ULMS Project Proposals

See below for a list of potential project proposals that would be overseen by academics in the University of Liverpool Management School.

If you have any queries or are interested in one of these proposed projects, please contact the Faculty PGR Team at hsspgr@liverpool.ac.uk in the first instance. Click here to visit the University of Liverpool Management School Postgraduate Research information pages.

Accounting and Finance

(Contact: busxc@liverpool.ac.uk; cc: florakis@liverpool.ac.uk; ulmsphdteam@liverpool.ac.uk)

Enhancing Financial Literacy and Management Skills Using Generative AI

This project explores how generative AI (GAI) can be leveraged to enhance financial literacy and improve financial management skills among disadvantaged groups in the UK. While there is growing research on the use of AI in education and personal finance, limited attention has been given to how GAI can bridge financial literacy gaps and guide individuals to appropriate support when facing financial challenges. This research will investigate the opportunities, limitations, and ethical concerns associated with GAI tools, including issues of data privacy, bias, and the potential for misinterpretation of outputs as financial advice.

As financial illiteracy exacerbates economic inequality, the use of advanced AI to provide personalized, accessible, and culturally sensitive financial education is of increasing importance. The candidate will be expected to conduct interviews with disadvantaged individuals, financial literacy educators, and AI developers; analyze existing datasets on financial behaviours; and develop and test prototypes of GAI-powered tools. Skills in mixed-method research, basic programming or AI-related tools, and ethical analysis are essential. A background in finance, education, or social policy is highly desirable, along with strong analytical and communication skills.

This research has the potential to contribute to global debates on the responsible use of AI for financial inclusion and to develop scalable tools that can address financial literacy gaps worldwide.

International financial markets and financial stability

Financial markets have become more difficult to predict following financial instability issues (related to the financial crisis of 2008-2009), supply-side pressures (since the recent pandemic), and rising geopolitical risks (since the war in Ukraine). All these challenges call for the use of large economic and financial information sets (such as financial stress and economic uncertainty measures) in quantitative models of predicting sovereign interest rates, and domestic as well as global stock market indices. The successful candidate should have good knowledge of theoretical and applied econometric techniques and be able to handle large datasets in statistical software packages like Matlab and R.

Short-expiry options and financial stability

There has recently been a tremendous increase in the offering and trading of short-expiry financial options. Investors trade these options to hedge underlying risks and/or profit from their own beliefs, especially around macroeconomic and political events. However, little is known about the aggregate impact of this trading activity on financial stability. This is the research question this project intends to address. A suitable candidate for this research topic should be able to handle large databases and conduct econometric analysis using time-series models.

The Information Content of Commodity Derivatives

Commodity derivatives are highly liquid and exchanges are launching more and more VIX-related products in the commodity space. This project aims to investigate the information content of commodity derivatives. Specifically, the researchers will use options and futures data, as well as the position of various financial market participants, to analyse the predictability of key state variables in the commodity market. In doing so, the research contributes to a vibrant literature that includes seminal contributions such as that of Hong and Yogo (2012).

Reference: Hong, H. and Yogo, M., 2012. What does futures market interest tell us about the macroeconomy and asset prices?. Journal of financial economics, 105(3), pp.473-490.

Strategic investment decisions and innovation in the supply chain

This project analyses how vertical organisation of production interacts with innovation, growth, financing and other related aspects in firms at different layers of the supply chain. There is some evidence to indicate that technological progress and lower fixed costs of research and development of products and processes have dispersed the concentration of innovation from large firms selling final consumption goods to the different layers of upstream suppliers. Relatively little research has been done to scrutinise the strategic financial decisions that is leading to relationships that promote supplier innovations and its effect on disclosure of research and development (R&D) in and valuation of upstream firms. The aim is to understand the management of supply chain relationships, innovation, and disclosure regarding R&D at the firm level driven by financial motives given developments of technology. The candidate needs to be able to develop a strong foundation of the theoretical and empirical basis of Accounting and Finance research and conduct analyses using large datasets that contain patent and citation information, firm level financials, supply chain and textual disclosure data.

