Responsive image
博碩士論文 etd-0730121-122500 詳細資訊
Title page for etd-0730121-122500
論文名稱
Title
後「既」無力? 醫療品牌態度一致性對態度極化的影響
Breaking through 'bias': The influence of hospital brand attitude consistency on attitude polarization
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
131
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2021-07-27
繳交日期
Date of Submission
2021-08-30
關鍵字
Keywords
醫療口碑、態度一致性、偏見同化、態度極化、社會線索、Facebook表情符號反應
medical electronic word-of-mouth, brand attitude consistency, bias assimilation, attitude polarization, social cues, Facebook emoji reactions
統計
Statistics
本論文已被瀏覽 116 次,被下載 25
The thesis/dissertation has been browsed 116 times, has been downloaded 25 times.
中文摘要
近年人工智慧醫療的發展愈來愈受市場矚目,對數位時代的我們而言,藉由網路搜尋口碑訊息已顯然成為一種習慣,消費者的醫療決策亦非常容易受到網路資訊影響。針對社群媒體探究既存態度與醫療口碑的相關研究甚少,故本研究應用偏見同化理論(Bias assimilation theory)調查消費者既存態度對醫療口碑說服力及態度極化的影響,並將社會線索作為干擾變數。
本研究採2(評論效價:正面vs.負面)x5(Facebook表情符號反應類型:無表情符號vs.讚vs.大心vs.哇vs.同時顯示讚+大心+哇)x2(醫院品牌:台大vs.榮總)x2(評論試驗)的實驗設計,共招募了754位研究對象。本研究發現,醫療品牌態度一致性藉由口碑說服力影響態度極化有顯著效果。此外,相較無表情符號,同時顯示讚、大心及哇表情符號反應更能夠弱化說服力對態度極化的影響。本研究結果彌補品牌態度一致性與醫療口碑間關係的空白,藉由結合社會線索拓展網路口碑之研究範疇,同時為態度極化做出貢獻。最後,本研究提供醫院品牌在社群平台進行醫療資訊傳播相關的實務建議。
Abstract
In recent years, the development of artificial intelligence in medical fields has attracted more and more attention in the market. For us in the digital age, it has become a habit to search for electronic word of mouth on the Internet, and consumers' medical decisions are also easily influenced by the electronic word of mouth on the Internet. There are few studies on social media to explore prior attitudes and electronic word-of-mouth about medical services, so this study based on the Bias assimilation theory to investigate the influence of consumers' prior attitudes on persuasiveness and attitude polarization through medical electronic word-of-mouth and examine the effect of social cues as a moderator variable.
In this study, a 2(eWOM valence: positive vs. negative) x5 (Facebook emoji reactions type: no emoji vs. like vs. love vs. wow vs. both like+ love + wow) x2 (Hospital brand: National Taiwan University Hospital vs. Veterans General Hospital) x2 (eWOM trial) experimental design was conducted to test the hypotheses and clarify the research question. A total of 754 participants were recruited for the design. The experimental results show that hospital brand attitude consistency has a significant effect on attitude polarization through eWOM persuasiveness. In addition, the effect of persuasiveness on attitude polarization was significantly weakened by "like, love and wow" Facebook emoji reactions compared to no emojis ones. The results of this study fill the gap in the relationship between brand attitude consistency and medical electronic word-of-mouth. Finally, this study makes theoretical contributions to the blank of combination of electronic word-of-mouth, social cues, and attitude polarization. It also offers practical suggestions for the hospital brands to disseminate medical information on social media.
目次 Table of Contents
論文審定書.....................................................................................................................i
中文摘要......................................................................................................................iii
英文摘要.......................................................................................................................iv
壹、緒論........................................................................................................................1
第一節 研究背景....................................................................................................1
第二節 研究動機....................................................................................................3
第三節 研究目的....................................................................................................5
貳、文獻探討................................................................................................................6
第一節 AIoT 科技診療..........................................................................................6
第二節 網路口碑..................................................................................................14
第三節 既存態度..................................................................................................18
第四節 社會線索..................................................................................................24
參、研究方法..............................................................................................................34
第一節 研究架構..................................................................................................34
第二節 假說推論..................................................................................................34
第三節 研究方法..................................................................................................38
第四節 各變項衡量方法......................................................................................49
第五節 資料分析方法..........................................................................................51
肆、資料分析與結果..................................................................................................52
第一節 敘述性統計分析......................................................................................52
第二節 操弄性檢驗..............................................................................................54
第三節 信效度分析..............................................................................................56
第四節 研究假說之檢驗......................................................................................58
伍、結論與建議..........................................................................................................64
第一節 研究結論..................................................................................................64
第二節 研究貢獻..................................................................................................68
參考文獻......................................................................................................................77
附錄 A 正式問卷......................................................................................................97
附錄 B 正式問卷之實驗口碑貼文........................................................................102
參考文獻 References
王若樸(2019)。AI浪潮席捲醫療業 醫療影像AI開發流程大公開。取自 https://www.ithome.com.tw/news/129974
財團法人台灣網路資訊中心(2017)。2017年台灣寬頻網路使用調查報告。取自https://www.twnic.tw/doc/twrp/20170721e.pdf
財團法人台灣網路資訊中心(2019)。2019台灣網路報告。取自 https://report.twnic.tw/2019/TrendAnalysis_globalCompetitiveness.html
陳靜君、陶振超(2018)。偏見同化效果:網路新聞不文明留言對態度極化的影響。中華傳播學刊,33,137-179。
彭子珊(2019)。面對「李開復的詛咒」 這位榮總名醫為何選擇「擁抱敵人」?取自https://www.cw.com.tw/article/5096463
黃浥暐(2018)。日本將明定人工智慧(AI)法規,醫生將負起決定最終的診斷和治療方針之責任。取自 http://www.angle.com.tw/ahlr/discovery/post.aspx?ipost=3027
黃聖筑(2018)。AI人工智慧可以幫助病患提前發現癌症。取自https://cancer.heho.com.tw/archives/19371
資誠聯合會計師事務所(2017)。2017全球人工智慧研究報告。取自https://www.pwc.tw/zh/news/press-release/press-20170627.html
資誠聯合會計師事務所(2018)。2018 全球消費者洞察報告。取自https://www.pwc.tw/zh/news/press-release/press-20180313.html
資誠聯合會計師事務所(2019)。 2019全球消費者洞察報告。取自 https://csrone.com/news/5464
裴有恆(2017)。台灣在智慧醫療進展上的阻礙。取自https://www.commonhealth.com.tw/blog/blogTopic.action?nid=2445&from=search
蔡騰輝(2019)。台灣人工智慧醫療發展 林百里:五大方向必須克服。取自https://www.digitimes.com.tw/iot/article.asp?cat=158&cat1=20&cat2=20&id=0000552869_46h7gjb779j8tf866sgj1
遠見雜誌(2017)。醫學中心以台大、林口長庚、台北榮總最受青睞。取自 https://www.gvm.com.tw/article/39006
衛福部(2020)。108年國人死因統計結果。取自https://www.mohw.gov.tw/cp-16-54482-1.html
蕭文龍(2013)。統計分析入門與應用:SPSS 中文版+ PLS-SEM (SmartPLS)。台北市:碁峯資訊。
OpView(2019年6月13日)。「大數據開講 Bar」解析網路口碑與社群聆聽–會後整理。取自 https://www.opview.com.tw/activity-highlights/20190617/10519
Trendmonitor(2017)。2017年消費者評論影響力調查。取自 https://www.trendmonitor.co.kr/tmweb/trend/allTrend/detail.do?bIdx=1599&code=0201&trendType=CKOREA
Yahoo奇摩新聞(2020年7月15日)。擔憂疾病排行榜:2020肺病從10名外躍升第2!男最怕肺病 女擔心婦科更甚肺。取自https://news.campaign.yahoo.com.tw/lung-health/arti.php?id=33dcef34-310b-3cf9-8325-814760584189

英文文獻
Aerts, G., Smits, T., & Verlegh, P. W. J. (2017). How online consumer reviews are influenced by the language and valence of prior reviews: A construal level perspective. Computers in Human Behavior, 75, 855-864. doi:10.1016/j.chb.2017.06.023
Ahluwalia, R. (2000). Examination of Psychological Processes Underlying Resistance to Persuasion. Journal of Consumer Research, 27(2), 217-232.
