Knowledge that Transforms

Information systems scholars typically frame the delegation of tasks to AI-based bots as a means of improving efficiency and supporting decision-making. Yet when evaluative tasks are delegated to supervisory bots, authority is transferred to them as well. This study examines how such authority, once conferred on bots, is contested and redistributed by the humans whose work is subject to the bot’s evaluation. We investigate this process in the context of Wikipedia, an online peer-production community in which an antivandalism bot was given the task of autonomously reverting vandalized, i.e., maliciously edited, contributions. Drawing on the concept of performative authority, we traced how community members negotiated the enactment of the bot’s newfound authority with the bot’s developers through authority-negotiation design moves. We found that these moves were the outcome of a recursive process marked by the redistribution of authority across actors in decisions on vandalism, the institutionalization of structures that shaped how authority was influenced, and the evolving actions of the bot itself, which triggered renewed contestation over time. Within the context of a peer-production community, our study offers fresh insight into how authority delegated to bots evolves as a dynamic cocreation process after delegation and how humans reclaim authority once it has been ceded to a supervisory bot. We shed light on how humans preserve authority, professional discretion, and the meaningfulness of their work in AI-mediated settings.

This commentary explores multiple perspectives on the potential use of digital technologies to improve organizational resilience in the context of climate change. Such an approach is needed to address this complex problem space, especially since it encompasses a wide variety of phenomena, including floods and landslides, disruptions to global supply chains, heat waves, biodiversity loss, greenhouse gas emissions, and food insecurity. We assembled a diverse set of five scholarly teams specializing in multiple problem topics, research approaches, and theoretical perspectives on this project. Each team identified and problematized a specific facet of digital resilience for the climate crisis. The perspectives cover a range of rich narratives, including digital resilience in the context of floods and landslides in Brazil and Indonesia, conceptual development efforts incorporating the natural environment with people and technology, reconceptualization of the problem space in terms of time and type, and two applications of digital resilience in the domains of global supply chains and carbon emissions tracking. This research commentary thus presents a multi-perspective examination and interrogation of digital resilience for addressing the climate crisis, out of which four transcending themes emerge: the need to integrate nature into sociotechnical thinking, the need to examine actions at both micro and macro levels, the need to include both reactive and proactive strategies, and the need to view climate crisis as a process rather than a series of events. This commentary aims to motivate other scholars who take diverse theoretical perspectives to join us in developing fundamental knowledge and practical solutions needed to achieve digital resilience for the climate crisis.

The classic digital divide theory asserts that unequal access to and unequal experience with information technologies may lead to unequal user outcomes. This paper introduces a new perspective to extend this theory: outcome divides can persist despite equal access and equal experience if users differ in their analytical ability to analyze and interpret available data for decision-making. We term this new data-to-decision skill as analytical ability and integrate it into the classic digital divide framework. We develop a new approach to operationalize analytical ability by contrasting humans’ actual performance against that of a standard machine learning model that makes similar analytical decisions based on the same information available to humans, essentially emulating a quasi-random counterfactual setting. To minimize the confounding impact of other divides, we validate the role of analytical ability in information-transparent environments like the blockchain-based trading markets, where all historical trading data is equally available to all users on the blockchain. We leverage data from EnjinX, a blockchain-enabled non-fungible token (NFT) marketplace that records all historical NFT transactions. We measure user outcomes by their flip trading performance, a standard metric captured via the percentage of exploited flipping opportunities. Our empirical analysis reveals that disparities in analytical ability may become the new bottleneck for outcome equity: flip trading performance could decrease by 66.86% when traders are incapable of analyzing the available blockchain information effectively. Our study contributes to the literature by extending the digital divide theory with the notion of the analytical ability divide. Moreover, we are among the first to rigorously quantify analytical ability and empirically test its impact based on the extended digital divide framework. Our study also offers important practical implications for platforms and policymakers to bridge this new divide in order to foster outcome equity.

Despite the prevalence of single average ratings on online rating platforms, they mask nuanced insights in online ratings. This paper contributes to the burgeoning literature on this topic by illustrating the importance of critical, yet overlooked, information obscured by single average ratings: trend information in online ratings. Online ratings are not static; they change over time as product/service quality changes and consumer expectations and preferences shift. The trend information embedded in online ratings enables consumers to go beyond past ratings to extrapolate expected ratings in the future. I focus on this aspect of online ratings and investigate (a) the effects of displaying trends in online ratings on consumer purchase decisions, (b) the mechanism mediating these trend effects, and (c) the situational contingency factors moderating these trend effects. Drawing on psychological momentum (PM) theory and four randomized controlled experiments (total n=2,015), this paper shows that the presence and explicit presentation of trends in online ratings create a PM experience that sways consumer decisions. It further demonstrates that the potency of these trend effects depends on the trend’s norm alignment, valence, surface forms of online ratings, and trend continuity. I will discuss the contributions of these findings to the research and practice of online ratings.

Declining circulation and advertising revenue have led many newspapers to cut costs by reducing in-house staff and relying more on wire content. In-house production provides unique content, while wire (non-exclusive) content offers broad coverage at lower cost. Using data from a regional U.S. newspaper, we examine how the mix of in-house and wire content affects subscription decisions. To address reader-side selection bias, we use local precipitation as an excluded variable to indirectly randomize readers’ exposure to content mix. We find that a 1% increase in the share of in-house articles raises daily subscriptions by 0.024% (≈9% of baseline) and by another 0.009% (≈4% of baseline) under a paywall. Against this overall trend, wire Business and Sports articles also increase conversion, suggesting opportunities for selective sourcing. Effects are weaker for local readers and for visitors from social media. Finally, readers from aggregators respond to in-house content and paywalls much like direct visitors, indicating that differentiation and paywalls remain effective even as readers turn to aggregators to discover news.

