Sharing Economy - Making Supply Meet Demand

Preface

Acknowledgments

Contents

Contributors

1 Introduction

1.1 Overall Structure

1.2 Chapter Highlights

1.2.1 Part I: Impact of Sharing Economy

1.2.1.1 Economic Impact

1.2.1.2 Operational Opportunity and Challenge

1.2.2 Part II: Intermediary Role of a Sharing Platform

1.2.2.1 Intermediation via Pricing and Matching

1.2.2.2 Intermediation via Information and Payment

1.2.2.3 Intermediation in the Presence of Self-Scheduling Suppliers

1.2.3 Part III: Crowdsourcing Management

1.2.3.1 Group Buying and Crowdfunding

1.2.3.2 Crowdsourcing Contest

1.2.4 Part IV: Context-Based Operational Problems in Sharing Economy

1.2.4.1 Bike Sharing

1.2.4.2 Vehicle Sharing

1.2.4.3 Short-Term Rental

1.2.4.4 Online Advertising

References

Part I Impact of Sharing Economy

2 Peer-to-Peer Product Sharing

2.1 Introduction

2.2 Literature Review

2.3 Model Description

2.3.1 Matching Supply with Demand

2.4 Equilibrium Analysis

2.4.1 Impact of Collaborative Consumption on Ownership and Usage

2.4.2 Impact of Collaborative Consumption on Consumers

2.5 The Platform's Problem

2.5.1 The For-Profit Platform

2.5.2 The Not-for-Profit Platform

2.5.3 Systems with Negative Externalities

2.5.4 The Impact of Extra Wear and Tear and Inconvenience Costs

2.6 Concluding Comments

References

3 The Strategic and Economic Implications of Consumer-to-Consumer Product Sharing

3.1 Introduction

3.2 Modeling Framework

3.3 Effects of Sharing on Firm's Pricing Strategy, Profit, and Consumer Surplus

3.4 Effects of Sharing on Product Quality and Distribution Channel

3.4.1 Effects of Sharing on Product Quality

3.4.2 Effects of Sharing on Distribution Channel

3.5 Conclusions and Discussions

References

4 Operational Factors in the Sharing Economy: A Framework

4.1 Introduction

4.2 The Framework

4.3 Examples

4.3.1 Ride Sharing

4.3.2 Group Buying

4.4 Concluding Remarks

References

5 Ride Sharing

5.1 Introduction

5.2 Anatomy of a Modern Ridesharing Platform

5.2.1 Timescales

5.2.2 Strategic Choices

5.2.3 Operation and Market Design

5.3 A Modeling Framework for Ridesharing Platforms

5.3.1 Modeling Stochastic Dynamics of the Platform

5.3.2 Platform Controls

5.3.3 Platform Objectives

5.3.4 Local Controls and Closed Queueing Models

5.3.5 Modeling Endogenous Entry of Drivers

5.4 Analyzing the Model: Key Findings

5.4.1 Fast-Timescale Control of Platform Dynamics

5.4.2 The Slow Timescale: Pricing and Driver Entry

5.5 Related Literature

5.6 Conclusion

References

Part II Intermediary Role of a Sharing Platform

6 The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity

6.1 Introduction

6.2 Literature Review

6.3 Model

6.4 Profitability of Commission Contract

6.5 Impact of Dynamic Prices on Consumers

6.6 Conclusion

References

7 Time-Based Payout Ratio for Coordinating Supply and Demand on an On-Demand Service Platform

7.1 Introduction

7.2 Literature Review

7.3 A Model of Wait-Time Sensitive Demand and Earnings Sensitive Supply

7.3.1 Customer Request Rate ? and Price Rate p

7.3.2 Number of Participating Providers k and Wage Rate w

7.3.3 Problem Formulation

7.4 The Base Model

7.4.1 Special Case 1: When the Payout Ratio w/p Is Fixed

7.4.2 Special Case 2: When the Service Level Is Exogenously Given

7.5 Numerical Illustrations Based on Didi Data

7.5.1 Background Information

