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In the practice of live streaming e-commerce, the consumer demand is usually uncertain, and the inventory and prices can be decided by brand owners or streamers. To this end, this study examines the inventory and pricing decisions of the brand owner and streamer in a live streaming e-commerce supply chain under demand uncertainty.

In this study, four scenarios are considered, i.e. the brand owner determines the inventory and price (Scenario BB), the brand owner determines the inventory and the streamer determines the price (Scenario BS), the streamer determines the inventory and the brand owner determines the price (Scenario SB), and the streamer determines the inventory and price (Scenario SS).

The results show that the inventory and prices, as well as the profits of the brand owner and streamer increase with the consumer sensitivity to streamer’s sales effort level under the four scenarios. The inventory (price) is the highest under Scenario SS (SB), while that is the lowest under Scenario BB (BS). In addition, when the sensitivity is low, the brand owner’s profit is the highest under Scenario BB, otherwise, the profit is the highest under Scenario SS. Regardless of the sensitivity, the streamer’s profit is always the highest under Scenario SS.

Few studies focused on the inventory and pricing decisions of brand owners and streamers in live streaming e-commerce supply chains under demand uncertainty, while this work bridges the research gap. This study can provide theoretical basis and decision support for brand owners and streamers.

Over the past decade, artificial intelligence (AI) technologies have rapidly advanced organizational management, with many organizations adopting AI-based algorithms to enhance employee management efficiency. However, there remains a lack of sufficient empirical research on the specific impacts of these algorithmic management practices on employee behavior, particularly the potential negative effects. To address this gap, this study constructs a model based on the psychological ownership theory, aiming to investigate how algorithmic management affects employees’ knowledge hiding.

This study validates the model through a situational experiment and a multi-wave field study involving full-time employees in organizations implementing algorithmic management. Various analytical methods, including analysis of variance, regression analysis and path analysis, were used to systematically test the hypotheses.

The study reveals that algorithmic management exerts a positive indirect influence on knowledge hiding through the psychological ownership of personal knowledge. This effect is particularly pronounced when employees have lower organizational identification, highlighting the critical role of organizational culture in the effectiveness of technological applications.

This study is among the first empirical investigations to explore the relationship between algorithmic management and employee knowledge hiding from an individual perception perspective. By applying psychological ownership theory, it not only addresses the current theoretical gap regarding the negative effects of algorithmic management but also provides new theoretical and empirical support for the governance and prevention of knowledge hiding within organizations in the context of AI algorithm application. The study highlights the importance of considering employee psychology (i.e. psychological ownership of personal knowledge) and organizational culture (i.e. organizational identification) under algorithmic management. This understanding aids organizations in better managing knowledge risks while maximizing technological advantages and effectively designing organizational change strategies.

To maximize health synergistic benefits and provide a scientific foundation for enhancing air pollution control policies, this research proposed a causal inference framework with a decomposition ensemble prediction model for comprehensive policy assessment.

We introduced the CD-OASVR-Ensemble-CC method, a policy assessment approach that combined machine learning-based decomposition ensemble prediction (CD-OASVR-Ensemble) with counterfactual contrast (CC) analysis. The CD-OASVR-Ensemble model incorporated support vector regression (SVR) optimized through swarm intelligent optimization algorithms (OA), complete ensemble empirical model decomposition of adaptive noise (CD) and a linear ensemble model (Ensemble). This approach was applied to analyze patterns in historical PM2.5 data and predict the future trend. The predicted PM2.5 served as a counterfactual trend, enabling an evaluation of the policy’s environmental and health effects.

Analysis of prediction errors confirmed the proposed CD-OASVR-Ensemble model’s accuracy and robustness in predicting PM2.5 levels. The findings indicated that the implementation of “Regulation 2019” led to a significant reduction in PM2.5 concentration, preventing an estimated 7,960 premature deaths and saving approximately 2,602.95 m yuan in health-related economic costs.

This study introduces an innovative framework for evaluating the environmental effect of policies, with a specific emphasis on health synergistic benefits.