Economics

(Contact: ruijunbu@liverpool.ac.uk; cc: Giuseppe.De-Feo@liverpool.ac.uk; ulmsphdteam@liverpool.ac.uk)

Testable Implications for Markets with Indivisibilities

This PhD project aims to investigate the positive implications of one-sided matching theory, originally developed by Shapley and Scarf in 1974. While existing applications of this theory have primarily focused on normative questions related to market design, our goal is to establish testable implications for the theory. We will present results similar to the revealed preference tests used in consumer theory. The objective of our research is to create tests for markets characterized by indivisibilities in abstract settings. These tests can be applied to actual data to evaluate the assumptions made by applied researchers. Furthermore, they will serve as the foundation for a statistical theory of testing market outcomes, offering valuable insights into how markets can be redesigned.

Supervisor: Professor Michele Lombardi

Portfolio selection under climate risk

This research addresses the growing challenge of integrating climate risks into portfolio decision-making. Traditional models like the Markowitz mean-variance framework fail to account for climate-related risks, including physical hazards (e.g., hurricanes, floods) and policy uncertainties. To enhance investment resilience, this study develops an advanced portfolio selection model incorporating global climate risks into risk and reward measures. Using machine learning, including neural networks, we quantify climate risks and modify traditional metrics (e.g., Sharpe ratio) to reflect climate risk premiums. The proposed model optimizes portfolios by balancing financial performance with climate risk exposure. Rigorous back-testing using historical financial and climate data across various scenarios will evaluate its effectiveness. This research aims to provide a robust framework for climate-risk-informed portfolio optimization, improving financial performance under climate stress conditions.

Supervisor: Professor Abderrahim Taamouti

Assessing the ECB’s Targeted Long-term Refinancing Operations

The ECB’s targeted longer-term refinancing operations (TLTROs, from 2014 to 2022) provided long-term funding to banks at favourable conditions, linking the amount a bank can borrow, and the interest rate applied, to the bank’s lending pattern to non-financial corporations and households. While researchers have studied the effects of these policies using reduced-form empirical techniques, no systematic study of their design using a structural framework has yet been done. This project will develop a dynamic general equilibrium economic model with a richly specified heterogeneous banking sector, to analyse the optimal design of these types of “targeted” monetary operations. The successful candidate will require a strong background in a quantitative subject such as mathematics, computer science, or economics, and have a broad interest in macroeconomics and finance.

Supervisor: Dr Alexey Gorn

Fiscal-monetary policy interaction in HANK-SAM models

Modern policy analysis relies on the class of models that feature heterogeneous agents, nominal rigidities and labour market frictions (HANK-SAM models). Transmission and effectiveness of fiscal and monetary policy measures in these models have been studied separately so far. This project will investigate the interaction between the two policies theoretically, quantitatively, and empirically. First, it will establish the key margins of interactions theoretically and analyse what equilibrium outcomes and model fundamentals affect them. Second, it will estimate these key margins in the data using both aggregate and individual level datasets. Third, it will use the estimates to calibrate the model and assess the contribution of the interaction for effectiveness of each policy quantitatively.

Supervisor: Dr Alexey Gorn

Offshore Finance Networks and their Impact on Political Institutions

The role of offshore finance in the global economy has been fundamentally reassessed in recent years and it is increasingly attracting media coverage and public scrutiny due to its use by oligarchs, criminals and political leaders to evade taxes, launder money, hide assets, and move wealth out of their countries. In this project we will focus on the connections between money laundering, organized crime, and political corruption. We will explore information available about offshore finance, money-laundering activities and their use by criminal organizations to assess their effect on corruption and political institutions. This research is particularly timely given increasing global efforts to combat financial crime and protect democratic institutions from corrupting influences.

Supervisor: Professor Giuseppe De Feo

Price-sharing and coordination

Price-sharing platforms can empower consumers to find the cheapest available price and thereby exert competitive pressure on firms. However, price sharing platforms also facilitate coordination between ostensibly competing firms. Government-administered price-sharing platforms operate in retail petrol markets in several countries, including Germany and Australia. The recent Competition Markets Authority report into the road fuel market in the UK (https://www.gov.uk/cma-cases/road-fuel-market-study) recommends the introduction of a comprehensive price-sharing platform in the UK. This project will build an extensive database of high-frequency pricing data from price-sharing platforms to systematically study the nature of coordination in retail petrol markets.

