Factors affecting YouTube influencer marketing credibility: a heuristic-systematic model
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doi.org/10.1080/16522354.2018.1501146 →Countries where authors are citing Factors affecting YouTube influencer marketing credibility: a heuristic-systematic model
This map shows the geographic impact of Factors affecting YouTube influencer marketing credibility: a heuristic-systematic model. 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 Factors affecting YouTube influencer marketing credibility: a heuristic-systematic model with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Factors affecting YouTube influencer marketing credibility: a heuristic-systematic model more than expected).
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This network shows the impact of Factors affecting YouTube influencer marketing credibility: a heuristic-systematic model. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Factors affecting YouTube influencer marketing credibility: a heuristic-systematic model.
About Factors affecting YouTube influencer marketing credibility: a heuristic-systematic model
This paper, published in 2018, received 371 indexed citations . Written by Min Xiao, Rang Wang and Sylvia M. Chan‐Olmsted covering the research area of Sociology and Political Science and Communication. It is primarily cited by scholars working on Sociology and Political Science (313 citations), Marketing (156 citations), Information Systems and Management (84 citations), Literature and Literary Theory (57 citations) and Communication (44 citations). Published in Journal of Media Business Studies.
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.
This paper is also available at doi.org/10.1080/16522354.2018.1501146.