The impact of continued use of an Enterprise Systems (ES) on job satisfaction
Bibliographic record
Abstract
Organizations have invested heavily in implementing Information Systems (IS) such as enterprise systems (ES) but experienced significant challenges in realizing the potential benefits from these systems. Despite the maturity of research in ES, little research has examined the impact of continued use of ES on job satisfaction. With increased use and dependency on systems such as ES, recent research has shown that system use can impact employees’ satisfaction especially during the earlier stages of the system implementation. This is because the implementation of this system is usually accompanied with drastic change in work duties and tasks in which employees might have to learn new skills to navigate the new system. This disruptive event can influence employees’ attitudes about their jobs following ES implementation. However, past the initial stage of implementation not much is known about the impact of continued use of ES on employee job satisfaction. This research, by drawing on theoretical models on IT continued usage and IT adoption (e.g., Unified Theory of Acceptance and Use of Technology [UTAUT]), theorizes the impact of perceived usefulness (PU) on user satisfaction, IS continuance intention, and job satisfaction, and tests a model through a survey of 108 ES users at a manufacturing company in Canada. The results suggest that facilitating conditions are a salient predictor of perceived ease of use, perceived usefulness and user satisfaction. Additionally, the results support that user satisfaction has a positive and significant effect on continuance intention and employee job satisfaction.
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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 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".