|
中文部份
韓乾(譯)(民101)。研究方法原理(原作者：Earl Babbie)。台北市：五南文化。(原著出版年：1975)
原文部份
Ahn, T., S. Ryu, et al. (2007). The impact of Web quality and playfulness on user acceptance of online retailing. Information & Management, 44(3), 263-275.
Akcayol, M. A., Utku, A., Aydogan, E., & Mutlu, B.(2018) A weighted multi-attribute-based recommender system using extended user behavior analysis. Electronic Commerce Research and Applications, vol 28, 86-93
Al-Taie, M. Z., & Kadry, S. (2014). Visualization of Explanations in Recommender Systems. Journal of Advanced Management Science, 2(2), 140-144.
Aversano, L., & Tortorella, M. (2013). Quality evaluation of floss projects： Application to ERP systems. Information and Software Technology, 55(7), 1260-1276.
Balaban, I., Mu, E., & Divjak, B. (2013). Development of an electronic Portfolio system success model: An information systems approach. Computers & Education, 60, 396-411.
Belanche, D., Casaló, L. V., & Guinalíu, M. (2012). Website usability, consumer satisfaction and the intention to use a website: The moderating effect of perceived risk. Journal of Retailing and Consumer Services, 19(1), 124-132.
Berry, L. L., Seiders, K., & Grewal, D. (2002). Understanding service convenience. Journal of Marketing, 66(3), 1–17.
Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model, MIS Quarterly, 25(3), 351-370.
Blanchard, A.L., Markus, M.L., The experiences sense of a virtual community: characteristics and processes, The Database for Advances in Information Systems 35 (2004) 65–79
Boakye, K. G., McGinnis, T., Prybutok, V. R., & Paswan, A. K. (2014). Development of a service continance model with IT service antecedents, Journal of Retailing and Consumer Services, 21,717-724.
Bobadilla, J., Ortega, F., Hernando, A. & Glez-de-Rivera, G. (2013). A similarity metric designed to speed up, using hardware, the recommender systems k-nearest neighbors’ algorithm. Knowledge-Based Systems, 51, 27-34.
Bøe, T., Gulbrandsen, B., & Sørebø, Ø. (2015). How to stimulate the continued use of ICT in higher education: Integrating Information Systems Continuance Theory and agency theory. Computers in Human Behavior, 50, 375–384).
Brown, L.G. (1990). Convenience in services marketing. Journal of Services Marketing, 4(1), 53–59.
Brown, L.G.(1989)The strategic and tactical implications of convenience in consumer product market,international Journal of Consumer Marketing, 6, 13–19.
Calli, L., Balcikanli, C., Calli, F., Cebeci, H., & Seymen, O. (2013). Identifying factors that contribute to the satisfaction of students in e-learning. Turkish Online Journal of Distance Education, 14(1), 85-101.
Card, S. K., Mackinlay, J. D., & Shneiderman, B. (Eds.). (1999). Readings in information visualization: using vision to think. Morgan Kaufmann Publishers Inc.
Chen, C. C. & Tsai, J. L.(2017) Determinants of behavioral intention to use the Personalized Location-based Mobile Tourism Application: An empirical study by integrating TAM with ISSM. Future Generation Computer Systems, In Press.
Chen, S. C. (2012). To use or not to use: understanding the factors affecting continuance intention of mobile banking. International Journal Mobile Communications, 10(5), 490-507.
Chen, Y., Yang, L., Zheng, M. & Yang, Jun.(2018) Central or Peripheral? Cognition Elaboration Cues’ Effect on Users’ Continuance Intention of Mobile Health Applications in the Developing Markets. International Journal of Medical Informatics, In Press.
Chiu, C. M., Hsu, M. H., Lai, H. C., & Chang, C. M. (2012). Re-examining the influence of trust on online repeat purchase intention: The moderating role of habit and its antecedents. Decision Support Systems, 53(4), 835-845.
Chiu, C.M., Sun, S.Y., Sun, P.C., & Ju, T.L. (2007). An empirical analysis of the antecedents of web-based learning continuance. Computers & Education, 49(4), 1224-1245.
Clark, J.M. & Paivio, A., (1991) Dual Coding Theory and Education. Educational Psychology Review, 3(3), 149-210.
