Inhibitors and Enablers as Dual Factor Concepts in Technology Usage
Bibliographic record
Abstract
Information systems (IS) research has focused extensively on the factors that foster adoption and usage. A large body of work explores overall beliefs about system usage, antecedents of system satisfaction, and other perceptions that enable system success, create positive attitudes, and encourage usage. However, much less attention has been given to what perceptions uniquely inhibit usage. In large part, this is due to the implicit assumption that the inhibitors of usage are merely the opposite of the enablers. This paper proposes a theory for the existence, nature, and effects of system attribute perceptions that lead solely to discourage use. I posit that usage inhibitors deserve an independent investigation on the basis of three key arguments. One, there exist perceptions that serve solely to discourage usage, and these are qualitatively different from the opposite of the perceptions that encourage usage. Two, these inhibiting and enabling perceptions are independent of one another and can coexist. Three, inhibiting and enabling perceptions have differing antecedent and consequent effects.. As unique beliefs, inhibiting perceptions can add to our understanding of the antecedents of usage or outright rejection. Further, such inhibitors may not only be important to the IS usage decision, they may be more important than enabling 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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".