Impact of IT User Behavior: Observations through a New Lens
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 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 it