Importance of Technology–Job Fit on the Sustained Use of E-Government
Bibliographic record
Abstract
Success in e-government technology implementation offers many benefits for both governments and citizens; however, the real-world implementation showcases a high failure rate. Such failures are mainly attributed to a lack of use and adequate management expertise in implementation. The authors argue that this deficiency is because of a lack of fit between the technology and the jobs of employees in public institutions. Drawing on foundational work in person-job and person-organization fit, the authors conceptualize technology-job fit (TJF) as a two-dimensional construct: task relevance and workstyle compatibility. Using data from Thai government employees across core administrative functions, they test a moderated model and uncover a quality-fit paradox: high system and information quality only translate into positive outcomes when TJF is perceived as high. When TJF is low, even well-designed systems fail to generate enthusiasm or sustained use. These findings reframe e-government implementation challenges as issues of misalignment rather than technical inadequacy.
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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 | 0.002 | 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 | 0.001 | 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".