Winter Research Projects
Team up with some of the University's leading academics and researchers to participate in research-related activities for a selected project. You'll have the opportunity to progress and apply your degree-specific knowledge, whilst developing valuable research and professional capabilities.
Applications for the 2024 Winter Research Projects open on 25 March.
Accounting for modern slavery in hospitality: narratives and performance gap
Project duration and delivery
Six weeks, 36 hours per week. St Lucia, with hybrid/remote working arrangements possible.
Project overview and significance of the study
This study investigates how organizations address societal concerns regarding modern slavery in supply chains and operations. Modern slavery includes multiple forms of people exploitation such as forced labour, child labour, and human trafficking. It is explicitly addressed in the United Nations Sustainable Development Goal 8, target 8.7. (United Nations, 2015). Given its importance, the trend of mandatory reporting on modern slavery risks is on the rise worldwide. Prior research has concentrated on high-risk sectors like manufacturing, revealing limited adherence to the transparency criteria.
Centered on the Australian Modern Slavery Act (2018), this project assesses evolving disclosures in under-researched vulnerable sectors like hospitality and tourism-related transport. The research aims to uncover textual and visual narratives vis-à-vis organizational effectiveness in addressing the risks, thus contributing to the literature on social and environmental accounting.
Given that accounting for modern slavery is an area of growing concern globally, participation in this research offers the applicant the opportunity to contribute to an impactful research project with practical implications for the industry.
Expected outcomes and deliverables
The selected applicant will strengthen her/his data collection and qualitative analysis and interpretation skills, critical reasoning, ability to work independently, and knowledge of human rights and modern slavery. The student will also have the opportunity to familiarise her/himself with analytical software such as MAXQDA.
The applicant will work closely with the supervisor to gather the documentation and test and refine the qualitative coding framework (a system that consistently categorizes information) through literature review and coding a sample of documents. Upon the performance and demonstrated results, the applicant may have an opportunity to engage in a publication from this research in the later stages of the project.
Suitable for:
This project is open to applications from students with a background in business, communication, accounting, tourism, and hospitality. We are looking for 4th-year undergraduate or 2nd-year post-graduate students who are familiar with research methods and have an interest in human rights or the social responsibilities of organizations.
To engage in qualitative content analysis, prospective candidates should have strong organizational skills and meticulous attention to detail for capturing subtle nuances and variations in statements. Additionally, they should exhibit adaptability to modify the analysis approach and the willingness to revise the analysis if required.
Primary Supervisors
Further information
Contact the supervisor priot to submitting an application at m.guix@uq.edu.au
Advancing AI Governance – An Australian Perspective
Project duration and delivery
Expected hours of engagement will be 20 hours per week. The project can be offered on-site, remotely or through a hybrid arrangement.
Project overview and significance of the study
This research project aims to investigate and develop an understanding of the current landscape of Artificial Intelligence governance frameworks, tailored for Australia’s unique social, legal, business, and cultural landscape.
The objectives of this research project include:
- Define the concept of Artificial Intelligence Governance within the broader context of contemporary business operations.
- Explore the literature associated with AI Governance, highlighting emerging themes, opportunities, risks, and challenges.
- Identify relevant case studies across the Australian economy that can be used to showcase specific features of AI governance, including lessons learnt around how to best implement AI governance practices.
Expected outcomes and deliverables
Scholars participating in this project will gain invaluable insight into the dynamics of AI governance within Australia. It is anticipated that scholars will explore intersections across the literature of information systems, social sciences, ethics, and law as part of this research project.
Through hands-on experience, students will also have the opportunity to analyse existing AI governance frameworks, engage with academics in the BIS and Management disciplines, and contribute to the development of learning materials across the UQ Business School.
Scholars may gain skills in data collection, data analysis and report writing. They may also be an opportunity to generate publications from their research. Students may be asked to produce a report or oral presentation at the end of their project.
Suitable for:
This project is open to applications from students with a background in information systems, preferably third year students. An understanding of qualitative research methods is desirable.
Primary Supervisors
Further information
Contact the supervisor at katie.williams@uq.edu.au for further details.
Corporate Culture and Insider Trading
Project duration and delivery
The project will require 36 hours of engagement every week for the duration of 4 weeks. The project will be offered through a hybrid arrangement.
