What the paper studied
The [paper](https://doi.org/10.1016/j.jretconser.2026.104997) examines how hospitality businesses can design humanoid robot services to remain dependable when operations are strained or guest interactions become uncertain.
It combines Random Forest analysis with a two-phase Quality Function Deployment framework – a structured way to translate customer needs into design priorities. The analysis used 1,355 valid customer survey responses and interviews with an 11-member expert panel. It linked customer needs to risks associated with human-like robots, then connected those risks to resilience mechanisms.
The supplied affiliations are Universitas Bunda Mulia, Universitas Ciputra, John von Neumann University, and Western Sydney University; individual author affiliations were not specified.
Key findings
- The study identified 22 customer needs, 14 anthropomorphic risks, and eight resilience mechanisms.
- Trustworthiness, safety, precision, comfort, and automation were leading customer priorities. Aesthetics, human-likeness, responsiveness, personalisation, social presence, and context awareness also showed strong relative associations with satisfaction in this dataset.
- Important risks included mismatches between a robot’s human-like presentation and its capabilities, operational downtime, excessive autonomy, unclear instructions, and weak engagement. Other risks involved opaque decisions, unsettling human-like characteristics, navigation safety, delayed responses, contextual inflexibility, and difficulty recognising emotions.
- The highest-priority resilience mechanisms were transparent interaction, multimodal communication, user profile modelling, fault-tolerant architecture, dynamic allocation of autonomy, and situational awareness.
These results establish priorities within the study; they do not demonstrate that implementing any particular mechanism will produce a specific improvement in guest satisfaction or operating performance.
Why it matters for hospitality
A human-like appearance does not remove the need for dependable service. It can create expectations that a robot cannot consistently meet. For hotels and restaurants, service design therefore needs to address both technical reliability and the guest’s understanding of the interaction.
A robot may function technically yet still cause confusion if guests cannot tell what it can do, why it made a decision, or how to reach a staff member. Clear task boundaries and visible human support are therefore relevant operational considerations, not merely interface details.
The framework gives managers a way to connect guest expectations with specific failure risks before choosing design responses. It supports decisions about task allocation, communication, escalation, and service continuity rather than treating humanoid robots as stand-alone replacements for staff.
Practical takeaways
- Start with bounded tasks. Reception information, routine concierge guidance, ordering support, and queue assistance are suggested applications, not outcomes tested by this study.
- Make capabilities and limits clear. Explain what the robot handles and when a person will take over.
- Define autonomy and escalation rules before deployment. Keep staff available for complaints, special requests, and ambiguous situations.
- Ask technology providers how they support understandable communication, fault tolerance, navigation awareness, and flexible human intervention.
- Balance personalisation and social interaction with dependable execution. Human-like behaviour should not take priority over safety, accuracy, or guest comfort.
- Adapt priorities locally. The supplied implications locate the customer research in Greater Jakarta. Different customer profiles, service formats, technology readiness, and staff capabilities may require different choices.