Racial discrimination in crowdfunding is a significant barrier to equitable access to capital, as racial minorities face greater challenges in achieving their fundraising goals. While prior research has documented discriminatory patterns in crowdfunding outcomes, the underlying mechanisms driving this discrimination remain unclear. This is a critical gap that must be addressed to develop effective interventions. Drawing on economic theories of taste-based and statistical discrimination, we examine how discrimination mechanisms vary across crowdfunding types. We posit that donation crowdfunding primarily exhibits taste-based discrimination, while investment crowdfunding manifests statistical discrimination. We tested these predictions through three preregistered randomized experiments. Confirming our prediction, the first experiment revealed taste-based discrimination against Black fundraisers in donation crowdfunding. The second experiment demonstrated statistical discrimination in investment crowdfunding. However, contrary to prior research showing negative discrimination against minorities, our second experiment revealed positive statistical discrimination toward Asian fundraisers. We posit that this positive discrimination is due to the decision frame participants adopt, and this explanation is supported by Experiment 3: The direction of discrimination is reversed when task instructions are modified to elicit a negative decision frame. Our research advances both the crowdfunding and the racial discrimination literatures while providing insights for platform design and debiasing interventions.
Participants on tokenized platforms (i.e., platforms with blockchain implementation) can simultaneously take multiple roles, such as user, investor, and laborer, and draw income from the last two roles. Unlike traditional markets that typically prioritize one means of profitable participation, participants on such platforms need to allocate their efforts on the platform to increase revenue. We developed a decision framework for determining participants’ strategic participation on tokenized platforms to maximize earnings from investment and labor. Individual participants were distinguished from the platform-average participant, and decision-making is cast into two subproblems: (1) ignoring individual actions’ impact on platform state, we constructed strategies based on metrics that characterized model projections of future platform development and derived the metrics from Monte Carlo ensembles; (2) considering individuals’ actions as explicitly influencing the platform state, we formulated the control problem as a Markov decision process and solved it via reinforcement learning (RL). The framework addresses parameter uncertainty from model estimation, system uncertainty in model projection, and input uncertainty during participant-platform interaction. We compared metric-based and RL strategies from the two solution approaches using historical token price series; the results suggest good performance of our decision framework.
Cyber breaches pose an increasing concern for executives and boards of directors, as they involve the exposure, damage, or loss of critical organizational data. The risk of a cyber breach is particularly acute for large corporations, which are prime targets for cybercriminals due to their vast data reserves. Prior literature has examined the role of CEOs and boards in cybersecurity management. However, their cybersecurity effectiveness may depend on firm size, which can provide more resources but also create challenges such as bureaucracy and resistance to change. To test these theoretical perspectives, this study develops a contingency theory by exploring the moderating role of firm size in the relationship between CEOs with IT functional experience, board interlock ties with technological firms, and the likelihood of cyber breaches. Analyzing a sample of U.S. Fortune 500 public firms from 2009 to 2018, our findings reveal that compared with smaller firms, CEO IT functional experience and board technological ties in larger firms reduce the likelihood of a cyber breach. These effects are also complementary, representing the potential for collaborative IT governance. Our findings provide important insights into IT governance and cybersecurity.1
As new technologies diffuse within an industry, they impact the value of worker skills, and in turn may influence the mobility of workers between firms in that industry. This mobility may be particularly pronounced for individuals whose jobs are complementary to these new technologies and therefore increase demand for these individuals. In this paper, we investigate the impact of a small set of development tools diffusing within an industry and investigate whether this was associated with an increase in worker mobility for individuals with complementary skills, compared to those with (partly) substitutable ones. Specifically, we study the diffusion of middleware tools within the video game industry that made human capital more general by making it industry-specific (rather than firm-specific) and was complementary to the tasks being performed by Creatives in this industry, compared to Programmers. We exploit the uneven diffusion of these tools across genres to investigate whether workers experienced greater mobility in the genre where the technologies diffused more broadly. We also find greater mobility between projects that used the same middleware components. We find that the diffusion of these tools was associated with greater mobility for creatives than for programmers. These results are robust to a variety of empirical checks and speak to the impact of broad diffusion of IT tools, which may enable workers to more easily move between companies.
