Decision, Operations & Information Technologies
Permanent URI for this communityhttp://hdl.handle.net/1903/2230
Prior to January 4, 2009, this unit was named Decision & Information Technologies.
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Item TWO ESSAYS ON THE ROLE OF INFORMATION TRANSPARENCY IN MARKETPLACE OPERATIONS(2024) Jiang, Jane Yi; Elmaghraby, Wedad J.; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation encompasses two studies on the crucial role of information within marketplace operations. Collaborating with two platforms, we deliver empirical evidence and offer prescriptive insights into how information is conveyed to and perceived by customers, and the consequent impacts on sellers and the marketplace at large.The first study analyzes the introduction of the novel blockchain tracing technology into an online grocery marketplace. Our findings indicate that credible supply chain transparency encourages consumers to more readily buy traced products, especially those that are handling-sensitive or offered in less-trusted markets. Consequently, adopting third-party sellers experienced an average monthly revenue increase of up to 23.4\%. By utilizing structural estimation to understand how consumers assess product attributes and quality, we highlight that consumer responses (and welfare effects) vary in sophistication and size based on their prior experience with the product category. Additionally, we establish that consumers deem blockchain-based. The second study analyzes the unintended transparency issue associated with the pricing structure of bundle discounts and its consequences on product purchases and returns. Our findings reveal that customers tend to overlook complex pricing structures, leading to impulsive buying and increased returns. Enhancing customer attentiveness of pricing can decrease the Retailer's return rates by 20.9\%. Moreover, improving customer attentiveness to pricing benefits retailers by enabling them to create more versatile bundle offers, further optimizing their sales strategy.Item An Operations Management Framework to Improve Geographic Equity in Liver Transplantation(2022) Akshat, Shubham; Raghavan, S.; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In the United States (U.S.), on average three people die every day awaiting a liver transplant for a total of 1,133 lives lost in 2021. While 13,439 patients were added to the waiting list in 2021, only 9,236 patients received liver transplantation. To make matters worse, there is significant geographic disparity across the U.S. in transplant candidate access to deceased donor organs. The U.S. Department of Health and Human Services (HHS) is keen to improve transplant policy to mitigate these disparities. The deceased donor liver allocation policy has been through three major implementations in the last nine years, but yet the issue persists. This dissertation seeks to apply operations management models to (i) understand transplant candidate behavior, and (ii) suggest improvements to transplant policy that mitigate geographic disparity. In the first essay, we focus on reducing disparities in the organ supply to candidate demand (s/d) ratios across transplant centers. We develop a nonlinear integer programming model that allocates organ supply to maximize the minimum s/d ratios across all transplant centers. We focus on circular donation regions that address legal issues raised with earlier organ distribution frameworks. This enables reformulating our model as a set-partitioning problem and our proposal can be viewed as a heterogeneous donor circle policy. Compared to the current Acuity Circles policy that has fixed radius circles around donation locations, the heterogeneous donor circle policy greatly improves both the worst s/d ratio, and the range of s/d ratios. With the fixed radius policy of 500 nautical miles (NM) the s/d ratio ranges from 0.37 to 0.84 at transplant centers, while with the heterogeneous circle policy capped at a maximum radius of 500NM the s/d ratio ranges from 0.55 to 0.60, closely matching the national s/d ratio of 0.5983. Broader sharing of organs is believed to mitigate geographic disparity. Recent policies are moving towards broader sharing in principle. In the second essay, we develop a patient's dynamic choice model to analyze her strategic response to a policy change. First, we study the impact of the Share 35 policy, a variant of broader sharing introduced in 2013, on the behavioral change of patients at the transplant centers (i.e., change in their organ acceptance probability), geographic equity, and efficiency (transplant quality, offer refusals, survival benefit from a transplant, and organ travel distance). We find that sicker patients became more selective in accepting organs (acceptance probability decreased) under the Share 35 policy. Second, we study the current Acuity Circles policy and conclude that it would result in lower efficiency (more offer refusals and a lower transplant benefit) than the previous Share 35 policy. Finally, we show that broader sharing in its current form may not be the best strategy to balance geographic equity and efficiency. The