Muhammad Noman Shafique’s research while affiliated with Multimedia University and other places

The industrial shift from Industry 4.0 to Industry 5.0 has transformed organizational thinking, moving the focus from purely technological implementation to a more human-centered approach. The current study has focused on the Industry 5.0 technological capabilities to bring into circular economy practices aligned with sustainable development goals, aiming to enhance sustainable performance. Moreover, the resource-based theory has grounded the development of the comprehensive framework on Industry 5.0 technological capabilities (artificial intelligence capabilities, big data analytical capabilities, Internet of Things capabilities, machine learning capabilities, and blockchain technology capabilities) and circular economy practices (eco-design, management system, and investment recovery) to achieve sustainable performance (environmental performance, social performance, and economic performance). Data have been collected from 179 respondents from the Chinese manufacturing industry. Additionally, data have been analyzed using the structural equation modeling technique. The results showed that Industry 5.0 technological capabilities directly affect sustainable performance. Moreover, circular economy practices played a dual, moderating, and mediating role between Industry 5.0 technological capabilities and sustainable performance. The current study has contributed to filling a gap in the literature on Industry 5.0 capabilities, especially in the circular economy and sustainable performance perspective. The practical contribution recommended is that if organizations focused on their Industry 5.0 technological capabilities, it would boost circular economy practices and sustainable performance to achieve sustainable development goals.

This study investigates the impact of rookie independent directors (RIDs) on agency costs. Utilizing a sample of Chinese listed firms, we employ panel ordinary least square estimations. Our findings reveal that increased RIDs is positively associated with agency costs, suggesting that rookie independent directors may exacerbate agency conflicts within Chinese firms. Also, qualified financial institutional investors and gender diversity within the board composition mitigate the positive effect of RIDs on agency costs. Overall, our study contributes to understanding corporate governance dynamics in Chinese listed firms by shedding light on the nuanced relationship between rookie independent directors and agency costs.

The present study aims to establish a link between digital and humanitarian supply chain management. This study has focused on using artificial intelligence-big data analytical capabilities and information alignment to develop and maintain supply chain collaboration to achieve supply chain agility in a dynamic environment like a disaster. The targeted population is humanitarian organizations in Pakistan. Simple random sampling method, data was collected from 242 respondents using an online questionnaire. The Partial Least Square – Structural Equation Modelling technique has been used for analysis. Resource Based Theory and Contingency Theory in this study have provided foundations to develop and test the relationships among information alignment, supply chain agility, artificial intelligence – big data analytical capabilities, and supply chain collaboration in disaster management. Findings showed the use of artificial intelligence – big data analytical capabilities are beneficial for information alignment and supply chain agility.

With the global digitalisation, big data has received growing attention from academicians and practitioners. However, only a few empirical studies examined the benefits of big data predictive analytics (BDPA) and its influence on supply chain collaboration (SCC) and supply chain performance (SCP). Addressing the identified gaps of the implementation of organisational information processing theory (OIPT), the current study provided the foundation to develop a conceptual framework. All relevant data were collected from 197 employees in the Chinese logistics industry. Partial least squares-structural equation modelling technique was performed. The obtained empirical results supported top management support and compatibility as critical factors for the adoption of BDPA. Moreover, BDPA exhibited positive influence on SCC and SCP. Additionally, SCC mediated the relationship between BDPA and SCP. This study presented significant theoretical contributions and provided guidelines that can benefit policymakers and organisations in the efforts of implementing BDPA for enhanced SCP. After all, improving SCP would benefit customers and the society in the case of reduction and wastage of resources.

The Sustainable Development Goals and circular economy are two critical aspects of the 2030 Agenda for Sustainable Development. They both seek to reduce the waste of natural resources and enhance society’s social, economic, and environmental goals. This study aims to identify, develop, test, and verify the significant antecedents that affect the adoption of supply chain analytics and its consequences for achieving the circular economy. We have divided the conceptual framework into two parts. In the first part, the relationship among data integration and scalability, organizational readiness, and policies and regulations as Technological–Organizational–Environmental factors as antecedents in adopting supply chain analytics. In the second part, the dynamic capabilities view grounded the relationship among supply chain analytics, supply chain integration, and sustainable supply chain flexibility effect directly and indirectly on the circular economy. Data have been collected using the survey method from 231 respondents from the manufacturing industry in Pakistan. Data have been analyzed using (i) partial least square structure equation modeling (ii) and artificial neural network approaches. The empirical findings proved that antecedents (data integrity and scalability, organizational readiness, and policy and regulation) and consequences (supply chain integration and sustainable supply chain flexibility) of supply chain analytics adoption would improve the circular economy performance. Additionally, artificial neural networks have supported these relationships. The adoption of supply chain analytics will enable organizations to supply chain integration. Additionally, organizations with more integration and analytics in their operations tend to have more flexibility and a circular economy. Moreover, organizations and society will obtain social, economic, and environmental benefits and reduce wastage and negative environmental impacts.

Artificial intelligence (AI) and big data are two emerging technological concepts in supply chain management. The current study has merged AI and big data as artificial intelligence-driven big data analytics capability (AI-BDAC). The integration of resource-based theory (RBT) and contingency theory (CT) has provided to formulate conceptual framework. The efficient use of AI-BDAC in the supply chain improves internal integration (II) organizational processes and achieves supply chain agility (SCA). The ultimate goal for utilizing AI-BDAC is to increase supply chain performance (SCP). Data has been collected data from Chinese logistic companies having sample size 150 using survey method. Partial least square-structural equation modeling (PLS-SEM) results showed that AI-BDAC has direct effect on SCP. Moreover, II and SCA have mediating effects between AI-BDAC and SCP. It is concluded that using advanced technologies like big data and AI will support organizations to improve their II to attain SCA, which will boost SCP, especially in emerging economies. Additionally, the improvement in SCP will also increase speed and efficiency in logistic industry, and reduce the wastage, and cost; these are social benefits.KeywordsSupply chain performanceArtificial intelligenceBig data analyticsSupply chain integrationSupply chain agility