Over the years, there has been a pervasive stigma associated with remote work. Remote workers are typically conferred low status in their organizations and are afforded fewer resources than their "on-site" colleagues. Yet, the COVID-19 pandemic has shaped new conceptions of remote workers and the viability of remote work for the future. As more and more organizations are adopting remote and hybrid work and as remote workers are no longer a minority group in many organizations, remote workers seem to have gained relative status in their organizations. There is a new understanding that remote work is "real" work and workers seem to have more authority than ever before to adopt remote work arrangements. Yet we have minimal understanding of the microdynamics underlying how these shifts related to remote workers' status in organizations are playing out. This dissertation draws on ethnographic methods to examine the status-ridden processes through which workers come to be remote workers and hybrid workers (or fail to become such). It demonstrates how these status dynamics play out through the materiality of technology and through high-status actors' "status contests" and theorizes the less visible ways in which remote workers are gaining and losing status in their organizations. This two-chapter dissertation contributes to research on occupational jurisdiction, the sociology of classification, and remote work, while also offering practical implications aimed at helping organizational leaders make strategic decisions about remote and hybrid work moving forward

In recent years, activists and policymakers have become increasingly concerned about the use of data and algorithms in criminal justice settings, fearing that their use will reinforce demographic disparities and perpetuate punitive policies. This dissertation demonstrates how the careful application of such approaches also has the potential to reduce disparities and incarceration in a series of real-world applications. I begin by reviewing a collection of analytic techniques to identify unnecessary and discriminatory police stop practices. Next, I review two new prosecutor-oriented algorithms to aid the decision to charge or dismiss a case after an arrest has occurred. I subsequently review a group of algorithms and analyses intended to reduce the use of incarceration, including risk assessment instruments, pretrial behavioral nudges, and post-prison re-entry programs. I conclude the dissertation by discussing the larger context surrounding these approaches, including some risks and limitations. Overall, my dissertation demonstrates that computational approaches are a valuable tool to advance reform in the criminal justice system

Operation of autonomous unmanned vehicles introduces new risks about which decisionmakers have neither exhaustive statistics nor similar systems from which to derive priors. This model-based risk analysis combines algorithms used for autonomous control, Monte Carlo simulations, and learning parameters from data to improve the risk model's performance. The results inform high-level decisionmakers on when and how best to employ autonomous unmanned vehicles in security and military applications where risk tolerance is higher than for civilian applications, while explicitly maintaining high-risk decisions as the responsibility of human decisionmakers, even when software is used in the process of executing those decisions. This risk analysis has applications in use of unmanned vehicles for localization of radio-frequency threats and maritime tracking of non-cooperative targets using linear array sonar

This dissertation focuses on frictions that arise in various dynamic marketplaces such as kidney exchange, labor markets, and logistics. The central question that we ask is how do heterogeneity, network structure, liquidity, and stochasticity, which cause these frictions, affect our ability to perform simple policies that can achieve efficient outcomes? In Chapters 2 and 3, we analyze dynamic matching markets. When agents arrive to the market over time, an inherent trade-off arises between short- and long-term allocative efficiency. For example, kidney exchange platforms, which arrange exchanges between incompatible patient-donor pairs, can form a match as soon as it becomes feasible, or wait for the market to thicken in order to generate exchanges that yield more life years from transplants. This trade-off raises several questions. How to optimally match agents over time? If the market is cleared periodically, how does the period length affect allocative efficiency at different times? How does stochastic demand impact desirable clearing times? We study these questions from a queueing perspective, and we propose simple batching and greedy policies with a strong performance guarantee: these policies (nearly) maximize the total match value simultaneously at all times. This suggests that the tension between short- and long-term allocative efficiency is essentially moot. In Chapter 4, we analyze scrip systems, where such systems serve an alternative to sustain cooperation, improve efficiency, and mitigate free riding in economies without monetary transfers. Agents request and provide service over time, and scrips are used as artificial currency to pay for service provision. We study the possibility of agents sustaining cooperation when the market is thin, in the sense that only few agents are available to provide the requested service. We analyze the stability of the scrip distribution of agents, assuming that among the available agents, the one with the minimum amount of scrips is selected to provide service. The analysis suggests that even with minimal liquidity in the market, cooperation can be sustained by balancing service provisions among agents. Simulations based on kidney exchange data propose that scrip systems can lead to efficient outcomes in kidney exchange by sustaining cooperation between hospitals

