Abstract The proliferation of fake news presents an emerging and complex cybersecurity challenge in the realm of information systems, with significant implications for security. This study investigates the intersection of fake news and risk management through a Multivocal Literature Review, drawing from academic research, professional practice, and grey literature. The findings demonstrate how fake news acts as a cyber-enabled threat, amplifying risks and complicating decision-making and risk mitigation. Considering the interdisciplinary nature of cybersecurity and modern management, addressing fake news requires integrated strategies that combine technological innovation, policy development, and education. This research emphasizes the importance of proactive prevention, mitigation, and strategic recovery to enhance resilience against information manipulation. By synthesizing diverse perspectives, the study contributes to the broader discourse on data, information, and management security, and advocates for collaborative solutions to strengthen digital trust and organizational integrity. It also identifies key directions for future research, underscoring the need for continuous adaptation in managing the evolving landscape of cyber-enabled disinformation.

Abstract Data and its valuation have gained vital significance in academia and enterprises, coinciding with diverse data valuation approaches encompassing various layers, dimensions, and characteristics. This paper assesses data value determination through a business capability lens based on the TOGAF standard. The paper encompasses (a) constructing a Data Valuation Business Capability (DVBC) taxonomy and (b) validating the taxonomy using two existing data valuation concepts from academia. The methodology involves information systems taxonomy development techniques backed by a previously conducted systematic literature review of 64 articles. The resultant taxonomy comprises four business capability layers, nine dimensions, and 36 characteristics. These layers and dimensions offer business, technology, and organizational perspectives, reflecting the interdisciplinary nature of data valuation alongside an enterprise architecture. Characteristics within these layers and dimensions are either exclusive or non-exclusive based on their contents. The compiled findings meet both objective and subjective quality criteria. The implications of the DVBC are multifaceted, influencing scholars and professionals alike. Scholars gain a cohesive tool enhancing transparency in the extensively debated data value domain, fostering linkages among information systems, enterprise architecture management, and data management. This empowers the progress in developing comprehensive data valuation concepts. Additionally, professionals may employ the DVBC taxonomy as a lighthouse and guiding tool, fostering internal dialog on data valuation. This entails elevating data valuation to a pivotal business capability, necessitating collaborative, regular assessment, and enhancement involving business and technological stakeholders. By adopting this taxonomy, the challenge of consistently determining data value can be effectively addressed in both academia and enterprises.

A metamodel is an important aspect of defining a modeling language. It specifies the language’s syntax through a set of constructs as well as how the language models are ought to be composed. Modeling languages, and thus their metamodels, are subject of constant evolution due to changing language requirements as consequence of business changes. Therefore, perceiving the essential aspects responsible for altering the structure of metamodels when a change requirement arises can become an issue. The Enterprise Ontology theory and its methodology (DEMO) provide ontological knowledge about organizations resulting in organizational self-awareness. Applying this methodology to the context of metamodeling can be a starting point for uncovering the essential aspects, i.e., the ontological knowledge regarding metamodel evolution. For that purpose, we modeled two diagrams using the DEMO methodology. The input for both diagrams was a set of coupled operations defined in Herrmannsdorfer’s evolutionary metamodeling research. In the end we stated the main conclusions of our work and themes for future work.