Toward a Deeper Understanding of System Usage in Organizations: A Multilevel Perspective1
Bibliographic record
Abstract
The objective of this paper is to contribute to a deeper understanding of system usage in organizations by examining its multilevel nature. Past research on system usage has suffered from a levels bias, with researchers studying system usage at single levels of analysis only (e.g., the individual, group, or organizational level). Although single-level research can be useful, we suggest that studying organizations one level at a time will ultimately lead to an unnatural, incomplete, and very disjointed view of how information systems are used in practice. To redress this situation, we draw on recent advances in multilevel theory to present system usage as a multilevel construct and provide an illustration for what it takes for researchers to study it as such. The multilevel perspective advanced in this article offers rich opportunities for theoretical and empirical insights and suggests a new foundation for in-depth research on the nature of system usage, its emergence and change, and its antecedents and consequences.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".