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Multi-variable time series (MTS) information is a typical type of data inference in the real world. Every instance of MTS is produced via a hybrid dynamical scheme, the dynamics of which are often unknown. The hybrid species of this dynamical service are the outcome of high-frequency and low-frequency external impacts, as well as global and local spatial impacts. These influences impact MTS’s future growth; hence, they must be incorporated into time series forecasts. Two types of normalization modules, temporal and spatial normalization, are recommended to accomplish this. Each boosts the original data’s local and high-frequency processes distinctly. In addition, all components are easily incorporated into well-known deep learning techniques, such as Wavenet and Transformer. However, existing methodologies have inherent limitations when it comes to isolating the variables produced by each sort of influence from the real data. Consequently, the study encompasses conventional neural networks, such as the multi-layer perceptron (MLP), complex deep learning methods such as LSTM, two recurrent neural networks, support vector machines (SVM), and their application for regression, XGBoost, and others. Extensive experimental work on three datasets shows that the effectiveness of canonical frameworks could be greatly improved by adding more normalization components to how the MTS is used. This would make it as effective as the best MTS designs are currently available. Recurrent models, such as LSTM and RNN, attempt to recognize the temporal variability in the data; however, as a result, their effectiveness might soon decline. Last but not least, it is claimed that training a temporal framework that utilizes recurrence-based methods such as RNN and LSTM approaches is challenging and expensive, while the MLP network structure outperformed other models in terms of time series predictive performance.

This research seeks to examine the purchase behaviour of environment-friendly automobiles in the Southern part of China. The researcher adopted a quantitative approach to analyse primary data using Smart PLS3. Independent variables of this study include environmental attitudes such as; environmental knowledge, environmental values and responsibility feeling. These variables were tested against purchase intention, which is regarded as the dependent variable of this study. Based on the findings, all the proposed hypotheses of this study are positive. Environmental knowledge has a significant effect on the establishment of environmental values. Environmental values have a positive effect on the formation of a responsibility feeling. Responsibility feeling has a significant effect on the purchase intention of environment-friendly automobiles. Individuals who possess adequate knowledge about their environment are likely to develop environmental values, which then transforms into a responsibility feeling towards the environment and then predict the purchase intention of environment-friendly automobiles. It is therefore recommended that the Government of the People's Republic of China should ensure that its citizens are imparted with adequate knowledge about the environment, through formal and informal education, and through socialization agencies and awareness campaigns, as this will help in boosting pro-environmental and sustainable human behaviours. Keywords: environment-friendly automobiles; purchase intention; environmental knowledge; environmental values; responsibility feeling.

Objective - This research seeks to examine the purchase behaviour of environment-friendly automobiles. It also identifies the key factors affecting the purchase intention of green vehicles. The study adopted a quantitative approach, and primary data was analysed on Smart PLS3. The researcher utilised a non-probability sampling technique to select the most appropriate sample for this study. This type of sampling is also known as purposive sampling, deliberate sampling, and or judgement sampling. Independent variables of this study include environmental attitudes such as; environmental knowledge, environmental values and responsibility feeling. These variables were tested against purchase intention, which is regarded as a dependent construct of this study. Finding – Based on the findings, all the proposed hypotheses of this study are proven significant. Environmental knowledge has a positive effect on environmental values. Environmental values have a significant effect on the formation of a responsibility feeling. Responsibility feeling has a significant effect on the purchase intention of green vehicles. Novelty – Individuals who possess adequate knowledge about their environment are likely to develop environmental values, which then transforms into a responsibility feeling towards the environment and then predict the purchase intention of environment-friendly automobiles. Citizens should thus be equipped with adequate knowledge about environmental issues, through formal and informal education, and through socialization agencies and awareness campaigns, as this will help in boosting pro-environmental and sustainable human behaviours. Type of Paper: Empirical. JEL Classification: O31, O32, O33. Keywords: Environment-friendly automobiles; purchase intention; environmental knowledge; environmental values; responsibility feeling. Reference to this paper should be made as follows: Mabaire, A.M; Guangquan, X.U; Moyo, N. (2021). Purchase Behaviour of Environment-Friendly Automobiles, GATR Global J. Bus. Soc. Sci. Review, 9(1): 65 – 72. https://doi.org/10.35609/gjbssr.2021.9.1(8)