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The purpose of this paper is to contribute to the tourism management literature by: first, developing a hybrid neural network that will be able to predict tourists' overall satisfaction of their travel experience; and second, prioritizing the travel attributes based on their proportional impact on tourists' overall satisfaction of their travel experience in Iran.

A total of 1,870 questionnaires were distributed amongst foreign tourists in the departure lounge of “Imam Khomeini International Airport” over a period of three months. The data were used to develop a hybrid neural network in which the “rough set” is used to reduce travel attributes and the neural network to predict tourists' overall satisfaction of travel experience. After the model proved its predictive accuracy, using the sensitivity analysis of the neural network travel attributes were prioritized based on their impact on tourists' overall satisfaction.

The results were quite promising in that the proposed hybrid neural network was able to predict tourists' overall satisfaction with a relatively low amount of error (RMSE=0.05246). Furthermore, it was demonstrated that rough sets theory is capable to be applied effectively to feature selection of large datasets in the tourism context. Finally, it was found that “improving tourism infrastructures of the country” in addition to “globally promoting the image of Iran” (as a secure and pleasant destination) are of the highest priority for Iran's tourism industry to reach to its full potential.

Besides developing a data mining tool which is an efficient means for predicting tourists' overall satisfaction, the paper's findings provide precious information for tourism policy makers in Iran by prioritizing those travel attributes that have the greatest impact on foreign tourists' overall satisfaction of their travel experience.

This study aims to address how the social structure of the hospitality management field has evolved from 1960 to 2016.

The informal social structure of the hospitality management literature was analyzed by collecting authorship data from seven hospitality management journals. Co-authorship analyses via network analysis were conducted.

According to the findings, throughout the history of hospitality management, international collaboration levels are relatively low. Based on social network analysis, the research community is only loosely connected, and the network of the community does not fit with the small-world network theory. Additional findings indicate that researchers in the hospitality management literature are ranked via degree centrality, closeness centrality and betweenness centrality. Cliques, which contain at least five researchers, and core researchers are identified.

This study helps both scholars and practitioners improve the informal structure of the field. Scholars must generate strong ties to strengthen cross-fertilization in the field; hence, they collaborate with authors who have strong positions in the field. Specifically, this provides a useful performance analysis. To the extent that institutions and individuals are rewarded for publications, this study demonstrates the performance and connectivity of several key researchers in the field. This finding could be interesting to (post)graduate students. Hospitality managers looking for advisors and consultants could benefit from the findings. Additionally, these are beneficial for journal editors, junior researchers and agencies/institutions.

As one of the first study in the field, this research examines the informal social structure of hospitality management literature in seven journals.