About

Onkar Malgonde has a background in both the academic and technical fields. After working as a Systems Engineer with Infosys Technologies, he pursued higher education to receive his MS and PhD in Information Systems from the University of South Florida. Prior to his position at Poole College of Management, Malgonde held faculty positions at Northern Illinois University and the University of North Texas. His research interests include the intersection of digital platforms, recommender systems, and software systems. His work has been published in several journals such as MIS Quarterly, Journal of Management Information Systems, Empirical Software Engineering, and Electronic Markets, as well as in the proceedings of premier Information Systems conferences and workshops.

Research topics

• Computer Science
• Artificial Intelligence
• Psychology
• Data science
• Sociology
• Political Science
• World Wide Web
• Social Science
• Mathematics
• Economics
• Economic growth
• Statistics
• Social psychology
• Theoretical computer science
• Public relations

Selected publications

• How Do Technology Paradigms Influence Configurations of Contract Characteristics for Success of Inter‐Organizational Outsourcing Projects, 1991–2009?

  Journal of Operations Management · 2026-01-06

  article1st author

  ABSTRACT What are the distinct configurations of contract characteristics associated with the success of inter‐organizational outsourcing projects across different technology paradigms? We examine information technology outsourcing contracts between 1991 and 2009 to address this question by using a relatively new approach based on qualitative comparative analysis. We consider four technology paradigms: pre‐Internet (1991–1996), pre‐Dotcom (1997–2000), post‐Dotcom (2001–2005), and Cloud Computing (2006–2009). We discuss issues related to adverse selection and moral hazard and identify five key contract characteristics that determine contract success: new contract, existing organizational relationship, long contract duration, fixed price, and competitive bidding. Our analyses document two key findings. First, we show that configurations of contract characteristics for success and failure of outsourcing projects are different across technology paradigms. Second, we identify three themes in configurations associated with outsourcing success—economic imperative, conservative relational, and conservative imperative. These themes extend prior work that draws on transaction cost economics, social exchange theory, and relational exchange theory and identify an increasing emphasis on the relational component to manage contracting risk for outsourcing success over time. From a managerial perspective, we provide context‐sensitive causal recipes to choose configurations of contract characteristics, considering technology paradigms. Together, our findings provide new insights for developing cumulative knowledge for understanding the determinants of success of interorganizational outsourcing projects while opening new avenues for further theorizing and empirical testing.

  PublisherDOI

• How Do Star Contributors Influence the Quality and Popularity of Artifacts in Online Collaboration Communities?

  Journal of the Association for Information Systems · 2026-01-01

  articleSenior author

  Online collaboration communities (OCCs) enable geographically distributed individuals, groups, and organizations to self-organize and contribute to community-owned artifacts. The significance of these artifacts has been underscored by recent advancements in large language models, which leverage community content for training sophisticated models across diverse domains, including productivity, healthcare, and education. This study investigates star contributors—individuals making disproportionately large contributions to focal OCC artifacts. Drawing on theories of collective action and strategic interactions, we hypothesize a curvilinear relationship between star contributors’ contributions and both artifact quality and popularity. Utilizing data from over 21,000 open-source software projects between 2015 and 2019, we find: (1) an inverted U-shaped relationship between the number of star contributors and artifact quality, (2) an inverted U-shaped relationship between the number of star contributors and artifact popularity, (3) that a higher proportion of star contributors’ contributions enhances artifact quality but not popularity, and (4) that environmental dynamism moderates the relationship between the number of star contributors and both artifact quality and popularity. This research advances the conceptualization of star contributors, offering a more nuanced understanding aligned with the fluid boundaries of OCCs compared to traditional core-periphery models. A key implication is that while star contributors positively impact artifact quality and popularity, an excessive proportion of their contributions negatively affects artifact quality.

