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Performance Measure Aggregation in Multi‐Task Agencies
Author(s) -
Şabac Florin,
Yoo Junwook
Publication year - 2018
Publication title -
contemporary accounting research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.769
H-Index - 99
eISSN - 1911-3846
pISSN - 0823-9150
DOI - 10.1111/1911-3846.12418
Subject(s) - personalization , aggregate (composite) , standardization , task (project management) , measure (data warehouse) , computer science , homogeneous , process (computing) , activity based costing , performance measurement , degree (music) , process management , data mining , mathematics , business , accounting , engineering , marketing , world wide web , materials science , physics , systems engineering , combinatorics , acoustics , composite material , operating system
In multi‐task environments, the efficiency of aggregating managerial performance information and the degree of customization/standardization are closely related. Aggregation without information loss (i.e., statistically sufficient) requires at least as many measures as there are effective tasks (which arise through a task aggregation process analogous to that applied to homogeneous activities in activity‐based costing) and can be used uniformly for evaluation across similar jobs. Aggregation without economic loss (i.e., economically sufficient) can be achieved with a single performance measure but requires customization even across similar jobs. The main implication is that job complexity, the number of aggregate performance measures, and the degree of customization in performance measurement are interrelated. In particular, at the same level of performance measure aggregation, we predict highly customized performance evaluation in complex multi‐task jobs and standardized (uniform) performance evaluation only in simpler jobs with fewer tasks. We discuss additional empirical implications in the conclusion.