Understanding User Responses to Information Technology: A Coping Model of User Adaptation1
Why this work is in the frame
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Bibliographic record
Abstract
This paper defines user adaptation as the cognitive and behavioral efforts performed by users to cope with significant information technology events that occur in their work environment. Drawing on coping theory, we posit that users choose different adaptation strategies based on a combination of primary appraisal (i.e., a user’s assessment of the expected consequences of an IT event) and secondary appraisal (i.e., a user’s assessment of his/her control over the situation). On that basis, we identify four adaptation strategies (benefits maximizing, benefits satisficing, disturbance handling, and self-preservation) which are hypothesized to result in three different individual-level outcomes: restoring emotional stability, minimizing the perceived threats of the technology, and improving user effectiveness and efficiency. A study of the adaptation behaviors of six account managers in two large North American banks provides preliminary support for our model. By explaining adaptation patterns based on users’ initial appraisal and subsequent responses to an IT event, our model offers predictive power while retaining an agency view of user adaptation. Also, by focusing on user cognitive and behavioral adaptation responses related to the technology, the work system, and the self, our model accounts for a wide range of user behaviors such as technology appropriation, avoidance, and resistance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it