Business administration

Permanent URI for this collectionhttps://digital.lib.washington.edu/handle/1773/4902

Browse

Now showing 1 - 20 of 201

Ideology-Driven Social Media Opinions and Market Responses to Polarizing Boycotts
(2026-02-05) Li, Xue; Blankespoor, Elizabeth
Boycotts triggered by public companies’ practices perceived as ideologically polarizing can lead to negative investor reactions. In this study, I examine how the stock market responds to such boycotts and whether ideology-driven social media discourse shapes this response, given investors’ increasing reliance on social media information for decision-making. On average, polarizing boycotts are associated with a 1% (2.3%) drop in equity value over the 7 (60) trading days after gaining online traction. Immediate price decline is more pronounced when social media discussions are dominated by users ideologically aligned with the boycotters, particularly when their posts attract online engagement, emphasize financial impact, or come from influential, prolific users. I also find modest evidence that return volatility following boycotts increases when the ideological beliefs of social media posters are more diverse. My findings suggest that polarizing boycotts against corporate actions have stock market ramifications, and that ideology-driven social media opinions seem to amplify both price decline and volatility.
UNDERSTANDING AND ADDRESSING PSYCHOLOGICAL BARRIERS TO RECEIVING AND RESPONDING TO CHALLENGING VOICE REGARDING SOCIAL INEQUALITY IN ORGANIZATIONS
(2025-10-02) Banerjee, Anusuya; Johnson, Michael; Hafenbrack, Andrew
Despite increasing awareness of issues related to social inequality in the workplace and the proliferation of efforts to combat barriers to equality between social groups, members of non-dominant social groups continue to be disadvantaged within organizations. In my dissertation, I focus on one key barrier to transforming practices, norms, systems and structures which sustain and perpetuate inequality between different social groups: the psychological threat of individuals who are gatekeepers to change. Drawing upon social psychological theories of self-concept threat, as well as theory and evidence in the voice and feedback literatures, I explore recipient-driven factors which shape responses to challenging voice regarding social inequality through two papers. In Paper 1, I examine individual response to challenging voice regarding gender inequality by varying the experience of self-concept threat. I also examine how values, beliefs, and preferences for growth may moderate the experience of self-concept threat. Results indicate that the impact of increased self-concept threat exposure varies in terms of gender-based advantage – with men responding more negatively under increased threat exposure. However, the impact of self-concept threat on response is moderated by individual values of growth for both men and women (albeit in different ways). In Paper 2, I compare the response of advantaged versus disadvantaged racial group members to challenging voice regarding racial inequality, and I examine how experiences of threat as opposed to empathy explain differences in response. I argue that empathy for the experience of being disadvantaged explains differences in response more than threat, and that this empathy deficit cannot be explained simply by ingroup favoritism/outgroup bias or anti-egalitarian beliefs. To interrogate the idea that the worsened response of advantaged group members can be explained by racial ingroup favoritism and bias against racial outgroups I vary the race of the voicer, control for anti-egalitarian beliefs and analyze differences in advantaged group member response at different levels of socioeconomic status. Results indicate that poorer ratings of voice and voicers by advantaged racial group members persist even after controlling for anti-egalitarian beliefs. Findings suggest that empathy mediates the relationship between advantaged racial group membership and voice response more than threat, but this empathy deficit may be greater for higher SES advantaged racial group members.
Foundations and Fault Lines: A Theorization of Certification Emergence Patterns and the Latent Hazards Facing New Ventures
(2025-10-02) Taylor, David Scott; Sirmon, David
Certifications are powerful institutional mechanisms that shape markets and guide firm behavior. However, we know little about their emergence patterns or whether they truly benefit the firms that adopt them. In this dissertation, I theorize how market ambiguity and stakeholder pressure set the stage for institutional agents to construct certification schemes. This combination influences whether emergence occurs and the design of the measurement scheme. I then examine the counterperformative potential of certification through an embedded case study of ethical certifications in the cosmetic industry. I use interviews, archival data, and direct observation to trace how certifications cascade through value chains, embedding heuristics and processes that can later backfire. I contribute to certification research by theorizing patterns of emergence and how certifications embed and activate latent hazards. In doing so, I illuminate the institutional work of agents and the tensions entrepreneurs face between securing legitimacy and maintaining flexibility.
