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The purpose of this study is to investigate the adoption of waqf technology (Waqftech) using blockchain smart contracts for corporate waqf crowdfunding. Despite the growing interest in Waqftech, Malaysian enterprises have not fully embraced this emerging technology because of uncertainty regarding the benefits it offers to contributors. The research incorporates two theoretical frameworks: the electronic data interchange (EDI) model for firms’ technology adoption, and the triple bottom line theory (TBL) for corporate social responsibility.

A quantitative method using a cross-sectional survey design with a five-point Likert scale questionnaire was used. Data was collected from 210 decision-makers representing small and medium-sized enterprises and analyzed using partial least squares-structural equation modeling.

The findings from this research suggest that Malaysian enterprises are influenced by both corporate and social predictive benefits when using blockchain crowdfunding, but not by environmental benefits. The adoption of blockchain smart contracts does not correlate with predictive environmental benefits because of misconceptions about the disruptive technology’s impact on biological and digital environmental preservation.

This research focuses on organizational behavior rather than individual users of waqf crowdfunding, and it is limited, primarily focusing within Malaysia and regions with similar waqf structures.

The Waqftech framework allows innovative mechanisms for executing corporate waqf investment returns to the intended beneficiaries through the smart contracts’ platform. In addition, this study supports relevant corporate social responsibility and creating shared value technology adoption theories, including EDI and TBL. Aside from this, the study provides empirical implications for waqf management using fintech platforms.

This groundbreaking study focuses on creating a Waqftech model for corporate waqf crowdfunding. The results of this study are important for the development of government policies that support the use of Waqftech in charitable fundraising. More research on biological and digital environmental perspectives is proposed to foster investors’ confidence in the visibility of digital tracking and lead to swift investments in future metaverse fundraising platforms.

This study attempts to explore the linkages between reliable big and cloud data analytics capabilities (RB&CDACs) and the comparative advantage (CA) that applies in the manufacturing sector in the countries located in North Africa (NA). These are considered developing countries through generating green product innovation (GPI) and using green process innovations (GPrLs) in their processes and functions as mediating factors, as well as the moderating role of data-driven competitive sustainability (DDCS).

To achieve the aim of this study, 346 useable surveys out of 1,601 were analyzed, and valid responses were retrieved for analysis, representing a 21.6% response rate by applying the quantitative methodology for collecting primary data. Convergent validity and discriminant validity tests were applied to structural equation modeling (SEM) in the CB-covariance-based structural equation modeling (SEM) program, and the data reliability was confirmed. Additionally, a multivariate analysis technique was used via CB-SEM, as hypothesized relationships were evaluated through confirmatory factor analysis (CFA), and then the hypotheses were tested through a structural model. Further, a bootstrapping technique was used to analyze the data. We included GPI and GPrI as mediating factors, while using DDCS as a moderated factor.

The empirical findings indicated that the proposed moderated-mediation model was accepted due to the relationships between the constructs being statistically significant. Further, the findings showed that there is a significant positive effect in the relationship between reliable BCDA capabilities and CAs as well as a mediating effect of GPI and GPrI, which is supported by the proposed formulated hypothesis. Additionally, the findings confirmed that there is a moderating effect represented by data-driven competitive advantage suitability between GPI, GPrI and CA.

One of the main limitations of this study is that an applied cross-sectional study provides a snapshot at a given moment in time. Furthermore, it used only one type of methodological approach (i.e. quantitative) rather than using mixed methods to reach more accurate data.

This study developed a theoretical model that is obtained from reliable BCDA capabilities, CA, DDCS, green innovation and GPrI. Thus, this piece of work bridges the existing research gap in the literature by testing the moderated-mediation model with a focus on the manufacturing sector that benefits from big data analytics capabilities to improve levels of GPI and competitive advantage. Finally, this study is considered a road map and gaudiness for the importance of applying these factors, which offers new valuable information and findings for managers, practitioners and decision-makers in the manufacturing sector in the NA region.

Micro, small, and medium enterprises (MSMEs) can play a significant role in achieving sustainable development goals (SDGs) as they have the ability to reduce unemployment. Digitalization helps MSMEs in a number of ways, including lowering transaction costs, quickening access to information, and bettering communication with extended supply chain members. This chapter aims to understand the level of digitalization in MSMEs in an emerging economy such as Bangladesh. MSMEs in Bangladesh account for 25% of the gross domestic product and employ 87% of civilians. This chapter builds on qualitative data from 60 MSMEs from various manufacturing and service sectors such as textile, retail, food delivery, IT companies, etc. The interviews were semi-structured and followed an interview protocol. The length of interviews varied between 40 and 50 minutes. Content analysis was used to analyze the data. Findings suggest that counterintuitively the level of digitization in MSMEs is not low in Bangladesh. Many micro and small enterprises use MS Excel to help them manage customer and product data. Medium Enterprises use Enterprise Resource Planning (ERP) software for planning enterprise-wide resources. Some medium enterprises also use powerful data analytics software such as Oracle, Power BI, Google Analytics, Python, and SPSS. Results also reveal barriers to digitization in MSMEs, which include a lack of employee awareness, training, and motivation of top management. This chapter maps the digitalization levels in MSMEs in Bangladesh and provides implications for SGDs. The chapter also presents policy recommendations for improving the digitalization level in emerging economies.

