Modelling the Relationships between Internal Marketing Factors and Employee Job Satisfaction in Oil and Gas Industry
Bibliographic record
Abstract
Employees have long been playing the pivotal role in service organizations to achieve a success-oriented goal. The oil and gas industry is included in the high rising sectors in the world’s economy. Due to economic turmoil in this sector, a fear of being laid off remains in an employee’s mind. Thus, the goal of this study is to assess the impact between internal marketing factors (e.g., extrinsic and intrinsic employee rewards, leadership, internal communication, and training and development), and employee job satisfaction in the oil and gas industry. There were 215 complete and usable questionnaires received, and the answers varied among the demographic and functional designation within the oil and gas industry. Multiple regressions were utilized for analysis of data. Results revealed that internal communication is recognized to have the strongest effect on employee job satisfaction in the oil and gas industry. Organizations must emphasize on communicating to all level of employees by setting clear directions and key priorities in the organization, provided that the communications are not misled through upward and downward streams. Furthermore, organizations are to create a space for employees to give clear instructions via e-mail, paper, telephones, and face-to-face communication. A management can utilize the research results by conducting such internal marketing practices to keep their top rated employees within the organization. ut the individual differences related to entrepreneurial intentions, it is necessary to continue studying this phenomenon, considering that the results are still scarce and inconclusive.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".