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Record W1986979783 · doi:10.1111/jpim.12261

Product Life‐Cycle Management and Distributor Contribution to New Product Development

2015· article· en· W1986979783 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Product Innovation Management · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsConcordia UniversityHEC Montréal
Fundersnot available
KeywordsNew product developmentProduct (mathematics)Product lifecycleBusinessTypologyProcess managementProduct life-cycle managementProduct managementProduct designMarketingTask (project management)Computer scienceKnowledge managementSystems engineeringEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.789
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.257
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it