The 10 most-cited articles in MIS Quarterly, 2021

Managing artificial intelligence (AI) marks the dawn of a new age of information technology management. Managing AI involves communicating, leading, coordinating, and controlling an ever-evolving frontier of computational advancements that references human intelligence in addressing ever more complex decisionmaking problems. It means making decisions about three related, interdependent facets of AI—autonomy, learning, and inscrutability—in the ongoing quest to push the frontiers of performance and scope of AI. We demonstrate how the frontiers of AI have shifted with time, and explain how the seven exemplar studies included in this special issue are helping us learn about management at the frontiers of AI. We close by speculating about future frontiers in managing AI and what role information systems scholarship has in exploring and shaping this future.

Online labor platforms (OLPs) can use algorithms along two dimensions: matching and control. While previous research has paid considerable attention to how OLPs optimize matching and accommodate market needs, OLPs can also employ algorithms to monitor and tightly control platform work. In this paper, we examine the nature of platform work on OLPs, and the role of algorithmic management in organizing how such work is conducted. Using a qualitative study of Uber drivers’ perceptions, supplemented by interviews with Uber executives and engineers, we present a grounded theory that captures the algorithmic management of work on OLPs. In the context of both algorithmic matching and algorithmic control, platform workers experience tensions relating to work execution, compensation, and belonging. We show that these tensions trigger market-like and organization-like response behaviors by platform workers. Our research contributes to the emerging literature on OLPs.

Information systems (IS) use, the dominant theoretical paradigm for explaining how users apply IS artifacts toward goal attainment, gives primacy to human agency in the user–IS artifact relationship. Models and theorizing in the IS use research stream tend to treat the IS artifact as a passive tool; lacking in the ability to initiate action and accept rights and responsibilities for achieving optimal outcomes under uncertainty. We argue that a new generation of “agentic” IS artifacts requires revisiting the human agency primacy assumption. Agentic IS artifacts are no longer passive tools waiting to be used, are no longer always subordinate to the human agent, and can now assume responsibility for tasks with ambiguous requirements and for seeking optimal outcomes under uncertainty. To move our theorizing forward, we introduce delegation, based on agent interaction theories, as a foundational and powerful lens through which to understand and explain the human– agentic IS artifact relationship. While delegation has always been central to human–IS artifact interactions, it has yet to be…

The introduction of machine learning (ML) in organizations comes with the claim that algorithms will produce insights superior to those of experts by discovering the “truth” from data. Such a claim gives rise to a tension between the need to produce knowledge independent of domain experts and the need to remain relevant to the domain the system serves. This two-year ethnographic study focuses on how developers managed this tension when building an ML system to support the process of hiring job candidates at a large international organization. Despite the initial goal of getting domain experts “out the loop,” we found that developers and experts arrived at a new hybrid practice that relied on a combination of ML and domain expertise. We explain this outcome as resulting from a process of mutual learning in which deep engagement with the technology triggered actors to reflect on how they produced knowledge. These reflections prompted the developers to iterate between excluding domain expertise from the ML system and including it. Contrary to common views that imply an opposition…

We analyze how advice from an AI affects complementarities between humans and AI, in particular what humans know that an AI does not know: “unique human knowledge.” In a multi-method study consisting of an analytical model, experimental studies, and a simulation study, our main finding is that human choices converge toward similar responses improving individual accuracy. However, as overall individual accuracy of the group of humans improves, the individual unique human knowledge decreases. Based on this finding, we claim that humans interacting with AI behave like “Borgs,” that is, cyborg creatures with strong individual performance but no human individuality. We argue that the loss of unique human knowledge may lead to several undesirable outcomes in a host of human–AI decision environments. We demonstrate this harmful impact on the “wisdom of crowds.” Simulation results based on our experimental data suggest that groups of humans interacting with AI are far less effective as compared to human groups without AI assistance. We suggest mitigation techniques to create environments…

