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.

Browse

Search Results

Now showing 1 - 10 of 92
  • Thumbnail Image
    Item
    The Effect of Perceived Attitude Similarity on Performance Ratings
    (1983) Feren, Dena Beatrice; Carroll, Stephen J.; Digital Repository at the University of Maryland; University of Maryland (College Park, Md)
    This research consists of a laboratory study designed to test the notion that variance in performance ratings can be accounted for by the perception of the rater that the persons/he is evaluating is attitudinally similar or dissimilar to himself or herself. Student subjects were led to believe that a certain manager either agreed or disagreed with them on a number of attitudinal issues. Subjects then viewed a videotaped performance of the manager conducting a performance review with one of his problem subordinates. Subjects were asked to rate his performance using two different rating instruments -- a trait rating scale and a Behavior Observation Scale -- and to indicate personal liking for the manager. Extent of attitude similarity was manipulated on two levels with a control group . That is, some subjects were led to believe that the ratee was attitudinal l y similar to self, others that the ratee was dissimilar to self, and a third group received no information about the ratee's attitudes. The ratee's performance was manipulated on three levels. Some subjects viewed only a high performin1; manager, others viewed a moderate performer, and a third group viewed a lo w performing manager. Three different vignettes were prepared to represent the three levels of performance. Finally, a hard-performance-data condition was included to test the robustness of the attitude similarity effect. Some subjects received hard performance data, in the form of bar graphs, that was consistent with the level of performance portrayed in their videotaped vignette (i.e., those viewing the low performer received hard data indicative of low performance). It was hypothesized that perceived attitude similarity would have its greatest effect when performance was moderate, and when subjects did not receive hard performance data. The results did not support these predictions. The effect of perceived attitude similarity on performance ratings was not significant under any of the experimental conditions. Perceived similarity had a small, but significant effect on attraction; however, level of performance accounted for a far greater proportion of variance in attraction measures than perceived similarity. It was concluded that the rating task in this experiment failed to create the conditions under which perceived similarity would be most likely to exert an influence on ratings. Specifically, the rating task was not sufficiently ambiguous for student raters.
  • Thumbnail Image
    Item
    DATA-DRIVEN OPTIMIZATION AND STATISTICAL MODELING TO IMPROVE DECISION MAKING IN LOGISTICS
    (2019) Sinha Roy, Debdatta; Golden, Bruce; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In this dissertation, we develop data-driven optimization and statistical modeling techniques to produce practically applicable and implementable solutions to real-world logistics problems. First, we address a significant and practical problem encountered by utility companies. These companies collect usage data from meters on a regular basis. Each meter has a signal transmitter that is automatically read by a receiver within a specified distance using radio-frequency identification (RFID) technology. The RFID signals are discontinuous, and each meter differs with respect to the specified distance. These factors could lead to missed reads. We use data analytics, optimization, and Bayesian statistics to address the uncertainty. Second, we focus on an important problem experienced by delivery and service companies. These companies send out vehicles to deliver customer products and provide services. For the capacitated vehicle routing problem, we show that reducing route-length variability while generating the routes is an important consideration to minimize the total operating and delivery costs for a company when met with random traffic. Third, we address a real-time decision-making problem experienced in practice. In one application, routing companies participating in competitive bidding might need to respond to a large number of requests regarding route costs in a very short amount of time. In another application, during post-disaster aerial surveillance planning or using drones to deliver emergency medical supplies, route-length estimation would quickly need to assess whether the duration to cover a region of interest would exceed the drone battery life. For the close enough traveling salesman problem, we estimate the route length using information about the instances. Fourth, we address a practical problem encountered by local governments. These organizations carry out road inspections to decide which street segments to repair by recording videos using a camera mounted on a vehicle. The vehicle taking the videos needs to proceed straight or take a left turn to cover an intersection fully. Right turns and U-turns do not capture an intersection fully. We introduce the intersection inspection rural postman problem, a new variant of the rural postman problem involving turns. We develop two integer programming formulations and three heuristics to generate least-cost vehicle routes.
  • Thumbnail Image
    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.
