This study develops a data-driven model to examine the determinants of tourist satisfaction and revisit intention by integrating social media analytics and service quality factors within a digital tourism context. The rapid growth of big data generated through social media platforms has transformed how tourists perceive destinations, evaluate experiences, and make revisit decisions. However, limited studies have incorporated social media-driven variables into structural behavioral models in tourism research. This research proposes a comprehensive framework that includes brand image based on local wisdom, product quality, social media analytics, attitude, experience, and perceived risk as key predictors of tourist satisfaction and revisit intention. Data were collected from 160 domestic and international tourists visiting Belimbing Tourism Village, Bali, using a purposive sampling technique. The proposed model was analyzed using Structural Equation Modeling with Partial Least Squares (SEM-PLS), which is well-suited for predictive and complex data-driven analysis. The findings reveal that social media analytics, brand image, and product quality have significant positive effects on both tourist satisfaction and revisit intention, highlighting the critical role of digital engagement and online content in shaping tourist behavior. Attitude significantly influences satisfaction but does not directly affect revisit intention, while experience and perceived risk show no significant effects. Satisfaction is found to be a strong predictor of revisit intention and partially mediates the relationships between key antecedents and behavioral outcomes. This study contributes to the literature by integrating big data perspectives into tourism behavior modeling and demonstrates how social media-derived insights can enhance predictive accuracy in tourism analytics. Practically, the results suggest that tourism managers should leverage social media data, digital engagement metrics, and data-driven marketing strategies to strengthen destination competitiveness and foster sustainable revisit behavior.

This paper investigates the effect of Strategic Management Accounting (SMA) and Business Analytics (BA) on Sustainable Competitive Advantage (SCA). Moreover, it examines the mediating effect of Dynamic Capabilities (DY) and the moderating effect of Competition Intensity (CI) on direct relationships. The study used the survey method to collect data from listed companies in the Amman Stock Exchange, and the hypotheses were tested using Partial-Least Squares-Structural Equation Modelling. Based on the findings, DC mediates the relationship between SMA and SCA as well as BA and SCA. CI moderate only the relationships between SMA and SCA. The study findings can be used as directions by management, policymakers and researchers to comprehend the positive influence of SMA, BA and DC on SCA.

This study aims to examine the influence of Big Data analytics, innovation & capabilities in the supply chain as well as to find the moderating effect of organisational flexibility on the performance of the supply chain (SCP) and find the effect of competitive intensity as a controlling variable on the performance of supply chain. This research aims to present a theoretical model based on the resource-based theory's relational view (RBV). The data was collected through a survey questionnaire and 25 manufacturer firms managers participate in this survey. According to the findings of this study BDA has a favourable and strong association with SCI and SCC, as well as SCP. The majority of the manufacturers' firms in this research employed BDA to speed up their standing algorithms quicker with massive data sets.

The purpose of this paper is to investigate potential links between Blockchain technology (BCT) and big data analytics (BDA) with supply chain risk management (SCRM) and supply chain performance (SCP) in the Jordanian Chemical and Cosmetic Industries Sector. Additionally, the paper tests a conceptual model that links SCRM to indirect effects. To test our proposition, data were collected from 364 employees working in Jordanian Chemical and Cosmetic Industries Sector. The data were analyzed using structural equation modeling with aid of the Lavaan R package. The results show that the influences of blockchain technology and big data analytics on supply chain performance do occur directly, and indirectly through the cascading of a supply chain risk management.

The objective of the study is to identify the impact of big data on sustainable supply chain management. The current research was conducted on hypermarkets in Jordan. Many of these hypermarket brands are widely scattered in Jordan, for instance, Carrefour, Kareem, Safeway and more. Accordingly, the target population in the current research was hypermarkets managers in Jordan as they are responsible for formulating such strategies in the companies they work for. A convenience sample was selected from the target population that included 770 managers based on the sample size formula. The study hypotheses were tested by covariance based structural equation modeling (CB-SEM). The study results showed the impact of each big data dimension on sustainable supply chain management. Based on this result, the researchers recommend the hypermarkets in Jordan to use modern and diverse methods for accurate collection of reliable data and save it in organized ways, and to employ advanced programs to analyze it and extract information of high value.

