Building Adoption of Visual Analytics Software

Determinants of visual analytics adoption in organizations

“…Common factors include having the infrastructure necessary to support the new visual analytics tool, customizing training for users, integrating the visual analytics tool into current processes, and securing the time, managerial support, and necessary funds to support the adoption process. Similarly, Chinchor et al (2012) argue that the transition of visual analytics technologies into broad analytical use requires addressing the security and privacy requirements, providing support for scalability so that the tool can be deployed in existing architectures, and ensuring that the tool fits into work processes of prospective adopters. A novel visualization approach might have been designed to improve work with combined and aggregated sources of data.…”

“…These specifics of visual analytics represent different impacts on their acceptance and use compared to enterprise information systems. Unlike other classes of enterprise information systems, visual analytics requires users to adapt a new ways of thinking and more reliance on automated analytic techniques (Chinchor et al , 2012). Business users must not only trust the visual representations and the sources of their data, but also have to be able to interact with these visual representations and understand their meaning.…”

“…Thomas and Cook (2005) emphasized the importance of understanding prospective adopters and the factors influencing their adoption–decision process as a critical step for moving visual analytics technologies from research into practice. Recognizing these factors delivers immense value for organizations in order for them to be successful in bringing visual analytics technologies to the workplace, and mitigating the risk of rejection or resistance to the implementation of new visual analytics platforms (Chinchor et al , 2012).…”

“…The crux of visual analytic tools is that they require extensive software development, systems integration, and configuration prior to their usage (Thomas and Cook, 2005; Daradkeh, 2018). Moreover, the highly interactive and visual nature of visual analytics poses particular technological and organizational challenges that represent significant constraints on the adoption of visual analytics technology in organizations (Chinchor et al , 2012). Thus, the motivation of this study is to extend the previous work on technology adoption by developing an adoption framework that is aligned with the specific nature and characteristics of visual analytics technology and the factors involved to increase the utilization and business value of visual analytics in organizations.…”

Towards a product design assessment of visual analytics in decision support applications: a systematic review

“…Although considerable research has focused on the design and evaluation of VA tools (Chinchor et al 2012;Jeong et al 2008;Kang and Stasko 2012;Kluse et al 2012;Konecni et al 2010;Plaisant et al 2008), the outcome is a research agenda characterized by a horde of experiential concepts that, to some extent, differ in terms of theoretical backgrounds, research directions, and design processes.…”