Robert H. Smith School of Business

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A theoretical model on how firms can leverage political resources to align with supply chain strategy for competitive advantage
(Wiley, 2022-03-10) Grover, Abhay K.; Dresner, Martin
The success of a firm's supply chain strategy depends on resources in the political environment and the supply network in which it operates. If the political environment is not conducive to a firm's supply chain strategy, a firm can either change its supply chain strategy or seek a political environment that is more favorable to its supply chain. This paper examines this second alternative. The structure-conduct-performance (SCP) paradigm and the competitive dynamics literature are used to explore the relationships between political actions that leverage supply network resources, supply chain strategies, and firm performance. We extend a well-known typology of political actions from the strategic management literature and suggest that beyond influencing or complying with the political environment, firms may choose to moderate the political environment (circumvent or submit) or stay neutral (free ride). An integrated model is developed to explore the relationships between political actions and supply chain strategy, along with a series of propositions outlining how political actions can facilitate supply chain risk management strategies. Finally, suggestions are provided for future research.
Absorptive Capacity And Open Source Software Project Performance
(2007-12-04) Daniel, Sherae Lee; Agarwal, Ritu; Stewart, Katherine; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The open source phenomenon is an exciting movement that is transforming traditional forms of software development. Some open source software (OSS) projects, such as Linux and Apache, are performing extremely well and rapidly replacing proprietary software in major corporations and governments. In addition to these highly publicized examples, there are legions of OSS projects that have not experienced a similar uptake. The purpose of this dissertation is to understand how and why some OSS projects are able to perform better than others. It explores antecedents of OSS project performance from a knowledge-focused perspective because software development is a knowledge-intensive activity. In particular, it examines the development and effects of absorptive capacity for an OSS project. Absorptive capacity captures the degree to which an organization is able to acquire and assimilate knowledge. In describing how OSS absorptive capacity is developed, this dissertation identifies characteristics and behaviors of project participants that indicate an OSS project's absorptive capacity. I underscore the importance of the characteristics and behaviors of two different sets of project participants in an OSS project: those in the Internet-based user community and those in the development group. To the extent that absorptive capacity influences OSS project performance, I argue that these characteristics and behaviors are critical for OSS project performance. Archival data about OSS projects that use the SourceForge platform are used to empirically test the model developed. This dissertation makes several contributions to theory and practice. The research informs project managers regarding the participants to target and behaviors to encourage that will lead to superior performance for their OSS project. In exploring the effect of absorptive capacity in an OSS project, this dissertation adds to the absorptive capacity literature by examining the interaction of two dimensions of this construct: knowledge acquisition and knowledge transfer. Finally, this dissertation extends the OSS literature by specifically exploring the effect of the Internet-based user community on OSS project performance.
ADMIRATION AND ENVY AS AN IMPETUS: JOINT EFFECTS OF LEADER-MEMBER EXCHANGE DIFFERENTIATION AND GROUP INCENTIVE PAY ON GROUP AFFECTIVE CLIMATES, COORDINATION, AND PERFORMANCE
(2014) Han, Joo Hun; Liao, Hui; Business and Management: Management & Organization; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Drawing upon cognitive appraisal theory of emotions in conjunction with incentive pay research, I examine the mechanisms and boundary conditions for the effects of group leaders' differentiated development of leader-member exchange (LMX) relationship on group coordination and performance. I propose that it is when groups receive a higher average proportion of group, as opposed to individual, incentive pay that LMX differentiation is more likely to foster group climate of admiration, rather than envy, which then enhances group coordination and subsequent performance. Using data on 828 sales groups in a major Chinese retailer, I find evidence that groups' use of group, rather than individual or hybrid (i.e., [1] incentive pay based on individual and group performance or [2] incentive pay based on individual, group, and store performance), incentive pay with a higher average proportion in total pay facilitated LMX differentiation to improve group coordination by cultivating group admiration climate. Also, group, as opposed to individual or hybrid, incentive pay buffered the negative effects of group envy climate on group coordination. Lastly, it was found that group coordination predicted groups' six-month lagged sales performance above and beyond prior sales performance. Several theoretical and practical implications are discussed.
