CV - Boston University

Davide Proserpio
Contact
Information
111 Cummington Mall, Room 136
Department of Computer Science
Boston University
Boston, MA 02215 USA
E-mail: [email protected]
Web: http://cs-people.bu.edu/dproserp
Research
My research seeks to empirically measure and quantify how digital information influences industries
and markets. Most of my work is carried out by conducting empirical analyses using data collected
from online marketplaces and leveraging concepts from computer science, statistics, and economics.
Education
Boston University, Boston, Massachusetts USA
Ph.D. Candidate, Computer Science, June 2011 - Present
• Advisor: Sharon Goldberg and John W. Byers
Carlos III University, Madrid, Spain and Universitat Polit`
ecnica de Catalunya, Barcelona,
Spain
M.S., Engineering, June, 2010
Politecnico di Milano, Milano, Italy
B.A., Engineering, March, 2008
Honors and
Awards
Hariri Graduate Fellows Program 2015
The Hariri Graduate Fellows Program recognizes outstanding PhD graduate students who pursue
computational and data-driven research at Boston University.
SIGCOMM Travel Grant, 2013
Erasmus Mundus Scholarship, 2006
Academic
Experiences
Boston University, Department of Computer Science, Massachusetts USA
Professional
Experiences
Microsoft Research Redmond
Teaching Assistant for Probability in Computing
Fall 2014
Duties at various times have included office hours, leading weekly discussions and preparing labs.
Summer Intern
Mentor: Scott Counts
June 2015 - August 2015
Telefonica Research Barcelona
Summer Intern
June 2014 - August 2014
Project: financial credit score prediction using users mobile phone behavior data
Mentors: Jose San Pedro and Nuria Olvier
Working papers
Proserpio, D. and Zervas, G. (2014). Online reputation management: Estimating the impact of
management responses on consumer reviews1 [Job Market Paper]
Abstract:
Failure to meet a consumer’s expectations can result in a negative review, which can have a lasting,
1 A version of this paper appears in the Proceedings of the 16th ACM Conference on Economics and Computation
(EC) 2015 (abstract-only publication to accommodate subsequent publication in journals).
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damaging impact on a firm’s reputation, and its ability to attract new customers. To mitigate the
reputational harm of negative reviews many firms now publicly respond to them. How effective
is this reputation management strategy in improving a firm’s reputation? We empirically answer
this question by exploiting a difference in managerial practice across two hotel review platforms,
TripAdvisor and Expedia: while hotels regularly respond to their TripAdvisor reviews, they almost
never do so on Expedia. Based on this observation, we use difference-in-differences to identify
the causal impact of management responses on consumer ratings by comparing changes in the
TripAdvisor ratings of a hotel following its decision to begin responding against a baseline of changes
in the same hotel’s Expedia ratings. We find that responding hotels, which account for 56% of hotels
in our data, see an average increase of 0.12 stars in the TripAdvisor ratings they receive after they
start responding. Moreover, we show that this increase in ratings does not arise from hotel quality
investments. Instead, we find that the increase is consistent with a shift in reviewer selection:
consumers with a poor experience become less likely to leave a negative review when hotels begin
responding.
Zervas, G., Proserpio, D., and Byers, J. W. (2014). The rise of the sharing economy: Estimating the
impact of airbnb on the hotel industry2
Abstract:
A number of decentralized peer-to-peer markets, now colloquially known as the sharing economy,
have emerged as alternative suppliers of goods and services traditionally provided by long-established
industries. A central question surrounding the sharing economy regards its long-term impact: will
peer-to-peer platforms materialize as viable mainstream alternatives to traditional providers, or will
they languish as niche markets? In this paper, we study Airbnb, a sharing economy pioneer offering
short-term accommodation. Combining data from Airbnb and the Texas hotel industry, we estimate
the impact of Airbnb’s entry into the Texas market on hotel room revenue, and study the market
response of hotels. To identify Airbnb’s causal impact on hotel room revenue, we use a difference-indifferences empirical strategy that exploits the significant spatiotemporal variation in the patterns
of Airbnb adoption across city-level markets. We estimate that in Austin, where Airbnb supply
is highest, the impact on hotel revenue is roughly 8-10%. We find that Airbnb’s impact is nonuniformly distributed, with lower-priced hotels, and hotels not catering to business travel being the
most affected segments. Finally, we find that affected hotels have responded by reducing prices, an
impact that benefits all consumers, not just participants in the sharing economy. Our work provides
empirical evidence that the sharing economy is making inroads by successfully competing with, and
acquiring market share from, incumbent firms.
