Access the full text.
Sign up today, get DeepDyve free for 14 days.
P. Leonardi, N. Contractor (2018)
Better people analyticsHarvard Business Review, 2018
(2020)
Editorial statement | information systems research
M. Seeber, M. Cattaneo, M. Meoli, P. Malighetti (2017)
Self-citations as strategic response to the use of metrics for career decisionsResearch Policy
Ertan Bütün, Mehmet Kaya, R. Alhajj (2017)
A Supervised Learning Method for Prediction Citation Count of Scientists in Citation Networks2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
L. Bornmann, Hans-Dieter Daniel (2006)
Selecting scientific excellence through committee peer review - A citation analysis of publications previously published to approval or rejection of post-doctoral research fellowship applicantsScientometrics, 68
B. Martinson, A. Crain, Melissa Anderson, R. Vries, B. Martinson (2009)
Institutions' Expectations for Researchers' Self-Funding, Federal Grant Holding, and Private Industry Involvement: Manifold Drivers of Self-Interest and Researcher BehaviorAcademic Medicine, 84
Dashun Wang, Chaoming Song, A. Barabási (2013)
Supplementary Materials for Quantifying Long-Term Scientific Impact
Shuai Xiao, Junchi Yan, Changsheng Li, Bo Jin, Xiangfeng Wang, Xiaokang Yang, Stephen Chu, H. Zha (2016)
On Modeling and Predicting Individual Paper Citation Count over Time
Lawrence Page, S. Brin, R. Motwani, T. Winograd (1999)
The PageRank Citation Ranking : Bringing Order to the Web, 98
Yi Bu, Dakota Murray, Jian Xu, Ying Ding, Peng Ai, Jinhua Shen, Fan Yang (2018)
Analyzing scientific collaboration with “giants” based on the milestones of careerProceedings of the Association for Information Science and Technology, 55
M. Biagioli (2016)
Watch out for cheats in citation gameNature, 535
B. Ponomariov, Hannes Toivanen (2014)
Knowledge flows and bases in emerging economy innovation systems: Brazilian research 2005–2009Research Policy, 43
G. Sabidussi (1966)
The centrality of a graph.Psychometrika, 31 4
Rui Yan, Cong Huang, Jie Tang, Yan Zhang, Xiaoming Li (2012)
To better stand on the shoulder of giants
B. Meyer, C. Choppy, J. Staunstrup, J. Leeuwen (2009)
ViewpointResearch evaluation for computer scienceCommun. ACM, 52
Sayed Shah, Myong-Soon Park, Wan-Sik Choi, Sajjad Hussain, A. Bashir (2013)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
R. Sinatra, Dashun Wang, Pierre Deville, Chaoming Song, A. Barabási (2016)
Quantifying the evolution of individual scientific impactScience, 354
A. Hoerl, R. Kennard (2000)
Ridge Regression: Biased Estimation for Nonorthogonal ProblemsTechnometrics, 42
A. Flanagin, Lisa Carey, P. Fontanarosa, S. Phillips, B. Pace, G. Lundberg, D. Rennie (1998)
Prevalence of articles with honorary authors and ghost authors in peer-reviewed medical journals.JAMA, 280 3
M. McNutt (2014)
The measure of research meritScience, 346
Janine Nahapiet, S. Ghoshal (1998)
Social Capital, Intellectual Capital, and the Organizational AdvantageAcademy of Management Review, 23
Aleksandre Asatiani, P. Malo (2020)
MIS Quarterly
L. Bromham, Russell Dinnage, Xia Hua (2016)
Interdisciplinary research has consistently lower funding successNature, 534
Daniel Acuna, S. Allesina, Konrad Kording (2012)
Future impact: Predicting scientific successNature, 489
F. Radicchi, Alexander Weissman, J. Bollen (2016)
Quantifying perceived impact of scientific publicationsJ. Informetrics, 11
(2012)
Washington DC: The George Washington University
Jian Wang (2013)
Citation time window choice for research impact evaluationScientometrics, 94
Feiheng Luo, Aixin Sun, Mojisola Erdt, Aravind Raamkumar, Y. Theng (2017)
Exploring prestigious citations sourced from top universities in bibliometrics and altmetrics: a case study in the computer science disciplineScientometrics, 114
