Value-added services exert a substantial impact on the interaction between startups and investors, leading to a win–win situation. Exploring the mechanisms through which investors exert their influence on the value-added services offered to startups can facilitate this goal. Such an investigation can aid startups in increasing their value, while also enabling investors to reap benefits through investment appreciation. Additionally, it can facilitate the growth of the equity investment industry in a healthy manner. As part of this study, six startups that have received value-added services from investors have been selected as typical cases. A SETC theoretical model has been constructed using grounded theory to extract the main influencing mechanisms of the value-added services’ effects. The research results show that value-added services, in addition to directly affecting the value-added service effect, may also instigate principal-agent conflicts between investors and startups, driven by the external environment and the both parties’ traits, which ultimately influence the value-added service effect. Value-added service is an internal direct driver of the value-added service effect; the external environment is a external direct driver, both parties’ traits are situational drivers, and principal-agent conflict is an intermediary driver.
Our main purpose is to investigate the dynamic control problem of a monopolist’s product and process innovation under reference quality. The main features of this article are: (i) a monopolist dealing with customer behaviour in the spirit of the principle of behaviour economics determines the product price, and carries out the activities of product and process innovation; (ii) the consumers’ demand depends on price, product quality and reference quality, and adopts an additive separable demand function form. Our main results show that under the cases of the monopolist optimum and the social planner optimum, (i) there exists an unique stable, which is a saddle-point steady-state equilibrium; (ii) the change rates of the monopolist’s investments in product and process innovation are increasing with the reference quality, while the monopolist’s steady-state investments in product and process innovation are decreasing with the reference quality; (iii) as the memory parameter increases with other parameters kept constant, it is very likely that the monopolist’s investment in process innovation be greater than the investment in product innovation; and (iv) the social incentive towards both investments in product and process innovation is always larger than the private incentive characterizing the profit-seeking monopolist.
To address the problem of uncertainty in decision information and the difficulty of complete rationality of decision-makers in emergency response decision-making,a novel approach to emergency response decision-making is proposed based on the prospect theory and probabilistic linguistic terms.First,the evaluation of decision-makers is represented as probabilistic linguistic terms,and a similarity measure of probabilistic linguistic terms based on Jensen-Shannon divergence is proposed.Then,the prospect theory,which can describe the mental behavior of decision-makers,is applied to the probabilistic linguistic environment,and a prospect decision matrix is constructed by combining the proposed similarity measure of probabilistic linguistic terms.Afterwards,a criterion weight model is constructed with the idea of deviation minimization to calculate the criterion weights,based on which,the overall prospect value of each alternative is obtained by combining the prospect decision matrix with the criterion weights,and the ranking of each alternative is obtained.Finally,a maritime accident case illustrates the effectiveness and superiority of the proposed method.
Aimed at an integrated problem of flight timetabling and fleet assignment under fluctuated multi-segment service demands in commercial airline service market,considering airline service attractiveness to consumers,a mixed integer linear programming model is constructed by replicating flights with multiple optional take-off time periods and using a time-space network.By predefining a discrete distribution for fluctuating demands as input data,flight timetabling and fleet assignment are taken as a whole and solved by CPLEX.Then a diving heuristic algorithm is designed and six examples are generated with 10 to 20 airports respectively for simulation test.The computational results show that when an example scale exceeds 12 airports,calculation speed and target value for the algorithm are better than CPLEX.Finally,sensitivity analysis is applied to verify the robustness of the algorithm,which indicates that the algorithm can help airlines find faster and better real-time decision-making solutions than CPLEX.
Studying the relationship between talent scale and enterprise innovation has an important reference value for the government to reasonably guide high-level labor factors to flow to enterprises and make enterprise innovative development.Based on the absorptive capacity theory and"Williamson"hypothesis,this paper finds that the high-level labor factor scale has a three-stage impact on enterprise innovation.The empirical analysis shows that the innovation of high-level labor factors in Chinese enterprises has a staged"breakpoint"feature,and is more inclined to the"breakpoint"feature indicated by the absorptive capacity theory.High-level labor factors flow to foreign enterprises,enterprises in eastern region and enterprises in industries with small innovation gap is more likely to make the high-level labor factor scale in enterprises break through the"breakpoint"value indicated by the absorptive capacity theory,which is beneficial for enterprise innovation.On the one hand,the government should introduce appropriate talent policies to guide the transfer of high-level labor factors from enterprises of phase Ⅲ to enterprises of phase Ⅱ.On the other hand,the government should implement appropriate industrial policies to build an innovation structure with a small innovation gap within the industry,create an ecological environment with fierce innovation competition,and guide high-level labor factors to flow to enterprise clusters with a small innovation gap for making enterprises innovative development.
