Amazon Marketplace Research Articles

Impact of Bilateral Rating System on Ride-Sharing Platforms

Traditional online platforms (e.g., Amazon Marketplace) use Unilateral Rating System (URS), where customers rate sellers. However, sharing economy platforms (e.g., Uber, Airbnb) have adopted Bilateral Rating System (BRS) that also allows service providers to rate customers, and even selects customers based on their ratings. BRS is often purported to be better than URS, as BRS unlocks more hidden information than URS by revealing ratings of customers to service providers before they make acceptance/rejection decisions. We compare URS and BRS in the context of the ride-sharing service to study its impact on all stakeholders in the system. Conventional wisdom suggests that BRS would harm riders, as riders can be rejected by drivers while platforms should benefit from BRS, since it unlocks the value in the riders’ rating that can be potentially extracted. We find that contrary to this intuition, BRS may increase riders’ welfare under some conditions, as drivers rejecting low-rating riders can reduce the excess demand and the associated inconvenience cost born by all riders in the system. BRS can both improve or reduce the platform’s revenue, as there is a tug-of-war between the platform and drivers on the revenue share. Under the right conditions, BRS can result in a win-win-win situation for the drivers, riders and the platform.

Segmenting two-sided markets

Recent years have witnessed the rise of many successful e-commerce marketplace platforms like the Amazon marketplace, AirBnB, Uber/Lyft, and Upwork, where a central platform mediates economic transactions between buyers and sellers. A common feature of many of these two-sided marketplaces is that the platform has full control over search and discovery, but prices are determined by the buyers and sellers. We summarize our results from [Banerjee et al. 2017] where, motivated by this application domain, we study the algorithmic aspects of market segmentation via directed discovery in two-sided markets with endogenous prices. We consider a model where an online platform knows each buyer/seller's characteristics, and associated demand/supply elasticities. Moreover, the platform can use discovery mechanisms (search, recommendation, etc.) to control which buyers/sellers are visible to each other. We develop efficient algorithms for throughput (i.e. volume of trade) and welfare maximization with provable guarantees under a variety of assumptions on the demand and supply functions.

To compete or to take over? An economic analysis of new sellers on e-commerce marketplaces

The rise of e-commerce has inspired entrepreneurs to start their businesses online. E-commerce platforms, such as eBay, Amazon Marketplace, and Taobao, feature low barriers of entry to their sellers, as there is very low cost involved in entering an online marketplace and becoming a seller. In the meantime, we observe that many sellers choose to incur a much higher cost and enter the marketplace by taking over an existing seller’s account. We identify an interesting problem faced by entrants to online marketplaces: should they enter as new or should they take over another seller? Entrants’ decisions are complicated by several factors, including the reputation of the existing seller, the entrant’s capability, and the information asymmetry between the entrant and the incumbent. We develop an economic model to study the interactions between an entrant and an incumbent in an e-commerce marketplace. We discover that reputation plays an important role in the entrant’s competition-takeover decision. Our findings carry useful insights for e-commerce platforms in understanding the impact of entrants on the existing community of sellers.

Should Platforms be Allowed to Charge Ad Valorem Fees?

Many platforms that facilitate transactions between buyers and sellers charge ad valorem fees in which fees depend on the transaction price set by sellers. Given these platforms do not incur significant costs that vary with transaction prices, their use of ad valorem fees has raised controversies about the efficiency of this practice. In this paper, using a model that connects platforms' use of ad valorem fees to third-degree price discrimination, we evaluate the welfare consequences of banning such fees. We find the use of ad valorem fees generally increases welfare, including for calibrated versions of the model based on data from Amazon's marketplace and Visa's signature debit cards.

Observing algorithmic marketplaces in-the-wild

In this letter, we briefly summarize two recent works from our group that use observational data to study the mechanisms used by two large markets. First, we examine Uber's surge price algorithm, and observe that its incentive model may not be effective at changing driver behavior. Second, we study the adoption of dynamic pricing strategies by sellers on Amazon Marketplace, and investigate how these strategies interact with Amazon's "Buy Box" matching algorithm. We make our data available to the research community.

