Table of Content

25 November 2023, Volume 32 Issue 11

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Decision Making and Optimization in the Digital Economy Era

Supply Chain Financing Strategies of Platforms under Uncertain Demands and Competitive Markets

YI Zelong, ZHANG Huijun, FU Yelin, LAI Kinkeung

2023, 32(11):  5-11.  DOI: 10.12005/orms.2023.0346

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The global economy is increasingly characterized by digitization and informatization. In the digital era, the integration of digital platform and supply chain is a promising direction for which the financial services of digital platforms are widely used in companies such as Tmall’s financing plan, Alibaba’s digital supply chain scheme as well as B2B and B2C transactions. Different from bank financing and credit financing, the digital platform provides online sales channels for suppliers and becomes a common party in the supply chain. Meanwhile, the platform holds data, technology and capital, and becomes a supplier of funds. With a closer combination of platform and supply chain, in the financing area, how to balance the functions of financing providers and sales channels at the same time, and how to design a perfect transaction credit contract and achieve the ultimate optimal equilibrium, are of great practical significance. Based on these, we propose the research question as follows:
1)What is the impact of capital structure of suppliers on the optimal operational decision-making and profits of each participant,namely,will the platform choose financing?
2)From the view of platform, how to control the interest rate and commission fee to make a larger production and a higher profit?
In this paper, we construct a supply chain model consisting of a digital platform and two competing suppliers to examine the impacts of direct financing of the platform on profits of supply chain members and the platform. We set up a random demand environment and a competitive market structure. The demand follows a downward sloping function and the market size is random; on the other hand, two suppliers make their own production decisions and form a Cournot competition in horizontal level. The suppliers’ funding structure is also an important influencing factor in our game. As such, we consider four cases of symmetrical and sufficient capital structure, symmetrical and insufficient capital structure and asymmetric capital structure. In addition, as the leader of supply chain, the platform can flexibly design the financing interest rate and the commission rate firstly, and then sign the financing contract with the suppliers requiring that after the completion of sales, it can obtain the interest and part of the sales revenue. If the suppliers eventually default, the platform will receive all the sales revenue. As for methodology, we use backward induction to find the production equilibrium of the suppliers and the optimal profit of each participant. Then, we make numerical simulation to analyze the influence of exogenous factors such as platform commission rate and product substitution rate on supply chain equilibrium output, the suppliers’ equilibrium profits and optimal platform profit under different scenarios.
Through theorical analysis and numerical study, the results show that the platform can always make more profits by supporting the financially short-running suppliers, and the platform prefers suppliers with weaker financial positions. The platform has a tendency to reach cooperative financing contracts with both suppliers at the same time, because the profit of the platform created by financing two supplyers is more than that by only one. Platform commissions have two opposite effects onsuppliers’production quantity decisions: a lower commission rate can incentivize well-funded suppliers to produce more, while the production quantity of underfunded suppliers increases as commission rate rises. In terms of competition intensity, a smaller product differentiation of suppliers would bring more intense competition, which negatively influences the suppliers’production incentive. There is a negative correlation between the supplier’s profit and the platform commission. Obviously, the higher the commission fee, the greater the exploitation of suppliers by the platform. Then, a larger part of the suppliers’revenue flows to the platform, while production is reduced and profit decreased for suppliers. The above conclusions can provide useful implications for digital platform financing.

Research on Manufacturer’s Encroachment Strategy When Considering the Dual Goals of Retailers

CAO Yu, SONG Deshan, YI Chaoqun

2023, 32(11):  12-17.  DOI: 10.12005/orms.2023.0347

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With the rapid development of e-commerce, more and more manufacturers have begun to open direct sales channels to sell products directly to downstream consumers, thereby directly competing with retailers. By establishing direct sales channels, manufacturers can avoid retailers, thereby understanding market demand and consumer feedback more quickly, and then adjusting product production and marketing strategies in a timely manner to improve their market response speed. Retailers are more likely to pursue consumer benefit goals than manufacturers because they are closer to downstream consumers.
This paper constructs a supply chain consisting of for-profit manufacturers and retailers pursuing dual goals, and studies the impact of retailers’ dual goals on manufacturers’ channel encroachment strategies. A supply chain composed of for-profit manufacturers and retailers is constructed, in which the retailer has the dual goals of pursuing economic benefits and social benefits (consumer surplus), while the manufacturer decides whether to conduct channel encroachment and studies the impact of retailers’ social target preferences on manufacturers’ channel encroachment strategies. In the manufacturer’s non-channel encroachment mode (strategy N), the profit-oriented manufacturer makes green investments in products and sells the products at wholesale prices to retailers with dual objectives, and the retailers make decisions based on wholesale prices,order quantities of products and sell the products to consumers at sales prices. In the manufacturer channel encroachment model (Strategy E), in addition to retail channels, manufacturers can also choose to sell products directly to consumers through their direct sales channels (supplier encroachment). Under this model, manufacturers will make green investments in products and sell products at wholesale prices to retailers with dual goals. Retailers will decide the quantity of products to order based on wholesale prices. At the same time, manufacturers will decide the sales quantity of their direct sales channels, and manufacturers and retailers and sell products to consumers at sales prices.
The study has found that the retailer’s preference for consumer surplus and the unit sales cost of the manufacturer’s direct sales channel are the key factors affecting the encroachment of the manufacturer’s channel. When the unit sales cost of the manufacturer’s direct sales channel is lower, the manufacturer will always choose channel encroachment. However, when the unit sales cost of the manufacturer’s direct sales channel is higher than that on the given threshold, the higher the retailer’s preference for consumer surplus, the higher the manufacturer’s preference for consumer surplus. Business is more inclined to channel encroachment,that is to say, as the retailer’s preference for consumer surplus increases, the manufacturer will choose not to conduct channel encroachment and channel encroachment in sequence. Consumers’ sensitivity to green investment levels will also affect manufacturers’ channel encroachment strategies. If the level of consumers’ green investment is lower than that on a given threshold, the manufacturer’s channel encroachment strategy depends on the retailer’s preference for consumer surplus. At this time, as the retailer’s preference for consumer surplus increases, the manufacturer will choose not to engage in channel encroachment. But when consumers are more sensitive to green investment levels, manufacturers will always choose channel encroachment.

Study on Strategies Selection of Retailer’s E-coupon Distribution Considering the Influence of Channel Preference and Cross Elasticity

TIAN Yingdong, YANG Wensheng, HOU Xinru, CHEN Mengze

2023, 32(11):  18-25.  DOI: 10.12005/orms.2023.0348

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The popularization of the Internet, the advancement of information technology, and the development of e-commerce have given rise to online retail channels. The dual-channel consisting of offline and online plays a significant role as an intermediary in facilitating transactions between retailers and consumers, bringing numerous conveniences to shoppers. However, as society gradually transforms from the information age to the digital age, consumers’ shopping habits undergo a comprehensive transformation, and competition in the retail industry becomes increasingly fierce. Obviously, the dual-channel retail model can no longer meet the needs of companies for normal operations and competitiveness, and the entire retail industry is actively exploring new retail models that can support future development. At this time, as a new retail model that can promote the integration of online and offline development while providing consumers with a all-round and seamless shopping experience, omni-channel retail has rapidly gained application and practice in the industry, especially the “Buy-Online-Pick-up-in-Store”(BOPS) model. Retailers sell their products through dual-channel and omni-channel approaches inevitably triggers intense market competition. Although price is widely used as a common competitive tool, a single pricing strategy cannot effectively identify consumer segments or flexibly implement differential pricing. Coupons, as an effective tool for price regulation and channel coordination, can form a dual pricing mechanisms in conjunction with consumer price discrimination and market segmentation, in order to increase sales volume and expand market share by enhancing pricing flexibility, thus helping retail enterprises to maintain an advantageous position in the competition. This study can effectively guide retail enterprises to conduct omni-channel practices and enhance their competitiveness when facing various complex situations.
Dual-channel and omni-channel retailers achieve differential pricing across different channels by distributing coupons. Firstly, we construct a duopoly Bertrand game model to optimize the retailer’s single coupon distribution strategies by solving the optimal prices and coupon face values as binary decision variables for both dual-channel and omni-channel retailers. Secondly, we construct an asymmetric evolutionary game model for a two-population of retailers based on the channel quantity characteristics. This model is aimed to explore the long-term evolutionarily stable coupon distribution strategies of the retailer population. Finally, we make a sensitivity analysis of the evolutionarily stable strategies based on channel preferences and cross-price elasticity parameters. We also depict the evolutionary trends of retailer prices, coupon face values, consumer actual payments, and profits under the scenarios of pure strategies and mixed strategy evolutionarily stable points.
　　This paper has several interesting research findings. Specifically, when dual-channel retailers choose not to distribute coupons, they may gain substantial unit profits but remain at a disadvantage compared to omni-channel retailers. On the other hand, by distributing coupons, dual-channel retailers can intensify competition and actually gain a profit advantage. The evolutionary game model shows the existence of four groups of dual-population and one group of single-population pure strategy evolutionarily stable points. Due to the significant differences in consumer actual payments caused by whether coupons are distributed, which subsequently affects retailer profits, a mixed strategy equilibrium cannot be reached. The evolutionarily stable coupon distribution strategies are constantly adjusting dynamically to changes in channel preferences and cross-price elasticity coefficients. The continually evolving and updating coupon distribution strategies result in differentiated pricing across retail channels, leading to varying levels of competitiveness. As the competition becomes more intense, the evolutionarily stable prices and coupon face values decrease. However, the evolutionarily stable consumer actual payments increase. When differential pricing is implemented across all channels, it will be easy for two population retailers to be passively trapped in a lose-lose “prisoner’s dilemma” situation, resulting in a serious loss of evolutionary profits.

Third-degree Price Discrimination Based on Sellers’Homing Choices in Duopoly Competition between Digital Platforms

ZHU Feng, LI Guopeng, CAO Zhigang

2023, 32(11):  26-32.  DOI: 10.12005/orms.2023.0349

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The digital economy has become an increasingly pivotal sector in the growth trajectory of China’s overall economy. Digital platforms serve as the principal arenas for transactions within this digital economy, effectively acting as its backbone. While the implementation of stringent antitrust regulations has increasingly limited the capacity of these platforms to openly enforce exclusivity contracts, digital platforms still retain the ability to subtly influence users through the traditional economic tactic of discriminatory pricing. This approach is more covert. This paper aims to investigate the impact of price discrimination by digital platforms, particularly targeting sellers based on their affiliations, as a more covert means of influencing user behavior.
This paper develops a competitive model for duopolistic digital platforms where both sellers and buyers exhibit heterogeneous preferences. We explore the impact on social welfare and platform pricing strategies when sellers and buyers can opt for multi-homing—that is, joining multiple platforms. Specifically, the model investigates the effects of third-degree price discrimination by the platforms based on whether sellers choose to multi-home or not. To capture the preferences of sellers and buyers, we employ the Hotelling model and position the duopolistic platforms at the two trisection points on the Hotelling line. This approach allows us to mitigate the interference caused by varying preferences when investigating the impact of price discrimination.
The findings indicate that third-degree price discrimination leads to an increase in seller surplus while simultaneously reducing buyer surplus and overall social welfare. The primary reason for this outcome is that price discrimination intensifies competition between the duopolistic platforms for sellers, thereby driving down the prices set for them.From the perspective of sellers, the duopolistic platforms find themselves in a Prisoner’s Dilemma, intensifying competitive pressures. These escalated competitive forces subsequently cascade to the buyers, ultimately burdening buyers who are not subject to price discrimination. Specifically, the platforms raise Prices targeted at these buyers, resulting in a reduction in buyer surplus. The third-degree price discrimination also alters the platforms’pricing strategies for both sellers and buyers, giving rise to skewed pricing phenomena: both platforms lower prices for sellers and raise prices for buyers, while simultaneously reducing the number of sellers and buyers that join both platforms.An increase in the number of sellers (or buyers) joining a platform leads to enhanced positive externalities for buyers (or sellers), thereby improving overall social welfare. However, the price reductions induced by platform price discrimination fail to increase the number of sellers joining the platforms. Instead, they elevate the costs of multi-homing for sellers, leading to a reduction in their numbers and a consequent detrimental impact on social welfare. These conclusions hold stable under the conditions of asymmetry between sellers and buyers, as well as varying levels of service heterogeneity provided by the platforms.
In future research, we will examine the impact of third-degree price discrimination by platforms based on whether sellers are multi-homing, specifically focusing on its impact on platform profitability. We intend to analyze this impact using methods that do not rely on explicit solutions. Furthermore, we aim to distill economically meaningful and more concise conditions to ensure the existence and uniqueness of symmetric equilibria. Additionally, we will explore the effects of price discrimination on user surplus and social welfare under alternative distributions of user preferences.

