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Special education in China has lagged behind regular education for many years, however, the past few decades, the government has made considerable efforts to develop and improve the special education system. While the citizens of China have had a generic moral interest in disability since ancient times, the development of special education schools did not occur until American and European missionaries started schools for the visually and hearing impaired in the 19th century. The next major influence in the development of the special education system occurred with China’s Cultural Revolution in 1978. Interestingly, there is not any exclusive legislation on special education but in the 1980s, the government started Learning in Regular Classrooms (LRC), which is China’s version of inclusion. LRC has progressed rapidly the past two decades; however, the quality of instruction is low due to a lack of specialists, a shortage of personnel, inadequate funding, and limited technology as well as other barriers that are delineated in the chapter. The chapter emphasizes the government’s recent efforts in in-service teacher training, the preparation of preservice teachers, working with families, developing community rehabilitation training programs, and implementing evidence-based practices. Special education in China today is at a good place but it has quite a way from the ideal situation.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

There is a growing literature in nonparametric econometrics in the recent two decades. Given the space limitation, it is impossible to survey all the important recent developments in nonparametric econometrics. Therefore, we choose to limit our focus on the following areas. In Section 2, we review the recent developments of nonparametric estimation and testing of regression functions with mixed discrete and continuous covariates. We discuss nonparametric estimation and testing of econometric models for nonstationary data in Section 3. Section 4 is devoted to surveying the literature of nonparametric instrumental variable (IV) models. We review nonparametric estimation of quantile regression models in Section 5. In Sections 2–5, we also point out some open research problems, which might be useful for graduate students to review the important research papers in this field and to search for their own research interests, particularly dissertation topics for doctoral students. Finally, in Section 6 we highlight some important research areas that are not covered in this paper due to space limitation. We plan to write a separate survey paper to discuss some of the omitted topics.

This chapter offers a comprehensive review the literature on authentic leadership (AL). The authors employ a bibliometric approach to identify, classify, visualise and synthesise relevant scholarly publications and the work of a core group of interdisciplinary scholars who are key contributors to the research on AL. They review 264 journal articles, adopting a clustering technique to assess the central themes of AL scholarship. They identify five distinct thematic clusters: authenticity in the context of leadership; structure of AL; social perspectives on AL; dynamism of AL; and value perceptions of AL. Velt and Sinkovics assert that these clusters will help scholars of AL to understand the dominant streams in the literature and provide a foundation for future research.

All firms operating in the global economy are exposed to a multitude of risks including financial crisis, cyberattack, social instability, governance failure, extreme weather events, etc. As a consequence, international organizations assume many (new and evolving) exposures that must be addressed, where some firms are able to adjust and thrive against these adverse odds, whereas many others fail. It appears like some (a few) firms are able to repeatedly outperform the market, where a great many of them struggle, and quite a few register negative returns every year. As a consequence, the authors typically observe leptokurtic negatively skewed distributions of financial returns with extreme negative tails of poor performing firms, where the performance data fall way beyond the requirements of a normal distribution. The authors investigate this phenomenon based on a comprehensive dataset of European firms retrieved from Compustat Global for the 25-year period 1995–2019. The analysis shows that there is indeed a consistent pattern of many underperforming firms across different industry classifications and time intervals and a few outperformers. This provides evidence of a regularly observed phenomenon that often is overlooked in mainstream management studies. The results have implications for academic research that often relies on assumptions of data normality in statistical analysis and for corporate management that has to deal with a risk-prone business environment.