This piece of research highlights a contextual understanding of employee performance’s concept by identifying factors affecting employee performance in the organization. This achieved through analyzing literature in ISI (Web of Knowledge) from 2015 until 2019, after that determine factors influencing employee performance. The definition of employee performance is given, furthermore, the description of each factor which has an influence on employee performance. The result indicates that knowledge management, information and communication technology, employee’s empowerment, innovation and creativity and organization culture have a significant impact on employee performance. On the other hand, the correlation between these factors has a vital role in maintaining employee’s performance and attitude.

Artificial intelligence (AI) has revolutionized the way employees and managers work. This paper studies how AI is transforming Human Resource Management (HRM) functions: Staffing, Learning & Development and, Motivation. Using recent advances in science mapping, this article analyses 30 journals and proceedings using three main keywords: “Artificial intelligence”; “Human Resource Management”; and “Transformation”. All the consulted papers have been published in Scopus databases between 1998 to 2021 in order to explore and understand topic content and intellectual structure of how AI is transforming HRM functions. The results reveal a gap in literature to build a complete framework for the transforming role of AI in HRM functions. Particularly, Strategic HR Planning, Job Design and Compensation. This study gives insights and foundations for researchers to expand their study on the role of AI in HRM.

Artificial intelligence (AI) has become a valuable tool for facilitating Human Resource Management (HRM) functions. Although, it should be noted that AI has a specific character side away from other technology. Publications covering this knowledge area have grown sharply, however the scholarly covering the impact of AI bias inHRM is scarce. This paper studies this area and goes deeper to explore the future research areas by conducting a systematic literature review for 598 papers from Scopes and Emerald insight databases of which 34 articles were selected after implementing the PRISMA tool and quality evaluation stage. Results generated revealed that biased AI applications are negatively affecting performance management, compensation, staffing and training and development. Apart from that future research domains and questions have been outlined and identified from organizations’ and employees’ perspectives.

The phenomenon of artificial intelligence has been widely studied in several areas. In opposite, in terms of AI in HRM, the literature shows limited research on the adoption factors of artificial intelligence (AI) in HRM. AI has been enrolled in several HRM’s areas starting from staffing till management performance or compensation. A set of suggestions on how to adopt AI in HRM has been raised. This piece of research aims to identify the adoption factors of six scenarios of AI in HRM. These scenarios are turnover prediction with artificial neural networks, candidate search with knowledge-based search engines, staff rostering with genetic algorithms, HR sentiment analysis with text mining, résumé data acquisition with information extraction and employee self-service with interactive voice response. As a result, compatibility, relative advantage, complexity, managerial support, government involvement, and vendor partnership are determinants affected factors of AI adoption in HRM. This paper tries to address new insights for practitioners and academics by minimizing the risks associated with AI adoption in some areas of HRM through exploring determinant factors of adoption.

The world is witnessing new technological advancements, which significantly impacts organizations across different departments. Artificial intelligence (AI) is one of these advancements that is widely heralded as a revolutionary technology in Human Resource Management (HRM). Professionals and scholars have discussed the bright role of AI in HRM. However, deep analysis of this technology in the HR process is still scarce.Therefore, the main goal of this thesis is to investigate the status of AI in HRM and derive concrete implementation key factors. Through, first, building an academic framework for AI in HRM; second, analyzing the most commonly used AI applications in HR process; third, identifying the optimal ways to transfer the knowledge of AI implementation processes.The methodology used for the investigation combines a systematic literature review and a qualitative research technique. As a basis and preparatory measure to address the research questions, an extensive literature analysis in the AI-HRM field was carried out, with a particular focus on publications of AI in HRM, HR-Big data analysis, AI applications/solutions in HRM and AI implementation. Along similar lines, the author published papers in several conference proceedings to improve the maturity of research questions.Based on this work, the published studies illustrate the gap between the promise and reality of AI in HRM, taking into account the requirements of AI implementation as well as the applications and limitations. Subsequently, HR experts and AI consultants, who had already gained first-hand experience with HR processes in an AI environment, were interviewed to find out the truth of the dominant AI's application in HR process.The main findings of this thesis are the derivation of a complete definition of AI in HRM as well as the status of the adoption strategies of AI applications in HRM. As a further result, it explores the usefulness and limitations of chatbots in the recruitment processes in India. In addition, derived the key factors to transfer the knowledge of AI implementation process to HR managers and employees. Challenges associated with AI implementation in the HR process and the impact of COVID-19 on AI implementation were also concluded.