Mobile application e-grocery retail adoption challenges and coping strategies: a South African small and medium enterprises’ perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper explores how small and medium-sized e-grocery mobile application retailers evolving within the geographical context of South Africa and operating in the urban, township, and rural areas respond to theoretically and emerging field-based e-business and e-grocery adoption challenges, respectively. The study used semi-structured qualitative interviews to explore the coping strategies of e-grocery mobile application retailers to mitigate technological, organizational, and environmental (TOE) adoption challenges. The significance of small grocery adoption strategies related to context informs e-grocery adoption from the evidence generated in other small e-grocers and for the superior grade of TOE (or theoretical) knowledge sought from the inevitable evolving mobile application and digital grocery markets. The findings reveal that specialist skills and unified team production are crucial conduits for lowering the TOE barriers to e-business and e-grocery adoption. They also reveal the interconnected resource orchestration, shared value, and social inclusion strategies used to mitigate various e-business and e-grocery challenges.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it