Supervisor: Professor Nick de Roos

Operations and Supply Chain Management

(Contact: hugolam@liverpool.ac.uk; cc: etmalyon@liverpool.ac.uk; ulmsphdteam@liverpool.ac.uk)

Data-driven Optimisation and Analytics for Port Operations

Ports are crucial nodes in maritime transportation networks and global supply chains. Growing demands for operational efficiency and environmental sustainability have driven ports toward data-driven operations. While recent advances in automation and digitalisation within ports have generated enormous data, much remains underutilised to be of real value. Current literature extensively employs Operations Research (OR) techniques to optimise port operations. Meanwhile, Machine Learning (ML) is increasingly adopted for predictive analytics related to various port activities. However, the synergy between ML and OR remains relatively unexplored, particularly in harnessing ML-driven insights for improved decision-making. This project aims to develop innovative data-driven optimisation and analytical frameworks that leverage ML and OR methods, enhancing decision-making, operational efficiency, and sustainability of port logistics systems. Potential methodological lenses include but are not limited to:

Integrated ML and OR frameworks for uncertainty challenges.
Prescriptive application of ML for real-time operational decisions.
ML-assisted optimisation for enhanced computational efficiency.

Lead Supervisor: Dr Yuanjun Feng

Shaping the Future of Creativity: The Economics of Generative AI and Platform Regulation

The emergence of generative AI is transforming the creative industries by enabling machines to produce diverse forms of human expression, from visual art and music to literature and design. This project explores the economic and strategic implications of generative AI through the lens of copyright regulation, platform governance, and innovation policy. Using analytical modeling, it examines how these mechanisms influence the incentives and outcomes for human creators and AI developers, with implications for creativity and the future of the industry. The research investigates how interventions such as copyright enforcement, disclosure requirements, and innovation incentives can support a sustainable creative ecosystem, preserve authentic creativity, and position AI as a driver of innovation. Findings will offer insights into designing balanced policies that benefit creators, AI developers, and society. This project is ideal for applicants interested in the intersection of technology, economics, and intellectual property, and who are keen to apply analytical tools, such as game theory and dynamic modeling, in their research.

Lead Supervisor: Dr Yudi Zhang

Digital technology in multi-tier sustainable supply chains

Sustainable supply chains are critical for achieving environmental and social responsibility goals. However, managing sustainability across multiple tiers of a supply chain, i.e., multi-tier sustainable supply chains (MT-SSCM), which presents challenges such as a lack of transparency, inefficient coordination, and data silos. Digital technologies, including blockchain, IoT, and artificial intelligence (AI), have the potential to enhance sustainability by improving traceability, real-time monitoring, and data-driven decision-making.

This project aims to explore the role of digital technology in fostering sustainability within multi-tier supply chains. The key objectives are:

To assess how blockchain, IoT, and AI contribute to transparency and accountability in MT-SSCM.
To analyze the impact of digital tools on reducing environmental footprints and enhancing circular economy practices.
To identify challenges and best practices for implementing digital solutions in MT-SSCM.
To explore the governance mechanisms of managing MT-SSCM by using digital technologies.

The study is suitable for applying qualitative research methods. This includes a literature review, providing a comprehensive analysis of existing research on digitalization in supply chains; case studies, which examine real-world applications of digital technologies in multi-tier supply chains across industries such as fashion, electronics, and food.

Lead Supervisor: Dr Shenghao Xie

The Ethics of Shared Agency and Hybrid Decision-Making in the Age of AI

As AI systems increasingly participate in high-stakes professional decision-making, a gap remains in our understanding of how human autonomy and AI agency are ethically balanced. This research will explore the ethics of ‘shared agency’ between professionals and AI systems, examining how professional expertise, autonomy, and responsibility are redistributed in hybrid decision-making contexts when AI tools are treated as epistemic agents. Adopting an interdisciplinary approach, the research will, on the one hand, engage with concepts of moral responsibility, accountability, and algorithmic authority to assess the normative implications of delegating decision-making to machines. On the other hand, it will examine how professionals navigate AI-generated recommendations in practice, including tensions between acceptance and resistance. The research aims to develop an understanding of shared human-AI agency, contributing to both theory and practice in the design of AI systems that support—rather than undermine—professional integrity, responsibility, and autonomy in decision-making environments.

Lead Supervisor: Dr Atif Sarwar

The Impact of Tariff Barriers on Global Supply Chain Networks

Practitioners, policymakers, and academics widely acknowledge that global supply chains are undergoing significant restructuring across industries and geographies due to turbulent geopolitical environments. Recent U.S. tariff policies have introduced new obstacles that are reshaping the flows of supply chains. While existing research has examined the macroeconomic impacts of tariffs and trade wars, there is a limited understanding of how these policies specifically influence global supply chain management. This research project will address these gaps by exploring how tariff policies are disrupting established global supply chain structures and prompting firms to reconsider sourcing decisions, relocate production, and diversify their supplier networks. The study will adopt a mixed-methods approach, beginning with a systematic literature review and followed by quantitative analyses of the effects of tariff barriers. Ultimately, this research project aims to generate insights into supply-chain level impacts of tariff policies and inspire avenues for future research in global supply chain management.