7.5.2 Number of Rides and Drivers Across Different Hours

7.5.3 Travel Distance and Travel Speed

7.5.4 Pricing and Wage Rates

7.5.5 Strategic Factors and Their Implications

7.5.6 Numerical Examples for Illustrative Purposes

7.6 Conclusion

References

8 Pricing and Matching in the Sharing Economy

8.1 Introduction

8.1.1 Two-Sided Pricing

8.1.2 Two-Sided Matching

8.1.3 Pricing and Matching Under Strategic Behavior

8.2 Two-Sided Pricing and Fixed Commission

8.2.1 The Price and Wage Optimization Problem

8.2.2 The Fixed Commission Contract

8.2.3 Numerical Study

8.3 Dynamic Matching with Heterogeneous Types

8.3.1 Priority Properties of the Optimal Matching Policy

8.3.1.1 Unidirectional Horizontal Types

8.3.1.2 Vertical Types

8.3.2 Bound and Heuristic

8.3.2.1 Numerical Study

8.4 Pricing and Matching with Strategic Suppliers and Customers

8.4.1 Upper Bound of the Intermediary's Optimal Profit

8.4.2 A Simple Dynamic Policy: Asymptotic Optimality

8.4.2.1 Optimal Policy in an Auxiliary Setting

8.4.2.2 Waiting Adjusted Fixed Pricing Policy

8.5 Conclusion

References

9 Large-Scale Service Marketplaces: The Roleof the Moderating Firm

9.1 Introduction

9.2 Literature Review

9.3 Model Formulation

9.4 No-Intervention Model

9.4.1 Characterization of SPNE

9.5 Operational Efficiency Model

9.5.1 Characterization of the Market Equilibrium

9.5.1.1 Buyer's Market

9.5.1.2 Seller's Market

9.6 Communication Enabled Model

9.6.1 Characterization of the (?,?)-Market Equilibrium

9.7 A Marketplace with Non-identical Agents

9.8 Conclusion

References

10 Inducing Exploration in Service Platforms

10.1 Introduction

10.2 Related Literature

10.3 Illustrative Example

10.4 Benchmark Model

10.5 Inducing Exploration

10.5.1 Strategic Information Disclosure

10.5.2 The Value of Information Obfuscation

10.5.3 Minimizing Regret

10.5.4 Incentivizing Customers Using Payments

10.6 Promising Directions

10.6.1 Learning in Dynamic Contests

10.6.2 Dealing with Misinformation

10.7 Concluding Remarks

References

11 Design of an Aggregated Marketplace Under Congestion Effects: Asymptotic Analysis and Equilibrium Characterization

11.1 Introduction

11.1.1 Background and Motivation

11.1.2 Overview of Results

11.1.3 Literature Review

11.2 Model

11.2.1 Description of the Market

11.2.2 Problems to Address

11.3 Asymptotic Analysis of Marketplace Dynamics

11.3.1 Background: Revenue Maximization for an M/M/1 Monopolistic Supplier

11.3.2 Setup for Asymptotic Analysis

11.3.3 Transient Dynamics via a Fluid Model Analysis

11.3.4 State-Space Collapse and the Aggregate Marketplace Behavior

11.3.5 Limit Model and Discussion

11.3.6 A Numerical Example

11.4 Competitive Behavior and Market Efficiency

11.4.1 Suppliers' First-Order Payoffs and the Capacity Game

11.4.2 Suppliers' Second-Order Payoffsand the Pricing Game

11.4.3 Centralized System Performance

11.4.4 Competitive Equilibrium

11.4.4.1 Homogeneous Service Rate Case

11.4.4.2 Heterogeneous Service Rate Case

11.4.4.3 Numerical Results

11.4.4.4 A Remark on the Suppliers' Participation

11.4.5 Coordination Scheme

11.4.5.1 Sufficient Condition for Coordination

11.4.5.2 ``Compensation-While-Idling'' Mechanism That Achieves Coordination

11.4.6 Simulation Results

11.5 Conclusions

References

12 Operations in the On-Demand Economy: Staffing Services with Self-Scheduling Capacity

