Semantic Processing Theory Could Help Explain the Effectiveness of Creativity Incentives: A Numerical Experiment

ABSTRACT Two seminal accounting studies find that output creativity is insensitive to creative effort: workers simplify creativity-weighted contracts by focusing on increasing creativity, but they cannot increase output creativity enough to compensate for sacrifices they make to output quantity. Other studies, however, find evidence that output creativity is sensitive to creative effort. To examine these differing results, I use a numerical experiment: I model workers in quantity contracts and creativity-weighted contracts, and I proxy creative effort as workers' choice between fine semantic processing (less creative) and coarse semantic processing (more creative). My numerical experiment's results show (1) output creativity's sensitivity to creative effort when the task is less inherently creative and (2) output creativity's insensitivity to creative effort when the task is more inherently creative. More inherently creative tasks effectively require coarse semantic processing in both contracts, limiting workers' ability to give incrementally more creative effort in response to creativity incentives. Data Availability: Data are available upon request.

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A Comparison of Activity-Based Costing and Time-Driven Activity-Based Costing

Journal of Management Accounting Research ◽

10.2308/jmar-51686 ◽

2017 ◽

Vol 30 (1) ◽

pp. 143-167 ◽

Cited By ~ 10

Author(s):

Sophie Hoozée ◽

Stephen C. Hansen

Keyword(s):

Numerical Experiment ◽

Data Availability ◽

Activity Based Costing ◽

Analytical Comparison ◽

Resource Information ◽

Two Systems ◽

System Maps ◽

The Impact ◽

The Relationship ◽

Activity Information

ABSTRACT The relationship between activity-based costing (ABC) and time-driven activity-based costing (TDABC) has not been systematically investigated. We compare the two systems analytically and via a numerical experiment. Our analytical comparison generates formulas that describe how each system maps resources to activities and finally to products. We demonstrate that ABC aggregates resource-to-activity information by resources (columns), while TDABC selects partitions of activity-by-resource information. Our numerical experiment shows that TDABC is more accurate than ABC when traceability of resources to activities is high and activity traceability to products is low, while ABC is more accurate when activities are more traceable to products, irrespective of the level of resource traceability to activities. Finally, we examine the impact of hybridizing an ABC (TDABC) system with TDABC (ABC). We find that adding one ABC element into a TDABC system usually improves accuracy. However, adding one TDABC element into an ABC system usually substantially degrades accuracy. Data Availability: The simulated datasets are available from the first author on request.

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