TOWARDS EXTENDING THE ORIGINAL TECHNOLOGY ACCEPTANCE MODEL (TAM) FOR A BETTER UNDERSTANDING OF EDUCATIONAL TECHNOLOGY ADOPTION

Vilma Sukackė

Abstract


Technology acceptance model (TAM) is arguably the most widely used intention theory that explains the individual’s acceptance of a certain technology.  Since Davis introduced TAM in 1986, it has been applied and validated in a variety of disciplines, including educational sciences. However, scholars note that depending on a specific context, the original TAM needs to be extended, which has been done by introducing external variables and other theories. Despite the existent TAM2 and TAM3, numerous scholars still opt for the original TAM, extending it with the variables and theories that are relevant to the specific context of their study. The aim of the present paper is to provide an overview of validated TAM extensions, which might later help to further the understanding of educational technology acceptance, which is a prerequisite of its adoption. Since interdisciplinarity in various contexts is becoming more and more common, the overview presents TAM extensions that come from a number of different disciplines. The overview is based on 108 papers that were retrieved from the Web of Science (Clarivate Analytics) by searching for the keywords ‘extended Technology Acceptance Model’, ‘extended TAM’, and ‘TAM extension’.

 


Keywords


Technology acceptance model; TAM; extended TAM; technological innovations in education

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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.

Ajzen, I. (1991). The Theory of Planned Behavior. Organization Behavior and Human Decision Processes. Academic Press, Inc. 179-211.

Ajzen, I.M. Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Prentice-Hall, Englewood Cliffs, NJ.

Alalwan, A.A., Baabdullah, A.M., Rana, N.P., Tamilmani, K., & Dwivedi, Y.K. (2018). Examining adoption of mobile internet in Saudi Arabia: Extending TAM with perceived enjoyment, innovativeness and trust. Technology in Society, 55, 100-110.

Al-Ammary, J. (2010, March). Factors affecting the acceptance and use of computers and the internet by elderly people in the Kingdom of Bahrain. In International Conference on Information Management and Evaluation (p. 9). Academic Conferences International Limited.

Al-Azawei, A., & Lundqvist, K. (2015). Learner Differences in Perceived Satisfaction of an Online Learning: An Extension to the Technology Acceptance Model in an Arabic Sample. Electronic Journal of e-Learning, 13(5), 408-426.

Al-Azawei, A., Parslow, P., & Lundqvist, K. (2017). Investigating the effect of learning styles in a blended e-learning system: An extension of the technology acceptance model (TAM). Australasian Journal of Educational Technology, 33(2).

Alenezi, A.R., & Karim, A. (2010). An empirical investigation into the role of enjoyment, computer anxiety, computer self-efficacy and internet experience in influencing the students' intention to use e-learning: A case study from Saudi Arabian governmental universities. Turkish Online Journal of Educational Technology-TOJET, 9(4), 22-34.

Al-Harby, F., Qahwaji, R., & Kamala, M. (2009, September). The effects of gender differences in the acceptance of biometrics authentication systems within online transaction. In CyberWorlds, 2009. CW'09. International Conference on (203-210). IEEE.

Alkali, A.U., & Abu Mansor, N.N. (2017). Interactivity and Trust as Antecedents of E-Training Use Intention in Nigeria: A Structural Equation Modelling Approach. Behavioral Sciences, 7(3), 47.

Al-Khateeb, F.B. (2007, May). Predicting internet usage in two emerging economies using an extended technology acceptance model (TAM). In Collaborative Technologies and Systems, 2007. CTS 2007. International Symposium on (143-149). IEEE.

Al-Mushasha, N.F.A. (2013, May). Determinants of e-learning acceptance in higher education environment based on extended technology acceptance model. In e-Learning" Best Practices in Management, Design and Development of e-Courses: Standards of Excellence and Creativity", 2013 Fourth International Conference on (261-266). IEEE.

Alnajjar, G. (2017). Facilitating conditions and cost in determining M-Commerce acceptance in Jordan: Initial findings. In Leadership, Innovation and Entrepreneurship as Driving Forces of the Global Economy (345-351). Springer, Cham.