12.1 Introduction

12.2 Model

12.3 Analysis

12.3.1 The Cost of Self Scheduling

12.3.2 Earnings Constraint and Agent Flexibility

12.3.3 Time-Varying Demand

12.3.4 The Benefit of Flexible Capacity

12.4 Variants of the Base Model

12.4.1 Volume-Dependent Compensation Schemes

12.4.2 Price-Dependent Newsvendor

12.4.3 When Maintaining a Larger Pool Costs More

12.4.4 Period-Dependent Threshold Distributions

12.5 Concluding Remarks

Appendix

Proof of Theorem 1

Proof of Lemma 2

Proof of Lemma 3

Proof of Theorem 2

Proof of Theorem 3

Proof of Lemma 4

References

13 On Queues with a Random Capacity: Some Theory, and an Application

13.1 Introduction

13.2 Theoretical Background: Queues with Uncertain Parameters

13.2.1 Self-Scheduling Servers: A Binomial Distribution

13.2.2 What Do the Asymptotic Results Mean?

13.3 Self-Scheduling Agents: A Long-Term Staffing Decision

13.3.1 The Model

13.3.2 Fluid Formulation

13.3.3 Optimal Staffing Policy

13.3.3.1 No Self-Scheduling

13.3.3.2 Self-Scheduling Capacity

13.4 Short-Term Controls

13.4.1 Delay Announcements: Performance Impact

13.4.1.1 When Do the Announcements Reduce the Cost of Self-Scheduling?

13.4.1.2 A New Staffing Problem

13.5 Joint Control of Compensation and Delay Announcements

13.6 Jointly Optimizing Long and Short-Term Controls

13.6.1 Low Minimum Wage

13.6.2 High Minimum Wage

13.7 Conclusions

Technical Appendix

Proof of Theorem 1

The Overloaded Regime

The Underloaded Regime

The Critically-Loaded Regime

Proofs of Propositions

References

Part III Crowdsourcing Management

14 Online Group Buying and Crowdfunding: Two Cases of All-or-Nothing Mechanisms

14.1 Introduction

14.2 Consumer Behavior Under All-or-Nothing Mechanisms

14.2.1 Empirical Model

14.2.1.1 The Base Model

14.2.1.2 The Extended Model with Lagged Variables

14.2.2 Results

14.2.3 Potential Mechanisms Behind Threshold Effects

14.3 Coordination Under All-or-Nothing Mechanisms

14.3.1 Information Disclosure

14.3.1.1 Model Setup

14.3.1.2 Equilibrium Analysis Under Simultaneous Mechanism

14.3.1.3 Equilibrium Analysis Under Sequential Mechanism

14.3.1.4 Mechanism Design: Simultaneous or Sequential?

14.3.2 Pricing

14.3.2.1 Model Setup

14.3.2.2 Alternative Pricing Policies

14.3.2.3 Optimal Pricing Strategy

14.4 Conclusion

References

15 Threshold Discounting: Operational Benefits, Potential Drawbacks, and Optimal Design

15.1 Introduction

15.2 Literature Review

15.3 The Model

15.3.1 Preliminaries

15.3.2 The Traditional Approach: Seasonal Closure or Regular Discounting

15.3.3 Threshold Discounting

15.3.3.1 Sequence of Events

15.3.3.2 Customer Continuation Game

15.3.3.3 Optimal Announcement and Equilibrium Outcome

15.3.4 Comparing Threshold Discounting with the Traditional Approach

15.3.4.1 Responsive Duality

15.3.4.2 Strategic Scarcity Effect

15.3.4.3 A Novel Operational Advantage

15.3.5 Impact of Strategic Customers on Threshold Discounting Performance

15.3.6 Mediated Threshold Discounting

15.3.7 Design Considerations in ThresholdDiscounting Offers

15.3.7.1 Opaque Activation Rule

15.3.7.2 Time When the Outcome of the Deal Is Announced

15.3.7.3 Time Restricted Discounts

15.3.7.4 Focused Threshold Discounting

15.4 Discussion

References

16 Innovation and Crowdsourcing Contests

16.1 Introduction

16.2 A General Model Framework for Innovation Contests

