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 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 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 ON THE IMPLICATIONS OF NEW POLICIES, MARQUEE SELLERS, AND GREEN NUDGES IN ONLINE SECONDARY MARKETS FOR DURABLE IT PRODUCTS: EVIDENCE FROM EMPIRICAL STUDIES(2021) Alhauli, Abdullah; Gopal, Anandasivam; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The rapid pace of product development in the IT sector has led to a volume surge of product returns, giving rise to critical environmental threats that can potentially have significantly adverse ecological effects. One possible avenue to mitigate these negative effects pertains to the establishment of robust secondary markets for these products, so that their useful life can be enhanced. My dissertation seeks to study multiple aspects aimed at enhancing the efficiency of online secondary markets for durable IT products, using economic and behavioral theories. The first essay examines the extent to which firm policies in the primary market mitigate inefficiencies caused by adverse selection in the secondary market for IT products. I find that policies implemented by firms in the primary market with respect to their products can have beneficial effects in addressing adverse selection in the secondary markets. The second essay studies how adding a marquee seller to a B2B secondary market platform for IT products affects other sellers, in terms of the prices they obtain for comparable products. I show that the entry of a marquee seller has a positive effect on the prices obtained by other sellers on the platform. I further show that this positive effect on final prices is moderated by bidders multi-homing activity, and their level of involvement in the marquee seller’s site. Finally, through behavioral experiments performed on Amazon MTurk, my third essay examines the extent to which the use of behavioral interventions, in the form of green nudges, can enhance the propensity of used IT products being purchased in the secondary market, thereby increasing the lifetime of these products. I find that the efficacy of using green nudges to impact consumer behavior depends on the kind of motivation (i.e., internal versus external motivation) the nudge is delivering. I further find that the effectiveness of green nudges can vary based upon product price and perceived quality, and consumer demographics and latent personalities. Collectively, the findings from these studies in my dissertation provide valuable theoretical as well as practical insights about the effectiveness of different mechanisms for enhancing the efficiency of online secondary markets for durable IT products.Item EMPIRICAL INVESTIGATION OF USERS’ SUCCESSFUL STRATEGIES IN ONLINE PLATFORMS - EVIDENCE FROM CROWD-SOURCING AND SOCIAL MEDIA PLATFORMS(2021) Lysyakov, Mikhail; Viswanathan, Siva; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)With the proliferation and constant growth of online platforms, there has been an increasing interest among academicians and practitioners to understand various aspects of these platforms, including the effective design of platforms, their governance and user engagement. This dissertation seeks to add to this stream of research by leveraging large-scale unstructured data and corresponding data analytics and econometric techniques to examine users’ strategies in online social media and crowdsourcing platforms and gain insights into factors that lead to successful outcomes. The first essay examines the content strategies of closely competing firms on Twitter with a focus on how the similarity/dissimilarity of their content strategies impacts their online outcomes. I find that firms that are more adept at leveraging higher-level social media affordances, such as interactivity, collaboration, and online contests to differentiate their content strategies experience better outcomes as compared to their closest rivals that only leverage the basic technological affordances of social media. The second essay examines successful strategies of users (designers) in a crowdsourcing platform wherein clients post contests to solicit design solutions for a monetary reward. This study uses state-of-the-art deep learning and image analysis techniques to examine the strategies of experienced and less-experienced designers in open contests where later-entrants can potentially leverage information spillovers from earlier design submissions within a contest. I find that while later-entrants typically leverage information spillovers from earlier submissions in a contest, only experienced designers who are able to integrate information from multiple highly-rated early submissions are more likely to be successful. The third essay examines users’ strategies in response to the introduction of an Artificial Intelligence system for logo design in an online crowdsourcing design platform. In analyzing what differentiates successful contestants from the others, I find that the successful contestants significantly increase focus (i.e., the number of re-submissions per contest) and increase the emotional content as well as the complexity of their designs, in response to the introduction of the AI system. Collectively, the findings from these studies add to our understanding of successful strategies in