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Infrastructure financialization plays a critical role in infrastructure development and urban growth around the world. However, on the one hand, the existing research on the infrastructure financialization focuses on qualitative and lacks quantitative country-specific studies. On the other hand, the spatial heterogeneity and influencing factors of infrastructure financialization are ignored. This study takes China as a typical case to identify and analyze the spatial characteristics, development process and impact factors of infrastructure financialization.

To assess the development and characteristics of infrastructure financialization in China, this study constructs an evaluation index of infrastructure financialization based on the infrastructure financialization ratio (IFR). This study then analyzes the evolution process and spatial pattern of China's infrastructure financialization through the spatial analysis method. Furthermore, this study identifies and quantitatively analyzes the influencing factors of infrastructure financialization based on the spatial Dubin model. Finally, this study offers a policy suggestion as a governance response.

The results demonstrate that infrastructure financialization effectively promotes the development of infrastructure in China. Second, there are significant spatial differences in China’s infrastructure financialization. Third, many factors affect infrastructure financialization, with government participation having the greatest impact. In addition, over-financialization of infrastructure has the potential to lead to government debt risks, which is a critical challenge the Chinese Government must address. Finally, this study suggests that infrastructure financialization requires more detailed, tailored,and place-specific policy interventions by the government.

This study not only contributes to enriching the knowledge body of global financialization theory but also helps optimize infrastructure investment and financing policies in China and provides peer reference for other developing countries.

Entrepreneurial ecosystem is a frontier issue in the field of enterprise strategy and entrepreneurship. As suggested by bottom of the pyramid (BOP) theory, entrepreneurs from base of the pyramid can gain the benefits of economic growth by obtaining equal entrepreneurial opportunities with appropriate support and motivation. However, theoretical framework to understand the ecosystem and help the people from the BOP to benefit from ecosystem is under-researched. Based on the investigation of the Taobao ecosystem case study, this paper developed a multi-layer framework to fill in the research gap.

This research adopts case study methodology for several reasons. First, case study methodology fits the explorative nature of this research to understand “what” and “how” a phenomenon happened (Yin, 2017). Second, case study research design can specify gaps or holes in existing theory with the ultimate goal of advancing theoretical explanations (Ridder, 2016). And third, it can provide researchers with contextual richness (Davison and Martinsons, 2016; Spigel, 2017).

Based on the literature review of the entrepreneurial ecosystem and the theory of the BOP, the initial framework of the inclusive entrepreneurial ecosystem is proposed. And then, based on the investigation of the Taobao village which is the typical inclusive entrepreneurial ecosystem, this paper explored how the inclusive entrepreneurial ecosystem emerges, develops and realises the co-creation between multiple actors. The emergence of Taobao village entrepreneurial ecosystem is because of the fact that ICT empowers BOP entrepreneurs. The development of entrepreneurial ecosystem presents a point-line-plane diffusion path, and co-creation to enhance inclusive entrepreneurship is realised by interaction and cooperation within social networks and integration of resources. Then, the framework of inclusive entrepreneurial ecosystem is further modified.

The complexity of entrepreneurial ecosystem facing BOP poses a major challenge to its actual operation. Therefore, it is necessary to study the driving factors of inclusive entrepreneurial ecosystems. In particular, ecosystem is a purposeful collaborative network of dynamic interactive systems, which has a set of changing dependencies in a given context. Research is still limited on socioeconomic actors’ interaction with each other in each stage to promote the evolution of entrepreneurial ecosystem. The extent to which they are intentionally designed or organically produced is still unclear, which is recommended for future study in this field.

It provides theoretical understanding on how to successfully form sustainable entrepreneurial ecosystem by integrating BOP entrepreneurs in value chain. The successful experience of Taobao village can provide contributions and implications for the management in practice. On the one hand, this can provide theoretical guidance for other countries and regions to build inclusive entrepreneurship ecosystems and help them to check and fill the gaps and build inclusive entrepreneurship ecosystems based on their local characteristics. On the other hand, this study provides theoretical guidance for solving the problem of poverty at the BOP, transforming the poor from the objects of help to successful entrepreneurs and thus realising regional sustainable development.

The significance of this study is to provide theoretical understanding on how to successfully form entrepreneurial ecosystem by practical investigation of entrepreneurial “habitat” at the BOP.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.