Sensors, actuators, and controllers are becoming deeply embedded across nearly every aspect of life, from transportation and manufacturing to energy systems, consumer technologies, and healthcare. This expansion of cybernetic systems carries two important implications. First, the category of “computing machines” is broadening in both form and scope, now including a diverse array of computational architectures that sense and actuate across increasingly complex physical environments. Second, the growing integration of cybernetics is driving higher degrees of computational autonomy, enabling systems to operate with greater independence—both augmenting and, in some cases, replacing human agency. At the core of cybernetic systems are translational actions (TAs) that facilitate the detection and transformation of the energies of the physical world, across the strata of technology artifacts, to ultimately become virtualized representations in the computational world. However, the concept of TA remains underspecified, offering scant insight into its concrete forms, the value and costs these different forms create, and ultimately, how physical-digital TAs function in real-world systems. In response, we conducted an inductive study of 188 detection and computational technologies originating from leading scientific research institutions to develop a more nuanced vocabulary of how translational actions govern the relationship between digital representations and their physical referents. Our analysis identifies six distinct TA forms and three hierarchical levels at which they operate, each with specific trade-offs that we characterize as fidelity costs. These insights enable us to theorize how TAs shape the technical construction of data as inputs to digital representations, and, in turn, how such representations attain performative value in the physical world.
Organizations increasingly rely on social media bots for real-time monitoring. Yet, configuring bots for effective information retrieval remains challenging. Too much data creates noise; too little risks missing insights. We address this tradeoff by examining how action triggers—the search terms bots use—shape retrieval outcomes. We introduce volume-adjusted relevance, which weights relevance against retrieved volume and explore three design dimensions: semiotic specificity (hashtags vs. no-hashtags), semantic specificity (hypernyms vs. hyponyms), and trigger expansion (single vs. paired terms). In a large-scale randomized field experiment on X, a custom-built master bot retrieved over 8 million posts using 204 triggers across 50 objectives for one week. Results show that hashtags improve volume-adjusted relevance, semantic specificity provides limited benefit, and combining semantically related hashtags yields the best performance. These findings advance understanding of bot-based retrieval and offer a framework for reducing noise, avoiding blind spots, and enhancing social media monitoring.
Social media’s dominant economic model relies on curating content that appeals to users’ emotions to capture and hold their attention, resulting in experiences that are primarily affective. User behavior drives content propagation, which is the core process underlying major social media phenomena—both beneficial and harmful. While IS research recognizes the importance of affective factors in content propagation, this research is marked by diverse theoretical foundations, a focus on content-level factors, and a complex landscape of findings. To unpack the affective factors underlying user behavior in content propagation and integrate insights from prior research, we develop an affective model of social media content propagation grounded in the affective response model (ARM) and extend ARM’s propositions to the omnibus context of social media. We examine two highly relevant, theoretically nuanced, and countervailing affective forces—humor and politics—by developing testable hypotheses in the discrete context of humorous and political memes. Specifically, we consider the role of human factors (mood, ideology), affective dispositions (sense of humor, affinity for political humor), content factors (political nature), and induced affective states (mirth) to predict propagation intentions. We test the model using an online experiment involving 289 participants, balanced across gender and political orientation, and 48 memes, balanced across presence and partisan leaning of political content. Using cross-classified mixed-effects regression, we find general support for our hypotheses and uncover interesting differences in politically relevant factors. The broader proposed model, supported by the study’s findings, offers a general multilevel framework to illuminate the role of affective processes in social media’s promise and peril, situate prior IS research, and support future studies of social media user behavior. We contribute to the broader social media literature by identifying emotionality as a contextual stimulus, and to ARM by explicating its multilevel nature, unpacking indirect effects, and contextualizing it to social media content propagation.
