Occupational moderation in food delivery platforms: a UTAUT-based analysis of consumer purchase intentions
Bibliographic record
Abstract
Purpose This study investigates the moderating effects of consumers’ occupations on their purchase intentions (PIs) for food takeout services using a modified unified theory of acceptance and use of technology model. It evaluates how different occupations influence the relationships between social influence (SI), expectation confirmation (EC), facilitating conditions (FC) and PI. Design/methodology/approach The study collected data from individuals in various occupations, including technical/associate professionals, executives/professionals, administrative/service workers and manual/operative workers. The data were analyzed using structural equation modeling, while hierarchical analysis assessed how occupation moderated the relationships between latent variables (SI, EC and FC) and PI. Findings Different occupations have a certain moderating effect on the relationships between SI/EC/FC and PI. For the technical and associate professionals and manual and operative occupations, the moderating effect of FC on PI is stronger than that of EC and SI. For executives and professionals and administrative and service occupations, the moderating effect of EC on PI is stronger than that of SI and FC. Originality/value This study provides new insights into how occupational differences influence consumer behavior in the context of online food ordering services. The results expand the application of the unified theory of acceptance and use of technology model and the understanding of the influence of occupation on consumer’ behavior.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".