Marketing at the Human-Technology Frontier: Essays on AI Acceptance, Digital Twins, and Brain-Computer Interfaces

We are living through a moment when technology is no longer something we simply use; it is something we increasingly live with. From smart assistants and autonomous systems to rapidly evolving generative AI and early-stage brain-computer interfaces (BCIs), the boundary between human thought and machine intelligence is becoming more fluid than ever. As these technologies advance, they are quietly reshaping how people think, decide, shop, and interact. Marketing, which has always sought to understand people as consumers, must now learn to understand these new forms of human-technology relationships. This dissertation explores that transformation through three interconnected studies, moving from today's AI, to emerging AI-driven digital replicas of consumers, to a future where neural interfaces may connect the human mind directly with digital systems. The first study asks a simple but consequential question: How do people feel about interacting with AI as it becomes more capable and more human-like? Drawing on data from more than one hundred thousand people, this study identifies which features of AI, such as its perceived intelligence, transparency, role, and human-likeness, most strongly influence acceptance. The results reveal a small but shrinking hesitation toward AI and highlight the specific design choices that can help build trust and comfort as agentic AI becomes more common. The second study moves from understanding consumers to re-creating them. It introduces a new framework for building "digital twins" of real consumers, which are AI-powered replicas that can mimic how individuals think, choose, and respond. By combining large language models with a person's own digital footprint like reviews they've written, these digital twins can learn a consumer's preferences and personality, while also incorporating new situational information at the moment of prediction. Tested on hundreds of real shoppers, the digital twins accurately forecast what people would buy and even generate product reviews that closely match each person's writing style and opinions. This approach opens a path to more privacy-friendly, psychologically grounded personalization, without needing constant data tracking. The final study looks ahead to technologies that are only beginning to emerge: brain-computer interfaces. As BCIs develop, they may fundamentally alter how people interact with digital content, devices, brands, and each other. This chapter presents a forward-looking framework called "interface-enabled marketing," which maps how neural interfaces could transform the consumer journey across four layers: from the user's mind, to the hardware they wear, to the software that interprets neural signals, to the AI systems making sense of it all. The chapter outlines both the opportunities and the profound ethical dilemmas that may arise when everyday interactions can be shaped or even partially inferred from neural activity. Together, this dissertation examines marketing at the human-technology frontier. It not only analyzes how technology reshapes consumer behavior but also offers marketers, researchers, and society a roadmap for navigating a world where humans and intelligent systems become deeply interconnected.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Business, Marketing

Department dc:contributor.department

Marketing

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2025

Author dc:creator

• Li, Bingqing

Chair dc:contributor.committeechair

• Wang, Xin

Committee members dc:contributor.committeemember

• Thomson, Matthew
• Bagchi, Rajesh
• Chakravarti, Dipankar

• emerging technology
• artificial intelligence
• machine learning
• digital marketing

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:45109

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/139820