Analysis of Platform Subsidy Strategy under the Shrinking Growth Rate of Users

LIU Zhuoqi, PENG Geng

2023, 32(11):  33-39.  DOI: 10.12005/orms.2023.0350

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The accelerated evolution of digital platforms catalyzes profound transformations in production modalities, commercial protocols, and the distribution and refinement of industrial assets. This has contributed to successful platform-centric enterprises in the marketplace. Subsidies by e-commerce platforms have attracted attention nationwide.During an initial expansion of a platform, subsidy strategy generally promotes rapid growth, allowing the platform to quickly take the market share. Nevertheless, as the platform reaches maturation, it has raised a question regarding the sustainable advantage of a predatory subsidy approach. Drawing upon principles from economics and management studies, this article investigates the following questions: Firstly, does the magnitude of subsidy strategy profoundly influence the velocity of platform escalation? Secondly, amidst the mechanisms wherein subsidy strategy stimulates platform evolution, which mitigating variables could temper this progression? Thirdly, does the strategy of subsidy hold empirical merit? Fourthly, what determinants shape the sustainability of a subsidy strategy approach?
This research formulates a model encapsulating the dynamics of a monopolistic platform landscape, and carefully analyzes the sustainability of aggressive subsidy tactics. From an economic vantage point, this paper scrutinizes the selective pricing modalities embraced by platform-centric enterprises. By making a framework in a bilateral marketplace, including e-commerce infrastructures, vendors, and digital clientele, the investigation suggests, via model refinement and elucidation, the determinants modulating subsidy paradigms by online commerce establishments. This paper proposes a paradigm of a monopolistic platform marketplace to carefully examine the viability of subsidy stratagems. It scrutinizes the disparate pricing stratagems by platform enterprises through an economic lens. Within a monopolistic marketplace,considering a diminishing influx of new users, this paper quantitatively assesses the ramifications of subsidies including three scenarios: customary charges to users, inducements for newcomers, and the partial exodus of seasoned users. Our investigation suggests that the determinants, including the conversion metrics of user incentives, the cumulative expenditure in formulating user subsidy profiles, and the chronology of subsidies, influence the efficacy of the stratagem. The inquiry includes: Firstly, within a monopolistic market framework, the incentives increase with the rising conversion metrics of novice users. Secondly, a predatory inducement stratagem is embraced when the fiscal outlay for user profiling remains beneath a designated benchmark. Thirdly, predatory incentive stratagems predominantly operate on an early market phases, and their merits decrease over temporal progression. Grounded upon these findings, we proffer the subsequent suggestions: for government, emphasis should be placed on championing platform autonomy, and thwarting undue bureaucratic encroachments. Moreover, against the background of digitalized economy, oversight agencies must bolster their supervisory rigor to safeguard the rights of consumers. Pertaining to platform, perpetual vigilance towards market dynamics is imperative, acknowledging the latent prospects for market evolution, and maintaining profound cognizance of their incentive stratagem ramifications.

Research on the Business Model Evolution Strategy of High-tech Incumbent Enterprises under the Background of Digital Economy

CAO Luping, DING Xiaozhou, GUO Tao

2023, 32(11):  40-47.  DOI: 10.12005/orms.2023.0351

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The rapid development of digital economy has changed the business ecology that enterprises rely on for survival and development. As the important implementation subjects of national innovation-driven development strategy, high-tech incumbent enterprises are faced with a rapidly changing and uncertain environment full of opportunities and challenges. Therefore, high-tech incumbent enterprises need to continuously innovate business model to realize business model evolution, and then realize the dynamic match between business model and environment, so as to maintain and improve growth. Business model evolution has two evolution modes, including radical business model evolution and incremental business model evolution. Due to the great changes in the motivation and mode of business model evolution of high-tech incumbent enterprises under the background of digital economy, the evolution strategy has become an important issue that urgently needs an in-depth analysis. However, although the research on business model evolution has made some progress, there are still the following shortcomings: Firstly, the research on business model evolution in the new context of digital economy is far from enough. Secondly, the existing research on business model evolution mainly adopts the qualitative research method, by which it is difficult to describe the internal mechanism of business model dynamic evolution comprehensively, deeply and systematically. Thirdly, the research focusing on the choice of business model evolution strategy of high-tech incumbent enterprises based on the heterogeneities of micro-entities is particularly insufficient. Therefore, it is of great significance both in theory and practice to use evolutionary game analysis to explore the choice of business model evolution strategy of high-tech incumbent enterprises under the background of digital economy from a dynamic perspective based on the characteristics of enterprises.
Business model iceberg theory provides a theoretical framework for this study. Digital factors such as digital economy development level, digital capability and digital resource belong to the category of business model tacit knowledge. Introducing different digital factors into the research of business model evolution is helpful for deeply revealing the dynamic mechanism of choice of business model evolution strategy of high-tech incumbent enterprises under the background of digital economy. Therefore, rooted in the new context of digital economy, based on business model iceberg theory and evolutionary game theory and on the premise of cost differences between enterprises, this paper analyses the stable equilibrium strategy of business model evolution of enterprises under different situations by means of Stackelberg competition model and Cournot competition mode. On this basis, this paper discusses the evolution path and evolution stability of choice of business model evolution strategy of enterprises under the effects of digital economy development level, digital capability and digital resource through case analysis and simulation analysis. The results show that digital economy development level, digital capability and digital resource are the important factors affecting the choice of business model evolution strategy of enterprises. The higher the levels of digital economy development, digital capability and digital resource, the more inclined enterprises are to choose radical business model evolution. Under the same condition, enterprises with cost advantages are more willing to choose radical business model evolution than those with cost disadvantages. Digital economy development level, digital capability and digital resource have different effects on the choice of business model evolution strategy, among which digital economy development level has the most significant effect, followed by digital capability.
The marginal contributions of this paper are as follows: Firstly, this paper makes up for the shortage of a few existing researches on the business model evolution behavior of high-tech incumbent enterprises under the background of digital economy and on the basis of considering the heterogeneities of micro-entities, and provides a new perspective for the research of business model evolution behavior. Secondly, this paper supplements digital economy development level, digital capability and digital resource as the important contents of business model tacit knowledge, reveals the dynamic mechanisms of those three factors on the choice of business model evolution strategy of high-tech incumbent enterprises, and further enriches business model iceberg theory.
Based on the research conclusions, this paper puts forward the following policy suggestions: Firstly, managers of high-tech incumbent enterprises should combine their own professional knowledge to predict and weigh the expected benefits generated by the two business model evolution strategies under the effects of digital factors, and choose the most appropriate business model evolution strategy in line with the cost characteristics, resources and capabilities, development needs of enterprises and development of digital economy to realize the dynamic match between business model and environment. Secondly, high-tech incumbent enterprises need to predict the technical risk and market risk brought by the two business model evolution strategies in advance, take preventive measures to deal with different risks, and build a relatively stable business model that is compatible with technological innovation. Thirdly, high-tech incumbent enterprises should make full use of the favorable conditions created by digital economy to build digital resource space, improve digital capability, and then seize the opportunities of technological progress and market demand change, so as to realize business model evolution. Fourthly, when the condition is relatively mature, high-tech incumbent enterprises should actively use digital economy condition to support radical business model evolution, carry out pioneering innovation, and build core competitive advantage. When the condition of digital economy is insufficient, enterprises should gradually optimize business model through the iterative adjustment of business model to achieve sustainable development.

Game Analysis of Information Disclosure Based on Blockchain under the Ultra-long Advance Selling of E-commerce

WANG Yajing, LI Jian, ZHU Shichao, XIA Bing

2023, 32(11):  48-55.  DOI: 10.12005/orms.2023.0352

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Advances in information and communication technology have driven the rapid development of the digital economy, leading to profound changes in the consumption field. Among them, advance selling of e-commerce has attracted much attention and developed rapidly in recent years, forming a sales model of “collecting goods, reducing costs and selling accurately”. However, it has also caused problems.For example, the ultra-long pre-sale time leads to a lack of consumer confidence. In order to cope with competition between multiple platforms and within platforms, in addition to relying on price advantages, reducing inventory has also become an inevitable choice for merchants. Merchants usually organize production after receiving orders. Since it is pre-sale production, consumers have great uncertainty about the product quality and the time of receiving goods, so their purchase intention will decrease. For a long time, e-commerce sellers and consumers are full of great pressure under advance selling.
To promote the sales of pre-sale goods in the ultra-long advance selling, it is vital to disclose product information. By disclosing product information, consumers’ perceived risk is reduced and their willingness to buy increases. This can not only improve the revenue of sellers and the e-commerce platform, but also promote the more standardized implementation of pre-sale activities. Compared to the traditional methods, blockchain guarantees the data after being linked, non-tampering and traceable, ensuring the authenticity of information disclosed. At present, the usage cost is one of the key factors restricting the application of blockchain by the e-commerce platform and sellers. So, to reward the seller for using blockchain, the e-commerce platform offers the subsidy price. Therefore, this paper introduces the authenticity of information disclosure guaranteed by blockchain into the ultra-long advance selling, and constructs a three-party game model dominated by one e-commerce platform and two competing sellers. Specifically, considering the four scenarios in which neither of the two sellers uses blockchain (NN), only the dominant seller uses blockchain (BN), only the inferior seller uses blockchain (NB), and both of the two sellers use blockchain (BB), the optimal information disclosure strategy of the two presale sellers and the optimal price subsidy of the e-commerce platform are investigated based on the impacts of the three factors of the pre-sale time, the level of information disclosure, and the consumer’s trust of disclosed information on the consumer’s utility by solving the single-stage Stackelberg game model.
The findings of this paper are as follows: (1)The equilibrium outcomes under four scenarios are obtained, and further the impacts of blockchain use and sellers’competition on the three-party decision-making are discussed. (2)In the case of BB, the e-commerce platform has free riding behavior. That is, the unit subsidy price is zero, and its profit is higher than that of NN. (3)The cases NN and BB constitute the set of implementation schemes of two sellers. The specific scheme choice is determined by consumers’trust in disclosed information and cost coefficient of applying blockchain. When consumers’trust is low, even if cost coefficient of blockchain application is high, two sellers are willing to adopt BB scheme; when consumers’trust is at a high level, both sellers will no longer use blockchain. (4)Through numerical examples, this paper discusses the influence of other parameters on the choice of implementation schemes. The results show that when two sellers tend to be homogeneous, the e-commerce platform and two sellers only adopt BB scheme.
In the era of digital economy, using game theory to study competition and cooperation helps to accelerate the high-quality development of digital economy. This paper aims to provide theoretical reference for e-commerce platforms and sellers that adopt advance selling on how to implement blockchain for information disclosure. In the subsequent research, the impact of the subsidy price on blockchain application could be further studied. For example, it is reasonable that the e-commerce platform first decides the subsidy price, and then two competing sellers decide whether to apply blockchain. Moreover, the impact of blockchain application on the pre-sale price decision-making could also be discussed.

Study of the Cooperation between E-commerce Platform and Merchants Based on Differential Game

CHENG Yukun, WANG Xinxin, TIAN Xiaoming, CHEN Jinmian

2023, 32(11):  56-63.  DOI: 10.12005/orms.2023.0353

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Under the backdrop of the digital economy, the rapid development of the e-commerce industry has played a significant driving role in societal progress. With the advancement of social intelligence, online shopping has become increasingly convenient, leading to a yearly increase in the volume of online transactions. Despite the COVID-19 pandemic, the overall transaction scale during “6.18” e-commerce festival in 2022 reached a new high, maintaining a positive growth trend. With the flourishing growth of the e-commerce sector, an increasing number of merchants actively seek opportunities to collaborate with e-commerce platforms. Therefore, how to appropriately manage the collaborative coordination between e-commerce platforms and the merchants joining these platforms to achieve the optimal allocation of social resources has become a focal point of concern for all stakeholders in society.
To study the optimal cooperation mode between e-commerce platforms and merchants while considering both of the commodity goodwill and the continuity of time, this article constructs a differential game model consisting of an e-commerce platform and n merchants selling similar goods. By employing the Hamilton-Jacobi-Bellman equation, we discuss the optimal decision-making regarding effort levels, reputation values, and optimal revenue values for both the e-commerce platform and merchants under the decentralized decision-making model, the Stackelberg leader-follower game model, and the centralized decision-making model. After conducting a theoretical analysis of the effort levels of participants, the commodity goodwill, and the overall system revenue under these three decision-making models, combining it with numerical experimental analysis by applying real data used by JD Open Platform, we have reached the following conclusions:
(1)Compared to the decentralized decision-making model, when the e-commerce platform adopts the Stackelberg leader-follower game decision-making model, the optimal effort level of the platform will remain unchanged. However, the platform can motivate merchants to invest more effort by sharing their costs. In the centralized decision-making model, both the e-commerce platform and merchants exert higher effort levels than those under the decentralized and Stackelberg leader-follower game models.
(2)In terms of commodity goodwill jointly established by e-commerce platform and merchants, the value of commodity goodwill is the highest under centralized decision model, followed by Stackelberg master-slave game model. In the centralized decision-making model, the e-commerce platform and merchants are aligned, leading to maximum effort exertion in selling commodities. This, in turn, results in the highest commodity goodwill. Enhanced goodwill increases consumer trust, which is a valuable asset for both the platform and merchants.
(3)In terms of the overall benefit to the e-commerce ecosystem, the centralized decision-making model generates the highest system-wide revenue. In the Stackelberg leader-follower game model, the e-commerce platform’s cost-sharing with merchants leads to increased overall revenue compared to the decentralized decision-making model.
Through the comparative analysis of theory and numerical experiments, it is evident that the Stackelberg leader-follower game model and the centralized decision-making model, compared to the decentralized model focused on individual gains, promote cooperation between e-commerce platforms and merchants, thus favoring the long-term development of the e-commerce ecosystem. Partial or deep cooperation between e-commerce platforms and merchants can yield higher returns for both parties and contribute to the sustainable development of the entire e-commerce ecosystem.

