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Record W2173394018 · doi:10.1509/jmkg.70.4.37

Creating a Market Orientation: A Longitudinal, Multifirm, Grounded Analysis of Cultural Transformation

2006· article· en· W2173394018 on OpenAlexaff
Gary F. Gebhardt, Gregory S. Carpenter, John F. Sherry

Bibliographic record

VenueJournal of Marketing · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsMarket orientationMarketingBusinessMarket orientedOrientation (vector space)Market analysisNonmarket forcesOrganizational culturePublic relationsEconomicsFactor marketPolitical scienceMarket economyGeometry

Abstract

fetched live from OpenAlex

Market orientation is a foundation of marketing and is increasingly important in other fields, such as strategic management. Research in marketing has identified the characteristics of market-oriented organizations. However, how organizations change to become more market oriented has received less attention. In this article, the authors conduct an in-depth, longitudinal, multifirm investigation of firms that have successfully created a market orientation. Grounded by this in-depth understanding, they develop a theoretical model to explain how firms create a market orientation. The model identifies four path-dependent stages of change. In contrast to current conceptualizations, the authors find that creating a market orientation requires dramatic changes to an organization's culture and the creation of organizationally shared market understandings. The findings offer new insights into how organizations develop a greater market orientation, organizational change, and the nature of market orientation, including the role of intraorganizational power and organizational learning in creating and sustaining a market orientation.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.256
Teacher spread0.238 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations383
Published2006
Admission routes1
Has abstractyes

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