Daniel P. Gross

649 citations
32 papers · 247 · h-index 10

Impact in

Papers in

Daniel P. Gross

28 papers receiving 232 citations

Peers

Daniel P. Gross
Comparison fields: 5 of 78
  • Computer Science Applications 26
  • Economics and Econometrics 97
  • Management of Technology and Innovation 25
  • Safety Research 29
  • Information Systems and Management 14
Replace Maciej Workiewicz with:
Maciej Workiewicz France
Ernest N. Biktimirov Canada
Devendra Kodwani United Kingdom
Benjamin J. Bates United States
Nozibele Gcora South Africa
Ad Notten Netherlands
Irene Comeig Spain
James A. Dearden United States
Wolfram Schrettl Germany
Jaya Josie South Africa
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Citations per field
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Citations per year

Countries citing papers authored by Daniel P. Gross

Since Specialization
Citations

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

Fields of papers citing papers by Daniel P. Gross

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel P. Gross. 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 Daniel P. Gross. The network helps show where Daniel P. Gross may publish in the future.

Co-authors

The 12 scholars most cited alongside Daniel P. Gross, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel P. Gross Line = papers co-authored together Daniel P. Gross links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202342
2 201727
3 201926
4 201522
5 202014
6 202412
7 201412
8
Pop!: Why Bubbles Are Great For The Economy
200711
9 202010
10 20229
11 20189
12 20217
13 20207
14 20205
15 20165
16 20205
17 20235
18 20204
19 20143
20
Dumb Money: How Our Greatest Financial Minds Bankrupted the Nation
20093

About Daniel P. Gross

Daniel P. Gross is a scholar working on Economics and Econometrics, Strategy and Management, Management Science and Operations Research, Management of Technology and Innovation and Information Systems and Management, having authored 32 papers that have together received 247 indexed citations. Recurring topics across this work include Economic Growth and Productivity (7 papers), Innovation Policy and R&D (6 papers), Intellectual Property and Patents (4 papers), Digital Platforms and Economics (4 papers), Experimental Behavioral Economics Studies (3 papers), Auction Theory and Applications (3 papers), Research, Science, and Academia (3 papers) and Innovation and Knowledge Management (3 papers). The work is most often cited by research in Computer Science Applications (26 citations), Economics and Econometrics (97 citations), Management of Technology and Innovation (25 citations), Safety Research (29 citations) and Information Systems and Management (14 citations). Daniel P. Gross has collaborated with scholars based in United States and France. Frequent co-authors include Bhaven N. Sampat, James Feigenbaum, Kevin Clark, Steven W. Jones, Maureen H. Beresini, Till Maurer, Marya Liimatta, Amy Gustafson, Yichin Liu and Nicholas J. Skelton. Their work appears in journals such as Management Science, Research Policy, The RAND Journal of Economics, The Review of Economics and Statistics and American Economic Review.

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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