Ahluwalia, R. (2002). How Prevalent Is the Negativity Effect in Consumer Environments? Journal of Consumer Research, 29(2), 270-279.
Ajina, A. S. (2019). The perceived value of social media marketing: an empirical study of online word-of-mouth in Saudi Arabian context. Entrepreneurship and Sustainability Issues, 6(3), 1512-1527.
Aldunate, N., & González-Ibáñez, R. (2017). An Integrated Review of Emoticons in Computer-Mediated Communication. Frontiers in Psychology, 7(2061). doi:10.3389/fpsyg.2016.02061
Alhabash, S., McAlister, A. R., Lou, C., & Hagerstrom, A. (2015). From Clicks to Behaviors: The Mediating Effect of Intentions to Like, Share, and Comment on the Relationship Between Message Evaluations and Offline Behavioral Intentions. Journal of Interactive Advertising, 15(2), 82-96.
AlliedMarketResearch. (2020). Artificial Intelligence (AI) Market: Global Opportunity Analysis and Industry Forecast, 2018-2025. Retrieved from https://www.alliedmarketresearch.com/artificial-intelligence-market
Amblee, N., & Bui, T. (2011). Harnessing the Influence of Social Proof in Online Shopping: The Effect of Electronic Word-of-Mouth on Sales of Digital Microproducts. International Journal of Electronic Commerce, 16. doi:10.2307/23106395
ArmSurvey. (2017). Robots to Enhance, not Replace Humans in most Jobs: ARM Survey. Retrieved from https://www.businesswire.com/news/home/20170627005941/en/Robots-Enhance-Replace-Humans-Jobs-ARM-Survey
Arning, K., & Ziefle, M. (2009, 2009//). Different Perspectives on Technology Acceptance: The Role of Technology Type and Age. Paper presented at the HCI and Usability for e-Inclusion, Berlin, Heidelberg.
Beneke, J., Mill, J., Naidoo, K., & Wickham, B. (2015). The impact of willingness to engage in negative electronic word-of- mouth on brand attitude: a study of airline passengers in South Africa. Journal of Business and Retail Management Research, 9(2), 69-84.
Bone, P. F. (1995). Word-of-Mouth Effects on Short-Term and Long-Term Product Judgment. Journal of Business Research, 32(3), 213–223.
Boutet, I., LeBlanc, M., Chamberland, J. A., & Collin, C. A. (2021). Emojis influence emotional communication, social attributions, and information processing. Computers in Human Behavior, 119, 106722. doi:https://doi.org/10.1016/j.chb.2021.106722
Boysen, G., & Vogel, D. (2007). Biased Assimilation and Attitude Polarization in Response to Learning About Biological Explanations of Homosexuality. Sex Roles, 57, 755-762. doi:10.1007/s11199-007-9256-7
Bright ideas. (2020). Local Consumer Review Survey. Retrieved from https://www.brightlocal.com/research/local-consumer-review-survey/#top
Bronner, F., & de Hoog, R. (2011). Vacationers and eWOM: Who Posts, and Why, Where, and What? , 50(1), 15-26. doi:10.1177/0047287509355324
Buil, I., Chernatony, L., & Hem, L. (2009). Brand extension strategies: Perceived fit, brand type, and culture influences. European Journal of Marketing, 43, 1300-1324. doi:10.1108/03090560910989902
Butterworth, S. E., Giuliano, T. A., White, J., Cantu, L., & Fraser, K. C. (2019). Sender Gender Influences Emoji Interpretation in Text Messages. Frontiers in Psychology, 10(784). doi:10.3389/fpsyg.2019.00784
Byron, K., & Baldridge, D. C. (2007). E-mail recipients’ impressions of senders’ likability: The interactive effect of nonverbal cues and recipients’ personality. The Journal of Business Communication, 44(2), 137–160.
Cao, X., Liu, Y., Zhu, Z., Hu, J., & Chen, X. (2017). Online selection of a physician by patients: Empirical study from elaboration likelihood perspective. Computers in Human Behavior, 73, 403-412. doi:https://doi.org/10.1016/j.chb.2017.03.060
Castelo, N. (2019). Blurring the Line Between Human and Machine: Marketing Artificial Intelligence. (doctoral dissertation). Columbia University, Retrieved from https://academiccommons.columbia.edu/doi/10.7916/d8-k7vk-0s40
Castelo, N., Bos, M., & Lehmann, D. (2019). Let the Machine Decide: When Consumers Trust or Distrust Algorithms. NIM Marketing Intelligence Review, 11, 24-29. doi:10.2478/nimmir-2019-0012
Chan, M. P. S., Jones, C., & Albarracín, D. (2017). Countering false beliefs: An analysis of the evidence and recommendations of best practices for the retraction and correction of scientific misinformation. In The Oxford Handbook of the Science of Science Communication (pp. 341-350). Oxford, UK: Oxford University Press.
Chandrasekaran, R., Katthula, V., & Moustakas, E. (2020). Patterns of Use and Key Predictors for the Use of Wearable Health Care Devices by US Adults: Insights from a National Survey. Journal of Medical Internet Research, 22(10), 1-11.
Chawla, N. (2020). AI, IOT and Wearable Technology for Smart Healthcare –A Review. International Journal of Recent Research Aspects, 7(1), 9-13.
