Online Yorumlardan Algılanan Faydanın İncelenmesi: Yorumun Faydalı Bulunma İhtimali ve Aldığı Faydalı Oy Sayısı

Year 2017, Volume: 46 Issue: 2, 179 - 187, 01.11.2017

Meltem Yetkin Özbük , Eyyup Yaraş , Akif Şen

Abstract

Bu çalışmada, bir yorumun faydalı oy alma
ihtimali ile aldığı faydalı oy sayısı üzerinde etkili olan değişkenler
incelenmiştir. Bu amaçla, online yorumun olumluluğu/olumsuzluğu, uzunluğu,
internet sitesinde yayınlandığı süre ve yorumcunun uzmanlığı değişkenlerinin
bir yorumun faydalı oy alma ihtimali ile aldığı faydalı oy sayısı üzerindeki
etkileri araştırılmıştır. Booking.com adlı internet sitesinde yer alan İstanbul
otellerini değerlendirmek amacıyla yazılmış 59.163 adet online yorum için eşik
regresyonu analizi uygulanmıştır. Analiz sonuçlarına göre, bir online yorumun
olumsuz içerikte, uzun ve güncel olması ile yorumcunun uzmanlığı, yorumun
faydalı oy alma ihtimalini arttırırken; bir yorumun olumlu olması ve internet
sitesinde uzun süre yayınlanması ile yorumcunun uzmanlığının az olması, o
yorumun aldığı faydalı oy sayısını arttırmaktadır. Bu araştırma sonucunda, bir
yorumun aldığı faydalı oy sayısı için yorum ve yorumcunun inandırıcılığının da
önemli bir etken olduğu belirlenmiştir.

Keywords

Online Yorum, Algılanan Fayda, Eşik Regresyonu, Elektronik Ağızdan Ağıza İletişim, Hizmet Pazarlaması

Examination of the Online Reviews’ Perceived Helpfulness: The Review’s Possibility of Being Helpful and the Number of Helpful Votes Taken

Abstract

In this study, the variables effective on
the online review’s possibility of being helpful and the number of helpful
votes that a review taken were examined. For this purpose, the effects of
online review’s valence, length, the period that a review has been published on
the website, and the expertise of a reviewer on the online review’s possibility
of being helpful and the number of helpful votes that a review has taken were
investigated. Hurdle regression was applied for 59,163 online reviews written to
evaluate the Istanbul’s hotels and posted on Booking.com website. According to
the results of the analysis, while negative, long, recent online reviews and
online reviews written by expert reviewers have higher possibility of being
helpful; positive, old reviews and the reviews written by nonexpert reviewers
have higher number of helpful votes.  As
a result of this research, the review’s and reviewer’s credibility are also
found to be as effective determinants of the number of helpful votes that a
review has taken.

Keywords

Online Review, Perceived Helpfulness, Hurdle Regression, Electronic Word of Mouth Communication, Service Marketing

