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Due to product diversity, traditional quality signals in the hotel industry such as star ratings and brand affiliation do not work well in the accommodation booking process on the sharing economy platform. From a suppliers’ perspective, this study aims to apply the signaling theory to the booking of Airbnb listings and explore the influence of quality signals on the odds of an Airbnb listing being booked.

A binomial logistic model is used to describe the influences of different attributes on the market demand. Because of the large sample size, sequential Bayesian updating method is utilized in hospitality and tourism field for the first attempt.

Results show that, in addition to host-specific information such as “Superhost” and identity verification, attributes including price, extra charges, region competitiveness and house rules are all effective signals in Airbnb. The signaling impact is more effective for the listings without any review comments.

This study contributes to the literature by incorporating the signaling theory in the analysis of booking probability of Airbnb accommodation. The research findings are valuable to hosts in improving their booking rates and revenue. In addition, government and industrial management organizations can have more efficient strategy and policy planning.

This study aims to progress scholarly understanding of the staycation phenomenon by examining customer segments and documenting local customers’ attribute preferences.

A stated choice experiment is used to examine customer preferences for staycation package attributes. Latent class discrete choice modeling is deployed to classify customers into market segments based on their preferences. The profile of each segment is enhanced by documenting customer characteristics and consumption styles.

Six prominent market segments are identified using a combination of sociodemographics, consumption styles and staycation attribute preferences. The findings draw on consumer experiences during the COVID-19 pandemic to generate theoretical insights into preferred staycation packages. Empirically, the estimation results from the research framework and choice experimental method demonstrate that staycation market segments exhibit distinct preference structures.

Practitioners and policymakers can incorporate the findings of this study in designing and/or assessing staycation packages. This can ensure differentiated products for defined segments that resonate within local communities through positive word of mouth, thus offering prospective spillovers to visiting friends and relatives.

This is a pioneering study on preference heterogeneity from the customer perspective, with a focus on staycation markets. The findings can encourage and assist hotel sector leaders to capitalize on local market developments to achieve a more resilient hospitality business model.

The impact of demand fluctuation during crisis events is crucial to the dynamic pricing and revenue management tactics of the hospitality industry. The purpose of this paper is to improve the accuracy of hotel demand forecast during periods of crisis or volatility, taking the 2019 social unrest in Hong Kong as an example.

Crisis severity, approximated by social media data, is combined with traditional time-series models, including SARIMA, ETS and STL models. Models with and without the crisis severity intervention are evaluated to determine under which conditions a crisis severity measurement improves hotel demand forecasting accuracy.

Crisis severity is found to be an effective tool to improve the forecasting accuracy of hotel demand during crisis. When the market is volatile, the model with the severity measurement is more effective to reduce the forecasting error. When the time of the crisis lasts long enough for the time series model to capture the change, the performance of traditional time series model is much improved. The finding of this research is that the incorporating social media data does not universally improve the forecast accuracy. Hotels should select forecasting models accordingly during crises.

The originalities of the study are as follows. First, this is the first study to forecast hotel demand during a crisis which has valuable implications for the hospitality industry. Second, this is also the first attempt to introduce a crisis severity measurement, approximated by social media coverage, into the hotel demand forecasting practice thereby extending the application of big data in the hospitality literature.

The purpose of this study is to conduct a comparative analysis of the economic determinants of the compensation for chief executive officers (CEOs) between the pre- and post-financial crisis periods. To conduct the comparative analysis, the authors consider five years before and five years after the financial crisis of 2008. The authors use the data from the US financial service institutions and run separate regressions for the pre- and post-crisis periods to check if there is any significant difference in the economic determinants of executive compensation before and after the financial crisis. The authors find that total compensation and its incentive components decreased significantly in the post-crisis period. In the pre-crisis period, total compensation was determined by stock performance, accounting profit, growth, and leverage, whereas in the post-crisis period stock returns and leverage are the major factors influencing total compensation. The authors also find that firms’ leverage negatively influences the sensitivity of the pay for performance, but the influence of leverage on pay for performance is weaker in the post-crisis period. Our research is significant in the context of the US economy, the regulatory reforms of financial institutions, and the perspectives of the executive compensations. This is the first study that compares the relationship between compensation and firm performance over the pre- and post-crisis periods. It is an explicit attempt to develop a theoretical understanding of the compensation/performance relationship for the financial industry, which is blamed for the financial crisis and is affected by the Dodd–Frank regulation after the crisis.

