Search results

This article assesses the impact of the internal and external Business Environment on the Digital Entrepreneurial Activity (DEA) in the European tourism industry.

A sample of 125 European tourism entrepreneurs in Germany and France was studied. Data were analyzed using quantitative methods.

The results indicate that a firm experiencing losses due to theft and vandalism has a positive relationship with the DEA, and there is Liquidity or Cash flow that contributes positively to DEA. The outcomes shows that there is a specific limit to the institution having liquidity or cash flow, the costs of inspection by tax officials, and the average management time with government regulations that affect digital entrepreneurs. The total cost of labor contributes significantly to the digital productivity of entrepreneurs in the tourism sector.

These findings have significant and practical implications for entrepreneurs and academics in the tourism industry, providing them with valuable insights for decision-making.

Our comprehensive study, which is highly relevant to the current state of the tourism industry, investigated the extensive impact of tourism entrepreneurial ecosystems (TEE) on sales growth in European countries based on a sample of 109 entrepreneurs.

We studied 109 entrepreneurs in the tourism sector in Germany and France and analyzed the data using quantitative methods.

The results showed that financing and funding, networks, technological and international trade affect the sales growth of the tourism entrepreneurs. In addition, the uneducated workforce, legal and political factors as well as TEE do not influence the sales growth.

These findings have significant and practical implications for entrepreneurs and policymakers in the tourism industry, providing them with valuable insights for decision-making.

Studies in higher education tend to use different methods and methodologies, from documentary analysis to auto/biographical and observational studies. Most studies are either qualitative or qualitative. A mixed-methods approach has emerged in recent years, in which the qualitative approach generally plays an important role. The purpose of this chapter is to show the potential of a new methodology that is also appropriate for higher education research and widely used in the social sciences: probabilistic topic models. A probabilistic method can be used to analyse and categorise thousands of words. After collecting large sets of texts, content analysis is used to deeply analyse the meaning of these words. The huge number of texts published today pushes researchers to employ new techniques in their search for hidden structures built upon a set of core ideas. These methods are called topic modelling algorithms, with Latent Dirichlet Allocation being the basic probabilistic topic model. The application of these new techniques to the field of higher education is extremely useful, for two reasons: (1) studies in this area deal in some cases with a great volume of data and (2) these techniques allow one to devise models in a way that is unsupervised by humans (even when researchers operate on the resulting model); thus they are less subjective than other types of analyses and methods used for qualitative purposes. This chapter shows the foundations and recent applications of the technique in the higher education field, as well as challenges related to this new technique.