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研究生(外文):Chiang-Yu Cheng
論文名稱(外文):What Makes Helpful Online Reviews to the Formation of Trustworthiness? An Experimental Study on the Effectiveness of Review Arrangement across Products
外文關鍵詞:review helpfulnessHomophilyreview valencereview framingtrustworthinesscognitive process
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消費者通常仰賴他人產品使用經驗來進行購物決策,在網路環境中更是不難發現此現象,例如網路評論即為相當普遍的例子。網路評論係由具產品使用經驗之消費者所撰寫,潛在消費者可藉由他人經驗得知特定產品是否值得購買。若評論確實為消費者帶來若干幫助,則消費者可以透過評價功能來讚許評論之助益性 (review helpfulness)。助益性一詞可以擁有多種面向,例如有助於了解產品、有助於節省資訊處理心力、有助於社交臨場感等。遺憾地,目前大多數實務評論網站皆以數字評價來表達評論助益性,此舉並無法清楚表達助益性所指為何。我們相信一般消費者於閱讀網路評論時,至少會關注上述三項評論助益性維度,故其重要性不言而喻。類似於輸入 (input)、處理 (process)、輸出 (output) 模式,本研究將同質理論 (homophily theory) 視為輸入、認知負荷理論 (cognitive load theory) 為處理,以及信任理論 (trust theory) 為輸出,以探究消費者如何處理網路評論之助益性。研究以2 (評論格式) × 2 (評論價鍵) × 2 (評論框架) 完全隨機因子為實驗設計,共募得480位受試者。研究結果顯示,“價值-狀態” 同質評論對助益性與信任具有較高之影響,此結果在評論價鍵與評論框架方向一致的情況下更為顯著 (例如多數負面評論與負面評論優先),然而這些研究發現會隨著產品屬性不同而產生不同地變化。相關理論義涵、實務義涵以及研究限制將於文中一併探討。
Consumers usually rely on others’ experiences to make their purchase decisions. One of the prevalent examples is online reviews. Reviews generated by reviewers serve as an informant to notify consumers that the product is worthy or unworthy of buying. The reviews are in turn praised by consumers with the helpfulness rating. The term “helpfulness” can have different meanings, for example, helpful to product understanding, cognitive effort saving, or perceived social presence which receive no considerations from the modern practices. We believe that consumers should at least care about these three helpfulness factors when reading reviews online. Similar to the input-process-output model, the current study applies homophily theory to the input of review helpfulness factors, while cognitive load theory pertains to the process required to transform the inputted reviews into the outputs. As for trust theory, it serves as outcomes of the cognitive process in the reviews. By connecting these theories, we can understand the way in which consumers interpret helpful reviews. A 2 (review format) × 2 (review valence) × 2 (review framing) full factorial experiment was conducted with 480 participants. The results indicate that value-status homophilous reviews introduce higher review helpfulness as well as review trustworthiness, particularly when reviews are valenced and framed with the same direction. Academic and practical implications are discussed.
Abstract in Chinese i
Abstract ii
Acknowledgement iii
Table of Content iv
List of Figures vi
List of Tables vii
Chapter 1. Introduction 1
1.1 Research background 1
1.2 Research motivation 2
1.3 Research questions and purposes 6
Chapter 2. Theory and hypotheses development 11
2.1 Homophily 12
2.1.1 Qualitative value homophily 13
2.1.2 Quantitative value homophily 15
2.1.3 Value-status homophily 17
2.2 Review valence and framing 19
2.2.1 Review valence 19
2.2.2 Review framing 20
2.3 Cognitive process 22
2.4 Review trustworthiness 23
2.5 Research model and hypotheses 25
2.5.1 Independent and dependent variables 25
2.5.2 The effects of review presentation formats on review helpfulness 28
2.5.3 Moderating effects 29
2.5.4 The effects of review helpfulness on review trustworthiness 31
Chapter 3. Research method 33
3.1 Experimental products 33
3.2 Design and measures 35
3.2.1 Consumer reviews 35
3.2.2 Review presentation formats 36
3.2.3 Review valence 37
3.2.4 Review framing 39
3.2.5 Dependent variables 40
3.1 Experimental websites 43
3.4 Control variables 45
3.5 Experimental procedures 45
3.5 Research methods 46
Chapter 4. Data analysis 50
4.1 ANCOVA analysis 51
4.2 PLS analysis 57
Chapter 5. Discussion 59
5.1 Search type product 60
5.2 Experience type product 64
Chapter 6. Implications 66
6.1 Theoretical implications 66
6.2 Practical implications 69
6.3 Limitations and future research 72
References 74
[1]S. Ba and P. A. Pavlou, “Evidence of the effect of trust building technology in electronic markets: price premiums and buyer behavior”, MIS Quarterly, Vol 26, pp. 243-268, 2002.
