Visual Analytics Adoption in Business Enterprises: An Integrated Model of Technology Acceptance and Task-Technology Fit

Mohammad Daradkeh
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Mohammad Daradkeh: Department of Management Information Systems, Faculty of Information Technology and Computer Science, Yarmouk University, Irbid, Jordan

International Journal of Information Systems in the Service Sector (IJISSS), 2019, vol. 11, issue 1, 68-89

Abstract: Visual analytics is increasingly being recognized as a source of competitive advantage. Yet, limited research has examined the factors deriving it organizational adoption. By integrating the technology acceptance model (TAM) with the task-technology fit (TTF) model, this research developed a model for visual analytics adoption in business enterprises. To test the research model, data was collected through a questionnaire survey distributed to 400 business professionals working in a variety of industries in Jordan. Collected data were tested and analyzed using structural equation modeling (SEM) technique. Findings of this research confirmed the applicability of the integrated TAM/TTF model to explain the key factors that affect the adoption of visual analytics systems for work-related tasks. Specifically, the results of this research demonstrated that the task, technology, and user characteristics are fundamental and influential antecedents of TTF, which in turn has a significant positive effect on the perceived usefulness and perceived ease of use of visual analytics systems. Additionally, there are significant positive effects from perceived usefulness and perceived ease of use toward users' intention to adopt visual analytics systems, and a firm relationship between perceived ease of use and perceived usefulness of visual analytics systems. Together all these constructs explain 59.9% of the variance in user's intention to adopt visual analytics systems at the workplace. Findings of this research provide several important implications for research and practice, and thus should help in the design and development of more user-accepted visual analytics systems and applications.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jisss0:v:11:y:2019:i:1:p:68-89

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