1. Introduction
In recent years, in order to solve the financing difficulties of small- and medium-sized enterprises (SMEs), the supply chain finance (SCF) business has been vigorously developed. Among them, the finance, transportation and warehouse (FTW) financing model, which takes enterprise inventory as collateral, is favored due to its strong availability of collateral and its ability to bring more business to third-party logistics enterprises (3PLs) [
1]. Although the intervention of 3PLs has built a bridge between financial institutions (FIs) and SMEs, reducing information asymmetry between each other and providing convenience for financing businesses [
2], there are still many problems in the implementation process of FTW financing, such as repeated pledge, insufficient supervision, market value fluctuations, etc., and the implementation process is lengthy and cumbersome [
3,
4]. When SMEs have relative information advantages with 3PLs, they may also collude and conduct fraud [
5]. Among them, repeated pledge causes the greatest degree of risk loss to FIs, which is the most unfavorable aspect for the benign development of financing businesses. For example, in the famous “Shanghai Steel Trade Case”, Shanghai Shensai Materials Co., Ltd. used the same collateral to obtain loans from Huaxia Bank and Industrial and Commercial Bank of China, impacting the rights and interests of Industrial and Commercial Bank of China, which later executed the rights and interests. The reason for this is due to information asymmetry.
With the development of blockchain technology (BT), there have been innovations in the traditional financing model, and the construction of online service platforms can directly provide convenient query methods for those demanding information [
6]. At present, BT is widely applied in the supply chain field. The supply chain accounts receivable service platform launched by Pingan Bank provides accounts receivable management, trading, and other services for CEs and their upstream SMEs participating in the SCF business [
7]. The microenterprise chain platform jointly built by Tencent and Linklogis truthfully and completely records the entire process of asset listing, circulation, splitting, and redemption based on accounts payable of CEs through blockchain [
8]. They are all based on the accounts receivable financing model in SCF, with less involvement in the FTW model. BT has the characteristics of immutability, decentralization, and traceability [
9], which can increase information transparency, simplify information flow, and reduce credit and operational risks in the financing process [
10]. Collaborating with partners in different fields can generate higher business efficiency and benefits [
11]. Shi et al. [
12] proposed that FIs should increase cooperation with online platforms such as blockchain in order to facilitate the development of their business. Some scholars believed that the application of BT in the SCF field can bring huge benefits to FIs and financing enterprises [
13,
14].
Evolutionary game (EG) theory originated from the biological evolution theory [
15]. It holds that participants are bounded rational and have limited information ownership, and they choose strategies through a certain transmission mechanism [
16]. Supply chain financing generally has information asymmetry and is a financing activity involving multiple entities. Scholars often use the EG method to study their strategic interaction process. In the field of SCF, this includes accounts receivable pledge financing [
17] and factoring financing [
18], with less involvement in the field of FTW financing. Recently, scholars have begun to study SCF by combining BT, but this has also focused on accounts receivable pledge financing and factoring financing [
19,
20]. The strong availability of collateral in FTW, a type of SCF financing model, means that we should not overlook the importance of studying it. Therefore, the motivation of this study was based on the empowerment capability of BT. The aim is to construct a strategic evolution model of FTW financing and to solve the problem of repeated pledge in the financing process of FTW so as to reduce the risk of losses to FIs participating in the financing business and promote the benign development of the FTW business.
The main contributions of this paper are as follows: (1) We have paid attention to the significant risks of repeated pledge in FTW. Through the perspective of blockchain empowerment, this study investigated the dynamic evolution of repeated pledge in FTW to contribute to the resolution of risks. (2) Based on the perspective of blockchain empowerment, a tripartite EG model involving FIs, SMEs, and 3PLs was constructed to address the issue of repeated pledges, which fully and comprehensively considers the dynamic behavior strategies of major players in the system. (3) Through stability analysis and simulation of the game results, the driving factors for FIs to access the blockchain platform and the influencing factors for SMEs and 3PLs to choose the active financing strategy of FTW were identified. (4) According to the research results, policy recommendations are proposed to promote the conscientious development of the FTW financing business.
The rest of this paper are arranged as follows.
Section 2 is the literature review.
Section 3 outlines the construction of the tripartite EG model.
Section 4 explains the stability analysis.
Section 5 provides details of the numerical simulation. The results, suggestions, and future work are described in
Section 6.
