臺灣博碩士論文加值系統

本研究的目的是了解如何在社交媒體“YouTube”中增加eWOM意圖（eWOM 採用、eWOM 可信度）。為了達到目的，我們採用推敲可能性模型（ELM）並測試了傳播特性（eWOM 論證品質、eWOM 來源可靠性、eWOM 聯繫力）是否可以提高利用促進因子（eWOM 有用性、eWOM 易用性)在 YouTube上。
本研究透過網路問卷在台灣收集510份樣本，並使用結構方程式模型 (SEM)對概念模型進行測試。
根據我們的實證結果，大多數假設都得到支持。說明eWOM 聯繫力為利用促進因子的主要傳播特性，對eWOM意圖也有正向影響。最後，本研究也進一步討論eWOM的理論意義與管理意涵。

The objective of this research is to realize how to increase eWOM intention (eWOM Adoption and eWOM Credibility) in social media “YouTube”. In order to meet the purpose, we adopt the Elaboration Likelihood Model (ELM) and test the communication features (eWOM Argument Quality, eWOM Source Expertise, and eWOM Tie Strength) whether can enhance the utilization promoted factors (eWOM Usefulness and eWOM Ease of Use) in YouTube. The 510 samples were collected in Taiwan via online questionnaire platforms and the conceptual model was tested by using structural equation modeling (SEM).
Based on our empirical results, most of the them are supported. It indicated that eWOM Tie Strength is the key communication features to the utilization promoted factors, which also have positive effects on the eWOM intentions. Theoretical and managerial implications of the examination of eWOM are further discussed in the discussion of this research.

Contents

Chapter 1 Introduction 1
1.1 Research Motivation 1
1.2 Research Gap and Value 2
1.3 Research Process 5
Chapter 2 Literature review 6
2.1 Communication Features 6
2.2 Utilization Promoted Factors 10
2.3 Intention to use 12
2.4 Implicit eWOM 15
2.5 Explicit eWOM 16
2.6 Summary 17
Chapter 3 Research Methodology 18
3.1 Research Framework 18
3.2 Research Hypothesis 20
3.3 Research Scale and Measurement 30
3.4 Data Collection and Sampling Design 36
3.5 Statistical Analysis Method 38
3.6 Basic Statistics Analysis of Pretest 44
Chapter 4 Empirical Results 46
4.1 Sample Collection and Descriptive Statistic Analysis 46
4.2 One-Way ANOVA Analysis 46
4.3 Measurement Model 47
4.4 Result of Research Hypothesis 53
4.5 Empirical Result of Mediation Effects 56
4.6 Rival Model 61
4.7 Empirical Analysis of Moderating Effect 63
4.8 Summary 67
Chapter 5 Conclusions 68
5.1 Discussion 68
5.2 Theoretical Implications 70
5.3 Managerial Implications 71
5.4 Limitations and Future Research 72
Reference List 74
Appendix 89

張珮甄 (2021年)。Youtuber 之體驗行銷對閱聽者品牌價值、新鮮感及購買意願之相關研究-以 Costco 美食開箱文為例 。

財團法人台灣網路資訊中心 (n.d.). 2020 台灣網路報告. Retrieved November 26, 2021, from https://report.twnic.tw/2020/TrendAnalysis_globalCompetitiveness.html

Ab Hamid, M., Sami, W., & Sidek, M. M. (2017). Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion. Journal of Physics: Conference Series, 890(1), 10-18.

Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ perceived ease of use (PEOU) and perceived usefulness (PU) of e-portfolios. Computers in Human Behavior, 63, 75-90.

Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 227-247.

Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies?. Decision Sciences, 30(2), 361-391.

Aghakhani, N., Karimi, J., & Salehan, M. (2018). A unified model for the adoption of electronic word of mouth on social network sites: Facebook as the exemplar. International Journal of Electronic Commerce, 22(2), 202-231.

Agerström, J., Björklund, F., & Allwood, C. (1991). Aiken, LS & West, SG (1991). multiple regression: testing and interpreting interactions. Journal of Applied Psychology, 73, 305-307.

Ahmed, E., & Ward, R. (2016). Analysis of factors influencing acceptance of personal, academic and professional development e-portfolios. Computers in Human Behavior, 63, 152-161.

Ali, B. M., & Younes, B. (2013). The impact of information systems on user performance: An exploratory study. Journal of Knowledge Management, Economics and Information Technology, 3(2), 128-154.

