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Intelligent automation implementation and corporate sustainability performance: the enabling role of corporate social responsibility strategy
Although Intelligent Automation (IA) represents the future of business automation, the organizational implementation and sustainability performance of this emerging technological innovation is vastly understudied. Understanding the implications of IA for sustainability is critical since leveraging these technologies shapes operations and policies that can promote sustainable digitalization and automation practices. We study how firms’ technological, organizational, environmental, and human resource contexts impact IA implementation. The study further explains how IA may associate with the firm’s triple bottom line while accounting for the moderating role of corporate social responsibility strategy. The study surveyed 207 multinational firms in 2022 and used partial least square-structural equation modeling to test the hypothesized relationships. Results showed that IA implementation is mainly determined by the characteristics of the firm’s internal environment, such as absorptive capacity, employee socio-behavioral concerns, and social capital competency. IA may offer valuable opportunities for boosting the firm’s economic and environmental sustainability performance. Nonetheless, IA is a double-edged sword for social sustainability, harming social values in implementing firms with informal corporate social sustainability strategies. Conversely, firms with formal corporate social sustainability strategy have a significantly higher opportunity to transform the value of IA into social sustainability performance. Findings are expected to assist managers and decision-makers with streamlining an impartial and sustainable transition of organizations toward automation.
Intelligent automation implementation and corporate sustainability performance: the enabling role of corporate social responsibility strategy
Although Intelligent Automation (IA) represents the future of business automation, the organizational implementation and sustainability performance of this emerging technological innovation is vastly understudied. Understanding the implications of IA for sustainability is critical since leveraging these technologies shapes operations and policies that can promote sustainable digitalization and automation practices. We study how firms’ technological, organizational, environmental, and human resource contexts impact IA implementation. The study further explains how IA may associate with the firm’s triple bottom line while accounting for the moderating role of corporate social responsibility strategy. The study surveyed 207 multinational firms in 2022 and used partial least square-structural equation modeling to test the hypothesized relationships. Results showed that IA implementation is mainly determined by the characteristics of the firm’s internal environment, such as absorptive capacity, employee socio-behavioral concerns, and social capital competency. IA may offer valuable opportunities for boosting the firm’s economic and environmental sustainability performance. Nonetheless, IA is a double-edged sword for social sustainability, harming social values in implementing firms with informal corporate social sustainability strategies. Conversely, firms with formal corporate social sustainability strategy have a significantly higher opportunity to transform the value of IA into social sustainability performance. Findings are expected to assist managers and decision-makers with streamlining an impartial and sustainable transition of organizations toward automation.
Intelligent automation implementation and corporate sustainability performance: the enabling role of corporate social responsibility strategy
Ghobakhloo, Morteza (author) / Asadi, Shahla (author) / Iranmanesh, Mohammad (author) / Foroughi, Behzad (author) / Mubarak, Muhammad Faraz (author) / Yadegaridehkordi, Elaheh (author)
2023-01-01
Technology in society, Oxon : Elsevier, 2023, vol. 74, art. no. 102301, p. 1-20 ; ISSN 0160-791X ; eISSN 1879-3274
Article (Journal)
Electronic Resource
English
DDC:
690
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