Understanding consumer emotions arising from robot-customers encounters and shared through online reviews is critical for forecasting consumers’ intention to adopt service robots. Qualitative analysis has the advantage of generating rich insights from data, but it requires intensive manual work. Scholars have emphasized the benefits of using algorithms for recognizing and differentiating among emotions. This study critically addresses the advantages and disadvantages of qualitative analysis and machine learning methods by adopting a hybrid machine-human intelligence approach. We extracted a sample of 9707 customers reviews from two major social media platforms (Ctrip and TripAdvisor), encompassing 412 hotels in 8 countries. The results show that the customer experience with service robots is overwhelmingly positive, revealing that interacting with robots triggers emotions of joy, love, surprise, interest, and excitement. Discontent is mainly expressed when customers cannot use service robots due to malfunctioning. Service robots trigger more emotions when they move. The findings further reveal the potential moderation effect of culture on customer emotional reactions to service robots. The study highlights that the hybrid approach can take advantage of the scalability and efficiency of machine learning algorithms while overcoming its shortcomings, such as poor interpretative capacity and limited emotion categories.

In an era of rapid change and sustainable development, reform of technical and vocational education and training (TVET) systems worldwide is essential. Integrating green technologies into TVET is key to training a competent workforce in this field. This study aims to determine the extent of faculty knowledge, attitudes, and practices towards green technology to better integrate green technology into future labor. Sixteen faculty from a public technical and vocational institute in China were interviewed, and the contents were analyzed with NVIVO 11 software. However, our research reveals a daunting challenge: 68% of vocational education teachers lack the necessary knowledge and understanding of green technologies. Despite this, the attitudes of these teachers toward integrating green technology into education vary widely, ranging from education about environmental protection to education about green production to education about good green habits. The majority of faculty recognize the need for green technology education due to governmental requirements, industry transformation, improving student employability, and creating environmentally friendly citizens. Unfortunately, faculty commitment to green technologies remains low. A few faculty members have integrated green technologies into their teaching, but research in this area remains limited, primarily due to heavy workloads, course and program irrelevance, lack of college support, and lack of industry demand. To address this problem, faculty members have proposed embedding green technology education into the talent development system rather than treating it as a stand-alone course. They also emphasize the need for closer collaboration with the industry to improve green technology education. This study serves as a foundation for understanding the current state of green technology in vocational education and a pathway for its improvement.

Although research on hotel service robots has been continuously increasing, the existing literature still lacks a systematic exploration of the multiple concurrent mechanisms involved in the formation of user experience. Based on the theory of value co-creation, this study first extracted the key service robot attributes that affect user experience by analyzing 3200 online user notes from the Chinese platform Xiaohongshu. Then, using 433 valid questionnaires, it employed SEM and fsQCA to examine the influence mechanism of service robot comprehensively attributes on user experience. The results of the SEM study showed that the attributes of perceived usefulness, perceived ease of use, anthropomorphism, and service remediation ability of service robots positively influenced customers’ willingness to co-create value, thereby further enhancing the user experience. The perceived privacy risk attribute did not significantly affect customers’ willingness to co-create value. The fsQCA analysis further identified multiple effective configurations, including antecedent configurations with high customer willingness to co-create value, high usage intention, high satisfaction, and high forgiveness intention as outcome variables. This study, through a combination of methods, revealed the complex experiences users encounter during interactions with service robots and regarded customers’ willingness to participate in value creation as a front-end psychological mechanism, providing a new theoretical perspective on the value co-creation process in human–machine collaboration. At the same time, this study, from the user perspective, provided strategies for optimizing user experience and service deployment for Chinese hotel managers.