Advances in Mathematical Models in Marketing
(2007-04-18) Aravindakshan, Ashwin; Rust, Roland T; Business and Management: Marketing; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
This dissertation comprises a series of three essays that relate advances made to both theoretical and empirical issues in marketing. The first essay discusses the issue of endogeneity of market share and price in logit models and provides a theoretical procedure to solve this problem. The inseparability of demand and price make the possibility of drawing definite conclusions about either almost impossible. We employ a recently rediscovered mathematical function called the 'LambertW' to solve this problem of endogeneity and in turn yield logit models more conducive to theoretical study. We also employ this methodology to the problem studied by Basuroy and Nguyen (1998). The second essay deals with the issue of pricing implicit bundling. Implicit bundles are products that are sold separately but provide an enhanced level of satisfaction if purchased together. We develop a model that would account for the possible relationships of the products across the different product lines. We show that accounting for these relationships would decrease the amount of price competition in the market and also allow the Firm to enjoy higher profits. We also account for the endogeneity of price and market share when deriving the optimal solutions. We show that optimal prices first increase as the relationship between the firm's two products become stronger and then decrease as the two products become more exclusive to each other. Finally, we also find that a firm's prices increase as the competitor's contingent valuations increase. The third essay helps improve the efficacy of CRM interventions by analyzing the latent psychological loyalty states of the customer. We use state space models to predict these latent loyalty states using observed data. We then use the predicted values of loyalty to derive the probability of repurchase of the customer. We also identify the types of CRM interventions that play a role in improving the loyalty of the customer to the firm and those interventions that have no effect. We compare our model's predictions to those derived from two other estimation methods. We find that our predictions are better than those computed from the other methods discussed.
ADVERSE EFFECTS OF COMPETITION WITH COWORKERS: THE ROLE OF THIRD-PARTY TIES
(2020) Yan, Taiyi; Venkataramani, Vijaya; Tangirala, Subra; Business and Management: Management & Organization; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Employees rely on coworkers for support. Through workflow ties and friendship ties with coworkers, employees acquire task support and emotional support that allows them to be effective in their work. At the same time, employees often find themselves having to compete with those very coworkers for limited rewards and recognition (e.g., bonuses, promotion) that organizations provide. In this dissertation, I delineate the negative effects that competition with coworkers who are closely connected to employees in their workflow and friendship networks has on employees’ task performance. I note that such competition can prevent employees from obtaining critical task and emotional support required to remain effective in their roles. Using a social embeddedness perspective, I further highlight that these negative effects of competition can be avoided when employees and their competitors are connected to third-party peers in their teams who can act as mediators and allow for continued flow of task and emotional support via workflow and friendship ties between employees and their competitors. I test these hypotheses in the field (using a sample of 394 employees embedded in 39 R&D teams) and in two experimental studies (using 694 participants). I will discuss implications of my model for theory and practice.
Air Transportation System Performance: Estimation and Comparative Analysis of Departure Delays
(2006-11-22) Tu, Yufeng; Ball, Michael; Jank, Wolfgang; Decision and Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The U.S. National Airspace System (NAS) is inherently highly stochastic. Yet, many existing decision support tools for air traffic flow management take a deterministic approach to problem solving. In this study, we focus on the flight departure delays because such delays serve as inputs to many air traffic congestion prediction systems. Modeling the randomness of the delays will provide a more accurate picture of the airspace traffic situation, improve the prediction of the airspace congestion and advance the level of decision making in aviation systems. We first develop a model to identify the seasonal trend and daily propagation pattern for flight delays, in which we employ nonparametric methods for modeling the trends and mixture distribution for the residual errors estimation. This model demonstrates reasonable goodness of fit, robustness to the choice of the model parameters, and good predictive capabilities. We emphasize that a major objective is to produce not just point estimates but estimates of the entire distribution since the congestion estimation models envisioned require delay distribution functions, e.g. to produce probability of certain delays or expected traffic levels for arbitrary time intervals. Local optima problems are typically associated with mixture distribution estimation. To overcome such problems, we develop a global optimization version of the Expectation Maximization algorithm, borrowing ideas from Genetic Algorithms. This optimization algorithm shows the ability to escape from local traps and robustness to the choice of parameters. Finally, we propose models to estimate the so called "wheels-off delays" for flights within the NAS while incorporating a dynamic update capability. Approaches are evaluated based on their ability to reduce variance and their predictive accuracy. We first show that how a raw histogram can be misleading when a trend is present and how variance can be reduced by trend estimation. Then, various techniques are explored for variance reduction. The multiple seasonal trends method shows great capability for variance reduction while staying parsimonious in parameters. The downstream ripple effect method further enhances the variance reduction capability and makes real-time prediction practical and accurate. A rolling horizon updating procedure is described to accommodate the arrival of new information. Finally different models are compared with the current model adopted by the ETMS systems and the predictive capabilities of all models are shown.
