Competition Between Two-Sided Platforms Under Demand and Supply Congestion Effects

2020 ◽  
Author(s):  
Fernando Bernstein ◽  
Gregory A. DeCroix ◽  
N. Bora Keskin
Keyword(s):  
Service Providers ◽  
Supply And Demand ◽  
Relative Number ◽  
Sharing Economy ◽  
Problem Definition ◽  
Ride Sharing ◽  
Managerial Implications ◽  
The Impact ◽  
Number Of Customers ◽  
Congestion Effects

Problem definition: This paper explores the impact of competition between platforms in the sharing economy. Examples include the cases of Uber and Lyft in the context of ride-sharing platforms. In particular, we consider competition between two platforms that offer a common service (e.g., rides) through a set of independent service providers (e.g., drivers) to a market of customers. Each platform sets a price that is charged to customers for obtaining the service provided by a driver. A portion of that price is paid to the driver who delivers the service. Both customers’ and drivers’ utilities are sensitive to the payment terms set by the platform and are also sensitive to congestion in the system (given by the relative number of customers and drivers in the market). We consider two possible settings. The first one, termed “single-homing,” assumes that drivers work through a single platform. In the second setting, termed “multihoming” (or “multiapping,” as it is known in practice), drivers deliver their service through both platforms. Academic/practical relevance: This is one of the first papers to study competition and multihoming in the presence of congestion effects typically observed in the sharing economy. We leverage the model to study some practical questions that have received significant press attention (and stirred some controversies) in the ride-sharing industry. The first involves the issue of surge pricing. The second involves the increasingly common practice of drivers choosing to operate on multiple platforms (multihoming). Methodology: We formulate our problem as a pricing game between two platforms and employ the concept of a Nash equilibrium to analyze equilibrium outcomes in various settings. Results: In both the single-homing and multihoming settings, we study the equilibrium prices that emerge from the competitive interaction between the platforms and explore the supply and demand outcomes that can arise at equilibrium. We build on these equilibrium results to study the impact of surge pricing in response to a surge in demand and to examine the incentives at play when drivers engage in multihoming. Managerial implications: We find that raising prices in response to a surge in demand makes drivers and customers better off than if platforms were constrained to charge the same prices that would arise under normal demand levels. We also compare drivers’ and customers’ performance when all drivers either single-home or multihome. We find that although individual drivers may have an incentive to multihome, all players are worse off when all drivers multihome. We conclude by proposing an incentive mechanism to discourage multihoming.

Drivers, Riders, and Service Providers: The Impact of the Sharing Economy on Mobility

2021 ◽  
Author(s):  
Saif Benjaafar ◽  
Harald Bernhard ◽  
Costas Courcoubetis ◽  
Michail Kanakakis ◽  
Spyridon Papafragkos
Keyword(s):  
Operations Management ◽  
Service Providers ◽  
Sharing Economy ◽  
Car Ownership ◽  
Full Time ◽  
Ride Sharing ◽  
Finite Set ◽  
Nonatomic Game ◽  
Payoff Functions ◽  
The Impact

It is widely believed that ride sharing, the practice of sharing a car such that more than one person travels in the car during a journey, has the potential to significantly reduce traffic by filling up cars more efficiently. We introduce a model in which individuals may share rides for a certain fee, paid by the rider(s) to the driver through a ride-sharing platform. Collective decision making is modeled as an anonymous nonatomic game with a finite set of strategies and payoff functions among individuals who are heterogeneous in their income. We examine how ride sharing is organized and how traffic and ownership are affected if a platform, which chooses the seat rental price to maximize either revenue or welfare, is introduced to a population. We find that the ratio of ownership to usage costs determines how ride sharing is organized. If this ratio is low, ride sharing is offered as a peer-to-peer (P2P) service, and if this ratio is high, ride sharing is offered as a business-to-customer (B2C) service. In the P2P case, rides are initiated by drivers only when the drivers need to fulfill their own transportation requirements. In the B2C case, cars are driven all the time by full-time drivers taking rides even if these are not motivated by their private needs. We show that, although the introduction of ride sharing may reduce car ownership, it can lead to an increase in traffic. We also show that traffic and ownership may increase as the ownership cost increases and that a revenue-maximizing platform might prefer a situation in which cars are driven with only a few seats occupied, causing high traffic. We contrast these results with those obtained for a social welfare-maximizing platform. This paper was accepted by Charles Corbett, operations management.

