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Scholarship Productivity: Identifying Challenges and Overcoming Barriers

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• January 14 Book Club and Writing Group Kickoff: Writing Your Journal Article in 12 Weeks
• January 20 Write Here, Write Now Kickoff
• January 28 Writing Compelling Award and Funding Narratives
• February 4 Scholarship Productivity: Identifying Challenges and Overcoming Barriers
• April 14 Thinking Intentionally about Your Academic Brand

Thursday, February 4th, 2021 | 3:30 p.m. - 4:30 p.m.

Event Type: Webinar Facilitator: Este Jordan

While many of us enjoy our scholarship and creative activity, at times impediments to productivity arise that can prove challenging to overcome. In this workshop we will discuss the research on scholarly and creative productivity to identify barriers and effective practices for overcoming them -- including procrastination, impostor syndrome, shame, questioning relevance or institutional/disciplinary alignment, reinvigorating a stalled research agenda, finding time to do that which feels important but not urgent, responding to negative reviews, and beyond.

 Registration for this event has closed. Please contact [email protected] for more information.

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Departmental structure, cooperative scholarship, and productivity: a fuzzy set qualitative-comparative analysis of selected sociology departments.

1. Introduction

2. theoretical framework, 3.1. fuzzy set analysis, 3.2. research cases, 3.4. variables, 3.5. calibration, 4. findings, scholarship productivity, 5. discussion and conclusions, acknowledgments, author contributions, conflicts of interest.

Variables (Sets)	Full Membership	Crossover	Full Non-Membership
Productivity	5.0	2.5	0.5
Cooperative Scholarship	0.90	0.70	0.25
Individualistic Scholarship	0.75	0.30	0.10
Tenured Dominant	0.76	0.85	0.65
Department’s Type (Public)	1	-	0

University	Public	Tenured Dominant	Individualistic Scholarship	Cooperative Scholarship	Scholarly Productivity
Arizona	1	0.46	0.7	0.3	0.43
Berkeley	1	0.73	0.95	0.05	0.50
Brown	0	0.77	0.21	0.79	0.59
Chicago	0	0.26	0.69	0.31	0.46
Columbia	0	0.18	0.84	0.16	0.39
Cornell	0	0.39	0.32	0.68	0.67
Duke	0	0.77	0.18	0.82	0.81
Harvard	0	0.73	0.16	0.84	0.84
Indiana	1	0.5	0.26	0.74	0.26
Iowa	1	0.55	0.18	0.82	0.43
Johns Hopkins	0	0.55	0.46	0.54	0.21
Miami	0	0.43	0.05	0.95	0.95
Michigan	1	0.46	0.43	0.57	0.59
Northwestern	0	0.83	0.53	0.47	0.56
Notre Dame	0	0.07	0.78	0.22	0.23
NYU	0	0.55	0.82	0.18	0.32
Ohio State	1	0.32	0.29	0.71	0.59
Penn State	1	0.5	0.1	0.9	0.84
Pennsylvania	0	0.6	0.55	0.45	0.91
Princeton	0	0.46	0.39	0.61	0.81
Rutgers	1	0.26	0.72	0.28	0.65
Stanford	0	0.18	0.68	0.32	0.46
SUNY-Albany	1	0.26	0.39	0.61	0.32
Temple	1	0.32	0.82	0.18	0.10
Texas-Austin	1	0.77	0.21	0.79	0.81
U of Washington	1	0.43	0.11	0.89	0.62
UCLA	1	0.69	0.5	0.5	0.65
UC-Santa Barbara	1	0.95	0.91	0.09	0.43
UNC	1	0.16	0.29	0.71	0.65
Wisconsin Madison	1	0.12	0.57	0.43	0.26
Yale	0	0.05	0.32	0.68	0.53

	Outcome: Productivity
	Consistency	Coverage
COOPERATIVE SCHOLARSHIP	0.83	0.84
cooperative scholarship	0.58	0.68
INDIVIDUALISTIC SCHOLARSHIP	0.58	0.68
individualistic scholarship	0.83	0.84
PUBLIC	0.48	0.51
public	0.52	0.58
TENURED DOMINANT	0.70	0.83
tenured dominant	0.70	0.71

