The cultural acceptance of digital food shopping: conceptualisation, scale development and validation
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide an alternative framework that will assist in understanding the adoption of digital food shopping. The coronavirus disease 2019 (COVID-19) pandemic has exacerbated the demand for digital shopping, but the adoption of digital shopping for food has not accelerated as fast as in other product categories. This study considered the role of socio-cultural factors to understand the reason for slow adoption of digital technology to access food. A cultural framework that can be used to investigate socio-cultural factors in this context was lacking, however, this paper provides a discussion of social and cultural factors and developed measurement scales to assist in understanding cultural change acceptance in consumers' adoption of digital technology to purchase food. Design/methodology/approach Using Hayes' process analysis, this paper investigated how cultural acceptance – mediated by consumer affection and appeal and measuring the moderated effects of digital trust (DT) – determined the eventual impact on consumer intention to adopt digital food retailing. This paper also considered moderated mediation with parallel mediations (consumer affection and appeal, digital convenience (DC) and consumer digital readiness) interacting with DT and consumer learning. Findings The authors found that cultural acceptance of digital technology (CADT) is an antecedent to the adoption of digital shopping for food, but this is also mediated by consumers' appeal and affection for digital technology and consumers' digital readiness. Practical implications This study also indicates that DT influences consumer appeal and affection (CAA), especially amongst female consumers. Originality/value The paper represents an empirical investigation of a new conceptual framework that considers socio-cultural factors to understand consumers' use of digital technology in food shopping which has been an existing knowledge gap in current literature.
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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 | 0.030 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".