The remarkable connectivity and embeddedness of digital technologies enable innovations undertaken by a broad set of actors, often beyond organizational and industry boundaries, whose relationships mimic those of interdependent species in a natural ecosystem. These digital innovation ecosystems, if successful, can spawn countless innovations of substantial social and economic value, but are complex and prone to often surprising failure. Aiming to understand ecosystems as a new organizational form for digital innovations, I develop a theory that addresses an underexplored but important question: In a digital innovation ecosystem, how are the efforts of autonomous parties integrated into a coherent whole and what role do digital technologies play in this integration? By synthesizing ecological and information perspectives, this information ecology theory identifies several key functions that digital technologies serve in providing the information needed to support the interactions and tasks for innovation in ecosystems of varying scales. This theory contributes to digital innovation…

This paper applies upper echelons theory to investigate whether chief information officers (CIOs) and boards of directors affect the development of AI orientation, which represents firms’ overall strategic direction and goals regarding the introduction and application of artificial intelligence (AI) technology. We tested our model using a dataset drawn from 1,454 publicly listed firms in China. Our findings show that the presence of a CIO positively influences AI orientation and that board educational diversity, R&D experience, and AI experience positively moderate the CIO’s effect on AI orientation. Our post hoc analysis further demonstrates that these board characteristics represent contingencies that impact AI orientation but not conventional IT orientation. This paper contributes to the upper echelons literature and IT management research by offering contextualized arguments that explain new business and IT strategies such as AI orientation. Further, our findings suggest important implications about how to build top management teams and boards capable of effectively developing…

Machine learning (ML) tools reduce the costs of performing repetitive, time-consuming tasks yet run the risk of introducing systematic unfairness into organizational processes. Automated approaches to achieving fairness often fail in complex situations, leading some researchers to suggest that human augmentation of ML tools is necessary. However, our current understanding of human–ML augmentation remains limited. In this paper, we argue that the Information Systems (IS) discipline needs a more sophisticated view of and research into human–ML augmentation. We introduce a typology of augmentation for fairness consisting of four quadrants: reactive oversight, proactive oversight, informed reliance, and supervised reliance. We identify significant intersections with previous IS research and distinct managerial approaches to fairness for each quadrant. Several potential research questions emerge from fundamental differences between ML tools trained on data and traditional IS built with code. IS researchers may discover that the differences of ML tools undermine some of the fundamental…

Advances in artificial intelligence (AI) could potentially reduce the complexities and costs in drug discovery. We conceptualize an AI innovation capability that gauges a firm’s ability to develop, manage, and utilize AI resources for innovation. Using patents and job postings to measure AI innovation capability, we find that it can affect a firm’s discovery of new drug-target pairs for preclinical studies. The effect is particularly pronounced for developing new drugs whose mechanism of impact on a disease is known and for drugs at the medium level of chemical novelty. However, AI is less helpful in developing drugs when there is no existing therapy. AI is also less helpful for drugs that are either entirely novel or those that are incremental “followon” drugs. Examining AI skills, a key component of AI innovation capability, we find that the main effect of AI innovation capability comes from employees possessing the combination of AI skills and domain expertise in drug discovery as opposed to employees possessing AI skills only. Having the combination is key because developing and…

Ongoing digital innovations are transforming almost every aspect of our contemporary societies—rendering our lives and work evermore fluid and dynamic. This paper is an invitation to likewise remake our theorizing of socio-technological transformation by shifting from actor-centric orientations toward a flow-oriented approach and vocabulary. Such a shift from actors to the flows of action allows us to offer an innovative theory of socio-technological transformation that does not rely on self-contained actors or technologies as originators of transformation. To do this, we turn to the work of social anthropologist Tim Ingold to advance a theoretical vocabulary of flowing lines of action and their correspondences. We expound three modalities of correspondence, namely: timing, attentionality, and undergoing, which together explain the dynamics of creation, sensing, and actualization of (trans)formative possibilities for action along socio-technological flows. We demonstrate the application and utility of this vocabulary through an empirical illustration and show how it reveals novel…