人工神经网络在企业社会责任决策中的应用

Q1 Economics, Econometrics and Finance Intelligent Systems in Accounting, Finance and Management Pub Date : 2023-10-03 DOI:10.1002/isaf.1542
Nguyen Thi Thanh Binh
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引用次数: 0

摘要

深度学习中的神经网络正在改变我们与世界互动的方式。本文的重点是构建一个对数人工神经网络(ANN),并通过该网络找出影响企业加入企业社会责任(CSR)决策的因素。本研究有助于为人工智能(AI)时代公司治理与企业社会责任之间关系的研究提出新的方向。本研究分析了 2014-2020 年间 817 家台湾电子企业的数据集。实证结果表明,当董事会、监事和首席执行官的权力较高时,企业不会选择参与企业社会责任。独立董事会尚未促进其对企业社会责任参与的企业监督。当企业更具备会计、金融和法律背景时,它们会做出不参与企业社会责任的决定。只有负债、资产价值和盈利能力较高的企业才愿意参与企业社会责任。这些研究结果为未来的可持续发展政策改革提出了一些重要观点。
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An application of artificial neural networks in corporate social responsibility decision making

Neural networks in deep learning are changing the way we interact with the world. This paper focuses on building a logit artificial neural network (ANN) and through it finds out the factors affecting the decision to join corporate social responsibility (CSR) of firms. This study contributes to suggesting new directions for research in the artificial intelligence (AI) era on the relationship between corporate governance and CSR. The dataset of 817 Taiwanese electronic firms is analyzed for the period 2014–2020. The empirical results show that when the power of the board of directors, supervisors, and CEOs are higher, firms do not choose to participate in CSR. The independent board has not yet promoted its corporate oversight of CSR participation. The decision not to participate in CSR of the firms is made when they are more equipped with the background of accounting, finance, and law. Only firms with higher debt, asset value, and profitability are willing to join CSR. These research results suggest some important points for future policy reforms towards sustainability.

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来源期刊
Intelligent Systems in Accounting, Finance and Management
Intelligent Systems in Accounting, Finance and Management Economics, Econometrics and Finance-Finance
CiteScore
6.00
自引率
0.00%
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0
期刊介绍: Intelligent Systems in Accounting, Finance and Management is a quarterly international journal which publishes original, high quality material dealing with all aspects of intelligent systems as they relate to the fields of accounting, economics, finance, marketing and management. In addition, the journal also is concerned with related emerging technologies, including big data, business intelligence, social media and other technologies. It encourages the development of novel technologies, and the embedding of new and existing technologies into applications of real, practical value. Therefore, implementation issues are of as much concern as development issues. The journal is designed to appeal to academics in the intelligent systems, emerging technologies and business fields, as well as to advanced practitioners who wish to improve the effectiveness, efficiency, or economy of their working practices. A special feature of the journal is the use of two groups of reviewers, those who specialize in intelligent systems work, and also those who specialize in applications areas. Reviewers are asked to address issues of originality and actual or potential impact on research, teaching, or practice in the accounting, finance, or management fields. Authors working on conceptual developments or on laboratory-based explorations of data sets therefore need to address the issue of potential impact at some level in submissions to the journal.
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