Abstract:By applying the complex network theory to the input-output model and introducing it into the macroeconomic production function theoretical model, this paper investigates and simulates the internal mechanism of anti-dumping embedded in the global production network and its cascading effect. The analysis results reveal that from the network perspective, the effect of anti-dumping shocks on intermediate products is significantly greater than that on final products, and the intermediate products that cross borders multiple times will cumulatively increase the trade cost caused by anti-dumping. The effects caused by anti-dumping, including lower induced demand, suppressed export trade, and fluctuations in aggregate output levels across industries and countries, will spread along the input-output relationships in the global production networks. This results in a significant cascading effect that decay with the connection (trade volume) between different country-sector nodes. The cascading effect of anti-dumping not only involves the direct connection nodes, such as the domestic upstream and downstream industries of the countries involved in the anti-dumping case of the global production network, but also affects the indirect connection nodes, such as the third-party countries and regions through the production network, thereby affecting the development of global multilateral trade. The cascading effect also prevents network nodes from participating in the division of production in the global value chain, resulting in the extension of the domestic value chain of the country (region) to which the node belongs. After the anti-dumping measures are embedded in the global production network, they generate a total fluctuation effect through the entire production network, thereby changing the topological structure of the global production network. In other words, the global production network becomes relatively sparse, and the network distance between nodes becomes longer.

Abstract:In the era of innovation-driven development, it is of great significance to the S&T innovation platform and social economy to provide supporting services for resource sharing for innovation and to realize the effective use of scientific and technological innovation resources through the platform supply chain, thereby revitalizing social resources. The level of supporting services provided by science and technology resource providers on the S&T innovation platform not only affects their innovation output, but also affects the number of users who join the platform. Considering the actual situation of China’s science and technology innovation, this paper examines the preference of scientific research for the quality of the supporting services provided by resource providers on the platform. Game theory is applied to studying the pricing decisions of the S&T innovation platform. Two strategies, platform subsidies and piggybacking alliances, are extensively examinedbased on ordinary pricing strategies. Based on the realistic situation of the supply and demand characteristics of users on both sides of the platform, the profit maximization strategies of different types of S&T innovation platforms are revealed. The research has found that under the three strategies, the platform pays commission to resource providers within a certain range of unit operating costs. The commission increases with the increase of user quality preference coefficient, and the optimal subsidy amount and drainage quantity also increase. Comparing the three strategies, the service fee of the platform is the lowest under SS and the highest under AS, both of which can bring higher returns to the platform under certain conditions. Different types of S&T innovation platforms should choose different pricing strategies to maximize platform profits when the proportion of high-quality resource providers in the market is different. In addition, after considering the characteristics of innovation risk, a higher quality preference coefficient is required to achieve a substantial increase in the profits of the S&T innovation platform.

Abstract:In order to provide customers with a better shopping experience, many retailers have introduced the buy online and pick up in store (BOPS) shopping channel, so that the limited offline inventory is faced with multiple channels demand. Whether the introduction of BOPS channel is beneficial to retailers and how to fully utilize offline limited inventory is still lacking research. This study considers a retailer and two customer segments: physical store customers who purchase goods only through physical stores, and omnichannel customers who choose among various channels strategically. Most existing omnichannel research assume that retailers’ offline inventory satisfies offline channels with priority. This paper takes three service strategies into consideration, including store customer priority strategy(SPS), omnichannel customer priority strategy(OPS), and equal priority strategy(EPS). Compared with the traditional literature, it is found that the introduction of BOPS is not entirely beneficial to the retailer. The density of physical store (determining the customer’s store visiting cost) and distribution center (determining the customer’s online waiting cost) affect the retailer’s revenue. Moreover, it is found that if customers’ online waiting costs are low, and store visit costs are even lower, the introduction of BOPS channels with SPS and EPS can increase the retailer’s profits, while simultaneously increasing the consumer surplus for both store customers and omnichannel customers.

Abstract:Firstly, from the perspective of multiplex network theory, a multi-channel diffusion model of the biased perceived iatrogenic risk (BPIR) in the context of the Internet of Healthcare Systems (IHS) is constructed in this paper. The model considers the interactive effects of perceived diffusion of BPIR among various information channels under IHS. Secondly, the threshold to distinguish whether BPIR spreads among the public under the IHS is obtained. Finally, the parameters of the established theoretical model are estimated and the case analysis is conducted using actual data. The results indicate that: 1) Compared with mere the publicity of correct cognition, the diffusion efficiency of BPIR under IHS would be significantly reduced when the correct cognition dissemination is combined with cognitive correction; 2) Compared with the in-depth control of the diffusion of BPIR in only one or two channels, moderate intervention across all channels results in a more significant reduction in the diffusion efficiency of BPIR.

