A Longitudinal Analysis of Customer Satisfaction and Share of Wallet: Investigating the Moderating Effect of Customer Characteristics
Bibliographic record
Abstract
Customer loyalty is an important strategic objective for all managers. Research has investigated the relationship between customer satisfaction and loyalty in various contexts. However, these predominantly cross-sectional studies have focused on customer retention as the primary measure of loyalty. There has been little investigation into the impact on share of wallet. Using data from the Canadian banking industry, this research aims to (1) provide the first longitudinal examination of the impact of changes in customer satisfaction on changes in share of wallet and (2) determine the moderating effects of customer age, income, education, expertise, and length of relationship. Data from 4319 households using 12,249 observations over a five-year period indicate a positive relationship between changes in satisfaction and share of wallet. In particular, the initial satisfaction level and the conditional percentile of change in satisfaction significantly correspond to changes in share of wallet. Two variables, income and length of the relationship, negatively moderate this relationship. Other demographic and situational characteristics have no impact.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".