Use of dynamic programming to improve the detection of means-end chains from laddering data

Use of dynamic programming to improve the detection of means-end chains from laddering data This article, based on the logic of means-end chain (MEC) technique and dynamic programming analysis, proposes a new structural MEC technique (SMT) for enhancing the functions of traditional MECs. The SMT can not only analyze significant consumers’ means-end hierarchies (namely consumers’ preferences of product characteristics) toward a particular product for deducing effective marketing strategies, but can also overcome the problems found in traditional MEC studies. The purposes of the SMT model are: (1) to avoid making the cutoff value decision by the investigators’ subjective judgment, (2) to analyze consumer product knowledge without constructing the hierarchical value map (HVM),(3) to evade the investigators’ cognition bias derived from the HVM to reveal consumer product knowledge, and (4) to confirm the accuracy of HVMs constructed by the laddering technique. Quality & Quantity Springer Journals

Use of dynamic programming to improve the detection of means-end chains from laddering data

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Springer Netherlands
Copyright © 2010 by Springer Science+Business Media B.V.
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
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