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Scheller College of Business

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Now showing 1 - 10 of 506
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    Towards a Better Future of Work: Enhancing Efficiency, Promoting Fairness, and Upholding Labor Standards in the Contemporary Workplace
    (Georgia Institute of Technology, 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 Fintech, AI, and Innovation in Finance
    (Georgia Institute of Technology, 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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    A Threat or a Promise?: Essays on Consumer Perception of Emerging Marketplace Technologies
    (Georgia Institute of Technology, 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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    Essays on Resources and Innovation
    (Georgia Institute of Technology, 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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    Essays on the Wisdom of the Crowd in Crowdfunding
    (Georgia Institute of Technology, 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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    Information Sharing and Operational Transparency on On-Demand Service Platforms
    (Georgia Institute of Technology, 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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    Essays on Strategic Use of Intellectual Property Rights
    (Georgia Institute of Technology, 2023-05-02) Ortega Moncada, Leonardo G.
    This dissertation examines various aspects of firms' strategic responses in the management of their intellectual property rights to external factors, with each chapter focusing on a different aspect of this complex topic. The first chapter of the dissertation investigates whether markets for technology can provide an alternative to in-house innovation as a response to foreign escape competition. The findings indicate that while external technologies are important for innovative firms, low-productivity firms experience a negative impact on the demand for external technologies when exposed to import competition. The second chapter focuses on the tradeoff incumbents face in using patents as barriers to entry or as ex-post responses to competitors' entry moves. Using the U.S. pharmaceutical industry as the main empirical setting, this chapter shows that incumbents intentionally fragment and delay the full disclosure of their intellectual property rights through continuation patents. They disproportionately reveal continuation patents after a competitor entry threat becomes concrete, tailoring their response to the threat they have received and successfully delaying competitor entry through litigation. Finally, the third chapter investigates how firms manage information asymmetry in their patent prosecution source using exposure to patent litigation. This chapter shows that firms exposed to patent litigation are more likely to change the sourcing of patent prosecution legal services relative to unexposed firms working with the same prosecuting law firm.
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    Employee Evaluation and Performance Management
    (Georgia Institute of Technology, 2023-04-27) Green, Christopher M.
    The three essays in this dissertation focus on understanding how the structural policies and organizational procedures set by management may impact the performance of the workforce. Specifically, this dissertation: (i) employs analytical (e.g. game-theoretical and optimization models) and experimental techniques and (ii) encompasses the following three areas: performance management systems, rating systems, and sequential selection. Performance and Talent Management: The U.S. Army is at an inflection point with its talent management process. The Secretary of the Army has stated that a focused priority of the Army is to revolutionize the decades old process that was established in the 1980s under the Defense Officer Personnel Management Act (DOPMA) which has fallen woefully behind current talent and personnel management programs. One of the most promising areas for change is in the arena of performance management. To support this effort, I review performance management and evaluation systems studied in literature and currently in use within industry to identify how organizational and structural characteristics impact their effectiveness. I then apply this analysis to the unique case of the U.S. Army. The procedural limitations and legal constraints set on the military services make understanding and incorporating the factors of the external environment and organization structure critical to constructing a performance management system that meets the unique needs of these organizations. Rating Systems: Performance evaluation has long been a key mechanism for supervisors to grade the proficiency of employees at executing the tasks associated with their jobs. To conduct these evaluations firms often rate workers compared to their peers or against an objective standard. Which of these rating systems leads to higher workforce performance? To answer this question, I construct game-theoretic models of two performance rating systems. First, for a Relative rating system where workers compete with each other for a constrained number of high ratings. Second, for an Absolute rating system where workers are awarded high ratings by performing at or above a standard threshold. I derive the workers’ equilibrium performance as a function of their ability and the characteristics of the rating pool. From a firm’s perspective, I find that an Absolute rating system can lead to higher performance than a Relative rating system when the rating pool size is small or the workers’ cost of effort relative to their efficiency rate is low, and the reverse holds true otherwise. When considering the workers’ perspective, I find that higher ability workers prefer an Absolute system due to its predictable nature, while lower ability workers prefer a Relative system as it provides them an opportunity to outperform other workers. Sequential Selection: Enhancing workforce performance is the key to success for professional firms. Many firms employ competitive rating systems where supervisors can only award promotions or bonuses to a certain percentage of their subordinates. In many cases, such as the evaluation system of the U.S. Army, supervisors evaluate subordinates' performances over time and in sequence (e.g., based on employee’s work anniversary). As such, supervisors must make decisions based on incomplete information due to the temporal nature of the evaluation process. In this paper, I study how managers react under such sequential evaluation systems. I construct a theoretical model of a sequential selection problem to generate the optimal solution. I then conduct a set of experimental studies and evaluate the impact of pool size on the accuracy of each participant's decisions as compared to the state dependent optimal solutions. Despite theoretical increases in performance with larger pools, experimental performance did not yield an increase. Indeed, the average performance of subjects was the highest in the treatment that had the smallest pool size. I conduct multiple decision mechanism analyses to provide insights about the approaches subjects take and the nature of the behavioral traits leading to sub-optimal outcomes. Those comparisons suggest that the search fatigue mechanism may account for subjects’ sub-optimal behavior across treatments.
