고객 만족도와 고객 충성도의 관계에서 공간 의존성과 공간 변화성에 대한 연구
Bibliographic record
Abstract
For recent decades, a plethora of research in marketing literature has explored the relationship between customer (or market) metrics and firm financial performance. These studies have found that customer metrics have a positively significant impact on firm profitability. The literature has identified that among customer metrics, customer loyalty is one of the most important drivers for firm profitability, and customer satisfaction is a key antecedent to customer loyalty. However, the literature is challenged by the finding that customer satisfaction does not always equate to customer loyalty. This challenge may be due to researchers’ failure to identify the spatial dependence and spatial variation in customer satisfaction-loyalty data. Research on the customer satisfaction-loyalty relationship, in general, employs classical global empirical models that do not account for spatial dependence across customers’ satisfaction-loyalty behavior or spatial variation in customers’ behavior across different geographic spaces and product categories. Therefore, the parameter estimates of customer satisfaction obtained from these models could be biased and inconsistent, which leads to inconsistent empirical results. To remedy this problem, we employ global and multi-level spatial regression models to examine the customer satisfaction-customer loyalty relationship across different product categories, given that these spatial models can effectively handle spatial dependence and spatial variation. The empirical results indicate that customer satisfaction is a very significant antecedent to customer loyalty, and its impact is positive after controlling for spatial dependence. However, the strength of this positive customer satisfaction-loyalty association varies over geographic space and, more importantly, product category. These results provide significant academic and managerial implications for retailers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".