Doctor of Business Administration (DBA)

Module Introduction

This module develops an advanced understanding of management theory and its application in complex organisational contexts.

Module Aims

• Develop advanced understanding of management theory.
• Critically evaluate contemporary management practices.
• Integrate theory with professional practice.
• Develop doctoral-level critical thinking.
• Support research-informed managerial decision-making.

Learning Outcomes

• Critically evaluate major management theories.
• Apply theory to complex organisational issues.
• Demonstrate integrative and critical analysis.
• Produce scholarly arguments.
• Reflect on implications for practice.

Indicative Content

• Contemporary management theory
• Strategy and performance
• Organisational design
• Leadership and governance
• Ethics and sustainability
• Evidence-based management

Learning and Teaching Strategy

Learning and teaching will be delivered through doctoral seminars, workshops, guided reading, case-based discussions, and independent study. Emphasis is placed on critical analysis, integration of theory and professional practice, and the development of advanced research-informed judgement appropriate to doctoral-level study.

Assessment Strategy

Assessment 1: Applied or conceptual paper / project (40%). Assessment 2: Major research-informed assignment or portfolio (60%).

Recommended Reading

• Boddy, D. (2020). Management: An Introduction. Pearson. Johnson, G., Scholes, K., & Whittington, R. (2020). Exploring Strategy. Pearson. Grant, R.M. (2021). Contemporary Strategy Analysis. Wiley.

Module Introduction

This module develops doctoral-level understanding of research philosophy, design, and methodology for applied research.

Module Aims

• Develop understanding of research philosophies.
• Critically evaluate research designs.
• Select appropriate methods.
• Develop ethical research practice.
• Prepare for doctoral research.

Learning Outcomes

• Explain research paradigms.
• Design rigorous research frameworks.
• Justify methodological choices.
• Apply ethical principles.
• Produce a coherent methodology chapter outline.

Indicative Content

• Research paradigms
• Qualitative and quantitative designs
• Mixed methods
• Ethics and governance
• Validity and reliability
• Research writing

Learning and Teaching Strategy

Learning and teaching will be delivered through doctoral seminars, workshops, guided reading, case-based discussions, and independent study. Emphasis is placed on critical analysis, integration of theory and professional practice, and the development of advanced research-informed judgement appropriate to doctoral-level study.

Assessment Strategy

Assessment 1: Applied or conceptual paper / project (40%). Assessment 2: Major research-informed assignment or portfolio (60%).

Recommended Reading

• Saunders, M., Lewis, P., & Thornhill, A. (2019). Research Methods for Business Students. Pearson. Creswell, J.W. (2018). Research Design. Sage. Bryman, A., & Bell, E. (2015). Business Research Methods. OUP.

Module Introduction

This module examines strategic leadership and its impact on organisational performance and transformation.

Module Aims

• Analyse leadership theories.
• Evaluate leadership in strategy.
• Link leadership to performance.
• Develop strategic judgement.
• Apply leadership in complex contexts.

Learning Outcomes

• Critically analyse leadership approaches.
• Evaluate performance implications.
• Apply frameworks to cases.
• Formulate strategic recommendations.
• Demonstrate scholarly communication.

Indicative Content

• Leadership theories
• Strategy execution
• Performance management
• Change leadership
• Culture and governance
• Contemporary challenges

Learning and Teaching Strategy

Learning and teaching will be delivered through doctoral seminars, workshops, guided reading, case-based discussions, and independent study. Emphasis is placed on critical analysis, integration of theory and professional practice, and the development of advanced research-informed judgement appropriate to doctoral-level study.

Assessment Strategy

Assessment 1: Applied or conceptual paper / project (40%). Assessment 2: Major research-informed assignment or portfolio (60%).

Recommended Reading

• Northouse, P.G. (2021). Leadership: Theory and Practice. Sage. Kaplan, R.S., & Norton, D.P. (2004). Strategy-Focused Organization. HBS Press. Burnes, B. (2017). Managing Change. Pearson.

Module Introduction

This module supports the development of a rigorous and viable DBA research proposal.

Module Aims

• Formulate research problems.
• Conduct systematic literature review.
• Design methodology.
• Develop project planning skills.
• Prepare for confirmation of candidature.

Learning Outcomes

• Produce a coherent proposal. 2 2. Critically review literature.
• Justify methods.
• Plan project delivery.
• Demonstrate ethical awareness.