Study of Community E-commerce Efficiency Evaluation and Improvement Paths Based on Two-stage DEA Model: The Case of Xinjiang Community E-commerce Enterprises

QIAO Han, LYU Haoyi, LUO Wenchao, LI Zhuolun, ZHANG Shuo

2023, 32(11):  64-71.  DOI: 10.12005/orms.2023.0354

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Community e-commerce, a new business model, has been bred by the amalgamation of e-commerce and social media. There exists a great difference between community e-commerce and traditional e-commerce. One is that user traffic comes from different sources. While public domain traffic is the main source of traffic on traditional e-commerce platforms, traffic on community e-commerce platforms is mainly private. The second difference is decentralization. Community owners connect the platforms and consumers, weakening the centralized characteristics of traditional e-commerce platforms. There have been studies of traditional e-commerce, but how to scientifically evaluate the efficiency of community e-commerce generated by private domain traffic still remains unclear. This paper explores how to improve traditional efficiency evaluation methods for community e-commerce and studies the quantitative interpretation of operation efficiency based on the internal mechanisms of community e-commerce.
According to the characteristics of community e-commerce, this paper defines two stages: “Recruitment” and “Transformation”, to describe the process of enterprises acquiring users, building communities, operating communities, and promoting products to achieve value creation and value capture. In this paper, to build a two-stage correlated DEA model, we introduce “the number of community owners” and “the number of community members” as two intermediate indicators which can reflect the scale of community. In the Recruitment stage, the number of employees and the cost of main business are selected as input variables, while the number of community owners and community members are selected as output variables to measure the company’s ability to acquire user bases and cultivate communities. In the Transformation stage, the number of community owners and community members are selected as input variables, while the income of main business and the rate of purchase again are selected as output variables to measure the ability of the enterprise to convert community resources into revenue and profits. We measure the efficiency of community e-commerce based on the survey questionnaire about community e-commerce enterprises in Xinjiang Uygur Autonomous Region.
The results show that there exists divergence in the efficiency of community e-commerce enterprises in Xinjiang Uygur Autonomous Region. Most enterprises possess low-level general efficiency and scale efficiency, and merely half of them can achieve effective technical efficiency. To compare the two stages, the general efficiency and scale efficiency of the “Recruitment stage” are relatively lower than that of the “Transformation stage”, but the technical efficiency is not much different in the two stages. We provide managerial insights for enterprises: We conduct an input-output analysis of representative individual enterprises, and further propose corresponding improvement measures. The high logistics costs and slow delivery caused by geographical location affect scale efficiency and constrain the development of community e-commerce in Xinjiang. Policy recommendations are also provided for the government. Based on the two-stage analysis of efficiency, this paper proposes a reasonable path to improving the efficiency of community e-commerce in Xinjiang: Enterprises should get through the dilemma of low efficiency by improving scale efficiency and forming scale effects. Specifically, increasing the category of products supplied, improving the capability of community service, and intensifying the development of logistics can promote the formation of scale effects, so as to improve the efficiency of community e-commerce enterprises.

Investment Strategies for Competitive Two-sided Platform with Data Empowerment Value-added Service

JIA Wenpeng, NIU Xiaxia, LIANG Mengkun, LIU Jiajia, WU Jun

2023, 32(11):  72-78.  DOI: 10.12005/orms.2023.0355

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In the era of digital economy, data has become an important production factor for platform enterprises. E-commerce platforms implement data empowerment strategies to achieve value co-creation between the platform and users. This article is based on the bilateral market theory and the Hotelling model, considering whether the platform provides data enabled value-added services, constructs investment competition models for e-commerce platforms under two different attribution scenarios, and analyzes the equilibrium strategy choices of different platforms. The main research conclusions of this article are as follows:
(1)When competitive platforms adopt the same data empowerment value-added service investment strategy and both bilateral users are single-homing, the market share obtained by both platforms and the pricing to bilateral users will be equal. When bilateral users have significantly different preferences for the platform, the price by platforms that adopt investment strategies will be lower than that by platforms that only provide basic service. In order to achieve the optimal profit of the platform, the service fee charged by the platform to bilateral users decreases with the increase of cross-side network effects, increases with the improvement of the investment level of data-enabled value-added service, and increases with the increase in the unit preference cost.
(2)When competitive platforms adopt the same data empowerment value-added service investment strategy in the case of single-homing for consumers and partial multi-homing for sellers, the platform will have lower pricing for consumers and higher service fee for sellers. The higher the perceived level of platform data empowerment by sellers, the more conducive it is to encouraging them to adopt a multi-homing strategy. The fees charged by the platform to bilateral users is related to the cross-side network effects and platform differentiation. When the network externality coefficient on the consumer side is greater than the user preference cost, the service fee charged by the platform to sellers will increase with the increase in value-added service investment level. When the network externality coefficient on the consumer side is less than the user preference cost, the service fee charged by the platform to sellers will decrease with the increase in value-added service investment level, and the fee charged by the platform to consumers will also be the same.
(3)When competitive platforms adopt different data empowerment value-added service investment strategies, whether in the case of single-homing or partial multi-homing, in order to maintain the platform’s own competitive advantage and make the most of data empowerment investment, the platform will not only need to comprehensively consider the cross-side network effects, user preference costs, and the data empowering investment levels, but also require attention to the investment strategies adopted by competitive platforms.
The management insights obtained in this are the following: Firstly, in the process of providing data empowerment value-added services, the platform should focus on the utility analysis of bilateral users of different attribution, and adopt different service charging strategies for users of different attribution. Secondly, the platform should make good use of its own service investment level, create a differentiated data empowerment value-added service strategy, and enhance user stickiness. Finally, the platform’s data empowerment strategy helps to enhance the utility of bilateral users, and the platform should implement the data empowerment strategy in conjunction with its own and competitors’ investment level strategies.

Research on the Evaluation Method of Senior Care Service Resources Allocation Scheme Based on the Perspective of Data Elements

XIONG Honglin, CHEN Hongmin, YANG Yunpeng, FAN Chongjun, HUANG Nai, HUANG Aiguo

2023, 32(11):  79-86.  DOI: 10.12005/orms.2023.0356

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With the deepening of the digital economy, data has become a factor of production and plays an important role. Nowadays, population aging brings new challenges to the construction of senior care service resources. Combining advanced technology to realize data-driven senior care services has attracted the extensive attention of the fields of sociology, public administration, and data science. For the assessment of resource allocation for the construction of senior care services, most of the existing studies have considered the situation in which the assessment indicators are independent of each other. The weights and interaction coefficients of the indicators are usually based on the preferences of decision makers, which is inconsistent. One challenge is to determine the weights of interdependent criteria based only on a given set of objective data without the subjective judgment of decision makers. While traditional perspectives focus more on collecting, cleaning, and analyzing data to extract valuable information, data element perspective starts with objective data. It identifies relevant data elements, establishes relationships between data elements, and provides insights into the connotations of data elements.
In view of this, to reduce the influence of decision-makers’subjective factor judgments, this study proposes a multi-indicator evaluation method for senior care service resources construction and allocation schemes. The method is based on the data element perspective and uses data-driven livelihood service thinking. It integrates the respective characteristics of fuzzy, Shapley value and Marichal entropy. The method addresses the demand characteristics of senior care services and constructs the evaluation index system from the three dimensions of daily care, medical care, and spiritual comfort respectively. It then determines the objective weights of the indexes and uses the optimization model to objectively obtain the interaction coefficients, weights, and program satisfactions of the criteria on the multi-level structure. This approach effectively avoids the influence of inconsistent subjective preferences. It can accurately reflect the complexity of objective senior care service resources construction and can also more accurately reflect the interrelationship between different variables in the system. According to the change of fuzzy measure, it can reflect the existence of the mutual influence relationship between different evaluation indexes of senior care service resources. By using this feature of fuzzy measure, it can better analyze the change of each index in the evaluation indexes of the senior care service resources construction program. Compared with traditional evaluation methods for all kinds of senior care services, the method proposed in this paper realizes the use of objective data and objective weights. It ensures that the evaluation results are not affected by the inconsistency or uncertainty of subjective judgments. The proposed method effectively avoids interdependent interactions in the evaluation indexes and weights interfered with by subjective factors and can more objectively respond to the evaluation program. In addition, data on the construction of senior care service resources in 16 administrative districts of Shanghai are selected for empirical analysis to further verify the feasibility and effectiveness of the proposed method.
According to the experimental results, the following conclusions can be drawn: 1)The quantitative analysis of elderly service resource data based on the constructed objective weighting model method presents to a certain extent the interplay between various types of senior care service resources. The interactions between all the sub-indicators are positive. 2)Fuzzy measurements react to the assessment of different senior care service resources construction program evaluation indexes. There is a cross-influence phenomenon between the variables, and their values affect the overall performance of the overall senior care service resources construction evaluation system. The weight of spiritual comfort is the largest in the evaluation of senior care service resources construction allocation, followed by medical care, and finally daily care. 3)The evaluation method of the senior care service resources construction allocation program constructed in this paper is feasible and effective. The results of this study are useful for the government in promoting the construction of senior care service resources. It can help scientifically and reasonably utilize resources and improve the level of service.
The findings of this paper are of guiding significance for the construction of senior care service resources in data-driven livelihood. However, there is still room for further optimization. In practice, due to the different characteristics of the structure of the elderly population, economic conditions, and assessment mechanisms of the relevant departments in various places, the original impetus in the implementation of the construction in various places is different. This leads to different evaluation results. The follow-up research work will consider an in-depth advancement for these characteristics of the factors.

High-quality Data Resource Identification of Network Trading Platform Based on K-medoids-NCA-SMOTE-BSVM Model

NI Yuan, LI Siyuan, XU Lei, ZHANG Jian, FANG Jinyu

2023, 32(11):  87-93.  DOI: 10.12005/orms.2023.0357

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As an emerging factor of production, the demand for trading and circulation of data resources has shown explosive growth. The problem of data quality has sparked widespread concern along with the exponential growth of data scale, and a lot of low-quality data is flooding into different types of data resource trade platforms. How to identify high-quality data resources in the massive resources has become the key for data trading platforms to gain competitive advantages and improve the efficiency of factor allocation. Existing research has provided a basis for high-quality data identification in the platform trading context, but there are still two deficiencies: Firstly, it is challenging to meet the requirements for large-scale data resources’quality identification because the existing identification methods, which are only applicable to the quality evaluation of small-scale and homogeneous data resources, have more manual participation components and insufficient automation. Secondly, the existing identification methods ignore the problem of uneven distribution of data resources of different quality, which easily triggers the bias of classification results and is difficult to meet the robustness requirements of heterogeneous sample classification. This paper is to clarify the mechanism of high-quality data resource formation in the context of platform transactions, discover the key factors for high-quality data resource formation in the context of platform transactions, and propose a method for efficiently recognizing high-quality data from large-scale and heterogeneous data resources.
Data circulation and transaction are necessary for the realization of the value of data resources, and in a platform economy, data circulation is in the form of an open market with numerous participants. This paper studies the flow of data resources and the generation process of high-quality data within the platform environment and builds a high-quality data resource identification index system of “intrinsic quality-commodity characterization”. After that, it suggests a K-medoids-NCA-SMOTE-BSVM fusion model, handles the identification of high-quality data as a pattern recognition problem, and uses supervised machine learning to identify high-quality data and solve the issue. Four primary sections make up the model: (1)Synthesizing the number of views, collections and downloads of data resources as the basis of discrimination, using K-medoids to cluster the samples of data resources, automatically creating the classification labels for data resources, and calculating the ideal number of classification labels by combining them with profile coefficients. (2)The built metrics are downscaled using Nearest Neighbor Component Analysis (NCA) to come up with a new set of features, taking into account the possibility that the chosen metrics contain elements that are less important for the categorization of high-quality data resources and may therefore influence the model’s effectiveness. (3)After clustering division, the number of samples from different classes fluctuates widely, and therefore, to maintain the balance of data between classes, the few class oversampling method (SMOTE) is used to increase the amount of data from the few sample classes under the new feature set. (4)A nonlinear high-quality data resource identification model based on Bayesian optimization support vector machine (BSVM) is constructed, and it achieves classification prediction of high, medium, and low quality data of various calibers by using the data resource identification indexes after feature dimensionality reduction, the clustered data resources as input, labeling the clustered data resources with categories, and balancing the dataset as the model’s output. Finally, based on the API datasets of real data trading platform, Python is used to crawl the request parameters, return parameters, update frequency, data sources, data descriptions, labels, application scenarios, specifications, registered capital of service merchants, views, downloads, and favorites of data resources to carry out the empirical research.
The results show that: a)SMOTE balanced processing can improve the effect of data resource quality identification and improve the classification accuracy of the optimization model based on the comparison of unbalanced with balanced datasets. b) Whether based on imbalanced or balanced datasets, BSVM outperforms SVM, WOA-SVM, PSO-SVM, MLP, and CNN approaches in terms of prediction accuracy, and BSVM has higher algorithmic efficacy with less training time than other optimization algorithms. In summary, this paper, which is an innovative attempt and a significant addition to the theory of data resource quality assessment, clarifies the meaning of high-quality data resources, builds a high-quality data resource identification index system, and fully verifies the validity of the index system with the aid of trading platform data. It also builds a high-quality data resource identification model, which can effectively generate the quality labels of massively parallel data sets. It has significant guiding relevance for encouraging the active trading of data resources and can effectively develop quality labels for vast data resources. It can also increase the recognition accuracy of heterogeneous data resources.

