Nina Mažar

132 total papers · 5.6k total citations
62 papers, 2.9k citations indexed

About

Nina Mažar is a scholar working on Safety Research, General Decision Sciences and Economics and Econometrics. According to data from OpenAlex, Nina Mažar has authored 62 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Safety Research, 17 papers in General Decision Sciences and 16 papers in Economics and Econometrics. Recurrent topics in Nina Mažar's work include Experimental Behavioral Economics Studies (19 papers), Decision-Making and Behavioral Economics (17 papers) and Psychology of Moral and Emotional Judgment (14 papers). Nina Mažar is often cited by papers focused on Experimental Behavioral Economics Studies (19 papers), Decision-Making and Behavioral Economics (17 papers) and Psychology of Moral and Emotional Judgment (14 papers). Nina Mažar collaborates with scholars based in United States, Canada and United Kingdom. Nina Mažar's co-authors include Dan Ariely, Chen‐Bo Zhong, Uri Gneezy, George Loewenstein, On Amir, Pankaj Aggarwal, Sonya Sachdeva, Jennifer Jordan, Mara Mather and Nichole R. Lighthall and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Clinical Investigation and Journal of Marketing.

In The Last Decade

Nina Mažar

53 papers receiving 2.7k citations

Hit Papers

Do Green Products Make Us... 2010 2026 2015 2020 2010 200 400 600

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Nina Mažar 709 690 641 606 557 62 2.9k
Kimberly A. Wade‐Benzoni 411 0.6× 1.0k 1.5× 314 0.5× 213 0.4× 309 0.6× 55 2.8k
Anuj Shah 439 0.6× 791 1.1× 444 0.7× 533 0.9× 333 0.6× 48 3.2k
Denis Hilton 392 0.6× 1.6k 2.3× 355 0.6× 422 0.7× 731 1.3× 89 4.4k
Ziv Carmon 286 0.4× 777 1.1× 1.3k 2.1× 501 0.8× 364 0.7× 43 3.0k
Cynthia Cryder 306 0.4× 1.2k 1.8× 731 1.1× 355 0.6× 356 0.6× 29 3.1k
Katherine White 216 0.3× 1.1k 1.6× 1.3k 2.0× 318 0.5× 382 0.7× 41 3.7k
Amar Cheema 260 0.4× 1.2k 1.7× 1.1k 1.8× 395 0.7× 289 0.5× 36 3.6k
Stefan Palan 392 0.6× 730 1.1× 212 0.3× 475 0.8× 383 0.7× 42 2.7k
Paul Webley 381 0.5× 813 1.2× 357 0.6× 1.3k 2.2× 154 0.3× 95 3.8k
Gal Zauberman 230 0.3× 704 1.0× 936 1.5× 571 0.9× 472 0.8× 77 3.3k

Countries citing papers authored by Nina Mažar

Since Specialization
Citations

This map shows the geographic impact of Nina Mažar'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 Nina Mažar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nina Mažar more than expected).

Fields of papers citing papers by Nina Mažar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nina Mažar. 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 Nina Mažar. The network helps show where Nina Mažar may publish in the future.

Co-authorship network of co-authors of Nina Mažar

This figure shows the co-authorship network connecting the top 25 collaborators of Nina Mažar. A scholar is included among the top collaborators of Nina Mažar 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 Nina Mažar. Nina Mažar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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.

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