Segmenting Two-Sided Markets

Recent years have witnessed the rise of many successful e-commerce marketplaces like the Amazon marketplace, Uber, AirBnB, and Upwork, where a central platform mediates economic transactions between buyers and sellers. A common feature of many of these two-sided marketplaces is that the platform has full control over search and discovery, but prices are determined by the buyers and sellers. Motivated by this, we study the algorithmic aspects of market segmentation via directed discovery in two-sided markets with endogenous prices. We consider a model where an online platform knows each buyer/seller's characteristics, and associated demand/supply elasticities. Moreover, the platform can use discovery mechanisms (search/recommendation/etc.) to control which buyers/sellers are visible to each other. This leads to a segmentation of the market into pools, following which buyers and sellers endogenously determine market-clearing transaction prices within each pool. The aim of the platform is to maximize the resulting volume of transactions/welfare in the market. We develop efficient algorithms with provable guarantees under a variety of assumptions on the demand and supply functions. We also test the validity of our assumptions on demand curves inferred from NYC taxicab log-data, as well as show the performance of our algorithms on synthetic experiments.

Price-Based Prominence under Inventory Constraint

This paper studies a price-based prominence tool (i.e., buy-box) provided by online shopping platforms like Amazon Marketplace, and the role of sellers’ inventory constraints in sellers’ price competition and the platform’s profitability. Buy-box allows a seller to make its product more prominent to consumers by charging a lower price. Intuitively, the buy-box seems to intensify price competition among sellers, reduce the platform’s profit, and benefit consumers. However, our findings challenge this intuition. When sellers are asymmetric in inventory constraints, a platform benefits from the buy-box in equilibrium because of the lessened price competition: the seller without constraint might strategically raise its price to accelerate the depletion of its rival’s inventory so as to exploit the monopoly benefit once the rival leaves the market. This lessened competition also encourages the rival who wins the buy-box to charge a higher price. In contrast, in symmetric cases in which both sellers are either with or without inventory constraints, providing the buy-box is not profitable to the platform. Furthermore, consumer surplus always decreases when the platform finds it profitable to provide the buy-box, suggesting that the price-based prominence tool actually harms rather than protects consumers. Finally, social welfare always increases when the buy-box is provided.

Sondologics: Product Diversion and MAP Violations in Internet Channels

Sondologics, a manufacturer of video, audio, and gaming accessories products, was experiencing pricing and distribution problems in its channels. Numerous retailers were complaining about unfair price competition from unauthorized retailers, i.e., gray marketers, on standalone websites or Amazon's Marketplace, offering discounts of up to 30% off list price. The company estimated that about 10% of its retail volume in the United States was being generated by unauthorized retailers. Compounding the problem, gray marketers and authorized retailers alike were selling at below-list prices, which violated the Sondologics MAP (minimum advertised pricing) policy. Sondologics was considering numerous initiatives to address the MAP and gray-market problems, including retaining a third-party service to monitor pricing and distribution in the channel. Students are asked to develop recommendations that would promote sales while protecting the name-brand image and price points of Sondologics' products.

Restrictions of Third-Party Platforms within Selective Distribution Systems

Third-party platforms, such as Amazon Marketplace, have not only brought a wider variety of choice to consumers, but most importantly prompted a fierce intra-brand and price competition between various suppliers and distributors resulting primarily in a lower price level. However, this development particularly affected suppliers of luxury products, who were prompted to protect the brand image and price level of their products by means of selective distribution. As a result, distributors within such distribution systems were prohibited from selling products via such platforms. As is the case for most vertical restraints, neither the European Commission nor the EU courts have dealt with this issue in their decisional practice. On the other hand, restrictions of third-party platforms have been subject to controversial case law in Germany, though the final word on the issue has still not been spoken. Against this background, this article examines the compatibility of the absolute ban of third-party platforms within selective distribution systems, as well as qualitative requirements that may exist if they are allowed with EU Competition law.