5. Simulations
In order to observe the sensitivity of FIs, SMEs, and 3PLs to the key parameter changes more intuitively, this study used MATLAB R2022a software for numerical simulation to explore the key influencing factors of each player’s strategy selection. The parameters were assigned as follows: , , , , , , , , , , , , , , , , and . Without losing generality, the probability of selecting strategies for each subject in the initial condition setting of the simulation process is 0.5. The sensitivity of one variable was simulated while keeping the values of other variables unchanged.
5.1. The Influence of Initial Probability
In order to study the influence of initial strategy selection probabilities of each participant on other participants, we assigned values of 0.1, 0.3, 0.5, 0.7, and 0.9 to each initial probability. To ensure the consistency of the result output, when exploring the influence of changes in the initial strategy selection probability of one player, the probability of other players was kept at 0.5. The simulation results are shown in
Figure 6. It can be seen that the greater the probability of FIs accessing the platform, the more likely SMEs are to make a good-faith pledge and the more inclined 3PLs will be to not cover. The higher the probability of a good-faith pledge by SMEs, the slower the speed at which FIs choose to access the platform and the faster the speed of 3PLs choosing not to cover when it is unprofitable. The higher the probability that 3PLs choose not to cover, the slower the speed of FIs choosing to access the platform and the faster the speed at which SMEs choose to pledge with integrity. It can be inferred that FIs accessing blockchain platform is effective in facilitating SMEs and 3PLs to choose positive financing strategies. From the simulation results, it can also be seen that the time for SMEs to evolve into a good-faith pledge is always longer than the time for 3PLs to evolve into not cover. From this, it can be inferred that the choice of the pledge strategy of SMEs depends to a certain extent on the strategy choice of 3PLs, manifested as a state that is followed closely behind. Therefore, enhancing the enthusiasm of 3PLs to not cover can effectively avoid repeated pledge behavior of SMEs, thereby reducing financing risks.
5.2. The Influence of Blockchain Access Cost
The evolution trajectory of FIs is shown in
Figure 7. We can see that if the cost of accessing blockchain is lower than the cost of conducting FTW financing business, FIs will eventually tend to access blockchain, and the lower the cost, the faster FIs will tend to access blockchain. When the cost of accessing blockchain is greater than the cost of conducting FTW financing business, FIs will eventually tend to not access blockchain, and the higher the cost, the faster FIs will tend to not access blockchain. It can be seen that when the cost of accessing blockchain is lower than the threshold (FTW business development cost), FIs eventually tend to adopt the strategy of “access blockchain”.
From the above sensitivity analysis, we can conclude that the blockchain accessing cost significantly affects the choice of strategy of FIs and whether FIs accessing blockchain will have a direct impact on the repeated pledge behavior of SMEs and 3PLs. Therefore, the sensitivity of the change in key parameters of the simulation of SMEs’ and 3PLs’ strategy selection can be distinguished by whether FIs access blockchain or not. When FIs choose to access blockchain, the value of is 10, and when they do not access blockchain, the value of is 50. The sensitivity of additional income by repeated pledge and reward by honest pledge was simulated as was the sensitivity of additional income by covering and reward by not covering.
5.3. The Influence of Additional Income of Repeated Pledge
The evolution trajectory of SMEs and 3PLs are shown in
Figure 8 and
Figure 9 when the additional income of repeated pledge changes. It can be seen that the additional income of repeated pledge is sensitive to the influence of SMEs’ strategy selection. As shown in
Figure 8a, with the increase in additional income from a repeated pledge, the speed at which SMEs tend to adopt good-faith pledge slows down. When the additional income from a repeated pledge exceeds a certain threshold, SMEs’ strategy selection shows a trend of repeated pledge, and the larger the additional income, the more obvious the trend. However, ultimately, it stabilizes to good-faith pledge due to FIs choosing to access blockchain. As shown in
Figure 9a, as FIs choose to access blockchain, the impact of additional income of repeated pledge on 3PLs is not obvious. As shown in
Figure 8b, with the increase in additional income from a repeated pledge, the speed at which SMEs tend to adopt good-faith pledge slows down. When the additional income from a repeated pledge exceeds a certain threshold, SMEs finally choose repeated pledge, and the larger the value, the faster they tend to choose repeated pledge. As shown in
Figure 9b, 3PLs seem to be followers of SMEs. If SMEs pledge in good faith, 3PLs will not cover; if SMEs pledge repeatedly, 3PLs will cover. Therefore, in the blockchain model, as the additional income of repeated pledge increases, the model eventually stabilizes to “access blockchain”, “good-faith pledge”, and “no cover”. In the traditional model, when the additional income of repeated pledge increases but falls below a certain threshold, the model eventually stabilizes to “do not access blockchain”, “good-faith pledge”, and “not cover”. If it exceeds a certain threshold, the model eventually stabilizes to “do not access blockchain”, “repeated pledge”, and “cover”.
