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The Channel Strategy And Delivery Capacity Allocation Strategy In The O2O Takeout Industry

Posted on:2020-01-09Degree:DoctorType:Dissertation
Country:ChinaCandidate:Z Y TaoFull Text:PDF
GTID:1369330578983055Subject:Management Science and Engineering
Abstract/Summary:PDF Full Text Request
Since 2012,O2O(Online to Offline)takeout mode has become the favorite of the market.Many platforms rush into the 020 takeout industry,establishing their app channel with self-delivery logistics system or crowdsourcing logistics system.The lo-cal sellers adopt the app channel to sell products.Consumers have more choices for the app channel's introducing.Some new problems arise in the 020 takeout mode.How should the local seller introduce the app channel strategy?What about the channel structure the 020 takeout platform should adopt for its entering the retailing market?How should the platform balance the self-delivery logistics and crowdsourcing logis-tics system?This dissertation incorporates the local seller's delivery mode decision and channel members' pricing strategy as well as the channel strategy.This dissertation also discusses the 020 takeout platform's optimal delivery capacity allocation.This dissertation investigates the following main content revolving around the new problems mentioned above.The third chapter from the perspective of the local seller,investigates the effects of the local seller's delivery model on the channel strategy.Under the 020 takeout industry,consumers can purchase from the traditional store,or make orders on the app delivered off-line.The local seller can adopt the app channel with self-delivering or the 3rd delivery company.Under this situation,whether the local seller should adopt the app channel in the 020 takeout industry?If yes,how about the delivery model?The local seller delivers itself?Or outsources the delivery service to the 3rd party company?And how about the delivery distance decision?In this chapter,we use Economic Utility Theory and Hotelling model to model consumers' purchase choice and the delivery distance decision's effects,and derive the sales and profit of the local seller.By solving the local seller's maximizing problem,we find that the local seller should adopt the app channel,and its delivery capacity as well as the pricing authority will affect its app channel strategy.What's more,we get an interesting conclusion:a high efficient local delivery system can promote the prosperity of the 020 takeout industry,but also be the condition that the offline channel co-exist with the app channel.Chapter 4 investigates the channel strategy and its impacts for an 020 takeout platform's entering into the retailing market.When the 020 takeout platform enters the retailing market,it has three channel structures:centralized system,channel decen-tralized system and total decentralized system.Chapter 4 uses a linear demand function and Game Theory to investigate the equilibrium strategy under the three channel struc-tures.This chapter also analyzes the impacts of the product's channel substitution and the platform's efficiency.This chapter finds that the 020 takeout mode can improve the system's profit of the retailing market,and this chapter also finds that the increase of the profit as well as the 020 takeout platform's channel structure varies with the product's channel substitution and the platform's efficiency.Chapter 5 discusses the 020 takeout platform's optimal delivery capacity alloca-tion considering the crowdsourcing delivery.When the platform uses crowdsourcing delivery,the platform can hire full-time delivery drivers and utilize the part-time deliv-ery drivers simultaneously.The salary compensation plan will affect the decisions of the potential delivery drivers' choice and the platform's human resource cost.The inter-action between the delivery drivers and the platform will affect the platform's expected revenue and cost significantly.This chapter discusses the potential delivery drivers' de-cision and the platform's optimal delivery capacity allocation under the framework of Newsvendor Model when the salary compensation plan is exogenous.This chapter gets an important result:when the expected revenue that the total full-time delivery capacity can create is equal to that of the total part-time delivery capacity,the platform's deliv-ery capacity allocation is optimal.What's more,the platform can lead those potential delivery drivers' choices to be consistent with its optimal delivery capacity allocation.Chapter 5 also finds that the potential delivery drivers' decisions are affected by the salary compensation plan,and the compensation per delivery order has greater impact than the fix salary.This dissertation focuses on the 020 takeout industry,and does a series of research on the channel strategy and the logistics delivery strategy.First,this dissertation firstly jointly consider the local seller's delivery mode and pricing strategy,as well as their impacts on channel strategy.We found that a high efficient local delivery system can promote the prosperity of the 020 takeout industry,but also be the condition that the offline channel co-exist with the app channel.Second,this dissertation investigates ef-fects of the market and the platform's characteristics and finds that the platform's entry strategy will be affected by the two factors,and the platform's entry can increase the system profits.Lastly,the platform's delivery capacity allocation also been discussed in this dissertation under the crowdsourcing environment.We firstly investigate the in-teraction of the delivery drivers' decision and the platform's decision.The results imply that the optimal delivery capacity allocation is when profit that the full-time delivery capacity and the part-time delivery capacity can create are the same.Furthermore,the platform's delivery capacity allocation can be optimal when the compensation of the part-time delivery is slightly higher than that of the full-time delivery.
Keywords/Search Tags:Delivery Service, Channel Structure Decision, O2O Takeout Industry, Crowdsourced Delivery, Delivery Driver, Delivery Capacity Allocation
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