12826: Optimizing Performance: Task-Technology Fit of Generative AI in Global Virtual Team Student Projects (session 26457)

Tuesday, July 29, 2025

08:30 - 10:00 CEST (GMT+2)

Location: Bella Center: Hall B- B4-m2

Session Type: Paper Session
Orientation: Teaching,International,Research
Shareable link: https://cdmcd.co/pM65Wk

Author(s)

WF

Wendy Farrell

Management Center Innsbruck, Austria
ET

Ernesto Tavoletti

U. of Macerata, Italy
DS

Dhruv Pratap SINGH

NEOMA Business School, France
VT

Vas Taras

U. of North Carolina, Greensboro, United States
JL

Jennifer Leigh

Nazareth U., United States

Designated as a “Best Paper” by MED
view abridged “best paper” (if available)

The study investigates the impact of generative AI (GAI) on team performance and individual peer evaluations in global virtual teams completing international business projects. Using a large international sample (N=3,193 people), the study applies the task- technology fit theory to examine how different GAI applications affect team report grades and peer assessments. The findings reveal that using GAI for proofreading and learning enhances both team and individual performance, while recreational use negatively impacts performance. The study highlights the importance of aligning GAI usage with specific task requirements to enhance both individual and team performance in academic settings. It emphasizes that simply using GAI does not guarantee better results, suggesting the need to educate students on how to effectively utilize GAI in a manner consistent with task-technology fit principles and good academic practices.

view paper (if available)