Bibliographic record
Abstract
Information systems research has focused extensively on the factors that foster adoption and usage. This research has focused on overall beliefs about system usage, antecedents of system satisfaction, and other factors that facilitate system success, create positive attitudes, and encourage usage. However, little attention has been given to what inhibits usage. The inhibitors of usage are implicitly assumed to be the opposite of the facilitators. The position taken in this paper is that usage inhibitors deserve their own independent investigation and are proposed to exist and act uniquely apart from the extensive set of positively oriented beliefs well established in the information systems literature. A theory that proposes inhibitors as beliefs about an information system that uniquely discourage technology use both directly as well as by negatively influencing other beliefs about the system is developed and tested. To test the theory, an empirical field study involving 387 participants in a scenario-based exercise involving a variety of actual e-Business Websites was conducted. The results support that usage inhibitors are qualitatively different from established system attributes and that they act uniquely to negatively bias these beliefs. The theory and results add to our understanding of IS design and functionality and why users may choose not to use a system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".