MétaCan
Menu
Back to cohort
Record W1987146443 · doi:10.1002/smj.814

Transaction cost implication of private branding and empirical evidence

2009· article· en· W1987146443 on OpenAlexaff
Shih‐Fen S. Chen

Bibliographic record

VenueStrategic Management Journal · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Market Behavior and Pricing
Canadian institutionsWestern University
FundersUniversity of Illinois at Urbana-ChampaignUniversity of Oregon
KeywordsTransaction costReputationBusinessProduct (mathematics)MarketingAsset specificityDatabase transactionAsset (computer security)Empirical researchPrivate labelIndustrial organizationFinance

Abstract

fetched live from OpenAlex

Abstract Branding and transaction cost economics represent two research streams that rarely cross paths in the literature. In this study, I explore the transaction cost implication of private branding, a practice whereby products supplied by unaffiliated manufacturers are sold under private brands owned by retailers. The main thesis is that private branding can preempt a special case of asset specificity called brand specificity, where retailers also invest in the marketing of an outsourced product, but subsequent reputation effects (positive or negative) are specific to the manufacturer who brands the product. Retailers, thus, will not be fully motivated to optimize their investment in product marketing unless they take over the branding right. With potential barriers to private branding being controlled, data obtained from a national chain reveal that the retailer deploys its marketing resources according to the branding status of a product, implying that private branding can deflect the transaction cost of solving the brand specificity problem. The results offer new theoretical insights into branding and transaction cost analysis. This efficiency‐based approach to private branding also provides practitioners with useful guidelines for crafting a branding strategy that will facilitate cooperation between manufacturers and retailers. Copyright © 2009 John Wiley & Sons, Ltd.

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 score0.637
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.096
GPT teacher head0.322
Teacher spread0.226 · 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

Citations17
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueStrategic Management JournalSame topicConsumer Market Behavior and PricingFrench-language works237,207