Enhancing Customer Retention in Retail through Logistic Service Quality

The role of Omnichannel Capability, Operational Flexibility, Technological Innovation, and the Moderating Effect of Entrepreneurial Orientation

Abstract

This research aims to contribute to the current Dynamic Capability theory within strategic management, focusing on an organization's ability to integrate, build, and reconfigure internal and external competencies to address rapidly changing environments. This framework, developed by David Teece, Gary Pisano, and Amy Shuen in their 1997 paper, emphasizes the importance of adaptive, innovative, and absorptive capacities in creating and sustaining competitive advantage, especially in industries characterized by fast-paced change. In the context of omnichannel retailing, it involves providing a seamless shopping experience to customers across various channels, including online platforms, mobile apps, and physical stores. Exploring the intersection of dynamic capabilities theory with omnichannel retailing and logistic service quality presents several promising avenues for future research. As customer retention becomes an increasingly crucial goal for retailers, understanding how these elements can be optimized and integrated offers significant potential for theoretical and practical advancements. The dissertation research analyses variables on omnichannel capability, operational flexibility, and technological innovation that impact logistic service quality and how it affects on customer retention in retail, with entrepreneurial orientation as a moderating factor. Each of these areas not only contributes to the academic understanding of how dynamic capabilities influence omnichannel retailing and logistic service quality but also provides actionable insights for practitioners looking to enhance customer retention. By addressing these gaps, researchers can help retailers better navigate the complexities of today's market environments, ultimately leading to more resilient and customer-centric business models.