Causal Inference and Decision Making on Digital Platforms
(2025-08-01) Zhang, Jingwen; Tan, Yong
This thesis examines advanced methodological approaches for addressing complex inference and decision-making challenges in digital and economic environments characterized by uncertainty, measurement error, and endogeneity. The development of robust methodological solutions in this domain is increasingly critical as organizations rely on algorithmic decision-making for high-stakes business decisions, platforms design experimentation systems for continuous optimization, and researchers leverage AI-generated variables for econometric analyses. Traditional methods often fail in these contexts, potentially leading to biased inferences, suboptimal decisions, and misguided strategies that can significantly impact business outcomes and research validity. Through three interconnected essays, this thesis demonstrates novel frameworks that enhance both theoretical understanding and practical applications in causal inference and decision making for online and offline settings, providing essential methodological tools for researchers and practitioners navigating the complexities of modern digital economics. The first essay introduces the $\epsilon$-BanditIV algorithm to address endogeneity issues in online dynamic decision-making contexts. By incorporating instrumental variables into the Multi-Armed Bandit (MAB) framework, this approach achieves both optimal regret minimization and asymptotically consistent parameter estimation, establishing a methodological foundation for unbiased causal inference in adaptive experimentation settings with endogenous covariates. The second essay addresses the critical challenge of measurement error in machine learning (ML) or artificial intelligence (AI) generated regressors within partially linear models. By developing estimators that utilize Two-Stage Least Square (TSLS) and Generalized Method of Moments (GMM) under the Double Machine Learning (DML) framework, this work provides a robust solution for debiasing predictions, advancing causal inference where ML/AI tools are used to generate feature variables for econometric analysis. The third essay investigates decision-making processes in livestream e-commerce, focusing on hosts' product selection and presentation timing behaviors. Using structural models that combine online learning frameworks with survival analysis, this research reveals how livestream hosts balance exploration-exploitation trade-offs and optimize product transitions to maximize sales performance, providing both theoretical insights and practical recommendations for livestream commerce optimization. Together, these essays contribute to advancing methodological approaches across econometrics, machine learning, and information systems, offering frameworks that address fundamental challenges in causal inference and decision-making. The first two essays develop theoretical solutions to estimation challenges that threaten causal validity in both sequential learning environments and static models with ML/AI-generated variables. The third essay then demonstrates how structural modeling of online learning processes can reveal opportunities for improved decision-making in practice. This complementary relationship between theoretical advances in causal inference and empirical applications in decision optimization creates a cohesive contribution with significant implications for marketing, e-commerce, and digital platform design in environments characterized by dynamic interactions, endogeneity concerns, and imperfect information.
Nationality-Based Homophilic Preferences Against U.S. Venture Capital Firms in the Chinese Venture Capital Industry
(2025-08-01) Xia, Wei; Steensma, Kevin
My dissertation focuses on homophilic preferences based on shared nationality and includes two papers. The first paper focuses on the formation of venture capital investment syndicates. Both Chinese and U.S. investment firms generally prefer including their fellow compatriot firms over comparable non-compatriots in the syndicates that they assemble. When a Chinese investor initiates a collaborative first move by including a U.S. investor in a syndicate, however, the U.S. investor no longer prefers comparably familiar U.S. investors over the Chinese investor when it subsequently chooses among prospective partner firms to include in its investment syndicates. In such cases, familiarity triggers impartiality; the experiential trust that was garnered from the collaborative first-move engagement initiated by the Chinese investor diminishes the nationality-based homophilic preferences of the U.S. investor. Similar dynamics when the tables are turned are not found. When a U.S. investor initiates a collaborative first move by including a Chinese investor in a syndicate, the Chinese investor subsequently remains partial to fellow compatriot firms that are otherwise comparable to the U.S. investor. The homophilic preferences and identity-based trust between Chinese investment firms grounded in shared nationality are resilient to any goodwill created by U.S. investment firms when they initiate collaborative first moves. Shifting to new ventures’ perspectives, the second paper shows that Chinese new ventures seeking funding may also be biased against U.S. investment firms. Although conventional wisdom suggests that foreign firms can overcome such bias by being highly qualified, this study suggests otherwise. It finds that Chinese new ventures are more inclined to choose a Chinese investment firm as their initial venture capital investor over more qualified U.S. firms when these competing investors are of higher quality. When competing investors are of high quality, any sacrifice in investor quality that a new venture makes by rejecting a more qualified U.S. firm is relatively inconsequential in proportion to a somewhat lesser qualified Chinese firm. To affirm that the empirical results are due to a distaste toward U.S. investors due to their foreignness, this study shows that they are significantly stronger for Chinese new ventures embedded in provinces that are less cosmopolitan and, thus, less accepting of foreigners in general.
