| With the development of information technology,many traditional industries and information technology are combined together to be some new business models,on-demand ride service is one of them.Through information technology this emerging mode achieves more efficient resource sharing and connects leisure drivers with customers who have travel needs efficiently.Although on-demand service platform has not been developed for a long time,it has become one of the most important travel modes for people.But with the rapid development,there are many problems in the operation process.This paper reviewed the development of on-demand ride service platform,summarized several problems in its development process,and explored the optimization pricing decision for the platform driven by the main problems.First of all,with the rapid development of on-demand ride service platform,operational problems have not been fully considered,including different service quality of drivers.The safety of on-demand ride service platform is a practical issue of concern to customers,and the platform has issued a lot of regulations to certify the service qualification of drivers.On the premise of ensuring the quality of drivers,the platforms also need to balance the supply and demand.Then,with the rapid development of on-demand ride service platform,the platform gradually found different service preferences of customers.While facing with different needs of customer groups,the platform also faced with different types of drivers,for example vehicle types and drivers’ service quality.Through differentiated services,different types of drivers can meet the needs of the corresponding types of customers.Therefore,how to arrange different kinds of drivers to provide different types of services is a problem that needs to be reconsidered when the platform has reached a certain stage of development.In recent years,with the development of on-demand ride service platform and the end of the price war,many other platforms have been developed.But the dominance situation has been continued until a new model emerging—aggregate platform model,which will bring an integration of ride service platforms and the subversion of competition.This is important for the small and medium-sized platforms.We all know that two-sided market has cross network externalities,but some of the platforms are lack of bilateral customers and even be in a state of low profit.Through the form of cooperation with ride platforms,aggregation platform shares customers’ volume with them,which is a form of universal benefits for all.What kind of cooperative optimization decisions should be made by the platforms participating in this mode is not involved in the current research yet.Based on market survey and expert interviews,this paper discovers the practical problems encountered in the development process of on-demand ride service platform.By refining the problems,we study on several problems lacking theoretical guidance at present.For the above problems,decision processes of various parties are described.Then using the method of game theory and model optimization,analysing each parties’ utility or profit functions(including customers,drivers,and platform),we modeled the main problems with mathematical model.Then optimization methods are used to obtain optimal solutions for the problems.Explaining the optimal decisions and giving some management suggestions for the proposed problems are the goals of this thesis.From the perspective of the development of on-demand ride service platform,this paper explores the following problems gradually:First,the decision of the service threshold under customers’ service quality preference homogeneity or heterogeneity is studied.In the face of passenger safety problems arising from platforms operation,society urgently needs platforms to provide corresponding measures.This paper first puts forward a concept of the minimum service quality level required for drivers – service quality threshold.Service quality threshold can improve customers’ expectation about quality,so from this aspect it can attract more customers to participate.But it means platform will set restrictions on drivers,from this aspect it may inhibit drivers to participate.In the model,we put it in the customer’s utility and driver’s earnings,so the threshold of service quality not only affect the customer’s utility through customer’s expectations of service quality,but also affect the driver returns through minimum requirements.We also explore the problem while customer service preference is homogeneous/ heterogeneous,the service quality provided by the driver and costs are independent/ related respectively.The findings are: When platform sets threshold of service quality,if customers expect service quality is higher,the platform will attract more customers to participate in.Then the increasing of the number of customers will also motivate the driver to involve in,so the number of both parties involved in will increase.The research shows that from the perspective of platform,the optimal profit is better when the service quality threshold is set.With service quality,the platform needs to balance customers’ waiting time,service quality,price and salary.For example,when the customers’ sensitivity of service quality is high enough,through high expected service quality level platform can benefit more,the platform will not hesitate to set a higher service requirements and wages to attract all the qualified drivers to participate in.Even attracting high retained costs drive may be costly,the platforms can also maximize profits.In the paper,the independent and correlation between the retention cost of drivers and the service quality they can provide are all considered.It is found that the correlation between the two is not conducive to the optimal profit of the platform.In this case,the platform usually does not select drivers in terms of service quality or simply selects drivers who are willing to participate.The paper also considers the heterogeneity of customer service quality sensitivity,in which the variability of customer valuation is in effect increased.The number of potential drivers on the platform determines whether the heterogeneity of customer preference is conducive to the platform’s optimal profits or not.When the number of drivers involved in providing services limited and the customer waiting time cost is large enough,then some customers are not willing to seek service because of high waiting time cost,the profit of the platform increases with the variability of customer service valuation.So in this case,the higher customers’ preference heterogeneous is,the better the platform profit is.It is also shown that setting the service quality threshold for drivers is better for the platform’s profit than without it through numerical examples.The second topic is about quality differences of service.When entering the market,platform usually provides only one service without identifying the service types of the provider.All the providers are mixed together to provide services for customers,and the decision variables of the platform are only a set of prices and wages.With the development of the platform,the platform finds that different providers have different service levels and different retention costs.Of course,providers that can provide high-quality services have higher retention costs.Therefore,the platform will consider whether to seek greater profits through service differentiation of providers.Assuming that there are two types of drivers in the market,respectively high quality(H type)and low quality(L type)service,then the platform are faced with the decision only one type or both types to participate,to price separately or pooling together.This paper first takes the provider’s service quality into consideration.Customer’s heterogeneity of preference for service is also considered to see how platform makes decisions by integrating various factors.The main conclusions are as follows:Firstly,the