Adoption and non-adoption motivational risk beliefs in the use of mobile services for health promotion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it