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This study aims to explore when and for what purpose is social media used by tourists in their decision-making; and to further interpret the nature of such usage.

An online questionnaire was administered to 530 Indians who qualified as recent tourists and social media users. Partial least square structural equation modeling (PLS-SEM) is used to analyse hypothesised relationships; the results helped us to unveil the tourist social media cycle which is a conceptual model of the results obtained.

PLS-SEM results indicate that social media is predominantly used in the pre-travel stage for evaluation purposes, during travel stage for purchase purposes and post-travel stage for post-purchase behaviour. Also, it is noted that social media use by a tourist is an ongoing process, and thus, a conceptual model in the form of tourist social media cycle is unveiled.

This study reveals the unique behaviour of Indian tourists with respect to using social media for travel decision-making, which establishes a different perspective of understanding and further strategizing social media’s use in tourism for a developing nation. Also, the tourist social media cycle stresses on the importance of social media as a digital repository where continuously the user-generated content is used by self (existing) or “others” (potential tourists).

To the best of the author’s knowledge, this is the first study that addresses the population and knowledge gap of understanding social media–based tourist behaviour providing a perspective of a developing nation, specifically India. Further, it unveils the nature of social media use by tourist in the form of a first ever tourist social media cycle.

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.

The COVID-19 outbreak reached a critical stage when it became imperative for public health systems to act decisively and design potential behavioral operational strategies aimed at containing the pandemic. Isolation through social distancing played a key role in achieving this objective. This research study examines the factors affecting the intention of individuals toward social distancing in India.

A correlation study was conducted on residents from across Indian states (N = 499). Online questionnaires were floated, consisting of health belief model and theory of planned behavior model, with respect to social distancing behavior initially. Finally, structural equation modeling was used to test the hypotheses.

The results show that perceived susceptibility (PS), facilitating conditions (FC) and subjective norms are the major predictors of attitude toward social distancing, with the effect size of 0.277, 0.132 and 0.551, respectively. The result also confirms that the attitude toward social distancing, perceived usefulness of social distancing and subjective norms significantly predict the Intention of individuals to use social distancing with the effect size of 0.355, 0.197 and 0.385, respectively. The nonsignificant association of PS with social distancing intention (IN) (H1b) is rendering the fact that attitude (AT) mediates the relationship between PS and IN; similarly, the nonsignificant association of FC with IN (H5) renders the fact that AT mediates the relationship between FC and IN.

The results of the study are helpful to policymakers to handle operations management of nudges like social distancing.

The research is one of its kind that explores the behavioral aspects of handling social nudges through FC.