  PublisherDOI

• Unraveling the Privacy Paradox: a Comprehensive Review of Factors Behind the Discrepancy in Online Concerns and Disclosure Behavior

  Information Systems Frontiers · 2025-06-23 · 6 citations

  review
  PublisherDOI

• Foreign-Born Information Technology Professionals’ Pay and Mobility in the European Labor Market

  Academy of Management Proceedings · 2025-07-01

  article

  Although prior studies have studied how foreign-born or immigrant workers fare in terms of their wages in the United States compared to native or American Information technology (IT) professionals, we know very little about wage differences in compensation of foreign-born and native IT professionals in other geographies. In addition, in the last two decades or so, there have been significant changes in underlying technologies, and it is not clear how such changes influenced any wage differences and mobility between foreign-born and native IT professionals. Against this backdrop, our goal in this exploratory study is to leverage a new longitudinal dataset from Finland to explore wages and mobility of foreign-born IT professionals. Our analyses of individual-level data from Statistics Finland (1995-2020) suggests that female employees, and employees with immigration status earn less than their counterparts. However, these effects are significantly weaker in ICT jobs compared to other job categories. The pay gap between employees with and without immigration status in ICT jobs decreases over time, and this trend is more pronounced in ICT jobs compared to other job categories. For mobility, we find that foreign-born IT professionals are less likely to be promoted or leave the company. We discuss implications for research and practice.

  PublisherDOI

• How to Mitigate Misinformation Diffusion on Blockchain‐Based Decentralized Social Network Platforms? Insights From an Agent‐Based Simulation Model

  Journal of Operations Management · 2025-07-21 · 1 citations

  article1st author

  ABSTRACT Although the spread of misinformation on centralized social media platforms has received significant attention, few studies compare centralized and decentralized platforms, and how to mitigate misinformation diffusion in newly emerging blockchain‐based decentralized social network platforms. We study misinformation diffusion between the decentralized and the centralized platforms and identify three decentralized governance mechanisms to mitigate the spread of misinformation in decentralized networks: user flagging, user article ratings, and user reputation. Our empirical experiments using agent‐based simulations leveraging real‐world data from two platforms reveal two findings. First, comparing misinformation diffusion between the decentralized Steemit and the centralized Pokec platforms suggests that in the absence of any mitigation mechanisms, misinformation in decentralized platforms affects more users, at faster rates, and reaches shorter distance from the misinformation initiating user. Second, within the decentralized platforms, misinformation affects fewer users, at slower rates, and reaches shorter distance from the misinformation initiating user in the presence of mitigating mechanisms than in their absence. We discuss the implications of these results both for the understanding of misinformation diffusion and for the governance of decentralized social network platforms.

  PublisherDOI

• CoMPHI: A Novel Composite Machine Learning Approach Utilizing Multiple Feature Representation to Predict Hosts of Bacteriophages

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-08-02 · 1 citations

  preprintOpen accessSenior authorCorresponding

  Abstract Phage therapy has reemerged as a compelling alternative to antibiotics in treating bacterial infections, especially for superbugs that have developed antibiotic resistance. The challenge in the broader application of phage therapy is identifying host targets for the vast array of uncharacterized phages obtained through next-generation sequencing. To solve this issue, this paper introduces an innovative Composite Model for Phage Host Interaction, CoMPHI, to predict phage-host interactions by combining the accuracy of alignment-based methods with the efficiency and flexibility of machine learning techniques. The model initially generates multiple feature encodings from nucleotide and protein sequences of both phages and hosts to enhance prediction accuracies. It is further enriched by incorporating alignment scores between phage-phage, phage-host, and host-host, creating a composite model. During the 5-fold cross-validation, the composite model exhibited an Area Under the ROC Curve (AUC) of 94%, 96.4%, 96.5%, 96.6%, 96.6%, and 96.7% and accuracy of 92.3%, 93.3%, 93.6%, 94%, 94.9%, and 95.1% at the Species, Genus, Family, Order, Class, and Phylum levels, respectively. A comparative analysis revealed a 6-8% increase in model performance due to the inclusion of alignment scores. Additionally, an ablation study highlighted that including both nucleotide and protein sequences from both phages and hosts increased the prediction accuracy of the model. Another ablation study provided evidence that phage-host and host-host alignment scores, combined with phage-phage scores, equally contributed to enhancing the composite model’s performance. In conclusion, this paper presents a robust and comprehensive composite model advancing the use of phage therapy in modern medicine.

