Abstract

In the rapidly evolving field of Information Systems (IS) research, effective management and organization of research activities, data, and collaboration among researchers are imperative. Traditional approaches, often characterized by fragmented tools and ad-hoc workflows, are inadequate to address the complexities of modern IS research, which involves interdisciplinary teams, vast datasets, and diverse methodological approaches (Johnson et al., 2019). Integrating data management, collaboration, project tracking, and analytical tools in a common platform can aid in streamlining research workflows, enhancing productivity, and fostering innovation. Furthermore, comprehensive data security ensures confidentiality, integrity, and compliance with research ethics and regulations. These functionalities are central to developing a modern research management platform meeting the dynamic demands of contemporary IS research. Augmenting current workflows is critical in the design of the platform; resistance to change can be reduced by addressing the perceived threats that arise when new technologies disrupt existing practices (Lapointe & Rivard, 2005). Potential losses of expertise, autonomy, or control over work routines are examples of threats that can be mitigated by integrating tools already used by researchers, preserving their established ways of working. Substitutes for currently used tools may also be considered to ensure functional continuity. A modular architecture would best support these needs, as it maximizes the retention of existing workflows, reducing resistance and facilitating adoption. Furthermore, such an architecture promotes interoperability with existing systems, thereby lowering development costs and timelines. It also allows for adaptability and future expansion, enabling the platform to evolve with researchers' needs and technological advances (Hodapp & Hanelt, 2022). The design of this platform can draw from industry practices, particularly best practices from enterprise software solutions, such as agile methodologies, cloud-based collaboration tools, and advanced analytics. By leveraging proven strategies from technology-driven industries, this approach aims to offer IS researchers collaborative, scalable, and reliable systems. The incorporation of continuous feedback loops and iterative development processes will ensure that the platform remains adaptable to evolving research needs and technological advancements.

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