Under Pressure: Essays on Stakeholder Suppression as a Stakeholder Engagement Tactic
(2025-08-01) Prabhu, Sanjana Ganesh; Tan, David
A fundamental question in non-market strategy literature is how firms engage with non-market actors to avoid accountability for socially costly activities and outputs. While the literature has explored tactics like cooperation, lobbying, compliance, and voluntary disclosures, it has largely ignored the use of comparatively antagonistic tactics such as suppression. This dissertation explores the threat of suppression tactics and how this threat affects the generation (or abatement of generation) of environmental negative externalities. Across three empirical chapters, I focus on how the threat of litigation, in particular, influences the generation of a relatively well-studied externality: toxic waste. In the first chapter, I examine whether firms change how toxic waste is managed when they have less power to threaten stakeholders through litigation. I find that, following the introduction of constraints on the ability to effectively use litigation to stifle dissent, facilities shift how the toxic waste generated through the production process is managed rather than the overall generation of that waste. I find that the shift in how waste is managed is driven by the expected stakeholder concerns and the potential to shift liability. In chapter 2, I continue my examination of the effect of the introduction of constraints on the ability to effectively use litigation as a suppression topic. In this chapter, I leverage the fact that these constraints are introduced at different times at a state level and examine how firms leverage that heterogeneity across jurisdictions. I focus specifically on how this heterogeneity affects the movement of waste from the source locations to other offsite locations. I find that following the introduction of constraints on the effectiveness of litigation as a suppression tactic, facilities significantly increase the transfer of waste to locations without those constraints and significantly decrease the transfer of waste to locations with these constraints. This suggests that facilities leverage the state-level heterogeneity in the introduction of these constraints and engage in regulatory arbitrage. In chapter 3, I move away from the context of constraints on the effectiveness of litigation as a suppression tactic and focus on the threat of litigation itself. I explore how regulatory scrutiny may lead to unintended consequences and how prior filings of litigation may be perceived by the firm as a barrier to regulatory scrutiny. I leverage county-level designation data based on the National Ambient Air Quality Standards (NAAQS) for ground ozone. I find that following increased regulatory scrutiny related to ground ozone, facilities significantly increase the generation of pollutants that are not associated with the scrutiny. I do not find that the prior filings of litigation influence the generation of non-ozone-related pollution following increased scrutiny. Together, these chapters examine how the threat of litigation may be perceived as a means to suppress dissent and how the costs associated with that threat affect the generation and management of negative externalities.
Stochastic Optimization in Disaster Operations Management: Medical Capacity Planning During Epidemic, Optimal Subsidy Policy For Renewal Technology Adoption and Data-driven Robust Supply Chain Optimization
(2025-08-01) Rayal, Swapnil; Jain, Apurva
Each of the problems addressed in this thesis falls under the broader umbrella of disaster operations management, where the focus is on minimizing the impact of unforeseen disruptions on critical systems. Whether it’s managing the surge in healthcare demand during a pandemic, promoting sustainable energy adoption to mitigate long-term environmental crises, or optimizing supply chains to withstand production disruptions, the core challenge remains the same: to design resilient systems capable of navigating uncertainty and resource constraints. These problems highlight the need for well-informed, strategic decision-making to ensure that essential services continue to function even in the face of crises. The first problem deals with managing medical equipment capacity during the early spread of an infection in a region, a critical research problem during COVID pandemic. After a brief introduction to infectious disease modeling, we develop a model for a regional decision- maker to analyze the requirement of medical equipment capacity in the early stages of a spread of infections. We use the model to propose and evaluate ways to manage limited equipment capacity. Early stage infection growth is captured by a stochastic differential equation (SDE) and is part of a two-period community spread and shutdown model. We use the running-maximum process of a geometric Brownian motion to develop a performance metric, probability of breach, for a given capacity level. Decision-maker estimates costs of economy versus health and the time till the availability of a cure; we develop a heuristic rule and an optimal formulation that use these estimates to determine the required medical equipment capacity. We connect the level of capacity to a menu of actions, including the level and timing of shutdown, shutdown effectiveness, and enforcement. Our results show how these actions can compensate for the limited medical equipment capacity in a region. We next address the sharing of medical equipment capacity across regions and its impact on the breach probability. In addition to traditional risk-pooling, we identify a peak-timing effect depending on when infections peak in different regions. We show that equipment sharing may not benefit the regions when capacity is tight. A coupled SDE model captures the messaging coordination and movement across regional borders. Numerical