Bibliographic record
Abstract
The purpose of this research is to refine the notion of Supply Chain Orientation (SCO) as originally posited by Mentzer et al. (2001) and Min and Mentzer (2004). Supply chain orientation is defined to be “the extent to which there is a predisposition among chain members toward viewing the supply chain as an integrated entity and on satisfying chain needs in an integrated way” (Hult et al., 2008, p. 527). This orientation (management philosophy), when implemented, manifests as Supply Chain Management (SCM) within and across organizations. \n \n The process of ‘refining’ supply chain orientation involved three stages: determining additional SCO factors / indicators beyond those already in existence, refining the total set of factors / indicators through factor analysis techniques, and associating the SCO concept to other SCM-related concepts. Determining additional SCO factors and the vetting of the existing SCO model was done through a qualitative method (structured interviews with industry experts). Analysis of the interview data resulted into two new SCO factors—SCM Capability and Measurement Propensity—being identified. The high accuracy / low generalizability nature of the interview process required an industrywide survey in order to gather su cient quantitative data for a meaningful analysis. The new SCO factors were developed into survey questionnaire measurement items. \n \n An invitation to participate in a web-based, quantitative survey was e-mailed to executive at roughly a third of the manufacturing companies in Canada. The results of that data gathering exercise were analyzed in a multi-stage process. First, after removing ‘motherhood statements’ from the indicator set, an exploratory factor analysis (EFA) was conducted to determine the underlying structure of SCO. Three factors—Benevolence (Trust), Internal SCM Focus, and Partner Reliability—emerged through this process. This “refined” SCO construct was then subject to a rigourous confirmatory factor analysis (CFA) process. \n \n The CFA process found the SCO factors to be reliable. A dependent variable, Supply Chain Operational Performance (SCOP) was found to be positively influenced by changes in SCO. SCO was found to be a unique strategic orientation through the literature review process and validated as its own construct through a discriminant validity process. SCO was determined to be a second-order reflective latent variable, and top management support was found to be an antecedent to SCO. \n \n Of interest to SCM practitioners and academics, SCO was found to be statistically invariable between respondents who were or were not members of a SCM industry association. As well, SCO did not vary outside statistical bounds across the supply chain from ultimate supplier (Earth) to ultimate customer. However, SCO was found to be stronger in companies that employed an “e cient” supply chain strategy (using the taxonomy of Lee (2002)) versus other generic strategies (like “agile” supply chain strategy). \n \n The contributions of this research to academics include a parsimonious definition of SCO which meets the criteria of Wacker (1998), an operationalization of the Lee (2002) model, and additional evidence of the power of Parallel Analysis (PA) of Thompson (2004) in determining factors in an EFA. Supply chain orientation is an important theoretical ‘building block’ from which SCM theory can be built and through the refinement process, SCO was tied into the dynamic capabilities area of the larger resource-based view (RBV) theoretical framework. \n \n Supply chain orientation was found to positively influence SCOP. The Council of Supply Chain Management Professionals reported that business logistics (SCM) costs in the United States alone in 2009 were 1.3 trillion dollars. Hence, improving upon the understanding of the mechanisms of supply chain management and its components can have substantial economic consequences.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".