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Dashboards are increasingly used by governments and health organizations to provide important information to the general public during a crisis. However, in contrast to organizational settings, the majority of the general population has not or rarely used dashboards before and therefore often struggles to interact effectively with these dashboards....
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... on their conceptualization, effective use is an aggregate construct formed by the following three dimensions: (1) transparent interaction, (2) representational fidelity, and (3) informed action (Burton-Jones and Grange 2013). As illustrated in Figure 1, the three dimensions of effective use form a hierarchy because "each lower-level dimension is necessary but not sufficient for the higher-level dimension" (Burton-Jones & Grange, 2013, p. 642). Initially, the unimpeded access to the system's representations (transparent interaction) enables one to obtain representations that faithfully reflect the domain (representational fidelity). ...
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... This research effort was initially motivated by our own DSR project. In this project, we designed and evaluated a conversational dashboard for the COVID-19 pandemic to improve less tech-savvy user's interaction with the dashboard and enhance their effectiveness and efficiency in finding the information they need [16]. In the course of publishing our DSR project, we were asked by reviewers to not only demonstrate the novelty of our dashboard artifact relative to dashboards described in the literature, but also to show that its features are novel compared to existing public crisis response dashboards used in practice (e.g., the Johns Hopkins University COVID-19 dashboard [17]). ...
... Motivated by the lack of methodological guidance in our own DSR project [16], we took a first step toward a method for reviewing software artifacts from practice. Drawing on the generic steps of the literature review process [10], we proposed a seven-step method and applied it in our own review of public crisis response dashboards. ...
Solving real-world problems with innovative and novel artifacts is at the core of design science research (DSR). Given DSR’s emphasis on a strong connection to the real-world, artifacts for solving a particular problem may not only be described in extant literature, but also exist in practice. This is particularly the case for software artifacts. Therefore, DSR scholars need to explore the state of the art and demonstrate the novelty of their software artifact relative to existing artifacts in research and practice. However, while methodological guidance for conducting literature reviews is abundant, there is little guidance on how to review software artifacts from practice. This paper takes a first step toward addressing this gap by proposing and illustrating a seven-step method for reviewing software artifacts from practice. Our research provides actionable guidance for DSR scholars on how to support the claim that their software artifact constitutes a substantial contribution to knowledge.
Forecasting support systems (FSSs) support demand planners in important forecasting decisions by offering statistical forecasts. However, planners often rely on their judgment more than on system-based advice which can be detrimental to forecast accuracy. This is caused by a lack of understanding and subsequent lack of trust in the FSS and its advice. To address this problem, we explore the potential of extending the traditional static assistance (e.g., manuals, tooltips) with conversational assistance provided by a conversational assistant that answers planners' questions. Drawing on the theory of effective use, we aim to conduct a framed field experiment to investigate whether conversational (vs. static) assistance better supports planners in learning the FSS, increases their trust, and ultimately helps them make more accurate forecasting decisions. With our findings, we aim to contribute to research on FSS design and the body of knowledge on the theory of effective use.
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