AI & Consumer-Machine Relationships
Understanding why consumers fall in love with machines โ and what it means for the future of marketing.
Consumer-machine relationships describe the emotional bonds, parasocial connections, and patterns of psychological dependence that form when people interact with AI-powered agents such as chatbots, social companions, robo-advisors, and voice assistants. This research area sits at the intersection of marketing, psychology, and computer science, drawing on theories of anthropomorphism, attachment, and trust calibration to explain why consumers increasingly treat machines as relationship partners. My research demonstrates that these relationships follow developmental stages remarkably similar to those observed in human relationships, progressing from curiosity through engagement to attachment. Through mixed-method studies combining qualitative depth interviews with quantitative survey instruments, I have mapped the mechanisms by which consumers assign personality traits, emotional capacities, and social roles to AI agents.
Why This Matters Now
The proliferation of generative AI and social companion apps like Replika, Character.AI, and ChatGPT has made consumer-machine relationships a mainstream phenomenon rather than a niche curiosity. Millions of users now engage in daily conversations with AI companions, some forming attachments strong enough to affect their real-world social behavior. Marketers, therapists, and policy-makers urgently need evidence-based frameworks for understanding when these relationships are beneficial, when they become pathological, and how ethical boundaries should be drawn around AI-mediated intimacy.
Dr. Pentina's Contribution
My contribution to this field centers on three pillars. First, I co-authored one of the earliest and most highly cited empirical studies of relationship formation with AI chatbot Replika, employing a sequential mixed-method design that combined grounded theory interviews with structural equation modeling. This work identified loneliness, trust, and chatbot personification as the primary drivers of engagement and showed that sustained engagement can lead to psychological dependence. Second, I led a systematic literature review of 117 studies on consumer-machine relationships published in Psychology & Marketing, establishing a taxonomy of relationship types across chatbots, service robots, voice assistants, and robo-advisors. This review has become a foundational reference for researchers entering the field. Third, my experimental research comparing human financial advisors with robo-advisors demonstrated that consumers prefer high-expertise humans but rate robo-advisors as more trustworthy than low-expertise human advisors, revealing important boundary conditions for AI adoption in high-stakes service contexts. Collectively, this research stream has accumulated over 700 citations and continues to guide both academic inquiry and industry practice in the design of consumer-facing AI.
Key Findings
- Users develop meaningful emotional connections with AI chatbots that follow stages paralleling human relationship development
- Loneliness and social anxiety are significant predictors of deeper chatbot engagement and potential psychological dependence
- Attachment theory provides a robust framework for understanding human-AI relationships, with users displaying secure, anxious, and avoidant attachment styles
- Robo-advisors outperform low-expertise human advisors on trust and competence measures, suggesting AI has a viable role in financial services
- A systematic review of 117 studies identifies anthropomorphism, trust, and attachment as the dominant theoretical lenses in consumer-machine relationship research
- Ethical concerns around AI companion design include addiction potential, impact on real-life relationships, and unclear boundaries of machine empathy
Selected Publications
Exploring relationship development with social chatbots: A mixed-method study of replika
Comput. Hum. Behav., vol. 140
Who do you choose? Comparing perceptions of human vs robo-advisor in the context of financial services
Journal of Services Marketing
Attachment Theory as a Framework to Understand Relationships with Social Chatbots: A Case Study of Replika
Friend, mentor, lover: does chatbot engagement lead to psychological dependence?
Journal of Service Management
Consumerโmachine relationships in the age of artificial intelligence: Systematic literature review and research directions
Psychology & Marketing
Service robots or human staff? The role of performance goal orientation in service robot adoption
Comput. Hum. Behav., vol. 134
Interplay of rationality and morality in using ChatGPT for academic misconduct
Behaviour & Information Technology, vol. 44
Research Summary
Dr. Pentina's research on AI and consumer-machine relationships provides empirical evidence that humans form genuine emotional bonds with AI agents, following developmental patterns analogous to human relationship formation. Her work spans social chatbots, robo-advisors, and service robots, employing mixed-method designs that combine qualitative depth with quantitative rigor. Key findings include the identification of loneliness and anthropomorphism as primary drivers of AI attachment, the demonstration that robo-advisors occupy a distinct trust niche between expert and novice human advisors, and a comprehensive taxonomy of consumer-machine relationship types derived from systematic review. This research stream, which has accumulated over 700 citations, provides foundational knowledge for the responsible design of AI systems that interact with consumers.