The Symmetrical and Asymmetrical Relationship of Technology Acceptance Model (TAM) on Consumer Emotional Value, and Service Innovation in Supporting Consumer Purchase Decisions

(1) * Nurliina Nurlina Mail (STIMI YAPMI, Makassar, Indonesia)
(2) Aditya Halim Perdana Kusuma Putra Mail (Universitas Muslim Indonesia, Indonesia)
*corresponding author


This study aims to determine the symmetrical and asymmetrical relationships between technology, acceptance model (TAM), and consumer emotional value and service innovation in supporting consumer purchase decisions. This research approach uses quantitative research. The primary data sources used in this study are preliminary data obtained from questionnaires and secondary data. This research was conducted in the city of Makassar. The population in this study is based on the infinite population, with a sample of 231 respondents spread across various provinces in Indonesia. Data analysis used validity, reliability, R-square, F-square, direct effect, and partial least square (PLS) hypothesis submission. The results of this study indicate that the Technology Acceptance Model (TAM) variable has a positive and significant effect on the Emotional Value and Service Innovation variables. Likewise, the Technology Acceptance Model (TAM) variable positively and significantly impacts the Consumer Purchase Decision variable by making the Emotional Value variable and Service Innovation intervene. The results of this study also show that the Technology Acceptance Model (TAM) variable has no positive and insignificant effect on the Consumer Purchase Decision variable.


Technology Acceptance Model (TAM); Emotional Value; Service Innovation; Purchase Decision



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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.

Assauri, S. (2008). Manajemen produksi dan operasi.

Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588.

Bentler, P. M., & Chou, C.-P. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16(1), 78–117.

Casidy, R., Nyadzayo, M., & Mohan, M. (2020). Service innovation and adoption in industrial markets: An SME perspective. Industrial Marketing Management, 89(June), 157–170.

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

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.

F. Hair Jr, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review, 26(2), 106–121.

Fauzi, H. (2021). The Effect of Experiential Marketing, Perceived Quality, as Well Advertising of Purchase Decisions (Study on Wardah Cosmetics User in Kadipaten District). Enrichment: Journal of Management, 11(2), 392–395.

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, 120002.

Ferdinand, A. (2002). Structural equation model. Semarang: CD Indoprint.

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

Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems.

Hoogland, J. J., & Boomsma, A. (1998). Robustness studies in covariance structure modeling: An overview and a meta-analysis. Sociological Methods & Research, 26(3), 329–367.

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.

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.

Loehlin, J. C., & Martin, N. G. (1998). A comparison of adult female twins from opposite-sex and same-sex pairs on variables related to reproduction. Behavior Genetics, 28(1), 21–27.

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.

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.

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.

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.

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.

Stevens, W. (1996). Letters of Wallace Stevens. Univ of California Press.

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.

Weijters, B., Cabooter, E., & Schillewaert, N. (2010). Intern . J . of Research in Marketing The effect of rating scale format on response styles : The number of response categories and response category labels. International Journal of Research in Marketing, 27(3), 236–247.

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.

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