Coping with Spatial Mismatch: Subsidy Design for Electric Vehicle and Charging Markets

2021 ◽  
Author(s):  
Wei Zhang ◽  
Yifan Dou
Keyword(s):  
Electric Vehicle ◽  
Service Providers ◽  
Supply And Demand ◽  
Problem Definition ◽  
Environmental Benefits ◽  
Third Party ◽  
Theoretic Model ◽  
Spatial Mismatch ◽  
The Government ◽  
Ev Charging

Problem definition: We study how the government should design the subsidy policy to promote electric vehicle (EV) adoptions effectively and efficiently when there might be a spatial mismatch between the supply and demand of charging piles. Academic/practical relevance: EV charging infrastructures are often built by third-party service providers (SPs). However, profit-maximizing SPs might prefer to locate the charging piles in the suburbs versus downtown because of lower costs although most EV drivers prefer to charge their EVs downtown given their commuting patterns and the convenience of charging in downtown areas. This conflict of spatial preferences between SPs and EV drivers results in high overall costs for EV charging and weak EV adoptions. Methodology: We use a stylized game-theoretic model and compare three types of subsidy policies: (i) subsidizing EV purchases, (ii) subsidizing SPs based on pile usage, and (iii) subsidizing SPs based on pile numbers. Results: Subsidizing EV purchases is effective in promoting EV adoptions but not in alleviating the spatial mismatch. In contrast, subsidizing SPs can be more effective in addressing the spatial mismatch and promoting EV adoptions, but uniformly subsidizing pile installation can exacerbate the spatial mismatch and backfire. In different situations, each policy can emerge as the best, and the rule to determine which side (SPs versus EV buyers) to subsidize largely depends on cost factors in the charging market rather than the EV price or the environmental benefits. Managerial implications: A “jigsaw-piece rule” is recommended to guide policy design: subsidizing SPs is preferred if charging is too costly or time consuming, and subsidizing EV purchases is preferred if charging is sufficiently fast and easy. Given charging costs that are neither too low nor too high, subsidizing SPs is preferred only if pile building downtown is moderately more expensive than pile building in the suburbs.

Incentives for Shared Services: Multiserver Queueing Systems with Priorities

2021 ◽  
Author(s):  
Hanlin Liu ◽  
Yimin Yu
Keyword(s):  
Cooperative Game ◽  
Cost Allocation ◽  
Service Providers ◽  
Queueing Systems ◽  
Service Level ◽  
Problem Definition ◽  
Shared Services ◽  
Scheduling Policy ◽  
Shared Service ◽  
Managerial Implications

Problem definition: We study shared service whereby multiple independent service providers collaborate by pooling their resources into a shared service center (SSC). The SSC deploys an optimal priority scheduling policy for their customers collectively by accounting for their individual waiting costs and service-level requirements. We model the SSC as a multiclass [Formula: see text] queueing system subject to service-level constraints. Academic/practical relevance: Shared services are increasingly popular among firms for saving operational costs and improving service quality. One key issue in fostering collaboration is the allocation of costs among different firms. Methodology: To incentivize collaboration, we investigate cost allocation rules for the SSC by applying concepts from cooperative game theory. Results: To empower our analysis, we show that a cooperative game with polymatroid optimization can be analyzed via simple auxiliary games. By exploiting the polymatroidal structures of the multiclass queueing systems, we show when the games possess a core allocation. We explore the extent to which our results remain valid for some general cases. Managerial implications: We provide operational insights and guidelines on how to allocate costs for the SSC under the multiserver queueing context with priorities.

