The transition toward sustainable organizations has intensified the need for advanced knowledge systems capable of addressing complex economic, environmental, and social challenges. In this context, intelligent knowledge—defined as the integration of human expertise, organizational learning, and digital technologies such as artificial intelligence (AI) and data analytics—has emerged as a critical enabler of informed decision-making and long-term organizational resilience. This paper examines the research methods most suitable for investigating sustainable organizations through the lens of intelligent knowledge, with particular attention to their ability to capture complexity, dynamism, and socio-technical interactions.
The study adopts an integrative methodological perspective, combining insights from quantitative, qualitative, mixed-methods, design science, and participatory research approaches. Quantitative methods support the analysis of measurable relationships between knowledge capabilities and sustainability performance, while qualitative approaches provide in-depth understanding of organizational processes, cultural dynamics, and meaning-making practices. Mixed-methods designs enable the triangulation of findings, enhancing both validity and explanatory power. In addition, design science and action research approaches contribute to bridging theory and practice by supporting the development and evaluation of intelligent knowledge systems in real organizational contexts.
A central argument of the paper is that no single methodological approach is sufficient to address the multifaceted nature of sustainability challenges. Instead, the research highlights the necessity of methodological pluralism, emphasizing longitudinal and process-oriented designs, multi-level analysis, and the integration of ethical considerations related to AI and data use. The paper also underscores the importance of reflexivity and interdisciplinary collaboration in developing robust and context-sensitive research frameworks.
By providing a comprehensive overview of methodological approaches and their implications, this study contributes to the advancement of sustainability research and offers a coherent framework for analyzing how intelligent knowledge can support sustainable organizational transformation. The proposed methodological orientation is both theoretically grounded and practically relevant, enabling researchers and practitioners to better understand and navigate the complexities of sustainability in an increasingly data-driven world.