intuition is that by indiscriminately enlarging the pool of supply locations from where patients can receive offers, they tend to become more selective, resulting in more offer rejections and less efficiency. We illustrate that the heterogeneous donor circles policy that equalizes the s/d ratios across geographies is better than Acuity Circles in achieving geographic equity at the lowest trade-off on efficiency metrics. The previous two essays demonstrate the benefit of equalizing the s/d ratios across geographies. In December 2018 the Organ Procurement and Transplantation Network (OPTN) Board of Directors approved the continuous distribution framework as the desired policy goal for all the organ allocation systems. In this framework, the waiting list candidates will be prioritized based on several factors, each contributing some points towards the total score of a candidate. The factors in consideration are medical severity, expected post-transplant outcome, the efficient management of organ placement, and equity. However, the respective weights for each of these potential factors are not yet decided. In the third essay, we consider two factors, medical severity and the efficient management of organ placement (captured using the distance between the donor hospital and transplant center), and we design an allocation policy that maximizes the geographic equity. We develop a mathematical model to calculate the s/d ratio of deceased-donor organs at a transplant center in a continuous scoring framework of organ allocation policy. We then formulate a set-partitioning optimization problem and test our proposals using simulation. Our experiments suggest that reducing inherent differences in s/d ratios at the transplant centers result in saving lives and reduced geographic disparity.Item Evolution and Current Practices of Ride Services(2021) Noh, Daehoon; Tunca, Tunay I; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In the past decade we have seen a rapid transformation in the ride service market with the advent of decentralized Ride Hailing (RH) services (e.g., Uber, Lyft), who gained significant market share at the expense of traditional Vertically Integrated (VI) taxi companies.However, it is not clear what the endgame of this competition between these two models will be. What is more, the current development of driverless car technology, which leads to another form of Vertically Integrated service model, is posing further questions on how the industry will shape in the future. In the first chapter, we analyze this question by game-theoretically modeling entry and competition between decentralized Ride Hailing and Vertically Integrated ride services. First, by comparing monopoly models, we find that due to the advantage of centralization, the VI model is more efficient in increasing firm profits, reducing delays and increasing social welfare compared to the RH model. However, in competition, the RH firm predominantly reverses this disadvantage, and gains the upper hand in the market with lower delays, higher market share, and higher profits due to its flexibility in setting its supply compared to the VI firm, which projects the success of the ride-hailing firms compared to the traditional taxi model. Furthermore, the entry of the RH firm into the market always improves social welfare, while the entry of an inefficient VI firm may reduce industry service levels and surprisingly decrease social despite introducing competition. Our results suggest that entry of a costly self-driving car technology may in fact hurt the industry as a whole and social welfare in a market that is served predominantly by a ride-hailing company, and this technology should be approached carefully by the industry and the regulators. In the second chapter, we examine the effect of offering ride distance information to the drivers in the ride-hailing two-sided market.This is of our interest because currently, the drivers cannot observe the riders' destination. However the leading companies in the ride-hailing business such as Uber and Lyft are experimenting the effect of offering this information to the drivers, but the effect is yet uncertain. It may introduce efficiency, or it may aggravate the cherry picking behavior which will be detrimental to the firm. We develop a game theoretical model of (i) a ride-hailing firm where the drivers do not observe the riders' ride distance (the unobservable case), and (ii) where they can observe (the observable case). We compare the two cases to determine when it would benefit the ride-hailing firm to offer this information to the drivers. We also compare consumer surplus and social welfare to see if the firm's decision may be in conflict with the social planner's. Our main finding is that when consumers are patient, the drivers' cherry picking behavior introduces efficiency to the operations of the observable case, making it optimal for the firm to offer observability to the drivers. However as consumers become impatient, this cherry picking behavior becomes burdensome for the observable case, and thus the firm's optimal decision is to not give observability. Furthermore, under certain parameter