Knowledge that Transforms

This paper proposes a new type of blockchain, generative blockchain, that aims to transform the conventional blockchain from the focus of transaction-recording to transaction generation. The key to our design of generative blockchain lies in a novel consensus mechanism, proof-of-merit (PoM), that is uniquely designed for environments where businesses must solve complex problems before transactions find their place on the blockchain. PoM integrates the generation and recording of transactions within a unified blockchain system, differing fundamentally from prevailing consensus mechanisms whose primary objective is to record existing transactions. Specifically, PoM delegates the task of solving complex transaction-generating problems to a pool of independent problem solvers, whose solutions govern the generation of transactions, and selects the winning solutions based on merit. The winning solvers register these transactions onto the blockchain and are rewarded accordingly. We introduce a decentralized control parameter (DCP) to balance two key performance metrics of a generative blockchain: efficiency and equity. The applicability of our designed generative blockchain is illustrated through a ridesharing context, where a group of matchers (solvers) take on the task of matching riders with drivers. We demonstrate PoM’s performance and nuanced properties using agent-based simulation and explore how to find the proper value of DCP toward achieving a desirable balance of efficiency and equity in a generative blockchain. We also implement a prototype blockchain using PoM mechanism to validate its feasibility and evaluate its performance.

This research theorizes and documents for the first time a cyber risk that may arise from strategic alliances with Big Tech, a grouping of the most dominant companies in the information technology industry. This research proposes that alliances with Big Tech tend to involve leveraging and integrating Big Tech’s innovative technological capabilities into a focal firm’s data management, operations, products, or services. This process can be understood as an inter-organizational system of systems integration and is presented as a mechanism that makes focal firms vulnerable to external cyberattacks. In addition, this research theorizes and shows that the effect of strategic alliances with Big Tech on the risk of cyberattacks is not linear; instead, it increases up to a certain point and then starts decreasing. Finally, this research suggests that intangible asset intensity strengthens the relationship between strategic alliances with Big Tech and the risk of cyberattacks. The findings of this research provide timely and important insights into cyber risks arising from strategic alliances centered on integrating systems and technologies across partner firms’ boundaries.

We study how app competitiveness evolves through intertemporal shifts between proprietary code and platform-sourced functionality. We introduce the novel mechanism of boundary morphing—apps expanding their boundaries by adding proprietary code or contracting them by increasing platform-sourced functionality—by integrating transaction cost economics (TCE) with platforms theory. Using seven years of data from over 600 AndroidOS apps, we show that platform specificity and market uncertainty drive boundary contraction, while frequent updates counterintuitively catalyze boundary expansion. App modularity—acting as a shift parameter—reshapes this calculus, enabling boundary expansion amid uncertainty and rapid updates. We contribute: (1) boundary morphing as a mechanism to straddle synergy-differentiation tradeoffs, (2) IS-native insights in a TCE scaffold to explain morphing, and (3) modularity’s role in reshaping it. Together, these findings explain how apps morph boundaries as an architectural lever to stay competitive.

Software packages can be susceptible to attack whenever they utilize dependencies containing security vulnerabilities (vulnerable dependencies). We theorize that the likelihood of relying on vulnerable dependencies is heightened by two structural dimensions of dependency networks: complexity (dependency count) and tight coupling (interdependence). Analyzing 40,049 packages, we find that complexity is positively associated with this likelihood and that vertical complexity (depth) plays a more prominent role than horizontal complexity (breadth). However, tight coupling is negatively associated with the likelihood of vulnerable dependencies. Further analyses reveal that this relationship turns positive with greater complexity but becomes more strongly negative as the number of package developers and the average number of developers per dependency increase. Our findings identify key boundary conditions under which tight coupling may be beneficial and offer a nuanced understanding of how dependency network structure influences the security of dependencies and, more broadly, the security of software supply chains.

Malware detectors are the first line of defense against cyber-attacks that damage Information Technology (IT) infrastructure. Recently, deep learning (DL)-based malware detectors have yielded breakthrough results in identifying unseen attacks without requiring feature engineering and expensive dynamic malware analysis in a sandbox. However, these detectors are susceptible to adversarial malware attacks. Emulating effective adversarial malware variants is instrumental in revealing the vulnerabilities of such systems and developing automated cyber defense. Current methods for launching such attacks often assume scenarios that require accessing insider knowledge about the architecture of the malware detector and/or cannot operate directly on raw malware files. We propose Adversarial Malware example Generation and Defense (AMGD), a novel framework to defend the detectors by automatically generating malware variants from raw executables without assuming any prior detector knowledge. AMGD is generalizable to multiple detectors as it can be trained on multiple malware detectors simultaneously. AMGD employs Independent Recurrent Neural Nets (IndRNNs) to offer a novel generative byte-level malware sequence model, named Mal-IndRNN, to evade DL-based malware detectors. Mal-IndRNN effectively evades three renowned DL-based malware detectors and outperforms benchmark methods. We utilize malware variants generated by Mal-IndRNN to improve the robustness of malware detectors against adversarial attacks on a real dataset. AMGD offers a practical approach to proactively accounting for the Artificial Intelligence (AI)-enabled adversary in the design and development phase of DL-based malware detectors rather than reactive measures after deployment.

