Key Areas of Research
Transforming Products into Platforms: Unearthing New Avenues for Business Innovation
NIM Marketing Intelligence Review, October 2024
It is impossible for brands to ignore digital platform opportunities. Network effects are one of the strongest sources of power and defensibility ever invented and underlie some of the most valuable businesses in the world. Managers and entrepreneurs can leverage the power of platforms by adding some platform elements to their existing products or services, by distributing their brands via existing platforms or by developing their own new platforms. By using one’s own brands as platforms requires creativity but can help businesses unlock new value and build resilient ecosystems around their products. There are three key methods. The first is to invite third-party sellers to enhance existing products. Examples include selling advertising space around products or creating app stores to extend offers. The second is to connect one’s customers by enabling interactions among users to add value. Third, brands might reach out to customers’ customers by enhancing the end-user experience in a way that benefits both themselves and their direct customers. If thoughtfully implemented, any platform strategy will create self-reinforcing feedback loops sparking growth and keeping competitors at bay.
Andrei Hagiu, Associate Professor of Information System, Boston University; Bobby Zhou, Associate Professor of Marketing, University of Maryland
How to Talk When a Machine Is Listening: Corporate Disclosure in the Age of AI
The Review of Financial Studies, March 2023
Growing AI readership (proxied for by machine downloads and ownership by AI-equipped investors) motivates firms to prepare filings friendlier to machine processing and to mitigate linguistic tones that are unfavorably perceived by algorithms. Loughran and McDonald (2011) and BERT available since 2018 serve as event studies supporting attribution of the decrease in the measured negative sentiment to increased machine readership. This relationship is stronger among firms with higher benefits to (e.g., external financing needs) or lower cost (e.g., litigation risk) of sentiment management. This is the first study exploring the feedback effect on corporate disclosure in response to technology.
Sean Cao, Associate Professor (with tenure), Robert H. Smith School of Business, University of Maryland, United States of America
Improved LISA Analysis for Zero-Heavy Crack Cocaine Seizure Data
INFORMS Journal of Data Science
Local Indicators of Spatial Association (LISA) analysis is a useful tool for analyzing and extracting meaningful insights from geographic data. It provides informative statistical analysis that highlights areas of high and low activity. However, LISA analysis methods may not be appropriate for zero-heavy data, as without the correct mathematical context the meaning of the patterns identified by the analysis may be distorted. We demonstrate these issues through statistical analysis and provide the appropriate context for interpreting LISA results for zero-heavy data. We then propose an improved LISA analysis method for spatial data with a majority of zero values. This work constitutes a possible path to a more appropriate understanding of the underlying spatial relationships. Applying our proposed methodology to crack cocaine seizure data in the U.S., we show how our improved methods identify different spatial patterns, which in our context could lead to different real-world law enforcement strategies. As LISA analysis is a popular statistical approach that supports policy analysis and design, and as zero-heavy data is common in these scenarios, we provide a framework that is tailored to zero-heavy contexts, improving interpretations and providing finer categorization of observed data, ultimately leading to better decisions in multiple fields where spatial data is foundational.
