Analyzing the Interaction of Industry 4.0 and Sustainable Global Marketing Channel Development with Necessary Condition Analysis: The Role of Inter-Organizational Trust
Bibliographic record
Abstract
The purpose of this study is to examine the interaction between Industry 4.0 technologies, inter-organizational trust, sustainable distributor channel development, and marketing channel operational performance. The research employed a quantitative approach, collecting data from 131 respondents in Canadian and U.S. global firms with over 400 employees. The analysis utilized partial least squares structural equation modelling (PLS-SEM) and Necessary Condition Analysis (NCA). The study revealed that inter-organizational trust is both a significant determinant and a necessary condition for marketing channel operational performance. While Industry 4.0 technologies emerged as a significant determinant, they were not identified as a “must-have” necessary condition. Notably, distributor sustainability development proved to be an insignificant determinant, but still a “must-have” necessary condition for marketing channel operational performance. This study uniquely contributes to understanding Industry 4.0 and marketing channel dynamics by integrating inter-organizational trust analysis with NCA methodology. By identifying trust as a significant determinant and a “must-have necessary condition”, the research provides practical guidance for managers navigating technological adoption in global marketing channels. The findings challenge conventional assumptions about sustainable development while emphasizing trust’s crucial role in the digital age, offering valuable insights for achieving high marketing channel operational performance during the transformation to Industry 4.0.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".