Purpose The purpose of this paper is to discuss how the configurations of short life cycle, low quality, design and price, influence customer purchase intention in fast fashion and high technology industries in China. Design/methodology/approach The traditional thinking is that products with high quality and low price will win more customers. However, the authors can notice that high quality products usually have high cost. Therefore, it is necessary to do more research on how customers can accept low quality products. The authors take fast fashion products and smart phones as empirical studies, collecting data from customer’s online survey. Based on the methodology of fuzzy set qualitative comparative analysis, the authors analyse the relationship between the factors of short life cycle, low quality, design and price and influence customer purchase intention. Findings The authors find that price is the most important influencing factor. Low price is a strong competitive factor in the market. As to low quality products, low price can be achieved relatively more easily than with high quality products, resulting from relatively poorer raw material or configurations. Hence the connection between quality and price may give an idea to enterprises that customers will accept low quality products with low price. Moreover, according to the research, different generations are equally affected by the low price condition, regardless of customer gender and household income. Research limitations/implications Because the study only focuses on fast fashion and smart phones industries, future work needs to replicate this study with individual data for different industries and with alternative methods to reinforce the confidence in the research. Meanwhile , this research studied mainly the customer perspective, it would be desirable to extend the study to the enterprise perspective and find out the difficulties that limit them in using low quality products to meet market needs. This may revel some cultural differences in purchase behavior among different countries and the discussed industries can be expanded to a larger area. Practical implications The study offers a number of managerial implications. With the rapid changes in people’s aesthetic sense and developing high-tech, it is more and more necessary for companies to think about how to win more customers and earn more profits. Low quality products have advantages as they will lower companies’ costs in many dimensions, improving the speed of supply. It helps firms to take low quality products into consideration and think whether they will influence different aspects of the company assistance firms to get a deeper understanding of customer psychology and make better decisions on their products. Originality/value The paper fills the gap in the research field by exploring how consumer behavior is affected by different conditions.

Cropland abandonment is crucial in agricultural management and has a profound impact on crop yield and food security. In recent years, many cropland abandonment identification methods based on remote sensing observation data have been proposed, but most of these methods are based on coarse-resolution images and use traditional machine learning methods for simple identification. To this end, we perform abandonment recognition on high-resolution remote sensing images. According to the texture features of the abandoned land, we combine the method of statistical texture learning and propose a new deep learning framework called PSPNet-STL. The model integrates high-level semantic feature extraction and deep mining of low-level texture features to identify cropland abandonment. First, we labeled the abandoned cropland area and built the HRAC dataset, a high-resolution cropland abandonment dataset. Secondly, we improved PSPNet by fusing statistical texture learning modules to learn multiple texture information on lowlevel feature maps, and combined high-level semantic features for cropland abandonment recognition. Experiments are performed on the HRAC dataset. Compared with other methods, the proposed model has the best performance on this dataset, both in terms of accuracy and visualization, proving that deep mining of low-level statistical texture features is beneficial for crop abandonment recognition.

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. Design/methodology/approach -Driving behavior scoring model. Findings -The driving behavior scoring model could effectively reflect the risk level of driver's safe driving. Originality/value -A driving behavior scoring model for UBI.

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