A hybrid data mining model in analyzing corporate social responsibility

2015 ◽  
Vol 27 (3) ◽  
pp. 749-760 ◽  
Author(s):  
Ping-Feng Pai ◽  
Lei-Chun Chen ◽  
Kuo-Ping Lin
Keyword(s):  
Data Mining ◽  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
Hybrid Data ◽  
Corporate Social ◽  
Mining Model

scholarly journals Corporate social responsibility in international business literature: results from text data mining of the Journal of International Business Studies

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Karen Paul ◽  
Carlos M. Parra
Keyword(s):  
Data Mining ◽  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
International Business ◽  
Text Data ◽  
Text Data Mining ◽  
Business Studies ◽  
International Business Research ◽  
The 1960S ◽  
Corporate Social

AbstractCorporate social responsibility has been an important theme in management at least since the 1960s. International business became a recognized subfield in management around the same time. Logically, there might have been much dialogue about corporate social responsibility in international business research and publication, yet previous reviews of the literature indicate relatively little such research. This study complements previous literature reviews by employing text data mining to analyze a sample of 1188 articles published from 2000 to 2018 in the Journal of International Business Studies (JIBS). Results show that from 2000 to 2018 only 35 CSR focused articles appeared. CSR research has increased over time, highly influenced by editorial specification of special issues. These documents can be grouped into seven CSR topics, with corruption and embeddedness being the most salient. Strategies are suggested for increasing research on CSR in international business.

scholarly journals Visualizing Term Eigenvector Prominence in a Corporate Social Responsibility Context

2016 ◽  
Vol 2 (2) ◽  
pp. 31
Author(s):  
Carlos M Parra ◽  
Monica Tremblay ◽  
Arturo Castellanos
Keyword(s):  
Data Mining ◽  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
General Motors ◽  
Text Data ◽  
Text Data Mining ◽  
Core Business ◽  
The Core ◽  
Exxon Mobil ◽  
Corporate Social

In this study we develop a simplified technique for helping researchers and analysts visualize the alternative prominence of term eigenvectors obtained after exploring term associations (Term Clusters) while conducting Text Data Mining on a collection to Corporate Social Responsibility (CSR) reports. The collection analyzed is comprised of CSR reports produced by 7 US firms (Citi, Coca-Cola, Exxon-Mobil, General Motors, Intel, McDonald’s and Microsoft) in 2004, 2008 and 2012. The analysis is performed by year in order to discern how the prominence of term eigenvectors has evolved for each firm and for different CSR topics. Results indicate that term eigenvectors maintain their prominence when CSR topics are related to the core business of the firm in question.

Corporate social responsibility of Internet enterprises based on data mining

2020 ◽  
pp. 002072092094058
Author(s):  
Wenzhong Zhu ◽  
Yabo Shang ◽  
Sitong He ◽  
Wen-Tsao Pan
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
Decision Tree ◽  
The Internet ◽  
Decision Tree Analysis ◽  
Tree Analysis ◽  
Corporate Social ◽  
The Relationship

In the age of the Internet economy, Internet enterprises have attracted tremendous public attention, especially in China. In this paper, data mining through regression analysis, grey relational analysis, decision tree analysis and cluster analysis is implemented to further study the relationship between corporate social responsibility (CSR) and corporate financial performance (CFP) of Internet enterprises in China. This study collects and analyzes data of 20 Internet enterprises in China from the year of 2011 to 2016 and draws the following conclusions: (1) the relationship between CSR and CFP of the Internet enterprises is negative; (2) from the stakeholder perspective, CSR to shareholders, creditors and government is positively related to CFP; CSR to customers, suppliers and employees is not positively related to CFP; (3) through decision tree analysis, it is found that what affects the overall CSR performance of the Internet enterprises the most is CSR to customers and suppliers, while what affects the CFP of the Internet enterprises the most is CSR to creditors; (4) through cluster analysis, 20 enterprises can be divided into three types. This study has theoretical, methodological, practical and educational implications for future related research, business practitioners and educational institutions.

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