regions, the firm's decisions are in conflict with the social planner's objective, thus the government may need to devise a policy to maximize social welfare.Item Essays on Business Analytics(2019) Gu, Liyi; Ryzhov, Ilya O; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)With the rapidly increasing availability of business-related big data in recent years as well as the advancements in statistical and machine learning techniques, business analytics (BA) is becoming an essential practice to explain past events, predict future trends and optimize decision making. Using BA, the two essays in this dissertation aim to address some important questions in two emerging topics: humanitarian fleet management and social behaviors in online gaming industry. In the first essay, we analyse how vehicle management is carried out in a humanitarian setting. In humanitarian fleet management, the performance of purchase, assignment, and sales decisions is determined by dynamic interactions between the fleet composition, the time-varying and uncertain demands on the fleet, and the depreciation of the vehicles as they are exploited. We propose to evaluate purchase, assignment, and sales policies in a realistic simulation environment that directly models heterogeneous vehicle attributes and tracks their evolution over time. Using data from a large international humanitarian organization (LIHO), the simulator can identify the rationale behind seemingly ad-hoc decisions by field managers at LIHO. For instance, by selling vehicles later than LIHO recommends, managers are actually reducing their costs; similarly, managers decline to switch vehicles between mission types because the benefits to the operational cost turn out to be marginal at best. In the second essay, we conduct an empirical study of the relationship between social interaction and user engagement, retention, and purchase behavior, based on a high-resolution player-level dataset from a major international video game company for one of its premier titles. We engineer a set of features that characterize social behavior within the game, and link these behaviors to several measures of user engagement using statistical and econometric models. Our results show that user engagement is highly correlated with certain social dynamics; meanwhile, social interaction does not always translate to better retention rates or more purchases. In some cases, high dependence on a small set of friends is positively correlated with churn, indicating a tradeoff between engagement in one title and adoption of others. Early adopters are generally more responsive to the social experience than late adopters.Item Algorithms for Online Advertising Portfolio Optimization and Capacitated Mobile Facility Location(2017) Sahin, Mustafa; Raghavan, Subramanian; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation, we apply large-scale optimization techniques including column generation and heuristic approaches to problems in the domains of online advertising and mobile facility location. First, we study the online advertising portfolio optimization problem (OAPOP) of an advertiser. In the OAPOP, the advertiser has a set of targeting items of interest (in the order of tens of millions for large enterprises) and a daily budget. The objective is to determine how much to bid on each targeting item to maximize the return on investment. We show the OAPOP can be represented by the Multiple Choice Knapsack Problem (MCKP). We propose an efficient column generation (CG) algorithm for the linear programming relaxation of the problem. The computations demonstrate that our CG algorithm significantly outperforms the state-of-the-art linear time algorithm used to solve the MCKP relaxation for the OAPOP. Second, we study the problem faced by the advertiser in online advertising in the presence of bid adjustments. In addition to bids, the advertisers are able to submit bid adjustments for ad query features such as geographical location, time of day, device, and audience. We introduce the Bid Adjustments Problem in Online Advertising (BAPOA) where an advertiser determines base bids and bid adjustments to maximize the return on investment. We develop an efficient algorithm to solve the BAPOA. We perform computational experiments and demonstrate, in the presence of high revenue-per-click variation across features, the revenue benefit of using bid adjustments can exceed 20%. Third, we study the capacitated mobile facility location problem (CMFLP), which is a generalization of the well-known capacitated facility location problem that has applications in supply chain and humanitarian logistics. We provide two integer programming formulations for the CMFLP. The first is on a layered graph, while the second is a set partitioning formulation. We develop a branch-and-price algorithm on the set partitioning formulation. We find that the branch-and-price procedure is particularly effective, when the ratio of the number of clients to the number of facilities is small and the facility capacities are tight. We also develop a local search heuristic and a rounding heuristic for the CMFLP.Item Essays on Dynamic Pricing and