Pricing,Cooperation And Merger Strategies For On-demand Service Platforms

As resources are not fully utilized,people are trying to find effective ways to consume these surpluses.With the rapid development of information technology,we have witnessed the rise of sharing economy in recent years.Today more than 280 companies worldwide in 16 industries provide on-demand goods and services.In the US,the on-demand economy attracts annual spending of over $57 billion from more than 22 million consumers.In China,the latest data show that the size of the on-demand economy reached 57.22 billion CNY in 2017.Different from one-sided markets,(self-scheduling)service providers(or laborers)of on-demand service platforms in two-sided markets are free to decide whether and when to work.In addition,the platforms need to recruit sufficient service providers to meet(delay-sensitive)customers' instant demand.These two prominent features leads to various operational problems arising from demand and supply sides.To better coordinate demand and supply,pricing plays a key role in the development of the on-demand service platforms.Besides,how to increase supply to meet growing demand through cooperation or merger strategy is also important for the platforms.However,most of the platforms are not able to manage these issues in the actual operation management process.Therefore,pricing,cooperation and merger strategies for on-demand service platforms have important practical significance for the platforms,and is also a hot issue in the current operational management field.Based on the above two features,to coordinate demand and self-scheduling supply,the paper first studies pricing policy selection problems for on-demand service platforms providing horizontally and vertically differentiated services,respectively.Motivated by the cooperations and the horizontal mergers that have recently become prevalent in the on-demand economy,this paper then analyzes the impacts of these two operational decisions on the platforms' profits,consumer surplus and labor welfare.The specific research contents are as follows:(1)We study three pricing policies for a monopoly platform,such as Uber or Gett,who offers vertically differentiated services to customers via multiple types of self-scheduling providers.Ideally,the platform can employ a “dynamic pricing” policy,which pays providers wages and charges customers prices for the transactions of different services that both adjust based on prevailing demand conditions,to maximize its profit.Since it is challenging for the platform to implement and for providers to understand this policy,however,the other two pricing policies are commonly adopted in practice,i.e.,“surge pricing” policy(adopted by Uber)which pays providers a fixed commission of its dynamic prices,and “static pricing” policy(applied by Gett)which pays providers a fixed commission of its fixed prices.By observing these phenomena,we propose to study and discuss the platform's profit performance of these three pricing strategies.We show that the surge pricing policy does not always perform well for low customer size in the high demand state,which can explain why some on-demand platforms would implement the static pricing policy in practice.Also,although the dynamic pricing policy will significantly improve the platform's profit,we find that the profitability of the static(surge)pricing policy would approach that of the dynamic pricing policy if the platform can balance the number of different types of providers and/or reduce the commission rate.(2)We investigate three pricing policies for a monopoly firm,who acts as a transaction platform between customers and service providers.The platform provides multiple services with self-scheduling service providers and allows the customers to purchase different services based on their preferences.Ideally,for the transactions of different services,the platform can charge different rates of commissions accordingly,which is called the “optimal policy”,to maximize its profit.However,in practice,the other two contracts are commonly adopted,i.e.,the freecustomers-commission(FCC)policy and the dynamic-customers-commission(DCC)policy,and both of them do not apply the optimal commission policy.In particular,the three contracts all regulate the numbers of providers who join the platform.By observing these phenomena,we propose to study the commission and recruitment strategies for different policies.We analyze the reason why the platforms adopt the FCC or DCC policy,but not the optimal policy,over different development stages.Compared to the optimal policy,our results show that the DCC policy hurts the customer surplus but improves the provider surplus,while the FCC policy can improve the benefits of both parties.We also extend our base model to incorporate the price endogeneity and competition concerns.The results of our study can be used to explain the underlying mechanisms of the proposed three contracts and provide guidance to the practitioners to select the best-fit one.(3)This paper studies the impact of another source of drivers for a ride-sharing platform on platform profit,consumer surplus and labor welfare.One driver source is from those providers who have their own cars(the with-car drivers),and another one comes from those who do not have cars but are willing to provide services on the platform(the without-car drivers).The without-car drivers can offer services by renting cars from a car-rental company only when the platform cooperates with the company.In this on-demand service market,the platform first decides whether to cooperate with the car-rental company and chooses a price charged to customers and a wage paid to(with-and without-car)drivers.The company then announces a price charged to the without-car drivers and pays an exogenous commission rate to the platform.We analyze a stylized model in which customers decide whether to use the platform based on the price and expected waiting time,and drivers base decisions about whether to work for the platform on the wage and probability of getting jobs.Driven by these two features,we find that another driver source can lead to a win-win-win outcome,in which the platform,customers and drivers are all better off,for a high commission rate or a low fixed payout ratio.Due to social concerns motivating ride-sharing platforms to improve their consumer and driver surpluses,this finding can explain the reason why most ride-sharing platforms would cooperate with carrental companies in practice.Moreover,we observe that the win-win-win outcome can be easily achieved for a large customer size or delay cost but a small(with-and without-car)driver size or service rate.(4)This paper studies the impact of mergers between on-demand service platforms on platform profits,consumer surplus and labor welfare.We analyze a game-theoretical model in which customers choose between platforms based on prices and expected waiting times,and agents base decisions about which platform to work for on wages and the probability of getting jobs.Driven by these two features,we find that mergers between on-demand service platforms have several welfare implications that have not been documented by previous research.While a merger reduces competition,we show that customers may benefit from a merger due to the riskpooling effect and reduced waiting times;moreover,if customers are very sensitive to delay,this benefit can spill over to the labor force via cross-side network effects.We further establish that a win-win-win outcome,in which merging firms,customers and agents are all better off,can always be achieved if the merged platform commits to certain ratios between prices and wages.This implies that antitrust agencies can enforce restrictions on the payout ratios to protect both consumers and agents.Finally,we illustrate our main insights by implementing our model in numerical experiments calibrated using real data from large on-demand ride-sharing platforms.