Alrasheedi, N. M. (2021). A conceptual model development for persuasive e-wom reviews: Challenges and future prospect. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(10), 2124-2133.

Amin, H. (2007). An analysis of mobile credit card usage intentions. Information Management & Computer Security, 15(4), 260-269.

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411.

Aren, S., Güzel, M., Kabadayı, E., & Alpkan, L. (2013). Factors affecting repurchase intention to shop at the same website. Procedia-Social and Behavioral Sciences, 99, 536-544.

Arora, N., & Lata, S. (2020). YouTube channels influence on destination visit intentions: An empirical analysis on the base of information adoption model. Journal of Indian Business Research, 12(1), 23-42.

Ashraf, A. R., Thongpapanl, N. T., & Spyropoulou, S. (2016). The connection and disconnection between e-commerce businesses and their customers: Exploring the role of engagement, perceived usefulness, and perceived ease-of-use. Electronic Commerce Research and Applications, 20, 69-86.

Ashrafi, A., Ravasan, A. Z., Trkman, P., & Afshari, S. (2019). The role of business analytics capabilities in bolstering firms’ agility and performance. International Journal of Information Management, 47, 1-15.

Auxier, B., Monica, & Erson. (2021, April 7). Social Media Use in 2021. Pew Research Center: Internet, Science & Tech. https://www.pewresearch.org/internet/2021/04/07/social-media-use-in-2021/

Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of The Academy of Marketing Science, 16(1), 74-94.

Bennett, R., Härtel, C. E., & McColl-Kennedy, J. R. (2005). Experience as a moderator of involvement and satisfaction on brand loyalty in a business-to-business setting 02-314R. Industrial Marketing Management, 34(1), 97-107.

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238.

Bhattacherjee, A., & Sanford, C. (2006). Influence processes for information technology acceptance: An elaboration likelihood model. MIS Quarterly, 805-825.

Bristor, J. (1990). Exhanced explanations of word of mouth communications; the power of relations. Research in Consumer Behavior, 4, 51-83.

Bryman, A., & Bell, E. (2011). BUSINESS RESEARCH METHODS (3rd ed.): Oxford University Press.

Bueno, S., & Gallego, M. D. (2021). eWOM in C2C platforms: Combining IAM and customer satisfaction to examine the impact on purchase intention. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1612-1630.

Camilleri, M. A. (2022). Walking the talk about corporate social responsibility communication: An elaboration likelihood model perspective. Business Ethics, the Environment & Responsibility.

Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81.

Canhoto, A. I., & Clark, M. (2013). Customer service 140 characters at a time: The users' perspective. Journal of Marketing Management, 29(5-6), 522-544.

Chen, J., Chen, M. H., Li, S., Guo, Y. L., Zhu, C. G., Xu, R. X., Zhang, Y., Sun, J., Qing, P., & Liu, G. (2014). Usefulness of the neutrophil-to-lymphocyte ratio in predicting the severity of coronary artery disease: A Gensini score assessment. Journal of Atherosclerosis and Thrombosis, 25940.

Chen, T.-Y., Yeh, T.-L., & Chang, C.-I. (2020). How different advertising formats and calls to action on videos affect advertising recognition and consequent behaviours. The Service Industries Journal, 40(5-6), 358-379.

Chetioui, Y., & Lebdaoui, H. (2021). COVID-19 cause-related campaigns and consumers’ purchase intention: Does religiosity matter?. Journal of Islamic Marketing.

Cheung, C. M., & Thadani, D. R. (2012). The impact of electronic word-of-mouth communication: A literature analysis and integrative model. Decision Support Systems, 54(1), 461-470.

Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.

Choi, G., & Chung, H. (2013). Applying the technology acceptance model to social networking sites (SNS): Impact of subjective norm and social capital on the acceptance of SNS. International Journal of Human-Computer Interaction, 29(10), 619-628.

Chu, S. C., & Kim, Y. (2011). Determinants of consumer engagement in electronic word-of-mouth (eWOM) in social networking sites. International Journal of Advertising, 30(1), 47-75.

Cooper, D. R., Schindler, P. S., & Sun, J. (2006). Business research methods (Vol. 9). Mcgraw-Hill New York.