experiments on this model show that under certain conditions, such movement and coordination can synchronize the infection trajectories and bring the peaks closer, reducing the benefit of sharing capacity. We then turn our attention to the problem of devising optimal subsidy to encourage adoption of solar technology in a region. Each household in a population characterized by income heterogeneity faces random demand for electricity and decides if and when it should adopt a solar product, rooftop solar or community solar. A central planner, aiming to meet an adoption level target within a set time, offers net metering and subsidy on solar products and minimizes its total cost. Our focus is on analyzing the interactions of three new features we add to the literature: income diversity, availability of community solar, and consideration of adoption timing. We develop a bilevel optimization formulation to derive the optimal subsidy policy. The upper level (planner’s) problem is a constrained non-linear optimization model in which the planner aims to minimize the average subsidy cost. The lower level (household’s) problem is an optimal stopping formulation, which captures the adoption decisions of the households. We derive a closed-form expression for the distribution of optimal adoption time of households for a given subsidy policy. We show that the planner’s problem is convex in the case of homogeneous subsidy for the two products. Our results underscore the importance for planners to consider three factors - adoption level target, time target, and subsidy budget - simultaneously as they work in tandem to influence the adoption outcome. The planners must also consider the inclusion of community solar in their plans because, as we show, community and rooftop solar attract households from different sides of the income spectrum. In the presence of income inequality, the availability of community makes it easier to meet solar adoption targets.The third problem we consider is a classic mixed integer programming formulation for sup- ply chain optimization of a semi-conductor supplier in presence of supply disruptions. We provided a deterministic formulation for optimal outbound network routing. We integrate the past publicly available data in the optimization model and show the shortcomings of its optimal solution using a numerical experiment. We then designed a practical robust formulation to handle uncertain production capacity. We numerically study the optimal solutions for low and high tolerance to deviation from deterministic optimal solution and generate optimal production and transportation plan which is not only more resilient to random shocks but also requires marginal change from current plan.
Mental Health in the Modern Workplace: An Exploration of Individual and Relational Consequences of Employee Depression
(2025-08-01) Kaur, Ekonkar; Fehr, Ryan
Employee mental health is an increasing topic of conversation and concern in today’s workplace. This dissertation consists of two chapters that explore employee mental health, specifically depression, as an important determinant of both individual and relational workplace consequences. In the first chapter, I seek to understand how remote work uniquely impacts employees who are experiencing depression. Results from three studies, using both archival and online panel data, found that remote work does not uniquely hinder a depressed employee’s interpersonal well-being, but had inconsistent detrimental impacts to their intrapersonal well-being. In the second chapter, I seek to understand how the coworker of a depressed employee is perceived by another coworker when they attempt to engage in—and involve others in—social support behaviors. Across three experiments using both online panel and field data sources, results highlighted the critical role of depression stigma in determining the extent to which social support behaviors are evaluated as empathetic and appropriate. This dissertation extends an understanding of the ways in which depression as a mental health condition is consequential to both the employee experiencing the condition, as well as their coworkers and surrounding climate.
Let My People Go Hunting and Gathering: How Alaskan Employees Create Sustainable Careers Balancing Traditional Heritage Work and Wage Employment
(2025-08-01) Quan, Xiaoshi; Schabram, Kira; Barnes, Christopher
Born to “walk in two worlds”, Indigenous employees in Alaska sit at the intersection of dominant U.S. culture and Alaska Native culture. Drawing from in-depth interviews, a month-long ethnographic participant observation in rural Alaska, and archival data from employees and employers, I explore how workers conceptualize modern and traditional careers and sustainably navigate multiple possible livelihood strategies amid rapid change. From my analysis emerged two diverging individual approaches: In “cultural modeling ”, workers view traditional work as an inherited right, prioritize subsistence hunting and gathering, and engage in employment for financial needs; in “cultural translating”, workers view heritage practices as privileges to be earned, put wage work first, and only prioritize subsistence seasonally. I illustrate the importance of organizational and communal support in maintaining this balance, as well as the individual and societal consequences when the balance fails. This research advances theories in tradition, sustainable careers, culture, and Indigenous management literature by developing a theory to explain how workers balance traditional and modern work approaches. It examines how individuals leverage temporal, ecological, organizational, and social resources to create sustainable careers that fulfill obligations across multiple domains, and demonstrates how societal culture shapes individuals' conceptualization of careers and daily domain prioritization decisions.
