From amusement parks to casinos, cruises to video games, large sectors of the economy market consumer fun. Yet surprisingly, little research has been devoted to understanding the consumer psychology of fun—both the experience and its main psychological drivers in marketplace settings. This dissertation aims to develop a psychological theory of consumer fun that can help inform how fun experiences are engineered and managed by both businesses and consumers. I use a multimethod approach combining in-depth interviews and narrative analyses with controlled experiments, structural equation modeling, and field data analysis of consumer selfies. Two psychological pillars of consumer fun are identified: (1) hedonic engagement and (2) a sense of liberation. Each pillar in turn rests on two sub-pillars: (1a) perception of novelty and (1b) connectedness, and (2a) a sense of spontaneity and (2b) impressions of boundedness. My dissertation research shows that fun is an experience of liberating engagement, a temporary release from psychological restriction via a hedonically engaging activity. Importantly, a digital ethnography of consumer selfies showed that compared to other positive experiences such as happiness, pride, or relaxation, fun is much more likely to be situated in commercial settings, thus substantiating the business relevance of fun. Through five experiments, I show that marketers can engineer fun by directly activating feelings of liberation through situational cues such as boundedness. Using a proprietary dataset by Brand Asset Valuator, I show that fun emerges as a major brand image attribute that is significantly related to brand preference and key financial outcomes such as revenue. Broadly, my dissertation reveals that fun leads to increased consumer well-being, independently from the meaningful, eudaimonic path toward happiness.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/d8-b5e0-f942 |
| Date | January 2020 |
| Creators | Oh, Tae Seok |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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