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The purpose of this paper is to develop a comprehensive understanding of the public safety risks of international construction projects (ICPs) from the perspective of threat and vulnerability. A novel and comprehensive risk assessment approach is developed from a systemic perspective and applied to the Belt and Road Initiative (BRI) to improve the public safety risk management strategy for ICPs in BRI.

First, a public safety risk indicator system was constructed from the two dimensions, namely threat and vulnerability. Next, an integrated measurement model was constructed by combining the Genetic Algorithm-Backpropagation (GA-BP) neural network, fuzzy comprehensive evaluation method and matter-element extension (MME) method. Data from 49 countries involved in the BRI, as well as five typical projects, were used to validate the model. Finally, targeted risk prevention measures were identified for use at the national, enterprise and project levels.

The findings indicate that while the vulnerability risks of typical projects in each region of the BRI were generally low, threat risks were high in West Asia and North Africa, Commonwealth of Independent States (CIS) countries and South Asia.

First, the structure of the public safety risk system of ICPs was analyzed using vulnerability and system theories. The connotation of public safety risk was defined based on two dimensions, namely threat and vulnerability. The idea of measuring threat risk with public data and measuring vulnerability risk with project data was clarified, and the risk measurement was integrated into the measurement results to help researchers and managers understand and systematically consider the public safety risks of ICPs. Second, a public safety risk indicator system was constructed, including 18 threat risk indicators and 14 vulnerability risk indicators to address the gaps in the existing research. The MEE model was employed to overcome the problem of incompatible indicator systems and provide stable and credible integrated measurement results. Finally, the whole-process public safety risk management scheme designed in this study can help to both provide a reference point for the Chinese enterprises and oversea contractors in market selection as well as improve ICP public safety risk management.

Our research investigated how the perceived effectiveness of privacy, perceived privacy risk, and perceived security influenced consumers' behavioral intention to use mobile payment applications during the COVID-19 pandemic.

We applied a quantitative method using a cross-sectional online survey conducted over three years. We collected a sample of 1,471 survey responses focused on ages 18–39. Using descriptive statistics, confirmatory factor analysis, and structural equation modeling, we tested our hypotheses with SPSS 27 and AMOS 27.

Results of the study indicate that the perceived effectiveness of privacy positively influences perceived privacy risk, perceived security, and behavioral intention. Moreover, perceived privacy risk has a positive effect on perceived security. We found no significant relationship between perceived privacy risk and behavioral intention, although perceived security has a positive effect on behavioral intention. Further mediation analyses showed that perceived privacy risk and perceived security mediate the relationship between the perceived effectiveness of privacy and behavioral intention.

This research sheds new light on the role of perceived privacy effectiveness in mobile payment adoption in Hungary, particularly during the COVID-19 pandemic. Our research also explains why and how perceived privacy effectiveness influences consumers' perceived privacy risk, perceived security, and behavioral intention.

The recent advancements in smartphone technology and social media platforms have increased the popularity of artificial intelligence (AI) color cosmetics. Meanwhile, China is a lucrative market for various foreign beauty products and technological innovations. This research aims to investigate the adoption of AI color cosmetics applications and their electronic word-of-mouth (e-WOM) intention among Chinese social media influencers. Several key concepts have been proposed in this research, namely body esteem, price sensitivity, social media addiction and actual purchase.

An online questionnaire design was used in this research. A combination of purposive sampling and snowball sampling of AI color cosmetics users who are also social media influencers in China yields 221 respondents. To analyze the data, this research employs Structural Equation Modelling (SEM) method via SPSS and AMOS software. A 2-step approach, Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA), is implemented to prove the hypotheses and generate the results.

1) Social media addiction is a positive predictor of AI color cosmetics usage, (2) AI color cosmetics usage is a positive predictor of actual purchase, (3) actual purchase is a positive predictor of e-WOM intention and lastly, (4) there is a full mediation effect of actual purchase.

This research draws on the uses and gratification (U&G) theory to investigate how specific user characteristics affect Chinese social media influencers' adoption of AI color cosmetics, as well as how this may affect their decision to purchase branded color cosmetics and their e-WOM.

As one of the most cutting-edge technologies in the digital age, facial enhancement technology (FET) has greatly enhanced consumer online shopping experience and brought new e-commerce opportunities for cosmetics retailers. The purpose of this paper is to extend the unified theory of acceptance and use of technology (UTAUT) model in the context of FET. In addition to the concepts from the original model, the new FET-UTAUT model features (low) body esteem, social media addiction and FET adoption.

A purposive sampling of FET users in China via an online questionnaire yields 473 respondents. To analyze the data, this research uses the structural equation modeling method via statistical package for the social sciences and analysis of a moment structures software. A two-step approach, exploratory factor analysis and confirmatory factor analysis, was used to test the hypotheses and generate the findings.

Performance expectancy, effort expectancy, social influence, facilitating conditions and (low) body esteem have positive relationships with FET adoption. FET adoption has a positive relationship with online purchase intention of branded color cosmetics, and the empirical evidence for the moderating role of social media addiction in the relationship between FET adoption and online purchase intention is inconclusive.

This research extends the traditional UTAUT model by proposing a novel FET-UTAUT model that incorporates additional key concepts such as body esteem, FET adoption and social media addiction. Managerial implications of this research are provided for FET designers and branded color cosmetic retailers.