Cooper, D. & Schindler, P. (2013). Business Research Methods： McGraw-Hill Education.
Creswell, J.W. (2013). Research Design：Qualitative, Quantitative, and Mixed Methods Approaches (4ed.)：SAGE Publications, Inc.
Daniels, B. C., Flack, J. C. & Krakauer, D.C.(2017)Daul coding Theory Explains Biphasic Collective Computation in neural decision-making. Frontiers in Neuroscience, 11(313), 1-16.
Davis, F. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
Delone, W. H. & McLean, E. R. (2003). The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems, 19(4), 9-30.
Delone, W. H. & McLean, E. R.（1992）. Information Systems Success: The Quest for the Dependent Variable. Information Systems Research, 3（1）, 60-95.
Dwivedi, Y. K., Kapoor, K. K., Williams, M. D. & Williams, J. (2013). RFID systems in libraries: An empirical examination of factors affecting system use and user satisfaction. International Journal of Information Management, 33, 367-377.
Emerson, R. M. (1981). Social exchange theory. In M. Rosenberg, & R. H. Turner (Eds.), Social psychology: sociological perspectives (pp. 30-65). New York, NY: Basic Books.
Fang, Y. H., Chiu, C. M., & Wang, E. T. G. (2011). Understanding customers’ satisfaction and repurchase intention: An integration of IS success model, trust, and justice. Internet Research, 21(4), 479-503.
Flavian, C., Guinaliu, M. & Gurrea, R. (2006). The Role Played by Perceived Usability, Satisfaction, and Customer Trust on Website Loyalty. Information & Management, 43, 1-14.
Freeze, R.D. , Alshare, K.A. , Lane, P.L. & Wen, H.J. (2010). IS Success Model in E-Learning Context Based on Students' Perceptions. Journal of Information Systems Education , 21(2) , 173-184.
Gedikli, F., Jannach, D., & Ge, M. (2014). How should I explain? A comparison of different explanation types for recommender systems. International Journal of Human-Computer Studies, 72(4), 367-382.
Ghorab, M. R., Zhou, D., O’Connor, A. & Wade, V. (2013). Personalised Information Retrieval: survey and classification. User Modeling and User-Adapted Interaction, 23(4), 381-443.
Gorla, N. & Somers, T. M. (2014). The impact of IT outsourcing on information systems success. Information & Management, 51, 320-335.
Guo, G., Zhang, J., Thalmann, D. & Yorke-Smith, N. (2014). Leveraging prior ratings for recommender systems in e-commerce. Electronic Commerce Research and Applications. 13(6), 440-455.
Gulzar, Z., Leema, A. A., & Deepak, G.(2018) PCRS: Personalized Course Recommender System Based on Hybrid Approach. Procedia Computer Science, vol 125, 518-524
Gupta, J. & Gadge, J. (2015) Performance Analysis of Recommendation System Based On Collaborative Filtering and Demographics. 2015 International Conference on Communication, Information & Computing Technology.
Hadji, B. & Degoulet, P. (2016). Information system end-user satisfaction and continuance intention: A unified modeling approach. Journal of Biomedical Informatics, 61, 185–193.
Hadji, B., Martin, G., Dupui, I., Campoy, P., & Degoulet, P. (2016). 14 Years longitudinal evaluation of clinical information systems acceptance: The HEGP case. International Journal of Medical Informatics, 86, 20–29.
Hernández-Rodríguez, S., Hernández-Lira, C. A., Aguilar-Rico, A., Flores-Becerra, G., Flores-Méndez, J. & Vázquez-Cuchillo, O. (2016) A. Recommender system applied to the indirect materials selection process (RS-IMSP) for producing automobile spare parts. Computers in Industry, 82, 233–244.
Herz, M. F. & Diamantopoulos, A.(2013) Country-Specific associations made by consumers: a dual-coding theory perspective. Journal of International Marketing, 21(3), 95-121.
Hsu, H. H., & Chang, Y. Y. (2013). Extended TAM model: Impacts of convenience on acceptance and use of Moodle. Online Submission, 3(4), 211-218.