5.4. The Influence of Additional Income of Covering
Figure 10 and
Figure 11 show the evolution trajectory of 3PLs and SMEs. When FIs access blockchain, the smaller the additional income from covering, the faster 3PLs tend to not cover. They eventually stabilize to the “not cover” strategy due to FIs choosing to access blockchain (
Figure 10a). When the additional income obtained for covering gradually increases below a certain threshold, SMEs tend to slow down the speed of the good-faith pledge. When they gradually increase above a certain threshold, SMEs tend to accelerate the speed of the good-faith pledge (
Figure 11a). When FIs do not access blockchain, the smaller the additional income from covering, the faster 3PLs tend to not cover. When the additional income from covering exceeds a certain threshold, 3PLs will stabilize to the “cover” strategy. However, when the additional income from covering is large, exceeding the additional income from SMEs’ repeated pledge, 3PLs will eventually stabilize to the “not cover” strategy (
Figure 10b). For SMEs, when the additional income of covering exceeds the additional income of repeated pledge by SMEs, SMEs will quickly stabilize to the “good-faith pledge” strategy (
Figure 11b). Because the benefit of covering obtained by 3PLs comes from SMEs, when the covering cost paid by SMEs is high, the net benefit of repeated pledge that SMEs can obtain will decrease. The repeated pledge collusion behavior of SMEs and 3PLs cannot reach an agreement, and 3PLs will quickly stabilize to the “not cover” strategy. Therefore, in the blockchain model, no matter how much additional income is obtained by 3PLs from covering change, the model eventually stabilizes to “access blockchain”, “good-faith pledge”, and “not cover”. In the traditional model, the model eventually stabilizes to “do not access blockchain”, “good-faith pledge”, and “not cover” when the additional income obtained by 3PLs from covering are small or too large, and at an appropriate intermediate value, the model eventually stabilizes to “do not access blockchain”, “repeated pledge”, and “cover”.
5.5. The Influence of Penalty of Repeated Pledge
The evolution trajectory of SMEs and 3PLs under repeated pledge penalty changes are shown in
Figure 12 and
Figure 13. As can be seen from
Figure 12a, when accessing blockchain, the greater the penalty for repeated pledge, the faster SMEs will achieve good-faith pledge. When the penalty for repeated pledge is very small, SMEs tend to exhibit a trend of repeated pledge but eventually stabilize to good-faith pledge because of the blockchain model. As shown in
Figure 13a, the repeated pledge penalty has little impact on 3PLs. As can be seen from
Figure 12b, in the traditional FTW financing model, the greater the penalty for repeated pledge, the faster SMEs tend to make a good-faith pledge. When the penalty for repeated pledge is lower than a certain threshold, SMEs will eventually tend to make a repeated pledge. However, for 3PLs (as shown in
Figure 13b), when the repeated pledge penalty is higher than a certain threshold, it eventually stabilizes to the “not cover” strategy. When the repeated pledge penalty is lower than a certain threshold, it eventually presents a fluctuating and unstable state. Therefore, in the blockchain model, regardless of how SMEs’ repeated pledge penalty changes, the model eventually stabilizes to “access blockchain”, “good-faith pledge”, and “not cover”. In the traditional model, when SMEs’ repeated pledge penalty exceeds a certain threshold, the model eventually stabilizes to “do not access blockchain”, “good-faith pledge”, and “not cover”, and if it is below a certain threshold, the model has a fluctuating, unstable state.
