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Essays on Household Finance, FinTech, and Entrepreneurship

2024-07-27 , Zhang, Yafei

This dissertation consists of five essays that examine the intersection of household finance, FinTech, and entrepreneurship, leveraging comprehensive credit bureau data and novel empirical strategies. The first essay investigates the impact of banks' cloud technology adoption on credit card management and borrower outcomes, highlighting differential effects across credit segments. The second essay compares the long-term borrowing capacities and outcomes of marketplace lending (MPL) borrowers to those of traditional bank borrowers, emphasizing the limitations of data-driven lending models in mitigating information frictions. The third essay documents significant gender-based sorting across credit card products and its implications for gender gaps in borrowing capacities and consumption smoothing. The fourth essay studies the impact of the federal student loan forbearance program on distressed borrowers' debt accumulation and delinquency patterns, suggesting that extended forbearance may accelerate financial distress. The final essay examines the negative long-term consequences of entrepreneurship on entrepreneurs' personal credit, highlighting the role of increased personal borrowing and the potential costs of business-friendly policies.

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The Effect of Environmental Affinity and Environment-Related Disclosure Nature on Investor Perceptions and Judgments

2024-07-22 , Liu, Peina

While financial disclosures are generally deemed important by investors, the value placed on Environmental, Social, and Governance (ESG) matters can vary depending on investors’ environmental affinity. This study experimentally investigates whether environmental affinity (low vs. high) interacts with the voluntary vs. mandatory nature of environment-related disclosures to influence investor perceptions and judgments. I predict and find that high-environmental-affinity investors perceive environment-related risks as more significant than low-environmental-affinity investors. I also observe that a mandatory environment-related disclosure, as opposed to a voluntary one, increases investors’ perception of environment-related risks. As predicted, this effect primarily exists among investors with low rather than high environmental affinity. A moderated mediation analysis reveals that the perception of regulators’ views on environment-related risks mediates this effect. Additionally, I present preliminary evidence suggesting that gender plays a role in this context. This study accentuates how an individual characteristic influences investors’ consumption of mandatory disclosures.

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A Threat or a Promise?: Essays on Consumer Perception of Emerging Marketplace Technologies

2023-08-01 , Hyun, Na Kyong

Rapid and ongoing technological innovation is transforming the lives of ordinary consumers. My dissertation examines how consumers’ perception of and relationships with modern technologies (smart agents, AI, voice-based interfaces) influence persuasion, behavior, and well-being. As firms become “smarter” than ever, utilizing vast customer information and advanced technologies to improve their marketing efforts, my research aims to inform managerial decisions that generate economic and social value, while ensuring consumer welfare. Chapter 1: Personality Perceptions of Consumer Smart Agents The ongoing evolution of consumer smart agents into daily interaction partners is raising important new questions about “social” perception and cognition in the context of consumer technology. Building on research in social perception of both human and nonhuman entities, I investigate how consumers assign humanlike personality traits to smart agents. The goals of Essay 1 are to construct a parsimonious, psychometrically valid instrument that captures perceptions of smart agent personality and to demonstrate this instrument’s utility for addressing important, managerially-relevant questions regarding consumer-device interactions. Across a series of studies, I develop a hierarchical model of smart agent personality that contains two high-level factors (“friendly” and “reliable”) with seven underlying facets. I demonstrate the reliability and validity of the measurement instrument with multiple methods, and I use follow up experiments to document unique and theory-compatible antecedents to each dimension. In a final study, I document how different agent “voices” impact downstream interaction variables through perceptions of agent friendliness and reliability. My findings suggest that consumers perceive smart agent personalities in a stable and coherent manner, and that careful construction of these personalities is a means of differentiation, diversification, and targeting to specific segments. Chapter 2: Vocal Similarity, Trust, and Persuasion in Consumer-Recommender Interactions I extend the principle of similarity-based attraction to the domain of the human voice, by examining how similarity in voice (timbre) can influence consumer choice. Using machine learning, I generate an objective measure of vocal similarity between an individual consumer and a recommender using mel-frequency cepstral coefficients (MFCCs) which capture vocal timbre. First, using data from 2,791 Kickstarter campaigns, I show that a spokesperson’s voice that is closer to an average-voice (i.e., the average MFCC scores from a large sample of sampled voices) results in higher persuasion, as measured by fundraised amount and campaign success – a result driven by vocal similarity. These effects are attenuated when external signals of campaign validity (staff endorsements) are present. Then, in five laboratory studies, I show that vocal similarity with a recommender (both human and simulated-human through AI) leads to greater trust, and consequently a higher likelihood of accepting the recommendation. I also show that objective and perceived voice similarity have similar results, with objective similarity mapping on to perceived similarity. The methods and findings provide a deeper understanding of consumer and recommender interactions, including new tools for voice analytics. Together, my essays inform understanding regarding consumer perception of modern technologies, factors that drive persuasion and customer relationship formation, and opportunities for future research in this emerging area.

