2015 Informs Annual Meeting

TD52

INFORMS Philadelphia – 2015

2 - Long-Term Partnership for Achieving Efficient Capacity Allocation Fang Liu, Assistant Professor, Nanyang Business School, Nanyang Technological University, 50 Nanyang Avenue, South Spine S3-B2A-13, Singapore, 639798, Singapore, liu_fang@ntu.edu.sg, Tracy Lewis, Nataliya Kuribko, Jeannette Song We consider a manufacturer and a group of buyers who share a scarce but expensive-to-build capacity over a finite period. Each member has private history- dependent demand information and makes unverifiable investments. Because of the high uncertainty, achieving supply chain efficiency while sustaining under a dynamic environment is challenging for the partnership. We construct a membership agreement that enforces efficient capacity allocation and investments by introducing a novel breach remedy. 3 - The Perils of Sharing Information in a Trade-association Noam Shamir, Assistant Professor, Tel-Aviv University, Haim Levanon, Tel-Aviv, Israel, nshamir@post.tau.ac.il, Hyoduk Shin Studying the incentives of a group of retailers, organized as a trade association, to exchange forecast information, we compare between two industry policies: exclusionary and non-exclusionary information sharing. Although non- exclusionary policy has been advocated to promote information sharing, we show the opposite can happen and explain the reason. 4 - Aligning Incentives in Omni-channel Sale Elnaz Jalilipour Alishah, PhD Candidate, University of Washington, Seattle, Foster School of Business, Mackenzie Hall 358, Seattle, WA, 98195-3200, United States of America, jalilipo@uw.edu, Yong-Pin Zhou, Jingqi Wang We consider a retailer with both online and offline channels. While the online store exerts costly effort to attract customers, the offline store handles inventory for both locations – including fulfillment of online orders. We study how the retailer should appropriately credit both channels to align their incentives. TD51 51-Room 106B, CC Innovative and Entrepreneurial OM Sponsor: Manufacturing & Service Operations Management Sponsored Session Chair: Onesun Steve Yoo, University College London, Gower Street, London, WC1E 6BT, United Kingdom, o.yoo@ucl.ac.uk 1 - Pricing and Capacity Planning for Flexible Consumption Sanjiv Erat, UCSD, Gilman Drive, La Jolla, CA, United States of America, serat@ucsd.edu, Sreekumar Bhaskaran, Rajiv Mukherjee Motivated by the emergence of flexible consumption opportunities - such as the rollover cellphone plans offered by many mobile providers - we study a firm’s pricing and capacity planning decision when the timing of consumption is a choice variable for consumers. Subsequently, we explore the effect of heterogeneity in consumer preferences and its effect on a firm’s decision of how much flexibility to offer. 2 - Improving Supply Chain Compliance using Buyer Consortiums Prashant Chintapalli, Anderson School of Management, University of California, Los Angeles, CA, 90095, United States of America, prashant.chintapalli.1@anderson.ucla.edu, Kumar Rajaram, Felipe Caro, Chris Tang Motivated by the Accord on Fire and Building Safety in Bangladesh we study the effectiveness of buyer consortiums. We show that a consortium can increase factory compliance and improve the buyers’ profits, though possibly at the expense of the supplier. We also study the conditions under which a buyer should join the consortium and characterize the settings in which the whole supply chain is better off. 3 - Startup as a Process: Increase Your Chances of Success via a Just-in-time Approach Christophe Pennetier, PhD Student, INSEAD,

4 - Selling Fashionable Products: Change Price or Facilitate Learning?

Yufei Huang, PhD Student, University College London, Gower Street, London, United Kingdom, yufei.huang.10@ucl.ac.uk, Onesun Steve Yoo, Bilal Gokpinar, Chris Tang Firms selling new fashionable products are shifting their focus away from pricing and towards facilitating the learning process for customers. To understand this phenomenon, we present a stylized model with pricing and three channels through which customers learn. We find that for new fashionable products, facilitating learning can lead to greater profit than variable pricing. Moreover when firms facilitate learning, variable pricing has only a marginal effect on firm profits. TD52 52-Room 107A, CC Social Media and Internet Marketing Sponsor: Marketing Science Sponsored Session Chair: Michael Trusov, University of Maryland, 3454 Van Munching Hall, College Park, MD, United States of America, mtrusov@rhsmith.umd.edu 1 - Attribution Metrics and Return on Keyword Investment in Paid Search Advertising Hongshuang Li, Indiana University, Bloomington, IN, United States of America, lhshruc@gmail.com, Siva Viswanathan, Abhishek Pani, P.k. Kannan In this paper, we analyze the impact of the attribution metric used for imputing conversion credit to search keywords on the overall effectiveness of keyword investments in search campaigns. We model the relationship among the advertiser’s bidding decision for keywords, the search engine’s ranking decision for these keywords, and the consumer’s click-through rate and conversion rate on each keyword, and analyze the impact of the attribution metric used on the overall return-on-investment of paid search advertising. 2 - Controlling for Self Selection Bias in Customer Reviews Leif Brandes, University of Warwick, Coventry, CV4 7AL, United Kingdom, Leif.Brandes@wbs.ac.uk, David Godes, Dina Mayzlin Customers frequently use user online reviews as a valuable information resource before making a purchase. This observation has motivated a large number of empirical studies, and it is now a well-established finding that customer online reviews impact product sales. However, one possible criticism of online user reviews as a source of information is the self-selection inherent in the review process. That is, consumers self-select into choosing whether to review a product, which suggests that reviews may be prone to the extremity bias: the distribution of reviews may be more polarized than the true preference distribution . This of course implies that posted review valence may not always provide an unbiased representation of customers’ true product experiences. We provide survey evidence that demonstrates that customers who post an online review tend to have more extreme opinions than customers who never post a review. We hypothesize that the consumers who have more extreme opinions post their reviews quicker, while the consumers with more moderate opinions may take longer to post a review, which implies that in the limit some consumers with moderate opinions may never post a review. One implication of this is that reviews that arrive after a long time lapse are more similar to the opinions of the non-responders. Hence, a firm that is able to observe the time lapse between the experience and the review should be able to calculate the valence of reviews in a way that corrects for the non-response bias. That is, we suggest how to correct for the extremity bias by taking into account the latency of response data. To test our hypotheses, we use a new dataset from a large online travel portal. Overall, we have detailed information on 1.26 million bookings and 2.75 million reviews over the complete history of the firm (twelve years). Because we observe hotel bookings and review provision behavior at the individual customer level, we know for each customer the exact duration between her last travel day and the day that she provided the review. Based on our empirical results, we show how customer self-selection across time impacts her review behavior and suggest a method for controlling for this bias.

1 Ayer Rajah Avenue, Singapore, 138676, Singapore, Christophe.Pennetier@insead.edu, Karan Girotra, Serguei Netessine

Using a unique and novel dataset, we study the success of startups modeled as a process: what is the best configuration for batches of funding cash –in terms of size and frequency– to exit successfully? Our results suggest that founders should not be obsessed by the amount of money they raise in any single round. It is better to raise small batches more often than the other way around.

363