Alshare, K.A., Freeze, R., & Kwun, O. (2009). Student intention to use expert systems: An exploratory study. Journal of Computer Information Systems, 49(4), 105-113.

Altanopoulou, P., & Tselios, N. (2017). Assessing acceptance toward wiki technology in the context of Higher Education. The International Review of Research in Open and Distributed Learning, 18(6).

Asua, J., Orruño, E., Reviriego, E., & Gagnon, M. P. (2012). Healthcare professional acceptance of telemonitoring for chronic care patients in primary care. BMC medical informatics and decision making, 12(1), 139.

Autry, C.W., Grawe, S.J., Daugherty, P.J., & Richey, R.G. (2010). The effects of technological turbulence and breadth on supply chain technology acceptance and adoption. Journal of Operations Management, 28(6), 522-536.

Balouchi, M., Aziz, Y.A., Hasangholipour, T., Khanlari, A., Abd Rahman, A., & Raja-Yusof, R.N. (2017). Explaining and predicting online tourists’ behavioural intention in accepting consumer generated contents. Journal of Hospitality and Tourism Technology, 8(2), 168-189.

Bao, Y., Xiong, T., Hu, Z., & Kibelloh, M. (2013). Exploring gender differences on general and specific computer self-efficacy in mobile learning adoption. Journal of Educational Computing Research, 49(1), 111-132.

Barhoumi, C. (2016). User acceptance of the e-information service as information resource: A new extension of the technology acceptance model. New Library World, 117(9/10), 626-643.

Barnett, T., Kellermanns, F.W., Pearson, A.W., & Pearson, R.A. (2006). Measuring information system usage: Replication and extensions. Journal of Computer Information Systems, 47(2), 76-85.

Belanche, D., Casaló, L.V., & Flavián, C. (2012). Integrating trust and personal values into the Technology Acceptance Model: The case of e-government services adoption. Cuadernos de Economía y Dirección de la Empresa, 15(4), 192-204.

Bere, A., & Rambe, P. (2013, June). Extending technology acceptance model in mobile learning adoption: South African University of Technology students’ perspectives’. In International Conference on e-Learning (52-61). Academic Conferences International Limited.

Bhatiasevi, V., & Krairit, D. (2013). Acceptance of open source software amongst Thai users: an integrated model approach. Information Development, 29(4), 349-366.

Bhatiasevi, V., & Naglis, M. (2016). Investigating the structural relationship for the determinants of cloud computing adoption in education. Education and Information Technologies, 21(5), 1197-1223.

Calisir, F., Altin Gumussoy, C., Bayraktaroglu, A. E., & Karaali, D. (2014). Predicting the intention to use a web‐based learning system: Perceived content quality, anxiety, perceived system quality, image, and the technology acceptance model. Human Factors and Ergonomics in Manufacturing & Service Industries, 24(5), 515-531.

Cegarra-Navarro, J.G., Eldridge, S., Martinez-Caro, E., Teresa, M., & Polo, S. (2014). The value of extended framework of TAM in the electronic government services. Electronic Journal of Knowledge Management, 12(1), 14-24.

Cha, J. (2013). Predictors of television and online video platform use: A coexistence model of old and new video platforms. Telematics and Informatics, 30(4), 296-310.

Chang, C.C., & Chen, P.Y. (2018). Analysis of critical factors for social games based on extended technology acceptance model: a DEMATEL approach. Behaviour & Information Technology, 1-12.

Chang, C.C., Yan, C.F., & Tseng, J.S. (2012). Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. Australasian Journal of Educational Technology, 28(5).

Chin, J., & Lin, S.C. (2015). Investigating users’ perspectives in building energy management system with an extension of technology acceptance model: A case study in indonesian manufacturing companies. Procedia Computer Science, 72, 31-39.

Chin, J., & Lin, S.C. (2016). A behavioral model of managerial perspectives regarding technology acceptance in building energy management systems. Sustainability, 8(7), 641.

Cho, V., Cheng, T.C.E., & Hung, H. (2009). Continued usage of technology versus situational factors: An empirical analysis. Journal of Engineering and Technology Management, 26(4), 264-284.

Davis, F. (1986). A technology acceptance model for empirically testing new-end-user information systems: Theory and results. Massachusetts, United States: Sloan School of Management, Massachusetts Institute of Technology.