Hsu, M.H., Chang, C.M., Chu, K.K. & Lee, Y. J. (2014). Determinants of repurchase intention in online group-buying: The perspectives of DeLone & McLean IS success model and trust. Computers in Human Behavior, 36, 234-245
Huang, R. T.(2018) What motivates people to continuously post selfies? The moderating role of perceived relative advantage. Computers in Human Behavior, vol 80, 103-111
Huang, S. (2011). Designing utility-based recommender systems for e-commerce: Evaluation of preference-elicitation methods. Electronic Commerce Research and Applications, 10(4), 398-407.
Huang, T. L. & Chen, Y. M.(2014) Young audiences’ emotional experience on smartphone film: an application of dual-coding theory, 15(2), 193-208.
Indriana M. & Hwang C.S. (2014). Applying Neural Network Model to Hybrid Tourist Attraction Recommendations. ULTIMATICS, 6(2), 63-69.
Jamal, A. & Sharifuddin, J. (2014). Perceived value and perceived usefulness of halal labeling: The role of religion and culture. Journal of Business Research, 68(5) , 933-941.
Jensen, J. A., Walsh, P., Cobbs, J. & Turner B.A.(2015) The effects of second screen use on sponsor brand awareness: a dual coding theory perspective. Journal of Consumer Marketing, 32(2), 71-84.
Jia, Q., Guo, Y. & Barnes, S. J.(2017) Enterprise 2.0 post-adoption: Extending the information system continuance model based on the technology-Organizationenvironment framework. Computers in Human Behavior, 67, 95-105.
Jing, Y., Liang, Z. & Phelan, C.A. (2016) A Novel Recommendation Strategy for User-based Collaborative Filtering in Intelligent Marketing. Journal of Digital Information Management, 14(2), 81-91.
Jonathan L.H. , Joseph A.K. , Loren G.T. & John T.R. (2004). Evaluating Collaborative Filtering Recommender Systems. Journal ACM Transactions on Information Systems , 22(1) , 5-53.
Kardan, A. A. & Ebrahimi, M. (2013). A novel approach to hybrid recommendation systems based on association rules mining for content recommendation in asynchronous discussion groups. Information Sciences, 219, 93-110.
Kardan, A. A. & Ebrahimi, M. (2013). A novel approach to hybrid recommendation systems based on association rules mining for content recommendation in asynchronous discussion groups. Information Sciences, 219, 93-110.
Katerattanakul, P. & Siau, K. (1999). Measuring information quality of web sites: Development of an instrument. In Proceedings of the 20th International Conference on Information Systems, Charlotte, NC, 279-285.
Keller, K. L. (1987). Memory factors in advertising - The effect of advertising retrieval cues on brand evaluations. Journal of Consumer Research, 14(3), 316-333.
Kim, Y. A., & Phalak, R. (2012). A trust prediction framework in rating-based experience sharing social networks without a web of trust. Information Sciences, 191, 128–145.
Koc-Januchta, M., Hoffler, T., Thoma, G. B. & Prechct, H.(2017) Visualizers versus verbalizers: Effects of cognitive style on learning with texts and pictures e An eye-tracking study. Computers in Human Behavior, 68, 170-179
Konstan, J.A. &Riedl, J. (2012). Recommender systems from algorithms to user experience. User Modeling and User-Adapted Interaction, 22, 101-123.
Koo, C., Wati, Y. & Chung, N. (2013). A Study of Mobile and Internet Banking Service: Applying for IS Success Model. Asia Pacific Journal of Information Systems, 23(1), 65-86.
Ku, E.C.S. & Chen, C.D. (2013). Fitting facilities to self-service technology usage: evidence from kiosks in Taiwan airport. Journal of Air Transport Management, 32, 87-94.
Kwabena G. Boakye, Thomas McGinnis , Victor R. Prybutok , Audhesh K. Paswan.(2014). Development of a service continance model with IT service antecedents, Journal of Retailing and Consumer Services, 21 ,717-724.
Lee, G. & Lee, W. J. (2009). Psychological reactance to online recommendation services, Information & Management, 46,448-452.
Lee, G. & Lee, W. J. (2009). Psychological reactance to online recommendation services, Information & Management, 46,448-452.
Li, G. & Song, X. (2011). A New Visualization-oriented Knowledge Service Platform. Procedia Engineering, 15, 1859-1863.
