Logistics and distribution need to be more responsive and flexible to satisfy changing and demanding customer requirements due to e-commerce and customization trends. This work focuses in particular on warehousing, with the aim of understanding how emerging business models provide companies with additional ways to acquire warehouse space or fulfillment services. To do so, this work classifies and describes traditional warehouse models. Next, on-demand warehousing is analyzed as an emerging business-to-business (B2B) model that embraces the sharing economy principle of accessing resources rather than owning them. On-demand warehousing companies operate through online platforms connecting companies who have underutilized warehouses or fulfillment capacity to other ones searching for warehousing services. On-demand warehousing enables more flexible resource acquisition, as fixed cost investments are not necessary, and lengthy negotiations are eliminated through a standardized contract between the on-demand platform and the renter. This work contributes to the literature through an improved understanding and description of the main features of on-demand warehousing, representing a starting point for further research on this topic. Future developments are needed on the analysis of the main decisions a lender of space has to make when choosing an on-demand model.
Airbnb’s reputation system and gender differences among guests: Evidence from large-scale data analysis and a controlled experiment
Sharing economy platforms are rapidly scaling up by reaching increasingly diverse demographics. However, this expansion comes with great difficulties in adequately identifying and responding to everyone’s needs. In this paper, we study gender-related behaviors of guests on the currently most prominent home-sharing platform, Airbnb. While our results confirm the efficacy of Airbnb’s reputation system, we also find that the level of trust and participation on the platform varies by gender. In particular, female solo travelers are more likely to be conscious of review sentiment and choose more often female hosts than male solo travelers. Our findings are obtained by combining exploratory data analysis with large-scale experiment and call for further studies on the usage of sharing economy platforms among subpopulations, informing and improving both policy and practice in these growing online environments.
- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- Lecture notes in computer science
- Sponsoring Org:
- National Science Foundation
More Like this
The sharing economy has upset the market for housing and transportation services. Homeowners can rent out their property when they are away on vacation, car owners can offer ridesharing services. These sharing economy business models are based on monetizing under-utilized infrastructure. They are enabled by peer-to-peer platforms that match eager sellers with willing buyers. Are there compelling sharing economy opportunities in the electricity sector? What products or services can be shared in tomorrow’s Smart Grid? We begin by exploring sharing economy opportunities in the electricity sector, and discuss regulatory and technical obstacles to these opportunities. We then study the specific problem of a collection of firms sharing their electricity storage. We characterize equilibrium prices for shared storage in a spot market. We formulate storage investment decisions of the firms as a non-convex non-cooperative game. We show that under a mild alignment condition, a Nash equilibrium exists, it is unique, and it supports the social welfare. We discuss technology platforms necessary for the physical exchange of power, and market platforms necessary to trade electricity storage. We close with synthetic examples to illustrate our ideas.
This paper proposes a novel quantity-based demand management system that aims to promote ridesharing. The system sells a time-dependent permit to access a road facility (conceptualized as a bottleneck) by auction but encourages commuters to share permits with each other. The commuters may be assigned one of three roles: solo driver, ridesharing driver, or rider. At the core of this auction-based permit allocation and sharing system (A-PASS) is a trilateral matching problem (TMP) that matches permits, drivers, and riders. Formulated as an integer program, TMP is first shown to be tightly bounded by its linear relaxation. A pricing policy based on the classical Vickrey–Clarke–Groves (VCG) mechanism is then devised to determine the payment of each commuter. We prove that, under the VCG policy, different commuters pay exactly the same price as long as their role and access time are the same. Importantly, by controlling the number of shared rides, any deficit that may arise from the VCG policy can be eliminated. This may be achieved with a relatively small loss to system efficiency, thanks to the revenue generated from selling permits. Results of a numerical experiment suggest A-PASS strongly promotes ridesharing. As sharing increases, all stakeholders are better off: themore »
Involving the public in scientific discovery offers opportunities for engagement, learning, participation, and action. Since its launch in 2007, the CitSci.org platform has supported hundreds of community-driven citizen science projects involving thousands of participants who have generated close to a million scientific measurements around the world. Members using CitSci.org follow their curiosities and concerns to develop, lead, or simply participate in research projects. While professional scientists are trained to make ethical determinations related to the collection of, access to, and use of information, citizen scientists and practitioners may be less aware of such issues and more likely to become involved in ethical dilemmas. In this era of big and open data, where data sharing is encouraged and open science is promoted, privacy and openness considerations can often be overlooked. Platforms that support the collection, use, and sharing of data and personal information need to consider their responsibility to protect the rights to and ownership of data, the provision of protection options for data and members, and at the same time provide options for openness. This requires critically considering both intended and unintended consequences of the use of platforms, data, and volunteer information. Here, we use our journey developing CitSci.org tomore »
The emergence of the sharing economy in urban transportation networks has enabled new fast, convenient and accessible mobility services referred to as Mobilty-on-Demand systems (e.g., Uber, Lyft, DiDi). These platforms have flourished in the last decade around the globe and face many operational challenges in order to be competitive and provide good quality of service. A crucial step in the effective operation of these systems is to reduce customers' waiting time while properly selecting the optimal fleet size and pricing policy. In this paper, we jointly tackle three operational decisions: (i) fleet size, (ii) pricing, and (iii) rebalancing, in order to maximize the platform's profit or its customers' welfare. To accomplish this, we first devise an optimization framework which gives rise to a static policy. Then, we elaborate and propose dynamic policies that are more responsive to perturbations such as unexpected increases in demand. We test this framework in a simulation environment using three case studies and leveraging traffic flow and taxi data from Eastern Massachusetts, New York City, and Chicago. Our results show that solving the problem jointly could increase profits between 1% and up to 50%, depending on the benchmark. Moreover, we observe that the proposed fleet sizemore »