ADOPTION PROCESS FOR VoIP: THE UTAUT MODEL

Transcription

1 ADOPTION PROCESS FOR VoIP: THE UTAUT MODEL Eduardo Esteva-Armida, Instituto Tecnológico y de Estudios Superiores de Monterrey Gral. Ramón Corona 2514, Zapopan, Jalisco, México Phone: (5233) eesteva@itesm.mx Alberto Rubio-Sanchez, University of the Incarnate Word 4301 Broadway, San Antonio, Texas Phone: (210) rubiosan@uiwtx.edu In recent years, consumer research has used different models to find the variables that help consumers make decisions as to whether to adopt a new technology or not. Some of the more popular models used for this purpose are: Technology Acceptance Model (TAM), Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), and Innovations Diffusion Theory, among others. Recently, Venkatesh, Morris, Davis, and Davis [16] tried to unify the different constructs of all these models in one unique model, and with this in mind developed the Unified Theory of Acceptance and Use of Technology (UTAUT). It is the objective of this study to test the usefulness of this model in the context of end user consumption, a task not specifically attempted by Venkatesh, et al. The setting for this test is Voice over Internet Protocol (VoIP) technology. The Adoption Process As with any new technology, consumers adoption process of VoIP has been slow [8]. VoIP phones are not free of some disadvantages so suppliers need information about what factors are more important to customers. Suppliers can then develop strategies to increase the speed of the adoption process in order to maximize their profits. It was the intention of this study that as a byproduct of testing the UTAUT model, information would be developed that is useful to the companies and end-users of VoIP. The UTAUT Model With the intention to formulate a comprehensive model that considered the variables included in previous theory aimed at explaining adoption behavior, Venkatesh, et al. [16], developed a way to test each of the constructs from eight pre-existing models: Theory of reasoned action [7], technology acceptance model [5], motivational model [4] [6] [17], theory of planned behavior [1], combined TAM and TPB [11] [13], model of PC utilization [14], innovation diffusion theory [12], and cognitive theory [3]. They presented a summary of prior model comparison studies and an empirical synthesis of the different models. Finally, with the variables that showed the biggest impact, they described a new model called Unified Theory or Acceptance and Use of Technology (UTAUT). According to the authors, this model accounted for up to 70 percent of the variance (adjusted R 2 ) in usage intention and it is a definitive model that summarizes what is

2 known and forms a basis for direct future research in this area. Considering a theoretical point of view, UTAUT gives a perspective on how the variables related to intention and behavior change over time. But the main contribution of UTAUT is by unifying the theoretical perspectives common in the adoption literature and incorporating moderators to consider dynamic impacts, namely organizational context, user experience, and demographic characteristics such as age and gender. Because most of the key relationships in the model are moderated, the study of these variables is an important added value of UTAUT [16]. UTAUT claims that three main factors (Performance Expectancy, effort expectancy, and Social Influence) determine the intention toward using a new technology while facilitating conditions and the Behavioral Intention toward using relate to the use behavior. At the same time, some variables moderate these relationships, namely gender, age, experience and Voluntariness of Use. Based on the review of literature conducted, 4 hypotheses were developed: H1: Performance Expectancy will positively affect Behavioral Intention to adopt. H2: Effort Expectancy will positively affect Behavioral Intention to adopt. H3: Social Influence will positively affect Behavioral Intention to adopt. H4a: The impact of Performance Expectancy on Behavioral Intention will be moderated by Gender and Age. H4b: The impact of Effort Expectancy on Behavioral Intention will be moderated by Gender, Age, and Experience. H4c: The impact of Social Influence on Behavioral Intention will be moderated by Gender, Age, Experience, and Voluntariness of Use. RESULTS An online survey was used to test the above mentioned hypotheses. Participation was voluntary. Partial Least Squares (PLS) was used to evaluate the relationships in the model. The sample for this study consisted of 475 respondents out of 2000 contacts. This represented a response rate of 23.8%. The instrument used to measure the variables was adapted from previous work by Venkatesh et al. and proved to be valid and reliable [16]. The results show that hypothesis 1 should be accepted. Performance Expectancy is significantly and positively related to Behavioral Intention to adopt. Hypothesis 2, however, did not find the necessary support and is rejected. Strong support was provided for Hypothesis 3. The relationship between Social Influence and Behavioral Intention is, as predicted, positive and significant.

