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This study aims to analyze whether the Barcelona markets integrated with Europe during the 17th and 18th centuries.

This study uses the unit root tests with multiple structural breaks under both the null and alternative hypotheses proposed by Carrion-i-Silvestre et al. (2009) and Harvey et al. (2013). These tests are robust to multiple unknown breaks in the series.

The results suggest that the Barcelona wheat markets integrated with some European cities during the 18th century.

The results are important because they highlight the importance of considering nonlinearities and structural breaks in the series to study market integration with historical perspective. Contrary to the results obtained using conventional unit root tests, when this study applies unit root tests robust to structural breaks in the series, it finds that the law of one price holds in some cases.

The purpose of this article is to discover how false information has been received through mobile instant messaging services (MIMS), Facebook and Twitter and what sociodemographic factors have a stronger influence on the perception of the democratic effects generated by the disinformation on the citizens of three countries.

To do this, an online survey (n = 3,019) was developed for citizens of Spain (n = 1,015), Germany (n = 1,001) and the UK (n = 1,003). The sample is stratified according to the gender, age, income and ideology of the respondents.

The results show that the reception of false information is high in all three countries, especially on Facebook. Additionally, we found that the country of origin, genre, age and ideology influence the reception of disinformation in MIMS, but not in the rest of the platforms. Considering disinformation's effects on citizens, we observe how, in general terms, those surveyed perceive disinformation effects with a medium-low intensity. In this way, citizens do not believe that false information causes substantial changes in their thinking. An increase in mistrust has been detected toward social media and mainstream media, which are not considered reliable sources of information. At this point, the respondents' country of origin, income and ideology are conditioning factors.

This research provides some relevant trends that help to better understand how disinformation is received on digital platforms in three countries with different political and social traditions, as well as the effects that it has had on citizens and the sociodemographic and political factors that have a greater incidence.

This study aims to conduct a “real-time” investigation with user-generated content on Twitter to reveal industry challenges and business responses to the coronavirus (Covid-19) pandemic. Specifically, using the hospitality industry as an example, the study analyses how Covid-19 has impacted the industry, what are the challenges and how the industry has responded.

With 94,340 tweets collected between October 2019 and May 2020 by a programmed Web scraper, unsupervised machine learning approaches such as structural topic modelling are applied.

This study contributes to the literature on business response during crises providing for the first time a study of using unstructured content on social media for industry-level analysis in the hospitality context.