With the aim of engaging a range of business disciplines, the hub carries several themes driven by “Discipline Champions.”
Information systems
The Information System (IS) theme explores how analytics and AI can be better used to generate business value for a competitive advantage.
The rapid advancement in analytics and artificial intelligence (AI) has generated an unprecedented disruptive impact on organisations through changes at the task, process, and business model levels. Understanding how organisations can fully embrace analytics and AI is critical to driving business success.
Topics in this theme cover various domains of IS, including the affordance of analytics and AI; organisation readiness for the adoption of analytics and AI; design and implementation of analytics and AI applications; development of analytics and AI architectures, infrastructures and capabilities; cybersecurity, ethics, and privacy concerns in the use of analytics and AI; digital sustainability, business performance, and success factors in the age of analytics and AI.
Through working with industry partners, government, and policymakers, practical solutions and strategies can be formulated to advance the analytics and AI transformation journey of organisations.
Theme leader
Featured projects
- Digital work
- Digital sustainability
- Smart mobility
- SMEs digital transformation
- AI in education
- Navigating the future of artificial intelligence in Australian SMEs
Affiliate research
Enhancing the digital adaptability of Australian small retail businesses, Australian Communicatons Consumer Action Network.
Industry partners: Small Business Australia, Council of Small Business Organisations Australia, and National Retail Association
Publications
- Duan, S., Deng H., & Wibowo, S. (2022). Exploring the impact of digital work on job performance from a technology affordance perspective. Information Technology & People
- Deng H., Duan, S., & Wibowo, S. (2022). Artificial intelligence affordances in digital work. Pacific Asia Conference on Informa-on Systems. https://aisel.aisnet.org/pacis2022/7
- Duan, S., & Deng H (2022). Exploring privacy paradox in contact tracing apps adoption. Internet Research. https://doi.org/10.1108/INTR-03-2021-0160
- Chong, J., & Duan, S. X. (2022). Riding on the waves of the COVID-19 pandemic in re-thinking organizational design: a contingency-based approach. Journal of Strategy and Management. https://doi.org/10.1108/jsma-07-2021-0142
- Deng H., Duan, S., & Wibowo, S. (2022). Digital technology driven knowledge sharing for job performance. Journal of Knowledge Management. https://doi.org/10.1108/JKM-08-2021-0637/full/html
- Li, M., Molla, A., & Duan, S. (2021). AI Affordances Perception for the Transformation of Mobility Ecosystems, Australasian Conference on Informa-on Systems. https://aisel.aisnet.org/acis2021/76
PhD students
- Mingye Li: “AI Affordances Perception and Actualisation for the Transformation of Mobility Ecosystems: An Australian Study”
- Peiwen Li: “A Compliance-Based Framework for Digital Identity Management in Organisations”
Economics and Finance
The Economics and finance theme conducts research in spaces such as employing machine learning to forecast asset returns and predict default probability, in order to provide practical guidance to investors and financial regulators.
Other research spaces include:
- Applying textual analysis to extract information from financial and non-financial reports of firms, such as annual report, CSR report, credit rating report and so on, in order to evaluate firm value and guide on optimal corporate finance policy, or improve the market efficiency.
- Exploring the microstructure of cryptocurrencies, to understand the constituents of investors.
Theme leader
Dr Xiaolu Hu
Senior Lecturer of Finance, School of Economics, Finance and Marketing
Research team
Featured projects
- Understanding Credit Risk for Chinese Companies using Machine Learning: A Default-Based Approach
- CSR report and peer effect: An application of textual analysis. Machine learning and corporate bond return.
Affiliate research
- Enhancing the digital adaptability of Australian small retail businesses, Australian Communicatons Consumer Action Network.
- Industry partners: Small Business Australia, Council of Small Business Organisations Australia, and National Retail Association
Publications
- Chiah, Hu and Zhong (2022), Photo sentiment and stock returns around the world. Finance Research Letters, 46. https://doi.org/10.1016/j.frl.2021.102417
PhD students
- Yiwen Fang, supervised by Xiaolu Hu
- Xize Guo, supervised by Xiaolu Hu
Marketing
Theme leader
Dr Ashish Kumar
Senior Lecturer, School of Economics, Finance and Marketing
Research team
- Dr Daniel Rayne, Lecturer, School of Economics, Finance & Marketing
- Dr Fatima Madani, Lecturer, School of Economics, Finance & Marketing
Featured projects
- Dr Anish Kumar - Decentralised Finance and Market Innovation
- Investigation of NFT adoption by businesses
- FinTech and E-Commerce: The Effects of Buy Now, Pay Later (BNPL) on Customers’ Online Purchase Behavior
- Exploring the Impact of the Buy Now Pay Later (BNPL) Sector on the Economy
- Interplay of Technological Innovation and Marketing Capability in Creating Firm Value: Evidence using US Patent Data
- Dr Daniel Rayne - Non-Fungible Tokens (NFT)
- Marketing perspectives of NFT.
- Using NFT in sports industry (in partnership with Blockchain Hub)
- Dr Fatima Madani - Health Marketing
- The effects of household life cycle transitions on two important aspects of households’ food baskets – healthiness and freshness
PhD students
- Ching Sophia Yiu: "Livestream Shopping in E-commerce", supervised by Ashish Kumar and Angela Dobele
Accounting
Our accounting theme conducts research in areas such as the implications of business intelligence and analytics (BIA), artificial intelligence (AI) and machine learning (ML) on accounting and auditing.
Research in this theme also includes examining the ways in which BIA, AI and ML bring opportunities for accountants and auditors to provide more insights, improve efficiency, make effective decisions and deliver value to all types of organisations.
Theme leader
Featured projects
- Professor Gary Hecht and Professor KL - Examining the use of artificial intelligence and machine learning-based textual analytics method for qualitative data analysis.
- Professor Gary Hecht - The use of big data analytics by auditors.
- Dr Jan Svanberg and Professor Peter Öhman - Machine learning for predicting social controversies.
- Dr Md Jahidur Rahman and Dr Md Moazzem Hossain - Predicting Profitability Using Artificial Intelligence, Machine Learning and Principal Component Analysis — Evidence from China.
- Models of profitability prediction provide valuable information for investors and managers for tracking performance and making forward-looking decisions.
- Dr Chetan Singh and Dr Lutfa Ferdous - Stock price prediction using machine learning – A sentiment analysis and CandleStick chart representation.
- The machine learning algorithm information is collected from the historical data, candlestick charts, and social media data to predict the stock price movement. Further, this study applies deep neural network implementation in the investment which comprises stock technical indicators, sentiment, and candlestick to predict the stock.
Publications
- Svanberg, J., Ardeshiri, T., Samsten, I., Öhman, P., Neidermeyer, P. E., Rana, T., ... & Danielson, M. (2022). Corporate governance performance ratings with machine learning. Intelligent Systems in Accounting, Finance and Management, 29(1), 50-68, https://doi.org/10.1002/isaf.1505.
- Svanberg, J., Ardeshiri, T., Samsten, I., Öhman, P., Rana, T. & Danielson, M. (2021). Prediction of environmental controversies and development of a corporate environmental performance rating methodology. Journal of Cleaner Production, 344, https://doi.org/10.1016/j.jclepro.2022.130979.
- Kend, M., & Nguyen, L. A. (2020). Big data analytics and other emerging technologies: the impact on the Australian audit and assurance profession. Australian Accounting Review, 30(4), 269-282, https://doi.org/10.1111/auar.12305.
