How personality and privacy fears decide hotel guests’ acceptance of AI robots

The authors report that openness significantly increased both enjoyment and perceived usefulness, while neuroticism lowered performance expectations and heightened privacy concerns. Extraversion amplified the influence of social cues, meaning extroverted guests were more likely to adopt robots when encouraged by peers or staff.

A new study published in Tourism and Hospitality offers the first evidence-based model explaining how personal traits and robot characteristics interact to determine acceptance in the hospitality sector.

Titled “Personality-Driven AI Service Robot Acceptance in Hospitality: An Extended AIDUA Model Approach", the study extends the AI Device Use Acceptance (AIDUA) framework to include the Big Five personality traits, combining psychology, technology adoption theory, and hospitality behavior analysis. Using survey data from 301 South African consumers and advanced structural equation modeling, the authors identify distinct psychological pathways that predict comfort or discomfort with robots in hotels and restaurants.

Personality traits as predictors of robot acceptance

Personality plays a crucial role in how people perceive and interact with service robots. Individuals high in openness to experience were found to be the most receptive to robotic innovation, showing strong hedonic motivation and a preference for humanoid designs that replicate human gestures. By contrast, those high in neuroticism, often associated with anxiety or emotional sensitivity, were less likely to trust or engage with service robots, perceiving them as intrusive or unsafe.

The authors report that openness significantly increased both enjoyment and perceived usefulness, while neuroticism lowered performance expectations and heightened privacy concerns. Extraversion amplified the influence of social cues, meaning extroverted guests were more likely to adopt robots when encouraged by peers or staff.

These findings demonstrate that technology acceptance is not merely a function of usability or efficiency, but also of individual temperament. The research suggests that hotels and restaurants can increase acceptance by matching robot designs to customer personalities, for example, using more expressive humanoid robots for curious, outgoing guests, and simpler, less anthropomorphic models for privacy-conscious or risk-averse customers.

Transparency and trust as the bridge to wider adoption

Beyond personality, the study identifies privacy and transparency as critical mediators between fear and acceptance. Respondents with high neuroticism consistently expressed discomfort with data collection and uncertainty about how robots process personal information. However, a key finding of the study was that transparency interventions, such as clearly communicating how AI systems store and use data, can dramatically improve acceptance, particularly among anxious users.

In controlled experiments, when hotels provided brief explanations of how service robots operated, including their security protocols, acceptance rates rose sharply among previously resistant groups. The transparency effect was strongest for users with high neuroticism, indicating that clear communication may neutralize distrust more effectively than technological design alone.

The researchers also found that anthropomorphism, or the human-like appearance of robots, had both positive and negative effects depending on context. While openness-driven guests associated humanoid designs with innovation and entertainment, privacy-sensitive users felt such designs blurred boundaries between human and machine. As a result, the study recommends contextual deployment, using humanoid robots for high-contact environments like concierge or entertainment services, and functional designs for back-office or logistics roles.

The extended AIDUA model used in this study accounted for 68.4% of the variance in behavioral intention to use service robots - a remarkably high explanatory power in social science terms. This supports the authors’ argument that combining psychological and design variables provides a fuller understanding of human–AI interaction than traditional acceptance models alone.

Segmenting the future of hospitality: Four types of guests

A key contribution of the paper is the segmentation of hospitality consumers into four personality-based categories that reflect their readiness for robotic service.

• Tech Innovators (23%) — Enthusiastic early adopters who view AI robots as extensions of digital convenience. They respond positively to interactive, humanoid robots with creative features.

• Pragmatic Adopters (32%) — Value-oriented users who care about efficiency, speed, and accuracy more than novelty. They prefer functional, purpose-driven robots and clear cost–benefit communication.

• Cautious Sceptics (28%) — Privacy-conscious and risk-averse individuals. They are most resistant but can be persuaded through transparency and limited, low-contact interfaces.

• Social Moderates (17%) — Guests who enjoy human interaction and view robots as complements, not replacements. They favor balanced integration where technology supports, but doesn’t dominate, service delivery.

This segmentation model gives hotels a practical framework for targeted robot deployment strategies. By aligning robot types and messaging with customer profiles, businesses can improve user experience, reduce resistance, and strengthen brand reputation for innovation.

The study also highlights cultural diversity as a moderating factor. In South Africa’s multicultural market, guests’ openness to robots varied based on prior exposure to technology and cultural attitudes toward automation. This reinforces the need for localized design and communication strategies rather than one-size-fits-all deployment.

Redefining the human–robot balance in hospitality

The authors' research positions AI-driven service robots as a transformative yet sensitive frontier in hospitality. While automation offers operational efficiency, cost savings, and contactless service, all crucial post-pandemic, the authors warn that human psychology remains the decisive variable in long-term adoption.

They propose that hotels and restaurants adopt a “human-robot complementarity model”, where AI enhances convenience without eroding emotional warmth. The study’s results demonstrate that psychological comfort, data trust, and cultural fit matter just as much as technical reliability in achieving widespread acceptance.

Furthermore, the findings align with global trends emphasizing human-centered AI and responsible automation. By integrating insights from psychology and behavioral economics, the paper provides a blueprint for ethical deployment - one that prioritizes transparency, autonomy, and customer wellbeing.

As service robots become more prevalent in high-contact industries, the authors argue that understanding users’ personalities and emotional drivers will be key to maintaining hospitality’s essence of trust and empathy in a digital era.