Can High-Tech Companies Enhance Employee Task Performance through Organizational Commitment?
Bibliographic record
Abstract
Prior studies about task performance within the high-tech industry have focused mainly on the relationship among working stress, working characteristics, employee motivation, and the compensation system. This study, however, examines whether employee personality, organizational culture, and different leadership styles have an impact on organizational commitment and hence increase the employee’s task performance. To study this issue, 304 employees from high-tech public companies in Taiwan were selected as illustrative example, and LISREL software was used as the analytic tool. The research findings indicate that high-tech companies whose employees exhibit personality characteristics such as competition and high ambition (typically “Type A”) have a positive effect on organizational commitment. Second, high-tech companies exhibiting an innovative and supportive culture also have significant impact on organizational commitment. Third, employees with more value commitment and effort commitment show increased task performance. In addition, organizational commitment acted as an intermediary role between employee personality characteristics, organizational culture, and task performance; that is, employee personality characteristics and organizational culture indirectly influenced task performance through organizational commitment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".