We examine the methodological problem in which information systems (IS) research that describes itself as interpretive does not interpret meaning – in particular, the subjective meanings with which people come to understand not only the technologies they are using and managing, but also the overall lifeworld in which they live and work – with the result that meaning-related phenomena go undetected, uninvestigated, and therefore untheorized. We provide a review of how IS scholars originally emphasized meaning when interpretive research was first introduced to the IS discipline and we provide some motivating ideas pertaining to meaning that come from the phenomenology of Martin Heidegger and the phenomenology of Alfred Schutz. We cite Orlikowski (1993) as an exemplar with which to illustrate the difference that the interpretation of meaning makes to theory, as well as to point out the problem that would result if meaning were not interpreted or even acknowledged. We then examine how a study published by Califf et al. (2020) in MIS Quarterly can be considered to neglect meaning and we give additional examples of published articles that describe themselves as interpretive but are not transparent in reporting the details of any interpretation of meaning. We offer paths to correcting the methodological problem by returning to, and restoring the importance of, the basics of meaning.
This paper examines the influence of artificial intelligence (AI) on research and development (R&D) practices, proposing that AI enables firms to discover, evaluate, and make sense of information from their partners, thereby reducing information asymmetry and allowing alliances to flourish. Empirically, we approximate a firm’s AI resources using patents and job postings to analyze the relationship amongst AI, alliances, and drug innovation in the pharmaceutical industry. Our findings show that firms with greater AI resources shift their locus of innovation toward alliances and develop more drugs from their alliances. Furthermore, AI can be particularly useful in exploiting information held by counterparties within an alliance. Taken together, our study highlights the complementary role of AI and alliances, shedding light on how modern organizational and technological advancements jointly shape the production of innovation.
This study examines how the extent of business intelligence (BI) systems within business enterprises is associated with compliance risk. BI tools promote a risk-aware culture by providing real-time data and analytics, helping establish objectives aligned with compliance, and ensuring transparent communication of compliance information across the organization. Using BI, organizations can better address compliance challenges, strengthen risk management practices, and adhere to regulatory requirements more effectively. Using a BI implementation dataset, we find that firms with more widespread BI implementation in the organization have lower compliance risk, reporting fewer regulatory violations and lawsuits. We also provide evidence that some measures of strategic IT focus moderate the association between BI implementation and compliance risk outcomes. Delving into the particularities of the association between BI implementation and compliance risk, we find that greater BI implementation is associated with lower lawsuit settlement amounts, fewer lawsuit types, and shorter lawsuits. Further, the beneficial BI implication is associated mainly with operational compliance risk rather than financial accounting compliance risk. Our findings have crucial implications for the value of BI in corporate compliance risk management.
How does online trading intensity influence investors’ gambling preferences? How do financial advisors shape the relationship between online trading intensity and gambling preference? This study answers these important questions in response to concerns about whether digital technologies such as the Internet exacerbate the tendency of retail investors to gamble in the stock market. Leveraging transactional trading data of approximately 20,000 investors and more than 450,000 monthly observations, we find a U-shaped relationship between online trading intensity and investors’ holdings of lottery-like securities. Interestingly, we show that financial advisors flatten this relationship between online trading intensity and investors’ holdings of lottery-like securities. In other words, financial advisors can reduce the undesirable costs associated with online trading. These findings, which draw on literature at the intersection of information systems and behavioral finance, provide valuable insights on how the Internet influences stock trading. Our work suggests the continued relevance and importance of human capital (e.g., financial advisors) in the digital era increasingly shaped by artificial intelligence, and provides guidance as to when financial advisors are more effective in reducing investors’ gambling preferences.
Information diffusion in social networks is uneven: some content spreads much more than other content, shaping what people see. The mix of what gets shared can leave users with a misleading sense of how often things happen. Prior research primarily examines content attributes and user attributes but has largely overlooked the role of exposure frequency—how often a user encounters an event category relative to others in their information stream. We argue that exposure frequency is a key factor influencing sharing behavior. Drawing on perceptual bias and variety-seeking, we theorize that users are more likely to share low-exposure frequency (rare) event categories. As these individual decisions accumulate, rare categories become disproportionately represented—a systematic distortion that we call rareness-biased diffusion (RBD). Across six experiments and a network simulation, we show that individuals disproportionately share rare events. At the individual level, the tendency to share rare events weakens when perceptual bias or variety seeking is suppressed but strengthens when sharing opportunities increase. Temporal clustering of rare events further reduces sharing by making rare events seem common. At the network level, distortion amplifies with distance from the source and is most stable in chain networks, while outcomes in small-world and preferential-attachment networks show greater variability due to overlapping exposure. Together, these findings introduce category-level exposure frequency as a distinct predictor of sharing, establish RBD as a new diffusion construct, and highlight implications for platform design, where simple aggregation can amplify rare events and distort public understanding.
