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This study aims to address the gap in hospitality and tourism (H&T) research concerning green creativity (GC) and seeks to identify the ways in which the interaction between spiritual incentives (SI) and material incentives (MI) affects the relationships among green intrinsic motivation (GIM), green extrinsic motivation (GEM) and GC.

In accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, this paper examines studies related to GC in the H&T industry and analyses them using content analysis and critical analysis.

By integrating self-determination theory and the componential theory of creativity, this study enhances the understanding of the interactive moderating role played by SI and MI in the relationship between green motivation and GC. When the level of SI is high and the level of MI is low, GIM has the strongest positive impact on GC. When the levels of MI and SI are both high, GEM has the strongest positive effect on GC.

In practical terms, “high SI-low MI” is the optimal combination for achieving high GC and promoting sustainable long-term green-oriented incentives.

To the best of the authors’ knowledge, this paper represents the first investigation of the interactive moderating effects of SI and MI on the relationships among GIM, GEM and GC, thus enriching the research on the factors influencing green motivation and GC. In addition, this paper proposes a better decision-making basis for organizations facing a green-oriented incentive situation, according to which “high SI-low MI” can facilitate the achievement of high GC at a low cost.

Since the introduction of the outstanding web AI chat system, ChatGPT, it has caused a significant impact in both academia and the business world. Many studies have started to explore its potential applications in various fields. However, there is a lack of research from the perspective of user experience. To fill this theoretical gap and provide a theoretical basis for the operation and design of related services, this study plans to develop a set of evaluation scales for AI chat system user experience and explore the relationship between various factors and user satisfaction.

This study obtained 41 evaluation indicators through literature review and user research. Subsequently, these indicators were used as questionnaire items, combined with satisfaction metrics. A total of 515 questionnaires were distributed, and factor analysis and linear regression were employed to determine the specific elements influencing user experience and the user satisfaction model.

This study found that the factors influencing user experience are usefulness, accuracy, logical inference, interactivity, growth, anthropomorphism, convenience, credibility, ease of use, creativity, and security. Among these factors, only accuracy, anthropomorphism, creativity, and security indirectly influence satisfaction through usefulness, while the rest of the factors have a direct positive impact on user satisfaction.

This study provides constructive suggestions for the design and operation of related services and serves as a reference for future theoretical research in this area.