Abstract: Digital transformation is a key driver of business evolution, but it comes with significant challenges, particularly employee resistance to change. This resistance can manifest in various forms, ranging from explicit opposition to more subtle hesitation toward new practices. Its underlying causes are diverse, including fear of the unknown, loss of control, and dissatisfaction with perceived transformations. Understanding employee perceptions is, therefore, crucial to adapting digital initiatives and ensuring successful adoption. However, existing methods for assessing resistance, which rely on closed-ended questionnaires and binary classifications, have limitations. They restrict the expression of opinions and fail to provide a nuanced segmentation of employees’ stances toward change. In this context, this study proposes an innovative and automated methodology that combines specialized zero-shot LLMs and prompt engineering techniques to analyze resistance to change. It is based on the allies strategy, a concept derived from sociodynamic theory and widely applied in change management, which seeks to more precisely differentiate employee attitudes based on their level of synergy or antagonism toward a new project or transformation initiative. To evaluate the effectiveness of the proposed approach, an experiment was conducted on an annotated dataset comprising a hundred employee responses. Two prompt engineering strategies were explored and applied to six zero-shot models to assess their ability to accurately classify expressed attitudes. The findings underscored, on one side, the significance of prompt structuring in enhancing classification efficacy and, on the other side, the preeminence of DeBERTa-v3-large-zeroshot, which demonstrated itself as the most exemplary model, even exceeding GPT-4, one of the most sophisticated and cutting-edge language models currently accessible.

Bachira Abou El Karam, Tarik Fissaa and Rabia Marghoubi, “AI-Powered Assessment of Resistance to Change in the Context of Digital Transformation” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160653

@article{Karam2025,
title = {AI-Powered Assessment of Resistance to Change in the Context of Digital Transformation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160653},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160653},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Bachira Abou El Karam and Tarik Fissaa and Rabia Marghoubi}
}