Corona, R. (2009). Is Costco better than Walmart? A comparative analysis based on enterprise marketing efficiency. A Comparative Analysis Based on Enterprise Marketing Efficiency (April 15, 2009).

Chen, T.-Y., Yeh, T.-L., & Chang, C.-I. (2020). How different advertising formats and calls to action on videos affect advertising recognition and consequent behaviours. The Service Industries Journal, 40(5-6), 358-379.

Cheung, G. W., & Lau, R. S. (2008). Testing mediation and suppression effects of latent variables: Bootstrapping with structural equation models. Organizational Research Methods, 11(2), 296-325.

Clifford, N., Cope, M., Gillespie, T., & French, S. (2016). Key methods in geography. Sage.

Cohen, J. (1988). Statistical power analysis for the social sciences.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.

Data Sources. (2021). DataReportal – Global Digital Insights. Retrieved November 23, 2021, from https://datareportal.com/reports/digital-2021-taiwan

Doll, W. J., Xia, W., & Torkzadeh, G. (1994). A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Quarterly, 453-461.

Dou, X., Walden, J. A., Lee, S., & Lee, J. Y. (2012). Does source matter? Examining source effects in online product reviews. Computers in Human Behavior, 28(5), 1555-1563.

Ella, T., Agus, S., & Untrung, K. (2012). Analisis faktor-faktor kunci dari niat pembelian kembali secara online (study kasus pada konsumen fesh shop). Jurnal Bisnis dan Ekonomi, 19(2).

Erkan, I., & Evans, C. (2016). The influence of eWOM in social media on consumers’ purchase intentions: An extended approach to information adoption. Computers in Human Behavior, 61, 47-55.

Erkan, I., & Evans, C. (2018). Social media or shopping websites? The influence of eWOM on consumers’ online purchase intentions. Journal of Marketing Communications, 24(6), 617-632.

Eze, E., Echezona, B., & Uzodinma, E. (2011). Isolation and identification of pathogenic bacteria associated with frozen mackerel fish (Scomber scombrus) in a humid tropical environment. African Journal of Agricultural Research, 6(8), 1947-1951.

Fan, Y. W., Miao, Y. F., Fang, Y. H., & Lin, R. Y. (2013). Establishing the adoption of electronic word-of-mouth through consumers' perceived credibility. International Business Research, 6(3), 58.

Fishbein, M., Jaccard, J., Davidson, A. R., Ajzen, I., & Loken, B. (1980). Predicting and understanding family planning behaviors. In Understanding attitudes and predicting social behavior. Prentice Hall.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 51-90.

Germain Cassiere, S., McLain, D. A., Brooks Emory, W., & Hatch Jr, H. B. (1980). Metastatic carcinoma of the pancreas simulating primary bronchogenic carcinoma. Cancer, 46(10), 2319-2321.

Hair, J. F., Celsi, M., Ortinau, D. J., & Bush, R. P. (2010). ESSENTIAL of MARKETING RESEARCH (Vol. 2). McGraw-Hill/Irwin New York, NY.

Hair, J. F., Gabriel, M., & Patel, V. (2014). AMOS covariance-based structural equation modeling (CB-SEM): Guidelines on its application as a marketing research tool. Brazilian Journal of Marketing, 13(2).

Hair, J. F., Page, M., & Brunsveld, N. (2019). Essentials of business research methods. Routledge.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139-152.

Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414-433.

Hair Jr, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European business review.

Hajli, N. (2018). Ethical environment in the online communities by information credibility: A social media perspective. Journal of Business Ethics, 149(4), 799-810.

Hamid, S., Ijab, M. T., Sulaiman, H., Anwar, R. M., & Norman, A. A. (2017). Social media for environmental sustainability awareness in higher education. International Journal of Sustainability in Higher Education, 18(4), 474-491.

Hansen, J. M., Saridakis, G., & Benson, V. (2018). Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers’ use of social media for transactions. Computers in Human Behavior, 80, 197-206.

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.

Hong, S., & Park, H. S. (2012). Computer-mediated persuasion in online reviews: Statistical versus narrative evidence. Computers in Human Behavior, 28(3), 906-919.

Hong, S., & Pittman, M. (2020). eWOM anatomy of online product reviews: Interaction effects of review number, valence, and star ratings on perceived credibility. International Journal of Advertising, 39(7), 892-920.