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.

Di Benedetto, C.A., Calantone, R.J., & Zhang, C. (2003). International technology transfer: Model and exploratory study in the People's Republic of China. International Marketing Review, 20(4), 446-462.

Dishaw, M.T., & Strong, D.M. (1999). Extending the technology acceptance model with task–technology fit constructs. Information & management, 36(1), 9-21.

Dumpit, D.Z., & Fernandez, C.J. (2017). Analysis of the use of social media in Higher Education Institutions (HEIs) using the Technology Acceptance Model. International Journal of Educational Technology in Higher Education, 14(1), 5.

Dutta, B., Peng, M.H., & Sun, S.L. (2018). Modeling the adoption of personal health record (PHR) among individual: the effect of health-care technology self-efficacy and gender concern. Libyan Journal of Medicine, 13(1).

Egea, J.M.O., & González, M.V.R. (2011). Explaining physicians’ acceptance of EHCR systems: An extension of TAM with trust and risk factors. Computers in Human Behavior, 27(1), 319-332.

Fathali, S., & Okada, T. (2018). Technology acceptance model in technology-enhanced OCLL contexts: A self-determination theory approach. Australasian Journal of Educational Technology, 34(4).

Fayad, R., & Paper, D. (2015). The technology acceptance model e-commerce extension: a conceptual framework. Procedia Economics and Finance, 26, 1000-1006.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behaviour: An introduction to theory and research. Reading, Mass; Don Mills, Ontario: Addison-Weley Pub. Co.

Gefen, D., & Keil, M. (1998). The impact of developer responsiveness on perceptions of usefulness and ease of use: an extension of the technology acceptance model. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 29(2), 35-49.

Gefen, D., & Straub, D.W. (1997). Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MIS quarterly, 389-400.

Ghazizadeh, M., Lee, J.D., & Boyle, L.N. (2012). Extending the Technology Acceptance Model to assess automation. Cognition, Technology & Work, 14(1), 39-49.

Gillenson, M.L., & Sherrell, D.L. (2002). Enticing online consumers: an extended technology acceptance perspective. Information & management, 39(8), 705-719.

Govender, I., & Rootman-le Grange, I. (2015, October). Evaluating the Early Adoption of Moodle at a Higher Education Institution. In European Conference on e-Learning (p. 230). Academic Conferences International Limited.

Gupta, R., & Jain, K. (2015). Adoption behavior of rural India for mobile telephony: A multigroup study. Telecommunications Policy, 39(8), 691-704.

Ha, I., Yoon, Y., & Choi, M. (2007). Determinants of adoption of mobile games under mobile broadband wireless access environment. Information & Management, 44(3), 276-286.

Hernandez, B., Jimenez, J., & José Martín, M. (2009). Adoption vs acceptance of e-commerce: two different decisions. European Journal of Marketing, 43(9/10), 1232-1245.

Huang, L.K. (2017). A cultural model of online banking adoption: Long-term orientation perspective. Journal of Organizational and End User Computing (JOEUC), 29(1), 1-22.

Irani, Z., Dwivedi, Y.K., & Williams, M.D. (2009). Understanding consumer adoption of broadband: an extension of the technology acceptance model. Journal of the Operational Research Society, 60(10), 1322-1334.

Jackson, C.M., Chow, S., & Leitch, R.A. (1997). Toward an understanding of the behavioral intention to use an information system. Decision sciences, 28(2), 357-389.

Janiūnaitė, B. (2004). Edukacinės novacijos ir jų diegimas. Kaunas: Technologija.

Kabir, M.A., Saidin, S.Z., & Ahmi, A. (2017, October). An extension of technology acceptance model to determine factors that influence the intention to use electronic collection system in Nigerian federal hospitals. In AIP Conference Proceedings (Vol. 1891, No. 1, p. 020072). AIP Publishing.

Karavasilis, I., Vrana, V. G., & Zafiropoulos, K. (2016). An Extended Model of E-Government Adoption by Civil Servants in Greece. International Journal of Electronic Government Research (IJEGR), 12(1), 1-23.

