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.
Relationship between quality management practices and innovation
2012491 citationsDong‐Young Kim, Vinod Kumar et al.Journal of Operations Managementprofile →
e-Government Adoption Model (GAM): Differing service maturity levels
2010421 citationsMahmud Akhter Shareef, Vinod Kumar et al.profile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
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This map shows the geographic impact of Vinod Kumar'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 Vinod Kumar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vinod Kumar more than expected).
This network shows the impact of papers produced by Vinod Kumar. 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 Vinod Kumar. The network helps show where Vinod Kumar may publish in the future.
Co-authorship network of co-authors of Vinod Kumar
This figure shows the co-authorship network connecting the top 25 collaborators of Vinod Kumar.
A scholar is included among the top collaborators of Vinod Kumar 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 Vinod Kumar. Vinod Kumar is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kim, Dong‐Young, Vinod Kumar, & Uma Kumar. (2012). Relationship between quality management practices and innovation. Journal of Operations Management. 30(4). 295–315.491 indexed citations breakdown →
Murphy, Steven A., et al.. (2009). Unit of Analysis: A Case for Performance Measurement in Supply Chain Management. SSRN Electronic Journal.3 indexed citations
11.
Kumar, Vinod, et al.. (2009). Porter's Generic Strategies and their Application in Supply Chain Management. 30(7).1 indexed citations
12.
Maheshwari, Bharat, Raili Pollanen, & Vinod Kumar. (2009). Contrasting Information Systems and Financial Executive Perspective on Implementing Regulatory Controls. Journal of the Association for Information Systems. 401.1 indexed citations
13.
Kim, Dong‐Young, Vinod Kumar, & Steven A. Murphy. (2008). EUROPEAN FOUNDATION FOR QUALITY MANAGEMENT (EFQM) BUSINESS EXCELLENCE MODEL: A LITERATURE REVIEW AND FUTURE RESEARCH AGENDA. 29(7).3 indexed citations
14.
Kim, Dong‐Young, Vinod Kumar, & Young‐Ha Hwang. (2007). COMPARISON BETWEEN EFQM BUSINESS EXCELLENCE MODEL AND INTELLECTUAL CAPITAL MANAGEMENT: THE CASE OF A GOVERNMENT- SPONSORED LARGE R&D ORGANIZATION. 28(7).1 indexed citations
15.
Maheshwari, Bharat, et al.. (2007). E-GOVERNMENT PORTAL EFFECTIVENESS: MANAGERIAL CONSIDERATIONS FOR DESIGN AND DEVELOPMENT. 28(7).13 indexed citations
16.
Kumar, Vinod, et al.. (2007). ON MEASURING AND SUCCESSFUL IMPLEMENTATION OF PROCESS- ORIENTATION: A LITERATURE SYNTHESIS. 28(7).2 indexed citations
17.
Kumar, Vinod, Bhasker Mukerji, & Irfan Butt. (2007). Factors for Successful e‑Government Adoption: a Conceptual Framework. 5(1).248 indexed citations
18.
Misra, Subhas Chandra, Vinod Kumar, & Uma Kumar. (2005). Goal-Driven Measurement Framework for Software Innovation Processes.. Software Engineering Research and Practice. 710–716.2 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.