The interaction between Industry 4.0 practices, distributor sustainability development and performance: a necessary condition analysis
Bibliographic record
Abstract
Purpose The existing literature predominantly portrays a positive perspective on the influence of Industry 4.0 on sustainability, yet some studies have pinpointed unresolved concerns. This study aims to challenge the assumptions surrounding the Fourth Industrial Revolution by examining how Industry 4.0 impacts marketing channel operational performance, distributor sustainability development and social performance. Therefore, this research investigates the interaction among Industry 4.0 practices, distributor sustainability development and performance in the context of global distribution channels. Design/methodology/approach The sample population consisted of 131 marketing professionals from Canada and the US working in firms with at least limited experience deploying Industry 4.0 technologies. The researchers developed a survey questionnaire, where the constructs and their indicator variables were adapted from existing research. Smart partial least squares, jointly with the necessary condition analysis, was used for data analysis. Findings The results indicate that the distributor sustainability development and marketing channel operational performance constructs are necessary conditions for social performance. Against expectations, the Industry 4.0 technologies construct had a significant relationship with social performance but proved not to be a necessary condition. Originality/value This study is one of the few to systematically problematize the assumptions of the interaction among Industry 4.0 practices, distributor sustainability development and performance, generating research propositions that reveal several avenues for future research. Furthermore, the research findings enhance the resource-based view by enabling the distinction between necessary, “must-have” and “should-have” resources.
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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 | 0.022 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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".