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.
An Alternative Measure to Detect Intentional Earnings Management through Discretionary Accruals
(2005-06-10) Ibrahim, Salma Samir; Kim, Oliver; Accounting and Information Assurance; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
This study proposes an alternative measure of discretionary accruals that can be used in testing for intentional earnings management. Prior research has shown the prevalence of measurement error in all models used to estimate discretionary accruals (Healy (1985), DeAngelo (1986), Jones (1991) and modified Jones models (Dechow et al., 1995). The alternative measure proposed relies on the premise that managers use one or more components of accruals (accounts receivable, inventories, accounts payable, other working capital and depreciation) to manipulate bottom-line income in a given direction, consistent with their incentives. In other words, components of discretionary accruals are expected to be positively correlated. If they are not, this is an indication of high measurement error in the models estimating them. The alternative measure is tested in terms of its power (type II error) and specification (type I error) and compared to the traditional discretionary accruals measure. The power of the tests is measured in random samples with added accrual manipulation as well as a sample of firms targeted by the Securities and Exchange Commission for alleged fraud and a sample of firms that violated their debt covenants. The results indicate that the power of this alternative discretionary measure is higher than that of the traditional discretionary accruals measure. The specification (specificity) is tested in random samples chosen from the full sample as well as random samples chosen from extreme income and cash from operations observations and a sample in which discretionary accruals is a noisy measure of the estimated discretionary accruals. The results indicate that the specification of detecting earnings management behavior is improved by using the alternative discretionary accruals measure.
ANALYSIS OF DISTRIBUTION-FREE METHODS FOR REVENUE MANAGEMENT
(2008-10-14) Gao, Huina; Ball, Michael; Karaesmen, Itir; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Revenue management (RM) is one area of research and practice that has gained significant attention in the past decade. The practice originated in the airline industry, where the idea was to maximize revenues obtained from a fixed amount of resources through differentiation/segmentation and strategic use of pricing and capacity. While many of the research models take into account uncertainty, the uncertainty is modeled using random variables and known probability distributions, which is often difficult to estimate and prone to error for a variety of reasons. For instance, demand patterns can fluctuate substantially from the past,and characterizing demand from censored data is challenging. This dissertation focuses on the multifare single resource (leg) problem in RM.We consider the "limited information" case where the demand information available consists of lower and upper bounds rather than a characterization of a particular probability distribution or stochastic process. We first investigate the value of the amount and type of information used in solving the single-leg RM problem. This is done via extensive computational experiments. Our results indicate that new robust methods using limited information perform comparably to other well-known procedures. These robust policies are very effective and provide consistent results, even though they use no probabilistic information. Further, robust policies are less prone to errors in modeling demand. Results of our preliminary computations justify the use of robust methods in the multi-fare single-leg problem. We next apply this distribution-free approach to a setting where progression of demand is available through time-dependent bounds. We do not make any further assumptions about the demand or the arrival process beyond these bounds and also do not impose a risk neutrality assumption. Our analytical approach relies on competitive analysis of online algorithms, which guarantee a certain performance level under all possible realizations within the given lower and upper bounds. We extend the robust model from a problem using static information into a dynamic setting, in which time-dependent information is utilized effectively. We develop heuristic solution procedures for the dynamic problem. Extensive computational experiments show that the proposed heuristics are very effective and provide gains over static ones. The models and computations described above assume a single airline, disregarding competition. As an extension of robust decision-making, in the third part of this dissertation, we analyze a model with two airlines and two fare classes where the airlines engage in competition. The model does not use any probabilistic information and only the range of demand in each fare-class is known. We develop a game-theoretic model and use competitive analysis of online algorithms to study the model properties. We derive the booking control policies for both centralized and decentralized models and provide additional numerical results.