  PublisherOA PDFDOI

• Resilience in the Open Source Software Community: How Pandemic and Unemployment Shocks Influence Contributions to Others’ and One’s Own Projects

  MIS Quarterly · 2023 · 25 citations

  1st authorCorresponding
  • Political Science
  • Sociology
  • Public relations

  Contributions by individual open source software (OSS) community members are the lifeblood of the OSS projects that power today’s digital economy and are important for the very survival of such communities. Individual contributions by OSS community members to others’ projects and their own determine whether OSS communities are resilient in the face of major shocks. Arguably, if crises such as the COVID-19 pandemic prompt users to reduce their contributions to others’ projects relative to the contributions to their own projects, such behavior can have implications for the overall resilience of the OSS community. Therefore, whether and how individuals change their contributions in the face of a crisis is an important question. We examine whether members in an OSS community increased or decreased their contributions to others’ projects relative to their own in the face of the COVID-19 pandemic, a sudden and unexpected global health-related shock that has affected almost everyone. We also compare and contrast this behavior when the OSS community faced increasing unemployment, an economic cyclic shock that is arguably and relatively more personal. Drawing on the concept of prosocial behavior and conservation of resources (COR) theory, we hypothesize that the pandemic increased OSS community members’ contributions to others’ projects relative to their own; on the other hand, the threat of rising unemployment decreased OSS community members’ contributions to others’ projects relative to their own. Our empirical analyses of a longitudinal dataset of over 18,000 OSS community members on GitHub, with more than 1.4 million member-day observations, support our hypotheses. This study contributes by uncovering the differential effects of exogenous health-related and economic shocks on the resilience of the OSS community. We conclude with a discussion of our findings’ implications for OSS community resilience.

  PublisherDOI

• Managing Digital Platforms with Robust Multi-Sided Recommender Systems

  Journal of Management Information Systems · 2022 · 8 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Artificial Intelligence