Indicative Content

• Problem formulation
• Systematic review
• Methodology design
• Project management
• Ethics
• Proposal defence preparation

Learning and Teaching Strategy

Learning and teaching will be delivered through doctoral seminars, workshops, guided reading, case-based discussions, and independent study. Emphasis is placed on critical analysis, integration of theory and professional practice, and the development of advanced research-informed judgement appropriate to doctoral-level study.

Assessment Strategy

Assessment 1: Applied or conceptual paper / project (40%). Assessment 2: Major research-informed assignment or portfolio (60%).

Recommended Reading

• Saunders, M., Lewis, P., & Thornhill, A. (2019). Research Methods for Business Students. Pearson. Booth, A., Sutton, A., & Papaioannou, D. (2016). Systematic Approaches to a Successful Literature Review. Sage. Creswell, J.W. (2018). Research Design. Sage.

Module Introduction

This module provides a doctoral-level examination of artificial intelligence (AI) as a driver of business strategy and organisational transformation. It explores how AI technologies reshape value creation, processes, decision systems, and competitive advantage, while addressing governance, ethics, and risk. Candidates will critically engage with contemporary research and practice to design, evaluate, and lead AI-enabled transformation initiatives in complex organisational contexts.

Module Aims

• To develop advanced understanding of AI technologies and their strategic business implications.
• To critically evaluate AI-enabled business models and transformation initiatives.
• To analyse governance, ethical, and risk considerations in AI adoption.
• To integrate AI strategy with organisational change and performance management.
• To develop doctoral-level critical, analytical, and research-informed judgement in AI- driven transformation.

Learning Outcomes

• Critically evaluate AI technologies and their implications for business strategy and operations.
• Analyse AI-enabled business models and transformation pathways in complex organisations.
• Critically assess governance, ethical, and risk issues associated with AI adoption.
• Apply appropriate strategic and analytical frameworks to AI transformation challenges.
• Produce rigorous, research-informed strategic analysis and recommendations at doctoral level.

Indicative Content

• AI technologies and business applications
• Data, platforms, and decision intelligence
• Process automation and organisational redesign
• AI-enabled business models and competitive strategy
• Governance, ethics, bias, and accountability in AI
• Risk management, cybersecurity, and resilience
• Change management and capability development for AI
• Measuring value creation and performance impact of AI

Learning and Teaching Strategy

Learning and teaching will be delivered through doctoral seminars, workshops, case-based discussions, guided reading, and independent study. Candidates will critically analyse contemporary research and industry cases, evaluate AI transformation initiatives, and develop research-informed strategic perspectives. Emphasis is placed on scholarly critique, 3 integration of theory and practice, and the development of advanced professional judgement.

Assessment Strategy

Assessment 1: Critical review paper on AI strategy or governance (40%). Assessment 2: Applied AI transformation strategy or consultancy-style report (60%).

Recommended Reading

• Davenport, T.H. and Ronanki, R. (2018). Artificial Intelligence for the Real World. Harvard Business Review. Brynjolfsson, E. and McAfee, A. (2017). Machine, Platform, Crowd. New York: W.W. Norton & Company. Russell, S. and Norvig, P. (2020). Artificial Intelligence: A Modern Approach. 4th ed. Harlow: Pearson.

Module Introduction

This module provides a doctoral-level examination of leadership and policy in education systems and organisations. It explores governance, reform, quality assurance, and strategic leadership, and how policy is designed, implemented, and evaluated in complex educational contexts. Candidates will critically engage with contemporary research and practice to lead evidence-informed change and improvement initiatives.

Module Aims

• Develop advanced understanding of educational leadership and policy.
• Critically evaluate governance and reform in education systems.
• Analyse quality assurance and performance improvement strategies.
• Integrate evidence and research into policy and leadership decisions.
• Develop doctoral-level critical, analytical, and research-informed judgement.

Learning Outcomes

• Critically evaluate leadership and policy frameworks in education.
• Analyse governance, accountability, and reform agendas.
• Assess quality assurance and improvement strategies.
• Apply evidence-based approaches to educational leadership challenges.
• Produce rigorous, research-informed policy and leadership analysis at doctoral level.

Indicative Content

• Education policy analysis and governance
• Leadership theories in education
• Quality assurance and standards
• System reform and change management
• Accountability, performance, and evaluation
• Equity, inclusion, and ethics in education
• Stakeholder engagement and partnership
• International and comparative education policy

Learning and Teaching Strategy

Learning and teaching will be delivered through doctoral seminars, workshops, case-based discussions, guided reading, and independent study. Candidates will critically analyse contemporary research and organisational cases, evaluate strategic initiatives, and develop research-informed perspectives. Emphasis is placed on scholarly critique, integration of theory and practice, and the development of advanced professional judgement.

