Doing Business with Strangers: Reputation in Online Service Marketplaces

Antonio  Moreno; Christian  Terwiesch

Article | Information Systems Research

Doing Business with Strangers: Reputation in Online Service Marketplaces

by Antonio Moreno and Christian Terwiesch

Abstract

Online service marketplaces allow service buyers to post their project requests and service providers to bid for them. To reduce the transactional risks, marketplaces typically track and publish previous seller performance. By analyzing a detailed transactional data set with more than 1,800,000 bids corresponding to 270,000 projects posted between 2001 and 2010 in a leading online intermediary for software development services, we empirically study the effects of the reputation system on market outcomes. We consider both a structured measure summarized in a numerical reputation score and an unstructured measure based on the verbal praise left by previous buyers, which we encode using text mining techniques. We find that buyers trade off reputation (both structured and unstructured) and price and are willing to accept higher bids posted by more reputable bidders. Sellers also respond to changes in their own reputation through three different channels. They increase their bids with their reputation score (price effect) but primarily use a superior reputation to increase their probability of being selected (volume effect) as opposed to increasing their bid prices. Negative shocks in seller reputation are associated to an increase in the probability of seller exit (exit effect), but this effect is moderated by the investment that the seller has made in the site. We conclude that participants in this market are very responsive to the numerical reputation score and also to the unstructured reputational information, which behaves in a similar way to the structured numerical reputation score but provides complementary information.

Keywords: online service marketplace; procurement; Auctions; Reputation; Bids and Bidding;

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Moreno, Antonio, and Christian Terwiesch. "Doing Business with Strangers: Reputation in Online Service Marketplaces." Information Systems Research 25, no. 4 (December 2014): 865–886.

The Indian banknote demonetization in 2016 was one of the most significant international events of that year. Overnight, 86% of Indian currency in circulation was declared invalid unless exchanged for new bills. The sudden and unexpected demonetization constituted a large shock to the entire Indian economy. One effect of the ensuing cash shortage was a large and sustained increase in the adoption and usage of digital payments. We use detailed sales data consisting of more than two and a half million transactions from a leading Indian online retailer to empirically investigate the effects of payment digitization on the online retail industry. We take advantage of the demonetization as a source of exogenous variation that induced a subset of consumers to switch to digital payments from more commonly used cash-on-delivery payments. We show that consumers who switch to digital payments maintain their purchase frequency but spend more and are less likely to return their purchases. Our findings show that firms in emerging markets may enjoy gains from consumer demand in addition to operational gains resulting from payment digitization.

Article | Production and Operations Management | October 2018

The Operational Value of Social Media Information

Ruomeng Cui, Santiago Gallino, Antonio Moreno and Dennis J. Zhang

While the value of using social media information has been established in multiple business contexts, the field of operations and supply chain management have not yet explored the possibilities it offers in improving firms' operational decisions. This study attempts to do that by empirically studying whether using publicly available social media information can improve the accuracy of daily sales forecasts. We collaborated with an online apparel retailer to assemble a dataset that combines (1) detailed internal operational information, including data on sales, advertising, and promotions, as well as (2) publicly available social media information obtained from Facebook. We implement a variety of machine learning methods to forecast daily sales. We find that using social media information results in statistically significant improvements in the out-of-sample accuracy of the forecasts, with relative improvements ranging from 12.85% to 23.23% over different forecast horizons. We also demonstrate that nonlinear boosting models with feature selection, such as random forests, perform significantly better than traditional linear models. The best-performing method (random forest) yields an out-of-sample mean absolute percentage error (MAPE) of 7.21% when not using social media information and 5.73% when using social media information is used. In both cases, this significantly improves the accuracy of the company's internal forecasts (a MAPE of 11.97%). Combining these empirical results, we provide recommendations for forecasting sales in general as well as with social media information.

Keywords: social media; Machine learning; Information; Sales; Forecasting and Prediction;

Citation:

Cui, Ruomeng, Santiago Gallino, Antonio Moreno, and Dennis J. Zhang. "The Operational Value of Social Media Information." Special Issue on Big Data in Supply Chain Management. Production and Operations Management 27, no. 10 (October 2018): 1749–1774. View Details

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Doing Business with Strangers: Reputation in Online Service Marketplaces

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