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.

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Purpose-This study aims to evaluate the role of social media (SM) tools in the polio prevention in an Indian context, using a hybrid Delphi-DEMATEL approach. Design/methodology/approach-A preliminary list of suitable evaluation criteria was derived from an extensive literature review. Ten experts were then contacted to collect data and finalize the most prominent criteria using the Delphi method. To establish cause-effect relationships among the criteria, further data were collected from twenty-one experts. The decision-making trial and evaluation laboratory (DEMATEL) method was applied to process and interpret the data collected. Findings-The analysis grouped criteria into two sets, i.e. cause and effect. The results show that awareness of social cause and government utilization of resources fall into the cause group; these elements are critical since both directly affect the remaining criteria. These outcomes can help government and businesses to utilize SM for public health surveillance, e.g. to promote schemes/initiatives through sites concerning polio or related health issues. Practical implications-The findings of this research are useful for governments and individual companies to conceive their marketing initiatives akin to polio prevention issues using SM. Originality/value-Despite the emergence of SM, there has been little discussion in existing literature on their role for polio prevention; however, measuring such role could be useful in practice, to help decision makers (DMs) exploiting the potential of SM in the healthcare context. To fill this gap, this study aims to measure the role of SM in polio prevention in the Indian context and to create a cause-effect evaluation model. Using an integrated Delphi-1 DEMATEL framework for decision-making in the healthcare context is another novelty of this study.

Online reputation management (ORM) is a significant and proactive tool that can reinforce the credibility of the service provider. Literature existing today on this topic has rarely reported on the causal modeling analysis from an ORM perspective. Therefore, the objective of this paper is to build a factor structure of ORM and to build the inter-relationship map amongst the criteria of each factor. To allow for vague human judgment, a fuzzy concept is employed in a form of Fuzzy Delphi. The DEMATEL technique has been used to develop a Network Relationship Map (NRM) among the criteria of each factor. Data has been gathered through a structured questionnaire conducted with a survey of experts. The study divided the criteria of each factor into cause-effect criteria. Findings of the study show that criteria such as distributed reputation system, trust, online competitive branding, website management, customer relationship, search engine optimization, corporate social responsibility, users' reach, competition/page views, purchase discounted products and cash back or money back fall under the cause group of ORM's factors. The results of this study can not only help service providers to enhance their reputation but can also guide them towards targeting their customers in an online platform.

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.