Assessment Strategy

Assessment 1: Critical review paper on a core theme (40%). Assessment 2: Applied strategy/consultancy-style report or project (60%).

Recommended Reading

• Bush, T. (2011). Theories of Educational Leadership and Management. Sage. 4 Fullan, M. (2014). The Principal: Three Keys to Maximizing Impact. Jossey-Bass. OECD (2019). Education Policy Outlook. OECD Publishing.

Module Introduction

This module provides a doctoral-level exploration of financial technologies (FinTech) and their strategic implications for financial services and broader business ecosystems. It examines digital finance platforms, payments, blockchain, and data-driven financial innovation, alongside regulatory, risk, and governance considerations. Candidates will critically engage with contemporary research and industry practice to evaluate how FinTech reshapes business models, competition, and value creation, and apply these insights to complex organisational and policy contexts.

Module Aims

• To develop advanced understanding of FinTech ecosystems and digital finance strategies.
• To critically evaluate the strategic impact of financial technologies on business models and markets.
• To analyse regulatory, risk, and governance challenges in digital finance.
• To integrate data-driven and technology-enabled perspectives into financial strategy.
• To develop doctoral-level critical, analytical, and research-informed judgement in FinTech.

Learning Outcomes

• Critically evaluate key FinTech technologies and digital finance business models. 2 2. Analyse the strategic implications of FinTech for incumbents, start-ups, and regulators.
• Critically assess regulatory, risk, and governance issues in digital finance.
• Apply appropriate analytical frameworks to complex FinTech strategy problems.
• Produce rigorous, research-informed strategic analysis at doctoral level.

Indicative Content

• FinTech ecosystems and digital disruption in financial services
• Digital payments, platforms, and open banking
• Blockchain, crypto-assets, and distributed ledger technologies
• Data analytics, AI, and automation in finance
• RegTech, compliance, and regulatory innovation
• Risk management, cybersecurity, and resilience in digital finance
• Business model innovation and competitive strategy in FinTech
• Ethics, governance, and sustainability in financial innovation

Learning and Teaching Strategy

Learning and teaching will be delivered through doctoral seminars, workshops, case-based discussions, guided reading, and independent study. Candidates will critically analyse contemporary research and industry cases, evaluate strategic choices in digital finance, and develop research-informed perspectives on financial innovation. Emphasis is placed on scholarly critique, integration of theory and practice, and the development of advanced professional judgement.

Assessment Strategy

Assessment 1: Critical review paper on a FinTech strategy theme (40%). Assessment 2: Applied FinTech strategy analysis or policy/consultancy-style report (60%).

Recommended Reading

• Gomber, P., Kauffman, R.J., Parker, C. and Weber, B.W. (2018). On the FinTech Revolution. Journal of Management Information Systems. 4 Arner, D.W., Barberis, J. and Buckley, R.P. (2017). FinTech, RegTech, and the Reconceptualization of Financial Regulation. Northwestern Journal of International Law & Business. Philippon, T. (2016). The FinTech Opportunity. BIS Working Papers.

Module Introduction

This module develops advanced capabilities in business analysis and evidence-based decision making for senior leaders and practitioners. It focuses on the critical evaluation and integration of qualitative and quantitative evidence to support strategic and complex organisational decisions. The module strengthens analytical judgement, problem structuring, and the effective use of data, research, and professional evidence in uncertain and dynamic business contexts, and prepares candidates for doctoral-level applied inquiry and decision-making.

Module Aims

• To develop advanced analytical and critical thinking skills for complex business problems.
• To enhance the use of evidence-based approaches in strategic decision making.
• To integrate qualitative and quantitative evidence in managerial analysis.
• To strengthen problem structuring and decision design at executive level.
• To prepare candidates for doctoral-level applied research and inquiry.

Learning Outcomes

• Critically evaluate complex business problems using appropriate analytical frameworks. 2 2. Integrate qualitative and quantitative evidence to support strategic decisions.
• Design and justify evidence-based decision-making approaches in organisational contexts.
• Critically assess the quality, relevance, and limitations of different sources of evidence.
• Communicate rigorous, research-informed managerial analysis at doctoral level.

Indicative Content

• Problem structuring and decision framing in complex organisations
• Evidence-based management and decision-making models
• Qualitative and quantitative data for managerial analysis
• Critical appraisal of research and professional evidence
• Analytical tools for strategic and operational decision making
• Managing uncertainty, risk, and bias in decision processes
• Linking analysis to strategic and organisational outcomes

Learning and Teaching Strategy

Learning and teaching will be delivered through doctoral seminars, workshops, case-based discussions, guided reading, and independent study. Candidates will engage in critical analysis of complex business cases, apply analytical tools to real organisational problems, and develop research-informed decision frameworks. Emphasis is placed on scholarly critique, integration of theory and practice, and the development of advanced professional judgement.