References

• Agnihotri, A. & Bhattacharya, S. (2016). Online review helpfulness: Role of qualitative factors. Psychology & Marketing, 33(11), 1006-1017.
• Baker, A. M., Donthu, N. & Kumar, V. (2016). Investigating how word-of-mouth conversations about brands influence purchase and retransmission intentions. Journal of Marketing Research, 53(2), 225-239.
• Bigné, E., Caplliure, E. M. & Miquel, M. J. (2016). eWOM on travel agency selection: Specialized versus private label. Psychology & Marketing, 33(12), 1046-1053.
• Bowerman, B. L., O’Connell, R. T. & Hand, L. M. (2001). Business Statistics in Practice . McGraw-Hill.
• Brown, J., Broderick, A. J. & Lee, N. (2007). Word of mouth communication within online communities: Conceptualizing the online social network. Journal of Interactive Marketing, 21(3), 2-20.
• Cabosky, J. (2016). Social media opinion sharing: beyond volume. Journal of Consumer Marketing, 33(3), 172-181.
• Cameron, A. C. & Trivedi, P. K. (2009). Microeconometrics Using Stata. College Station, Texas: Stata Press.
• Casaló, L. V., Flavián, C., Guinalíu, M. & Ekinci, Y. (2015). Avoiding the dark side of positive online consumer reviews: Enhancing reviews’ usefulness for high risk-averse travelers. Journal of Business Research, 68(9), 1829-1835.
• Chen, J., Teng, L. Y. & Yu, X. (2016). The effect of online information sources on purchase intentions between consumers with high and low susceptibility to informational influence. Journal of Business Research, 69(2), 467-475.
• Chen, Z. ve Lurie, N. H. (2013). Temporal contiguity and negativity bias in the impact of online word of mouth. Journal of Marketing Research, 50(4), 463-476.
• Cheng, Y. H. ve Ho, H. Y. (2015). Social influence’s impact on reader perceptions of online reviews. Journal of Business Research, 68(4), 883-887.
• De Langhe, B., Fernbach, P. M. & Lichtenstein, D. R. (2016). Navigating by the stars: Investigating the actual and perceived validity of online user ratings. Journal of Consumer Research, 42(6), 817-833.
• Dünya Turizm Organizasyonu. (2016). UNWTO Tourism Highlights 2016 Edition. Madrid: World Tourism Organization.
• Felbermayr, A. & Nanopoulos, A. (2016). The role of emotions for the perceived usefulness in online customer reviews. Journal of Interactive Marketing, 36, 60-76.
• Filieri, R. (2015). What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. Journal of Business Research, 68(6), 1261-1270.
• Folse, J. A., Porter III, M., Godbole, M. B. & Reynolds, K. E. (2016). The effects of negatively valenced emotional expressions in online reviews on the reviewer, the review, and the product. Psychology & Marketing, 33(9), 747-760.
• Garrigos-Simon, F. J., Garrigos-Simon, F. J., Galdon, J. L., Galdon, J. L., Sanz-Blas, S. & Sanz-Blas, S. (2017). Effects of crowdvoting on hotels: the Booking. com case. International Journal of Contemporary Hospitality Management, 29(1), 419-437.
• Gottschalk, S. A. & Mafael, A. (2017). Cutting through the online review jungle—Investigating selective eWOM processing. Journal of Interactive Marketing, 37, 89-104.
• 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), 1041-1049.
• Hamby, A., Daniloski, K. & Brinberg, D. (2015). How consumer reviews persuade through narratives. Journal of Business Research, 68(6), 1242-1250.
• He, S. & Bond, S. (2013). Word-of-mouth and the forecasting of consumption enjoyment. Journal of Consumer Psychology, 23(4), 464-482.
• Huang, J. H. & Chen, Y. F. (2006). Herding in online product choice. Psychology & Marketing, 23(5), 413-428.
• Ismagilova, E., Dwivedi, Y. K., Slade, E. & Williams, M. (2017). Electronic Word of Mouth (eWOM) in the Marketing Context: A State of the Art Analysis and Future Directions. Springer.
• Ito, T. A., Larsen, J. T., Smith, N. K. & Cacioppo, J. T. (1998). Negative information weighs more heavily on the brain: the negativity bias in evaluative categorizations. Journal of Personality and Social Psychology, 75(4), 887-900.
• Jiménez, F. R. & Mendoza, N. A. (2013). Too popular to The influence of online reviews on purchase intentions of search and experience products. Journal of Interactive Marketing, 27(3), 226-235.