Karjaluoto, H., Töllinen, A., Pirttiniemi, J., & Jayawardhena, C. (2014). Intention to use mobile customer relationship management systems. Industrial Management & Data Systems, 114(6), 966-978.

Kim, Y.J., Chun, J.U., & Song, J. (2009). Investigating the role of attitude in technology acceptance from an attitude strength perspective. International Journal of Information Management, 29(1), 67-77.

Kitchen, P.J., Martin, R., & Che-Ha, N. (2015). Long term evolution mobile services and intention to adopt: a Malaysian perspective. Journal of Strategic Marketing, 23(7), 643-654.

Kowalewski, S., Arning, K., Minwegen, A., Ziefle, M., & Ascheid, G. (2013). Extending the engineering trade-off analysis by integrating user preferences in conjoint analysis. Expert Systems with Applications, 40(8), 2947-2955.

Lai, P.C. (2017). The literature review of technology adoption models and theories for the novelty technology. JISTEM-Journal of Information Systems and Technology Management, 14(1), 21-38.

Lau, S.H., & Woods, P.C. (2009). Understanding learner acceptance of learning objects: The roles of learning object characteristics and individual differences. British journal of educational technology, 40(6), 1059-1075.

Lee, E.Y., Lee, S.B., & Jeon, Y.J.J. (2017). Factors influencing the behavioral intention to use food delivery apps. Social Behavior and Personality: an international journal, 45(9), 1461-1473.

Lee, S.M., & Chen, L. (2010). The impact of flow on online consumer behavior. Journal of Computer Information Systems, 50(4), 1-10.

Lee, W., Xiong, L., & Hu, C. (2012). The effect of Facebook users’ arousal and valence on intention to go to the festival: Applying an extension of the technology acceptance model. International Journal of Hospitality Management, 31(3), 819-827.

Lee, Y.C. (2006). An empirical investigation into factors influencing the adoption of an e-learning system. Online information review, 30(5), 517-541.

Lin, F.T., Wu, H.Y., & Nga, T.T.N. (2013, September). Adoption of Internet banking: An empirical study in Vietnam. In E-Business Engineering (ICEBE), 2013 IEEE 10th International Conference on (pp. 282-287). IEEE.

Lin, F.T., Wu, H.Y., & Tran, T.N.N. (2015). Internet banking adoption in a developing country: an empirical study in Vietnam. Information Systems and e-Business Management, 13(2), 267-287.

Liu, D., Lu, W., & Niu, Y. (2018). Extended Technology-Acceptance Model to Make Smart Construction Systems Successful. Journal of Construction Engineering and Management, 144(6), 04018035.

Liu, L., & Ma, Q. (2005). The impact of service level on the acceptance of application service oriented medical records. Information & Management, 42(8), 1121-1135.

Liu, Y.C., Lin, C., Huang, Y., & Liu, CW. (2009). Extending the Technology Acceptance Model in a Context of Integrating Technology into a University Classroom. Creating Global Economies through Innovation and Knowledge Management: Theory & Practice, 550-563.

Lowry, P.B., Gaskin, J., Twyman, N., Hammer, B., & Roberts, T. (2012). Taking ‘fun and games’ seriously: Proposing the hedonic-motivation system adoption model (HMSAM).

Lwoga, E.T., & Lwoga, N.B. (2017). User Acceptance of Mobile Payment: The Effects of User‐Centric Security, System Characteristics and Gender. The Electronic Journal of Information Systems in Developing Countries, 81(1), 1-24.

Matemba, E.D., & Li, G. (2018). Consumers' willingness to adopt and use WeChat wallet: An empirical study in South Africa. Technology in Society, 53, 55-68.

Melas, C.D., Zampetakis, L.A., Dimopoulou, A., & Moustakis, V. (2011). Modeling the acceptance of clinical information systems among hospital medical staff: an extended TAM model. Journal of biomedical informatics, 44(4), 553-564.

Muthitcharoen, A., Palvia, P.C., & Grover, V. (2011). Building a model of technology preference: The case of channel choices. Decision Sciences, 42(1), 205-237.

Nagy, J.T. (2018). Evaluation of online video usage and learning satisfaction: An extension of the technology acceptance model. The International Review of Research in Open and Distributed Learning, 19(1).

