Ghaiath Altrjman

The study aims to detect the relative impact of the basic advertising elements on attaining advertisement objectives. It also seeks to determine if the location and quality of billboards have an essential moderating impact on the effectiveness of advertising elements concerning their ability to achieve desired advertisement objectives in a developing country such as Jordan.
A quantitative survey methodology and an online questionnaire were used to a convenient sample of 450 university students from different academic years and their family members and acquaintances in Amman, Jordan, to achieve the study goals. IBM SPSS version 25 and Smart PLS 3 were used to test the hypotheses. The study revealed a statistically significant impact (p ≤ 0.05) of three billboard advertising elements in achieving the goals of promoting advertisements, namely: headline (t = 3.483), color (t = 2.308), and the number of elements (t = 2.418). However, the study failed to prove the effectiveness of other two elements in achieving these objectives. The analysis did not confirm the effect of moderation of billboard locations and quality between independent variables and billboard’s advertising objectives; however, the location of billboards (independent variable) directly affects the achievement of advertising objectives. The study came up with a set of conclusions, the most important of which is that the billboard still has an important impact on customers’ purchasing behavior or power, regardless of the location and the quality of billboards as a moderator variable.

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.