Evolutionary Game Theory in the Digital Economy Era

Evolutionary Game Analysis of the Seller’s Price Cheating Behavior on Remanufactured Products Based on PT-MA Theory

YANG Mingge, YANG Zhuo, LIANG Xiaozhen

2023, 32(11):  94-101.  DOI: 10.12005/orms.2023.0358

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Digital economy promotes the rapid development of remanufacturing industry, but it also brings a lot of data abuse and infringement. For example, in order to obtain more benefits, the seller utilizes the virtual and hidden characteristics of the digital economy to conduct the price cheating. The price cheating on remanufactured products refers to the phenomenon that sellers, driven by profit, spend a certain camouflaged cost to disguise remanufactured products as new products and sell camouflaged remanufactured products at the price of new products. This behavior not only seriously damages the rights and interests of the consumer, but also undermines the fairness of the remanufactured market. In order to protect the rights and interests of consumers and prompt the healthy development of the remanufactured market, we discuss the price cheating on remanufactured products in closed-loop supply chain, and explore its internal mechanism and solutions.
Firstly, considering the impact of psychological factors and risk preference on individual decision making, we introduce the prospect theory and mental account (PT-MA) theory into the tripartite game of the seller, the government and the consumer, and construct a prospect benefit perception matrix. To be specific, we replace the utility function with a benefit function to construct a traditional benefit matrix. On this basis, we divide the benefits of individual decision making into a valence function representing “gain” perception and a cost function representing “loss” perception. The valence function and cost function together constitute the value function of individual decision making.In the traditional benefit matrix, benefit functions, for example, income, with “gain” perception are included in the valence function, while benefit functions, for example, cost and loss, with “loss” perception are included in the cost function to obtain the prospect benefit perception matrix.
　Secondly, we establish a tripartite evolutionary game according to the principle of dynamic replication equation, and try to find the evolutionarily stable strategy of the system through Lyapunov first method. We analyze the evolutionarily stable strategies of the system and obtain the following results: i)The government always chooses to strictly supervise and remains unchanged. ii)When the value perception that the seller chooses to camouflage is greater than the value perception that the seller chooses not to camouflage, and the value perception that the consumer chooses to purchase is greater than the value perception that the consumer chooses not to purchase, “camouflage, strict supervision and purchase” are the evolutionarily stable strategy for the system. This situation generally occurs in the early stage of development of remanufacturing industry. iii)When the value perception that the seller chooses to camouflage is greater than the value perception that the seller chooses not to camouflage, and the value perception that the consumer chooses to purchase is less than the value perception that the consumer chooses not to purchase, “camouflage and strict supervision but no purchase” are the evolutionarily stable strategy for the system. This situation generally occurs in the middle stage of development of remanufacturing industry. iv)When the value perception that the seller chooses to camouflage is less than the value perception that the seller chooses not to camouflage, and the value perception that the consumer chooses to purchase is less than the value perception that the consumer chooses not to purchase, “no camouflage or purchase but strict supervision” are the evolutionarily stable strategy for the system. This situation generally occurs in the middle and late stage of development of remanufacturing industry. v)When the value perception that the seller chooses to camouflage is less than the value perception that the seller chooses not to camouflage, and the value perception that the consumer chooses to purchase is greater than the value perception that the consumer chooses not to purchase, “strict supervision but no camouflage or purchase” are the evolutionarily stable strategy for the system. This situation generally occurs in the late stage of development of remanufacturing industry.
　Finally, we study the factors that impact the evolutionary paths of the seller, the government and the consumer through numerical simulation, and obtain the following results. i)Reducing the valence reference point or the cost reference point can prompt the seller to choose not to camouflage. ii)Increasing the risk preference coefficient of the seller, the government and the consumer can prompt the system to reach a stable state of “no camouflage by the seller, strict supervision by the government, and purchase made by the consumer”. iii)Increasing the degree of loss aversion can prompt the seller to choose not to camouflage, and the government to choose to strictly supervise. Reducing the degree of loss aversion can prompt the consumer to choose to purchase.In this paper, a numerical analysis is carried out in the case of pure simulation and does not combine with the actual data of the reproduct market. In the future, we can collect big data of the reproduct market and use econometrics and data mining to conduct an empirical analysis. So we can improve the research on the behavior of relevant participants in the reproduct market.

Evolutionary Game Analysis of the Product Quality of Live Streaming Commerce

LIANG Xiaoying, LIU Yumin, ZHAO Zheyun, TIAN Guangjie

2023, 32(11):  102-108.  DOI: 10.12005/orms.2023.0359

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Driven by digital technology, China’s short video and webcast users continue to expand, and the live streaming e-commerce industry has achieved rapid development. The new marketing model has gradually attracted everyone’s attention. In the context of the impact of the COVID-19 pandemic on the global economy, live streaming commerce can effectively stimulate domestic consumption and promote economic development. However, while the live streaming commerce industry is booming, product quality problems have arisen such as shoddy products and products without guarantee after sales. Therefore, exploring the reasons for frequent quality problems of live streaming is of great significance for guiding the development of the live streaming commerce industry and promoting domestic economic development.
Aiming at the problem of low product quality in live streaming commerce, the evolutionary game model is used to analyze the strategy choice of anchors, manufacturers, platforms and consumers. In the process of live streaming, the anchor charges a certain admission fee and commission from the manufacturer and uses the e-commerce platform to sell products to consumers through live streaming. The platform charges a certain percentage of fees for the live streaming activities of the anchors, and is responsible for the quality of the products produced by the manufacturers and the behavior of the anchors.Firstly, we assume that: (1)The strategic choice of anchors is {strict product selection, loose product selection}. (2)The strategic choice of the manufacturer is {self-disciplined production, not self-disciplined production}. (3)The strategic choice of the platform is {active regulation, negative regulation}. (4)Consumer behavior choices include {active rights protection,negative rights protection}. Secondly, the payment matrix of strategic choice among anchors, manufacturers, platforms and consumers is constructed, and the replication dynamic equation is presented. We solve an equilibrium strategy in an evolutionary game. And then, the stability analysis of anchor selection behavior, manufacturer production behavior, platform supervision behavior and consumer rights protection behavior is carried out respectively based on the possible equilibrium solution. Finally, the asymptotic steady state with different parameters is solved by a numerical simulation method, and the influence of the change of key parameters on the four-party strategy selection is analyzed.
The research result shows that: (1)The recognition efficiency and selection behavior of anchors for undisciplined manufacturers are important factors affecting the strategy selection of manufacturers and platforms. Strict product selection and improved identification efficiency of anchors reduce the probability of cooperation with undisciplined manufacturers, and can urge manufacturers to make self-disciplined production and improve product quality in the market. In addition, whether manufacturers are self-disciplined in production is also affected by the regulatory behavior of the platform. (2)The strict selection of anchors forms a certain alternative to the supervision of the platform. The improved identification efficiency of anchors reduces the possibility of unqualified products flowing into the market, and will prompt the platform to relax the supervision of product quality issues. (3)Consumer rights protection will cause the anchor and platform to suffer user losses, and have a positive impact on the behavior of the anchor and platform. With the increase of user losses, the behavior of anchors and platforms tend to be strict selection and active supervision respectively. In addition, the behavior of the platform is also affected by user stickiness. Reducing users’dependence on the platform can increase the willingness of the platform to actively supervise. (4)An appropriate increase in punishment on the platform will prompt anchors and manufacturers to change their negative behaviors, but increasing punishment alone cannot have a long-term impact on manufacturers’behaviors to stabilize them in the strategy of self-disciplined production. In addition to punishment, the platform should also consider taking other ways to control the status quo of frequent product quality problems in live streaming.

Evolutionary Game Model of Platform Enterprise-Union Co-opetition Relations

CHEN Wei, HU Enhua

2023, 32(11):  109-116.  DOI: 10.12005/orms.2023.0360

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Platform economy is an important gripper to promote the development of digital economy, and the flexible employment by platform enterprises has become an important channel to alleviate and solve the employment pressure of workers. However, driven by digital technology, platform enterprises have the problems of behavioral alienation and a lack of social responsibility, which makes it difficult for employees (the flexible employment personnel of platform enterprises) to fairly participate in the process of benefit distribution and share the development achievements of platform enterprises. This inevitably triggers a “trust crisis” among employees, threatening the sustainable development of platform enterprises. Therefore, under the situation that platform enterprises lack effective supervision and are difficult to self-correct, it has become a highly concerned issue that how to effectively solve the above problems to achieve a win-win situation for platform enterprises and employees. Under the realistic background of strong capital and weak labor, the effective intervention of union as the spokesperson of labor interests is expected to protect the legitimate rights and interests of employees. This paper establishes an evolutionary game model of platform enterprise-union co-opetition relations and numerical experiments are carried out with MATLAB to analyze the evolutionary process of platform enterprise-union co-opetition relations.
The results show that: (1)There is no evolutionary equilibrium in the process of co-opetition game between the platform enterprise and union, and the behavior strategies adopted by the two parties present a cyclical pattern. This shows that under the “strong capital and weak labor” reality, the evolution of the platform enterprise-union co-opetition relations is in a long-term dynamic process of continuous adjustment. Both parties optimize their own interests through selection of cooperative and competitive strategies, which reflects the dynamic relationship of competition and cooperation between the platform enterprise and the union. (2)A defect in the current platform enterprise-union co-opetition evolutionary mechanism is that the traditional weak union situation makes it difficult for both parties to establish effective cooperation, but a stronger status of the union will greatly improve this situation. This indicates that the crux of the imperfect mechanism of platform enterprise-union co-opetition evolution lies in the insufficient status of unions in platform enterprises.
To this end, countermeasures and suggestions are proposed from the perspectives of the union and platform enterprise. On the one hand, from the union’s perspective, the union may rely on the national legal system and exert its own initiative, and get rid of its old appearance as a puppet of the enterprise through direct election of its chairman and active cooperation with higher-level unions, thereby enhancing its independence of the platform enterprise. The union chairman directly elected by employees represents the will of most employees, and therefore will actively seek equality in communication and the game with the platform enterprise, effectively play the role of employee spokesperson, and safeguard the legitimate rights and interests of the employees. This should compensate for the inherent asymmetry in the status of the two parties. On the other hand, from the platform enterprise’s perspective, the platform enterprise should respect the status of its union and the interests and needs of its employees. Through actively establishing a cooperative relationship with the union and improving its labor condition, the platform enterprise will be able to stabilize the workforce and greatly reduce the possibility of both parties adopting a competitive strategy, therefore achieving a win-win goal among the platform enterprise, the union and the employees, and promoting the vigorous development of the digital economy.

