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평점이 수렴되지 않는 리뷰의 제품들이 더 좋을 수도 있을까?: 제품 리뷰평점의 분산과 소비자의 조절초점 성향에 따른 소비자 태도 변화

Could a Product with Diverged Reviews Ratings Be Better?: The Change of Consumer Attitude Depending on the Converged vs. Diverged Review Ratings and Consumer's Regulatory Focus

  • 이은주 (국민대학교 비즈니스IT전문대학원) ;
  • 박도형 (국민대학교 경영정보학부/비즈니스IT전문대학원)
  • Yi, Eunju (Graduate School of Business IT, Kookmin University) ;
  • Park, Do-Hyung (School of Management Information Systems / Graduate School of Business IT, Kookmin University)
  • 투고 : 2021.08.20
  • 심사 : 2021.09.28
  • 발행 : 2021.09.30

초록

팬데믹(Pandemic)으로 인해 온라인 시장의 규모가 급속하게 커졌다. 일상에서의 비대면화는그동안 기술수용에 늦은 소비자마저 온라인구매의 편리함을 경험하게 하는 계기가 되었고, 이들은 팬데믹 이후에도 온라인구매의 이점을 선호하게 될 것이다. 하지만 이러한 변화의 시기에 소비자가 취할 수 있는 제품 정보는 편평한 디스플레이상의 시각적 정보만으로 축소되었다. 회사들은 차별적이고 경쟁력 있는 정보를 제공하기 위해 AR/VR, Streaming 기술 등을 도입하고 있지만, 정직한 사용자들이 남긴 리뷰는 회사가 제공하는 잘 가공된 정보만큼 소비자에게 강력하게 인식되고, 회사의 상품개발과 마케팅 및 판매 전략을 위한 인사이트를 얻을 수 있다는 점에서 중요하게 인식될 필요가 있다. 그렇다면 소비자의 입장에서, 구매 의사결정 전에 참고하는 리뷰의 평점이 크게 어긋난다면, 소비자들은 어떻게 리뷰정보를 처리할까? 수렴되지 않은 평점은 늘 신뢰할 수 없고 가치 없는 것일까? 본 연구에서는 소비자의 개인 성향으로 볼 수 있는 조절초점 성향이 어떻게 사고방식을 지배하여 수렴되지 않은 정보를 수용하고 처리하는지 보이고자 하였다. 실험은 화장품을 대상으로 제품 리뷰 평점의 분산(높음 vs 낮음)이 소비자의 조절초점(예방초점 vs. 향상초점)에 따라 제품 태도에 어떤 영향을 미치는지 2x2 연구로 설계하였다. 연구결과, 예방초점의 소비자는 분산이 작을 때 높은 제품 태도를 보이지만, 향상초점의 소비자는 분산이 클 때 높은 제품 태도를 보인다는 것을 발견하였다. 이와 같은 연구로, 본 논문은 동일한 평균값의 평가점수를 가진 제품이라도 후기의 분산 값에 따라 소비자의 조절초점 성향이 영향을 미쳐 제품 태도가 달라진다는 것을 설명할 수 있다. 본 논문은 평점이 수렴되지 않는 정보에 대한 소비자의 정보처리의 메커니즘을 밝힌 이론적 공헌이 있으며, 실무적으로 기업은 리뷰가 축적됨에 따라 개인화되고 최적화된 상품 정보를 제공하는 등 빅데이터를 바탕으로 지식경영을 응용한 고객경험설계가 가능함을 시사한다.

