檢驗有幫助性評論與發訊者的效果之研究 ─以旅館產業為例

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姓名

趙晏慈(Yan-chi Chao) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

檢驗有幫助性評論與發訊者的效果之研究 ─以旅館產業為例
(Investigating the effectiveness of Helpful reviews and reviewers in hotel industry)

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摘要(中)

在網路的發展下，消費者可以將個人經驗或知識分享到網路上，網路評論是消費者提供真實經驗的資訊，其中旅館產業是較短時間的體驗性消費，消費者在購買決策的過程中非常依賴網路口碑。然而現今網路資料量過載，大量的網路評論反而干擾消費者決策，所以許多研究開始找尋哪些是有幫助性的評論，本研究與以往不同之處在於檢驗評論被標記為有幫助性的評論和發訊者，實際對旅館業者之影響。
以TripAdvisor全球旅遊評論網站上，北美前十大排名熱門都市的旅館評論為例，選取每地區排名前三十家旅館評論。對旅館評論分為正向、中立和負向，針對評論的有幫助性，以及發訊者的平均貢獻度，進行變數之間的相關分析。本研究發現「評論有幫助性(Helpfulness)」在正向與中立評論中對旅館業者有影響，以及發訊者的「平均貢獻度(Average contribution)」在正向、中立和負向評論中，對旅館業者有影響，而「評論有幫助性」與「評論等級」有顯著負相關。
另外，本研究還分析旅館等級與旅館排名之間的關係，發現「旅館等級標準差(Rating standard deviation)」與旅館排名有正相關性，表示旅館排名愈後面，等級標準差愈大，發訊者的意見愈不一致。而「旅館正負變化次數(Rating runs)」與旅館排名具有正向相關，說明旅館排名愈後面，評論正負變化次數愈高。

摘要(英)

With the growth of e-commerce, online consumer reviews have become important attributes that influence purchase decisions. Especially, hotel industry has been strongly influenced by online reviews due to most tourists cannot experience all hotels personally and the service levels among hotels can very significantly.
However, the inundation of online consumer reviews has caused information overload, making it difficult for consumers to choose reliable reviews. Therefore, helpful remarks of hotel review should potentially have strong influence on users. Previous research focused on how to predict the helpful scores of reviews but has not explore the influence of reviews marked with helpfulness. The aims of this study is to investigate whether the helpful reviews and reviewers who contribute many reviews really have effects on the marks hotel received.
With analysis of reviews contributed in Tripadvisor.com for three hundred hotels scattered in ten cities of U.S., this study found both reviewer contribution, and helpful review has a positive effect on marks of hotels. Moreover, the research also discovered that the helpfulness of reviews is negatively relates to the ratings. Also, the research found that the standard deviation of review mark is positively relates to hotel ranks.

關鍵字(中)

★ 評論等級
★ 有幫助性的評論
★ 旅館評論
★ Tripadvisor

關鍵字(英)

★ Online ratings
★ helpful reviews
★ online hotel reviews
★ Tripadvisor

論文目次

誌謝(v)
摘要(vi)
Abstract(vii)
目錄(viii)
表目錄(ix)
圖目錄(x)
第一章緒論(1)
1-1研究背景(1)
1-2研究動機(3)
1-3研究目的(4)
1-4研究架構(5)
第二章文獻探討(6)
2-1有幫助性評論的屬性(6)
2-2本研究使用的評論屬性(9)
2-3本研究使用的發訊者屬性(10)
第三章研究流程(11)
第四章資料分析與結果(16)
第五章結論與建議(24)
5-1研究結論(24)
5-2研究限制及未來研究建議(24)
參考文獻(26)

