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Mobile banking services have been a significant breakthrough in the electronic banking system and have many potential demands for online banking services to connect with consumers. Although there has been a rapid expansion of information technology (IT) in banking, which offers multiple opportunities in the global market, massive growth has not been seen in India’s m-banking adoption. Several kinds of research on m-banking adoption have been conducted in various countries, and it has been observed that India has great potential for m-banking. Nonetheless, users are not quite sure about its use for a few reasons. The present study extends the applicability of technology acceptance model (TAM) constructs in connection with customers’ awareness, perceived risk and perceived trust to investigate the user’s behavioural intention of m-banking adoption. The authors tested the proposed framework by using regression analysis in SPSS 23 and collected a sample of 311 mobile banking users by using convenience sampling. In support of the previous studies, findings revealed that perceived usefulness, perceived ease of use, customer awareness, perceived risk and perceived trust significantly adopted m-banking services in the Indian context.

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The performance degradation assessment of ball bearings is of great importance to increase the efficiency and the reliability of rotating mechanical systems. The large dimensionality of feature space introduces a lot of noise and buries the potential information about faults hidden in the feature data. This paper proposes a novel health assessment method facilitated with two compatible methods, namely curvilinear component analysis and self-organizing map network. The novelty lies in the implementation of a vector quantization approach for the sub-manifolds in the feature space and to extract the fault signatures through nonlinear mapping technique. Curvilinear component analysis is a nonlinear mapping tool that can effectively represent the average manifold of the highly folded information and further preserves the local topology of the data. To answer the complications and to accomplish reliability and accuracy in bearing performance degradation assessment, the work is carried out with following steps; first, ensemble empirical mode decomposition is used to decompose the vibration signals into useful intrinsic mode functions; second, two fault features i.e. singular values and energy entropies are extracted from the envelopes of the intrinsic mode function signals; third, the extracted feature vectors under healthy conditions, further reduced with curvilinear component analysis are used to train the self-organizing map model; finally, the reduced test feature vectors are supplied to the trained self-organizing map and the confidence value is obtained. The effectiveness of the proposed technique is validated on three run-to-failure test signals with the different type of defects. The results indicate that the proposed technique detects the weak degradation earlier than the widely used indicators such as root mean square, kurtosis, self-organizing map-based minimum quantization error, and minimum quantization error-based on the principal component analysis.