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Learning Resource Centre Monthly Bulletin |
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Learning Resource Centre Monthly Bulletin |
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| New Books |
 117657 Innovations in Human Resource Management By Sistare Hannah S |  115999 Human Resource Analytics : Strategic Decision Maki By Uppal, Nishant |  115899 Competency-Based Human Resource Management By Roy, Anindya Basu |
| Click Here to learn about the Recent Books added to our collection on different topics of Management. |
| Articles |
| Leadership Training for Department Chairs: Integrating Formal Training, Experiential Learning, and Mentorship. By Stawnychko, Leda Leadership Training for Department Chairs: Integrating Formal Training, Experiential Learning, and Mentorship. Abstract :The study explores the link between formal training, experiential learning, mentorship, and the leadership development of department chairs at a Canadian university. Employing a framework analysis approach grounded in transformative learning theory, data from 17 semi‐structured interviews revealed that formal training imparted essential knowledge and skills to new chairs. However, the training`s influence on the chairs` development varied, depending on individual career stages and previous leadership experiences. Participants identified experiential learning as a vital element of their leadership development, with prior leadership roles providing a solid base for a successful transition. Mentorship emerged as a transformative instrument, offering timely developmental opportunities through exchanges with experienced leaders. The study concludes that strategically combining learning approaches can enhance institutional leadership capacity and facilitate faculty members` transition into department chair roles, particularly in the context of the post‐COVID‐19 pandemic and the ongoing leadership crisis. | ||
| Women`s authorship in international human resource management research: Implications for responsible management education and emerging scholars. By Cooke, Fang Lee Human Resource Management Review. Sep2025, Vol. 35 Issue 3, pN.PAG-N.PAG. 1p. Abstract :Like many professional occupations, the participation of female scholars has steadily increased since the 1990s in the human resource management (HRM) fields. While it is widely acknowledged that workforce diversity brings different perspectives, we lack insight into the impact of such changes. In this paper, we explore the implications of gender in the authorship of scholarly articles for the knowledge base of this field, using an example of a content analysis of 890 articles in the international HRM field. We discuss the implications of gender in scholarly work both within and beyond the HRM field. We draw connections to the sustainability development agenda and responsible management education from a gender perspective and offer suggestions for the career development of emerging and future scholars. • The participation of female scholars has steadily increased in the field of IHRM but gender remains an under-research topic. • There is a discernible gender segregation in the engagement with IHRM research topics. • Gender research maybe undervalued in their scholarship and disadvantage women academics in their promotion. • Gender of the researchers influences the development of the knowledge base of the field. • Management education and research training should adopt a more inclusive approach to promoting gender-related topics. | ||
| The Strategy of Human Resource Management Based on the K-Means Clustering Algorithm. By Li, Lirong International Journal of High Speed Electronics & Systems. Sep2025, Vol. 34 Issue 3, p1-15. 15p. Abstract :With the continuous accumulation of enterprise data and the continuous development of technology, data-driven decision-making has become an important trend in enterprise management. Human resource management, as an important component of enterprise management, also requires the use of data analysis tools to optimize the decision-making process. Traditional human resource management strategy algorithms are often based on experience and intuition, lacking scientific data support. These algorithms often struggle to achieve ideal results when dealing with large and complex human resource data. This study aims to provide more scientific and accurate data support for human resource management by introducing the K-means algorithm. The main research purpose of this paper is to detect and identify the data in enterprise human resource management. Data mining can realize the comprehensive detection and analysis of poetry, but the application of data mining algorithms in human resource management data processing is not much. This paper hopes to analyze the anomaly of human resource data statistics by data mining method. According to the characteristics of the K-means algorithm and DBSCAN algorithm, a hybrid K-means algorithm is proposed in this study, which can automatically generate a more appropriate K value and realize parameter self-adaptation with less manual intervention. The optimized clustering K-means algorithm obtains the initial value of DBSCAN through calculation, and then the abnormal data of human resources statistics can be obtained through the second clustering calculation. The optimized K-means algorithm can detect human resource statistics data in real time, and give a new idea for human resource analysis of data anomalies. | ||
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| News |
| The human energy behind a Maharatna: Inside ONGC’s evolving HR strategy By Economic Times; Jule 30, 2025 |
| Breakdown Maintenance Vs Preventive Maintenance: A paradigm shift in employee wellbeing By Economic Times; 30 June, 2025 |
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