[2]G. Haubl and V. Trifts, “Consumer decision making in online shopping environments: the effects of interactive decision aids”, Marketing Science, Vol 19, pp. 4-21, 2000.
[3]E-tailing group (2010), Social Shopping Survey 2.0: Customer Trust and Online Shopping, Available at www.e-tailing.com/content/?p=1310. Last accessed April 15, 2011.
[4]P. Bharati and A. Chaudhury, “An empirical investigation of decision-making satisfaction in web-based decision support systems”, Decision Support Systems Vol 37, pp. 187-197, 2004.
[5]Webmarketing, Online Marketing Forecasts for 2010, E-Commerce to Continue to Grow, Available at webmarketinggroup.co.uk/Blog/ online-marketing-forecasts-1571.aspx. Last accessed April 15, 2011.
[6]F. Xue and J. E. Phelps, “Internet-facilitated consumer-to-consumer communication: the moderating role of receiver characteristics”, International Journal of Internet Marketing and Advertising, Vol 1, pp. 121-136, 2004.
[7]C. C. Yang, et al. “Visualization of large category map for Internet browsing”, Decision Support Systems, Vol 35, pp. 89-102, 2003.
[8]D. Fleder and K. Hosanagar, “Blockbuster culture’s next rise or fall: the impact of recommender systems on sales diversity”, Management Science, Vol 55, pp. 697-712 2009.
[9]Pennington, D. C. Social Cognition. Routledge, London, 2000.
[10]L. Xia and N. N. Bechwati, “Word of mouth: the role of cognitive personalization in online consumer reviews”, Journal of Interactive Advertising, Vol 9, pp. 3-13 2008.
[11]Wells, W. D. and Prensky, D. Consumer Behavior. John Wiley & Sons, NY, 1996.
[12]G. A. Miller, “The magical number seven, plus or minus two: some: limits on our capacity for processing information”, Psychological Review, Vol 63, pp. 81-97, 1956.
[13]D. R. Llgen, et al. “Teams in organizations: from input-process-output models to IMOI models”, Annual Review of Psychology, Vol 56, pp. 517-543 2005.
[14]Cheung, C. M. K. and Thadani, D. R. “The state of electronic word-of-mouth research: a literature analysis,” Pacific Asia Conference on Information Systems (PACIS), 2010.
[15]D. Godes and D. Mayzlin, “Using online conversations to study word-of-mouth communication”, Marketing Science, Vol 23, pp. 545-560, 2004.
[16]Y. Liu, “Word of mouth for movies: its dynamics and impact on box office revenue”, Journal of Marketing, Vol 70, pp. 74-89, 2006.
[17]J. Chevalier and D. Mayzlin, “The effect of word of mouth on sales: online book store”, Journal of Marketing Research, Vol 43, pp. 345-354 2006.
[18]D. Mayzin, “Promotional chat on the Internet”, Marketing Science, Vol 25, pp. 155-163 2006.
[19]Kumar, N. and Benbasat, I. “Para-social presence: a re-conceptulization of social Presence to Capture the Relationship between a Website and Her Visitors,” Proceedings of the 35th Hawaii International Conference on System Sciences. 2002.
[20]E. K. Clemons, et al. “When online reviews meet hyperdifferentiation: a study of the craft beer industry”, Journal of Management Information Systems, Vol 23, pp. 149-171 2006.
[21]B. Gu, et al. “Competition among virtual communities and user valuation: the case of investing-related communities”, Information Systems Research, Vol 18, pp. 68-85, 2007.
[22]X. X. Li and L. M. Hitt, “Self-selection and information role of online product reviews”, Information Systems Research, Vol 19, pp. 456-474 2008.
[23]C. Forman, et al. “Examining the relationship between reviews and sales: the role of reviewer identity disclosure in electronic markets”, Information Systems Research, Vol 19, pp. 291-313, 2008.
[24]N. F. Awad and A. Ragowsky, “Establishing trust in electronic commerce through online word of mouth: an examination across genders”, Journal of Management Information Systems, Vol 24, pp. 101-121, 2008.
