Task allocation involves questions of uncertainty, contextual judgement, explainability and risk, as well as technical performance. For HRM, it also changes job content, skill requirements and accountability. The programme is designed to compare human, AI and hybrid allocation pathways, connect HRM, organisational behaviour and AI-related management research, and develop external funding proposals from the initial project. The objective is a coherent programme that explains how organisations can distribute work and responsibility without losing the human capabilities on which effective oversight depends.
A next step would be to develop and test task-classification approaches against human-only, AI-only and rule-based alternatives. Building on the dependency paper's limitations, I would also seek longitudinal and experimental evidence on skill maintenance, verification and decision quality. These are proposed directions for developing the programme, rather than completed studies.
My current manuscript pipeline complements this agenda. Revisions have been submitted on digital leadership and organisational performance, workplace relationships and knowledge sharing, and AI awareness and employee performance. Other manuscripts listed as under review address algorithmic résumé anxiety, AI implementation and withdrawal behaviour, and AI perceptions, learning demands and professional efficacy. These remain submissions, not accepted publications.
Principal Investigator · 2027
Developing an Evidence-Based Online HR Task Bank Prototype for Responsible AI Task Allocation and Job Redesign
AURAK seed grant · BUSN/002/27 · AED 8,300
The agreement lists academic collaborators and Zand Bank PJSC. The project is intended to support the development of a prototype; it is not yet evidence of a validated allocation system or completed organisational deployment.
Research capacity
Postgraduate supervision and faculty training in qualitative comparative analysis are complementary forms of academic contribution. They build the capability to ask better questions, design stronger studies and interpret evidence with care.