Purpose: Blockchain technology can overcome many complicated problems related to confidentiality, integrity and availability of fast and secure distributed systems in the agri-food supply chain. In emerging economies like India, blockchain application in the agri-food supply chain is still new, and their adoption is underdeveloped. This paper aims to investigate the drivers of blockchain technology adoption and their effect on the behavioral intention of stakeholders in adopting blockchain technology among various stakeholders in the agri-food supply chain. The study also develops a framework to enhance understanding of blockchain adoption in the agri-food supply chain as well as the stakeholders' motivation in seeking blockchain solutions.Design/methodology/approach: Considering the most significant aspects of blockchain adoption in the agri-food supply chain, this study attempts to develop an adoption model by using the extended unified theory of acceptance and technology model with interfirm trust and transparency as additional factors. Data was collected from a sample of 200 stakeholders in the North Indian state of Punjab. The empirical analysis was carried out using structural equation modeling in Smart PLS3.Findings: The findings supported the developed framework and the results of SEM indicate that all the paths are supported. In particular, the findings of the study reveal that performance expectancy, effort expectancy, social influence, facilitating conditions, interfirm trust and transparency are the drivers of blockchain adoption and have a significant impact on the behavioral intention of stakeholders. Cumulatively, the results positively impact the performance of agri-food supply chain. From this study, it is found that the adoption of blockchain technology in agri-food supply chain enhances their performance.Originality/value: The originality of the study lies in the developed framework, technology adoption will help them focus in the right direction by eliminating manual methods and converting the agri-food supply chain into a digitalization system.

Purpose: The food supply chain has faced many challenges due to its complex and complicated nature. Blockchain technology is one of the mechanisms used to improve agri-food supply chain processes by evolving organization capabilities. A study is being conducted to scrutinize the adoption of blockchain technology in the agri-food supply chain through the lens of the operational capability approach. It further makes an attempt to identify the capabilities of blockchain to improve supply chain processes.Design/methodology/approach: The qualitative research method with semi-structured interviews was used to gather information from experts and professionals in the food supply chain and blockchain technology. The authors have adopted a systematic approach of coding using open, axial and selective methods to depict and identify the themes that represent the blockchain-enabled agri-food supply chain. The data were collected from 32 interviews of selected participants.Findings: The result shows five critical areas where blockchain can come up to enhance the agri-food supply chain performance by providing traceability, transparency, information security, transactions, and trust and quality. Further, the study reveals that blockchain will provide safety, lower the cost of transactions and can create trust among users to communicate within the whole supply chain without the intervention of a third party. This study demonstrated that the capabilities need to be considered when introducing technology into the practice.Research limitations/implications: The study implies thought-provoking implications for bridging the theory-practice gap by examining the empirical data to demonstrate how the operational capabilities of blockchain technology further strengthen the agri-food supply chain. Additionally, this study provides some suggestions for utilizing the results and proposes a framework to understand more about blockchain use cases in the agri-food supply chain as well as extend the application of blockchain using an operational capability approach for future academic researchers in this area.Practical implications: This study presented some more important managerial implications which reveal that the majority of organisations were in the initial stages of adoption process of blockchain technology. Further, the positive influence of managers and IT experts can help the information technology companies (IT) and stakeholders for developing and promoting blockchain solutions in the agri-food supply chain. The important implication of blockchain enabled agri-food supply chain is to maintain information security and incresae supply chain performance. Originality/value: The study shows the operational capabilities of agri-food supply chain using blockchain technology. Blockchain can contribute in enhancing the agri-food supply chain to increase traceability and transparency and helps to reduce the risk of disruptions.

Inherent complex topography and drastic weather patterns together have concocted various natural disasters worldwide. In difficult terrains such as those prevalent in the North-Eastern regions of India, coupled with the factors such as population explosion and improper land use, lead them to witness some of the world’s most drastic landslides with an astonishing frequency, reckoning landslide susceptibility assessment crucial in such regions. This paper focuses on exploring a promising machine learning ensemble technique of Majority-based voting which has seldom been employed for landslide susceptibility assessment. The ensemble comprises Logistic Regression (LR), Gradient Boosted Decision Trees (GBDT) and Voting Feature Interval (VFI) to prepare landslide susceptibility zonation maps for the Brahmaputra valley region (Assam & Nagaland) and its close vicinity. In the first stage of the study, a landslide inventory for the area comprising 436 landslide locations was prepared in geographic information system (GIS), substantiated by news reports and remote sensing data. In the second stage, 16 landslide causative thematic maps including Elevation, Slope, Slope Aspect, General Curvature, Plan Curvature, Profile Curvature, Surface Roughness, Topographic Wetness Index (TWI), Stream Power Index (SPI), Slope Length, Normalized Difference Vegetation Index (NDVI), Land Use/Land Cover (LULC), Distance from Roads, Rivers, Faults and Railways were prepared. In the third stage, the landslide inventory was annexed with the causative factor maps to obtain a dataset comprising coordinates of the locations and the values of aforementioned causative factors on the corresponding coordinates. The proposed model was then trained and tested on the prepared dataset (70%:30% split). Finally, the efficiency of the new model was tested using the area under receiver operating characteristic curve (AUC of ROC). The validation results demonstrate the mettle of the proposed majority-based voting ensemble LR-GBDT-VFI (AUC: 0.98) against the conventional techniques such as Decision Trees, Support Vector Machines, Random Forest, etc. Altogether, the study offers an approach with wide scope across the field of landslide hazard assessment.