The aim of this study is to explore the impact of supply chain 4.0 technologies (e.g., big data and Internet-of-things) of the strategic outcomes of supply chain 4.0. The required data was collected via a questionnaire from a sample consisting of 211 employees from construction companies in Jordan. The results point out that big data and the Internet of things have significant impacts on the strategic outcomes of supply chain 4.0. Hence, it was concluded that supply chain technologies are pivotal drivers of supply chain strategic outcomes. Based on these results companies are called to adopt and integrate Industry 4.0 technologies into their supply chains to improve supply chain long-run performance. Scholars were also encouraged to investigate the impact of supply chain 4.0 technologies on other constructs such as supply chain resilience.

Adopting the Supply Chain Operations Reference (SCOR) model, this study aims at investigating the impact of big data (volume, velocity, variety, veracity, and value) on supply chain performance through the mediating role of supply chain management (plan, source, make, deliver, and return) assuming four hypotheses. Data were collected using a questionnaire from managers of food processing companies. The results showed that big data affected supply chain management significantly and positively, which in turn affected supply chain performance significantly and positively. In addition, big data exerted a significant and positive impact on supply chain performance. Based on these links, it was found that supply chain management mediated significantly the effect of big data on supply chain performance. The study contributes to the literature showing that big data plays a pivotal role in improving supply chain performance and supply chain performance from the SCOR model perspective is critical for the relationship between these two constructs.

The organization needs to improve on a steady basis. For this purpose, organizations must gauge their performance regularly. To achieve this purpose, the agility of the supply chain may play a key role. Therefore, this study was designed to explore the relationship between big data analytics, organizational flexibility, supply chain agility, and organizational performance. This study assessed the mediation effect of supply chain agility as well. The research design of the cross-sectional and research approach was quantitative. The data of this study was gathered from the retail sector employees. In total, 516 questionnaires were distributed using simple random sampling. The usable response rate of the study was 54.90%. The gathered data was examined through smart PLS 3.3.2. The findings of the study revealed that Supply chain agility plays a crucial role in improving the performance of the organization. The study also confirmed the mediating effect of supply chain agility. The findings of the study are helpful for the policymakers of the retail sector.

This paper examines the complex interdependencies between the big data analytics capabilities, competitive intelligence, organizational agility, and organizational outcomes in the telecommunication industry in Jordan. Using a quantitative design and data gathered through 350 participants and processed by SmartPLS. The results show that big data analytics capabilities influence organizational outcomes in a positive manner both directly and indirectly via competitive intelligence. Although competitive intelligence does not influence organizational outcomes directly, it mediates the relationship between the big data analytics capabilities and organizational outcomes significantly. Moreover, organizational agility is identified to moderate positively between big data analytics capabilities and organizational outcomes, which underscores its significant contribution to boosting the success of analytics. These findings present important theoretical insights by clarifying the processes by which big data analytics competencies are transformed to generate better organizational performance, as well as have practical implications to telecommunication companies seeking to better exploit their data assets.

Intangible Cultural Heritage (ICH) is vital to cultural identity but difficult to preserve in a rapidly changing world. Artificial Intelligence (AI) and Big Data offer new opportunities to document and analyze ICH, yet the field lacks a comprehensive overview of research trends. No prior study has combined AI-driven topic modeling with bibliometric analysis to map ICH-AI scholarship. This paper addresses that gap by mining emerging topics and global trends in ICH-AI research over the past decade. We compiled ICH-related publications (2015–2024) from Web of Science and Scopus. Using BERTopic, an AI-based topic modeling technique, we extracted thematic topics from the corpus. We also analyzed publication and citation trends to gauge research growth and impact. Combining these approaches allowed us to track the evolution of research themes and identify emerging topics in ICH–AI research. ICH–AI research has grown steadily, with a surge in publications after 2018. BERTopic uncovered 18 distinct research topics, grouped into four broad thematic directions. Key emerging topics include deep learning for heritage preservation, knowledge graphs for cultural data integration, and immersive technologies (AR/VR) for intangible heritage engagement. These trends reflect a shift toward interdisciplinary, technology-driven approaches to ICH preservation. For researchers, the identified trends highlight new opportunities for collaboration and innovation; for policymakers and cultural institutions, they inform strategic support for AI-driven preservation initiatives. These results underscore the potential of AI and Big Data to enhance ICH safeguarding.