Bibliographic record
Abstract
The current work is an investigation into the effect of customer relationship management on an organization's competitive advantage, considering the mediating variable of customer loyalty.The statistical population of the study was composed of the customers of the branches of Alborz Insurance Company, Tehran, Iran.According to Cochran formula, the sample size in the study is 384 among whom questionnaires were distributed randomly.This is a descriptive survey conducted using field methods.Data collection instrument in the study was a three-page questionnaire with 31 items, including three questionnaires which were localized based on the questionnaires of customer relationship management standard, customer loyalty and gaining competitive advantage.After collecting the research data based on the research hypotheses, we analyzed the data using the confirmatory factor analysis (CFA) and structural equation modeling.The results of this study illustrated that customer relationship management and its dimensions have a positive impact on loyalty.Loyalty, in turn, has a positive impact on competitive advantage.In addition, the results obtained from the structural equations indicate the positive impact of customer relationship management, through loyalty, on competitive advantage.Relying upon the findings of the research, we suggest that the management of Alborz Insurance Company update its knowledge base with the help of its specialized team to improve the management of customer relationship.This will require more workforce, more up-to-date communication tools for creating loyalty among their customers to gain a better competitive advantage.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.934 | 0.916 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".