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This paper investigates the reviews posted by Airbnb customers in order to assess the customer satisfaction, and to understand the criteria of Airbnb customers in short-term accommodation rentals. The objective is to determine the characteristics that customers find important, and to provide the typologies of cities from the customer perspective. The analysis holds under scope the five most touristic cities in Europe. The opinion of Airbnb customers were retrieved from the Airbnb website. First, the satisfaction was assessed using sentiment analysis. Second, main characteristics that describe the tourism experience were defined through a set of Exploratory Factor Analysis. Next, segmentation of cities according to these features was settled. Results indicate that Airbnb customers are satisfied with the service they use, and that their choice of short-term accommodation on Airbnb is a multi-criterion decision process. The results of our research are of utmost importance for governmental institutions, tourism agencies, and Airbnb.

Social network analysis has been widely used by organizational behavior researchers to stress the importance of the context, social connections, and social structure on human behavior. In the last decade, social network analysis has emerged as one of the most useful techniques for exploring online social networks, world wide web, e-mail traffic, and logistic operations. In this chapter, the authors present an application of social network analysis techniques for academic research. The authors choose Kahneman and Tversky's prospect theory as the focus of their analysis and, based on that, develop a co-authorship structure that depicts in a clear manner the key authors and/or the researchers that dominate and bridge different sub-fields in the field of management. The authors discuss the implications of this study for academic research and management discipline.

Social media receives growing interest from sports executives. Yet, very little is known about how to make use of such user-generated, unstructured data. By exploring tweets generated during Turkish Airlines Euroleague's Final Four event, which broadcasted the four tournaments of championship among four finalist teams, the authors studied how fans respond to gains and losses and how engaged they were during games through the course of the event. The authors found that favorable reactions were received when teams won, but the magnitude of unfavorable reaction was larger when teams lost. When it came to the organizer rather than the teams, the organizer of the event received most of the positive feedback. The authors also found that main source of tweets was smartphones while tablets were not among real-time feedback devices.

Several studies have recently raised a common concern in the field of management, which is the overspending in marketing activities. In this paper, we propose and empirically test that overspending in marketing investments is an unfortunate outcome of information overload, in a sense that managers who confront too many risk informants in their decision environment tend to overinvest in marketing activities due to the overemphasis on the environmental risk. In a longitudinal experiment, where we manipulated the amount of information through marketing analytics, we demonstrate that firms employing simple marketing analytics are less prone to increase their marketing expenditures due to the fear of losing customers, and have a lower expectancy that their competitors will increase their brand-level advertising and promotional expenditures, compared to firms using a combination of simple and complex marketing analytics. Moreover, we demonstrate that firms employing simple marketing analytics keep their overall marketing spending at a lower level, and spend less in brand-level marketing, especially in promotional activities, compared to when using a combination of simple and complex marketing analytics.