The pricing of European airbnb listings during the pandemic: A difference-in-differences approach employing COVID-19 response strategies as a continuous treatment

The COVID-19 pandemic has been a major shock to the global tourism industry. Given its peculiarity, this paper analyzes one of the most intriguing questions in the Airbnb literature – the pricing of Airbnb listings – by taking advantage of a difference-in-differences methodology that largely draws on variations in country-level policy responses to the pandemic. Relying on a dataset containing weekly information from 130,999 continuously active listings across 27 European countries from 2019 to 2020, this study first investigates the exogenous impact of response policies (proxied by the COVID-19 Stringency Index) on demand. Secondly, accounting for the endogeneity of both demand and prices, this research analyzes pricing responses to demand variations. Results show that: i) increases in the COVID-19 Stringency Index cause significant declines in Airbnb demand; ii) increases in demand cause, on average, increases in Airbnb prices; and iii) pricing strategies between commercial and private hosts differ substantially.     

A decision support model for buying battery electric vehicles considering consumer learning and psychological behavior

Developments in battery electric vehicles (BEVs) have received more and more attentions in the last decades due to alleviating carbon emissions and energy crisis. Consequently, how to rank alternative BEVs to assist consumers make better purchasing decisions is a worthy research study. However, there are still some defects in the existing studies for ranking of BEVs: 1) the evaluation index system of BEVs is not comprehensive; 2) the determination of criteria weights cannot be well applied to the actual purchase scenarios; and 3) the psychological behavior of consumers is ignored. To address those shortcomings, this paper proposes a decision support model to assist with consumers to buy BEVs. First, a systematic evaluation criteria system of BEVs including quantitative and qualitative indicators from parameter configurations and online reviews is constructed. Then, a weight algorithm considering consumer learning is proposed to determine the criteria weights. Furthermore, a decision support process considering consumers' regret avoidance behavior is proposed. Finally, an actual BEV purchase case is given to illustrate the practicability of the decision support model. This can be seen in case studies the proposed support model can be well applied to consumers with different regret avoidance behaviours.     

Identifying the ‘knowledge base’ or ‘intellectual structure’ of research on international business, 2000–2015: A citation/co-citation analysis of JIBS

The purpose of this study consists in analyzing the existing literature on international business from all the research papers published in Journal of International Business Studies (JIBS) during the period of time comprised between 2000 and 2015. The authors apply bibliometric methods to that end –in particular, document citation and co-citation analyses (DCAs)– so that they can try and identify the main research lines within the aforementioned scientific field or domain; in other words, the ‘intellectual structure’ of the discipline examined. Social networks analysis was also used to carry out the representation or visualization of such structure or knowledge base. The highly interdisciplinary nature of the research undertaken, since the study field still keeps its characteristic diversity and interdisciplinarity or the need to integrate a variety of theoretical frameworks or approaches into the development of a widely-accepted theory about the field under study become evident. Amongst these theories would be the new institutional theory, the strategic theory or perspective or others such as the transaction cost theory or the agency theory, applied to the context of internationalization and to the field of international business, on whose integration the future development of this field may largely depend.     

Using artificial neural network and multivariate analysis techniques to evaluate road operating conditions

Regional paved roads are low volume roads with a prevalence of heavy traffic. In the world, these roads concern about 80% of the total road network; however, the traffic that affects these roads is about 20%. Since regional roads are characterized by weak demand, budget for their management/maintenance is very low. This produces considerable difficulties in the choice of strategies for maintenance planning and scheduling. For this reason, the recurring topics of research in this field deal with typical roads issues and aim to develop low cost tools and methods. The study proposes a decision support system to evaluate regional paved roads operating condition in relation to the hydrogeological situation. In particular, the system allows to evaluate in a quick and easy manner, the operating conditions of the road, through low-cost tools (i.e. using low economic resources). This is very useful in the case of LVRs because administrations for these roads have a limited budget. The procedure is developed on a regional paved roads network based on more than 80 roads located in Southern Italy. Data is collected by direct surveys in the field and is integrated with cartography and information available in road agency records. From data analysis, obtained using two different techniques, an easy and quick use procedure is made. In particular, Model 1 is built through multivariate analysis and Model 2 using the artificial neural network (ANN) technique. The results show the validity of the two models in Regional paved roads operating conditions estimation in relation to hydrogeological situations of sites. Both models show good reliability. In particular, the first model (Model 1) is characterized by a high level of significance (p < 0.01) and by a coefficient of determination equal to 0.82. Comparative tests between the second model (Model 2) on which standard tests cannot be performed for obvious reasons, and the first model (Model 1). The results show that the ANN model (model 2), characterized by lower residual, simulates more accurately than the second (Model 1).     

