An interaction and networks approach to developing sustainable organizations
Bibliographic record
Abstract
Purpose This paper aims to present an interactions and networks approach (INA) to the issue of change for sustainability, which can bring business out of the firm‐centric impasse and lead to collaborative action and transformation. Design/methodology/approach This paper builds upon the extant relational theories in management, and presents a holistic multi‐level framework (the system/network, issue‐based or strategic nets, dyadic relationships and the network organization) to conceptualize change for sustainability. Findings By adopting INA business is able to discuss: the nature and role of the network in building systems level change; the role of dyadic relations as a central mechanism for change; and the nature of organizational level capabilities necessary to enhance learning for sustainability. Research limitations/implications Areas of future inquiry include examination of the dynamics of intra‐stakeholder relationships over time, specifically the development of actors' attitudes, behavior and cognition in business networks alongside how actors perceive and capitalize on network embedded learning. Further scholarly attention in these areas can further the appreciation of how an INA can assist in building more sustainable organizational futures. Practical implications The paper builds on the concept of “ecological literacy” at an organizational level, and considers the specific capabilities required including network visioning, orchestration and the ability to perceive the “other” as partners in creating new market realities. Moreover, it discusses the role and importance of firm “change agent power” in this regard. Originality/value By building on an INA approach, the paper provides an important conceptual stepping stone towards the ongoing realization of sustainable organization and market forms.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".