This research examines how perceived reciprocity and perceived ethical AI characteristics – fairness, accountability, and transparency (FAT principles) – shape consumers’ willingness to share personal information and its downstream consequences in AI-mediated consumer-brand interactions. Grounded in social exchange theory, we conceptualize personal information sharing as a behavioral gateway through which ethical exchange cues translate into perceived algorithmic legitimacy and intention to co-create value. Using a two-study design, consisting of a scenario-based experiment (n = 184) and a cross-sectional survey (n = 612), the findings of this research show that reciprocity and FAT principles robustly increase willingness to share personal information. Their effects on perceived algorithmic legitimacy and co-creation intentions operate primarily through information sharing rather than directly. Results further indicate that willingness to share personal information functions as a legitimacy-conferring act, supporting a bottom-up, interactional view of legitimacy formation in AI-mediated exchanges. By distinguishing information sharing-based participation from downstream value co-creation, this study advances a process-based account of engagement and offers actionable insights for designing ethically grounded AI systems that encourage voluntary data sharing and sustained consumer collaboration. • Ethical AI cues and reciprocity jointly shape willingness to share personal information. • Information sharing functions as a gateway to legitimacy and value co-creation. • Experimental evidence shows reciprocity affects outcomes indirectly via disclosure. • Survey results confirm strong links between disclosure, legitimacy, and co-creation. • This research advances a bottom-up, exchange-based view of algorithmic legitimacy.
Hussain et al. (Fri,) studied this question.