Olugbara, Oludayo O.Thompson, Robyn Cindy2026-06-262026-06-262026-03-31https://hdl.handle.net/10321/6422Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy in Information Technology, Durban University of Technology, Durban, South Africa, 2026The construction of consistent and time-sensitive scenarios plays a crucial role in strategic planning, foresight, and decision making across a wide range of disciplines. By anticipating future trends and uncertainties, scenarios allow organizations and policymakers to better comprehend current situations, prepare for possible futures and mitigate possible risks. However, scenario construction methods are increasingly criticized for time intensive data collection and analysis processes, the influence of cognitive bias, difficulty in handling complex interactions, over-reliance on a limited pool of experts, and inconsistency in assigning judgements, all of which can affect the validity, inclusivity, and utility of the resulting scenarios. In addition to these methodological limitations, traditional scenario construction approaches lack a coherent theoretical foundation. Many methods are grounded in either overly rigid quantitative models or unstructured qualitative exercises, failing to adequately account for the interconnected, dynamic, and interpretive nature of complex socio-technical systems. This study addresses this gap by developing a methodology explicitly grounded in a complementary theoretical framework comprising general systems theory (GST), complexity theory, potential surprise theory (PST) and constructivism. This framework provides the conceptual basis for ensuring structural rigour, embracing dynamic uncertainty, and incorporating diverse perspectives, thereby enabling the construction of scenarios that are both analytically robust and strategically relevant. This study proposes a novel cross-impact analysis methodology for scenario construction that is based on the crowdsourcing process, advanced impact analysis (ADVIAN®), and cross-impact balance (CIB) analysis. The aim is to overcome the limitations of conventional methods by enabling faster, more diverse, and more theoretically grounded scenario development. The methodology was implemented and validated over two practical applications. The first was the adoption of renewable energy and the second artificial intelligence (AI) adoption in higher education. In addition to validating the robustness and scalability of the proposed approach, these applications generate practical, context-specific scenario outputs. For renewable energy adoption, the resulting scenarios provide structured insights into potential transition pathways under varying conditions. For AI adoption in higher education, the scenarios highlight alternative trajectories shaped by institutional, technological, and socio-cognitive factors. These outputs offer actionable value for policymakers, institutional leaders, and other stakeholders engaged in strategic planning within these domains. The primary contribution of this research lies in the development of a novel, theory-driven scenario construction methodology that systematically integrates the crowdsourcing process, ADVIAN®, and CIB analysis within a coherent analytical framework. The study demonstrates how this integration enables faster, more efficient scenario construction, reduces potential bias, and increases the diversity of perspectives. These advancements address the major challenges of traditional scenario planning, such as lengthy data collection and analysis processes and the risk of over-reliance on a narrow pool of expert opinions. This research makes a significant contribution by introducing a novel, scalable methodology, grounded in theory, that blends advanced analytical techniques with crowdsourcing to enhance scenario construction. By strengthening the theoretical foundation, improving methodological efficiency, and expanding the diversity of perspectives, the study provides a practical and academically rigorous approach to scenario planning. Future research can build on this approach to further refine scenario methods and explore broader applications across sectors facing complexity and uncertainty.264 penCrowdsourcingScenario planningScenario constructionStrategic foresightCross-impact analysisComplexity theoryStrategic planningTechnological forecastingArtificial intelligence--Social aspectsDecision makingThesishttps://doi.org/10.51415/10321/6422