dc.contributor.author | Carl, Johan Lagerkvist | |
dc.contributor.author | Julius, Okello | |
dc.contributor.author | Nancy, Karanja | |
dc.date.accessioned | 2013-02-27T06:10:08Z | |
dc.date.issued | 2012 | |
dc.identifier.citation | Food Quality and Preference 25 (2012) 29–40 | en |
dc.identifier.uri | http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/11923 | |
dc.description.abstract | Applying best–worst (BW) scaling to a multifaceted feature, e.g. food quality, is challenging as attribute non-attendance or lack of attribute discrimination risks invalidating the transformation of choice data to unidimensional scale. The relativism of BW scaling also typically prevents distinction of respondents or groups of respondents based on similarities to the study object. A dual-response BW scaling method employed here to obtain an anchored scale allowed comparisons of importance ratings across individuals.
Attribute importance ratings and rankings obtained were compared with those from relative BW scaling. Latent class (LC) and hierarchical Bayesian (HB) analyses of individual specific BW choice data were also compared for ability to consider within- and between-respondent choice heterogeneity. Personal interviews with 449 consumers provided data on the importance of 16 food quality attributes of kale produced
in peri-urban farming in Kenya. Major findings were that the anchoring model improved individual choice predictions compared with conventional relativistic BW scaling, i.e. was more reliable in measuring consumer preferences, and that HB analysis fitted the data better than LC analysis. HB analysis also successfully obtained individual parameter estimates from sparse data and is thus a promising tool for analysis of BW choices in sensory and consumer-orientated research. | en |
dc.language.iso | en | en |
dc.subject | Food quality | en |
dc.subject | Anchored best–worst scaling | en |
dc.subject | Peri-urban farming | en |
dc.subject | Hierarchical Bayesian estimation | en |
dc.subject | Latent class | en |
dc.title | Anchored vs. relative best–worst scaling and latent class vs. hierarchical Bayesian analysis of best–worst choice data: Investigating the importance of food quality attributes in a developing country | en |
dc.type | Article | en |
local.publisher | Department of Economics, Swedish University of Agricultural Sciences, P.O. Box 7013, Uppsala 750 07, Sweden | en |
local.publisher | Department of Agricultural Economics, University of Nairobi | en |
local.publisher | Department of Land Resource Management & Agricultural Technology, University of Nairobi | en |