Is Usage Predictable Using Belief-Attitude-Intention Paradigm?
Bibliographic record
Abstract
While much of the prior information system (IS) research has employed technology acceptance model (TAM) and theory of planned behavior (TPB) to explain user’s technology acceptance behavior, most of them use self-reported use intention to develop their investigation. The purpose of this paper is to empirically examine the validity of behavioral intention’s prediction on actual system usage under a voluntary context. By integrating constructs of the two closely related theoretical paradigm (TAM and TPB), we propose an integrated model to investigate the relationship. In doing so, we used questionnaire to gather the system usage perceptions of students who took an online management information system (MIS) course at a large Canadian university. At the same time, we also set up the e-learning system to record students’ actual usage. Using partial least square (PLS) approach, data collected from 105 students are tested against the model showing a very good fit with 60% explanation of the behavioral intention. The relationship between the intention and actual system use however was found to be insignificant and weak. Our study questions the validity of using self-reported intention to represent system usage and provides insight into future research directions on technology acceptance behavior.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".