Zervas, G., Proserpio, D., and Byers, J. (2015a). A first look at online reputation on airbnb, where
every stay is above average
Abstract:
Judging by the millions of reviews left by guests on the Airbnb platform, this “trusted community
marketplace” is fulfilling its mission of matching travelers seeking accommodation with hosts who
have room to spare remarkably well. Based on our analysis of ratings we collected for over 600,000
properties listed on Airbnb worldwide, we find that nearly 95% of Airbnb properties boast an average
user-generated rating of either 4.5 or 5 stars (the maximum); virtually none have less than a 3.5
star rating. We contrast this with the ratings of approximately half a million hotels worldwide that
we collected on TripAdvisor, where there is a much lower average rating of 3.8 stars, and more
variance across reviews. Considering properties by accommodation type and by location, we find
considerable variability in ratings, and observe that vacation rental properties on TripAdvisor have
ratings most similar to ratings of Airbnb properties. Last, we consider several thousand properties
that are listed on both platforms. For these cross-listed properties, we find that even though the
average ratings on Airbnb and TripAdvisor are similar, proportionally more properties receive the
2 A version of this paper appears in the Proceedings of the 16th ACM Conference on Economics and Computation
(EC) 2015 (abstract-only publication to accommodate subsequent publication in journals).
2
highest ratings (4.5 stars and above) on Airbnb than on TripAdvisor. Moreover, there is only weak
correlation in the ratings of individual cross-listed properties across the two platforms. Our work is
a first step towards understanding and interpreting nuances of user-generated ratings in the context
of the sharing economy.
Peer reviewed
Economics
conferences and
Proserpio, D. and Zervas, G. (2015). Online reputation management: Estimating the impact of manjournals
agement responses on consumer reviews. In Proceedings of the 16th ACM conference on electronic
commerce. ACM
Zervas, G., Proserpio, D., and Byers, J. W. (2015b). The impact of the sharing economy on the
hotel industry: Evidence from airbnb’s entry in texas. In Proceedings of the 16th ACM conference
on electronic commerce. ACM
Computer Science
San Pedro, J., Proserpio, D., and Oliver, N. (2015). Mobiscore: Towards universal credit scoring from
mobile data. Proceedings of the 23rd conference on User Modeling, Adaptation and Personalization
(UMAP)
Proserpio, D., Goldberg, S., and McSherry, F. (2014). Calibrating data to sensitivity in private data
analysis. Proceedings of the VLDB Endowment, 7(8)
Ruchansky, N. and Proserpio, D. (2013). A (not) nice way to verify the openflow switch specification:
formal modelling of the openflow switch using alloy. In Proceedings of the ACM SIGCOMM 2013
conference on SIGCOMM, pages 527–528. ACM
Proserpio, D., Goldberg, S., and McSherry, F. (2012). A workflow for differentially-private graph
synthesis. In Proceedings of the 2012 ACM workshop on Workshop on online social networks, pages
13–18. ACM
Proserpio, D., Diaz-Sanchez, D., Almenarez, F., Marin, A., and Guerrero, R. (2011). Achieving iptv
service portability through delegation. Consumer Electronics, IEEE Transactions on, 57(2):492–498
D´ıaz-S´
anchez, D., Almenarez, F., Mar´ın, A., Proserpio, D., and Arias Cabarcos, P. (2011). Media
cloud: an open cloud computing middleware for content management. Consumer Electronics, IEEE
Transactions on, 57(2):970–978
D´ıaz-S´
anchez, D., Sanvido, F., Proserpio, D., and Mar´ın, A. (2010). Dlna, dvb-ca and dvb-cpcm
integration for commercial content management. Consumer Electronics, IEEE Transactions on,
56(1):79–87
Proserpio, D., Sanvido, F., Arias Cabarcos, P., Guerrero, R. S., Almen´arez-Mendoza, F., DiazSanchez, D., and Marin-Lopez, A. (2010). Introducing infocards in ngn to enable user-centric identity
management. In Global Telecommunications Conference (GLOBECOM 2010), 2010 IEEE, pages
1–5. IEEE
D´ıaz-S´
anchez, D., Proserpio, D., Mar´ın-L´opez, A., Almen´arez-Mendoza, F., and Weik, P. (2009). A
general ims registration protocol for wireless networks interworking. Wireless and Mobile Networking,
pages 32–43
Languages
Italian (native), Spanish (bilingual), English (fluent)
References
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Sharon Goldberg, PhD
Associate Professor of Computer Science
Boston University
email: [email protected]
John Byers, PhD
Professor of Computer Science
Boston University
email: [email protected]
Georgios Zervas, PhD
Assistant Professor of Marketing
Questroom School of Business
email: [email protected]
Michael Luca, PhD
Assistant Professor of Business Administration
Harvard Business School
email: [email protected]
email (assistant): [email protected]
Last updated: June 28, 2015
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