Hong, W., Thong, J. Y., Wong, W. M., & Tam, K. Y. (2002). Determinants of user acceptance of digital libraries: an empirical examination of individual differences and system characteristics. Journal of Management Information Systems, 18(3), 97-124.

Hoskins, C., Gaff, C., McEwen, A., Macciocca, I., Pearn, A., Shalhoub, C., Salvemini, H., Berkman, J., Riley, K. E., & Williams, R. (2021). Professional regulation for Australasian genetic counselors. Journal of Genetic Counseling, 30(2), 361-369.

Hu, L. T., & Bentler, P. M. (1995). Evaluating model fit.

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.

Hu, Z., Yang, Z., Liang, X., Salakhutdinov, R., & Xing, E. P. (2017). Toward controlled generation of text. International Conference on Machine Learning, Sydney, Australia.

Huang, H.-F., & Lee, C.-C. (2021). Exploring consumers' purchase intentions on Facebook: The influence of characteristics of eWOM. Asian Journal of Research in Business and Management, 3(2), 59-68.

Hussain, S., Guangju, W., Jafar, R. M. S., Ilyas, Z., Mustafa, G., & Jianzhou, Y. (2018). Consumers' online information adoption behavior: Motives and antecedents of electronic word of mouth communications. Computers in Human Behavior, 80, 22-32.

Hussain, S., Huang, K., Ilyas, Z., & Niu, B. (2020). Exploring the novel input attributes affecting eWOM. Frontiers in Psychology, 11.

Huston, N. C., Wan, H., Strine, M. S., Tavares, R. D. C. A., Wilen, C. B., & Pyle, A. M. (2021). Comprehensive in vivo secondary structure of the SARS-CoV-2 genome reveals novel regulatory motifs and mechanisms. Molecular Cell, 81(3), 584-598.

Hull, C., & Nie, N. H. (1981). SPSS Up date 7-9new procedures and facilities for releases 7-9.

Ismagilova, E., Dwivedi, Y. K., & Rana, N. (2021). The Use of Elaboration Likelihood Model in eWOM Research: Literature Review and Weight-Analysis. Conference on e-Business, e-Services and e-Society,

Ismagilova, E., Slade, E., Rana, N. P., & Dwivedi, Y. K. (2020). The effect of characteristics of source credibility on consumer behaviour: A meta-analysis. Journal of Retailing and Consumer Services, 53, 101736.

Jöreskog, K. G. (1993). Testing structural equation models. Sage focus editions, 154, 294-294.

Jöreskog, K. G., & Sörbom, D. (1986). LISREL VI: Analysis of linear structural relationships by maximum likelihood, instrumental variables, and least squares methods. Scientific Software.

Jamil, R. A., & Farid Hasnu, S. A. (2013). Consumer's Reliance on Word of Mouse: A Review Paper. Journal of Business & Economics, 5(2), 171-205.

Jerslev, A. (2016). Media times in the time of the microcelebrity: Celebrification and the YouTuber Zoella. International Journal of Communication, 10, 19.

Kang, J. W., & Namkung, Y. (2018). The effect of corporate social responsibility on brand equity and the moderating role of ethical consumerism: The case of Starbucks. Journal of Hospitality & Tourism Research, 42(7), 1130-1151.

Khwaja, M. G., Mahmood, S., & Zaman, U. (2020). Examining the effects of eWOM, trust inclination, and information adoption on purchase intentions in an accelerated digital marketing context. Information, 11(10), 478.

Khwaja, M. G., & Zaman, U. (2020). Configuring the evolving role of ewom on the consumers information adoption. Journal of Open Innovation: Technology, Market, and Complexity, 6(4), 125.

Kim, M. J., Chung, N., Lee, C.-K., & Preis, M. W. (2016). Dual-route of persuasive communications in mobile tourism shopping. Telematics and Informatics, 33(2), 293-308.

Krackhardt, D. (1998). Super strong and sticky. Power and influence in organizations, 21.

Krasila, H. (2021). Examining the influence of consumer-to-consumer eWOM source credibility on brand awareness, purchase intention, eWOM intention, and eWOM behavior in Facebook. [Master’s thesis, Jyväskylä University School].

Kumala, D., & Tuwisna, S. (2021). Analisa tingkat kehandalan unique selling proportion, eWOM dan Facebook ads terhadap pembelian kopi kekenian menggunakan metode kruskal walls. Jurnal Bisnis Digital (BIDIG), 1(1), 62-79.

