With the extension of online time-limited promotion, an urgent problem for sellers to solve is how to formulate reasonable intertemporal pricing and advertising strategies targeting consumers with different characteristics. This paper constructs a two-stage dynamic pricing and advertising model with time-sensitive customers and price-sensitive customers in online time-limited promotions. In addition, it explores the optimal pricing and advertising level of sellers under a uniform pricing strategy or differential pricing strategy and performs numerical verification on the model. It is found that sellers’ product pricing for price-sensitive consumers in the first stage is higher than that of the second stage, regardless of what pricing strategy the sellers use in the first stage. Then, the seller’s advertising level for price-sensitive consumers in the first stage is lower than that of the second stage. It is worth noticing that only when the proportion of the time-sensitive customers is higher, would the advertising level for time-sensitive consumers in the first stage be higher than that of price-sensitive consumers. Besides, the seller’s intertemporal pricing and advertising level is concerned with the proportion of time-sensitive customers regardless of what pricing strategy the sellers use in the first stage. Moreover, compared with using the uniform pricing strategy, the overall profit of the sellers under the differential pricing strategy is higher than that of the non-price discrimination strategy. Furthermore, when sellers use a uniform pricing strategy, whether the consumer surplus of time-sensitive consumers is higher than that of price-sensitive consumers is determined by the proportion of the time-sensitive customers. When sellers use a differential pricing strategy, the consumer surplus of price-sensitive consumers is significantly higher than that of time-sensitive consumers. The research results can provide theoretical suggestions for intertemporal pricing and online advertising in time-limited promotion.

Product green investment （PGI）of start-ups in the post-pandemic era is inhibited and effective government support policies are required. This paper constructs a two-stage model for start-ups in demand uncertainty, and considers the characteristics that financially constrained start-ups will pursue survival under financial constraints in addition to the traditional profit-seeking objective. It introduces two types of policy tools, including financial supports (special funds, and research and development (R&D) subsidies) and information supports (consumption guidance, and information provision), to compare the PGI of start-ups under different operational objectives and supporting policies. The results show that in an environment with market volatility, start-ups under profit-seeking objective will simultaneously increase (decrease) their PGI and pricing relative to a deterministic environment to balance short-term survival pressure and long-term profits, and positively correlate PGI and pricing to balance the survival risk at each stage under profit-seeking objective. In terms of incentive policies, for profit-seeking start-ups, R&D subsidies have a more evident effect than special funds. Consumption guidance can exert a positive regulatory influence, whereas information provision might temporarily inhibit the drive of firms to pursue the risk-reward of green investment in products. For survival-seeking start-ups, special funds yield a more substantial effect than R&D subsidies, and there exists an optimal intensity for consumer guidance and information provision.

In this paper, the optimal decisions and profits of the supply chain under green credit financing and prepayment financing are analyzed respectively, in which the supplier has capital constraints and research and development (R&D) failure risk. On this basis, considering the subsidy of the government to the R&D cost of the supplier, the optimal subsidy and its impact on the choice of supply chain financing modes when the social welfare is maximized are explored. The results show that when the R&D cost of green products is high, more social welfare can be obtained under prepayment financing, but the supplier has no incentive to accept prepayment financing. After government subsidy, both the R&D effort of the supplier and the profit of the buyer increase, but the profit of the supplier first increases and then decreases. Therefore, the government should set the optimal subsidy and make the supply chain choose appropriate financing mode to maximize social welfare and capital utilization efficiency.

The globalization of digital economy and the networking of consumption have promoted the innovation of auction mechanisms. In view of the seller’s “impulse” to over set a buyout price, this paper constructs an online buyout price auction mechanism combining commissions and penalties based on the complementary characteristics of commissions and buy-in penalties. It uses the static game theory for recursive analysis. First, it investigates bidders’ optimal bidding strategies on the base of Nash equilibrium. Then, it analyzes the seller’s pricing decision of the buyout price and the setting of commission rate for the auction platform and buy-in penalties for the whole system. Finally, it verifies the superiority of the auction mechanism through a numerical experiment. The results indicate that there exist a bidders’ optimal symmetric equilibrium, a sellers’ optimal buyout price, and a reserve price strategy, as well as an optimal commission rate of auction platform and an optimal buy-in penalty that maximizes the expected revenue of the whole system. Compared with the buyout price auction setting without buy-in penalties, the reserve price and buyout price set by the seller are relatively low, which not only increases the opportunity for bidders to participate in the auction and reduces the probability of auction failure, but also promotes bidders to choose the buyout price, thus increasing the probability of a buyout price being executed and increasing bidders’ expected revenue. The proposed mechanism satisfies conditions of incentive compatibility and individual rationality, which is beneficial to the seller to set a reasonable buyout price and increase the probability of a successful auction.

