Search results

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.

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.

Generative Artificial Intelligence (Gen-AI) has provided new opportunities and challenges in using educational environments for students’ interaction and knowledge acquisition. Based on the expectation–confirmation theory, this paper aims to investigate the effect of different constructs associated with Gen-AI on engagement, satisfaction and word-of-mouth.

We collected data from 508 students in the UK using Qualtrics, a prominent online data collection platform. The conceptual framework was analysed through structural equation modelling.

The findings show that Gen-AI expectation formation and Gen-AI quality help to boost Gen-AI engagement. Further, we found that active engagement positively affects Gen-AI satisfaction and positive word of mouth. The mediating role of Gen-AI expectation confirmation between engagement and the two outcomes, satisfaction and positive word of mouth, was also confirmed. The moderating role of cognitive processing in the relationship between Gen-AI quality and engagement was found.

This paper extends the Expectation-Confirmation Theory on how Gen-AI can enhance students’ engagement and satisfaction. Suggestions for future research are derived to advance beyond the confines of the current study and to capture the development in the use of AI in education.

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 determine how cognitive diversity at the workplace influences team creativity. In this regard, the authors examined knowledge sharing and team-focused inclusion through which team members’ cognitive diversity was expected to elevate their positive work outcomes.

A quantitative method is used to accumulate the data. The authors surveyed workers and their respective managers at a single China-based food company. The supervisors rated the outcome variables (creativity and team effectiveness) regarding their employees, whereas employees were asked to rate the cognitive diversity, inclusion and knowledge sharing within the workgroup. The final valid sample size (n = 391) consisted of 137 workgroups with an adequate response rate (62.3%).

Cognitive diversity is related to team effectiveness but not creativity. The research found that cognitive diversity can increase creativity only through enhanced inclusion and knowledge sharing. Inclusion, likewise, explained the impact of cognitive diversity on effectiveness.

The originality of the current research lies in its contemporary exploration of inclusion and cognitive diversity and their pathways to team creativity and effectiveness. The social capital theory was applied to explain the proposed relationships.

This study investigated consumer perceptions of AI chatbots focusing on sentiment analysis across Twitter (X) and Reddit during ChatGPT3 through ChatGPT4 launches. It identifies user sentiments: positive, negative, or neutral and explores their impact on chatbot deployment in interactive marketing. The goal was to understand consumer engagement dynamics and provide insights for enhancing marketing strategies and consumer interactions with chatbots.

Using sentiment analysis, this research examined the nature and scope of discussions surrounding AI chatbots. This methodological approach allowed for a nuanced understanding of the predominant sentiments: positive, negative, or neutral, expressed by users providing insights into consumer engagement and interaction patterns.

The findings reveal a diverse range of consumer sentiments toward ChatGPT 3, reflecting varying degrees of acceptance and skepticism. These varied sentiments are crucial for organizations in shaping their interactive marketing strategies, particularly in how they deploy chatbots for consumer engagement and brand interaction.

Trust and positive sentiment toward ChatGPT, particularly on platforms like Twitter and Reddit, suggest it is becoming part of everyday life. However, concerns about its impact on human jobs and the lack of emotional intelligence persist. Users still weigh the benefits and drawbacks of ChatGPT, with negative and neutral sentiments reflecting these worries. For interactive marketers, this presents an opportunity to differentiate through human interaction in customer-facing roles. Addressing the risks and ethical concerns of AI, marketers can better engage consumers and refine strategies for future chatbot deployments, ensuring AI enhances rather than detracts from the customer experience.

This paper makes a unique contribution to the existing literature by demonstrating how consumer perceptions, rather than mere acceptance, directly influence the strategic use of AI chatbots in interactive marketing. By focusing on perceptions, this study offers deeper insights from social media sentiment analysis that can refine marketing campaigns and enhance consumer engagement with emerging technologies in the digital landscape.

Drawing on the technology affordance and affinity theories, this study proposes a framework explaining the antecedents and consequences of customers’ smart experiences (CSEs) in the artificial intelligence (AI) chatbot context.

The quantitative approach employing an online survey was adopted to obtain data from chatbot users (N = 761) and analyzed using structural equation modeling.

Results from a survey study show that chatbot affordances, including interactivity (two-way communication, active control and synchronicity), selectivity (customization and localization), information (argument quality and source credibility), association (connectivity and sense of safety) and navigation positively affect CSEs (hedonic and cognitive), leading to customer chatbot stickiness through affinity.

Our study provides evidence that supports and extends the affordances and affinity lens by highlighting the roles of specific chatbot affordances that contribute to a positive-smart experience and subsequently enhances customer chatbot stickiness through affinity.