The present study aimed to explore the moderating role of strategic momentum in the relationship between big data analytics capabilities and lean supply chain practices in eight textile companies in Jordan. A quantitative research methodology incorporating a cross-sectional design was adopted to gather questionnaire-based responses to investigate the hypotheses put forth. The sample for the study consisted of 116 respondents, who were selected from a diverse group of senior executives from various fields, including IT, logistics, marketing, production, and strategic planning. These individuals possessed both knowledge and skills in data and business analytics disciplines. The data were analyzed utilizing SPSS version 28 and the PROCESS v3.5 macro developed by Andrew F. Hayes. The results revealed that the strategic momentum positively moderates the relationship between big data analytics capabilities and lean supply chain practices. These findings indicate that high levels of strategic momentum allow an organization to increase resources and focus on investing in and developing its big data capabilities, thereby supporting the implementation of lean supply chain practices.

This paper aims to investigate the impact of strategic intelligence on strategic flexibility, in addition, to identify the effective decision-making on Logistics Performance. Nevertheless, partial least squares (Smart -PLS- 4.0.8.7) are used to examine the hypotheses, the results indicate that strategic intelligence (organized thinking, strategic vision, foresight, partnership, and motivation) significantly affect Strategic flexibility. In addition, effective decision-making mediates the effect of strategic intelligence and strategic flexibility towards the performance of logistics companies. Moreover, the influence of effective decision-making on logistics performance is therefore clear and significant towards logistics performance. However, the study supports managers in enhancing sustainability practices in logistics organizations while also offering policy guidance to decision-makers.

The practical application of business data analytics in human resource (HR) management activities is still limited and underutilized. The primary reason for this is the requirement for more analytical skills, which can be acquired through data literacy. These skills are necessary to ensure obtaining the potential benefits of human resource analytics linked to desired positive employee outcomes, such as fostering innovative behavior. To address this issue, this quantitative study has been conducted to delve into the prospective moderating role of data literacy in the interaction between human resource analytics and employees’ innovative behavior. The core objective of this study is to examine whether human resource professionals’ data literacy level significantly impacts the correlation between human resource analytics and the inclination of employees towards innovative behavior. A sample of 250 HR specialists from large companies in Jordan has been used to address this inquiry. A correlational-predictive design has been used in this study. Regression analysis using the SPSS macro-PROCESS software has been utilized to address the study hypotheses. The study reveals a positive connection between HR analytics and employees’ innovative behavior. Moreover, it uncovers a noteworthy moderating influence of data literacy on this association. These findings suggest that heightened data analysis proficiency among HR professionals amplifies the potential benefits derived from analytics, thereby enhancing employees’ innovative capabilities. As a result, the research suggests that companies upgrade their technical infrastructure for HR functions and concurrently utilize data analytics for informed HR decisions. Further, these insights do serve as necessary inputs in understanding HR analytics and their implications for prudent business strategy. In view of this dynamic nature of HR analytics, the contributions made by the study are immensely valuable; hence, further exploration is needed to fill in gaps within existing knowledge.