‘Notice and Choice’ has been a mainstay of policies designed to safeguard consumer privacy. This paper investigates distortions in consumer behavior when faced with notice and choice which may limit the ability of consumers to safeguard their privacy. We use data derived from a field experiment at MIT that distributed a new product, Bitcoin, to all undergraduates. There are two primary findings. First, small navigation costs have a tangible effect on how privacy-protective consumers’ choices are, often in sharp contrast with individual stated preferences about privacy. Second, the introduction of irrelevant, but reassuring, information about privacy protection makes consumers less likely to avoid surveillance, regardless of their stated preferences toward privacy.

Machine learning has long held intuitive appeal in aiding theory building, owing to its capacity to automatically uncover intricate patterns from vast amounts of observed data. Yet, recent literature in organizational research has acknowledged a potential hurdle in utilizing machine learning for theory building: a lack of consistency in the machine-learning output. This means that the same machine learning algorithm could learn predictively accurate yet theoretically contradicting patterns when applied to different samples from the same population, or even when run multiple times on the same sample. This article aims to address the fundamental question of whether, when, and how predictive accuracy implies theoretical pertinence in the context of using machine learning to support inductive theory building. Specifically, we offer theoretical arguments to establish the importance of ensuring that a machine learning algorithm’s assumptions regarding input data are properly aligned with the phenomena being studied. Building on these arguments, we develop a 2×2 framework that outlines the conditions under which four distinct types of machine learning algorithms may be better suited to facilitate inductive theory building in behavioral and organizational research.

The issue of smartphone addiction has been extensively studied, yet there is a lack of exploration into effective solutions to mitigate the impulsivity and temptation induced by these addictive technologies. Despite the proliferation of precommitment-based blocking apps introduced by major smartphone manufacturers, little is known about their effectiveness in curbing compulsive mobile indulgence—particularly among users with varying capacities for self-regulation. Drawing on a rational habit-formation framework, this study investigates how precommitment measures reduce susceptibility to mobile temptation over time, with particular attention to app characteristics, precommitment modes, and individual differences. To empirically validate our framework, we employ a structural model using a multiple discrete-continuous extreme value (MDCEV) approach, which endogenizes individuals’ choices of blocker modes (rigid vs. flexible) and simultaneously examines their app usage behaviors. Our unique dataset—capturing consumer app consumption with and without blocking features active—reveals that consistent use of precommitment apps not only fosters positive habit formation but also significantly reduces compulsive app usage across both hedonic and utilitarian categories. Furthermore, we find that flexible precommitment strategies outperform their rigid counterparts in reducing utilitarian app usage. Individual factors, such as gender and age, are found to play a moderating role in the efficacy of these precommitment strategies. Counterfactual simulations reveal that users who build stronger habit stock through repeated blocker use exhibit significantly lower app usage even after the tool is removed. Scaling the analysis to the population level further shows that expanding access to precommitment devices meaningfully reduces overall smartphone engagement and enhances digital well-being. Based on these empirical findings, we derived implications that can guide policymakers and managers in address excessive mobile dependence in public and workplace environments.

The trade-off between recommending specific versus diverse information to users has long been a challenging issue in recommendation systems. In this study, we probe into a novel problem—mixed-grained recommendation (MGR)—to address this challenge. MGR involves determining the optimal recommendation granularity that aligns with users’ needs for item exploitation and exploration. To this end, we propose a novel deep chain-of-preference learning strategy to infer a user’s choice across mixed-grained categories and items, based on category-aware demand-perception alignment in a top-down manner. Specifically, we design a chain-of-preference-empowered deep learning method (CoPDL) that can infer a user’s (1) dynamic and interrelated mixed-grained demands along a multi-granularity item tree, (2) self-adapted perception along the item tree, and (3) choice regarding mixed-grained nodes in the item tree by virtue of top-down category-aware inference. Empirical evaluation results demonstrate the superior performance of CoPDL over state-of-the-art deep learning alternatives for fine-grained, coarse-grained, and mixed-grained recommendations. Further explanatory investigations render insights into how CoPDL fulfills MGR in effectively balancing the trade-off between recommendation specificity and diversity.

Recently, a controversial new privacy policy on mobile app platforms, which requires app developers to display privacy labels and explicitly request data-tracking permissions from users, has sparked a heated discussion among practitioners in digital advertising. In this paper, we build a game-theoretic model to examine how this new policy impacts the key stakeholders of a mobile app platform (i.e., app developers, the platform, and consumers). The model captures how the new policy prompts developers to expand their strategies beyond pricing by introducing data-tracking levels as an additional competitive dimension, which in turn affects the intensity of price competition. We find that while implementing a policy restricting developers’ tracking of consumers may increase the platform’s advertising revenue, it can also put the platform at a disadvantage. This is because it may incentivize developers to lower their prices and engage in more intense price competition, consequently reducing the platform’s commission revenue. Regarding app developers, the new policy may prove beneficial despite its restriction to tracking only consumers who have opted in. Another noteworthy finding is that while platforms have asserted that the new policy is aimed at safeguarding consumers’ privacy, it does not always serve consumers’ best interests. Our paper provides meaningful implications for all key stakeholders of mobile app platforms regarding the implementation of the new privacy policy.