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AbstractExisting research suggests that housing demolition negatively affects labour supply through the wealth effect of the demolition compensation. However, there is limited research on the gender differences in the impact of housing demolition on labour force participation. This study investigates the effects of housing demolition on labour force participation, with a specific focus on the gender difference. Using data from the Chinese Family Panel Studies, this study reveals a significant decline in labour force participation resulting from housing demolition, primarily driven by females. Moreover, housing demolition exerts a stronger effect on the labour force participation of married women, who tend to take on more responsibilities for household chores, while its impact on married men is less evident. These findings offer valuable insights into the issue of labour shortage and underscore the importance of reducing the traditional gender‐based division of household labour.

AbstractCustomer citizenship behavior (CCB) in virtual brand communities is a topic of increasing importance in marketing management research. This type of behavior plays a critical role in the improvement of enterprises' marketing capabilities. In this study, we draw on regulatory focus theory—along with the perspectives of self‐presentation and regulatory fit in relation to social identity—to construct and test a model that investigates both the main effects of regulatory foci (promotion and prevention) and the indirect effects of online self‐presentation and community identification on CCB. The empirical results based on our online survey between 310 individual members of an well‐known online‐community in China demonstrate: (1) a promotion focus exerts a positive influence on CCB while a prevention focus exerts a negative impact; (2) the desire for online self‐presentation mediates the association between regulatory foci and CCB; and (3) community identification moderates the relationship between regulatory foci and the desire for online self‐presentation, as well as the mediation effect. These results have substantial implications for studying CCB within virtual brand communities.

We examined whether customers' attribution of positive and negative events was influenced by intergroup attributional bias toward enterprises and consumers. The participants ( s = 180 in Study 1 and 54 in Study 2) read fictional case studies of positive and negative events occurring
in relation to enterprises and consumers and then provided details of their attributions. The results supported the existence of both derogatory attributions toward enterprises and favorable attributions toward consumers. Thus, an intergroup attributional bias was generally observed. Furthermore,
stereotypes about enterprises influenced the formation of derogatory attributions toward enterprises. A more significantly negative enterprise stereotype was associated with an increased likelihood of showing a derogatory attribution toward enterprises. Whether the event was positive or negative,
participants' perception was more inclined toward assigning egoistic motivational attributions to enterprises rather than to consumers. This study provides a new, integrative perspective to understand consumer attributions in relation to enterprise events.

Although many studies have considered the effects of online reviews on tourists' decisions, none have directly investigated how to leverage open data analyses to create early choice sets and facilitate destination planning. This paper illustrates how salient characteristics can be mined from the shared experiences embedded in review data and incorporated into a predictive model to build a travel counseling approach. The model is designed by first defining a prediction-based mechanism from online reviews and then generating a multinomial classification problem on all candidate destinations of interest. The model is implemented by applying Natural Language Processing (NLP) and Deep Learning (DL) technologies to review textual features. The model is validated using 75,315 reviews from TripAdvisor along with destinations from 257 U.S. national parks. Empirical results indicate a best classification accuracy of 67%, outperforming two previous approaches. Findings shed light on how to exploit past tourists' experiences to generate early destination recommendations to identify items for choice sets and reduce tourists' travel-planning effort. Theoretical and managerial implications regarding social media analytics are provided based on online review meta-data in touristic management.