PhD Journey
My PhD journey is centred on understanding how generative AI is transforming managerial decision-making in small and medium-sized enterprises (SMEs), particularly under conditions of uncertainty, ambiguity, and constrained information environments.
This research direction has emerged from a sustained interest in how organisations interpret and act on information, rather than simply how they access it.
How This Research Began
My academic foundation in international business first developed through the study of consumer behaviour, value perception, and sustainability decisions in competitive markets.
During my MSc research, I explored how consumers interpret value signals in relation to sustainable packaging. This introduced a key insight that now underpins my doctoral work:
Decision-making is fundamentally interpretive, not purely rational or technical.
This shifted my focus from outcomes to processes of interpretation.
Transition to AI and Decision-Making
As generative AI systems became more widely integrated into business environments, I became increasingly interested in a deeper question:
How do decision-makers actually use AI-generated outputs when making strategic choices?
This question revealed a gap in existing literature:
- Most studies focus on adoption rates
- Fewer studies examine cognitive interpretation processes
- Very few explore how trust in AI is formed, adjusted, or broken in real time
This gap became the foundation of my current research direction.
Current PhD Research Focus
My doctoral research investigates:
How SME managers use generative AI in strategic decision-making under uncertainty, and how they calibrate trust in AI-generated outputs over time.
This involves examining:
- How AI outputs are interpreted in managerial contexts
- How trust in AI systems is developed and adjusted
- How organisational constraints shape AI use
- How uncertainty is managed in AI-supported decisions
Theoretical Development
My work is grounded in three core intellectual traditions:
- Organisational sensemaking within Organizational Theory
- Human–AI trust calibration and decision reliance
- Strategic decision-making under uncertainty and bounded rationality
Together, these perspectives allow me to examine AI not as a tool, but as part of a decision-making system shaped by human interpretation.
Why This Matters
SMEs are increasingly encouraged to adopt AI technologies as part of digital transformation strategies. However, adoption alone does not guarantee effective use.
My research addresses a critical gap:
- AI is often assumed to improve decisions directly
- In reality, its impact depends on interpretation, context, and organisational logic
This raises an important question:
When does generative AI actually improve decision-making — and when does it introduce new forms of uncertainty?
Research Direction Moving Forward
My PhD work is evolving toward a structured framework that explains:
- How AI becomes embedded in managerial cognition
- How trust is dynamically calibrated in practice
- How SMEs integrate (or resist) AI in strategic processes
- How decision outcomes emerge from human–AI interaction loops
This framework aims to contribute both theoretical clarity and practical insight for AI-enabled organisations.
Academic Positioning
This research positions me within interdisciplinary work across:
- Strategic Management
- Information Systems
- Organisational Theory
- Digital Transformation Studies
My goal is to contribute to a more realistic understanding of AI in organisations — one that reflects how decisions are actually made under uncertainty, rather than how they are assumed to be made in ideal models.
Current Status
I am currently refining my PhD proposal and preparing for supervisory discussions, with a focus on UK-based SME contexts and interpretive decision-making frameworks.
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