Mediating Role of Organizational Learning in the Relationship between Use of Artificial Intelligence Security Technology and Community Security

Abstract

This study focuses on developing a robust model to augment community security through the implementation of AI, while simultaneously investigating the mediating influence of organizational learning within the context of vital AI factors in the UAE. The model examines the interplay among key AI elements, such as compatibility (COMPAT), complexity (COMPLEX), management support (MS), ethics (ETH), and staff capabilities (SC), concerning their impact on the effectiveness of Community Security (ESC). Data collection was conducted via a questionnaire survey utilizing the Abu Dhabi Police as a representative case study for public organizations in the UAE, involving 138 participants spanning both managerial and operational roles, with responses acquired through randomized distribution using online tools. The amassed data was employed to construct the model using SmartPLS software, and its evaluation adhered to assessment criteria encompassing measurement and structural components. A goodness-of-fit score of 0.751 indicated a high level of overall predictive performance for the model. The study's findings revealed that organizational learning (OL) serves as a partial mediator in the relationship between the complexity construct (COMPLEX) and the effectiveness of Community Security (ESC), with no observed mediation effects in other relationships. The research outcomes culminated in the creation of a versatile model that enhances community security through AI technology, applicable across diverse scenarios, and benefiting individuals invested in AI and community security, such as academics, researchers, and practitioners. The study's methodology provides valuable insights for practitioners and researchers in the UAE and related fields, affording opportunities for replication or adaptation to suit specific investigative contexts.