Supervisor: Dr Geng Wang

Optimisation and Strategic Modelling of Vehicle-to-Grid Systems for Energy Networks

The rapid adoption of electric vehicles (EVs) is transforming urban transportation and contributes to global decarbonisation efforts, but it also poses challenges to electricity distribution networks, particularly in high-demand urban areas. Large-scale EV charging can strain grid infrastructure, especially during peak periods, while disruptions in energy supply chains may increase market volatility, affecting operational costs and supply chain resilience. This PhD research investigates the potential of vehicle-to-grid (V2G) systems to mitigate these instabilities by enabling bi-directional energy exchange, allowing EVs to supply energy back to the grid during high-demand periods. The study includes the development of robust optimisation models that address fluctuating energy demand, market price volatility, and EV battery degradation. The proposed models will integrate stochastic processes and game-theory approaches to evaluate strategic interactions among market participants, while supporting grid stability and cost-efficiency. Advanced computational strategies, including decomposition methods and math-heuristic algorithms, will be explored to deliver high-quality solutions within reasonable computational times. Simulations will cover both real-time and day-ahead trading scenarios to assess V2G effectiveness in peak load reduction and market optimisation. The findings aim to provide valuable insights for grid operators, energy providers, and policymakers, promoting resilient, efficient, and sustainable electricity markets.

Supervisors: Dr Greg Kasapidis & Dr Cagatay Iris

Marketing

(Contact: mgtlsudb@liverpool.ac.uk; cc: phj@liverpool.ac.uk; ulmsphdteam@liverpool.ac.uk)

The Trade-Off between AI and ESG Emphasis in the Firm’s Marketing Strategy

Companies are forced to balance between two competing forces—investments in artificial intelligence (AI) and alignment with environmental, social, and governance (ESG) commitments. AI can potentially bring multiple benefits to firms regarding marketing workforce optimization, better customer targeting and more alignment between brands and consumers. On the other hand, focusing on ESG can create issues with product placement due to environmental complaints, brand positioning due to stance on social issues and supply chain profitability due to poor governance. However, consumers might prefer firms with better ESG commitments, which can build brand trust. This trade-off presents a challenge for international firms: should firms prioritize investment in AI or ESG, and how would such prioritization impact firm performance and customer metrics? The study can use a sample of international firms (e.g., Chinese, Turkish, or Indonesian) to help find the right balance—integrating AI to align with ESG values rather than contradicting them.

Primary Supervisor: Professor Anatoli Colicev

Quantifying Marketing Accountability: Investor and Internal Stakeholder Perspectives

Despite growing investments in marketing, firms often face challenges in demonstrating marketing’s value to both external and internal stakeholders. This PhD project investigates how marketing accountability—defined as the ability to demonstrate and communicate the value of marketing—can be improved through rigorous empirical analysis. Building on recent work that links firms’ accountability for marketing assets to investor reactions (e.g., Guenther et al., 2024), and studies of internal performance accountability (e.g., Verhoef & Leeflang, 2021), this project explores how firms quantify, disclose, and defend marketing investments. Potential topics include: the role of marketing metrics in financial disclosures, the impact of marketing KPIs on internal resource allocation, and the effectiveness of marketing dashboards in shaping C-suite decisions. The project will adopt quantitative methods such as panel data econometrics, event study methodology, or structural equation modeling, using data from financial disclosures, investor calls, surveys, or internal dashboards.

Primary Supervisor: Dr Peter Guenther

The Strategic Role of B2B Advertising in Driving Firm Performance

This research investigates the strategic impact of B2B advertising on firm performance, with a particular focus on how emerging technologies such as artificial intelligence (AI) are shaping advertising effectiveness. While B2C advertising has been widely studied, B2B advertising remains underexplored, despite significant investments by industrial firms. Key topics include the financial performance outcomes of B2B advertising, the use of AI-driven targeting and creative optimization, and differences in effectiveness across industries and firm sizes. The study will adopt a quantitative methodology, utilizing panel data on firm-level advertising expenditures, financial performance metrics, and digital advertising adoption indicators. Econometric models, including fixed effects and instrumental variable approaches, will be employed to address potential endogeneity and uncover causal relationships. This research contributes to the literature by offering a data-driven assessment of B2B advertising as a strategic lever and by highlighting the role of AI in enhancing advertising efficiency in industrial markets.

