Blockchain technology (BT) is expected to bring a revolutionary paradigm shift in the manner the transactions are carried in the supply chains. BT provides better visibility and transparency by removing the disadvantages of trust related issues in a supply chain. In this paper, we advance the literature on BT and its adoption in the supply chain by developing, and statistically validating a model for understanding the user perceptions on BT adoption. The model is based on the integration of three adoption theories- technology acceptance model (TAM), technology readiness index (TRI) and the theory of planned behaviour (TPB). Based on a survey of 181 supply chain practitioners in India the proposed model was tested using structural equation modelling. The study found that the TRI constructs- Insecurity and discomfort have an insignificant effect on the perceived ease of use and usefulness. Perceived usefulness, attitude, and perceived behavioural control affect the behavioural intention. Subjective norm has a negligible impact on behavioural intention. This is one of the preliminary studies on BT adoption in supply chain and the findings imply that the supply chain practitioners perceive BT adoption free of efforts and would help them to derive maximum benefits for improving the supply chain effectiveness.

The current literature claims the direct effects of industry 4.0 technologies (I4 T) on lean manufacturing practices (LMP) and sustainable organisational performance (SOP). LMP are also found to have a positive influence on SOP. However, the integrated effect of I4 T and LMP on SOP has not been empirically investigated. To address this gap, this research study investigates the indirect effects of I4 T on SOP with LMP as the mediating variable; furthermore, it aims to confirm or not the direct effects of I4 T on LMP and SOP. The study is based on data collected from 205 managers, working in 115 manufacturing firms. The findings suggest significant direct and indirect effects of I4 T on SOP and confirm the presence of LMP as a strong mediating variable. The results of the study extend the literature on I4 T by identifying I4 T as an enabler of LMP, leading to enhancement of the SOP. Implications and future research directions for academicians, practitioners, and consultants are provided.

Purpose: The purpose of this paper is to examine when and how organizations build big data analytics capability (BDAC) to improve supply chain agility (SCA) and gain competitive advantage.Design/methodology/approach: The authors grounded the theoretical framework in two perspectives: the dynamic capabilities view and contingency theory. To test the research hypotheses, the authors gathered 173 usable responses using a pre-tested questionnaire.Findings: The results suggest that BDAC has a positive and significant effect on SCA and competitive advantage. Further, the results support the hypothesis that organizational flexibility (OF) has a positive and significant moderation effect on the path joining BDAC and SCA. However, contrary to the belief, the authors found no support for the moderation effect of OF on the path joining BDAC and competitive advantage.Originality/value: The study makes some useful contributions to the literature on BDAC, SCA, OF, and competitive advantage. Moreover, the results may further motivate future scholars to replicate the findings using longitudinal data.

This study examines the effects of supply chain agility (SCAG) and supply chain resilience (SCRES) on performance under the moderating effect of organizational culture. We have used the dynamic capability view (DCV) to conceptualize our theoretical models for different phases of humanitarian supply chain (HSC) (pre and post-disaster phases). We used partial least squares (PLS) to examine the proposed research hypotheses using 335 responses gathered from organizations in India using questionnaires designed for a single respondent. The results suggest that SCAG and SCRES are two important dynamic capabilities of supply chain, have significant effects on pre-disaster performance (PRE-DP). The control orientation does not have significant effect on the path joining SCAG and PRE-DP. However, the control orientation has a significant interaction effect on the path joining SCRES and PRE-DP. Similarly, SCRES has significant effect on post-disaster performance (POST-DP) but SCAG has no significant effect on POST-DP. In contrast, the flexible orientation has significant moderation effects on the paths SCAG/SCRES and POST-DP. These findings contribute to our understanding of the differential effect of SCAG/SCRES on supply chain performance in different contexts. The results provide further understanding to develop appropriate strategies for different phases. Finally, limitations of our study and future research are presented.

The study aims to identify the current trends, gaps, and research opportunities in research pertaining to the disruptive field of artificial intelligence (AI) applications in supply chain management (SCM). Since SCM represents managerial innovation due to its new way of integrated system thinking, SCM has emerged as one of the most fruitful business disciplines for AI applications. The study utilises bibliometric review in tracing the evolution of AI research in SCM and further synthesises decades of past AI research efforts to develop viable solutions for various supply chain problems and then proposes promising future research themes that would enrich supply chain decision-aid tools. The study identified five main research clusters through scholarly network and content analysis. The identified themes were: (a) supply chain network design (SCND), (b) supplier selection, (c) inventory planning, (d) demand planning, and (e) green supply chain management. As the role of AI in SCM continues to grow, there is a growing need for exploiting AI as a way to add value to supply chain process. The study proposes a research framework which will help academicians and practitioners in identifying current research patterns of AI in SCM.