AI in managerial decision-making : A qualitative exploration of cross-regional attitudes in a global tech company

This thesis explores how managers in a global tech company engage with Generative Artificial Intelligence (GenAI) in their daily decision‑making and whether attitudes toward AI vary across regions. Using a comparative qualitative design, nine managers within the same function across three regions participated in semi‑structured interviews that combined Likert‑scale statements and open‑ended questions. The study integrates TAM2 with theories of bounded rationality and intuitive decision-making to propose a combined framework, capturing both rational technology evaluations and cognitive realities of managerial judgment. The results show that managers consistently use AI as an augmentation tool rather than a decision‑maker: it supports data-intensive or time-pressured tasks but requires verification, as trust is conditional. Perceived Usefulness (PU) drives adoption, while Perceived Ease of Use (PEOU) remains modest due to prompting effort. Regional differences reflect perceived output quality; Region 2 reports high PU and usage despite low PEOU, whereas skepticism persists in Region 3. Across all regions, managers maintain firm boundaries around people‑related or ethically sensitive decisions, where human intuition and contextual experience prevail. Alongside the empirical insights, the thesis makes a central contribution through the combined theoretical model, offering a conceptual foundation for future research on AI-supported decision-making in organizations.