In alignment with the UN’s 17 Sustainable Development Goals and the UN Global Compact’s Ten Principles, nations have established platforms for environmental sustainability through financial routes, spotlighting green finance and FinTech. While there have been tangible advancements, academic discourse on these topics remains dispersed and lacks cohesion. Observing the frequent overlap in the implementation of green finance and FinTech, this paper offers a bibliometric analysis of research concerning green finance and FinTech up to 2022. The primary objective of this study is to identify some of the most pertinent research in these fields. The results assist in delineating several future research directions, including a greater focus on the investment facet of green finance, the application facet of FinTech, the regulatory environment in some developing countries, and an emphasis on Green FinTech research based on information from the Web of Science database.

Research indicates that perceived trust affects both behavioral intention to use chatbots and service satisfaction provided by chatbots in customer service contexts. However, it remains unclear whether perceived propensity to trust impacts service satisfaction in this context. Thus, this research aims to explore how customers’ propensity to trust influences trusting beliefs and, subsequently, their satisfaction when using chatbots for customer service. Through purposive sampling, individuals in Hong Kong with prior experience using chatbots were selected to participate in a quantitative survey. The study employed Necessary Condition Analysis, Importance-Performance Map Analysis, and Partial Least Squares Structural Equation Modelling to examine factors influencing users’ trusting beliefs toward chatbots in customer service settings. Findings revealed that trust in chatbot interactions is significantly influenced by propensity to trust technology, social presence, perceived usefulness, and perceived ease of use. Consequently, these factors, along with trusting belief, also influence service satisfaction in this context. Thus, Social Presence, Perceived Ease of Use, Propensity to Trust, Perceived Usefulness, and Trusting Belief are found necessary. By combining Importance-Performance Map Analysis, priority managerial action areas were identified. This research extends the Technology Acceptance Model by incorporating social presence, propensity to trust technology, and trusting belief in the context of AI chatbot use for customer service.

Many corporations have now revised their value statements to showcase their commitment to sustainable development and green business due to the pressing issues of climate and environmental damage. Despite the importance of value statements that can direct companies to achieve sustainable goals, which kind of values can be perceived as organizational orientations towards sustainability remains unknown. This study explores how the general public views the inclusion of sustainable components in value statements from listed companies in Hong Kong by using Q-sort methodology (n = 30). Analyzing the final sample of 40 value statements extracted from 27 listed companies, we identified three factors related to sustainability, namely, Empowering business process management, Empowering customer-centric excellence, and Empowering sustainable progress. This study provides an additional component to the traditional triple bottom line by suggesting that the internal operation of a company is an emerging idea in understanding corporate sustainability.

Purpose: This study aims to understand the factors influencing the adoption of DeepSeek to seek and utilize information within the company, with a focus on its impact on managing and utilizing information to enhance firm performance. Specifically, this study examines how fear of missing out (FOMO) and attitudes impact firm performance through the adoption of DeepSeek for improving task efficiency, streamlining workflows, supporting effective information management and enhancing firm performance. Design/methodology/approach: Data were collected from 568 full-time employees in managerial roles in China through SoJump, a widely recognized and robust online survey platform. The data analysis was conducted using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, utilizing the SmartPLS software. Findings: Results showed that personal FOMO and social FOMO have positive impact on value-expressive and utilitarian attitude to predict DeepSeek adoption in enhancing firm performance. However, social-adjustive attitude was found to be an insignificant predictor in DeepSeek adoption. The research highlighted that there is a need for AI developers to promote DeepSeek adoption from the perspectives of roles of attitudes and motivations to enhance work efficiency, thereby improving firm performance. Originality/value: By integrating self-determination theory (SDT) and functional theory of attitudes, this research provides a novel framework linking motivations, attitudes and firm performance in the context of Generative artificial intelligence (AI) (GenAI) adoption. Specifically, it highlights FOMO as a unique and powerful motivator, integrating it with functional attitude theories to predict user behavior in DeepSeek adoption This study contributes to the technology adoption literature by emphasizing the contextual relevance of China’s digital ecosystem and exploring the interplay between motivations and functional attitudes – an innovative approach in the AI context that advances our understanding of how GenAI adoption drives firm performance.