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Record W1543898739 · doi:10.1108/03090561011047535

Defensive strategy framework in global markets

2010· article· en· W1543898739 on OpenAlexaff
Fahri Karakaya, Peter Yannopoulos

Bibliographic record

VenueEuropean Journal of Marketing · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsBrock University
Fundersnot available
KeywordsCompetition (biology)Industrial organizationOrder (exchange)OriginalityTypologyBusinessMarketingVariety (cybernetics)Nonmarket forcesValue (mathematics)EconomicsFactor marketMicroeconomicsQualitative research

Abstract

fetched live from OpenAlex

Purpose The purpose of this study is to develop a conceptual framework for defensive strategy by integrating market entry modes and the typology of firms suggested by Day and Nedungandi, and to attempt to propose how local incumbent firms utilize their mental models in order to react against market entry of new competition in global markets. Design/methodology/approach The theoretical perspective adopted in the study is how mental models used by incumbent firms influence their reaction to market entry of new competition in developing defensive strategies to defend their markets. Findings Mental models of incumbent firms, categorized as self‐centered, competitor‐centered, customer‐oriented, and market‐driven firms, impact their reaction and the development of defensive marketing strategies against market entrants using a variety of market entry modes in global markets. Originality/value The paper presents an extensive review of the defensive marketing and mental models literature and shows how the way in which incumbent firms react to market entry of new competition contributes to understanding of incumbent reaction to market entry of new competition in global markets. Research directions for future research and managerial implications are also provided.

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 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)0.009
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.631
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.233
Teacher spread0.220 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations30
Published2010
Admission routes1
Has abstractyes

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