An Investigation of Information Systems Use Patterns: Technological Events as Triggers, The Effect of Time, and Consequences for Performance1
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
Information systems use represents one of the core concepts defining the discipline. In this article, we develop a rich conceptualization of IS use patterns as individuals’ emotions, cognition, and behaviors while employing an information technology to accomplish a work-related task. By combining two novel perspectives—the affect–object paradigm and automaticity—with coping theory, we theorize how different patterns appear and disappear as a result of different IT events—expected and discrepant—as well as over time, and how these patterns influence short-term performance. In order to test our hypotheses, we conducted two studies, one qualitative and the other quantitative, that combined different methods (e.g., open-ended questions, physiological data, videos, protocol analysis) to study the influence of expected and discrepant events. The synergistic properties of the two studies demonstrate the existence of two IS use patterns, automatic and adjusting. Most interactions are automatic, and adjusting patterns, triggered by discrepant IT events, fade over time and transition into automatic ones. Further, automatic patterns result in enhanced short-term performance, while adjusting ones do not. Our conceptualization of IS use patterns is useful because it addresses important questions (such as why negative IT perceptions persist) and clarifies that it is how (rather than how much) people use IT that is pertinent for performance.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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