Examining consumers' continuance and sharing intention toward food delivery apps
Bibliographic record
Abstract
Purpose A food delivery app (FDA) is a technological advancement connecting restaurants and consumers, making it possible to deliver food home conveniently. The current study seeks to identify the factors affecting consumers' continuance intention and sharing intention toward the FDA in the USA and Canada using an integrated framework built using trust transfer theory and a variety of constructs. Design/methodology/approach The authors collected data/inputs from 476 respondents in the USA and Canada who had used FDAs in the past and analyzed them using the structural equation modeling technique. Findings The results indicate that trust in FDA, trust in the user community and commitment affect continuance intention and sharing intention. Interestingly, trust in the seller does not influence commitment, continuance intention and sharing intention. Additionally, the trust disposition and reputation of the FDA play an important role in building trust in FDA. Research limitations/implications The present study combines the trust transfer theory with various important constructs such as commitment, trust disposition and reputation of the FDA to build an integrated framework to elucidate the continuance intention and sharing intention toward FDAs. Practical implications This study facilitates the FDA providers to understand how trust disposition, the reputation of the FDA and trust in the Internet build trust among FDA consumers. The study also helps them to fine-tune their trust-building strategy by considering several trust targets. It further enables them to appreciate how commitment results in continuance intention and sharing intention toward FDA. Originality/value It is an original study investigating the role of various constructs and trust transfer theory in shaping the consumers' continuance intention and sharing intention toward the FDA.
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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 | 0.002 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".