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Information Sharing and Operational Transparency on On-Demand Service Platforms

2023-07-27 , Kulkarni, Swanand

The three essays in this dissertation examine the operational practices of on-demand service platforms, pertaining to information sharing and operational transparency on the supply-side. On-demand service platforms such as ridesharing, food delivery, grocery delivery, and courier delivery platforms critically depend on the services of workers, who are independent contractors. Given that these workers have discretion over their labor supply decisions, the platform's information provision to workers plays a key role in influencing workers' decisions and in eventually meeting the customer demand. In this dissertation, I employ game-theoretic modeling and conduct incentivized experiments with human subjects to evaluate the implications of a platform's practices around information sharing and operational transparency with workers, for workers' decisions and potentially for the platform's operational outcomes. Demand-Supply Information Sharing: We investigate how an on-demand service platform's mechanism to share demand-supply mismatch information spatially affects drivers' relocation decisions and the platform's matching efficiency. We consider three mechanisms motivated by practice: the platform either shares demand-supply mismatch information about zones(s) with excess demand (i.e., surge zone(s)) with all drivers (surge information sharing, common practice today), all zones with all drivers (full information sharing), or about surge zone(s) only with drivers sufficiently close by (local information sharing). We develop a game-theoretic model with three zones; drivers in two non-surge zones decide whether to relocate to the surge zone. We incorporate two spatial aspects: drivers' relocation costs, and initial supply across non-surge zones. Theoretically, full can yield a lower matching efficiency than surge information sharing under low relocation costs because drivers do not relocate as much when demand in non-surge zones is high. Local information sharing is strictly dominated by other mechanisms on matching efficiency under limited supply near the surge zone, and weakly dominated otherwise by surge information sharing. We test these theoretical predictions in the lab with human participants as drivers. Experimentally, surge information sharing serves fewer customers than predicted because drivers relocate too often, compromising efficiency in the non-surge zones. The alternatives, full and local, are not dominated by surge information sharing, and serve more customers than theoretically predicted, providing support for their potential benefits. A behavioral equilibrium incorporating loss aversion through mental accounting and decision errors describes drivers' behavior in our experiments better than the rational equilibrium. Payment Algorithm Transparency: On-demand service platforms have been experimenting with algorithms to determine compensation for their workers. While some use commission- or effort-based algorithms that are intuitive to workers, others, in their efforts to better match customer demand, have transitioned to algorithms where pay is not strictly tied to effort, but depends on other, potentially exogenous factors. Platforms have also kept these algorithms opaque. Despite the move towards less-intuitive and opaque algorithms in practice, workers’ reactions to them are not systematically examined or understood. Through incentivized online experiments on Prolific, we present real-effort tasks as work opportunities for payment to human participants, and examine how individual features of a pay algorithm, specifically its intuitiveness to workers and transparency, affect workers' engagement (measured by work rejection rates and willingness to pay to accept a work opportunity) and perceptions of the platform. We also examine the effect of an algorithm change from intuitive to non-intuitive, and how transparency interacts with this change. For workers with prior experiences on the platform, intuitiveness, and transparency both are effective at sustaining engagement in our experiments. Transparency is particularly motivating for workers under a non-intuitive algorithm and can fully compensate for the reduction in worker engagement from implementing a non-intuitive algorithm. Furthermore, even though a transparent platform experiences a drop in worker engagement after switching to a non-intuitive algorithm, commitment to transparency is still beneficial: Worker engagement with transparency is at least as much as that without transparency, while transparency is more potent at motivating positive perceptions towards the platform. Platform Commission Transparency: On-demand service platforms in the role of an intermediary that matches service-seeking customers and service-providing independent contractors, typically charge workers a commission on each service request that they complete. Early on, most on-demand service platforms operated a fixed commission model, where the platform determines the price on a service request such that the worker completing it is compensated for effort, while the platform keeps a fixed percentage of the price as a commission from the worker. While several platforms continue to operate this model, some platforms transitioned to a model where the platform's commission is inconsistent across service instances. Thereby, while the platform continues to compensate workers based on effort-based factors, it utilizes several factors that do not influence workers' wage to determine the price, leading to the platform commission being variable across service instances. Platforms argue that this helps to improve customers' experience through better prices while drivers continue to earn for their effort. Anecdotal evidence suggests that workers are concerned about the large commission that platforms charge them in some service instances. In response, platforms have experimented with workers' visibility of the platform's commission under the variable commission model, which has reportedly contributed to worker suspicion and distrust. Motivated by these practices, we design incentivized experiments with human subjects to examine the influence of a platform's commission on workers' participation decisions under the fixed and variable commission models. We study the impact of consistency in platform commission on workers' participation decisions and their perceptions of the platform. Furthermore, we evaluate how the visibility of platform commission influences workers' participation decisions and their perceptions of the platform.

