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By adopting a cultural lens, this study aims to conceptualize destination-community hospitality to capture societal hospitality at the destination as an intangible asset. Destination-community hospitality comprises a set of attitudes and traits that are organically conveyed by community members and directly experienced by tourists. The paper contributes to efforts aimed at freeing hospitality from the confines of commercial lodging and food and beverage establishments.

This study proposes an affection–attention–awareness (AAA) framework by drawing on the pertinent literature and leveraging the affective–behavioral–cognitive model of attitudinal change. It focuses on the effects of cognition in conceptualizing destination-community hospitality as the core of the framework. Methodologically, the framework is illustrated with qualitative data including observational fieldwork and interviews conducted in Iran.

Illustration of the AAA framework in the context of Iran highlights how welcoming attitudes are leveraged at a destination as an ingrained community mindset even in the absence of formal initiatives or professional training. It underscores the hospitality advocacy role of local-community members irrespective of public-sector policy and capacity deficiencies.

Destinations that strive to attract tourists should tap into the significant wealth of intuitive hospitality within communities as an intangible resource. The ingenuity should be preserved to sustainably enhance the overall tourist experiences. The framework can serve as a guide to strategically facilitate destination-community hospitality.

Research on community-wide hospitality at destinations, as a major cultural asset in the context of strategic hospitality management, remains underdeveloped. With illustrative evidence, the findings elevate the critical role of community members as innate advocates of hospitality in developing destinations. The proposed framework of destination-community hospitality provides a new perspective and impetus for investigating hospitality beyond commercial domains.

As the cost of completing a transaction, the green merger and acquisition (M&A) premium paid on mergers can influence whether the acquisition creates value or not. However, studies linking M&A premiums to firm value have had mixed results, even fewer studies have examined the effect of green M&A premiums on bidders’ firm value. The purpose of this paper is to investigate whether and how green M&A premiums affect firm value in the context of China’s heavy polluters.

Using 323 deals between 2008 and 2019 among China’s heavy polluters, this paper estimates with correlation analysis and multiple regression analysis.

Green M&A premiums are negatively associated with firm value. The results are more significant when firms adopt symbolic rather than substantive corporate social responsibility (CSR) strategies. Robustness and endogeneity tests corroborate the findings. The negative relation is stronger when acquiring firms have low governmental subsidy and environmental regulation, when firms have overconfident management, when firms are state-owned and when green M&A occurs locally or among provinces in the same region. This study also analyzes agency cost as an intermediary in the relationship between green M&A premium and firm value, which lends support to the agency-view hypothesis.

This study provides systemic evidence that green M&A premiums damage firm value through agency cost channel and the choice of CSR strategies from the perspective of acquirers. These findings enrich the literature on both the economic consequences of green M&A premiums and the determinants of firm value and provide a plausible explanation for mixed findings on the relationship between green M&A premiums and firm value.

Since crude oil is crucial to the nation's economic growth, crude oil futures are closely related to many other markets. Accurate forecasting can offer investors trustworthy guidance. Numerous studies have begun to consider creating new metrics from social networks to improve forecasting models in light of their rapid development. To improve the forecasting of crude oil futures, the authors suggest an integrated model that combines investor sentiment and attention.

This study first creates investor attention variables using Baidu search indices and investor sentiment variables for medium sulfur crude oil (SC) futures by collecting comments from financial forums. The authors feed the price series into the NeuralProphet model to generate a new feature set using the output subsequences and predicted values. Next, the authors use the CatBoost model to extract additional features from the new feature set and perform multi-step predictions. Finally, the authors explain the model using Shapley additive explanations (SHAP) values and examine the direction and magnitude of each variable's influence.

The authors conduct forecasting experiments for SC futures one, two and three days in advance to evaluate the effectiveness of the proposed model. The empirical results show that the model is a reliable and effective tool for predicting, and including investor sentiment and attention variables in the model enhances its predictive power.

The data analyzed in this paper span from 2018 through 2022, and the forecast objectives only apply to futures prices for those years. If the authors alter the sample data, the experimental process must be repeated, and the outcomes will differ. Additionally, because crude oil has financial characteristics, its price is influenced by various external circumstances, including global epidemics and adjustments in political and economic policies. Future studies could consider these factors in models to forecast crude oil futures price volatility.

In conclusion, the proposed integrated model provides effective multistep forecasts for SC futures, and the findings will offer crucial practical guidance for policymakers and investors. This study also considers other relevant markets, such as stocks and exchange rates, to increase the forecast precision of the model. Furthermore, the model proposed in this paper, which combines investor factors, confirms the predictive ability of investor sentiment. Regulators can utilize these findings to improve their ability to predict market risks based on changes in investor sentiment. Future research can improve predictive effectiveness by considering the inclusion of macro events and further model optimization. Additionally, this model can be adapted to forecast other financial markets, such as stock markets and other futures products.

The authors propose a novel integrated model that considers investor factors to enhance the accuracy of crude oil futures forecasting. This method can also be applied to other financial markets to improve their forecasting efficiency.