The goal of this thesis is to study key questions arising in matching and experimentation in time-varying stochastic models. In the first part, we study optimal design and statistical inference of switchback experiments. In the second part, we focus on an optimal matching policy of a centralized dynamic matching market

Given i.i.d samples from an unknown distribution, estimating its symmetric properties is a classical problem in information theory, statistics, operations research and computer science. Symmetric properties are those that are invariant to label permutations and include popular functionals such as entropy and support size. Over the past decade, the study of time and sample complexities for estimating properties of distributions has received great attention leading to computationally efficient and sample optimal estimators for various symmetric properties. Most of these estimators were property specific and the design of a single estimator that is sample optimal for any symmetric property remained a central open problem in the area. In a seminal result, Acharya et al. showed that computing an approximate profile maximum likelihood (PML) distribution, a distribution that maximizes the likelihood of the observed multiset of frequencies, allows statistically optimal estimation of various symmetric properties. However, since its introduction by Orlitsky et al. in 2004, efficient computation of approximate PML distributions remained a well-known open problem. In our work, we resolved this question by designing the first efficient algorithm for computing an approximate PML distribution. More broadly our investigation has led to a deeper understanding of various computational and statistical aspects of PML and universal estimators. Additional results include the design of better algorithms for deterministic permanent approximation, new rounding algorithms, faster optimization methods and novel techniques for statistical analysis

The bond market is one of the largest financial markets, with $52.9 trillion of debt outstanding for the US market as of 2021. The implied interest rate for borrowing at different horizons is the fundamental object for this market. However, a complete set of interest is not observed and must be estimated from the noisy market data. In two papers, we develop machine learning methods to precisely estimate the term structure of interest rates and to understand and manage interest-rate related risks. In the first paper, we introduce a robust, flexible and easy-to-implement method for estimating the yield curve from Treasury securities. This method is non-parametric and optimally learns basis functions in reproducing Hilbert spaces with an economically motivated smoothness reward. We provide a closed-form solution of our machine learning estimator as a simple kernel ridge regression, which is straightforward and fast to implement. We show in an extensive empirical study on U.S. Treasury securities, that our method strongly dominates all parametric and non-parametric benchmarks, which positions our method as the new standard for yield curve estimation. In the second paper, we develop a sparse factor model for bond returns, that unifies non- parametric term structure estimation with cross-sectional factor modeling. Building on the modeling framework of the first paper, we estimate an optimal set of sparse basis functions, which maps into a cross-sectional conditional factor model. Our estimated factors are investable portfolios of traded assets, that replicate the full term structure and are sufficient to hedge against interest rate changes. In an extensive empirical study on U.S. Treasury securities, we show that the term structure of excess returns is well explained by four factors. We introduce a new measure for the time-varying complexity of bond markets based on the exposure to higher-order factors

