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Record W2287978267 · doi:10.1108/md-12-2014-0667

Spillover effects of marketing expertise on market performance of domestic firms and MNEs in emerging markets

2016· article· en· W2287978267 on OpenAlexaff
Qiang Lu, Chinmay Pattnaik, Mengze Shi

Bibliographic record

VenueManagement Decision · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMultinational corporationSpillover effectBusinessDomestic marketLoyaltyMarket shareMarketingEmerging marketsValue (mathematics)Nonmarket forcesMarket share analysisFactor marketOriginalityIndustrial organizationEconomicsMarket microstructureOrder (exchange)Market economyInternational tradeMicroeconomics

Abstract

fetched live from OpenAlex

Purpose – The purpose of this paper is to study the spillover effects of marketing expertise on the market performance of domestic firms and multinational enterprises (MNEs). Specifically, this study examines how the adoption of frequency loyalty programs by a domestic firm following an MNE affects the competitive dynamics and the market performance of both firms in a Chinese retail gasoline market. Design/methodology/approach – This study is based on empirical data that were obtained from a quasi-field experiment in which the MNE entered the market with a frequency loyalty program and the domestic firm later responded with a similar loyalty program. The authors measured the impact of the adoption of a frequency loyalty program by the domestic firm on the market performance of both the domestic firm and the MNE. Findings – The authors find that the domestic firm’s adoption of a similar loyalty program significantly increased its market share in the regular gasoline market. The domestic firm’s adoption of a loyalty program also increased the market performance of the MNE in the premium gasoline market. Originality/value – This study explicitly demonstrates the spillover benefits through demonstration effects and provides empirical evidence on specific spillover benefits to domestic firms and MNEs based on their competencies in distinct market segments where they compete.

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)0.001
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.694
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.005
GPT teacher head0.219
Teacher spread0.213 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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