Towards a “Narratology of Otherness”: Colum McCann, Ireland, and a New Transcultural Approach

This essay gestures towards a new type of analysis of contemporary transcultural fiction. While existing analyses of such fiction tend to evaluate the ethical or political import thereof (often via the work of Gayatri Spivak or Homi Bhabha), by devising a narratological lens the focus may be shifted away from such ideological concerns. This is not to disavow the importance of such concerns; rather it is to illuminate the formal ingenuities enacted by certain transcultural writers, the majority of which have been heretofore overlooked. Taking Ireland as a case study, this new lens—this ‘Narratology of Otherness’—will be devised by examining the work of Irish-American author Colum McCann. This will at once shed new light on the formal nuances of McCann’s oeuvre, particularly his 2006 novel Zoli , whilst also revealing the implications such an approach may have for both Irish and transcultural literary debates more broadly.

Coopetition within and between value networks - a typology and a modelling framework

This study develops a typology of coopetition in value networks, wherein a distinction is made based on two factors. Firstly, whether coopetition takes place inside a particular value network (i.e., intra-value network coopetition) or between value networks (i.e., inter-value network coopetition); and secondly, whether the nature of collaboration is focused on value leveraging or value co-creation. We present empirical examples from the global ICT sector (Amazon Services, Amazon Marketplace, AIM Alliance, and Windows Mobile Community) to illustrate the four categories identified in the typology. Each example is graphically represented using a modelling framework in order to aid the understanding of various organisational and network structures that can accommodate coopetition as a complex inter-organisational relationship. We suggest that the developed typology and modelling methodology can help researchers and practitioners to better grasp and communicate the role and benefits as well as the network and organisational structures of coopetition within and between value networks.

Coopetition-based business models: The case of Amazon.com

Coopetition (collaboration between competing firms) is a phenomenon that has recently captured a great deal of attention due to its increasing relevance to business practice. However, current research on coopetition is still short on explaining how the potential advantages of coopetition can be realized over time as part of an individual firm's business model. In order to gain insights into this, we conduct a longitudinal, in-depth case study on the coopetition-based business models of Amazon.com. We find evidence of three distinct coopetition-based business models: (1) Amazon Marketplace, (2) Amazon Services and Web Services, and (3) the collaboration between Apple and Amazon on digital text platforms. We conclude by forwarding several propositions on how value can be created and captured by involving competitors in a firm's business model. As a whole, the results contribute to the current understanding of how firms – as well as their stakeholders – can better benefit from coopetition.