optimal driver types decisions corresponding to the platform are given under different levels of retention cost,service quality and external effects.For example,when the external effect is small or external effect is large but L type driver keeps a high cost,the result is same that H or L type driver to provide is the best decision.But the internal driving factors are different,when the external effect is small,the platform can attract customers by improving service quality.Another case is external effect is large but L type driver keeps a high cost,the platform is more considering the cost factors,that is while two classes cost difference is not that much,platform tend to use higher type driver to participate in.But if H type driver involved in ”value” is not high,the platform will attract only L type.Because in this case externalities are high enough to engage customers.Furthermore,the changing process of platform optimal decision is given with changes of parameters.The unit retention cost of L type drivers can be subdivided into low,medium or high-level.When L type drivers’ cost is at a low or medium level and the external effects of L and H type are close to each other,the platform needs to implement differentiated pricing when there is a large difference in service levels.When difference between H and L type service network effects is large,the platform can make differentiated pricing while two types of service levels are approaching because of the differentiated network effects.When L type drivers’ reservation cost is at a higher level,regardless of the size of the external effect,the optimal decision attract only H type or L type driver to participate in.That is,the platform will measure the retention cost and service quality of the driver when choosing which type to provide service.Finally,Competitive strategy under aggregation mode is analyzed.In recent years,the rise of aggregate mode platform enables customers to see multiple ride service platforms on one app,among which there are many small and mediumsized platforms.The relatively inclusive and fair traffic distribution method has brought new opportunities for them.Aggregation platform will charge fees according to certain standards.Joining in the aggregation platform brings benefits to the platform,and doing so also improves the competitiveness of competitors.Based on this scenario,the paper analyses optimal strategy of cooperation between ride platform and aggregation platform under different market conditions.The main conclusions are as follows:The models comparison shows that if one cooperate with the aggregation platform while the another not,profits of the party with cooperation will be higher than before while another one’ profit depends on different market conditions.When the two platforms haven’ t cover all the market yet,its profit is the same as before;when just cover all market or encroached on each other’s market,if the platform with cooperation is very crowded,the optimal profit of the platform without cooperation may also increase.If the bargaining power is considered,the profit of the platform with cooperation will increase compared with before,because the cooperation will help it to obtain an additional external utility and enhance the competitiveness of the platform,so cooperation is the optimal decision.Then for the platform without cooperation,only in certain circumstances,its profit is higher than before,so the platform will also consider whether to cooperate or not.Then through the model of both sides cooperating with aggregation platform,the research explores the optimal decision of all parties.The conclusions were:When the market is not completely covered yet,both the platforms and the aggregation platform will choose to cooperate,so that all participants can obtain the optimal profits.Because the market is very broad,the cooperation can help to satisfy more markets.When all the market is all covered,competition will becoming stiff and the aggregation platform has the right to choose cooperation.When the profit distribution is more while cooperating with one than both of them,aggregation platform will only choose one to cooperate.When the crowding degree and additional network externalities brought by the cooperation for the platforms cooperating with aggregation platforms are all large,the profits of platform without cooperation may be higher than before(with no aggregation platform in the market).Taking on-demand ride service platform as the background,based on the existing theoretical research and taking the development process of platform as the main line,this paper studies the main problems of platform at each stage and analyzes the optimal strategies.The possible innovations are as follows:First,most of the current research about service quality of on-demand ride service platform are about the quality of service provided by platform influence on bilateral customers.Based on some security events,this paper put forward the concept of service quality threshold,which is the request of drivers’ service quality.At the same time,the customer’s preference(homogeneous or heterogeneous)of service quality is also considered,so the customer utility is affected by waiting time,service quality and price.So while exploring the optimal strategies of the platform,the paper takes price,wage and service quality threshold into account at the same time.Therefore,the difficulty and innovation in the model lies in the decision variables of the platform including price,wage and service quality requirements,and the three variables have mutual influence.Studies usually took the number of drivers as exogenous as it is difficult while analysing.In this problem we took it as endogenous,although it is also difficult to solve,the influence of some parameters on decision variables can still be obtained.Compared with the model without considering this variable,it is concluded that the platform can achieve higher profits by adjusting this variable,although adding service quality requirements will inhibit the enthusiasm of drivers to participate.Second,some scholars have made researches on customers’ preference for service,but most of them focus on the influence of platform service quality or differentiated service on bilateral customers’ decision-making.In view of the characteristics of on-demand ride service platform,the passenger preference of the service more from driver with ride experience,this article considers customers preferences and type of service provider,and explore how the different preferences and different types of driver matching,so that the various parties can make optimal decisions.It is found that although the service provided by mixing all consumers together can achieve higher cross-network externality,platforms may choose some drivers to provide services,in the form of one-class service or differentiated service.As a result,platforms make decisions based on trade-offs between cost,quality of service,and cross-network externalities.Third,aggregation model springs up in recent years,which has broken rigid form of competition.It brings a greater number of customers for ride platform and brings additional cross network externalities for the customer.As number of customers rises,the increase in congestion is also taken into account.Under the new competition pattern and different market conditions,there is no theoretical guidance for platform to refer to.In this paper,models under three kinds of situations(original market competition,only one platform cooperated with aggregation platform,and both cooperated with aggregation platform)were contrasted.The research explored that platforms will make what kind of cooperation decisions.The platforms are always willing to cooperate,but when the aggregation platform can choose which platform to cooperate with,it may be only one platform had the chance to cooperate,and the case for the platform without cooperation is not always bad. |
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