Data-driven decision-making systems are deployed ubiquitously in practice, and they have been drastically changing the world and people's daily life. As more and more decisions are made by automatic data-driven systems, it becomes increasingly critical to ensure that such systems are \textit{responsible} and \textit{trustworthy}. In this thesis, I study decision-making problems in realistic contexts and build practical, reliable, and trustworthy methods for their solutions. Specifically, I will discuss the robustness, safety, and fairness issues in such systems. In the first part, we enhance the robustness of decision-making systems via distributionally robust optimization. Statistical errors and distributional shifts are two key factors that downgrade models' performance in deploying environments, even if the models perform well in the training environment. We use distributionally robust optimization (DRO) to design robust algorithms that account for statistical errors and distributional shifts. In Chapter 2, we study distributionally robust policy learning using historical observational data in the presence of distributional shifts. We first present a policy evaluation procedure that allows us to assess how well the policy does under the worst-case environment shift. We then establish a central limit theorem for this proposed policy evaluation scheme. Leveraging this evaluation scheme, we further propose a novel learning algorithm that is able to learn a policy that is robust to adversarial perturbations and unknown covariate shifts with a performance guarantee based on the theory of uniform convergence. Finally, we empirically test the effectiveness of our proposed algorithm in synthetic datasets and demonstrate that it provides the robustness that is missing using standard policy learning algorithms. We conclude the paper by providing a comprehensive application of our methods in the context of a real-world voting dataset. In Chapter 3, we focus on the impact of statistical errors in distributionally robust optimization. We study the asymptotic normality of distributionally robust estimators as well as the properties of an optimal confidence region induced by the Wasserstein distributionally robust optimization formulation. In the second part, we study the A/B tests under a safety budget. Safety is crucial to the deployment of any new features in online platforms, as a minor mistake can deteriorate the whole system. Therefore, A/B tests are the standard practice to ensure the safety of new features before launch. However, A/B tests themselves may still be risky as the new features are exposed to real user traffic. We formulated and studied optimal A/B testing experimental design that minimizes the probability of false selection under pre-specified safety budgets. In our formulation based on ranking and selection, experiments need to stop immediately if the safety budgets are exhausted before the experiment horizon. We apply large deviations theory to characterize optimal A/B testing policies and design associated asymptotically optimal algorithms for A/B testing with safety constraints. In the third part, we study the fairness testing problem. Algorithmic decisions may still possess biases and could be unfair to different genders and races. Testing whether a given machine learning algorithm is fair emerges as a question of first-order importance. In this part, We present a statistical testing framework to detect if a given machine learning classifier fails to satisfy a wide range of group fairness notions. The proposed test is a flexible, interpretable, and statistically rigorous tool for auditing whether exhibited biases are intrinsic to the algorithm or due to the randomness in the data. The statistical challenges, which may arise from multiple impact criteria that define group fairness and are discontinuous on model parameters, are conveniently tackled by projecting the empirical measure onto the set of group-fair probability models using optimal transport. This statistic is efficiently computed using linear programming, and its asymptotic distribution is explicitly obtained. The proposed framework can also be used to test composite fairness hypotheses and fairness with multiple sensitive attributes. The optimal transport testing formulation improves interpretability by characterizing the minimal covariate perturbations that eliminate the bias observed in the audit

Online platforms often rely on experiments to aid decision-making. When considering a new change, they test the intervention on a subset of the users before deciding whether to launch platform-wide. However, in the setting of marketplace platforms, prior work shows that treatment effect estimates can be biased. Users in a market interact with each other, which violates the Stable Unit Treatment Value Assumption (SUTVA), creates biased estimates, and may impact the resulting decisions made from these experiments. We develop models to capture market dynamics and investigate the effect of interference on different designs and estimators. In particular, we are able to highlight and formalize the relationship between the magnitude of the treatment effect bias in commonly run experiments and the level of supply and demand imbalance in the market. Building on these insights, we propose a novel class of experimental designs and estimators using two-sided randomization (TSR), as a method to reduce bias. In addition, we show that the commonly used standard error estimates are also biased in these marketplace settings. We analyze the impact of the statistical biases on the resulting decisions based on the experiment, show that both forms of biases interact to negatively impact decision-making, and propose practical methods to mitigate such biases

This thesis explores the role of information and fairness in resource allocation settings with applications for social good. The thesis has two parts. At a high level, the first part asks whether and how we can use information as a natural lever to maximize social welfare in the presence of strategic incentives. More specifically, we consider a mechanism design model of objects and agents arriving over time. Agents have private types and waiting costs. We show that the welfare-maximizing mechanism can be implemented in two simple ways that both require some pooling of information. Furthermore, we partially extend this result to a setup where agents have heterogeneous outside options. The second part asks whether and how inherent informational differences across different individuals can explain documented disparities in the allocation of social goods, and if so, what alternative policies can mitigate such disparities. Motivated by disparities in college admissions, we introduce a theoretical framework to study how a decision-maker, concerned with both merit and diversity, selects candidates under imperfect information, limited capacity, and possibly legal constraints. We apply this framework to study the tradeoffs of existing policies (dropping standardized testing, using the top percent rule) as well as to find the optimal selection policy for a generalized optimization problem

This dissertation is motivated by the deceased donor kidney allocation system in the United States. Through stylized models, I develop theories to examine the interplay of learning and incentives in such allocation markets. In Chapter 1, I consider a mechanism design problem where a social planner needs to decide whether and how to allocate a single object to a queue of strategic and privately informed agents. Using tools from operations research and voting literatures, I propose a simple mechanism that can effectively crowdsource agents' private information by balancing their strategic incentives and planner's learning goal. In Chapter 2, I add inter-temporal dynamics to study the equilibrium and optimal strategy in the presence of both dynamic incentives and observational learning in first-come-first-served waitlists. On the one hand, the availability of object implies preceding agents' remaining on the waitlist, which incentivizes agents to accept. On the other hand, it may also imply negative object quality via observational learning, incentivizing agents to reject. I show how these opposing forces come into play. Finally, in Chapter 3 I develop machine learning models to predict hard-to-place kidneys based on donor characteristics and signals from waitlist candidates