  • Thumbnail Image
    Item
    A Configurational Approach to Examining the Influence of Information Technology Management and Governance on Organization Performance
    (2019) Aljazzaf, Salman; Mithas, Sunil; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Information technology (IT) is becoming an increasingly crucial part of modern organizations. This dissertation includes two essays that examine how effective IT management and decision-making structure are associated with better organizational performance. The first essay examines the complementarity between IT management and human resource (HR) management capabilities and discusses the mechanisms through which these two capabilities jointly lead to better organizational performance. The unique contribution of this study is the use of direct measures of IT management and HR management capabilities to estimate their joint impact on organizational performance. Furthermore, I disaggregate HR capability into two specific dimensions: (1) work systems such as employee performance management systems and hiring and promotion systems, and (2) employee learning and development. The main results confirm the complementarity between IT management and both HR management dimensions, and show that work systems more positively moderates the impact of IT management on organizational performance based on financial and market measures. The study is supplemented with a configurational analysis that examines the complex relationships between the organizational capabilities and explain how the complementarity between IT management, work systems, and employee learning varies across sectors and relies also on the presence and absence of other capabilities such as leadership and strategic planning. The study compares the results of the conventional and configurational methods and highlights the unique insights derived from each approach. The second essay discusses the optimal IT reporting structure in a firm, that is, whether the IT head should report to the chief executive officer or some other executive. This study proposes that there are several factors that determine the optimal IT reporting structure such as firm size, industry, IT investment intensity, and whether IT is viewed as strategic to the firm. The study argues that the relationship between these factors and the optimal IT reporting structure is too complex to be represented by linear models that rely on the correlation-based approach. Instead, there is a need to study configurations that lead to better performance based on different combinations of firm-level and industry-level conditions. The study uses a novel configurational approach and a corresponding method, the fuzzy-set qualitative comparative analysis, to determine the optimal IT reporting structure of different configurations. The study results shed light on the complex relationship between IT reporting structure and the conditions defining various firm configurations. Together the two essays provide new insights on how successful IT management and governance structure lead to organizational success.
  • Thumbnail Image
    Item
    DESIGNING INFORMATION STRATEGIES FOR DIGITAL PLATFORMS: FINDINGS FROM LARGE-SCALE RANDOMIZED FIELD EXPERIMENTS
    (2019) Shi, Lanfei; Viswanathan, Siva; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The rise of digital platforms has transformed our economy and reshaped consumer behaviors and experiences. While practitioners and researchers have a growing interest in understanding digital platforms, there is still a dearth of research on how platforms can design effective information strategies to mitigate fundamental issues such as information asymmetry and search frictions by leveraging granular data. My dissertation seeks to fill this gap. Specifically, by focusing on significant real-world problems on digital platforms, I aim to examine IT-enabled and analytics-driven information strategies and study the impact of these strategies on the users as well as on the platforms themselves. In collaboration with two different online platforms, I design and conduct three randomized field experiments to investigate the impact of informational interventions and provide actionable suggestions. In Essay 1, I examine incentive strategies for motivating effective mobile app adoptions, by comparing monetary incentives against informational incentives. I find that the usage after app adoption depends on how customers are motivated, and only information induced adoption leads to long-term increase in purchases. In Essay 2, I investigate the role of “verification” when it is made optional, and find that it serves as a very effective signaling device, especially in markets that lack other mechanisms such as reputation systems. I also find that users on the two sides of online platform use the same signal very differently, and that this is attributable to the difference in the credibility of their primary signaling-attribute of each side, viz. income in males and beauty in females. In Essay 3, I examine the effectiveness of three different recommendation systems in two-sided matching platforms with a focus on how the provisioning of potential candidates’ preference information impacts focal user’s decision-making and matching outcomes. I find that compared to “people you might prefer”, users act strategically towards “people who might prefer you” and “people who you might prefer and who might prefer you” by actively reaching out to less desirable candidates, which leads to improved outcomes. In short, the three studies present new empirical evidence of how platforms can leverage information as a tool to design effective incentives, signaling mechanisms and recommender systems to facilitate users’ decision-making, transactions and matching.
  • Thumbnail Image
    Item
    Online Doctor Reviews: Essays on their Economic Implications, Incidence of Fraud, and Motivation of Reviewers
    (2019) Shukla, Aishwarya Deep; Agarwal, Ritu; Gao, Guodong (Gordon); Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The ubiquity of WOM in the business world underscores how instrumental it has been as a consumer engagement lever. Therefore, it is imperative for business to understand the consequential role of WOM in influencing consumer behavior. There is also a great need to improve the quality and quantity of online reviews. I address three overarching questions in my dissertation: (1) What is the effect of WOM on consumer decision making; (2) How to detect fake reviews using the review text; and (3) How to encourage reviewers to reveal their identity and give higher quality reviews. In the first study, I estimate the causal effect of online WOM on consumer demand and uncover its mechanism in affecting the consumer decision making process. I utilize a natural experiment to examine the causal effect of online WOM on consumer demand. The setup allows me to gain granular understanding of how WOM affects the consideration set size and session duration. In addition, the availability of provider location in the dataset allows me to estimate the impact of online WOM on the consumer’s willingness to travel. In the second study, I detect fake online reviews. To identify fake reviews, I use an incidental honeypot that attracts fraudulent behavior by opening low-cost channels for fraudsters. This allows me to build a large training dataset for the machine learning classifier. Finally, in my third study, I explore how email message framing and assurance of user privacy affect the response rate and response quality of online WOM. I conduct a field experiment to uncover how the propensity of a user to give feedback for their doctor can be influenced by a motivational message and how privacy assurance affects identity revelation. Collectively, these studies advance our knowledge on the antecedents and consequences of online reviews, which helps business and society to better utilize WOM for greater value creation for consumers.
