Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
The adoption of artificial intelligence in human resources management practices
202456 citationsNishad Nawaz, Vijayakumar Gajenderan et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Nishad Nawaz's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nishad Nawaz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nishad Nawaz more than expected).
This network shows the impact of papers produced by Nishad Nawaz. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nishad Nawaz. The network helps show where Nishad Nawaz may publish in the future.
Co-authorship network of co-authors of Nishad Nawaz
This figure shows the co-authorship network connecting the top 25 collaborators of Nishad Nawaz.
A scholar is included among the top collaborators of Nishad Nawaz based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Nishad Nawaz. Nishad Nawaz is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Nawaz, Nishad, et al.. (2020). A Kaleidoscopic View on the Impact of Financial Knowledge on Investment Decision of Individual Investors. SSRN Electronic Journal.
8.
Nawaz, Nishad. (2020). Assessment of Emotional Intelligence Among the Primary Schools Teachers: A Comparative Study. SSRN Electronic Journal.5 indexed citations
9.
Nawaz, Nishad. (2020). HRD Practices Impact on Organizational Climate of Insurance Sector. SSRN Electronic Journal.
10.
Nawaz, Nishad. (2020). Entrepreneurship in the Age of Artificial Intelligence and Robotics. SSRN Electronic Journal.1 indexed citations
11.
Nawaz, Nishad. (2020). Impact of Leadership on Organizational Performance in Service Organizations. SSRN Electronic Journal.
12.
Nawaz, Nishad. (2019). How Far Have We Come With The Study Of Artificial Intelligence For Recruitment Process. International journal of scientific and technology research. 8(7). 488–493.12 indexed citations
13.
Nawaz, Nishad. (2017). A Comprehensive Literature Review of the Digital HR Research Filed. SSRN Electronic Journal. 7(4). 15–20.9 indexed citations
Nawaz, Nishad, et al.. (2017). Human Resource Information System: A Review of Previous Studies. SSRN Electronic Journal.2 indexed citations
16.
Nawaz, Nishad, et al.. (2015). An Empirical Study on Employee Competence in Relation to Emotional Intelligence in Bahrain. SSRN Electronic Journal.2 indexed citations
17.
Nawaz, Nishad, et al.. (2014). Review of Knowledge Management in Higher Education Institutions. SSRN Electronic Journal.14 indexed citations
18.
Nawaz, Nishad, et al.. (2014). An Effective Teaching Pedagogy in Changing Business Education. SSRN Electronic Journal.1 indexed citations
19.
Nawaz, Nishad, et al.. (2014). Automation of the HR Functions Enhance the Professional Efficiency of the HR Professionals - A Review. SSRN Electronic Journal.2 indexed citations
20.
Nawaz, Nishad. (2013). Impact of Talent Mobility on Employee Performance in Software Companies, Bangalore. SSRN Electronic Journal.4 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.