Primary Supervisor: Dr Miriam Guenther

An investigation of brand activism and consumers’ responses

Brand activism is gaining attraction with a growing number of brands willing to engage with it, and more people participating in relevant conversations. This project will aim to explore brand activism, and how it differs from existing marketing related concepts associated with social causes such as CSR, and cause-related marketing. The PhD student will examine its benefits for when brands engage successfully with it, as well as its challenges for when things do not go well, and brands end up being accused of woke-washing. Through an empirical investigation focusing on an international brand the student will provide insights about the persuasive or controversial strategies brands tend to employ when engaging in brand activism. The project will also focus on the role of various stakeholders and their reactions, with emphasis on consumers.

Primary Supervisor: Dr Athanasia Daskalopoulou

Strategy, International Business, and Entrepreneurship

(Contact: dilanij@liverpool.ac.uk; cc: J.Surroca@liverpool.ac.uk; ulmsphdteam@liverpool.ac.uk)

The Anatomy of Digital Disruption

This project aims to advance our understanding of 'digital disruption': the creation of (and consequent change in) market offerings, business processes, or models resulting from the use of digital technologies that fundamentally disturb or re-order the ways in which firms and their ecosystems operate. The challenges in identifying and managing digital disruption stem from our limited knowledge about how and why it unfolds over time. The project investigates three interrelated themes: (i) the emergence of new competitive dynamics resulting from the changing nature of relationships among innovation ecosystem actors; (ii) implications of new/changing competitive dynamics on firm-level capabilities, processes, routines, and business models; and (iii) responses and competing concerns within incumbent and challenger firms confronting digital disruption. Overall, this research project seeks to develop contributions (either conceptual, empirical or methodological) that disentangle 'digital disruption' to support the understanding of its unfolding and advance its manageability.

Primary Supervisor: Dr Anup Nair

Managing Ecosystem Complexity and Innovation through AI

Industries such as telecommunications, clean energy, and the Internet of Things (IoT) are becoming increasingly interconnected, forming dynamic ecosystems with complex interdependencies among products, technologies, and actors. These interdependencies create challenges for coordination, integration, and innovation. Artificial Intelligence (AI), particularly machine learning and natural language processing, has the potential to address these challenges. This PhD project explores how AI can help firms manage ecosystem complexity by optimizing product integration, enabling collaboration, and balancing standardization with customization. Potential directions include: (1) examining how AI can support innovation across industries by managing technological interdependencies; (2) investigating how AI fosters specialization and knowledge development while enhancing efficiency; and (3) analyzing AI’s role in shaping market power and collaboration, potentially reducing concentration risks and promoting more open, competitive ecosystems. The project aims to contribute to a better understanding of AI as a coordination and innovation tool in complex technological environments.

Primary Supervisor: Dr Francesca Hueller

Unleashing Green Innovation for Environmental Performance: A Study of Green Patents

This PhD project aims to advance the literature on strategy and innovation for environmental sustainability using secondary data on patents and patent litigation available in the public domain. The project will zoom in on technology spillover of green patents and the litigation strategy of green patents by addressing the following questions:

Given the power of corporate green innovation in addressing environmental issues, what factors facilitate (or inhibit) the technology spillover of green patents? What is the influence of institutional distance (Bruno et al., 2021) and the rising geopolitical tension (Luo, 2022) on the technology spillover of green patents?

As distinct technological features of green patents may attract litigation from non-practicing entities (i.e., patent trolls) do firms pursue escape strategy (Chen et al., 2023; Huang et al., 2024) or resort to other strategy to protect their green patents?

Primary Supervisor: Dr Irene Margaret

Executives’ Framing of Climate Change and Corporate Environmental Actions

This project explores CEO’s framing of climate change as a signal of corporate environmental actions. Framing refers to the language or symbolic gestures used by strategic actors to evoke meaning (Cornelissen & Werner, 2014). Prior research has identified framing by executives as a critical signal that helps distinguish between symbolic and substantive corporate actions (Crilly et al., 2015). Following this logic, CEO’s framing on climate change would determine whether a firm engages in more substantive environmental actions or they will focus more on the symbolic ones. The PhD project could be branched out as follows,

On the ‘technical’ aspect of framing, namely the types of language executives used in framing climate change, and then identity the conditions under which such framing leads to symbolic/substantive corporate environmental actions.