To ensure the smooth implementation of safety rescue after unconventional emergencies and improve emergency blood scheduling efficiency, aiming at the degree of disaster and the characteristics of blood products, and considering the priority of disaster areas, an emergency logistic system of two-level network with multiple blood banks and disaster areas which transported by multi-compartment mode is studied, a model of multi-compartment emergency blood scheduling with the goal of timeliness and fairness is established. In addition, an improved adaptive large neighborhood search algorithm is developed to solve the model. The validity of the algorithm improvement is demonstrated by numerical examples. Moreover, based on the actual case of the Wenchuan earthquake, the effectiveness of the model and algorithm proposed is verified through comparative analysis and sensitivity analysis, and some management implications are obtained

Consumer demand and user experience are the key focuses of the new format of e-commerce. As an important part of e-commerce, last mile delivery is cost-oriented and has a low rate of door-to-door distribution. Consumers’ door-to-door distribution needs have not been fully met. To better meet users’ door-to-door service needs and improve logistics service, a collaborative distribution optimization problem involving trucks and drones is proposed, taking into account the load capacity and flight range of drones. In addition, a mixed integer programming model is formulated with the objective of minimizing the comprehensive distribution cost of trucks and drones. Moreover, an improved hybrid ant colony algorithm is proposed to solve the problem and schedule the route of trucks and drones. Furthermore, the rationality of the model and the effectiveness of the algorithm are verified through a series of experiments, providing decision-making reference and basis for improving the service level of e-commerce logistics in the last mile delivery.

Travellers’ route choice behaviour often has the characteristics of bounded rationality such as regret avoidance under the situation that travel time is uncertain. The intensity of regret caused by the result of choice is closely related to travel habits. In this paper, travellers’ regret avoidance psychology and travel habits are considered simultaneously in the traffic assignment problem. Travellers are classified according to the familiarities of routes between origin-destination pairs. Then, a multi-class stochastic user equilibrium model is established based on the regret theory considering route familiarity. The equivalent variational inequality (VI) is presented, and the solution existence is shown. The result of numerical examples shows that the proportion of users with familiar routes and the difference of their familiarities to each route will significantly affect the equilibrium traffic flow. The new model proposed in this paper can provide theoretical support for travel behaviour analysis, network traffic flow analysis, and traffic control strategy formulation from the perspective of behavioural psychology.

Order picking is a key part of warehouse operation, and the congestion caused by multiple pickers picking simultaneously will greatly increase the working time. Additionally, it is of great significance to optimize the walking route of multiple pickers in the picker-to-parts picking systems while considering the congestion to improve the picking efficiency. Therefore, a route adjustment method based on the return routing policy is proposed, and the walking route optimization problem is converted from the one-dimensional physical network into a space-time network combining time and space. A route optimization model of multi-pickers picking system based on the space-time network is constructed, and a route optimization algorithm considering the congestion of the system is designed to solve the model. The validity of the route adjustment method considering congestion is verified by numerical analysis, and the proposed algorithm can achieve better solutions within a reasonable solution time range, especially suitable for the optimization of picker-to-parts picking systems for small and medium-sized e-commerce enterprises with a high picking density.

Perceived quality is the consumer’s perceptual knowledge of product attributes, which is very important to manufacturers. Considering that the product attributes perceived by consumers are different from the attribute system in enterprise production, a perceived quality attribute extraction method based on the grounded theory is constructed for online forum data. The method consists of two phases, i.e., extracting rules by inductive method process in the coding process to better adapt to the big data context, and proposing a three-stage testing process based on the text mining method in the theory saturation test to reduce the manual workload. Eight consumer-perceived car attributes are obtained in the instance analysis of a large amount of car forum data. The method ensures the completeness of attribute extraction and reduces the manual workload. This paper has theoretical significance for the research on perceived quality and attribute extraction, and improves the big data processing capability of manufacturers.

For the problem of regional prevention and control of infectious diseases in large cities, this paper proposes a novel spatiotemporal clustering model which can effectively identify local diffusion sources and divide diffusion boundaries. Taking the COVID-19 in Shanghai in March 2022 as an example, it is shown that developing regional prevention and control strategies at the scale of 0.8km, 1.5km, and 2.5km can effectively control the adjacent diffusion and relocation diffusion of the pandemic. The spatial risk factors of bus stop, shopping spot, and large-scale hospital can assist the formulation of regional prevention and control strategies. The spatial risk factors of metro station, shopping mall, and supermarket can help predict the migration path of the pandemic.

