한국IT서비스학회지 (Journal of Information Technology Services)

한국IT서비스학회 (Korea Society of IT Services)
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온라인 리뷰의 경제적 효과, 유용성과 유용성 투표수에 영향을 주는 결정요인

The Determinant Factors Affecting Economic Impact, Helpfulness, and Helpfulness Votes of Online

  • 이상재 (세종대학교 경영대학 e-비즈니스 전공) ;
  • 최준연 (세종대학교 디지털콘텐츠학과) ;
  • 최진호 (세종대학교 경영학과)
  • 투고 : 2013.11.07
  • 심사 : 2014.03.17
  • 발행 : 2014.03.31
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초록

More and more people are gravitating to reading products reviews prior to making purchasing decisions. As a number of reviews that vary in usefulness are posted every day, much attention is being paid to measuring their helpfulness. The goal of this paper is to investigate firstly various determinants of the helpfulness of reviews, and intends to examine the moderating effect of product type, i.e., search or experience goods on the product sales, helpfulness and helpfulness votes of online reviews. The determinants include product data, review characteristics, and textual characteristics of reviews. The results indicate that the direct effect exists for the determinants of product sales, helpfulness, and helpfulness votes. Further, the moderating effects of product type exist for these determinants on three dependent variables. The results of study will identify helpful online review and design review sites effectively.