Can Government Governance Resolve the Online Labor “Paradox”: An Evolutionary Game Analysis Based on a Model between Platforms and Workers

YIN Yan, LIU Yin, SHANGGUAN Zijian, LYU Benfu

2023, 32(11):  117-123.  DOI: 10.12005/orms.2023.0361

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Under the trend of deep integration of gig economy and digital technology, online labor platforms, which provide services on demand through precise, efficient, and large-scale operation with algorithms as the core means, have emerged, and gradually formed an “Algorithmic Management” (AM) model. The automatic and efficient, data-driven characteristics of AM have significantly improved the operational efficiency of the platform. However, while optimizing efficiency through algorithms, online labor platforms often undermine the autonomy and rights protection of workers, triggering a contradiction between the concept of flexible labor and algorithmic control.
Relevant studies have mainly analyzed the characteristics and impacts of algorithmic management. However, few have discussed how to govern the “paradox” from the government’s perspective. To answer the question of how governments can solve the online labor “paradox” by designing and combining different governance policies, this paper establishes an evolutionary game model between online labor platforms and workers based on two governance scenarios: regulation and incentives. Through model analysis, the governance effects of regulatory and incentive policies on platforms and workers’strategy selection are compared. Additionally, through numerical simulations, we analyze the evolution paths of platforms and workers under different model parameter settings to further indicate the effectiveness of regulatory and incentive policies.
The study finds that government regulation alone cannot solve the “paradox” and inappropriate regulation intensity will make the game evolve toward lower overall welfare. In contrast, a synergistic mechanism combining regulation with incentive policies is more effective which indicates that an appropriate level of incentives is needed to lead the game to evolve to a stable point with higher overall welfare. Furthermore,the simulation results show that a well-combined governance mechanism of both regulation and incentives is significantly better than the effect of only adopting one of the two governance measures. Finally,the simulation results also indicate that the extent of how well online labor platforms can effectively manage the arbitrage behaviors of workers will influence the final state of evolutionary system.
The conclusions of this paper suggest that the government should (1)keep regulatory intensity in an appropriate range to avoid excessive regulatory pressure on platforms, (2)provide differentiated support for platforms that adopt flexible algorithmic management and (3)actively integrate regulatory and incentive governance mechanisms to build a multidimensional and complementary governance matrix to promote the platform economy’s standardized, healthy, and sustainable development and effectively protect the rights and interests of workers in this new employment pattern. Finally, the platform should actively promote management model innovation and technology iteration, explore new management methods, and promote “algorithms for good” while optimizing algorithm efficiency. This paper expands the research in the field of algorithmic management and provides some theoretical support and decision-making basis for the government to carry out governance over online labor platforms.
The next step of this research is, firstly, to broaden the government governance scenarios; secondly, to further refine the government governance behaviors; and thirdly, to deepen the assessment of the effect of government governance by combining quantitative methodologies to carry out more empirical research.

Multi-agent Stochastic Evolutionary Game on Responsible Innovation: A Case Study of Coping with the Aging Digital Divide

LI Xiaodi, YUAN Yuan

2023, 32(11):  124-131.  DOI: 10.12005/orms.2023.0362

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Driven by the wave of the digital economy and the deepening of the aging population, the digital divide for the elderly has been exacerbated, making the elderly as a digitally vulnerable group out of touch with the digital information age, causing the elderly to fall into an information crisis and technological panic, and further affecting social security and efficiency. Therefore, in order to actively address the issue of the aging digital divide and better integrate them into the digital age, making an intelligent technology innovation for aging is an important measure to address the digital divide in the elderly. It can optimize the solution of high-frequency elderly related services such as online appointments and provide products that meet the needs of the elderly, and fully consider the special needs of the elderly population from the process of intelligent technology research and application. However, how to carry out innovation activities has become a focus of current attention. It is not only necessary to rely on enterprises to carry out technological innovation to achieve research on aging, but also require the government to play a guiding role and provide policy support and institutional guarantees. At the same time, this can stimulate elderly consumers to actively participate and provide feedback on their own needs, closely combine the supply and demand sides, and ultimately achieve the joint participation of multiple entities.
This paper builds a three-party evolutionary game for enterprises, elderly consumers and the government. It emphasizes the joint participation of multiple parties, which assumes the responsibility of product innovation for aging, meeting social needs, and creating social well-being. Considering the complexity and high uncertainty of the real situation, the Gaussian white noise is added to show the disturbance of the game subject during the evolution process, and the stochastic evolutionary game model is established. The stochastic differential equation theory is used to analyze the conditions for the stability of the game subject, and computer simulation is used to analyze the evolution process and strategy selection of the three-party game subject.
The results show that random factors interfere with the strategic choices of enterprises, elderly consumers and the government in responsible innovation. With the increase of interference intensity, the fluctuation range of the game subject to a stable state is larger and the evolution speed is slower. Under the situation of government supervision, the government’s cost subsidy and reward and punishment mechanism are conducive to mobilizing the enthusiasm of the participants to carry out responsible innovation. The changes in the losses of the three participants of enterprises, elderly consumers and the government have a consistent direction for their respective strategic choices. When the losses of enterprises, elderly consumers and the government increase, the three-party game subject is more inclined to choose the (participation, participation, supervision) strategy. Based on the above research conclusions, the following three countermeasures and suggestions are mainly proposed. Firstly, enterprises play a technology oriented role in responsible innovation, providing intelligent products and services to the elderly and meeting their diverse needs. Secondly, elderly consumers actively participate in responsible innovation, taking on the responsibility of maintaining public interests, meeting the needs of the elderly, and adapting to digital intelligent technology. Finally, the government plays a guiding role in responsible innovation. On the one hand, the government adopts cost subsidy measures to mobilize the participation enthusiasm of enterprises and elderly groups. On the other hand, the government attaches great importance to the implementation effect of various policies and adopts regulatory measures to ensure the effectiveness of these policies.

Evolutionary Game Analysis of Service Mechanism of Cloud-edge Collaboration System in the Digital Economy Era

LI Shiyong, XU Min, SUN Wei

2023, 32(11):  132-138.  DOI: 10.12005/orms.2023.0363

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It is agreed that the digital economy will bring subversive innovation to businesses and organizations, and stimulate the needs of enterprise digital transformation and endogenous power. However, due to the variety of the traditional enterprise products, the diversity of the user data and the differentiation of ability development, it is difficult to achieve digital upgrade in a short period of time. Fortunately, the digital technologies such as cloud computing provide an important technical support for the digital transformation of enterprises. In particular, the cloud-edge collaboration technology can provide faster business processing ability and better user service quality, thus attracting extensive attention from scholars and entrepreneurs from all fields. However, for the academic community, many scholars have studied more from the technical optimization perspective of computing offloading, task and resource scheduling, and resource allocation, and less on the inherent law of game behavior between digital technology enterprises which are mainly regarded as two parties: cloud service providers and edge operators.
Under the bounded rationality assumption, this article presents a dynamic evolution game model between cloud service providers and edge operators for the edge-cloud framework according to the evolutionary game theory, explores the unilateral evolutionary stability strategies under four different conditions based on the stability theorem for differential equations, and analyzes the evolution path of each player and the influence affected by the other’s strategy choice in the actual cases. The equilibrium of the system is calculated according to the replicated dynamic equations, and the Jacobian matrix is also constructed. In accordance with the criterion proposed by Friedman, the equilibrium is proven to be an evolutionary stability strategy under different conditions, and the combination of evolutionary stability equilibria under different conditions is also obtained. The evolution law of the system is analyzed and the evolution results are classified and summarized. Then the basic conditions for achieving the cloud-edge cooperative relationship are found. Furthermore, this article also investigates the influence factors of stable cooperative relationship between cloud service providers and edge operators in two kinds of evolutionary stability strategies, and verifies the theoretical analysis results through numerical simulation results.
The research results show that the final evolution of the cloud-edge collaboration system depends on the benefits and costs of the game matrix between the two parties as well as the system initial state values. The two parties will finally reach a stable cloud-edge collaborative relationship after continuous learning and adjustment of strategies in a long time enough, when the cost of constrained loss is at least greater than the difference between the benefit of both parties choosing to deal alone and the benefit of their cooperative treatment. The distribution coefficient of revenue and cost has different effects on the cooperative evolution of the system with different conditions, and needs to be adjusted dynamically in a real time according to market changes. The lower the risk coefficient of cloud edge collaborative processing for both parties, that is, the higher the safety coefficient, the more conducive to the construction of the cloud-edge collaboration system. Under a certain condition, the cloud-edge collaboration system can be eventually driven to be an ideal state by reducing the collaboration cost and improving the collaboration revenue, so as to effectively promote the long-term stability of cloud-edge collaboration system and better serve the enterprise digital transformation.

Evolutionary Game Analysis of Knowledge Sharing and Cooperation in Blockchain Autonomous Organizations Based on Prospect Theory

LI Zhihong, QIAO Guihong, XU Xiaoying, TIAN Minghao

2023, 32(11):  139-146.  DOI: 10.12005/orms.2023.0364

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In the age of the knowledge economy, online communities serve as quintessential crowd-sourcing platforms, fostering the emergence of collective wisdom and knowledge sharing that effectively propels open collaboration in knowledge innovation. Traditional social media platforms, mainly built on centralized organizational structures, inherently harbor numerous issues such as user privacy breaches, disparities between user contributions and earnings, and a dearth of efficacious incentive mechanisms. On one hand, conventional centralized platforms store all user data on centralized servers, thereby permitting platform administrators to exploit or sell this data for profit without user consent. On the other hand, user-generated content’s rights and benefits should naturally belong to creators, but most centralized platforms commodify user contributions while users receive negligible gains or no share of them. Furthermore, the uniform incentive mechanisms tend to conform to the “90-9-1” rule in community knowledge contributions, substantially dampening the enthusiasm of users for sharing high-quality knowledge.
The ascendance of blockchain technology has catalyzed a shift in organizational paradigms, giving rise to the Decentralized Autonomous Organization (DAO), a new form that addresses the knowledge governance limitations of conventional centralized communities. Hsieh characterizes blockchain-based autonomous organizations (hereinafter referred to as blockchain autonomous organizations) as entities capable of functioning autonomously devoid of centralized control or third-party intervention. Steemitis the most representative of blockchain autonomous organizations, having devised an incentive mechanism encompassing three underlying tokens. This structure dispenses token rewards and community privileges impartially to users who contribute to community development, encompassing knowledge creation, sharing, and dissemination. Contrasting with conventional knowledge communities, the Steemit community leverages the token’s value traits and governance capacities, not only augmenting anticipated user benefits but also conferring community governance rights. This strategy directly achieves knowledge realization and spurs user participation in collaborative knowledge sharing on a material level.
The decision-making process for users engaged in knowledge sharing entails intricate dynamics, with cooperative behavior contingent upon external influences. Given that token incentives underpin a dynamic reward mechanism within community user groups’collaborative cooperation, the variability of anticipated returns yields divergent perceptions of gains and losses among economically rational agents. Traditional evolutionary game models fall short in comprehensively explaining irrational psychological factors and knowledge sharing actors’expectations of income. Prospect theory, capable of effectively analyzing knowledge sharing subjects’decision-making within uncertain contexts and their preferences for gains and losses, is harnessed here to enhance the game income matrix for knowledge sharing subjects. This study probes the evolutionary stability strategies of user sharing behavior within blockchain autonomous organizations, scrutinizing their influence on user knowledge sharing choices through simulations and analyses of various parameter variables, and ultimately furnishing pertinent management recommendations.
Simulation reveals that users’willingness to partake in knowledge sharing and collaborative cooperation is significantly influenced by factors like token rewards, their distribution coefficient, and sharing costs. Notably, knowledge creators’perceived risks exert substantial sway over sharing strategies, whereas mutual potential benefits exert a relatively minor impact. Conclusively, this paper’s research insights provide actionable suggestions for the wholesome development of blockchain autonomous organizations. Future endeavors will extend this study to explore the ramifications of token incentives on user conduct within blockchain autonomous organizations based on the conclusions drawn herein.

