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ABSTRACTResearch Question/IssueAre companies monitored by common owners (i.e., institutional investors that block‐own [owning 5% or more] several companies in a single industry) more likely than other companies to record goodwill impairments when their assets are overstated?Research Findings/InsightsWe find that companies monitored by common owners are more likely than other companies to record goodwill impairments when their assets are overstated. The monitoring effect is stronger for common owners with a stronger incentive to monitor and with more industry knowledge and stronger for the co‐presence of multiple common owners. Our findings are in line with the notion that common owners have an economy of scale in monitoring and internalize the negative externality of delayed recording of goodwill impairment. We also find that common ownership is associated with lower information asymmetry, which in turn increases the timeliness of goodwill impairment.Theoretical/Academic ImplicationsOur research emphasizes the monitoring role of common ownership in recording goodwill impairments. We find support for the mechanisms enabling common owners to be better monitors.Practitioner/Policy ImplicationsThe prevalence of common ownership has prompted regulatory and societal concerns regarding under‐investment in the oversight of the companies. Our findings documenting the association between common ownership and the timely recording of goodwill impairments are relevant to the ongoing debate regarding the potential costs and benefits of common ownership.

"This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience, this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science, such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields, soft and programming skills in data science projects, and potential career paths. Traditional data-related practitioners such as statisticians, business analysts, and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes."

Intro -- Introduction: Spatial Synthesis in Computational Social Science and Humanities -- 1. Towards Computational Spatial Social Science and Humanities -- 2. Synthesis and Convergence -- 3. Spatial Synthesis in Humanities, Regional Science, and Urban Science -- 4. Conclusion -- References -- Contents -- Part IForeword -- 1 Foreword I: Charting Computational Social Science from a Spatial Perspective -- References -- 2 Foreword II: Convergence and Synthesis -- References -- Part IISpatial Synthesis in Humanities -- 3 The China Family Tree Geographic Information System -- 3.1 Family Tree and GIS -- 3.1.1 Family Tree -- 3.1.2 GIS and Family Tree Research -- 3.1.3 Concept and Objectives of Family Tree GIS -- 3.2 A Unified Spatial-Temporal Framework for Family Trees -- 3.2.1 Why Is a Unified Spatial-Temporal Framework Needed? -- 3.2.2 How Can a Unified Spatial-Temporal Framework Be Constructed? -- 3.3 FTGIS Data Model -- 3.3.1 Content and Information of Family Trees -- 3.3.2 Overview of the Models -- 3.4 Family Tree Information Specification and Sharing -- 3.4.1 Existing Specifications Associated with Family Trees -- 3.4.2 Family Tree Information Specification -- 3.5 Mass Family Tree Information Collection -- 3.6 FTGIS Platform -- 3.6.1 Architecture of the FTGIS Platform -- 3.6.2 Functions of the FTGIS Platform -- 3.7 Conclusions and Future Research -- References -- 4 GIS for Chinese History Research -- 4.1 The Construction of Typical Geographic Information Systems for China Study -- 4.2 The Research Regarding Climate, Rivers, Hydrology and Geomorphology Through the Application of the GIS Technology -- 4.2.1 The Historical Climate Research with the GIS -- 4.2.2 The Research of Rivers and Hydrology in History Through the GIS -- 4.2.3 The Geomorphology and the Research of Environmental Changes Through the GIS.

Based on the value of agricultural farm products produced by different subsectors in China, the foregoing analysis reveals the dynamic character of agricultural diversification by which, this study seeks to examine the evolutionary process of Chinese agriculture through a quantitative study of agricultural diversification at both national and provincial levels. In the initial stages of reform and opening up, the degree of agricultural diversification in the southwest was relatively high; then the center of agricultural diversification gradually moved to the southeast of China; finally, the degree of agricultural diversification in the economically developed eastern provinces was obviously higher than those in other regions in 2019. It was seen that some provinces in the eastern and central south regions moved toward increasing diversification in one direction, and other provinces changed direction, first moving toward diversification and later toward concentration or vice versa. These oscillations implied that there was a cyclic tendency of agricultural diversification along with an increase in per capita GDP. Generally speaking, the patterns of diversification differed across regions due to diversified agricultural subsectors resulting from different natural and socio-economic circumstances. In particular, in less developed regions with lower agricultural diversification levels, farming agriculture persistently dominated the leading position with relatively more resistance to modernizing trends in other aspects of agriculture. It is urgent for the Chinese government to figure out ways off reconciling agricultural productivity with environmental quality through the ecological intensification of agriculture.

Individual factors relating to dietary behaviors are widely explored. However, the effects of social environment on dietary patterns for the older people are less explored. The purpose of this study was to identify dietary patterns among older people in Taiwan and to examine the relationship of dietary patterns with social environment and individual factors. The current study used the 2013–2016 Nutrition and Health Survey in Taiwan. The sample was representative at the national and city levels. Only those who were aged 55 years old and above were included for analysis (n = 2922); the mean age of the participants was 68.62 (SD = 8.76). The city-level data, including population characteristics, food availability, and age-friendly city indicators, were obtained from the open data and survey report of government. Three dietary patterns were identified: high protein-vegetable (41.6%), high sweets and low protein-vegetables (37.9%), and high viscera and fats (20.5%). The results of multilevel multinomial logistic regressions showed that marital status, economic status, education, drinking alcohol, dietary belief, living a the city with more food availability, and bus accessibility were related to dietary patterns. Dietary patterns are related to the individual-level factors and social environment. Healthy dietary beliefs and age-friendly environments are beneficial to promoting healthy dietary patterns.