Main Article Content
Abstract
This article presents the results of the mapping carried out taken from various sources, with a total of 34 papers used as mapping materials. Based on the existing theory, TAM consists of several factors, namely risk factors, usability, and added value. The community can accept the stronger these three factors, the more robust technology. These factors can lead to an acceptance of feelings of emotional value or commonly refer to as perceived emotional value. A person can receive emotional value after using the technology, and an emotional value can occur because of innovation. These two things can make a decision someone will use the technology. This study aims to determine the symmetrical and asymmetrical relationship between TAM and Emotional Value, Service Innovators, and also Consumer Purchase Decisions.
Keywords
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References
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- Ashraf, A. R., & Thongpapanl, N. T. (2015). Connecting with and Converting Shoppers into Customers: Investigating the Role of Regulatory Fit in the Online Customer’s Decision-making Process. Journal of Interactive Marketing, 32, 13–25. https://doi.org/10.1016/j.intmar.2015.09.004
- Assauri, S. (2008). Manajemen produksi dan operasi.
- Casidy, R., Nyadzayo, M., & Mohan, M. (2020). Service innovation and adoption in industrial markets: An SME perspective. Industrial Marketing Management, 89(June), 157–170. https://doi.org/10.1016/j.indmarman.2019.06.008
- Celuch, K., Goodwin, S., & Taylor, S. A. (2007). Understanding small scale industrial user internet purchase and information management intentions: A test of two attitude models. Industrial Marketing Management, 36(1), 109–120. https://doi.org/10.1016/j.indmarman.2005.08.004
- 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.
- Delgado, M., & Mills, K. G. (2020). The supply chain economy: A new industry categorization for understanding innovation in services. Research Policy, 49(8), 104039. https://doi.org/10.1016/j.respol.2020.104039
- Distler, V., Lallemand, C., & Koenig, V. (2020). How Acceptable Is This? How User Experience Factors Can Broaden our Understanding of The Acceptance of Privacy Trade-offs. Computers in Human Behavior, 106, 106227. https://doi.org/10.1016/j.chb.2019.106227
- Elwalda, A., Lü, K., & Ali, M. (2016). Perceived derived attributes of online customer reviews. Computers in Human Behavior, 56, 306–319. https://doi.org/10.1016/j.chb.2015.11.051
- Feng, C., & Ma, R. (2020). Identification of the factors that influence service innovation in manufacturing enterprises by using the fuzzy DEMATEL method. Journal of Cleaner Production, 253. https://doi.org/10.1016/j.jclepro.2020.120002
- Gong, T., Park, J. K., & Hyun, H. (2020). Customer response toward employees’ emotional labor in service industry settings. Journal of Retailing and Consumer Services, 52(July 2019), 101899. https://doi.org/10.1016/j.jretconser.2019.101899
- Holmlund, M., Van Vaerenbergh, Y., Ciuchita, R., Ravald, A., Sarantopoulos, P., Ordenes, F. V., & Zaki, M. (2020). Customer experience management in the age of big data analytics: A strategic framework. Journal of Business Research, 116(January), 356–365. https://doi.org/10.1016/j.jbusres.2020.01.022
- Jang, H. Y., & Noh, M. J. (2011). Customer acceptance of IPTV service quality. International Journal of Information Management, 31(6), 582–592. https://doi.org/10.1016/j.ijinfomgt.2011.03.003
- Jeong, J. Y., Hwang, J., & Hyun, S. S. (2020). Customers’ relationships leading to brand tribalism and tribe behavioral intentions. International Journal of Hospitality Management, 88(January), 102529. https://doi.org/10.1016/j.ijhm.2020.102529
- Kotler, P. (2012). Kotler on marketing. Simon and Schuster.
- Kotler, S. (2014). The rise of superman: Decoding the science of ultimate human performance. Houghton Mifflin Harcourt.
