Georg von Krogh’s research while affiliated with ETH Zurich and other places

Machine learning is now widely used to guide decisions in processes where gauging the probability of a specific outcome-such as whether a customer will repay a loan-is sufficient. But because the technology, as traditionally applied, relies on correlations to make predictions, it offers managers no insight when it comes to understanding the impact of different choices on business outcomes. 1 Consider an R&D manager at a large company who is faced with deciding how much to invest in a new technology. Using traditional ML, they can ask what will happen when they increase their spending. They might find a strong correlation between higher levels of investment and higher revenue when the economy is growing. And they might conclude that, since economic conditions are favorable, they should increase the R&D budget. But should they, really? If so, by how much? External factors, such as levels of consumer spending, technology spillover from competitors, and interest rates, also influence revenue growth. Comparing how different levels of investment might affect revenue while considering these other variables is useful for the manager to determine an R&D budget that delivers the greatest benefit to the company. Causal ML-an emerging area of machine learning-can help to answer such what-if questions through causal inference. Similar to how marketers use A/B tests to infer which of two ads is likely to generate more sales, causal ML can inform what might happen if managers take a particular action. 2 This makes the technology useful in many of the same business functions that use traditional ML, including product development, manufacturing, finance, human resources, and marketing. 3 Traditional ML is still the go-to approach when making predictions-such as forecasting stock prices or recommending products that customers

Generative AI is upon us and is changing organizations and organizing. In this essay, we extend the relational perspective on technology, which argues for moving away from an entity-based view of technology to one that focuses instead on the evolving relations and functions between people, technologies, and organizations. We do so by introducing the concept of “progressive encapsulation” which captures GenAI’s potential ability to increasingly expand the “black box” and reduce visibility into and control over the relations and functions performed. We argue that progressive encapsulation is critical in our theorizing about GenAI. As an illustration and thought experiment we consider how GenAI and progressive encapsulation may necessitate changes in our theorizing about groups and teams in organizations.

The growth of digital platforms has led to the proliferation of Online Communities, providing individuals with opportunities to seek help and share knowledge. A key challenge of help-related platforms that address technical questions (i.e., utilitarian, rather than opinion or supportive) is to ensure the contributions address seekers’ specific information needs. Despite growing academic interest in such platforms, research has mainly focused on factors that influence the quantity of contributions, ignoring whether these contributions effectively helped the seekers. To fill this research gap, this study draws upon theories of self-determination and motivation crowding to examine contributing behaviors that result in successful helping. By analyzing a rich dataset collected from an online Q&A platform, we find that gains in a help provider’s past rewards positively influence the success of contribution. Further, while previous studies suggest that external rewards result in a high quantity of contribution, our findings show that an inflated frequency of contribution leads to a crowding-out effect. Specifically, the contribution frequency has a curvilinear relationship with the success of the contribution. Taken together, these findings demonstrate there is a need to revisit the gamification mechanism on help-related platforms to ensure the success of knowledge contribution. This is crucial for the sustainability of these platforms as low-quality answers can lead users to mistrust and eventually leave the platform.

Artificial intelligence (AI) applications have proliferated, garnering significant interest among information systems (IS) scholars. AI-powered analytics, promising effective and low-cost decision augmentation, has become a ubiquitous aspect of contemporary organisations. Unlike traditional decision support systems (DSS) designed to support decisionmakers with fixed decision rules and models that often generate stable outcomes and rely on human agentic primacy, AI systems learn, adapt, and act autonomously, demanding recognition of IS agency within AI-augmented decision making (AIADM) systems. Given this fundamental shift in DSS; its influence on autonomy, responsibility, and accountability in decision making within organisations; the increasing regulatory and ethical concerns about AI use; and the corresponding risks of stochastic outputs, the extrapolation of prescriptive design knowledge from conventional DSS to AIADM is problematic. Hence, novel design principles incorporating contextual idiosyncrasies and practice-based domain knowledge are needed to overcome unprecedented challenges when adopting AIADM. To this end, we conduct an action design research (ADR) study within an e-commerce company specialising in producing and selling clothing. We develop an AIADM system to support marketing, consumer engagement, and product design decisions. Our work contributes to theory and practice with a set of actionable design principles to guide AIADM system design and deployment.

Artificial intelligence (AI) offers new possibilities to augment human decision‐making under radical uncertainty. This viewpoint commentary explores how AI can relax limits of bounded rationality. It offers a framework for analyzing how AI can support human decision‐makers confronted with deep uncertainty by bolstering key decision‐making sub‐processes. Specifically, AI can help set agendas by scanning environments, formulate problems by providing contextual insights, identify creative alternatives through combinatorial abilities, select options by modelling scenarios and enable rapid experimentation cycles. Connecting the role of AI with the contributions in this special issue, this viewpoint commentary concludes by outlining directions for future research regarding the function of AI and augmented human intelligence in decision‐making under conditions of radical uncertainty.