Cheng, L. (2017). Do I mean what I say and say what I mean? A cross cultural approach to the use of emoticons & emojis in CMC messages. Fonseca: Journal of Communication, 15, 199–217.
Cheung, C. M. K., Xiao, B. S., & Liu, I. L. B. (2014). Do Actions Speak Louder than Voices? The Signaling Role of Social Information Cues in Influencing Consumer Purchase Decisions. Decision Support Systems, 65, 50-58. doi:10.1016/j.dss.2014.05.002
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. doi:10.2753/JEC1086-4415130402
Choi, J., & Kim, S. (2016). Is the smartwatch an IT product or a fashion product? A study on factors affecting the intention to use smartwatches. Computers in Human Behavior, 63, 777-786. doi:10.1016/j.chb.2016.06.007
Choi, Y. K., Seo, Y., & Yoon, S. (2017). E-WOM messaging on social media: Social ties, temporal distance, and message concreteness. Internet Research, 27, 495-505. doi:10.1108/IntR-07-2016-0198
Chong, K., Guo, J., Deng, X., & Woo, B. (2019). Consumer Perception of Wearable Technology Device: Retrospective Review and Analysis. JMIR mHealth and uHealth, 8. doi:10.2196/17544
Clarkson, J. J., Tormala, Z. L., & Rucker, D. D. (2008). A new look at the consequences of attitude certainty: the amplification hypothesis. Journal of Personality and Social Psychology, 95(4), 810-825.
Coursaris, C. K., Van Osch, W., & Albini, A. (2018). Antecedents and Consequents of Information Usefulness in User-generated Online Reviews: A Multi-group Moderation Analysis of Review Valence. AIS Transactions on Human-Computer Interaction, 10(1), 1-25.
Das, G., Wiener, H. J. D., & Kareklas, I. (2019). To emoji or not to emoji? Examining the influence of emoji on consumer reactions to advertising. Journal of Business Research, 96, 147-156. doi:https://doi.org/10.1016/j.jbusres.2018.11.007
De Bruyn, A., & Lilien, G. (2008). A Multi-Stage Model of Word-of-Mouth Influence Through Viral Marketing. International Journal of Research in Marketing, 25, 151-163. doi:10.1016/j.ijresmar.2008.03.004
Dubois, D., Bonezzi, A., & De Angelis, M. (2016). Sharing with Friends versus Strangers: How Interpersonal Closeness Influences Word-of-Mouth Valence. Journal of Marketing Research, 53(5), 712-727. doi:10.1509/jmr.13.0312
Dursun, İ., & Kabadayi, E. T. (2013). Resistance to persuasion in an anti-consumption context: Biased assimilation of positive product information. Journal of Consumer Behaviour, 12(2), 93-101. doi:10.1002/cb.1422
East, R., Hammond, K., & Lomax, W. (2008). Measuring the impact of positive and negative word of mouth on brand purchase probability. International Journal of Research in Marketing, 25. doi:10.1016/j.ijresmar.2008.04.001
East, R., Hammond, K., & Wright, M. (2007). The relative incidence of positive and negative word of mouth: A multi-category study. International Journal of Research in Marketing, 24, 175-184. doi:10.1016/j.ijresmar.2006.12.004
Edwards, K., & Smith, E. E. (1996). A disconfirmation bias in the evaluation of arguments. Journal of Personality and Social Psychology, 71(1), 5-24. doi:10.1037/0022-3514.71.1.5
Egebark, J., & Ekström, M. (2011). Like What You Like or Like What Others Like? Conformity and Peer Effects on Facebook. SSRN Electronic Journal. doi:10.2139/ssrn.1948802
Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The Benefits of Facebook “Friends:” Social Capital and College Students’ Use of Online Social Network Sites. Journal of Computer-Mediated Communication, 12(4), 1143-1168. doi:10.1111/j.1083-6101.2007.00367.x
Eranti, V., & Lonkila, M. (2015). The social significance of the Facebook Like button. First Monday, 20(6).
Esmaeilzadeh, P. (2020). Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives. BMC Medical Informatics and Decision Making, 20(1), 170. doi:10.1186/s12911-020-01191-1
Fang, W., & Yu, C.-S. (2017). Understand the Influence of Online Word-of-Mouth on Consumer Purchase Intention: The Moderating Effect of Conformity. Journal of Innovation and Management, 13(1), 1-31.
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. doi:https://doi.org/10.1016/j.jbusres.2014.11.006
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.
Filik, R., Țurcan, A., Thompson, D., Harvey, N., Davies, H., & Turner, A. (2016). Sarcasm and emoticons: Comprehension and emotional impact. Quarterly Journal of Experimental Psychology, 69(11), 2130-2146. doi:10.1080/17470218.2015.1106566
Fine, M., Gironda, J., & Petrescu, M. (2017). Prosumer Motivations for Electronic Word-of-Mouth Communication Behaviors. Journal of Hospitality and Tourism Technology, 8. doi:10.1108/JHTT-09-2016-0048
Frankenfield, J. (2020). Artificial Intelligence (AI). Retrieved from https://www.investopedia.com/terms/a/artificial-intelligence-ai.asp
Freeman, C., Alhoori, H., & Shahzad, M. (2020). Measuring the Diversity of Facebook Reactions to Research. Paper presented at the Proceedings of the ACM on Human-Computer Interaction. https://doi.org/10.1145/3375192
Fullerton, L. (2017). Online reviews impact purchasing decisions for over 93% of consumers, report suggests. Retrieved from http://www.thedrum.com/news/2017/03/27/online-reviews-impact-purchasing-decisions-over-93-consumers-report-suggests
Fullwood, C., Quinn, S., Chen-Wilson, J., Chadwick, D., & Reynolds, K. (2015). Put on a smiley face: textspeak and personality perceptions. Cyberpsychol Behav Soc Netw, 18(3), 147-151. doi:10.1089/cyber.2014.0463
Ganster, T., Eimler, S. C., & Krämer, N. C. (2012). Same same but different!? The dif- ferential influence of smilies and emoticons on person perception. Cyberpsychology, Behavior and Social Networking, 15(4), 226-230.
Gao, G., Greenwood, B. N., McCullough, J., & Agarwal, R. (2012). A Digital Soapbox? The Information Value of Online Physician Ratings.
Gao, S., He, L., Chen, Y., Li, D., & Lai, K. (2020). Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social Media. J Med Internet Res, 22(7), 1-11.
Gao, S., Zhang, X., & Peng, S. (2016). Understanding the Adoption of Smart Wearable Devices to Assist Healthcare in China (Vol. 9844).
