Abstract: New Internet-based markets enable consumer/owners to rent out their durable goods when not using them. Such markets are modeled to determine ownership, rental rates, quantities, and surplus generated. Both the short run, before consumers can revise their ownership decisions, and the long run, in which they can, are examined to assess how these markets change ownership and consumption. The analysis examines bringing-to-market costs, such as labor costs and transaction costs, and considers the operating platform's pricing problem. A survey of consumers broadly supports the modeling assumptions employed. For example, ownership is determined by individuals' forward-looking assessments of planned usage.
Abstract: Activities within the sharing economy (SE) are in a precarious situation due to the Covid-19 pandemic. Even though the SE is considered a disruptive phenomenon, especially in the accommodation and transport sectors, the Covid-19 has raised concerns about its survivability. Thousands of people have lost their jobs, the value of SE firms has dropped, and many service providers have no other option but to stop working. Understanding the effect of the Covid-19 pandemic on the SE sector is therefore essential. The objective of this study is therefore to examine the effect of the Covid-19 on sharing economy activities. We have used various publications-such as news articles, TV news items, YouTube videos, and blog posts-as data sources for this study purpose. Through content analysis, the study shows how the SE phenomenon is coping with the changing environment caused by the Covid-19. We analyzed the SE sector mainly from the perspective of four stakeholders: SE firms, service providers, service receivers (customers), and regulatory bodies. We explored the SE phenomenon based mainly on the following themes: anxiety, cancelation, job loss, income reduction, hygiene and safety, overcoming strategy, and outcomes. Based on the findings, we point out implications and avenues for future research.
TL;DR: This work shows why the best business model depends on whether consumer usage rates vary or not, and finds that when consumer variation in usage rates is intermediate, the manufacturer is surprisingly best off avoiding offering its own direct rentals option and instead, facilitating a peer-to-peer rental market where consumers can share among themselves.
Abstract: With peer-to-peer sharing of durable goods like cars, boats, and condominiums, it is unclear how manufacturers should react. They could seek to encourage these markets or compete against them by of...
Abstract: With the emergence of a series of physical sharing platforms such as Uber and Airbnb, the business model of the sharing economy (SE) is rapidly developing globally. Existing research on the sharing economy mainly involves three main aspects: the connotation of the sharing economy, the business model of the sharing economy, and the impact of the sharing economy. We begin by introducing the reasons why the sharing economy is booming and why customers from different positions participate in the sharing economy. Then we summarize the basic mechanisms of the consumer-to-consumer (C2C) and business-to-consumer (B2C) aspects of the sharing economy. Regarding the main causes of the sharing economy, we specifically focus on the impacts of the sharing economy on the environment, i.e., whether sharing economy is eco-friendly to the society. We consider product life cycle assessment (LCA) technology as one of the frontiers and key technologies in the fields of green design and green manufacturing. Furthermore, combined with the impact of COVID-19 on the sharing economy and the actual problems in real life, we present the future development directions of the sharing economy.
Abstract: The development of distributed generation technology is endowing consumers the ability to produce energy and transforming them into "prosumers". This transformation shall improve energy efficiency and pave the way to a low-carbon future. However, it also exerts critical challenges on system operations, such as the wasted backups for volatile renewable generation and the difficulty to predict behavior of prosumers with conflicting interests and privacy concerns. An emerging business model to tackle these challenges is peer-to-peer energy sharing, whose concepts, structures, applications, models, and designs are thoroughly reviewed in this paper, with an outlook of future research to better realize its potentials.
Abstract: On-demand transportation services have been developing in an irresistible trend since their first launch in public. These services not only transform the urban mobility landscape, but also profoundly change individuals’ travel behavior and demand for cars. In this paper, we propose an integrated model structure which integrates empirical analysis into a discrete choice based analytical framework to investigate a heterogenous population’s choices on transportation mode and car ownership with the presence of ride-hailing. Distinguished from traditional discrete choice models where individuals’ choices are only affected by exogenous variables and are independent of other individuals’ choices, our model extends to capture the endogeneity of supply demand imbalance between ride-hailing service providers and users. Through equilibrium searching and counterfactual analysis, we further quantify the magnitude of impacts of platform operations and government policies on car demand, usage and traffic conditions. The structure of the model and managerial insights are explained in detail.
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Abstract: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
TL;DR: This special section includes descriptions of five recommender systems, which provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients, and which combine evaluations with content analysis.
Abstract: Recommender systems assist and augment this natural social process. In a typical recommender system people provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients. In some cases the primary transformation is in the aggregation; in others the system’s value lies in its ability to make good matches between the recommenders and those seeking recommendations. The developers of the first recommender system, Tapestry , coined the phrase “collaborative filtering” and several others have adopted it. We prefer the more general term “recommender system” for two reasons. First, recommenders may not explictly collaborate with recipients, who may be unknown to each other. Second, recommendations may suggest particularly interesting items, in addition to indicating those that should be filtered out. This special section includes descriptions of five recommender systems. A sixth article analyzes incentives for provision of recommendations. Figure 1 places the systems in a technical design space defined by five dimensions. First, the contents of an evaluation can be anything from a single bit (recommended or not) to unstructured textual annotations. Second, recommendations may be entered explicitly, but several systems gather implicit evaluations: GroupLens monitors users’ reading times; PHOAKS mines Usenet articles for mentions of URLs; and Siteseer mines personal bookmark lists. Third, recommendations may be anonymous, tagged with the source’s identity, or tagged with a pseudonym. The fourth dimension, and one of the richest areas for exploration, is how to aggregate evaluations. GroupLens, PHOAKS, and Siteseer employ variants on weighted voting. Fab takes that one step further to combine evaluations with content analysis. ReferralWeb combines suggested links between people to form longer referral chains. Finally, the (perhaps aggregated) evaluations may be used in several ways: negative recommendations may be filtered out, the items may be sorted according to numeric evaluations, or evaluations may accompany items in a display. Figures 2 and 3 identify dimensions of the domain space: The kinds of items being recommended and the people among whom evaluations are shared. Consider, first, the domain of items. The sheer volume is an important variable: Detailed textual reviews of restaurants or movies may be practical, but applying the same approach to thousands of daily Netnews messages would not. Ephemeral media such as netnews (most news servers throw away articles after one or two weeks) place a premium on gathering and distributing evaluations quickly, while evaluations for 19th century books can be gathered at a more leisurely pace. The last dimension describes the cost structure of choices people make about the items. Is it very costly to miss IT IS OFTEN NECESSARY TO MAKE CHOICES WITHOUT SUFFICIENT personal experience of the alternatives. In everyday life, we rely on
Abstract: Many if not most markets with network externalities are two-sided. To succeed, platforms in industries such as software, portals and media, payment systems and the Internet, must “get both sides of the market on board.” Accordingly, platforms devote much attention to their business model, that is, to how they court each side while making money overall. This paper builds a model of platform competition with two-sided markets. It unveils the determinants of price allocation and end-user surplus for different governance structures (profit-maximizing platforms and not-for-profit joint undertakings), and compares the outcomes with those under an integrated monopolist and a Ramsey planner. (JEL: L5, L82, L86, L96)