Recorded Work Meetings and Algorithmic Tools: Anticipated Boundary Turbulence

Abstract

Meeting recordings and algorithmic tools that process and evaluate recorded meeting data may provide many new opportunities for employees, teams, and organizations. Yet, the use of this data raises important consent, data use, and privacy issues. The purpose of this research is to identify key tensions that should be addressed in organizational policymaking about data use from recorded work meetings. Based on interviews with 50 professionals in the United States, China, and Germany, we identify the following five key tensions (anticipated boundary turbulence) that should be addressed in a social contract approach to organizational policymaking for data use of recorded work meetings: disruption versus help in relationships, privacy versus transparency, employee control versus management control, learning versus evaluation, and trust in AI versus trust in people.

Artificial Intelligence in Business Communication: The Changing Landscape of Research and Teaching

“…Employees have expressed concerns about privacy when a third-party technology is collecting and analyzing content of communication between two or more people (Cambre et al, 2019). Things that could previously be said in confidence between group members may not be able to be confidential if a machine is recording, transcribing, and storing the conversation (Cardon et al, 2021). This could in turn lead to people choosing to not say things that they are worried about being recorded.…”

How algorithmic management influences worker motivation: A self-determination theory perspective.

“…The degree of acceptance of AM seems to vary from one context to another or from person to person (Alahmad & Robert, 2020; Basukie et al, 2020; Bhardwaj et al, 2019; Briône, 2017; Cardon et al, 2021; Cox, 2020; Gallagher, 2019; McDonald et al, 2020; Nagtegaal, 2021; Reid-Musson et al, 2020; Roshdy & Erhua, 2020; Wiblen & Marler, 2021; Woodcock, 2020), and the literature reports resistance behaviors on the part of workers that aim to counter or circumvent algorithmic management. Lehdonvirta (2018) shows that gig workers often develop practices and tools, as well as informal communities, to deal with the constraints imposed by algorithmic systems.…”

A Synthesized Perspective on Privacy and Transparency in the Digital Workplace

“…Similarly, tracing tools for e-mails [37], digital productivity assistances [39] or personalized assistance systems [46] cause privacy concerns at the workplace. When employees work remotely, online recordings can specifically unsettle them [51]. Lastly, quantified self-practices track physical data [28].…”