Doctor of Business
Administration (DBA)

Analyse how marketing strategy drives competitive advantage in the age of AI-powered personalisation, platform commerce, and shifting consumer trust. Apply strategic frameworks to real-world marketing challenges including digital ecosystem positioning, brand equity in saturated markets, and data-driven customer acquisition models.

Topics: AI-Driven Marketing 2026 · Platform Commerce Strategy · Customer Lifetime Value · Brand Positioning

Learning Outcomes: Critically evaluate strategic marketing theories; design and conduct marketing research; synthesise data to explain contemporary marketing phenomena.

Apply economic theory to managerial decision-making in an era of geopolitical realignment, supply chain restructuring, and monetary policy uncertainty. Evaluate how inflationary pressures, interest rate cycles, and emerging market dynamics shape strategic resource allocation and investment decisions.

Topics: Post-Pandemic Economic Shifts · Interest Rate Strategy · Supply Chain Economics · Pricing Under Uncertainty

Learning Outcomes: Apply economic theories to business research; evaluate the assumptions and limitations of economic models; synthesise results to explain real-world business phenomena.

Examine the forces reshaping international business in 2026: the rise of ASEAN as a manufacturing and investment hub, de-risking strategies in global supply chains, trade corridor realignment, cross-border digital commerce regulation, and the strategic implications of regional trade agreements such as RCEP and CPTPP.

Topics: ASEAN Economic Integration · RCEP & CPTPP Implications · Supply Chain De-Risking · Cross-Border Digital Trade

Learning Outcomes: Critically assess theories of international business; conduct research using appropriate methodologies; synthesise findings to explain contemporary global phenomena.

Navigate the evolving landscape of corporate finance, ESG reporting mandates, and sustainability-linked financial instruments. Address IFRS developments, integrated reporting frameworks, AI in financial analysis, capital structure optimisation, and the growing regulatory demands for climate-related financial disclosure.

Topics: ESG & Climate Disclosure · IFRS Updates 2026 · AI in Financial Analysis · Capital Structure Strategy

Learning Outcomes: Carry out computational exercises for accounting and finance approaches; evaluate theoretical assumptions; conduct research and synthesise results.

Develop advanced leadership capability for the hybrid, multigenerational, and AI-augmented workplace. Examine transformational, servant, and adaptive leadership models: leading distributed teams, navigating organisational restructuring, driving culture in post-M&A integration, and building psychological safety at scale.

Topics: Hybrid Workforce Leadership · AI-Augmented Decision-Making · Psychological Safety · Multigenerational Teams

Learning Outcomes: Critically evaluate organisational and leadership theories; conduct leadership research; synthesise findings to explain leadership phenomena.

Formulate and evaluate corporate and competitive strategy in an environment defined by technological disruption, sustainability imperatives, and geopolitical volatility. Apply strategic frameworks to AI integration strategy, platform business models, ESG-embedded strategy design, and strategic pivots in post-disruption industries.

Topics: AI Integration Strategy · Platform Business Models · ESG-Embedded Strategy · Strategic Disruption Response

Learning Outcomes: Explain and critically evaluate strategic management theories; conduct strategic management research; synthesise results to explain strategic phenomena.

Establish the philosophical foundations for doctoral-level research. Critically examine positivist, interpretivist, pragmatist, and critical realist paradigms, and evaluate their implications for business research design. Address research ethics in AI-generated data, algorithmic bias, and evolving standards for rigour in applied business research.

Topics: Research Paradigms · Ontology & Epistemology · AI Ethics in Research · Doctoral Rigour Standards

Learning Outcomes: Justify the appropriateness of research paradigms; evaluate assumptions and limitations of specific research approaches; design research using relevant philosophical methodologies.

Master the tools and procedures for doctoral-level business research: survey design, interview protocols, case study methodology, mixed-methods design, sampling strategy, and data management. Address AI-assisted literature review, digital ethnography, computational social science methods, and ethical data collection.

Topics: Mixed-Methods Design · AI-Assisted Literature Review · Digital Ethnography · Data Privacy Compliance

Learning Outcomes: Justify the choice of research tools and procedures; evaluate assumptions and limitations of each technique; adopt relevant techniques to analyse and synthesise results.

Develop proficiency in the statistical tools essential for doctoral research: hypothesis testing, ANOVA, regression analysis, non-parametric tests, and time-series analysis using SPSS, R, and Python. Address AI-augmented statistical analysis, automated model selection, and the critical importance of understanding statistical output.

Topics: Regression & ANOVA · SPSS, R & Python Tools · AI-Augmented Analysis · Statistical Interpretation

Learning Outcomes: Carry out computational exercises for each statistical tool; evaluate assumptions and limitations for different data types; compute, analyse, and synthesise results.

Master advanced multivariate techniques: factor analysis, cluster analysis, structural equation modelling (SEM), discriminant analysis, and MANOVA. Engage with PLS-SEM for applied business research, machine learning–assisted variable selection, and growing expectations for transparency and reproducibility in doctoral quantitative work.

Topics: Structural Equation Modelling · PLS-SEM for Business Research · Factor & Cluster Analysis · Reproducibility Standards

Learning Outcomes: Carry out computational exercises for each multivariate technique; evaluate assumptions for different data types; adopt relevant techniques to compute, analyse, and synthesise results.