A gamification approach for enhancing older adults' technology adoption and knowledge transfer: A case study in mobile payments technology

Siyang An, Chi Fai Cheung and Kelvin W. Willoughby

Technological Forecasting and Social Change, 2024, vol. 205, issue C

Abstract: Technology is assumed to be important for enhancing older adults' life quality and for ameliorating age-related problems, but older adults nevertheless typically exhibit lower technology adoption rates than young people. Gamification has the potential to address this problem by motivating older adults, but its value for the elderly has thus far been undermined through gamification design biases favoring young people. This study addressed this problem by developing a purpose-built gamified learning system, based on a popular mobile payment platform, to test the potential of employing a gamification-and-learning approach to the design of gamification systems for enhancing knowledge transfer and technology adoption by older adults. The research employed structural equation modeling, incorporating user knowledge and gamification-related constructs, drawing upon the established Technology Acceptance Model. Data were collected from older adults in Hong Kong with an appropriate demographic and market profile, following a one-group pretest–posttest research design. The results revealed notable gamification-induced improvements in the knowledge and technology adoption intentions of older adults, and significant positive relationships between gamification effectiveness and technology adoption constructs. The research demonstrates the significant positive effects which gamification may have on the acceptance and usage of technology by older adults and evokes policy implications for the silver-hair market.

Keywords: Gamification; Extended technology acceptance model; Mobile payments; Older adults; Knowledge transfer; System design (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:205:y:2024:i:c:s004016252400252x

DOI: 10.1016/j.techfore.2024.123456

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