Algorithm Transparency, Seller Trust, and Service Quality on Indonesian Marketplace Platforms

Tri Septiar Syamfithriani; Munir Munir; Chairul Furqon; Asep Wahyudin

doi:10.59261/jequi.v8i4.447

Authors

Tri Septiar Syamfithriani Universitas Pendidikan Indonesia
Munir Munir Universitas Pendidikan Indonesia
Chairul Furqon Universitas Pendidikan Indonesia
Asep Wahyudin Universitas Pendidikan Indonesia

DOI:

https://doi.org/10.59261/jequi.v8i4.447

Keywords:

Algorithm Transparency, Seller Trust, Seller Service Quality, Indonesian Marketplaces, SEM-PLS

Abstract

Background: Online marketplaces increasingly rely on algorithmic systems to determine product visibility, ranking positions, and seller performance evaluations. When sellers have a limited understanding of how these mechanisms operate, uncertainty may arise, potentially affecting the seller–platform relationship.

Objective: This study examines the relationship between algorithm transparency and seller service quality and assesses whether sellers’ trust in the platform mediates this relationship.

Methods: An explanatory quantitative research design was adopted, with 150 Indonesian marketplace sellers recruited through purposive sampling. A questionnaire comprising 16 items measured algorithm transparency, sellers’ trust in the platform, and seller service quality using a five-point Likert scale. The data were analyzed using partial least squares structural equation modeling (PLS-SEM), including assessments of the measurement and structural models and hypothesis testing.

Results: The findings indicate that algorithm transparency is positively and significantly associated with seller service quality (β = 0.569, t = 5.130, p < 0.001) and seller trust in the platform (β = 0.811, t = 23.451, p < 0.001). Seller trust in the platform is also positively and significantly associated with seller service quality (β = 0.346, t = 3.224, p = 0.001). Furthermore, seller trust in the platform mediates the relationship between algorithm transparency and seller service quality (β = 0.280, t = 3.109, p = 0.002).

Conclusion: Algorithm transparency is positively associated with seller service quality and sellers’ trust in the platform. The findings highlight the need for marketplaces to communicate clear and readily understandable information about algorithmic mechanisms, particularly those governing product rankings, visibility, performance evaluations, and algorithmic changes. Such transparency may help build sellers’ trust and improve the quality of services delivered to consumers.

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