參考文獻

Baek, H., Ahn, J., & Choi, Y. (2012). Helpfulness of online consumer reviews: Readers′ objectives and review cues. International journal of electronic commerce, 17(2), 99-126.
Bansal, H. S., & Voyer, P. A. (2000). Word-of-mouth processes within a services purchase decision context. Journal of service research, 3(2), 166-177.
Cao, Q., Duan, W., & Gan, Q. (2011). Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems, 50(2), 511-521.
Chen, Y.-L., Tang, K., Wu, C.-C., & Jheng, R.-Y. (2014). Predicting the influence of users’ posted information for eWOM advertising in social networks. Electronic Commerce Research and Applications, 13(6), 431-439.
Cheong, H. J., & Morrison, M. A. (2008). Consumers’ reliance on product information and recommendations found in UGC. Journal of Interactive Advertising, 8(2), 38-49.
Cheung, M. Y., Luo, C., Sia, C. L., & Chen, H. (2009). Credibility of electronic word-of-mouth: Informational and normative determinants of on-line consumer recommendations. International journal of electronic commerce, 13(4), 9-38.
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.
De Bruyn, A., & Lilien, G. L. (2008). A multi-stage model of word-of-mouth influence through viral marketing. International Journal of Research in Marketing, 25(3), 151-163.
Flanagin, A. J., & Metzger, M. J. (2013). Trusting expert-versus user-generated ratings online: The role of information volume, valence, and consumer characteristics. Computers in Human Behavior, 29(4), 1626-1634.
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. (2007). Designing novel review ranking systems: predicting the usefulness and impact of reviews. Paper presented at the Proceedings of the ninth international conference on Electronic commerce.
Ghose, A., & Ipeirotis, P. G. (2011). 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.
Hu, N., Koh, N. S., & Reddy, S. K. (2014). Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales. Decision Support Systems, 57, 42-53.
Hu, N., Liu, L., & Sambamurthy, V. (2011). Fraud detection in online consumer reviews. Decision Support Systems, 50(3), 614-626. doi:10.1016/j.dss.2010.08.012
Huang, P., Lurie, N. H., & Mitra, S. (2009). Searching for experience on the web: an empirical examination of consumer behavior for search and experience goods. Journal of marketing, 73(2), 55-69.
Insights, C. (2013). The value of Google result positioning. Online Advertising Network.
Kim, S.-M., Pantel, P., Chklovski, T., & Pennacchiotti, M. (2006). Automatically assessing review helpfulness. Paper presented at the Proceedings of the 2006 Conference on empirical methods in natural language processing.
Lee, H., Lee, D. I., Kim, T., & Lee, J. (2013). The moderating role of socio-semantic networks on online buzz diffusion. Journal of Business Research, 66(9), 1367-1374.
Lim, B. C., & Chung, C. M. (2011). The impact of word-of-mouth communication on attribute evaluation. Journal of Business Research, 64(1), 18-23.
Litvin, S. W., Goldsmith, R. E., & Pan, B. (2008). Electronic word-of-mouth in hospitality and tourism management. Tourism Management, 29(3), 458-468.
Liu, J., Cao, Y., Lin, C.-Y., Huang, Y., & Zhou, M. (2007). Low-Quality Product Review Detection in Opinion Summarization. Paper presented at the EMNLP-CoNLL.
Luo, Q., & Zhong, D. (2015). Using social network analysis to explain communication characteristics of travel-related electronic word-of-mouth on social networking sites. Tourism Management, 46, 274-282.
Maslowska, E., Malthouse, E. C., & Bernritter, S. F. (2017). Too good to be true: the role of online reviews’ features in probability to buy. International Journal of Advertising, 36(1), 142-163.
McAuley, J., & Leskovec, J. (2013). Hidden factors and hidden topics: understanding rating dimensions with review text. Paper presented at the Proceedings of the 7th ACM conference on Recommender systems.
McCarthy, L., Stock, D., & Verma Ph D, R. (2010). How travelers use online and social media channels to make hotel-choice decisions.
Mudambi, S. M., & Schuff, D. (2010). What makes a helpful review? A study of customer reviews on Amazon. com.
Murray, K. B. (1991). A test of services marketing theory: consumer information acquisition activities. The journal of marketing, 10-25.
O’Brien, J., & Ali, R. (2014). Skift report: State of travel 2014.
O’Mahony, M. P., & Smyth, B. (2010). A classification-based review recommender. Knowledge-Based Systems, 23(4), 323-329.
Otterbacher, J. (2009). ′Helpfulness′ in online communities: a measure of message quality. Paper presented at the Proceedings of the SIGCHI conference on human factors in computing systems.
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.
Rafaeli, S., & Raban, D. R. (2005). Information sharing online: a research challenge. International Journal of Knowledge and Learning, 1(1-2), 62-79.
Senecal, S., & Nantel, J. (2004). The influence of online product recommendations on consumers’ online choices. Journal of retailing, 80(2), 159-169.
Sundaram, D., & Webster, C. (1999). The role of brand familiarity on the impact of word-of-mouth communication on brand evaluations. NA-Advances in Consumer Research Volume 26.
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.
Weiss, A. M., Lurie, N. H., & MacInnis, D. J. (2008). Listening to strangers: whose responses are valuable, how valuable are they, and why? Journal of marketing research, 45(4), 425-436.
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.
Ye, Q., Law, R., & Gu, B. (2009). The impact of online user reviews on hotel room sales. International Journal of Hospitality Management, 28(1), 180-182.
Zhu, L., Yin, G., & He, W. (2014). Is this opinion leader′s review useful? Peripheral cues for online review helpfulness. Journal of Electronic Commerce Research, 15(4), 267.

指導教授

許秉瑜(Ping-Yu Hsu)

審核日期

2017-6-30

推文