Opportunismes påvirkning af relationer
Bibliographic record
Abstract
Supply chain relationships are very often complex and affected by a number of determinants,\ncomprising sociological, psychological, economic and organizational factors. Companies as well as\nuniversities are increasingly using the term SCM in order to describe the best case scenario. Trust is\nemphasized to be a crucial factor. Working from the theory of supply chain management and\ntransactions cost economics, this study investigates whether supply chain relations are affected by\nopportunism from a number of perspectives.\nIn order to answer the main question (i.e. whether opportunism affects relationships in a supply chain\nperspective), a number of research questions and hypothesis were developed. These four questions\nwere divided into two categories – ex ante and ex post, respectively. The first two questions\ninvestigates whether the need for and importance of contractual protection and regulations were\naffected by the risk of opportunism. While the first two questions are concerned with how opportunism\naffects the relationship before the contract has been sealed, the last two questions are more concerned\nabout how the parties actions are affected after the contract has been settled, or more precisely how it\naffects their willingness and interest in sharing critical information, and making complementary and\nrelation-specific investments.\nMethodologically, the study followed a multiple case study approach, with data collected from three\ndifferent Danish companies – Novozymes, Pandora and Alfa Laval, respectively. Qualitative data were\ncollected through semi-structured interviews, and the analysis was based on information from the\nrespondents.\nThe results of the study indicate that trust is an important player when it comes to developing a strong\nrelationship. But, all three companies give the impression that their decisions, to some point, are\naffected by the risk of opportunism. The contract is defined as a way to describe the process and how to\ncollaborate, but it is also used as a safeguard in order to protect the company against opportunistic\nbehavior.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".