Jong Kim, Kye-Hyeon Kim, Seungjin Choi (2015)
Supervised Learning
P. Merkus (2014)
Standing on shouldersEuropean Respiratory Journal, 43
D. Bertsimas, Erik Brynjolfsson, Shachar Reichman, J. Silberholz (2015)
OR Forum - Tenure Analytics: Models for Predicting Research ImpactOper. Res., 63
Marc Bertin, Iana Atanassova, Cassidy Sugimoto, V. Larivière (2016)
The linguistic patterns and rhetorical structure of citation context: an approach using n-gramsScientometrics, 109
Tian Yu, Guang Yu, Peng-Yu Li, Liang Wang (2014)
Citation impact prediction for scientific papers using stepwise regression analysisScientometrics, 101
Zhiya Zuo, Haifeng Qian, K. Zhao (2019)
Understanding the Field of Public Affairs through the Lens of Ranked Ph.D. Programs in the United StatesPolicy Studies Journal
M. Strathern (1997)
‘Improving ratings’: audit in the British University systemEuropean Review, 5
M. Kosmulski (2009)
A new Hirsch-type index saves time and works equally well as the original h-index
A. quick-service, Carla Arellano, Alexander DiLeonardo, Ignacio Felix (2017)
Using people analytics to drive business performance: A case study
L. Freeman (1977)
A set of measures of centrality based upon betweenness
T. Parsons (1938)
The Role of Theory in Social ResearchAmerican Sociological Review, 3
Tanmoy Chakraborty, Suhansanu Kumar, Pawan Goyal, Niloy Ganguly, Animesh Mukherjee (2014)
Towards a stratified learning approach to predict future citation countsIEEE/ACM Joint Conference on Digital Libraries
Xiaoli Li, Chuan-Sheng Foo, Kar Tew, See-Kiong Ng (2009)
Searching for Rising Stars in Bibliography Networks
T. Ayer, J. Chhatwal, O. Alagoz, C. E. Kahn, R. W. Woods, E. S. Burnside (2010)
Comparison of logistic regression and artificial neural network models in breast cancer risk estimation, 30
Zhiya Zuo, K. Zhao, Chaoqun Ni (2019)
Standing on the shoulders of giants? - Faculty hiring in information schoolsJ. Informetrics, 13
C. Stegehuis, N. Litvak, L. Waltman (2015)
Predicting the long-term citation impact of recent publicationsArXiv, abs/1503.09156
M. Matzke (2005)
F1000Prime recommendation of An index to quantify an individual's scientific research output.
Masoumeh Nezhadbiglari, Marcos Gonçalves, J. Almeida (2016)
Early prediction of scholar popularity2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL)
S. Taylor, B. Fender, K. Burke (2006)
Unraveling the Academic Productivity of Economists: The Opportunity Costs of Teaching and ServiceSouthern Economic Journal, 72
H. Mukougawa, T. Hirooka, T. Ichimaru, Y. Kuroda (2007)
on the Predictability
Luca Weihs, Oren Etzioni (2017)
Learning to Predict Citation-Based Impact Measures2017 ACM/IEEE Joint Conference on Digital Libraries (JCDL)
Yuxiao Dong, Reid Johnson, N. Chawla (2016)
Can Scientific Impact Be Predicted?IEEE Transactions on Big Data, 2
Ali Daud, Muhammad Ahmad, M. Malik, D. Che (2015)
Using machine learning techniques for rising star prediction in co-author networkScientometrics, 102
Stefan Hornbostel, Susan Böhmer, Bernd Klingsporn, J. Neufeld, M. Ins (2009)
Funding of young scientist and scientific excellenceScientometrics, 79
Radford Neal (2006)
Pattern Recognition and Machine LearningPattern Recognition and Machine Learning
A. Wilhite, E. Fong (2012)
Coercive Citation in Academic PublishingScience, 335
Samuel Way, D. Larremore, A. Clauset (2016)
Gender, Productivity, and Prestige in Computer Science Faculty Hiring NetworksProceedings of the 25th International Conference on World Wide Web
G. Abramo, Ciriaco D’Angelo, G. Felici (2019)
Predicting publication long-term impact through a combination of early citations and journal impact factorJ. Informetrics, 13
