Translating reinforcer dimensions and behavioral economic demand to inform incentive delivery in organizational behavior management

Recent research has effectively translated behavioral economic demand curve analyses for use with work-related behavior and workplace incentives (e.g., Henley, DiGennaro Reed, Reed, & Kaplan, 2016). The present experiments integrated a hypothetical and experiential demand preparation into a computerized task for use with Amazon Mechanical Turk Workers to evaluate the effects of parametric manipulations of reinforcer dimensions on performance using a behavioral economic demand framework. The first experiment examined the effects of three incentive magnitudes ($0.05, $0.10, and $0.20) on performance assessed with a progressive ratio schedule. Results indicate responding on the hypothetical and experiential demand assessments was sensitive to incentive magnitude, with higher responding in the higher incentive magnitude conditions. Participant responses on the hypothetical assessment were in general agreement with observed responding in the experiential assessment. The second experiment extended the methods of Experiment 1 to evaluate the effects of three parametric values of reinforcer probability (10%, 50%, and 90% probability of earning incentives). Responding was generally comparable for all three probability conditions. Experiment 3 evaluated the effects of three delays to incentive receipt (1, 14, and 28 days). Responding was higher in the condition in which incentives were delayed by 1 as compared to 28 days. Results of the current studies may inform the development of novel methods for measuring reinforcer efficacy in organizations.

Grantor dc:publisher

University of Kansas

Year dc:date.issued

2017

Author dc:creator

• Henley, Amy Jessica

Advisor dc:contributor.advisor

• DiGennaro Reed, Florence D.

• Behavioral sciences
• Behavioral economics
• Behavior analysis
• Demand
• Incentives
• Organizational behavior management
• Performance

Statement dc:rights

• Copyright held by the author.

Language dc:language.iso

en

Dc Identifier Other

http://dissertations.umi.com/ku:15304

OAI identifier oai:identifier

oai:kuscholarworks.ku.edu:1808/26327