The role of key workplace elements in determining individual and organizational success in Jordan Tourism Board
Bibliographic record
Abstract
The aim of this research was to investigate the impact of various work-related factors, such as task load, workers’ compensation, and organizational structure community on job success and the overall performance of the Jordan tourism board. The researchers conducted an analysis of 269 randomly selected samples from their study and discussed their findings to validate their hypotheses. The study revealed that factors like compensation levels, organizational structure, community, and task load significantly influence individual and organizational success. The first aspect examined was the task load, which was assessed by considering factors such as job completion rates, daily challenges faced, and the time required for tasks. Work-related compensation, encompassing factors like experience, skill, incentives, and rewards, emerged as the second most crucial factor after working hours. The third and most vital component to consider was the organization structure community, which includes communities of practice, collaborative efforts, and physical infrastructure. According to the research findings, improving employee performance can be achieved by reducing their task load, increasing work income, and developing the organization community that fosters teamwork and the formation of workgroups. These elements collectively impact overall productivity. The study provides valuable insights into an underexplored area, shedding light on how task load, compensation, and organizational structure community interplay. The research's focus on the Jordan tourism board is particularly significant, as it has the potential to help tourism companies enhance their operations and provide superior service to their customers.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".