Do I Matter?: The Impact of Individual Differences on a Technology-Mediated End User Training Process

Do I Matter?: The Impact of Individual Differences on a Technology-Mediated End User Training Process

Saurabh Gupta (Department of Management, University of North Florida, Jacksonville, FL, USA) and Rob Anson (College of Business and Economics, Boise State University, Boise, ID, USA)
Copyright: © 2014 |Pages: 20
DOI: 10.4018/joeuc.2014040104

Abstract

Advances in technology-mediated learning (TML) have created opportunities to personalize learning, based on the assumption that individual differences affect one's ability to learn. However, cumulative research on individual differences is equivocal, and largely excludes the learning process. The authors argue that without a model that incorporates the process of learning, the authors cannot empirically assess how individual differences influence learning outcomes. This study first models the learning process in terms of two sub-processes: process appropriation and content assimilation. Second, it partially tests the model within a technology-mediated learning (TML) environment used to provide end-user IT training. The results support the efficacy of individual differences' influence on learning outcomes, while shedding light on how this effect occurs. Individual differences affect the appropriation of TML supplied structures, which in turn impacts learning outcomes. The research method used in the study is a laboratory experiment. Data was analyzed using SEM.
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Literature Review

Ever since the early days of research into end user computing, researchers have argued for the importance of individual differences (Gupta et al., 2010; Sun, Zang, Galletta, & Zhang, 2005). The principle argument is that the effectiveness of any training method depends on interactions with individual idiosyncrasies. This is consistent with the predictions of the aptitude-training interaction paradigm in education psychology (Lehtinen, Hakkarainen, Lipponen, Rahikainen, & Muukkonen, 2001). Researchers have argued that these individual characteristics could influence learning outcomes by influencing the formation of mental models, or by interacting with training methods (Olfman & Pitsatron, 2000). Empirically though, while many individual difference constructs have been investigated in Education and IS (summarized in Gupta et al. (2010)), this stream of research has had limited success in either explaining the effects or influencing training method designs. Consequently, some researchers have called for focusing on constructs that are more in line with the targeted outcomes of the training, i.e., individual differences that are more oriented toward technology use in the context of end user training (Bostrom, Olfman, & Sein, 1993).

The context of this study is IT training in a TML environment. To this end, we use a set of four individual difference variables (personal innovativeness, computer self-efficacy, computer anxiety, and motivation to learn the specific end user application) that are specific to learning about technology and learning with technology. In addition, critics of training research have conceded that certain non-technology based variables, such as learning style, might also be important in IT training contexts (Kettanurak, Ramamurthy, & Haseman, 2001). Thus, learning style was included in this study. All five variables have been studied in the IT literature. Table 1 summarizes the previous research regarding these variables.

Table 1.
Individual differences research in information systems
Individual DifferenceDefinitionTarget System / Context & StudyFindings
Kolb learning styleAn individual’s approach towards gaining experience as a source of learningEmail (Sein & Bostrom, 1989); Email & Lotus 123 (Sein & Robey, 1991);
Word-processing (Bohlen & Ferratt, 1997)
Greater abstraction in learners leads to better performance under abstract and analogical training methods.
TML in general is better, except for assimilators’ (learners greater reflectivity and abstract conceptualization).
Computer Self-efficacyAn individual’s perception of how well he/she can execute some required course of action needed to deal with a perspective situationLotus 123, WordPerfect (Compeau & Higgins, 1995);
Excel (Johnson & Marakas, 2000); (Marakas et al., 1998);
Windows 3.1 (Martocchio & Judge, 1997)
Pre-training self-efficacy significantly influenced post-training learning outcomes.
Computer AnxietyAn individual’s resistance to and avoidance of computer technologyBasic IT skills (Szajna & Mackay, 1995); (Keeler & Anson, 1995)Anxiety was not related to learning performance.
Anxiety has significant interaction with training method.
Motivation to learnDirection, intensity and persistence of learning-directed behavior in training contextsExcel (Yi & Davis, 2003)Positive correlation between intrinsic motivation and learning outcome.
Personal InnovativenessWillingness of an individual to try new information technologyTechnology adoption (Agarwal & Prasad, 1998; Karahanna, Ahuja, Srite, & Galvin, 2002)Positive correlation between personal innovativeness in IT and adoption of technology.

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