Non-financial information, especially information carried in disclosure reports, plays an important role in conveying financial distress signals. Considering the rise of generative AI (GenAI) and its potential in capturing both surface and latent meanings of disclosure reports, we initiate a new research avenue, GenAI-enhanced financial distress prediction. We position GenAI as an information intermediary and propose a functional analogy framework to conceptualize the process of leveraging disclosure reports with four functions: perception, extraction, reasoning, and evaluation. We then provide a guideline with three GenAI use strategies (i.e., prompt engineering, knowledge injection, and fine-tuning) and design a deep learning method featuring a function-based bidirectional representation module, which explicitly and separately extracts representations for the emphasis information produced by the extraction function and insight information produced by the reasoning function, guided by tailored convergent and divergent mutual information criteria, respectively. Empirical evaluation at the model level and impact analysis at the application level demonstrate advantages of the proposed method over benchmarked state-of-the-art methods on all fronts. Mechanism-level analyses further reveal the core drivers underlying the utility of the proposed method.
While extant studies have investigated how digital industries influence the digital transformation of traditional firms, the impact of learning from the digitalization of traditional industries has been largely overlooked. This study addresses this gap by introducing the concept of digital transformation centrality (DTC) – a novel measure of the informational advantages and associated costs a firm accrues based on its position within a network of traditional firms interconnected through their shared digital suppliers. DTC captures a new type of IT spillover: digital suppliers accumulate digital transformation knowledge by serving traditional firms, and such knowledge then spills over to other traditional firms they serve. Using extensive longitudinal data on global supply chain relationships and firm fundamentals, we find that DTC has an inverted U-shaped relationship with firm value. This shows a non-monotonic impact of digital transformation learning from traditional industries. Moreover, we develop a curvilinear mediation model to uncover the underlying mechanisms, finding that the effect of DTC is partially mediated by both innovation output and the real value of innovations. These results demonstrate the essential roles of digital suppliers in both offering digital technologies and enabling the dissemination of digital transformation knowledge among traditional firms, and provide new insights for understanding digital transformation and IT spillover across industries.
The Management Discussion and Analysis (MD&A) section in Form 10-K offers highly valuable insight of a company’s fiscal health, operational performance, and future outlook. In today’s ever-changing business environment, both practitioners and academics have extensively studied this textual data, with particular interest in quantifying the evolving information encoded in MD&A narratives. In this work, we challenge the traditional cosine similarity-based method for measuring differences between a firm’s year-over-year MD&A disclosures. We propose a novel method, D3, short for Deep Learning Method for Disclosure Differences, that incorporates external expert evaluation signals, in the form of analyst recommendation updates, to guide the learning of variant information between consecutive MD&A reports. Instead of producing a dissimilarity score as in traditional cosine distance approaches, D3 learns the changing information directly as a variant vector. The variant vector derived by D3 serves as a multifunctional artifact for various downstream financial applications. In our experiments, we show that the variant vector demonstrates strong economic utility. Specifically, a long-short portfolio strategy that sorts firms based on the magnitude of their variant vectors generates significant excess returns, after controlling for common risk factors. Moreover, the variant vector exhibits predictive power for financial risk-related outcomes. We further introduce an interpretability technique based on D3 to uncover key evolving information disclosed in MD&A reports. Our work has important implications for information systems computational design research and the emerging FinTech literature.
