Consumer Adoption of Technology theories (TAM, UTAUT and UTAUT2)
When it comes to consumer adoption of Technology it is important to look into the Technology Acceptance Model (TAM) (Davis, 1986), Unified theory of acceptance and use of technology (UTAUT) (Venkatesh, et al., 2003) and the more recent UTAUT2 (Venkatesh, et al., 2012). These models are extended to many studies involving E-commerce and mobile payment adoption. Also, it is necessary to look into the innovation diffusion theory, which states that the consumer adoption rates of any type of technology depend on many factors such as perceived relative advantage compatibility, complexity, trialability, and observability (Rogers, 2003). TAM is an adaptation of the Theory of Reasoned Action. TAM aims to provide a general explanation of the determinants of computer acceptance, which can be capable of explaining user behavior towards a range of end-user computing technologies and user populations while being both parsimonious and theoretically justified at the same time. According to TAM, perceived usefulness and perceived ease of use are the two aspects of acceptance behavior (Davis, et al., 1989).
TAM postulates that the usage of a technology is determined by Behavioral Intention, and Behavioral intention(BI) is jointly determined by Attitude towards using the system (A) and perceived usefulness of the order (U).
UTAUT holds on four key constructs — Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating conditions (Venkatesh, et al., 2003). The initial three are determinants of the usage intention and behavior, while the fourth is a determinant of user behavior. This theory was developed and reviewed based on eight earlier models — theory of reasoned action, Technology acceptance model, Motivational model, social cognitive theory, the model of personal computer use, diffusion of innovation theory, the method of planned behavior, the model of personal computer use and a combined theory of planned technology/behavior acceptance model.
As seen in figure 3, The UTAUT model uses performance expectancy, effort expectancy, social influence, and facilitating conditions to determine the user’s behavior intention to use technology. Gender, experience, age, and voluntariness of use are the moderating variables assumed to influence the four key variables. It should be acknowledged that the initial UTAUT model was established in order to determine the implementation and usage of technologies in a corporate sense and that these considerations were not used in this model in relation to customer acceptance processes. Hence, UTAUT2 was developed with other three constructs — hedonic motivation, price value, and habit.
UTAUT2 reports better predictive capabilities than TAM. The direct effect hypothesis accounts for 44 percent of the variation in behavioral intent and using 35 percent of technologies. The researcher intends to use the various determinants from UTAUT2 and TAM as core aspects while developing a questionnaire as a part of the quantitative analysis of this research.
Apart from the core theories, various studies have been done which indicate influential factors relating to the consumers’ intention to adopt a mobile payment system. Subjective norms, Perceived ease of use, Perceived usefulness, Attitude to use, and Perceived security are some of the factors that influence the consumers’ intention to adopt a mobile payment system (Liébana-Cabanillas, et al., 2017). Other factors like Performance expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Habit, Price value, Hedonic Motivation, Perceived Risk, and Trust in providers play a significant role when it comes to adoption of the new system (Slade, et al., 2015). Another study claims that factors like Perceived technological uncertainly, Perceived information asymmetry, Perceived regulatory uncertainly, Perceived service intangibility, Perceived financial risk, Perceived privacy risk, Perceived performance risk, Perceived psychological risk, Perceived time risk and Perceived value act as a significant driver for consumers’ adoption of a payment system (Yang, et al., 2015). Another study claims that mobile banking application use is associated with low perceived risk, high compatibility, high perceived ease of use, and high perceived usefulness and the combination of low compatibility, little perceived usefulness, small perceived ease of use and high perceived risk is a sufficient condition for mobile banking application non-use (Manuel & Veríssimo, 2016).
REFERENCES-
Davis, F., 1986. A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results. Doctoral dissertation, Sloan School of Management, Massachusetts Institute of Technology .
Venkatesh, V., Morris, M. G., Davis, G. В. & Davis, F., 2003. User Acceptance of Information Technology: Toward a Unified View. MIS Quaterly, 27(3), pp. 425–478.
Venkatesh, V., Thong, J. Y. L. & Xu, X., 2012. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quaterly , 36(1), pp. 157–178.
Rogers, E., 2003. Diffusion of innovations. 5th ed. New York: NY: Free Press.
Liébana-Cabanillas, I., R. d. L. & F, M.-R., 2017. Intention to use new mobile payment systems: a comparative analysis of SMS and NFC payments. Econ. Res. Ekonomska istraživanja, 30(1), pp. 892–910.
Slade, E., Dwivedi, Y., Piercy, N. & Williams, M., 2015. Modeling consumers’ adoption intentions of remote mobile payments in the United Kingdom: extending UTAUT with innovativeness, risk and trust.. Psychol Market, 32(8), pp. 860–873.
Yang, Y., Liu, Y., Li, H. & Yu, B., 2015. Understanding perceived risks in mobile payment acceptance. Ind manag data syst., 115(2), pp. 253–269.
Manuel, J. & Veríssimo, C., 2016. Enablers and restrictors of mobile banking app use: A fuzzy set qualitative comparative analysis (fsQCA). Journal of Business Research, Volume 69, pp. 5456–5460.