3 The results show that the first order interactions for the four moderator variables with the main variables are not significant. Since the first order interactions are not significant, it is not expected than higher order interactions would be significant either. Hence, there is not support for hypotheses H4a, H4b or H4c. DISCUSSION The main objective of this study was to test the Unified Theory of Acceptance and Use of Technology (UTAUT) model in a reduced form in an end-use consumer context. The study worked with potential adopters of VoIP technology. The results generated in this work using the UTAUT model are congruent with the original study made by Venkatesh et al. [16]. In both, Performance Expectancy has an influence in the Behavioral Intention to adopt a technology (called information system technology or VoIP technology). Both studies were able to account around for 50% of the variance in Behavioral Intention to adopt. In both studies, the significance of the relationship between Effort Expectancy and Behavioral Intention to adopt is not clear. The results in this study show a weak and non significant relationship. The only difference between both studies is presented by the relationship between Social Influence and Behavioral Intention to adopt. In the original study, this relationship is not as strong and significant as that in the present research. One possible explanation can be related to the differences in characteristics of the sample. The study of Venkatesh et al. [15] was done with professionals in four organizations and the technology evaluated was something that is useful for their work. In comparison, the sample for this research was done with more heterogeneous respondents and the technology is something that they can use in daily life. The respondents disposition to accept other person s influence in the decision to adopt these technologies may be different and stronger than in the sample used in the original study. The results in this research provide support for almost all of the relationships specified in the model. Future research will be necessary to validate the relationship between Effort Expectancy and Behavioral Intention to adopt. Questions could be addressed to the sample, the model or to the scales used to measure one or both of these variables. This opens possibilities for future research. Another noteworthy aspect of testing the model is the effect of the moderator variables. In the past, other researchers had found it difficult to evaluate and provide support for moderators in the model [1] [9] [10]. This study found the same problem. Even though the moderator variables Gender, Age, Experience and Voluntariness of Use show some relationship with other variables in the model, none of them was statistically significant. Academics will need to be cautious in future research regarding these relationships.

4 REFERENCES [1] Ajzen, I. The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 1991, 50(2), [2] Anderson, J. E., Schwager, P. H., & Kerns, R. L. The Drivers for Acceptance of Tablet PCs by Faculty in a College of Business. Journal of Information Systems Education, 2006, 17(4), [3] Bandura, A. Self-Efficacy: Toward a Unifying Theory of Behavioral Change. Psychological Review, 1977, 84(2), [4] Calder, B. J. & Staw, B. M. Self-Perception of Intrinsic and Extrinsic Motivation. Journal of Personality and Social Psychology, 1975, 31(4), [5] Davis, F. D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 1989,13(3), [6] Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. Journal of Applied Social Psychology, 1992, 22(14), [7] Fishbein, M. & Ajzen, I. Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley, [8] Harbert, T. VoIP for the Masses. Electronic Business, 2005, 31(5), [9] Li, J. P. & Kishore, R. How Robust is the UTAUT Instrument? A Multigroup Invariance Analysis in the Context of Acceptance and Use of Online Community Weblog Systems. Proceedings of the 2006 ACM SIGMIS CPR Conference on Computer Personnel Research, 2006, [10] Marchewka, J. T., Liu, C., & Kostiwa, K. An Application of the UTAUT Model for Understanding Student Perceptions Using Course Management Software. Communications of the IIMA, 2007, 7(2), [11] Mathieson, K. Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior. Information Research Systems, 1991, 2(3), [12] Rogers, E.M. Diffusion of Innovations. New York, NY: The Free Press, [13] Taylor, S. & Todd, P. Assessing IT Usage: The Role of Prior Experience. MIS Quarterly, 1995, 19(4),

5 [14] Thompson, R. L., Higgins, C. A., & Howell, J. M. Personal Computing: Toward a Conceptual Model of Utilization. MIS Quarterly, 1991, 15(1), [15] Venkatesh, V. Determinants of Perceived Ease if Use: Integrating Perceived Behavioral Control, Computer Anxiety and Enjoyment into the Technology Acceptance Model. Information Systems Research, 2000, 11(4), [16] Venkatesh, V., Morris, M., Davis, G., Davis, F. User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 2003, 27 (3), [17] Venkatesh, V. & Speir, Ch. Computer Technology Training in the Workplace: A Longitudinal Investigation of the Effect of Mood. Organizational Behavior and Human Decision Processes, 1999, 79(1), 1-28.