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    Essays on Digital Goods and Online Markets
    (Georgia Institute of Technology, 2023-04-25) Hu, Hao
    Information technology has revolutionized the way in which sellers engage with potential customers and distribute their products through online channels. However, they also face increasing challenges to remain competitive. For example, in the software industry, the plethora of available applications leads to a highly competitive landscape, making it difficult for new entrants to gain visibility and attract consumer interest. For online platforms, the platform owner not only serves as an intermediary for sellers and buyers but also introduces its own private-label products, further intensifying competition with third-party sellers. This dissertation investigates the strategic actions sellers undertake to tackle these challenges. In the first essay, we build a game-theoretical model to examine two prevalent strategies, seeding and time-limited freemium, that developers can employ to spur adoption by helping consumers directly or indirectly learn the value of their products. We offer managerial recommendations on the optimal circumstances for implementing each strategy, considering factors such as social and self-learning dynamics, adoption costs, and product value depreciation. In the second essay, we study the impacts of Amazon launching its private-label products and engaging in self-preferencing for these products on third-party sellers. Our findings show that although Amazon favors its own products in search results, the average sales of third-party products in the affected categories increase more than those in unaffected categories. We then investigate several mechanisms that could contribute to this change. We find that Amazon's private-label products displace lower-quality sellers, foster variety in product designs, and serve as valuable references for third-party sellers to improve their searchability. These factors potentially lead to higher sales and ultimately an increase in consumer welfare, with little impact on prices.
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    Essays on responsible and sustainable finance
    (Georgia Institute of Technology, 2023-04-25) Malakar, Baridhi
    The dissertation consists of three essays on responsible and sustainable finance. I show that local communities should be seen as stakeholders to decisions made by corporations. In the first essay, I examine whether the imposition of fiduciary duty on municipal advisors affects bond yields and advising fees. Using a difference-in-differences analysis, I show that bond yields reduce by 9\% after the imposition of the SEC Municipal Advisor Rule. Larger municipalities are more likely to recruit advisors after the rule is effective and experience a greater reduction in yields. However, smaller issuers do not seem to significantly benefit from the SEC Rule in terms of offering yield. Using novel hand-collected data, I find that the average advising fees paid by issuers does not increase after the regulation. In the second essay, we analyze the impact of \$40 billion of corporate subsidies given by U.S. local governments on their borrowing costs. We find that winning counties experience a 15.2 bps increase in bond yield spread as compared to the losing counties. The increase in yields is higher (18 -- 26 bps) when the subsidy deal is associated with a lower jobs multiplier or when the winning county has a lower debt capacity. However, a high jobs multiplier does not seem to alleviate the debt capacity constraints of local governments. Our results highlight the potential costs of corporate subsidies for local governments. In the third essay, we provide new evidence that the bankruptcy filing of a locally-headquartered and publicly-listed manufacturing firm imposes externalities on the local governments. Compared to matched counties with similar economic trends, municipal bond yields for affected counties increase by 10 bps within a year of the firm’s bankruptcy filing. Counties that are more economically dependent on the industry of the bankrupt firm are more affected and do not immediately recover from the negative impact of the corporate bankruptcy. Our results highlight that local communities are major stakeholders in public firms and how they are adversely affected by corporate financial distress. The final 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.