Impact of IT User Behavior: Observations through a New Lens
Bibliographic record
Abstract
Despite the progress that research has made on acceptance and resistance, a need to further clarify what behaviors they translate into and their impacts beyond face value remains. Based on the extant literature on user acceptance and resistance, we developed a framework in which we map user behaviors in light of their conformity/non-conformity to organizational intent. Mapping the literature in this framework revealed mixed study results on impacts of IT user behaviors. Overall, we suggest that one should understand the impacts of user behaviors in light of organizational intent, which organizations’ IT terms of use generally embody. This new lens allows one to understand the contradictions in extant research results and to articulate a more nuanced account of IT use impacts. To conclude, we propose that researchers could add much value to current knowledge in this area by: 1) exploring how acceptance and resistance IT user behaviors relate to conformity/non-conformity with IT terms of use and delving in their impacts, 2) explaining the sometimes paradoxical impacts of conforming/non-conforming IT user behaviors, and 3) investigating the role of individual and organizational agency in relation with IT terms of use.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".