Research at the intersection of strategy, organizational theory, and entrepreneurship explores what facilitates the creation of high-performing entrepreneurial ventures. While prior research has recognized that the venture creation process is often characterized by change, the relationship between various forms of change and entrepreneurial performance remains an important area for further research. This dissertation begins to address this gap, investigating how the distinct changes faced by entrepreneurs relate to venture outcomes. This dissertation answers this question over two research projects, starting with a comprehensive literature review of how venture teams manage internal and external changes. The subsequent empirical study unpacks the role of external change. The empirical analysis uses a survey dataset comprising over one thousand entrepreneurial ventures to unpack the performance implications of a changing macroeconomic environment. This dissertation provides insight into how entrepreneurs address external changes and manage internal ones to create successful ventures. Overall, this dissertation contributes to entrepreneurship, strategy, and organizations research and practice

Infectious diseases are responsible for millions of deaths globally each year. Difficult decisions must be made about how to allocate resources efficiently to treat infection, prevent transmission, and save lives while also mitigating the negative impacts of an outbreak. Mathematical and decision analytic modeling help inform decision makers about the most effective and most cost-effective interventions to prepare for and respond to infectious disease outbreaks. In this dissertation, I present novel applications of a variety of model types to assess interventions for recent disease outbreaks. I develop cutting edge methodological improvements for decision making amid an outbreak and provide critical evidence on how model structure could impact predicted intervention effectiveness. Specifically, I assess the cost-effectiveness of plague control interventions for the 2017 plague outbreak in Madagascar including expanded access to antibiotic treatment with doxycycline, mass distribution of doxycycline prophylaxis, and mass distribution of malathion -- alone and in combination. I focus on the trade-off between intervention timing and coverage levels as measured in terms of costs, quality-adjusted life years (QALYs), and incremental cost-effectiveness ratios. Subsequently, I provide a novel framework for rapid decision making and advancing methods for meta-modeling in the infectious disease context. I derive the simple decision rule using a compartmental model framework and net monetary benefit to assess cost-effectiveness and compare the performance of the simple decision rule to machine learning metamodels. During the COVID-19 pandemic, I estimated the impact of various mitigation strategies on COVID-19 transmission in a large U.S. urban jail. I develop a stochastic dynamic transmission model and use this model to estimate the effectiveness of three interventions undertaken by the jail -- depopulation, increased single celling, and asymptomatic testing -- in reducing the spread of COVID-19. Finally, I explicitly address how the choice of model can influence estimates of intervention effectiveness in the short and long term for an endemic disease. I consider four disease models with different permutations of socially connected network vs. unstructured contact (mass-action mixing) model and heterogeneous vs. homogeneous disease risk. I calibrate the models to the same long-term equilibrium disease prevalence and consider a simple intervention with varying levels of coverage and efficacy. For each type of model, I measure the rate of prevalence decline post-intervention, the long-term equilibrium prevalence, and the long-term effective reproduction ratio at equilibrium

As online social networks become more influential in society, a new form of cyber threat continues to impact its users: platform manipulation. This occurs when social network users attempt to exploit other users through their online behavior on a social network platform. Current methods for detecting and eliminating platform manipulation rely on the use of machine learning (ML) and artificial intelligence (AI) models. While these methods are able to parse through very large data sets efficiently, they are often trained to only recognize specific traits within user behavior (e.g., spam, malicious links, hate speech), and may not be able to incorporate other forms of evidence (e.g., human observations) in real time without being re-trained and validated, which can be costly and time-consuming in practice. To make an accurate risk assessment of a user's long-term behavior which can be updated over time, decision-makers need a method for integrating multiple forms of evidence (AI and human) across space (by malicious trait) and time into a probability distribution over possible scenarios, as well as knowledge of the potential consequences to the platform from each scenario. In this research, we use probabilistic risk analysis to combine both AI-generated and human-generated evidence in order to determine the risk of individual users to the platform. This allows decision-makers to not only consider the probability that users are malicious, but also their range of potential consequences. We also develop a decision analysis framework for platform referral decisions (whether to request human intervention) using the value of clairvoyance on human agent observations to determine when to involve human agents in the decision-making process (and when to rely on automated decisions to remove users from the platform)

