Optimising Customer Service Delivery and Response Time through AI-Enhanced Chatbots in Facilities Management-A Mixed-Methods Research

Abstract

The present study aimed to assess the effect of AI-enhanced chatbots that optimize customer service delivery and response times on facility management. It utilised the Technology Acceptance Model (TAM) and Social Response Theory (SRT) for this purpose. The research adopted a mixed-methods methodology aimed to explore the multiple perspectives of 10 facility managers and facility service providers affiliated with facilities management departments in the UAE, Qatar and Saudi Arabia regarding the benefits and challenges of AI-enhanced chatbots. This research used correlation analysis and regression to examine the relationships between variables. Correlation analysis, using SPSS 24.0, showed strong positive correlations between five AI-enhanced chatbot factors: Perceived Usefulness (PU), Perceived Ease of Use (PEoU), Behavioural Intention to Use (BIoU), Responsiveness (RP), and User Satisfaction (US) (Pearson Correlation Coefficient>0.7). Regression analysis indicated a significant impact of all these variables on facilities management (p<0.05). The study found that AI-enhanced chatbots in facilities management improve communication, responsiveness, and operational efficiency. They automate workflows, handle manual tasks, predict failures, and respond to customer queries. However, challenges include technical issues, limited human-human interaction, system quality and security, and user adoption. Chatbots deliver productivity gains and are used for automated reporting, identifying hazards, conducting briefings, managing meetings, providing training, supporting teamwork, ensuring well-being, and enhancing customer service.