Analysts Unchained—Expanded Information Processing Capacity and Effort Transfer under Technology Adoption
(2020) Feng, Ruyun; Kimbrough, Michael; Business and Management: Accounting & Information Assurance; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Analysts acquire and disseminate information to assist investors in equity valuation. Despite their expertise in equity valuation, sell-side analysts are economic agents with limited time and cognitive resources. The constraint on an analyst’s information processing capacity is reflected by the previously documented negative association between an analyst’s forecast accuracy for a focal firm and the total number of firms the analyst covers. While prior research focuses on analysts’ attributes and portfolio firm characteristics as factors impinging on analysts’ information processing capacity, I examine whether information technology—an exogenous factor—can alleviate this constraint. Using the recent exogenous shock of XBRL adoption, I find that the widespread adoption of XBRL expands analysts’ information processing capacity. I document two consequences of this expanded capacity. As an analyst’s information processing capacity increases, the analyst either improvs the forecast accuracy for non-adopting firms in the existing portfolio or increases the size of the portfolio. This finding indicates that the adoption of XBRL generates a positive externality from the adopting firms due to the transfer of analyst effort away from those firms. This study provides the first evidence that exogenous factors such as the adoption of new technology can expand analysts’ information processing capacity, thereby allowing analysts to improve the overall quality of existing coverage and allowing more firms to enjoy the benefits of analyst coverage. The paper also provides the new insight that information externalities can exist among firms that are fundamentally unrelated by identifying another channel—the effort channel—as a source of such externalities.
Analysts' Superiority in Processing Public Information: Evidence from Recommendation Revisions
(2006-07-20) Wang, Zheng; Kim, Oliver; Accounting and Information Assurance; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
In this paper, I study analysts' superiority over the market in processing publicly disclosed earnings information by examining a sample of recommendation revisions issued subsequent to annual earnings announcements within a short period of thirty trading days. The main findings of this study are as follows: First, I provide strong evidence that these recommendation revisions convey valuable information to the market for clarifying the long term implications of recently released earnings. These revisions significantly alter the market's belief about the value implications of announced earnings, suggesting that analysts do have superiority over the market in processing pubic information. Also, the extent of this superiority is positively related to analysts' performance in picking stocks and forecasting earnings. Recommendation revisions issued by analysts with superior performance can make the market revise its assessment about the value implications of previous earnings to a much greater extent than those issued by analysts with moderate performance. Moreover, the extent of this superiority increases with the level of information complexity of earnings signals. Analysts' information is even more valuable to the market for reevaluating previous earnings when the earnings information is more difficult to analyze. Lastly, on average, the extent of this superiority declines after Regulation Fair Disclosure, but still remains significant, suggesting that analysts do not solely rely on inside information from the management to interpret public information. Actually, the decline in the extent of superiority is more likely due to a great increase in the number of revisions issued by analysts whose expertise is not in processing public information. Prior studies document that investors also use subsequent earnings announcements to adjust their estimate of the value implications of previous earnings. This study finds initial evidence that when analysts' information and subsequent earnings announcements provide consistent predictions on how previous earnings is misinterpreted, subsequent earnings announcements become less useful to investors for updating their beliefs regarding the implications of previously released earnings. This paper also compares the extent of analysts' superiority in processing publicly released earnings information across industries and find that analysts exhibit a greater degree of superiority for firms in the manufacturing and retail industry.