With the rise of the omnichannel model,the issue of omnichannel promotion considering the placement of e-coupon has attracted much attention.To this end,this paper considers opening online,offline and BOPS(buy online,pick-up in-store)omnichannel retailing systems.Then,it introduces consumer channel preferences,e-coupon,and constructs four decision models of coupons of the same face value and different face value in omnichannel mode by brand owners themselves or by joint efforts of retail enterprises.Finally,it further analyzes the optimal pricing mix strategy of firm-product pricing and coupon face value.The result shows that compared with the self-established brand mode,the product pricing and coupon face value are higher in co-retailer establish mode.Compared with the same face value strategy,targeting coupons of different face value based on consumer preference can obtain a higher brand profit,while the opposite is true of retailers.When and only when the subsides,which is given to retailers by brand,are less than a certain threshold value,co-retailers can build BOPS channels with greater profits.The product price is based on BOPS channel purchase overflow,which is the coupon combination strategy,but the coupon face value needs to be controlled within the threshold value.
This paper explored the effect of public innovation policy on the innovation performance of enterprises at different life cycle stages,using 1694 A-share listed enterprises from 2012 to 2017,and further explored the moderating effect of state-owned holdings using group regression.The result shows that the public innovation policy has a significant incentive effect on the innovation performance of Chinese listed enterprises,but it does not have a synergistic incentive effect.The public innovation policy has a synergistic incentive effect on the innovation performance of enterprises in the growing stage and a statistically insignificant synergistic incentive effect on the innovation performance of enterprises in the mature stage,but it has a repulsion effect on the innovation performance of enterprises in the declining stage.The result also shows that state-controlling has a significant moderating effect between public innovation policy synergy and the innovation performance of enterprises in the growing and mature stage.
With the continuous development of China's futures market,the types of listed commodity futures are increasing.A large number of financial capital invested in commodity futures has become a new feature of commodity market operation.The importance of studying the covariance matrix of multiple commodity futures has become increasingly prominent.In this paper,the Bayesian shrinkage and sparse methods in machine learning are integrated into the VAR model with time-varying parameters,and a new SS-TVP-VAR model is constructed.The model is used to dynamically model and forecast the realized covariance matrices of multiple commodity futures.The empirical results show that the SS-TVP-VAR model can effectively predict the realized covariance matrix of China's commodity futures market,and is superior to other fixed parameter models such as VAR-Lasso in terms of statistical accuracy and economic benefits.The variance and covariance of realized covariance matrix in China's commodity futures markets have different driving structures,and variance is mainly driven by its own lags,while covariance is mainly driven by other lags.This paper helps investors and market managers perform asset allocation and risk management in terms of both covariance matrix driven structure and predicted future covariance matrix,which is of great practical significance to promote the high-quality development of China's commodity futures market.
This paper analyzes the factors affecting employee behavior from the perspective of the motivation-hygiene theory and uses game models to characterize employee behavior.It establishes the cellular automata model of the employee group,and employs the cusp catastrophe model to explore the employee group behavior reversal.It finds the existence of catastrophe mechanisms in the evolution of behavior selections of employee groups of different sizes.The intelligent system not only affects the behavior selections of employees,but also affects the stability of employee group behavior.The catastrophe mechanism in the employee group behavior evolution can be explained by the cusp catastrophe model.If the intelligent system can reduce the cooperation cost,the employee behavior selection reversals will increase in labor-intensive scenarios,and the employee behavior selection reversals will decrease in technology-intensive scenarios.These conclusions reveal the intelligent system's impact on cooperation behavior of employee groups of different sizes in different scenarios.From the individual game of employees to the reversal of group behavior state,this paper analyzes employee cooperation behavior mechanism,enriching the theory of cooperation behavior.It integrates the game model,cellular automata,and the cusp catastrophe model,discovers new characteristics of the interaction among individual employees and the group behavior selection evolution,and provides practical guidance for enterprises.
