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Hotel servicescapes have been extensively examined in the literature; however, there has been less attention on green servicescapes that attract consumers to visit green hotels. This model explores the relationship among green servicescapes – green items, green surfaces, natural environment, green consumerism and their outcomes, including intentions to return and green evangelism with a moderating role of green perceived quality.

The multi-wave method was utilized to gather data from China's major cities, Beijing and Shanghai. A total of 462 responses were received over three waves. Subsequently, the data were analyzed employing structural equation modeling (SEM) in Smart PLS 4.

The findings indicated that green servicescape – green items, green surfaces and natural environment – have a positive impact on green consumerism. The authors have discovered that green consumerism leads to positive intentions among consumers to return and engage in green evangelism. Green perceived quality significantly moderated the relationship between green servicescape and green consumerism.

The study offers insightful contributions to academia and managerial fields, encompassing consumer psychology, consumer behaviour, the stimulus-organism-response (SOR) framework and servicescapes. Additionally, it assists hotel managers in addressing challenges stemming from the competitive environment and creating a more environmentally friendly atmosphere.

The research focused on the innovative reflective model of green consumerism model and adopted a pioneering approach to examine green servicescapes within the hotel industry. This study enhances understanding of consumer intentions to return and the influence of green consumerism on green evangelism, while also quantifying the significance of green perceived quality.

The topic of artificial intelligence (AI) has been expanding rapidly in recent years, gaining the attention of academics and practitioners. This study provides a structured literature review (SLR) on AI and management decisions (MDs) by analysing the scientific output and defining new research topics.

The study uses a rigorous methodological approach to summarise the state of the art of the past literature. The authors used Scopus as the database for data collection and utilised the Bibliometrix R package. In total, 204 peer-reviewed English articles were collected and analysed.

The results showed that literature in this field is emerging. Studies are focused on using AI as forecasting and classification for management decision-making, AI as a tool to improve knowledge management in organisations and extract information. The cluster analysis revealed the presence of five thematic clusters of studies on the topic.

The study’s originality lies in providing a new perspective on AI for MDs. In particular, the analysis reveals a new classification of research streams and provides fruitful research questions to continue research on the topic.