ANTECEDENTS AND CONSEQUENCES OF INSIDERS' EQUITY SHARE SELLING AT IPO: THREE ESSAYS
(2013) Li, Qiang; Goldfarb, Brent; Business and Management: Management & Organization; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Secondary share sales at IPO by insiders happen frequently and on a large scale. Current literature offers mixed explanations. For example, signaling theory (Leland & Pyle, 1977) suggests that secondary share sales at IPO by insiders signal poor quality of the IPO firm. The premise is that insiders have more private information about the firm than outsiders. Therefore, insider sales should be indicative of trouble in the firm. This implies that insiders' secondary share sales will be associated with poor pre- and post-IPO performance. Agency theory (Jensen & Meckling, 1976) suggests that insiders will lower their commitment to the firm after they sell part of their shares; after such sales, managerial and firm interests are more poorly aligned. This theory suggests that poor post-IPO performance is causally associated with insiders' secondary share sales at IPO. Finally, risk aversion may drive insiders to diversify their risk away from the focal firm by selling secondary shares at IPO. This would suggest that sales have nothing to do with firm quality or managerial commitment. Although the above theories provide different implications for practice, the mixed nature of their explanations prevent us from having a clear understanding of the phenomenon. Additionally, prior studies have been unable to tease them apart. To address this issue, this dissertation investigates the following related questions: what factors predict insiders' secondary share sales at IPO and how do such sales affect various firm performances. Only by looking at the antecedents and consequences of insiders' share sales at IPO, as well as finding exogenous variation that affects secondary share sales and is unrelated to the characteristics of the firm can we see if the sales are associated with firm quality or risk aversion or if insiders lower their commitment after sales. The answers to these questions are investigated in three essays. In Essay 1, I ask which CEOs sell shares at IPO and under what conditions? Using a sample of 651 U.S. software IPOs from 1990 to 2011, I find that when more of the CEO's wealth is tied up in the firm, they are more likely to sell. The effect is especially strong for CEO founders. Interestingly, when board members also engage in equity share sales at IPO, CEOs are more likely to sell. This latter result suggests weakened board oversight of the CEO. Using an instrumental variable approach, I tease apart cotemporaneous selling due to poor firm quality and selling that only occurs with the reduction of oversight. In Essay 2, I ask when equity share sales at IPO influence IPO underpricing. Through an analysis of 633 IPOs in the U.S. computer software industry, I find that the equity share sales by outside directors (VCs and other institutional investors) are associated with upward offer price revision pre-IPO and lower IPO underpricing. The interpretation is that outside directors may be able to bargain for a higher offer price when they attempt to sell part of their equity shares at IPO. As such, the upward offer price revision pre-IPO results from outsiders' bargaining leads to lower IPO underpricing. These results are robust to a Heckman two-stage approach that addresses potential selection bias. In Essay 3, I examine whether insiders' secondary share sales at IPO impacts a variety of performance measures post-IPO and the contingencies under which any impact may vary. Through the analysis of 500 IPOs of the U.S. computer software industry, in general, I find that insiders' secondary share sales at IPO are not associated with sales or sales growth. Rather, they are only associated with slower R&D growth in the year post-IPO. This effect is less negative for large firms. The results are robust to an instrumental variable approach to address the potential endogeneity issues. Taken together, this dissertation finds that insiders' secondary share sales are not significantly associated with post-IPO firm performances, providing no support to signaling theory or agency theory. The findings are more consistent with risk aversion theory and imply that insiders' secondary share sales at IPO are not a significant negative signal and traditional wisdom may overreact to the sales.
Antecedents and Effects of Retail Shelf Availability
(2019) Celebi, Heidi; Evers, Philip T; Business and Management: Logistics, Business & Public Policy; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Retail shelf availability research has been limited by the inability to measure stockouts. Not being able to fully capture stockout occurrences has led to studying either the effects of stockouts or their antecedents. It has also led to using various fundamentally different stockout attributes as measures across studies. The relationship between stockout attributes is not clear, making it difficult to have a consensus on either the drivers or the impact of stockouts. This thesis considers both antecedents and effects of stockouts by incorporating actual stockout events under two different risk pooling methods. The first set of models simulate stockout-based customer switching (the inventory effect) to study pooling by substitution for a retailer setting service level goals for two products. The second set of models study pooling by postponement, termed “instore logistics postponement,” using archival data from a new shelf sensor technology that captures actual stockout events. An extension to the second part of this study examines the nonlinear relationship between stockout attributes. Both parts of the dissertation contribute to the stockout literature in different ways. The simulation work contributes towards reconciling opposing views on the performance effect of risk pooling through substitution, also showing how different performance measures may accentuate or mask the impact of stockouts. The shelf technology work contributes to logistics postponement by studying how a two-tier inventory within the store may affect stockouts along more than one stockout attribute, and whether less frequent but longer stockouts are linked to better performance than shorter but more frequent stockouts.