16.3 A Brief Taxonomy of Contest Literature

16.4 Contests with Uncertainty

16.4.1 Optimal Award Scheme

16.4.2 Open Innovation and Agents' Incentives

16.5 Contests with Heterogenous Agents

16.5.1 Optimal Award Scheme

16.5.2 Open Innovation and Agents' Incentives

16.6 Conclusion and Future Research

Appendix

References

Part IV Context-Based Operational Problems in Sharing Economy

17 Models for Effective Deployment and Redistribution of Shared Bicycles with Location Choices

17.1 Introduction

17.1.1 Review of the Bicycle-Sharing Systems

17.1.2 Research Issues and Structure of the Chapter

17.2 The Stochastic Network Flow Model

17.2.1 Equilibrium State in Time Invariant System

17.2.2 Bicycle-Sharing System Design with Location Choice

17.3 Bicycle Sharing as Substitute for Train Rides

17.3.1 Bicycle Deployment and Utilization

17.3.2 Number of Bicycle Docks Needed

17.3.3 Effectiveness of Bicycle Redistribution

17.4 Case Study on Bicycle Sharing with Location Decisions

17.5 Concluding Remarks

References

18 Bike Sharing

18.1 Introduction

18.2 Data and Statistical Challenges

18.3 Motorized Rebalancing

18.3.1 User Dissatisfaction Function

18.3.2 Optimal Allocation Before the Rush

18.3.3 Resulting Routing Problems

18.4 Allocating Capacity

18.4.1 Model formulation

18.4.2 Long-Run Average

18.4.3 Measuring the Impact

18.5 Beyond Motorized Rebalancing

18.5.1 Incentives

18.5.2 Valets and Corrals

18.6 Expansion Planning

18.7 Conclusion

References

19 Operations Management of Vehicle Sharing Systems

19.1 Introduction

19.2 Service Region Design

19.2.1 Basic Model

19.2.2 Customer Adoption

19.2.3 Operational Profit

19.2.4 Numerical Results

19.3 Fleet Sizing

19.3.1 Two-Stage Stochastic Optimization Model

19.3.2 Numerical Results

19.4 Fleet Repositioning

19.4.1 Stochastic Dynamic Program Formulation

19.4.2 The 2-Region System

19.4.3 The N-Region System

19.5 Other Topics

19.5.1 Dynamic Pricing

19.5.2 Reservation Management

19.6 Discussion

References

20 Agent Pricing in the Sharing Economy: Evidence from Airbnb

20.1 Introduction

20.2 Literature Review and Hypothesis Development

20.2.1 Literature Review

20.2.2 Hypotheses Development

20.3 Empirical Setting and Data

20.3.1 Empirical Setting: The Airbnb Platform

20.3.2 Airbnb Data: Listings and Transactions

20.4 Performance of Professional vs. Nonprofessional Hosts: Econometric Specifications and Results

20.4.1 Daily Revenue

20.4.2 Occupancy Rate and Average Rent Price

20.4.3 Exit Probability

20.5 Understanding the Differences in Performance

20.6 Conclusion

References

21 Intermediation in Online Advertising

21.1 Introduction

21.1.1 Main Contributions

21.1.2 Literature Review

21.1.2.1 Intermediary Problems

21.1.2.2 Online Advertising and Ad Exchanges

21.1.2.3 Mechanism Design with Budget Constraints

21.2 Optimal Contracts for Intermediaries in Online Advertising

21.2.1 Mechanism Design Problem

21.2.2 Optimal Mechanism Characterization

21.2.2.1 Dual Problem

21.2.2.2 Support Function Characterization

21.2.2.3 Synthesis

21.2.2.4 Optimal Bidding Policy

21.2.3 Economic Insights

21.2.3.1 Intermediation Profit and the Advertiser Surplus

21.2.3.2 Market Efficiency

21.3 Multi-stage Intermediation in Display Advertising

21.3.1 Equilibrium Characterization

21.3.2 Economic Insights

21.4 Concluding Remarks

References