An Investigation of Information Systems Use Patterns: Technological Events as Triggers, The Effect of Time, and Consequences for Performance1
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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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".