Assessment Strategy

Assessment 1: Analytical position paper on a complex business problem (40%). Assessment 2: Applied evidence-based decision analysis project or report (60%).

Recommended Reading

• Pfeffer, J. and Sutton, R.I. (2006). Hard Facts, Dangerous Half-Truths, and Total Nonsense: Profiting from Evidence-Based Management. Boston: Harvard Business School Press. 4 Provost, F. and Fawcett, T. (2013). Data Science for Business. Sebastopol, CA: O’Reilly Media. Saunders, M., Lewis, P. and Thornhill, A. (2019). Research Methods for Business Students. 8th ed. Harlow: Pearson.

Module Introduction

This module provides an advanced, doctoral-level exploration of Strategic Human Resource Management (SHRM) and its role in creating sustainable organisational performance and competitive advantage. It examines how people strategies are designed, implemented, and evaluated in complex and dynamic environments, and how HRM integrates with corporate strategy, governance, and organisational change. Candidates will engage critically with contemporary research, analytics-driven HRM, and strategic workforce challenges, and apply these perspectives to their own professional contexts.

Module Aims

• To develop advanced understanding of SHRM theories and strategic people management.
• To critically evaluate the contribution of HR strategy to organisational performance and sustainability.
• To integrate evidence-based and analytics-informed approaches into people management decisions.
• To analyse complex workforce, talent, and capability challenges in contemporary organisations.
• To develop doctoral-level critical, analytical, and research-informed judgement in HRM.

Learning Outcomes

• Critically evaluate key theories, models, and debates in Strategic Human Resource Management.
• Analyse the alignment between HR strategy and organisational strategy in complex contexts.
• Apply evidence-based and analytics-informed approaches to strategic people management decisions.
• Critically assess contemporary workforce, talent, and capability challenges.
• Produce rigorous, research-informed strategic HR analysis at doctoral level.

Indicative Content

• Strategic HRM: theories, models, and critiques
• HR strategy and business strategy alignment
• Talent management, workforce planning, and capability development
• Performance management, reward, and high-performance work systems
• HR analytics and evidence-based people management
• Employee relations, engagement, and organisational culture
• International and comparative HRM
• Governance, ethics, and sustainability in people management

Learning and Teaching Strategy

Learning and teaching will be delivered through doctoral seminars, workshops, case-based discussions, guided reading, and independent study. Candidates will critically engage with contemporary research, analyse complex organisational cases, and apply strategic HRM frameworks to real-world contexts. Emphasis is placed on scholarly critique, integration of theory and practice, and the development of advanced professional judgement.

Assessment Strategy

Assessment 1: Critical review paper on a strategic HRM theme (40%). Assessment 2: Applied strategic HRM analysis or consultancy-style report (60%).

Recommended Reading

• Boxall, P., Purcell, J. and Wright, P. (2019). The Oxford Handbook of Human Resource Management. Oxford: Oxford University Press. Torrington, D., Hall, L., Taylor, S. and Atkinson, C. (2020). Human Resource Management. 11th ed. Harlow: Pearson. Armstrong, M. and Taylor, S. (2020). Armstrong’s Handbook of Human Resource Management Practice. 15th ed. London: Kogan Page.

Module Introduction

This module provides a doctoral-level examination of entrepreneurship and innovation as strategic processes. It explores opportunity recognition, business models, venture strategy, and innovation management, and how organisations create, scale, and sustain value in dynamic environments.

Module Aims

• Develop advanced understanding of entrepreneurship and innovation strategy.
• Critically evaluate venture creation and business model design.
• Analyse innovation management and growth strategies.
• Integrate evidence and research into entrepreneurial decision making.
• Develop doctoral-level critical, analytical, and research-informed judgement.

Learning Outcomes

• Critically evaluate theories of entrepreneurship and innovation.
• Analyse opportunity recognition, business models, and venture strategy.
• Assess growth, scaling, and innovation management challenges.
• Apply strategic frameworks to entrepreneurial contexts.
• Produce rigorous, research-informed entrepreneurial strategy analysis at doctoral level.

