Scholars around the world have studied electronic word of mouth (eWOM) heavily in the past decade. However, despite extensive knowledge of the effect of eWOM on consumer behavior, there is still a lack of understanding of how the eWOM source shapes those behaviors. There are many sources of eWOM, the influence of which may have different outcomes when it comes to consumers’ purchase intentions. Moreover, most studies have measured social tie strengths (strong/weak) as one variable. Weak-tie eWOM (W-tie) and strong-tie eWOM (S-tie) are always studied in contrast to each other. The versus characteristics of these two constructs have been considered in almost all studies; however, they may work together via different mechanisms. This study split social ties into two separate constructs, W-tie and S-tie, while adding another source of eWOM (celebrities eWOM), and investigated how W-tie moderated the effect of S-tie and celebrity eWOM on purchase intention, as well as the mediation effect of perceived value/perceived enjoyment on purchase intention. A total of 726 valid responses were analyzed using SPSS and AMOS. The results showed that all direct relationships in the model were supported. Moreover, perceived value mediated only the effect of celebrity eWOM on purchase intentions and did not successfully convey the effect of S-tie on purchase intentions. On the other hand, perceived enjoyment mediated both relationships. W-tie did not moderate the relationship between S-tie and perceived value, perceived enjoyment, or purchase intention. In contrast, W-tie moderated the positive relationship of celebrity eWOM with perceived value, perceived enjoyment, and purchase intention, while it also moderated the positive relationship of perceived value and perceived enjoyment with purchase intention. This research illuminates how different eWOM sources and social ties on social media platforms affect purchase intentions.

This study examines how AI influences the efficiency of strategic planning, with attention to the AI dimensions: task automation, decision‐making speed, resource use efficiency, the accuracy of automated predictions, the ability to handle complexity, scalability and flexibility, and integration into decision support. A quantitative research approach is adopted, using a structured questionnaire distributed to a sample of 218 senior managers from heterogeneous SMEs. The results suggest that artificial intelligence enhances the efficiency of strategic planning. considering the exploratory character of the investigation and the limited sample size. Further studies with larger and more diverse populations are needed for confirmation of these findings and to explore additional implications of artificial intelligence on strategic planning.

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. AcknowledgmentWe 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.

The current study focused on examining the role of TQMk (Total Quality Marketing) in increasing the effectiveness of e-marketing within Jordanian telecommunication sector; TQMk included variables of service quality, market orientation and the customer-focused approach. A quantitative approach was adopted through utilizing a questionnaire, which was distributed to 18 marketing and project managers within Jordanian telecommunication organizations (Zain, Umniah and Orange). Results of the study indicated that TQMk can have an influence in increasing the effectiveness and efficiency of e-marketing solutions within the organization and mainly within the social marketing and electronic marketing departments, through developing the variable of the customer-focused approach, which has the deepest influence on e-marketing approach’s effectiveness; it was followed by an influence of service quality, and the least influential factor was market orientation. The study recommended focusing on clients within the targeted markets through different aspects, including price, new products acceptance, customer behavior and purchase decision motivators.

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. AcknowledgmentThe 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.

This study aims to explore the impact of blockchain technology on project management practices. It also examines the moderating role of blockchain literacy in enhancing project management benefits derived from blockchain features. A quantitative research method was used drawing on data from project managers using blockchain across various industries from February to April 2024. An online questionnaire was sent through LinkedIn to the targeted respondents who use blockchain technology in their fields to ensure that they have enough knowledge about the research topic, therefore, guaranteeing the reliability of responses. 277 questionnaires were received, and then the data were analyzed through Smart-PLS 4.0. The results revealed a significant and positive relationship between blockchain features and improved project management practices. The results also showed that while blockchain literacy has enhanced the benefits achieved from blockchain adoption in projects, it has no impact on the ability to benefit from security, tracking and control. This study contributes to the knowledge base on blockchain in project management by providing empirical evidence of its beneficial impacts without and through the moderating role of blockchain literacy. The study offers insights into the distinct impacts of blockchain features, recommending areas where blockchain can provide the most value to project management.