Customer gratitude expressions and FLEs’ prosocial behavior: insights from delighted customers
Bibliographic record
Abstract
Purpose This study aims to investigate the impact of customers’ delight on the likelihood of frontline employees (FLEs) receiving expressions of gratitude from customers, as well as the subsequent effects on their customer-focused and coworker-focused behaviors. Additionally, it examines how customer orientation moderates the relationship between FLE’s likelihood of receiving customer gratitude expressions and their performance behaviors. Design/methodology/approach The study used a sample from a Canadian retailer specializing in the sale of artistic and creative materials for artists, crafters and hobbyists. Longitudinal data was collected through a survey administered to frontline employees, unit managers and customers, spanning 7 assessment waves over a 12-month period. In total, the data set comprised 1,609 individual observations and 3,533 customers nested within 35 business units. The hypotheses were tested by using a multilevel longitudinal modeling approach. Findings This research has yielded important insights. First, significant relationships emerged between enhanced customers’ delight and an increased likelihood of FLEs receiving expressions of gratitude from customers. Second, gratitude expressions received from customers were found to be positively associated with prosocial behaviors toward both customers and coworkers. Third, the findings indicate that the impact of receiving customer gratitude expressions on FLEs’ performance behaviors is more pronounced for employees with a high level of customer orientation. Practical implications This study highlights the importance of investing in relationship-building strategies aimed at enhancing customers’ delight. This can motivate customers to express their gratitude toward service employees and to elicit higher prosocial behaviors from employees. Originality/value This study offers theoretical insights into gratitude, customer behaviors and employee performance in the retail industry. A pivotal contribution of this study to marketing literature lies in its paradigm shift, redirecting attention from the traditional examination of firm-customer relationships to a nuanced exploration of customer–employee relationships.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".