What the paper studied
This research revises the traditional Love and Breakup Letter Method (LBM), a qualitative approach for exploring consumer-brand relationships, by introducing a third letter type: the 'intervention letter.' While LBM typically captures only positive (love) or negative (breakup) sentiments, this study recognizes that such a binary framework may overlook more nuanced consumer attitudes, particularly those involving constructive suggestions or a desire to improve services. The modified method was used to investigate how hotel guests perceive and interact with service robots, allowing participants to express not just affection or dissatisfaction, but also their willingness to help enhance the robot service.
Key findings
- A majority of participants (52.72%) chose to write intervention letters, outnumbering love letters (38.18%) and breakup letters (9.09%).
- Participants spent more time composing intervention letters, indicating deeper engagement and thoughtfulness.
- Linguistic analysis (using LIWC-22) revealed that intervention letters scored highest in analytic thinking and were less self-focused than the other letter types.
- Love letters were characterized by authenticity and emotional language, highlighting personal connections, while breakup letters were also emotional but carried a negative tone.
Why it matters for hospitality
The findings show that hotel guests interacting with service robots are not just forming emotional attachments or experiencing dissatisfaction—they are also eager to participate in improving these services. By recognizing and encouraging this desire for co-creation, hotels can foster stronger guest engagement and gather actionable feedback. This approach enables a shift from merely measuring satisfaction or dissatisfaction to actively involving guests in shaping their experiences with service robots, leading to more meaningful and effective service enhancements.
Practical takeaways
- Integrate intervention letters or similar feedback opportunities to capture guests’ constructive ideas for improving robot services.
- Use linguistic and content analysis to distinguish between emotional, negative, and practical feedback in guest communications.
- Encourage guests to participate in co-creating and refining service robot experiences, which can deepen their engagement and satisfaction.
- Apply insights from intervention letters to inform the design, training, and deployment of service robots, ensuring that improvements align with guest needs and expectations.