How to simultaneously secure economic prosperity and heed environmental sustainability may be one of the most pressing global challenges. Such societal level challenges often necessitate work across public, private and philanthropic organizational boundaries. However, prevailing theories of organization and management would suggest that such work is too complex to coordinate on a global scale. However, meta-organizations, or associations of autonomous organizations, are designed for this very purpose -- to operate in complex settings with multiple, diverse stakeholders. How can this form of organization be successful in the face of so much complexity? Though the literature on meta-organization focuses on structure and governance, I propose examining meta-organization as action and argue that work processes can illuminate how these complex interorganizational partnerships manage diverse interests. Rather than a narrow focus on the structure and governance of meta-organization, I shift the analysis to meta-organizing, the work activities and processes to build a network of organizations aimed at facilitating societal grand challenges. In a three-year inductive, qualitative field study, I investigate the mechanisms of organizational identity and boundary formation processes that facilitate meta-organizing for societal grand challenges. Using ethnographic methods, I study DevNet, an organization facilitating public-private-philanthropic partnerships to finance sustainable development. I uncover two work processes: (1) identity activation to manage externally-imposed identities that trigger latent attributes and, (2) boundary permeation to manage diverse stakeholders by changing when and how it distinguishes between audiences. In the identity activation process, I argue that DevNet shifts the meaning of imposed identities after latent attributes are activated. Ultimately, DevNet leverages multiple, emergent organizational identities. In the boundary permeation process, I argue that DevNet crafts "porous" boundaries, or social-symbolic distinctions, between itself as the organizer, its members, and general audiences. By simultaneously thinning and thickening its boundary, DevNet enhances the value of membership to the ecosystem. I observe that exploiting the porous boundary allows DevNet to benefit from openness and exclusivity while juggling diverse audiences with conflicting goals and advancing the broader mission. This dissertation contributes to the nascent literature on meta-organizations and to organizational theory that conceptualizes organizational boundaries and identities as dynamic, socially constructed processes. I challenge the scholarly focus on meta-organization as simply a governing structure. Instead, I propose building on studies of meta-organization as networks and ecosystems that facilitate collective efforts to address societal grand challenges and focus on action -- meta-organizing -- rather than structure. Through this shift, the dissertation develops new understanding about the development of multiple organizational identities and porous organizational boundaries for an emerging ecosystem

In recent years, many high-stakes societal decision-making systems have begun incorporating data and algorithms. This trend raises the question of how decision makers can do so in a way which creates equitable systems which ameliorate inequities. This dissertation considers two broad paths forward towards this goal. First, we review a series of interventions at various stages of the model-building and deployment process. Specifically, we consider how a model-builder might selectively acquire additional information, adaptively sample training data, and add personalization. We show that these interventions allow for model-builders to efficiently allocate resources to create decision-making systems which are inclusive of individuals from vulnerable groups. Second, we review two pieces of work where modern, online data sources give insights which can inform improvements for existing systems. In particular, we first consider how telematics data, containing records on the true prevalence of speeding, sheds light on inequities in traffic enforcement. Then, we see how online game records provide valuable insight into how users make decisions within social networks. Findings from both studies can be incorporated in future design of or interventions in decision-making systems within both spaces. Overall, this dissertation demonstrates two concrete paths for moving towards equitable decision making: intervening to efficiently improve outcomes for underserved groups, and leveraging insights from modern data sources to improve societal decision making systems

Although the demand for kidney transplants continues to greatly exceed supply, most cadaveric kidneys are offered to multiple patients before being accepted or discarded. The decision of whether to accept or decline an offered kidney is difficult because of uncertainty about the quality and length of the patient's remaining life. If they decline the offer, then it is uncertain whether they will ever receive a kidney, let alone one of higher quality than the one currently available. This thesis develops a decision-analytical approach to model the situation facing patients waiting for kidney transplants, based on twenty years of data on patients waiting for transplants in the US. When they are offered a matching kidney, they and their surgeons must decide whether to accept the offer. We model each patient's situation as a one-step decision-making problem that compares the observed remaining lifetime or landmark survival for similar patients who accepted an offered kidney of similar quality and those who declined such an offer. This information can facilitate a meaningful conversation between a patient and their surgeon that can address the particular preferences and circumstances of that patient. Our analysis shows that for patients in almost every situation, the observed remaining lifetime for similar patients was longer if they accepted the offer than if they declined it, and sometimes significantly longer. Because accepting an offer almost always reduces the time a patient will spend on dialysis, it also improves the quality of life for most patients. Therefore, we believe that our results should encourage patients to accept many more offered kidneys than they have in the past