- Kurnia, S., Choudrie, J., Mahbubur, R. M., & Alzougool, B. (2015). E-commerce technology adoption: A Malaysian grocery SME retail sector study. Journal of Business Research, 68(9), 1906–1918. https://doi.org/10.1016/j.jbusres.2014.12.010
- Kusumadewi, A. N., Lubis, N. A., Prastiyo, R., & Tamara, D. (2021). Technology Acceptance Model (TAM) in the Use of Online Learning Applications During the Covid-19 Pandemic for Parents of Elementary School Students. Edunesia: Jurnal Ilmiah Pendidikan, 2(1), 272–292.
- 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. https://doi.org/10.1016/j.ijhm.2011.09.018
- Mamonov, S., & Benbunan-Fich, R. (2017). Exploring factors affecting social e-commerce service adoption: The case of Facebook Gifts. International Journal of Information Management, 37(6), 590–600. https://doi.org/10.1016/j.ijinfomgt.2017.05.005
- Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81–95.
- Markovic, S., Jovanovic, M., Bagherzadeh, M., Sancha, C., Sarafinovska, M., & Qiu, Y. (2020). Priorities when selecting business partners for service innovation: The contingency role of product innovation. Industrial Marketing Management, 88(June), 378–388. https://doi.org/10.1016/j.indmarman.2020.06.001
- Mohammed, Z. A., & Tejay, G. P. (2017). Examining privacy concerns and ecommerce adoption in developing countries: The impact of culture in shaping individuals’ perceptions toward technology. Computers and Security, 67, 254–265. https://doi.org/10.1016/j.cose.2017.03.001
- Ortega Egea, J. M., & Román González, M. V. (2011). Explaining physicians’ acceptance of EHCR systems: An extension of TAM with trust and risk factors. Computers in Human Behavior, 27(1), 319–332. https://doi.org/10.1016/j.chb.2010.08.010
- Peña-García, N., Gil-Saura, I., Rodríguez-Orejuela, A., & Siqueira-Junior, J. R. (2020). Purchase intention and purchase behavior online: A cross-cultural approach. Heliyon, 6(6). https://doi.org/10.1016/j.heliyon.2020.e04284
- Porter, C. E., & Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59(9), 999–1007. https://doi.org/10.1016/j.jbusres.2006.06.003
- Sjödin, D., Parida, V., Kohtamäki, M., & Wincent, J. (2020). An agile co-creation process for digital servitization: A micro-service innovation approach. Journal of Business Research, 112(June 2019), 478–491. https://doi.org/10.1016/j.jbusres.2020.01.009
- Tseng, A. (2017). Why do online tourists need sellers’ ratings? Exploration of the factors affecting regretful tourist e-satisfaction. Tourism Management, 59, 413–424. https://doi.org/10.1016/j.tourman.2016.08.017
- Verma, P., & Sinha, N. (2018). Integrating perceived economic wellbeing to technology acceptance model: The case of mobile based agricultural extension service. Technological Forecasting and Social Change, 126(September 2016), 207–216. https://doi.org/10.1016/j.techfore.2017.08.013
- Wirtz, J. (2020). Organizational Ambidexterity: Cost-Effective Service Excellence, Service Robots, and Artificial Intelligence. Organizational Dynamics, 49(3). https://doi.org/10.1016/j.orgdyn.2019.04.005
- Xia, M., Zhang, Y., & Zhang, C. (2018). A TAM-based approach to explore the effect of online experience on destination image: A smartphone user’s perspective. Journal of Destination Marketing and Management, 8(April 2016), 259–270. https://doi.org/10.1016/j.jdmm.2017.05.002
- Xu, X., Li, Q., Peng, L., Hsia, T. L., Huang, C. J., & Wu, J. H. (2017). The impact of informational incentives and social influence on consumer behavior during Alibaba’s online shopping carnival. Computers in Human Behavior, 76, 245–254. https://doi.org/10.1016/j.chb.2017.07.018
- Zhao, Y., Wen, L., Feng, X., Li, R., & Lin, X. (2020). How managerial responses to online reviews affect customer satisfaction: An empirical study based on additional reviews. Journal of Retailing and Consumer Services, 57(June), 102205. https://doi.org/10.1016/j.jretconser.2020.102205
References
Arslanagic-Kalajdzic, M., Kadic-Maglajlic, S., & Miocevic, D. (2020). The power of emotional value: Moderating customer orientation effect in professional business services relationships. Industrial Marketing Management, 88(March), 12–21. https://doi.org/10.1016/j.indmarman.2020.04.017
Ashraf, A. R., & Thongpapanl, N. T. (2015). Connecting with and Converting Shoppers into Customers: Investigating the Role of Regulatory Fit in the Online Customer’s Decision-making Process. Journal of Interactive Marketing, 32, 13–25. https://doi.org/10.1016/j.intmar.2015.09.004
Assauri, S. (2008). Manajemen produksi dan operasi.