Accelerating 3B single-molecule super-resolution microscopy with cloud computing

To the Editor: The recently invented technique of Bayesian localization microscopy1 relaxes the requirement for an extremely high signal-to-background ratio for single-molecule super-resolution microscopy2–5. It enables a standard wide-field fluorescence microscope to generate super-resolution images from the stochastic bleaching and blinking6,7 of fluorescence dyes (3B analysis). Although the new 3B microscopy has enormous potential to put the power of super-resolution imaging into the hands of average biologists, its underlying algorithm can be an impediment. Calculating an area of 50 × 50 pixels takes approximately a full day on a state-of-the-art desktop computer (Fig. 1a). For larger image areas, the need for massive computing resources makes the 3B analysis inaccessible to many labs and limits its scope of application. We report here a simple solution for everyone: the Amazon Elastic Compute Cloud (Amazon EC2). Figure 1 3B analysis using Amazon EC2 Aside from its flexible and cost-effective super-computing power, the greatest advantages of Amazon EC2 are its accessibility and ease of use. Normally, porting a computational code to a new system involves the configuration of a system-dependent software environment. The convoluted configuration process—not to mention the increased complexity for cluster computing—often discourages scientists without programming backgrounds. Use of Amazon EC2 significantly lowers this entry barrier. By packaging the executable program together with its running environment in a single image file known as the Amazon machine image (AMI), we can distribute 3B analysis to the public through the Amazon Marketplace. Hundreds of processes can run on instances, with each instance configured as an exact copy of the AMI. A user can launch massively parallel 3B processes on the Cloud from a local computer in just a few minutes (Fig. 1b and Supplementary Video 1). Large computing power allows data analysis without compromising on field of view. We tested Amazon EC2 using the photoactivatable (PA) mCherry–tagged podosome data from ref. 1 (Fig. 1c). The recorded image was divided into small mosaics of 12 × 12 pixels. The mosaics were analyzed in parallel and recombined to form a super-resolution image of 9.6 × 9.6 µm. Given the same computation time, an eight-thread 3.40-GHz Intel Core i7 desktop computer could analyze a region of 2.715 × 2.715 µm. The image generated from 100 virtual cores from Amazon EC2 was 12 times as large. An entry-level user of the Amazon Cloud may obtain up to 100 instances at about $0.02–$0.06 per instance hour. Depending on the market price, reconstruction of a 25 × 25–µm image costs less than $25. We strongly recommend the use of our bash scripts to continuously monitor the reconstructed image (Supplementary Software and tutorial therein). The calculation should be terminated only when the structrual features have converged, as otherwise potentially serious image artifacts may still be present. Researchers may apply for Amazon Web Service research grants for additional computing hours. We performed a test with 300 instances of 600 virtual cores. An image of PAmCherry-tagged tubulins in U2OS cells (from our own experiments) with the size of 150 pixels × 100 pixels × 1,500 frames could be reconstructed in only 210 min (Fig. 1d), whereas on a state-of-the-art desktop computer, this process would have taken 9 d. Cloud computing has been transforming the information technology industry through its elastic and flexible on-demand services. Although cloud computing has contributed to the success of many companies such as Dropbox and Netflix, until now it has been little used in biological research. Through this Correspondence, we hope to highlight its huge potential for the imaging community.

Competition and Coordination in Online Marketplaces

Online marketplaces, such as those operated by Amazon, have seen rapid growth in recent years. These marketplaces serve as an intermediary, matching buyers with sellers, whereas control of the good is left to the seller. In some cases, e.g., the Amazon marketplace system, the firm that owns and manages the marketplace system will also sell competing products through the marketplace system. This creates a new form of channel conflict, which is a focus of this article. We consider a setting in which a marketplace firm operates an online marketplace through which retailers can sell their products directly to consumers. We consider a single retailer, who currently sells its product only through its own website, but who may choose to contract with Amazon to sell its product through the marketplace system. Selling the product through the marketplace expands the available market for the retailer, but comes at some expense, e.g., a fixed participation fee or a revenue sharing requirement. Thus, a key question for the retailer is whether she should choose to sell through the marketplace system, and if so, at what price. We analyze the optimal decisions for both the retailer and the marketplace firm and characterize the system equilibrium.

Aligning Value and Implementation in Service Design

The promise of service design is to enable a service supplier to prosper by delivering continuous value to customers. This prosperity is of strategic value to the service supplier. There is value in a service for both the service supplier and its service customers. The authors call these two values, service supplier value and service adopter value. When designing a service, it is necessary to align both value propositions with the service components and service features. In this paper, the authors propose a systemic method where whole (i.e., black box) and composite (i.e., white box) reasoning are interleaved on the organizational and functional dimensions. The authors begin by producing an as-is model that describes how the value is created for the customer and captured by the service supplier. Service improvement opportunities are identified in terms of value to both service adopter and service supplier. A to-be model is built that specifies the new interaction between actors and their new responsibilities and thereby, new service components and features. The method is illustrated with an example based on the service offering and implementation of Amazon.com in Amazon Marketplace.

Do reputation feedback systems really improve trust among anonymous traders? An experimental study

Feedback systems are claimed to be a crucial component of the success of electronic marketplaces like eBay or Amazon Marketplace. This article aims to compare the effects of various feedback systems on trust between anonymous traders, through a set of experiments based on the trust game. Our results indicate that trust is significantly improved by the introduction of a reputation feedback system. However, such mechanisms are far from being perfect and are vulnerable to strategic ratings and reciprocation. Our findings indicate that some changes in rating rules may significantly improve the efficiency of feedback systems, by avoiding strategic rating or reciprocation, and hence stimulate trust and trustworthiness among traders. In particular, a system in which individuals are not informed of their partner's rating decision before making their own decision provides better results, both in terms of trust and earnings.

