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, trust, impulsive buying, e-commerce, persuasion knowledge theoryAbstract
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 results confirm that algorithmic performance expectations significantly enhance consumer trust (β = 0,627, p < 0,05), which in turn, positively drives impulsive purchasing behavior (β = 0,423, p < 0,05). This findings suggest that consumer perceptions of algorithmic accuracy and transparency are pivotal in fostering trust and spontaneous consumption.
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