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The purpose of this paper is to investigate the relevance and the appropriateness of Turing-style tests for computational creativity.

The Turing test is both a milestone and a stumbling block in artificial intelligence (AI). For more than half a century, the “grand goal of passing the test” has taught the authors many lessons. Here, the authors analyze the relevance of these lessons for computational creativity.

Like the burgeoning AI, computational creativity concerns itself with fundamental questions such as “Can machines be creative?” It is indeed possible to frame such questions as empirical, Turing-style tests. However, such tests entail a number of intricate and possibly unsolvable problems, which might easily lead the authors into old and new blind alleys. The authors propose an outline of an alternative testing procedure that is fundamentally different from Turing-style tests. This new procedure focuses on the unfolding of creativity over time, and – unlike Turing-style tests – it is amenable to a more meaningful statistical testing.

This paper argues against Turing-style tests for computational creativity.

This paper opens a new avenue for viable and more meaningful testing procedures.

The novel contributions are: an analysis of seven lessons from the Turing test for computational creativity; an argumentation against Turing-style tests; and a proposal of a new testing procedure.

The purpose of the research is to conduct an exploratory investigation of the material handling activities of an Italian logistics hub. Wearable sensors and other smart tools were used for collecting human and environmental features during working activities. These factors were correlated with workers' performance and well-being.

Human and environmental factors play an important role in operations management activities since they significantly influence employees' performance, well-being and safety. Surprisingly, empirical studies about the impact of such aspects on logistics operations are still very limited. Trying to fill this gap, the research empirically explores human and environmental factors affecting the performance of logistics workers exploiting smart tools.

Results suggest that human attitudes, interactions, emotions and environmental conditions remarkably influence workers' performance and well-being, however, showing different relationships depending on individual characteristics of each worker.

The authors' research opens up new avenues for profiling employees and adopting an individualized human resource management, providing managers with an operational system capable to potentially check and improve workers' well-being and performance.

The originality of the study comes from the in-depth exploration of human and environmental factors using body-worn sensors during work activities, by recording individual, collaborative and environmental data in real-time. To the best of the authors' knowledge, the current paper is the first time that such a detailed analysis has been carried out in real-world logistics operations.