Managing Internal Marketing Channel Conflict: A Proposal for Narrative Epistemology
Bibliographic record
Abstract
This article calls for extending the understanding and management of the channel conflict that occurs between competing sales teams inside a manufacturer organization. This internal battle occurs as the sales teams try to sell manufacturer products to two different channel members (e.g., retailers, wholesalers, etc.) in the same market and, as a result, compete for quotas, sales targets, promotional budgets, etc. The article argues that by drawing on narrative epistemology, which has extensively been applied in management research, marketing scholars and practitioners can gain novel insights through which understanding and management of internal channel conflict could be enhanced. An epistemological review of the extant literature on the topic in the field of Industrial Marketing is presented. Drawing on the narrative method, three narratives told by competing groups in the context of Pakistan’s evolving fast moving consumer goods (FMCG) distribution channel are constructed, and an empirical model is developed for narrative analysis. It is shown that the understanding of various narrative logics and alignments can help in positive interventions in the channel story network.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.048 |
| Scholarly communication | 0.019 | 0.035 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".