This study aims to expand tourism value co-creation to include resident-tourist social interactions. Specifically, we aim to empirically verify the effects of residents' life satisfaction and their perceived benefits and costs of tourism development on their value co-creation with tourists. An online questionnaire survey was conducted with a sample of 380 urban residents in China. Partial Least Square structural equation modelling was adopted to analyze the data. The results show that residents' perceived economic and social-cultural benefits of tourism development have positive effects on both value co-creation and life satisfaction, while perceived costs have negative effects. Life satisfaction influences value co-creation. This study contributes to the literature by conceptualizing tourism value co-creation between tourists and residents and empirically investigating residents' participation in value co-creation in the tourism context. Moreover, we introduced broaden-and-build theory to analyze a tourism phenomenon for the first time. This study also provides important managerial implications

Understanding consumer emotions arising from robot-customers encounters and shared through online reviews is critical for forecasting consumers’ intention to adopt service robots. Qualitative analysis has the advantage of generating rich insights from data, but it requires intensive manual work. Scholars have emphasized the benefits of using algorithms for recognizing and differentiating among emotions. This study critically addresses the advantages and disadvantages of qualitative analysis and machine learning methods by adopting a hybrid machine-human intelligence approach. We extracted a sample of 9707 customers reviews from two major social media platforms (Ctrip and TripAdvisor), encompassing 412 hotels in 8 countries. The results show that the customer experience with service robots is overwhelmingly positive, revealing that interacting with robots triggers emotions of joy, love, surprise, interest, and excitement. Discontent is mainly expressed when customers cannot use service robots due to malfunctioning. Service robots trigger more emotions when they move. The findings further reveal the potential moderation effect of culture on customer emotional reactions to service robots. The study highlights that the hybrid approach can take advantage of the scalability and efficiency of machine learning algorithms while overcoming its shortcomings, such as poor interpretative capacity and limited emotion categories.

Analyzing carbon emissions is critical for successfully managing sustainable production and consumption. In a dual channel supply chain that includes traditional retailers and online e-tailers, consumer free riding often occurs when consumers enjoy the services provided by a traditional retailer but make purchases at a lower price from an e-tailer. The specific aim of this paper, therefore, is to evaluate the impact of consumer free riding on carbon emissions in a product's life cycle across a dual channel closed loop supply chain and to assess the effect of governmental e-commerce tax on carbon emissions. The study comprises a systematic comparison and numerical analysis of cases in which consumers do or do not free ride. Our results show that although manufacturers may gain economic benefits from consumer free riding behavior, total carbon emissions across the supply chain increase too, and a governmental tax on e-commerce can help reduce consumer free riding and total carbon emissions. Our research also suggests that, a government may have to subsidize the e-tailer in the consideration of maximizing the social welfare