現今消費者越來越倚賴透過線上消費者評論(OCRs)做出購買決策。因此現今有許多電子商務平台都允許消費者對每則評論進行有用性投票，可以使消費者在龐大的評論中找到大家最認可的評論，過去研究也指出其實具有評分極端性的評論是比中等評論更有用。因此本研究的目的是探討評分極端性對評論認知有用性的關係，以及這個關係會如何被評論特徵與評論者專業影響，評論特徵是在評論中使用人稱代名詞的比率以及評論順序進行探討，而評論者專業則是使用評論者在TripAdvisor 上累計評論數與有用評論數。
本研究透過爬蟲抓取在TripAdvisor 上55 間5 星級飯店共17600 則評論。研究結果顯示，與過去研究相同具有評分極端性的評論比中等評論有用並且在評論順序的調節作用中，具有評分極端性的評論越後期出現會比較沒有用，另外在累計評論數的調節作用中，評論者的累計評論數越高具有評分極端性的評論會被認為更有用。最後依據研究結果提出理論與實務上的建議，期望能夠給予電子商務平台實務上的建議。

Today's consumers increasingly rely on Online Consumer Reviews (OCRs) to
make purchasing decisions. Therefore, many e-commerce platforms today allow
consumers to vote on the usefulness of each review, allowing consumers to find the
most recognized reviews among the huge reviews. Past research have also pointed out
that reviews with extreme ratings are more useful than moderate reviews. Therefore, the
purpose of this research is to explore the relationship between extreme rating and the
cognitive of review helpfulness, and how this relationship is affected by reviewer
expertise and review features. Review features are the ratio of use of personal pronouns
in review and the order of review. The reviewer feature are the cumulative number of
reviews and useful reviews by reviewers on TripAdvisor.
This research crawls a total of 17,600 reviews of 55 5-star hotels on TripAdvisor in
Taiwan. The results shows that reviews with extreme ratings are more useful than
moderate reviews same as previous research; and in the moderating effect of review
order, reviews with extreme ratings appearing later are less useful; and in the
moderating effect of the cumulative number of reviews. The higher cumulative number
of reviews by reviewers, the more extreme reviews will be considered more useful.
Finally, the theoretical and practical suggestions are put forward according to the
research results, and it is expected to give practical suggestions to the e-commerce
platform.

論文審定書 i
摘要 ii
Abstract iii
目錄 iv
圖次 vi
表次 vii
1 第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究問題及目的 4
2 第二章 文獻探討 6
2.1 評論認知有用性 6
2.2 評分極端性與評論認知有用性之間的關係 7
2.3 推敲可能性模型(Elaboration Likelihood Model, ELM) 8
2.4 心智模型(Mental Models) 10
2.5 社會影響(Social Influence) 12
3 第三章 研究方法 14
3.1 研究架構 14
3.2 研究假說 15
3.3 研究流程設計 18
4 第四章 資料分析 29
4.1 樣本基本資料分析 29
4.2 模型分析結果 32
5 第五章 結論與建議 37
5.1 研究結果與討論 37
5.2 理論及實務意涵 38
5.3 研究限制與未來建議 40
參考文獻 42
附錄一 透過ngram找出自訂義字詞 48
附錄二 本研究飯店樣本名單與評論筆數 50

Agnihotri, A., & Bhattacharya, S. (2016). Online review helpfulness: Role of qualitative factors. Psychology & Marketing, 33(11), 1006–1017.
Ariffin, A. A. M., & Maghzi, A. (2012). A preliminary study on customer expectations of hotel hospitality: Influences of personal and hotel factors. International Journal of Hospitality Management, 31(1), 191–198.
Asch, S. E. (1951). Effects of group pressure upon the modification and distortion of judgments, in groups, leadership, men. Carnegie Press, Pittsburgh.
Baddeley, A. E., Aggleton, J. P., & Conway, M. A. (2002). Episodic memory: New directions in research. Oxford University Press.
Barbara Hemforth, & Konieczny, L. (2006). Language Processing: Construction of Mental Models or More? Advances in Psychology, 138, 189–204.
Bickart, B., & Schindler, R. M. (2001). Internet forums as influential sources of consumer information. Journal of Interactive Marketing, 15(3), 31–40.
Birnbaum, M. H. (1972). Morality judgments: Tests of an averaging model. Journal of Experimental Psychology, 93(1), 35.
Bower, G. H., & Morrow, D. G. (1990). Mental models in narrative comprehension. Science, 247(4938), 44–48.
Cantallops, A. S., & Salvi, F. (2014). New consumer behavior: A review of research on eWOM and hotels. International Journal of Hospitality Management, 36, 41–51.
Chaiken, S. (1980). Heuristic versus systematic information processing and the use of source versus message cues in persuasion. Journal of Personality and Social Psychology, 39(5), 752.
Chatterjee, P. (2001). Online reviews: Do consumers use them? Advances in Consumer Research, 28, 129–133.