Project overview and significance of the study
The project includes linking corporate culture with insider trading. Using machine learning approach, Li et al. (2021) identifies corporate culture in 5 different domains including innovation, integrity, quality, respect, and teamwork. Importantly, insider trading literature suggests that innovation and integrity play a major role in firms’ information environment which insiders either could exploit due to proprietary information in an innovative culture (Rahman et al., 2021) or couldn’t exploit due to strong ethical and integrity culture in firms. The research idea stems from this tension whether and how innovative and integrity cultures could influence insider trading behaviour.
To identify this relationship, we will use panel data regression model where insider trading profitability (measured by cumulative abnormal returns over 3- or 6-months period) is modelled as the dependent variable and the innovation and the integrity cultural indices (measures following the machine learning approach of Li et al., 2021) are used as the key independent variables. Following Rahman et al. (2021), the controls are size, book-to-market, past return, leverage, research intensity, analyst forecast dispersion, volatility, product market competition, and firm age.
Expected outcomes and deliverables
The potential scholar will work as a co-author in this project where expected skills gained are assumed to be data collection, data management, academic writing, modelling and analysis. This is a great opportunity for the scholar to attempt at least 1 publication in ABDC ranked ‘A’ journal which will help the scholar to pursue PhD in future. At the end of this project, we expect to see a working paper draft.
Suitable for:
This project is open to applications from students with a background in Finance or students enrolled in Master of Commerce program only.
Primary supervisors
Further information
Please email the supervisor your expression of interest at d.rahman@business.uq.edu.au.
References
- Li, K., Mai, F., Shen, R., & Yan, X. (2021). Measuring corporate culture using machine learning. The Review of Financial Studies, 34(7), 3265-3315.
- Rahman, D., Kabir, M., & Oliver, B. (2021). Does exposure to product market competition influence insider trading profitability?. Journal of Corporate Finance, 66, 101792.
How Multinational Enterprises Manage Diverse Stakeholders Through Strategic Information Disclosure
Project duration and delivery
The project lasts 4 weeks. The applicant can work remotely but on-site preferred.
Project overview and significance of the study
Multinational Enterprises (MNEs) face unique challenges for their information disclosure, due to the different even conflicted institutional pressures from different stakeholders globally. Despite the significant theoretical and managerial implications, it is surprising that there has been limited research on how MNEs may strategize their voluntary information disclosure to overcome these challenges. Drawing on insights from stakeholder theory and information disclosure literature, this project investigates whether MNEs would utilize the language barriers between home and host country stakeholders and adopt the selective information disclosure towards different stakeholders’ demands in respective language.
This project will use publicly listed firms as data and conduct empirical analysis. The Stata software is often used to conduct such data analysis.
Expected outcomes and deliverables
The main tasks will be around literature review, data collection through established database (e.g., WRDS; CSMAR), and preliminary data analysis based on Stata.
At the end of the project, a report of summarizing key findings will be expected. If the students are interested in further developing the project, we could continue to work on this project leading to a publication eventually.
Suitable for
This project is open to applications from students with a background in business research (honours and MPhil preferred). Please also be aware that this project will deal with data collection and analysis based on Stata software.
Primary supervisors
Further information
No need to be contacted before submission.
Intersectionality and belongingness: The lived experience of employees with multiple dimensions of diversity in organizations
Project duration and delivery
An estimate of 4 weeks and applicants will be required on-site for this project.
Project overview and significance of the study
This project extends our work in diversity, culturally heterogeneity in teams, conflict and leadership in organisations (see Ayoko & Fujimoto, 2024; Ayoko & Hartel, 2006; Ayoko, et.al.2002, 2008; 2021; Ayoko & Konrad, 2012; Caputo, et al., 2018; 2019) to explore the lived experience of employees intersectionality ( i.e. employees with multiple diversity categories such as individuals that are women, aged, from culturally and linguistic diverse backgrounds and/or neurodiverse) in the workplace. Intersectionality is a study of the ‘interaction between gender, race, and other categories of difference in individual’s lives, social practices, institutional arrangement and cultural ideologies and the outcomes of this interaction in terms of power (Davis, 2008), productivity, and wellbeing. Especially, our research project aims at unpacking the issues of belongingness, inclusion, and ostracism for those employees with multiple diversity dimensions in the workplace. We are aware that even for employees with single diversity category, workplace ostracism has adverse individual, relational, and organisational impacts (Sharma & Dhar, 2022). Outcomes of our findings will have practical and theoretical implications for HR diversity management/interventions/practices in organisation and HRM education. In this regard, our findings should assist in promoting the development of employee with multiple diversity categories, their growth, and sense of belonging while fostering their inclusion at work (Ayoko & Fujimoto, 2024).