The objective of this study is to improve the food supply chain performance taking into consideration the fundamental concepts of traceability by combining the current frameworks, its principles, its implications and the emerging technologies.

A narrative literature review of already existing empirical research on traceability systems was conducted resulting in 862 relevant papers. Following a step-by-step sampling process, the authors ended up with 46 final samples for the literature review.

The main findings of this study include the various descriptions of the architecture of traceability systems, the different sources enabling this practice, the common desirable attributes, and the enabling technologies for the deployment and implementation of traceability systems. Moreover, several technological solutions are presented, which are currently available for traceability systems, and finally, opportunities for future research are provided.

It provides an insight, which could affect the implementation process of traceability in the food supply chain and consequently the effective management of a food traceability system (FTS). Managers will be able to create a traceability system, which meets users' requirements, thus enhancing the value of products and food companies.

This study contributes to the food supply chain and the traceability systems literature by creating a holistic picture of where something has been and where it should go. It is a starting point for each food company to design and manage its traceability system more effectively.

The purpose of this paper is to develop a theoretical model that explains the impact of big data analytics capabilities (BDAC) on company's supply chain innovation capabilities and sustainable supply chain performance. BDAC is represented through two dimensions of big data technological capabilities (BDTC) and big data personal capabilities (BDPC). Moreover, the relationships between BDTC and BDPC with sustainable supply chain performance through the mediation effect of supply chain innovation capabilities are examined.

The study used a quantitative research approach. A survey of 400 Jordanian manufacturing companies was carried out to conduct this research. However, the responses of 207 managers were valid to be used in the analysis. In this study, the SmartPLS software was used to perform structural equation modeling using a partial least squares approach (PLS-SEM) and to examine the measurement and structural model's validity and reliability.

According to the results of this study, BDPC has a significant positive impact on supply chain innovation capabilities. Furthermore, the findings indicate that supply chain innovation capabilities are the most influential predictor of sustainable supply chain performance and act as a positive significant mediator in the relationship between BDPC and firm sustainable performance. Surprisingly, the study found that BDTC had no significant effect on supply chain innovation capabilities. Besides that, no significant relationship exists between BDTC and firm sustainable performance via the mediation effect of supply chain innovation capabilities.

This study provides an integrated research model that incorporates BDAC, supply chain innovation capabilities, and sustainable supply chain performance in order to analyze supply chain innovation and sustainable supply chain performance. This suggests that the scope of the study is broader in terms of predicting sustainable supply chain performance. As a result, the study intends to fill a gap in the literature by explaining how BDAC affects supply chain innovation capabilities and firms sustainable performance. In addition, the role of supply chain innovation capabilities as a mediator between BDAC and sustainable supply chain performance is investigated.

It is claimed that port supply chain integration (PSCI), thanks to its attributes, holds a potential to trigger a port to strategically sustain performance- and competitiveness-measures through strengthening and/or reinforcing an array of port sustainability aspects. This paper aims to empirically investigate the existent influence of PSCI on economic and environmental pillars of port sustainability.

Based on a resource-based view, this paper hypothesises that PSCI has a certain impact on port “economic” sustainability (PECS) and port “environmental” sustainability (PENS). Measures of PSCI, PECS and PENS are refined and validated using data collected from the maritime logistics industry in Egypt, and structural equation modelling is employed to test the hypothetical relationships.

The results indicate that a port having adopted an integrative strategy into supply chains could enhance its cost and operational efficiency, financial and investment situation, while offering high-quality services to its customers. Similarly, PSCI is emphatically correlated with water and air pollution management, energy efficiency and green port management practices.

Having identified the acute potential of PSCI for sustainable development in maritime logistics and supply chains, this line of research allows port operators and/or authorities to better understand strategic options with which they are able to improve their sustainability practices. This paper is, however, limited only to two dimensions of sustainability by not exploring the “social” aspect of port sustainability due to data-related issues.

This line of research could be regarded as an extended application from other industrial sectors to the port industry in a way to empirically examine the inclusive relationship of PSCI with economic and environmental parameters. The findings from this research make a due contribution to the field of port sustainability in general and Egyptian ports in particular.

The purpose of this paper is to understand the dark side of blockchain technology (BCT) adoption in small and mid-size enterprises. The focus of the authors is to decode the intricate relationship among the selected variables missing in the existing literature.

