Unveiling the nexus between customer social participation, mutually beneficial interactions, and resource integration within Indonesian brand communities
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DOIhttp://dx.doi.org/10.21511/im.21(1).2025.18
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Article InfoVolume 21 2025, Issue #1, pp. 225-235
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Creative Commons Attribution 4.0 International License
Although there is general consensus that social media can enhance collaboration among contributors, the debate continues regarding the optimal approach for integrating consumer resources into brand communities on these platforms. This paper investigates how customer social participation impacts customer resource integration and mutually beneficial interactions within social media brand communities. Utilizing the Partial Least Squares approach to Structural Equation Modeling (PLS-SEM) and involving 295 participants, the findings reveal that customer social participation positively affects both customer resource integration (β=0.415, p=0.000) and mutually beneficial interactions (β=0.753, p=0.000). Furthermore, mutually beneficial interactions significantly influence customer resource integration within the brand community (β=0.432, p=0.000). Notably, mutually beneficial interactions act as a mediator in the relationship between customer social participation and customer resource integration (β=0.325, p=0.000). These findings contribute to the emerging research stream on customer social participation, resource integration, and mutually beneficial interactions in marketing contexts, offering valuable insights for both scholars and practitioners. The study also provides practical implications for brand community activities and suggests several avenues for future research.
Acknowledgment
This research was supported by the Ministry of Education, Culture, and Technology Research of Indonesia under the Doctoral Dissertation Research Grant in 2022, Number 59/UN5.2.3.1/PPM/KPDRTPM/TI/2022.
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JEL Classification (Paper profile tab)M31, D91, O33
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References37
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Tables3
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Figures0
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- Table 1. Path analysis results
- Table 2. Explanatory power, predictive relevance of constructs, and model fit
- Table A1. Measurement model statistics: construct reliability, validity, and item loadings
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