Citation

Keong, Lai Mun and Ling, Chow Poh and Chan, Man Seong and Tan, Chi Hau (2026) AI-Driven Predictive Analytics in Business Risk Management. In: Sustainable Development Agenda 2030. Springer Nature Link, pp. 615-629. ISBN 978-3-032-11611-6, 978-3-032-11612-3

Full text not available from this repository.

Abstract

As business environments in the global context continue to become more uncertain and complex, organizations have been turning to artificial intelligence (AI)-based predictive analytics to help them improve their abilities to manage risk. This paper examines how AI-led predictive analytics and the mediating role of organizational readiness determines the effectiveness of how business risk can be managed, but also looks into how the moderating role of trusting AI systems in the hands of a manager limits that effectiveness. Based on the Technology-Organization-Environment (TOE) model and the Technology Acceptance Model (TAM), the study borrows the quantitative methodology to interpret the data sourcing conducted in 217 managers of diverse industries that face unpredictable markets in terms of strategic risks. The results demonstrate that AI enables much more effective risk identification, assessment, and mitigation processes when it hosts predictive analytics, and the impact depends on the willingness of the organization to use new technologies and the amount of trust that managers put in insights that are generated by AI. The findings can help develop the body of knowledge through the relevance of technological factors, organizational factors, and human factors in achieving the possibilities of AI in business risk management. They provide some practical implications that a manager, AI system developer, and policymaker may pursue in order to promote effective adoption of AI in risk-intensive businesses.

Item Type:	Book Section
Uncontrolled Keywords:	Artificial intelligence (AI), Predictive analytics
Subjects:	H Social Sciences > HF Commerce > HF5001-6182 Business
Divisions:	Faculty of Management (FOM)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	04 Jun 2026 05:45
Last Modified:	08 Jun 2026 07:17
URII:	http://shdl.mmu.edu.my/id/eprint/15929

Downloads

Edit (login required)