Lee, K. (2018). The Influence of Beauty-Related YouTube content on Consumers' Purchase Intention.

Leong, C. M., Loi, A. M. W., & Woon, S. (2021). The influence of social media eWOM information on purchase intention. Journal of Marketing Analytics, 1-13.

Leong, L. Y., Hew, T. S., Ooi, K. B., & Lin, B. (2019). Do electronic word-of-mouth and elaboration likelihood model influence hotel booking?. Journal of Computer Information Systems, 59(2), 146-160.

Li, C. Y. (2015). The effects of source credibility and argument quality on employees' responses toward information system usage. Asia Pacific Management Review, 20(2), 56-64.

Li, L. A. (2015). The edge of expertise: Representing barefoot doctors in cultural revolution China. Endeavour, 39(3-4), 160-167.

Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86(1), 114.

Lockwood, C. M., & MacKinnon, D. P. (1998). Bootstrapping the standard error of the mediated effect. Proceedings of the 23rd annual meeting of SAS Users Group International, 997–1002.

Lule, I., Omwansa, T. K., & Waema, T. M. (2012). Application of technology acceptance model (TAM) in m-banking adoption in Kenya. International Journal of Computing & ICT Research, 6(1).

Machdar, N. M. (2019). The effect of information quality on perceived usefulness and perceived ease of use. Business and Entrepreneurial Review, 15(2), 131-146.

Mahardika, A. A. & Saino, 2014, Analisis faktor yang mempengaruhi niat beli di zalora online shop. Jurnal Ilmu Manajemen, 2(3), 917-927.

Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common method variance in IS research: A comparison of alternative approaches and a reanalysis of past research. Management Science, 52(12), 1865-1883.

Mao, E., & Palvia, P. (2006). Testing an extended model of IT acceptance in the Chinese cultural context. ACM SIGMIS Database: The Database for Advances in Information Systems, 37(2-3), 20-32.

Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness-of-fit indexes in confirmatory factor analysis: The effect of sample size. Psychological Bulletin, 103(3), 391.

Mooney, C. Z., Mooney, C. F., Mooney, C. L., Duval, R. D., & Duvall, R. (1993). Bootstrapping: A nonparametric approach to statistical inference. sage.

Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58(3), 20-38.

Moslehpour, M., Pham, V. K., Wong, W.-K., & Bilgiçli, İ. (2018). E-purchase intention of Taiwanese consumers: Sustainable mediation of perceived usefulness and perceived ease of use. Sustainability, 10(1), 234.

Muda, M., & Hamzah, M. I. (2021). Should I suggest this YouTube clip? The impact of UGC source credibility on eWOM and purchase intention. Journal of Research in Interactive Marketing, 15(3), 441-459.

Naujoks, A., & Benkenstein, M. (2020). Who is behind the message? The power of expert reviews on eWOM platforms. Electronic Commerce Research and Applications, 44, 101015.

Noor, M. M. (2011). Determining critical success factors of mobile banking adoption in Malaysia. Australian Journal of Basic and Applied Sciences, 5(9), 252-265.

Nov, O., & Ye, C. (2008). Users' personality and perceived ease of use of digital libraries: The case for resistance to change. Journal of the American Society for Information Science and Technology, 59(5), 845-851.

Ohanian, R. (1990). Construction and validation of a scale to measure celebrity endorsers' perceived expertise, trustworthiness, and attractiveness. Journal of Advertising, 19(3), 39-52.

Ortiz, J., Chih, W. H., & Teng, H. C. (2017). Electronic word of mouth in the Taiwanese social networking community: Participation factors. Internet Research,27(5), 1058-1084.

Petty, R. E., & Cacioppo, J. T. (1979). Issue involvement can increase or decrease persuasion by enhancing message-relevant cognitive responses. Journal of Personality and Social Psychology, 37(10), 1915.

Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In Communication and persuasion. 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.

Pitasari, Y., & HH, S. R. (2020). Factors influencing customers’ continued mobile app use intention from the internet service provider perspective: Information adoption model. Contemporary Research on Business and Management: Proceedings of the International Seminar of Contemporary Research on Business and Management (ISCRBM 2019), Jakarta, Indonesia.

Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879.

Prendergast, G., Ko, D., & Siu Yin, V. Y. (2010). Online word of mouth and consumer purchase intentions. International Journal of Advertising, 29(5), 687-708.