Antecedents of Dishonest Consumer Behavior
(2019) Kang, In Hye; Kirmani, Amna; Business and Management: Marketing; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Consumers engage in a wide range of dishonest behaviors, such as cheating or lying to companies for financial rewards. These dishonest behaviors are costly for companies and consumers. However, relatively little research in marketing has paid attention to consumer dishonesty. In this dissertation, we enhance the understanding of dishonest consumer behavior by examining a few prominent antecedents: a company’s corporate social responsibility (CSR) initiatives and construal level. The first essay examines how a company’s CSR initiatives impacts consumers’ dishonest behavior toward the company. Companies are proclaiming their values by taking stands on controversial issues in their CSR. We examine a novel way with which consumers respond to a company’s polarizing CSR: dishonest behavior toward the company. We demonstrate that when the CSR cause is congruent with the consumer’s self-concept, CSR (vs. no-CSR) decreases dishonest behavior by increasing anticipatory self-threat (i.e., if I cheat the company, I will feel like I am a bad person). In contrast, when the CSR cause is incongruent, CSR (vs. no-CSR) increases dishonest behavior by decreasing anticipatory self-threat. We demonstrate an asymmetric effect such that the effect of incongruent CSR is larger than the effect of congruent CSR. Building on the anticipatory self-threat mechanism, we identify a boundary condition in which the backfiring effect of incongruent CSR is attenuated: situational salience of moral identity. The second essay investigates how construal level—the extent to which people’s thinking about a situation is abstract or concrete—influences dishonest consumer behavior. We show that the effect of construal level on dishonest behavior is moderated by the importance of moral values. We find that compared to concrete construal, abstract construal reduces dishonest behavior when the importance of moral values is high but not when the importance of moral values is low. Importance of moral values is measured as individual differences and situationally primed. These essays provide valuable insights into consumer dishonesty by demonstrating that different types of factors (characteristics of a company such as CSR and contextual factors such as construal level) influence dishonest consumer behavior. Moreover, these essays provide practical implications for companies seeking to reduce dishonest consumer behaviors.
APPLYING OPERATIONS RESEARCH MODELS TO PROBLEMS IN HEALTH CARE
(2015) Price, Stuart Patrick; Golden, Bruce; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Intensity- modulated radiation therapy is a form of cancer treatment that directs high energy x-rays to irradiate a tumor volume. In order to minimize the damage to surround-ing tissue the radiation is delivered from multiple angles. The selection of angles is an NP-hard problem and is currently done manually in most hospitals. We use previously evaluated treatment plans to train a machine learning model to sort potential treatment plans. By sorting potential treatment plans we can find better solutions while only evalu-ating a fifth as many plans. We then construct a genetic algorithm and use our machine learning models to search the space of all potential treatment plans to suggest a potential best plan. Using the genetic algorithm we are able to find plans 2% better on average than the previously best known plans. Proton therapy is a new form of radiation therapy. We simulated a proton therapy treatment center in order to optimize patient throughput and minimize patient wait time. We are able to schedule patients reducing wait times between 20% and 35% depending on patient tardiness and absenteeism. Finally, we analyzed the impact of operations research on the treatment of pros-tate cancer. We reviewed the work that has been published in both operations research and medical journals, seeing how it has impacted policy and doctor recommendations.
APPROPRIATING VALUE FROM INFORMATION TECHNOLOGY IN HEALTHCARE
(2011) Goh, Jie Mein; Agarwal, Ritu; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
The value potential of information technology (IT) in healthcare settings has generated considerable optimism yet, significant questions remain unanswered. This dissertation employs the lens of social structure to investigate the value of information technology in healthcare situated in two distinct contexts: hospitals, that exemplify the traditional institutional form for the delivery of healthcare services, and online patient communities that represent new organizational forms enabled by IT. It seeks to address the following fundamental research questions "What is the impact of information technology in healthcare settings? How does social structure influence the appropriation of the value of information technology in healthcare?" Each of the two contexts is investigated in a separate essay, drawing upon distinct bodies of literature and using both qualitative and quantitative analytical methods. Essay 1: Evolving Work Routines: A Theory of Successful Adaptation to Information Technology in Healthcare The first essay investigates the impact of healthcare technologies such as electronic medical record systems in the traditional hospital environment. It traces the development of changes in social structure before and after an IT implementation. Using a longitudinal field study, the process of how information technology and routines interact is deconstructed. A theory of the co-evolution of routines and technology is proposed and described. Essay 2: The Social Value of Online Health Communities The second essay examines the impact of health information technology in the form of online patient communities by uncovering the social structure of the community. Using data collected from a popular online patient community, I identify the generative processes using support patterns between patients within the community. I find that online patient communities yield social value through information and emotional support to patients by enabling the transfer of support between patients with differential needs. Results also provide descriptive insights into the attributes of patients that contribute to variation in the provision of support within such online patient communities. The two studies in this dissertation make theoretical and empirical contributions. They shed light on the impact of information technology in healthcare, and further inform us about the appropriation of HIT value from a social structure perspective.
