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Megaproject's construction is essential for the development and economic growth of any country, especially in the developing world. In Kuwait, megaprojects are facing many restrictions that discourage their execution causing a significant delay in bidding, design, construction and operation phases with the execution quality being affected. The objective of this study is to develop a complexity measurement model using analytic hierarchy process (AHP) for megaprojects in Kuwait, with a focus on the New Kuwait University multi-billion campus Shadadiyah (College of Social Science, Sharia and Law (CSSL)) as a case study.

The study applies a hybrid fuzzy analytic hierarchy process (FAHP) method to compare the results with those obtained using the conventional AHP method. This can facilitate the project management activities during the different stages of construction. Data were collected based on the results of a two-round Delphi questionnaire completed by seniors and experts of the selected project.

It was found that project modeling methodology was responsible for complexity. It was grouped under several categories that include technological, goal, organizational, environmental and cultural complexities. The study compares complexity degrees assessed by AHP and FAHP methods. “Technological Complexity” scores highest in both methods, with FAHP reaching 7.46. “Goal Complexity” follows closely behind, with FAHP. “Cultural Complexity” ranks third, differing between methods, while “Organizational” and “Environmental Complexity” consistently score lower, with FAHP values slightly higher. These results show varying complexity levels across dimensions. Assessing and understanding such complexities were essential toward the completion of such megaprojects.

The contribution of this study is on providing the empirical evidential knowledge for the priority over construction complexities in a developing country (Kuwait) in the Middle East.

The purpose of this study is to revisit the conventional wisdom about a key contribution [i.e. strengths, weaknesses, opportunities, threats (SWOT) analysis] in the field of strategic management. The societal context and the role of academics, consultants and executives is taken into account in the emergence of SWOT analysis during the 1960–1980 period as a pivotal development within the broader context of the satisfactory, opportunities, faults, threats (SOFT) approach. The authors report on both the content and the approach, so that other scholars seeking to invigorate indigenous theories and/or underreported strategy practices will thrive.

Applying a historiographic approach, the authors introduce an evidence-based methodology for interpreting historical sources. This methodology incorporates source criticism, triangulation and hermeneutical interpretation, drawing upon insights from robust evidence through three iterative stages.

The underreporting of the SOFT approach/SWOT analysis can be attributed to several factors, including strategy tools being integrated into planning frameworks rather than being published as standalone materials; restricted circulation of crucial long-range planning service/theory and practice of planning reports due to copyright limitations; restricted access to the Stanford Research Institute Planning Library in California; and the enduring popularity of SOFT and SWOT variations, driven in part by their memorable acronyms.

In the spirit of a renaissance in strategic planning research, the authors unveil novel theoretical and social connections in the emergence of SWOT analysis by combining evidence from both theory and practice and delving into previously unexplored areas.

Caution is advised for scholars who examine the discrete time frame of 1960–1980 through mere bibliometric techniques. This study underscores the risks associated with gathering incomplete and/or inaccurate data, emphasizing the importance of triangulating evidence beyond scholarly databases. The paradigm shift of strategic management research due to the advent of large language models poses new challenges and the risk of conserving and perpetuating academic urban legends, myths and lies if training data is not adequately curated.