opportunity produce gaming only under particular motive configurations, and do motives produce gaming only when opportunity permits; (ii) how do the distinct forms of motivation identified by the framework (instrumental motives; intrinsic motives, which subsume self-interested and other-interested (prosocial) orientations; and conformity-based motives) combine or trade off, and do particular combinations yield distinct gaming behaviors (e.g., effort substitution versus outright data manipulation); and (iii) how do the opportunity structures created by the performance measurement system itself, the organization, and the broader operating environment interact — are they substitutes or complements in enabling gaming. The causal account to be established addresses all three facets jointly: how motivations and opportunities interact (i), how the framework's motive forms combine or trade off and with what distinct behaviors (ii), and how the three opportunity sources interact (iii). It would strengthen such a test if the evidence also speaks to whether these relationships hold across different public-sector settings (sectors, countries, administrative levels) and levels of analysis (individual officials, work units, organizations, whole performance regimes), or whether the framework requires refinement for particular settings and levels; cross-setting and cross-level coverage is a strengthening property of the test rather than a component of the account. Extension of the account to other stages of performance management beyond the measurement of performance, including the use of performance information in decision-making, is an optional follow-on research direction beyond the scope of Taylor (2020)'s framework. The target of the account is an empirically supported, refined version of the causal model — evidence capable of confirming, rejecting, or delimiting its propositions — together with the resulting theoretical revision.

References

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