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.
An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes
2022162 citationsWaymond Rodgers, Abraham Stefanidis et al.Human Resource Management Reviewprofile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
citations ·
hero ref
Countries citing papers authored by Waymond Rodgers
Since
Specialization
Citations
This map shows the geographic impact of Waymond Rodgers'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 Waymond Rodgers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Waymond Rodgers more than expected).
This network shows the impact of papers produced by Waymond Rodgers. 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 Waymond Rodgers. The network helps show where Waymond Rodgers may publish in the future.
Co-authorship network of co-authors of Waymond Rodgers
This figure shows the co-authorship network connecting the top 25 collaborators of Waymond Rodgers.
A scholar is included among the top collaborators of Waymond Rodgers 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 Waymond Rodgers. Waymond Rodgers is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Rodgers, Waymond, et al.. (2022). An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes. Human Resource Management Review. 33(1). 100925–100925.162 indexed citations breakdown →
Rodgers, Waymond, et al.. (2020). Does the Role of Board Commissioners Can Increase Executive Compensation and Company Financial Performance in Indonesia Commercial Banking. Solid State Technology. 63(3). 3155–3179.1 indexed citations
Rodgers, Waymond, et al.. (2019). THE INFLUENCE OF BOARD OF COMMISSIONERS STRUCTURE ON CORPORATE SUSTAINABILITY CONCERNS AND FINANCIAL PERFORMANCE IN INDONESIAN COMMERCIAL BANKS. Journal of Advanced Research in Dynamic and Control Systems. 11. 352–371.1 indexed citations
Guiral, Andrés, Emiliano Ruiz Barbadillo, & Waymond Rodgers. (2008). To What Extent is the Going Concern Judgment Influenced by the Self-Fulfilling Prophecy?. SSRN Electronic Journal.1 indexed citations
13.
Guiral, Andrés, et al.. (2008). A Cognitive Model Testing Moral Seduction Theory: Unconscious Bias and the Role Played by Expertise. eScholarship (California Digital Library). 30(30).1 indexed citations
Rodgers, Waymond, et al.. (2007). Do Business Schools' Theories Negatively Influence Students Ethical Positions?. Borås Academic Digital Archive (University of Borås). 259–274.1 indexed citations
16.
Rodgers, Waymond, et al.. (2005). Auditors' forecasting in going concern decisions: framing, confidence and information processing. 1.
Rodgers, Waymond & Thomas J. Housel. (2004). The Effects of Environmental Risk Information on Auditors' Decisions about Prospective Financial Statements. SSRN Electronic Journal.1 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.