Cultivating Diverse Perspectives or Inhibiting Them? The Unexamined Consequences of Leader Voice Solicitation for Organizations and Their Members
(2025-08-01) Middlebrook, Blair; Farh, Crystal
In this dissertation, I examine the practice of leader voice solicitation—leaders’ individually-directed requests to employees for improvement-oriented ideas—and consider its value as a means for transforming employees’ unique perspectives into organizational value. Departing from the prior literature’s focus on the extent to which leader voice solicitation promotes greater voice quantity, I assess the resulting organizational value of this behavior through the outcome of voice quality. I hypothesize that leader voice solicitation can positively affect voice quality by increasing felt social worth, yet also argue that women experience this positive effect to a lesser extent due to the gender-based challenges to voice that they can face. I further describe the specific mechanisms behind these gendered inequities and explore potential mitigating factors to present a theoretical model of these hypothesized effects. Using a set of newly developed and validated scales, I test my hypotheses in two empirical studies. Study 1 consisted of a three-wave, multi-source field survey in a male-dominated organizational setting, and Study 2 consisted of in-person, interactive laboratory experiment featuring manipulations of leader voice solicitation through the work of a professional actor. Results from the two studies indicate that leader voice solicitation is indeed associated with higher voice quality, suggesting that this leader behavior can offer meaningful organizational value, via the performance-enhancing potential provided by voice. Additional analyses provide further insight into the differences in these effects for men and women under varied conditions and uncover numerous opportunities for future research. Taken together, this dissertation offers valuable contributions to the leader voice solicitation literature, as well as to the voice, diversity, gender, and inclusion literatures.
Understanding Human–Generative AI Interaction
(2025-08-01) Ma, Lijia; Tan, Yong
This thesis offers a thorough analysis of multiple scenarios of human-generative AI interaction, including large-language-model-based search engine optimizations, heterogeneous adoption of generative AI, and the impact of generative AI tools on work. First, I investigate how LLM-powered chat search, exemplified by Bing Chat, selects its information sources. By comparing thousands of Bing Chat citations with traditional search results, I show that it systematically prefers text that is highly readable, well-structured, and of low perplexity. I replicate these findings using a GPT-4 RAG API, demonstrating that these preferences arise from the language models themselves rather than from bespoke engineering. I also find that RAG-cited sites are more homogeneous than those surfaced by classic search algorithms. These insights highlight the distinctive sourcing behavior and economic implications of chat-based search engines. Second, I examine how recent generative AI advances such as ChatGPT create a digital divide. Specifically, I distinguish between a learning divide, referring to differences in how quickly users update beliefs about ChatGPT’s value, and a utility divide, referring to variation in actual per-use benefit. By estimating a Bayesian learning model on six months of clickstream data, I find that lower-educated and non-white users gain greater utility per use but update their beliefs more slowly, whereas younger, male, and IT-background users excel on both fronts. I also identify a belief trap in which persistent underuse results from underestimated utility, and I show that targeted training can reduce this outcome divide. Third, I investigate how daily engagement with large-language-model-based generative AI tools reshapes the structure and focus of work. Drawing on clickstream records, I conduct an empirical analysis of the effect of using generative AI tools on working duration and distraction levels. I find that engagement with these tools significantly increases working hours while simultaneously reducing distraction rates. These findings offer practical guidance for employers and individual employees considering the adoption of generative AI tools for work. Taken together, this thesis lays a solid foundation for future research on human-generative AI interactions and on the broader challenges of AI alignment.
