Knowledge that Transforms

Human-AI ecologies involve human and AI-based agents that coordinate their interactions in part by following social norms. Social norms, therefore, are important for establishing the guardrails that ensure desirable interactions in a way that is consistent with essential values, such as human safety. Managing human-AI ecologies requires specifying norms to enable coordination in known situations but also allowing for the emergence of norms to enable coordination in unspecified, unstructured situations. We integrate predictive processing theory and social norm theory to explain how existing norms are enacted and reinforced based on agents’ predictive models and how new norms emerge as agents update their predictive models in response to prediction errors in uncertain coordination scenarios. Rooted in this perspective, we develop a design theory that emphasizes design for predictability and propose a set of design principles for managers and developers to encode norms to evolve in human-AI ecologies, monitor outcomes, and intervene when necessary.

Users’ disregard of security warnings is a critical problem in cybersecurity. This problem worsens when people confuse security warnings with common, non-security-related notifications, which they learn to routinely disregard. We investigate this problem through the neurobiological phenomenon of generalization of habituation, where habituation to one stimulus transfers to another stimulus that shares similar characteristics. Generalization of habituation suggests that because of habituation to frequent notifications, people may also be deeply habituated to security warnings they have never seen before, leading to warning disregard. Furthermore, because generalization of habituation occurs unconsciously at the neurobiological level, this may occur even though a person can consciously distinguish security warnings from notifications. We address this problem through three experiments—two in the field and one using functional magnetic resonance imaging. These experiments demonstrate how generalization of habituation occurs and can be mitigated by differentiating warnings from notifications in terms of their visual appearance or mode of interaction. These findings provide guidance to software developers for designing warnings that resist generalization of habituation and promote greater warning adherence.

We live in an age of unprecedented opportunities to use existing data for tasks not anticipated when those data were collected, resulting in widespread data repurposing. This commentary defines and maps the scope of data repurposing to highlight its importance for organizations and society and the need to study data repurposing as a frontier of data management. We explain how repurposing differs from original data use and data reuse and then develop a framework for data repurposing consisting of concepts and activities for adapting existing data to new tasks. The framework and its implications are illustrated using two examples of repurposing, one in healthcare and one in citizen science. We conclude by suggesting opportunities for research to better understand data repurposing and enable more effective data repurposing practices.

Over the last few years, MISQ

Digital platform ecosystems thrive on their ability to acquire and leverage user data across multiple data-driven services. This enables dominant platforms to harness insights obtained from their primary markets, where user data is collected, thus gaining a competitive advantage in secondary markets, where they exploit this data. While data cross-use brings about efficiencies, policymakers worldwide have expressed concerns about the economic power and the potential distortion of competition and innovation incentives associated with it. To address these concerns, two distinct and targeted policy interventions have been suggested: data siloing, which restricts the cross-use of data within platform ecosystems, and mandated data sharing with competitors. Using an analytical model that examines data cross-use in digital platform ecosystems, we analyzed the impact of data siloing and data sharing obligations, and their interaction on competition, innovation, consumer welfare, and overall social welfare. Our findings indicate that an optimal policy involves data sharing without data siloing, whereas the EU’s Digital Markets Act currently mandates both types of data cross-use regulation.

Augmented reality (AR) is rapidly emerging as a transformative display technology, blending computer-generated content with the real-world environment in real time. Using divided attention theory, this study investigates how different information delivery channels (i.e., AR vs. mobile phone) and the nature of information (i.e., dependence on specific physical context and complexity) affect work performance. A field experiment in the aircraft maintenance context demonstrates that the effect on work performance of providing information via AR vs. a mobile phone is mediated by work attentiveness. The findings reveal that the effectiveness of AR is particularly pronounced when information is highly dependent on the specific physical context but diminishes when information complexity is high. This research deepens our understanding of how presenting information directly in front of users’ eyes (i.e., via AR) affects their attention management and work performance. The findings have significant implications for firms in terms of how to leverage AR to enhance work performance in industrial settings.

Algorithms are increasingly seen as capable of autonomously initiating and managing interactions with humans—for example, through delegating the rights and responsibilities for successful outcomes of shared tasks without human intervention. While research into such interactions primarily focuses on dyadic configurations, complex settings where multiple agents work together have become a nexus of more nuanced interactions that go beyond the dyad. This paper explores such interactions through the lens of delegation by investigating how many algorithms delegate to many humans in a multi-agent setting. Analyzing patent data and interviews with drivers and passengers, we unpack delegation in the context of the ride-hailing application Uber. We theorize distributed delegation as a construct capturing collective hybrid appraisal, collective hybrid distribution, and collective hybrid coordination, in which a collective of algorithms delegates by drawing on inputs from multiple human agents. Our findings highlight that distributed delegation is collective, hybrid, and relational by nature, and demonstrate the extent to which human inputs are necessary for collectives of algorithms to exercise the capacity to delegate. Distributed delegation as a continuum of algorithmic and human involvement poses a challenge for recent theories suggesting the unprecedented autonomy of algorithms from humans.

Researchers must ensure that the claims about the knowledge produced by their work are valid. However, validity is neither well-understood nor consistently established in design science, which involves the development and evaluation of artifacts (models, methods, instantiations, and theories) to solve problems. As a result, it is challenging to demonstrate and communicate the validity of knowledge claims about artifacts. This paper defines validity in design science and derives the Design Science Validity Framework and a process model for applying it. The framework comprises three high-level claim and validity types-criterion, causal, and context-as well as validity subtypes. The framework guides researchers in integrating validity considerations into projects employing design science and contributes to the growing body of research on design science methodology. It also provides a systematic way to articulate and validate the knowledge claims of design science projects. We apply the framework to examples from existing research and then use it to demonstrate the validity of knowledge claims about the framework itself.

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