Eunseong Jang, The Robert H. Smith School of Business, University of Maryland
Margret Bjarnadottir, The Robert H. Smith School of Business, University of Maryland
Marcus Boyd, National Consortium for the Study of Terrorism and Responses to Terrorism, University of Maryland
S. Raghavan, The Robert H. Smith School of Business & Institute for Systems Research, University of Maryland
Large language models and synthetic health data: progress and prospects
JAMIA Online, October 2024
There is growing interest in the application of machine learning models and advanced analytics to various healthcare processes and operations, including the generation of new clinical discoveries, development of high-quality predictions, and optimization of administrative processes. Machine learning models for prediction and classification rely on extensive and robust datasets, particularly for deep learning models common in health, creating an urgent need for large health datasets. Yet datasets can be insufficiently large due to the rapid evolution of diseases, such as coronavirus disease 2019 (COVID-19), rarity of disease, or the myriad obstacles to sharing and acquiring existing health data, including ethical, legal, political, economic, cultural, and technical barriers. Synthetic data provide a unique opportunity for health dataset expansion or creation by addressing privacy concerns and other barriers. In this paper, we review prior literature and discuss the landscape of machine learning models used for synthetic health data generation (SHDG), outlining challenges and limitations. We build on existing research on the state of the art in SHDG and prior broad explorations of the potential risks and opportunities for large language models (LLMs) in healthcare. We contribute to the literature with a focused assessment of LLMs for SHDG, including a review of early research in the area and recommendations for future research directions. Six promising research directions are identified for further investigation of LLMs for SHDG: evaluation metrics, LLM adoption, data efficiency, generalization, health equity, and regulatory challenges
Daniel Smolyak, Department of Computer Science, University of Maryland
Margret V. Bjarnadottir, Robert H. Smith School of Business, University of Maryland
Kenyon Crowley, Accenture Federal Services
Ritu Agarwal, Center for Digital Health and Artificial Intelligence, Carey Business School
Seductive Language for Narcissists in Job Postings
Management Science
Prior research indicates that narcissistic executives engage in earnings management and other negative organizational behaviors, and many studies ponder why firms hire such individuals, especially into corporate accounting positions. Utilizing a selection of terms from real-world job postings that we characterize as either describing a “Rule-Bender” or “Rule-Follower" candidate, we first conduct several validation studies which reveal that these terms vary predictably across types of job postings, that people generally agree with our categorization of these terms, and that Rule-Benders are viewed as possessing worse managerial skills but a higher proclivity for unethical behavior. We then demonstrate that narcissistic job seekers are more attracted to job postings that describe the ideal candidate using Rule-Bender terms for both general positions (Experiment 1) and senior accounting positions (Experiment 2). Finally, we examine firm characteristics that might lead professional recruiters to incorporate Rule-Bender language into Chief Accounting Officer job postings and find that Rule-Bender terms are preferred for higher-growth, higher-innovation firms (Experiment 3), and when more aggressive reporting would benefit the firm (Experiment 4). Our results suggest that recruiters’ language choices can attract Rule-Bending narcissists to firms, perhaps even in unintended circumstances.
Jonathan Gay (University of Mississippi), Scott Jackson (University of South Carolina), Nick Seybert (University of Maryland)
Bayesian Ensembles of Exponentially Smoothed Life-Cycle Forecasts
Manufacturing and Servoce Operations Management
We study the problem of forecasting an entire demand distribution for a new product before and after its launch. Firms need accurate distributional forecasts of demand to make operational decisions about capacity, inventory and marketing expenditures. We introduce a unified, robust, and interpretable approach to producing these pre- and post-launch distributional forecasts. Our approach is inspired by Bayesian model averaging. Each candidate model in our ensemble is a life-cycle model fitted to the completed life cycle of a comparable product. A pre-launch forecast is an ensemble with equal weights on the candidate models’ forecasts, while a post-launch forecast is an ensemble with weights that evolve according to Bayesian updating. Our approach is part frequentist and part Bayesian, resulting in a novel form of regularization tailored to the demand forecasting challenge. We also introduce a new type of life-cycle or product diffusion model with states that can be updated using exponential smoothing. The trend in this model follows the density of an exponentially tilted Gompertz random variable. For post-launch forecasting, this model is attractive because it can adapt itself to the most recent changes in a product’s life cycle. We provide closed-form distributional forecasts from our model. In two empirical studies, we show that when the ensemble’s candidate models are all in our new type of exponential smoothing model, this version of the ensemble outperforms several leading approaches in both point and quantile forecasting. In a data-driven operations environment, our model can produce accurate fore- casts frequently and at scale. When quantile forecasts are needed, our model has the potential to provide meaningful economic benefits. In addition, our model’s interpretability should be attractive to managers who already use exponential smoothing and ensemble methods for other forecasting purposes.
