An Investigation of Trust Dynamics in Human-AI Collaborative Software Engineering Teams in Nigerian Technology Companies

The increasing integration of AI coding assistants and automated decision tools into software engineering workflows raises fundamental questions about trust dynamics between human engineers and AI systems, with significant implications for team performance, accountability, and professional identity. This dissertation investigates trust dynamics in human-AI collaborative software engineering teams at Nigerian technology companies, making an original theoretical and empirical contribution to the sociology of AI-integrated engineering work. A mixed-methods longitudinal study is conducted at six technology companies in Lagos, Abuja, and Kano, spanning South West, North Central, and North West zones, over 18 months. Quantitative data from 120 engineers captures trust calibration trajectories using adapted instruments from Muir's trust in automation framework, while qualitative ethnographic observations of 8 development teams and 40 in-depth interviews provide contextual depth. The study develops an original theoretical model of Cultural Trust Modulation (CTM), positing that Nigerian engineers' trust calibration of AI tools is distinctively shaped by collectivist professional identity norms, prior experiences with unreliable digital infrastructure, and regulatory uncertainty regarding AI-generated code accountability. CTM explains trust dynamics not captured by existing Western-centric automation trust theories. Findings show that trust over-reliance events are more frequent in junior engineer cohorts, while senior engineers exhibit systematic trust scepticism aligned with CTM predictions. The dissertation contributes CTM theory, validated measurement instruments, and managerial implications for responsible AI tool integration in African technology companies. Keywords: human-AI collaboration, trust dynamics, software engineering teams, Nigeria, automation trust