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- P-ISSN1738-3110
- E-ISSN2093-7717
- SCOPUS, ESCI
ISSN : 1738-3110
Determinants of Online Review Helpfulness for Korean Skincare Products in Online Retailing
Abstract
Purpose: This study aims to examine how to review contents of experiential and utilitarian products (e.g., skincare products) and how to affect review helpfulness by applying natural language processing techniques. Research design, data, and methodology: This study uses 69,633 online reviews generated for the products registered at Amazon.com by 13 Korean cosmetic firms. The authors identify key topics that emerge about consumers' use of skincare products such as skin type and skin trouble, by applying bigram analysis. The review content variables are included in the review helpfulness model, including other important determinants. Results: The estimation results support the positive effect of review extremity and content on the helpfulness. In particular, the reviewer's skin type information was recognized as highly useful when presented together as a basis for high-rated reviews. Moreover, the content related to skin issues positively affects review helpfulness. Conclusions: The positive relationship between extreme reviews and helpfulness of reviews challenges the findings from prior literature. This result implies that an in-depth study of the effect of product types on review helpfulness is needed. Furthermore, a positive effect of review content on helpfulness suggests that applying big data analytics can provide meaningful customer insights in the online retail industry.
- keywords
- Big Data Analytics, Online Consumer Reviews (OCRs), Review Helpfulness, Product Type, Online Retailing
Reference
Bickart, B., & Schindler, R. M. (2001). Internet forums as influential sources of consumer information. Journal of Interactive Marketing, 15(3), 31-40.
Bjering, E., Havro, L. J., & Moen, Ø . (2015). An empirical investigation of self-selection bias and factors influencing review helpfulness. International Journal of Business and Management, 10(7), 16-30.
Bhakkad, A., Dharamadhikari, S. C., & Kulkarni, P. (2013). Efficient approach to find bigram frequency in text document using E-VSM. International Journal of Computer Applications, 68(19), 9-11.
Cheung, C. M., & Lee, M. K. (2012). What drives consumers to spread electronic word of mouth in online consumer-opinion platforms. Decision Support Systems, 53(1), 218-225.
Cheung, C. M., Xiao, B. S., & Liu, I. L. (2014). Do actions speak louder than voices? The signaling role of social information cues in influencing consumer purchase decisions. Decision Support Systems, 65, 50-58.
Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345-354.
Clemons, E. K., Gao, G. G., & Hitt, L. M. (2006). When online reviews meet hyperdifferentiation: A study of the craft beer industry. Journal of Management Information Systems, 23(2), 149-171.
Crowley, A. E., & Hoyer, W. D. (1994). An integrative framework for understanding two-sided persuasion. Journal of Consumer Research, 20(4), 561-574.
Chua, A. Y., & Banerjee, S. (2016). Helpfulness of user-generated reviews as a function of review sentiment, product type, and information quality. Computers in Human Behavior, 54, 547-554.
Eisend, M. (2006). Two-sided advertising: A meta-analysis. International Journal of Research in Marketing, 23(2), 187-198.
Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19(3), 291-313.
Ghose, A., & Ipeirotis, P. G. (2010). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 23(10), 1498-1512.
HAN, S. S. (2020). A factors effecting online social decisions in online consumer behavior. The Journal of Distribution Science, 18(3), 67-76.
Hirschman, E. C., & Holbrook, M. B. (1982). Hedonic consumption: emerging concepts, methods and propositions. Journal of Marketing, 46(3), 92-101.
Hu, Y. H., & Chen, K. (2016). Predicting hotel review helpfulness:The impact of review visibility, and interaction between hotel stars and review ratings. International Journal of Information Management, 36(6), 929-944.
Hunt, J. M., & Smith, M. F. (1987). The persuasive impact of twosided selling appeals for an unknown brand name. Journal of the Academy of Marketing Science, 15(1), 11-18.
Mudambi, S. M., & Schuff, D. (2010). Research note: What makes a helpful online review? A study of customer reviews on Amazon. com. MIS Quarterly, 34(1),185-200.
Nelson, P. (1970). Information and consumer behavior. Journal of Political Economy, 78(2), 311-329.
Nelson, P. (1974). Advertising as information. Journal of Political Economy, 82(4), 729-754.
Oh, Y. K. (2017). The impact of initial eWOM growth on the sales in movie distribution. The Journal of Distribution Science, 15(9), 85-93.
Pan, Y., & Zhang, J. Q. (2011). Born unequal: A study of the helpfulness of user-generated product reviews. Journal of Retailing, 87(4), 598-612.
Park, Y. S. (2015). Does the Rise of the Korean Wave Lead to Cosmetics Export? Journal of Asian Finance, Economics and Business, 2(4), 13-20.
Pavlou, P. A., & Dimoka, A. (2006). The nature and role of feedback text comments in online marketplaces: Implications for trust building, price premiums, and seller differentiation. Information Systems Research, 17(4), 392-414.
Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30-40.
Sen, S., & Lerman, D. (2007). Why are you telling me this? An examination into negative consumer reviews on the web. Journal of Interactive Marketing, 21(4), 76-94.
Schindler, R. M., & Bickart, B. (2012). Perceived helpfulness of online consumer reviews: The role of message content and style. Journal of Consumer Behavior, 11(3), 234-243.
Siering, M., Muntermann, J., & Rajagopalan, B. (2018). Explaining and predicting online review helpfulness: The role of content and reviewer-related signals. Decision Support Systems, 108, 1-12.
Singh, J. P., Irani, S., Rana, N. P., Dwivedi, Y. K., Saumya, S., & Roy, P. K. (2017). Predicting the “helpfulness” of online consumer reviews. Journal of Business Research, 70, 346-355.
Strahilevitz, M., & Myers, J. G. (1998). Donations to charity as purchase incentives: How well they work may depend on what you are trying to sell. Journal of Consumer Research, 24(4), 434-446.
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty:Heuristics and Biases. Science, 185(4157), 1124-1131.
YOON, S. H., SONG, S. Y., & KANG, M. S. (2020). The Growth and Change of Korean Cosmetics Market in Distribution Structure. The Journal of Distribution Science, 18(1), 5-13.
Wakamiya, S., Morita, M., Kano, Y., Ohkuma, T., & Aramaki, E.(2019). Tweet classification toward Twitter-based disease surveillance: new data, methods, and evaluations. Journal of Medical Internet Research, 21(2), e12783.
Willemsen, L. M., Neijens, P. C., Bronner, F., & De Ridder, J. A.(2011). “Highly recommended!” The content characteristics and perceived usefulness of online consumer reviews. Journal of Computer-Mediated Communication, 17(1), 19-38.
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