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 DEVELOPING MULTIMODAL LEARNING METHODS FOR VIDEO UNDERSTANDING(2024) Sun, Mingwei; Zhang, Kunpeng; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In recent years, the field of deep learning, with a particular emphasis on multimodal representation learning, has experienced significant advancements. These advancements are largely attributable to groundbreaking progress in areas such as computer vision, voice recognition, natural language processing, and graph network learning. This progress has paved the way for a multitude of new applications. The domain of video, in particular, holds immense potential. Video is often considered the most potent form of digital content for communication and the dissemination of information. The ability to effectively and efficiently comprehend video content could prove instrumental in a variety of downstream applications. However, the task of understanding video content presents numerous challenges. These challenges stem from the inherently unstructured and complex nature of video, as well as its interactions with other forms of unstructured data, such as text and network data. These factors contribute to the difficulty of video analysis. The objective of this dissertation is to develop deep learning methodologies capable of understanding video across multiple dimensions. Furthermore, these methodologies aim to offer a degree of interpretability, which could yield valuable insights for researchers and content creators. These insights could have significant managerial implications.In the first study, I introduce an innovative network based on Long Short-Term Memory (LSTM), enhanced with a Transformer co-attention mechanism, designed for the prediction of apparent emotion in videos. Each video is segmented into clips of one-second duration, and pre-trained ResNet networks are employed to extract audio and visual features at the second level. I construct a co-attention Transformer to effectively capture the interactions between the audio and visual features that have been extracted. An LSTM network is then utilized to learn the spatiotemporal information inherent in the video. The proposed model, termed the Sec2Sec Co-attention Transformer, outperforms several state-of-the-art methods in predicting apparent emotion on a widely recognized dataset: LIRIS-ACCEDE. In addition, I conduct an extensive data analysis to explore the relationships between various dimensions of visual and audio components and their influence on video predictions. A notable feature of the proposed model is its interpretability, which enables us to study the contributions of different time points to the overall prediction. This interpretability provides valuable insights into the functioning of the model and its predictions. In the second study, I introduce a novel neural network, the Multimodal Co-attention Transformer, designed for the prediction of personality based on video data. The proposed methodology concurrently models audio, visual, and text representations, along with their intra-relationships, to achieve precise and efficient predictions. The effectiveness of the proposed approach is demonstrated through comprehensive experiments conducted on a real-world dataset, namely, First Impressions. The results indicate that the proposed model surpasses state-of-the-art methods in performance while preserving high computational efficiency. In addition to evaluating the performance of the proposed model, I also undertake a thorough interpretability analysis to examine the contribution across different levels. The insights gained from the findings offer a valuable understanding of personality predictions. Furthermore, I illustrate the practicality of video-based personality detection in predicting outcomes of MBA admissions, serving as a decision support system. This highlights the potential importance of the proposed approach for both researchers and practitioners in the field. In the third study, I present a novel generalized multimodal learning model, termed VAN, which excels in learning a unified representation of \textbf{v}isual, \textbf{a}coustic, and \textbf{n}etwork cues. Initially, I utilize state-of-the-art encoders to model each modality. To augment the efficiency of the training process, I adopt a pre-training strategy specifically designed to extract information from the music network. Subsequently, I propose a generalized Co-attention Transformer network. This network is engineered to amalgamate the three distinct types of information and to learn the intra-relationships that exist among the three modalities, a critical facet of multimodal learning. To assess the effectiveness of the proposed model, I collect a real-world dataset from TikTok, comprising over 88,000 videos. Extensive experiments demonstrate that the proposed model surpasses existing state-of-the-art models in predicting video popularity. Moreover, I have conducted a series of ablation studies to attain a deeper comprehension of the behavior of the proposed model. I also perform an interpretability analysis to study the contributions of each modality to the model performance, leveraging the unique property of the proposed co-attention structure. This research contributes to the field by proffering a more comprehensive approach to predicting video popularity on short-form video platforms.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 Estimating the Tour Length for the Close Enough Traveling Salesman Problem(MDPI, 2021-04-12) Roy, Debdatta Sinha; Golden, Bruce; Wang, Xingyin; Wasil, EdwardWe construct empirically based regression models for estimating the tour length in the Close Enough Traveling Salesman Problem (CETSP). In the CETSP, a customer is considered visited when the salesman visits any point in the customer’s service region. We build our models using as many as 14 independent variables on a set of 780 benchmark instances of the CETSP and compare the estimated tour lengths to the results from a Steiner zone heuristic. We validate our results on a new set of 234 instances that are similar to the 780 benchmark instances. We also generate results for a new set of 72 larger instances. Overall, our models fit the data well and do a very good job of estimating the tour length. In addition, we show that our modeling approach can be used to accurately estimate the optimal tour lengths for the CETSP.Item WHITHER WONDER WOMEN? ESSAYS ON GENDER DIVERSITY IN IT-ENABLED PROFESSIONAL AND CREATIVE