Drawing upon resource-based view (RBV) and attribution theoretical lenses, this chapter provides a paradigm for examining the interplay among environmental investment towards green innovation, environmental disclosure as well as firm performance using the structural equation modelling (SEM) methodology. This chapter demonstrate a growing environmental awareness among stakeholders of the relevance of environmental performance to share value. It is also suggested that the mediating power of environmental disclosure between environmental investment and firm value as well as incremental goodwill is crucial. The findings of this chapter provide critical implications for several stakeholders that if environmental performance is hypothesised to affect the firm's value, companies may take proactive measures to avert potential environmental-related violations. Besides, investors may trade based on the evidence as to how firm value and its goodwill from acquisition will be affected by news of its environmental performance.

The fundamental change in accounting rules for equity-based compensation (EBC) instituted by SFAS 123, SFAS 123r, and IFRS 2 has allowed for new insights related to a variety of research questions. This paper discusses the empirical evidence generated in the wake of the new regulation and categorizes it into two broad streams. The first stream encompasses research on the changed use of EBC and the incentives provided. The second stream addresses how firms account for EBC, including the underreporting phenomenon and how it was affected by the mandatory recognition of EBC expenses. I discuss where research delivers unanimous findings versus contradictory results. Using these insights, I make recommendations for further research opportunities in the area of EBC.

This study aims to evaluate the consumer perceptions related to sanitation in the five-star hotels due to the COVID-19 pandemic. It also identified the most significant sanitation dimensions and their influence on the pertinent emotions experienced by the hotel consumers.

It identified seven sanitation dimensions (i.e. exterior of the hotel, guestrooms, washrooms/restrooms, in-house restaurant dining, hotel employees, public areas and handling of food) which were assessed through 10 positive and negative consumer sanitation emotions. A structured online survey was conducted to collect data from the 763 five-star hotel consumers. Exploratory factor analysis was applied on the 35 parameters of the seven identified sanitation dimensions, and analysis of variance (ANOVA) was used to determine the most significant dimensions among the hotel consumers.

Results revealed that among the seven sanitation dimensions, “exterior of the hotel” and “public area” dimensions were found to be statistically less significant compared to the other sanitation dimensions. Findings related to consumer emotions showed that a significantly high percentage of consumers revealed strong negative emotions, i.e. disgust and discontent toward the poor standards of sanitation in the hotels.

The study results may be helpful for the hotel administrators and managers to adequately plan the training sessions for their employees based on the consumer perception and emotions toward the identified sanitation dimensions. Further, it may also help in the implementation of the COVID-19 awareness program based on the consumer inputs and perceptions.

Although a few studies have been performed to explore the sanitation dimensions in the hospitality sector before, but this will be a first attempt to identify and measure the sanitation dimensions and corresponding consumer emotions due to the COVID-19 pandemic.

Trumpism seeks to maintain white domination. President Trump's policies aimed to restore white power at a time when it seemed to be in jeopardy. This chapter examines Trump's policy record and its impact on the US Black population, focusing on voting rights, policing, and criminal justice. I also discuss the far right's attack on history curricula and public education, specifically its demonization of Critical Race Theory. These efforts to protect and extend white power are not new. They are based on the principles articulated by the Founding Fathers, who asserted the right of white settlers to control the nation. More recent precedents for Trump's racism include the presidencies of Richard Nixon and Ronald Reagan, who, like Trump, ascended politically by mobilizing white racism. While many have labeled Trumpism a fascist movement, I argue that it is better understood as a precursor to fascism. It represents a continuation of the racist origins and traditions of the United States, where the national oppression of African Americans is core to the operation of capitalism. In closing, I offer a strategic proposal for stopping this reactionary movement and preventing it from developing into a full-fledged fascist movement.