In order to optimize the configuration of large medical equipment in hospitals,improve the use efficiency,and avoid issues such as idle waste and repeated purchases of equipment,a method for evaluating the use efficiency of large medical equipment based on cloud models and the improved evidence theory was proposed,taking MRI equipment from 14 hospitals as examples.First,an evaluation index system is constructed for the use efficiency of large medical equipment from three dimensions:operational efficiency,social benefits,and cost control.Then,the cloud model is applied to generate the membership degree of each evaluation index and convert it into a basic reliability allocation function.Afterwards,in order to reduce the conflict between evidence,the evidence theory is improved based on the game theory.The dynamic and static weights are integrated in the game,and the conflicting evidence is identified,corrected,and fused through the combination of weight values.Finally,by referring to the idea of average fit,the closeness values of the evaluated object is compared with the optimal and worst solutions,to obtain a comprehensive evaluation result.A comparison of the cloud model fuzzy comprehensive evaluation method with the cloud model traditional evidence theory indicates that this method has advantages in solving evidence conflicts and handling uncertainty problems,providing decision-making basis for improving the efficiency of large-scale medical equipment use and optimization configuration.
Brands provide a lasting driving force for enhancing the corporate export resilience and achieving an upward trend in foreign trade.The data of Chinese listed enterprises from 2009 to 2013 are used and the negative impact caused by the 2008 global financial crisis is taken to empirically investigate the influence and mechanism of brand strength on corporate export resilience.The results show that the improvement of brand strength can significantly enhance corporate export resilience.This conclusion is still valid after the robustness tests and endogeneity treatment.Brand strength can enhance corporate export resilience by reducing financing constraints and enhancing export diversification.The increase in the proportion of female directors can positively regulate the relationship between brand strength and corporate export resilience and has a significant co-moderating effect on brand strength to restrain financing constraints and enhance export diversification
In order to systematically and objectively evaluate big data governance of government public services in the transformation of smart cities,a maturity evaluation method of city safety big data governance is proposed based on case source evidence reasoning.First,taking big data governance activities as the basic object,key process areas are extracted from different perspectives such as organization,regulation,process and technology,and the capacity goal sets of key process areas are defined.Then,in order to avoid the impact of subjective factors of expert scoring on the evaluation results,the case reference idea is introduced into the maturity evaluation.Based on the structural expression of the historical case source,the case-source evidence is retrieved according to case similarity,and synthesized to achieve capacity maturity evaluation using the evidence reasoning theory.The rationality of the proposed case-based method is analyzed by a case study of urban intelligent waterlogging prevention in Puyang,Henan Province.The result shows that the proposed method can realize the intelligent generation of maturity evaluation results,and the generated results have a strong differentiation and scenario adaptability,which helps governance decision-makers to accurately understand the weak points in the current city safety big data governance,and provides decision-making support for the updating and improvement of governance.