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참고문헌

  1. Aciar, S., D. Zhang, S. Simoff, and J. Debenham, "Informed recommender : Ba sing recommendations on consumer product reviews", IEEE Intelligent Systems, Vol.22(2007), pp.39-47. https://doi.org/10.1109/MIS.2007.55
  2. Aladag, C. H., E. Egrioglu, and S. Gunay, "A new architecture selection strategy in solving seasonal autoregressive time series by artificial neural networks", Hacettepe Journal of Mathematics and Statistics, Vol. 37, No.2(2008), pp.185-200.
  3. Baek, H., J. Ahn, and Y. Choi, "Helpfulness of online consumer reviews : readers' objectives and review cues", International Journal of Electronic Commerce, Vol.17, No.2(2012-2013), pp.99-126.
  4. Cao, Q., W. Duan, and Q. Gan, "Exploring determinants of voting for the 'helpfulness' of online user reviews : A text mining approach", Decision Support Systems, Vol.50, No.2(2011), pp.511-521. https://doi.org/10.1016/j.dss.2010.11.009
  5. Chevalier, J. A. and D. Mayzlin, "The effect of word of mouth on sales : online book reviews", Journal of Marketing Research, Vol.43, No.3(2006), pp.345-354. https://doi.org/10.1509/jmkr.43.3.345
  6. Chevalier, J. A. and A. Goolsbee, "Measuring prices and price competition online : Amazon.com and BarnesandNoble. com", Quantitative Marketing and Economics, Vol.1, No.2(2003), pp.203-222. https://doi.org/10.1023/A:1024634613982
  7. Clemons, E., G. Gao, and L. Hitt, "When online reviews meet hyperdifferentiation : A study of the craft beer industry", Journal of Management Information Systems, Vol.23, No.2(2006), pp.149-171. https://doi.org/10.2753/MIS0742-1222230207
  8. Chung, W. and T.-L. Tseng, "Discovering business intelligence from online product reviews : A rule-induction framework", Expert Systems with Applications, Vol.39, No.15(1, 2012), pp.11870-11879. https://doi.org/10.1016/j.eswa.2012.02.059
  9. Duan, W., B. Gu, and A. B. Whinston, "The dynamics of online word-of-mouth and product sales : an empirical investigation of the movie industry", Journal of Retailing, Vol.84, No.2(2008), pp.233-242. https://doi.org/10.1016/j.jretai.2008.04.005
  10. Forman, C., A. Ghose, and B. Wiesenfeld, "Examining the relationship between reviews and sales : the role of reviewer identity disclosure in electronic markets", Information Systems Research, Vol.19, No. 3(2008), pp.291-313. https://doi.org/10.1287/isre.1080.0193
  11. Ghose, A. and P. G. Ipeirotis, "Estimating the helpfulness and economic impact of product reviews : Mining text and reviewer characteristics", IEEE Transactions on Knowledge and Data Engineering, Vol.23, No.10(2011), pp.1498-1512. https://doi.org/10.1109/TKDE.2010.188
  12. Ghose A. and A. Sundararajan, "Evaluating pricing strategy using e-commerce data : Evidence and estimation challenges," Statistical Science, Vol.21, No.2(2006), pp. 131-142. https://doi.org/10.1214/088342306000000187
  13. Huang, P., N. H. Lurie, and S. Mitra, "Searching for experience on the Web : An empirical examination of consumer behavior for search and experience goods", Journal of Marketing, Vol.73, No.2(2009), pp.55-69. https://doi.org/10.1509/jmkg.73.2.55
  14. Jiang, Z. and I. Benbasat, "Investigating the influence of the functional mechanisms of online product presentations", Information Systems Research, Vol.18, No.4(2007), pp.221-244.
  15. Khashei, M. and M. Bijari, "An artificial neural network(p, d, q) model for time series forecasting", Expert Systems with Applications, Vol.37, No.1(2010), pp.479-489. https://doi.org/10.1016/j.eswa.2009.05.044
  16. Leahy, P., G. Kiely, and G. Corcoran, "Structural optimisation and input selection of an artificial neural network for river level prediction", Journal of Hydrology, Vol.355 (2008), pp.192-201. https://doi.org/10.1016/j.jhydrol.2008.03.017
  17. Liu, Y., "Word of mouth for movies : Its dynamics and impact on box office revenue", Journal of Marketing, Vol.70, No. 3(2006), pp.74-89. https://doi.org/10.1509/jmkg.70.3.74
  18. Min, H.-J. and J. C. Park, "Identifying helpful reviews based on customer's mentions about experiences", Expert Systems with Applications, Vol.39(2012), pp.11830-11838. https://doi.org/10.1016/j.eswa.2012.01.116
  19. Mudambi, S. M. and D. Schuff, "What makes a helpful online review? A study of customer reviews on Amazon.com", MIS Quarterly, Vol.34, No.1(2010), pp.185-200. https://doi.org/10.2307/20721420
  20. Pan, Y. and, J. Q. Zhang, "Born Unequal : A Study of the Helpfulness of User-Generated Product Reviews", Journal of Retailing, Vol.87, No.4(2011), pp.598-612. https://doi.org/10.1016/j.jretai.2011.05.002
  21. Park, D. H., J. Lee, and I. Han, "The effect of on-line consumer reviews on consumer purchasing intention : The moderating role of involvement", International Journal of Electronic Commerce, Vol.11, No.4(2007), pp.125-148. https://doi.org/10.2753/JEC1086-4415110405
  22. Pavlou, P., H. Liang, and Y. Xue, "Uncertainty and mitigating uncertainty in online exchange relationships : A principalagent perspective", MIS Quarterly, Vol. 31, No.1(2007), pp.105-131. https://doi.org/10.2307/25148783
  23. Reinstein, D. and C. M. Snyder, "The influence of expert reviews on consumer demand for experience goods : a case study of movie critics", Journal of Industrial Economics, Vol.53, No.1(2005), pp. 27-51. https://doi.org/10.1111/j.0022-1821.2005.00244.x
  24. Weathers, D., S. Sharma, and S. L. Wood, "Effects of online communication practices on consumer perceptions of performance uncertainty for search and experience goods", Journal of Retailing, Vol.83, No.4(2007), pp.393-401. https://doi.org/10.1016/j.jretai.2007.03.009
  25. West, D., S. Dellana, and J. Qian, "Neural network ensemble strategies for financial decision applications", Computers and Operations Research, Vol.32(2005), pp.2543-2559. https://doi.org/10.1016/j.cor.2004.03.017
  26. Yoon, Y., T. Guimaraes, and G. Swales, "Integrating artificial neural networks with rule-based expert systems", Decision Support Systems, Vol.11(1994), pp.497-507. https://doi.org/10.1016/0167-9236(94)90021-3
  27. Yuen, C. W. M., W. K. Wong, S. Q. Qian, L. K. Chan, and E. H. K. Fung, "A hybrid model using genetic algorithm and neural network for classifying garment defects", Expert Systems with Applications, Vol.36, No.2(2009), pp.2037-204. https://doi.org/10.1016/j.eswa.2007.12.009
  28. Zhang, R. and T. Tran, "Helpful or unhelpful : a linear approach for ranking product reviews", Journal of Electronic Commerce Research, Vol.11, No.3(2010), pp. 220-230.
  29. Zhang, Z., "Weighing stars : Aggregating online product reviews for intelligent ecommerce applications", IEEE Intelligent Systems, Vol.23(2008), pp.42-49.