Indicative Content

• Entrepreneurship and innovation theory
• Opportunity recognition and business models
• Venture strategy and competitive positioning
• Innovation management and product development
• Growth, scaling, and internationalisation
• Financing ventures and investment readiness
• Entrepreneurial ecosystems and partnerships
• Governance, risk, and sustainability

Learning and Teaching Strategy

Learning and teaching will be delivered through doctoral seminars, workshops, case-based discussions, guided reading, and independent study. Candidates will critically analyse contemporary research and organisational cases, evaluate strategic initiatives, and develop research-informed perspectives. Emphasis is placed on scholarly critique, integration of theory and practice, and the development of advanced professional judgement.

Assessment Strategy

Assessment 1: Critical review paper on a core theme (40%). Assessment 2: Applied strategy/consultancy-style report or project (60%).

Recommended Reading

• Tidd, J. and Bessant, J. (2020). Managing Innovation. 6th ed. Wiley. 4 Burns, P. (2016). Entrepreneurship and Small Business. 4th ed. Palgrave Macmillan. Osterwalder, A. and Pigneur, Y. (2010). Business Model Generation. Wiley.

Module Introduction

This module provides a doctoral-level examination of strategic marketing and brand management in competitive and global markets. It explores how organisations create, communicate, and sustain value through market-oriented strategy, brand leadership, and customer-centric capabilities. Candidates will critically engage with contemporary research and practice to evaluate strategic choices in branding, positioning, and market development, and apply these insights to complex organisational contexts.

Module Aims

• To develop advanced understanding of strategic marketing and brand management theory and practice.
• To critically evaluate market-oriented and customer-centric strategies.
• To analyse branding, positioning, and value creation in competitive markets.
• To integrate data, analytics, and research evidence into marketing strategy.
• To develop doctoral-level critical, analytical, and research-informed judgement in strategic marketing.

Learning Outcomes

• Critically evaluate strategic marketing and brand management theories and frameworks.
• Analyse market dynamics, competitive positioning, and value propositions.
• Critically assess branding strategies and their organisational implications.
• Apply appropriate strategic frameworks to complex marketing challenges.
• Produce rigorous, research-informed strategic marketing analysis at doctoral level.

Indicative Content

• Strategic marketing planning and market orientation
• Brand equity, brand architecture, and brand leadership
• Value propositions, positioning, and differentiation
• Customer-centric strategy and market segmentation
• Digital and data-driven marketing strategy
• Global and cross-cultural marketing strategy
• Ethics, governance, and sustainability in marketing
• Measuring marketing performance and value creation

Learning and Teaching Strategy

Learning and teaching will be delivered through doctoral seminars, workshops, case-based discussions, guided reading, and independent study. Candidates will critically analyse contemporary research and organisational cases, evaluate strategic marketing and customer experience initiatives, and develop research-informed strategic perspectives. Emphasis is placed on scholarly critique, integration of theory and practice, and the development of advanced professional judgement.

Assessment Strategy

Assessment 1: Critical review paper on a strategic marketing or CX theme (40%). Assessment 2: Applied marketing/CX strategy or consultancy-style report (60%).

Recommended Reading

• Kotler, P. and Keller, K.L. (2016). Marketing Management. 15th ed. Harlow: Pearson. Kapferer, J.-N. (2012). The New Strategic Brand Management. 5th ed. London: Kogan Page. Aaker, D.A. (2014). Aaker on Branding. New York: Morgan James.

Module Introduction

This module provides a doctoral-level examination of advanced analytics, machine learning, and decision support systems and their application in complex organisational contexts. It focuses on how data-driven models are designed, governed, and deployed to support strategic and operational decision making, while addressing issues of bias, explainability, ethics, and risk. Candidates will critically engage with contemporary research and practice to evaluate the strategic value and limitations of analytics-driven decision systems.

Module Aims

• To develop advanced understanding of analytics and machine learning for executive decision making.
• To critically evaluate decision support systems and data-driven models in organisations.
• To analyse governance, ethical, and risk issues in analytics and AI-enabled decision systems.
• To integrate advanced analytics into strategic and operational decision processes.
• To develop doctoral-level critical, analytical, and research-informed judgement in data- driven decision making.

Learning Outcomes

• Critically evaluate advanced analytics and machine learning approaches for organisational decision making.
• Analyse the design and use of decision support systems in complex organisational contexts.
• Critically assess governance, ethical, and risk issues associated with data-driven decision systems.
• Apply appropriate analytical and strategic frameworks to decision system challenges.
• Produce rigorous, research-informed analysis and recommendations at doctoral level.