Choice in the Internet and Sharing Economy(2017) Ming, Liu; Tunca, Tunay I; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The widespread use of the Internet, social networking, mobile technology and big data has improved people's ability to obtain and use information to an unprecedented level. Influencing consumer behavior and changing concepts of consumption, the Internet and Sharing Economy has carved itself a significant and growing place in the daily life and the economy. The race to commercialize the value of this change has brought about numerous innovations and creative operational solutions in emerging industries worldwide. The first chapter of my dissertation theoretically and empirically studies consumer equilibrium, pricing, and efficiency of these events. Modeling a continuous time customer arrival and sign-up process, we start by deriving the stochastic dynamic consumer equilibrium. Based on this equilibrium and utilizing sign-up level data from a major Chinese retailer's group buying events, we then structurally estimate consumer arrival rates and utility distributions for 266 events, and empirically verify the fit and predictive power of the model. Utilizing the estimated arrival rates and consumer utility distributions, we then employ a doubly stochastic Generalized Linear Regression Model to provide empirical evidence for consumer network effects in group buying, and estimate 15.4% increase in consumer demand attributable to the employment of a group buying mechanism. Through counterfactual analysis, we further estimate that employing group buying increased retailer profits by 11.21% on average, corresponding to an annual monetary gain of approximately $4.32M for the 266 events in the data set. We further demonstrate that low deal discounts offered by the retailer for very low and very high consumer arrival rates boost profitability, suggesting that an inverse U-shaped deal discount pattern as a function of consumer arrival rate is recommendable when employing group buying events. Ride-sharing platforms, such as Uber and Lyft and their Chinese counterpart Didi, set prices dynamically to balance the demand and supply for their services. In the second chapter, we provide an empirical model and analysis of price formation and surplus generation of these services. We first develop a two-sided-market discrete choice model, capturing the formation of mutually dependent demand (consumer) and supply (driver) sides that jointly determine the pricing. Based on this model, we then use a comprehensive data set obtained from Didi to estimate consumer and driver price elasticities as well as other factors that affect market participation. Based on the estimation results and counterfactual analysis, we demonstrate that surge pricing has a significant role in improving the welfare of consumers and Kuaiche drivers, i.e., by 21.80% and 22.02%, respectively. In terms of government regulations, proposed regulation imposing price caps that match current Taxi rates can decrease consumer surplus by 39.84% while causing a relatively moderate 5.66% decrease in Kuaiche driver surplus. Further, we estimate that restricting driver capacity to equal local Taxi levels would have more severe consequences, resulting in 18.07% and 23.40% reductions in consumer and Kuaiche driver surpluses respectively.Item Mathematical Programming Models for Influence Maximization on Social Networks(2016) Zhang, Rui; Raghavan, Subramanian; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation, we apply mathematical programming techniques (i.e., integer programming and polyhedral combinatorics) to develop exact approaches for influence maximization on social networks. We study four combinatorial optimization problems that deal with maximizing influence at minimum cost over a social network. To our knowl- edge, all previous work to date involving influence maximization problems has focused on heuristics and approximation. We start with the following viral marketing problem that has attracted a significant amount of interest from the computer science literature. Given a social network, find a target set of customers to seed with a product. Then, a cascade will be caused by these initial adopters and other people start to adopt this product due to the influence they re- ceive from earlier adopters. The idea is to find the minimum cost that results in the entire network adopting the product. We first study a problem called the Weighted Target Set Selection (WTSS) Prob- lem. In the WTSS problem, the diffusion can take place over as many time periods as needed and a free product is given out to the individuals in the target set. Restricting the number of time periods that the diffusion takes place over to be one, we obtain a problem called the Positive Influence Dominating Set (PIDS) problem. Next, incorporating partial incentives, we consider a problem called the Least Cost Influence Problem (LCIP). The fourth problem studied is the One Time Period Least Cost Influence Problem (1TPLCIP) which is identical to the LCIP except that we restrict the number of time periods that the diffusion takes place over to be one. We apply a common