Artificial intelligence (AI) algorithmic tools that analyze and evaluate recorded meeting data may provide many new opportunities for employees, teams, and organizations. Yet, these new and emerging AI tools raise a variety of issues related to privacy, psychological safety, and control. Based on in-depth interviews with 50 American, Chinese, and German employees, this research identified five key tensions related to algorithmic analysis of recorded meetings: employee control of data versus management control of data, privacy versus transparency, reduced psychological safety versus enhanced psychological safety, learning versus evaluation, and trust in AI versus trust in people. More broadly, these tensions reflect two dimensions to inform organizational policymaking and guidelines: safety versus risk and employee control versus management control. Based on a quadrant configuration of these dimensions, we propose the following approaches to managing algorithmic applications to recording meeting data: the surveillance, benevolent control, meritocratic, and social contract approaches. We suggest the social contract approach facilitates the most robust dialog about the application of algorithmic tools to recorded meeting data, potentially leading to higher employee control and sense of safety.

The rapid, widespread implementation of artificial intelligence technologies in workplaces has implications for business communication. In this article, the authors describe current capabilities, challenges, and concepts related to the adoption and use of artificial intelligence (AI) technologies in business communication. Understanding the abilities and inabilities of AI technologies is critical to using these technologies ethically. The authors offer a proposed research agenda for researchers in business communication concerning topics of implementation, lexicography and grammar, collaboration, design, trust, bias, managerial concerns, tool assessment, and demographics. The authors conclude with some ideas regarding how to teach about AI in the business communication classroom.

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.

Collaboration platforms for teams, such as Slack, are increasingly used in virtual teams. Conventional wisdom suggests attitudes about adopting these types of platforms is primarily driven by their affordances. Our project emerged from the premise that psychological safety and personality traits can also significantly influence attitudes related to technology adoption. This research of roughly 300 global virtual teams showed that psychological safety influences views of collaboration platforms in terms of performance expectancy, effort expectancy, and hedonic motivation. In addition, this research showed that personality traits influence views of collaboration platforms. These findings about psychological safety and personality traits suggest a team-development approach is an integral component of the technology adoption process. Recommendations for future research are provided.

Objectives: The purpose of this paper is (a) to examine the relationship between employee voice and management receptiveness with employee engagement; (b) to explore changes in internal vertical communication during the COVID-19 pandemic; and (c) to examine how less formal communication influences employee engagement. Methods: A survey of 344 Chinese professionals in the Shanghai region was conducted to measure employee voice; management receptiveness; internal vertical communication via DingTalk, WeChat, online meetings, and face-to-face (F2F) meetings; and use of informal communication (frequency of WeChat Moments between managers and employees). ANOVA analysis was used to compare changes across the three time periods, and hierarchical regression analysis was used to explore predictors of employee engagement. Results: Across the pandemic, managers increased their communication with employees via DingTalk and online meetings but decreased their communication with employees via F2F meetings. Employee voice and management receptiveness were the most significant predictors of employee engagement. Perceptions of employee voice grew significantly from the pre-COVID period until the present. The increased sharing and liking of WeChat Moments among managers and employees significantly predicted higher employee engagement. Conclusions: This is the first known study to explore the connection between employee voice and management receptiveness with employee engagement in the Chinese context. It also explores how two communication platforms, DingTalk and WeChat, with similar affordances are used with varying amounts of formality in the workplace. It highlights how the use of WeChat Moments, an informal form of communication, drives higher engagement.