Emre Sarigöl, René Pfitzner, Ingo Scholtes, A. Garas, F. Schweitzer (2014)
Predicting scientific success based on coauthorship networksEPJ Data Science, 3
G. Sabidussi (1966)
The centrality index of a graphPsychometrika, 31
A. Rajkomar, Eyal Oren, Kai Chen, Andrew Dai, Nissan Hajaj, Michaela Hardt, Peter Liu, Xiaobing Liu, J. Marcus, Mimi Sun, Patrik Sundberg, H. Yee, Kun Zhang, Yi Zhang, Gerardo Flores, Gavin Duggan, Jamie Irvine, Quoc Le, Kurt Litsch, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, S. Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, N. Shah, A. Butte, M. Howell, Claire Cui, Greg Corrado, Jeffrey Dean (2018)
Scalable and accurate deep learning with electronic health recordsNPJ Digital Medicine, 1
Clint Kelly, M. Jennions (2006)
The h index and career assessment by numbers.Trends in ecology & evolution, 21 4
(1999)
Journal impact factor: a brief review.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne, 161 8
M. García-Pérez (2013)
Limited validity of equations to predict the future h indexScientometrics, 96
E. Aversa (1985)
Citation patterns of highly cited papers and their relationship to literature aging: A study of the working literatureScientometrics, 7
A. Raan (2004)
Sleeping Beauties in scienceScientometrics, 59
B. McInnes, Jannene McBride, N. Evans, David Lambert, A. Andrew (1999)
Emergence of Scaling in Random Networks
Samreen Ayaz, N. Masood, Muhammad Islam (2018)
Predicting scientific impact based on h-indexScientometrics, 114
Amin Mazloumian (2012)
Predicting Scholars' Scientific ImpactPLoS ONE, 7
Mikko Packalen, Jay Bhattacharya (2015)
Age and the Trying Out of New IdeasJournal of Human Capital, 13
Eldon Li, C. Liao, H. Yen (2013)
Co-authorship networks and research impact: A social capital perspectiveResearch Policy, 42
D. Price (1976)
A general theory of bibliometric and other cumulative advantage processesJ. Am. Soc. Inf. Sci., 27
E. Garfield (1955)
Citation Indexes for Science: A New Dimension in Documentation through Association of IdeasScience, 122
E. Gaidar (2003)
State and evolution
Zhiya Zuo, K. Zhao, D. Eichmann (2017)
The state and evolution of U.S. iSchools: From talent acquisitions to research outcomeJournal of the Association for Information Science and Technology, 68
Zhiya Zuo, K. Zhao (2018)
The more multidisciplinary the better? - The prevalence and interdisciplinarity of research collaborations in multidisciplinary institutionsJ. Informetrics, 12
F. Rijnsoever, L. Hessels (2011)
Factors associated with disciplinary and interdisciplinary research collaborationResearch Policy, 40
T. Ayer, J. Chhatwal, O. Alagoz, C. Kahn, R. Woods, E. Burnside (2010)
Informatics in radiology: comparison of logistic regression and artificial neural network models in breast cancer risk estimation.Radiographics : a review publication of the Radiological Society of North America, Inc, 30 1
L. Waltman (2015)
A review of the literature on citation impact indicatorsJ. Informetrics, 10
J. Katz, B. Martin (1997)
What is research collaborationResearch Policy, 26
Jasleen Kaur, Emilio Ferrara, F. Menczer, A. Flammini, F. Radicchi (2014)
Impact, productivity, and scientific excellenceJ. Informetrics, 9
Rui Yan, Jie Tang, Xiaobing Liu, Dongdong Shan, Xiaoming Li (2011)
Citation count prediction: learning to estimate future citations for literature
J. Hirsch (2005)
An index to quantify an individual's scientific output
P. Bonacich (1987)
Power and Centrality: A Family of MeasuresAmerican Journal of Sociology, 92
M. Newman (2004)
Coauthorship networks and patterns of scientific collaborationProceedings of the National Academy of Sciences of the United States of America, 101
G. Cawley, N. Talbot (2010)