Responsible innovation is an important engine for balancing economic and social benefits and achieving sustainable development. It emphasizes that a wide range of stakeholders should be included in the innovation decision-making process to reduce the potential harm of emerging technologies. Multi-agent cooperation is a key element for responsible innovation. However, it is not clear how multi-agent can cooperate more effectively to promote responsible innovation. Based on the differential game theory, this paper discusses the responsible innovation behavior of enterprises, government, and the public under three cooperation mechanisms: enterprise-dominated, government traction, and deep collaboration, while simultaneously considering the influence of changes in technology and economy levels and social ethical satisfaction. The results show that the optimal strategy of responsible innovation is negatively correlated with cost coefficient and recession coefficient, and positively correlated with the sensitivity coefficient of science and technology economy and support coefficient. Moreover, the optimal strategies, optimal benefits, technology and economy levels and social ethical satisfaction of the three game parties are the highest under the deep collaboration mechanism. There is a threshold for the optimal strategy of the public, and when the threshold is exceeded, the strategy under the enterprise-dominated mechanism is more optimal. Furthermore, the three cooperation mechanisms have different promoting effects on responsible innovation. The enterprise-dominated mechanism can only slowly promote the responsible practice under the low level of responsible innovation, while the deep collaboration mechanism with the close integration of members can still significantly promote the cooperation among subjects and accelerate the process of responsible innovation on the basis of a high level of responsible innovation.

Industrial policy is an important policy tool for the government to guide the green innovation strategy, accelerate the supply-side structural reform, and promote the adjustment and upgrading of industrial structure. How to improve the total factor productivity of enterprises through industrial policy optimization is an important issue in the construction of a modern economic system. Combining traditional econometric and machine learning methods, this paper systematically explores the effects and trends of industrial policy choices on enterprise total factor productivity from policy content, policy objectives, and policy means. An empirical research of 1238 listed companies from 2007 to 2020 shows that differences in industrial policy content, objectives, and means have different impacts on the total factor productivity of enterprises. Public services, capital investment, target planning, the introduction of policies such as financial support and consumption subsidies can promote the total factor productivity of enterprises, while the introduction of management policies for overseas institutions will inhibit the improvement of total factor production of enterprises. The setting of output targets in policy objectives can inhibit the total factor productivity of enterprises. The energy efficiency target and labor productivity target can promote the total factor productivity of the enterprise. Tax incentives and government subsidies in policy measures can both promote the total factor productivity of the enterprise. A comparison of the linear regression and machine learning methods indicates that the conclusions of the two are basically the same, but the goodness of fit and mean square error of the machine learning method are better than linear regression, which helps to improve the accuracy of the analysis of industrial policy effects and is applicable to industrial policy selection and decision-making scenarios. This paper can provide literature support and analysis tools for improving the intelligent level of government policy decision-making and strengthening government governance capabilities.

With the increase of digital content copyright and users’ awareness of payment, the subscription model of online video platforms is gradually accepted by users. This paper, focusing on the industry chain structure of video platforms in the three digital content provision modes of platform exclusive subscription, platform exclusive free, and dual-platform syndication competition, investigates the interactive decision-making of optimal subscription fee and advertisement placement between content providers and video platforms in the three modes. The results show that the choice of a specific profit mode for video platforms depends on the interaction between content characteristics such as digital content production cost, unit advertising revenue, and the discount factor for consumers to watch free videos. When the cost of digital content production is low, the solo-subscription mode can maximize video platform profits. When the cost of digital content production is high, the dual-platform syndication-competition mode is conducive to achieving a Pareto improvement in the levels of consumer utility, content provider profits, video platform profits, and social welfare. The solo-free mode reduces the overall level of social welfare to a certain extent. These findings can provide useful management insights for operational decision-making of video platforms and content providers as well as the development of the online content industry.

This paper, based on the social network analysis technology to identify opinion leaders in the online stock forum, uses the text analysis technology to classify the type of posted message of opinion leaders, and develops two types of peer firm relationship based on industry and common analyst coverage. It examines the impact and mechanism of sentiment of opinion leaders on the stock price of peer firms, in order to test whether the sentiment of opinion leaders in the online forum spread among peer firms and their dissemination characteristics and patterns. Based on the sample data from 2013 to 2017, it is found that positive sentiment of opinion leaders from peer firms has a significantly positive impact on both short-term and long-term stock returns of the target firm, and this impact exists within both types of peer-firm relationship. Further research indicates that the more complex the relationships between firms, the stronger the effect, and the spread of sentiments among opinion leaders may be the explanatory mechanism. This paper reveals the impact of opinion leaders on the stock price and their formation mechanism of peer firms, and provide empirical evidence for the phenomena of information diffusion in the market.

