David T. Newman

518 total citations · 1 hit paper
6 papers, 346 citations indexed

About

David T. Newman is a scholar working on Safety Research, Cognitive Neuroscience and Information Systems and Management. According to data from OpenAlex, David T. Newman has authored 6 papers receiving a total of 346 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Safety Research, 3 papers in Cognitive Neuroscience and 2 papers in Information Systems and Management. Recurrent topics in David T. Newman's work include Ethics in Business and Education (2 papers), Ethics and Social Impacts of AI (2 papers) and Experimental Behavioral Economics Studies (2 papers). David T. Newman is often cited by papers focused on Ethics in Business and Education (2 papers), Ethics and Social Impacts of AI (2 papers) and Experimental Behavioral Economics Studies (2 papers). David T. Newman collaborates with scholars based in United States. David T. Newman's co-authors include Nathanael J. Fast, Derek Harmon, Scott S. Wiltermuth, Jared L. Peifer, Peter H. Kim, Hengchen Dai, Jennifer M. Logg, Yongwei Yang, Patrick Gage Kelley and Courtney Heldreth and has published in prestigious journals such as Academy of Management Review, Organizational Behavior and Human Decision Processes and Current Opinion in Psychology.

In The Last Decade

David T. Newman

6 papers receiving 331 citations

Hit Papers

align trajectories

What are hit papers?

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.

When eliminating bias isn’t fair: Algorithmic reductionism and procedural justice in human resource decisions

2020 296 citations David T. Newman, Nathanael J. Fast et al. Organizational Behavior and Human Decision Processes profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
David T. Newman United States	5	152	112	89	51	46	6	346
Alina Köchling Germany	5	124 0.8×	87 0.8×	123 1.4×	21 0.4×	22 0.5×	10	328
David L. Tomczak United States	6	68 0.4×	129 1.2×	166 1.9×	11 0.2×	86 1.9×	6	392
Ifeoma Ajunwa United States	10	135 0.9×	150 1.3×	44 0.5×	13 0.3×	27 0.6×	24	358
Jack H. Zhang Singapore	5	52 0.3×	65 0.6×	127 1.4×	26 0.5×	95 2.1×	6	350
Meghan E. Norris Canada	5	81 0.5×	96 0.9×	17 0.2×	16 0.3×	40 0.9×	11	362
Yalçın Açıkgöz United States	5	63 0.4×	62 0.6×	152 1.7×	13 0.3×	32 0.7×	16	289
Ainslie E. Schultz United States	5	42 0.3×	62 0.6×	27 0.3×	23 0.5×	38 0.8×	11	266
Thorsten Busch Switzerland	4	76 0.5×	87 0.8×	56 0.6×	27 0.5×	8 0.2×	5	219
Michael Gerlich Switzerland	9	49 0.3×	100 0.9×	18 0.2×	18 0.4×	23 0.5×	27	350
Johannes Boegershausen Netherlands	7	31 0.2×	167 1.5×	86 1.0×	48 0.9×	81 1.8×	11	360

Countries citing papers authored by David T. Newman

Since Specialization

Citations

This map shows the geographic impact of David T. Newman'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 David T. Newman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David T. Newman more than expected).

Fields of papers citing papers by David T. Newman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David T. Newman. 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 David T. Newman. The network helps show where David T. Newman may publish in the future.

Co-authorship network of co-authors of David T. Newman

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

All Works

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6 of 6 papers shown

1.

Newman, David T., Nathanael J. Fast, & Derek Harmon. (2020). When eliminating bias isn’t fair: Algorithmic reductionism and procedural justice in human resource decisions. Organizational Behavior and Human Decision Processes. 160. 149–167. 296 indexed citations breakdown →

2.

Kelley, Patrick Gage, et al.. (2020). "Happy and Assured that life will be easy 10years from now.": Perceptions of Artificial Intelligence in 8 Countries.. 6 indexed citations

3.

Peifer, Jared L. & David T. Newman. (2020). Making the business case for corporate social responsibility and perceived trustworthiness: A cross‐stakeholder analysis. Business and Society Review. 125(2). 161–181. 11 indexed citations

4.

Dai, Hengchen, et al.. (2019). The Future of Work: How People Respond to Digital Actors and Algorithms. Academy of Management Proceedings. 2019(1). 14111–14111. 2 indexed citations

5.

Kim, Peter H., Scott S. Wiltermuth, & David T. Newman. (2019). A Theory of Ethical Accounting and Its Implications for Hypocrisy in Organizations. Academy of Management Review. 46(1). 172–191. 13 indexed citations

6.

Wiltermuth, Scott S., et al.. (2015). The consequences of dishonesty. Current Opinion in Psychology. 6. 20–24. 18 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.

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