Chen, Y., & Xie, J. (2008). Online consumer review: Word-of-mouth as a new element of marketing communication mix. Management Science, 54(3), 477–491.
Cheung, C. M., Lee, M., & Rabjohn, N. (2008). The adoption of online opinions in online customer communities. Internet Research, 18(3), 229–247.
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.
Chua, A. Y., & Banerjee, S. (2015). Understanding review helpfulness as a function of reviewer reputation, review rating, and review depth. Journal of the Association for Information Science and Technology, 66(2), 354–362.
Craciun, G., Zhou, W., & Shan, Z. (2020). Discrete emotions effects on electronic word-of-mouth helpfulness: The moderating role of reviewer gender and contextual emotional tone. Decision Support Systems, 130, 113226.
Dellarocas, C. (2003). The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management Science, 49(10), 1407–1424.
Deutsch, M., & Gerard, H. B. (1955). A study of normative and informational social influences upon individual judgment. The Journal of Abnormal and Social Psychology, 51(3), 629.
Fang, B., Ye, Q., Kucukusta, D., & Law, R. (2016). Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics. Tourism Management, 52, 498–506.
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.
Filieri, R. (2016). What makes an online consumer review trustworthy? Annals of Tourism Research, 58, 46–64.
Filieri, R., Alguezaui, S., & McLeay, F. (2015). Why do travelers trust TripAdvisor? Antecedents of trust towards consumer-generated media and its influence on recommendation adoption and word of mouth. Tourism Management, 51, 174–185.
Filieri, R., Hofacker, C. F., & Alguezaui, S. (2018). What makes information in online consumer reviews diagnostic over time? The role of review relevancy, factuality, currency, source credibility and ranking score. Computers in Human Behavior, 80, 122–131.
Filieri, R., Raguseo, E., & Vitari, C. (2018). When are extreme ratings more helpful? Empirical evidence on the moderating effects of review characteristics and product type. Computers in Human Behavior, 88, 134–142.
Filieri, R., Raguseo, E., & Vitari, C. (2019). What moderates the influence of extremely negative ratings? The role of review and reviewer characteristics. International Journal of Hospitality Management, 77, 333–341.
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. (2006). Designing ranking systems for consumer reviews: The impact of review subjectivity on product sales and review quality. Proceedings of the 16th Annual Workshop on Information Technology and Systems, 303–310.
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.
Gretzel, U., Yoo, K. H., & Purifoy, M. (2007). Trip Advisor online travel review study: The role and impacts of online travel review for trip planning. College Station, TX: Laboratory for Intelligent Systems in Tourism.
Heckman, J. J. (1976). The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. Annals of Economic and Social Measurement, 4, 475–492.
Heiss, F., ,. (2016). Using R for introductory econometrics. Retrieved from. http://www.urfie.net/
Henningsen, D. D., & Henningsen, M. L. M. (2003). Examining social influence in information-sharing contexts. Small Group Research, 34(4), 391–412.
Hong, H., Xu, D., Wang, G. A., & Fan, W. (2017). Understanding the determinants of online review helpfulness: A meta-analytic investigation. Decision Support Systems, 102, 1–11.
Hu, N., Liu, L., & Sambamurthy, V. (2011). Fraud detection in online consumer reviews. Decision Support Systems, 50(3), 614–626.
Huang, A. H., Chen, K., Yen, D. C., & Tran, T. P. (2015). A study of factors that contribute to online review helpfulness. Computers in Human Behavior, 48, 17–27.
Hyland, K. (2008). Persuasion, interaction and the construction of knowledge: Representing self and others in research writing. International Journal of English Studies, 8(2), 1–23.
Jiang, Z., & Benbasat, I. (2007). Research note—Investigating the influence of the functional mechanisms of online product presentations. Information Systems Research, 18(4), 454–470.
Johnson-Laird, P. N. (1983). Mental models: Towards a cognitive science of language, inference, and consciousness. Cambridge, UK: Harvard University Press.
Kang, H., Yoo, S. J., & Han, D. (2012). Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews. Expert Systems with Applications, 39(5), 6000–6010. https://doi.org/10.1016/j.eswa.2011.11.107
Kaplan, K. J. (1972). On the ambivalence-indifference problem in attitude theory and measurement: A suggested modification of the semantic differential technique. Psychological Bulletin, 77(5), 361.
Kaplan, M. F., & Miller, C. E. (1987). Group decision making and normative versus informational influence: Effects of type of issue and assigned decision rule. Journal of Personality and Social Psychology, 53(2), 306.
Karimi, S., & Wang, F. (2017). Online review helpfulness: Impact of reviewer profile image. Decision Support Systems, 96, 39–48.
Kaushik, K., Mishra, R., Rana, N. P., & Dwivedi, Y. K. (2018). Exploring reviews and review sequences on e-commerce platform: A study of helpful reviews on Amazon.in. Journal of Retailing and Consumer Services, 45, 21–32.
