Multilevel assessment of restaurant profitability: Evidence with European data

• Published in: Data in brief Vol. 30; p. 105426
• Main Authors: Díaz-Puche, Miguel, Fernández-Miguélez, Sergio M., Campos-Soria, Juan A., Fernández-Gámez, Manuel A.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.06.2020; Elsevier
• Subjects: business management; Business, Management and Accounting; corporations; data collection; European data; experimental design; finance; Financial performance; globalization; hospitality industry; macroeconomics; Multilevel analysis; profitability; Restaurant profitability; restaurants; tourism; Tourism industry; European data; Restaurant profitability; Multilevel analysis; Financial performance; Tourism industry
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2020.105426

Previous literature has analysed the effects of establishment regressors on different measures of restaurant financial performance [1,2]. However, as [3] stated, these studies have focused on the analysis at establishment level rather than at corporate level. Determining the factors that explain the restaurant profitability is not only an important phenomenon for establishments but also for companies because they adapt their products, services and strategies to obtain additional benefits and cash flow [1]. Additionally, progressive globalization has forced companies to operate in countries with environments that differ from the companies’ country of origin [4]. In this context, the dataset presented in this paper sought to contribute to the existing literature in two ways. First, it allows us to investigate the factors that determine the profitability of restaurant corporations using advanced measures of financial performance. Second, a multilevel experimental design may be helpful when understanding country heterogeneity in companies´ profitability. The dataset contains a sample of 860 restaurant corporations operating in 18 European countries. From each corporation in the sample 6 financial variables were collected, and from each country, 10 context variables associated with economic conditions and tourism environment were considered. Due to the lack of data that allow a global analysis of the factors that determine profitability in the restaurant industry, this dataset can play an important role for business management, which should control not only their financial ratios but also the macroeconomic conditions and tourism environment where the companies operate.