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Economic Analysis of Mobile Advertising Networks: Targeting Precision, Revenue Models, Heterogeneous Privacy Concerns, and Costly Development of Targeting Capability

2024-07-26 , Kang, Chunghan

This dissertation develops a game-theoretic modeling framework involving three-stage decision making among an ad network, an app developer, and consumers. The ad network decides the optimal levels of targeting precision and revenue sharing; the app developer responds by choosing its optimal revenue model among three options, which are the free, the paid, and the hybrid; and consumers with heterogeneous privacy sensitivity make their choices. It contains two essays based on the developed modeling framework. The first essays investigates whether the free revenue model persists as more consumers become privacy sensitive. We find that as more consumers become privacy sensitive, the ad-supported free revenue model prevails in equilibrium. Interestingly, we identify the existence of economic rents for the app developer when the proportion of highly privacy-sensitive consumers reaches a certain threshold. Consumer surplus is also maximized at this threshold. The second essay studies whether more stricter data privacy regulations are necessary to improve consumer surplus. We find that the hybrid revenue model is induced as costs decrease, where consumers obtain suboptimal surplus. By applying more stronger data privacy regulations, the optimal revenue model shifts back to free where consumer surplus could be maximized. This shift, however, could disproportionately disadvantage one consumer type while benefiting the other, necessitating a subtle calibration of the strength of data privacy regulations.

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Towards a Better Future of Work: Enhancing Efficiency, Promoting Fairness, and Upholding Labor Standards in the Contemporary Workplace

2024-04-27 , Ding, Li

The three essays in this dissertation examine critical issues at the intersection of the future of work and labor practices. As the nature of employment and the workplace continues to evolve, it is essential to consider how these changes might affect organizational efficiency, worker welfare, and broader societal implications. In the dissertation, I focus on three important contexts that shape the future of work, which are digital service platforms, gig economy platforms, and global supply chains, in each essay. I adopt empirical methodologies including structural estimation, policy simulations, online experiments, and econometric analyses to offer actionable insights for organizations and policymakers to navigate the complexities of the changing work landscape and work towards a better future of work.

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Essays on Resources and Innovation

2023-07-31 , Zhou, Shibo

External resources play a crucial role in fostering innovation by allowing individuals and firms to actively seek new knowledge and create novel products alongside their routine operations. In my dissertation, I investigate the impact of external resources in three distinct forms: consumers, award authorities, and foreign governments. Online user reviews are an important external information source for both consumers and producers. While the impact of reviews on consumer purchasing behavior has drawn much attention in the literature, whether it can influence producers in terms of future product development remains unclear. In Chapter 1, I examine the role of user reviews on product development and assess how the impact varies across different types of reviews. Analyzing textual data from a two-sided platform using NLP techniques, I evaluate the effect of review ratings on video game updates. The empirical results show that games with more design-related reviews have a higher probability of updates in the following month when users are not satisfied. Moreover, incumbent firms with more resources and capabilities can learn from reliability-related reviews for more complicated product development. The developed updates are positively correlated with the re-engagement of inactive users. My findings show that producers learn from users to absorb ideas about subsequent product development, and the relationship is heterogeneous across different dimensions of the reviews and producers. I discuss the strategic implications of the results for further development by producers, as well as the importance of platform review systems governance. While innovations are often critical to the growth of firms, such firm-level outcomes emerge from the actions of organizational members who seek novel knowledge. Chapter 2 (co-authored with Jessica Li) develops and tests a model examining how status gain impacts individual novel knowledge adoption and subsequent performance. The model is tested using longitudinal data from a sample of book authors. Results indicate that status gain is associated with a higher level of novelty adoption, and the effect is more pronounced when the authors do not have other sources of income. Adoption is positively associated with subsequent performance, measured by online ratings and easiness of passing through the publishers. This chapter contributes to knowledge management literature by demonstrating the unique effect of status gain on individual-level knowledge searching and adding to the evidence on how these two activities are present at the individual level. The last external resource that I am examining is government support, which has been accounting for regional innovation growth. in Chapter 3 (co-authored with Kedong Chen and Xiaojin Liu), I analyze the unintended consequences of foreign government policies on domestic inventors. In 2009, the Chinese government launched the policy of ``national innovative cities'' to support the innovation of firms in selected regions. But the unintended consequence of the policy is unclear at the inventor level, in particular on those foreign inventors who have experience working with Chinese firms that are exposed to the policy intervention. Our research is guided by the research question: \textit{How does government support influence foreign inventors who have collaborated with domestic firms before?} By employing the difference-in-differences (DiD) technique in the quasi-experimental setting, we examine the influence of government intervention on foreign partners. We find that foreign inventors who have established relationships with firms in selected cities experience an increase in collaborators and innovations. We further show that inventors with less patent stock take better advantage of cross-border government support. Taken together, the findings of the study suggest that government support can facilitate unintended cross-border knowledge flows and strengthen the innovation performance of ``treated'' foreign inventors. Overall, this dissertation enhances our comprehension of how firms and individuals respond to changes in external resources.

