The contemporary research in the area of technology adoption mainly focuses on commercial supply chains. However, limited research focuses on the context of humanitarian supply chains. This calls to develop structural models that can scrutinize the technology adoption behaviour in the humanitarian context. Therefore, this study is an attempt to empirically examine the technology adoption behaviour of humanitarian organizations. It extends the unified theory of the acceptance and use of technology (UTAUT) model by integrating personal innovativeness and trust in technology with the behavioural intention to adopt technology. Data from 192 humanitarian practitioners, who have experienced a large number of disasters, is utilized to empirically validate the conceptual model. The structural equation modelling results show that -out of four constructs namely performance expectancy, effort expectancy, social influence and facilitating conditions under UTAUT -performance expectancy and effort expectancy significantly affect the IT adoption. Contrary to expectations, trust and personal innovation do not affect the behavioural intention. Also, personal innovation does not moderate the relationship between performance expectancy and effort expectancy. This underlines the need to foster a learning culture within these organizations. The efforts made by involved humanitarian organizations may be directed towards improving the level of education, skills and facilitating them with other resources such as appropriate IT and data mining training, so that the technology adoption becomes an integral part of their daily activities. Finally, detailed implications for humanitarian organizations are discussed.

No abstract

Purpose – The purpose of this paper is to explore the barriers to coordination in humanitarian supply chain management (HSCM), proposes solutions and prioritizes them to overcome the barriers particularly in the Indian context. Design/methodology/approach – This study adopts a comprehensive and rigorous procedure to explore the barriers and solutions to coordination in HSCM. The research design is divided into three phases; first, the barriers and solutions are collected through an extensive literature review; second, barriers and solutions were verified with experts involved in relief operations of the disaster that occurred in Uttarakhand (a Northern state in India) on June 14, 2013 and finally, based on the weight of barriers estimated by fuzzy analytic hierarchy process, solutions to overcome the barriers are prioritized using fuzzy technique for order performance by similarity to ideal solution that considers uncertainty and impreciseness rather than a crisp value. Findings – This study explored 23 barriers to coordination in HSCM and grouped into five categories i.e., strategic barriers, individual barriers, organizational barriers, technological barriers and cultural barriers, and finally 15 solutions were proposed and prioritized to overcome the barriers so decision makers can focus on overcoming these barriers and realize the benefits of coordination in HSCM. Practical implications – This study provides a more efficient, effective, robust and systematic way to overcome barriers to coordination and improve the competencies of humanitarian supply chain (HSC). Originality/value – This is the first kind of study that prioritizes the solutions to enhance coordination in HSC based on the weight of the barriers.

The increase in the occurrence of natural disasters worldwide is of growing concern for the social and economic development of the countries involved. Disasters are unavoidable but actions can be taken to mitigate the impact of disaster on the country concerned. The efficiency of disaster relief operations cannot be enhanced as a whole; the organizations involved in the process of disaster management process should adjust, modify and reconfigure their supply chains in order to improve the performance of the humanitarian supply chain (HSC). Empirical research into the context of HSC which explores the relationship between critical factors to humanitarian supply chain management performance is often limited. Hence, this study aims to investigate the relationships among information technology (IT) utilization, mutual trust, agility, flexibility, adaptability, and performance in the context of the humanitarian supply chain. Data were collected using a structured questionnaire from the respondents involved in the relief operations of the disaster that occurred in Uttarakhand (a Northern state in India) on June 13, 2013 to test the aforementioned relationship. This study indicates that the agility and flexibility of the organizations involved in a HSC are associated with the utilization of IT, which in turn is associated with the performance.

A novel paradigm in the service sector i.e. services through the web is a progressive mechanism for rendering offerings over diverse environments. Internet provides huge opportunities for companies to provide personalized online services to their customers. But prompt novel web services introduction may unfavorably affect the quality and user gratification. Subsequently, prediction of the consumer intention is of supreme importance in selecting the web services for an application. The aim of study is to predict online consumer repurchase intention and to achieve this objective a hybrid approach which a combination of machine learning techniques and Artificial Bee Colony (ABC) algorithm has been used. The study is divided into three phases. Initially, shopping mall and consumer characteristic's for repurchase intention has been identified through extensive literature review. Secondly, ABC has been used to determine the feature selection of consumers' characteristics and shopping malls' attributes (with > 0.1 threshold value) for the prediction model. Finally, validation using K-fold cross has been employed to measure the best classification model robustness. The classification models viz., Decision Trees (C5.0), AdaBoost, Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN), are utilized for prediction of consumer purchase intention. Performance evaluation of identified models on training-testing partitions (70-30%) of the data set, shows that AdaBoost method outperforms other classification models with sensitivity and accuracy of 0.95 and 97.58% respectively, on testing data set. This study is a revolutionary attempt that considers both, shopping mall and consumer characteristics in examine the consumer purchase intention.