• Jin, L., Hu, B. & He, Y. (2014). The recent versus the out-dated: An experimental examination of the time-variant effects of online consumer reviews. Journal of Retailing, 90(4), 552-566.
• Kim, J. & Gupta, P. (2012). Emotional expressions in online user reviews: How they influence consumers’ product evaluations. Journal of Business Research, 65(7), 985-992.
• King, R. A., Racherla, P. & Bush, V. D. (2014). What we know and don’t know about online word-of-mouth: A review and synthesis of the literature. Journal of Interactive Marketing, 28(3), 167-183.
• Koo, D. M. (2015). The strength of no tie relationship in an online recommendation: Focused on interactional effects of valence, tie strength, and type of service. European Journal of Marketing, 49(7/8), 1163-1183.
• Kostyra, D. S., Reiner, J., Natter, M. & Klapper, D. (2016). Decomposing the effects of online customer reviews on brand, price, and product attributes. International Journal of Research in Marketing, 33(1), 11-26.
• Leal, G. P., Hor-Meyll, L. F. & de Paula Pessôa, L. A. (2014). Influence of virtual communities in purchasing decisions: The participants’ perspective. Journal of Business Research, 5(882-890), 67.
• Lee, M., Rodgers, S. & Kim, M. (2009). Effects of valence and extremity of eWOM on attitude toward the brand and website. Journal of Current Issues & Research in Advertising, 31(2), 1-11.
• Long, J. S. & Freese, J. (2014). Regression Models For Categorical Dependent Variables Using Stata. Collage Station, Texas: Stata Press.
• Martin, W. C. & Lueg, J. E. (2013). Modeling word-of-mouth usage. Journal of Business Research, 66(7), 801-808.
• März, A., Schubach, S. & Schumann, J. H. (2017). “Why Would I Read a Mobile Review?” device compatibility perceptions and effects on perceived helpfulness. Psychology & Marketing, 34(2), 119-137.
• Mert, M. (2016). Yatay Kesit Veri Analizi Bilgisayar Uygulamaları. Ankara: Detay Yayıncılık.
• Moore, S. G. (2015). Attitude predictability and helpfulness in online reviews: The role of explained actions and reactions. Journal of Consumer Research, 42(1), 30-44.
• Munzel, A. (2016). Assisting consumers in detecting fake reviews: The role of identity information disclosure and consensus. Journal of Retailing and Consumer Services, 32, 96-108.
• 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, C. & Lee, T. M. (2009). Antecedents of online reviews’ usage and purchase influence: An empirical comparison of US and Korean consumers. Journal of Interactive Marketing, 23(4), 332-340.
• Peng, L., Cui, G., Zhuang, M. & Li, C. (2016). Consumer perceptions of online review deceptions: an empirical study in China. Journal of Consumer Marketing, 33(4), 269-
• Purnawirawan, N., De Pelsmacker, P. & Dens, N. (2012). Balance and sequence in online reviews: How perceived usefulness affects attitudes and intentions. Journal of Iinteractive Marketing, 26(4), 244-255.
• Purnawirawan, N., Eisend, M., De Pelsmacker, P. & Dens, N. (2015). A meta-analytic investigation of the role of valence in online reviews. Journal of Interactive Marketing, 31, 17-27.
• Rozin, P. & Rosyzman, E. (2001). Negativity bias, negativity dominance, and contagion. Personality and Social Psychology Review, 5(4), 296-320.
• Senecal, S. & Nantel, J. (2004). The influence of online product recommendations on consumers’ online choices. Journal of retailing, 80(2), 159-169.
• Singh, V. K., Nishant, R. & Kitchen, P. J. (2016). Self or simulacra of online reviews: an empirical perspective. Psychology & Marketing, 33(12), 1112-1118.
• Sussman, S. W. & Siegal, W. S. (2003). Informational influence in organizations: An integrated approach to knowledge adoption. Information Systems Research, 14(1), 47-65.
• Tripadvisor. (2017). Tripadvisor. 15 Eylül 2017 tarihinde www.tripadvisor.com sitesinden alınmıştır.
• Wathen, C. N. & Burkell, J. (2002). Believe it or not: Factors influencing credibility on the Web. Journal of the American Society for Information Science and Technology, 53(2), 134-144.
• Wu, P. (2013). In search of negativity bias: An empirical study of perceived helpfulness of online reviews. Psychology & Marketing, 30(11), 971-984.
• Zhao, Y., Yang, S., Narayan, V. & Zhao, Y. (2013). Modeling consumer learning from online product reviews. Marketing Science, 32(1), 153-169.