Key Areas of Research

Prompt Adaptation as a Dynamic Complement in Generative AI Systems
Information Systems Research, April 2026

As generative-AI models become more powerful, organizations will only realize a portion of that improvement unless users learn to adjust how they interact with the models — prompt-adaptation becomes a critical skill for unlocking full value. 

To capture the full benefits of new AI technologies, companies should invest not only in the latest models and infrastructure, but also in user training and workflow design — enabling teams to use the new technologies effectively.

Eaman Jahani (UMD), Benjamin S. Manning (MIT), Joe Zhang (Stanford), Hong-Yi TuYe (MIT), Mohammed Alsobay (Microsoft), Christos Nicolaides (University of Cyprus), Siddharth Suri (Microsoft), David Holtz (Columbia)

First-Party Content Production in a Competitive Media Market
Journal of Marketing

Streaming platforms are pouring money into original content, but whether it pays off depends on two things: how much their content already overlaps with competitors and how flexible their pricing is. When prices are fixed (e.g., standard subscription tiers), platforms are more likely to invest in originals—especially if competitors offer similar libraries—because originals help differentiate. But when platforms can easily adjust prices, heavy content overlap actually reduces the incentive to invest in originals, since pricing can be used instead to compete. Notably, when pricing is flexible, original and licensed content can work together to benefit platforms, content producers, and consumers.

Practical takeaways:

* If your pricing is rigid, invest in original content to stand out in crowded markets.
* If you can adjust prices flexibly, don’t overinvest in originals in crowded markets; use pricing strategically instead.
* Treat original and licensed content as complementary (not competing) when pricing flexibility allows it.

Xuelian Qin, Assistant Professor, Business School, Central South University; Lin Tian
Professor, School of Management, Fudan University; Bobby Zhou
Associate Professor, Robert H. Smith School of Business

Abortion Restriction Laws and Mobility of Scientists
Strategic Management Journal

We track the enactment of targeted regulation of abortion providers (TRAP) laws in the U.S. and analyze 4.98 million person-year mobility records for 535,568 biomedical scientists from 1990 to 2018. Our estimations reveal a 0.8-1.6 percentage-point increase in scientists’ relocation probability after states enacted abortion-restrictive laws, with substantially stronger effects among junior scientists (1.6-3.9 percentage points). Anti-abortion states also became less likely to be chosen as relocation destinations, particularly by higher-quality scientists. These responses appear driven by ideological misalignment and research-related concerns in fields affected by abortion-related regulation. We further find that states that have adopted TRAP laws experienced declines in scientific research quality, federal research funding, and patenting in relevant technological fields and among local firms.

Beril Yalcinkaya, postdoc, The Wharton School, University of Pennsylvania; Waverly W. Ding, Associate Professor of Strategy and Entrepreneurship, R.H. Smith School of Business, University of Maryland;

If We Build It, We Will Come: Strategies for Developing Academic Institutions and the Evolution of Career Choices by Top Talent During Japan’s Industrialization
CJEB Columbia University Working paper series

Modern day economies rely on academia—with its focus on generating new knowledge and training future work forces—as a critical complement to industry in contributing to endogenous growth. How well academia performs this role, however, depends on its ability to recruit and retain talented faculty who have lucrative alternative options in industry; moreover, such allocation of talent in academia vs. industry is conditioned by path-dependencies in the evolution of these sectors. We complement existing literature that has focused on factors in mature scientific labor markets by examining the endogenous evolution of academic institutions concurrent with industrialization in Japan during the turn of the 20th century. Our study combines historical methods with estimation of a dynamic occupational choice model and utilizes unique data on the census of university-educated engineers from the first 40 cohorts since the inception of higher technical education in Japan. The historical analysis reveals systematic shaping strategies to build institutions that catered to both monetary and non-monetary preferences, and the quantitative estimations highlight that the latter were particularly important in academia disproportionately attracting top talent in later cohorts, despite an increasing pay gap with industry.

Takuya Hiraiwa, Serguey Braguinsky, Rajshree Agarwal, University of Maryland Smith School

Technology (Non-) Emergence: The Role of (Mis-)Alignment of Uncertainty Dimensions In Alternative Solar Technology Trajectories
Organization Science

We examine industry and technology (non-) emergence by integrating actor-centric and systems perspective literature streams. We use historical methods to analyze rich data tracking investments by actors spanning private, public and academic sectors in the solar PV context. The industry took several decades after commercialization to emerge; moreover silicon and thin film technologies experienced divergent fates despite firm takeoff. By uncovering critical interdependencies across activities by different actors, we show that while attention by all actors to developing various elements of technological systems is necessary for emergence, it may not be sufficient. The industry emerged after activities by technology producers, industry associations and government agencies ensured stable institutional support that stimulated latent demand (by utilities and end consumers) and created reinforcing loops among activities by technology producers and research institutes for solar technologies to become a viable alternative to fossil fuels. Moreover, silicon experienced additional reinforcing loops in demand side and supply-side ecosystems, wherein technology producers and equipment manufacturers leveraged adjacent mature supply chains to meet demand-side scale and reliability requirements in fast growing markets. In contrast, thin film experienced balancing loops wherein nascent, firm-specific supply side alliances could not address these demand side needs. These findings showcase how dominant designs may emerge even when there is no ex-ante competitive dynamics among technology producers: while silicon may have benefited from first mover advantage at the technology level, our study highlights that ecosystem first mover advantages of silicon relative to thin film were particularly salient in their divergent fates.

