PhD scholarship funded research projects 2024

Civil Society and Public Discourse: Artificial Intelligence and Networks as Information Filters

Supervisory team: Simona Fabrizi (Main supervisor, ECON), Steffen Lippert (Co-supervisor, ECON), and Prasanna Gai (Co-supervisor, ECON)

Abstract: Civil society and public discourse are not immune to contradictions, clashes, and conflicts, from how to fight a pandemic and how to combat climate change to how to preserve democracies. Various disciplines have attempted to rationalise this phenomenon separately. Yet, it would be beneficial to gain a coherent, unified framework that explains these generalised contradictions across alternative spheres of human lives. Key to that is acknowledging that individuals and society at large form and revise beliefs based on information which, although often available at everyone’s fingertips, might be manipulated by self-interested agents along its path, through propaganda or populism. Furthermore, the individuals’ positioning in the society and how they revise beliefs that would challenge their world’s view should matter to explain the origin and persistence of these apparent contradictions. To expand knowledge in the field, we propose to implement a systematic, interdisciplinary approach, integrating otherwise distinct perspectives, theories, and methods from mathematical science, network, and computer science. Examining strategic information transmission mechanisms within networks will inform the design of suitable novel ‘information filters’ to restore a high quality of the public discourse and civil societies, including filters harvesting human-machine cooperation to assist sound decision-making.

Employee-(ro)bot collaboration in service: An institutional perspective

Supervisory Team: Joseph Yan ( Main supervisor, MIB) and Laszlo Sajtos (Mentor co-supervisor, Marketing) and Sihong Wu (Co-supervisor, MIB)

Abstract: Society evolves as technologies develop. Organisations across industries and around the globe are consistently under pressure to make sense of and adapt to new technologies. Workplaces in various industries have been augmented with smart, digital, and automated technologies, where human employees work alongside machines, robotics, and other types of artificial intelligence (AI) ‘co-workers’. The implementation of employee-robot collaboration is especially prominent in today’s service industries, and particularly at the business frontline where employees directly interact with and deliver value to consumers. It is well-known that this can create new workflows for employees, new experiences for consumers, and new images and reputations for the company. However, what remains understudied is how these dynamics can become more complex when the service provider operates across different regions in the world, where major differences in regulations, cultural norms, religious beliefs, and other institutions (i.e., social rules) can significantly shape firm strategy and performance. As such, this project adopts an institutional perspective and seeks to examine the impact of employee-(ro)bot collaboration on the performance of international service firms; and how this impact can change due to various institutional factors faced by the firm in global operations.

A Novel Pension Scheme That Incentivises Childbirth and The Quality of Human Capital To Provide Sustainable Old-Age Care and Equitable Macroeconomic Welfare

Supervisory Team: Debasis Bandyopadhyay (Main-supervisor, ECON) and Erwann Sbai (Co-supervisor, ECON)

As technology advances, people live longer, but fertility and the average child quality decline because the opportunity cost of a mother’s time for having children and supervising their education increases. It causes two economic problems.

First, the population may go extinct if fertility falls below the replacement rate. Second, as the population ages, a structural deficit emerges in the macroeconomy with too few taxpayers with reduced productivity to support a growing number of elderly adults alive.

A policy tradeoff emerges for the macroeconomy to achieve sustainable economic development: (a) upskilling experienced workers and raising their retirement age versus (b) rewarding mothers to raise the number and quality of future taxpayers.

The PhD thesis will investigate how best to provide sustainable old age care by raising the number and the average quality of taxpayers in the macroeconomy. It will identify macroeconomic factors that incentivise childbirth and parental supervision of education to ensure sustainable economic growth with equity. Then, use microeconomic panel data estimates of relevant parameters for a quantitative analysis of the model.

Our supervision team, with expertise in theoretical and quantitative economics, will empower students to achieve Taumata Teitei’s objective of pursuing “ambitious research confronting humanity’s greatest challenges”.

Sustainable urban growth with land rent and social dynamics under Globalisation

Supervisory Team: Zhi Dong (Main-supervisor, Property) and Edward Yiu Co-supervisor, Property)

Abstract: It is critical to investigate how globalisation affects sustainability of urban growth. The impact of globalisation on urban growth is multi-facets. It produces positive effects like urban and economic growth, poverty reduction and cultural exchange, but it also brings in challenges to people like income inequality, social dislocation and gentrification.