Gao, Y., Li, H., & Luo, Y. (2015). An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems, 115(9), 1704-1723. doi:10.1108/IMDS-03-2015-0087
Garrett, P. M., Wang, Y., White, J. P., Hsieh, S., Strong, C., Lee, Y.-C., . . . Yang, C.-T. (2021). Young Adults View Smartphone Tracking Technologies for COVID-19 as Acceptable: The Case of Taiwan. International Journal of Environmental Research and Public Health, 18(3). doi:10.3390/ijerph18031332
Gawne, L., & McCulloch, G. (2019). Emoji as Digital Gestures. Language@Internet, 17, article 2.
Go, E., Jung, E. H., & Wu, M. (2014). The effects of source cues on online news perception. Computers in Human Behavior, 38, 358-367. doi:https://doi.org/10.1016/j.chb.2014.05.044
Gong, X., Lee, M., Liu, Z., & Zheng, X. (2018). Examining the Role of Tie Strength in Users’ Continuance Intention of Second-Generation Mobile Instant Messaging Services. Information Systems Frontiers, 22, 1-22. doi:10.1007/s10796-018-9852-9
Graf, J., & Aday, S. (2008). Selective attention to online political information. Journal of Broadcasting & Electronic Media, 52(1), 86-100. doi:10.1080/08838150701820874
Grahl, J., Rothlauf, F., & Hinz, O. (2013). How do social recommendations influence shopping behavior? A field experiment. Johannes Gutenberg University Mainz. Mainz, Germany.
Greco, L., Percannella, G., Ritrovato, P., Tortorella, F., & Vento, M. (2020). Trends in IoT based solutions for health care: Moving AI to the edge. Pattern Recognition Letters, 135, 346-353. doi:https://doi.org/10.1016/j.patrec.2020.05.016
Greitemeyer, T., Fischer, P., Frey, D., & Schulz-Hardt, S. (2009). Biased assimilation: the role of source position. 39(1), 22-39. doi:10.1002/ejsp.497
Greitemeyer, T., & Mügge, D. O. (2014). Video games do affect social outcomes: A meta-analytic review of the effects of violent and prosocial video game play. Personality and Social Psychology Bulletin, 40(5), 578–589.
Guo, X., Chen, S., Zhang, X., Ju, X., & Wang, X. (2020). Exploring Patients' Intentions for Continuous Usage of mHealth Services: Elaboration-Likelihood Perspective Study. JMIR Mhealth Uhealth, 8(4), 1-15.
GWI. (2021). Social media marketing trends in 2021. Retrieved from https://www.gwi.com/reports/social
Hamilton, R. W., Schlosser, A., & Chen, Y.-J. (2017). Who's Driving this Conversation? Systematic Biases in the Content of Online Consumer Discussions. 54(4), 540-555. doi:10.1509/jmr.14.0012
Hareli, S., & Rafaeli, A. (2008). Emotion Cycles: On the social influence of emotion in organizations. Research in Organizational Behavior, 28, 35-59.
Hart, W., Albarracín, D., Eagly, A. H., Brechan, I., Lindberg, M. J., & Merrill, L. (2009). Feeling validated versus being correct: A meta-analysis of selective exposure to information. Psychological Bulletin, 135(4), 555-588. doi:10.1037/a0015701
Hauthal, E. B., D.; Dunkel, A. . (2019). Analyzing and Visualizing Emotional Reactions Expressed by Emojis in Location-Based Social Media. . International Journal of Geo-Information, 8, 113.
Hayes, R. A., Carr, C. T., & Wohn, D. Y. (2016). It’s the Audience: Differences in Social Support Across Social Media. Social Media + Society, 2(4), 2056305116678894. doi:10.1177/2056305116678894
Heejae, S., & Dahana, W. D. (2017). The Moderating Roles of Prior Attitude and Message Acceptance in Electronic Word of Mouth. International Journal of Business and Information, 12, 183. doi:10.6702/ijbi.2017.12.2.4
Hejlskov, M. (2017). Emoticons as Social Influence: The Effects of Valence and Quantity of Emoticon Reactions on Message Credibility and Brand Attitude. (Master's thesis). Universiteit van Amsterdam, Amsterdam, Netherlands.
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the Internet? Journal of Interactive Marketing, 18(1), 38-52. doi:https://doi.org/10.1002/dir.10073
Hill, J. H. (2016). The Impact of Emojis and Emoticons on Online Consumer Reviews, Perceived Company Response Quality, Brand Relationship, and Purchase Intent. (M.A.). University of South Florida, United States.
Hong, S., Jahng, M. R., Lee, N., & Wise, K. R. (2020). Do you filter who you are?: Excessive self-presentation, social cues, and user evaluations of Instagram selfies. Computers in Human Behavior, 104, 106159. doi:https://doi.org/10.1016/j.chb.2019.106159
Hong, S., & Kim, S. H. (2016). Political polarization on twitter: Implications for the use of social media in digital governments. Government Information Quarterly, 33(4), 777-782. doi:https://doi.org/10.1016/j.giq.2016.04.007
Hong, S., Tandoc, E., Kim, E. A., Kim, B., & Wise, K. (2012). The real you? The role of visual cues and comment congruence in perceptions of social attractiveness from Facebook profiles. Cyberpsychology, Behavior, and Social Networking, 15(7), 339-344.
Hsueh, M., Yogeeswaran, K., & Malinen, S. (2015). “Leave Your Comment Below”: Can Biased Online Comments Influence Our Own Prejudicial Attitudes and Behaviors? Human Communication Research, 41(4), 557-576. doi:10.1111/hcre.12059
Hu, T., Guo, H., Sun, H., Nguyen, T.-v., & Luo, J. (2017). Spice up Your Chat: The Intentions and Sentiment Effects of Using Emoji. Paper presented at the Proceedings of the Eleventh International AAAI Conference on Web and Social Media(ICWSM 2017), Montreal, Quebec, Canada.
Huang, A. H., Yen, D. C., & Zhang, X. (2008). Exploring the potential effects of emoticons. Information & Management, 45(7), 466-473. doi:https://doi.org/10.1016/j.im.2008.07.001
Huisman, M., Biltereyst, D., & Joye, S. (2020). Sharing is caring: the everyday informal exchange of health information among adults aged fifty and over. Information Research, 25.
Hussain, S., Song, X., & Niu, B. (2019). Consumers' Motivational Involvement in eWOM for Information Adoption: The Mediating Role of Organizational Motives. Front Psychol, 10, 3055. doi:10.3389/fpsyg.2019.03055
Hussein, Z., Oon, W., & Fikry, A. (2017). Consumer Attitude: Does It Influencing the Intention to Use mHealth? Procedia Computer Science, 105, 340-344. doi:10.1016/j.procs.2017.01.231
IDC. (2020). Shipments of Wearable Devices Reach 118.9 Million Units in the Fourth Quarter and 336.5 Million for 2019. Retrieved from https://www.idc.com/getdoc.jsp?containerId=prUS46122120
IDTechEx. (2019). IDTechEx Research: The Future of Wearables is Medical. Retrieved from https://www.prnewswire.com/news-releases/idtechex-research-the-future-of-wearables-is-medical-300909440.html
Jackson, S. A. (1992). Message effects research: Principles of design and analysis. New York: Guilford Press.