Nasir, S., & Yurder, Y. (2015). Consumers’ and physicians’ perceptions about high tech wearable health products. Procedia-Social and Behavioral Sciences, 195, 1261-1267.

Naspetti, S., Mandolesi, S., Buysse, J., Latvala, T., Nicholas, P., Padel, S., ... & Zanoli, R. (2017). Determinants of the acceptance of sustainable production strategies among dairy farmers: Development and testing of a modified technology acceptance model. Sustainability, 9(10), 1805.

Nasser AL-Subari, Saleh & Mohamed Zabri, Shafie & Ahmad, Kamilah. (2018). Factors Influencing Online Banking Adoption: The Case of Academicians in Malaysian Technical University Network (MTUN). Advanced Science Letters. 24. 3193-3197. DOI: 10.1166/asl.2018.11342.

Oh, H., Jeong, M., Lee, S., & Warnick, R. (2016). Attitudinal and situational determinants of self-service technology use. Journal of Hospitality & Tourism Research, 40(2), 236-265.

Patel, K.J., & Patel, H.J. (2018). Adoption of internet banking services in Gujarat: An extension of TAM with perceived security and social influence. International Journal of Bank Marketing, 36(1), 147-169.

Qi, J., Li, L., Li, Y., & Shu, H. (2009). An extension of technology acceptance model: Analysis of the adoption of mobile data services in China. Systems Research and Behavioral Science: The Official Journal of the International Federation for Systems Research, 26(3), 391-407.

Räckers, M., Hofmann, S., & Becker, J. (2013, September). The influence of social context and targeted communication on e-government service adoption. In International Conference on Electronic Government (pp. 298-309). Springer, Berlin, Heidelberg.

Ramkumar, M., & Jenamani, M. (2015). Organizational Buyers’ Acceptance of Electronic Procurement Services—An Empirical Investigation in Indian Firms. Service Science, 7(4), 272-293.

Rawashdeh, A. (2015). Factors affecting adoption of internet banking in Jordan: Chartered accountant’s perspective. International Journal of Bank Marketing, 33(4), 510-529.

Rejón-Guardia, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2011). Motivational Factors that influence the Acceptance of Microblogging Social Networks: The µBAM Model (No. 06/11). Faculty of Economics and Business (University of Granada).

Rigopoulou, I.D., Chaniotakis, I.E., & Kehagias, J.D. (2017). An extended technology acceptance model for predicting smartphone adoption among young consumers in Greece. International Journal of Mobile Communications, 15(4), 372-387.

Rogers, E.M. (2010). Diffusion of innovations. New York: Free Press.

Saadé, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model. Information & management, 42(2), 317-327.

Saeed, K.A., & Abdinnour-Helm, S. (2008). Examining the effects of information system characteristics and perceived usefulness on post adoption usage of information systems. Information & Management, 45(6), 376-386.

Salajan, F. & Welch, A. & Peterson, Cl. & Ray N.C. (2011). Faculty Perceptions of Teaching Quality and Peer Influence in the Utilization of Learning Technologies: An Extension of the Technology Acceptance Model. World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2011, 2500-2509.

Shan, S., Shen, H. & Lu, X. (2008). Evaluating business intelligence acceptance with the technology acceptance model. 38th International Conference on Computers and Industrial Engineering 2008, 3, 2756-2767.

Shih, Y.Y., & Huang, S.S. (2009). The actual usage of ERP systems: An extended technology acceptance perspective. Journal of Research and Practice in Information Technology, 41(3), 263.

Shin, D.H. (2008). Understanding purchasing behaviors in a virtual economy: Consumer behavior involving virtual currency in Web 2.0 communities. Interacting with computers, 20(4-5), 433-446.

Shin, D.H., Biocca, F., & Choo, H. (2013). Exploring the user experience of three-dimensional virtual learning environments. Behaviour & Information Technology, 32(2), 203-214.

Son, H., Park, Y., Kim, C., & Chou, J.S. (2012). Toward an understanding of construction professionals' acceptance of mobile computing devices in South Korea: An extension of the technology acceptance model. Automation in construction, 28, 82-90.

