Lu’ay Al-Mu’ani

Data-driven strategies have become essential for brand valuation optimization in today’s rapidly evolving virtual economy, where organizations face increasing pressure to gain real-time, accurate insights to maintain a competitive edge. The purpose of the study examine the impact of machine learning in monitoring key market factors to predict future brand value, addressing the growing need among industry professionals for tools that enhance strategic decision-making. From April to September 2024, a purposive sample of 350 upper-level brand managers and sales marketing directors from various Jordanian companies targeted due to their direct involvement in brand evaluation and marketing strategy. 229 completed and valid responses were collected through a self-administered questionnaire. The data analyzed using AMOS software and Structural Equation Modeling (SEM) to test the research hypotheses. Results indicated that all proposed factors significantly influenced the prediction of future brand value, with purchase frequency (β = 2.681), industry trend monitoring (β = 2.228), consumer behavior (β = 0.353), and social media metrics (β = 0.345) showing statistically significant effects (p < 0.05). These findings demonstrate the effectiveness of machine learning in identifying predictive patterns relevant to brand performance and provide a practical framework for leveraging digital tools to enhance brand valuation strategies. The study concludes that integrating machine learning with key performance monitoring enables organizations to make more informed, timely, and impactful branding decisions in a dynamic digital landscape.

Acknowledgment
We would like to thank the Business School at Al Ahliyya Amman University, Jordan. Specifically, many thanks go to the Department of E-marketing and Digital Communications.

Type of the article: Research Article

Abstract
Customer loyalty is a pressing priority and strategic imperative in the Jordanian hotel sector. With the rapid technological transformation in consumer behavior, social media marketing activities have emerged as a tool for engaging with hotel guests and building long-term relationships. This paper aims to explore the impact of Social Media Marketing activities (SMM activities) on brand loyalty through the mediating role of brand love and brand trust in the five-star hotel sector. The study adopted a quantitative design, employing a descriptive correlational approach to examine the relationship between study variables. Data were collected from June 1, 2024, to September 30, 2024, using a structured online questionnaire from guests of five-star hotels in Jordan, in Amman. A purposive sample was used to select hotel guests, using social media platforms for 280 respondents. The questionnaire was designed to measure the variables of the study. Statistical analysis was conducted using Smart PLS to test the hypotheses. The study results indicate a significant impact of social media marketing activities (β = 0.299, p < 0.001) on brand loyalty, as well as the mediating role of brand trust (β = 0.09, p < 0.001) and brand love (β = 0.18, p < 0.001) in mediating the relationship between marketing activities and customer loyalty. The results also revealed a positive relationship between social media marketing activities and brand loyalty in the hotel sector. The results also indicate that brand love and trust mediate the relationship between social media marketing activities and brand loyalty, enhancing brand loyalty in the hotel industry.

Acknowledgment
The authors would like to thank all the participants involved in this research work, and the authors are grateful to the Philadelphia University, Amman, Jordan for the financial support granted to cover the publication fee of this article.