In view of the shortcomings of the research on electric vehicle routing optimization in combination with charging station and vehicle sharing,this paper proposes several optimization strategies,including the charging station and electric vehicle sharing with multiple service periods and centralized transportation among multiple centers.In addition,it studies the electric vehicle routing optimization problem of multi-center joint distribution based on resource sharing.First,it establishes a bi-objective optimization model to minimize the operating cost,including the rental cost of electric vehicles,the cost of electricity consumption,the cost of service,the penalty cost for violating the time windows,and the number of electric vehicles.Then,it designs a 3D-K-means spatial-temporal clustering algorithm based on the characteristics of the model considering the geographic location and demand time windows of customers.Next,it proposes a hybrid algorithm consisting of the Clarke-Wright(CW)saving algorithm and the multi-objective particle swarm optimization(CW-MOPSO).It uses the CW saving algorithm to generate the initial solutions,and devises the charging station insertion strategy,the external archive update strategy,and the resource sharing strategy in MOPSO to improve the quality of Pareto optimal solutions.Afterwards,it verifies the proposed CW-MOPSO algorithm by comparing it with the non-dominated sorting genetic algorithm,the multi-objective genetic algorithm,and the multi-objective gradient evolution algorithm.Finally,it conducts a case study of the electric vehicle routing optimization problem of multi-center joint distribution based on the resource sharing modes with the actual data of a logistics enterprise in Chongqing,China.It analyzes and discusses the changes of the operating cost,the number of electric vehicles,the electricity consumption,and the number of used charging stations of the multi-center joint distribution network under the conditions of the uncertainty of the waiting time of electric vehicles in the charging station,the stepwise relationship between the electric vehicle power consumption and speed,and the different resource sharing modes.The results show that in the scenario where some charging stations queue up and the rest do not queue up,some electric vehicles will choose a charging station with a longer distance for charging in order to reduce the waiting time in the queue,which increases the driving distance of electric vehicles and has a higher penalty cost for customer delay.However,when the electric vehicles have different speed states in different service time periods,the power consumption of electric vehicles in the process of long-distance constant speed driving is less than that of short-distance driving in different speed states.Moreover,the proposed model and algorithm can achieve the sharing of charging stations,the shared scheduling of electric distribution vehicles,and the reasonable electric vehicle routing optimization.Furthermore,the proposed model and algorithm can improve the operation efficiency of the multi-center joint distribution network and reduce the operating cost,thus providing theoretical support and decision-making reference for urban logistics and distribution enterprises to realize the rational configuration of charging stations and optimal scheduling of the electric distribution vehicles.
Ecolabeling has become an important tool for manufacturers to disclose green quality information.Information asymmetry leads to consumers'skepticism on ecolabel,which becomes a key factor affecting manufacturers'ecolabel strategy.By establishing game models among the label originator,the manufacturer,and the retailer,this paper investigates the ecolabeling strategies and differences of design-type and marginal-type green products based on consumer skepticism.The results show that ecolabeling strategies of green products depend on the cost type.Specifically,design-type product prefers industry labeling.However,the ecolabeling strategy of marginal-type product is still related to consumers'skepticism.If consumers'skepticism degree is relatively low,governmental labeling will be a better choice;otherwise,industry labeling might be better.In addition,with a relatively higher green level,wholesale/retail prices and green sales effort decisions,governmental labeling outperforms industry labeling from the perspectives of the retailer,consumer surplus,and social welfare.It is more advantageous for manufacturers to choose design-type product when the cost budget of green innovation is high.However,when the cost budget is low or constrainted,manufacturers will choose marginal-type product.
In the digital economy era,the cultural industry has experienced a rapid growth.However,the specific mechanisms through which the digital economy facilitates the development of the cultural industry remain unclear.This paper adopts a micro-level perspective to theoretically deduce and empirically test these mechanisms using data from the film industry.On the supply side,digital technology enhances the diversity and quantity of cultural market supply by lowering the barriers to entry.On the demand side,digital platforms decrease consumers'search costs and improve consumption structure.The quantile regression analysis indicate that at the lower quantiles of box office revenue,the effect of comment quantity on box office revenue is insignificant or weak,whereas at the higher quantiles,the positive effect of comment quantity on box office revenue is significantly stronger.This paper provides a novel perspective and empirical evidence for analyzing the high-quality development of the cultural industry driven by the digital economy.
Aimed at a multi-objective disassembly sequence optimization problem,considering the precedence relations between the disassembly tasks,a transformed AND/OR graph(TAOG)based and Pareto based multi-objective dynamic programming approach(MODP)is proposed.Compared with other optimization algorithms,the proposed method does not need to adjust parameters and to consider the effects of the changing parameters.In addition,MODP can obtain the Pareto front of the multi-objective disassembly sequence optimization problem and is an exact algorithm for the problem.The simulation results show that the proposed algorithm is feasible and effective.
