Sustainable development is a powerful impetus to achieve the coordination between economic advancement and environmental protection. Therefore, the promotion and adoption of green consumption has attracted increasing attention from academics. As an important tool for multivariate data analyses, structural equation modelling has been extensively used in purchase intention for green products. However, most previous research has recognized green products as a general class when studying their purchase intention, which focused little attention on comparing purchase intention for different kinds of green products. This study extends and tests the theory of planned behavior (TPB) and includes another cognitive construct, that is, environmental concern, to explore purchase intention for different kinds of green products. First, the effect of TPB constructs was examined on purchase intention for utilitarian green products and hedonic green products. Second, a cognitive factor, that is, environmental concern, was used to test its different effects on these two kinds of green products. Third, the indirect effect of environmental concern on purchase intention was measured for these two kinds of green products. The study showed that extended TPB constructs, that is, attitude, perceived behavior control, and environmental concern positively and significantly affected purchase intention for both utilitarian green products and hedonic green products, while subjective norm only significantly and positively affected purchase intention for utilitarian green products. Moreover, the cognitive factor, that is, environmental concern, had a higher effect on purchase intention for utilitarian green products than purchase intention for hedonic green products, and it also had a similar indirect effect on both kinds of green products. This study provides an insight to policymakers, entrepreneurs, and marketers into how to increase purchase intention for different kinds of green products.

As part of the increasing efforts toward the prevention and control of motor vehicle pollution, the Chinese government has practiced a range of policies to stimulate the purchase and use of battery electric vehicles (BEVs). Zhejiang Province, a key province in China, has proactively implemented and monitored an environmental protection plan. This study aims to contribute toward streamlining marketing and planning activities to introduce strategic policies that stimulate the purchase and use of BEVs. This study considers the nature of human behavior by extending the theory of planned behavior model to identify its predictors, as well as its non-linear relationship with customers’ purchase intention. To better understand the predictors, a substantial literature review was given to validate the hypotheses. A quantitative study using 382 surveys completed by customers in Zhejiang Province was conducted by integrating a structural equation model (SEM) and a neural network (NN). The initial analysis results from the SEM revealed five factors that have impacted the customers’ purchase intention of BEVs. In the second phase, the normalized importance among those five significant predictors was ranked using the NN. The findings have provided theoretical implications to scholars and academics, and managerial implications to enterprises, and are also helpful for decision makers to implement appropriate policies to promote the purchase intention of BEVs, thereby improving the air quality.

Stock prices forecasting is a topic research in the fields of investment and national policy, which has been a challenging problem owing to the multi-noise, nonlinearity, high-frequency, and chaos of stocks. These characteristics of stocks impede most forecasting models from extracting valuable information from stocks data. Herein, a novel hybrid deep learning model integrating attention mechanism, multi-layer perceptron, and bidirectional long-short term memory neural network is proposed. First, the raw data including four types of datasets (historical prices of stocks, technical indicators of stocks closing prices, natural resources prices, and historical data of the Google index) are transformed into a knowledge base with reduced dimensions using principal component analysis. Subsequently, multi-layer perceptron is used for the fast transformation of feature space and rapid gradient descent, bidirectional long-short term memory neural network for extracting temporal features of stock time series data, and attention mechanism for making the neural network focus more on crucial temporal information by assigning higher weights. Finally, a comprehensive model evaluation method is used to compare the proposed model with seven related baseline models. After extensive experiments, the proposed model demonstrated its good forecasting performance.