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

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.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Business, Executive Business Research

Department dc:contributor.department

Business

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2023

Author dc:creator

• Wang, Jeremy Fei

Chair dc:contributor.committeechair

• Lowry, Paul Benjamin

Committee members dc:contributor.committeemember

• Zobel, Christopher W.
• Wang, Alan Gang
• Chakravarti, Dipankar

• Recommendation agents (RAs)
• RAs 2.0
• affordance
• design artifacts
• digital companionship
• artificial intelligence (AI)
• deep learning (DL)

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:36247

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/113014