Date of Award


Degree Type


Degree Name

Doctor of Philosophy


Business Administration

Major Professor

Pratibha A. Dabholkar

Committee Members

Daniel J. Flint, Funda Sahin, Michael A. Olson


Online product recommendation agents are gaining greater strategic importance as an innovative technology to deliver value-added services to consumers. Yet the active role of consumers as the participants in using this technology is not well understood. This dissertation builds on the technology-based self-service (TBSS) literature, consumer participation literature, the service-dominant logic, and the trust literature on recommendation agents to develop a research framework that explains the role of consumer participation in using online product recommendation agents.

The objective of this dissertation is three-fold: (1) to examine the effects of consumer participation and privacy/security disclosures in using online product recommendation agents, (2) to explore the mediating effects of trust, perceived control, and perceived risk in providing personal information, and (3) to test the trust transference process within the current research context.

A field experiment using existing recommendation agents was conducted with multiple sessions in computer labs to collect data from university students, a representative sample of the online population. 67 undergraduate students participated in the pretest, and 117 participated in the main study. Structural equation modeling with AMOS 7.0 was used to test the research hypotheses.

The results showed that consumer participation was a contributing factor in building consumers’ trust in recommendation agents and that privacy/security disclosures decreased consumers’ perceived risk in providing personal information. Moreover, the trust transference process was validated among the three different types of consumer trust within the agent-mediated environment, that is, trust in the recommendation agent, trust in the Web site, and trust in recommendations. Finally, perceived control was shown to be a salient factor in increasing consumers’ trust and motivating consumers to reuse the recommendation technology

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