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.

In the technological and digital revolution, the world is witnessing unprecedented environmental uncertainty as big data becomes more complex in the labor market. Hence, the study examined the relationship between business analytics, big data, and decision-making insights. The study design used a quantitative approach through a questionnaire distributed to a sample of 412 management levels from international organizations located in King Hussein Business Park in Jordan, named CISCO, Microsoft, Oracle, MBC, Samsung, Migrate, Aramex, Experia, and Ericsson. The data were managed through PROCESS Micro v3.5 software via SPSS packages to investigate the total effects of the study variables. The results confirmed the positive relationship between business analytics, big data, and decision-making insights at a statistically significant level (p < 0.01). The study presented a theoretical development of the role of management in achieving mature visions based on big data that constitute solutions to the complex interactions between technology and human orientation, facilitating the organizational complexities supported by the digital age and transforming them in favor of business decisions in the organizational environment of business companies.

The study aimed to explore The Role of Big Data in Improving the Balanced Scorecard in Jordanian Commercial Banks. The descriptive approach was employed, and the quantitative method was adopted to achieve the study’s objectives and test its hypotheses. A questionnaire tool was developed, consisting of four sections for big data and four sections for the balanced scorecard, comprising a total of 48 items. The validity and reliability of the tool were verified. The questionnaire was allocated to a sample of 400 employees of the study community which is the Jordanian commercial banks. The study's findings revealed that big data has a statistically significant impact on enhancing the balanced scorecard in Jordanian commercial banks. Dimensions of big data, such as "variety" and "veracity," had a positive and direct effect on improving all aspects of the balanced scorecard, including financial performance, customer service, learning, and growth. On the other hand, the impact of "volume" and "velocity" was limited or statistically insignificant in some aspects. According to multiple regression analyses, big data contributes to explaining 82% of the improvements observed in the balanced scorecard, highlighting the importance of investing in big data to enhance operational and financial performance.

Accurate crime prediction is crucial for effective law enforcement and security, enabling proactive resource allocation and risk reduction. Criminal behavior is influenced by complex, diverse socio-economic factors, necessitating advanced models capable of extracting intricate patterns from large datasets. This research presents a methodological and applied comparison of four primary categories of time series forecasting models: Statistical Models (AutoARIMA), Machine Learning models (AutoLightGBM), Deep Learning models (N-HiTS), and Foundation Models (TimeGPT). The study’s innovation lies in (1) integrating these diverse categories in a single comparative framework tailored for security decision-makers, (2) explicitly applying cutting-edge AI, particularly Foundation Models (TimeGPT) with pre-training on vast, multi-domain time series, for crime prediction for the first time, and (3) demonstrating a comprehensive application using daily crime data from Chicago (2017–2019), with the final month serving as a challenging test set for assessing robustness against sudden fluctuations. Results indicate that Foundation (TimeGPT) and Deep Learning (N-HiTS) models outperform in accuracy, effectively capturing nonlinear relationships and complex seasonality. Statistical (ARIMA) and traditional ML (LightGBM) models offer greater interpretability and faster training but are less adept at handling unexpected surges. This comparative, automated approach offers a practical solution for security agencies seeking AI adoption without significant programming complexity. The research underscores time series modeling’s role in enhancing security operations and explores new avenues for AI-driven proactive crime prevention using big data.