The effect of customer relationship management (CRM) on business profitability in Jordanian logistics industries: The mediating role of customer satisfaction
Bibliographic record
Abstract
In today's competitive business environment, the implementation of Customer Relationship Management (CRM) strategies is essential for firms to succeed. The purpose of this study is to investigate the relationship between CRM, customer satisfaction, and business profitability in the Jordanian logistics industry. Specifically, the study examines how customer satisfaction mediates the effect of three key CRM on business profitability. To achieve this, Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyze data collected from 384 employees of logistics firms in Jordan. The results of the study suggest that CRM positively affects customer satisfaction, and that customer satisfaction has a significant mediating effect on the relationship between CRM and business profitability. The findings of this study have important implications for logistics firms seeking to improve their business profitability through the adoption of CRM. By focusing on the three key CRM (Customer Identification, Customer Acquisition, and Customer Analytics), firms can improve their customer satisfaction levels, which, in turn, can lead to improved business profitability. This study adds to the growing body of literature on CRM practices and their impact on business profitability, particularly in the context of the Jordanian logistics industry.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".