Publications
Gai, Phyliss J. and Stefano Puntoni, “Language and Consumer Dishonesty: A Self-Diagnosticity Theory," forthcoming, Journal of Consumer Research.
A theory of when using a foreign language would increase, decrease, and not change lying behavior, compared to using one's native language.
* Created an original paradigm on cheating, summarized here and adapted for neural studies.
* Early findings incorporated in a meta-analysis by Köbis et al. (2019).
* A short summary in layperson language on JCR website
Gai, Phyliss J. and Anne-Kathrin Klesse (2019), “Making Recommendations More Effective through Framings: Impacts of User- versus Item-based Framings on Recommendation Click-throughs," Journal of Marketing, 83 (6), 61-75. [link]
Framing the same recommendation as "People who like this also like" versus "Similar items" improves the click-through rate of recommendations, under certain conditions.
Working papers
Gai, Phyliss J. and Amit Bhattacharjee, “Willpower signals moral goodness"
People infer moral goodness from the success in non-moral self-control, but not moral badness from the failure.
Gai, Phyliss J. and Gita Johar, "Using mobile devices leads to more discriminative sharing of information"
Analyses of Twitter data and experiments reveal that people are more discriminative in sharing information of high (versus low) quality on mobile devices as opposed to non-mobile devices.
Gai, Phyliss J., (Mirjam Tuk, and Steven Sweldens)* "Choosing within vice and virtue"
Choosing within the vice or virtue category is fundamentally different from choosing between vice and virtue categories.
*(Note: I no longer collaborate with the latter two researchers for personal reasons. Their contribution to the early stages of the project is gratefully acknowledged. )
*(Note: I no longer collaborate with the latter two researchers for personal reasons. Their contribution to the early stages of the project is gratefully acknowledged. )
Gai, Phyliss J., Eugina Leung, and Anne Klesse, "Diversity signaling to algorithmic versus human recommenders"
Algorithmic (versus human) recommenders are perceived as lacking the purpose as well as the ability to recommend diverse products. In turn, consumers are less likely to indicate their diverse preferences.
Yu Feng, Yiqi Yu, Phyliss Jia Gai, "Lucky Machine: Task Delegation to AI versus Human with Uncertainty" (in Chinese)