MétaCan
Menu
Back to cohort
Record W1970074098 · doi:10.1108/17410390610658450

Issues in supplier partner selection

2006· article· en· W1970074098 on OpenAlex
Anne Banks Pidduck

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Enterprise Information Management · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOutsourcing and Supply Chain Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNegotiationReputationSelection (genetic algorithm)AllianceKnowledge managementDocumentationIdentification (biology)Supply chainProcess (computing)Process managementBusinessManagement sciencePsychologyMarketingComputer scienceEngineeringPolitical science

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.213
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it