Liaw, S. S. & Huang, H. M. (2013).Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments, Computers & Education, 60(1), 14-24.
Limayem, M., & Cheung, C. M. (2011). Predicting the continued use of Internet- based learning technologies: The role of habit. Behaviour & Information Technology, 30(1), 91-99.
Liu, Y. & Jang, S. (2009). Perceptions of Chinese restaurants in the U.S.: What affects customer satisfaction and behavioral intentions? International Journal of Hospitality Management, 28(3), 338-348.
Loiacono, E. T., Watson, R. T., and Hoodhue, D. L. (2002). WEBQUAL: Measure of web site quality. Paper presented at the Marketing Educators Conference: Marketing Theory and Applications. Retrieved May 12, 2011, from the World Wide Web: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.86.4410&rep=rep1&type= pdf
Lu, C.C., & Tseng, V. S. (2009). A novel method for personalized music recommendation. Expert Systems with Applications, 36(6), 10035-10044.
Lu, J., Wu, D., Mao, M., Wang, W. & Zhang, G. (2015) Recommender system application developments: A survey. Decision Support Systems, 74, 12–32.
Lu, J., Wu, D., Mao, M., Wang, W. & Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems, 74, 12-32.
Lwin, M. O., Morrin, M. & Krishna, Aradhna.(2010) Exploring the superadditive effects of scent and pictures on verbal recall: an extension of dual coding theory. Journal of Consumer Psychology, 20, 317-326.
Malhotra, N., Sahadev, S. & Purani, K.(2017) Psychological contract violation and customer intention to reuse online retailers: Exploring mediating and moderating mechanisms. Journal of Business Research, 75, 17-28.
Mason, R. O. (1978). Measuring information output: A communication systems approach. Information and Management, 1(5), 219-234.
Mathur, T., Das, G. & Paul, U. K.(2016) Convenience, Satisfaction, and Post-Purchase Behavior in India's Health Insurance Market. Journal of Global Marketing, 29(4), 218-232
Mayer, R. E. & Sims, K. (1994). For whom is a picture worth a thousand words? Extensions of a dual-coding theory of multimedia learning. Journal of Educational Psychology, 86(3), 389-401.
Meyer, R. (2010). Knowledge Visualization, in Trends in Information Visualization (edited by Baur, M., Sedlmair, M., Wimmer, R., Chen, Y., Streng, S., Boring, S., De Luca, A., & Butz A.). Hauptseminar Medieninformatik WS.
Moradi, P.& Ahmadian, S. (2015). A reliability-based recommendation method to improve trust-aware recommender systems. Expert Systems with Applications, 42, 7386–7398
Moreno, M.N. Segrera, S., López, V.F., Muñoz, M.D. & Sánchez, A.L. (2016). Web mining based framework for solving usual problems in recommender systems. A case study for movies' recommendation. Neurocomputing, 176, 72–80.
Munemasa, I., Tomomatsu, Y., Hayashi, K., & Takagi, T.(2018). Deep reinforcement learning for recommender systems. 2018 International Conference on Information and Communications Technology (ICOIACT)
Najjar, L. (1996). Multimedia information and learning. Journal of Educational Multimedia and Hypermedia, 5, 129-150.
Ochirbat, A., Shih, T. K., Chootong, C., Sommool, W., Gunarathne, W. K. T. M., Wang, H. H., & Ma, Z. H.(2015) Hybrid occupation recommendation for adolescents on interest, profile, and behavior. Telematics and Informatics, vol 35(3), 534-550
Oh, H., Jeong, M., & Baloglu, S. (2013). Tourists’ adoption of self-service technologies at resort hotels. Journal of Business Research, 66 (6), 692-699.
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of marketing research, 460-469.
Ong, C. S., Lai, J. Y., & Wang, Y. S. (2004). Factors Affecting Engineers’ Acceptance of Asynchronous E-Learning Systems in High-Tech Companies. Information & Management, 41(6), 795-804.
Ortega, F. ,SáNchez, J.L. , Bobadilla, J.&GutiéRrez A. (2013). Improving Collaborative Filtering Based Recommender Systems Using Pareto Dominance. Information Sciences: an International Journal , 239 , 50-61.
