Investigate the Impact of Relationship Marketing Orientation on Customer Loyalty: The Customer's Perspective
Bibliographic record
Abstract
In today's high competitive and globalize banking context, increasing Customer loyalty emerges as the mostimportant challenges faced by marketers. Cultivating loyal customers is frequently argued to be the single mostimportant driver of organizations’ long-term financial performance, which can lead to increased sales andcustomer share, lower costs, and higher prices. Therefore marketing scholars emphasize the influence ofrelationship marketing as a strategically important tool from which customer loyalty can be secured and, as aresult, the attainment of higher competitiveness and enhanced customer satisfaction can be achieved. Thepurpose of this study is to empirically investigate the impact of relationship marketing orientation on customerloyalty. The study also aims to test the impact of demographic variables, in association with relationshipmarketing dimensions, on customer loyalty. The study was carried out in 2008 on a convenience sample of 450respondents through the distribution of structured questionnaires to bank customers within the area of Amman,Jordan. The data were factor analyzed to determine the key dimensions of relationship marketing orientationmeasurement scale. Results confirm that the five dimensions scale (Bonding, trust, communication, satisfactionand commitment) possess adequate reliability and internal consistency as well as convergent validity. Results ofregression analysis show that relationship marketing orientation is significant in explaining the variation incustomer loyalty. In addition, sex and income displayed a significant impact on the relationshipmarketing-customer loyalty relationship. The findings contribute to understanding the relationships betweendifferent dimensions of relationship marketing orientation, customer loyalty and demographic variables; providecritical implications for bank managers; and highlight directions for future research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.004 | 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".