Interdependencies between People and Information Systems in Organizations
Bibliographic record
Abstract
Abstract This article argues that while people and information systems (ISs) represent the two single largest areas of investment for many organizations and are increasingly interconnected resources, there has been very little research on the nature of their interdependencies and how these interdependencies affect their functioning and complementarity. It discusses how a better understanding of the dynamics of interdependencies between people and ISs can help researchers study organizations and help organizations improve the interoperation of their human and technological assets, and thus returns on investments in them. The article begins by reviewing the concept of capital and its application to people – human capital – and information systems: ISs capital. Next, it surveys past literature on interdependencies and recent literature relating to interdependencies between people and information systems. Based on the analysis, the article proposes an agenda for future research aiming to conceptualize interdependencies between people and ISs in a richer fashion.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".