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The essential investments in new product development (NPD) made by industrial companies entail effective management of NPD activities. In this context, performance measurement is one of the means that can be employed in the pursuit of effectiveness.

This chapter critically examines whether it may be possible to create an AI-based authentic leader, questioning the inherent contradiction between artificial and authentic. The authors pose central research questions: Does the application of AI – even just as a powerful resource – challenge the tenets of authentic leadership? What are the possibilities and limitations of the concept of authenticity in AI-based management systems? Moreover, with the help of three vignettes illustrating practical applications of AI-based systems in leadership and management tasks, the authors illustrate how technology may be used to either control or empower workers and leaders. The authors call for research to assess whether the search for authenticity in AI-based leadership could lead anywhere, warning that it could entrap us in unresolvable existential and conceptual ambiguity, ultimately diverting our focus from the essence of leadership altogether.

Three themes dominate Hunting Causes. The first is that cause is a plural concept. The methods and metaphysics of causation, Cartwright believes, are context dependent. Different causal accounts seem to be at odds with one another only because the same word means different things in different contexts. Every formal approach to causality uses a conceptual framework that is “thinner” than causal reality. She lists a bewildering variety of approaches to causation: probabilistic and Bayes-net accounts (of, for example, Patrick Suppes, Clive Granger, Wolfgang Spohn, Judea Pearl, and Clark Glymour), modularity accounts (Pearl, James Woodward, and Stephen LeRoy), invariance accounts (Woodward, David Hendry, and Kevin Hoover), natural experiments (Herbert Simon, James Hamilton, and Cartwright), causal process accounts (Wesley Salmon and Philip Dowe), efficacy accounts (Hoover), counterfactual accounts (David Lewis, Hendry, Paul Holland, and Donald Rubin), manipulationist accounts (Peter Menzies and Huw Price), and others. The lists of advocates of various accounts overlap. Nevertheless, she sometimes treats these accounts as if they were so different that it is not clear why they should be the subject of a single book. And she fails to explain what they have in common. If, as she apparently believes, they do not have a common essence, do they have a Wittgensteinian family resemblance? She fails to explore in any systematic way the complementarities among the different approaches – for example, between invariance accounts, Bayes-nets, and natural experiments – that frequently make their advocates allies rather than opponents.