[25]E. K. Clemons, “How Information changes consumer behavior and how consumer behavior determines corporate strategy”, Journal of Management Information Systems, Vol 25, pp. 13-40, 2008.
[26]M. Trusov, et al. “Effects of word-of-mouth versus traditional marketing: findings from an Internet social networking site”, Journal of Marketing, Vol 73, pp. 90-102, 2009.
[27]P. Huang, et al. “Searching for experience on the web: an empirical examination of consumer behavior for search and experience goods”, Journal of Marketing, Vol 73, pp. 55-69, 2009.
[28]F. Zhu and X. M. Zhang, “Impact of online consumer reviews on sales: the moderating role of product and consumer characteristics”, Journal of Marketing, Vol 74, pp. 133-148, 2010.
[29]S. M. Mudambi and D. Schuff, “What makes a helpful online review? a study of consumer reviews on Amazon.com”, MIS Quarterly, Vol 34, pp. 185-200, 2010.
[30]S. Highhouse, “Designing experiments that generalize”, Organizational Research Method, Vol 12, pp. 554-566, 2009.
[31]P. Nelson, “Advertising as information”, Journal of Political Economy, Vol 82, pp. 729-754, 1974.
[32]M. L. Tushman and D. A. Nadler, “Information processing as an integrating concept in organizational design”, The Academy of Management Review, Vol 3, pp. 613-264, 1978.
[33]Galbraith, J. K. The Age of Uncertainty. Houghton Mifflin, Boston, 1977.
[34]Dennis, A. R. and Valacich, J. S. “Rethinking Media Richness: Towards a Theory of Media Synchronicity,” Proceedings of the 32nd Hawaii International Conference on System Sciences, 1999.
[35]Shannon, C. E. and Weaver, W. The mathematical theory of communication. University of Illinois, Urbana, 1949.
[36]M. Keil, et al. “Escalation: the role of problem recognition and cognitive bias”, Decision Science, Vol 38, pp. 391-421, 2007.
[37]P. A. Pavlou and A. Dimoka, “The nature and role of feedback text comments in online marketplaces: implications for trust building, price premiums, and seller differentiation”, Information Systems Research, Vol 17, pp. 392-414, 2006.
[38]Y. S. Kang and P. M. Herr, “Beauty and beholder: toward an integrative model of communication source effects”, Journal of Consumer Research, Vol 33, pp. 123-130, 2006.
[39]E. M. Rogers and D. K. Bhowmik, “Homophily-heterophily: relational concepts for communication research”, Public Opinion Quarterly, Vol 34, pp. 523-538, 1970.
[40]M. C. Gilly, et al. “A dyadic study of interpersonal information search”, Journal of the Academy of Marketing Science, Vol 26, pp. 83-100, 1998.
[41]L. B. Dorothy, “Experts as negative opinion leaders in the diffusion of a technological innovation”, Journal of Consumer Research, Vol 11, pp. 914-926, 1985.
[42]E. W. Anderson, et al. “Customer satisfaction, market share, and profitability: findings from Sweden”, Journal of Marketing, Vol 58, pp. 53-664, 1994.
[43]S. Y. Lam, et al. “Customer value, satisfaction, loyalty, and switching costs: an illustration from a business to business service context”, Journal of the Academy of Marketing Science, Vol 32, pp. 239-311, 2004.
[44]Z. Yang and R. Peterson, “Customer perceived value, satisfaction, and loyalty: the role of switching costs”, Psychology and Marketing, Vol 21, pp. 799-822, 2004.
[45]R. B. Woodruff, “Customer value: the next source for competitive advantages”, Journal of Academy of Marketing Science, Vol 25, pp. 139-153, 1997.
[46]B. J. Babin, et al. “Work and/or fun: measuring hedonic and utilitarian shopping value”, Journal of Consumer Research, Vol 20, pp. 644-656, 1994.
[47]B. J. Babin and W. R. Darden, “Consumer self-regulation in a retail environment”, Journal of Retaining, Vol 71, pp. 47-70, 1995.
[48]J. J. Cronin, et al. “Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments”, Journal of Retailing, Vol 76, pp. 193-205, 2000.
[49]M. A. Jones, et al. “Hedonic and utilitarian shopping value: investigating differential effects on retail outcomes”, Journal of Business Research, Vol 59, pp. 974-981, 2006.
[50]J. W. Overby and E. J. Lee, “The effects of utilitarian and hedonic online shopping value on consumer preference and intentions”, Journal of Business Research, Vol 59, pp. 1160-1166, 2006.