Many online platforms have introduced markers that identify and highlight minority-owned businesses. This study examines whether such markers produce observable benefits for the targeted minority-owned businesses, whether there are unintended effects, and what mechanisms may explain these outcomes. Such markers may have differing effects on users depending on their level of motivation to support minority-owned businesses. Additionally, these markers may serve as a reminder of biases and lead consumers to form expectations about the quality of products or services solely based on the ethnicity of the business owner. Our analysis of Yelp Open Data suggests that the markers have varied effects on different types of businesses, with observable benefits concentrated in specific categories. Subsequently, the results from our online experiments show that the markers may have the intended effect of increasing users’ preference rank and likelihood of choosing minority-owned businesses, but primarily among individuals who were motivated to support such businesses. For those who were not motivated to support such businesses, the markers have an inconsistent impact, even when businesses align with what people expect minorities to excel at. Even worse, the markers may have a backfire effect, resulting in a decreased preference rank and likelihood of choosing businesses that operate outside of these expectations. We discuss the research and practical implications of these findings.

In this study, we develop insights on three key aspects related to omni-channel assortment: product popularity, demand fulfillment level, and channel primacy. Regarding product popularity, we probe whether conventional wisdom of selling popular products through the physical channel and niche products through the online channel is always optimal. Our results show that this suggestion may be sub-optimal when the price in the online channel is greater or customers are heterogeneous in their preference for channel. For demand fulfillment level, we examine whether popular products should always have greater demand fulfillment level. We find that the fulfillment level depends on the cost structure of the channel through which the product is sold and not on demand volume. Finally, we develop insights on when it is optimal to sell most or all of the products through one of the channels and explore how this is affected by changes in model parameters.

Material to inform the decisions on the job promotion of open source software (OSS) developers is available on platforms. Digital features increase the visibility of OSS developer technical and interpersonal skills. Digital features also make it possible for OSS developers to visibly receive endorsements and recommendations from others about those skills. It is unclear how well the visibility of technical versus interpersonal skills through such digital features facilitates OSS developer job promotions. Leveraging Turner’s contest-sponsored mobility framework [66], we theorize and empirically examine these concerns using a sample of 269 OSS developers. We find that a developer making their technical skills visible on digital platforms is associated with job promotions. In contrast, the visibility of interpersonal skills through recommendations affects job promotions. Our findings contribute to theory and practice by providing a lens to understand the differential effects of technical versus interpersonal skill visibility offered by different digital features and their effect on OSS developer job promotions.

In the digital economy, understanding user behavior in post-adoption stages is essential for sustained revenue. This paper investigates the role of passive innovation resistance for digital service usage during these stages, an area that has received limited attention. Through a predictive field study (nt1 = 511, nt2 = 356) over five weeks and an experiment (n = 190), we analyze how passive innovation resistance affects user loyalty when modular changes to service business models are introduced. Our findings show that users with high passive innovation resistance may remain loyal despite better alternatives, challenging the belief that such resistance only hinders adoption. We also identify strategies to mitigate negative reactions to business model changes in post-adoption stages. These insights enhance the understanding of post-adoption user behavior, bridging the gap between information systems and innovation resistance research, and offer practical guidance for digital service providers to reduce churn and enhance customer retention.

Social media firestorms, characterized by the rapid spread of negative electronic word of mouth (eWOM), can ignite widespread criticism of a firm’s brand transgressions, service failures, product-harm crises, or ideological conflicts. This study, grounded in the sociotechnical and social learning perspectives, examines the drivers and outcomes of social learning in ideologically charged firestorms, which represent an emerging form. We investigate the effects of social consensus and message persuasiveness, platform content centricity, and individual–firm relationship closeness on the social learning of negative eWOM behavior and purchase behavior. Two studies, comprising longitudinal quantitative and qualitative surveys, were conducted. The results indicate that message persuasiveness is strongly associated with the social learning of negative eWOM behavior. Negative eWOM behavior subsequently influences post-firestorm purchasing behavior. The moderating effects of platform content centricity and individual–firm relationship closeness are discussed. These findings advance the literature and offer practitioners actionable insights into managing digital crises.