Why Do Professionals Go the Extra Mile? Testing Intrinsic and Extrinsic Incentives with Generative AI

Overview

Health and education systems increasingly rely on staff contributing effort beyond their formal job requirements, particularly during staffing shortages, emergency pressures, administrative backlogs and periods of organisational change. However, it remains unclear which forms of support are most likely to encourage this extra contribution. Financial incentives may matter, but flexibility, career opportunities, recognition, professional development, autonomy and wellbeing supports may be equally or more important depending on the context.

This interdisciplinary project combines behavioural economics, organisational psychology, health and education policy, labour economics, and artificial intelligence. It uses an existing synthetic-agent dataset in which teacher and nurse profiles responded to a large set of standardised workplace scenarios. The scenarios vary both the source of the staffing shortage and the incentive offered in return for working additional hours. For example, agents are asked whether they would contribute extra time when shortages arise from illness, resource cuts, emergency demand, administrative pressure or organisational mismanagement, and whether responses change when incentives involve overtime pay, flexible scheduling, career progression, recognition or wellbeing benefits.

The project provides an opportunity to examine how the structure and framing of incentives shape model-generated willingness to contribute extra work. It will also explore whether patterns differ between teaching and nursing settings, between intrinsic and extrinsic motivators, and across different model temperature settings. The study is designed as a methodological and substantive contribution: it investigates both workplace incentives and the usefulness of generative agents for systematically pre-testing behavioural hypotheses. Findings will be interpreted as evidence about synthetic agent responses rather than direct estimates of real teacher or nurse behaviour.

Research engagement

The student will:

• Conduct a review existing literature to identify gaps and contextualise the current study;
• Work on data using quantitative data analysis techniques;
• Document the findings of the study, interpreting the data and drawing conclusions based on the analysis.

Research activities

The student will work with Professor Benno Torgler and members of the behavioural economics/AI research team at the ARC BITA Centre. They will gain practical experience in Stata, Python or R, reproducible data cleaning, regression modelling, visualisation, research documentation and academic writing.

• The project aims to produce a draft manuscript that includes the following:
  • Introduction: Overview of the study’s background and significance.
  • Literature Review: Review of the existing research literature and identification of gaps.
  • Research Aims/Objectives and Questions/Hypotheses: Clear articulation of the study’s aims/objectives and research questions/hypotheses.
  • Methodology: Description of the data collection process and analysis
  • methods.
  • Results and Discussion: Presentation and interpretation of findings.
  • Conclusions and Future Work: 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 of their research to an audience of 100+ academics and industry partners in the annual BITA conference in February/March 2027, provided the student is keen and interested to.
• (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 to. However, it should be noted that the primary deliverable is a final draft manuscript. No work beyond the VRES period is required. Any later contribution to a manuscript would be optional and by mutual agreement.

Research skills

They will gain practical experience in Stata, Python or R, reproducible data cleaning, regression modelling, visualisation, research documentation and academic writing.

Outcomes

• The project aims to produce a draft manuscript that includes the following:
  • Introduction: Overview of the study’s background and significance.
  • Literature Review: Review of the existing research literature and identification of gaps.
  • Research Aims/Objectives and Questions/Hypotheses: Clear articulation of the study’s aims/objectives and research questions/hypotheses.
  • Methodology: Description of the data collection process and analysis
  • methods.
  • Results and Discussion: Presentation and interpretation of findings.
  • Conclusions and Future Work: 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 of their research to an audience of 100+ academics and industry partners in the annual BITA conference in February/March 2027, provided the student is keen and interested to.
• (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 to. However, it should be noted that the primary deliverable is a final draft manuscript. No work beyond the VRES period is required. Any later contribution to a manuscript would be optional and by mutual agreement.

Skills and experience

• An interest in data analysis, economics, psychology, health, education or AI;
• Some proficiency in data entry, data analysis, and statistical techniques; and
• Experience using software such as Stata, R and Python would be beneficial for this role.

Start date

2 November, 2026

End date

19 February, 2027

Location

QUT Gardens Point campus (Z Block, Level 7), with some work able to be completed online/remotely subject to mutual agreement between the student and their VRES supervisor.

Additional information

The project provides the existing simulation data, code templates, regular supervision and relevant readings. Before commencement, the student will read a short set of papers on incentives, prosocial work behaviour and synthetic agent research.

Keywords

• Behavioural economics
• Workplace incentive design
• Synthetic agents
• Generative AI
• Intrinsic and Extrinsic Motivation

Contact

Prof. Benno Torgler

+61 7 3138 2517

benno.torgler@qut.edu.au