In this editorial, we examine the role of causal machine learning in Information Systems research. We show how causal machine learning extends traditional causal inference by using machine learning methods to estimate causal effects in complex and high-dimensional settings. We identify two main opportunities for Information Systems scholars: using causal machine learning to study causal relationships and develop theory, and investigating causal machine learning as a sociotechnical phenomenon in practice. In doing so, we highlight the potential of causal machine learning to shape future research in informatin systems.

Smart durable goods are products embedded with software and connectivity enabling post-sale control, like smartphones, cars or machinery. We examine how producers can leverage smart product capabilities to implement enforceable resales fees, creating a novel opportunity to reclaim value from platform-dominated secondary markets, a shift that we call de-platformization. We develop an analytical model to investigate the implications of resale fees on smart durable goods markets and show that despite theoretical promise, implementing resale fees can paradoxically reduce profits for both producers and reselling platforms due to demand distortions. However, producers can increase their profits by strategically managing used goods’ devaluation, either expanding the secondary market to maximize resale fee profit or curtailing it to optimize profit from new goods sales. These findings demonstrate the complex implications of enforceable resale fees and provide immediate relevance, as major smart durable goods producers increasingly embed digital capabilities into their products.

Online gambling platforms exhibit increasing revenues, exacerbating societal issues while evading regulatory oversight. The emergence of blockchain gambling platforms aggravates these issues, luring users with the promise of transparent and verifiable gambling rules. To provide insights for regulators and user protection measures, we strive to understand how users interact with these emerging platforms. We analyze over 22,800 rounds of YOLO, a gambling platform implemented as a smart contract. In our sample, 3,306 unique users lost a total volume of USD 5.1 million. Generalized linear mixed model estimation results reveal the effects of users’ cognitive biases while engaging in on-chain gambling, demonstrating the substantial risks of gambling smart contracts in exploiting users with gambling inclinations, especially given the lack of user protection measures. Enforcing user protection remains challenging for decentralized gambling smart contracts, which might exacerbate the lack of oversight, user protection, and law enforcement on traditional online gambling platforms.

Digital ecosystems (DEs) are pivotal for an intelligent digital future, fostering co-created value through collaboration among diverse stakeholders. By sharing and recombining technologies, knowledge, and services, DEs enable the creation of innovative products and offerings that surpass what isolated efforts can achieve. However, the sustainability of DEs and the value co-created in these DEs depends on fair value distribution mechanisms, as inequitable allocation of rewards can deter participation and hinder growth. Addressing this gap, our research project draws on prior literature and extant theories to design a fair mechanism for value distribution in DEs. This Research-in-Progress outlines the theoretical foundation of the proposed mechanism and identifies the next steps needed to achieve the overall goals of our project. However, the mechanism itself remains to be refined and empirically validated.

This research explores the emerging phenomenon of “Phygitals” - bundled products combining physical goods with their unique digital counterparts (tokenized digital products) in the context of bundling theory. We examine how superadditive utility created by the integration of physical and digital components affects optimal bundling strategies, considering the associated technical integration costs. Through analytical modeling, we demonstrate that producers can increase profits by introducing superadditivity in pure and mixed bundling strategies, despite integration costs. Our findings suggest that mixed bundling consistently outperforms other strategies, while the effectiveness of pure bundling versus pure components depends on the degree of superadditivity. We conceptualize different levels of Phygital functionality, ranging from basic authentication to advanced digital twin applications, and discuss real-world implementation examples. This study contributes to understanding the implications of the ontological reversal in Information Systems, specifically how producers can strategically bundle superadditive Phygitals for profit maximization.