Product Life‐Cycle Management and Distributor Contribution to New Product Development
Bibliographic record
Abstract
After the initial launch of a new product, distributors are frequently among the first to learn about product‐related problems through the information they get about how it is perceived and used by customers, and how it might be improved or adapted for broader market coverage. For producers, such information, which has the potential to impact new product development (NPD) activities during the product life‐cycle management (PLM) phase that follows launch, can be decisive for ensuring the continued viability of the product in the medium to longer term. The goal of this article is to better understand how distributors contribute to producer PLM activities by engaging in product‐related information processing. A typology of four distinct scenarios is developed by integrating three conceptual themes: organizational information processing, dynamic capabilities, and task complexity. Each scenario results from the interplay of the distributor's level (low/high) of capability—specifically, a combination of information coordination and management of interorganization relations—and of the degree (low/high) of complexity of the product‐related problem. The four scenarios are analyzed and described in terms of NPD‐related information processing. According to the typology, distributors act as “problem informers” (low capability/high complexity), “solution advisors” (low capability/low complexity), “solution implementers” (high capability/low complexity), or “solution managers” (high capability/high complexity). Fourteen in‐depth interviews with distributors and producers in industrial goods provide empirical evidence for the analysis, description, and support of each scenario. The article contributes to NPD by shedding light on the role of distributors in terms of incremental innovation in the context of PLM. Developers of new products can use the typology in planning for distributor involvement in PLM activities; distributors can use it to map out their current and future level of engagement in PLM‐related activities.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".