Kuran, T., & Sunstein, C. R. (1998). Availability cascades and risk regulation. Stan. L. Rev., 51, 683.
Lee, J., & Lee, J.-N. (2009). Understanding the product information inference process in electronic word-of-mouth: An objectivity–subjectivity dichotomy perspective. Information & Management, 46(5), 302–311.
Lee, Y.-J., Hosanagar, K., & Tan, Y. (2015). Do I follow my friends or the crowd? Information cascades in online movie ratings. Management Science, 61(9), 2241–2258.
Liu, Z., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism Management, 47, 140–151.
Mudambi, S., & Schuff, D. (2010). What makes a helpful online review? A study of customer reviews on amazon. Com. MIS Q 34 (1): 185–200.
Na, J., & Choi, I. (2009). Culture and first-person pronouns. Personality and Social Psychology Bulletin, 35(11), 1492–1499.
Nemeth, C. J., & Wachtler, J. (1983). Creative problem solving as a result of majority vs minority influence. European Journal of Social Psychology, 13(1), 45–55.
Ngo-Ye, T. L., Sinha, A. P., & Sen, A. (2017a). Predicting the helpfulness of online reviews using a scripts-enriched text regression model. Expert Systems with Applications, 71, 98–110.
Ngo-Ye, T. L., Sinha, A. P., & Sen, A. (2017b). Predicting the helpfulness of online reviews using a scripts-enriched text regression model. Expert Systems with Applications, 71, 98–110.
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, D.-H., & Lee, J. (2008). EWOM overload and its effect on consumer behavioral intention depending on consumer involvement. Electronic Commerce Research and Applications, 7(4), 386–398.
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.
Park, S., & Nicolau, J. L. (2015). Asymmetric effects of online consumer reviews. Annals of Tourism Research, 50, 67–83.
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.
Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In Communication and persuasion (pp. 1–24). Springer.
Petty, R. E., Cacioppo, J. T., & Schumann, D. (1983). Central and peripheral routes to advertising effectiveness: The moderating role of involvement. Journal of Consumer Research, 10(2), 135–146.
Plotkina, D., & Munzel, A. (2016). Delight the experts, but never dissatisfy your customers! A multi-category study on the effects of online review source on intention to buy a new product. Journal of Retailing and Consumer Services, 29, 1–11.
Presser, S., & Schuman, H. (1980). The measurement of a middle position in attitude surveys. Public Opinion Quarterly, 44(1), 70–85.
Reeder, G. D., & Brewer, M. B. (1979). A schematic model of dispositional attribution in interpersonal perception. Psychological Review, 86(1), 61.
Ren, G., & Hong, T. (2019). Examining the relationship between specific negative emotions and the perceived helpfulness of online reviews. Information Processing & Management, 56(4), 1425–1438.
Riegner, C. (2008). Wired China: The Power of the World’s Largest Internet Population. Journal of Advertising Research, 48(4), 496.
Rubin, D. C. (1988). Autobiographical memory. Cambridge University Press.
Saumya, S., Singh, J. P., Baabdullah, A. M., Rana, N. P., & Dwivedi, Y. K. (2018). Ranking online consumer reviews. Electronic Commerce Research and Applications, 29, 78–89.
Sherif, M. (1936). The psychology of social norms.
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.
Tavassoli, N. T., & Lee, Y. H. (2003). The differential interaction of auditory and visual advertising elements with Chinese and English. Journal of Marketing Research, 40(4), 468–480.
Tulving, E. (2002). Episodic memory: From mind to brain. Annual Review of Psychology, 53(1), 1–25.
Wang, F., & Karimi, S. (2019). This product works well (for me): The impact of first-person singular pronouns on online review helpfulness. Journal of Business Research, 104, 283–294.
Wang, S., Cunningham, N. R., & Eastin, M. S. (2015). The impact of eWOM message characteristics on the perceived effectiveness of online consumer reviews. Journal of Interactive Advertising, 15(2), 151–159.
Watts, S. A., & Zhang, W. (2008). Capitalizing on content: Information adoption in two online communities. Journal of the Association for Information Systems, 9(2), 3.
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.
Wooldridge, J. M. (2015). Introductory econometrics: A modern approach. Cengage learning.
Wyer, R. S., & Hinkle, R. L. (1976). Informational factors underlying inferences about hypothetical persons. Journal of Personality and Social Psychology, 34(3), 481.
Xie, K. L., & So, K. K. F. (2018). The effects of reviewer expertise on future reputation, popularity, and financial performance of hotels: Insights from data-analytics. Journal of Hospitality & Tourism Research, 42(8), 1187–1209.
Zeithaml, V. A., Parasuraman, A., & Berry, L. L. (1985). Problems and strategies in services marketing. Journal of Marketing, 49(2), 33–46.
Zhou, S., & Guo, B. (2017). The order effect on online review helpfulness: A social influence perspective. Decision Support Systems, 93, 77–87.