research paradigm to each of these four problems. First, we work on special graphs: trees and cycles. Based on the insights we obtain from special graphs, we develop efficient methods for general graphs. On trees, first, we propose a polynomial time algorithm. More importantly, we present a tight and compact extended formulation. We also project the extended formulation onto the space of the natural vari- ables that gives the polytope on trees. Next, building upon the result for trees---we derive the polytope on cycles for the WTSS problem; as well as a polynomial time algorithm on cycles. This leads to our contribution on general graphs. For the WTSS problem and the LCIP, using the observation that the influence propagation network must be a directed acyclic graph (DAG), the strong formulation for trees can be embedded into a formulation on general graphs. We use this to design and implement a branch-and-cut approach for the WTSS problem and the LCIP. In our computational study, we are able to obtain high quality solutions for random graph instances with up to 10,000 nodes and 20,000 edges (40,000 arcs) within a reasonable amount of time.Item VOX POPULI: THREE ESSAYS ON THE USE OF SOCIAL MEDIA FOR VALUE CREATION IN SERVICES(2016) Mejia, Jorge; Gopal, Anandasivam; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Prior research shows that electronic word of mouth (eWOM) wields considerable influence over consumer behavior. However, as the volume and variety of eWOM grows, firms are faced with challenges in analyzing and responding to this information. In this dissertation, I argue that to meet the new challenges and opportunities posed by the expansion of eWOM and to more accurately measure its impacts on firms and consumers, we need to revisit our methodologies for extracting insights from eWOM. This dissertation consists of three essays that further our understanding of the value of social media analytics, especially with respect to eWOM. In the first essay, I use machine learning techniques to extract semantic structure from online reviews. These semantic dimensions describe the experiences of consumers in the service industry more accurately than traditional numerical variables. To demonstrate the value of these dimensions, I show that they can be used to substantially improve the accuracy of econometric models of firm survival. In the second essay, I explore the effects on eWOM of online deals, such as those offered by Groupon, the value of which to both consumers and merchants is controversial. Through a combination of Bayesian econometric models and controlled lab experiments, I examine the conditions under which online deals affect online reviews and provide strategies to mitigate the potential negative eWOM effects resulting from online deals. In the third essay, I focus on how eWOM can be incorporated into efforts to reduce foodborne illness, a major public health concern. I demonstrate how machine learning techniques can be used to monitor hygiene in restaurants through crowd-sourced online reviews. I am able to identify instances of moral hazard within the hygiene inspection scheme used in New York City by leveraging a dictionary specifically crafted for this purpose. To the extent that online reviews provide some visibility into the hygiene practices of restaurants, I show how losses from information asymmetry may be partially mitigated in this context. Taken together, this dissertation contributes by revisiting and refining the use of eWOM in the service sector through a combination of machine learning and econometric methodologies.Item IT ENABLED SERVICE INNOVATION: STRATEGIES FOR FIRM PERFORMANCE(2013) Khuntia, Jiban; Agarwal, Ritu; Mithas, Sunil; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation seeks to understand firm strategies and implications for sustainability and success in the context of IT-enabled service innovations. The first essay examines how business models evolve to influence the financial sustainability and maturity of health information exchanges (HIE), a new organizational form in the United States healthcare landscape to facilitate electronic health information sharing across multiple stakeholders such as hospitals, doctors, laboratories, and patients. The study focusses on two components of business models of HIEs: the customer value proposition that is manifested through three categories of service offerings (e.g., foundational, vendor driven and advanced), and two revenue model approaches to earn profits (e.g., subscription and transaction-based revenues models). Using an unique archival data set constructed from surveys of HIEs in the US from 2008 to 2010 for empirical analysis; we find that foundational IT enabled service offerings have higher positive influence on operational maturity and financial sustainability, compared to vendor driven or advanced service offerings. Further, findings show subscription-based revenue models are more advantageous for sustainability in early stages, while transaction-based revenue models lead to higher operational maturity in later stages. The second essay investigates how two dimensions of IT enabled service