With the proliferation of live streaming, there is evidence that online impulse buying is becoming an emerging phenomenon. Although many studies have investigated impulse buying in the context of offline shopping and business-to-consumer e-commerce, online impulse buying in live streaming has attracted little attention. In this study, we aim to explore the effect of social presence in live streaming on customer impulse buying based on the stimulus–organism–response framework. The research model presented here identifies pleasure and arousal as the mediation of impulse buying in live streaming. We use the AMOST and IBM SPSS PROCESS software to estimate our model based on data at the minute level from 189 customers, who watched live streaming in the past three months. The results suggest that the social presence of the broadcaster and the social presence of the live streamer positively affect impulse buying directly and indirectly via pleasure and arousal, promoting consumer online impulse buying in live streaming, but the social presence of the viewers has no significant effect on pleasure and arousal. For practice, our results can help policymakers and operators of the live streaming platform alleviate impulse buying in the digital world.

As artificial intelligence (AI) technology becomes increasingly prevalent in the tourism sector, an in-depth exploration of its opportunities and potential risks for the United Nations Sustainable Development Goals (SDGs) is urgently needed. However, existing research falls short in constructing an integrated knowledge framework that systematically clarifies how AI can effectively advance sustainable tourism, leaving theoretical understanding and practical pathways relatively fragmented. To address this gap, this study employs a systematic literature review following the strict SPAR-4-SLR protocol and integrates a domain-based TCCM (i.e., theories, contexts, characteristics, and methods) analysis framework. A total of 177 core articles from the Scopus and Web of Science databases were rigorously analyzed. This study first examines publication trends, key journals, and the citation impact of AI in tourism. It then systematically synthesizes the theoretical foundations, research contexts, characteristics, and methodologies. Most importantly, this review delves into the antecedents, decision-making processes (including mediating and moderating variables), and outcomes of AI applications in the tourism industry. This study not only delineates a clear direction and agenda for future academic inquiry but also provides theoretical support and practical guidance for policymakers and tourism managers to design and implement AI-driven sustainable tourism strategies.

In the wake of the COVID-19 pandemic, hospitality institutions are striving for legitimacy, which leads them to organizational hypocrisy, generating perceptions of inducement breach, future anxiety, and ultimately reduced Job Embeddedness. This study has identified industry and environmental situation-specific constructs in a mutual relationship to fill a theoretical gap. An electronic survey of 2100 frontline employees was administered among which 842 completed surveys were retained for analysis. The validity of the measures and the absence of common method bias were established. SPSS PROCESS was used to compute the serial mediation effects. Contrary to existing knowledge, the results of this study indicate that organizational hypocrisy increases employee job embeddedness. Three reasons identified for this result are Asian culture sample, prevalence of COVID-19 pandemic, and the necessity of hypocrisy emphasized by scholars. The study also presents an underlying mechanism that makes this relationship negative through perceived inducement breach and future anxiety. This study focuses on HOW and IF organizational hypocrisy has detrimental effects, thus adding empirical evidence to otherwise exploratory literature. For hospitality industry, employees are an irreplaceable resource that provides competitive advantages; they need to align their values with that of their employees by word and actions or risk losing them.

The low-carbon development (LCD) of energy markets not only serves as a critical enabler in combating global climate change and advancing the green economy but also enhances global industrial competitiveness. Grounded in prospect theory, this study develops a tripartite evolutionary game model involving three core energy market stakeholders, i.e., energy enterprises, the public, and the government, to investigate the determinant factors and decision-making mechanisms underlying the LCD of energy enterprises, with subsequent simulation analyses conducted through MATLAB R2024a. The research findings indicate that loss aversion serves as the primary driver for energy enterprises’ adoption of LCD strategies. Public supervision demonstrates optimal effectiveness only under conditions of low risk and low loss, while risk sensitivity remains the dominant factor influencing the government’s strategic choices. Notably, government incentives combined with public supervision demonstrate significant synergistic effects in accelerating the corporate transition toward LCD. Accordingly, the government should actively promote LCD strategies to mitigate transformation risks for energy enterprises while concurrently optimizing regulatory frameworks to reduce public supervision costs and amplify incentive benefits, thereby fostering active public participation in LCD.

Based on resource orchestration theory, this paper examines the influence of data-intelligence empowerment on enterprise knowledge recombinant capabilities empirically, using A-share-listed high-technology enterprises from 2012 to 2021 as research examples. Research shows that data-intelligence empowerment can promote both enterprise knowledge recombinant reuse capability and knowledge recombinant creation capability. Furthermore, knowledge depth diversification acts as a mediator between data-intelligence empowerment and knowledge recombinant reuse capability, and knowledge breadth diversification plays a mediating role between data-intelligence empowerment and knowledge recombinant creation capability. The heterogeneity analysis demonstrates that the data-intelligence empowerment has a more promotive impact on knowledge recombinant reuse capability in non-state-owned enterprises and eastern region enterprises, and there is a promotion effect on the knowledge recombinant creation capability across enterprises with different ownership structures and regions. This study provides theoretical guidance as well as practical references for enterprises to take advantage of digital intelligence technology to empower themselves, match internal and external resources and capabilities, and ultimately accomplish sustainable recombinant innovation.