Casidy, R., Nyadzayo, M., & Mohan, M. (2020). Service innovation and adoption in industrial markets: An SME perspective. Industrial Marketing Management, 89(June), 157–170. https://doi.org/10.1016/j.indmarman.2019.06.008
Celuch, K., Goodwin, S., & Taylor, S. A. (2007). Understanding small scale industrial user internet purchase and information management intentions: A test of two attitude models. Industrial Marketing Management, 36(1), 109–120. https://doi.org/10.1016/j.indmarman.2005.08.004
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.
Delgado, M., & Mills, K. G. (2020). The supply chain economy: A new industry categorization for understanding innovation in services. Research Policy, 49(8), 104039. https://doi.org/10.1016/j.respol.2020.104039
Distler, V., Lallemand, C., & Koenig, V. (2020). How Acceptable Is This? How User Experience Factors Can Broaden our Understanding of The Acceptance of Privacy Trade-offs. Computers in Human Behavior, 106, 106227. https://doi.org/10.1016/j.chb.2019.106227
Elwalda, A., Lü, K., & Ali, M. (2016). Perceived derived attributes of online customer reviews. Computers in Human Behavior, 56, 306–319. https://doi.org/10.1016/j.chb.2015.11.051
Feng, C., & Ma, R. (2020). Identification of the factors that influence service innovation in manufacturing enterprises by using the fuzzy DEMATEL method. Journal of Cleaner Production, 253. https://doi.org/10.1016/j.jclepro.2020.120002
Gong, T., Park, J. K., & Hyun, H. (2020). Customer response toward employees’ emotional labor in service industry settings. Journal of Retailing and Consumer Services, 52(July 2019), 101899. https://doi.org/10.1016/j.jretconser.2019.101899
Holmlund, M., Van Vaerenbergh, Y., Ciuchita, R., Ravald, A., Sarantopoulos, P., Ordenes, F. V., & Zaki, M. (2020). Customer experience management in the age of big data analytics: A strategic framework. Journal of Business Research, 116(January), 356–365. https://doi.org/10.1016/j.jbusres.2020.01.022
Jang, H. Y., & Noh, M. J. (2011). Customer acceptance of IPTV service quality. International Journal of Information Management, 31(6), 582–592. https://doi.org/10.1016/j.ijinfomgt.2011.03.003
Jeong, J. Y., Hwang, J., & Hyun, S. S. (2020). Customers’ relationships leading to brand tribalism and tribe behavioral intentions. International Journal of Hospitality Management, 88(January), 102529. https://doi.org/10.1016/j.ijhm.2020.102529
Kotler, P. (2012). Kotler on marketing. Simon and Schuster.
Kotler, S. (2014). The rise of superman: Decoding the science of ultimate human performance. Houghton Mifflin Harcourt.
