Evaluating the effects of task–individual–technology fit in multi-DSS models context: A two-phase view
Research Highlights
► We conducted an experiment to examine effects of task-individual-technology fit. ► Increasing task-technology fit and individual-technology fit will improve attitude. ► Increasing task-individual fit will decrease attitude. ► Increasing task-technology fit will improve decision agreement and decision time. ► Increasing individual-technology fit will reduce decision time.
Introduction
Throughout the history of information systems (IS) development, there has remained great interest in developing accurate insights into how individuals interact with information technology (IT) to complete varying tasks [8], [25], [32], [34]. Myriad studies have proposed a variety of “fit models” to describe and explain these complex interrelations including various forms of cognitive fit [59], [60], task–technology fit [18], [19], user style–task structure–information support fit [46], agent–task–technology fit [11], and capability–task–strategy fit [16], [56]. While these models help our understanding of the “IT fit” phenomena, they commonly have underlying limitations. For example, previous fit models have been criticized for: (1) applying only to low-level spatial and symbolic tasks, (2) having decidedly rational perspective, (3) having not touched core individual differences, and (4) being without sufficient empirical support [e.g., [18], [56]]. We consider various shortcomings of the literature to date which, together, suggest that there remains significant need and opportunity for researchers to advance the knowledge in the area of task–individual–technology fit.
We begin by noting a major limitation to date in the literature on human–technology interaction which has been the use of an oversimplified focus on what factors should be included or excluded while ignoring how factors interact with each other. A rather old, but still valid example is the unresolved debate over whether individual differences should be considered for IS design [see [26], [45]]. We suggest that this debate is more salient in the context of decision support systems (DSS), where researchers question whether we should design a DSS to fit needs of each type of decision makers [30] or we should not [26]. We conjecture that a better starting point to address this issue is to carefully investigate how the individual difference factor interacts with other relevant factors from a fit perspective. As some authors have pointed out: “the quality of interaction, […] between human and computer […] is affected very slowly, if at all, by technological advance” [5]. Thus, in this study we investigate the effects of individual difference with the framework of task–individual–technology fit using a two-phase view. Because DSS models are in the core of decision support systems, we investigate the phenomenon under a multi-DSS models context. Our research question is: in addition to task–technology fit, does individual–technology fit or individual–task fit matter in users' attitude and performance in the multi-tasks and multi-DSS models context?
In order to answer this question, we first divide the concept of task–individual–technology fit into three dimensions – task–technology fit (TTF), individual–technology fit (ITeF), and task–individual fit (TaIF) – so that we can explore mechanisms and effects of interaction among these factors (i.e., task, individual difference, and technology). While prior fit studies [e.g., [18], [46]] considered interactions among task, individual difference, and technology at the same level, we propose a two-phase view and contend that those above-mentioned fit dimensions take effect differently in different phases. The two-phase view is based on Kuhn's concept of revolutionary science [33]. Kuhn defined a paradigm as what members of a scientific community, and they alone, share. According to Kuhn, there are pre-paradigm and paradigm phases. In the pre-paradigm phase, there are several incompatible and incomplete theories and there is no consensus on any particular theory. If the actors in the pre-paradigm community eventually reach a consensus, then the phase, paradigm phase (or normal science), begins. Basically, this paradigm revolution view postulates that science activities present different characteristics in different phases. In the paradigm phase, the focus is problem-solving with accepted paradigm; in the pre-paradigm phase, however, problem-solving is less efficient and the major focus is paradigm competition.
Traditionally, tasks can be characterized as structured, semi-structured, and unstructured based on whether we can clearly define a task and identify a process to complete the task [20]. Our two-phase view of task–individual–technology fit refers to the first phase as the pre-paradigm phase where tasks are not clearly defined and problem-solving processes are hardly specified (so called semi- and unstructured task setting). We refer to the second phase as the paradigm phase where tasks are structured and problem-solving processes are specified so that technologies can be designated to carry out specific tasks (so called structured task setting). We suggest that the two-phase view can facilitate a better lens to examine conflicting arguments and provide new insights on the effects of individual difference on IS design. We further elaborate on the mechanisms under each phase in the next section.
The remainder of the paper is organized as follows. Section 2 presents research background and theoretical foundation for our work. Section 3 introduces a variety of specific research hypotheses. 4 Methodology, 5 Data analysis and results discuss methodology and experiment results, respectively. Section 6 indicates limitations of this research and directions for future research. The final section concludes this study.
