The Effect of Revisiting Technology Acceptance Model on the Behavioral Targeting Declaration
Main Article Content
Abstract
The technology acceptance model (TAM) encompasses seven factors that contribute to its effectiveness: subjective norms, output quality, voluntariness, job relevance, image, result demonstrability, and experience. Nevertheless, the evolution of the technology acceptance model into TAM2 resulted in a significant boost in its explanatory power, surpassing the original TAM by 20%. Various industries, including electronic commerce, have embraced this model. Exploring the role of the TAM2 model in social commerce is a crucial topic discussed in this article.
Article Details

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Afrasiabi Rad, A., & Benyoucef, M. (2011). A model for understanding social commerce. Journal of Information Systems Applied Research, 4(2), 63.
Antonietti, C., Cattaneo, A., & Amenduni, F. (2022). Can teachers’ digital competence influence technology acceptance in vocational education?. Computers in Human Behavior, 132, 107266. https://doi.org/10.1016/j.chb.2022.107266
Baby, A., & Kannammal, A. (2020). Network Path Analysis for developing an enhanced TAM model: A user-centric e-learning perspective. Computers in Human Behavior, 107, 106081. https://doi.org/10.1016/j.chb.2019.07.024
Baier, D., & Stüber, E. (2010). Acceptance of recommendations to buy in online retailing. Journal of Retailing and Consumer Services, 17(3), 173-180. https://doi.org/10.1016/j.jretconser.2010.03.005
Busalim, A. H. (2016). Understanding social commerce: A systematic literature review and directions for further research. International Journal of Information Management, 36(6), 1075-1088. https://doi.org/10.1016/j.ijinfomgt.2016.06.005
Chang, G., & Caneday, L. (2011). Web-based GIS in tourism information search: Perceptions, tasks, and trip attributes. Tourism Management, 32(6), 1435-1437.
Choi, J. A., & Lim, K. (2020). Identifying machine learning techniques for classification of target advertising. ICT Express, 6(3), 175-180. https://doi.org/10.1016/j.icte.2020.04.012
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. https://doi.org/10.2307/249008
Dönmez-Turan, A., & Kır, M. (2019). User anxiety as an external variable of technology acceptance model: A meta-analytic study. Procedia Computer Science, 158, 715-724. https://doi.org/10.1016/j.procs.2019.09.107
Elhajjar, S., & Ouaida, F. (2019). An analysis of factors affecting mobile banking adoption. International Journal of Bank Marketing, 38(2), 352-367.
Elshafey, A., Saar, C. C., Aminudin, E. B., Gheisari, M., & Usmani, A. (2020). Technology acceptance model for Augmented Reality and Building Information Modeling integration in the construction industry. J. Inf. Technol. Constr., 25, 161-172.
En, A., Govindarajo, N., & Dileep, M. (2021, May). Factors of Purchase Intention Toward Social Commerce Among Young Generation in Malaysia. In Proceedings of the 1st International Conference on Law, Social Science, Economics, and Education, ICLSSEE 2021, March 6th 2021, Jakarta, Indonesia.
Eslami, M., Krishna Kumaran, S. R., Sandvig, C., & Karahalios, K. (2018, April). Communicating algorithmic process in online behavioral advertising. In Proceedings of the 2018 CHI conference on human factors in computing systems (pp. 1-13). https://doi.org/10.1145/3173574.3174006
Esteban-Millat, I., Martínez-López, F. J., Pujol-Jover, M., Gázquez-Abad, J. C., & Alegret, A. (2018). An extension of the technology acceptance model for online learning environments. Interactive Learning Environments, 26(7), 895-910. https://doi.org/10.1080/10494820.2017.1421560
Estrada-Jiménez, J., Parra-Arnau, J., Rodríguez-Hoyos, A., & Forné, J. (2019). On the regulation of personal data distribution in online advertising platforms. Engineering Applications of Artificial Intelligence, 82, 13-29. https://doi.org/10.1016/j.engappai.2019.03.013
Farahat, T. (2012). Applying the technology acceptance model to online learning in the Egyptian universities. Procedia-Social and Behavioral Sciences, 64, 95-104.
