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Abstract:The recent global increase in the competitiveness of tourism has made the implementation of eco-innovations a differentiating element among both the destinations and companies in the sector, with quality management and contribution to sustainable development being increasingly valued. However, the eco-innovations that have been developed and implemented in tourist industries have rarely been studied. In this study, the eco-innovations that have been developed and implemented by 57 tourism businesses worldwide are analysed. The identified eco-innovations are classified by using different qualitative methodologies. The obtained results shed light on the limited development of eco-innovations in the tourism industry and the industry focuses mainly on product eco-innovations. Several examples by the tourist sub-industry and types of eco-innovation are analysed. Furthermore, this study provides practical information about measures that both businesses and governmental organisations can adopt to promote eco-innovation in the sector.

Business simulators are powerful tools for both supporting the decision-making process of business managers as well as for business education. An example is SIMBA (SIMulator for Business Administration), a powerful simulator which is currently used as a web-based platform for business education in different institutions. In this paper, we propose the application of reinforcement learning (RL) for the creation of intelligent agents that can manage virtual companies in SIMBA. This application is not trivial, given the particular intrinsic characteristics of SIMBA: it is a generalized domain where hundreds of parameters modify the domain behavior; it is a multi-agent domain where both cooperation and competition among different agents can coexist; it is required to set dozens of continuous decision variables for a given business decision, which is made only after the study of hundreds of continuous variables. We will demonstrate empirically that all these challenges can be overcome through the use of RL, showing results for different learning scenarios.

Thus, SIMBA opens up a wide field of research between Artificial Intelligence and Business Management aimed at developing efficient intelligent agents humans can compete with.