The Prediction of Corporate Social Responsibility Impact on Competitive Advantage: An Artificial Neural Network Approach

Albahussain, Sami Abdullah; Garaihy, Wael Hassan El; Mobarak, Abdel-Kader Mohamed

doi:10.6007/ijarems/v3-i5/1197

Cited by 3 publications

(2 citation statements)

References 64 publications

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“…D'Oca et al [210] emphasized the need for further study to incorporate human factors into building design and operation procedures with the aim of improving occupant comfort and productivity. An ANN model was developed by Albahussain [211] to study the influence of corporate social responsibility (CSR) on the competitive advantage (CA) of the industry.…”

Section: Society and Human Values In Construction Industrymentioning

confidence: 99%

Artificial Neural Networks for Sustainable Development of the Construction Industry

et al. 2022

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Artificial Neural Networks (ANNs), the most popular and widely used Artificial Intelligence (AI) technology due to their proven accuracy and efficiency in control, estimation, optimization, decision making, forecasting, and many other applications, can be employed to achieve faster sustainable development of construction industry. The study presents state-of-the-art applications of ANNs to promote sustainability in the construction industry under three aspects of sustainable development, namely, environmental, economic, and social. The environmental aspect surveys ANNs’ applications in sustainable construction materials, energy management, material testing and control, infrastructure analysis and design, sustainable construction management, infrastructure functional performance, and sustainable maintenance management. The economic aspect covers financial management and construction productivity through ANN applications. The social aspect reviews society and human values and health and safety issues in the construction industry. The study demonstrates the wide range of interdisciplinary applications of ANN methods to support the sustainable development of the construction industry. It can be concluded that a holistic research approach with comprehensive input data from various phases of construction and segments of the construction industry is needed for the sustainable development of the construction industry. Further research is certainly needed to reduce the dependency of ANN applications on the input dataset. Research is also needed to apply ANNs in construction management, life cycle assessment of construction projects, and social aspects in relation to sustainability concerns of the construction industry.

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Section: Society and Human Values In Construction Industrymentioning

confidence: 99%

Artificial Neural Networks for Sustainable Development of the Construction Industry

et al. 2022

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“…Research conducted by Albahussain, El-Garaihy, and Mobarak (2014) has proven that ANN can be a significant method for understanding corporate CSR evaluations and providing an approach that encourages data processing in CSR. The research focused on the triple bottom line in CSR alone.…”

Section: Introductionmentioning

confidence: 99%

Prediction Sustainability Company Using Artificial Neural Network

Rifa

Muslih

2018

RIMA

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The study aims to find out how to predict company sustainability using Artificial Neural Networks (ANN) and looks at the relationship of board tenure, board age, company size, profitability, leverage, and liquidity in predicting sustainability of Asia Sustainability Reporting Rating (ASRR) participants in Indonesia period 2009-2018. Previous studies state that ANN is considered to provide accurate results in predictions. Therefore, this study uses ANN modeling for predicting sustainability. Company sustainability is assessed by analyzing sustainability performance in the Company's Sustainability Report based on the Global Reporting Initiatives (GRI) G4 guidelines. The results of predictions using ANN indicate that company size, liquidity, and leverage contribute more than 50% in predicting company sustainability. Meanwhile, profitability, board age, and biard tenure contribute less than 50%.

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Women Fighting the Crisis: Antecedents of Women's Leadership Performance in Facing the Covid-19 Pandemic

Mamduh,

Pratikto,

Handayati

et al. 2024

RGSA

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Objective: This study aims to empirically investigate the interplay between financial literacy, entrepreneurial orientation, learning orientation, and the performance of food and beverage Micro, Small, and Medium Enterprises (MSMEs) in Bontang City, East Kalimantan Province. Method: The research, grounded in Resource-Based Theory (RBT), explores the mediation role of innovation ability in this relationship. The study encompasses 272 MSME owners as its population. Utilizing Structural Equation Modeling for data analysis. Research Findingsand Discussions: The findings show that financial literacy, entrepreneurial orientation, and learning orientation positively influence innovation capability and MSME performance. In addition, innovation capability emerges as an important mediator that bridges the impact of financial literacy, entrepreneurial orientation and learning orientation on MSME performance. The results of this study contribute to enriching the understanding of the Resource-Based Theory, highlighting that effective utilization of resources, such as financial literacy, entrepreneurial orientation, learning orientation, and innovation capability, can improve MSME performance. Research Implications: This study is a valuable reference for MSME owners in the food and beverage sector, emphasizing the importance of a proactive approach. Responding quickly to market opportunities, introducing new products to new markets, and developing a learning-oriented mindset all play an important role in driving innovation and ultimately improving MSME performance. Originality/Value: This research underscores the importance of strategic resource utilization for MSME success, in line with the principles of Resource-Based Theory. Owners of food and beverage MSMEs are encouraged to develop a proactive and learning-oriented attitude, encouraging innovation as a key driver to improve business performance.

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The Prediction of Corporate Social Responsibility Impact on Competitive Advantage: An Artificial Neural Network Approach

Cited by 3 publications

References 64 publications

Artificial Neural Networks for Sustainable Development of the Construction Industry

Artificial Neural Networks for Sustainable Development of the Construction Industry

Prediction Sustainability Company Using Artificial Neural Network

Women Fighting the Crisis: Antecedents of Women's Leadership Performance in Facing the Covid-19 Pandemic

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