The Relationship Between Disclosing Purchase Information and Reputation Systems in Electronic Markets

In this work we investigate how the introduction of the Verified Purchase (VP) badge on Amazon.com affected both the review helpfulness and the product ratings. We first conduct a propensity score matching study and find that all else equal, camera reviews are on average ranked 7 positions higher than non-VP reviews, while book VP reviews are on average ranked 11 positions higher than non-VP reviews. Next, we use a natural experiment setting to identify whether the entry of the VP feature had an effect on the (1) overall review helpfulness (both VP and non-VP reviews), and (2) average product rating. Our results show that the introduction of VP caused an increase in review helpfulness of 7.7% for books, and 1.7% for electronics. Furthermore, it caused on average an increase of 20 and 18 positions in the ranks on book and electronic products respectively

Realizing the Activation Potential of Online Communities

Online communities suffer from the 1-9-90 principle, which states that 1% of the community\u27s user base generates original content, an additional 9% is limited to interacting with existing content, while the remaining 90% of the participants is passively lurking. In this work we present a data-driven stochastic framework that estimates (1) the activation potential (i.e., the users that are currently lurkers but present a high likelihood of becoming heavy contributors) of an online community and (2) when and which users are more likely to become heavy contributors. Our proposed framework captures the transitional evolution of a user by a Hidden Markov Model, and estimates each user\u27s propensity to become a heavy contributor by employing parametric survival models. We build and evaluate our models on a unique large dataset of a specialized online community about diabetes

Economic impact and policy implications from urban shared transportation: The case of Pittsburgh’s shared bike system

During the last years the number of cities that have installed and started operating shared bike systems has significantly increased. These systems provide an alternative and sustainable mean of transportation to the city dwellers. Apart from the energy sustainability benefits, shared bike systems can have a positive effect on residents' health, air quality and the overall condition of the currently crumbling road network infrastructure. Anecdotal stories and survey studies have also identified that bike lanes have a positive impact on local businesses. In this study, driven by the rapid adoption of shared bike systems by city governments and their potential positive effects on a number of urban life facets we opt to study and quantify the value of these systems. We focus on a specific aspect of this value and use evidence from the real estate market in the city of Pittsburgh to analyze the effect on dwellers' properties of the shared bike system installed in the city in June 2015. We use quasi-experimental techniques and find that the shared bike system led to an increase in the housing prices (both sales and rental prices) in the zip codes where shared bike stations were installed. We further bring into the light potential negative consequences of this impact (i.e., gentrification) and discuss/propose two public policies that can exploit the impact of the system for the benefit of both the local government as well as the city dwellers

Determining systematic differences in human graders for machine learning-based automated hiring

Firms routinely utilize natural language processing combined with other machine learning (ML) tools to assess prospective employees through automated resume classification based on pre-codified skill databases. The rush to automation can however backfire by encoding unintentional bias against groups of candidates. We run two experiments with human evaluators from two different countries to determine how cultural differences may affect hiring decisions. We use hiring materials provided by an international skill testing firm which runs hiring assessments for Fortune 500 companies. The company conducts a video-based interview assessment using machine learning, which grades job applicants automatically based on verbal and visual cues. Our study has three objectives: to compare the automatic assessments of the video interviews to assessments of the same interviews by human graders in order to assess how they differ; to examine which characteristics of human graders may lead to systematic differences in their assessments; and to propose a method to correct human evaluations using automation. We find that systematic differences can exist across human graders and that some of these differences can be accounted for by an ML tool if measured at the time of training

The Utility of Skills in Online Labor Markets

In this work, we define the utility of having a certain skill in an Online Labor Market (OLM), and we propose that this utility is strongly correlated with the level of expertise of a given worker. However, the actual level of expertise for a given skill and a given worker is both latent and dynamic. What is observable is a series of characteristics that are intuitively correlated with the level of expertise of a given skill. We propose to build a Hidden Markov Model (HMM), which estimates the latent and dynamic levels of expertise, based on the observed characteristics. We build and evaluate our approaches on a unique transactional dataset from oDesk.com. Finally, we estimate the utility of a series of skills and discuss how certain skills (e.g. ‘editing’) provide a higher expected payoff once a person masters them over others (e.g. ‘microsoftexcel’)

Reputation Spillover from Agencies on Online Platforms: Evidence from the Entertainment Industry

Digital markets have proliferated in recent years, overcoming many market inefficiencies by facilitating direct interactions between consumers and creators. Thanks to this disintermediation, consumers now have access to a vast number of alternatives, while creators can efficiently reach huge markets. However, the success of digital markets has created a concomitant challenge for creators: differentiation. In crowded markets, agencies (e.g., publishing companies in books, freelance agencies in online labor markets, independent labels in music) can differentiate creators by signaling product quality. But how do agencies’ reputations affect product success for creators? Can some agencies do more harm than good? To investigate these research questions, we theorize how variation in creator and agency reputation leads to asymmetric and heterogeneous effects, namely that (1) more reputable agencies have a stronger positive effect on less reputable creators than they have on more reputable creators, and (2) less reputable agencies hurt more reputable creators more than they hurt less reputable ones. Analyses of more than one million observations from two digital markets provide empirical support for these theory-driven arguments. The findings have design implications for markets and contribute to our understanding of how agencies, depending on creator reputation, can either benefit or hurt product success

The Invisible Barrier: The Effect of Promoting Agencies on Sales in Electronic Markets

Electronic marketplaces have been booming in the past decade. Many of them (e.g., Amazon, Apple App Store and Google Play) create an almost zero barrier-to-entry en- vironment, facilitating independent authors, programmers and artists to list their books, software and songs respec- tively for sale. However, existing literature supports the hypothesis that promoting a product has a strong effect on sales. As a result, many of the users that participate in these environments collaborate with promoting agencies, such as publishers, software companies and music labels. Hence, even though these markets create an outlet for independent pro- fessionals to rise, broker-style third-party companies might create an invisible barrier for them. In this work, we study the effect of these promoting agents on the placement of a song on the charts. By collecting and analyzing a unique dataset from a major marketplace of electronic music we first deploy an Accelerated Failure Time survival model to estimate the probability of a song to appear on the charts. Next, we employ a multidimensional tree-based causal infer- ence approach and we identify how the interplay of different music label’s characteristics (treatment) affect the placement of a song. Our results indicate that certain combinations of treatments increase a song’s probability to get in the charts by a factor of six. Our work provides insights (i) on the importance of promoting agents in these marketplaces, as well as (ii) on potential actionable ways for overcoming the invisible barrier that could pave the way for independent artists to succeed