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Digital communication technologies have become ubiquitous for various firm processes related to international business (IB) and global strategy. However, IB and strategy scholars lack an encompassing and theory-based typology of these technologies that facilitates analysis and discussion of their uses and effects. Likewise, managers have a large choice of technologies at their disposal making it difficult to determine what technology to use in different IB areas. This paper aims to develop a typology of digital communication technologies based on the synchronicity and interactivity of these technologies and capture their fundamental social and temporal dimensions. This results in four ideal types: broadcasting, corresponding, aggregating and collaborating technologies.

This is a conceptual paper incorporating theoretical perspectives to theorize about four ideal types of digital communication technologies. A subsequent empirical test of this typology has been provided in the appendix.

The authors discuss how the typology might be applied in IB decisions and some of the contingencies that impact this choice. Building on that, the authors develop directions for future research to increase their understanding of the use of digital communication technologies to help improve IB functions. Overall, the authors suggest future research explores contingencies about where and when different types of digital communication technologies should be used. Finally, the authors provide implication of having a unified typology for both academics and managers.

The authors offer a robust framework for thinking about and capturing different types of digital communication technologies that can be applied by researchers and used by managers when making decisions related to IB. The authors also provide some initial testing of the typology with a three-country study design helping to determine its validity.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

Though the circular economy (CE) is a current buzzword, this still lacks a precise definition. In the absence of a clear notion of what that term includes, actions taken by the government and companies may not be well informed. In particular, those actions need to consider what people mean when people talk about the CE, either to refocus people's decisions or to undertake a more effective communications strategy.

Since people voice people's opinions mainly through social media nowadays, special attention has to be paid to discussions on those media. In this paper, the authors focus on Twitter as a popular social platform to deliver one's thoughts quickly and fast. The authors' research aim is to get the perceptions of people about the CE. After collecting more than 100,000 tweets over 16 weeks, the authors analyse those tweets to understand the public discussion about the CE. The authors conduct a frequency analysis of the most recurring words, including the words' association with other words in the same context and categorise them into a set of topics.

The authors show that the discussion focuses on the usage of resources and materials that heavily endanger sustainability, i.e. carbon and plastic and the harmful habit of wasting. On the other hand, the two most common good practices associated with the CE and sustainability emerge as recycling and reuse (the latter being mentioned far less). Also, the business side of the CE appears to be relevant.

The outcome of this analysis can drive suitable communication strategies by which companies and governments interested in the development of the CE can understand what is actually discussed on social media and call for the attention.

This paper addresses the lack of a standard definition the authors highlighted in the Introduction. The results confirm that people understand CE by looking both at CE's constituent activities and CE's expected consequences, namely the reduction of waste, the transition to a green economy free of plastic and other pollutants and the improvement of the world climate.

The purpose of this study is to investigate the recovery and resilience tourism strategies and possible future development of four main Chinese tourism cities.

The authors collected data from the official accounts of tourism administrations of these cities, tourist attractions and opinions from media and newspapers in Sina Weibo platform. The authors adopted an inductive approach in observing relevant social media posts and applied content analysis to identify main China’s tourism prevention and recovery strategies.

During the mass pandemic infection period, top-down prevention and control measures were implemented by the Chinese central and local governments, with feasible and regional recovery policies and protocols being adapted according to local situations. Measures related to tourism industrial re-employment, improvement of international images and governmental financial supports to re-boost local tourism in Chinese cities were paid great attention. Digitalization, close-to-nature and cultural heritages became important factors in the future development of China’s tourism. Dark tourism, as a potential tourism recovery strategy, also obtained huge emergence, for the memory of people deceased in the pandemic and for the inheritance of national patriotism.

This study enriches the current literature in urban tourism recovery studies analyzing the specific case of Chinese tourism cities and fulfill some voids of previous research mostly focused on the first wave of the pandemic and the recovery strategies mainly of Western cities. It also provides valuable suggestions to tourism practitioners, destinations and urban cities in dealing with regional tourism recession and finding possible solutions for the scenario associated to the COVID-19 and other similar health crisis.

The study aims to analyze the relationship between tourists' satisfaction and their sense of belonging in a specific cultural destination by also investigating the moderating role of their on-site involvement, both offline (on-site activities at the destination) and online (social media activities). A further objective lies in exploring the relationship between tourists' sense of belonging and their adoption of environmentally responsible behavior while at the destination. Finally, the paper attempts to determine whether said behavior can predict tourists' pro-environmental behavior and recommendation intention.

To reach these objectives, a survey and a structural equation model, based on a sample of 647 visitors of an important world Heritage site UNESCO (i.e. Urbino), have been adopted.

Findings reveal a positive influence of sense of belonging on satisfaction and environmentally responsible behavior, which, in turn, influences pro-environmental behavior and Recommendation intention, thus triggering a virtuous process in the tourists' formation as responsible and loyal travelers. Moreover, results underline how the relationship between satisfaction and sense of belonging is significantly strengthened when tourists' on-site activity involvement increases. Conversely, the moderating role of social media involvement is not supported.

The present study offers important implications for different actors in the tourism sector, such as policy makers, destination marketing organizations (DMOs) and tourism operators.

The present study explores the role of social media involvement, specifically during the tourist's holiday.

This study enriches the empirical evidence in the cultural tourism through an analysis focused on the tourists' perspective, especially by investigating the relationship between satisfaction and emotional constructs (i.e. sense of belonging) that are able to bring environmental and loyalty benefits to the destination. Moreover, although existing research has highlighted the positive influence of the tourists' involvement on their experience, there is a paucity of studies jointly analyzing the on-site and online activities in the sustainability field.