5.6. The Influence of Penalty of Covering
The evolution trajectories of 3PLs and SMEs when covering penalty changes are shown in
Figure 14 and
Figure 15. As shown in
Figure 14a, the greater the covering penalty, the faster 3PLs will tend to not cover, but this effect is not obvious in the blockchain model. Similarly, the sensitivity of SMEs to cover penalties is not significant (as shown in
Figure 15a). As can be seen from
Figure 14b, under the traditional FTW model, the greater the covering penalty, the faster 3PLs will tend to not cover, but the effect is particularly obvious. When the covering penalty is below a certain threshold, 3PLs tend to the “cover” strategy, and the speed of this trend increases as the penalty decreases. For SMEs, the willingness of 3PLs to cover means that they have an opportunity to take advantage of it. Therefore, when the cost of covering is below a certain threshold, SMEs will definitely choose the strategy of “repeated pledge”. When the cost of covering exceeds a certain threshold, SMEs will try to make a repeated pledge. However, because 3PLs are stable in the strategy of “not cover”, they finally choose the “good-faith pledge” strategy, which can be seen from
Figure 15b, and the time for SMEs to reach stability is longer than that for 3PLs. Therefore, in the blockchain model, no matter how the covering penalty changes, the model eventually stabilizes to “access blockchain”, “good-faith pledge”, and “not cover”. In the traditional model, when the covering penalty exceeds a certain threshold, the model eventually stabilizes to “do not access blockchain”, “good-faith pledge”, and “not cover”, and if it is below a certain threshold, the model eventually stabilizes to “do not access blockchain”, “repeated pledge”, and “cover”.
5.7. Discussion of Simulation Results
Through the above simulation analysis, it can be seen that the blockchain access cost is the main factor affecting the choice of FI strategy. When the blockchain access cost is lower than the cost of developing the FTW financing business, FIs will choose to access blockchain. When FIs choose to access blockchain, SMEs and 3PLs cannot collude to repeated pledges, which can reduce risk to FIs. At this point, no matter how much additional income is obtained from a repeated pledge and covering the pledge, they will eventually stabilize to the ideal stability strategy of “good-faith pledge” and “not cover”. The EG model will finally stabilize to “access blockchain”, ”good-faith pledge”, and “not cover”. When FIs do not choose to access blockchain, the EG model may eventually stabilize to two states: (1) “do not access blockchain”, “good-faith pledge”, and “not cover” and (2) “do not access blockchain”, “repeated pledge”, and “cover”. At this point, enhancing the enthusiasm of 3PLs to not cover can effectively avoid repeated pledge behavior of SMEs. When both SMEs’ repeated pledge and 3PLs’ decision to cover can obtain great additional income, they will eventually choose repeated pledge and cover, but the additional income of 3PLs’ decision to cover cannot exceed the income of SMEs’ repeated pledge. Without accessing blockchain, FIs can increase the punishment for dishonest behavior by SMEs and 3PLs, which can play a positive incentive role.
6. Conclusions
6.1. Results
To solve the problem of repeated pledge in the process of FTW financing, this study built an EG model based on BT involving FIs, SMEs, and 3PLs. Through theoretical deduction and simulation results, some conclusions were made as follows.
First, the factors that drive FIs to access the blockchain platform are the blockchain access cost being lower than the financing business development cost of FTW financing and the measures in the blockchain FTW financing model that prevent collusion and defaults by SMEs and 3PLs. According to the results of evolutionary equilibrium and the sensitivity of the change of the blockchain platform access cost, the strategy selection of FIs can be obtained. This is consistent with the research conclusion of Wang et al. [
39]. However, by establishing an EG model with the participation of 3PLs, this study considered the key participants involved in the default behavior. Although this finding came from constructing a tripartite EG of FTW, it is also applicable to any field that needs to combine BT to resolve information asymmetry because cost is an important issue that no industry can avoid.
Second, the driving force of a good-faith pledge by SMEs and decision not to cover by 3PLs is the punishment mechanism formulated by FIs under the traditional FTW model. This is similar to the research conclusion of Zhao et al. [
46]. From the equilibrium stability analysis and parameter sensitivity simulation, it can be seen that under the traditional model, FIs giving a larger penalty for breach of contract can reduce the probability of defaults by SMEs and 3PLs, and both of them stabilize to the ideal state of “good-faith pledge” and “not cover”, respectively. The difference of this study is that we conducted not only theoretical analysis of the equilibrium results but also the numerical simulation. This is a general rule that can be applied to any field. Because the function of the punishment mechanism is to form a restraining effect on the subject’s breach of contract, when this degree of restraint is reached, the subject will naturally choose to keep the agreement.
