A Conceptual Model of Sustainable Technology Use: The Role of Confirmation and Perceived Usefulness in the Hospital X Management Information System in Padang
Bibliographic record
Abstract
Background and Objective: The adoption and use of Management Information Systems (MIS) in healthcare settings, like Hospital X in Padang, are crucial for improving operational efficiencies and patient care. Task-Technology Fit (TTF) measures how well technology supports its intended tasks and significantly influences user satisfaction and system use continuity. Key factors include Confirmation, assessing post-adoption user expectations, and Perceived Usefulness (PU), evaluating job performance enhancement. This study explores TTF's impact on Continuance Intention (CI), mediated by Confirmation and PU, within Hospital X's MIS context. Methods: Data were gathered from staff at H.B. Saanin Mental Hospital, one of West Sumatera's public hospitals. A total of 158 questionnaires were distributed, with 150 deemed analyzable using structural equation modeling. Result: The study finds no statistically significant relationship between TTF and PU. However, a marginally significant relationship between TTF and Confirmation suggests modest evidence that alignment between tasks and technology influences users' confirmation of their expectations. Notably, PU does not directly impact CI within Hospital X's MIS, nor does Confirmation significantly affect users' intention to continue using the system. Overall, the direct influence of technology-task alignment on users' intention to continue using MIS is inconclusive in this study context. Conclution: This study reveals complex relationships among TTF, Confirmation, PU, and CI within Hospital X's MIS framework. Despite the theoretical significance of TTF and Confirmation, their direct impacts on PU and users' intention to continue system use are not statistically significant. These findings emphasize the ongoing need to evaluate and adapt MIS strategies to better align with user needs and ensure sustained effectiveness in healthcare operations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".