Zahra Salimi

Purpose: The purpose of this paper is to investigate and provide in-depth understanding of the relationships between performance, integration and dynamic capabilities in the supply chain domain. The motivation of the study was that their combined relationships have not been explored, although they are closely related. Design/methodology/approach: Using multiple-case study research, data were gathered from practitioners in three distinct supply chains, achieving variation and diversity of cases. Several key grounded theory coding techniques and features were drawn upon for data coding, complementing the interpretive research. Findings: Sustainable performance is a trajectory - a trend believed to be a change catalyst in contemporary supply chain performance management and managing this requires a focused approach to developing integration and dynamic capabilities. The findings also revealed a cyclical approach to achieving robust supply chain performance rather than a purely linear view. Performance was found to depend upon input rather than output, informing the development of integration and dynamic capabilities. Research limitations/implications: Although the research is theoretically substantiated, the relationships of the phenomena can be tested to achieve statistical generalisation. Practical implications: The research provides insights for managers to improve sustainable supply chain performance using targeted integrative efforts while considering a special form of capability. This helps firms capitalise on efficiency while driving innovation, excelling in a changing business environment via an empirically grounded yet practitioner-friendly model. Originality/value: An emerging conceptual framework was developed, contributing to an extension of a middle-range theory. Using explanatory research to uncover the depth and richness of the phenomena under investigation is a novel approach, more common in quantitative studies.

Autonomous network functions (ANFs) are activated to achieve a specific objective. (e.g.: Load balancing, coverage and capacity optimization, energy saving across the network). Many times, activating the ANFs does not meet the specific objective predominantly due to external factors [1]. This paper introduces how explainable AI (xAI) methods such as feature impact analysis, dependency plot and other interpretable machine learning algorithms can be used for identifying such external factors and in turn sequencing the ANFs for meeting the objective. The paper concludes by introducing counterfactual and recourse algorithms as further research possibilities that goes beyond xAI for getting favorable outcome from ANFs.

At society level, the findings of this study indicate societal problems such as corruption and business environment which require wide level approaches to deal with these barriers. In addition, if TQM applied in road construction projects, the quality of the roads will be improved, this in turn will have direct impact on quality of life in the society, better roads means easier access to hospitals, schools and public places, better transport and movements of goods and services, etc. It can also save money for the country in long run and economic benefits to the society.