Positive moods and word-of-mouth in the banking industry
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the mediating effect of functional value and symbolic value between positive moods and word-of-mouth (WOM) referrals in the context of Taiwan’s banking industry. In addition, this study investigates the moderating effect of relational benefits on the relationship between perceived value and WOM. Design/methodology/approach The research model was tested using data collected from customers ( n =362) of the top 10 domestic banks in Taiwan. Structure equation modeling was employed to test and validate the conceptual model. Findings Positive moods are found to be an important predictor of functional value, symbolic value and WOM in this banking service study. Four types of relational benefits are identified including social, special treatment, confidence and face. Note that two distinct segments of bank customers are identified in terms of relational benefits: those who appreciate face benefits ( n 1 =169), and those who appreciate general relational benefits ( n 2 =193). The findings reveal the existence of partial mediation between a banking customer’s mood and WOM through functional value and symbolic value in the overall sample ( n =362). However, it was found that functional value partially mediates the influence of positive moods on WOM among respondents in the “general relational benefits” segment only. That is, relational benefits are found to moderate the relationship between functional value and WOM. Originality/value This study expands the existing body of knowledge on customers’ perceptions of value by differentiating types of value perceptions.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".