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Complex problems and new challenges, caused by an increasingly connected world, newly emerging technologies and ambitious customer demands, require companies to establish beneficial teamwork and collaboration (Dulebohn & Hoch, 2017; Finkbeiner & Morner, 2015). Information and communication technology (ICT) enable such collaboration to take place anywhere and at any time, resulting in digital collaboration. Such digital collaboration is now part of the day-to-day business of many knowledge workers, in which different team members collaborate using certain information technologies for communication, information sharing and overall collective value creation (Driskell, Radtke, & Salas, 2003; Dulebohn & Hoch, 2017; Fiol & O’Connor, 2005). Research in this field has been conducted over the past decades, leading to a variety of guidelines, technologies and computer systems that support tasks, like decision making, project and knowledge management or creativity (Resnick et al., 2005; Siemon, Becker, Eckardt, & Robra-Bissantz, 2017; Voigt & Bergener, 2013).
The continuous improvement of computing power and the general development of computer technology has significantly matured artificial intelligence (AI) (Russell & Norvig, 2016). This improvement has led to various new prototypes, systems or services, like Amazon’s Alexa, Apple’s Siri or IBM’s Watson. With Google’s Tensorflow, Facebook’s Wit.AI or other services, software developers are now able to implement AI within their products or services more easily. This results in smarter services using AI to interact and even collaborate with customers (Loebbecke & Picot, 2015; McTear, Callejas, & Griol, 2016; Spinella, 2018). This upswing of AI challenges research and existing theories on collaboration mechanisms, methods and phenomena in group- or teamwork.
The interdisciplinary research field of computer-supported collaborative work (or collaboration technology) has already revealed the various mechanisms required for successful collaboration via information systems (Borghoff & Schlichter, 2000; Grudin, 1994). A number of design principles have emerged and been developed from this research, proposing guidelines and different characteristics that support interaction and group dynamics, for example to reduce negative cognitive or social group effects such as production blocking, evaluation apprehension or social loafing (Diehl & Stroebe, 1987; Voigt & Bergener, 2013). Evaluation apprehension for example, or the fear of critics, is a phenomenon that appears when individuals hold back ideas, because they apprehend negative comments and critic. This can lead to ideas and thoughts that are hold back and might be valuable for a beneficial innovation (Diehl & Stroebe, 1987; Jessup, Connolly, & Galegher, 1990). Using anonymity has been proven to positively impact evaluation apprehension, as users can anonymously contribute and are less or not afraid of criticism. However, anonymity increases social loafing, a phenomenon of individuals exerting less effort in a group (Jessup et al., 1990). A study from 2015 used an AI-like support system in order to overcome the phenomena of evaluation apprehension in a group setting (Siemon, Eckardt, & Robra-Bissantz, 2015). The researchers implemented a pseudo AI within a creativity support system, examining whether participants fear to contribute when interacting and being supported by an artificial collaborator. The phenomena of evaluation apprehension were not observed within the experiment (Siemon et al., 2015). Even though, the study is limited by virtue of the small number of participants and the results only show a tendency, it shows that novel mechanisms need to be further analyzed.