Indicative Content

• Advanced analytics and machine learning concepts
• Predictive, prescriptive, and cognitive analytics
• Decision support systems and decision intelligence
• Model governance, explainability, and accountability
• Bias, fairness, and ethical considerations in algorithms
• Risk management and validation of analytical models
• Implementation and change management for analytics systems
• Measuring value and performance impact of decision systems

Learning and Teaching Strategy

Learning and teaching will be delivered through doctoral seminars, workshops, case-based discussions, guided reading, and independent study. Candidates will critically analyse contemporary research and organisational cases, evaluate analytics-driven decision systems, and develop research-informed perspectives on data-enabled strategy. Emphasis 3 is placed on scholarly critique, integration of theory and practice, and the development of advanced professional judgement.

Assessment Strategy

Assessment 1: Critical review paper on analytics-driven decision systems (40%). Assessment 2: Applied analytics and decision systems strategy or consultancy-style report (60%).

Recommended Reading

• Shmueli, G., Bruce, P., Gedeck, P. and Patel, N.R. (2020). Data Mining for Business Analytics. 2nd ed. Hoboken: Wiley. Provost, F. and Fawcett, T. (2013). Data Science for Business. Sebastopol, CA: O’Reilly Media. Goodfellow, I., Bengio, Y. and Courville, A. (2016). Deep Learning. Cambridge, MA: MIT Press.

Module Introduction

This module provides a doctoral-level exploration of innovation and digital transformation in education organisations. It examines learning systems, educational technologies, organisational change, and capability development, and how digital strategies are designed and implemented to enhance learning and institutional performance.

Module Aims

• Develop advanced understanding of innovation and digital transformation in education.
• Critically evaluate learning systems and educational technologies.
• Analyse organisational change and capability development in education contexts.
• Integrate data and evidence into digital and innovation strategies.
• Develop doctoral-level critical, analytical, and research-informed judgement.

Learning Outcomes

• Critically evaluate innovation and digital transformation frameworks in education.
• Analyse learning systems, pedagogies, and technologies.
• Assess organisational change and capability development strategies.
• Apply evidence-based approaches to digital transformation challenges.
• Produce rigorous, research-informed analysis and recommendations at doctoral level.

Indicative Content

• Innovation in education and learning systems
• Digital platforms, blended and online learning
• Change management and organisational development
• Learning analytics and data-informed improvement
• Capability development and professional learning
• Quality enhancement and evaluation
• Ethics, privacy, and digital governance
• Scaling and sustaining innovation

Learning and Teaching Strategy

Learning and teaching will be delivered through doctoral seminars, workshops, case-based discussions, guided reading, and independent study. Candidates will critically analyse contemporary research and organisational cases, evaluate strategic initiatives, and develop research-informed perspectives. Emphasis is placed on scholarly critique, integration of theory and practice, and the development of advanced professional judgement.

Assessment Strategy

Assessment 1: Critical review paper on a core theme (40%). Assessment 2: Applied strategy/consultancy-style report or project (60%).

Recommended Reading

• Selwyn, N. (2016). Education and Technology. Bloomsbury. 4 Weller, M. (2020). 25 Years of EdTech. Athabasca University Press. Fullan, M. and Langworthy, M. (2014). A Rich Seam. Pearson.

Module Introduction

This module provides a doctoral-level examination of advanced data analytics and their role in driving financial innovation and strategic decision making. It explores analytical methods, machine learning applications, and data governance within financial services and adjacent sectors. Candidates will critically evaluate how analytics capabilities create value, reshape products and processes, and influence risk management, compliance, and performance, while addressing ethical, regulatory, and organisational implications.

Module Aims

• To develop advanced understanding of data analytics and their application in financial innovation.
• To critically evaluate analytical methods, tools, and platforms for financial decision making.
• To analyse governance, ethical, and regulatory issues associated with data-driven finance.
• To integrate analytics into strategic and innovation-oriented financial management.
• To develop doctoral-level critical, analytical, and research-informed judgement in analytics-driven finance.

Learning Outcomes

• Critically evaluate advanced analytics and machine learning applications in financial contexts.
• Analyse how data-driven approaches support financial innovation and strategic decision making.
• Critically assess data governance, ethical, and regulatory challenges in analytics-driven finance.
• Apply appropriate analytical frameworks to complex financial innovation problems.
• Produce rigorous, research-informed analysis and recommendations at doctoral level.

