A structural model of student continuance intentions in ChatGPT adoption
Bibliographic record
Abstract
ChatGPT has experienced unprecedented acceptance and use, capturing popular and academic attention. With this growth in use comes the need to focus on the determinants of ChatGPT use as the success of a technology or service depends largely on users’ continuance intentions. Modeling what influences students’ intention to continue using ChatGPT is important to better understand how students search for information and their decision-making process. Using a sample of 106 students, we test a structural model developed using the unified extended-confirmation model. The research model included the following elements: subjective norm, perceived usefulness of continued use, disconfirmation of their expectations from prior use, satisfaction with prior use, and continuance intention. The findings demonstrate support for the proposed research model as the research model explains 60.5% of the variance in continuance intention. In terms of the direct influence on continuance intention, the role of perceived usefulness and satisfaction were documented. The present study has the potential to serve as a starting point for improving our understanding of antecedents of continuance intentions in the context of ChatGPT.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".