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Record W2136606988 · doi:10.1080/10454440801986256

Examination of Factors Moderating the Success of Private Label Brands: A Study of the Packaged Food Market in China

2008· article· en· W2136606988 on OpenAlexaboutno aff
Huei-Chen Hsu, Chi‐Shiun Lai

Bibliographic record

VenueJournal of Food Products Marketing · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Market Behavior and Pricing
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessMarketingChinaPrivate labelAdvertisingChinese market

Abstract

fetched live from OpenAlex

ABSTRACT The major objective of this study is to explore how different determinants of perceived risk help explain variations in purchasing preferences for national brands versus private label brands (PLB) of the packaged food market in urban China. We selected the Chinese packaged food market because it is “one of the most rapidly fastest growing markets” in the world (Wu & Deng, 2002 Wu, S. and Deng, H. . Do you want a Big Mac or rice?. Report on the fast food industry in China. Agriculture and Agri-Food Section: Canadian Consulate General in Shanghai. April, [Google Scholar]). Following a description of the Chinese market, we build our conceptual framework by combining the PLB literature with searching versus experience, price consciousness, and product quality literature. Using the data we collected in GuangZhou, Shenzhen, and Shanghai cities, we find both their direct and indirect effects. Supporting theory-based expectations, we find that (1) PLB purchase in a category increases when consumers perceive reduced consequences of making a mistake in brand choice in that category; (2) when that category has more “search” than “experience” characteristics; and (3) consumer's degree of price consciousness in that category, through which we brought in PLB-favoring variables such as lower incomes, high deal-proneness, and a decreased belief in price-quality associations. We discuss our results in light of the managerial and theoretical implications, especially the important role played by “experience” attributes in leading consumers to favor national brands over PLBs.

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)0.007
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.031
GPT teacher head0.231
Teacher spread0.200 · 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

Citations28
Published2008
Admission routes1
Has abstractyes

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