Paper Type
ERF
Abstract
Older adults are often stereotyped as technophobic and reluctant to embrace new technologies, a view potentially exacerbated by the rise of artificial intelligence (AI). However, older adults are not a homogenous group. The present study aims to explore the heterogeneity of technophobia and technology adoption among older adults. Additionally, it investigates the impact of demographic characteristics on potential categories. Data from 143 Chinese older adults were analyzed using latent profile analysis (LPA), revealing three distinct profiles: tech-confident adopters (30%), cautious moderates (40%), and tech-averse avoiders (30%). One-way ANOVA identified significant subgroup differences in domain-specific adoption (e.g., task completion, communication, information seeking, recreation). Among demographic variables, education level and volunteering experience emerged as significant predictors of these profiles. These findings underscore the need for tailored interventions to mitigate technophobia and enhance technology adoption among older adults, particularly in an AI-driven era.
Paper Number
1921
Recommended Citation
Dai, Shuo; XI, Wanyu; and Xue, Xiang, "Older, But Not All Fear: Latent Profile Analysis on Technophobia and Technology Adoption Among Older Adults" (2025). AMCIS 2025 Proceedings. 41.
https://aisel.aisnet.org/amcis2025/intelfuture/intelfuture/41
Older, But Not All Fear: Latent Profile Analysis on Technophobia and Technology Adoption Among Older Adults
Older adults are often stereotyped as technophobic and reluctant to embrace new technologies, a view potentially exacerbated by the rise of artificial intelligence (AI). However, older adults are not a homogenous group. The present study aims to explore the heterogeneity of technophobia and technology adoption among older adults. Additionally, it investigates the impact of demographic characteristics on potential categories. Data from 143 Chinese older adults were analyzed using latent profile analysis (LPA), revealing three distinct profiles: tech-confident adopters (30%), cautious moderates (40%), and tech-averse avoiders (30%). One-way ANOVA identified significant subgroup differences in domain-specific adoption (e.g., task completion, communication, information seeking, recreation). Among demographic variables, education level and volunteering experience emerged as significant predictors of these profiles. These findings underscore the need for tailored interventions to mitigate technophobia and enhance technology adoption among older adults, particularly in an AI-driven era.
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