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This study mainly aims to explore the causal nexus between big data-driven organizational capabilities (BDDOC) and supply chain innovation capabilities (SCIC) and innovation performance (IP), then explore the indirect effect of SCIC and also test the moderating effects for both internal supply chain integration (ISCI) and external supply chain integration (ESCI) into the relationship between BDDOC and SCIC.

In order to test the conceptual model and the hypothesized relationships between all the constructs, the data were collected using a self-reported questionnaire by workers in Jordanian small and medium manufacturing enterprises. Partial least squares-structural equation modeling (PLS-SEM) was employed to test the model.

The paper reached a set of interesting results where it was confirmed that there is a positive and statistically significant relationship between BDDOC, SCIC and IP in addition to confirming the indirect effect of SCIC between BDDOC and IP. The results also showed that there is a moderating role for both ESCI and ISCI.

This study can be considered the first study in the current literature that investigates these constructs as shown in the research model. Therefore, the paper presents an interesting set of theoretical and managerial contributions that may contribute to covering part of the research gap in the literature.

This paper aims to empirically examine the impact of dynamic capabilities on Industry 4.0 capabilities in small and medium enterprises (SMEs) in Jordan. It also aims to examine the potential impact of industry 4.0 capabilities on technological innovation.

Data were collected from 210 respondents who work and own SMEs in Jordan. SmartPLS software based on the partial least squares-structural equation modeling approach was used to test hypotheses.

The findings reveal the positive effects of the three components of dynamic capabilities, including sensing, seizing and reconfiguring, on Industry 4.0 capabilities. They also confirm the positive effect of Industry 4.0 capabilities on technological innovation.

This study provides valuable practical implications and enriches the literature on the determinants of Industry 4.0 capabilities and its role in developing the dynamic capabilities of SMEs, such as technological innovation.

The current work sought to investigate the mediating effect of supply chain ambidexterity on the relationship between Industry 4.0 capabilities and operational performance of manufacturing firms in Jordan.

Data collection was carried out through a survey with 253 respondents from manufacturing firms in Jordan through the first quarter in 2023. The quantitative approach and structural equation modeling (SEM) were applied to analyze the collected data. Dynamic capabilities view (DCV) theory was the adopted theoretical lens for this work.

The results demonstrated that Industry 4.0 capabilities positively and significantly affect exploration, exploitation and operational performance. In addition, the results confirmed that exploration and exploitation positively and significantly affect operational performance. Further, it is also found that exploration and exploitation in the supply chain positively and significantly mediate the relationship between Industry 4.0 capabilities and operational performance.

This study focuses on this gap to deepen the understanding of operational performance in a recent manufacturing environment under various factors and perspectives (Industry 4.0 capabilities and supply chain ambidexterity).

This study investigates the impact of big data analytics capabilities on export performance. Moreover, it assesses the mediating effect of the supply chain innovation and moderating effect of supply chain agility.

This study is based on primary data that were collected from the manufacturing sector operating in Jordan. A total of 327 responses were used for the final data analysis. Data analysis was performed via a partial least square structural equation modeling (PLS-SEM) approach.

The results of the data analysis supported a positive relationship between big data analytics capabilities and the export performance as well as a mediating effect of supply chain innovation. It was confirmed that supply chain agility moderated the relationship of supply chain innovation and export performance.

This study developed a theoretical and empirical model to investigate the relationship between big data analytics capabilities, export performance, supply chain innovation and supply chain agility. This study offers new theoretical and managerial contributions that add value to the supply chain management literature by testing the moderated-mediated model of these constructs in the manufacturing sector in Jordan.

The purpose of this study is to investigate the impact of big data (BD) analytics capabilities (BDACs) on green supply chain integration (GSCI) and green innovation (GI) in the context of a developing country, Jordan. In addition, the mediating effect of GSCI on the relationship between BDAC and GI is investigated.

Data collection was carried out through a survey with 300 respondents from food and beverages manufacturing firms located in Jordan. Partial least squares-structural equation modeling (PLS-SEM) technique was applied to analyze the collected data. Natural resource-based view (NRBV) theory was the adopted theoretical lens for this study.

The results revealed that BDAC positively and significantly affects both GSCI and GI. In addition, the results demonstrated that GSCI positively and significantly affects GI. Further, it is also found that GSCI positively and significantly mediates the relationship between BDAC and GI.

This study developed a theoretical and empirical model to investigate the relationship between BDAC, GSCI and GI. This study offers new theoretical and managerial contributions that add value to the supply chain (SC) management literature by testing the mediation model in food and beverages manufacturing firms located in Jordan.