Today e-commerce has become crucial element to transform some of the world countries into an information society. Business to consumer (B2C) in the developing countries is not yet a normalcy as compared to the developed countries. Consumer behaviour research has shown disappointing results regarding the overall use of the Web for online shopping, despite its considerable promise as a channel for commerce. As the use of the Internet continues to grow in all aspects of daily life, there is an increasing need to better understand what trends of internet usage and to study the barriers and problem of ecommerce adoption. Hence, the purpose of this research is to define how far Technology

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Purpose This paper aims to identify and review the critical success factors (CSFs) for successful Six Sigma implementation in service and manufacturing companies given in published literature. Design/methodology/approach A descriptive evaluation of the literature body is followed by future research opportunities. Studies published on the topic of Six Sigma during 2005-2016 are reviewed to retrieve identified CSFs. Findings From published literature, 48 CSFs vital for Six Sigma implementation were identified. The identified CSFs were classified under eight core categories. As per the categorization, 35 out of 48 CSFs identified are either Company Strategy related, Six Sigma Project related or Human Resources related. Only a limited number of studies are carried out mainly focusing on the stated three core areas. Hence, it is vital that these three core areas are further explored in future research. Originality/value With the outcome of this paper, the identified CSFs for Six Sigma will be taken for further studies, where they will be applied to service and manufacturing companies based in Sri Lanka. Hence, it would provide Sri Lankan service and manufacturing companies guidelines for successful implementation of Six Sigma for future endeavors.

Heart disease is recognized as one of the leading factors of death rate worldwide. Biomedical instruments and various systems in hospitals have massive quantities of clinical data. Therefore, understanding the data related to heart disease is very important to improve prediction accuracy. This article has conducted an experimental evaluation of the performance of models created using classification algorithms and relevant features selected using various feature selection approaches. For results of the exploratory analysis, ten feature selection techniques, i.e., ANOVA, Chi-square, mutual information, ReliefF, forward feature selection, backward feature selection, exhaustive feature selection, recursive feature elimination, Lasso regression, and Ridge regression, and six classification approaches, i.e., decision tree, random forest, support vector machine, K-nearest neighbor, logistic regression, and Gaussian naive Bayes, have been applied to Cleveland heart disease dataset. The feature subset selected by the backward feature selection technique has achieved the highest classification accuracy of 88.52%, precision of 91.30%, sensitivity of 80.76%, and f-measure of 85.71% with the decision tree classifier.