Due to the COVID-19 pandemic, the size of the e-commerce has been increased rapidly. This pandemic, which made contact-less communication culture in everyday life made the e-commerce market to be opened even to the consumers who would hesitate to purchase and pay by electronic device without any personal contacts and seeing or touching the real products. Consumers who have experienced the easy access and convenience of the online purchase would continue to take those advantages even after the pandemic. During this time of transformation, however, the size of information source for the consumers has become even shrunk into a flat screen and limited to visual only. To provide differentiated and competitive information on products, companies are adopting AR/VR and steaming technologies but the reviews from the honest users need to be recognized as important in that it is regarded as strong as the well refined product information provided by marketing professionals of the company and companies may obtain useful insight for product development, marketing and sales strategies. Then from the consumer's point of view, if the ratings of reviews are widely diverged how consumers would process the review information before purchase? Are non-converged ratings always unreliable and worthless? In this study, we analyzed how consumer's regulatory focus moderate the attitude to process the diverged information. This experiment was designed as a 2x2 factorial study to see how the variance of product review ratings (high vs. low) for cosmetics affects product attitudes by the consumers' regulatory focus (prevention focus vs. improvement focus). As a result of the study, it was found that prevention-focused consumers showed high product attitude when the review variance was low, whereas promotion-focused consumers showed high product attitude when the review variance was high. With such a study, this thesis can explain that even if a product with exactly the same average rating, the converged or diverged review can be interpreted differently by customer's regulatory focus. This paper has a theoretical contribution to elucidate the mechanism of consumer's information process when the information is not converged. In practice, as reviews and sales records of each product are accumulated, as an one of applied knowledge management types with big data, companies may develop and provide even reinforced customer experience by providing personalized and optimized products and review information.

키워드

과제정보

이 논문은 2020년 대한민국 교육부와 한국연구재단의 인문사회분야 중견연구자지원사업의 지원을 받아 수행된 연구임(NRF-2020S1A5A2A01040055)