This project is significant for three main reasons. First, and so far, a complete understanding of intersectionality for employees with multiple categories of diversity is limited. There is evidence that culturally heterogenous team is associated with conflict (Ayoko et al., 2002) but the full impact of intersectionality of employees with multiple diversity categories on belongingness, inclusion, productivity, and wellbeing remains elusive. The lack of intersectionality research means that this group of people may be excluded from diversity interventions in organisations (Ghavami & Peplau, 2013).
Second, we know that diversity is associated with increased innovation (Boehm, Dwertmann, & McAlpine, 2021), creativity (Hundschell et al., 2022), and great decision making (Hakstian, et al., 2022). Most of these studies examined diversity within one-dimensional analytical framework and are therefore invisible in the intersectionality literature (see Lawton et al., 2015). Existing literature in this area also seems to have missed the opportunity to examine the impact of a combined diversity categories on employee’s wellbeing and productivity. We argue that no one category of diversity explains the operation of social institutions or actors (Lawson et al., 2015). The poor understanding of the experience of intersectionality suggests that we miss the opportunity to track the lived experience of this category of employees and how they can be managed.
Third, research in intersectionality is promising in terms of awareness but in terms of how employee caught up with intersectionality can be assisted with organisational HR interventions is still lacking (Ayoko & Fujimoto, 2024). The authors call for more research on belongingness and internationality as it affects diversity, inclusion, and equality practices in organisations. We answer this call in this project by exploring the lived experience of employees that may be impacted by intentionality and what HR practices may be of assistance in their journey through organisations.
Altogether, the current project aims at identifying qualitatively (1) the lived experience of employees with diversity intersectionality within the workplace, (2) the connection between their experience, productivity, and wellbeing and (3) HR practices/interventions that may be useful in managing intersectionality at work.
The above culminates into 3 research questions:
- What is the lived experience of employees who experience multiple dimensions of diversity in the workplace?
- What is the relationship between their experiences, their productivity and wellbeing?
- Which HR practices/interventions may be effective in managing intersectionality at work and how may they be enacted?
Answers to the above questions should provide directions for managers and organisational leaders on how to manage intersectionality, employee well-being and productivity.
Approach and methodology
The current study adopts a qualitative approach to answer the above research questions. Given the exploratory nature of the study, we aim to collect 30-50 in-depth interviews from both employees who have single diversity category and those with multiple diversity categories at various organisational levels. This will enable us to compare the two groups of participants.
Two sets of interview questions will be prepared: one for the employees with single diversity category (e.g., gender or neurodivergent) and the those with multiple diversity categories (e.g., one individual who a woman but aged, and neuro-diverse).
Sample interview questions for employees will include:
- Please describe your experience of working in your organisation as a person with multiple dimensions of diversity.
- Please describe the specific experience or events related to your intersectionality and the context, frequencies, and duration of this experience/events.
- What actions have you taken about the way you experience intersectionality at work. Did they work, if yes, how and if not why?
- Please describe what you think is the impact of your lived experience about your intersectionality on your productivity, wellbeing, intention to stay and commitment to your organisation.
- What HR practices (e.g., training and development, staffing issues, diversity management, turnover/ retention, performance management) do you think might be helpful in managing the influence of intersectionality on your career and interactions with colleagues at work?
- How do you think your supervisor/manager may be helpful in managing your intersectionality experience.
The sample questions for employees with single diversity category will replace the word “intersectionality” in the above questions with “single diversity category” e.g.:
- Please describe your experience of working in your organisation as a person with a single dimension of diversity.
- How do you think your experience with a single dimension of diversity (e.g., being a woman) is impacting your well-being and productivity?
Sample:
Data for the study will be collected from 30 participants and sourced from various organization levels (i.e., executives, middle-level managers, and employees) and from a variety of industries. We will collect data from some of our current organization partners such as Dexus, Aurecon, Ergoton, Qld Transport and Telstra. It is expected that the participants would have both single and multiple diversity categories. Participants will be between the ages 18-65 years, 50-60% of which be females.
