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Medium Minimization Effect of Medium-type Reward in the Online Referral Reward Programs: A General Evaluability Perspective.

Authors :
Shouwang Lu
MengXiang Li
Kanliang Wang
Source :
Proceedings of the International Conference on Information Systems (ICIS); 2023, p1-17, 17p
Publication Year :
2023

Abstract

Medium-type reward is a token that people receive as an immediate reward for their effort and can be traded for a desired outcome, and has been widely used in various promoting campaigns. However, our understanding of its impact remains limited. This research focuses on the effect of medium-type reward on individuals’ referral intention in online reward referral programs. Based on general evaluability theory, we propose the medium minimization effect, i.e., individuals have higher referral intention when the numerical value of medium-type reward is small (vs. large) and that the effect will be attenuated when the reward strategy does not care whether referral is successful or the actual reward is uncertain. Results of three experimental studies support our hypotheses. Findings put forward new insights into the medium effect, as well as its potential mechanism, and individuals’ referral behavior, and can help firms optimize the design of online reward referral program systems. [ABSTRACT FROM AUTHOR]

Details

Language :
English
Database :
Complementary Index
Journal :
Proceedings of the International Conference on Information Systems (ICIS)
Publication Type :
Conference
Accession number :
179005155