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Natural Language Processing, such as speech-to-text technology, is increasingly implemented in collaboration software that is used by global virtual teams (GVT). GVT collaboration has become ubiquitous and has additionally accelerated during the COVID-19 pandemic. The main issues of global virtual teams are technology difficulties, language and time zone differences, and lower levels of psychological safety. Advances in collaboration technology aim at improving collaboration for GVT. But we know little about the acceptance of these technologies. Therefore, the objective of this study is to explore how Millennial and Gen Z members of GVT accept speech-to-text technology; namely, automated captions in virtual conferences and automated meetings transcripts. Particularly, we are comparing antecedents of acceptance across levels of language proficiency and psychological safety. We surveyed 530 users of speech-to-text technology in GVT both before and after they used the technology. The pre-survey was administered before the COVID-19 pandemic hit; when participants completed the post-survey all were under some degree of lockdown. Results suggest that use of the technology reduces anxiety and effort, but decreases performance expectation and hedonic motivation. Non-native speakers rate the technology more positively. The impact of psychological safety is limited to self-efficacy and anxiety.
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.