• Abramo, Giovanni, Ciriaco A. D’Angelo, and Flavia D. Costa. Research Collaboration and Productivity: Is there Correlation? Higher Education 57: 155–71.
• Arunachalam, Subbiah, and B. Viswanathan. 2008. South-South Cooperation: The Case of Indo-Chinese Collaboration in Scientific Research. Current Science 95: 311–13. [ Google Scholar ]
• Barblan, Andris. 2002. Academic Cooperation and Mobility in Europe: How it was and how it will be. Higher Education in Europe 27: 31–58. [ Google Scholar ] [ CrossRef ]
• Beerkens, Eric, and Marijk Derwende. 2007. The Paradox in International Cooperation: Institutionally Embedded Universities in a Global Environment. Higher Education 53: 61–79. [ Google Scholar ] [ CrossRef ]
• Bloch, Francis, Garance Genicot, and Debraj Ray. 2007. Reciprocity in Groups and the Limits to Social Capital. The American Economic Review 97: 65–69. [ Google Scholar ] [ CrossRef ]
• Bourdieu, Pierre. 1984. Homo Academicus . Palo Alto: Stanford University Press. [ Google Scholar ]
• Bourdieu, Pierre, and Loic Wacquant. 1992. Invitation to Reflexive Sociology . Chicago: University of Chicago Press. [ Google Scholar ]
• Cook, Karen S. 2005. Networks, Norms, and Trust: The Social Psychology of Social Capital 2004 Cooley Mead Award Address. Social Psychology Quarterly 68: 4–14. [ Google Scholar ] [ CrossRef ]
• Fleck, Ludwik. 1935. Entstehung und Entwicklung Einer Wissenschaftlichen Tatsache: Einfuehrung in die Lehre vom Denkstil und Denkkollektiv . Basel: Benno Schabe Co. [ Google Scholar ]
• Fleck, Ludwik. 1979. The Genesis of Scientific Fact . Chicago: The University of Chicago Press. [ Google Scholar ]
• Fox, Mary F., and Catherine A. Faver. 1984. Independence and Cooperation in Research: The Motivations and Costs of Collaboration. The Journal of Higher Education 55: 347–59. [ Google Scholar ] [ CrossRef ]
• Garfinkel, Harold. 1957. Studies in Ethnomethodology . Malden: Blackwell Publishers Inc. [ Google Scholar ]
• Grothus, Ulrich. 2003. The Contribution of International Academic Cooperation and Exchange to the Development and Advancement of Society. Higher Education in Europe 28: 109–11. [ Google Scholar ] [ CrossRef ]
• Hagen, Jon. 1987. University Co-Operation and Academic Recognition in Europe: The Council of Europe and the Communities. European Journal of Education 22: 77. [ Google Scholar ] [ CrossRef ]
• Hunt, Allcott, Dean Karlan, Markus M. Möbius, Tanya S. Rosenblat, and Adam Szeidl. 2007. Community Size and Network Closure. The American Economic Review 97: 80–85. [ Google Scholar ]
• Katz, J. Sylvan, and Ben R. Martin. 1997. What is Research Collaboration? Research Policy 26: 1–18. [ Google Scholar ] [ CrossRef ]
• Kollock, Peter. 1998. Social Dilemmas: The Anatomy of Cooperation. Annual Review of Sociology 24: 183–214. [ Google Scholar ] [ CrossRef ]
• Kuhn, Thomas S. 1970. The Structure of Scientific Revolutions . Chicago: University of Chicago Press. [ Google Scholar ]
• Lawler, Edward J., and Jeongkoo Yoon. 1996. Commitment in Exchange Relations: Test of a Theory of Relational Cohesion. American Sociological Review 61: 89–108. [ Google Scholar ] [ CrossRef ]
• Lawler, Edward J., Shane R. Thye, and Jeongkoo Yoon. 2000. Emotion and Group Cohesion in Productive Exchange. American Journal of Sociology 106: 616–57. [ Google Scholar ] [ CrossRef ]
• Lawler, Edward J., Shane R. Thye, and Jeongkoo Yoon. 2006. Commitment in Structurally Enabled and Induced Exchange Relations. Social Psychology Quarterly 69: 183–200. [ Google Scholar ]
• Leahey, Erin. 2016. From Sole Investigator to Team Scientist: Trends in the Practice and Study of Research Collaboration. Annual Review of Sociology 42: 81–100. [ Google Scholar ] [ CrossRef ]
• Leahey, Erin, and Ryan C. Reikowsky. 2008. Research Specialization and Collaboration Patterns in Sociology. Social Studies of Science 38: 425–40. [ Google Scholar ] [ CrossRef ]
• Lee, Sooho, and Barry Bozeman. 2005. The Impact of Research Collaboration on Scientific Productivity. Social Studies of Science 35: 673–702. [ Google Scholar ] [ CrossRef ]