The construction industry is notorious for high risks and accident rates, prompting professionals to adopt emerging technologies for improved construction workers’ health and safety (CWHS). Despite the recognized benefits, the practical implementation of these technologies in safety management within the Construction 4.0 era remains nascent. This study aims to investigate the mechanisms influencing the implementation of Construction 4.0 technologies (C4.0TeIm) to enhance CWHS in construction organizations.

Drawing upon integrated institutional theory, the contingency resource-based view of firms and the theory of planned behavior, this study developed and tested an integrated C4.0TeIm-CWHS framework. The framework captures the interactions among key factors driving C4.0TeIm to enhance CWHS within construction organizations. Data were collected via a questionnaire survey among 91 construction organizations and analyzed using partial least squares structural equation modeling to test the hypothesized relationships.

The results reveal that: (1) key C4.0TeIm areas are integrative and centralized around four areas, such as artificial intelligence and 3D printing, Internet of Things and extended reality; and (2) external coercive and normative forces, internal resource and capability, business strategy, technology competency and management (BST), organizational culture and use intention (UI) of C4.0 technologies, collectively influence C4.0TeIm-CWHS. The findings confirm the pivotal roles of BST and UI as mediators fostering positive organizational behaviors related to C4.0TeIm-CWHS.

Practically, it offers actionable insights for policymakers to optimize technology integration in construction firms, promoting industrial advancement while enhancing workforce well-being.

The novel C4.0TeIm-CWHS framework contributes to the theoretical discourses on safety management within the C4.0 paradigm by offering insights into internal strategic deployment and compliance challenges in construction organizations.

With recent advances in artificial intelligence, the hospitality industry has introduced the concept of unmanned smart hotels staffed by service robots instead of human employees. Research is needed to understand consumers’ receptivity to such an innovation. This paper examines factors associated with consumers’ potential resistance to using automated service hotels via two sequential studies. Given that younger generations of consumers are typically early adopters of advanced technology and innovative services, our sampling approach focused on this consumer group.

Two studies were conducted. Study 1 proposed and empirically tested a theoretical model. Results revealed that attitude, subjective norms and perceived behavioral control each positively influenced individuals’ intentions to use unmanned smart hotels. In Study 2, we further investigated aspects informing perceived security, a key variable in the use of unmanned smart hotels.

Findings showed how people’s beliefs about unmanned smart hotels and security control assurances led to perceived security. These perceptions were shaped by perceived physical risks, privacy concerns, website design and hotel reputation. Overall, this research provides theoretical and practical implications for various stakeholders associated with unmanned smart hotels.

Findings of this study suggested that managers of unmanned smart hotels should design user-friendly, secure processes and offer comprehensive support resources to enhance customer experience and usage.

The findings provide a holistic understanding of consumers’ receptivity to unmanned smart hotels.

The aim of this paper is to explore how information shared by SMIs affects consumers' food tasting intentions. To achieve this, it integrates the IAM and TAM, in conjunction with trust and EWOM.

This paper utilized a convenience sampling technique, employing a survey instrument to gather data online. The questionnaire was distributed across the social media pages of food bloggers from September 11 to November 30, 2023. The collected data was analyzed using SPSS and AMOS.

We developed a research framework that integrates IAM, TAM, Trust, and EWOM variables to assess how information shared by SMIs influence consumers' intentions to explore new food tastes. The model demonstrated enhanced predictive and explanatory capabilities.

This study enriches the existing literature on information adoption and technology acceptance by advancing our understanding of how SMIs influence consumers’ food tasting intentions. Additionally, it aids SMIs in comprehending their role in endorsing new food products and restaurants, fostering trust and reliability among their followers. This study enables consumers to make more informed decisions about trying new food products or dining establishments, empowering them to evaluate influencer recommendations critically.

This study uniquely focuses on the influence of information shared by SMIs on consumers' intentions to taste new foods. While SMIs have been extensively studied in various contexts, such as fashion, beauty, and travel, this research offers a fresh perspective on understanding their impact on consumer behavior within the food industry.