Guerra, M. (Bayes Business School) & Agarwal, R. (Maryland Smith)

Flying High or Crashing Down: Pre-Entry Knowledge and the Distribution of Startup Performance
Strategic Management Journal

We examine variation in high-technology startups’ performance based on founders’ pre-entry experiences by developing a formal model and using confidential employee-employer linked microdata from the United States to examine the empirical consistency of the model propositions. The model posits that relative to insiders, a lack of industry-specific experience creates greater epistemic uncertainty regarding optimal business models at time of entry for outsiders and thus, higher post-entry adjustment costs associated with necessary pivots.  Consequently, outsiders have a higher selection threshold for the value creation potential of the underlying technical ideas. Together, these mechanisms yield propositions that relative to insiders, outsiders have lower odds of survival on average, but higher growth and probability of being acquired. The empirical results indicate strong and robust support for these propositions.

Agarwal R. (Maryland Smith) Carnahan, S., (Wash. U. at St. Louis) Campbell, B. (Ohio State University), and Choi, J. (Federal Reserve

Dynamic Investment and Product Market Rivalry: The Network Q Model
February 2026

We present a new dynamic model of corporate investment in imperfectly-competitive product markets, extending the neoclassical (Q) theory of investment to a multi-firm, multi-product, fully structural model.  The model provides an explicit formula to quantify corporate investment and characterize investment spillovers for the entire network of firms in any economy.  The model therefore provides a tool for future for researchers and policymakers alike, to understand relevant issues such as the way in which a specific merger affects all interconnected firms in the same network and consumer welfare, or how changes in aggregate discount rates affect markups and product market concentration both over time and in the cross section of firms.

In the paper, we take our model to the data for the universe of U.S. publicly traded companies and obtain five novel insights: 1) product market competition is a key force driving aggregate investment and capital allocation; 2) the persistence of firm's capital stocks increased over the past 25 years (i.e. capital has became ""stickier""); 3) monopoly rents account for a large, rising share of firms' value; 4) positive shocks to firms' cost of capital increase markups and concentration; 5) mergers consummated since 1995 have led to a modest decline in aggregate capital formation.  These findings contribute to the existing economics and finance literature analyzing the secular decline in product market competition in the US, and the impact of product markets on firms' valuations

Maria Cecilia Bustamante (UMD) and Bruno Pellegrino (Columbia)

Three Strategic Bets on AI’s Future

This paper examines competition in the consumer AI assistant market using worldwide iOS and Android app-store data from seven major AI assistants from May 2023 through December 2025. Rather than finding a winner-take-all market, we show that major product launches tend to coincide with growth in the overall category, with little evidence of direct cannibalization across leading models. In other words, the “AI war” appears less zero-sum than commonly assumed.

The analysis identifies three distinct strategic positions that currently appear viable to date. ChatGPT is pursuing scale, with by far the largest market share and substantial revenue generated from a very large user base. Google Gemini is pursuing an ecosystem defense strategy, using broad distribution and low direct monetization to support Google’s wider platform. Claude is pursuing a differentiated premium niche strategy, with a much smaller user base but much higher revenue per user.

There are three practical takeaways for managers and investors. First, firms should not assume that AI markets will necessarily converge to a single dominant winner. Second, the right AI strategy depends on structural advantage: scale, ecosystem leverage, or premium differentiation. Third, as market growth slows and capital becomes less abundant, each strategy will face different risks, making monetization quality and strategic fit more important than raw user growth alone.

Maxime C. Cohen, Professor, Desautels Faculty of Management, McGill University
Eddy Hage-Youssef, McGill University
Daniel M. McCarthy, Associate Professor of Marketing, Robert H. Smith School of Business, University of Maryland, College Park
D. Daniel Sokol, Professor, USC Gould School of Law; USC Marshall School of Business

The Public Pension Crisis: Contractual Rights and Constitutional Limits
Cambridge University Press, March 2026

A timely response to the pressing issue of public pension reform, The Public Pension Crisis explores the complex relationship between contract law and government pensions, specifically focusing on the Contract Clause and related state Pension Clauses. Analyzing over a decade of litigation, the book highlights the evolving role of pension contracts in constitutional law and examines more than 70 landmark cases to establish a clear, principled framework for determining when pension benefits qualify as contractual obligations. T. Leigh Anenson presents a unified theory to consistently treat public and private pensions, balancing the interests of employees’ earned benefits with the financial challenges facing governments. Combining legal scholarship with practical policy insights, Anenson not only provides a much-needed legal perspective on pension reform but also calls for a systematic approach to addressing the retirement security crisis.

T. Leigh Anenson, J.D., LL.M., Ph.D.

Setting Higher Referral Targets Increases the Number of Women Recommended: Evidence From the Field and Lab
Journal of Applied Psychology

Women continue to be underrepresented in numerous occupations and in the highest echelons of many organizations. This may be due, in part, to disadvantages they face in referral-based hiring and promotion processes. We propose a low-cost and easily scalable intervention to boost referrals of women in male-dominated contexts: requesting a greater target number of referrals (e.g., at least four instead of at least two referrals). Across six experiments (including two field experiments embedded in an organization’s referrals process), requesting double the number of referrals increased the number of women referred by 17%-88%. Our intervention provides a versatile, low-cost, and low-risk option for managers and leaders looking to recruit from the full range of talent available to them.

Aneesh Rai (Assistant Professor, University of Maryland, College Park); Erika Kirgios (Assistant Professor, University of Chicago); Brian Lucas (Associate Professor, Cornell University); Katherine Milkman (Professor, University of Pennsylvania)

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