Overcoming Resistance to Organizational Change: Strong Ties and Affective Cooptation
Bibliographic record
Abstract
We propose a relational theory of how change agents in organizations use the strength of ties in their network to overcome resistance to change. We argue that strong ties to potentially influential organization members who are ambivalent about a change (fence-sitters) provide the change agent with an affective basis to coopt them. This cooptation increases the probability that the organization will adopt the change. By contrast, strong ties to potentially influential organization members who disapprove of a change outright (resistors) are an effective means of affective cooptation only when a change diverges little from institutionalized practices. With more divergent changes, the advantages of strong ties to resistors accruing to the change agent are weaker, and may turn into liabilities that reduce the likelihood of change adoption. Analyses of longitudinal data from 68 multimethod case studies of organizational change initiatives conducted at the National Health Service in the United Kingdom support these predictions and advance a relational view of organizational change in which social networks operate as tools of political influence through affective mechanisms. This paper was accepted by Jesper Sørensen, organizations.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".