참고문헌

  1. 곽지훈, 간형식 (2012). 조절초점 이론의 고찰 및 새로운 연구 방향. 글로벌경영연구, 25(1), 1-15.
  2. 김은희, 유승엽 (2018). SNS 광고 구성요인이 광고 신뢰도와 구매의도에 미치는 영향: 페이스북을 중심으로. 디지털융복합 연구, 16(5), 163-172.
  3. 박도형, 정재권 (2014). 조절일치가 소비자 제품태도에 미치는 영향: 광고 조절일치 vs. 소비자 후기 조절일치. e-비즈니스연구, 15(4), 127-148. https://doi.org/10.15719/GEBA.15.4.201408.127
  4. 박지영, 홍태호 (2021). 레스토랑의 온라인 리뷰를 통해 감성과 감정이 리뷰 유용성에 미치는 영향에 관한 연구. 지식경영연구, 22(1), 243-267. https://doi.org/10.15813/KMR.2021.22.1.012
  5. 유창조, 안광호, 박성휘 (2011). 온라인 구전정보가 소비자 구매의도에 미치는 영향에 대한 실증연구: 제품관여도, 조절초점, 자기효능감의 조절효과를 중심으로. Asia Marketing Journal, 13(3), 209-231.
  6. 유창조, 정혜은 (2002). 인터넷 쇼핑몰에서의 쇼핑경험의 질이 재방문의사와 구매의사에 미치는 영향에 관한 연구: 효용적 가치(utilitarian value)와 쾌락적 가치(hedonic value)의 구분. 소비자학연구, 13(4), 77-100.
  7. 이민철, 윤현식 (2020). 머신러닝을 활용한 가짜리뷰 탐지 연구: 사용자 행동 분석을 중심으로. 지식경영연구, 21(3), 177-195. https://doi.org/10.15813/KMR.2020.21.3.010
  8. 이애리 (2021). 언택트 시대 라이브 커머스 이용 활성화 영향요인 고찰: 다차원적 상호작용성, 현장감, 리뷰 신뢰도를 중심으로. 지식경영연구, 22(1), 269-286. https://doi.org/10.15813/KMR.2021.22.1.013
  9. 정재권, 박도형 (2013). 자기조절초점에 따른 온라인 제품 리뷰의 효과에 관한 연구. e-비즈니스연구, 14(3), 77-93.
  10. 정희정, 이현애, 정남호, 구철모 (2018). 유용한 온라인 리뷰에서 어느 것이 더 중요한가? 휴리스틱-체계적 모델 관점. 지식경영연구, 19(4), 3-19.
  11. 최지은, 여민선 (2018). 온라인 평점의 분산이 클 경우 소비자들이 항상 부정적으로 반응할까?: 영화 평점의 분포와 영화 관람동기의 상호작용이 소비자 반응에 미치는 영향. 광고학연구, 29(4), 7-25.
  12. Aaker, J. L., & Lee, A. Y. (2001). "I" seek pleasures and "we" avoid pains: The role of self-regulatory goals in information processing and persuasion. Journal of Consumer Research, 28(1), 33-49. https://doi.org/10.1086/321946
  13. Ayeh, J. K., Au, N., & Law, R. (2013). "Do we believe in TripAdvisor?" Examining credibility perceptions and online travelers' attitude toward using user-generated content. Journal of Travel Research, 52(4), 437-452. https://doi.org/10.1177/0047287512475217
  14. Bronner, F., & De Hoog, R. (2010). Consumer-generated versus marketer-generated websites in consumer decision making. International Journal of Market Research, 52(2), 231-248. https://doi.org/10.2501/S1470785309201193
  15. Chatterjee, P. (2001). Online review: Do consumers use them? Advances in Consumer Research, 28, 129-133.
  16. Cheng, M., & Jin, X. (2019). What do Airbnb users care about? An analysis of online review comments. International Journal of Hospitality Management, 76, 58-70. https://doi.org/10.1016/j.ijhm.2018.04.004
  17. 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. https://doi.org/10.2753/MIS0742-1222230207
  18. Cui, G., Lui, H. K., & Guo, X. (2012). The effect of online consumer reviews on new product sales. International Journal of Electronic Commerce, 17(1), 39-58. https://doi.org/10.2753/JEC1086-4415170102
  19. Foerster, J., Higgins, E. T., & Bianco, A. T. (2003). Speed/accuracy decisions in task performance: Built-in trade-off or separate strategic concerns? Organizational Behavior and Human Decision Processes, 90(1), 148-164. https://doi.org/10.1016/S0749-5978(02)00509-5
  20. Gupta, P., & Harris, J. (2010). How e-WOM recommendations influence product consideration and quality of choice: A motivation to process information perspective. Journal of Business Research, 63(9-10), 1041-1049. https://doi.org/10.1016/j.jbusres.2009.01.015
  21. Higgins, E. T. (1997). Beyond pleasure and pain. American Psychologist, 52(12), 1280-1300. https://doi.org/10.1037/0003-066X.52.12.1280
  22. Higgins, E. T. (2008). Culture and personality: Variability across universal motives as the missing link. Social and Personality Psychology Compass, 2(2), 608-634. https://doi.org/10.1111/j.1751-9004.2007.00075.x
  23. Higgins, E. T., Cesario, J., Hagiwara, N., Spiegel, S., & Pittman, T. (2010). Increasing or decreasing interest in activities: The role of regulatory fit. Journal of Personality and Social Psychology, 98(4), 559-572. https://doi.org/10.1037/a0018833
  24. Jeong, S. C., Kim, S. H., Park, J. Y., & Choi, B. (2017). Domain-specific innovativeness and new product adoption: A case of wearable devices. Telematics and Informatics, 34(5), 399-412. https://doi.org/10.1016/j.tele.2016.09.001
  25. Katz, E., & Lazarsfeld, P. F. (1955). Personal influence: The part played by people in the flow of mass communications, Glencoe, Ill. Free Press.