Indicative Content

• Advanced data analytics and machine learning in finance
• Predictive and prescriptive analytics for financial decision making
• Risk modelling, fraud detection, and compliance analytics
• Data platforms, cloud, and financial information systems
• Data governance, privacy, and ethical considerations
• Innovation in financial products and services through analytics
• Performance measurement and value creation using data
• Implementation challenges and organisational capabilities

Learning and Teaching Strategy

Learning and teaching will be delivered through doctoral seminars, workshops, case-based discussions, guided reading, and independent study. Candidates will critically analyse contemporary research and industry cases, evaluate analytics-driven innovation initiatives, and develop research-informed perspectives on data-enabled financial strategy. Emphasis 3 is placed on scholarly critique, integration of theory and practice, and the development of advanced professional judgement.

Assessment Strategy

Assessment 1: Critical review paper on analytics-driven financial innovation (40%). Assessment 2: Applied analytics strategy or innovation project / consultancy-style report (60%).

Recommended Reading

• Provost, F. and Fawcett, T. (2013). Data Science for Business. Sebastopol, CA: O’Reilly Media. Shmueli, G., Bruce, P., Gedeck, P. and Patel, N.R. (2020). Data Mining for Business Analytics. 2nd ed. Hoboken: Wiley. Marr, B. (2016). Big Data in Practice. Chichester: Wiley.

Module Introduction

This module provides a doctoral-level examination of talent management and organisational development (OD) as strategic levers for sustained organisational performance and transformation. It explores how organisations identify, develop, and retain talent, and how planned change and development interventions are designed, implemented, and evaluated. Candidates will critically engage with contemporary research, leadership development, learning systems, and change management, and apply these perspectives to complex organisational contexts.

Module Aims

• To develop advanced understanding of talent management and organisational development theories and practices.
• To critically evaluate approaches to leadership, learning, and capability development.
• To analyse organisational change and development interventions in complex contexts.
• To integrate evidence-based approaches into people and organisation development strategies.
• To develop doctoral-level critical, analytical, and research-informed judgement in OD and talent management.

Learning Outcomes

• Critically evaluate key theories and models of talent management and organisational development.
• Analyse leadership development, learning systems, and capability-building strategies in organisations.
• Critically assess the design and impact of organisational development and change interventions.
• Apply evidence-based approaches to talent and organisational development challenges.
• Produce rigorous, research-informed analysis and recommendations at doctoral level.

Indicative Content

• Talent management strategies and systems
• Leadership development and succession planning
• Organisational development theory and practice
• Learning and development systems and cultures
• Planned change and OD interventions
• Evaluating people and organisation development initiatives
• Culture, engagement, and organisational capability
• Ethics, governance, and sustainability in people development

Learning and Teaching Strategy

Learning and teaching will be delivered through doctoral seminars, workshops, case-based discussions, guided reading, and independent study. Candidates will critically analyse contemporary research, evaluate organisational cases, and design evidence-based 3 development and change interventions. Emphasis is placed on scholarly critique, integration of theory and practice, and the development of advanced professional judgement.

Assessment Strategy

Assessment 1: Critical review paper on talent management or organisational development (40%). Assessment 2: Applied organisational development or talent strategy project / consultancy- style report (60%).

Recommended Reading

• Cummings, T.G. and Worley, C.G. (2019). Organization Development and Change. 11th ed. Boston: Cengage. Garavan, T.N., Carbery, R. and Rock, A. (2012). Mapping Talent Development. European Journal of Training and Development. Day, D.V., Fleenor, J.W., Atwater, L.E., Sturm, R.E. and McKee, R.A. (2014). Advances in Leader and Leadership Development. The Leadership Quarterly.

Module Introduction

This module provides a doctoral-level exploration of growth, scaling, and business development strategies. It examines how firms expand markets, build partnerships, scale operations, and manage risk while sustaining performance and innovation.

Module Aims

• Develop advanced understanding of growth and scaling strategies.
• Critically evaluate business development and partnership models.
• Analyse operational, financial, and organisational challenges of scaling.
• Integrate evidence and research into growth strategy decisions.
• Develop doctoral-level critical, analytical, and research-informed judgement.

Learning Outcomes

• Critically evaluate growth and scaling frameworks.
• Analyse business development, partnerships, and market expansion strategies.
• Assess operational and organisational implications of scaling.
• Apply strategic tools to complex growth challenges.
• Produce rigorous, research-informed growth strategy analysis at doctoral level.

Indicative Content

• Growth strategies and scaling models
• Market entry and expansion
• Partnerships, alliances, and ecosystems
• Operations and capability scaling
• Finance for growth and performance management
• Risk management and resilience
• Governance and organisational design
• Sustainable growth and long-term value creation

Learning and Teaching Strategy

Learning and teaching will be delivered through doctoral seminars, workshops, case-based discussions, guided reading, and independent study. Candidates will critically analyse contemporary research and organisational cases, evaluate strategic initiatives, and develop research-informed perspectives. Emphasis is placed on scholarly critique, integration of theory and practice, and the development of advanced professional judgement.

