Achieving Customer Satisfaction Drive-Thru Services through Digital Twins in America

Abstract

In today’s dynamic business environment, comprehending consumer behavior stands as a pivotal factor for achieving success. However, traditional methods of gathering consumer insights often prove to be time-consuming, expensive, and limited in depth. Digital Twins technology emerges as a promising solution, offering virtual representations of consumers and real-time insights into their behaviors and preferences. This study, which has yielded promising results, investigates the implementation of Digital Twins in enhancing efficiency, performance, and user experience within drive-thru services, focusing on consumer perspectives. Additionally, it evaluates the technology’s impact on operational management and customer interactions while addressing data security and privacy concerns. Employing a quantitative approach, the study surveys 2000 drive-thru consumers in the United States through purposive sampling. Data collected covers consumer satisfaction, perceptions of Digital Twins’ effectiveness, and drive-thru service preferences, analyzed using Structural Equation Modeling (SEM-PLS). Findings indicate the significant impacts of Digital Twins on drive-thru services, including purchase accuracy, interactivity, process efficiency, and overall customer satisfaction. Reliability and validity tests affirm the robustness of the research model. This research contributes to understanding Digital Twins technology’s potential benefits and challenges in drive-thru services. It underscores the importance of strategic approaches to maximize its advantages across industries. Furthermore, it offers a comprehensive framework for securely and effectively implementing Digital Twins, considering broader aspects beyond technical considerations.