The Level of Desirability of Information Technology Systems and Its Relation with Organizational Commitment
Bibliographic record
Abstract
Purpose – This paper aims to define Information Technology (IT) desirability and determine IT relationship with organizational commitment. The existence of such a relationship between IT & organizational commitment can guide the organizational leaders to promote and to develop the IT potentials in order to improve the performance of employees, and elevate job satisfaction, commitment and organizational effectiveness.Design /methodology /approach –The design of the research is descriptive – correlational and the statistical sample consists of 84 educational service experts working at educational service offices in the University of Isfahan. They were selected based on purposeful sampling.Findings – The results showed that among IT desirability components, two components of training and fitness had a significant relationship with continuance commitment (one of the organizational commitment dimensions).Research limitations – since the statistical sample was selected purposefully and the research was done on educational service experts, any generalization of the results to other organizations and employees requires caution.Practical implications – Since the results of the research show the existence of only one of the dimensions of organizational commitment ( continuance commitment ) and two components of desirability of IT ( fitness and training), managers can try to improve the levels of these dimensions of IT.Originality / value – many researchers have investigated different effects and relations between IT and employees, but in this research the aim has been to investigate a more complete account as well as different aspects of employees’ perceptions about IT (i.e. desirability of the system).
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".