2026
doi: 10.12720/jait.17.9.1783-1797
An LLM-based Behavioral Intelligence Framework for Assessing Internal Team Conflict and Predicting Project Performance Outcomes
Santosh Reddy1
,
Ashwini R Malipatil2 ,
Srinivasa Suresh Sikhakolli3 ,
Manoj Meghrajani4 ,
Samar Mansour Hassen5 ,
Vaibhav Sharma6 ,
Mohammed Saleh Al Ansari7,* ,
Mohd Aarif8
- 1Department of Computer Science & Technology, Dayananda Sagar University, Bangalore, India
- 2Department of Computer Science & Engineering, BNM Institute of Technology, Bangalore, India
- 3Department of Business Analytics, Kirloskar Institute of Management, Pune, India
- 4Department of Business Administration, Ramchandran International Institute of Management (RIIM), Pune, India
- 5Department of Management Information Systems, College of Business Administration, Jazan University, Jazan, Saudi Arabia
- 6School of Engineering & Technology, Shri Guru Ram Rai University, Dehradun, India
- 7Department of Chemical Engineering, College of Engineering, University of Bahrain, Zallak, Bahrain
- 8Department of Commerce, Aligarh Muslim University, Aligarh, India
- Manuscript receivedJanuary 26, 2026
- revisedMay 13, 2026
- acceptedJune 9, 2026
- publishedSeptember 24, 2026
Abstract
Team conflict within organizations is a vital predictor of project outcomes in today’s organizations, especially in digitally mediated settings where interaction mostly happens through text-based communication. Determining the conflict state within projects and predicting project outcomes based on these interactions is a difficult task. Currently, there exist no methods that use automated analyses, dynamic linguistic features, and multivariate prediction models to understand behavioral dynamics, temporal relations, and cross-variables associations between conflict states and project outcomes. Moreover, many available datasets are not annotated for conflict and performance states and cannot be used for supervised learning methods. In order to tackle these issues, this paper suggests a new behavioral intelligence system based on a Large Language Model (LLM) to predict team conflicts and project outcomes from meetings’ transcripts. This paper proposes an approach that incorporates an easily interpretable proxy labeling scheme for the generation of conflict and project outcome labels based on measurable signs of conversation, including sentiment polarity changes, interruptions, speaker dominance, and agenda management actions. Linguistic, interactional, and contextual cues are combined with transformer contextual embedding produced by a shared DeBERTa encoder. Multi-task learning is used to benefit from the common representation of the behavior across the two prediction tasks. Experiments show that the model reaches high levels of predictive power, with macro-average precision, recall, and F1-Scores of 91%, 89%, and 90%, respectively, on predicting conflict intensity and accuracy of 93% with a macroROC-AUC of 95% on predicting project outcomes. This approach is implemented using Python and PyTorch.
Keywords
- team conflict detection
- project outcome prediction
- behavioral intelligence
- large language models
- multi-task learning
How to Cite
Santosh Reddy, Ashwini R Malipatil, Srinivasa Suresh Sikhakolli, Manoj Meghrajani, Samar Mansour Hassen, Vaibhav Sharma, Mohammed Saleh Al Ansari, and Mohd Aarif, "An LLM-based Behavioral Intelligence Framework for Assessing Internal Team Conflict and Predicting Project Performance Outcomes," Journal of Advances in Information Technology, vol. 17, no. 9, pp. 1783-1797, 2026. https://doi.org/10.12720/jait.17.9.1783-1797
Copyright & License
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
