Bibliographic record
Abstract
Purpose The purpose of this conceptual paper is to attempt to answer the related questions of how and why supply chain partners are chosen. Research objectives are to understand how and why collaborative partners are chosen, by learning the actual decision‐making processes and key factors in partner selection. Design/methodology/approach A mixed methods approach was chosen, comprising: a focused literature review, to identify key issues, and informal interviews, leading to the development of a Partner Negotiation Model; a multiple case study approach, involving formal interviews about two partnerships, supplemented by documentation, contracts, correspondence and other records; and some manual data analysis and a qualitative research tool. The whole resulted in identification of significant issues for partner negotiation and selection. Findings Contrary to accepted theory in the alliance, partner selection, and decision‐making literature, the results show that alliance partners are chosen through a complex negotiation process rather than rational selection. The research and interviews with software industry collaborators suggest roles for factors such as complexity, cyclic negotiation, several types of partners, several levels of alliance formation, and hidden factors, such as personal friendship or perceived reputation. Overall, the problem of collaborative partner selection was found to be much more complex than expected. Research limitations/implications Research results are limited by the small sample of partnerships reviewed, but the results can be used as a starting‐point for further larger‐scale studies. Practical implications Supply chain partners in business can use these results to help them better understand the process and criteria for future supply partner selection. Originality/value The results may be used to develop a set of partner selection recommendations for practitioners. For specific firms that become involved in organizing supply chain alliances, the results of this work will provide decision support in terms of choosing among partners or indeed whether to engage in a particular relationship.
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 | 0.055 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".