18. September 2021 : 2 Papers accepted at ICIS 2021 in Austin, Texas, USA
We are proud to announce that the paper "Understanding the Operational Value of Big Data Analytics Capabilities for Firm Performance: A Meta-Analytic Structural Equation Modeling Approach" by Eduard Anton, Thuy Duong Oesterreich, and Frank Teuteberg as well as the paper "Let's get immersive: How Virtual Reality can encourage user engagement in Process Modeling" by Ludger Pöhler, Julian Schuir, Pascal Meier and Frank Teuteberg have been accepted for publication at the 42th International Conference on Information Systems (ICIS 2021).
ICIS 2021 will take place December 12th-15th 2021 in Austin, Texas, USA. The ICIS conference has an “A” ranking according to the VHB Jourqual 3 ranking and an "A" ranking according to the WKWI journal and conference ranking.
Let's get immersive: How Virtual Reality can encourage user engagement in Process Modeling
Business process modeling plays a fundamental role for organizations to restructure their processes to meet the challenges of increasing digitalization and globalization. However, the geographic distribution of process stakeholders, the abstract non-contextual modeling languages and the resulting low motivation to participate make process documentation difficult. With this paper we present a design science research approach that resolves these issues by using virtual reality. To do this, we first developed design principles based on proposed solutions to increase employee engagement. Based on this, a virtual reality application was developed, which enables the placing of process models in realistic and immersive working environments. We developed it continuously in four evaluation cycles and finally tested it in three field studies with regard to its usefulness. The results of this study contribute to more context awareness in business process management and at the same time provide design knowledge for future virtual reality applications.
Understanding the Operational Value of Big Data Analytics Capabilities for Firm Performance: A Meta-Analytic Structural Equation Modeling Approach
To uncover the key mechanisms of how value is created through big data analytics (BDA), our main research objective is to integrate prior empirical findings on the relationship between BDA capabilities and firm performance. We conducted meta-analytic structural equation modeling based on 271 correlations and 33,281 observations collected from 63 individual studies. The findings confirm that creating business value from BDA is a complex and dynamic process affected by various value creation mechanisms. Aside from direct relationships between BDA capabilities and firm performance, we highlight the mediating role of operational performance in the value transmission to market and financial performance. Our study contributes to the rising debate on the business value of BDA by providing an integrated and novel picture of the value-adding pathways emanating from BDA capabilities. This informs future information systems research on theory building and assists practitioners in effectively formulating their objectives of BDA initiatives.