Arbitrage Free Approximations to Candidate Volatility Surface Quotations
(MDPI, 2019-04-21) Madan, Dilip B.; Schoutens, Wim
It is argued that the growth in the breadth of option strikes traded after the financial crisis of 2008 poses difficulties for the use of Fourier inversion methodologies in volatility surface calibration. Continuous time Markov chain approximations are proposed as an alternative. They are shown to be adequate, competitive, and stable though slow for the moment. Further research can be devoted to speed enhancements. The Markov chain approximation is general and not constrained to processes with independent increments. Calibrations are illustrated for data on 2695 options across 28 maturities for 𝑆𝑃𝑌 as at 8 February 2018.
Are entrepreneurs penalized during job searches? It depends on who is hiring
(Wiley, 2023-09-17) Ding, Waverly W.; Lee, Hyeun J.; Shapiro, Debra L.
How do job-applicants with entrepreneurship experience—“post-entrepreneurs”—fare in the wage labor job market? We propose an “entrepreneurship-experience penalty” generally occurs yet varies in strength depending on the recruiters faced by post-entrepreneurs in their job application process. In an experiment utilizing the selection-decisions of 275 recruiters (experimental study participants) in reaction to objectively-identical job-applicants' resumes whose differences relate to whether their last-held job was as a Founder or as an Executive, we found that: (a) resumes of Founders (compared to Executives) are about 23%–29% less likely to be picked as top-choice for hire, (b) this entrepreneurship penalty is weaker for recruiters with (rather than without) entrepreneurial aspirations, and (c) this recruiter moderator-effect is stronger for recruiters in smaller (rather than larger) firms.
ARE INDIVIDUAL INVESTORS INFORMED TRADERS? EVIDENCE FROM THEIR MIMICKING BEHAVIOR
(2020) Polat, M. Fikret; Hann, Rebecca; Business and Management: Accounting & Information Assurance; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Evidence from a large stream of research suggests that individual investors are uninformed noise traders, who push equity prices away from fundamentals. Recent studies, however, find that individual investors are sophisticated—their trades contain information about future stock prices. In this study, I shed light on this debate by examining a potential channel through which individual investors become informed: trading disclosures of other market participants. Specifically, I examine whether individual investors exploit the proprietary trading strategies of institutional investors revealed in 13F filings. Using a large sample of retail trades, I find a significant positive association between the order imbalance of retail investors and the first buys of institutional investors around the 13F disclosure deadline, with the positive association concentrated among transient and growth-style institutions. The results suggest that not only do individual investors engage in mimicking trading, but their mimicking behavior is selective. I further find that retail investors’ order imbalance predicts future stock returns, with this predictive ability more pronounced in the week around the 13F disclosure deadline, which suggests that individual investors benefit from their selective mimicking trading. Lastly, I find that mimicking trading accelerates the price discovery of upcoming earnings news. Overall, this study enhances our understanding of how individual investors use public disclosures to become informed and contributes to the debate on whether individual investors are informed traders as well as work on the role of SEC disclosures in leveling the informational playing field.
Are the voices of customers louder when they are seen? Evidence from CFPB complaints
(2022) Mazur, Laurel Celastine; Hann, Rebecca; Business and Management: Accounting & Information Assurance; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
This paper exploits a unique policy change in the banking sector – the first disclosure of the customer complaints submitted to the Consumer Financial Protection Bureau (CFPB) – to examine whether regulatory scrutiny represents one mechanism through which the disclosure of customer complaints can affect bank behavior. I find that banks with a higher complaint volume on the disclosure date increase mortgage approval rates relative to banks with fewer complaints in the same county, and that this effect is strongest in financially underserved communities. I further find that the disclosure effect is larger for banks under more regulatory scrutiny, namely, those operating in states with stronger consumer financial protection enforcement and those with prior consumer affairs violations. Taken together, the results suggest that the public disclosure of customer complaints, especially when accompanied by regulatory pressure, can serve as a mechanism for customers to influence banks’ consumer lending behavior.