Technology as a Double-Edged Sword: From Behavior Prediction with UTAUT to Students’ Outcomes Considering Personal Characteristics
Bibliographic record
Abstract
Aim/Purpose: We aim to bring a better understanding of technology use in the educational context. More specifically, we investigate the determinants of webinar acceptance by university students and the effects of this acceptance on students’ outcomes in the presence of personal characteristics such as anxiety, attitude, computer self-efficacy, and autonomy. Background: According to literature in information systems, understanding the determinants of technology use and their effect on outcomes can help ensure their effective deployment, which might yield productivity payoffs. Methodology: Data collection with an online quantitative questionnaire yielded to 377 valid responses from students enrolled in an undergraduate management information systems course. SPSS software allowed obtaining descriptive statistics and Smart-PLS was used for validity and hypotheses testing. Contribution: Previous studies assessed either the determinants of technology use or the effect of their use on students’ outcomes, and often omitted to assess the role of personal characteristics. This research fulfills the gap about the scarcity of studies that link goals to intentions and behavior, while considering personal cognitive characteristics. Findings: Results showed that performance expectancy, social influence, facilitating conditions, and voluntariness of use explained the behavioral intention and webinar usage. Some of these relationships were direct and others were moderated. Satisfaction was the only student outcome affected by the use of webinars. Anxiety, attitude, and autonomy are the personal characteristics that exerted direct and moderating effects on the relationships between the main variables of the research model. Recommendations for Practitioners: Results gave rise to interesting managerial recommendations for adopting technologies in universities. Among them, teachers are encouraged to promote the webinars’ advantages and to exert less pressure on students to use webinars. Recommendation for Researchers: On the theoretical side, we brought a holistic view of the use of technologies in higher education by linking goals to intentions and behavior, and integrating personal cognitive characteristics into the same model. Results allowed enriching the literature about technology adoption in the educational context. Future Research: Future research should follow closely the results of studies on generation Z to find better explanatory variables of technology adoption. We also propose to consider new variables from the updated technology acceptance models to further understand the derteminants of technology use by students.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".