Research on the Evolution Game of the “Regulatory Dilemma” Strategy for Profit-making Rights Protection of Bulk Commodity Spot Trading Platform

ZHANG Jian, ZHANG Qianyu, LIAO Mengjie, WAN Zhenlong

2023, 32(11):  147-154.  DOI: 10.12005/orms.2023.0365

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In the era of the digital economy, the development trend of e-commerce is getting better. With the help of the “four trillion” economic stimulus and the integration of the digital economy under the policy of innovation after the financial tsunami, China’s traditional commodity trading market through the digital transformation of the industry has actively evolved into a commodity electronic trading market. Commodity spot trading platform has a low barrier to entry, operating products with strong consumer attributes, and users with strong investment and profit-making purposes. Relying on the flexible operation mode, the platform has led to an increase in market turnover, and at the same time, it has also become the main target of the “grey industry chain of illegal rights”, which seriously restricts the orderly operation and benign development of the commodities spot trading platform. Intelligent regulatory technology in the digital economy era for commodities spot trading platform to break through the profit-making rights investment “regulatory dilemma” provides a powerful weapon, but the platform of intelligent regulatory technology threshold is high, the transition is difficult, so how to promote China’s commodities spot trading platform to fulfill its regulatory obligations through government regulation and guidance and enhance the level of regulatory intelligence is crucial.
Based on the theory of responsive regulation, this paper constructs a dynamic evolutionary game model with limited rationality “investment user-platform” as the main body of the game, starting from the analysis of the responsive regulatory mechanism of intelligent regulation on profit-making rights protection. The dynamic evolution path of the governance strategy of commodity spot trading platform and the investment strategy of investment user groups under the influence of key factors such as the government’s intelligent regulatory subsidies, the ability of intelligent regulatory technology to identify profit-making rights protection, and the cost of users’profit-making rights protection are comprehensively considered. In order to break through the limitation of the mainstream evolutionary game research simulation experiment of “describing the linearity of structural equations”, the multi-agent simulation method is chosen to construct simulation experiment, explain the complex nonlinear iterative process of system emergence phenomena, and discuss how to reasonably control investors’demand for preserving and increasing value while controlling transaction risks and realizing reasonable supervision of multiple innovative trading modes. It researches services for the healthy development of spot trading platforms for commodities, and the return to the positioning of electronic trading platforms for commodities to promote physical circulation and strengthen real economy services.
It is found that the improvement of the intelligent supervision level of commodity spot trading platforms has a certain positive influence on maintaining the benign development of the market. However, when market investors have a high incidence of profit-seeking rights protection, and the trading environment is relatively harsh, relying solely on the platform itself to enhance its intelligent regulatory technology cannot make the market’s operating status develop in a favorable manner. At this time, government support is particularly important for the intelligent transformation of commodity spot trading platform supervision. In addition, curbing the development of the gray industry chain to enhance the cost of investors’rights, is an effective way to solve the dilemma of the commodity spot trading platform.
Based on the simplified analysis, this paper constructs an evolutionary game model with platform and consumers as the game subjects. However, the governance of the commodity spot trading platform for profit rights protection should also involve third-party service agencies and other multi-entities. Therefore, from the perspective of profit-making rights protection speculative costs, it will be the focus of future research to consider the optimization of control strategies for third-party institutions providing rights protection services as game players.

Stochastic Evolutionary Game Analysis of Collective Actions for Digital Collaborative Supervision of Safety Production

DONG Changqi, LIU Jida, MI Jianing

2023, 32(11):  155-162.  DOI: 10.12005/orms.2023.0366

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In the context of the current superimposed risk society and the digital age, improving emergency early warning and response, handling capabilities through digital and information technology, reducing the adverseimpact of risks, crises and disasters on society, and improving public safety performance have gradually become the direction for development in the field of safety production. The “Industrial Internet+Safety Production” Action Plan (2021—2023) clearly aims to improve the digital, networked, and intelligent level of safety production in industrial enterprises. Through the industrial Internet, the new infrastructure in the digital economy era, a new format and model of safety production will be built.
According to the “14th Five-Year Plan for National Work Safety”, the promotion of digital supervision of safety production relies on the joint efforts of comprehensive supervision departments, industry supervision departments, and production work units, showing the characteristics of collective action of multi-subject collaboration. However, safety production is essentially a public good, with the distinctive features of non-competitiveness and non-exclusivity. Advancing the digital transformation process is influenced by stakeholder factors. In the pursuit of collective action, the regulator and the regulated party are affected by problems such as information asymmetry, responsibility sharing, and lack of coordination, which may lead to free-rider behavior, moral hazard, and other tendencies that undermine collective action, showing a tripartite game relationship in the digital supervision of safety production. Coupled with the contingency and randomness of accidents and disasters, the complexity of risks exacerbates the randomness and uncertainty of the game system. How to find the game stability of the collective action of safety production supervision under the digitalization process among comprehensive supervision departments, industry supervision departments, and production work units, and analyze the evolutionary logic of multi-party achieving collective action is the core proposition of this paper, which can provide a certain theoretical reference for the construction of intelligent emergency development under the background of the digital economy.
Based on the institutional collective action framework, this paper constructs a tripartite random evolutionary game model, discusses the strategic impact and evolution trend of different factors on the digital collaborative supervision of safety production from the perspectives of cooperation benefits and transaction costs, and then analyzes the cooperation mechanism of various parties to achieve collective action. The Gaussian white noise is introduced into the three-party evolutionary game replication dynamic system to reflect that the process of digital collaborative supervision of safety production is disturbed by random factors. Thus, a stochastic evolutionary game model is established, and the model is solved by stochastic process theory. The system dynamics is further used for simulation analysis, and a causal feedback loop among comprehensive supervision departments, industry supervision departments, and production work units is established, parameter assignments are set according to the model solving conditions, and simulation analysis of initial probability and intervention by different factors is carried out respectively.
The study results show that the initial willingness has a certain impact on the implementation of the digital transformation of production work units. However, with the cost input and the action cycle lengthening, the regulatory authorities and production units gradually reduce the collective action willingness in the absence of further incentives. Production work units are the key entities that consider cost factors, and their sensitivity to the impact of relevant transaction costs is much higher than that of the two types of regulatory entities. At the same time, incentive intervention has a more significant effect on the realization of consistent motivation, especially the credibility benefit as a collective benefit has a greater continuous incentive for collective action. Accumulating collaborative relationship capital by strengthening communication and establishing institutional or informal multilateral cooperation, consultation and coordination mechanisms can effectively remove the collaborative barriers to collective action, thereby promoting the game system to a positive and stable state.
According to the conclusions, this paper proposes to rely on the existing safety production deliberation and coordination institutions to establish a cross-departmental cooperation mechanism for safety production digitalization, at the same time increase the government’s special support for the digital transformation of safety production in production work units, and further coordinate the responsibility boundaries of the regulatory authorities and strengthen the main responsibility of the production units.

Lotka-Volterra Evolution Model of Platform Enterprise Data Resource Development and Supervision

CHEN Tingqiang, YANG Qinghao, HOU Yuejuan, WANG Lei

2023, 32(11):  163-169.  DOI: 10.12005/orms.2023.0367

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With the evolution of information technology and the expansion of data capital, novel forms of unfair competition behaviors, such as the utilization of “big data to stifle maturity” and the imposition of a “pick one or the other” dilemma on users, continue to emerge. Therefore, it becomes paramount to enhance or innovate the supervision and the methods employed by government regulators concerning the development and utilization of enterprise data resources. This imperative action is aimed at safeguarding user rights and interests, fostering the industry’s robust growth, and enhancing overall social welfare. Elevating or innovating the level or mode of government regulators’ regulation concerning the development and utilization of enterprise data resources is significant in protecting user rights, promoting the industry’s healthy development, and improving social welfare. The concept of data resource development refers to the utilization of historical data by enterprises to analyze, summarize, and draw conclusions about existing purchasing population profiles. This process vividly portrays the distribution of user populations for products, thereby validating product positioning’s appropriateness and enabling timely adjustments. These insights lay the groundwork for the design of effective marketing strategies and product solutions. While the development and utilization of data resources by platform enterprises yield economic utility, they also bring associated risks. Within the market environment of information technology, big data, and industrial integration, enterprises leverage data resource development and utilization to achieve cross-domain competitive advantages. However, this practice not only significantly encroaches upon user rights and interests but also disrupts market equilibrium and, in certain cases, reduces social benefits. Hence, the primary focus of research is directed toward enhancing government supervision levels on the development and utilization of enterprise data resources while concurrently innovating the regulatory approach.
This research objective aims to advance industry health, protect user rights, and elevate overall social welfare. Given these considerations, the present paper undertakes a theoretical analysis of how the development and utilization of data resources impact social welfare through the number of platform users. It constructs a Lotka-Volterra evolutionary model that encompasses government regulation and data resource development and utilization. By merging the concepts of evolutionary economics with dynamic methodologies, it evaluates the effectiveness of governmental regulation, the stability of regulatory outcomes, and the market volatility inherent in the evolutionary process. Through theoretical derivation and simulation research, the study discovers the following insights:
(1)A slower pace of data resource development and utilization corresponds to heightened sensitivity to government regulation. A higher preset level of regulatory oversight by government regulators renders it more challenging for enterprises to undertake data resource development and utilization.
(2)As long as enterprises engage in the development and utilization of data resources, government regulators cannot automatically eliminate the risks arising from such development occurring without any supervision.
(3)When challenges arise in regulating data resource development and utilization by enterprises, or when regulatory authorities exhibit limited tolerance for regulatory costs, or when the sensitivity of regulatory costs to the development and utilization of data resources is inadequate, or when government regulatory authorities are insufficiently sensitive to the formation of industry monopolies by enterprises, the low-cost regulatory approach alone is insufficient to curb the development and utilization of data resources by enterprises.
(4)The level of development and utilization of data resources plays a role in initially promoting and then inhibiting the enhancement of social welfare levels. Government regulation exerts a facilitating effect on the improvement of social welfare levels. Addressing this issue requires two-pronged action: on one hand, they should implement robust regulatory procedures to strictly curb data misuse and unreasonable monopoly behavior by platform enterprises. During the initial stages of regulation, regulatory authorities need to enhance the visibility of maximum regulatory penalties for excessive data resource development and utilization within regulatory laws and regulations. This approach establishes a strong deterrent, thereby reducing the likelihood of platform enterprises violating regulations and fostering positive social effects through the development and utilization of data resources. On the other hand, government regulators should conduct random inspections on the development and utilization of data resources by enterprises in key areas with a certain probability. In cases where excessive development and utilization of data resources by platform enterprises are identified, they should be subjected to legal punishment and required to rectify their practices to prevent harm to social welfare.

Mechanism Design and Regulatory Governance in the Digital Economy Era

Auction Pricing Mechanism of Data Transactions under Demand Information Asymmetry

GUO Xinxin, LI Qianru, WANG Haiyan, DU Jianguo

2023, 32(11):  170-175.  DOI: 10.12005/orms.2023.0368

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At present, various data trading platforms in the market promote the market flow of data products, and also provide a place for potential data demanders to obtain data products or services. However, the price of data products is affected by a variety of subjective and objective factors, which makes it difficult to price data products in the way of traditional commodities. From the perspective of data seller, the same data product can be sold several times, so that it cannot be priced according to the transaction price equal to marginal cost. From the perspective of data buyers, the real value of data products can be defined only after use, and there is a certain value lag. In the face of the lack of historical experience in the pricing process of data products, exploring the pricing mechanism of data products has become the key to promoting the sustainable development of data trading platforms.
Considering the existence of information asymmetry in data transactions, the pricing by data trading platforms can easily lead to unfairness. By analyzing the trading behavior between data trading platforms and potential data demanders, we consider the pricing problem of data transactions as a coordination problem under information asymmetry. In other words, for data products that cannot be traded repeatedly, how does the data trading platform coordinate the purchase volume of many potential data demanders to maximize social welfare? A challenge encountered in the coordination process is that the utility of the potential data demanders is private information, making it rather challenging for the data trading platform to achieve group objectives with incomplete information.
In this paper, we formulate the coordination problem as a mechanism design problem. The data demanders are modeled as individual utility maximizers, while the group objective is encoded in the social choice function, which is to maximize the social welfare subject to a maximum supply constraint. We then design an auction mechanism to determine the optimal trading price of data products. Specifically, the information space of auction mechanism is a function space monotonically decreasing on data trading price, and the result function is determined by the demand functions of the data demander bidding and the maximum supply of the data trading platform. We prove that the proposed auction pricing mechanism can implement the social choice function in dominant strategy equilibrium. Finally, the effectiveness of the designed auction mechanism is further verified by a numerical experiment.
In summary, the research methods proposed in this paper does not require iterative information exchanges between the data trading platform and data demanders, and can be implemented with limited communication resources. The research conclusion can provide theoretical guidance for the data trading platform to set trading prices for data products that cannot be traded repeatedly. In addition, the research methods and conclusions proposed in this paper can be applied to similar trading problems, such as carbon emission trading, water rights trading, etc. The commonality of such problems is that the participants are usually one seller and many buyers, and there is information asymmetry between them.