[51]J. R. Hauser and D. Clausing, “The house of quality”, Harvard Business Review, Vol 66, pp. 63-73, 1988.
[52]B. T. Ratchford, “The new economic theory of consumer behavior: an interpretative essay”, Journal of Consumer Research, Vol 2, pp. 65-75, 1975.
[53]Enis, B. M., and Roering, K. J. Product classification taxonomies: synthesis and consumer implications. In C. W. Lamb and P. M. Durane (ed.), Theoretical Developments in Marketing, American Marketing Association, Chicago, 1980, pp. 186-189.
[54]W. J. Severin, “Another look at cue summation”, Audio Visual Communications Review, Vol 15, pp. 233-245, 1967.
[55]A. Paivio, “Mental imagery in associative learning and memory”, Psychological Review, Vol 76, pp. 241-263, 1969.
[56]Lazarsfeld, P. F., and Merton, R. K. Friendship a social process: a substantive and methodological analysis. In M. Berger, (ed.), Freedom and Control in Modern Society, pp. 18-66, 1954.
[57]R. Lefkoff-Hagius and C. H. Mason, “Characteristic, beneficial, and image attributes in consumer judgments of similarity and preference”, The Journal of Consumer Research, Vol 20, pp. 100-110, 1993.
[58]L. Xia and N. N. Bechwati, “Word of mouth: the role of cognitive personalization in online consumer reviews”, Journal of Interactive Advertising, Vol 9, pp. 3-13, 2008.
[59]D.H. Park, et al. “The effect of online consumer reviews on consumer purchasing intention: the moderating role of involvement”, International Journal of Electronic Commerce, Vol 11, pp. 125-148, 2007.
[60]T. Hennig-Thurau and G. Walsh, “Electronic word-of-mouth: motives for and consequences of reading customer articulations on the Internet”, International Journal of Electronic Commerce, Vol 8, pp. 51-74, 2004.
[61]S. Standifird, “Reputation and e-commerce: eBay auctions and the asymmetrical Impact of positive and negative ratings”, Journal of Management, Vol 27, pp. 279-295, 2001.
[62]E. Yao, et al. “Effects of customer feedback level and consistency on new product acceptance in click-and-mortar context”, Journal of Business Research, Vol 62, pp. 1281-1288, 2009.
[63]R. J. Donovan and G. Jalleh, “Positively versus negatively framed product attributes: in influence of involvement”, Psychology and Marketing, Vol 16, pp. 613-630, 1999.
[64]D. Kahneman and A. Tversky, “Choices, values and frames”, American Psychologist, Vol 39, pp. 341-350, 1984.
[65]Pennington, D. C. Social Cognition. Routledge, London, 2000.
[66]J. Sweller and P. Chandler, “Why some material is difficult to learn”, Cognition and Instruction, Vol 12, pp. 185-223, 1994.
[67]D. H. McKnight, et al. “Developing and validating trust measures for e-commerce: an integrative typology”, Information Systems Research, Vol 13, pp. 334-359, 2002.
[68]S. Y. X. Komiak and I. Benbasat, “The effects of personalization and familiarity on trust and adoption of recommendation agents”, MIS Quarterly, Vol 30, pp. 941-960, 2006.
[69]W. Wang and I. Benbasat, “Attributions of trust in decision support technologies: a study of recommendation agents for e-commerce”, Journal of Management Information Systems, Vol 24, pp. 249-273, 2008.
[70]M. K. O. Lee and L. E. Turban, “A trust model for consumer Internet shopping”, International Journal of Electronic Commerce, Vol 6, pp. 75-91, 2001.
[71]D. J. Lewis and A. Weigert, “Trust as a social reality”, Social Forces, Vol 63, pp. 967-985, 1985.
[72]R. C. Mayer, et al. “An integrative model of organizational trust”, Academy of Management Review, Vol 10, pp. 709-734, 1995.
[73]D. Gefen, “Reflections on the dimensions of trust and trustworthiness among online consumers”, ACM SIGMIS Database, Vol 33, pp. 38-53, 2002.
[74]Z. Jiang and I. Benbasat, “The effect of presentation formats and task complexity on online consumers’ product understanding”, MIS Quarterly, Vol 31, pp. 475-500, 2005.
[75]W. Wang and I. Benbasat, “Interactive decision aids for consumer decision making in e-commerce: the influence of perceived strategy restrictiveness”, MIS Quarterly, Vol 33, pp. 293-320, 2009.
