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Record W1999396449 · doi:10.1108/08858621011077736

The effect of market orientation on innovation speed and new product performance

2010· article· en· W1999396449 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 Business and Industrial Marketing · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicInnovation Diffusion and Forecasting
Canadian institutionsYork University
Fundersnot available
KeywordsMarket orientationMarket intelligenceProduct innovationProduct (mathematics)OriginalityNew product developmentMarketingBusinessValue (mathematics)Industrial organizationComputer scienceCreativityPsychologyMathematics

Abstract

fetched live from OpenAlex

Purpose It has been argued that innovation speed has been inappropriately absent in models of market orientation. The present study seeks to provide new insights into whether and how market orientation's three main components: intelligence generation, intelligence dissemination, and responsiveness affect innovation speed and new product performance, and about the mediating role of innovation speed. Design/methodology/approach Data were collected from a sample of 247 firms in a variety of manufacturing industries. A mail survey was developed to collect the data. Findings The results indicate that intelligence generation has an indirect positive effect on innovation speed via intelligence dissemination and responsiveness. Intelligence dissemination influences innovation speed positively, both directly and indirectly through responsiveness. Findings report a curvilinear ( J ‐shaped) relationship between responsiveness and innovation speed. With regard to the effect of the market orientation's components on new product performance, the findings indicate a positive relationship between responsiveness and new product performance. The parameter estimates for the direct paths linking intelligence generation and intelligence dissemination with new product performance were found to be not significant. Instead, the findings show that intelligence generation and intelligence dissemination influence new product performance indirectly through responsiveness. Finally, a positive relationship was found between innovation speed and new product performance. Originality/value The research makes three important contributions to the marketing strategy and new product development literatures. First, by splitting market orientation into the components of intelligence generation, intelligence dissemination and responsiveness, the study provides a closer examination into the effect of market orientation on innovation speed and new product performance. Second, the results indicate that the effects of intelligence generation and intelligence dissemination on innovation speed and new product performance are mediated by responsiveness to market intelligence. Third, findings support the argument that innovation speed partially mediates the effect of market orientation's three main components on new product performance.

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)0.018
metaresearch head score (Gemma)0.042
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.883
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.082
GPT teacher head0.324
Teacher spread0.241 · 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