Large-scale capital programs frequently underperform against cost, schedule, and risk expectations despite advances in technical planning and project controls. While traditional explanations emphasize structural and operational challenges, this research examines the behavioral drivers that influence decision-making in these environments. Grounded in behavioral economics, this study explores how cognitive biases—specifically optimism bias, loss aversion, and sunk cost fallacy—shape decision-making under conditions of uncertainty, complexity, and organizational pressure. Drawing on a practice-based case study of the City of Atlanta, the research identifies recurring decision patterns and examines their relevance in large-scale capital program environments. Findings demonstrate that decision-making distortions are systematic rather than incidental, influencing both the timing and quality of decisions across program phases. The study introduces a Behaviorally Informed Decision Framework that illustrates how bias manifests at key decision points and provides practical mechanisms for improving decision discipline. This research contributes to engaged management scholarship by bridging behavioral theory and executive practice, offering a transferable framework for improving decision quality and performance outcomes in complex, capital-intensive environments.

The banking industry is ripe for disruption (Gomber, Kauffman, Parker, & Weber, 2018). A primary threat to the banking model is industry leadership not adapting to the paradigm shift (Mackintosh, 2014) in consumer and employee demands towards a collective good and businesses that make a difference (Bhattacharya & Sen, 2004). Only a few studies have focused on values-based banking leadership. Thirty-four CEOs in seven countries were interviewed for this study. The findings are 1) positive organizational development, 2) doing well by doing good and creating a 3) vibrant ecosystem. The research contribution includes a framework for CEOs to transition to a values-based financial institution.

This study investigates the influence of prior knowledge on entrepreneurs’ ability to identify green business opportunities through the theory of planned behaviour. It explores how sustainability-related knowledge impacts opportunity recognition and provides insights for fostering entrepreneurship in the green economy, focusing on Asia.

This study investigates the role of prior knowledge in entrepreneurs’ ability to identify green business opportunities, focusing on the theory of planned behaviour. It aims to explore how knowledge in green and sustainability-related topics impacts opportunity identification, providing insights to encourage entrepreneurship in the green economy, particularly in Asia.

Why are skilled migrants at risk of precarity, despite their competence being in demand? We investigate this at the level of organisations and how they evaluate skilled migrants’ labour worth. Drawing on a 36-month qualitative investigation, our study explores how a HR initiative for recruiting skilled immigrants assesses their competences. With Lamont’s sociology of evaluation, we approach recruitment as a social process of recognition of migrants’ professional worth and identify four instances of recognition gaps: the worth of immigrants’ labour, the worth of their academic skills, their expected social position, and the value attached to the cultural diversity they bring. This low social valuation can be linked to precarious positions. Our contribution to research on skilled migrants’ precarity illuminates how organisational process play a role in their market worth devaluation. Contribution to HR inclusive recruitment literature stresses how the evaluative design of the process, rather than individual bias, reproduces discrimination.

This dissertation study examines how organizations in emerging economies, operating under persistent resource constraints, achieve integrated operational reliability and innovation. Integrating ambidexterity, emerging-economy, organizational action and learning, and pragmatist perspectives, it uses a longitudinal qualitative case study of a digital infrastructure firm over 15 years (2008–2023), guided by a Departure-Based Pragmatist Action Framework that traces breakdowns, departures from established practice, and evaluation and adjustment. It aims to explain ambidexterity under constraint as an emergent, practice-based accomplishment and to offer practitioners a process-oriented approach to reliability–innovation tensions.

Examines the impact of Generative Artificial Intelligence (GenAI) on productivity and human resources in Agile Software Development teams, exploring system dynamics and feedback loops to predict outcomes.

This research analyzes the effects of Human Resources Outsourcing (HRO) on small businesses, focusing on employee retention, engagement, and the corporate culture. The study uses a mixed-methods approach to examine HRM knowledge, Organizational Support Theory, and the interplay of HRO in workforce dynamics.

This study investigates the integration of Generative AI in Agile software engineering, highlighting its potential to improve productivity, innovation, and ethical implementation practices. The research includes qualitative analysis through interviews with global technology leaders.

This study investigates the integration of Generative AI in Agile software engineering, highlighting its potential to improve productivity, innovation, and ethical implementation practices. The research includes qualitative analysis through interviews with global technology leaders.