  ABSTRACTDigital platforms have replaced traditional markets in most industries and orchestrate socioeconomic aspects of our lives. We address the problem of negative direct side network effects that arise with an increased number of agents on one side of the platform. Negative effects, if unaddressed, lead to undesired long-term consequences for the platform by developing a positive vicious cycle. Addressing negative effects require dynamic solution mechanisms that adapt to the changing landscape of platforms. The recommender systems literature has proposed multi-sided recommender systems (MSR) as a dynamic solution to many problems on platforms. However, current state-of-the-art MSRs do not consider uncertainty in predicting agents' choices, resulting in limited efficacy. We present a robust multi-sided recommender system that considers estimation errors in agents' choice to address this concern. Extensive experiments with agent-based models—ride-pooling and education platform—provide support for the efficacy and generalizability of the robust MSR to address negative effects.KEYWORDS: Digital platformsnetwork effectsnegative side effectsmulti-sided platformsmulti-sided recommendersrobust optimizationagent-based simulation AcknowledgmentsWe thank the Editor-in-Chief, Dr. Vladimir Zwass, and the three anonymous reviewers for their constructive suggestions throughout the review process. We also thank participants of the 2019 Winter Conference on Business Analytics (WCBA) and seminar participants at the University of Wisconsin at Milwaukee and Northern Illinois University for their valuable feedback on earlier versions of this paper. Onkar Malgonde acknowledges financial support for this research from the G. Brint Ryan College of Business.Supplementary materialSupplemental data for this article can be accessed online at https://doi.org/10.1080/07421222.2022.2127440Disclosure StatementNo potential conflict of interest was reported by the authors.Notes1 A positive direct side effect refers to the "positive benefits received by users when the number of users of the same kind increases—for example, the effect that arose as the number of subscribers to the Bell Telephone network grew" [40, p. 29].2 One-sided recommender systems have relied on data mining and optimization approaches [Citation41]. For example, Adomavicius and Kwon [Citation1] develop a candidate optimization model to balance diversity with the traditional measure of recommender quality, such as accuracy. As a relatively newer field of research within the recommender systems domain, the MSR literature has extensively relied on optimization models.3 For brevity, this model is based on the method proposed by [Citation45]. In our empirical study, we use the method proposed by Bertsimas and Sim [Citation10] for its efficacy in balancing the optimality of the solution and its protection against constraint violation.4 As a first step in addressing the challenges of uncertainty in data, Soyster [Citation45] proposed a method that traded the optimality of the solution in favor of feasibility for all data. To address this limitation, Ben-Tal and Nemirovski [Citation8] and El-Ghaoui et al. [Citation21] proposed methods that consider robust counterparts of the nominal problem. However, the proposed methods are computationally expensive. To address these computational challenges and retain the optimality of the solution, Bertsimas and Sim [Citation10] propose a method that allows the user to vary the conservatism of the solution. Robust optimization is applied in various fields such as finance (portfolio optimization), supply chain management (inventory control), and engineering design problems [Citation9]. To the best of our knowledge, robust optimization has not yet been introduced to the recommender systems domain.5 Popular platforms include Waze Carpool in the US, BlaBlaCar in Europe, Grab in Southeast Asia, Hitch in China, and Jrney in Africa, among others.6 Similar mechanisms and dynamics of (a) making an offer, (b) accepting/rejecting an offer, (c) using two-way ratings (buyers and sellers rate each other), and (d) agents' objectives, preferences, and constraints are at play on multiple other types of platforms such as lodging marketplaces (Airbnb), freelancing platforms (Upwork, Fiverr), and crowdsourcing platforms (Amazon Turk, Toloka), among others.7 Among other differences, one of the key differences between ride-pooling and ride-hailing platforms is how matches are made: identified by agents (ride-pooling) or determined by the platform (ride-hailing).8 Fitness has been used in prior agent-based simulation studies in the Information Systems domain [Citation20, Citation35, Citation38].9 We thank an anonymous reviewer for recommending this point.10 https://www.forbes.com/sites/jeffbercovici/2014/08/14/what-are-we-actually-rating-when-we-rate-otherpeople/?sh=78550e4debca11 https://www.businessinsider.com/leaked-charts-show-how-ubers-driver-rating-system-works-2015-212 https://www.uber.com/en-TW/blog/5starriders/13 https://www.uber.com/newsroom/rider-ratings-breakdown/14 An earlier version of the manuscript used flexible tuples (e.g. [Citation1,0,0,Citation1,0]) to represent a focal agent's preferences. To generate the preference scores between two agents, we took the ratio of the number of common elements over the tuples' length (e.g., for a rider [Citation1,0,0,Citation1,0] and driver [Citation1,Citation1,Citation1,0,0], the preference match score is 2/5 = 0.4). Although we note the heterogeneity of ratings for a focal driver across all riders using this approach, the mean rating for all drivers is within the range [0.48, 0.53] and may not represent the preference dynamics on ride-pooling platforms. We appreciate the comments of an anonymous reviewer in identifying this point.15 We choose 0.98 as the default parameter because it translates to an average rating of 4.9 (the average rating of riders and drivers based on anecdotal evidence) on a 5-point scale used by ride-pooling platforms.16 A random value drawn from a uniform distribution; redrawn for each composite measure in a period; the same for all drivers/riders across all simulation universes for a period.17 A random fraction of the existing fitness value; the same for all drivers/riders across all simulation universes.18 For example, we assume that a focal driver's fitness at the start of a period is 0.6. Also, assume that the average capacity utilization across all rides offered by the focal driver exceeds a specified valuexv, and that the focal driver's average quality of a match is greater than a specified valuexv. If we determine that the fitness increment should be 0.25xvii, then the fitness of the focal driver will be (0.6+(0.6*0.25)) = 0.75. We use a similar logic for riders.19 Although unavailable for ride-pooling platforms, estimates from a related platform suggest a ratio of 20 riders per driver (https://www.earnestresearch.com/behind-the-ridesharing-wheel/; accessed: 05/10/2022)20 In Tables 3 and 4, Gk is the average fitness of agents in the k universe, where k is either no recommender, a one-sided recommender, a multi-sided recommender, or a robust multi-sided recommender system. Each cell is a Wilcoxon signed-rank test comparing agents' fitness between the Robust Multi-sided Recommender System and the other system. When statistically significant, we conclude that the difference between the two systems' fitness is statistically significant and that the system with greater average fitness (Gk) outperforms the other system. The Wilcoxon signed-rank test is appropriate because (a) we need to compare systems' performance across multiple simulation runs and (b) acomparison should be paired to a simulation run—initial parameters of a simulation running across different systems are the same.21 We compute Cohen's d for each pair of compared systems. The literature suggests that the effect size can be small (d ≤ 0.2), medium (d = 0.5), or large (d ≥ 0.8), and provides broad categories that should be informed by the study's context [Citation46]. Each cell shows the probability of superiority and Cohen's d for the compared systems.22 We thank Dr. Vladimir Zwass, JMIS Editor-in-Chief, and the three anonymous reviewers for this discussion.Additional informationNotes on contributorsOnkar S. MalgondeOnkar S. Malgonde (onkar.malgonde@unt.edu; corresponding author) is an Assistant Professor in the Information Technology & Decision Sciences Department, G. Brint Ryan College of Business, University of North Texas. He received his Ph.D. in Information Systems from the University of South Florida. Before starting his graduate studies, Dr. Malgonde was a Systems Engineer with Infosys Technologies. His research interests are at the intersection of data analytics, digital platforms, and software systems. His work has been published in such journals as MIS Quarterly, Empirical Software Engineering, and Electronic Markets, and in the proceedings of premier Information Systems conferences and workshops.He ZhangHe Zhang (hezhang@usf.edu) is an Assistant Professor in the Information Systems and Decision Sciences Department in the Muma College of Business at University of South Florida. His research interests include healthcare information management, big data, and production and inventory management. Dr. Zhang's research has been published in several journals, including MIS Quarterly, Mathematical Programming, Decision Support Systems and ACM Transactions on Management Information Systems.Balaji PadmanabhanBalaji Padmanabhan (bp@usf.edu) is the Anderson Professor of Global Management in the Information Systems Decision Sciences Department and Director of the Center for Analytics & Creativity at University of South Florida. He received his Ph.D. from the Stern School of Business of New York University. Dr. Padmanabhan's interests include analytics and business intelligence, designing analytics algorithms for business applications, building and evaluating predictive models, patterns discovery in data, enabling citizen data science and applications of analytics in healthcare, recommender systems, fraud detection and elections. His research has been published in the premier Computer Science and Business journals and conference proceedings. He serves on the editorial boards and program committees of many leading academic journals and conferences.Moez LimayemMoez Limayem (mlimayem@unf.edu) is the President of the University of North Florida. Until June 2022, he was the Lynn Pippenger Dean of the Muma College of Business at the University of South Florida, which he joined coming from the Sam M. Walton College of Business at the University of Arkansas. Dr. Limayem's research was published in many top journals, such as Information Systems Research, Journal of Management Information Systems, MIS Quarterly, Journal of the AIS, and Management Science.

