Publication database

To study the effect of congestion on the fundamental trade-off between diagnostic accuracy and speed, we empirically test the predictions of a formal sequential testing model in a setting where the gathering of additional information can improve diagnostic accuracy, but may also take time and increase congestion as a result. The efficient management of such systems requires a careful balance of congestion-sensitive stopping rules. These include diagnoses made based on very little or no diagnostic information, and the stopping of diagnostic processes while waiting for information. We test these rules under controlled laboratory conditions, and link the observed biases to system dynamics and performance. Our data shows that decision makers (DMs) stop diagnostic processes too quickly at low congestion levels where information acquisition is relatively cheap. But they fail to stop quickly enough when increasing congestion requires the DM to diagnose without testing, or diagnose while waiting for test results. Essentially, DMs are insufficiently sensitive to congestion. As a result of these behavioral patterns, DMs manage the system with both lower-than-optimal diagnostic accuracy and higher-than-optimal congestion cost, underperforming on both sides of the accuracy/speed trade-off.

© 2023, INFORMS

We introduce a model of a rational credit card user's rather complex usage choices and develop an empirical framework to test its predictions. Employing a large national database of U.S. card accounts, we estimate how prices impact card usage and find that price effects are mostly well explained within our model. An exception is less borrowing in response to declining late-fees among low credit-score (subprime) users. Extension of our model based on "focusing theory" predicts this behavior. It also implies substantial indirect benefits of the CARD Act's late-fee cap due to subprime users re-focusing toward reducing their debt.

Artificial intelligence systems are increasingly demonstrating their capacity to make better predictions than human experts. Yet, recent studies suggest that professionals sometimes doubt the quality of these systems and overrule machine based prescriptions. This paper explores the extent to which a decision maker (DM) supervising a machine to make high-stake decisions can properly assess whether the machine produces better recommendations. To that end, we study a set-up in which a machine performs repeated decision tasks (e.g., whether to perform a biopsy) under the DM’s supervision. Because stakes are high, the DM primarily focuses on making the best choice for the task at hand. Nonetheless, as the DM observes the correctness of the machine’s prescriptions across tasks, she updates her belief about the machine. However, the DM is subject to a so-called verification bias such that the DM verifies the machine’s correctness and updates her belief accordingly only if she ultimately decides to act on the task. In this set-up, we characterize the evolution of the DM’s belief and overruling decisions over time. We identify situations under which the DM hesitates forever whether the machine is better, i.e., she never fully ignores but regularly overrules it. Moreover, the DM sometimes wrongly believes with positive probability that the machine is better. We fully characterize the conditions under which these learning failures occur and explore how mistrusting the machine affects them. These findings provide a novel explanation for human-machine complementarity and suggest guidelines on the decision to fully adopt or reject a machine.

© 2023, INFORMS

What defines success in executive education? Satisfied participants? Great scores on a post-training quiz? Participants using what they learned back on the job to influence important organizational metrics and KPIs? The article examines the case for taking a more results orientated approach to training design and evaluation for executive level education. Using the example of a TRATON executive education program, it sheds light on how to define a successful return via an impact map and how to shift from an event to a journey paradigm by combining a learning, a behavioral change, and a support layer.

View the book “Grasselli, N. (Ed.) (2023). The future of executive education. Learning leadership in a digital age” here.

An increased focus on co-creation in program design seeks to better meet the needs of the modern knowledge worker—setting a new standard for leadership development

View the book “Grasselli, N. (Ed.) (2023). The future of executive education. Learning leadership in a digital age” here.

What are the skills needed to lead a digital transformation today & how can leaders avoid digital transformation traps? This chapter brings forward an actionable route to making digital transformation work.

View the book “Grasselli, N. (Ed.) (2023). The future of executive education. Learning leadership in a digital age” here.

Why we cry at movies and what Hollywood can teach us about transferring our skills from the stage to the camera”. Tips for effective virtual communication in the “new normal” by using the ABC of virtual emotional intelligence:
A: Authorize emotion;
B: Be aware of the tacit;
C: Care and be compassionate.

View the book “Grasselli, N. (Ed.) (2023). The future of executive education. Learning leadership in a digital age” here.

How to achieve ownership among program participants and their sponsors – and why doing so can lead to better program outcomes.

View the book “Grasselli, N. (Ed.) (2023). The future of executive education. Learning leadership in a digital age” here.

The COVID-19 pandemic has reshaped the executive education industry, prompting a major transformation in program delivery. This article examines ESMT Berlin's successful adaptation to remote learning and the lessons learned from the experience. Through the case study of the "Enter the Eurozone" program, the article addresses common assumptions about online learning and presents strategies to enhance engagement and knowledge retention. Networking and peer-to-peer learning were prioritized. The article emphasizes the importance of small-group interactions and gradual integration of non-task related conversations to build trust and foster meaningful relationships. Contrary to the belief that scalability sacrifices customization, the article showcases how online learning can offer a higher degree of individualization. ESMT's approach involved providing a general framework and encouraging participants to apply it to their specific business challenges. In conclusion, the article highlights the positive outcomes of remote learning when supported by a dedicated team of professionals who prioritize learner outcomes and embrace lifelong learning.

View the book “Grasselli, N. (Ed.) (2023). The future of executive education. Learning leadership in a digital age” here.