Abstract:This paper proposes the “platform dominance” green subsidy and develops a sequential game model involving three players: the buyer, the seller, and the C2C second-hand e-commerce platform. It not only solves the optimal green subsidy strategy and the optimal level of integrity construction effort for C2C second-hand e-commerce platforms, but also analyzes how various factors, such as charging rules, influence the optimal decisions and profits. The results show that: 1) If the C2C second-hand e-commerce platform imposes a proportional fee, it should provide green subsidies to buyers. If not, offering green subsidies to either buyers or sellers would have the same effect on platform profits； 2) The government’〖KG-*5〗s green incentives for C2C second-hand e-commerce platforms should fall within a feasible range, not too low or too high. Additionally, charging a proportional fee could generate the “leverage effect” of the government’〖KG-*5〗s green incentive； 3) When C2C second-hand e-commerce platforms provide green subsidies, imposing proportional fee could promote integrity construction, while charging fixed fee could inhibit integrity construction. If the cost coefficient of integrity construction decreases, the platform should intensify its integrity construction. These findings contribute to advancing the internalization of green consumption externality by platforms in the context of the “Carbon neutrality”. Additionally, they provide valuable practical insights for the integrity construction of information intermediary platforms.

Abstract:This paper studies the variation of commute behaviors caused by unmanned vehicles from the perspective of activities. Considering the in-vehicle utility and the utilization efficiency of in-vehicle time, it formulates the activity-based utility function of commuters riding unmanned vehicles. Then it derives the departure and parking patterns, optimal dynamic bottleneck charge, and the model properties with constant and linear marginal-activity utility, respectively.These are compared with the corresponding results in the trip-based model.The results show that, with the constant marginal-activity utility,the early departure rate is an increasing function of in-vehicle activity utility and the utilization efficiency of the in-vehicle time, but the late departure rate reverses it. Meanwhile, with the linear marginal-activity utility, the number of early arriving commuters is not affected by the in-vehicle time loss. Still, it is positively related to the working start time. After implementing the optimal bottleneck toll, the trip-based model overestimates the upper bound of total toll revenue and underestimates the lower bound of total toll revenue. To maximize the net system utility, the government needs to adjust the parking density based on the parking social cost and the specific situation.

Abstract:The essence of recommender systems is to model the implicit preferences in consumer behavior. The human behavior is inseparable from psychology, and there are rich internal motives behind the superficial behavior. However, the current studies mainly focus on the behavioral data modeling, rarely involving the internal psychological activities and the information processing process of decision-making. Therefore, this paper studied a new idea of recommender systems by introducing AIDMA decision model from the perspective of consumer decision journey. A new deep review-based recommender system is proposed, which applies the AIDMA decision journey into the deep learning framework. Experiments showed that the recommendation performance of the proposal is significantly better than the state-of-the-art methods. This paper follows the big data-driven research paradigm of “model driven+data-driven”, realizing in-depth methodological innovation with theoretical support.

Abstract:An in-depth understanding of the factors influencing the earnings management of listed firms can help optimize China’s capital market ecology and ensure the full implementation of the registration system. The existing literature pays less attention to the characteristics of earnings management as a collective task, and ignores the impact of the intra-organizational informal relationships(IR) on earnings management by reducing collusion costs.This paper examines for the first time the impact of IR on the earnings management of listed firms.The results show that: 1)the stronger the IR, the higher the degree of earnings management, and this finding still holds after a series of robustness tests; 2)The more firms need to reduce the cost of collusion, the more pronounced the positive effect of IR on earnings management; 3) Providing higher levels of employee benefits is an important channel through which IR influence earnings management.This paper provides a new perspective for understanding the factors influencing earnings management.

Abstract:From the perspective of top managers’behavoir, this paper investigates the impacts of local air pollution on a firm’s stock price crash risk. To establish causality, we introduce an exogenous shock of spatial changes for air pollution and conduct spatial regression discontinuity design across the Qinling-Huai River boundary in China. The results show that air pollution has a significant positive effect on a firm’s stock price crash risk. A plausible mechanism behind the effect is that worsened air conditions increase management turnover, leading to the disclosure of accumulated bad information. In addition, firms located in polluted areas have a higher rate of managers leaving due to health reasons than those in less polluted areas. The effects of air pollution on stock price crash risk are more pronounced in firms with lower level of managers’ salary, firms located in areas with less medical resources, firms with poorer information disclosure quality and non-state-owned firms. This study adds empirical evidence on how external environment affects financial market through the behavior of firms’ managers, and provides policy implications regarding the importance of improving local environmental regulation enforcement and financial market development.