Exploring Scholarly Impact Metrics in Receipt of Highly Prestigious Awards

• Published in: Information in Contemporary Society Vol. 11420; pp. 147 - 153
• Main Authors: Lee, Dong Joon, Mutya, Kartik, Herbert, Bruce E., Mejia, Ethelyn V.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Highly prestigious awards; Impact metrics; Research information management system
• ISBN: 3030157415; 9783030157418
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-15742-5_13

The authoritative data that underlies research information management (RIM) systems supports fine-grained analyses of faculty members’ research practices and output, data-driven decision making, and organizational research management. Administrators at Texas A&M University (TAMU) asked the University Libraries to develop institutional reports that describe faculty research practices and identify their research strengths. The library runs Scholars@TAMU (https://scholars.library.tamu.edu/) based on VIVO, a member-supported, open source, semantic-web software program, as the university’s RIM system. This paper explores the scholarly impact and collaboration practices of senior faculty members in the College of Engineering at TAMU and identifies relationships between their impact metrics and collaboration practices. Full professors were divided into three groups: (1) those who received highly prestigious (HP) awards, (2) those who received prestigious (P) awards, and (3) those who did not receive any awards categorized as either HP or P by the National Research Council. The study’s results showed that the faculty members with HP awards had significantly higher mean ranks for their total citation count, the citation count of their top cited article, their h-index, and their total number of publications than did the faculty members without any HP or P awards. The findings from this study can inform researchers, university administrators, and bibliometric communities about the use of awards as an indicator that corresponds to other research performance indicators. Furthermore, researchers could also use the study’s results to develop a machine-learning model that could identify those faculty who are on track to win HP awards in the future.