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The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data.

Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features.

The two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches.

The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.

This study integrates the providers' perspective as well as the patient's perspective in developing and validating a scale to measure hospital service quality in multispecialty hospitals.

An exploratory sequential mixed-method approach was used in this study. The strategies used included a thematic literature review, semi-structured interviews, modified Delphi and confirmatory factor analysis.

The reliability coefficient of 41 item scale was 0.963 with each attribute, that is, pivotal, core and peripheral, having a Cronbach's alpha of 0.907, 0.91 and 0.891, with scale content validity (S-CVI Ave) of 0.9151. The composite reliability scores of all constructs were greater than 0.7, with an Average Variance Explained (AVE) of all items greater than 0.5.

The instrument can be used to measure the difference between what service providers believe customers expect and customers’ actual needs and expectations. The scale can be used to measure the difference between what is delivered (as perceived by the provider) and what customers perceive they have received (because they are unable to accurately evaluate service quality). The dyadic approach of administering this questionnaire in measuring hospital service quality will lead to the identification of a knowledge gap and a perception gap in delivering hospital service quality.

The purpose of this paper is to examine the effects of corporate social responsibility, social media marketing, sales promotion, store environment and perceived value on a purchase decision in the retail sector.

A quantitative research methodology was used and the data were collected from 278 customers of retail stores in Malaysia. The collected data were analysed using SPSS 19 and structural equation modelling on AMOS.

The findings showed that corporate social responsibility has significant positive effects on a purchase decision, whereas sales promotion has a negative effect on purchase decision. The outcomes of this study also indicated that store environment has a significant positive effect on consumers’ purchase decisions. Contrary to expectations, the findings revealed that the effect of social media marketing on purchase decision is insignificant. Finally, the results showed that perceived value has a significant positive effect on a purchase decision.

The findings of this study contribute to an understanding of the importance of the selected factors in affecting a consumer’s purchase decision in the retail industry.