Faculty/School

Faculty of Business and Law

School of Economics and Finance

Topic status

We're looking for students to study this topic.

Research centre

Centre for Behavioural Economics, Society and Technology

Centre for Behavioural Economics, Society and Technology

Supervisors

Dr Steve Bickley
Position
Postdoctoral Research Fellow
Division / Faculty
Faculty of Business & Law
Dr Ben Chan
Position
Postdoctoral Research Fellow
Division / Faculty
Faculty of Business & Law

Overview

In today's rapidly evolving world, where innovation serves as a catalyst for economic growth, understanding the dynamics between firms' skills, job hiring practices, and innovation becomes paramount. This exciting VRES project offers a unique opportunity to delve into the wealth of real-world data collected by our research team at the ARC Centre for Behavioural Insights for Technology Adoption (ARC BITA), which intertwines firms' skills and job hiring information from LinkedIn with their innovation levels, as measured by patent data from Lens.

At its core, this project builds upon a commissioned study by IP Australia, which initially aimed to analyse the technology transfer of AI into Australia and its adoption across diverse industries, and explore the role of intellectual property (IP) in fostering innovation within AI and advanced analytics among Australian firms. As an extension of this groundbreaking study, this VRES project focuses on a comprehensive literature review, identification of research gaps, and the application of quantitative data analysis techniques to empirical data of the Australian AI ecosystem, including over 600 unique company IDs, approximately 40k+ unique employee skill sets, and over 450k patent records and/or scientific publication records.

The ultimate goal is to generate insights that can inform policies and support systems within the research development and entrepreneurship communities, fostering a healthier and more productive environment for innovation and technology adoption. Through this research, you will contribute to cutting-edge knowledge at the intersection of social science, human behaviour, and online studies/data analysis. Additionally, there is an option to co-author and submit the manuscript to an academic journal (see ‘Outcomes’ section below).

Research engagement

  • Conduct an extensive review of existing literature to identify gaps and contextualize the current study.
  • Work on real-world data using quantitative data analysis techniques.
  • Document the findings of the study, interpreting the data and drawing conclusions based on the analysis.

Outcomes

  • The project aims to produce a draft manuscript that includes the following:
    • Introduction (1-2 pages): Overview of the study's background and significance.
    • Literature Review (3-5 pages): Detailed analysis of existing research and identification of gaps.
    • Research Aims/Objectives and Questions/Hypotheses (1-2 pages): Clear articulation of the study's goals and hypotheses.
    • Methodology (2-5 pages): Description of the data collection process and analysis methods.
    • Results and Discussion (5-8 pages): Presentation and interpretation of findings.
    • Conclusions and Future Work (2-4 pages): Summary of key insights and recommendations for future research.
    • Reference List/Bibliography
    • Appendices (optional)
  • Generate a brief, 2-3 slide presentation to present your research at the Faculty of Business and Law VRES Showcase to conclude the program.
  • (Optional) The student will be eligible to present the findings to an audience of 100+ academics and industry partners in the annual BEST conference in 2025.
  • (Optional) The ultimate goal is to co-author and submit the manuscript with the student to an academic journal, provided the student is keen and interested. However, it should be noted that the primary deliverable is a final draft manuscript. As it is not certain that the student will be paid beyond the completion of the VRES program in early 2025, any further work on the paper would be entirely voluntary and optional for the student.

Skills and experience

  • Some proficiency in data entry, data analysis, and statistical techniques
  • Experience using software such as STATA, R, python and/or an interest in artificial intelligence (AI), entrepreneurship, and innovation research would be beneficial for this role.
  • An interest in artificial intelligence (AI), entrepreneurship and innovation research, and business, management and operation studies, and/or the Science of Science (SciSci) and Intellectual Property would be beneficial.

Start date

20 November, 2024

End date

7 February, 2025

Location

Hybrid and flexible working options available - we can organise a temporary desk at Level 7, Z Block within the ARC BITA Centre working space.

Keywords

Contact

Dr Steve Bickley - s.bickley@qut.edu.au