The Role of Networks in FinTech Applications
(2025-08-01) Liu, Ye; Tan, Yong
Networks are increasingly central to how individuals make investment decisions, how prices are formed, and how platforms design user experiences in the context of modern financial technologies (FinTech). Rather than viewing market participants in isolation, this dissertation examines how surrounding social structures shape economic outcomes across three FinTech applications: social trading, cryptocurrency, and non-fungible tokens (NFTs). Each essay highlights how network position, peer behavior, and relational dynamics can influence attention, adoption, and valuation — often in ways that rival or exceed the impact of individual intrinsic characteristics. First, rather than focusing solely on individual attributes such as financial performance, I examine how strategy similarity among traders affects their popularity. I construct dynamic similarity networks and use a coevolution model to analyze how traders’ positions within these networks shape follower growth. I find that as more peers adopt similar strategies to a focal trader, that trader tends to attract fewer followers over time. This substitution effect is particularly strong for high-performing traders. Counterfactual simulations suggest that promoting a more even distribution of followers and reducing network density can mitigate substitution and increase overall platform engagement. This suggests that platform design should consider not only individual performance but also the network positioning of users to foster diversity and sustain long-term engagement. Second, I investigate how social learning drives cryptocurrency adoption. Using a large dataset from a social trading platform, I analyze how investment opinions and behaviors of network neighbors influence an individual’s decision to trade Bitcoin. The results show that people are more likely to adopt Bitcoin when their peers’ actions align with their stated views. Under higher uncertainty, individuals rely more on optimistic opinions, even if behaviors are not consistent. This suggests that social media plays a dual role — as a source of behavioral signals and emotional reinforcement — particularly when individuals face ambiguity. It also highlights the importance of credibility and alignment between opinion and action in peer influence. Third, I study the role of transactional networks in shaping NFT value. Drawing on data from a leading NFT platform, I construct monthly networks linking NFTs based on shared traders and utilize a permutation-invariance-based method to circumvent the dimensionality issue of network data. The results show that incorporating transactional network information substantially improves NFT price prediction, beyond what image or metadata features can explain. This chapter highlights the importance of social dynamics in digital asset valuation and offers implications for both pricing models and platform design. Together, these chapters demonstrate that financial decision-making and asset valuation cannot be fully understood without accounting for the social context in which they occur. Whether through imitation and substitution among traders, peer-driven learning under uncertainty, or value formation via transactional ties, networks serve not just as conduits of information but as fundamental structures shaping market outcomes. By integrating structural modeling, behavioral analysis, and machine learning on large-scale digital traces, this dissertation offers new insights into how financial technologies operate when embedded in social systems. These findings contribute to emerging conversations at the intersection of finance, platforms, and computational social science, and underscore the need for theories and tools that reflect the relational nature of modern digital markets.
Interactivity and Illusions of Ability: How Using Generative AI Affects Investor Judgments
(2025-08-01) Croom, Joseph; Grant, Stephanie
In this study, I use the setting of Generative AI (GenAI) to examine how processing tool interactivity affects investors’ self-assessments of ability and willingness to invest. Although GenAI can help investors process financial information, I theorize that the interactive nature of GenAI blurs the boundaries between investors’ own abilities and those of GenAI, prompting investors to discount their reliance on GenAI and misattribute its abilities to themselves. I rely on the advantages of a laboratory setting to disentangle the interactive element of GenAI from the mere presence of GenAI assistance. Across three experiments, I find that the interactivity underpinning GenAI heightens investors’ self-assessments of their own abilities and increases their willingness to invest, despite this interactivity not improving, and in fact hindering, their actual processing of information provided by GenAI. My study thus highlights one potential cost of using GenAI and other highly interactive processing tools.
Essays on Index Investing
(2025-08-01) Nurisso, George; Levit, Doron
This dissertation consists of two chapters about the economic consequences of index investing. The first chapter examines the impact of index investing on short selling. Short sellers convey negative information to securities lenders when borrowing shares. I model how this information generates novel interactions between institutional investors’ equilibrium lending, trading, and governance decisions. Index funds lenders cannot trade on lending market information, allowing them to attract more shorting demand and thereby improve price efficiency—despite increasing lending fees. The second chapter explores how index investing impacts managerial compensation in a moral hazard setting. With index investing, effort is reflected not only in the manager’s stock price but also in the stock prices of all other constituent firms through the synchronized asset demands of index investors. Thus, the prices of other constituent firms are positively related to and contain unique information about the manager’s effort. The optimal contract puts a positive weight on the index’s price to enhance the effort sensitivity of the manager’s pay, not to reduce risk.
