Bibliographic record
Abstract
Purpose Despite intense research interest in supply chain management (SCM) over two decades, there is still uncertainty as to what SCM is and how behavioural determinants have an impact on it. The purpose of this study is to explore the linkages between the behavioural and marketing determinants of SCM and their impact on commitment and process integration. Design/methodology/approach The approach takes the form of descriptive research leading into causal research, using survey data and testing relationships with Structural Equation Modelling. Findings The study found that there are significant behavioural dimensions in SCM and identified the impact of those dimensions on supply chain commitment and process integration. One noticeable finding of this study was the nature of supply chain commitment within Confucian culture. Research limitations/implications Data for the study were drawn from one single industry, so the findings are indicative but not representative of all supply chains. Also, the results cannot be generalised to other countries and industries. However, this study acts as a starting‐point to understand how behavioural and marketing determinants may impact supply chain commitment and business process integration. A series of future studies may follow this study to develop a comprehensive understanding of the nature, structures and strategies of supply chain commitment in Confucian culture. Practical implications This study will enable supply chain managers to understand the role of behavioural and marketing factors in managing supply chains. The root of Confucian culture lies in Asia, and Asia is an integral part of global supply chains. An understanding of Confucian dynamics will enable practitioners to manage these supply chains efficiently. Originality/value This paper contributes to an understanding of the behavioural/soft determinants in managing supply chains, particularly in Asia. This study also highlights the role of Confucian dynamism in shaping supply chain commitment. Both these areas had previously been under‐researched.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".