Consumer adoption of digital grocery shopping: what is the impact of consumer’s prior-to-use knowledge?
Bibliographic record
Abstract
Purpose Smart retail technology adoption models are largely focused on consumer perceptions of the technology and the characteristics of digital technologies. However, the impact of the prior-to-use knowledge of consumers on the adoption of the technologies has been understudied. This research examined to what extent social acceptance and consumer learning can facilitate consumer adoption of digital grocery shopping (DGS). Design/methodology/approach This paper builds on the innovation–decision model to develop a framework to examine the impact of social acceptance and consumer learning on DGS. The research tested a structural model based on data collected from 611 North American participants. Findings This study found that the social acceptance of DGS directly and consumer learning indirectly affects the appeal of grocery shopping to consumers and consequently increases their intention to adopt this new shopping method. Furthermore, the results indicated that both hypothesised directions are parallelly mediated by digital convenience, the consumer’s digital readiness and digital trust. Originality/value This study extends the understanding of consumer adoption of DGS by highlighting the influence of consumer knowledge about DGS on their behavioural intention. Several important theoretical and practical implications are provided to help retail managers to develop service strategies.
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.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".