战术面向服务架构概念及相关技术

战术面向服务架构（TSOA）是实现战术云平台服务基础设施的重要支撑概念。由于战术环境的复杂性，目前对TSOA实施过程中面临问题和相关实现技术的总结还较少。为此，开展了针对TSOA的概念以及相关技术的研究。首先，从战术环境下的TSOA架构需求背景出发，介绍了TSOA架构的组成以及适应性特征；然后，通过分析战术环境下设备和网络等多方面存在的限制性因素，明确实施TSOA架构所面临的挑战以及需要解决的关键问题，并对实现过程中涉及的服务发现、消息传输机制、网络状态自适应等相关技术进行归纳；最后，对国内外的相关研究工作进行总结，期望对推动TSOA架构的具体实施提供一定参考。     

Happiness and freedom in direct action: critical mass bike rides as ecstatic ritual,play, and temporary autonomous zones

Most leisure research does not consider collective action and social change, while most social movement research overlooks carnivality and spontaneity. A counter-example is the alternative bicycling movement critical mass (CM), a rebellious, liberatory leisure and cultural event. CM is goal-oriented and a libidinal expression of participant desire. This paper investigates CM’s production of happiness and freedom via three heterodox theories. Ecstatic rituals are repetitive, cathartic collective events that people engage in to express joy and flaunt convention. Political theories of play interpret individual’s physically-embodied, creative, and performative actions. Temporary autonomous zones are spatial locations of resistance to authority that evade state detection and suppression. CM – unlike standard bike races or conventional movement protest – transcends the limitations of physical exercise, lackadaisical leisure, and militant direct action. Instead it is a hybrid form of collective action, combining premeditated ritual, rebellious play, and mobile freedom-seeking.     

Applying the counseling-learning approach to a tourism-related massive open online course

ABSTRACT

This study discusses how to apply counseling-learning (CL) principles to a particular e-learning solution for teaching a tourism-related subject via a massive open online course (MOOC). Several effective caring patterns in achieving learning-related goals and being part of a community according to the CL principles are presented and discussed using the MOOC “eTourism: Communication Perspectives” as a case study. The study underlines how the MOOC platform can act as a place for contents enjoinment and active learning. Moreover, the active role of social media for increasing the engagement of learners in the proposed activities and for developing a sense of the community is identified. In particular, Facebook and Twitter can act as places for community building and informal social interactions among learners that last beyond the completion of the course, which in turn can be a valid aid for continuing the relationships among instructors/learners and learners/learners, and for reaching new ones.     

A comparative study of eight COVID-19 protective measures and their impact on Swiss tourists’ travel intentions

A comparative vignette-based experimental survey design incorporating various socio-psychological factors, linked to the Theory of Planned Behavior (TPB), the Health Belief Model (HBM) and the Domain-Specific Risk-Taking scale (DOSPERT) was carried out to test variations in eight travel-related COVID-19 protective measures on Swiss tourists’ travel intentions. Among the tested measures, vaccination passports, surgical masks and quarantining are those that stand out the most, with surgical masks having the greatest acceptance and willingness to adopt while traveling. Quarantining, on the other hand, appears to have a deterrent influence on travel intentions, and vaccination passports have the lowest perceived barriers during travel, but the highest perceived benefits in mitigating the spread of the infection. The discussion of individual differences has specific implications for tourism management against the background of our empirical findings.     

Collaborative delay management towards demand-capacity balancing within User Driven Prioritisation Process

The concept of User Driven Prioritisation Process (UDPP) was introduced to give Airspace Users (AUs) more flexibility under demand-capacity imbalance. This paper presents two UDPP-DCB models, built on the UDPP principle, using the Selective Flight Protection (SFP) approach to minimize the total delay cost. AUs are enabled to adapt their operations in a more cost-efficient way in the presence of capacity constraints in airspace, optimizing their flights to keep the priorities track. Then, ATFM integrates the AUs’ decision to reassign the time slots and the preferred rerouting trajectories. Results suggest that the delay cost for AUs can be largely reduced through applying the UDPP-DCB models proposed in this paper, while allowing rerouting proves effective in reducing the system delay cost.     

TrAffic LIght system for systemic Stress: TALIS3

For the purposes of financial stability, identifying financial institutions that, when in distress, could have a significant adverse impact on financial markets is important. A TrAffic LIght System for Systemic Stress (TALIS-cube) is proposed that provides a comprehensive color-based classification for grouping companies according to both the stress reaction level of the system when the company is in distress and the company’s stress level. TALIS³ can integrate multiple signals from the interaction between different risk metrics. Starting from specific risk indicators, companies are classified by combining two loss functions—one for the system and one for each company—evaluated over time and as a cross section. An aggregated index is also obtained from the color-based classification of companies. TALIS³ can be used to enhance the performance and robustness of existing systemic risk measures. An empirical analysis of the U.S. market is also provided.