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.
This map shows the geographic impact of Paul E. Green'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 Paul E. Green with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paul E. Green more than expected).
This network shows the impact of papers produced by Paul E. Green. 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 Paul E. Green. The network helps show where Paul E. Green may publish in the future.
Co-authorship network of co-authors of Paul E. Green
This figure shows the co-authorship network connecting the top 25 collaborators of Paul E. Green.
A scholar is included among the top collaborators of Paul E. Green 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 Paul E. Green. Paul E. Green is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Carmone, Frank J. & Paul E. Green. (2009). Multidimensional scaling and related techniques in marketing analysis. Allyn and Bacon eBooks.24 indexed citations
Lenk, Peter, Wayne S. DeSarbo, Paul E. Green, & Martin R. Young. (1996). Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs. SSRN Electronic Journal.17 indexed citations
7.
Green, Paul E., Abba Μ. Krieger, & Catherine Μ. Schaffer. (1993). A Hybrid Conjoint Model With Individual-Level Interaction Estimation. Advances in consumer research. 20(1). 149–154.9 indexed citations
Green, Paul E. & Catherine Μ. Schaffer. (1991). Importance Weight Effects on Self-Explicated Preference Models: Some Empirical Findings. ACR North American Advances.5 indexed citations
Green, Paul E., Stephen M. Goldberg, & James B. Wiley. (1983). A Cross-Validation Test of Hybrid Conjoint Models. ACR North American Advances.24 indexed citations
Green, Paul E. & Wayne S. DeSarbo. (1980). Two Models For Representing Unrestricted Choice Data. SSRN Electronic Journal.1 indexed citations
14.
Green, Paul E.. (1974). On the Design of Multiattribute Choice Experiments Involving Large Numbers of Factors Or Factor Levels. ACR North American Advances.4 indexed citations
15.
Green, Paul E. & Yoram Wind. (1973). Multiattribute decisions in marketing : a measurement approach.194 indexed citations
Green, Paul E. & Vithala R. Rao. (1972). Applied multidimensional scaling : a comparison of approaches and algorithms. Holt, Rinehart and Winston eBooks.103 indexed citations
18.
Green, Paul E., et al.. (1970). A note on the Spaeth-Guthery multidimensional scaling analysis of synthetic data. Marketing Science Institute eBooks.1 indexed citations
19.
Green, Paul E. & William T. Morris. (1970). Individual difference models in multidimensional scaling : an empirical comparison. Marketing Science Institute eBooks.1 indexed citations
20.
Green, Paul E. & Vithala R. Rao. (1970). Nonmetric approaches to multivariate analysis in marketing. Marketing Science Institute eBooks.8 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.