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Pursuit of Entrepreneurship: Operational Models

2024-07-24 , Wang, Zeya

This dissertation comprises three essays that explore innovative operational solutions for addressing entrepreneurial challenges. Building on the existing entrepreneurial operations literature, it emphasizes managing the inherent uncertainties of entrepreneurship through entrepreneurial learning. Specifically, the dissertation (i) employs diverse analytical approaches such as game theory, stochastic processes, and optimization models, and (ii) covers three critical topics: operational challenges faced by entrepreneurs, advising entrepreneurs, and hybrid entrepreneurship. Operational Challenges Faced by Entrepreneurs: Despite the significance of entrepreneurship in today’s economies, the harsh reality is that most entrepreneurial ventures ultimately fail. In recent years, there has been a growing awareness among researchers and practitioners that a significant portion of these failures can be attributed to execution and operational issues. Startups face unique challenges that differ from those of established firms, and these obstacles can vary significantly depending on their stage in the entrepreneurial process. To organize the existing entrepreneurial operations literature, we first review the literature on entrepreneurial stages. We then delve into the operational challenges faced at each stage. Lastly, we propose some future research opportunities in entrepreneurial operations. Advising Entrepreneurs: When novel and urgent challenges arise, entrepreneurs often lack the expertise needed to identify potential solutions. Additionally, they typically have a limited timeframe, or “runway,” in which to implement a viable solution. Consequently, startups often seek help from external experts, known as mentors or advisors, who can identify candidate solutions. Advisors are experienced professionals who have either engaged in or provided counsel to businesses in related fields. While substantial research in operations management has focused on entrepreneurial challenges, there is an important gap that remains: understanding the role of advisors in guiding entrepreneurs. This chapter addresses this gap by exploring how advisors should recommend options to entrepreneurs, considering the entrepreneur’s capability to execute and the iterative nature of the solution validation process. Hybrid Entrepreneurship: One of the primary reasons behind the high failure rate of startups is the significant uncertainty they face. To navigate these challenges, entrepreneurs must engage in extensive exploratory work. They often encounter two contrasting strategies for pursuing their ventures. The first strategy, endorsed by prominent venture capitalists and seasoned operators, advocates for unwavering commitment to one’s entrepreneurial vision, based on the belief that hesitation or half measures can undermine potential success. The second strategy, suggested by other experts in entrepreneurship, promotes a more fiscally cautious approach, advising entrepreneurs to maintain employment until their venture’s viability is firmly established, a concept also referred to as Hybrid Entrepreneurship. In this chapter, we complement existing studies by incorporating the dimension of entrepreneurial learning into Hybrid Entrepreneurship and examining the circumstances under which this approach should be adopted.

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Essays on Fintech, AI, and Innovation in Finance