A focused group approach is initiated by the authors to identify the barriers. Total interpretive structural modeling, Matrice d'impacts croisés multiplication appliquée á un classment, that is, matrix multiplication applied to classification and decision-making trial and evaluation laboratory are used to analyze the complex relationships among identified barriers.

This study finds that implementation of BCT reduces maintenance cost by withdrawing manual effort, as BCT has better capability to quantify the internal status of the system (observability characteristic). The observability characteristic of BCT provides high compatibility to the system. This study also finds that the compatibility of BCT with the organization reduces implementation cost and facilitates project management. The findings of this study recommend analyzing maintenance cost and compatibility of BCT before implementing it. Small and mid-size enterprises can select complex BCT depending on the sophistication level of IT usage and IT project management capabilities.

This study comes with various limitations, where the model developed by the authors may not be conclusive, as it is based exclusively on expert opinion. The samples collected may not help in validating the model statistically. Though the model has its limitations, it can still be considered as a nascent initiative for further investigation using structural equation modeling.

The outcomes of the theoretical and managerial contributions of the study can be categorized into three levels. This study can be used both by the industrialists and researchers to understand the barriers and the recovery methods thereafter. Suggestions that serve as future directives are also discussed by the authors.

The objective of this paper is to evaluate whether the data from consignors, logistics service providers (LSPs) and consignees contribute to the prediction of air transport shipment delays in a machine learning application.

The research database contained 2,244 air freight intercontinental shipments to 4 automotive production plants in Latin America. Different algorithm classes were tested in the knowledge discovery in databases (KDD) process: support vector machine (SVM), random forest (RF), artificial neural networks (ANN) and k-nearest neighbors (KNN).

Shipper, consignee and LSP data attribute selection achieved 86% accuracy through the RF algorithm in a cross-validation scenario after a combined class balancing procedure.

These findings expand the current literature on machine learning applied to air freight delay management, which has mostly focused on weather, airport structure, flight schedule, ground delay and congestion as explanatory attributes.

This study attempts to examine the relationship between reliable big and cloud data analytics capabilities (RB&CDACs) and comparative advantages (CA) of manufacturing firms (MFs) in the Middle East region as developing countries using green product innovation (GPI) and green process innovations (GPrI) mediating factors, further assess the role of data-driven competitive sustainability factor as a moderated factor.

436 useable online surveys were analyzed using the quantitative approach for the data-gathering process, applying structural equation modeling in the Smart-PLS program as an analysis tool. The sample unit for analysis included all middle- and senior-level managers and employees within MFs. The authors performed convergent validity and discriminant validity tests, bootstrapping also was applied. The authors included GPI and GPrI as mediating factors while using data-driven competitive sustainability as a moderated factor.

The findings of this study indicated that there is a positive significant effect in the relationship between reliable big and cloud data analytics capabilities and comparative advantages, which is supported by the formulated hypothesis. Furthermore, the findings confirmed that there was a positive and significant effect through the mediating factors (i.e. GPI and GPrI) on comparative advantage, additionally, it confirmed and supported that the moderating factor represented by data-driven competitive advantage suitability has significant effect as well.

This study has some limitations represented by using only one type of methodological approach (i.e. quantitative), further, it was conducted on only Asian countries in the Middle East region.

This piece of work improved the proposed conceptual research model and included several factors such as reliable big and cloud data analytics capabilities, comparative advantage, data-driven competitive sustainability, GPI and GPrI. This research offered new and valuable information and findings for managers, practitioners and decision-makers in the MFs in the Middle East region as a road map and gaudiness for the importance to apply these factors in their firms for enhancing the comparative advantages in their firms. Further, this research fills the gap in SCM literature and makes a bridge of knowledge and contribution to the existence of previous studies.

Minimizing the impact on the surrounding environment and maximizing the use of production raw materials while ensuring that the relevant processes and services can be delivered within the specified time are the contents of enterprise supply chain management in the green financial system.

With the continuous development of China's economy and the continuous deepening of the concept of sustainable development, how to further upgrade the enterprise supply chain management is an urgent need to solve. How to maximize the utilization of resources in the supply chain needs to be realized from the whole process of raw material purchase, transportation and processing.

It was proved that digital twin technology had a partial intermediary role in the role of supply chain big data analysis capability on corporate finance, market, operation and other performance.

This paper focused on describing how digital twin technology could be applied to big data analysis of enterprise supply chain under the green financial system and proved its usability through experiments. The experimental results showed that the indirect effect of the path big data analysis capability digital twin technology enterprise financial performance was 0.378. The indirect effect of the path big data analysis capability digital twin technology enterprise market performance was 0.341. The indirect effect of the path big data analysis capability digital twin technology enterprise operational performance was 0.374.