Algorithmic Performance Expectations and Impulsive Buying in E-Commerce: Trust in Algorithm-Generated Recommendations as a Mediator
DOI:
https://doi.org/10.21831/jim.v23i1.95607Keywords:
algorithmic performance expectations, e-commerce, impulsive buying, persuasion knowledge theory, trustAbstract
This study investigates the impact of algorithmic performance expectations on impulsive buying behavior within e-commerce platforms, with trust in algorithm-generated recommendations serving as a mediating variable. A structured questionnaire was administered to 116 online shoppers in Yogyakarta, Indonesia. The hypothesized relationships were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The empirical findings indicate that algorithmic performance expectations significantly enhance consumer trust, which subsequently drives spontaneous purchasing decisions. These insights suggest that consumers’ perceptions of the accuracy and transparency of AI recommendation systems are crucial factors for e-commerce businesses in building platform trust, reducing user skepticism, and effectively encouraging spontaneous consumption among digital shoppers.
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