DOMAIN(2023) Wang, Yifei; Ramaprasad, Jui; Gopal, Anandasivam; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Working towards equality and inclusion around gender and race in society is critically important. Despite the increasing number of conversations around these issues, more work is needed to evaluate the causes of unequal participation of men and women in organizations, markets, and economies. In particular, the lack of equity in terms of representation and participation within the important information technology (IT) sector has often been viewed as an ongoing problem. My dissertation focuses on this specific sector and explores potential remedies to enhance the participation and representation of women in specific segments of IT-enabled work, albeit in three different empirical contexts. In my first essay, I investigate the unequal participation of women in IT labor markets and whether they are less willing to compete for complex and risky IT projects. Through multiple experiments on technically trained students, I find that women in the IT industry are more willing to participate in bidding for riskier projects, and their bids are higher than those of men. My second essay studies the issue of unequal representation by women within the digital music industry, where inequitable representation has been clearly noted. Women artists are often faced with less attention, respect, and market share. The study shows that TikTok dance challenges offer a low-cost, effective way to promote artists and increase visibility. The challenges are particularly beneficial for women music artists. My third essay examines the intersection of gender and race in digital music consumption after Floyd's death. I explore whether music can raise awareness of social justice issues and the role of Black artists as sensemaking agents. I find that hip-hop listeners increased after Floyd's death, particularly in less racially diverse cities. Black artists received more listenership across all genres, but consumption was skewed towards Black men artists, highlighting the underrepresentation of women in Black-dominated music genres. Collectively, the findings from these studies in my dissertation will provide valuable theoretical contributions, practical insights, and actionable solutions to bind the gender gap and make the digital markets more diverse and inclusive.Item ECONOMETRIC ANALYSIS OF BIKE SHARING SYSTEMS(2022) Cao, Huan; Tunca, Tunay TT; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)I study the efficiency of the dockless bike sharing system, and how to utilize the operational decisions to improve system efficiency and profit. In the first chapter, I empirically analyze riders' economic incentives in a dockless bike sharing system and explore how to improve the efficiency of this business model. Specifically, I aim to answer three main questions: (i) What is the impact of the number of bicycles in the system on efficiency? (ii) How can bike relocation be best used to improve utilization? (iii) How do the efficiency of dockless and dock-based systems compare? To address these questions, I first build a microeconomic model of user decision-making in a dockless bike sharing system. I then use this model together with transaction-level data from a major dockless bike-sharing firm to structurally estimate the customer utility and demand parameters. Using this estimation in counterfactual analysis, we find that the company can decrease the bicycle fleet size by 40% while maintaining 90% of transactions, leading to estimated savings of $6.5 Million. We further find that a spatial bicycle rebalancing system based on our customer utility model can improve daily transactions by approximately 19%. Finally, we demonstrate that without bicycle redistribution, a smartly designed dock-based system can significantly outperform a dockless system. Our model provides a utility-based model that allows companies to estimate not only transactions, but also the time and location of lost potential demand, which can be used to make targeted improvements to the geographic bike distribution. It also allows managers to fine-tune bicycle fleet sizes and spatial rebalancing parameters. Further, our structural demand modeling can be used to improve the efficiency of dock-based systems by helping with targeted dock location decisions. In the second chapter, using data from a major dockless bike sharing system in Beijing, I study the subscription behavior and its relationship with the service level and price. Specifically, I develop an econometric model to study the subscription behavior for both existing subscribers and new sign-ups, respectively, and build up a functional relationship between the service level and system demand level and reveal the dynamics and interplays among subscription, system demand, and service level, which helps to recover the evolution of the number of subscribers over time. Based on all the estimation results and functional relationships, I then construct an empirical framework and straighten out the relationship between company profit and bicycle fleet size/subscription price. The counterfactual results show that the marginal benefit of deploying a larger bicycle fleet is decreasing, and the company should be cautious in determining and adjusting the bicycle fleet size. In addition, the examination demonstrates that the current price is too low, and raising the price properly can achieve about a 25% profit increase. The results also show the value of understanding riders' sensitivity to price and how to use it to better their operational decisions and accomplish better financial results. The counterfactual framework I proposed can be utilized in various policy evaluations and provides important insights and recommendations to the bikeshare companies and regulators.