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Export Reference (APA) 
Mota, F., Ferreira, N., Dabić, M., Ferreira, F. & Santos, M. (2026). “The dark side of the moon”: A structured analysis of generative AI | chatbots’ negative effects on SMEs. Technological Forecasting and Social Change. 224, 1-19
Export Reference (IEEE) 
F. L. Mota et al.,  "“The dark side of the moon”: A structured analysis of generative AI | chatbots’ negative effects on SMEs", in Technological Forecasting and Social Change, vol. 224, pp. 1-19, 2026
Export BibTeX 
@article{mota2026_1766612984877,
	author = "Mota, F. and Ferreira, N. and Dabić, M. and Ferreira, F. and Santos, M.",
	title = "“The dark side of the moon”: A structured analysis of generative AI | chatbots’ negative effects on SMEs",
	journal = "Technological Forecasting and Social Change",
	year = "2026",
	volume = "224",
	number = "",
	doi = "10.1016/j.techfore.2025.124490",
	pages = "1-19",
	url = "https://www.sciencedirect.com/science/article/pii/S0040162525005219"
}
Export RIS 
TY  - JOUR
TI  - “The dark side of the moon”: A structured analysis of generative AI | chatbots’ negative effects on SMEs
T2  - Technological Forecasting and Social Change
VL  - 224
AU  - Mota, F.
AU  - Ferreira, N.
AU  - Dabić, M.
AU  - Ferreira, F.
AU  - Santos, M.
PY  - 2026
SP  - 1-19
SN  - 0040-1625
DO  - 10.1016/j.techfore.2025.124490
UR  - https://www.sciencedirect.com/science/article/pii/S0040162525005219
AB  - Generative Artificial Intelligence (GenAI) has undergone a remarkable evolution, now standing as one of the most transformative technologies of our time. Among its applications, chatbots have emerged as pivotal tools for enhancing business competitiveness and efficiency. However, every innovation comes with its own set of challenges, often revealing a “dark side of the moon” that can prove catastrophic for small- and medium-sized enterprises (SMEs). Unlike larger companies, SMEs often have limited resources for implementing such tools and lack information tailored to their specific needs. This study aims to uncover the pain points and enhance SME performance by establishing a structured framework that identifies cause-and-effect relationships and prioritizes the challenges posed by GenAI chatbots in these companies. To achieve this, the study follows a Multiple Criteria
Decision Analysis (MCDA) approach based on group discussions among a panel of experts. The approach applies cognitive mapping and Interpretive Structural Modeling (ISM), complemented by the Matrice d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis. The findings are subsequently consolidated by neutral experts to ensure the robustness of the final model and to offer pertinent suggestions. The originality of this work lies in its use of expert group discussions to co-construct actionable insights, validated by neutral experts to reinforce reliability. By bridging the gap between technological advancements and the realities faced by SMEs, this study sheds light on an underexplored area, offering both theoretical and practical contributions to understanding and mitigating the risks associated with GenAI chatbots.
ER  -