Exploring the Roles of Benefits, Practices, Digitalization, and Sustainability on Employee Satisfaction in the Malaysian Oil and Gas Industry
Bibliographic record
Abstract
This study assesses the impact of benefits and rewards, new working practices, digitalization, and sustainability on employee satisfaction and expectations in the Malaysian oil and gas industry. A quantitative research methodology was employed, utilizing a structured survey distributed to 391 industry professionals. Statistical analyses were used to evaluate the relationships between the independent variables (benefits and rewards, new working practices, digitalization, and sustainability) and the dependent variable (employee satisfaction and expectations). The findings revealed that all four hypotheses were supported, indicating a strong and positive impact of each independent variable on employee satisfaction and expectations. Benefits and rewards were found to have the most substantial influence, followed closely by new working practices, sustainability, and digitalization. This research contributes significantly to the understanding of employee satisfaction dynamics in the post-pandemic era within this critical sector. It provides actionable insights for organizational leaders seeking to enhance work environments and align with evolving employee expectations. The study highlights the importance of a holistic approach to employee engagement, emphasizing the need for comprehensive strategies that address diverse aspects of the work environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".