Ramadania, S., & Braridwan, Z. (2019). The influence of perceived usefulness, ease of use, attitude, self-efficacy, and subjective norms toward intention to use online shopping. International Business and Accounting Research Journal, 3(1), 1-14.

Reagans, R., & McEvily, B. (2003). Network structure and knowledge transfer: The effects of cohesion and range. Administrative Science Quarterly, 48(2), 240-267.

Reyes-Menendez, A., Palos-Sanchez, P., Saura, J. R., & Santos, C. R. (2021). Revisiting the impact of perceived social value on consumer behavior toward luxury brands. European Management Journal.

Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23-74.

Shuang, Y. (2013). Effects of information quality and source credibility on EWOM adoption in context of virtual community. 2013 International Conference on Management Science and Engineering 20th Annual Conference Proceedings,

Siddiqui, J., Mehjabeen, M., & Stapleton, P. (2021). Emergence of corporate political activities in the guise of social responsibility: Dispatches from a developing economy. Accounting, Auditing & Accountability Journal, 34(5), 1137-1162.

Sussman, S. W., & Siegal, W. S. (2003). Informational influence in organizations: An integrated approach to knowledge adoption. Information Systems Research, 14(1), 47-65.

Ten Kate, S., Haverkamp, S., Mahmood, F., & Feldberg, F. (2010). Social network influences on technology acceptance: A matter of tie strength, centrality and density. Bled eConference,

Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302-312.

Tien, D. H., Rivas, A. A. A., & Liao, Y. K. (2019). Examining the influence of customer-to-customer electronic word-of-mouth on purchase intention in social networking sites. Asia Pacific Management Review, 24(3), 238-249.

Van der Heijden, H. (2003). Factors influencing the usage of websites: The case of a generic portal in The Netherlands. Information & Management, 40(6), 541-549.

Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365.

Venkatesh, V., & Morris, M. G. (2000). Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 115-139.

Wang, Q., Makaroff, D. J., Edwards, H. K., & Thompson, R. (2003). Workload characterization for an E-commerce web site. Cascon, 3, 313-327.

Wang, W., Chen, X. L., & Zhong, L. F. (2018). Social contagions with heterogeneous credibility. Physica A: Statistical Mechanics and Its Applications, 503, 604-610.

Wang, Y. (2016). Information adoption model, a review of the literature. J. Econ. Bus. Manag, 4(11), 618-622.

Wang, Y. S., Lin, H. H., & Luarn, P. (2006). Predicting consumer intention to use mobile service. Information Systems Journal, 16(2), 157-179.

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.

Wibowo, A., Chen, S. C., Wiangin, U., M, Y., & Ruangkanjanases, A. (2021). Customer behavior as an outcome of social media marketing: The role of social media marketing activity and customer experience. Sustainability, 13(1), 189.

Wijaito, D. S. H. (2008). Pengaruh kualitas sistem informasi, perceived usefulness, dan kualitas informasi terhadap kepuasan pengguna akhir software akuntansi. Jurnal Simposium Nasional Akuntansi XI.

Xerri, M. J., & Brunetto, Y. (2018). The impact of the perceived usefulness of workplace social networks upon the innovative behaviour of SME employees a social capital perspective. In Promoting Innovation in New Ventures and Small-and Medium-Sized Enterprises. World Scientific.

Yang, H. C., & Zhou, L. (2011). Extending TPB and TAM to mobile viral marketing: An exploratory study on American young consumers’ mobile viral marketing attitude, intent and behavior. Journal of Targeting, Measurement and Analysis for Marketing, 19(2), 85-98.

Zha, X., Li, J., & Yan, Y. (2013). Information self-efficacy and information channels: Decision quality and online shopping satisfaction. Online Information Review, 37(6), 872-890.

Zhang, H., Lai, P. J., Paul, S., Kothawade, S., & Nikolaidis, S. (2019). Learning collaborative action plans from YouTube videos. ISRR.

Zhang, H., Zhou, S., & Shen, B. (2014). Public trust: A comprehensive investigation on perceived media credibility in China. Asian Journal of Communication, 24(2), 158-172.

Zhang, K. Z., Zhao, S. J., Cheung, C. M., & Lee, M. K. (2014). Examining the influence of online reviews on consumers' decision-making: A heuristic–systematic model. Decision Support Systems, 67, 78-89.