This research identifies key aspects towards sustainable urban growth under the effect of globalisation. It explores globalisation with supply side driven migration, immigration and urban growth. The theory of global commodity chains is blended with spatial and location theory to explain urban growth. Dynamic land rents for residential property sector are analysed under the forces of urban intensification and growth. The scale of sustainability for urban development is investigated alongside demographic changes, dynamic social interactions and urban intensification.

The study develops a new theory explaining the dynamic drivers for sustainable urban growth. Empirical evidence is drawn based on datasets from Stats NZ and CoreLogic, using econometric modelling. Effective policies for sustainable urban growth and development are proposed and discussed. The research sheds light on how to achieve sustainable urban growth and value creation for societies through policy implications and potential novel policy design.

Models of Waste Management in Healthcare Systems

Supervisory Team: Mahdi Mahmoudzadeh (Main-supervisor, ISOM) and Timofey Shalpegin (Co-supervisor)

Abstract: Medical practices in many countries have advanced to delivery via healthcare systems, where several parties/entities interact throughout a chain to accomplish an outcome. These chains of interactions often resemble those of supply chains for services and physical goods. Managing the waste generated throughout such a chain is essential to ensure its sustainability. For healthcare, often the waste is in form of hazardous waste, where improper waste handling can result in transmission of numerous dangerous materials to the environment. This project aims to develop models of waste management for healthcare systems. The focus will be on incorporating aspects that are crucial and specific for healthcare systems that makes them different from other sectors. All methodologies, including analytical modelling (e.g., logistics models, dynamic programming, game theory, etc.), simulation, and empirical methods, can potentially be employed, while the focus will be on developing models. The project will commence with a critical review of the models developed so far and will then advance to developing models. The outcome of the project can help practitioners and supply chain managers of healthcare sectors better manage the waste generated throughout the chain to minimize their hazardous impact.

Micro-level research on emerging space industries to better understand how and when entrepreneurial ecosystems promote sustainable innovation

Supervisory Team: Stefan Korber (Main-supervisor, MIB) and Lisa Callagher (Mentor Co-supervisor, MIB)

Abstract: New space industries have attracted significant interest from a wide range of stakeholders. Unlike governmental-led space programmes, this sector is driven by commercial organisations that leverage advancements in technology to make space more accessible. While some critics highlight the adverse environmental impacts of space industries, services and products delivered are seen as pivotal for sustainability transitions. Examples include satellites that monitor pollution and the use of AI to analyse space data.

Whether and how space-related activities contribute to a more sustainable future depends not only on focal organizations but also on contextual factors -  the innovation and entrepreneurship ecosystem (I&EE) - that support them. Yet, our understanding of micro-level dynamics in I&EEs that empower actors to co-create sustainable solutions is limited. This research project will employ qualitative methods and investigate the interactions of key actors - startups, universities, and investors - and their impact on the nature and magnitude of sustainable innovation and entrepreneurship within the 'new space' sector. The project aligns with 'leading the transition to sustainable ecosystems,' one of four priorities in Taumata Teitei, and integrates two strategic themes: Innovation & Value Creation and Productivity & Sustainable business. Apart from theory development, the project promises practical implications for managers and policymakers.

Multimodal Fusion for Financial Time Series Forecasting Based on Deep Learning

Supervisory Team: Dulani Jayasuriya (Main-supervisor - A&F) and Lina El-Jahel (Co-supervisor - A&F)

In a rapidly evolving business landscape, this PhD project delves into the intricate web connecting Corporate Social Responsibility (CSR), Environmental, Social, and Governance (ESG) criteria, green investing, and the emerging challenge of greenwashing. Leveraging advanced machine learning, the research aims to demystify and quantify the interplay between genuine green investments and CSR/ESG initiatives, while distinguishing them from greenwashing practices. This study intends to provide groundbreaking insights into how machine learning can enhance the transparency and effectiveness of green investing, ensuring that CSR and ESG commitments lead to substantial, verifiable environmental and social impacts.