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How User‐Generated Content and Platform Shop Metadata to Influence Transaction Volume: A Multilevel Study of Online Consumer Behavior

Fang‐Yi Lo & Ivan Prayoga

Journal of Consumer Behaviour2026https://doi.org/10.1002/cb.70136article
AJG 2ABDC A
Weight
0.50

Abstract

This research investigates the intricate relationship between User‐Generated Content (UGC), shop metadata, and transaction volumes within the context of online shopping platforms. Beyond the UGC's influence on consumer perception, the gap in the literature presents a compelling opportunity for further investigation, particularly in understanding the influence of shop metadata and its implications for real consumer purchase decisions. In this study, we aim to address this gap by adopting a two‐fold approach. In the first study, we seek to deepen our understanding of how UGC factors influence consumer behavior. In the second study, we then extend to the role of shop metadata. Data retrieved from transactional data from Shopee Indonesia provides observable, behavior‐based evidence, enhancing the accuracy and validity of insights into consumer decision‐making. The study employs a hierarchical linear modeling (HLM) approach to analyze objective data sources from Shopee, the foremost online shopping platform in Indonesia. The dataset comprises 8552 product observations nested within 312 distinct platform shop profiles. A combination of the product and shop databases reduces common method bias and elucidates how UGC and shop metadata collectively influence transaction volumes in a hierarchical manner. Through this comprehensive approach, we aim to provide valuable insights into the multifaceted nature of consumer behavior in the online shopping platform landscape, ultimately contributing to a deeper understanding of how online shopping experiences are shaped and influenced.

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https://doi.org/https://doi.org/10.1002/cb.70136

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@article{fang‐yi2026,
  title        = {{How User‐Generated Content and Platform Shop Metadata to Influence Transaction Volume: A Multilevel Study of Online Consumer Behavior}},
  author       = {Fang‐Yi Lo & Ivan Prayoga},
  journal      = {Journal of Consumer Behaviour},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1002/cb.70136},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.