The Evolving Retail Ecosystem: Spatiotemporal Data, Partnerships, and Crowd Wisdom for Strategic Advantage
(2024-10-16) Zhang, Mingrui; Tan, Yong
The retail landscape is undergoing a profound transformation driven by technological advancements, changing consumer behaviors, and intensifying competition between online and offline channels. This dissertation explores three critical aspects of this evolving ecosystem: leveraging spatiotemporal data for strategic decision-making, forging partnerships between physical retailers and e-tailers for consumer returns, and harnessing crowd wisdom in the crowdfunding context. Chapter 3 employs tensor completion methods to estimate the treatment effect of deploying smart vending machines across various urban settings, demonstrating the superiority of this approach over traditional causal inference techniques. Chapter 4 investigates the impact of a returns partnership between a physical retailer and an e-tailer on their demand-side competition, revealing conditions under which both firms can benefit, but consumer surplus may decrease. Chapter 5 examines how atypical idea combinations on crowdfunding platforms influence project success, identifying an optimal balance between familiarity and novelty. Collectively, these chapters contribute to the literature on retail strategy, data-driven decision-making, and platform economies, offering valuable insights for firms navigating the complex and dynamic retail ecosystem.
Strategic Decisions at the Intersection of AI and Marketing
(2024-09-09) Smith, Evelyn Olivia; Shulman, Jeffrey D
Artificial Intelligence (AI) systems are widely adopted by firms to automate tasks such as recommending content, creating advertisements, and conducting marketing research. The primary objective of this research is to broaden our understanding of the interactions among firms, consumers, and AI. This objective is achieved through the development and analysis of a game theory model of firm and consumer reactions to an algorithmic policy, and through the analysis of portrait graphics created by generative AI. Modeling the strategic behaviors of content firms and consumers, I show that an algorithmic constraint requiring recommendation equality between groups can benefit consumers from both groups, increase AI learning investment, or inadvertently harm the consumers it aims to protect. Using generative AI as an example, I empirically show how AI exhibits single-identity and intersectional biases in the depiction of portraits for different occupations. The research extends the marketing literature through theoretical contributions and provides implications of marketing decisions for firms and users interacting with AI systems.
Psychology of Disposal and its Influence on Consumer Behavior
(2024-09-09) Chang, Sylvia; Jain, Shailendra Pratap; Agrawal, Nidhi
Consumption is a multi-stage process encompassing acquisition, usage, and disposal, and the issues surrounding consumers' disposal are as complex and far-reaching as those of acquisition and usage. Yet research on disposal remains limited, making it imperative to study related issues not only to offer a richer perspective on disposal but also because mindless disposal has contributed to dire environmental and social problems. My dissertation aims to enhance the understanding of disposal by illuminating the psychology behind it, providing insights into how consumers conceptualize disposal and how this influences decision-making at various consumption stages. Chapters 1 and 2 consolidate past research on disposal and provide an integrative framework to help researchers make novel predictions and ask new questions. This framework incorporates ways in which disposal exerts influence across the entire consumption cycle and identifies illustrative questions that can guide the ongoing development of disposal research. This presents an opportunity for consumer research to claim ownership in a domain that is, by definition, a key aspect of consumption. Chapter 3 then demonstrates an example of how the integrative framework can be implemented to broaden the disposal literature by investigating how referencing disposal as part of product information (i.e., disposal reference) influences consumers' product evaluations at purchase. Across eight studies, I show that highlighting disposal at the point of product acquisition negatively impacts consumers' product evaluations by affecting perceptions of the product's wastefulness. I also demonstrate that morality underlies this disposal reference effect. Referencing disposal imbues the product with moral significance, an effect amplified among consumers with a strong moral identity. In documenting a novel effect of considering disposal on product evaluations at purchase, these findings advance our understanding of disposal, wastefulness, and morality. In the concluding chapter, I delineate disposal’s substantive import and discuss further research directions on disposal-related phenomena and areas of inquiry. Overall, my dissertation theoretically and empirically expands our understanding of disposal while highlighting its importance across consumers, businesses, society, and the ecosystem.