An ethics application for the current study will be initiated once this application is successful. Once identified, the participants for the study will be provided with an information sheet about the study, and a consent form to sign. Also, once consent is obtained, the participants will be slated for an interview based on their availability. Interviews will take place virtually using Zoom. Each interview will take an average of about 30-40 minutes to complete. Interview data will be collected within a period of 3-4 weeks. We expect that data will reach saturation by the time we collect 20 interviews. If successful with the current application, we plan to devote a significant amount of time to conducting these interviews.
Data Analysis: Leximancer® analyses textual corpora of any size using an automatic concept selection process, most often used by researchers as a starting point for focusing their research question. Researchers usually customise the automatic analysis using linguistic strategies (McKenna & Waddell, 2007). We will adopt a similar approach to analyse our data. Additionally, we will employ Nvivo to reveal the nuances in the interview data around intersectionality, HR practices employee wellbeing, and productivity that Leximancer may have missed.
Future Research: Findings from this qualitative study will form the basis of the development of conceptual model of the relationship between intersectionality experience, ostracism, isolation, inclusion, belongingness, HR practices, productivity and well-being that will be tested quantitatively and for generalisability.
Expected outcomes and deliverables
By participating in the project, scholars can expect to gain skills in conducting a systematic literature review, developing ethics application, qualitative data collection (including recruiting participants), qualitative analytical skills (i.e., the use of Leximancer and Nvivo). Outcomes of the project may include conference papers and book chapters.
Suitable for:
This project is open to application from UQ Students ONLY such as Third-year undergraduate, Honours, Master, and Ph.D. students enrolled in the Faculty of Business, Law, and Economics (BEL). Applicants must also have some basic interviewing skills and some familiarity with a qualitative approach to data analysis (i.e., Leximancer and Nvivo).
Primary supervisors
Associate Professor Remi Ayoko
Further information
Contact the supervisors at r.ayoko@business.uq.edu.au for more clarifications if need be.
References
- Ayoko, O. B., & Fujimoto, Y. (2023). Diversity, Inclusion, and Human Resource Management: A call for more belongingness and intersectionality research. Journal of Management & Organization, 29(6), 983-990.
- Ayoko, O.B. and Härtel, C.E.J. (2006), “Cultural diversity and leadership: A conceptual model of leader intervention in conflict events in culturally heterogeneous workgroups”, Cross Cultural Management: An International Journal, Vol. 13 No. 4, pp. 345-360.
- Ayoko, O. B., Härtel, C. E., & Callan, V. J. (2002). Resolving the puzzle of productive and destructive conflict in culturally heterogeneous workgroups: A communication accommodation theory approach. International Journal of Conflict Management, 13(2), 165-195.
- Ayoko, O. B., Callan, V. J., & Härtel, C. E. (2008). The influence of team emotional intelligence climate on conflict and team members’ reactions to conflict. Small Group Research, 39(2), 121-149.
- Ayoko, O. B., & Konrad, A. M. (2012). Leaders’ transformational, conflict, and emotion management behaviors in culturally diverse workgroups. Equality, Diversity, and Inclusion: An International Journal, 31(8), 694-724.
- Ayoko, O. B., Zhang, Y., & Nicoli, J. (2022). Conflict and socio-cultural adaptation: the mediating and moderating role of conflict communication behaviors and cultural intelligence. The International Journal of Human Resource Management, 33(17), 3451-3491.
- Boehm, S. A., Dwertmann, D. J., & McAlpine, K. L. (2021). How disability diverse teams can drive innovation through mutual perspective taking. In Academy of Management Proceedings (Vol. 2021, No. 1, p. 15451). Briarcliff Manor, NY 10510: Academy of Management.
- Caputo, A., Ayoko, O. B., & Amoo, N. (2018). The moderating role of cultural intelligence in the relationship between cultural orientations and conflict management styles. Journal of Business Research, 89, 10-20.
- Caputo, A., Ayoko, O. B., Amoo, N., & Menke, C. (2019). The relationship between cultural values, cultural intelligence, and negotiation styles. Journal of business research, 99, 23-36.