Determinants of prices in an online marketplace

PurposeDespite the availability of side‐by‐side price comparisons, online retailers often charge very different prices for the same product. The purpose of this paper is to identify the drivers of price differences in an online retail marketplace by examining pricing information from a sample of sellers in the market.Design/methodology/approachAn empirical, quantitative research study of Amazon Marketplace, using 498 observations of online sellers of a variety of electronics products was conducted. A regression analysis is employed to determine the drivers of these sellers' prices.FindingsThe results provide a set of factors associated with deviation from the mean price Amazon Marketplace retailers charge for a given product. The authors find that online retailers that charge higher prices post additional channels of customer service, post their return policy, have lower reputation scores, display a retail brand logo, offer more products, and are not electronic specialists.Originality/valueThe paper contributes to the theoretical understanding of the effects of information quality and governance structures on prices. This is the first study to focus on these issues in an online marketplace setting.

A Probabilistic Approach for Maintaining Trust Based on Evidence

Leading agent-based trust models address two important needs. First, they show how an agent may estimate the trustworthiness of another agent based on prior interactions. Second, they show how agents may share their knowledge in order to cooperatively assess the trustworthiness of others. However, in real-life settings, information relevant to trust is usually obtained piecemeal, not all at once. Unfortunately, the problem of maintaining trust has drawn little attention. Existing approaches handle trust updates in a heuristic, not a principled, manner. This paper builds on a formal model that considers probability and certainty as two dimensions of trust. It proposes a mechanism using which an agent can update the amount of trust it places in other agents on an ongoing basis. This paper shows via simulation that the proposed approach (a) provides accurate estimates of the trustworthiness of agents that change behavior frequently; and (b) captures the dynamic behavior of the agents. This paper includes an evaluation based on a real dataset drawn from Amazon Marketplace, a leading e-commerce site.

Online Price Dispersion: What Can We Learn from Amazon Marketplace?

11 April 2012 Abstract Online price dispersion has remained a puzzle for many years. In this article, we test the effects of a range of factors identified in the literature including vendor strategy, market structure, vendor heterogeneity and the time dimension. We use a dataset collected from the Amazon.com, Amazon.uk and the Amazon.fr marketplaces for books, CDs and DVDs from March to November 2006. First, using posted prices, we show that vendors make few modifications to their prices over time (once every 20 to 30 days), and those variations that they do make are relatively small (on average 15 cents). Secondly, we compare posted prices to transaction prices and we show that transaction prices do not necessarily correspond to the lowest posted price but rather centered around the median prices. Thirdly, we show that online price dispersion increases with the number of sellers mainly because new sellers price their product significantly lower that the current median price. Fourth, we show that there is a significant reputation premium of about 10 to 15%. A well established seller can charge a price 10 to 20% higher than the average seller. Our estimation results support the idea that price dispersion is sensitive to the maturity of the market. Consistent with our finding that price dispersion increases with the number of sellers, less mature markets, such as France with fewer sellers, exhibit lower price dispersion. Similarly, we find that large sellers account for a greater proportion of transactions in France than in the U.S. Lastly, we find that public policy can affect price dispersion: a French law limiting discounting on new books results in lower price dispersion in the French book market relative to book markets in the US and UK and relative to the French market for other goods.

Are Durable Goods Consumers Forward-Looking? Evidence from College Textbooks*

We test whether textbook consumers are forward-looking, using a large new data set on textbooks sold in college bookstores during the ten semesters from 1997 to 2001. The data strongly support the hypothesis that students are forward-looking with low short-run discount rates and that they behave as if they have rational expectations of publishers' revision behavior. Data from a second new data set on the market prices of used books at Amazon Marketplace also support the hypothesis of rational, forward-looking behavior. Simulation results indicate that students are sufficiently forward-looking that publishers cannot consistently raise revenue by accelerating current revision cycles.