2024-04-25 , Du, Wendi

The dissertation consists of five essays on FinTech, AI and innovation in finance. These essays center around how innovation and capital market influence each other, and how to use cutting-edge technologies like machine learning and AI to address the economic questions that would otherwise remain unanswered. In the first essay, I investigate the redeployability channel of trademarks' collateral value. Using a novel court decision that exogenously weakens trademark redeployability, I find a 3.4 percentage point reduction in affected firms’ book leverage, equivalent to a 16.9\% decrease in their average book leverage. By using firm-level trademark portfolio data and employing natural language processing (NLP) techniques, including ChatGPT, I show that firms with more licensed trademarks (i.e., those more exposed to the court ruling), experience a stronger negative impact. Additionally, affected firms are less likely to pledge their registered trademarks as collateral afterward. When they do pledge, they pledge a greater number of trademarks, as well as more valuable ones. Affected firms also register fewer new trademarks in the future. In sum, my results highlight the value of trademark collateral in enhancing firms' debt capacity through its redeployability channel. In the second essay, we develop a text-based measure of firm-level inflation exposure from earnings calls. Our deep learning model identifies sentences discussing price changes, while distinguishing price increases from decreases and inputs from outputs. Our aggregate inflation exposure measure strongly correlates with official inflation measures. Firms with higher inflation exposure experience negative stock price reactions to earnings calls. The price reaction is attenuated when a firm has pricing power. Further, firms with higher inflation exposure have higher future costs of goods sold and lower operating cash flows. They perform worse on Consumer Price Index (CPI) release days when CPI exceeds the consensus forecast. In the third essay, we explore the area of emerging technologies which can potentially transform business and society but are difficult to identify and prone to hype and uncertainty. We construct a dictionary of emerging technology phrases from earnings calls using deep learning techniques and document an immediate positive stock market reaction to firms’ discussions of emerging technologies. We find that the positive reaction is more pronounced when firms discuss emerging technologies early in their life cycle. Firms with lower ex-ante credibility, such as a prior history of earnings management, innovate less ex-post and experience poorer long-term returns. Overall, our results highlight when firms' discussions of emerging technologies convey credible information to investors. The fourth essay examines whether managers walk the talk on the environmental and social discussion. We train a deep-learning model on various corporate sustainability frameworks to construct a comprehensive Environmental and Social (E\&S) dictionary. Using this dictionary, we find that the discussion of environmental topics in the earnings conference calls of U.S. public firms is associated with higher pollution abatement and more future green patents. Similarly, the discussion of social topics is positively associated with improved employee ratings. The association with E\&S performance is weaker for firms that give more non-answers and when the topic is immaterial to the industry. Overall, our results provide some evidence that firms do walk their talk on E\&S issues. In the final essay, we address the limitations of generic training schemes in the realm of financial language models. We propose a novel domain specific Financial LANGuage model (FLANG) which uses financial keywords and phrases for better masking. We further extend it to include span boundary objective and in-filing objective, utilizing the fact that many financial terminologies are phrases. Additionally, the evaluation benchmarks in the field have been limited. To this end, we contribute the Financial Language Understanding Evaluation (FLUE), an open-source comprehensive suite of benchmarks for the financial domain. These include new benchmarks across 5 NLP tasks in financial domain as well as common benchmarks used in the previous research. Experiments on these benchmarks suggest that our model outperforms those in prior literature on a variety of NLP tasks.

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Essays on the Wisdom of the Crowd in Crowdfunding

2023-07-31 , Yao, Jiayu

The rapidly growing crowdfunding has redefined financial behaviors and revolutionized traditional industries such as banking. My dissertation studies crowd behaviors in online crowdfunding and the impact of crowdfunding on entrepreneurial development. In my first essay, I propose several easily scalable variables derived from the heterogeneity of investors’ bids in terms of size and timing. I show that loans funded with larger bids relative to the typical bid amount in the market, or to the bidder’s historical baseline, particularly early in the bidding period, are less likely to default. More importantly, these variables improve the predictive performance of state-of-the-art models that have been proposed in this context. In my second essay, I study the impact of peer behavior information display on lenders’ decision-making in crowdfunding. Utilizing two online controlled experiments and a real-world dataset, I examine lenders’ platform abandonment, decision time, investment willingness, and risk preference under different display formats of peer information. The results call attention to the potential information overload that detailed peer information may cause to investors: they are more likely to abandon the platform and require a longer decision time when presented with prior investment transactions. The results also highlight the benefits and risks of aggregated peer information: Compared with completely no peer information or extensive peer information, a moderate amount of peer information saves lenders’ decision time, increases participation rate, and also amplifies the influence of peers. In my third essay, I study if features from crowdfunding projects can predict entrepreneurs’ mass market potential. I also examine if and how market reactions, especially their non-financial aspects, contribute to the prediction of mass market potential. I build classification and interpretable machine learning models to predict and explain entrepreneurs’ market success using project and crowd factors of entrepreneurs’ crowdfunding campaigns. The results suggest that crowd features, especially non-financial features, play an important role in predicting mass market launch and market evaluation. The analyses of non-financial features suggest that crowdfunding success does not always translate into mass market success. Taken together, the dissertation contributes to a better understanding of crowd intelligence in crowdfunding and its value.

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