1. Learning new uses of technology: Situational goal orientation matters Presenter: Che-Yu Lin Advisor: Min-Puu Chen Date: 03/09/2009 Loraas, T., & Diaz, M.C. (2009). Learning new uses of technology: Situational goal orientation matters. International Journal of Human-Computer Studies, 67(1), 50–61. 1

2. Introduction(1/5) 2 Businesses invest large sums of money in technology (up to 50% of firms’ capital budgets) (Rockartetal., 1996), yet do not see comparable gains in productivity due to lack of full implementation by employees (Devaraj and Kohli, 2003). We answer a call in the literature by studying the impact of a potential managerial intervention on the decision to learn a new use of technology to increase efficiencies on he job (Jasperson et al., 2005). We study whether potential users are more likely to switch from a known means of completing a task to learn a new systems approach based on their situational goal orientation and ease of learning perceptions.

3. Introduction(2/5) 3 Goal orientation theory describes how the type of goals pursued by an individual affects decision making (Nicholls, 1984; Dweck, 1986). A learning goal is defined by eagerness to learn for the sake of self- improvement, whereas a performance goal is defined by wanting to appear better (or at least no worse than) one’s peers. With learning goals, failure is deemed a part of the learning process and is not feared. With performance goals, anything that might jeopardize performance is considered a threat, so failure is feared (Button et al., 1996). This study investigates the efficacy of situational goal orientation as a practical managerial intervention that can motivate users to expand their use of technology to increase effectiveness and efficiencies on the job, even when the technology is deemed difficult to use.

4. Introduction(3/5) 4 Our goal is to extend these findings by more closely examining the characteristics of the post-adoption context and to explore the efficacy of situational goal orientation as a managerial intervention to promote system users to learn new uses of technology. A learning orientation focuses on the importance of gaining knowledge, a performance orientation focuses on completing tasks correctly (Dweck, 1986). A learning goal orientation has been shown to influence perceptions regarding ease of use via self-efficacy in the technology acceptance setting (Yi and Hwang, 2003).

5. Introduction(4/5) 5 Learning manipulations have resulted in higher effort, enhanced challenge seeking, and a predisposition for learning strategies in complex tasks (Ames andArcher,1988; Winters and Latham, 1996). Learning manipulations have been found to contribute to the development of knowledge structure, where performance manipulations did not affect knowledge acquisition (Kozlowskietal., 2001). Prior research has shown that a learning orientation is associated with positive effects in terms of motivation to learn and effort expended to learn, while a performance orientation has a negative influence on these elements (Colquitt and Simmering, 1998; Fisher and Ford, 1998).

6. 6 Introduction(5/5)

7. Methods(1/2) 7 The experimental task environment consisted of decoding numbers into letters in a Microsoft Excel application using paper decoding sheets. Participants were given three options for completing the final five decoding tasks. - First, they could simply follow the manual method that they knew and had successfully used; - Second, they could use 5 min of the first decoding period to try to learn the Excel procedure; - Third, they could use the entire 15 min of the first decoding period to try to learn the Excel procedure. Ease of learning is a dummy variable with the value of one assigned to high ease of learning (zero is low ease of learning). Situational goal orientation is a sum score, with higher (lower) scores indicating a higher performance (learning) situational goal orientation. Computer confidence and risk preferences are covariates.

8. Methods(2/2) 8

9. Results(1/3) 9 The correlation between the computer confidence factor and the risk preference factor is -.218. Computer confidence has a Cronbach’s alpha of.854, risk preferences has an alpha of.748, and goal commitment has an alpha of.844.

10. Results(2/3) 10

11. Results(3/3) 11

12. 12 Discussion(1/2) We extend the literature by investigating the traditional technology acceptance parameter, ease of use, in the post-adoption context. Future research might consider perceptions regarding perceived usefulness. When faced with a situational performance-oriented supervisor, the usefulness of learning the technology may be discounted. In this case, perceived usefulness of the technology may be reduced sufficiently to deter learning. Recent research provides evidence that staff accountants are likely to follow supervisor suggestions due to their motivation to comply with said referents’ beliefs.

13. 13 Discussion(2/2) This situational goal orientation can be influenced via a simple supervisor instruction. Performance orientation is described in two dimensions, wanting to appear better than peers do and not appearing incompetent (Elliott and Dweck, 1988). This suggests that an organizational culture that creates competition between employees may deter learning new uses of technology when failure may be likely, as employees will be unwilling to look bad in comparison to others. Our findings suggest that managers who universally espouse performance can modify an employee’s situational goal orientation and as such may unwittingly curtail the learning of difficult, yet beneficial new software solutions by their employees.

14. Conclusion We found that individuals primed for learning choose to learn without regard to perceived ease of learning. We do find that the situational goal of the potential user influences intent to learn when ease of learning was low. This suggests that looking for more effective ways to modify situational goals may be an effective avenue for future research. Additionally, participants were required to make a decision in a single period, and did not have the opportunity to ‘‘change their mind’’. Our experimental task setting is not as rich as a real-world decision context, and actual system usage was never required or observed in the experiment. There are a number of studies that indicate intention is a predictor of behavior, which suggests that our measure of intent is meaningful (Dholakia and Bagozzi, 2002). 14