Xiaojia Guo (Assistant professor, Robert H. Smith School of Business, UMD), Casey Lichtendahl (Google), Yael Grushka-Cockayne (Professor, Darden school of business, University of Virginia)
Marketplace Expansion Through Marquee Seller Adoption: Externalities and Reputation Implications
Management Science
In the race to establish themselves, many early-stage online marketplaces choose to accelerate their growth by adding marquee (established brand name) sellers. We study the implications of marquee seller entry on smaller, unbranded sellers in a marketplace when both unbranded sellers and marquee sellers can vary vertically across reputation (referred to as sellers’ quality). While recent literature has shown that higher-quality unbranded sellers fare better than their lower-quality peers, we posit that this may not hold for entrants of any quality. To this end, we collaborate with an online business-to-business platform and exploit the entry of two marquee sellers of vastly differing quality. Using a difference-in-difference-in-differences framework, we causally identify the effect. We find that while higher-quality unbranded seller revenues increase relative to low-quality unbranded sellers when the entrant is of superior quality (consistent with the literature), the effect is reversed when the entrant is of inferior quality. Further, unbranded sellers change their supply quantities such that the platform’s average supply quality shifts in the direction of entrant quality. Using a stylized theoretical model, we identify two mechanisms that drive our findings – (i) new buyers brought in by the entrant disproportionately favor unbranded sellers who are quality neighbors to the entrant, and (ii) the unbranded seller’s ability to adjust their supply quantities. Most notably, the choice of marquee sellers, examined through the lens of their externality on unbranded sellers, can foster or undermine the platform’s long-term growth objectives.
Wenchang Zhang (Kelly School of Business, Indiana University), Wedad Elmaghraby and Ashish Kabra (University of Maryland)
The Financial Consequences of Pretrial Detention
Review of Financial Studies
In the United States, a significant number of criminal defendants are held in pretrial detention and face substantial financial burdens. Matching individual-level criminal case records to household-level financial data, we exploit the quasi-random assignment of court commissioners to study how pretrial detention affects household solvency. We find that pretrial detention results in higher rates of household insolvency, driven by higher rates of Chapter 7 bankruptcies and judgment liens, and higher foreclosure rates during periods of decreasing house prices. We document that the effects spill over to family members and show that home equity can cushion households from insolvency.
Pablo Slutzky (UMD), Sheng-Jun Xu (University of Alberta)
Liability of Foreignness in Immersive Technologies: Evidence from Extended Reality Innovations
Journal of International Business Studies
This study investigates the persistence of the Liability of Foreignness (LOF) in the realm of immersive technologies like Extended Reality (XR), which includes Augmented Reality (AR) and Virtual Reality (VR). Challenging the assumption that digitalization eliminates traditional barriers for foreign firms, we argue that LOF in XR stems from foreign companies' difficulties in providing a "mentally fluent" experience to consumers in foreign markets. Cultural mismatches can disrupt smooth information processing and diminish the effectiveness of XR innovations. Our research identifies specific XR technological features—realism, interactivity, and vividness—and brand-related factors like brand newness and platform orientation that can either exacerbate or mitigate LOF. Confirming the existence of LOF in XR innovations, we find that foreign brands in the South Korean beauty market are at a disadvantage in generating positive brand engagement through XR compared to local brands. XR innovations that are less realistic, more interactive, and highly vivid tend to amplify LOF due to the need for deeper cultural understanding. Conversely, higher realism in XR experiences helps reduce LOF by offering universally relatable content. Newer foreign brands and those using communication-centered platforms experience less LOF, as consumers may overlook cultural mismatches to resolve information uncertainty and develop attitudinal loyalty.
Hyoryung Nam, Assistant Professor, Martin J. Whitman School of Management at Syracuse University (Ph.d. from Smith – Marketing Department) Yiling Li, Doctoral Student, Yonsei Business School, Yonsei University, Seoul, Korea P.K. Kannan, Dean’s Chair in Marketing Science, Robert H. Smith School of Business, University of Maryland Jeonghye Choi, Professor of Marketing, Yonsei Business School, Yonsei University, Seoul, Korea
Distributed Ledgers and Secure Multi-Party Computation for Financial Reporting and Auditing
August 2024
To understand the disruption and implications of distributed ledger technologies for financial reporting and auditing, we analyze firm misreporting, auditor monitoring and competition, and regulatory policy in a unified model. A federated blockchain for financial reporting and auditing can improve verification efficiency not only for transactions in private databases but also for cross-chain verifications through privacy-preserving computation protocols. Despite the potential benefit of blockchains, private incentives for firms and first-mover advantages for auditors can create inefficient under-adoption or partial adoption that favors larger auditors. Although a regulator can help coordinate the adoption of technology, endogenous choice of transaction partners by firms can still lead to adoption failure. Our model also provides an initial framework for further studies of the costs and implications of the use of distributed ledgers and secure multiparty computation in financial reporting, including the positive spillover to discretionary auditing and who should bear the cost of adoption.
Author: Sean Cao, Associate Professor (with tenure), Robert H. Smith School of Business, University of Maryland, United States of America