Presenting a Model and Identifying Key Dimensions of Social Media in Customer Repurchase Behavior

Objective
The rapid evolution of e-commerce and the widespread adoption of social media platforms have fundamentally transformed consumer behavior and business strategies worldwide. Among these platforms, Instagram has emerged as a pivotal channel for online shopping, offering businesses a dynamic space to engage with customers and foster long-term relationships. This study seeks to address a critical gap in the literature by presenting a comprehensive model that identifies the key dimensions of social media influencing customer repurchase behavior, with a specific focus on Instagram users. The primary objective is to explore how social media contributes to building trust and encouraging repurchase intentions, which sustains customer retention and enhances business profitability. By examining the interplay of psychological, experiential, and marketing-related factors, this research aims to provide actionable insights for businesses leveraging social media for digital marketing. The study addresses two main research questions: (1) What is an appropriate model for customers’ repurchase intentions on social media? and (2) What are the dimensions and components of this repurchase intention model?
Research Methodology
To achieve its objectives, this research employs a mixed-method approach, integrating qualitative and quantitative strategies to ensure a robust analysis. The qualitative phase utilizes a meta-synthesis method to synthesize findings from existing studies on social media and repurchase behavior. A systematic literature review was conducted using Google Scholar, targeting English-language articles published between 2013 and 2023, a period aligned with Instagram’s rise as a key e-commerce platform. Keywords such as “repurchase intention,” “social media,” and “Instagram” were used to retrieve relevant studies, resulting in an initial pool of 383 records. Following the PRISMA 2020 guidelines, the selection process involved multiple stages—identification, screening, eligibility, and synthesis—narrowing the dataset to 13 high-quality articles. These were analyzed using Atlas TI 9 software, where 131 initial codes were extracted, refined to 63 unique codes, and subsequently organized into 17 core codes and 6 overarching categories, forming the conceptual model.
In the quantitative phase, a field survey was conducted using a researcher-designed questionnaire based on the qualitative findings. The questionnaire, structured on a five-point Likert scale, assessed variables identified in the model. The target population comprised Instagram users who had made at least two purchases through the platform. Using Cochran’s formula for an infinite population, a sample size of 384 was calculated (with 365 responses analyzed), ensuring a 95% confidence level and a 5% margin of error. The questionnaire’s validity was confirmed by marketing and consumer behavior experts, while its reliability was verified with Cronbach’s alpha exceeding 0.7 for all constructs. Data were collected online via links and QR codes shared on Instagram and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed model’s fit and relationships.
Findings
The qualitative analysis yielded a model comprising six key categories influencing repurchase behavior: psychological factors, experience-related factors, marketing-related factors, social media characteristics, trust, and loyalty. These categories were derived from 17 core codes, including sharing, ease of use, social influence, satisfaction with social media, online customer attention, e-service quality, information, product-related psychological factors, company-related psychological factors, quality, perceived usefulness, brand, incentives and promotions, word-of-mouth advertising, trust, and loyalty. The quantitative results validated this model, demonstrating strong reliability and validity. Factor loadings for all indicators exceeded 0.4, Cronbach’s alpha and composite reliability values confirmed internal consistency, and Average Variance Extracted (AVE) values above 0.5 affirmed convergent validity. Discriminant validity was established using the Fornell-Larcker criterion, with the square root of AVE surpassing inter-construct correlations.
The structural model’s fit was robust, with R² values indicating strong explanatory power (e.g., 0.471 for repurchase behavior) and Q² values confirming predictive relevance. The Goodness of Fit (GOF) index of 0.311 suggested a strong overall model fit. Key findings highlighted that trust and loyalty serve as pivotal mediators in the relationship between social media dimensions and repurchase behavior. Psychological factors, such as product satisfaction and emotional attachment to brands, significantly enhance trust, while experiential factors like perceived quality and brand experience foster positive customer perceptions. Marketing efforts, including brand content and word-of-mouth promotion, amplify awareness and engagement, further driving repurchase intentions. Social media characteristics—such as ease of use, responsiveness, and information availability—underpin trust and loyalty, reinforcing their role as foundational elements in the repurchase ecosystem.
Discussion & Conclusion
This study advances the understanding of customer repurchase behavior in the context of social media by proposing and validating a multidimensional model tailored to Instagram users. Unlike prior research, which often examined isolated factors, this study integrates psychological, experiential, and marketing dimensions into a cohesive framework, emphasizing trust and loyalty as critical drivers of repurchase intentions. The findings underscore that businesses can enhance repurchase behavior by cultivating trust through consistent quality, responsive customer service, and transparent communication, while fostering loyalty via meaningful engagement and personalized experiences. The cost-effectiveness of retaining existing customers—highlighted as five times less expensive than acquiring new ones—further amplifies the practical significance of these insights.
For practitioners, the model offers a strategic roadmap to optimize social media marketing. Companies should prioritize interactive content, leverage influencers for word-of-mouth promotion, and ensure seamless user experiences to strengthen customer relationships. The study’s novelty lies in its mixed-method approach and Instagram-specific focus, addressing a gap in the literature where comprehensive models of social media-driven repurchase behavior were lacking. However, limitations include its focus on a single platform and reliance on self-reported data, suggesting avenues for future research across diverse platforms and cultural contexts. In conclusion, this research provides a theoretically grounded and empirically supported model that equips businesses with the tools to harness social media’s potential in driving customer repurchase, ultimately enhancing profitability and competitive advantage in the digital marketplace.