Faroqi, H., Mesbah, M., & Kim, J. (2019). Behavioural advertising in the public transit network. Research in Transportation Business & Management, 32, 100421. https://doi.org/10.1016/j.rtbm.2019.100421
Fuentes Martinez, A. (2020). From a technology acceptance model to a practice acceptance model. Ars Educandi, 17(3), 61-66.
Goldberg, R., & Kotze, A. (2022). The Influence Of Reference Groups On Millennials’social Commerce Buying Behaviour. Malaysian E Commerce Journal (Mecj), 6(1), 24-28.
Gupta, K. P., Singh, S., & Bhaskar, P. (2016). Citizen adoption of e-government: a literature review and conceptual framework. Electronic Government, an International Journal, 12(2), 160-185. https://doi.org/10.1504/EG.2016.076134
Hua, L., & Wang, S. (2019). Antecedents of consumers’ intention to purchase energy-efficient appliances: An empirical study based on the technology acceptance model and theory of planned behavior. Sustainability, 11(10), 2994.
Huang, Z., & Benyoucef, M. (2013). From e-commerce to social commerce: A close look at design features. Electronic Commerce Research and Applications, 12(4), 246-259. https://doi.org/10.1016/j.elerap.2012.12.003
Hussein, Z. (2017). Leading to intention: The role of attitude in relation to technology acceptance model in e-learning. Procedia Computer Science, 105, 159-164.
Iqbal, J., & Sidhu, M. S. (2019). A taxonomic overview and pilot study for evaluation of augmented reality based posture matching technique using technology acceptance model. Procedia Computer Science, 163, 345-351. https://doi.org/10.1016/j.procs.2019.12.117
Izuagbe, R., Ifijeh, G., Izuagbe-Roland, E. I., Olawoyin, O. R., & Ogiamien, L. O. (2019). Determinants of perceived usefulness of social media in university libraries: Subjective norm, image and voluntariness as indicators. The Journal of Academic Librarianship, 45(4), 394-405. https://doi.org/10.1016/j.acalib.2019.03.006
Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212. https://doi.org/10.1016/j.techsoc.2019.101212
Khalifa, M., & Ning, K. (2008). Demographic changes in is research productivity and impact. Communications of the ACM, 51(4), 89-94.
Lee, J., Kim, J., & Choi, J. Y. (2019). The adoption of virtual reality devices: The technology acceptance model integrating enjoyment, social interaction, and strength of the social ties. Telematics and Informatics, 39, 37-48. https://doi.org/10.1016/j.tele.2018.12.006
Lian, S., Cha, T., & Xu, Y. (2019). Enhancing geotargeting with temporal targeting, behavioral targeting and promotion for comprehensive contextual targeting. Decision Support Systems, 117, 28-37. Https://doi.org/10.1016/j.dss.2018.12.004
LIU, J. Y., Wang, C., Zhang, R., & Zhao, X. Y. (2014). Research on behavioral targeted advertising in mobile Internet. The Journal of China Universities of Posts and Telecommunications, 21, 1-5.
Liu, Y., Li, H., & Carlsson, C. (2010). Factors driving the adoption of m-learning: An empirical study. Computers & Education, 55(3), 1211-1219. https://doi.org/10.1016/j.compedu.2010.05.018
Llewellyn, G. (2020). Social commerce 2021 trends: tactics and tools to grow your e-commerce strategy through social media. Smart Insights.
Maia, C., Lunardi, G., Longaray, A., & Munhoz, P. (2018). Factors and characteristics that influence consumers’ participation in social commerce. Revista de Gestão, 25(2), 194-211. https://doi.org/10.1108/REGE-03-2018-031
Makudza, F., Sandada, M., & Madzikanda, D. (2021). Augmenting social commerce acceptance through an all-inclusive approach to social commerce drivers. Evidence from the hotel industry. Malaysian Journal of E-Commerce, 5(2), 55-63.
Mijoska, M. (2017). Applying TAM to study online shopping adoption among youth in the Republic of Macedonia.
Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information systems research, 2(3), 192-222.