online platforms and provide valuable insights to theory and practice.Item Marriages Made in Silico: Essays on Social Norms, Technology Adoption, and Institutions in Online Matrimonial Matching Platforms(2020) Karmegam, Sabari Rajan; Gopal, Anandasivam; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Online matrimonial platforms have emerged as a way to take the highly institutionalized process of arranged marriages online while preserving the offline social, cultural, and gender norms. While there is a rich body of empirical work on online dating, the corresponding literature on online matrimonial platforms is sparse. My dissertation seeks to fill this gap. In my first essay, I look at mobile adoption's role in online matrimonial platforms' engagement and matching outcomes. The analysis shows that unlike the dating market where the market's transaction costs are eased by the ubiquity and personal nature of the mobile device for all users, here subgroups associated with strong endogamous preferences benefit with mobile adoption. My work extends the mobile ecosystem study to the societal context where institutional norms take precedence and influence mobile adoption outcomes. In my second essay, I study how the search frictions, social norms, and disempowerment that results from the gender skew in online matching platforms can be mitigated by using appropriate market design. I use a quasi-experimental methodology by relying on two interventions designed by the platform to reduce women's cognitive load. The interventions improved the overall well-being of women on platforms. My work here aims to increase awareness on the role platforms needs to play to improve women's well-being while ensuring that online platforms do not unravel. In my third essay, I look at whether the sanctity of institutional norms and traditional markers of status - involvement of multiple stakeholders through parental involvement and social norms related to endogamy and gender roles are retained in online matrimonial platforms. I find that "platformization" leads to institutional unbundling, with outcomes guided by more liberal ethos. This essay extends the platform literature on institutional contexts and shows that transition to online settings may not be seamless. My dissertation thus contributes to the literature on Information Systems by highlighting the need to consider the societal, cultural, and gender norms to further our understanding of the market design and technology adoption in highly institutionalized contexts.Item Mechanism Designs to Mitigate Disparities in Online Platforms: Evidence from Empirical Studies(2020) Mayya, Raveesh K; Viswanathan, Siva; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)With the rising ubiquity of online platforms, there is an increasing focus on platforms’ role in enabling fair exchanges between buyers and sellers. Traditionally, platforms have inbuilt mechanisms such as screening or upfront data-gathering disclosure that encourage transactions between unfamiliar participants. Since such mechanisms can introduce power disparities between different sides, platforms have enacted policy changes to fix the imbalance. Extant literature hasn’t studied the unintended consequences of such policy changes. My dissertation seeks to fill this gap by examining platforms’ decisions to enact policy/mechanism changes that level the playing field by decentralizing choices for different sides. Using empirical studies, my dissertation seeks to causally identify the impact of such changes on outcomes for participants as well as for the platform. The first essay in my dissertation examines the impact Airbnb’s decision to make screening optional. There is increasing evidence that two-way screening mechanism has been used as a tool by users on the platform to discriminate against some users on the other side. In making screening optional, I find that African American hosts and female hosts are more likely to forgo screening and they benefit the most (in terms of occupancy, price and/or ratings) from forgoing screening, indicating that making screening optional can serve as a useful mechanism in helping alleviate reverse discrimination of hosts by guests. The second essay studies platforms’ attempts to provide smartphone users with better choice over which sensitive information can mobile apps access. In particular, I examine the timing of mobile apps' decisions to upgrade to Android 6.0, which restricts the ability of mobile apps from seeking blanket permissions to sensitive user information at download, instead requiring them to request à la carte permissions at run-time. I find that apps that over-seek (access information that are non-essential to their functionality) sensitive information from users strategically delay upgrading to Android 6.0. However, these apps suffer popularity and reputational costs in the Android marketplace. Collectively, the findings in my dissertation provides valuable theoretical as well as practical insights about the welfare implications of choice decentralization on all sides in online platforms, not just the intended side.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.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.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.
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