Jaeger, S. R., Roigard, C. M., Jin, D., Vidal, L., & Ares, G. (2019). Valence, arousal and sentiment meanings of 33 facial emoji: Insights for the use of emoji in consumer research. Food Research International, 119, 895-907. doi:https://doi.org/10.1016/j.foodres.2018.10.074
Jahng, M. R., & Littau, J. (2015). Interacting Is Believing: Interactivity, Social Cue, and Perceptions of Journalistic Credibility on Twitter. Journalism & Mass Communication Quarterly, 93(1), 38-58. doi:10.1177/1077699015606680
Jeong, H., Kim, H., Kim, R., Lee, U., & Jeong, Y. (2017). Smartwatch Wearing Behavior Analysis: A Longitudinal Study. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 1(3), Article 60. doi:10.1145/3131892
Jibril, T. A., & Abdullah, M. H. (2013). Relevance of Emoticons in Computer-Mediated Communication Contexts: An Overview. Asian Social Science, 9(4), 201-207.
Jiménez, F., & Mendoza, N. (2013). Too Popular to Ignore: The Influence of Online Reviews on Purchase Intentions of Search and Experience Products. Journal of Interactive Marketing, 27, 226-235. doi:10.1016/j.intmar.2013.04.004
Jin, C. Y. (2019). A review of AI Technologies for Wearable Devices. IOP Conference Series: Materials Science and Engineering, 688(4), 1-6. doi:10.1088/1757-899x/688/4/044072
Kaiser, J., Keller, T., & Kleinen-von Königslöw, K. (2018). Incidental News Exposure on Facebook as a Social Experience: The Influence of Recommender and Media Cues on News Selection. Communication Research, 45, 1-23.
Kanthawala, S., Vermeesch, A., Given, B., & Huh, J. (2016). Answers to Health Questions: Internet Search Results Versus Online Health Community Responses. Journal of Medical Internet Research, 18(4), e95-e95. doi:10.2196/jmir.5369
Kaufmann, G., Drevland, G. C. B., Wessel, E., Overskeid, G., & Magnussen, S. (2003). The importance of being earnest : Displayed emotions and witness credibility. Applied Cognitive Psychology, 17(1), 21-34. doi:10.1002/acp.842
Kaye, L. K., Wall, H. J., & Malone, S. A. (2016). Turn that frown upside-down: A contextual account of emoticon usage on different virtual platforms. Computers in Human Behavior, 60, 463-467. doi:https://doi.org/10.1016/j.chb.2016.02.088
Kim, J., & Lee, C. (2017). Examining the role of relationship factors on eWOM effectiveness in social media. International Journal of Internet Marketing and Advertising, 11(2), 103-123.
King, G., Pan, J., & Roberts, M. (2017). How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, Not Engaged Argument. American Political Science Review, 111, 484-501. doi:10.1017/S0003055417000144
Knobloch‐Westerwick, S., & Meng, J. (2011). Reinforcement of the political self through selective exposure to political messages. Journal of Communication, 61(2), 349-368. doi:10.1111/j.1460-2466.2011.01543.x
Kobayashi, K. (2010). Strategic use of multiple texts for the evaluation of arguments. Reading Psychology, 31(2), 121-149. doi:10.1080/02702710902754192
Krey, N., Rauschnabel, P., Chuah, S. H., Nguyen, B., Hein, D. W. E., Rossmann, A., & Lade, S. (2016). Smartwatches: Accessory or Tool? The Driving Force of Visibility and Usefulness. Paper presented at the Mensch & Computer.
Krug, S. (2016). Reactions now available globally. Retrieved from https://about.fb.com/news/2016/02/reactions-now-available-globally/
Kummer, T.-F., Recker, J., & Bick, M. (2017). Technology-induced anxiety: Manifestations, cultural influences, and its effect on the adoption of sensor-based technology in German and Australian hospitals. Information & Management, 54(1), 73-89. doi:https://doi.org/10.1016/j.im.2016.04.002
Laï, M. C., Brian, M., & Mamzer, M. F. (2020). Perceptions of artificial intelligence in healthcare: findings from a qualitative survey study among actors in France. Journal of Translational Medicine, 18(1), 14. doi:10.1186/s12967-019-02204-y
Lee, C., Shin, J., & Hong, A. (2018). Does social media use really make people politically polarized? Direct and indirect effects of social media use on political polarization in South Korea. Telematics and Informatics, 35(1), 245-254. doi:https://doi.org/10.1016/j.tele.2017.11.005
Lee, J., & Hong, I. (2016). Predicting positive user responses to social media advertising: The roles of emotional appeal, informativeness, and creativity. International Journal of Information Management, 36, 360-373. doi:10.1016/j.ijinfomgt.2016.01.001
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, 341-352. doi:10.1016/j.elerap.2007.05.004
Lee, K. T., & Koo, D. M. (2012). Effects of attribute and valence of e-WOM on message adoption: Moderating roles of subjective knowledge and regulatory focus. Computers in Human Behavior, 28(5), 1974-1984.
Lee, S. M., & Lee, D. (2020). Healthcare wearable devices: an analysis of key factors for continuous use intention. Service Business. doi:10.1007/s11628-020-00428-3
Lee, S. Y., & Lee, K. (2018). Factors that influence an individual's intention to adopt a wearable healthcare device: The case of a wearable fitness tracker. Technological Forecasting and Social Change, 129, 154-163. doi:https://doi.org/10.1016/j.techfore.2018.01.002
Lee, Y.-I., Phua, J., & Wu, T.-Y. (2020). Marketing a health Brand on Facebook: Effects of reaction icons and user comments on brand attitude, trust, purchase intention, and eWOM intention. Health Marketing Quarterly, 37(2), 138-154. doi:10.1080/07359683.2020.1754049
Liao, Q. V., & Fu, W.-T. (2014). Can You Hear Me Now? Mitigating the echo chamber effect by source position indicators. . Paper presented at the Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, New York, NY.
Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to Medical Artificial Intelligence. Journal of Consumer Research, 46(4), 629–650.
