Knowledge that Transforms

Ride-sharing platforms that match riders with drivers have become one of the most celebrated examples of disruptions caused by information technology (IT). A rider’s willingness-to-pay (WTP) for the platform’s service is affected by the expected waiting time, which in turn depends on the match made by the platform (“match quality”). While some riders may have low sensitivity to match quality (“low segment”), others may be highly sensitive (“high segment”). Without knowing an individual rider’s sensitivity, ride-sharing platforms often use priority matching to offer differentiated services, allowing riders to self-select the service that best suits them. While such a strategy is akin to quality-differentiated products in conventional markets, the ride-sharing context exhibits unique features. For instance, one-to-one matching of riders and drivers imposes constraints on the match qualities the platform can offer. Moreover, any differentiation in driver wage that accompanies service differentiation on the rider side can induce drivers to anticipate a possibly higher future wage (”forward-looking behavior”), which can lead to supply-side cannibalization analogous to well-known demand-side cannibalization resulting from rider self-selection. As a consequence of these features, service differentiation generates novel implications for ride-sharing platforms. We find that a platform would enhance the match quality intended for the high segment and degrade the match quality intended for the low segment to mitigate demand-side cannibalization, whereas a conventional firm would degrade only the quality intended for the low segment. If drivers are not forward-looking, the platform would also differentiate wages when it offers differentiated services. However, if forward-looking drivers wait to obtain the higher wage, the platform may find it optimal to offer a uniform wage to drivers even though it may offer differentiated services to riders. Moreover, the platform could degrade the match quality intended for the high segment and enhance the match quality intended for the low segment to mitigate supply-side cannibalization. We further find that the demand-side and supply-side cannibalization have qualitatively similar same-side effects, but they have asymmetric cross-side effects.

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.

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.

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.

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.

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.

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.