Section snippets
Task characteristic, individual difference, and DSS models
Task characteristic and individual difference are two factors widely studied in DSS research. Scholars investigated their interaction with DSS from varying perspectives, including technology use [14], [17], [29], [36], decision-making effectiveness [10], [38], performance [3], [37], [47], [52], implementation [2], model formulation [4], and decision-making process [58]. The effects of interaction between task and DSS (i.e., technology) are generally consistent, as can be seen in the research
Hypotheses
We approach the concept of task–individual–technology fit with three dimensions – task–technology fit (TTF), individual–technology fit (ITeF), and task–individual fit (TaIF) – and investigate their effects on user attitude and performance in different phases under the two-phase view outlined above. Fig. 1 presents our conceptual model.
In the paradigm phase, tasks are structured and problem-solving processes are specified so that sophisticated applications can be developed to solve problems. In
Manipulation of task, individual difference, and DSS models
We manipulate task, individual difference, and DSS models based on literature review of existing DSS studies [e.g., [21], [36], [48], [52]]. First, three decision tasks were selected, including structured, semi-structured, and unstructured tasks [20], [30]. Each task was used in one or more previous DSS studies. We selected Gorry et al.'s [20] warehouse location task as the structured task because factors that affect the warehouse location decision are clearly defined and decision models are
Validity and reliability
An exploratory factor analysis with Varimax rotation was conducted to investigate convergent and discriminant validity and reliability. Convergent validity indicates “whether the items comprising a scale behave as if they are measuring a common underlying construct” and discriminant validity refers to “the ability of a measurement item to differentiate between constructs being measured” [12, p. 327]. As shown in bold numbers in Table 7, all factor loadings are above .6, thus supporting
Limitations and future research
This study suffers from certain limitations. First, we used student subjects and conducted an experiment in a lab environment. Although student subjects can successfully represent knowledge workers to complete the tasks by using DSS models and a lab experiment is pertinent to precisely examine the effect of task–individual–technology fit on users' attitude to DSS model and performance, it can be important to use knowledge workers in real business settings to provide comparative results. In this
Conclusion
In this study, we propose and examine a two-phase view of task–individual–technology fit in a lab environment. The results confirmed our arguments that in the paradigm phase, the effects of individual differences on user attitudes toward DSS models can be ignored and that in the pre-paradigm phases, individual differences play an important role. We believe that this view has both theoretical and practical implications for DSS research. First, it confirms the effects of individual difference in
Acknowledgments
The authors wish to sincerely thank the editors and the anonymous reviewers for their invaluable comments. The authors also thank Dr. James Marsden, whose suggestions were gratefully appreciated.
Yucong Liu is a PhD candidate of information systems at the University of Kansas. He worked in IT industry more than 10 years before starting his doctoral education. His research interests include human-computer interaction, IT business value, business intelligence, and information security. He has presented his research work at Americas Conference of Information Systems (AMCIS), Pacific Asia Conference on Information Systems (PACIS), Big XII plus MIS Symposium, and IS-CoRE Workshop.
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Yucong Liu is a PhD candidate of information systems at the University of Kansas. He worked in IT industry more than 10 years before starting his doctoral education. His research interests include human-computer interaction, IT business value, business intelligence, and information security. He has presented his research work at Americas Conference of Information Systems (AMCIS), Pacific Asia Conference on Information Systems (PACIS), Big XII plus MIS Symposium, and IS-CoRE Workshop.
Younghwa Lee is an Associate Professor of Management at the University of Northern Iowa College of Business Administration. He received his PhD from University of Colorado/Boulder in 2005. His research interest is in website usability, technology acceptance, and IT ethics and security. He is an ICIS 2003 Doctoral Consortium fellow. He has published in Communications of the ACM, Decision Support Systems, European Journal of Information Systems, Journal of Organizational Computing and Electronic Commerce, MIS Quarterly among others.
Andrew Chen is an associate professor of information systems at the School of Business at the University of Kansas. His current teaching and research interests include knowledge management, IT business value, human computer interface design, database management, and business and Web programming applications. His research work appears in Decision Sciences, Decision Support Systems, European Journal of Operational Research, Journal of Electronic Commerce Research, Journal of Management Information Systems, and MIS Quarterly.