Paivio, A. (2007). Mind and its evolution: A dual coding theoretical approach. New York: Psychology Press. Previously published by Lawrence Erlbaum Associates.
Paivio, A.(1971). Imagery and Verbal Processes. New York:Holt, Rinehart & Winston
Paivio, A.(1986). Mental representations:a dual coding approach , Oxford, England:Oxford University Press.
Paivio, A.(2013) Dual coding theory, word abstractness, and emotion: a critical review of Kousta et al. (2011). Journal of Experimental Psychology: General, 142, 282-287.
Pallud, J. & Straub, D. W. (2014). Effective website design for experience-influenced environments: The case of high culture museums. Information & Management, 51(3), 359-373.
Pan, S., & Cornell, R. (2002). Optimum Use of Video in Light of Cognitive Learning Theories. Journal of Educational Media & Library Sciences, 40(2), 156-169.
Parent, M. M., MacDonald, D., & Goulet, G. (2014). The theory and practice of knowledge management and transfer： The case of the Olympic Games. Sport Management Review, 17(2), 205-218.
Park, J., Kim, J., & Koh, J. (2010). Determinants of continuous usage intention in web analytics services. Electronic Commerce Research and Applications, 9(1), 61-72.
Park, S. , Zo, H., Ciganek, A. P. & Lim, G. G. (2011). Examining success factors in the adoption of digital object identifier systems. Electronic Commerce Research and Applications, 10, 626-636.
Pérez-Gallardo, Y., Alor-Hernandez, G. & Cortes-Robles, G. (2013). Collective intelligence as mechanism of medical diagnosis: The iPixel approach. Expert Systems with Applications, 40, 2726-2737.
Petter, S., DeLone, W., & McLean, E. (2008). Measuring information systems success: Models, dimensions, measures, and interrelationships. European Journal of Information Systems, 17(3), 236-263.
Premkumar, G., & Bhattacherjee, A. (2008). Explaining information technology usage: A test of competing models. Omega, 36(1), 64-75.
Ramírez-Correa, P.E., Rondan-Cataluña, F.J., Arenas-Gaitán, J., & Alfaro-Perez, J.L. (2017). Moderating effect of learning styles on a learning management system’s success. Telematics and Informatics, 34, 272–286.
Rana, C., & Jain, S. K. (2014). An evolutionary clustering algorithm based on temporal features for dynamic recommender systems. Swarm and Evolutionary Computation, 14, 21-30.
Rashid, A. M., Albert, I., Cosley, D., Lam, S. K., McNee, S. M., Konstan, J. A., & Riedl, J. (2002). Getting to know you: learning new user preferences in recommender systems. In Proceedings of the 7th international conference on Intelligent user interfaces, 127-134.
Reji, K. G., & Ravindran, D. S. (2012). An empirical study on service quality perceptions and continuance intention in mobile banking context in India. Journal of Internet Banking & Commerce, 17(1), 1-22.
Roscoe, J. T. (2004). Fundamental Research Statistics for the Behavioral Sciences.
Rose, S., Clark, M., Samouel, P., & Hair, N. (2012). Online customer experience in eretailing: An empirical model of antecedents and outcomes. Journal of Retailing, 88(2), 308-322.
Rosen, T., Fullwood, H. L. & Henley, T.B.(2012) Dual coding theory and split attention in the Learning of abstract words. International Journal of lnstructional Media, 39(3), 181-186.
Roy, S.K., Lassat, W.M. & Shekhar, V.(2016) Convenience and satisfaction: mediation of fairness and quality.The Service Industries Journal, 36(5-6), 239-260
Sanford, D., Ross, D., Rosenbloom, A. & Singer, D.(2017) Course convenience, perceived learning, and course satisfaction across course formats. e-Journal of Business Education and Scholarship of Teaching, 11(1), 69-84.
Shannon, C. E., and Weaver, W. The Mathematical Theory of Communication, Urbana, IL: University of Illinois Press, 1949.
Shinde, S. K. & Kulkarni, U. (2012). Hybrid personalized recommender system using centering-bunching based clustering algorithm. Expert Systems with Applications, 39(1), 1381-1387.
Son, J. & Kim, S.B.(2017) Content-based filtering for recommendation systems using multiattribute networks. Expert Systems With Applications, 89, 404-412.
