The effect of task–individual–technology fit on user attitude and performance: An experimental investigation

Abstract

Decision support research explores interactions between individuals, tasks, and technology. In this paper, I deconstruct the task–technology–individual fit model into three two-way interactions and ascertain how these interactions affect user attitude and performance. Performance is conceptualized as consisting of two dimensions, technology performance and task performance. The paper reports a controlled laboratory experiment involving 94 subjects using a purpose built decision support system. The results demonstrate several important principles. User attitude is affected by the fit between individual and technology whereas technology performance is affected by the fit between task and technology, and task and individual. Users of technology fitted to them as an individual can perceive it as more useful than it actually is, in terms of improving task performance. Finally, technology performance translates into task performance. Technology performance is a necessary but not sufficient precursor to task performance.

Introduction

This paper uses a well established model [23] to frame an in-depth investigation of the effects of fit on decision support outcomes. Goodhue and Thompsons' [23] task–technology–individual fit model is deconstructed into three two-way interactions, task–technology fit (TTF), individual–technology fit (ITeF), and task–individual fit (TaIF) after Liu et al. [34]. The degree of granularity obtained by using this deconstructed model creates an opportunity to address fundamental decision support questions from a fresh perspective.

DSS performance is contextualized into two dimensions. One relates to the use of the DSS to obtain a better outcome or recommendation from the technology (technology performance), the other relates to performance on the decision task (task performance). Technology performance is about using the system; task performance is about using the outputs of the system. Prior empirical evidence demonstrates that improved technology performance leads to improved task performance [26], [50]. This relationship is not necessarily direct given that a user could use the system but not rely on the information it provides, in which case the DSS would affect technology performance but not task performance. Alternatively, a user could elect to use the system and to rely on the outputs, affecting both technology performance and task performance.

If technology performance is a necessary, but not sufficient, condition for improved task performance other questions arise. What prompts users of DSS to reply on DSS outputs? Under what conditions does technology performance affect task performance? The answer appears to lie in the attitude of users towards the DSS, with many prior studies linking users' attitude to technology with their performance. [18], [19], [23], [29], [31], [34]. In particular, the consideration of users' beliefs and attitude is particularly important where the use of the DSS is not mandated, and where the task solution is non-normative [27]. This paper looks at the impact of fit and user attitude on performance, exploring both technology and task performance. The primary research question addressed is:

How do task–technology–individual fit affect DSS user attitude and performance?

The remainder of the paper is organized as follows. The research background and theoretical foundations for the work are presented in Section 2. Section 3 details the specific hypotheses while Section 4 discusses the methods and materials used to test these hypotheses. Results are presented in 5 Results, 6 Discussion and conclusion contains a discussion of the results and limitations, and concludes the study.