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance EvaluationJ. Mach. Learn. Res., 11
Y. Gingras, V. Larivière, Benoit Macaluso, J.P. Robitaille (2008)
The Effects of Aging on Researchers' Publication and Citation PatternsPLoS ONE, 3
L. Bornmann, Alexander Tekles (2018)
Productivity does not equal usefulnessScientometrics, 118
O. Penner, R. Pan, A. Petersen, K. Kaski, S. Fortunato (2013)
On the Predictability of Future Impact in ScienceScientific Reports, 3
Ali Daud, Rashid Abbasi, Faqir Muhammad (2013)
Finding Rising Stars in Social Networks
Nicole Mitchell (2008)
Library 2.0: A guide to participatory library serviceJournal of the Association for Information Science and Technology, 59
Qing Ke, Emilio Ferrara, F. Radicchi, A. Flammini (2015)
Defining and identifying Sleeping Beauties in scienceProceedings of the National Academy of Sciences, 112
Xuanyu Cao, Yan Chen, K. Liu (2016)
A data analytic approach to quantifying scientific impactJ. Informetrics, 10
S. Dorogovtsev, J. Mendes (2015)
Ranking scientistsNature Physics, 11
Fabian Pedregosa, G. Varoquaux, Alexandre Gramfort, V. Michel, B. Thirion, O. Grisel, Mathieu Blondel, Gilles Louppe, P. Prettenhofer, Ron Weiss, Ron Weiss, J. Vanderplas, Alexandre Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay (2011)
Scikit-learn: Machine Learning in PythonArXiv, abs/1201.0490
L. Bornmann, R. Mutz, H. D. Daniel (2008)
Are there better indices for evaluation purposes than the h index? A comparison of nine different variants of the h index using data from biomedicine, 59
R. Miller (2018)
The ranking of scientistsJournal of biomedical informatics, 79
Tehmina Amjad, Ying Ding, Jian Xu, Chenwei Zhang, Ali Daud, Jie Tang, Min Song (2017)
Standing on the shoulders of giantsJournal of Organisational Transformation & Social Change, 14
Arun Rai (2018)
Editor's Comments: Beyond Outdated Labels: The Blending of IS Research TraditionsManagement Information Systems Quarterly, 42
E. Fong, A. Wilhite (2017)
Authorship and citation manipulation in academic researchPLoS ONE, 12
L. Egghe (2006)
Theory and practise of the g-indexScientometrics, 69
Performance assessment is ubiquitous and crucial in people analytics. Scientific impact, particularly, plays a significant role in the academia. This paper attempts to understand researchers' career trajectories by considering the research community as a social network, where individuals build ties with each other via coauthorship. The resulting linkage facilitates information flow and affects researchers' future impact. Consequently, we systematically investigate the career trajectories of researchers with respect to research impact using the social capital theory as our theoretical foundation. Specifically, for early‐stage and mid‐career academics, we find that connections with prominent researchers associate with greater impact. Brokerage positions, in addition, are beneficial to a researcher's research impact in the long run. For senior researchers, however, the only social network feature that significantly affects their future impact is the reputation of their recently built ties. Finally, we build predictive models on future research impact which can be leveraged by both organizations and individuals. This paper provides empirical evidence for how social networks provide signals on researchers' career dynamics guided by social capital theory. Our findings have implications for individual researchers to strategically plan and promote their careers and for research institutions to better evaluate current as well as prospective employees.
Journal of the Association for Information Science and Technology – Wiley
Published: Apr 1, 2021
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.