Although option market is becoming increasingly important, its complex structure and abundant underlying assets make it very difficult to obtain relatively accurate pricing formulas or algorithms. The first part of this thesis focuses on a theoretical stochastic volatility model for option pricing. We propose a continuous-time stochastic volatility model based on an arithmetic Brownian motion: a one-parameter extension of the normal stochastic alpha-beta-rho (SABR) model. Using two generalized Bougerol's identities in the literature, the study shows that proposed model has a closed-form Monte-Carlo simulation scheme and that the transition probability for one special case follows Johnson's SU distribution---a popular heavy-tailed distribution originally proposed without stochastic process. It is argued that the SU distribution serves as an analytically superior alternative to the normal SABR model because the two distributions are empirically similar. The second part of this thesis gives a bias corrected algorithm of the least square Monte Carlo (LSM) algorithm for pricing American options. The traditional LSM estimator contains undesirable look-ahead bias, and the conventional technique of removing it necessitates doubling simulations. We present the leave-one-out LSM (LOOLSM) algorithm for efficiently eliminating look-ahead bias. We also show that look-ahead bias is asymptotically proportional to the regressors-to-simulation paths ratio. Our findings are demonstrated with several option examples, including the multi-asset cases that the LSM algorithm significantly overvalues. The LOOLSM method can be extended to other regression-based algorithms that improve the LSM method. In addition to option pricing, the third part of this thesis studies item response theory (IRT) and its application to foraging data analysis. A fundamental problem in biology is to understand how the differences among components, or variation in function, contribute to collective behavior. To tackle this problem, we first give a new approach to latent trait modeling in IRT, then design corresponding experiments and finally apply IRT to data analysis. We also give detailed explanation and further discussion for experiment results

The innovation performance of entrepreneurial ventures critically depends upon their management of internal and external relationships. Within organizational boundaries, how cofounders collectively contribute resources and efforts can significantly impact the process of technological ideation and commercialization. Beyond organizational boundaries, how entrepreneurial ventures manage their external relationships with partners, such as product users, is also vital for innovation and overall performance. Across three inter-linked papers, this dissertation investigates collaborative innovation both within and beyond the organizational boundary of entrepreneurial ventures, focusing on the antecedents, consequences, as well as valuation of technological resource contributions. Using a quasi-natural experiment, the first paper examines how innovation-driven collaborations between ventures and their product users can be undermined by legitimacy concerns stemming from another domain within the multiplex relationship between the two parties. The second paper analyzes the organizational consequences of venture-user collaborations and suggests heterogeneous effects on different types of successes (initial public offering vs. acquisitions). Finally, the third paper explores how the type of resources contributed (technological vs. non-technological) shapes initial equity share division among cofounders as well as equity dilution over time. Together, this dissertation contributes to the literature on interorganizational relationships, innovation, and entrepreneurship and provides policy implications

As the threats of climate change and environmental degradation worsen, policy makers and non-profits worldwide have developed a plethora of incentive-based solutions that induce environmental efforts from individuals. This dissertation studies these incentive-based solutions using the theoretical modeling tools of contract theory, mechanism design and queueing theory. In the first two sections of this dissertation, I ask how to better design contracts and payment structures to increase the efficiency and effectiveness of environmental payment programs. First, I develop a stylized principal-agent model to study Payment for Ecosystem Services programs, which are used by governments worldwide to pay forest owners to conserve forest. I focus on simple and robust contracts that have performance guarantees even when the government has limited information. Next, I investigate the optimal payment structures in long-term and large-scale afforestation programs with smallholder farmers, finding that paying farmers early can increase the environmental benefits generated per dollar spent. Lastly, motivated by operational challenges in the palm oil value chain, I study the impact of travel delay and real-time announcements on queues at a congested server, finding that travel delay can cause reduction in customer welfare in a queueing system