Stefanovic, D., Marjanovic, U., Delic´, M., Culibrk, D., & Lalic, B. (2016). Assessing the success of e-government systems: An employee perspective. Information & Management,53, 717–726.
Stone, R. W., & Baker-Eveleth, L. (2013). Students’ expectation, confirmation, and continuance intention to use electronic textbooks. Computers in Human Behavior, 29(3), 984-990.
Sun, Y. & Mouakket, S. (2015). Assessing the impact of enterprise systems technological characteristics on user continuance behavior: An empirical study in China. Computers in Industry, 70, 153–167.
Sun, Y. & Mouakket, S. (2015). Assessing the impact of enterprise systems technological characteristics on user continuance behavior: An empirical study in China. Computers in Industry, 70, 153–167.
Tang, J.T. &Chiang, C.(2009)Perceived innovativeness, perceived convenience and TAM: Effects on mobile knowledge management, in: The 5th International Workshop on Mobile Commerce and Services (2009 WMCS), 413–420.
Tejeda-Lorente, A. , Bernab´e-Moreno, J. , Porcel, C. & Herrera-Viedma, E. (2014). Integrating Quality Criteria in a Fuzzy Linguistic Recommender System for Digital Libraries. Procedia Computer Science , 31 , 1036- 1043.
Thorat P.B. , Goudar R.M. & Sunita B. (2015). Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System. International Journal of Computer Applications , 110(4), 31-36.
Tseng, S. M. (2015). Exploring the intention to continue using web-based self-service. Journal of Retailing and Consumer Services, 24, 85-93.
Ullah, F., Sarwar, G. Lee, S. (2014). N-Screen Aware Multicriteria Hybrid Recommender System Using Weight Based Subspace Clustering. Scientific World Journal. http://dx.doi.org/10.1155/2014/679849.
Wang, H. C., Jhou, H. T., & Tsai, Y. S.(2018) Adapting topic map and social influence to the personalized hybrid recommender system. Information Science, In press
Wang, Y. S., Liao, Y. W. (2008). Assessing eGovernment systems success: A validation of the DeLone and McLean model of information systems success. Government Information Quarterly, 25, 717-733.
Wu, J.H. & Wang, Y.M. (2006). Measuring KMS success: A respecification of the DeLone and McLean's model. Information & Management , 43(6) , 728-739.
Xu, C.(2018) A novel recommendation method based on social network using matrix factorization technique. Information Processing and Management, 54, 463-474.
Xu, F., & Du, J. T.(2018)Factors influencing users’ satisfaction and loyalty to digital libraries in Chinese universities.Computers in Human Behavior, vol 83, 64-72
Xu, J. (2014). The nature and consequences of trade-off transparency in the context of recommendation agents. MIS Quarterly, 38(2), 379-406.
Xu, Y., Guo, X., Hao, J., Ma, J., Lau, R. Y. K., & Xu, W. (2012). Combining social network & semantic concept analysis for personalized academic researcher recommendation. Decision Support Systems, 54(1), 564-573.
Yoon, C., & Kim, S. (2007). Convenience and TAM in a ubiquitous computing environment: The case of wireless LAN. Electronic Commerce Research & Applications, 6(1), 102-112.
Zhao, X., Mattila, A.S. & Tao, L.S.E. (2008). The role of post-training self-efficacy in customers’ use of self service technologies. International Journal of Service Industry Management, 19(4), 492-505.
Zhao, X., Mattila, A.S. & Tao, L.S.E. (2008). The role of post-training self-efficacy in customers’ use of self service technologies. International Journal of Service Industry Management, 19(4), 492-505.
Zheng, Y.M., Zhao K. & Stylianou A. (2013). The impacts of information quality and system quality on users’ continuance intention in information-exchange virtual communities: an empirical investigation. Decision Support System, 56, 513-524.
Zheng, Y.M., Zhao K. & Stylianou A. (2013). The impacts of information quality and system quality on users’ continuance intention in information-exchange virtual communities: an empirical investigation. Decision Support System, 56, 513-524.
Zhu, Y., Heynderickx, I., & Redi, J. A. (2015). Understanding the role of social context and user factors in video Quality of Experience. Computers in Human Behavior, 49, 412-426.
|