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Explaining article influence: capturing article citability and its dynamic effects
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DOI：10.1007/s11747-014-0392-7.png)
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摘要
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Researchers from diverse disciplines have examined the many factors that contribute to the influence of published research papers. Such influence dynamics are in essence a marketing of science issue. In this paper, we propose that in addition to known established, overt drivers of influence such as journal, article, author, and Matthew effects, a latent factor citability influences the eventual impact of a paper. Citability is a mid-range latent variable that captures the changing relationship of an article to a field. Our analysis using a discretized Tobit model with hidden Markov processes suggests that there are two states of citability, and these dynamic states determine eventual influence of a paper. Prior research in marketing has relied on models where the various effects such as author and journal effects are deemed static. Unlike ours, these models fail to capture the continuously evolving impact dynamics of a paper and the differential effect of the various drivers that depend on the latent state a paper is in at any given point of time. Our model also captures the impact of uncitedness, which other models fail to do. Our model is estimated using articles published in seven leading marketing journals during the years 1996-2003. Findings and implications are discussed.
Keyword:
Citation
Citability
Matthew effect
Uncitedness
Scientometrics
Hidden Markov model
Tobit
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.1
论文数:
3.4K
被引数:
2.2W
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