Doxing, the malicious exposure of personal information online to threaten or intimidate individuals, is an escalating global threat. A particularly consequential form is politically motivated doxing (PMD), where perpetrators— known as doxers—target individuals for their viewpoints and beliefs, shaping sociopolitical discourse in the process. Despite PMD’s profound impact, research on the perspective of victims (referred to as doxees) remains scarce. Drawing on affective events theory, this study argues that doxees perceive PMD as a breach of their fundamental civil rights. We theorize that PMD triggers an affective duality—fear and anger—in doxees, which drives divergent behavioral shifts in their civic engagement across online and offline domains. Additionally, we examine how doxees’ strength of conviction regarding the PMD issue moderates their affective reaction, revealing distinct responses among individuals with moderate versus extreme views. Using a monostrand mixed-methods approach with a dominant quantitative and a complementary qualitative component, we conduct a personalized, immersive online vignette study in the context of U.S. school boards to empirically test our model. Our findings contribute to the IS literature by: (1) advancing theoretical and empirical understanding of doxees’ perceptions of PMD, (2) linking these perceptions to their affective and behavioral responses, and (3) demonstrating that PMD has a particularly suppressive effect on civic engagement among doxees with less firmly held convictions, highlighting PMD as a mechanism through which malicious actors exacerbate sociopolitical polarization. Our work offers timely implications for policymakers and online platforms.
Understanding the role of gender in business interactions is of significant importance to companies and society. Inspired by practical concerns of how agent gender might affect customer service interactions, the present study investigates this question using a unique dataset consisting of all public customer service interactions handled by Southwest Airlines’ Twitter account from March 2018 to September 2019. Leveraging the online text-based customer service setting where an agent’s first name serves as the only gender cue, we are able to identify the gender effect on customer behaviors and service outcomes. The identification relies on two unique features of the research context: the assignment of a customer to the next available agent is independent of agent gender; and there is no significant variation, especially gender-induced differences, in an agent’s first response. Both assumptions are supported by the data. Empirical analyses reveal that customers are more likely to continue interactions with female agents than with male agents, yet are more negative in valence of their second tweet towards female agents. Mediation tests further show that customer gender bias in their second tweets leads to downstream effects on service outcomes: interactions with female agents tend to be longer but result in lower resolution rates. Moreover, the treatment effects are moderated by customer personality and public visibility. These results offer valuable lessons to practitioners and academics regarding the unique role of gender in online customer service.
Digital platforms in repeat-purchase categories face a tension between facilitating inertial purchases from the same seller and encouraging variety seeking across sellers. Inertia toward a seller may increase contemporaneous conversion but may elevate the risk of platform disintermediation. Variety seeking can strengthen platform stickiness but may come at the expense of foregoing the conversion benefits of familiar options. It is unclear which tendency better serves the platform and how these tendencies unfold across stages of a purchase episode. Using clickstream data on consumers' path to purchase from a mobile food-ordering platform, we show the coexistence of these tendencies within a single purchase episode: variety seeking during consideration-set formation and inertia at final choice. We identify this pattern with a two-stage structural model that accommodates heterogeneous consumer preferences and correlated unobservables across stages. Counterfactual analyses indicate that nudging the ranking in the impression set to encourage exploration increases revenue by 4.56%. Moreover, contextualized ranking interventions based on consumers' category experience, inter-session elapsed time, and platform-wide promotions further amplify these gains, yielding revenue increases of 5.59% to 6.12%. In contrast, a conventional one-stage model detects inertia but misses the consideration-stage variety seeking tendency, biasing policy toward reinforcing inertia and thereby forgoing substantial revenue. These findings contribute to theories of state dependence by conceptualizing inertia and variety seeking as stage-contingent behaviors rather than fixed consumer types and have direct implications for platform ranking design.
This paper explores how different types of fact-checkers (i.e., AI or human) impact users’ perception of the believability of news that is flagged as false. Building on source credibility theory, we evaluate how the reputation of the news source and the user's political orientation (i.e., progressive or conservative) moderate the impact of AI versus human fact-checkers. We examined this interaction in two separate 3×2×2 online quasi-experiments conducted in the United States and the United Kingdom. In both studies, we found differences in the impact of fact-checker type and in the moderating effects of poster reputation and user political orientation. Our results show that AI fact-checkers are more effective than human fact-checkers in reducing users’ perceptions of news believability, particularly among progressives. We also found that if the news poster has a high reputation, this can further enhance this impact. By investigating the interplay among fact-checker type, the poster, and users’ political orientation, and by comparing results across two countries, we extend understanding of how different fact-checker types affect news believability regarding false news on social media platforms (and user engagement in robustness checks). Finally, we derive managerial implications for mitigating the spread of false news on social media platforms.
