Consumers' intention to adopt virtual grocery shopping: do technological readiness and the optimisation of consumer learning matter?
Bibliographic record
Abstract
Purpose It has generally been anticipated that the growth of Internet technology and e-commerce would result in virtual grocery shopping (VGS) becoming a normal way of life for consumers worldwide. However, the adoption of VGS, except in China and other Asian countries, has been quite slow and there is little understanding for this reason. Using Canada as a research context, the purpose of this study was to investigate the attitudes of consumers towards VGS with a focus on their technological readiness and the impact of the optimisation of consumer learning. Design/methodology/approach A quantitative research methodology was undertaken using cluster analysis with descriptive statistics to segment the different groups of consumers from a sample of 1,034 adult respondents. Structural equation modelling (SEM) was then used to test a theoretical model for consumers’ intention to adopt VGS. Findings The study found that the attitudes of consumers towards virtual shopping, convenience motivation, perceived ease of use (PEOU), perceived risk and consumer learning are all factors that impact consumers' intention to adopt virtual food shopping. The research also identified four segments of consumers in the Canadian market based on their attitudes and intention to adopt VGS. These results allow grocers to target the consumer groups favourable to VGS and provide insights on the factors that can be manipulated via marketing strategies to reach these consumers. Practical implications Retailers are provided with insights on consumers behaviour that will allow them to target specific segments with shopping modalities. Originality/value This research investigated VGS, focussing on consumer learning as a socio-cultural influence as well as the consumer's technological readiness as an intention to adopt to this modality of shopping for food. These constructs have not been investigated by previous studies on food grocery shopping.
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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 | 0.009 |
| 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 | 0.001 | 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".