Background: Cognitive biases are systematic errors in reasoning that can lead to inaccurate decision-making across all areas of software production, regardless of the domain, programming language, or development method. Given the central role of software across all sectors, such biases can result in largescale inefficiencies, delays, and increased costs. Goal: While prior software engineering (SE) research predominantly focuses on specific tasks and quantitative methods, the vision of this paper is to study qualitatively how cognitive biases emerge, persist, and can be mitigated to improve decision-making throughout the Software Development Life Cycle (SDLC). Method: This vision uniquely applies the concept of reconception as a theoretical lens to explore how software professionals' cognitive models influence their management of dialectical oppositions, such as project velocity vs product quality and open-source vs proprietary control. Utilising Socio-Technical Grounded Theory (STGT) and dialectical inquiry, it examines how reconceiving these oppositions affects cognitive models and decision-making processes. Expected Outcome: A broad and high-level theoretical framework that explains how selected cognitive biases influence SE decision-making across the SDLC. The framework is developed through a two-phase STGT process: identifying salient bias categories in the first phase and focusing on the role of specific biases in shaping decision patterns in the second. This highlevel theory will open new research opportunities for future investigations into bias-informed decision-making in SE. Conclusion: This theoretical framework will support the development of empirically grounded debiasing strategies for more reliable decision-making in software engineering.

Digital twin (DT) ecosystems are rapidly evolving, connecting many stakeholders, such as manufacturers, customers, and application platform providers. These ecosystems require collaboration and interaction between diverse actors to create value. This study delves into the collaboration of such stakeholders within DT-focused ecosystems. This research aims to understand stakeholder collaboration within DT ecosystems, identify potential challenges, and provide insights for managing these stakeholders. It also seeks to define the DT ecosystem and its implications for both research and practice. A systematic literature review was conducted, supplemented by empirical evidence gathered from interviews with DT experts who were knowledgeable about the DT ecosystem. The study also analyzed DT systems, stakeholder roles, and the challenges with ecosystem-focused DT development. The study identified various stakeholders and their roles in adding value to a DT ecosystem. It highlighted the benefits of stakeholder collaboration, such as knowledge gain during DT system development. The research also revealed the technical and non-technical challenges encountered in ecosystem-focused DTs, emphasizing the importance of standardization as a solution. A new definition of the DT ecosystem was proposed, emphasizing its data-driven nature, interconnected DTs, stakeholder value creation, and technology enablement. Stakeholder collaboration is pivotal in DT ecosystems, with each actor playing a distinct role. Addressing challenges, especially through standardization (OPC UA and ISO 23247), can lead to more efficient and coherent DT ecosystems. The insights provided by this study can guide industries in designing, developing, and maintaining their DT ecosystems, ensuring value creation and stakeholder satisfaction. Future research avenues that emphasize the importance of understanding the challenges involved and deploy appropriate solutions were suggested.