Dynamic Type Matching

2021 ◽  
Author(s):  
Ming Hu ◽  
Yun Zhou
Keyword(s):  
Sufficient Conditions ◽  
Sharing Economy ◽  
Problem Definition ◽  
Periodic Review ◽  
Optimal Matching ◽  
Demand And Supply ◽  
Holding Costs ◽  
Current Period ◽  
Dynamic Type ◽  
Managerial Implications

Problem definition: We consider an intermediary’s problem of dynamically matching demand and supply of heterogeneous types in a periodic-review fashion. Specifically, there are two disjoint sets of demand and supply types, and a reward for each possible matching of a demand type and a supply type. In each period, demand and supply of various types arrive in random quantities. The platform decides on the optimal matching policy to maximize the expected total discounted rewards, given that unmatched demand and supply may incur waiting or holding costs, and will be fully or partially carried over to the next period. Academic/practical relevance: The problem is crucial to many intermediaries who manage matchings centrally in a sharing economy. Methodology: We formulate the problem as a dynamic program. We explore the structural properties of the optimal policy and propose heuristic policies. Results: We provide sufficient conditions on matching rewards such that the optimal matching policy follows a priority hierarchy among possible matching pairs. We show that those conditions are satisfied by vertically and unidirectionally horizontally differentiated types, for which quality and distance determine priority, respectively. Managerial implications: The priority property simplifies the matching decision within a period, and the trade-off reduces to a choice between matching in the current period and that in the future. Then the optimal matching policy has a match-down-to structure when considering a specific pair of demand and supply types in the priority hierarchy.

Product Flexibility Strategy Under Supply and Demand Risk

2021 ◽  
Author(s):  
Yimin Wang ◽  
Scott Webster
Keyword(s):  
Supply Chain ◽  
Supply And Demand ◽  
Problem Definition ◽  
Base Case ◽  
Supply Network ◽  
Aggregation Effect ◽  
Risk Aggregation ◽  
Managerial Implications ◽  
Product Flexibility ◽  
Global Uncertainty

Problem definition: With heightened global uncertainty, supply chain managers are under increasing pressure to craft strategies that accommodate both supply and demand risks. Although product flexibility is a well-understood strategy to accommodate risk, there is no clear guidance on the optimal flexibility configuration of a supply network that comprises both unreliable primary suppliers and reliable backup suppliers. Academic/practical relevance: Existing literature examines the value of flexibility with primary and backup suppliers independently. For a risk-neutral firm, research shows that (a) incorporating flexibility in a primary supplier by replacing two dedicated ones (in absence of backup supply) is always beneficial and that (b) adding flexibility to a reliable backup supplier (in absence of product flexibility in primary suppliers) is always valuable. It is unclear, however, how flexibility should be incorporated into a supply network with both unreliable primary suppliers and reliable backup suppliers. This research studies whether flexibility should be incorporated in a primary supplier, a backup supplier, or both. Methodology: We develop a normative model to analyze when flexibility benefits and when it hurts. Results: Compared with a base case of no flexibility, we prove that incorporating flexibility in either primary or backup suppliers is always beneficial. However, incorporating flexibility in both primary and backup suppliers can be counterproductive because the supply chain performance can decline with saturated flexibility, even if flexibility is costless. A key reason is that the risk-aggregation effect of consolidating flexibility in an unreliable supplier becomes more salient when flexibility is already embedded in a backup supplier. Managerial implications: This research refines the existing understanding of flexibility by illustrating that flexibility is not always beneficial. When there is a choice, a firm should prioritize incorporating flexibility in a reliable backup supplier.

scholarly journals Nonbinding Goals in Teams: A Real Effort Coordination Experiment

2020 ◽  
Vol 22 (5) ◽  
pp. 1026-1044
Author(s):  
James Fan ◽  
Joaquín Gómez-Miñambres
Keyword(s):  
Energy Use ◽  
Weak Link ◽  
Human Subjects ◽  
Individual Performance ◽  
Problem Definition ◽  
Reference Points ◽  
Team Production ◽  
Team Members ◽  
Managerial Implications ◽  
The Impact

