Safeguarding mechanisms in a supply chain network
Bibliographic record
Abstract
Purpose Building on the transaction cost theory and power structure literature, this paper aims to investigate the extent to which firms use two safeguarding mechanisms (supply chain relational investments and electronic collaboration) in different network dependency contexts in order to protect their portfolios of business relationships. Design/methodology/approach Empirical evidence is gathered though a survey data conducted with 159 firms in the wireless communication sector. The paper tests the assumption that the two safeguarding mechanisms are used to a greater extent in interdependency‐intensive networks than in other supply chain contexts. Findings This empirical study suggests that: in a network‐dependent context, relational investments allow firms to safeguard their portfolios of relationships; electronic collaboration seems to be a safeguarding mechanism for firms in downstream‐dependent network contexts; in general, firms appear to use both relational investments and electronic collaboration to manage their relationships in a supply chain network; and the knowledge‐based theory may explain the strong relationship between upstream and downstream use of electronic collaboration. Research limitations/implications Overall, the present study complements the extant literature on supply chain management and inter‐firm electronic collaboration by showing how an important structural characteristic of supply chain networks (i.e. dependency) operates on the choice of using two key safeguarding mechanisms. Practical implications Results stress the importance of these safeguarding mechanisms in joint actions such as collaborative planning, forecasting and replenishment. Originality/value The paper addresses interdependencies from a network perspective which encompasses the firms' complete portfolio of relationships.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".