A literature review exploring values alignment as a proactive approach to conflict management
Bibliographic record
Abstract
Purpose The research aim has two purposes: to clarify the concepts of values, collaboration and conflict and their relationship with one another within organizations; and to provide data that supported or disconfirm values alignment as a proactive approach to conflict management. Design/methodology/approach An interdisciplinary review of literature was undertaken, as current literature on the topic of values as it relates to conflict was very limited in scope. The key concepts investigated were the connection between values (including alignment and congruence) and decision making, behavior, collaboration, strategy, prioritization and conflict within an organization. Research was guided using constructionism, chaos and complexity theories within a framework of Chaordic systems thinking. Findings The paper provides documentation that previous values research practices have been fragmented and have had limited practical applications. Support is provided indicating that values alignment fosters collaboration and could be a proactive approach to conflict management. Research implications/limitations No long‐term studies were found on the topic of inquiry, although some documentation on business performance is starting to appear. Further research using values alignment as an organizational process would be beneficial. Practical implications – The framework presented appears to have a pragmatic application that would benefit organizational development and effectiveness. Originality/value – This paper expands previous studies by examining values research across domains and suggesting a different research approach. A model is discussed that provides meaningful linkage between business strategy and organizational values.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".