- Davis, K. (2008). Intersectionality as buzzword: A sociology of science perspective on what makes a feminist theory successful. Feminist Theory, 9(1), 67–85.
- Hakstian, A. M., Evett, S. R., Hoffmann, J. S., Marshall, J. M., Boyland, E. A., & Williams, J. D. (2022). Racial diversity and group decision‐making in a mock jury experiment. Journal of Empirical Legal Studies, 19(4), 1253-1292.
- Hundschell, A., Razinskas, S., Backmann, J., & Hoegl, M. (2022). The effects of diversity on creativity: A literature review and synthesis. Applied Psychology, 71(4), 1598-1634.
- Lawton, N. R., Calveley, M., & Forson, C. (2015). Untangling multiple inequalities: intersectionality, work and globalisation. Work Organisation, Labour and Globalisation, 9(2), 7-13.
- Sharma, N., & Dhar, R. L. (2022). From curse to cure of workplace ostracism: A systematic review and future research agenda. Human Resource Management Review, 32(3), 100836.
Machine Learning in Business: Impact of Social Media on Bitcoin Value
Project duration and delivery
For the Winter program, students will be engaged for 4 weeks only. Hours of engagement must be between 20 – 36 hrs per week. The delivery mode will be hybrid, allowing for both on-site and remote participation.
Project overview and significance of the study
This project aims to explore the relationship between social media sentiment and the value of Bitcoin. Students will apply text analytics techniques to a dataset of tweets related to Bitcoin, aiming to uncover patterns and correlations between social media activity and cryptocurrency price fluctuations. The project will involve data preparation, feature generation, text analysis, and the application of machine learning techniques to evaluate the significance of social media sentiment on Bitcoin's value.
Expected outcomes and deliverables
Participants can expect to gain skills in data preparation, text analytics, machine learning, and data visualization. Deliverables include a comprehensive report detailing the methods used, analysis performed, and conclusions drawn, presented in the format of a Python script.
Suitable for:
This project is suitable for students with a background in Information Science, commerce, business analytics, or related fields. Familiarity with Python and text analytics is preferred.
Primary supervisors
Dr Ali Darvishi and Dr Morteza Namvar
Further information
If you would like to know more about the project, please contact a.darvishi@uq.edu.au or m.namvar@business.uq.edu.au.
Transforming Healthcare: Leveraging AI for Enhanced Discharge Decision Prediction from Intensive Care Clinician Notes
Project duration and delivery
4 weeks and applicant will be required on-site for the project.
Project overview and significance of the study
This research project aims to leverage the ongoing digital transformation in the healthcare industry, presenting unprecedented opportunities for AI and machine learning to revolutionize patient care and advance healthcare research.
We will delve into textual data analysis within healthcare research, exploring a database of anonymized electronic health records (EHR) from over 60,000 adult patients treated in intensive care units. The data, sourced from bedside monitors, clinician documentation, and hospital information systems, offers a rich resource for analysis. With Natural Language Processing (NLP), we aim to extract valuable insights from textual data, including patient feedback, using NLP techniques such as topic modeling.
Our goal is to develop a method to enhance the prediction of discharge decisions based on clinician note analysis, offering participants an opportunity to engage in cutting-edge research at the intersection of healthcare and technology. If you are passionate about making a meaningful impact on healthcare outcomes through innovative data analysis techniques, we encourage you to apply and join us on this transformative journey.
Expected outcomes and deliverables
The selected applicant will improve her/his technical and data analytics skills and use this project’s outcomes as evidence of successful teamwork in an impactful research project with practical implications. In collaboration with the selected applicant, we aim to publish the results of this multidisciplinary study in IS or health-informatics journals. The student will work closely with the supervisors in developing the literature review and conceptual model of machine learning in social media monitoring. To ensure research impact and to communicate the outcomes to a broader audience, we plan to publish the results of this research in journals, such as Decision Support Systems and Health Informatics Journal.
Suitable for:
This project is open to applications from students with a background in business and analytics. We are looking for 3rd – 4th year undergraduate or 2nd year post-graduate students who is familiar with research methods, literature review development and machine learning (to the extent that machine learning and its tools are taught in the Business School).
Primary supervisors
Further information
Applicants are welcome to contact the supervisor at m.namvar@business.uq.edu.au if they would like further information.