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This study explores managerial behavioral responses associated with the extent to which a firm's performance measurement system is linked to its strategy (SPMS). We hypothesize that an SPMS is positively associated with higher levels of job-relevant information (JRI) and lower levels of role stressors, which are then associated with higher levels of managerial performance. Using survey data from over 700 respondents, we find that an SPMS positively affects performance through its relations with JRI and role ambiguity (RA). Managers perceive that they have higher levels of JRI and lower levels of both role conflict (RC) and RA when they have an SPMS closely linked to strategy. In turn, performance is higher when managers perceive that their RA is lower. Additionally, we find that the link to the evaluative process, complexity, and managerial experience moderate the relations between an SPMS and JRI, RA, and RC.

Strategic performance measurement systems (SPMS) that translate a firm's strategy to its employees are increasingly used. We examine whether the extent to which an SPMS is coupled with strategy affects employee performance indirectly through motivational characteristics including perceived self-efficacy and perceived psychological contract. Using data from 242 employees, we find evidence that the extent to which an SPMS is tightly coupled with strategy affects employee performance through perceived self-efficacy and perceived psychological contract. Self-efficacy is a critical dimension of intrinsic motivation. Thus, an implication of our findings is that tightly coupling an SPMS-based incentive plan with strategy facilitates internalized motivated behaviors. We also find that our hypothesized results hold across varying levels of two types of employee climate. However, the workforce's age and education levels serve as boundary conditions since we find that the relation between self-efficacy and employee performance holds only for the older, less-educated employees. Data Availability: Data used in this study cannot be made public due to a confidentiality agreement with the participating firm.

Accounting information systems (AIS) research data may suffer from severe non-normality, which, if not handled properly, may lead to incorrect statistical inferences. To address this problem, we empirically evaluate the relative merits of a Two-Step normality transformation proposed by Templeton (2011) compared to four alternative distributions available to researchers (random-normal, original, natural log transformed, and winsorization transformed). Using 45 corporate financial performance ratios (CFP), we investigated three perspectives on measurement validity: construct validity, reliability, and difference testing. We then examined the efficacy of the Two-Step method in the context of business value of IT research—we regressed four IT investment and three control variables on 31 of theoretically relevant CFP indicators. The preponderance of our evidence shows that the Two-Step method consistently outperforms the prominently used alternatives in achieving statistical normality, retaining original series means and standard deviations, exhibiting validity and reliability, and theory testing. Our findings strongly suggest that AIS researchers consider adopting the Two-Step normality transformation when utilizing non-normally distributed data to obtain a more accurate understanding and interpretation of results.

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