Contextualizing Team Adaptation for Fostering Creative Outcomes in Multicultural Virtual Teams: A Mixed Methods Approach
Rapid developments in real-time collaborative technologies coupled with the quest for innovation and creativity, has made global virtual teams (GVTs) a viable workplace collaboration option, that many companies are turning to. Though diverse team member perspectives in GVTs are expected to foster creativity, cultural diversity within GVTs also poses significant challenges related to knowledge exchange and integration among team members. Grounding our work in team adaptation and cultural intelligence (CQ) literatures, we suggest CQ as a plausible modality for cultural adaptation in GVTs. Specifically, we propose a nomological network comprising CQ dimensions (motivation, cognition, metacognition, and behavior) serving as a cultural adaptation mechanism for fostering creativity in GVT outcomes. We contextualize and extend prior CQ theory, suggested for face-to-face contexts, to the virtual collaborative GVT environment. For this, we conceptualize the significant role of deep-level implicitly negotiated adaptative behavior (role structure adaptation) in GVTs —in addition to surface-level explicitly displayed adaptative behavior (CQ behavior). We test the proposed model through a sequential mixed methods approach that integrates the results from a quantitative two-wave survey study with findings from a qualitative study comprising expert interviews, to arrive at rich and robust inferences and meta-inferences. The proposed CQ-for-GVT framework, along with delineated boundary conditions and associated propositions, explicates an integrative model explaining the role of CQ for GVT creativity performance. The delineated model not only has theoretical implications but also provides useful directions to GVT practitioners.
Shirish, Anuragini; Srivastava, Shirish C.; and Boughzala, Imed, "Contextualizing Team Adaptation for Fostering Creative Outcomes in Multicultural Virtual Teams: A Mixed Methods Approach" (2023). JAIS Preprints (Forthcoming). 86.
Available at: https://aisel.aisnet.org/jais_preprints/86