What makes you feel attached to smartwatches? The stimulus–organism–response (S–O–R) perspectives
Bibliographic record
Abstract
Purpose The purpose of this paper is to answer the question of whether smartwatches will survive and gain their own niche within the consumer electronics market. Based on the stimulus–organism–response (S–O–R) framework, this study identifies and validates the impacts of both technological and fashion-related factors (interactivity, autonomy, visual aesthetics and self-expression) on product attachment towards smartwatches through user satisfaction and pleasure derived from their smartwatches. Design/methodology/approach The authors collected the survey data via online surveys from 198 respondents and tested measurement and structural models with the partial least square technique. Findings The authors found that both technological characteristics (interactivity and autonomy) and fashion-related characteristics (visual aesthetics and self-expression) have an impact on product attachment through pleasure. Research limitations/implications Several other important characteristics of traditional wrist-watches such as durability or workmanship are not considered in this study, but should be included in future studies. The three-item measure of autonomy may be insufficient for more sophisticated wearable devices in the future. In future studies, the impact of product attachment on users’ continued usage should be examined. Practical implications This study provides important practical implications for smartwatch makers interested in product development, as users were found to consider fashion-related characteristics to be as important as technological characteristics. Originality/value This study is the first study that considers both aesthetic and technological factors for IT acceptance in the context of wearable devices. Also, instead of traditional IT acceptance measures such as continued use, this study investigates users’ product attachment, which is more relevant to the case of wearable devices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".