On the relationship between framing and internal conflicts within TMT, such as matching framing between CEOs and chief sustainability officers.

Primary Supervisor: Dr Irene Margaret

How organizational design may help create synergies between multiple goals?

Organizations often pursue multiple goals that might not be correlated with each other. An implication is that such conflicting goals place conflicting demands on managers, and the structure of organizations. Therefore, managers experience a trade-off in the pursuit of conflicting goals. Existing research has highlighted characteristics of the macro environment which may affect such trade-offs.

However, we know little about the processes and mechanisms through which organizations can create synergies between different goals. Organizations may leverage different features of organizational design and design decision-making structures that may help creating synergies and avoid trade-offs between multiple goals. This is an important area of research which could produce implications for public as well as private sector organizations and help them play their role in addressing societal challenges – if they find a way to resolve tension between different goals.

Primary Supervisor: Nauman Asghar

Work, Organisation, Management

(Contact: M.Miraglia@liverpool.ac.uk; cc: Charlotte.Croft@liverpool.ac.uk)

Leadership

The WOM group welcomes doctoral proposals on the topic of leadership in organisations. Possible topics include but are not limited to:

Leader-follower relationship and, more generally, relational perspectives on leadership;
Diversity and leadership, with a specific focus on gender and, more generally, inclusive leadership;
Collective leadership;
Paradoxical leadership;
Follower-centric leadership styles (e.g., servant leadership);
New perspectives on the relationship between leader’s characteristics and states (e.g., traits, cognition, and emotions) and a range of employee outcomes;
New challenges in leadership in relation to remote working, AI and technology, health and well-being at work.

Proposals can adopt a variety of methodologies, including quantitative, qualitative or mixed-methods. If considering quantitative methods, the focus should be on state-of-the-art quantitative techniques (e.g., longitudinal and/or multi-level designs, experience sampling methods, consequential experiments).

Primary Supervisors: Dr Mariella Miraglia & Dr Joanne Lyubovnikova

The Body and Organization: Exploring embodiment and paid work

This proposed PhD topic would seek to investigate the role of the body as central to organizational life, recognizing the relationships between embodiment and power dynamics, personal lives and everyday and material interactions. Research questions could focus on how bodies are managed, controlled and disciplined within organizational contexts.

Topics could include:

Parenthood, the body and work: Exploring health and wellbeing as well as how parents balance personal lives and fathering/mothering/parenting that involves managing the embodied needs of dependent children
Embodied Work Practices: Exploration of physicality in paid work, from manual tasks to digital work.
The Managed Body: Analyzing dress codes, wellness programs, and surveillance practices.
Bodies, Power and Resistance: Examining how bodies are sites of control and agency.
Inclusion, Diversity, and the Body: Intersectional analyses of gendered, racialized, and disabled bodies in organizations.

Primary Supervisor: Professor Caroline Gatrell

Organizational and Employee Well Being

We wish to attract applicants wanting to specialise on research devoted to questions of well-being in management and organization and who want to join the Centre for Organizational and Employee Well-Being (COEW).

A fundamental concern of the research conducted by centre members is the relation between wellbeing at a micro-level, in the working lives of employees, and the wider well-being of society, traditionally conceived as a macro-realm. We encourage students to propose daring and imaginative research that stimulates debate in management and organization studies and contributes to its major paradigms or ‘schools’ of research enquiry including institutional theory, critical management studies, critical human resource management, social studies of science and technology, and actor-network theory.

Primary Supervisor: Professor Damian O’Doherty

Professionals and professional work

We seek potential PhD candidates interested in the future of professionals and professional work. Indicative topics may include consideration of changing models of professionalism in the 4^th industrial revolution, including the role of technology and AI in challenging or enhancing understandings of professional work. Issues of identity, leadership, collaboration, equity and diversity may also be considered. Colleagues within WOM are interested in shifting professional jurisdictions and associations with understandings of politics and power, forms and expressions of ‘expert’ knowledge, and the relationship between professionals and wider society. We are particularly interested in candidates seeking to engage in advanced qualitative methodologies.

Primary Supervisor: Professor Charlotte Croft