Longoni, C., & Morewedge, C. K. (2019). AI Can Outperform Doctors. So Why Don’t Patients Trust It? Retrieved from https://www.hbrtaiwan.com/article_content_AR0009339.html
Lord, C. G., Ross, L., & Lepper, M. R. (1979). Biased assimilation and attitude polarization: The effects of prior theories on subsequently considered evidence. Journal of Personality and Social Psychology, 37(11), 2098-2109. doi:10.1037/0022-3514.37.11.2098
Lowe-Calverley, E., & Grieve, R. (2018). Thumbs up: A thematic analysis of image-based posting and liking behaviour on social media. Telematics and Informatics, 35(7), 1900-1913. doi:https://doi.org/10.1016/j.tele.2018.06.003
Lu, N., & Wu, H. (2016). Exploring the impact of word-of-mouth about Physicians’ service quality on patient choice based on online health communities. BMC Medical Informatics and Decision Making, 16(1), 151. doi:10.1186/s12911-016-0386-0
Lunney, A., Cunningham, N. R., & Eastin, M. S. (2016). Wearable fitness technology: A structural investigation into acceptance and perceived fitness outcomes. Computers in Human Behavior, 65, 114-120. doi:https://doi.org/10.1016/j.chb.2016.08.007
Luo, X., Raithel, S., & Wiles, M. (2013). The impact of brand rating dispersion on firm value. Journal of Marketing Research, 50(3), 399-415.
Lupton, D. (2021). Young People’s Use of Digital Health Technologies in the Global North: Narrative Review. Journal of Medical Internet Research 23(1), 1-12.
Mafael, A., Gottschalk, S. A., & Kreis, H. (2016). Examining Biased Assimilation of Brand-related Online Reviews. Journal of Interactive Marketing, 36, 91-106. doi:10.1016/j.intmar.2016.06.002
Manganari, E. E., & Dimara, E. (2017). Enhancing the impact of online hotel reviews through the use of emoticons. Behaviour & Information Technology, 36(7), 674-686. doi:10.1080/0144929X.2016.1275807
MarketsandMarkets. (2020). IoT in Healthcare Market- Global Forecast to 2025. Retrieved from https://www.marketsandmarkets.com/PressReleases/iot-healthcare.asp
Martin, W. C. (2017). Positive Versus Negative Word-of-mouth: Effects on Receivers. Academy of Marketing Studies Journal, 21(2). Retrieved from https://www.abacademies.org/articles/positive-versus-negative-wordofmouth-effects-on-receivers-6732.html
Matthews, K. (2020). How AI and IoT Are Changing Daily Operations in Hospitals. Retrieved from https://www.hcinnovationgroup.com/analytics-ai/article/21132663/how-ai-and-iot-are-changing-daily-operations-in-hospitals
McHoskey, J. W. (1995). Case Closed? On the John F. Kennedy Assassination: Biased Assimilation of Evidence and Attitude Polarization. Basic and Applied Social Psychology, 17(3), 395-409. doi:10.1207/s15324834basp1703_7
Meier, D. Y., Barthelmess, P., Sun, W., & Liberatore, F. (2020). Wearable Technology Acceptance in Health Care Based on National Culture Differences: Cross-Country Analysis Between Chinese and Swiss Consumers. Journal of Medical Internet Research, 22(10), 1-15.
Messing, S., & Westwood, S. J. (2014). Selective exposure in the age of social media: Endorsements trump partisan source affiliation when selecting news online. Communication Research, 41(8), 1042-1063.
Metzger, M. J., Flanagin, A. J., & Medders, R. B. (2010). Social and heuristic approaches to credibility evaluation online. Journal of Communication, 60(3), 413-439. doi:10.1111/j.1460-2466.2010.01488.x
Mohammed Abubakar, A. (2016). Does eWOM influence destination trust and travel intention: a medical tourism perspective. Economic Research-Ekonomska Istraživanja, 29(1), 598-611. doi:10.1080/1331677X.2016.1189841
Munar, A. M., & Jacobsen, J. K. S. (2013). Trust and involvement in tourism social media and web- based travel information sources. Scandinavian Journal of Hospitality and Tourism, 13(1), 1-19.
Munro, G. D. (2010). The scientific impotence excuse: Discounting belief-threatening scientific abstracts. Journal of Applied Social Psychology, 40(3), 579-600. doi:10.1111/j.1559-1816.2010.00588.x
Munro, G. D., & Ditto, P. H. (1997). Biased Assimilation, Attitude Polarization, and Affect in Reactions to Stereotype-Relevant Scientific Information. Personality and Social Psychology Bulletin, 23(6), 636–653.
Munro, G. D., Ditto, P. H., Lockhart, L. K., Fagerlin, A., Gready, M., & Peterson, E. (2002). Biased Assimilation of Sociopolitical Arguments: Evaluating the 1996 U.S. Presidential Debate. Basic and Applied Social Psychology, 24(1), 15-26. doi:10.1207/S15324834BASP2401_2
Nan, X., & Daily, K. (2015). Biased Assimilation and Need for Closure: Examining the Effects of Mixed Blogs on Vaccine-Related Beliefs. Journal of Health Communication, 20(4), 462-471. doi:10.1080/10810730.2014.989343
Nusairat, N., Akhorshaideh, A., Rashid, T., Sahadev, S., & Rembielak, G. (2017). Social Cues-Customer Behavior Relationship: The Mediating Role of Emotions and Cognition. International Journal of Marketing Studies, 9, 1. doi:10.5539/ijms.v9n1p1
Osuna Ramírez, S. A., Veloutsou, C., & Morgan-Thomas, A. (2019). I hate what you love: brand polarization and negativity towards brands as an opportunity for brand management. Journal of Product and Brand Management, 28(5), 614-632.
Park, J., Konana, P., Gu, B., Kumar, A., & Raghunathan, R. (2013). Information Valuation and Confirmation Bias in Virtual Communities: Evidence from Stock Message Boards. Information Systems Research, 24, 1050-1067. doi:10.1287/isre.2013.0492
Park, M., Shin, J., & Ju., Y. (2014). The effect of online social network characteristics on consumer purchasing intention of social deals. Global Economic Review, 43, 25-41.
Pedersen, S., Razmerita, L., & Colleoni, E. (2014). Electronic Word-of-Mouth Communication and Consumer Behaviour: A Study of Danish Social Media Communication Influence. LSP Journal - Language for special purposes, professional communication, knowledge management and cognition, 5(1), 112-131.
Petty, R. E., & Cacioppo, J. T. (1984). The effects of involvement on responses to argument quantity and quality: Central and peripheral routes to persuasion. Journal of Personality and Social Psychology, 46(1), 69-81. doi:10.1037/0022-3514.46.1.69
Pew Research Center. (2016). Online Shopping and E-Commerce. Retrieved from https://www.pewresearch.org/internet/2016/12/19/online-reviews/
Phaneuf, A. (2020). Latest trends in medical monitoring devices and wearable health technology. Retrieved from https://www.businessinsider.com/wearable-technology-healthcare-medical-devices
Phua, J., & Ahn, S. J. (2016). Explicating the ‘like’ on Facebook brand pages: The effect of intensity of Facebook use, number of overall ‘likes’, and number of friends' ‘likes’ on consumers' brand outcomes. Journal of Marketing Communications, 22(5), 544-559.
