Collaborative Synergy Between Humans and Artificial Intelligence: Impact on Team Performance

This study investigates the dynamic interaction between human- artificial intelligence collaboration and team performance at Qatar Airways, focusing on the integration of digital management tools and artificial intelligence techniques within the organization's unique socio-cultural context. Using a combination of qualitative and quantitative research methods, the study draws on secondary research and employs a Likertscale questionnaire administered to 187 participants selected through random sampling. The questionnaire explores demographics, teamwork quality, team performance. By leveraging artificial intelligence, including machine learning algorithms and sentiment analysis tools, the study enhances data analysis, providing deeper and more accurate insights. Digital management systems are used to perform regression analysis and test hypotheses, revealing statistically significant relationships between artificial intelligence -driven tools and both teamwork quality and overall team performance. The findings highlight the crucial role of human- artificial intelligence synergy in improving team dynamics and boosting overall team productivity at Qatar Airways. This research offers valuable insights into how artificial intelligence integration can enhance leadership and collaboration, providing innovative strategies for improving team performance and organizational effectiveness in modern workplaces

Authors

Institutions

Publication Details

Published
2025-04-13
DOI
https://doi.org/10.1109/wids-psu64963.2025.00023
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Collaborative Synergy Between Humans and Artificial Intelligence: Impact on Team Performance

Roya Anvari, Saba Khergiani
Big Data and Business Intelligence
article

Collaborative Synergy Between Humans and Artificial Intelligence: Impact on Team Performance

Roya Anvari, Saba Khergiani
article en

Abstract

This study investigates the dynamic interaction between human- artificial intelligence collaboration and team performance at Qatar Airways, focusing on the integration of digital management tools and artificial intelligence techniques within the organization's unique socio-cultural context. Using a combination of qualitative and quantitative research methods, the study draws on secondary research and employs a Likertscale questionnaire administered to 187 participants selected through random sampling. The questionnaire explores demographics, teamwork quality, team performance. By leveraging artificial intelligence, including machine learning algorithms and sentiment analysis tools, the study enhances data analysis, providing deeper and more accurate insights. Digital management systems are used to perform regression analysis and test hypotheses, revealing statistically significant relationships between artificial intelligence -driven tools and both teamwork quality and overall team performance. The findings highlight the crucial role of human- artificial intelligence synergy in improving team dynamics and boosting overall team productivity at Qatar Airways. This research offers valuable insights into how artificial intelligence integration can enhance leadership and collaboration, providing innovative strategies for improving team performance and organizational effectiveness in modern workplaces

Caucasus University (GE), University of Georgia (GE)
Openalex Percentile: Top 80%
Big Data and Business Intelligence
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.