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Personality or Value: A Comparative Study of Psychographic Segmentation Based on an Online Review Enhanced Recommender System

1,†, 1,2,†, 3, 1, 1,4 and 1,*
1
Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Central China Normal University, Wuhan 430079, China
2
School of Information Management, Central China Normal University, Wuhan 430079, China
3
Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
4
Department of Psychology, The University of Memphis, Memphis, TN 38152, USA
*
Author to whom correspondence should be addressed.
Hui Liu and Yinghui Huang are co-first authors.
Appl. Sci. 2019, 9(10), 1992; https://doi.org/10.3390/app9101992
Received: 1 March 2019 / Revised: 28 April 2019 / Accepted: 8 May 2019 / Published: 15 May 2019
(This article belongs to the Special Issue Sentiment Analysis for Social Media)
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Abstract

Big consumer data promises to be a game changer in applied and empirical marketing research. However, investigations of how big data helps inform consumers’ psychological aspects have, thus far, only received scant attention. Psychographics has been shown to be a valuable market segmentation path in understanding consumer preferences. Although in the context of e-commerce, as a component of psychographic segmentation, personality has been proven to be effective for prediction of e-commerce user preferences, it still remains unclear whether psychographic segmentation is practically influential in understanding user preferences across different product categories. To the best of our knowledge, we provide the first quantitative demonstration of the promising effect and relative importance of psychographic segmentation in predicting users’ online purchasing preferences across different product categories in e-commerce by using a data-driven approach. We first construct two online psychographic lexicons that include the Big Five Factor (BFF) personality traits and Schwartz Value Survey (SVS) using natural language processing (NLP) methods that are based on behavior measurements of users’ word use. We then incorporate the lexicons in a deep neural network (DNN)-based recommender system to predict users’ online purchasing preferences considering the new progress in segmentation-based user preference prediction methods. Overall, segmenting consumers into heterogeneous groups surprisingly does not demonstrate a significant improvement in understanding consumer preferences. Psychographic variables (both BFF and SVS) significantly improve the explanatory power of e-consumer preferences, whereas the improvement in prediction power is not significant. The SVS tends to outperform BFF segmentation, except for some product categories. Additionally, the DNN significantly outperforms previous methods. An e-commerce-oriented SVS measurement and segmentation approach that integrates both BFF and the SVS is recommended. The strong empirical evidence provides both practical guidance for e-commerce product development, marketing and recommendations, and a methodological reference for big data-driven marketing research. View Full-Text
Keywords: psychographic segmentation; user preference prediction; lexicon construction; online review; recommender system; big data-driven marketing psychographic segmentation; user preference prediction; lexicon construction; online review; recommender system; big data-driven marketing
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Liu, H.; Huang, Y.; Wang, Z.; Liu, K.; Hu, X.; Wang, W. Personality or Value: A Comparative Study of Psychographic Segmentation Based on an Online Review Enhanced Recommender System. Appl. Sci. 2019, 9, 1992.

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