Voice analytics in the wild: Validity and predictive accuracy of common audio-recording devices.

The use of voice recordings in both research and industry practice has increased dramatically in recent years-from diagnosing a COVID-19 infection based on patients' self-recorded voice samples to predicting customer emotions during a service center call. Crowdsourced audio data collection in participants' natural environment using their own recording device has opened up new avenues for researchers and practitioners to conduct research at scale across a broad range of disciplines. The current research examines whether fundamental properties of the human voice are reliably and validly captured through common consumer-grade audio-recording devices in current medical, behavioral science, business, and computer science research. Specifically, this work provides evidence from a tightly controlled laboratory experiment analyzing 1800 voice samples and subsequent simulations that recording devices with high proximity to a speaker (such as a headset or a lavalier microphone) lead to inflated measures of amplitude compared to a benchmark studio-quality microphone while recording devices with lower proximity to a speaker (such as a laptop or a smartphone in front of the speaker) systematically reduce measures of amplitude and can lead to biased measures of the speaker's true fundamental frequency. We further demonstrate through simulation studies that these differences can lead to biased and ultimately invalid conclusions in, for example, an emotion detection task. Finally, we outline a set of recording guidelines to ensure reliable and valid voice recordings and offer initial evidence for a machine-learning approach to bias correction in the case of distorted speech signals.

Friend or fiend? Disentangling upward humor's (De)stabilizing effects on hierarchies.

Humor research in organizations focuses on leaders' humor, but we know far less about followers' humor. Here, we review and synthesize the scattered work on this "upward humor," offering a novel framing of it as a strategy for followers to deal with hierarchies. We propose a continuum of upward humor from stabilizing (i.e., a friend who uses upward humor to reinforce hierarchies, make hierarchies more bearable or stable) to destabilizing (i.e., a fiend who uses upward humor to question or reshape existing hierarchies) depending on perceived intent (i.e., from benevolent to malicious, respectively) and outline key factors that shape these interpretations. We close with novel questions and methods for future research such as power plays, multi-modal data, and human-robot interactions.

Human-Like Movements of Industrial Robots Positively Impact Observer Perception.

The number of industrial robots and collaborative robots on manufacturing shopfloors has been rapidly increasing over the past decades. However, research on industrial robot perception and attributions toward them is scarce as related work has predominantly explored the effect of robot appearance, movement patterns, or human-likeness of humanoid robots. The current research specifically examines attributions and perceptions of industrial robots-specifically, articulated collaborative robots-and how the type of movements of such robots impact human perception and preference. We developed and empirically tested a novel model of robot movement behavior and demonstrate how altering the movement behavior of a robotic arm leads to differing attributions of the robot's human-likeness. These findings have important implications for emerging research on the impact of robot movement on worker perception, preferences, and behavior in industrial settings.