Corporate social responsibility and customer loyalty during the Covid-19 pandemic: evidence from pharmacy practice
Bibliographic record
Abstract
Purpose In recent years, corporate social responsibility (CSR) has taken on a more prominent role in both large and small businesses because of its significant impact on various aspects of business performance. To date, a growing body of literature has demonstrated the mechanisms whereby CSR practices affect organizational outcomes; however, there has been little research examining how CSR practices contribute to customer loyalty within the pharmacy context. As such, this study aims to explore how CSR practices influence the loyalty of pharmacy customers, particularly in relation to the mediatory effects of customer-company identification (CCI) and customer trust. Design/methodology/approach A survey questionnaire was developed and administered to collect the required data from the pharmacy context. The resultant data were subjected to exploratory factor analysis to identify the scale dimensions, followed by multiple regression analysis to test the hypotheses. Findings Analysis of the results (n = 528) revealed that perceived CSR indirectly impacts loyalty through the mediatory effects of trust and CCI. All hypothesized effects were also confirmed via empirical testing. Originality/value The findings of this research suggest that not only are CSR activities responsive to societal concerns, but they can also promote customer identification with pharmacies and strengthen customer trust, which can, in turn, lead to long-term customer loyalty.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 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".