Role of Algorithm Awareness in Privacy Decision-Making Process: A Dual Calculus Lens

• Sujun Tian ^[1] ; Bin Zhang ^[1] ; Hongyang He ^[1]
  1. [1] Beijing University of Posts and Telecommunications

     Beijing University of Posts and Telecommunications

     China

     
• Localización: Journal of Theoretical and Applied Electronic Commerce Research, ISSN-e 0718-1876, Vol. 19, Nº. 2, 2024, págs. 899-920
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen

  • In the context of AI, as algorithms rapidly penetrate e-commerce platforms, it is timely to investigate the role of algorithm awareness (AA) in privacy decisions because it can shape consumers’ information-disclosure behaviors. Focusing on the role of AA in the privacy decision-making process, this study investigated consumers’ personal information disclosures when using an e-commerce platform with personalized algorithms. By integrating the dual calculus model and the theory of planned behavior (TPB), we constructed a privacy decision-making model for consumers. Sample data from 581 online-shopping consumers were collected by a questionnaire survey, and SmartPLS 4.0 software was used to conduct a structural equation path analysis and a mediating effects test on the sample data. The findings suggest that AA is a potential antecedent to the privacy decision-making process through which consumers seek to evaluate privacy risks and make self-disclosure decisions.

    The privacy decision process goes through two interrelated trade-offs—that threat appraisals and coping appraisals weigh each other to determine the (net) perceived risk and, then, the (net) perceived risk and the perceived benefit weigh each other to decide privacy attitudes. By applying the TPB to the model, the findings further show that privacy attitudes and subjective norms jointly affect information-disclosure intention whereas perceived behavioral control has no significant impact on information-disclosure intention. The results of this study give actionable insights into how to utilize the privacy decision-making process to promote algorithm adoption and decisions regarding information disclosure, serving as a point of reference for the development of a human-centered algorithm based on AA in reference to FEAT.