Building upon uses and gratifications (UG) theory and social exchange theory, the current study establishes a theoretical model to examine the underlying relationship between customer motivations, active participation and electronic word-of-mouth (e-WOM) and purchase intentions on WeChat.

The data were gathered in a web-based survey of 301 WeChat users in mainland China. To empirically verify the proposed hypotheses, confirmatory factor analysis (CFA) and structural equation modeling (SEM) were employed using online data.

Path analysis outcomes demonstrate that functional, hedonic and social motivations positively affect WeChat users' active participation. In addition, active participation significantly influences e-WOM intention while showing no correlation with purchase intention.

Theoretically, this article can enrich the extant system of relevant theories and offer a fresh perspective for further research on the generation of consumers' e-WOM intention and purchase intention in the WeChat context. Practically, the research outcomes provide insight for companies on how to motivate customers to participate in online activities, which subsequently improve WeChat users' willingness in conducting e-WOM communication and making purchase decisions.

Although mobile social media could serve as an influential marketing vehicle for individuals' engagement in social and commercial activities in today's mobile-matured environment, the substantial impact of active engagement on the relationship between customer motivation and purchase intention remains insufficiently explored. The outcomes not only contribute to the current body of knowledge, but also offer several managerial guidance for companies that pay attention to mobile social media marketing in a contemporary mobile media-saturated society.

This study examines the complex realm of customers’ acceptance of AI-powered robot chefs. It aims to enhance the understanding of customers’ motivational factors that influence their acceptance of AI-powered robots for food preparation in casual-dining restaurants.

This research was conducted using a comprehensive approach. Data were collected from 520 participants at casual dining restaurants in Florida, USA. After rigorous data cleaning, 489 valid responses were obtained. The study employed partial least squares structural equation modeling (PLS-SEM) utilizing SmartPLS Ver. 4.0 to examine both the measurement and the proposed research model, ensuring a thorough and robust investigation.

The research results show that customers’ motivational factors, such as functionality and hedonic and cognitive innovativeness, positively influence their knowledge and attitude toward accepting robotic chefs in casual-dining restaurants. Social motivation negatively impacts customers’ knowledge, but on the other hand, it positively affects customers’ attitudes. In addition, customer knowledge and attitudes positively influence the acceptance behavior of AI-powered robot chefs.

This study provides a practical understanding of the presence and embodiment of robot chefs in casual-dining restaurants. In the restaurant industry, customer intention to accept robot chefs is a significant factor, and this study not only sheds light on this crucial aspect but also provides actionable insights for industry professionals and researchers.

Based on the existing hospitality literature, this study is one of the first to address this issue. It presents the antecedents of motivated customers’ acceptance of robot chefs based on their knowledge and attitudes toward robot chefs. In addition, this research addressed the factors that influence customers’ robot chef acceptance behavior in casual-dining restaurants.

This study aims to examine the impact of AI service robots on restaurant customers' engagement and acceptance and the moderating role of robot anthropomorphism on the relationship between AI robot service quality and customer engagement.

Using a three-wave time-lagged design, 416 customers of service robots-enabled restaurants participated in the study. Mplus was used to examine the hypotheses.

The results confirmed that customers' perception regarding automation, personalization, efficiency and precision of robot service quality determine customer engagement, which further influences customer acceptance of AI service robots. Additionally, robot anthropomorphism moderates the relationships between AI robot service quality in terms of automation, personalization, efficiency and precision and customer engagement. This study confirms that AI service robots-customer engagement contributes to better acceptance of AI service robots.

The proposed framework can be used as a diagnostic tool to enhance customer acceptance of AI service robots in restaurant settings. This research provides guidelines to restaurant owners to employ AI service robots in front-line services that provide better quality, ultimately enhancing customer engagement and acceptance.

This study fills the gap in the literature by investigating the influence of AI robot service quality on customer engagement and customer acceptance with the moderating effect of robot anthropomorphism in an emerging market context.

The increased usage of smartphones has made mobile payment services (MPS) popular among millennials, but it is uncertain if this shift in behavior is temporary due to situational factors like the pandemic or a long-term trend due to technological advances. This study uses the diffusion of innovation (DOI) literature to assess an integrated model including smartphone addiction, technology-related and consumer-related factors that influence consumers' intention to use MPS. It also explores the interplay of situational variables and smartphone addiction in shaping this relationship.

The authors base the conceptual model on the theory of rational addiction and the theory of optimal flow. This model is then empirically validated through data collected from seven hundred users of MPS in India. Research hypotheses are tested using structural equation modeling and multi-group analysis.

The study finds that smartphone addiction has a positive impact on millennials' perceived advantages, compatibility, trialability and observability of MPS but does not significantly affect their perceived complexity or risk. The results also suggest that the relationship between smartphone addiction and MPS is moderated by situational factors. In low-priority situations, smartphone addiction strengthens millennials' perceptions and intentions to use MPS, while in high-priority situations, situational factors overshadow the impact of smartphone addiction.

The findings of the study enable organizations to capitalize on smartphone addiction-driven MPS adoption behavior to sustain long-term usage behavior by appropriately understanding the context/situation which drives MPS adoption.