Mpinganjira, M., & Maduku, D. K. (2019). Ethics of mobile behavioral advertising: Antecedents and outcomes of perceived ethical value of advertised brands. Journal of Business Research, 95, 464-478. https://doi.org/10.1016/j.jbusres.2018.07.037
Oturakci, M., & Oturakci, M. (2018). Developing New Technology Acceptance Model With Multi-Criteria Decision Technique: An Implementation Study. Engineering Management Research, 7(2), 43.
Ozcelik, Ayse Bengi; Varnali, Kaan (2018). Effectiveness of Online Behavioral Targeting: A Psychological Perspective. Electronic Commerce Research and Applications, (), S156742231830084X–. https://doi.org/10.1016/j.elerap.2018.11.006
Paramaeswari, R. P. I., & Sarno, R. (2020, September). Analysis of e-commerce (Bukalapak, Shopee, and Tokopedia) acceptance models using TAM2 method. In 2020 International Seminar on Application for Technology of Information and Communication (iSemantic) (pp. 505-510). IEEE. https://doi.org/10.1109/iSemantic50169.2020.9234271
Purnama, I. W. J. W., & Ginardi, R. V. H. (2019). Analysis of Application Based on Cloud Computing in Banking Industries in Indonesia Using Technology Acceptance Model (TAM) 2 Method Case Study The National Private Banks in Surabaya and Bali Region. IPTEK Journal of Proceedings Series, (5), 519-526. http://dx.doi.org/10.12962/j23546026.y2019i5.6425
Rahaman, S. U., Badugula, N. M., Wang, T. W., & Somarajan, N. C. (2018). The current development of technology model in e-commerce and suggestion for future research. MWAIS 2018 Proceedings, 27.
Rezaei, R., Safa, L., & Ganjkhanloo, M. M. (2020). Understanding farmers’ ecological conservation behavior regarding the use of integrated pest management-an application of the technology acceptance model. Global Ecology and Conservation, 22, e00941. https://doi.org/10.1016/j.gecco.2020.e00941
Rouibah, K., & Al-Qirim, N. (2017). Factors affecting social ecommerce adoption in an Arab country: Findings from a qualitative study. Issues in Information Systems, 18(2).
Rui-Hsin, K., & Lin, C. T. (2018). The usage intention of e-learning for police education and training. Policing: an international journal.
Saahar, S., Ahmad, R. P. N., Omar, N. H., & Hanim, P. S. (2019). Online Behavioral Targeting (OBT) and its Influence on Young Malaysian Consumers Experience. International Journal of Social Science Research, 1(1), 34-46
Sagnier, C., Loup-Escande, E., Lourdeaux, D., Thouvenin, I., & Valléry, G. (2020). User acceptance of virtual reality: an extended technology acceptance model. International Journal of Human–Computer Interaction, 36(11), 993-1007. https://doi.org/10.1080/10447318.2019.1708612
Schierz, P. G., Schilke, O., & Wirtz, B. W. (2010). Understanding consumer acceptance of mobile payment services: An empirical analysis. Electronic commerce research and applications, 9(3), 209-216. https://doi.org/10.1016/j.elerap.2009.07.005
Sharif, S. P., & Naghavi, N. (2021). Online financial trading among young adults: integrating the theory of planned behavior, technology acceptance model, and theory of flow. International Journal of Human–Computer Interaction, 37(10), 949-962. https://doi.org/10.1080/10447318.2020.1861761
Shirazi, F., Adam, N. A., Shanmugam, M., & Schultz, C. D. (2021). The importance of trust for electronic commerce satisfaction: an entrepreneurial perspective. British Food Journal, 123(2), 789-802. https://doi.org/10.1108/BFJ-07-2020-0626
Singer, D., Avery, A., & Baradwaj, B. (2008). Management innovation and cultural adaptivity in international online banking. Management research news, 31(4), 258-272. https://doi.org/10.1108/01409170810851339
Smith, S. M., Zhao, J., & Alexander, M. (2013). Social commerce from a theory of planned behavior paradigm: An analysis of purchase intention. International Journal of E-Adoption (IJEA), 5(3), 76-88. https://doi.org/10.4018/ijea.2013070104
Sonntag, M., Mehmann, J., & Teuteberg, F. (2022). AI-based conversational agents for customer service–A study of customer service representative’perceptions using TAM 2.
Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & Hakim, H. (2020). Using an extended Technology Acceptance Model to understand students’ use of e-learning during Covid-19: Indonesian sport science education context. Heliyon, 6(11), e05410.
Usman, H., Mulia, D., Chairy, C., & Widowati, N. (2020). Integrating trust, religiosity and image into technology acceptance model: the case of the Islamic philanthropy in Indonesia. Journal of Islamic Marketing.
Utami, T. L. W. (2021). Technology adoption on online learning during Covid-19 pandemic: implementation of technology acceptance model (TAM). Diponegoro International Journal of Business, 4(1), 8-19. https://doi.org/10.14710/dijb.4.1.2021.8-19
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
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. https://doi.org/10.2307/3250981
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. https://doi.org/10.2307/30036540
Verma, S., Bhattacharyya, S. S., & Kumar, S. (2018). An extension of the technology acceptance model in the big data analytics system implementation environment. Information Processing & Management, 54(5), 791-806. https://doi.org/10.1016/j.ipm.2018.01.004
Vijayasarathy, L. R. (2004). Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model. Information & management, 41(6), 747-762. https://doi.org/10.1016/j.im.2003.08.011
Wang, Y., Rod, M., Ji, S., & Deng, Q. (2019). Applying Bourdieu's Practice Theory to Social Commerce: toward a Dynamic Process-Oriented Research Framework. In CONF-IRM (p. 35).
Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information systems research, 16(1), 85-102. https://doi.org/10.1287/isre.1050.0042
Wu, J., & Song, S. (2021). Older adults’ online shopping continuance intentions: Applying the technology acceptance model and the theory of planned behavior. International Journal of Human–Computer Interaction, 37(10), 938-948. https://doi.org/10.1080/10447318.2020.1861419
Wu, Y., Xu, Y., & Li, J. (2021). Fraudulent traffic detection in online advertising with bipartite graph propagation algorithm. Expert Systems with Applications, 185, 115573.
Xiong, W., Xiong, Z., & Tian, T. (2022). Who to show the ad to? Behavioral targeting in Internet advertising. Journal of Internet and Digital Economics, 2(1), 15-26. https://doi.org/10.1108/JIDE-12-2021-0023
Yang, J., Zhao, X., Fan, J., Chen, G., Peng, C., Yao, S., & Du, X. (2021, April). A human-in-the-loop approach to social behavioral targeting. In 2021 IEEE 37th International Conference on Data Engineering (ICDE) (pp. 277-288). IEEE.
Yang, Y., & Wang, X. (2019). Modeling the intention to use machine translation for student translators: An extension of Technology Acceptance Model. Computers & Education, 133, 116-126. https://doi.org/10.1016/j.compedu.2019.01.015
Yoon, C. (2009). The effects of national culture values on consumer acceptance of e-commerce: Online shoppers in China. Information & management, 46(5), 294-301. https://doi.org/10.1016/j.im.2009.06.001
Zaineldeen, S., Hongbo, L., Koffi, A. L., & Hassan, B. M. A. (2020). Technology acceptance model'concepts, contribution, limitation, and adoption in education. Universal Journal of Educational Research, 8(11), 5061-5071.
Zhang, K. Z., & Benyoucef, M. (2016). Consumer behavior in social commerce: A literature review. Decision support systems, 86, 95-108. https://doi.org/10.1016/j.dss.2016.04.001
Zhang, T., Shen, D., Zheng, S., Liu, Z., Qu, X., & Tao, D. (2020). Predicting unsafe behaviors at nuclear power plants: An integration of Theory of Planned Behavior and Technology Acceptance Model. International Journal of Industrial Ergonomics, 80, 103047. https://doi.org/10.1016/j.ergon.2020.103047
Zhu, M., & Zhang, Y. (2022). Medical and public health instructors’ perceptions of online teaching: A qualitative study using the Technology Acceptance Model 2. Education and Information Technologies, 27(2), 2385-2405. https://doi.org/10.1007/s10639-021-10681-2