Article Preview
TopIntroduction
Supply chain financial services have been growing in literature. For instance, a robust Supply Chain Financial Logistics Supervision System was constructed by harnessing the power of Internet of Things (IoT) technology to examine the practical implementation of IoT technology in enabling customers to supervise the logistics process effectively (Liu et al., 2023). Also, a comprehensive supply chain finance (SCF) framework introducing two novel coordinating contracts that leverage trade credit financing was designed to address different problem settings within the supply chain (Emtehani et al., 2023).
FinTech has gained popularity as it refers to innovative technologies adopted by financial service institutions. The emergence of online peer-to-peer (P2P) lending platforms has introduced a promising FinTech business model that connects investors with capital recipients within supply chains (SCs) (Taleizadeh et al., 2022). Recent advancements in financial technology, known as FinTech, have emerged as solutions to various challenges. These FinTech-driven business models, including crowdfunding, peer-to-peer lending, invoice trading, mobile wallets and payments, and platform-driven SCF, are reshaping the landscape for small businesses (Chang et al., 2021; Chen, Li et al., 2021; Leung, Cho, & Wu, 2022; Liu Panfilova et al., 2022; Liu, Sakulyeva et al., 2022; Malaquias et al., 2021; Shankar, 2022; Sharma et al., 2023; Wamba et al., 2021). The effects of dynamic employee capabilities, FinTech, and innovative work behavior on employee and supply chain performance in the Vietnamese financial industry were analyzed in terms of impact (Phan et al., 2022).
The “prediction” and “adoption” approaches have been gaining cult in the FinTech market. Using structural modeling equations and neural networks has become popular in the financial sector by researchers aiming to identify users’ behavior intentions of digital services. The “single” methods have a fundamental difference in relation to the mixed approach, as they are based on a single approach and depend on a single model built based on the acquired knowledge. For example, partial least squares structural equation modeling was used to understand the intention of behavioral use of the FinTech services by companies, a causal-predictive analysis (Irimia-Diéguez et al., 2023). A correlation-regression analysis scenario method for forecasting was used to describe the number of FinTech companies in the finance sector (Taujanskaitė & Kuizinaitė, 2022). In addition, a deep learning-based prediction model is implemented to predict the price movement of fund classes based on the classification results in China FinTech (Chen et al., 2021).
The unified technology acceptance and utilization theory (UTAUT) model has been used to investigate behavioral intentions in several contexts. Recently, studies examined the behavioral intentions of extension professionals from two extension systems to foster the adoption of precision farming (Lee et al., 2023). Prior studies have examined the factors that impact the acceptance of mobile learning technology for 21st-century skills-based training among teachers in Saudi Arabia and Pakistan (Dahri et al., 2023).