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• Working Paper | HBS Working Paper Series | 2019

  The Effect of Payment Choices on Online Retail: Evidence from the 2016 Indian Demonetization

  Chaithanya Bandi, Antonio Moreno, Donald Ngwe and Zhiji Xu

  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.

  Keywords: cash on delivery; online retail; product returns; payment methods; digitization; Emerging Markets; Currency; Online Technology; Demand and Consumers; Retail Industry; India;

  Citation:

  Bandi, Chaithanya, Antonio Moreno, Donald Ngwe, and Zhiji Xu. "The Effect of Payment Choices on Online Retail: Evidence from the 2016 Indian Demonetization." Harvard Business School Working Paper, No. 19-123, June 2019.  View Details

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• Case | HBS Case Collection | April 2019

  Afterpay U.S.: The Omnichannel Dilemma

  Antonio Moreno, Donald Ngwe and George Gonzalez

  Citation:

  Moreno, Antonio, Donald Ngwe, and George Gonzalez. "Afterpay U.S.: The Omnichannel Dilemma." Harvard Business School Case 519-086, April 2019.  View Details

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• 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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