Analysis of Revenue Incentive Dynamic Mechanism of Financial Supply Chain from the Perspective of the Internet of Things

With the rapid development of society, all walks of life need the support of the Internet of Things, and the financial industry is no exception. This article integrates blockchain technology with supply chain finance and builds a supply chain financial alliance architecture based on blockchain technology and an underlying model of the Ethereum blockchain system suitable for supply chain finance. We innovated new supply chain finance models and operating mechanisms and proposed business scenarios for supply chain finance from the perspective of blockchain. Taking into account the actual operation of the blockchain supply chain financial platform, the principal-agent model and the incentive theory are applied, and the supply chain financial accounts receivable model is taken as an example in the case of complete information and incomplete information. The incentive mechanism between the service provider of the chain supply chain financial platform and the core enterprise promotes the better implementation of blockchain technology and supply chain finance. Based on the existing theoretical research, this paper identifies the key influencing factors of the supply chain’s cross-enterprise incentive mechanism. These influencing factors system includes two dimensions: transaction factors and relationship factors. Transaction factors include resource dependence, uncertainty, and cooperation experience; relationship factors include corporate reputation, trust level, and relationship commitment. Based on the nature of the incentive mechanism, information sharing and revenue sharing are extracted as the measurement dimensions of the supply chain’s cross-enterprise incentive mechanism. On this basis, this article draws on the existing enterprise life cycle division method and constructs a hypothetical model of the influencing factors of the incentive mechanism in the incubation period, the growth period, and the maturity period. Relevant data was collected through questionnaires, and SPSS and AMOS software were used to perform statistical analysis, reliability analysis, exploratory factor analysis, confirmatory factor analysis, and structural equation hypothesis testing on the data. The performance of each influencing factor in different stages of the enterprise’s life cycle and the importance of each influencing factor in the same life cycle stage are obtained.