{"links":{"self":"http://dataportal.arc.gov.au/NCGP/API/grants/DE260100134"},"data":{"type":"grant-details","id":"DE260100134","attributes":{"code":"DE260100134","administering-organisation":"Monash University","announcement-administering-organisation":"Monash University","scheme-name":"Discovery Early Career Researcher Award","grant-status":"Active","funding-commencement-year":2026,"years-funded":3,"project-start-date":"2026-01-01","anticipated-end-date":"2028-12-31","grant-summary":"Shaping the future of work: improving work design in human-AI collaboration. Without appropriate work design, the integration of AI-powered tools into workplace will primarily focus on replacing human work. This project aims to reveal the fundamental role of mental model of technology in shaping work design, contributing to the development of work design theory within the context of human-AI collaboration. Anticipated outcomes include: a measure of mental model, a deeper understanding of the antecedents of work design from both top-down and bottom-up perspectives, and intervention tools to mitigate biased mental models. Significant benefits include identifying feasible strategies to harness the respective advantages of both human employees and AI, thereby improving work effectiveness and well-being in the AI era. ","funding-current":528152.00,"funding-at-announcement":523978,"investigators-current":[{"title":"Dr","firstName":"Bin","familyName":"Wang","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0002-9459-1328 "}],"investigators-at-announcement":[{"title":"Dr","firstName":"Bin","familyName":"Wang","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0002-9459-1328 "}],"organisations-current":[{"organisationName":"Monash University","roleName":"Administering Organisation","state":"VIC"}],"organisations-at-announcement":[{"organisationName":"Monash University","roleName":"Administering Organisation","state":"VIC"}],"field-of-research":[{"isPrimary":false,"code":"350303","name":"Business Information Systems","type":"FOR20"},{"isPrimary":false,"code":"350503","name":"Human Resources Management","type":"FOR20"},{"isPrimary":true,"code":"3507","name":"Strategy, Management and Organisational Behaviour","type":"FOR20"},{"isPrimary":false,"code":"350710","name":"Organisational Behaviour","type":"FOR20"}],"socio-economic-objective":[{"code":"150302","name":"Management","type":"SEO20"},{"code":"150304","name":"Productivity (Excl. Public Sector)","type":"SEO20"},{"code":"150502","name":"Human Capital Issues","type":"SEO20"}],"international-collaboration":["China (excludes SARs and Taiwan)","Hong Kong (SAR of China)","United States of America"],"lief-register":[],"achievement-summary":null,"national-interest-test-statement":"AI is expected to contribute up to $600 billion annually to Australia’s GDP and automate more than 50 percent of activities in Australia by 2030. To capitalize on this strategic opportunity, the Department of Industry, Science and Resources aims to “support AI capability in Australia and drive responsible AI adoption”. However, AI adoption does not necessarily mean using AI to replace humans. A key challenge lies in designing high-quality jobs that enable both human employees and AI to leverage their respective strengths without compromising well-being. By integrating research on work design and mental models (i.e., cognitive representations about the effects of technology), this project will address this knowledge gap by revealing the crucial role of human mental model in shaping work design in human-AI collaboration from both top-down and bottom-up perspectives. In doing so, the research findings will support Australian organizations in improving job quality and productivity in the AI age. Specifically, this project will guide managers in overcoming cognitive biases and making appropriate work design decisions, while also equipping employees to proactively adapt to challenges arising from human-AI collaboration by crafting their jobs. Findings and intervention tools from this project will be disseminated to policymakers and industry leaders via open-access video and management cases, and to the broader public through media engagement and popular science articles. "}}}