Investigating the Impact of Digital Transformation
(2024-09-09) Kan, Yu; Tan, Yong
Digital transformation is fundamentally changing the way companies operate and engage with their customers, leading to significant shifts in various aspects of society. This thesis offers a thorough analysis of the multifaceted impact of digital transformation on businesses and society by focusing on three distinct yet interconnected perspectives: the role of artificial intelligence (AI) in customer care, the influence of user-generated content (UGC) on informational accessibility and inclusivity, and the effect of crowdsourcing on operational efficiency. First, I study how the identity cues of AI agents influence customers' strategic expression of emotions and satisfaction in customer care interactions. By emphasizing the significance of transparency and the potential challenges of non-disclosure in AI-mediated customer care, this research contributes to the growing literature on the social and behavioral implications of AI in business settings. Second, the thesis examines the underrepresentation of plus-sized users in online reviews and its consequences for informational asymmetry and purchasing behavior. By highlighting the role of privacy concerns arising from societal stigma and the need for platforms to actively promote inclusivity, this research sheds light on the crucial issue of informational accessibility and inclusivity in the digital era. Third, the thesis explores how leveraging crowdvoting in assortment planning can enhance a subscription-based platform's operational performance and user engagement. By demonstrating the transformative potential of crowdsourcing in retail operations, this research contributes to the literature on the impact of digital transformation on business processes and customer engagement. By synthesizing insights from these three perspectives, this thesis contributes to the complex and multifaceted impact of digital transformation on businesses and society. It underscores the importance of balancing technological innovation with social responsibility, efficiency with equity, and progress with inclusivity. Ultimately, this thesis serves as a foundation for future research on the evolving impact of digital transformation, emphasizing the need for ongoing, interdisciplinary investigations that keep pace with the rapid advancements in technology and their far-reaching implications for business and society.
Decoding Corporate Green Bonds: What Issuers Do With the Money and Their Real Impact
(2024-09-09) Mao, Yufeng; Harford, Jarrad
I investigate the use of proceeds and the real impact of global corporate green bonds issued by non-financial firms, with a focus on greenhouse gas (GHG) emissions. The research reveals that green bond proceeds are allocated at a slower pace, are not used for shareholder payouts, and are less likely to be used for debt rollover compared to conventional bonds. This unveils a distinct motivation for issuing green bonds in contrast to conventional bonds. Employing market-level greenium as an instrumental variable in a Difference-in-Differences (DID) framework, I investigate the causal impact of green bond issuance on firm-level GHG intensity. Although improvements in GHG intensity are observed through Two-Way Fixed Effects (TWFE) and Event-study DID analyses, these improvements are not causally attributed to green bond issuance and are likely due to green initiatives that would have been funded regardless. I further explore the underlying mechanisms in this self-regulated market and find that repeat issuers voluntarily comply with the green bond framework, achieving tangible environmental improvements and giving credibility to the signal at issuance. The findings challenge the view that green bonds are simply conventional bonds with a “green” label and the view that green bonds causally lead to incremental sustainable outcomes.
Unpacking Data-driven Technologies and Resource Mobilization Landscape Shifts in New Ventures
(2024-09-09) Park, Sung ho; Hallen, Benjamin BLH
Over the last two decades, the entrepreneurial landscape has undergone significant change. New ventures now are in the presence of low-cost data-driven technologies, novel entrepreneurial support systems, and evolving fundraising landscapes. While these developments have made entrepreneurship more accessible, they have simultaneously increased the complexity of the venture creation and growth process. As a result, these changes raise important questions about how they affect a venture’s ability to gain traction, mobilize resources, and pursue growth. My dissertation examines several facets of these changes. In the first chapter, I examine whether and when data analytics technologies enhance the visibility of a venture. I find what while these technologies are beneficial on average, there is significant variation among ventures in terms of the informational value gained by data analytics and the founding team's capability to effectively leverage the technology. In the second chapter, with my co-authors, I examine the factors contributing to venture valuation, focusing on the relative explanatory power of environmental conditions, founding team characteristics, and venture progress. We find that a significant amount of variance in valuation is linked to current and previous investment rounds, whereas the contributions of the founding team and venture progress are relatively minor. Lastly, in the third chapter, with my co-authors, I assess the role of seed accelerators in helping ventures match with high-status investors. Our results indicate that while some seed accelerators are effective in facilitating these connections, others are not. Further analysis provides a nuanced view of the benefits of seed accelerators in the investment matchmaking process.

Browse

Recent Submissions