Plous, S. (1991). Biases in the Assimilation of Technological Breakdowns: Do Accidents Make Us Safer? Journal of Applied Social Psychology, 21(13), 1058-1082. doi:10.1111/j.1559-1816.1991.tb00459.x
Qahri-Saremi, H., & Montazemi, A. (2019). Factors Affecting the Adoption of an Electronic Word of Mouth Message: A Meta-Analysis. Journal of Management Information Systems, 36, 969-1001. doi:10.1080/07421222.2019.1628936
Qiu, L., Wang, W., Pang, J., & Jiang, Z. J. (2016). The Persuasive impact of emoticons in online word-of-mouth communication. Paper presented at the The 20th Pacific Asia Conference on Information Systems (PACIS 2016), Chiayi Taiwan.
Quintly. (2016). Facebook Reactions Study. Retrieved from https://www.quintly.com/blog/facebook-reactions-study
Reeder, B., & David, A. (2016). Health at hand: A systematic review of smart watch uses for health and wellness. Journal of Biomedical Informatics, 63, 269-276. doi:https://doi.org/10.1016/j.jbi.2016.09.001
Riordan, M. A. (2017). Emojis as Tools for Emotion Work: Communicating Affect in Text Messages. Journal of Language and Social Psychology, 36(5), 549-567. doi:10.1177/0261927x17704238
Robson, K., Farshid, M., Bredican, J., & Humphrey, S. (2013). Making sense of online consumer reviews: A methodology. International Journal of Market Research, 55(4), 521-537.
Rupp, M. A., Michaelis, J. R., McConnell, D. S., & Smither, J. A. (2018). The role of individual differences on perceptions of wearable fitness device trust, usability, and motivational impact. Applied Ergonomics, 70, 77-87. doi:https://doi.org/10.1016/j.apergo.2018.02.005
Saleem, A., & Ellahi, A. (2017). Influence of Electronic Word of Mouth on Purchase Intention of Fashion Products on Social Networking Websites. Pakistan Journal of Commerce and Social Sciences, 11(2), 597-622.
Sandoval-Almazan, R., & Valle-Cruz, D. (2020). Sentiment Analysis of Facebook Users Reacting to Political Campaign Posts. Digit. Gov.: Res. Pract., 1(2), Article 12. doi:10.1145/3382735
Sarkar, T., Shetty, N., & Humstoe, M. K. (2014). Emoticons and emotions: Contextual interpretation in text messages and consensus of meaning. The Learning Curve, 3(24), e33.
Sasmita, J., & Mohd Suki, N. (2015). Young consumers’ insights on brand equity: Effects of brand association, brand loyalty, brand awareness, and brand image. International Journal of Retail & Distribution Management, 43(3), 276-292. doi:10.1108/IJRDM-02-2014-0024
Schmäh, M., Wilke, T., & Rossmann, A. (2017). Electronic word of mouth: A systematic literature analysis. Digital Enterprise Computing, 147-158.
Scissors, L., Burke, M., & Wengrovitz, S. (2016). What's in a Like? Attitudes and behaviors around receiving Likes on Facebook. Paper presented at the Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, San Francisco, California, USA.
Settanni, M., & Marengo, D. (2015). Sharing feelings online: studying emotional well-being via automated text analysis of Facebook posts. Frontiers in Psychology, 6(1045). doi:10.3389/fpsyg.2015.01045
Shah, P. (2018). Facebook's new Reactions are being used more – a lot more. Retrieved from https://www.quintly.com/blog/new-facebook-reaction-study
Sherman, L., Payton, A., Hernandez, L., Greenfield, P., & Dapretto, M. (2016). The Power of the Like in Adolescence: Effects of Peer Influence on Neural and Behavioral Responses to Social Media. Psychological Science, 27(7), 1-9.
Song, H., & Boomgaarden, H. G. (2017). Dynamic Spirals Put to Test: An Agent-Based Model of Reinforcing Spirals Between Selective Exposure, Interpersonal Networks, and Attitude Polarization. 67(2), 256-281. doi:10.1111/jcom.12288
Sparks, B., & Browning, V. (2011). The impact of online reviews on hotel booking intentions and perception of trust. Tourism Management, 32(6), 1310-1323. doi:10.1016/j.tourman.2010.12.011
Spottswood, E., & Wohn, D. (2019). Beyond the “Like”: How People Respond to Negative Posts on Facebook. Journal of Broadcasting & Electronic Media, 63, 250-267. doi:10.1080/08838151.2019.1622936
Statista. (2020). Customers having daily AI-enabled interactions with organizations as of 2020, by age. Retrieved from https://www.statista.com/statistics/1155970/customers-daily-ai-enabled-interactions-with-organizations-by-age/
Sumner, E. M., Hayes, R. A., Carr, C. T., & Wohn, D. Y. . (2020). Assessing the cognitive and communicative properties of Facebook Reactions and Likes as lightweight feedback cues. First Monday, 25(2).
Sumner, E. M., Ruge-Jones, L., & Alcorn, D. (2017). A functional approach to the Facebook Like button: An exploration of meaning, interpersonal functionality, and potential alternative response buttons. New Media & Society, 20(4), 1451-1469. doi:10.1177/1461444817697917
Sundar, S. S. (2008). The MAIN model: A heuristic approach to understanding technology effects on credibility. Cambridge, MA: The MIT Press.
Sung, K. H., & Lee, M. J. (2015). Do Online Comments Influence the Public's Attitudes Toward an Organization? Effects of Online Comments Based on Individuals’ Prior Attitudes. The Journal of Psychology, 149(4), 325-333.
Swayne, M. (2018). Love actually: Computer model may decode Facebook emoticons. Retrieved from https://techxplore.com/news/2018-02-decode-facebook-emoticons.html
Taber, C. S., & Lodge, M. (2006). Motivated Skepticism in the Evaluation of Political Beliefs. American Journal of Political Science, 50(3), 755-769. doi:10.1111/j.1540-5907.2006.00214.x
Tanis, M., & Postmes, T. (2003). Social Cues and Impression Formation in CMC. Journal of Communication, 53(4), 676-693. doi:10.1111/j.1460-2466.2003.tb02917.x
Tata, S. V., Prashar, S., & Gupta, S. (2020). An examination of the role of review valence and review source in varying consumption contexts on purchase decision. Journal of Retailing and Consumer Services, 52. doi:https://doi.org/10.1016/j.jretconser.2019.01.003
Teng, S., Khong, K. W., Chong, A. Y.-L., & Lin, B. (2017). Examining the Impacts of Electronic Word-of-Mouth Message on Consumers’ Attitude. Journal of Computer Information Systems, 57(3), 238-251. doi:10.1080/08874417.2016.1184012
Tian, Y., Galery, T., Dulcinati, G., Molimpakis, E., & Sun, C. (2017). Facebook sentiment: Reactions and Emojis. Paper presented at the Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media, Valencia, Spain.
