Exploring a New Determinant of Task Technology Fit: Content Characteristics (original) (raw)
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The Impact of Task-Technology Fit in Technology Acceptance and Utilization Models
In a recent paper, Venkatesh et al. (2003) examine a series of models that explain or predict user acceptance of information technology. These models included the Technology Acceptance Model (Davis et al., 1989), Computer Self Efficacy (Compeau and Higgins, 1995) and other models of user behavior, intention, or affect. Their study combined these models to form a Unified Model, which the authors call UTAUT. The underlying models as well as the combined model fail to explicitly include task constructs. Typically, users intend to use an information technology if it meets their task requirements. A model that explicitly includes task characteristics is the Task-Technology Fit (TTF) model (Goodhue, 1995), which has been shown to add explanatory power to the Technology Acceptance Model (Dishaw and Strong, 1999). Our study adds TTF constructs to the UTAUT with the goal of determining whether this addition produces an improvement in explanatory power, similar to that reported by Dishaw and Strong (1999).
Is more always better? Investigating the task-technology fit theory in an online user context
We used Task-Technology Fit (TTF) theory to examine the drivers and consequences of successful task completion by a user in an online context. The theory suggests that the fit between characteristics of the task and those of the website predicts user performance and behavioral intentions. Our hypotheses were tested using the input of two large scale studies performed in twelve industries and involving 13,135 participants. Results, which were replicated in a proximate culture, lend support to the predictions of Task-Technology Fit theory. The site information quality and ease of use were the only technology factors that significantly drove the users to a successful completion of their information tasks, rather than the site's graphical attractiveness, interactivity, security and privacy factors. The findings further suggested that focusing on the enhancement of site characteristics that have low fit with the task is not effective as it resulted in slowing the successful completion of the online task. ß
Extending the technology acceptance model with task–technology fit constructs
Information & Management, 1999
During the past decade, two signi®cant models of information technology (IT) utilization behavior have emerged in the MIS literature. These two models, the technology acceptance model (TAM) and the task±technology ®t model (TTF), provide a much needed theoretical basis for exploring the factors that explain software utilization and its link with user performance. These models offer different, though overlapping perspectives on utilization behavior. TAM focuses on attitudes toward using a particular IT which users develop based on perceived usefulness and ease of use of the IT. TTF focuses on the match between user task needs and the available functionality of the IT. While each of these models offers signi®cant explanatory power, a model that integrates constructs from both may offer a signi®cant improvement over either model alone. We discuss the theoretical foundation of both these models and present a theoretical rationale for an integrated model. The result is an extension of TAM to include TTF constructs. We test our integrated IT utilization model using path analysis. Our integrated model provides more explanatory power than either model alone. Research using the integrated model should lead to a better understanding of choices about using IT.
British Journal of Educational Technology, 2010
Understanding learners' behaviour, perceptions and influence in terms of learner performance is crucial to predict the use of electronic learning systems. By integrating the task-technology fit (TTF) model and the theory of planned behaviour (TPB), this paper investigates the online learning utilisation of Taiwanese students. This paper provides a better understanding of individual, technological and social factors regarding online learning system performance. A total of 870 students who were earlier introduced to e-learning were surveyed after a period of exposure to the system. The results of the research model were analysed using a structural equation modelling approach to verify 10 hypotheses; support was found for eight of them. This paper offers a new perspective on the mechanisms through the TTF and TPB model constructs, which facilitates e-learning learner performance and offers important implications for understanding learner performance in online learning environments. Several information technology concepts (eg, computer self-efficacy, normative technology fit, compatibility, computer-based tutorials), stemming from social psychology and cognitive fit theories, can help to explain the electronic learning system adoption and usage. While Kim and Malhotra (2005) indicated that task-technology fit (TTF) may be useful in explaining performance, its central tenets, such as task, technology and performance, have been directly tested.There is little research providing empirical evidence about how new technology characteristics, including communication and information representation support, may affect user adoption of online learning
Extending the task-technology fit model with self-efficacy constructs
Eighth Americas Conference …, 2002
MIS researchers have developed a number of models for studying the software utilization choices of end users, including the Task-Technology Fit Model and the Technology Acceptance Model. We are exploring the similarities and differences among ...
Scientific Journal of Informatics
In the information systems field, the fit between the components of information systems is a topic that has attracted the attention of many researchers. Various concepts of the fit such as Task-Technology Fit (TTF), Fit between Individuals, Tasks, and Technology (FITT), and Human Organization Technology Fit (HOT-Fit) are proposed and studied in various studies. In those various concept, the fit is one of the keys to the successful implementation and acceptance of information systems. Through a study of relevant literature, this study proposes a model consisting of human, organization, and technology characteristics, and adds the Perceived User Technology Organization Fit (PUTOF) variable as the initiated variable that influences the intention to use. In subsequent research, this model can be tested quantitatively with case studies of the information system implementation in an organization.
International Journal of Human-Computer Studies, 2012
Virtual learning system (VLS) is an information system that facilitates e-learning have been widely implemented by higher education institutions to support face-to-face teaching and self-managed learning in the virtual learning and education environment (VLE). This is referred to a blended learning instruction. By adopting the VLS, students are expected to enhance learning by getting access to courserelated information and having full opportunities to interact with instructors and peers. However, there are mixed findings revealed in the literature with respect to the learning outcomes in adopting VLS. In this study, we argue that the link between the precedents of leading students to continue to use VLSs and their impacts on learning effectiveness and productivity are overlooked in the literature. This paper aims to tackle this question by integrating information system (IS) continuance theory with task-technology fit (TTF) to extend our understandings of the precedents of the intention to continue VLS and their impacts on learning. By doing it, factors of technology-acceptance-to-performance, based on TAM (technology acceptance model) and TTF and post-technology-acceptance, based on expectation-confirmation theory, models can be included to test in one study. The results reveal that perceived fit and satisfaction are important precedents of the intention to continue VLS and individual performance. Later, a discussion and conclusions are provided. This study sheds light on learning system design as assisted by IS in VLE and can serve as a basis for promoting VLS in assisting learning.