Exploring loyalty program discourse on Instagram: a social network analysis approach

Abstract

Purpose - This study aims to analyze interaction patterns, network structures, and information dissemination related to loyalty program discussions on Instagram using a Social Network Analysis (SNA) approach based on the hashtag #LoyaltyProgramResearch. Design/methods/approach - The study applies a quantitative network-based approach by collecting 100 Instagram data points through the SocialX platform. The methodology includes data pre-processing, network construction (nodes and edges), and analysis using SNA metrics such as degree centrality, betweenness centrality, and closeness centrality. Additional analyses, including text network analysis, word cloud, sentiment, and emotion analysis, are used to enrich interpretation. Findings - The results show that discussions are centered on key themes such as loyalty, programs, customers, and rewards. Influential actors and clustered communities play a significant role in information dissemination. Sentiment analysis indicates a dominance of neutral (63%) and positive (36%) sentiments, with minimal negative responses (1%), while emotion analysis highlights happiness as the dominant emotion. Research limitations/implications - This study is limited by the relatively small dataset and the use of a single hashtag. Future research should incorporate larger datasets and multiple platforms to improve generalizability. Practical implications - The findings provide insights for marketers to design more engaging and data-driven loyalty strategies by leveraging user interaction patterns and sentiment insights from social media. Originality/value - This research integrates SNA with text and sentiment analysis to provide a comprehensive understanding of digital conversations on loyalty programs, offering both theoretical and practical contributions.