Predicting consumer-technology ownership without a diffusion history
Summary
This paper tests whether perceived attributes of consumer technologies, rated by humans and frontier language models, predict ownership prevalence better than years-since-launch, finding modest improvements but limitations for short-term forecasts.
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# Predicting consumer-technology ownership without a diffusion history Source: [https://arxiv.org/abs/2608.12344](https://arxiv.org/abs/2608.12344) [View PDF](https://arxiv.org/pdf/2608.12344) > 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\. ## Submission history From: Pontus Strimling \[[view email](https://arxiv.org/show-email/17f087a4/2608.12344)\] **\[v1\]**Wed, 3 Jun 2026 17:58:00 UTC \(1,075 KB\)
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