Kurnia, S., Choudrie, J., Mahbubur, R. M., & Alzougool, B. (2015). E-commerce technology adoption: A Malaysian grocery SME retail sector study. Journal of Business Research, 68(9), 1906–1918. https://doi.org/10.1016/j.jbusres.2014.12.010
Kusumadewi, A. N., Lubis, N. A., Prastiyo, R., & Tamara, D. (2021). Technology Acceptance Model (TAM) in the Use of Online Learning Applications During the Covid-19 Pandemic for Parents of Elementary School Students. Edunesia: Jurnal Ilmiah Pendidikan, 2(1), 272–292.
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. https://doi.org/10.1016/j.ijhm.2011.09.018
Mamonov, S., & Benbunan-Fich, R. (2017). Exploring factors affecting social e-commerce service adoption: The case of Facebook Gifts. International Journal of Information Management, 37(6), 590–600. https://doi.org/10.1016/j.ijinfomgt.2017.05.005
Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81–95.
Markovic, S., Jovanovic, M., Bagherzadeh, M., Sancha, C., Sarafinovska, M., & Qiu, Y. (2020). Priorities when selecting business partners for service innovation: The contingency role of product innovation. Industrial Marketing Management, 88(June), 378–388. https://doi.org/10.1016/j.indmarman.2020.06.001
Mohammed, Z. A., & Tejay, G. P. (2017). Examining privacy concerns and ecommerce adoption in developing countries: The impact of culture in shaping individuals’ perceptions toward technology. Computers and Security, 67, 254–265. https://doi.org/10.1016/j.cose.2017.03.001
Ortega Egea, J. M., & Román González, M. V. (2011). Explaining physicians’ acceptance of EHCR systems: An extension of TAM with trust and risk factors. Computers in Human Behavior, 27(1), 319–332. https://doi.org/10.1016/j.chb.2010.08.010
Peña-García, N., Gil-Saura, I., Rodríguez-Orejuela, A., & Siqueira-Junior, J. R. (2020). Purchase intention and purchase behavior online: A cross-cultural approach. Heliyon, 6(6). https://doi.org/10.1016/j.heliyon.2020.e04284
Porter, C. E., & Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59(9), 999–1007. https://doi.org/10.1016/j.jbusres.2006.06.003
Sjödin, D., Parida, V., Kohtamäki, M., & Wincent, J. (2020). An agile co-creation process for digital servitization: A micro-service innovation approach. Journal of Business Research, 112(June 2019), 478–491. https://doi.org/10.1016/j.jbusres.2020.01.009
Tseng, A. (2017). Why do online tourists need sellers’ ratings? Exploration of the factors affecting regretful tourist e-satisfaction. Tourism Management, 59, 413–424. https://doi.org/10.1016/j.tourman.2016.08.017
Verma, P., & Sinha, N. (2018). Integrating perceived economic wellbeing to technology acceptance model: The case of mobile based agricultural extension service. Technological Forecasting and Social Change, 126(September 2016), 207–216. https://doi.org/10.1016/j.techfore.2017.08.013
Wirtz, J. (2020). Organizational Ambidexterity: Cost-Effective Service Excellence, Service Robots, and Artificial Intelligence. Organizational Dynamics, 49(3). https://doi.org/10.1016/j.orgdyn.2019.04.005
Xia, M., Zhang, Y., & Zhang, C. (2018). A TAM-based approach to explore the effect of online experience on destination image: A smartphone user’s perspective. Journal of Destination Marketing and Management, 8(April 2016), 259–270. https://doi.org/10.1016/j.jdmm.2017.05.002
Xu, X., Li, Q., Peng, L., Hsia, T. L., Huang, C. J., & Wu, J. H. (2017). The impact of informational incentives and social influence on consumer behavior during Alibaba’s online shopping carnival. Computers in Human Behavior, 76, 245–254. https://doi.org/10.1016/j.chb.2017.07.018
Zhao, Y., Wen, L., Feng, X., Li, R., & Lin, X. (2020). How managerial responses to online reviews affect customer satisfaction: An empirical study based on additional reviews. Journal of Retailing and Consumer Services, 57(June), 102205. https://doi.org/10.1016/j.jretconser.2020.102205