Food private label brands: the role of consumer trust on loyalty and purchase intention
Bibliographic record
Abstract
Purpose – Private label brands of food products are an important component of many consumers’ purchases, as well as an integral element of the retail industry. The purpose of this paper is to examine the moderating role of trust on food private label brands’ purchase intention and loyalty. Design/methodology/approach – For this purpose, the authors propose and empirically test a conceptual model comprising variables such as price, familiarity and store image. A sample of 445 respondents was gathered, and the hypotheses were tested performing structural equation modelling. Findings – The findings highlight the moderating influence of trust on consumers’ loyalty to food private label brands. In addition, the results obtained reveal the substantially great influence of private label brand familiarity on purchase intention and loyalty. So, it seems that consumer trust and loyalty are strongly associated regarding food private label brands. Research limitations/implications – The authors suggest that trust of food private label brands allows retailers to increase consumer loyalty. Practical implications – Consequently retail managers should consider the enhancement of trust in the context of a marketing strategy formulation for food private label brands. Originality/value – The present study provides insights into the moderating effect of trust on loyalty to food private label brands, as well as evidence of the strong influence of familiarity on private label brands’ proneness, related to food products.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".