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Record W1982125237 · doi:10.1080/13657305.2014.903312

DETERMINANTS OF RETAIL PRICE AND SALES VOLUME OF CATFISH PRODUCTS IN THE UNITED STATES: AN APPLICATION OF RETAIL SCANNER DATA

2014· article· en· W1982125237 on OpenAlexaff
Madan M. Dey, Abed G. Rabbani, Kehar Singh, Carole R. Engle

Bibliographic record

VenueAquaculture Economics & Management · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsUniversity of Prince Edward Island
FundersNational Institute of Food and Agriculture
KeywordsRetail salesBusinessCatfishRetail tradeVolume (thermodynamics)ScannerCommerceMarketingEconometricsEconomicsFish <Actinopterygii>Computer science

Abstract

fetched live from OpenAlex

Abstract Catfish market research is important in terms of the viability and sustainability of the catfish aquaculture industry in the U.S. Analysis of market trends, retail level price and sales volume have particular importance in the context of the industry's efforts to promote and market their products. Using retail level scanner data, this study examined the market trends of different catfish product forms. The study also investigated the factors affecting prices and sales for frozen catfish products. The present study is a pioneer in using market/city level retail scanner data to study marketing behavior of catfish products under different product categories (i.e., breaded, unbreaded and entrees). The results of the study highlight a need for a non-price competition strategy for catfish retail market. This includes several activities that attempt to provide added value or incentives to consumers, wholesalers, retailers, or other organizational customers to stimulate immediate sales. Keywords: catfishmarket trendsretail scanner datasales and price response model ACKNOWLEDGMENTS The authors gratefully acknowledge the able research support provided by Mr. Prasanna Surathkal, including his critical help in organizing the data. The findings, opinions, and recommendations expressed are those of the authors and not necessarily those of the Southern Regional Aquaculture Center, the Mississippi Agricultural and Forestry Experiment Station, or the U.S. Department of Agriculture. Notes Note: Figures in parenthesis show % change in prices over 2005–2006. X = represents interaction. ***Significant at 1%, **significant at 5%, *significant at 10%. X = represents interaction. ***Significant at 1%, **significant at 5%, *significant at 10%. ***Significant at 1%, **significant at 5%, *significant at 10%. Note: Elasticities were calculated from significant coefficients of prices of different fish and interaction of prices and region for unbreaded catfish sales model. The term catfish used in this article refers to Ictalurus species, which includes channel catfish and its hybrids. A.C. Nielsen Inc. collects weekly store-level scanner data at the market level (52 markets in the U.S.) and national level. However, national-level data is not an aggregate of the data from the 52 individual markets and includes few more markets than those for which individual market-level data are provided. We used market level data for econometric analysis. But, for brevity in presentation and for space consideration, we have reported the trends at national level only. Wal-Mart has recently started sharing scanner data with A.C. Nielsen Inc in a limited scale for 10 U.S. markets. The sampling framework of the 10 markets where World-Mart participates and that of the 52 markets data we used in this paper are different and are not compatible. A similar approach has also been used at other levels in the seafood supply chain. This includes studies at the harvest level (Carroll et al., Citation2001; McConnell & Strand, Citation2000) and the wholesale level (Asche & Guillen, Citation2012). These studies have found that the attributes and their values differ at different levels in the chain. If the series are integrated of same order, they can be co-integrated. Since dependent variables are price and sales volumes, therefore, we did not test the order of integration and hence co-integration with other variables. The retail price model used in this study shows retailers' pricing behavior and its does not include quantities of seafood sales as explanatory variables. We, therefore, cannot estimate own- and cross-price elasticties directly from the retail price model. In the scanner data a product type is denoted as 'regular' if specific 'type' information is not mentioned in the product description. For example, "CTL BR CTF FLT FZ 12 OZ" is a store brand 12 oz regular catfish frozen fillet; "CTL BR CTF FLT BL SL FZ 32 OZ" is a store brand 32 oz boneless-skinless catfish frozen fillet. Singh et al. (2014) used the geographical disaggregation at the U.S. census division (9 in number) level; whereas this paper uses disaggregation at the U.S. census regions level (4 in number). In the scanner data a product type is denoted as 'regular' if specific 'type' information is not mentioned in the product description. For example, "CTL BR CTF NGT BR 80 OZ" is a store brand 80 oz catfish frozen nugget with regular breading; "CTL BR CTF STR NGT BR 13 OZ" is a store brand 13 oz catfish frozen nugget with cornmeal breading.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.029
GPT teacher head0.218
Teacher spread0.189 · 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

Citations37
Published2014
Admission routes1
Has abstractyes

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