Predicting consumer-technology ownership without a diffusion history
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Computer Science > Computation and Language
Title:Predicting consumer-technology ownership without a diffusion history
Abstract:We test whether the perceived attributes of a consumer technology predict how widely it is owned. In a 2022 Prolific survey of US adults (n = 678), respondents rated 65 consumer technologies on six attributes. We then elicited the same ratings from two frontier language models, Anthropic Claude Opus 4.7 and OpenAI GPT-5.5. We regress ownership prevalence on four UTAUT2 acceptance attributes plus a log-age covariate with a sign-constrained penalized regression and evaluate it by holding out one technology at a time. The attribute model improves on a baseline of years-since-launch: mean absolute error falls by 17% with the human ratings, and by more with either model, most with Opus 4.7. Over the short 2022-to-2025 window, where ownership moved little, the same attributes do not improve on a no-change baseline. We set out the limitations of the approach, including the possibility that language-model ratings reflect prior knowledge of these technologies rather than independent attribute reasoning. We include a deployment illustration: 2027 ownership predictions for eleven products launched in 2025 and 2026.
| Comments: | 31 pages, 4 figures, supplementary material included (Tables S1-S6, Figure S1), data and code at this https URL |
| Subjects: | Computation and Language (cs.CL); Computers and Society (cs.CY); Applications (stat.AP) |
| Cite as: | arXiv:2608.12344 [cs.CL] |
| (or arXiv:2608.12344v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.12344
arXiv-issued DOI via DataCite
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