Based on the joint patent applications of 41 cities in the Yangtze River Delta Urban Agglomeration from 2004 to 2020,a cross-regional technology collaborative innovation network is constructed,and the temporal exponential random graph model(TERGM)is used to empirically analyze the endogenous and exogenous dynamic factors that promote the evolution of the cross-regional technology collaborative innovation network.The result shows that the evolution of inter-city technological collaborative innovation relationships is the result of a combination of endogenous and exogenous factors.Inter-city technological innovation cooperation tends to be embedded in a triangle structure rather than a star-shaped structure,with prominent"small-world"and"high-agglomeration"characteristics.The evolution of the network has both stability and variability.The higher the level of economic development,advanced industrial structure and openness of cities in the network,the higher the probability of forming innovation cooperation with other cities.The cities with similar human capital levels are more inclined to establish new innovative cooperative relationships.Geographical,institutional,and organizational proximity have positive effects on inter-city technology innovation cooperation.Extending from cross-sectional to vertical dimensions and introducing time-dependent terms for the evolution dynamics of cross-regional technological collaborative innovation network,the findings provide a path reference for the development of regional integrated innovation,as well as a new perspective for the paradigm of social science research in the network economy era.
Aimed at the problems of incomplete indicators selected by the existing enterprise digital maturity evaluation model and unreasonable setting of subjective and objective weights,a digital transformation maturity evaluation model based on power grid enterprises is proposed.Considering the key factors that affect the digital transformation of power grid enterprises,a model index system is built for evaluating the maturity of digital transformation of power grid enterprises.Using the analytic hierarchy process and the anti-entropy weight method,the calculation of the subjective and objective weights of the evaluation indicators is completed,based on which,in combination with the idea of dynamic feedback,the panel threshold model is used to deeply integrate the proportion of subjective and objective weights with the dynamic goals of the corresponding stage,so that the accuracy of the evaluation results is greatly improved.The case analysis shows that the model can realize dynamic adjustment based on the goals of each stage,ensure the accuracy of the evaluation results of the digital transformation maturity of power grid enterprises,and provide decision-making support and theoretical support for optimizing the digital transformation route of power grid enterprises.
In the era of digital intelligence economy， privacy protection has become an important dimension for enterprises to gain competitive advantage. This paper constructs a multi-dimensional differentiated competition model of duopoly enterprises under the dominant position of brand preference or privacy preference， and examines how the intelligent decision efficiency affects the privacy protection strategy and the equilibrium profit. It is found that if only one enterprise makes intelligent decision， unlike the complementary relationship between privacy protection behavior and intelligent decision behavior of high privacy protection enterprises dominated by brand preference， there is a substitute relationship between the two under the dominant position of privacy preference; At the same time， under the dominant position of brand preference， only when the intelligent decision efficiency is higher than a certain threshold， the equilibrium profit of enterprises taking intelligent decision is positively related to the intelligent decision efficiency; In addition， under the dominant position of privacy preference， the equilibrium profit of enterprises adopting intelligent decision is positively related to the intelligent decision efficiency， while the equilibrium profit of enterprises adopting experience decision is negatively related to the intelligent decision efficiency. Lastly， if both enterprises adopt experience decision under the dominant position of privacy preference， the enterprise equilibrium profit is negatively related to the information security cost. However， under the dominant position of brand preference， the enterprise equilibrium profit is positively related to the information security cost.
新一轮科技革命和产业变革趋势对交叉学科知识生产形成了迫切需求,由此催生了以动态合作网络为主要形态的组织结构,既有研究在成员认知异质性与组织学习绩效的关系上存在争论,而该争论尚未在动态合作网络型组织中加以探讨.引入重复合作概率这一因素构建动态合作网络,基于 Agent-Based Model模拟仿真,研究了成员认知异质性对动态网络型组织学习绩效的影响,得到三方面主要发现:首先,成员认知异质性对组织学习绩效均值有正向影响,但同时也会导致组织学习绩效方差增大,可靠性降低,提高重复合作概率可以对这一负面影响起到减缓作用,让组织学习获得"高均值"与"低方差"的兼顾.其次,当认知共识化程度较高时,组织奉行同质化成员认知的策略能够有效降低学习绩效的方差;当认知共识化程度较低时,组织奉行同质化成员认知的策略对于降低学习绩效方差的作用不大.最后,在重复合作概率较高的组织中,认知同质性组织在发展初期的学习表现要优于认知异质性组织,但随着时间推移,认知异质性组织的学习表现曲线会相交并超过认知同质性组织的曲线,在长期中达到更高的水平.通过引入"重复合作概率"这一变量,在一定程度上弥合了现有关于成员认知异质性与组织学习绩效的理论争论,为合作网络型组织收获高效且稳定的学习提供了理论指导.
