Challenges Faced in Inter-Organizational Collaboration Process. A Case Study of Region Skåne
Bibliographic record
Abstract
The increase in the complexity of social and societal problems that even a large actor cannot solve alone has caused pressure on many sectors, organizations and entities making the need for collaboration to be more urgent. This is because collaboration enables merging financial resources, human resources and expertise needed to tackle complex problems. However, the increased failure of collaborations requests greater consideration and investigation of the challenges in collaboration. The purpose of this study is to investigate the challenges in inter-organizational collaboration at management and employee level with a focus on the Thomson and Perry (2006), model of collaboration. To fulfil this purpose, inter-organizational collaboration towards open Skåne 2030 strategy was used as a case study. The empirical data showed that there are challenges in both the management and employee level in inter-organizational collaboration. Further, the study also found that political influence is a major challenge in inter-organizational collaboration. The study makes a contribution with the adaption of the model of collaboration process. The model serves to enlighten collaborators that challenges in inter-organizational collaboration are inter-linked.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".