  PublisherDOI

• Controls and Novelty on Digital Platforms: Two Case Studies

  EPiC series in computing · 2021-03-02

  articleOpen access1st authorCorresponding

  Software system projects face challenges to rapidly meet user requirements while adding novel values to the application domain. Value appropriation focuses on exploiting existing knowledge to develop software that meets market requirements. Value creation focuses on exploring the solution space to innovate and attract new customers. In this pilot research, we study the tension between software project controls and the goal of novelty in the software product. Two case studies provide preliminary evidence that a well-balanced portfolio of controls can result in the design and implementation of novel product features. We position the case studies in the context of digital platforms to bound our definitions of control mechanisms and novelty. Based on analysis of data collected from two case studies, we find that formal and informal control modes can positively influence novelty in software applications on digital platforms. We conclude with a discussion on implications for software development and future research directions.

  PublisherOA PDFDOI

• Taming Complexity in Search Matching: Two-Sided Recommender Systems on Digital Platforms

  MIS Quarterly · 2020 · 65 citations

  1st authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  We study digital multisided platforms as complex adaptive business systems (CABS) where multiple sides have different and evolving objectives, preferences, and constraints. CABS are characterized by irreducible uncertainty, which cannot be reduced by the traditional approaches of collecting and processing data. Irreducible uncertainty in the system gives rise to a complex search matching problem between agents and value enhancing transactions. This paper presents a recommender systems-based approach for taming the complexity by allowing agents to coevolve and learn in the system. We propose a novel two-sided recommender system framework, which considers emergence on both sides of the platform and adapts to the changing environment to influence agents. An agent-based simulation model is developed based on popular internet-based educational platforms to study this complex system and test our hypotheses. Our results show the value of a two-sided recommender system to tame complex search matching in platforms. We discuss implications for information systems and complexity science research.

  PublisherDOI

Frequent coauthors

• Alan R. Hevner

  University of South Florida

  9 shared

• Anol Bhattacherjee

  University of South Florida

  2 shared

• Rosann Webb Collins

  2 shared

• Balaji Padmanabhan

  2 shared

• Moez Limayem

  University of North Florida

  2 shared

• Kaushal Chari

  2 shared

• Terence Saldanha

  University of Georgia

  1 shared

• He Zhang

  Peking University

  1 shared

Labs

• Poole College of ManagementPI

Education

• M.S., Business Administration

  Poole College of Management