Adoption and non-adoption motivational risk beliefs in the use of mobile services for health promotion
Bibliographic record
Abstract
Purpose The purpose of this paper is to validate empirically a theoretical model that integrates an innovative construct capturing consumers’ non-adoption risk belief associated with not using a mobile service designed to support them in a non-leisure activity. Design/methodology/approach A theoretical model contrasting perceived non-adoption risk to perceived adoption risk of a mobile service supporting health promotion was developed and tested with a sample of potential consumers in North America. Findings Results show that non-adoption risk is a moderately strong antecedent of motivational factors in contrast to adoption risk that hinders the acceptance of a mobile service supporting health promotion. Research limitations/implications Healthcare is a highly sensitive social sector, so possible negative consequences of not using the support of a mobile service are an additional motivation for adopting this service. Future research should test the role of non-adoption risk in other contexts of technology use, including non-leisure settings. Practical implications Making potential users see the possible negative consequences of not using a mobile service designed to support them in a non-leisure activity increases their motivation and, subsequently, intention to use the service. Social implications Educational efforts to making consumers see the risks of not using a supporting technology application appear to be justified. Originality/value This study demonstrates the significant role of non-adoption risk belief that captures the negative consequences individuals may perceive if they fail to use as expected a mobile service application designed specifically to help them.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".