A Three-Perspective Model of Culture, INFORMATION Systems, and Their Development and Use1
Bibliographic record
Abstract
Culture plays an increasingly important role in information systems initiatives, and it receives considerable attention from researchers who have studied a variety of aspects of its role in IS initiatives. Notwithstanding the contributions of research to date, our knowledge of how culture influences— and is influenced by—the development and use processes and an information system itself remains fragmented. Knowledge fragmentation is amplified by the fact that conceptualizations of culture differ among researchers. Indeed, most researchers agree that culture consists of patterns of meaning underlying a variety of manifestations. Researchers diverge, however, on the degree of consensus on these interpretations that they assume to be reached within a collective. In order to integrate these divergent conceptualizations of culture, we adopt the view that no single perspective is sufficient to capture the complexity of interplay between culture, the processes of developing and using an IS, and the IS itself. We have, therefore, adopted a conceptualization that views culture from three perspectives—integration, differentiation, and fragmentation—that come into play simultaneously and jointly. Using this conceptualization, the paper synthesizes what is known about the role of culture in IS initiatives, and proposes a model of the relationships between culture, the development and use processes, and an information system.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".