Visual Analytics Adoption in Business Enterprises

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.

Factors Influencing the Adoption Intention of Blockchain and Internet-of-Things Technologies for Sustainable Blood Bank Management

“…Hence, H5 is supported, which confirms that an easy to use Blockchain and IoT based BBMIS will lead to higher perceived benefits of such a system. This finding conforms to prior research outcomes in that PEU has positively affected PUF (Daradkeh, 2019;Ma et al, 2013;Wu & Chen, 2017;Yen et al, 2010). H6 tested PEU's impact on BINT.…”

“…The analysis also unveiled that PUF significantly affects BINT, implying that an improved perceived performance of BBMIS will lead to positive BINT to adopt Blockchain and IoT based systems. Thus, H7 is supported and confirms that PUF positively affects BINT, as shown in prior research outcomes (Chang et al, 2016;Chen, 2019;Daradkeh, 2019;Ma et al, 2013).…”

“…Further, results of the study also unveiled that TTF significantly affects PUF, which indicates that Blockchain and IoT enabled BBMIS will lead to perceived performance improvements in blood bank operations. Thus, H3 is accepted and confirms that TTF positively affects PUF, as evidenced in prior research (Chen, 2019;Daradkeh, 2019;Wu & Chen, 2017;Yen et al, 2010). Similarly, results of the study indicated that TTF significantly affects PEU, which implies that Blockchain and IoT enabled BBMIS will be easy to use with no significant learning efforts.…”

“…Similarly, results of the study indicated that TTF significantly affects PEU, which implies that Blockchain and IoT enabled BBMIS will be easy to use with no significant learning efforts. Thus, H4 is supported, which follows the prior research results with a positive relationship between TTF and PEU (Chang et al, 2016;Chen, 2019;Daradkeh, 2019;Ma et al, 2013;Vanduhe, Nat & Hasan, 2020). H5 tested the relationship between PEU and PUF.…”

“…The analysis results suggested that PEU significantly influences BINT, implying that the blood bank stakeholders intend to embrace the Blockchain and IoT enabled BBMIS only when it is convenient to use. Hence, H6 is accepted, which follows the prior research outcomes demonstrating a positive relationship between PEU and BINT (Daradkeh, 2019;Ma et al, 2013;Wu & Chen, 2017;Yen et al, 2010).…”