Assessment Strategy

Assessment 1: Critical review paper on a core theme (40%). Assessment 2: Applied strategy/consultancy-style report or project (60%).

Recommended Reading

• Bridge, S. and O’Neill, K. (2017). Understanding Enterprise. Palgrave. 4 Storey, D. and Greene, F. (2010). Small Business and Entrepreneurship. Pearson. Isenberg, D. (2010). How to Start an Entrepreneurial Revolution. Harvard Business Review.

Module Introduction

This module provides a doctoral-level exploration of digital marketing and customer experience (CX) strategy in omnichannel and platform-based environments. It examines how organisations design, manage, and measure customer journeys, leverage data and analytics, and integrate digital technologies to create superior experiences and sustainable value. Candidates will critically engage with contemporary research and practice to evaluate and lead CX and digital transformation initiatives.

Module Aims

• To develop advanced understanding of digital marketing and customer experience strategy.
• To critically evaluate omnichannel and platform-based customer strategies.
• To analyse the role of data, analytics, and technology in shaping customer experiences.
• To integrate CX design with organisational strategy and performance management.
• To develop doctoral-level critical, analytical, and research-informed judgement in digital and CX strategy.

Learning Outcomes

• Critically evaluate digital marketing and customer experience theories and frameworks.
• Analyse customer journeys and omnichannel experience design.
• Critically assess the use of data and analytics in CX and digital marketing.
• Apply appropriate strategic frameworks to complex CX and digital transformation challenges.
• Produce rigorous, research-informed strategic analysis and recommendations at doctoral level.

Indicative Content

• Digital platforms, ecosystems, and omnichannel strategy
• Customer journey mapping and experience design
• Data-driven marketing and customer analytics
• Personalisation, automation, and marketing technologies
• Customer engagement, loyalty, and relationship management
• Privacy, ethics, and regulation in digital marketing
• Measuring and managing customer experience performance
• Leading digital and CX transformation initiatives

Learning and Teaching Strategy

Learning and teaching will be delivered through doctoral seminars, workshops, case-based discussions, guided reading, and independent study. Candidates will critically analyse contemporary research and organisational cases, evaluate strategic marketing and customer experience initiatives, and develop research-informed strategic perspectives. Emphasis is placed on scholarly critique, integration of theory and practice, and the development of advanced professional judgement.

Assessment Strategy

Assessment 1: Critical review paper on a strategic marketing or CX theme (40%). Assessment 2: Applied marketing/CX strategy or consultancy-style report (60%).

Recommended Reading

• Chaffey, D. and Ellis-Chadwick, F. (2019). Digital Marketing. 7th ed. Harlow: Pearson. Lemon, K.N. and Verhoef, P.C. (2016). Understanding Customer Experience. Journal of Marketing. Ryan, D. (2016). Understanding Digital Marketing. 4th ed. London: Kogan Page.

Module Introduction

This module requires candidates to develop, submit, and defend a doctoral-level research proposal that will form the basis of the DBA thesis. It ensures methodological rigor, ethical compliance, and feasibility before candidates enter the Research Phase.

Module Aims

• To develop a rigorous and viable DBA research proposal.
• To ensure methodological and ethical soundness of the proposed study.
• To prepare candidates for confirmation of candidature.
• To develop advanced academic writing and defence skills.
• To align the research project with professional and organisational practice.

Learning Outcomes

• Formulate a clear and significant research problem and objectives.
• Conduct a critical and systematic literature review.
• Design and justify an appropriate research methodology.
• Demonstrate ethical awareness and compliance in research design.
• Defend the research proposal before an academic panel.

Indicative Content

• Research problem formulation and research questions
• Systematic literature review and theoretical framework
• Research design and methodology
• Methods of data collection and analysis
• Research ethics and governance
• Project planning and risk management
• Proposal writing and defence preparation

Learning and Teaching Strategy

Delivery will include doctoral seminars, proposal workshops, supervision meetings, and independent study, focusing on critical review, methodological design, and scholarly presentation.

Assessment Strategy

Assessment 1: Written DBA research proposal (70%). Assessment 2: Oral defence / viva of the proposal (30%).

Recommended Reading

• Saunders, M., Lewis, P. and Thornhill, A. (2019). Research Methods for Business Students. Pearson. Creswell, J.W. (2018). Research Design. Sage. Bryman, A. and Bell, E. (2015). Business Research Methods. Oxford University Press.