Examination of Factors Moderating the Success of Private Label Brands: A Study of the Packaged Food Market in China
Bibliographic record
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".