Search results

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.

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 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.

Sustainable management has become a strategic and popular requirement for stakeholders, as the ability to create competitive advantages in the current dynamic business environment and the pressures of digital transformation need to be increased attention in achieving the highest level of sustainable performance. The main objective of this paper is to explore the direct effects of the industrial Internet of things and supply chain visibility on sustainable performance (economic performance, environmental performance and social performance), in addition to evaluating the indirect effects that supply chain visibility plays in these relationships.

To achieve the purposes and objectives of this work, a conceptual model was developed by referring to the current works in the literature, and the theoretical model was developed, and then data were collected through the questionnaire and by the snowball sampling method, as 380 questionnaires were used. In the data analysis, the multivariate analysis technique was used via Covariance-based Structural Equation Modeling (CB-SEM), as hypothesized relationships were evaluated through confirmatory factor analysis (CFA), and then the hypotheses were tested through a structural model.

The results reached the acceptance of all hypotheses, and this means that all hypothesized relationships were positive, as the impact of the industrial Internet of things was positive on each of the supply chain visibility and economic performance, environmental performance and social performance, and also the supply chain visibility mediated the relationship between each of the industrial Internet of things and economic performance, environmental performance and social performance.

The current paper reported a range of theoretical and practical contributions, as this study is the first to examine these hypothesized relationships together in this way and thus helped this study to develop new perceptions about Industry 4.0 technologies and their potential impacts on supply chain and sustainable performance. Also, this study had a variety of management recommendations that allow managers of pharmaceutical manufacturing companies operating in Jordan to develop their technological capabilities and use them to enhance sustainable performance.

The objective of this paper is to examine the impact of big data analytics capabilities (BDAC) on green radical supply chain innovation (GRSCI), green incremental supply chain innovation (GISCI), and green supply chain performance (GSCP) in the context of a developing country, Jordan. In addition, the mediating effect of GRSCI and GISCI on the relationship between BDAC and GSCP is tested.

Data collection is carried out through a survey with 303 respondents from manufacturing firms located in Jordan. Partial least squares-structural equation modelling approach is applied to analyse the collected data. Resource-based view and natural resource-based view theory form the adopted theoretical lens for this study.

The results reveal that BDAC positively and significantly affects GRSCI, GISCI, and GSCP. In addition, the results demonstrate that GRSCI and GISCI positively and significantly affect GSCP. Further, it is also found that GRSCI and GISCI positively and significantly mediate the relationship between BDAC and GSCP.

This study's author develops a theoretical and empirical model to investigate the relationship among BDAC, GRSCI, GISCI, and GSCP. This study offers new theoretical and managerial contributions that add value to the supply chain management literature by testing the mediation model in manufacturing firms located in Jordan.

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.

Recently, the concept of the circular economy (CE) has witnessed significant momentum in academic and professional circles. However, there is a dearth of research that studies the enabling factors of the CE in the era of digital transformation. The existing research aimed to identify the impact of Industry 4.0 readiness on the CE in manufacturing firms operating in Jordan, as well as to identify the mediating role of the industrial Internet of things and big data analytics.

For this work objectives, 380 questionnaires were analyzed. Convergent validity and discriminant validity tests were performed through partial least squares-structural equation modelling (PLS-SEM) in the Smart-PLS programme. Data reliability was confirmed. A bootstrapping technique was used to analyze the data and then hypothesis testing was performed.

The results indicate that Industry 4.0 readiness, industrial Internet of things (IIoT) and big data analytics positively enable CE, also the IIoT and big data analytics positively mediate the nexus between Industry 4.0 readiness and CE.

This study promotes the idea of focusing on Industry 4.0 readiness to enhance CE in the Jordanian manufacturing sector and knowing the effect of IIoT and big data analytics in this relationship.

This research developed a theoretical model to understand how Industry 4.0 readiness might enhance the CE in manufacturing firms by invoking the IIoT and big data analytics as mediating constructs in the relationship between Industry 4.0 readiness and CE. This paper offers new theoretical and practical contributions that add value to industry 4.0 and CE literature by testing these constructs' mediation models in the manufacturing sector.

The current study aimed to identify the impact of the dynamic capabilities on the circular economy in Jordan's small- and medium-sized manufacturing enterprises sector as and the mediating role of industrial Internet of things.

This study surveyed small and medium manufacturing enterprises operating in Jordan and collected 459 responses through an online self-administered questionnaire. The effects of dynamic capabilities and the industrial Internet of things on the circular economy were analyzed using covariance-based structural equation modeling (CB-SEM).

Empirical results showed that all hypotheses are accepted. The results also revealed that the relationship between dynamic capabilities (sensing, seizing and reconfiguring) on a circular economy is mediated by the industrial Internet of things.

This work expanded the application of the dynamic capabilities view to investigate the antecedent effects of the industrial Internet of things and the circular economy and deepened understanding of the dynamic capabilities view theory in assessing the dynamic activities of these firms based on the three dimensions of sensing, seizing and reconfiguring. In general, this study made a theoretical and managerial contribution to supply chain and circular economy literature.

This paper aims to investigate hypothesized relationships between the Internet of things (IoT) and big data analytics (BDA) with supply chain visibility (SCV) and operational performance (OP) in the pharmaceutical manufacturing sector in Jordan. The paper also aims to test the conceptual model related to the indirect effects of SCV.

To achieve the objectives of this paper, a conceptual model was developed through a review of the current literature. Data analysis was performed by covariance-based structural equation modelling using Amos 25 software, and the convergent validity, discriminant validity, reliability and confirmatory factor analysis were verified. Then the hypotheses were tested.

The results of the study indicate that there is a positive and statistically significant relationship between the IoT and BDA on SCV and OP. The relationship was positive and statistically significant between SCV and OP. In addition, support for the mediation hypothesis that SCV mediates the relationship among IoT, BDA and OP was confirmed.

This paper provides new theoretical and managerial contributions that have not been covered in previous studies, and it is considered the first study that uses this conceptual model with this mechanism in terms of the theoretical lens and empirical application. This paper contributes to understanding the dynamic mechanisms of the IoT and BDA in enhancing OP, which contributes to creating a supply chain capable of facing various environmental fluctuations and pressures. This study presents new implications that can be used in the supply chain literature.

In this study, the authors investigate the effect of big data analytics capability (BDAC) on supply chain performance (SCP) to assess the mediating effect of supply chain innovation (SCI) and the moderating effect of a data-driven culture (DDC).

The authors collected the primary data through an online questionnaire survey from the manufacturing sector operating in Jordan. The authors used 420 samples for the final data analysis, which the authors performed via partial least squares structural equation modelling using SmartPLS 3.3.9 software.

The results indicate that BDAC has a strong relationship with SCI and SCP. SCI shows a positive relationship with SCP as well as a mediating effect on SCI. The authors confirmed that DDC moderated the relationship between SCI and SCP.

The authors developed a conceptual and empirical model to investigate the relationship between BDAC, SCI, DDC and SCP. The authors contributed new theoretical and managerial insights that add value to the supply chain management literature through testing the moderated-mediated model of these constructs in Jordan’s manufacturing sector.