Stern, B.B., Royne, M.B., Stafford, T.F., & Bienstock, C.C. (2008). Consumer acceptance of online auctions: An extension and revision of the TAM. Psychology & Marketing, 25(7), 619-636.

Sternad, S., Gradisar, M., & Bobek, S. (2011). The influence of external factors on routine ERP usage. Industrial Management & Data Systems, 111(9), 1511-1530.

Schwab, K. (2016). The fourth industrial revolution. New York: Crown Business.

Tamboli, M.A., & Biswas, P.K. (2015, October). Mobile Learning Applications' Acceptance Model (MLAAM). In Computing and Communication (IEMCON), 2015 International Conference and Workshop on (pp. 1-6). IEEE.

Tan, G. W. H., Ooi, K. B., Chong, S. C., & Hew, T. S. (2014). NFC mobile credit card: the next frontier of mobile payment? Telematics and Informatics, 31(2), 292-307.

Tarhini, A., Hone, K., & Liu, X. (2013). User acceptance towards web-based learning systems: Investigating the role of social, organizational and individual factors in European higher education. Procedia Computer Science, 17, 189-197.

Tarhini, A., Hone, K., & Liu, X. (2014). Measuring the moderating effect of gender and age on e-learning acceptance in England: A structural equation modeling approach for an extended technology acceptance model. Journal of Educational Computing Research, 51(2), 163-184.

Tarhini, A., Hone, K., & Liu, X. (2014). The effects of individual differences on e-learning users’ behaviour in developing countries: A structural equation model. Computers in Human Behavior, 41, 153-163.

Teo, T. (2016). Modelling Facebook usage among university students in Thailand: the role of emotional attachment in an extended technology acceptance model. Interactive Learning Environments, 24(4), 745-757.

Teo, T., Doleck, T., & Bazelais, P. (2018). The role of attachment in Facebook usage: a study of Canadian college students. Interactive Learning Environments, 26(2), 256-272.

Teo, T., Milutinović, V., Zhou, M., & Banković, D. (2017). Traditional vs. innovative uses of computers among mathematics pre-service teachers in Serbia. Interactive Learning Environments, 25(7), 811-827.

Tseng, A.H., & Hsia, J.W. (2008, September). The impact of internal locus of control on perceived usefulness and perceived ease of use in e-learning: An extension of the technology acceptance model. In Cyberworlds, 2008 International Conference on (815-819). IEEE.

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., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273-315.

Venkatesh, V., & Davis, F.D. (1986). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481.

Venkatesh, V., & Davis, F.D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.

Venkatesh, V., Morris, M.G., Davis, G.B., & Davis, F.D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.

Wahyuni, R. (2017, August). Explaining acceptance of e-health services: An extension of TAM and health belief model approach. In Cyber and IT Service Management (CITSM), 2017 5th International Conference on (1-7). IEEE.

Web of Science. Accessed through https://clarivate.com/products/web-of-science/

Werber, B., Baggia, A., & Žnidaršič, A. (2018). Factors Affecting the Intentions to Use RFID Subcutaneous Microchip Implants for Healthcare Purposes. Organizacija, 51(2), 121-133.

YeolShim, G. (2015). An Empirical Study on Factors Affecting Customer Adoption of Virtual Store in Extended Technology Acceptance Model: Focusing on the role of Trust and Playfulness. International Conference on Business and Economics (ICBE 2015), 381-382.

Zeba, F., & Ganguli, S. (2016). Word-Of-Mouth, Trust, and Perceived Risk in Online Shopping: An Extension of the Technology Acceptance Model. International Journal of Information Systems in the Service Sector (IJISSS), 8(4), 17-32.

Zhang, N., Guo, X., & Chen, G. (2008, September). An Extended IT Adoption Model and Two Empirical Studies in Chinese Cultural Contexts. In Advanced Management of Information for Globalized Enterprises, 2008. AMIGE 2008. IEEE Symposium on (pp. 1-5). IEEE.

Zhang, X. (2013). Income disparity and digital divide: The Internet Consumption Model and cross-country empirical research. Telecommunications Policy, 37(6-7), 515-529.




DOI: https://doi.org/10.17770/sie2019vol5.3798

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