Problem definition: We investigate the impact of nonbinding (wage-irrelevant) goals, set by a manager, on a team of workers with “weak-link” production technology. Can nonbinding goals improve team production when team members face production complementarity? Academic/practical relevance: Nonbinding goals are easy to implement and ubiquitous in practice. These goals have been shown to improve individual performance, but it remains to be seen if such goals are effective in team production when there is production complementarity among workers. Methodology: We first develop a theoretical model where goals act as reference points for workers’ intrinsic motivation to complete the task. We then test our hypotheses in a controlled, human-subjects experiment. In our experiment, participants act as managers or workers, and we examine the impact of nonbinding goals on team outcomes. Results: Consistent with our model, we find evidence that team production does increase when managers are able to set goals. This effect is strongest when goals are challenging but attainable for weak-link workers. However, we also find evidence that many managers assign goals that are too challenging for weak-link workers, resulting in suboptimal team production, lower profits, and higher wasted performance (performance above the weak-link level). Managerial implications: Our analysis indicates that goals are effective motivators in teams, but some managers may have difficulty overcoming personal biases when setting goals. The task of setting team goals is more complex than setting individual goals, and many managers can benefit from training on how to set good goals for the team. Moreover, our finding that suboptimal goals also increase wasted performance suggests that improving goal-setting strategies is especially important in production settings where overperformance is costly for the firm (scrap, energy use, inventory costs, lower prices as a result of oversupply, etc.).

Pricing and Matching with Forward-Looking Buyers and Sellers

2020 ◽  
Vol 22 (4) ◽  
pp. 717-734 ◽  
Author(s):  
Yiwei Chen ◽  
Ming Hu
Keyword(s):  
Operations Management ◽  
Sharing Economy ◽  
Problem Definition ◽  
Fixed Base ◽  
Managerial Implications ◽  
Dynamic Stochastic ◽  
Bid Prices ◽  
Supply Costs ◽  
Heuristic Policy ◽  
Forward Looking

Problem definition: We study a dynamic market over a finite horizon for a single product or service in which buyers with private valuations and sellers with private supply costs arrive following Poisson processes. A single market-making intermediary decides dynamically on the ask and bid prices that will be posted to buyers and sellers, respectively, and on the matching decisions after buyers and sellers agree to buy and sell. Buyers and sellers can wait strategically for better prices after they arrive. Academic/practical relevance: This problem is motivated by the emerging sharing economy and directly speaks to the core of operations management that is about matching supply with demand. Methodology: The dynamic, stochastic, and game-theoretic nature makes the problem intractable. We employ the mechanism-design methodology to establish a tractable upper bound on the optimal profit, which motivates a simple heuristic policy. Results: Our heuristic policy is: fixed ask and bid prices plus price adjustments as compensation for waiting costs, in conjunction with the greedy matching policy on a first-come-first-served basis. These fixed base prices balance demand and supply in expectation and can be computed efficiently. The waiting-compensated price processes are time-dependent and tend to have opposite trends at the beginning and end of the horizon. Under this heuristic policy, forward-looking buyers and sellers behave myopically. This policy is shown to be asymptotically optimal. Managerial implications: Our results suggest that the intermediary might not lose much optimality by maintaining stable prices unless the underlying market conditions have significantly changed, not to mention that frequent surge pricing may antagonize riders and induce riders and drivers to behave strategically in ways that are hard to account for with traditional pricing models.

scholarly journals The Consequences of Participating in the Sharing Economy: A Transparency-Based Sharing Framework

2020 ◽  
Vol 47 (1) ◽  
pp. 317-343 ◽  
Author(s):  
Nils C. Köbis ◽  
Ivan Soraperra ◽  
Shaul Shalvi
Keyword(s):  
Global Economy ◽  
Service Providers ◽  
Empirical Work ◽  
Sharing Economy ◽  
Third Parties ◽  
Future Research ◽  
Psychological Consequences ◽  
Negative Consequences ◽  
For Profit ◽  
Managerial Implications

The sharing economy is estimated to add hundreds of billions of dollars to the global economy and is rapidly growing. However, trust-based commercial sharing—the participation in for-profit peer-to-peer sharing-economy activity—has negative as well as positive consequences for both the interacting parties and uninvolved third parties. To share responsibly, one needs to be aware of the various consequences of sharing. We provide a comprehensive, preregistered, systematic literature review of the consequences of trust-based commercial sharing, identifying 93 empirical papers spanning regions, sectors, and scientific disciplines. Via in-depth coding of the empirical work, we provide an authoritative overview of the economic, social, and psychological consequences of trust-based commercial sharing for involved parties, including service providers, users, and third parties. Based on the aggregate insights, we identify the common denominators for the positive and negative consequences. Whereas a well-functioning infrastructure of payment, insurance, and communication enables the positive consequences, ambiguity about rules, roles, and regulations causes non-negligible negative consequences. To overcome these negative consequences and promote more responsible forms of sharing, we propose the transparency-based sharing framework. Based on the framework, we outline an agenda for future research and discuss emerging managerial implications that arise when trying to increase transparency without jeopardizing the potential of trust-based commercial sharing.

