The influence of training courses, customer relationship, and human resource management on customer focus among construction companies
Bibliographic record
Abstract
Reflecting upon language allows teachers, not only to have a greater insight on how English, the language they This study focuses on the influence of training courses, customer relationship and human resource management on customer focus among construction companies. This is due to the lack of information on its effectiveness. These problems may explain why the main players are less responsive to the implementation and practice of a mediating effect of Customer Relationship Management (CRM) and Human Resource management (HRM) on the relationship between Training Courses (TC) and Customer Focus (CF). It is essential that an appropriate model of CRM and HRM be used by administrators and professionals. The proposed model is based on the dependent variable, CF, and the independent variables, TC and mediator (CRM, HRM). This research is a descriptive-survey and inferential type based on the data collection method where parametric tests were used with the help of SPSS. The results of this research can be used in decision making, policy making, and also planning. In conclusion, it can be inferred that the relationship between CRM and HRM is still at its infancy stage, and as such, serious attention is needed among the players in the development of construction companies.
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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 | 0.007 |
| 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 | 0.001 | 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".