Third, under the blockchain FTW financing model, SMEs will stabilize to the “good-faith pledge” strategy, while 3PLs will stabilize to the “not cover” strategy. This is consistent with the idea that blockchain can form a strict regulatory environment to prevent the occurrence of defaults, as was obtained by Sun et al. [
19]. Theoretical analysis shows that under the traditional FTW financing model, SMEs and 3PLs may choose the nonideal financing strategy of repeated pledge and cover or the ideal financing strategy of honest pledge and not cover due to differences in additional income, penalties, and incentives for breach of contract. And the blockchain FTW model will only stabilize at the ideal strategy. The results of sensitivity analysis were also consistent.
Fourth, under the traditional FTW financing model, increasing the additional income of 3PLs to cover the repeated pledge behavior of SMEs can destroy their cooperation in collusion behavior. The results of sensitivity analysis show that when the additional income obtained by 3PLs’ cover behavior exceeds the additional income obtained by the repeated pledge behavior of SMEs, 3PLs will quickly change from covering to not covering strategy. This is because the additional income of 3PLs’ cover comes from the additional income obtained from the repeated pledge behavior of SMEs. In the real world, any participant in the cooperation will carry out benefit distribution. When the benefit distribution is uneven or unreasonable, cooperation will naturally collapse, which is applicable to any cooperation business.
6.2. Suggestions
Based on the above research results, this paper puts forward the following policy suggestions to promote the sound development of the FTW financing business.
First, there should be preferential access fees for FIs to the blockchain platform. Although the blockchain platform has advantages in the supervision of default behaviors in the FTW financing business, FIs will only choose to access the blockchain platform when the cost of access is lower than the cost of developing the FTW financing business. Therefore, measures such as reducing the average cost of use through trial offers, publicity, and promotion of BT to increase the number of users can motivate FIs to use BT.
Second, FIs should increase rewards for 3PLs for honest behavior of not providing cover. The reason 3PLs cover the repeated pledge behavior of SMEs is to obtain more additional income. When 3PLs require a large amount of earnings, the additional income from the repeated pledge behavior of SMEs will decrease and the two parties cannot reach an agreement. Therefore, an increase in FIs’ reward for not covering can indirectly increase the additional income expectation of 3PLs for the cover behavior and destroy the collusion between 3PLs and SMEs. In order to reduce the cost to FIs, the reward can be a long-term principal–agent agreement signed between FIs and 3PLs.
Third, information transparency and authenticity of the blockchain platform must be constantly enhanced. Although BT is immutable and decentralized, it can supervise the default behaviors of SMEs and 3PLs. However, when the coverage of blockchain platform users is narrow and the uploaded information is not true, the supervision effect will be greatly reduced. Therefore, information transparency and authenticity should be enhanced through integration with the internet, Internet of Things, and intelligent terminals and the establishment of a strict information uploading system.
6.3. Limitation of the Research
Firstly, although the tripartite EG model established in this paper obtained the factors that drive FIs to access the blockchain platform and the key factors that influence SMEs and 3PLs to choose the active FTW financing strategy, only the repeated pledge by SMEs and 3PLs covering it were considered in the construction of the model. In the process of FTW financing, the default behaviors of SMEs also include nonrepayment, false pledges, etc., and the default behaviors of 3PLs also include goods damage caused by lax supervision and wrong evaluation of the pledged inventory value. Secondly, in the model assumptions, referring to [
39], this study assumed that SMEs cannot carry out repeated pledge behavior under the blockchain FTW model. Although the immutable characteristics of BT can effectively prevent some dishonest behaviors, the correct degree of source information and the degree of information sharing of various subjects are worth thinking about. In addition, in the simulation experiment, the initial parameter assignment of the variable was carried out based on the evolutionary stability condition combined with the real situation, and the validity of the model was only verified theoretically. Finally, in the process of model construction, the influence of uncertain factors, such as group sentiment changes, social interests, and policy changes, on the model was not considered.
6.4. Potential Directions for Further Research
Based on the limitations of the research, several possible future research directions are proposed. Firstly, the EG model can be further improved based on the default behavior of the participants in the FTW financing business. Secondly, by describing information variables such as information authenticity and information sharing amount, combining them into the construction process of the payment matrix, and considering the disturbance of stochastic perturbation to EG, Gaussian white noise can be introduced into the deterministic EG replication dynamic model and a stochastic EG replication dynamic model can be constructed. Finally, by researching relevant practice case data, parameters can be assigned to the model, the simulation experiment can be completed, and a combination of theory and practice can be realized.