Tidwell, L. C., & Walther, J. B. (2002). Computer-mediated communication effects on disclosure, impressions, and interpersonal evaluations: Getting to know one another a bit at a time. Human Communication Research, 28(3), 317-348. doi:10.1111/j.1468-2958.2002.tb00811.x
TripAdvisor. (2019). Review Transparency Report. Retrieved from https://www.tripadvisor.co.uk/TripAdvisorInsights/wp-content/uploads/2019/09/TripAdvisor_Review_Transparency_Report_Full-GB-1.pdf
Tsao, W.-C., Hsieh, M.-T., Shih, L.-W., & Lin, T. M. Y. (2015). Compliance with eWOM: The influence of hotel reviews on booking intention from the perspective of consumer conformity. International Journal of Hospitality Management, 46, 99-111.
Tu, H. T., & Lauer, J. R. (2008). Word of mouth and physician referrals still drive health care provider choice. Res Brief(9), 1-8.
Van Kleef, G., Van Doorn, E., Heerdink, M., & Koning, L. (2011). Emotion is for influence. European Review of Social Psychology, 22, 114-163. doi:10.1080/10463283.2011.627192
Van Kleef, G. A. (2009). How Emotions Regulate Social Life:The Emotions as Social Information (EASI) Model. Current Directions in Psychological Science, 18(3), 184-188. doi:10.1111/j.1467-8721.2009.01633.x
Van Kleef, G. A., van den Berg, H., & Heerdink, M. W. (2015). The persuasive power of emotions: Effects of emotional expressions on attitude formation and change. J Appl Psychol, 100(4), 1124-1142. doi:10.1037/apl0000003
Van Strien, J., Kammerer, Y., Brand-Gruwel, S., & Boshuizen, H. (2016). How attitude strength biases information processing and evaluation on the web. Computers in Human Behavior, 60, 245-252. doi:10.1016/j.chb.2016.02.057
Van Strien, J. L. H., Brand-Gruwel, S., & Boshuizen, H. P. A. (2014). Dealing with conflicting information from multiple nonlinear texts: Effects of prior attitudes. Computers in Human Behavior, 32, 101-111. doi:10.1016/j.chb.2013.11.021
Waddell, T. F. (2018). When Comments and Quotes Collide: How Exemplars and Prior Attitudes Affect News Credibility. Journalism Studies, 20, 1598-1616.
Walther, J., & Parks, M. (2002). Cues Filtered Out, Cues Filtered In: Computer-Mediated Communication and Relationships. Thousand Oaks: CA: Sage.
Wang, L. C., Baker, J., Wagner, J. A., & Wakefield, K. (2007). Can a retail web site be social? . Journal of Marketing, 71(3), 143-157.
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.
WegoHealth. (2017). Role of Patient Influencers: How do patients truly share information? Retrieved from https://www.wegohealth.com/2018/04/02/social-media-healthcare-statistics-to-watch/
Westerman, D., Spence, P. R., & Van Der Heide, B. (2014). Social media as information source: Recency of updates and credibility of information. Journal of Computer-Mediated Communication, 19(2), 171-183.
Wiener, R. L., Wiener, A. T. F., & Grisso, T. (1989). Empathy and biased assimilation of testimonies in cases of alleged rape. Law and Human Behavior, 13(4), 343-355. doi:10.1007/BF01056407
Winter, S., & Kramer, N. C. (2014). A question of credibility–Effects of source cues and recommendations on information selection on news sites and blogs. The European Journal of Communication Research, 39(4), 435-456.
Wu, L., Mattila, A. S., Wang, C.-Y., & Hanks, L. (2016). The Impact of Power on Service Customers’ Willingness to Post Online Reviews. Journal of Service Research, 19(2), 224-238. doi:10.1177/1094670516630623
Wu, P. F. (2013). In Search of Negativity Bias: An Empirical Study of Perceived Helpfulness of Online Reviews. 30(11), 971-984. doi:10.1002/mar.20660
Wu, T. Y., & Lin, C. A. (2017). Predicting the effects of eWOM and online brand messaging: Source trust, bandwagon effect and innovation adoption factors. Telematics and Informatics, 34(2), 470-480. doi:https://doi.org/10.1016/j.tele.2016.08.001
Xiao, N., Sharman, R., Rao, R., & Upadhyaya, S. (2014). Factors influencing online health information search: An empirical analysis of a national cancer-related survey. Decision Support Systems, 57, 417–427. doi:10.1016/j.dss.2012.10.047
Xu, Q. (2013). Social Recommendation, Source Credibility, and Recency: Effects of News Cues in a Social Bookmarking Website. Journalism & Mass Communication Quarterly, 90(4), 757-775. doi:10.1177/1077699013503158
Xu, Q. (2014). Should I trust him? The effects of reviewer profile characteristics on eWOM credibility. Computers in Human Behavior, 33, 136–144. Retrieved from https://doi.org/10.1016/j.chb.2014.01.027
Yan, Q., Wu, S., Zhou, Y., & Zhang, L. (2018). How differences in eWOM platforms impact consumers’ perceptions and decision-making. Journal of Organizational Computing and Electronic Commerce, 28(4), 315-333. doi:10.1080/10919392.2018.1517479
Yang, H., Guo, X., Wu, T., & Ju, X. (2015). Exploring the effects of patient-generated and system-generated information on patients' online search, evaluation and decision. Electron. Commer. Rec. Appl., 14(3), 192–203. doi:10.1016/j.elerap.2015.04.001
Yin, D., Mitra, S., & Zhang, H. (2016). When do consumers value positive vs. negative reviews? An empirical investigation of confirmation bias in online word of mouth. Information Systems Research, 27, 131-144. doi:10.1287/isre.2015.0617
Yousaf, O., & Gobet, F. (2016). The effect of personal attitudes on information processing biases in religious individuals. Journal of Cognitive Psychology, 28(3), 1-8.
Zhang, X., Guo, X., Lai, K. H., Guo, F., & Li, C. (2014). Understanding gender differences in m-health adoption: a modified theory of reasoned action model. Telemed J E Health, 20(1), 39-46. doi:10.1089/tmj.2013.0092
Zhou, T., Lu, Y., & Wang, B. (2016). Examining online consumers’ initial trust building from an elaboration likelihood model perspective. Information Systems Frontiers, 18(2), 265-275.
電子全文 Fulltext
本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。
論文使用權限 Thesis access permission:校內校外完全公開 unrestricted
開放時間 Available:
校內 Campus: 已公開 available
校外 Off-campus: 已公開 available


紙本論文 Printed copies
紙本論文的公開資訊在102學年度以後相對較為完整。如果需要查詢101學年度以前的紙本論文公開資訊,請聯繫圖資處紙本論文服務櫃台。如有不便之處敬請見諒。
開放時間 available 已公開 available

QR Code