AI and Procurement

2021 ◽  
Author(s):  
Ruomeng Cui ◽  
Meng Li ◽  
Shichen Zhang
Keyword(s):  
Price Discrimination ◽  
Recommendation System ◽  
Wholesale Price ◽  
Problem Definition ◽  
Practical Relevance ◽  
Trading Company ◽  
Smart Control ◽  
Managerial Implications ◽  
The Impact ◽  
Price Quotes

Problem definition: In this research, we study how buyers’ use of artificial intelligence (AI) affects suppliers’ price quoting strategies. Specifically, we study the impact of automation—that is, the buyer uses a chatbot to automatically inquire about prices instead of asking in person—and the impact of smartness—that is, the buyer signals the use of a smart AI algorithm in selecting the supplier. Academic/practical relevance: In a world advancing toward AI, we explore how AI creates and delivers value in procurement. AI has two unique abilities: automation and smartness, which are associated with physical machines or software that enable us to operate more efficiently and effectively. Methodology: We collaborate with a trading company to run a field experiment on an online platform in which we compare suppliers’ wholesale price quotes across female, male, and chatbot buyer types under AI and no recommendation conditions. Results: We find that, when not equipped with a smart control, there is price discrimination against chatbot buyers who receive a higher wholesale price quote than human buyers. In fact, without smartness, automation alone receives the highest quoted wholesale price. However, signaling the use of a smart recommendation system can effectively reduce suppliers’ price quote for chatbot buyers. We also show that AI delivers the most value when buyers adopt automation and smartness simultaneously in procurement. Managerial implications: Our results imply that automation is not very valuable when implemented without smartness, which in turn suggests that building smartness is necessary before considering high levels of autonomy. Our study unlocks the optimal steps that buyers could adopt to develop AI in procurement processes.

Prepositioning and Local Purchasing for Emergency Operations Under Budget, Demand, and Supply Uncertainty

2021 ◽  
Author(s):  
Mahyar Eftekhar ◽  
Jing-Sheng Jeannette Song ◽  
Scott Webster
Keyword(s):  
Optimal Solution ◽  
Rapid Onset ◽  
Problem Definition ◽  
Supply Uncertainty ◽  
Approximate Methods ◽  
Humanitarian Organizations ◽  
Solution Methods ◽  
Local Supply ◽  
Managerial Implications ◽  
The Impact

Problem definition: Considering a mix of prepositioning and local purchasing, common to cover humanitarian demands in the aftermath of a rapid-onset disaster, we propose policies to determine preposition stock. These formulations are developed in the presence of demand, budget, and local supply uncertainties and for single-items delivery. Academic/practical relevance: The immediate period aftermath of a disaster is the most crucial period during which humanitarian organizations must supply relief items to beneficiaries. Yet, because of many unknowns such as time, place, and magnitude of a disaster, supply management is a significant challenge, and these decisions are made intuitively. The features and complexities we examine have not been studied in the literature. Methodology: We derive properties of the optimal solution, identify exact solution methods, and determine approximate methods that are easy to implement. Results: We (i) characterize the interplay of supply, demand, and budget uncertainties, as well as the impact of product characteristics on optimal prepo stock levels; (ii) show in what conditions the prepo stock is a simple newsvendor solution; and (iii) discuss the value of emergency funds. Managerial implications: We show that budget level is a key determinant of the optimal policy. When it is above a threshold, inventory increases in disaster frequency and severity, but the reverse is true otherwise. When budget is limited, the rate of savings from improved forecasts is amplified (attenuated) for critical (noncritical) items, reflecting opposing directional effects of mismatch cost and cost of insufficient funding. Our model can also be used to estimate the value of initiatives to mitigate constraints on local spend (e.g., a line of credit underwritten by large donors that is available during the immediate relief period).

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