Latest Research

Sameer Srivastava, Assistant Professor

Enculturation Trajectories: Language, Cultural Adaptation, and Individual Outcomes in Organizations

How do people adapt to organizational culture, and what are the consequences for their outcomes in the organization? These fundamental questions about culture have previously been examined using self-report measures, which are subject to reporting bias, rely on coarse cultural categories defined by researchers, and provide only static snapshots of cultural fit.

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By contrast, Srivastava and co-authors develop an interactional language use model that overcomes these limitations and opens new avenues for theoretical development about the dynamics of organizational culture. They trace the enculturation trajectories of employees in a midsized technology firm based on analyses of 10.24 million internal emails. The language-based model of changing cultural fit (1) predicts individual attainment; (2) reveals distinct patterns of adaptation for employees who exit voluntarily, exit involuntarily, and remain employed; (3) demonstrates that rapid early cultural adaptation reduces the risk of involuntary, but not voluntary, exit; and (4) finds that a decline in cultural fit for individuals who had successfully enculturated portends voluntary departure.

Przemyslaw Jeziorsk, Assistant Professor

Mobile Money in Tanzania

In developing countries, mobile telecom networks have emerged as major providers of financial services, bypassing the sparse retail networks of traditional banks. Jeziorski and his coauthor analyze a large individual-level data set of mobile money transactions in Tanzania to provide evidence of the impact of mobile money on alleviating financial exclusion in developing countries.

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The authors identify three types of transactions:(i) money transfers to others; (ii) short distance money self-transportation; and (iii) money storage for short to medium periods of time. The study takes advantage of a “natural experiment” – an unanticipated increase in transaction fees makes it is possible to examine how user behavior changes. The authors find that the willingness to pay to avoid walking with cash an extra mile is 2% of an average transaction. The same figure is 0.8% for storing money at home for an extra day. An important implication is that mobile money ameliorates significant amounts of crime-related risk related to handling cash.

Jose Guajardo, Assistant Professor

Mobile Technology and Social Media in Retail: Decomposing the Value of Geolocation Information

Consumer response to mobile and social media promotions can vary at different stages of the decision-making process. Guajardo and his coauthor examines this question based on a proprietary dataset reflecting the operations of a mobile platform that enables firms to send geolocated promotions.

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In this platform, information is gradually delivered to consumers, which allows for decomposing the value of geolocation information in influencing consumer’s awareness, consideration, purchase intention and recommendation behavior. The results indicate that geolocation information makes a significant difference in early stages of the purchase process (opening rates), but not in the conditional likelihood of accepting or recommending a promotion. These results suggest that the value of geolocation is reflected primarily in increased consumer awareness, and not in changing consumer behavior in later stages of the conversion funnel. Further analysis reveals that the impact of design features of geolocated campaigns varies throughout the purchase funnel. For example, timing variables have an influence on opening rates, while value proposition attributes are the main drivers of acceptance rates. The paper also shows that the performance of geolocated campaigns is in-between social media (Facebook) and non-geolocated campaigns, suggesting that geolocation information can be useful in resolving the trade-off between reach and effectiveness.


Ernesto Dal Bó

Political Economics, Influence and Corruption

Omri Even-Tov

Financial Accounting, Corporate Debt

Paul Gertler

Impact Evaluation, Public Economics

Andrea Gorbatai

Knowledge Diffusion, Information Sharing

Jose Guajardo

Operations Analytics, Operations-Marketing Interface

Przemyslaw Jeziorski

Marketing Analytics, Mobile Marketing

Zsolt Katona

Network Analytics, Social Media and Mobile Marketing

Jonathan Kolstad

Healthcare Analytics, Public Economics

Yaniv Konchitchki

Macro-Accounting, Financial Information

Alastair Lawrence

Accounting, Financial Reporting Analytics

Ming Leung

HR Analytics, Labor Markets

Ross Levine

Financial Regulation, Entrepreneurship

Abhishek Nagaraj

Information Economics, Digital Mapping

Hoai-Luu Q. Nguyen

Consumer Credit Analysis, Banking

Minjung Park

Marketing Analytics, Microeconometrics

Panos Patatoukas

Financial Statement Analysis, Economic forecasting

Richard Sloan

Earning Management, Financial Accounting

Sameer B. Srivastava

Organizational Sociology, Computational Linguistics

Toby Stuart

Entrepreneurship, Innovation, Network Analysis

Mathijs de Vaan

Social Network Analysis, Economic Sociology

Johan Walden

Networks in Capital Markets

Reed Walker

Environmental Economics, Public Economics

Grants and Fellowships

The Fisher Center for Business Analytics has a limited amount of funding available for research proposals from Haas faculty members, and invites you to submit a proposal. Priority will be given to junior faculty and to proposals that are willing to share data or otherwise collaborate with other interested Haas faculty members. This year the Center seeks to award $50,000.


Through the support of the Ryoichi Sasakawa Foundation, the Center administers Fellowships to PhD students applying analytics in their doctoral research.

More about Doctoral Fellows


(closed for the year)



Research that applies analytics to business – broadly defined as:

  • Addressing a business problem
  • By using empirical methods to analyze data of non-trivial scale or
  • Constructing novel explanatory variables using analytics (e.g. text mining)
  • Applying analytics to integrate previously disparate data sets in novel ways.
  • Proposal may be limited to data collection for future research – priority given to researchers willing to share data

Funding Availability

Funds will be transferred to individual faculty research accounts by June 2017 to be used before June 30, 2018.

Grants Should Include:

  • A 2-3 page description of the research your project
  • Recent CV with related papers highlighted
  • Project budget (maximum $15,000)

Reporting Requirements

Grant recipients are required to

  • Produce a progress report or working paper before June 30, 2018
  • Present their work at a seminar upon request


Email proposals to:

Conferences and Seminars

The Center sponsors conferences that invite scholars from around the world to visit Berkeley and share their research by applying analytics to business challenges.  The Center also hosts a cross-disciplinary research seminar series for faculty and PhD students.  Alternating between Haas faculty and industry data scientists, speakers introduce an algorithm in a particular problem domain and then engage the audience in a discussion of methodology, seeking new applications in different contexts.




Marketplace Optimization Data Science at Uber

Thursday May 11, 2017: 11AM – 12:30PM, Room C330



Connan Snider  Senior Data Scientist at Uber and was previously an Assistant Professor of Economics at UCLA.

Abstract: The Marketplace Optimization team at Uber builds the intelligent systems that determine rider and driver pricing and incentives, the dispatch of drivers to riders, and the matching of Pool passengers with each other, among others. Data scientists, with diverse backgrounds in Economics, Operations Research, Statistics, and Computer Science, are constantly confronting challenging substantive problems associated with incentive design on a two(+) sided platform and the design of experiments intended to inform it. In addition, there are unique challenges associated with working so close to actual product. Namely, data scientists have to work closely with software engineers and are forced to think about engineering constraints like latency and scalability.

Research Computing

Haas Computing Support

Data Management and Preprocessing
Secure Data Hosting
Cloud Provisioning

Campus Computing Support

Savio Cluster

When necessary, navigating campus administration to negotiate Non-Disclosure Agreements, Data Use Agreements, Publication, and Intellectual Property