بررسی اثرات تناسب کار ـ فرد ـ فن آوری در زمینه مدل های چندگانه سیستم پشتیبانی تصمیم: دیدگاه دو فازی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|5550||2011||13 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Decision Support Systems, Volume 51, Issue 3, June 2011, Pages 688–700
We investigate the effects of individual difference with the framework of task–individual–technology fit under a multi-DSS models context using a two-phase view. 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? 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). We then propose a two-phase view of task–individual–technology fit (i.e., pre-paradigm phase and paradigm phase) based on Kuhn's concept of revolutionary science. We conducted a controlled laboratory experiment with multiple DSS models and decision-making tasks to test our hypotheses. 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. In addition, we found that effects of individual difference can be a two-blade sword: ITeF can enhance but TaIF can diminish users' attitude to DSS model (i.e., technology). Our results also suggested that different dimensions of fit may affect performance directly or indirectly.
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 , ,  and . Myriad studies have proposed a variety of “fit models” to describe and explain these complex interrelations including various forms of cognitive fit  and , task–technology fit  and , user style–task structure–information support fit , agent–task–technology fit , and capability–task–strategy fit  and . 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.,  and ]. 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  and ]. 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  or we should not . 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” . 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.,  and ] 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 . 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 . 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 and 5 discuss methodology and experiment results, respectively. Section 6 indicates limitations of this research and directions for future research. The final section concludes this study.
نتیجه گیری انگلیسی
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 the pre-paradigm phase and provides a clearer view for the old debate of individual difference on DSS  and . Second, it indicates the complexity of interaction among task, individual, and technology and provides valuable insights on how those factors interact. Third, it suggests future directions for IS practitioners. In the structured task setting, the efforts should contribute to task–technology fit. In the semi- and unstructured task setting, however, individual difference has to be taken into account. Our findings also challenge the popular view on task–individual–technology fit, which implies that more fit is better [e.g., ,  and ]. The results of this study show that effects of individual difference may be a two-blade sword: ITeF can increase but TaIF can decrease users' attitude to DSS model (i.e., technology). Finally, this study indicates that different dimensions of fit may affect performance through different paths. We look forward to future research on these topics and additional insights on these important issues.