Will Sutherland

16 papers receiving 1.2k citations

Will Sutherland's Hit Papers

Algorithmic management in a work context 2021 · 187 citations
1870+2+5Years since publication100200300400

Peers

Will Sutherland
Comparison fields: 5 of 84
  • Marketing 627
  • Sociology and Political Science 826
  • Automotive Engineering 181
  • Safety Research 98
  • Human-Computer Interaction 63
Replace Gemma Newlands with:
Gemma Newlands Norway
Alex Rosenblat United States
Mareike Möhlmann United States
Lior Zalmanson Israel
Isis Hjorth United Kingdom
Jason Chan United States
Manon Arcand Canada
Siliang Tong China
Daniel Schlagwein Australia
Lars Meyer‐Waarden France
Will Sutherland relative to Gemma Newlands Norway Gemma Newlands's profile →
Citations per field
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Gemma Newlands · 1×
Citations per year

Countries citing papers authored by Will Sutherland

Since Specialization
Citations

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

Fields of papers citing papers by Will Sutherland

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 19 scholars most cited alongside Will Sutherland, 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 Will Sutherland Line = papers co-authored together Will Sutherland links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1
The sharing economy and digital platforms: A review and research agenda
Hit paper breakdown →
2018459
2
Algorithmic management in a work context
Hit paper breakdown →
2021187
3 2019152
4 2019123
5 201774
6 201957
7 201952
8 202149
9 202048
10 202117
11 201913
12 202011
13 20214
14 20212
15 20241
16 20231

About Will Sutherland

Will Sutherland is a scholar working on Sociology and Political Science, Information Systems, Marketing, Information Systems and Management and Automotive Engineering, having authored 16 papers that have together received 1.3k indexed citations. Recurring topics across this work include Digital Economy and Work Transformation (7 papers), Sharing Economy and Platforms (6 papers), Scientific Computing and Data Management (5 papers), Research Data Management Practices (4 papers), Transportation and Mobility Innovations (3 papers), Team Dynamics and Performance (2 papers), Innovative Human-Technology Interaction (2 papers) and Digital and Traditional Archives Management (1 paper). The work is most often cited by research in Marketing (627 citations), Sociology and Political Science (826 citations), Automotive Engineering (181 citations), Safety Research (98 citations) and Human-Computer Interaction (63 citations). Will Sutherland has collaborated with scholars based in United States, Canada and Denmark. Frequent co-authors include Mohammad Hossein Jarrahi, Sarah Beth Nelson, Steve Sawyer, Michael Dunn, Min Kyung Lee, Gemma Newlands, Christine T. Wolf, Seyram Avle, Robert Soden and David Ribes. Their work appears in journals such as Proceedings of the ACM on Human-Computer Interaction, Computer Supported Cooperative Work (CSCW), International Journal of Information Management, Human Behavior and Emerging Technologies and Big Data & Society.

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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