Building Sustainable Global Marketing Channels: Exploring the Role of Inter-Organizational Trust and Performance Metrics in the Age of Industry 4.0
Bibliographic record
Abstract
This research explores the interaction between inter-organizational trust, marketing channels, and market and financial performance (FP) in establishing sustainable global marketing channels using Industry 4.0 technologies. It is conducted within the relational exchange theory (RET) framework and transaction cost economics (TCE). The sample (N = 131) was collected through the marketing research firm Centiment. PLS-SEM and Necessary Condition Analysis (NCA) were utilized as statistical methods. All hypotheses except the relationship between marketing channel operational performance and FP were accepted. This research highlights the vital role of inter-organizational trust in enhancing operational efficiency, profitability, and sustainability. It finds that trust fosters collaboration in global distribution channels, improving performance across multiple dimensions. Specifically, trust positively impacts marketing channel operations, boosting market performance. Nevertheless, all exogenous constructs were essential—“must-have” conditions for the endogenous FP construct. Applying the novel NCA is distinctive, primarily as it demonstrates that the relationship between marketing channel operational performance and FP is a necessary “must-have” condition, despite the insignificant path coefficient between the constructs. This is a crucial finding, as further investment in marketing channel operational performance and other antecedents of FP may be futile if the necessary conditions have not been met.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".