PurposeIn the modern era, brand authenticity is one of the most powerful positioning strategies for sustainable business growth. This study investigated the impacts of perceived brand authenticity dimensions (i.e., quality commitment, heritage, sincerity) on brand love to predict Generation Y's behavior from the Asian context.Design/methodology/approachThis is new empirical research that tested the proposed hypotheses through PLS-SEM, as PLS is the most robust technique for predicting consumer behavior. Importantly, consumers (of Generation Y) from five Asian countries contributed to this study, and data collected from 427 Asian millennials on global brands.FindingsThe results analysis revealed that perceived brand authenticity dimensions significantly impacted brand love, which positively affected Asian millennials' behavioral outcomes (i.e., continuous purchase intention and price premium).Research limitations/implicationsThis study investigated dimensions of perceived brand authenticity to predict Asian millennials' behavioral outcomes in a broader perspective. Future researchers may investigate a specific culture with a larger sample size to predict millennials behavior.Practical implicationsThis study has several implications that guide the global managers of several service and manufacturing industries to develop various positioning and relationship strategies for global brands to target Asian markets effectively.Originality/valueUsing attribution theory, this is the first novel research study that empirically discussed the dimensions of perceived brand authenticity, brand love, and Asian millennials' behavior toward global brands.

With the world calling for environmental protection, China has to follow an innovation-driven development path in order to achieve its own high-quality and sustainable development. During this period, the problem of inefficient land use resulting from rapid progress in urbanisation is difficult to ignore. This study uses data from 30 provinces in mainland China to analyse the environmental protection effects of land use transition towards innovation-driven development, using spatial econometric models and entropy method. The results show that the innovation-oriented land use transition in four dimensions, human capital, material capital, urban function and government, is conducive to reducing industrial pollution emissions in the region, but this effect does not have a spillover effect. The results of this study provide some insights into the “triple-win” (environmental protection, innovation and land-use optimisation) approach to economic development in China.

Haze pollution is a problem that cannot be ignored in the process of building sustainable cities, and while shifting industrial enterprises can solve the problem at the root, it is not conducive to the sustainable development of urban economies. This paper discusses the role of industrial agglomeration on urban pollution amelioration (haze pollution) using a sample of 253 prefecture-level cities in China. The highlight of this paper is the study of economic and environmental factors in the development of sustainable cities in the same framework and a series of econometric treatments that greatly increase the accuracy of the empirical evidence. Research intuitively shows that China’s haze pollution is clustered in spatial distribution and is spatially heterogeneous in concentration. With the passage of time, haze pollution has a tendency to move from an H–H concentration area to an L–L concentration area. The regression results show that an increase in the scale of local industrial agglomeration will lead to a decrease in local haze pollution; but an increase in the scale of local industrial agglomeration will lead to an increase in haze pollution in adjacent areas. Industrial agglomeration has significant spatial spillover effects, which are spatially heterogeneous. In addition, spillover effects between regions are greater than those within regions. After replacing the spatial weight matrix and controlling the endogenous problem using the instrumental variable method, the conclusion is still robust.

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