Exploring excessive institutional dependences is the key to preventing and controlling large-scale outbreaks of systemic risks, but long-term correlations between the institutions are inevitable, and financial crises can also trigger short-term excessive correlations between institutions. This paper, employing the DCC-MIDAS model and dynamic conditional correlation coefficient decomposition approach, studies the long-term and short-term correlations within a unified framework. Through the analysis of the time-varying and persistent differences of short-term correlations, as well as the sensitive factors of long-term correlations, it explores the changes in the scope and duration of risk contagion between institutions in China’s financial system during different periods of structural changes, as well as the macro and micro characteristics of long-term correlations between financial institutions. It is found that the contagion during the shocks is temporary, and with the diversification of China’s financial institutions, the magnitude and persistence of the contagion have decreased. The size differences can reduce the long-term correlation and indirectly reduce the contagion. However, with the increasing marketization, the influence of market factors on long-term correlation increases in China’s system. It is suggested to use institutional differentiation as a reference factor for risk control strategies and to monitor and regulate the impact of market indicators on the degree of association to prevent institutions from forming excessively high long-term associations, in order to control potential contagion in the future.

Life time poverty has become a new dilemma that people are faced with. Existing research has demonstrated the potential outcomes of life time poverty, neglecting the exploration of its antecedents. Based on the grounded theory and using a sample from an Internet company, this paper constructed a theoretical model of the influencing factors on life time poverty, consisting of seven main categories and 22 sub-categories. The results show that social environment, organizational context, work team, overload at work, overload at family, task management ability, and work motivation are antecedents of life time poverty. Social environment, organizational context, and work team may influence life time poverty mediated by the overload at work. Task management ability and work motivation may moderate the effects of overload at work and overload at family on life time poverty. Finally, this paper also discussed theoretical contributions of the model of influencing factors on life time poverty and its implications for management practices.

Stable supply chain is an important guarantee for sustainable development of family businesses. This paper investigates the impact of family involvement in management on customer stability and explores the moderating role of intergenerational succession process by using family firms listed in China’s Shanghai and Shenzhen A-share markets from 2009 to 2022. The empirical results show that the increase in family involvement in management could significantly improve the customer stability of family firms. Both the second-generation participation in management and two generations’ co-governance strengthen the positive effect, while second-generation succession does not exert moderating effect. The mechanism test shows that the increased family involvement in management can strengthen trust and boost efficiency, thereby stabilizing customer relationship. It is also found that the customer stability enhanced by the increased family involvement in management could remarkably improve long-term value of family firms. This paper reveals how family involvement in management affects customer stability. Family firms will also benefit from taking effective measures to optimize the top management team to improve the quality of supply chain management by establishing a stable relationship with supply chain partners, which contributes to the virtuous circle of domestic economy from a micro perspective.

Becoming an ingroup brand is an important approach to obtain consumer loyalty. This paper, innovatively adopting the trilateral perspective of consumers, domestic brands, and foreign brands, explores the influence of brand social categorization on consumers’ purchase of Chinese and foreign co-branded products, as well as the moderating mechanism of product fit and cultural distance. A combination of field study, laboratory experiment, and online survey has found that consumers’ purchasing intention towards Chinese-foreign co-branded products is in a descending order of joint products of domestic ingroup brand and foreign ingroup brand, joint products of domestic ingroup brand and foreign outgroup brand, and joint products of domestic outgroup brand and foreign ingroup brand. In addition, as the product fit between the co-branded brands increases and the cultural distance between the source countries decreases, the advantages of social categorization fit will increase, while the advantages of domestic ingroup brands will decrease. This paper not only fills the gap in the literature, but also provides practical guidance for domestic brands to implement cross-cultural co-branding strategies.

There are two models of resale and agency to choose when manufacturers introduce livestreaming channels, and livestreaming-retailers also have two livestreaming marketing methods to choose: non-selection and selection. When making decisions to maximize their own profits, conflicts of interest are inevitable. Aiming at the problem of optimal decision-making and coordination when the sales model (resale/agency) is combined with the livestreaming marketing methods (non-curated/curated), a two-echelon game model consisting of manufacturers and livestreaming retailers is established to compare the optimal decision-making of the members of the livestreaming supply chain selected by different sales models of manufacturers and different marketing methods of livestreaming retailers, and to design coordination contracts for these members. It is found that regardless of the manufacturer’s choice of resale or agency sale model, when the product similarity is less than a certain threshold, curated methods are the optimal decision for retailers. The retail price, market demand, supply chain members, and overall profit of curated methods are superior to those of non-curated model. In the non-curated marketing mode, resale is the optimal decision of the manufacturer, and only the agent can achieve the optimal profit of the livestream retailer. The manufacturer’s optimal decision damages the profit of the livestreaming retailer and causes interest conflict, while in the curated marketing mode, the conflict of interest can only be caused within a certain threshold. The cost-sharing contract can coordinate part of the conflict of interest caused by the manufacturer’s decision, which not only enables the manufacturer and the live streaming retailer to achieve a win-win situation, but also enables the decision of both sides to reach an agreement.