Customer engagement in a retail setting: an examination of antecedents and outcomes
Bibliographic record
Abstract
Purpose Studies examining customer engagement (CE) with retailers have been sparse. We propose and test a comprehensive model of CE in three separate retail contexts. Building on the relationship marketing framework, we propose that satisfaction, trust and affective commitment are antecedents of CE. CE, a second order construct, manifests in terms of attitudinal loyalty, as well as intention to repurchase, willingness to advocate and actual spending. Design/methodology/approach Two national surveys were conducted in Canada. Study 1 focused on apparel (N = 225) and grocery (N = 229) retail sectors. In Study 2, survey responses from customers of one specific pharmacy retail chain (N = 464) were combined with their actual spending with the retailer. Structural equation modeling was used to test CE as a second-order construct. Findings Data from both studies supported the proposed model and hypotheses across all datasets. CE requires retailers to provide customer experience that builds trust and emotional attachment, which in turn leads to brand advocacy and increased spending with the retailer. Practical implications These studies show that CE is determined by satisfaction, trust and commitment. Retailers must focus on meeting and exceeding customer expectations, so they are highly satisfied. By consistently delivering the brand promise retailers can build trust and elicit affective commitment from customers which are crucial for developing CE. Originality/value This study demonstrates the process of CE creation in retail as well as the outcomes of engagement, with validation in three different retail settings. We combine data on consumer spending from a retailer's CRM system with their survey responses to demonstrate the real-world validity of our model.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".