Research on Innovative Customer Incentive Contracts in Network Community under Digital Economy

ZHANG Fenghua, DU Helen S, ZHANG Depeng

2023, 32(11):  176-182.  DOI: 10.12005/orms.2023.0369

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With the rapid development of digital technologies such as big data, artificial intelligence, Internet of Things and block chain, digital economy has become a new engine for national economic growth. Compared with traditional economy, in the context of digital economy, knowledge sharing becomes more efficient and convenient, and the rapid development of digital technology enables customers, as enterprises’end users, to participate in the innovation process of new products (or services). Customers are no longer passive buyers, but are new types of customer groups that form a complex social network through highly interconnected interaction. Their participation behavior reflects the dynamic integration of social media technology and customers’co-creation scene. The establishment of network innovation community enables the contact between enterprises and innovative customers to be free from the restrictions of time, geographical boundaries and organizational types, and provides more convenient platforms for innovative customers to share product knowledge, exchange experience and submit ideas for improvement. At the same time, in order to stimulate the participation enthusiasm of innovative customers and obtain the creative ideas related to product innovation, more and more enterprises begin to set up corresponding incentive mechanisms for innovative customers in online communities.
Although digital economy can effectively bridge the information gap between innovative customers and enterprises, enhance the willingness and ability of enterprises to realize innovation through innovative customers, and thus reduce the uncertainty of obtaining innovative profits, there are still some problems, such as insufficient investment in innovative customers’effort level, insignificant incentive effect of remuneration, and unsatisfactory incentive effect, although most enterprises set up incentive schemes for innovative customers who participate in innovation activities in network communities. How to formulate scientific and systematic incentive contracts for innovative customers in network community has become an urgent problem for enterprises. In addition, interaction behavior of innovative customers in network community depends on trust relationship to a large extent, so trust relationship is an important psychological driving factor for knowledge sharing or participation in innovation. However, existing studies tend to treat trust relationship as an intermediate variable from an empirical perspective and explore how it can affect the degree of knowledge sharing and the innovation performance of enterprises. It is not integrated into the incentive mechanism as an important variable and the studies highlight the psychological elements of innovative customers, so that they fail to make the research conclusion more close to management practice. In addition, driven by digital technology, social media makes the way for innovative customers to obtain information resources more efficient, and thus improves the innovation efficiency of innovative customers to a certain extent in the network community innovation activities. The innovation efficiency is the productivity of innovation results and refers to productivity. Therefore, when setting incentive contracts for innovative customers in network communities, we should pay attention to the influence of the productivity and the trust degree of innovative customers.
To sum up, the incentive model of innovative customers is built from the perspective where innovation customers of online community participate in enterprise’s innovation activities under digital economy, exploring the relationship among the productivity, the trust degree of innovative customers, the type of incentive contracts and the effect of incentive mechanism. This study explores the impact of the productivity and the trust degree of innovative customers on the setting of optimal incentive contracts by model solving and analysis. In addition, the results of model analysis are verified according to simulation experiments. The results show that when productivity is low and trust degree is high, the team reward contract with peer supervision advantage will be the optimal incentive contract. When productivity is high and trust degree is high, the firm will choose to provide team reward contract. With the decrease in trust degree, the relative performance contract with commitment advantage is the optimal incentive contract. As low productivity shifts to high productivity, the relative performance contract will be the optimal incentive contract more frequently.
This study only considers the influence of the trust degree and the productivity of innovative customers on optimal incentive contracts when innovative customers are risk neutral. In future studies, the risk aversion of innovative customers can be considered to further optimize the model. In addition, the influence of innovative customers’ subjective perception on the contract, including the perception of fairness in remuneration and the perception of reciprocity among participants, can be incorporated to supplement and make a comparative analysis of the research on innovative customers’incentive, so as to make the research conclusions more universal and operable.

Research on Knowledge Interaction and Evolution Mechanism of Cloud Manufacturing Platform Considering Collaborative Tax Supervision

ZHONG Qi, WANG Hongxue

2023, 32(11):  183-190.  DOI: 10.12005/orms.2023.0370

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The cloud manufacturing platform achieves collaborative resource sharing among agents through knowledge interaction, which is the power and source to improve the innovation ability of manufacturing enterprises. At the same time, influenced by the complex characteristics of platform network externality and mobility, regional differences in tax systems, incomplete tax knowledge systems of cloud providers, and a relatively weak awareness of taxation have gradually become prominent issues. Many tax-related pieces of information are inaccessible to tax authorities, making it difficult to establish effective oversight. How to obtain complete tax-related information from cloud providers has become an important part to ensure the continuous and healthy development of cloud manufacturing platforms, and it is also the most significant challenge in tax supervision of platforms.
The cloud manufacturing platform can obtain relevant data and information related to all business links of cloud providers. The disclosure of tax-related information by the platform can effectively reduce the management costs and provide a solid foundation for effective supervision by tax authorities. At the same time, tax-related information disclosure will also have an impact on the collaborative and knowledge interaction between the cloud manufacturing platform and cloud providers. Therefore, how to disclose tax-related information on the cloud manufacturing platform, help the government to improve tax supervision, ensure the willingness of cloud providers to interact with knowledge and enhance the overall performance of the cloud manufacturing platform, has become a practical issue in the development of cloud manufacturing platforms and an important subject that academia needs to further explore. In the face of the practical need for platform regulation and supervision, it is of great theoretical value and practical significance to study the evolution mechanism of knowledge interaction among the entities of cloud manufacturing platforms under the government-platform collaborative supervision.
Based on the cloud manufacturing platform with strong network externalities, this paper introduces the knowledge gap theory and social interdependence theory. It takes into account factors such as the revenue of network externalities and taxes and fees. It builds upon the architecture of the cloud manufacturing platform and simultaneously considers both positive knowledge interaction and negative knowledge concealment behaviors. It establishes a dynamic evolutionary game model involving three parties: the cloud manufacturing platform, cloud providers, and the government, to analyze the behavioral strategies and evolutionary paths of the three-party. Using MATLAB for model simulation and analysis, the article further investigates the impact of internal and external factors such as tax regulation, knowledge interaction costs and benefits, and network externalities on the evolution of knowledge interaction strategies within the cloud manufacturing platform.
The research findings indicate that: (1)In comparison to other factors, the cost of knowledge interaction and the benefits of knowledge interaction have the most significant impact on the choice of knowledge interaction strategies of cloud providers. The willingness for knowledge interaction decreases as the cost of knowledge interaction increases, and it increases with the benefits of knowledge interaction income. (2)The greater the cost of active government regulation, the higher the willingness of cloud providers to choose active knowledge interaction strategies. However, the government’s reward for the platform’s disclosure of tax-related information has a relatively little impact on the willingness of cloud manufacturers for knowledge interaction. (3)Network externalities have a positive impact on the strategic choices of cloud manufacturing platforms, cloud providers, and the government. Among these, the influence on cloud providers adopting knowledge interaction strategies is the most significant.

Collaboration Mechanism and Strategy for Digital Transformation of Tourism

CHEN Yuting, ZHANG Nan

2023, 32(11):  191-196.  DOI: 10.12005/orms.2023.0371

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In the backdrop of the evolving digital economy and the emergence of smart cities, the cultural and tourism industry finds itself at the crossroads of new opportunities and challenges. The integration of digital technologies has ushered in novel modes of tourism, creativity, entertainment, and art display, reshaping the industry’s landscape. As this sector undergoes a digital transformation, the urgency to embrace collaborative governance models becomes evident, while simultaneously raising questions surrounding their efficacy and optimal strategies. This study seeks to navigate this complex terrain by investigating the synergy between digital transformation and collaborative mechanisms within the cultural and tourism industry.
This study employs a comprehensive and innovative methodology to delve into the digital transformation of the cultural and tourism industry, with a specific emphasis on the impact of cross-industry and cross-regional collaborations. Drawing insights from consumer utility theory and game theory, we make an extended Hotelling model. This model serves as the bedrock for unraveling the intricate decision-making dynamics within a competitive terrain. Central to our methodology is the incorporation of heterogeneous consumers and a diverse array of competitive tourism attractions and associated enterprises. This holistic approach allows us to dissect the optimal strategies for digital transformation under varying scenarios of collaboration across industries and regions. Drawing from the rich tapestry of consumer utility theory, our model captures the nuanced preferences of consumers, factoring elements such as product quality, distance, and price. This foundation is then extended and tailored to the cultural and tourism context, enabling us to simulate and analyze the behavior of enterprises in response to changing digitalization paradigms. The analysis undertaken within our methodology entails a granular exploration of the interplay between competition and collaboration. Through rigorous mathematical examinations, we elucidate the dynamics that drive enterprises to opt for cross-industry and cross-regional cooperation. By uncovering the tipping points and equilibrium conditions, we provide insights into the circumstances under which collaborative strategies prove most advantageous. Moreover, we investigate the intricacies of the “prisoner’s dilemma” scenario that arises in the context of cross-industry collaboration. Our model unveils the pivotal role played by the cost coefficient of digital technology investment in influencing enterprise decisions. By systematically altering these factors within the model, we gain a comprehensive understanding of the strategic landscape.
The culmination of our methodology yields valuable findings that shed light on the intricate relationship between digital transformation and collaborative governance in the cultural and tourism industry. Notably, our analysis reveals that under cross-industry collaboration, increased digitalization efforts lead to higher investment levels, with the degree of investment rising proportionally to the number of collaborative competitors. Furthermore, enterprises opting for collaboration consistently outperform those not doing so in equivalent competitive environments, highlighting the inherent benefits of such cooperative approaches. Interestingly, our study unveils a counterintuitive yet fascinating phenomenon: even a single competitor’s choice of collaboration triggers a “prisoner’s dilemma” scenario, prompting all market players to embrace collaboration ultimately. This collective decision resolves the prisoner’s dilemma and promotes cooperative behavior. To address the challenges associated with collaborative decisions, the study suggests that mitigating the cost coefficient of digital technology investment can effectively enhance efficiency and promote collaborative efforts. Moreover, the analysis underscores the positive impact of cross-industry collaborations on consumer welfare. The potential for increased profits through cross-regional collaboration depends on the number of competitors; a smaller pool of competitors tends to foster a preference for such collaborative ventures.
The implications of our findings are far-reaching and hold significant implications for the future of the cultural and tourism industry. The identified strategies and influencing factors provide a roadmap for stakeholders navigating the digital transformation landscape. It becomes evident that the traditional competitive paradigm is being redefined by the allure of collaboration, illustrating a paradigm shift in the industry’s operational dynamics. It is imperative for industry leaders and policymakers to recognize the subtle yet powerful impact of collaborative governance models in guiding the digital transformation journey. Furthermore, the study underscores the need for a balanced approach to digital investment costs, suggesting avenues for policy interventions that can foster a collaborative ecosystem while mitigating potential challenges. As we navigate the uncharted waters of a digitally driven cultural and tourism landscape, the insights provided by this study serve as a compass, directing industry stakeholders toward collaborative strategies that not only enhance their individual performance but also contribute to the overall growth and dynamism of the sector.

Digital Transformation and Integrated Enterprise Risk

LIU Chunxia, TAN Ronghui, CHEN Youyu, DENG Chao

2023, 32(11):  197-205.  DOI: 10.12005/orms.2023.0372

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The external environment is currently characterized by heightened uncertainty. Global economic and trade growth momentum has slowed. The development of enterprises in China has been affected by many unpredictable factors both at home and abroad, and faces enormous risk challenges. Can digital transformation reduce enterprise risk? Existing research has primarily examined this from a single risk perspective, and has found that digital transformation can have a dual impact on enterprise risk. Considering that risks at different levels are not independent of each other, but influence and constrain each other, this paper breaks through the single risk research perspective and analyzes the mitigation mechanism of digital transformation on the integrated risk of enterprises from the perspective of risk portfolio.
First, this paper incorporates five types of key risks, including information risk, operational risk, financial risk, financial risk, and supply chain risk, into the enterprise risk portfolio system, applies multi-compartment principal component analysis to decompose, downgrade, and integrate them to reflect the integrated risk of the enterprise, studies the mitigation mechanism of digital transformation on the integrated risk of the enterprise, and finally obtains the following conclusions of the study: digital transformation reduces an organization’s integrated risk. A moderating effect analysis reveals that the integrated risk reduction effect of digital transformation is more significant for highly innovative firms, highly competitive industries, and firms without internal control deficiencies.Heterogeneity analysis shows that the digital transformation process in the service sector is ahead of the manufacturing sector, with a high degree of digital transformation and greater risk mitigation in the eastern part of China.
Second, based on the integrated risk, this paper introduces technology risk and human resource risk, constructs a new type of integrated risk, and studies the mitigation mechanism of digital transformation on the integrated risk of enterprises. The following research conclusions have been drawn: Although digital transformation can reduce the new integrated risks of enterprises, the fit between the two is poor. The linear correlations between digital transformation and various types of individual risks are characterized by heterogeneity. Through the introduction of machine learning models, it is found that there is a certain nonlinear relationship between digital transformation and new types of integrated risks, and the machine learning integrated model based on the stacking method has the best fitting effect.
Finally, the following management insights are drawn: In the current environment of increased uncertainty and risk, companies should integrate all types of risk into a whole and implement risk portfolio management so that risk information can truly play a decision-support role. Companies need to capitalize on the opportunities for digital transformation by leveraging cohort effects and “learning by doing” approaches to innovation investments and changes in innovation activities. Enterprises in the industrial chain and supply chain should collaborate on the pace of digital transformation, and build a joint risk control mechanism.

