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"e-HRM merupakan suatu teknologi informasi yang dapat meningkatkan efektivitas dan efisiensi sistem HRM perusahaan dengan melaksanakan fungsi sistem tersebut secara elektronik atau online. Penelitian ini bertujuan untuk menganalisis pengaruh penggunaan electronic human resource management (e-HRM) terhadap efektivitas HRM di perusahaan perbankan. Unified Theory of Acceptance and Use of Technology (UTAUT) digunakan sebagai teori untuk menganalisis pengaruh determinan e-HRM (performance expectancy, effort expectancy, dan social influence) terhadap penggunaan sistem e-HRM. Data empiris diperoleh melalui kuesioner yang disebarkan ke salah satu perusahaan perbankan di Jakarta. Analisis hipotesis dilakukan dengan teknik Structural Equation Modeling (SEM) menggunakan perangkat lunak Lisrel 8.80. Hasil penelitian ini menunjukkan bahwa performance expectancy dan effort expectancy dapat memengaruhi penggunaan sistem e-HRM oleh karyawan. Penggunaan sistem e-HRM juga terbukti memiliki pengaruh positif dan signifikan terhadap efektivitas HRM pada tingkat kebijakan dan praktik

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e-HRM is an information technology that increased the effectiveness and efficiency of company’s HRM system by carrying out HRM system’s function electronically or online. The purpose of this study is to analyze the effect of using electronic human resource management (e-HRM) on human resource management (HRM) effectiveness in Banking Company. Unified Theory of Acceptance and Use of Technology (UTAUT) was used as a theory to analyze the effect of e-HRM determinants (performance expectancy, effort expectancy, and social influence) toward e-HRM usage. Empirical data was obtained through questionnaires that distributed to one of the banking company in Jakarta. Hypothesis was examined using structural equation modeling (SEM) technique using Lisrel 8.80 software. The findings indicate that performance expectancy and effort expectancy influence e-HRM usage. e-HRM usage also has positive and significant effect on HRM effectiveness at policy and practice level"

"This study aims to explore the determinants of AI adoption and its impact on HRM effectiveness in Tanzanian medium enterprises (MEs). With a focus on providing insights for HR professionals and decision makers, data from 185 respondents comprising HR professionals, IT professionals, and CEOs who have adopted AI were analyzed using PLS-SEM, which includes factors of Relative Advantage, Complexity, Compatibility, Security/ Privacy, Top management, Organizational readiness, Competitive pressure, External support, and Government support for AI adoption. The results highlight relative advantage, compatibility, and competitive pressure as the key drivers of AI adoption in the Tanzanian context, which in turn enhances the effectiveness of HR systems. The study bridges the existing gap and offers recommendations for the integration of AI into HRM practices. Implications for managers and solution providers are discussed to facilitate a better understanding of the determinants influencing the adoption process in SEs in Tanzania. The study builds on theoretical knowledge of AI adoption by utilizing the TOE model, which incorporates technological, organizational, and environmental factors. The study recommends future exploration of additional factors and inclusion of a larger sample to enhance the universality of the results.

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This study intends to explore the determinants of AI adoption and its impact on HRM effectiveness in Tanzanian medium enterprises (MEs). With a focus on providing insights for HR professionals and decision-makers, data from 185 respondents comprising HR professionals, IT professionals, and CEOs who have already adopted AI was analyzed using PLS-SEM, in which factors of Relative advantage, Complexity, Compatibility, Security/Privacy, Top management, Organization readiness, Competitive pressure, External support and Government support were tested for the adoption of AI. Results highlight relative advantage, compatibility, and competitive pressure as key drivers of AI adoption in Tanzania's context, subsequently enhancing HR systems' effectiveness. The study bridges the existing gaps and offers recommendations for AI integration into HRM practices. Implications for managers and solution providers were discussed to facilitate a better understanding of the determinants influencing the adoption process within Tanzanian MEs. The study underlies the theoretical knowledge of AI adoption by utilizing the TOE model, incorporating technological, organizational, and environmental factors. This study recommends future exploration of additional factors and including a larger sample to enhance the universality of results."