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.Item THE PRICE OF FRESH AIR: ESSAYS ON THE INTERACTIVE EFFECTS OF TECHNOLOGY AND AIR POLLUTION ON ECONOMIC ACTIVITY(2022) Jeong, Jaehoon; Gopal, Anandasivam; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)With recent dramatic industrialization around the world, air quality has become a global issue. In my dissertation, I investigate the effects of air pollution on the omni-channel retail business and public transportation.In the first essay, I study how diminished air quality affects the substitutive relationship between offline and online sales associated with a cosmetics retailer located in South Korea. I specifically test how air pollution may affect the actual demand that occurs during these promotion days across the offline and online channels. Interestingly, polluted air boosts online sales and online promotion effectiveness. Unexpectedly, air pollution is unlikely to hurt offline sales, and even increase offline sales and offline promotion effectiveness. I also find a notion of the inverted-U shaped reaction to the seriousness of polluted air consistently in offline sales. The second essay examines the effect of mobile nudges on behavioral changes focusing on public transportation ridership. I study the effect of air quality categories with easy-to-interpret user interface and air quality notification using a regression discontinuity design. I show that mobile nudges effectively help users make better decisions to protect themselves. My additional analyses suggest that the effect of mobile nudges may vary by schedule flexibility and travel purposes. I also observe adaptation behavior to air pollution over the years. In my third essay, I study the interaction between air pollution, channels, and product categories in the online retail context. Combined with environmental changes, differences in product characteristics and channel fit can create varied patterns of demand shift. Considering air pollution-driven shopping motivation, I examine how air pollution affects relative product category sales across mobile and PC channels. My results show that air pollution can increase mobile sales volume more than PC sales volume in urban areas. Also, air pollution creates a larger effect on skin care products and lower priced items than on makeup products and higher priced items accordingly. Overall, my dissertation suggests theoretical and practical implications for the business and social impacts of air pollution, which should aid decision-makers in formulating business and sound policy.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 PRICING AND EMPLOYMENT ISSUES IN THE PROVISION OF RIDE SERVICES(2022) Cao, Ziwei; Ball, Michael; Kannan, P.K.; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Recent years have witnessed the emergence and dramatic growth of platform businesses. This dissertation addresses two challenges of significance to ride service companies: 1) it investigates promotion effects on two-sided platforms; and 2) it models platform pricing and staffing strategies under a hybrid employment mode.In the first chapter, I broadly discuss the new challenges faced by the ride service platforms in recent years and provide perspectives on related research questions. In the second chapter, I study the network effects of different promotion methods in two-sided markets. Using data from a transportation-service platform, I specify a structural model that quantifies the respective promotional effects for price discounts and service upgrades. The results show that the primary effect of price discounts is to increase demand within the same service tier, whereas upgrades have stronger stickiness effects and spillover effects. Based on the estimates, I calculate the return on investment (ROI) and find that the ROI for upgrades is higher than that for discounts. Our counterfactual analyses show that as the platform matures, the importance of upgrades increases while the importance of price discounts decreases. These results provide important managerial implications for platforms on how to design optimal promotions to grow their business. In the third chapter, I model an on-demand platform that adopts a hybrid employment mode. This work is motivated by the recent public debate over the status of drivers for the major ride- hailing platforms as contract workers. My hybrid employment environment includes both contractors and full-time employees who receive a benefits package. In the hybrid model with driver control, drivers have the flexibility to decide how long to work and consequently whether to be an employee or a Contractor. Those who work over a certain number of hours will be classified as employees and receive a benefits package. The platform is a profit-maximizer and decides the optimal price based on the required benefit amount. As the benefit amount increases, the platform's profit decreases, which is consistent with strong gig company opposition to providing benefits. Moreover, I show that higher benefits make consumers and full-time drivers better off but decrease part-time drivers' welfare as well as overall social welfare. I consider a second model to better balance the platform's profitability and drivers' welfare. Under the hybrid model with platform control, the platform hires a limited number of full-time employees while guaranteeing that a minimum proportion of all work will be fulfilled by those employees. In this way, the profit loss due to the required benefits is capped, making this alternative policy a potentially viable solution from the platform’s perspective.Item DATA-DRIVEN ESSAYS: ROLE OF PRICING AND RETURNS(2022) Hemmati, Sahar; Elmaghraby, Wedad J; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this dissertation, we identify leavers in retailers’ operations such as their shipping policy design as well as markdown strategy which can significantly impact their product returns and the costs associated with it. We study how a high free shipping thresholds can induce shoppers to place orders with an intention to return a part of the order later. Using an empirical approach, which exploits natural experiments, we confirm that high threshold and/or high shipping fees induce a substantial order padding behavior, which leads to lower sales revenue, after adjusting for returns. However, we find that such behavior can be largely alleviated with frictions to returns. We propose that retailers looking to design their shipping policy should correctly account for return environment features. We also explore the link between a retailer’s markdown pricing strategies and its impact on customers’ purchase and return behavior. We specifically study the distinction between regular price markdowns and bundle price markdowns and the key contributors to such distinctions, and how they contribute to sales and customers’ return behavior. Capturing this notion and the heterogeneous impact of bundle and regular discounts on merchandise with different substitutability or complementarity as well as correlation between items at return stage, we offer recommendations on how a retailer should approach the design of their markdown strategies.