Abstract
Quality management (QM), Human capital management (HCM) and Corporate Social Responsibility (CSR) fields are closely related. Traditionally QM has been captured in the relationship between companies and customers. However, corporate sustainability is believed to depend on not only customers but also on various company stakeholders. In addition, QM is expected to widely affect its customer effect and the various stakeholder evaluations. CSR has been captured in the relationship between companies and stakeholders. However, Strategic CSR concept is underway. Strategic CSR is a corporate strategy concept of selecting CSR activities according to whether they have a positive impact on corporate performance. Employees who make quality improvements by themselves, are indispensable to increase the effectiveness of QM. Human capital is thought to be one of the most important factors for the success of CSR activities. A lot of previous researches have analyzed on a psychometric basis. However, the approach has problems, such that the psychological influence and actual influence may differ. Therefore, this research analyzes the data from the CSR data book and financial reports which are based on the fact basis by using Bayesian Structural Equation Modeling. It demonstrates the impact of each factor more objectively. Analysis showed how each factor affects each other. Particularly, this study suggests that environmental CSR has a positive impact on corporate performance.
Original language | English |
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Pages (from-to) | 695-701 |
Number of pages | 7 |
Journal | Procedia Manufacturing |
Volume | 39 |
DOIs | |
Publication status | Published - 2019 |
Externally published | Yes |
Event | 25th International Conference on Production Research Manufacturing Innovation: Cyber Physical Manufacturing, ICPR 2019 - Chicago, United States Duration: 2019 Aug 9 → 2019 Aug 14 |
Keywords
- Bayesian SEM
- Corporate performance
- Corporate social responsibility
- Human capital
- Quality management
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
- Artificial Intelligence