2018 – 2022 Multi-university Collaboration
- Business Science Institute Luxembourg, ASBL, Luxembourg
- SKEMA Business School, France
- IAE Lyon, University Jean Moulin, France
A Conceptual Model of a Stigmergic Information System Network for Social Sustainability: An Inductive Top-down Theorizing Approach
This thesis aims to conceptualize the patterns that bring together different sets of social value and social impact indicators for systemic alignment with social sustainability. This research explored the missing big-picture technology link in harmonizing measurement and reporting for comparability and integration across a monitoring system. This initial conceptual model offers a minimum set of foundational structures, relationships, and interactions that can tolerate and balance dynamic social complexity and constant change.
This conceptual model is an early step in the foundational structure of an information system network that can connect the information to support decision-making and cross-disciplinary data integration for socially relevant data. We address academics, business managers, policymakers, computer scientists, and sustainability practitioners.
The inductive to-down theory building approach constructed the conceptual model with three data analysis Building Blocks of Theoretical Modeling. In the first stage, Building Block number I explores the relationships, roles, and patterns for assessing social value and social impact at a societal scale. This analysis resulted in relationship mapping and societal requirements for conceptual modeling. Building Block II’s metaphorical reasoning introduced pragmatic technical solutions considering dynamic societal complexity and ongoing change. Building Block III applied scalable distributed technology theories to tease out components of conceptual representation for the systemic alignment of social sustainability information.
The Conceptual Model for a Stigmergic Information System Network for Social Sustainability synergizes activities across society to alleviate social problems by design. The model offers predictive, corrective, and retrospective analytics with system dynamic feedback loops applied against control module parameters to act as systemic thermostats for social stability, reduce perception bias, and monitor exploitative effects. The distributed architecture considers self-organizing communication micro-processes. Systemic interaction promises cost efficiency improvements for the assessment of social aspects. It resolves the foundational big-picture technical issues associated with dynamic social complexity and continual change. It introduces the capacity for increased transparency, data comparability, and social information sharing.