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 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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".