Explicit and Implicit Antecedents of Users' Behavioral Beliefs in Information Systems: A Neuropsychological Investigation
Bibliographic record
Abstract
Behavioral beliefs—perceived usefulness and perceived ease of use—have been identified as the most influential antecedents of individuals' information systems use intentions and behaviors within the technology acceptance model. However, little research has been aimed at investigating the implicit (automatic or unconscious) determinants of such cognitive beliefs, and more importantly, the potential nonlinear relationships of such antecedents with explicit (perceptual) ones. As such, this paper theorizes that implicit neurophysiological states—memory load and distraction— and explicit—engagement and frustration—antecedents interact in the formation of perceived usefulness and perceived ease of use. To test the study's hypotheses, we conducted an experiment that measured neurophysiological states while individuals worked on instrumental and hedonic tasks using technology. The results show that, as theorized, implicit and explicit constructs interact together, and thus have a nonlinear effect on behavioral beliefs. Specifically, when engagement is high, neurophysiological distraction does not statistically significantly affect perceived usefulness, whereas when engagement is low, neurophysiological distraction has a negative and significant effect on usefulness. The results also show that when frustration is high, neurophysiological memory load has a negative effect on perceived ease of use, whereas when it is low, neurophysiological memory load has a positive effect on perceived ease of use. This study makes several contributions to acceptance research and the emerging field of NeuroIS, including demonstration of the importance of emotional perceptions for moderating the effects of neurophysiological states on behavioral beliefs.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".