Enhance understandings of Online Food Delivery's service quality with online reviews

This research presents an integrative approach, leveraging large-scale user-generated content (online reviews) to decipher consumers' quality perceptions in the burgeoning Online Food Delivery (OFD) sector. Utilizing the advanced BERTopic machine learning algorithm, we first qualitatively identify key service topics (qualities) pertaining to consumers' OFD experience. Different from prior studies that overlook the synergies between cutting-edge machine learning and traditional methods, our findings are reflected against current scales estab-lished with traditional methods such as interviews and surveys. This practice allows us to highlight several topics overlooked by existing framework, such as corporate social responsibility, and identify low-importance service dimensions like personalization experience. Following the narrative analysis, an importance-performance analysis is undertaken to discern the priority of quality improvement for OFD platforms. Collectively, our in-sights offer pivotal theoretical and practical implications for practitioners and researchers in the OFD domain. Besides, our integrative approach balances theoretical development and practical applicability and can be readily extended to wider service scenarios.

Automated summary

This research uses large-scale user-generated content to decipher consumers' quality perceptions in the online food delivery sector. By applying machine learning algorithms, key service topics related to the consumer experience are identified. Ultimately, our findings provide crucial theoretical and practical implications for practitioners and researchers in this field.