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Artificial intelligence is one of the most significant and active fields of study in the last few years. Artificial intelligence-derived robotic technologies known as chatbots are gaining interest from both academic and industry sectors. By analyzing the development and patterns of research on the chatbot phenomena within the tourism field, this study seeks to develop a theoretical framework for the interaction between chatbots and tourism.

The Web of Science (WoS) database’s 33 articles on chatbots related to travel and hospitality were examined between 2019 and 2024 using VOSviewer software for bibliometric and thematic content analysis.

Research on chatbots for tourism and hospitality appears to be in its early stages. The factors influencing tourists' intentions to use chatbots have been thoroughly researched; the attitudes, perceptions and behavioral intentions of destinations, travel agencies and restaurant patrons regarding chatbots were examined, and it was found that the quantitative research approach was dominant. In addition, the majority of the studies are based on a particular theory or model.

This is one of the first attempts to directly comprehend and depict the interconnected structures of studies on the interaction between chatbots and tourism through the use of network analysis. Furthermore, the study’s findings can offer academics a comprehensive viewpoint and a reference manual for more accurate assessment and oversight of the chatbot-tourism interaction. Regarding the lack of research on the topic and the fragmented structure of the studies that exist, it is imperative to provide both a comprehensive overview and a roadmap for future investigations into the usage of chatbots in the travel and hospitality sector.

In a recent quasi-experimental study, the effects of a large German public startup support measure entitled “EXIST – Business Startup Grant” (EGS) on a variety of outcomes were determined, but without examining which factors are responsible for these program effects. The present study investigates the contribution of several factors to the success of the program in promoting product development and business planning.

By means of a two-wave panel design and fixed-effects panel regressions, evidence is generated that provides unique insights into the effect mechanisms of a publicly funded startup grant. The data for the study come from the program monitoring of the startup support measure.

Several factors were identified that significantly drive the effects of the program on the product development and business planning stages, namely the program-induced improvement of the skills of the startup team, intensification of cooperation with pilot customers/users, increase in the degree of networking and advice/support from third parties and the effort put into business plan preparation.

Startup support programs are a crucial aspect of technology and innovation policies, which are often evaluated in order to find out whether they generate effects. Assessing whether a program is effective or not, however, does not usually allow specific recommendations on how to improve the measure to be developed. Further information on the mechanisms of intervention is needed for this purpose. The present study takes up on this idea and provides this information for a specific type of public startup support measure.