[76]N. Kumar and I. Benbasat, “Research note: the influence of recommendations and consumer reviews on evaluations of websites”, Information Systems Research, Vol 17, pp. 425-439, 2006.
[77]Schacter, S. The Psychology of Affiliation: Experimental Studies of the Source of Gregariousness. Stanford University Press, Stanford, CA, 1959.
[78]M. Ruef, et al. “The structure of founding teams: homophily, strong ties and isolation among U.S. entrepreneurs”, American Sociological Review, Vol 68, pp. 195-222, 2003.
[79]R. E. Burnkrant and A. Consineau, “Informational and normative social influence in buyer behavior”, Journal of Consumer Research, Vol 2, pp. 206-214, 1975.
[80]D. N. Lascu and G. Zinkhan, “Consumer conformity: review and applications for marketing theory and practice”, Journal of Marketing Theory and Practice, Vol 7, pp. 1-12, 1999.
[81]B. Sternthal, et al. “The persuasive effect of source credibility: tests of cognitive response”, Journal of Consumer Research, Vol 4, pp. 252-260, 1978.
[82]M. Granovetter and S. Roland, “Threshold models of diversity: Chinese restaurants, residential segregation, and the spiral of silence”, Sociological Methodology, Vol 18, pp. 69-104, 1988.
[83]V. W. Mitchell and P. McGoldrick, “Consumers’ risk-reduction strategies: a review and synthesis”, The International Review of Retail, Distribution and Consumer Research, Vol 6, pp. 1-33, 1996.
[84]A. E. Crowley and W. D. Hoyer, “An integrative framework for understanding two-side persuasion”, Journal of Consumer Research, Vol 20, pp. 561-574, 1994.
[85]F. D. Davis, et al. “User acceptance of computer technology: a comparison of two theoretical models”, Management Science, Vol 35, pp. 982-1003, 1989.
[86]D. Gefen, et al. “Trust and TAM in online shopping: an integrated model”, MIS Quarterly, Vol 27, pp. 51-90, 2003.
[87]J. Chevalier and D. Mayzlin, “The effect of word of mouth on sales: online book store”, Journal of Marketing Research, Vol 43, pp. 345-354, 2006.
[88]Viswanathan, M. Measurement Error and Research Design. Sage, Thousand Oaks, CA, 2005.
[89]J. Lee, et al. “The effect of negative online consumer reviews on product attitude: an information process view”, Electronic Commerce Research and Applications, Vol 7, pp. 341-352, 2008.
[90]Hong, Y. and Pavlou, P. A. “Fit Does Matter! An Empirical Study on Product Fit Uncertainty in Online Marketplaces,” Working paper, Temple University, United States, 2010. Available at papers.ssrn.com/sol3/papers.cfm?abstract_id=1600523. Last accessed April 15, 2011.
[91]Hair, J. F., Black, B., Babin, B., Anderson, R. E., and Tatham, R. L. Multivariate Data Analysis. Prentice Hall, New Jersey, 2006.
[92]C. Fornell and D. F. Larcker, “Evaluating structural equation models with unobservable and measurement error”, Journal of Marketing, Vol 18, pp. 39-50, 1981.
[93]R. Bagozzi and L. Phillips, “Assessing construct validity in organizational research”, Administrative Science Quarterly, Vol 36, pp. 421-458, 1991.
[94]D. C. Barclay, et al. “The partial least squares approach to causal modeling: personal computer adoption and use as an illustration”, Technology Studies, Vol 2, pp. 285-308, 1995.
[95]C. J. Huberty and J. D. Morris, “Multivariate analysis versus multiple univariate analyses”, Psychological Bulleting, Vol 105, pp. 302-308, 1989.
[96]J. W. Payne, “Task complexity and contingent processing in decision making: an information search and protocol analysis”, Organizational Behavior and Human Performance, Vol 16, pp. 366-387, 1976.
[97]Alba, J. W., Hutchinson, W., and Lynch, J. G. Memory and decision making. In T. S. Robson and H. H. Kassarjkan (ed.), Handbook of Consumer Behavior, Prentice Hall, Englewood Cliffs, NJ, 1991, pp. 1-49.
[98]E. Breugelmans, et al. “Shelf sequence and proximity effects on online grocery choices”, Marketing Letters, Vol 18, pp. 117-133, 2007.
[99]E. Brynjolfsson and M. D. Smith, “Frictionless commerce? a comparison of Internet and conventional retailers”, Management Science, Vol 46, pp. 563-585, 2000.
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