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<p>Doctoral research in Strategic Human Resources Management (SHRM) has significant impact to organisational productivity and employee performance. Employees’ attitude towards work changes dynamically depending on multiple factors and hence suitable SHRM measures need to be instituted optimally. Predicting metrics for HRM is vital and sensitive due to handling of personal data. Assessment of digital personal data, analysis and interpretation to produce suitable decision strategies have strong association to human factors. Integration of Artificial Intelligence (AI)/ Machine Learning (ML) utilities, tools and methods can significantly reduce the research time complexity. AI/ML methods are inherently black box in nature, which reduces visibility, consistency and accountability due to lack of technical maturity in controlling biases. While ethical and privacy concerns in using AI/ML for research persist, the critical analytic capability is not too far. This research aims to propose a framework with various concepts and tenets for deployment of AI/ML methods to address various SHRM research issues. A qualitative study using purposive sampling was implemented through in-depth interviews among twelve researchers including research supervisors, scholars, junior, and senior researchers from startups, MNCs, and academic institutions, ensuring participants representing both academic and industry backgrounds. Deductive thematic analysis and inter-rater reliability revealed that AI/ML tools are most beneficial in research design, data management, literature review, and research support activities. Ethical considerations and responsible use of AI/ML for doctoral research have been assessed to prove feasibilities for future employment. Findings suggest mitigation of new challenges is a collective responsibility of all stakeholders of research community for humanity as a whole approach towards a responsible innovation for future of SHRM research. The calibrated application of AI/ML is essential to address risks and societal implications inherent to managerial studies. This research provides a conceptual framework to utilise capabilities of AI/ML tools for research in SHRM with emphasis on ethical concerns and challenges. This framework will be instrumental in advancing the usage of AI for future SHRM research over traditional methods by increasing the acceptance by addressing its existential and impending threats.</p>

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Ethical AI framework for future SHRM research

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月1日

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  • Gowri R.,
  • Senthil Kumar S.,
  • Neeraj Kumar Dubey

摘要

Doctoral research in Strategic Human Resources Management (SHRM) has significant impact to organisational productivity and employee performance. Employees’ attitude towards work changes dynamically depending on multiple factors and hence suitable SHRM measures need to be instituted optimally. Predicting metrics for HRM is vital and sensitive due to handling of personal data. Assessment of digital personal data, analysis and interpretation to produce suitable decision strategies have strong association to human factors. Integration of Artificial Intelligence (AI)/ Machine Learning (ML) utilities, tools and methods can significantly reduce the research time complexity. AI/ML methods are inherently black box in nature, which reduces visibility, consistency and accountability due to lack of technical maturity in controlling biases. While ethical and privacy concerns in using AI/ML for research persist, the critical analytic capability is not too far. This research aims to propose a framework with various concepts and tenets for deployment of AI/ML methods to address various SHRM research issues. A qualitative study using purposive sampling was implemented through in-depth interviews among twelve researchers including research supervisors, scholars, junior, and senior researchers from startups, MNCs, and academic institutions, ensuring participants representing both academic and industry backgrounds. Deductive thematic analysis and inter-rater reliability revealed that AI/ML tools are most beneficial in research design, data management, literature review, and research support activities. Ethical considerations and responsible use of AI/ML for doctoral research have been assessed to prove feasibilities for future employment. Findings suggest mitigation of new challenges is a collective responsibility of all stakeholders of research community for humanity as a whole approach towards a responsible innovation for future of SHRM research. The calibrated application of AI/ML is essential to address risks and societal implications inherent to managerial studies. This research provides a conceptual framework to utilise capabilities of AI/ML tools for research in SHRM with emphasis on ethical concerns and challenges. This framework will be instrumental in advancing the usage of AI for future SHRM research over traditional methods by increasing the acceptance by addressing its existential and impending threats.