  • Thumbnail Image
    Item
    The Interplay Between Social Connections and Digital Technologies: Three Essays Examining Healthy Behaviors and Income Mobility
    (2018) Liu, Chewei; Agarwal, Ritu; Mithas, Sunil; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In the past few decades, digital technologies have profoundly altered virtually every aspect of human life. While the direct impact of digital technologies on individuals’ economic welfare or personal behaviors has attracted considerable attention, the interplay of digital technologies with social connections remains underexplored. Indeed, regardless of whether formed offline or online, social connections in the form of personal ties and affiliations that have long been the bedrock of human society continue to shape human behaviors and outcomes. To the extent that digitization will only continue to grow in scale and scope, an understanding of such effects is important for scholars, practitioners, and policymakers. I address two overarching research questions in my dissertation: (1) Whether, and to what extent digital technologies affect individuals’ economic welfare and habituated behavior, and (2) How social connections such as personal ties and affiliations condition the impact of the digital technologies. My studies are conducted in two distinct contexts: mobile interventions for health, and computer ownership for social and economic welfare. Drawing on diverse bodies of literature and using various econometric methods, I seek to answer questions related to how interventions orchestrated on mobile platforms help individuals form healthy behaviors, and how computer ownership affects long-term income mobility. In the first essay, I show that a social norms intervention on a mobile platform is effective in increasing individuals’ physical activity. In the second study, I investigate how the motivational incentive of reciprocity can be leveraged to promote healthy behavior. Finally, in my third essay, I show that computer ownership generates both private and social returns (IT spillovers) on individuals’ income mobility. All three papers then consider how individuals’ social connections condition the direct effects of digital technologies. The first two studies explore how online social ties and social relationships moderate the impact of mobile interventions, and the third study examines how caste groups affect the positive spillover effects of computer ownership. Collectively, the three studies advance our understanding of the heterogeneous effects of digital technologies on individuals and provide implications for researchers and practitioners.
  • Thumbnail Image
    Item
    Individual differences in regulatory mode moderate the effectiveness of a pilot mHealth trial for diabetes management among older veterans
    (PLoS (Public Library of Science), 2018-03-07) Dugas, Michelle; Crowley, Kenyon; Gao, Guodong Gordon; Xu, Timothy; Agarwal, Ritu; Kruglanski, Arie W.; Steinle, Nanette
    mHealth tools to help people manage chronic illnesses have surged in popularity, but evidence of their effectiveness remains mixed. The aim of this study was to address a gap in the mHealth and health psychology literatures by investigating how individual differences in psychological traits are associated with mHealth effectiveness. Drawing from regulatory mode theory, we tested the role of locomotion and assessment in explaining why mHealth tools are effective for some but not everyone. A 13-week pilot study investigated the effectiveness of an mHealth app in improving health behaviors among older veterans (n = 27) with poorly controlled Type 2 diabetes. We developed a gamified mHealth tool (DiaSocial) aimed at encouraging tracking of glucose control, exercise, nutrition, and medication adherence. Important individual differences in longitudinal trends of adherence, operationalized as points earned for healthy behavior, over the course of the 13-week study period were found. Specifically, low locomotion was associated with unchanging levels of adherence during the course of the study. In contrast, high locomotion was associated with generally stronger adherence although it exhibited a quadratic longitudinal trend. In addition, high assessment was associated with a marginal, positive trend in adherence over time while low assessment was associated with a marginal, negative trend. Next, we examined the relationship between greater adherence and improved clinical outcomes, finding that greater adherence was associated with greater reductions in glycated hemoglobin (HbA1c) levels. Findings from the pilot study suggest that mHealth technologies can help older adults improve their diabetes management, but a “one size fits all” approach may yield suboptimal outcomes.
  • Thumbnail Image
    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.
  • Thumbnail Image
    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.