dc.contributor.author	Wang, Jeremy Fei	en
dc.contributor.committeechair	Lowry, Paul Benjamin	en
dc.contributor.committeemember	Zobel, Christopher W.	en
dc.contributor.committeemember	Wang, Alan Gang	en
dc.contributor.committeemember	Chakravarti, Dipankar	en
dc.contributor.department	Business	en
dc.date.accessioned	2023-01-04T09:00:19Z	en
dc.date.available	2023-01-04T09:00:19Z	en
dc.date.issued	2023-01-03	en
dc.description.abstract	Rapid technological advances in artificial intelligence (AI), data analytics, big data, the semantic web, the Internet of Things (IoT), and cloud and mobile computing have given rise to a new generation of AI-driven recommendation agents (RAs). These agents continue to evolve and offer potential for use in a variety of application domains. However, extant information systems (IS) research has predominantly focused on user perceptions and evaluations of traditional non-intelligent product-brokering recommendation agents (PRAs), supported by empirical studies on custom-built experimental RAs that heavily rely on explicit user preference elicitations. To address the lack of research in the new generation of intelligent RAs (RAs 2.0), this dissertation aims to study consumer responses to AI-driven RAs using an affordance perspective. Notably, this research is the first in the IS discourse to link RA design artifacts, RA affordances, RA outcomes, and user continuance. It examines how actualized RA affordances influence user engagements with and evaluations of these highly personalized systems, which increasingly focus on user experiences and long-term relationships. This three-essay dissertation, consisting of one theory-building paper and two empirical studies, conceptually defines "RAs 2.0," proposes a comprehensive theoretical framework with testable propositions, and conducts two empirical studies guided by smaller carved-out models to test the validity of the comprehensive framework. The research is expected to enrich the IS literature on RAs and identify potential areas for future research. Moreover, it offers key implications for industry professionals regarding the effective system development of the new generation of intelligent RAs.	en
dc.description.abstractgeneral	Rapid technological advances in artificial intelligence (AI), data analytics, big data, the semantic web, the Internet of Things (IoT), and cloud and mobile computing have given rise to a new generation of AI-driven recommendation agents (RAs). These agents continue to evolve and offer potential for use in a variety of application domains. This three-essay dissertation, consisting of one theory-building paper and two empirical studies, conceptually defines "RAs 2.0," proposes a comprehensive theoretical framework with testable propositions, and conducts two empirical studies guided by smaller carved-out models to test the validity of the comprehensive framework. The research is expected to enrich the IS literature on RAs and identify potential areas for future research. Moreover, it offers key implications for industry professionals regarding the effective system development of the new generation of intelligent RAs.	en
dc.description.degree	Doctor of Philosophy	en
dc.format.medium	ETD	en
dc.identifier.other	vt_gsexam:36247	en
dc.identifier.uri	http://hdl.handle.net/10919/113014	en
dc.language.iso	en	en
dc.publisher	Virginia Tech	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Recommendation agents (RAs)	en
dc.subject	RAs 2.0	en
dc.subject	affordance	en
dc.subject	design artifacts	en
dc.subject	digital companionship	en
dc.subject	artificial intelligence (AI)	en
dc.subject	deep learning (DL)	en
dc.title	The New Generation of Recommendation Agents (RAs 2.0): An Affordance Perspective	en
dc.type	Dissertation	en
thesis.degree.discipline	Business, Executive Business Research	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	doctoral	en
thesis.degree.name	Doctor of Philosophy	en

The New Generation of Recommendation Agents (RAs 2.0): An Affordance Perspective

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