Can Digital Transformation Improve the Quality of Corporate Information Disclosure?——Empirical Evidence Based on Management Earnings Forecast

WAN Qingqing, SUN Guangguo

2023, 32(11):  206-211.  DOI: 10.12005/orms.2023.0373

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Under the wave of digitization, an increasing number of enterprises are integrating digital technologies into their daily operations, including procurement, production, and sales. Digital transformation enables businesses to promptly and accurately collect and process information generated at various stages, reshaping management thinking and models, and driving industrial upgrading and high-quality economic development.
Digital transformation promotes the deep integration of digital technology and production development, attracting the widespread attention of academia. The changes brought about by digital technology in various aspects of business, such as production management, marketing management, operational environments, and decision-making, have become a hot topic of discussion. For instance, sensors and wireless technology capture data in production processes, guiding production through smart devices. 3D printing enhances product design capabilities, shortening the time from design to production, and thereby increasing efficiency. Machine learning makes a predictive analysis and effective decision-making for businesses. The Internet of Things can transform traditional production and business processes, leading to organizational restructuring and improved operational efficiency. Big data technology uses distributed architecture for mining and processing massive data. The information effect is the most direct impact of digital transformation on businesses, providing digital support for their operational decision-making and information disclosure. In the increasingly complex modern economy, the information governance effect of digital transformation is particularly important.
Digital transformation not only reshapes production and business processes but also has a positive impact on information disclosure within enterprises. It breaks down information silos and enhances the internal information acquisition capabilities. In complex businesses, the information acquisition effect of digital transformation is even more prominent. Additionally, digital transformation helps businesses efficiently handle vast, non-standard, and unstructured data, enabling precise data processing and improving internal information utilization and processing efficiency. In summary, digital transformation can have an information governance effect, enhancing information collection and processing capabilities, and ultimately improving the quality of management’s performance forecasts. Currently, no literature has provided a definitive answer to whether digital transformation can realize its information governance effect, and this requires further investigation.
In light of this, this study examines manufacturing companies listed on the Shanghai and Shenzhen stock exchanges from 2013 to 2020. It measures the degree of digital transformation using text analysis techniques and investigates the information governance effect of digital transformation from the perspective of management’s performance forecasts. The research reveals that digital transformation has a significant information governance effect and can enhance the quality of performance forecasts. Specifically, companies with higher degrees of digital transformation have more accurate and timely performance forecasts. These conclusions remain robust after a series of robustness tests. Furthermore, the impact of digital transformation on the quality of performance forecasts is more pronounced for companies with higher business complexity, especially those with numerous subsidiaries and diversified operations. Digital transformation also increases the willingness and quality of voluntary performance forecasts but does not significantly affect the quality of mandatory performance forecasts. Lastly, the information governance effect of digitization is particularly prominent in the context of the COVID-19 pandemic.

Systemic Financial Risk Monitoring and Early Warning Based on Machine Learning Model

LI Hongquan, ZHOU Liang

2023, 32(11):  212-219.  DOI: 10.12005/orms.2023.0374

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With the rapid development of financial technology, the financial industry has experienced or is undergoing major changes at many levels. In the field of financial risk management, due to the increasing complexity of the modern financial system, the limitations of traditional risk modeling methods have become increasingly prominent, while machine learning methods are good at capturing the complex nonlinear relationship between variables, have many inherent advantages over traditional economic analysis and prediction technologies, and therefore can better meet people’s modeling requirements and analysis and prediction demands for economy and finance, a typical complex and open giant system. So, we aim to give a more effective risk analysis system using machine learning methods.
This paper proposes a new systemic financial risk monitoring and early warning system based on machine learning techniques, selecting early warning indicators from eight levels: economic fundamentals, money supply, fiscal conditions, securities and interest rate markets, price indices, foreign exchange and exchange rate markets, leverage and banking system, and using five classical machine learning models and its integrated models to forecast systemic financial risk. In order to open the black box of machine learning, we deconstruct the machine learning early warning model using feature importance, and partial dependency plots(PDP). Feature importance is commonly used in tree models to analyze the importance of variables, while PDP is applied to different models, and its core idea is to examine the effect of different values of a feature on the output value of the model. The PDP method can not only identify the relative importance of variables, but also examine the non-linear effects of variables. Our sample interval is from January 2005 to December 2020, and all raw data are obtained from the Wind database.
The research results show that: (1)Compared with traditional linear models, machine learning models are good at capturing nonlinear relationships, and perform well both in and out of the sample. (2)Compared with Lasso model, SVM and other single model, integrated models have better prediction capabilities by improving the robustness of prediction results. (3)PDP model can effectively identify the nonlinearity and importance of features, thereby helping to open the black box of machine learning; among all the early warning variables, exchange rate, money supply, market interest rates and industrial product prices are key factors affecting systemic financial risks. Monitoring these key variables will help prevent systemic financial risks in an early stage. Our research work is helpful for promoting the application of artificial intelligence in the field of finance by providing a new technical framework for the systemic risk monitoring and early warning.

Research on Multi Governance of Online Catering Food Safety from the Perspective of Game Theory

QIU Yuxia, LI Hongyong

2023, 32(11):  220-226.  DOI: 10.12005/orms.2023.0375

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With the development of the “stay-at-home economy” and the advancement of internet technology, people’s dining consumption habits have undergone significant changes, gradually shifting from traditional offline catering to online one, promoting the rapid development of the online catering industry and expanding market size year by year. However, the issue of food safety in online catering has become increasingly serious with the growth of the industry market size, and has become a key issue of public concern. The governance of this issue is of practical significance for improving people’s confidence in online catering consumption and promoting the good and sustainable development of the online catering industry.
Due to the fact that the issue of food safety in online catering involves multiple stakeholders such as platforms, catering merchants, and consumers, the market position and information level of different stakeholders are not completely symmetrical, resulting in differences in the governance attitudes of each stakeholder towards the issue. There is a complex intertwined relationship of interests among governance decisions of each stakeholder concerned. How to balance the interests of each stakeholder in governance of online catering food safety issues and form a multi-party governance mechanism with interest linkage, plays an important role in achieving effective governance of food safety issues in online catering.
In response to the issue of multi-party participation in the governance of online catering food safety, this paper constructs a tripartite static game model among online catering platforms, catering merchants, and consumers. By solving the expected returns of different strategy choices by various parties, analyzing the role and influencing factors of decision-making by all parties, and summarizing the necessary conditions and combination strategies for achieving effective governance of online catering food safety issues, and collecting and organizing practical cases formed by various parties under different strategic combinations as citations, this paper explores the theoretical basis and implementation mechanism for achieving multi co-governance of online catering food safety. Furthermore, it takes the Meituan takeout platform in real situations as a representative of online catering platforms, and a numerical simulation analysis is conducted using Meituan takeout platform related data to further verify the relationship among game party strategy choices.
The research results indicate that the basis for achieving long-term governance of online food safety issues through the participation of online catering platforms, catering merchants, and consumers lies in the balance of interests among participants. The necessary condition for achieving tripartite co-governance is to ensure the interests of catering merchants in compliant production. The governance of food safety in online catering can form four supervision states: strong supervision, weak supervision, inability to supervise, and optimal supervision. Different supervision states can form different governance results, and the ideal state is tripartite co-governance. From a non-ideal state, it can be transferred and transformed from controlling and adjusting influencing factor variables to the optimal governance state.
The research results promote the theoretical research of platforms’multi-party co-governance, and provide theoretical support for solving the issue of food safety in online catering, which can provide a certain reference for the governance of food safety issues in online catering in reality.

Analysis of Governance of E-commerce Platform’s Big Data Price Discrimination under the Data Rights Confirmation

LI Guohao, LIANG Yongtao, SU Jialu

2023, 32(11):  227-232.  DOI: 10.12005/orms.2023.0376

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With the recent rise of mobile payments, the increasing frequency of e-commerce platforms employing big data price discrimination has attracted widespread attention from various sectors of society. The ability of e-commerce platforms to engage in extensive price discrimination is attributable, on one hand, to the inherentinformation asymmetry between these platforms and consumers, and on the other hand, to the insufficient emphasis on personal information protection and data security by the government, which allows companies to engage in big data price discrimination without restraint. The practice of e-commerce platforms using big data for price discrimination not only harms consumer welfare and disrupts market order but also hinders the long-term development of the platforms themselves.
In response to the current governance challenges posed by big data price discriminationon e-commerce platforms, the concept of data ownership was first introduced in China by the Shenzhen Special Economic Zone in 2021. Personal data protection in China has entered a substantive stage, opening a new chapter in the governance of the phenomenon of big data price discrimination by e-commerce platforms and providing consumers with highly advantageous legal tools. Therefore, based on this regulation, this paper constructs an evolutionary game model with the government, e-commerce platforms, and consumers as the main stakeholders to explore the impact of strategies adopted by these stakeholders on the governance of big data price discrimination in the context of data rights confirmation.
Through the construction of dynamic replication equations and Jacobian matrices, this paper seeks to find the equilibrium points of the model. Using real-life data, it conducts a simulation analysis to assess the effectiveness of the model and the impact of relevant factors, which concludes: Firstly, whether consumers engage in price comparison is pivotal to the phenomenon of big data price discrimination by e-commerce platforms. Secondly, under the conditions of evolutionary stability, the government’s strategy choice is contingent upon the costs it incurs in market regulation and the promotion of data rights development. Thirdly, government penalties play a suppressive role in addressing big data price discrimination by e-commerce platforms, with harsher penalties resulting in stronger suppression. Lastly, the exercise of personal data rights by consumers can effectively mitigate instances of big data price discrimination by e-commerce platforms.
Based on the aforementioned conclusions, this paper presents the following policy recommendations. From a government perspective, the government in China should prioritize public awareness campaigns and engage in collaborative oversight with consumers, aiming to enhance consumers’ awareness of rights protection and their willingness to engage in price comparison. Additionally, government penalties for big data price discrimination by e-commerce platforms should not only concern the monetary aspect but also involve the disclosure of e-commerce platforms engaged in such practices through official channels, so as to lead public opinions to exerting pressure. From a consumer’s perspective, on one hand, it is essential to enhance one’s awareness of rights protection and remain vigilant against unethical conduct by e-commerce platforms. On the other hand, even after making a purchase, consumers can compare prices and actively exercise their personal data rights, thereby restraining big data price discrimination by e-commerce platforms from a post-purchase control standpoint.

Study of Game Issues in Cross-border Trade Empowered by Blockchain Digital Ecosystem

YU Tao, YAO Fanjun, GAO Hongwei

2023, 32(11):  233-239.  DOI: 10.12005/orms.2023.0377

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The Government Work Report underscores the imperative to vigorously promote the development of the digital economy and bolster the comprehensive construction of a digital government and digital China. The 14th Five-Year Plan for the Development of the Digital Economy seeks to harness the potential of the digital economy to provide substantial impetus for the sustainable and sound development of both the economy and society. In this paper, we have crafted a tripartite game tree model encompassing the customs department, financial institutions, and trade enterprises, with the aim of scrutinizing the digital transformation of trade enterprises and the customs department’s establishment of a blockchain-based digital ecosystem to empower cross-border trade.
Through our analysis of the outcomes, several management implications have come to the fore. First and foremost, we have ascertained that the readiness of enterprises to undertake digital transformation hinges on the cost of digital transformation and investment in digital infrastructure. As the expenses associated with digital transformation surge, firms necessitate increased financial backing for their day-to-day operations and digital endeavors. Consequently, financial institutions may emerge as a potential source of investment. Second, our analysis posits that trade enterprises may actively seek support from financial institutions during their digital transformation efforts. However, financial institutions tend to direct their attention toward trade enterprises exhibiting promising prospects and superior economic performance. This strategic approach enables financial institutions to bolster their future profitability by expediting the digital transformation of these enterprises. Third, the customs department’s establishment of a blockchain-based digital ecosystem to empower trade enterprises and financial institutions can catalyze the development of a digital real economy.
We have also formulated some recommendations for the stakeholders. It is important to note that the empowerment of trade enterprises within the ecosystem and the provision of preferential loan rates by banks should not be regarded as the primary drivers of a company’s digital transformation. Interestingly, the predominant factor influencing a company’s digital transformation is the cost associated with blockchain integration. Paradoxically, higher integration costs tend to increase a company’s willingness to adopt this technology. A higher integration cost signifies a greater financial obstacle to digital transformation. Trade enterprises can secure loans from financial institutions at favorable interest rates to support their digital transformation efforts, enabling them to become active participants in the ecosystem. Financial institutions are more inclined to offer preferential loan rates to trade enterprises that incur significant blockchain integration costs but exhibit strong profitability after achieving digital transformation. Moreover, when trade enterprises face a substantial funding gap during their digital transformation, it piques the interest of financial institutions to provide loans at favorable interest rates. This support assists enterprises in establishing digital infrastructure, fostering innovation in their business models, and effectively executing the digital transformation of traditional industries. Financial institutions place a greater emphasis on enterprises that, while building upon their traditional business foundations, strategically pursue digital transformation.
Through the utilization of blockchain technology, customs authorities have established a “Blockchain+Customs” digital ecosystem, offering a platform for trade enterprises and financial institutions to embrace digital operations. The provision of data services to financial institutions significantly influences their willingness to offer preferential loan interest rates, thereby encouraging trade enterprises to integrate blockchain technology and achieve a digital customs transformation. However, as the costs associated with customs data services increase, the likelihood of customs providing these services to financial institutions decreases.