Collective Information Systems Use: A Typological Theory1
Bibliographic record
Abstract
As the nature of information systems (IS) has evolved from primarily standalone, to enterprise, and distributed applications, the need for a better understanding of collective IS use has become a research and practical necessity. In view of contributing to this understanding, we conceptually define collective IS use as a unit level construct, rooted in instances of individual-level IS use within the context of a common work process. Its emergence from the individual to the unit level is shaped by different configurations of task, user, and system interdependence between instances of individual-level IS use. On the basis of this definition, we propose a typology of collective IS use that comprises four ideal types, namely siloed use, processual use, coalesced use, and networked use. For each ideal type, we theorize on the emergence process from the individual to the unit level and we consider the measurement implications for each.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".