augmentation, i.e., value added service and customer care, interplay with core services to influence customer satisfaction with cell phone services in base-of-the-pyramid (BOP) markets. Arguing for price- and relational- evaluations, we develop hypotheses for a substitution effect of value added services, and a complementary effect of customer care, on the relationship between core service and customer satisfaction. Specific to the BOP market context, we further proposed a differentiated influence of service augmentation for different categories of providers based on their institutional contexts and investment strategies. We empirically examine and find support for the hypothesized relationships using an archival data set from surveys of over 3,400 cell phone customers across 34 providers in seven South Asian countries. The two studies contribute to existing literature in exploring the factors associated with firm performance, and derive managerial implications to effectively manage and profit from IT enabled service innovations. Overall, the dissertation has research and practice implications to gain an understanding of the appropriate strategies to increase firm performance in the context of IT enabled service innovations.Item Essays on Information Flows and Auction Outcomes in Business-to-Business Market: Theoretical and Empirical Evidence(2013) Pilehvar, Ali; Elmaghraby, Wedad; Gopal, Anandasivam; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation, I have three separate essays in the context of Business-to Business (B2B) auctions; in each I introduce a complex problem regarding the impact of information flows on auction's performance which has not been addressed by prior auction literature. The first two essays (Chapter 1 and 2) are empirical studies in the context of online secondary market B2B auctions while the third essay (Chapter 3) is a theoretical investigation and will contribute to the B2B procurement auction literature. The findings from this dissertation have managerial implications of how/when auctioneers can improve the efficiency or success of their operations. B2B auctions are new types of ventures which have begun to shape how industries of all types trade goods. Online B2B auctions have also become particularly popular for industrial procurement and liquidation purposes. By using online B2B auctions companies can benefit by creating competition when auctioning off goods or contracts to business customers. B2B Procurement auctions− where the buyer runs an auction to procure goods and services from suppliers− have been documented as saving firms millions of dollars by lowering the cost of procurement. On the other hand, B2B auctions are also commonly used by sellers in `secondary market' to liquidate the left-over goods to business buyers in a timely fashion. In order to maximize revenues in either both industrial procurement or secondary market settings, auctioneers should understand how the auction participants behave and react to the available market information or auction design. Auctioneers can then use this knowledge to improve the performance of their B2B auctions by choosing the right auction design or strategies. In the first essay, I investigate how an online B2B secondary market auction environment can provide several sources of information that can be used by bidders to form their bids. One such information set that has been relatively understudied in the literature pertains to reference prices available to the bidder from other concurrent and comparable auctions. I will examine how reference prices from such auctions affect bidding behavior on the focal auction conditioning on bidders' types. I will use longitudinal data of auctions and bids for more than 4000 B2B auctions collected from a large liquidator firm in North America. In the second essay, I report on the results of a field experiment that I carried out on a secondary market auction site of another one of the nation's largest B2B wholesale liquidators. The design of this field experiment on iPad marketplace is directly aimed at understanding how (i) the starting price of the auction, and (ii) the number of auctions for a specific (model, quality), i.e., the supply of that product, interact to impact the auction final price. I also explore how a seller should manage the product differentiation so that she auctions off the right mix and supply of products at the reasonable starting prices. Finally, in the last essay, I study a norm used in many procurement auctions in which buyers grant the `Right of First Refusal' (ROFR) to a favored supplier. Under ROFR, the favored supplier sees the bids of all other participating suppliers and has the opportunity to match the (current) winning bid. I verify the conventional wisdom that ROFR increases the buyer's procurement cost in a single auction setting. With a looming second auction in the future (with the same participating suppliers), I show that the buyer lowers his procurement cost by granting the ROFR to a supplier. The analytical findings of this essay highlights the critical role of information flows and the timing of information-release in procurement auctions with ROFR.