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.
The Emergence of Deepfake Technology: A Review
2019517 citationsMika WesterlundSHILAP Revista de lepidopterologíaprofile →
A systematic review of living lab literature
2018300 citationsMokter Hossain, Seppo Leminen et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Mika Westerlund
Since
Specialization
Citations
This map shows the geographic impact of Mika Westerlund'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 Mika Westerlund with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mika Westerlund more than expected).
This network shows the impact of papers produced by Mika Westerlund. 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 Mika Westerlund. The network helps show where Mika Westerlund may publish in the future.
Co-authorship network of co-authors of Mika Westerlund
This figure shows the co-authorship network connecting the top 25 collaborators of Mika Westerlund.
A scholar is included among the top collaborators of Mika Westerlund 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 Mika Westerlund. Mika Westerlund is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Westerlund, Mika. (2019). The Emergence of Deepfake Technology: A Review. SHILAP Revista de lepidopterología. 9(11). 39–52.517 indexed citations breakdown →
McPhee, Chris, Seppo Leminen, Dimitri Schuurman, Mika Westerlund, & K.R.E. Huizingh. (2016). Editorial : Living Labs and User Innovation. Technology Innovation Management Review. 6(1). 3–6.3 indexed citations
10.
McPhee, Chris, Seppo Leminen, Dimitri Schuurman, Mika Westerlund, & K.R.E. Huizingh. (2015). Living Labs and User Innovation. Technology Innovation Management Review. 5(12).1 indexed citations
11.
McPhee, Chris, Mika Westerlund, & Seppo Leminen. (2013). Living Labs and Crowdsourcing. Theseus (Ammattikorkeakoulujen).1 indexed citations
Westerlund, Mika, Risto Rajala, & Arto Rajala. (2011). Security service adoption in B2B context: Do clients and providers hold congruent views?. 11(3). 42–51.2 indexed citations
15.
Westerlund, Mika, Risto Rajala, & Arto Rajala. (2011). International Academy of Business and Economics Annual Conference (IABE) 2011, Las Vegas, USA, October 16-18, 2011.1 indexed citations
Westerlund, Mika & Risto Rajala. (2006). Innovative Business Models and Offerings Based on Inconclusive Evidence. SHILAP Revista de lepidopterología.3 indexed citations
18.
Rajala, Risto, et al.. (2006). Determinants of OSS Revenue Model Choices. Journal of the Association for Information Systems. 1839–1850.2 indexed citations
19.
Westerlund, Mika, et al.. (2006). Managing Networked Business Models in the Software Industry. 7(1). 53–57.3 indexed citations
20.
Rajala, Risto & Mika Westerlund. (2005). Business Models: A New Perspective on Knowledge-Intensive Services in the Software Industry.. Journal of the Association for Information Systems. 10.10 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.