Sharing Economy of Online Food Delivery: Revealing the Underlying Attributes of the Customer Experience through Semantic Network Analysis

Abstract

Research Aims: This study aimed to reveal the underlying attributes of the customer experience in the sharing economy of online food delivery (OFD). Design/Methodology/Approach: After collecting 45,116 reviews from the Google Play store, a semantic network analysis was conducted. Python programming language and text mining were utilised to extract keywords from online reviews, a frequency analysis was performed, and a CONvergence of iterated CORrelations (CONCOR) analysis was conducted using Ucinet 6.0. Research Findings: The keywords ‘food,’ ‘order,’ ‘driver,’ and ‘application’ had the highest frequency and centrality. Customer experience attributes were classified into four clusters: ‘Delivery Procedure’, ‘OFD Platform’, ‘Payment Process’ and ‘Value of Money’. Theoretical Contribution/Originality: This study provides a relevant and novel assessment of customer experience using semantic network analysis, which should be more broadly used in academic research. Managerial Implications in the Southeast Asian Context: Given the predicted growth of online food delivery in Southeast Asia and shared cultural values with Indonesia, the findings of this study may have implications for developing strategies of sustainability in the sharing economy of online food delivery enterprises. Research Limitations & Implications: This study only collected online customer reviews from the Google Play store, and because the method focused on word frequency, understanding of the additional meaning of words is lacking.