Experience Richness: Effects of Training Method on Individual Technology Acceptance Andy Luse Iowa State University andyluse@iastate.edu Brian E. Mennecke Iowa State University mennecke@iastate.edu Anthony Townsend Iowa State University amt@iastate.edu Abstract The nature of the training experience a person is exposed to is an important contextual element influencing technology acceptance, but little research has explored the differences in technology acceptance for different types of training experiences. This research investigates the difference in technology acceptance for individuals experiencing different types of training. Our work builds on prior research by Venkatesh, Bandura, and others examining the technology acceptance model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and self-efficacy and this paper is focused on addressing several remaining questions raised by this research stream. Specifically, our research addresses the nature of training and user acceptance and our findings show that individuals who are exposed to vicarious experience training differ on many dimensions with regard to technology acceptance when compared to individuals experiencing direct, hands-on training. 1. Introduction Research addressing individual adoption of technology has had an extensive history within the IS discipline [1, 2]. The technology acceptance model (TAM), and its derivatives, predicts individual technology adoption and use with a high degree of accuracy and reliability [2-5]. Further, TAM and related theories have been extended to include various contextual variables that help to explain individual technology acceptance in specific settings [1]. One important contextual variable is experience, with research showing that the nature of subjects’ interactions with a technology affects the relationship among model constructs [2]. Experience adds a needed dimension to technology acceptance, but the nature of the experience construct has yet to be adequately explored. Previous research has focused on usage time as a measure of experience [2]; however, while the amount of time utilizing a product is an important dimension of experience, duration remains a non- specific measure of the experience construct. Training is one specific type of experience and scholars have called for additional research examining the different types of experience-based training and their relationship with IT adoption. Specifically, Venkatesh and Bala [1] state, “…there is still a need for more granular understanding of the effects of different training modes on the determinants of IT adoption” [1]. Our research builds on this call by examining how the nature of training experience influences user technology acceptance. 2. Background 2.1. Training and Experience Traditionally, the level of experience with a technology has been measured using temporal metrics, with more time using a product equating to greater experience. Differences in the amount of time of experience have been shown to have an impact on technology acceptance. For example, studies applying the Theory of Reasoned Action found that attitude became more influential with increasing experience (i.e., usage time) while the role of subjective norms diminished in importance [2, 6]. Additionally, research shows that the ease-of-use construct in the TAM model becomes insignificant with increasing experience [7, 8]. Variables such as complexity, affect toward use, social factors, and facilitating conditions were all less significant with more experience [9]. Differences were also found in adoption versus usage behavior with regard to the impact of innovation characteristics under different experience conditions [6]. Venkatesh and colleagues [2] show that the significance of the effect of some constructs decreases or sometimes disappears with increasing experience under a range of theories (i.e., Theory of Reasoned Action, Technology Acceptance Model, Theory of Planned Behavior, Model of PC Utilization, and Innovation and Diffusion Theory). Also, the Unified Theory of Acceptance and Use of Technology (UTAUT) model showed that experience influenced the effect of both Effort Expectancy and Social Influence on Behavioral Intent, where having less experience strengthened the effect of both Effort Expectancy and Social Influence on Behavioral Intent [2]. Training and experience have similar characteristics with regard to attitudes toward a new 2013 46th Hawaii International Conference on System Sciences 1530-1605/12 $26.00 © 2012 IEEE DOI 10.1109/HICSS.2013.213 852 2013 46th Hawaii International Conference on System Sciences 1530-1605/12 $26.00 © 2012 IEEE DOI 10.1109/HICSS.2013.213 853