Daniel Eyers

46 papers receiving 1.3k citations

Daniel Eyers's Hit Papers

Exploring collaborative decision-making: A quasi-experimental study of human and Generative AI interaction 2024 · 68 citations
680+1Years since publication204060

Peers

Daniel Eyers
Comparison fields: 5 of 92
  • Industrial and Manufacturing Engineering 588
  • Automotive Engineering 639
  • Strategy and Management 465
  • Management Information Systems 270
  • Management of Technology and Innovation 124
Replace Siavash H. Khajavi with:
Siavash H. Khajavi Finland
Saeed Mansour Iran
Anbesh Jamwal India
Vishal Ashok Wankhede India
Rajeev Agrawal India
Tan Ching Ng Malaysia
Jian Qin United Kingdom
Shaw C. Feng United States
Mohd Suhaib India
Elias Ribeiro da Silva Denmark
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Citations per year

Countries citing papers authored by Daniel Eyers

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Eyers

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Daniel Eyers, 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 Eyers Line = papers co-authored together Daniel Eyers links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 48 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020258
2 2017171
3 201794
4 201989
5 201880
6 201073
7 201972
8 201568
9
Exploring collaborative decision-making: A quasi-experimental study of human and Generative AI interaction
Hit paper breakdown →
202468
10 201938
11 201329
12 202124
13 202122
14 201822
15 201020
16 201218
17 202118
18 202116
19 201713
20 202011

About Daniel Eyers

Daniel Eyers is a scholar working on Industrial and Manufacturing Engineering, Automotive Engineering, Strategy and Management, Management of Technology and Innovation and Management Information Systems, having authored 48 papers that have together received 1.3k indexed citations. Recurring topics across this work include Additive Manufacturing and 3D Printing Technologies (24 papers), Manufacturing Process and Optimization (18 papers), Sustainable Supply Chain Management (14 papers), Digital Transformation in Industry (11 papers), Product Development and Customization (11 papers), Big Data and Business Intelligence (5 papers), Design Education and Practice (4 papers) and Quality and Supply Management (4 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (588 citations), Automotive Engineering (639 citations), Strategy and Management (465 citations), Management Information Systems (270 citations) and Management of Technology and Innovation (124 citations). Daniel Eyers has collaborated with scholars based in United Kingdom, Italy and China. Frequent co-authors include Andrew Potter, Mia Delić, Jonathan Gosling, Krassimir Dotchev, Emrah Demir, Josip Mikulić, Mohamed Mohamed Naim, Michael J. Ryan, Laura Purvis and Paolo Minetola. Their work appears in journals such as The International Journal of Logistics Management, International Journal of Operations & Production Management, Production Planning & Control, International Journal of Production Research and Journal of Manufacturing Technology Management.

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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