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Social media marketing has become a crucial component of contemporary business strategies, significantly influencing brand visibility, customer engagement, and sales growth. The aim of this study is to investigate and determine the key factors guiding customer attitudes towards social media influencers, and, on that basis, to explore their effects on purchase intentions regarding advertised products or services. A total of 376 filled-in questionnaires from an online survey were analysed. The main characteristics of digital influencers' behaviour that affect consumer perceptions have been systematized and categorized through a combination of both traditional and advanced data analysis methods. Structural equation modelling (SEM), machine learning and multi-criteria decision-making (MCDM) methods were selected to uncover the hidden dependencies between variables from the perspective of social media users. The developed models elucidate the underlying relationships that shape the acceptance mechanism of influencers' messages. The obtained results provide specific recommendations for stakeholders across the social media marketing value chain. Marketers can make informed decisions and optimize influencer marketing strategies to enhance user experience and increase conversion rates. Working collaboratively, marketers and influencers can create impactful and successful marketing campaigns that resonate with the target audience and drive meaningful results. Customers benefit from more tailored and engaging influencer content that aligns with their interests and preferences, fostering a stronger connection with brands and potentially affecting their purchase decisions. As the perception of customer satisfaction is an individual and evolving process, stakeholders should organize regular evaluations of influencer marketing data and explore the possibilities to ensure the continuous improvement of this e-marketing channel. [ABSTRACT FROM AUTHOR]

The objective of this study is to investigate and determine factors influencing user perception and acceptance of electronic government services in the context of technological advancements. The research focuses on classifying the main features of e-administrative systems with an emphasis on user satisfaction by integrating both traditional and modern data analysis techniques. Structural Equation Modelling (SEM), machine learning (ML) techniques, and multi-criteria decision-making (MCDM) methods have been applied to survey data to uncover the interdependencies between variables from the perspective of online users. The developed models discover and explain the underlying relationships in user attitudes towards e-government services. As the perception of customer satisfaction is subjective and dynamic, stakeholders should conduct regular measurements and data analysis to ensure continuous improvement of e-public services. [ABSTRACT FROM AUTHOR]