  26. Khare, A., Labrecque, L. I., & Asare, A. K. (2011). The assimilative and contrastive effects of word-of-mouth volume: An experimental examination of online consumer ratings. Journal of Retailing, 87(1), 111-126. https://doi.org/10.1016/j.jretai.2011.01.005
  27. Langan, R., Besharat, A., & Varki, S. (2017). The effect of review valence and variance on product evaluations: An examination of intrinsic and extrinsic cues. International Journal of Research in Marketing, 34(2), 414-429. https://doi.org/10.1016/j.ijresmar.2016.10.004
  28. Lee, J., Park, D. H., & Han, I. (2008). The effect of negative online consumer reviews on product attitude: An information processing view. Electronic commerce research and applications, 7(3), 341-352. https://doi.org/10.1016/j.elerap.2007.05.004
  29. Lee, J., Park, D. H., & Han, I. (2011). The different effects of online consumer reviews on consumers' purchase intentions depending on trust in online shopping malls: An advertising perspective. Internet Research, 21(2), 187-206. https://doi.org/10.1108/10662241111123766
  30. Lee, M., & Youn, S. (2009). Electronic word of mouth (eWOM) how eWOM platforms influence consumer product judgement. International Journal of Advertising, 28(3), 473-499. https://doi.org/10.2501/S0265048709200709
  31. Li, H., Kuo, C., & Rusell, M. G. (1999). The impact of perceived channel utilities, shopping orientations, and demographics on the consumer's online buying behavior. Journal of Computer-Mediated Communication, 5(2), JCMC521.
  32. Liu, Y. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70(3), 74-89. https://doi.org/10.1509/jmkg.70.3.074
  33. 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. https://doi.org/10.2307/20721420
  34. Nelson, P. (1970). Information and consumer behavior. Journal of Political Economy, 78(2), 311-329. https://doi.org/10.1086/259630
  35. Park, C., & Lee, T. M. (2009). Information direction, website reputation and eWOM effect: A moderating role of product type. Journal of Business Research, 62(1), 61-67. https://doi.org/10.1016/j.jbusres.2007.11.017
  36. Park, D. H., & Kim, S. (2008). The effects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews. Electronic Commerce Research and Applications, 7(4), 399-410. https://doi.org/10.1016/j.elerap.2007.12.001
  37. Park, D. H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125-148. https://doi.org/10.2753/JEC1086-4415110405
  38. Park, S. B., & Park, D. H. (2013). The effect of low-versus high-variance in product reviews on product evaluation. Psychology & Marketing, 30(7), 543-554. https://doi.org/10.1002/mar.20626
  39. Purnawirawan, N., De Pelsmacker, P., & Dens, N. (2012). Balance and sequence in online reviews: How perceived usefulness affects attitudes and intentions. Journal of Interactive Marketing, 26(4), 244-255. https://doi.org/10.1016/j.intmar.2012.04.002
  40. Shimul, A. S., Cheah, I., & Lou, A. J. (2021). Regulatory focus and junk food avoidance: The influence of health consciousness, perceived risk and message framing. Appetite, 166, 105428. https://doi.org/10.1016/j.appet.2021.105428
  41. Sun, M. (2012). How does the variance of product ratings matter? Management Science, 58(4), 696-707. https://doi.org/10.1287/mnsc.1110.1458
  42. Susan, M. M., & David, S. (2010). What makes a helpful online review? A study of customer reviews on amazon. Com. MIS Quarterly, 34(1), 185-200. https://doi.org/10.2307/20721420
  43. Trusov, M., Bucklin, R. E., & Pauwels, K. (2009). Effects of word-of-mouth versus traditional marketing: Findings from an internet social networking site. Journal of Marketing, 73(5), 90-102. https://doi.org/10.1509/jmkg.73.5.90
  44. Winer, R. S. (2009). New communications approaches in marketing: Issues and research directions. Journal of Interactive Marketing, 23(2), 108-117. https://doi.org/10.1016/j.intmar.2009.02.004
  45. Wu, P. F. (2013). In search of negativity bias: An empirical study of perceived helpfulness of online reviews. Psychology & Marketing, 30(11), 971-984. https://doi.org/10.1002/mar.20660
  46. IT조선 (2021). http://it.chosun.com/site/data/html_dir/2021/01/06/2021010601154.html