Asymmetry of stakeholders’ perceptions as an obstacle for collaboration in inter-organizational projects
Bibliographic record
Abstract
Purpose The purpose of this paper is to overlook influential factors associated with the collaboration itself, and to explore the effect of these factors on inter-organizational relationship. Design/methodology/approach This paper analyses two different technology projects requiring inter-organization collaboration for implementing medicine traceability: end-to-end verification system and e-pedigree. Based on a survey where 72 pharmaceutical organizations exposed their perceptions about each technological project, collaboration factors are identified. Findings This paper shows that pharmaceutical organizations in this study perceived differently the cost and benefits from traceability project. Organizations involved experience neither organizational nor technological proximity, impacting negatively collaboration in the inter-organizational project. Practical implications To strengthen collaboration, organization from different levels should consider how close they are each other, and this is at the geographic, organizational and technological level. Geographic proximity is defined as physical closeness, organizational proximity can be understood as the degree to which organizations are similar in interests and structure, and technological proximity concerns the similarity between the systems used to mediate communication and store information. Originality/value This paper presents empirical evidence on inter-organizational collaboration for industrial projects (i.e. implementing medicine traceability systems). It demonstrates proximity is a significant factor in producing inter-organizational collaboration success. Indeed, organizations experiencing proximity have a better knowledge of actors involved in the inter-organization project.
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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 | 0.023 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| 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".