Impact of Knowledge Management on Success of Customer Relationship Management (Staff, Leadership, Organizational Structure)
Bibliographic record
Abstract
The aim of this study was to analyze the impact of knowledge management on success of customer relationship management: mediating impacts of organizational factors (staffs, leadership, organizational structure) in capital bank by descriptive-correlation method. Accordingly, to measure knowledge management, the Fang and Choai (2009) questionnaire was applied, to evaluate organizational variables (staffs, leadership, organizational structure), Greve & Albers (2006) questionnaire was used and to assess technology of customer relationship management, Chang et al (2006) questionnaire was employed and regarding success of customer relationship management, Bang (2005) and Chen and Chung (2004) questionnaires were used. The population of this study included all staffs of capital bank in Tehran city that were 642 staffs which based on Cochran formula, 240 staffs were selected by cluster sampling. To analyze data, Pearson correlation test and structural equation model were employed by using SPSS and AMOS software. The obtained results of this study indicated that the knowledge management affected significantly on organizational factors (staffs, leadership, organizational structure). Furthermore, the technology of interaction management affected significantly on success of customer relationship management and also technology of customer relationship management influenced significantly on organizational factors (staffs, leadership, organizational structure). Organizational factors (staffs, leadership, organizational structure) affected significantly on success of customer relationship management. The findings of this study showed that the knowledge management did not affect significantly on success of customer relationship management. Technology of customer relationship management by mediating variable of organizational factors did not affect significantly on success of customer relationship management.
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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 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".