• Lewis, Jenny, Sandy Ross, and Thomas Holden. 2012. The How and Why of Academic Collaboration: Disciplinary Differences and Policy Implications. Higher Education 64: 693–708. [ Google Scholar ] [ CrossRef ]
• Logsdon, John M. 1984. U.S.-European Cooperation in Space Science: A 25-Year Perspective. Science 223: 11–16. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Merton, Robert K. 1973. The Sociology of Science: Theoretical and Empirical Investigations . Chicago: University of Chicago Press. [ Google Scholar ]
• Milton, Laurie P., and James D. Westphal. 2005. Identity Confirmation Networks and Cooperation in Work Groups. The Academy of Management Journal 48: 191–212. [ Google Scholar ] [ CrossRef ]
• Molm, Linda D. 1981. The Conversion of Power Imbalance to Power Use. Social Psychology Quarterly 44: 151–63. [ Google Scholar ] [ CrossRef ]
• Molm, Linda D. 2006. The Social Exchange Framework. In Contemporary Social Psychology Theories . Edited by Peter J. Burke. Palo Alto: Stanford University Press. [ Google Scholar ]
• Molm, Linda D., David R. Schaefer, and Jessica L. Collett. 2007. The Value of Reciprocity. Social Psychology Quarterly 70: 199–217. [ Google Scholar ] [ CrossRef ]
• Moody, James. 2004. The Structure of Social Science Collaboration Network: Disciplinary Cohesion from 1963 to 1999. American Sociological Review 69: 213–38. [ Google Scholar ] [ CrossRef ]
• The National Research Council Rankings of Sociology Programs. Available online: http://chronicle.com/article/nrc-sociology/124663 (accessed on 29 September 2010).
• O’Brien, Timothy L. 2012. Change in Academic Co-Authorship, 1953–2003. Science, Technology, and Human Values 37: 210–34. [ Google Scholar ] [ CrossRef ]
• Papon, Pierre. 2004. European Scientific Cooperation and Research Infrastructures: Past Tendencies and Future Prospects. Minerva: A Review of Science, Learning & Policy 42: 61–76. [ Google Scholar ]
• Peretroukhine, V.F., F. David, and C. Madic. 2004. Development of the French-Russian Scientific Cooperation in Radiochemistry. Radiochemistry 46: 315–23. [ Google Scholar ] [ CrossRef ]
• Peterson, Mark F. 2001. International Collaboration in Organizational Behavior Research. Journal of Organizational Behavior 22: 59–81. [ Google Scholar ] [ CrossRef ]
• Presser, Stanley. 1980. Collaboration and the Quality of Research. Social Studies of Science 10: 95–101. [ Google Scholar ] [ CrossRef ]
• Radda, George. 2002. Biomedical Research and International Collaboration. Science 295: 445–46. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ragin, Charles C. 1987. The Comparative Method . Berkeley: University of California Press. [ Google Scholar ]
• Ragin, Charles C. 2000. Fuzzy-Set Social Science . Chicago: University of Chicago Press. [ Google Scholar ]
• Ragin, Charles C. 2003a. Recent Advances in Fuzzy-Set Methods and Their Application to Policy Questions. COMPASSS Working Paper WP2003-9. Available online: http://www.compasss.org/wpseries/Ragin2003a.pdf (accessed on 31 August 2003).
• Ragin, Charles C. 2008. Redesigning Social Inquiry: Fuzzy Sets and Beyond . Chicago: University of Chicago Press. [ Google Scholar ]
• Rawlings, Craig M., and Daniel A. McFarland. 2011. Influence Flows in the Academy: Using Affiliation Networks to Assess Peer Effects among Researchers. Social Science Research 40: 1001–17. [ Google Scholar ] [ CrossRef ]
• Rihoux, Benoît, and Charles. C. Ragin. 2009. Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques . Thousand Oaks: Sage. [ Google Scholar ]
• Teune, Henry. 1966. Advantages and Problems of International Collaboration in Social Science Research. Background 10: 177–92. [ Google Scholar ] [ CrossRef ]
• Uller, Angela. 1993. Scientific Co-Operation between Europe and Latin America. European Journal of Education 28: 61. [ Google Scholar ] [ CrossRef ]
• Van Rijnsoever, Frank J., and Laurens K. Hessels. 2011. Factors Associated with Disciplinary and Interdisciplinary Research Collaboration. Research Policy 40: 463–72. [ Google Scholar ] [ CrossRef ]
• Wolfe, Alan. 1990. Books vs. Articles: Two ways of Publishing Sociology. Sociological Forum 5: 477–89. [ Google Scholar ] [ CrossRef ]
• Yamagishi, Toshio, and Karen S. Cook. 1993. Generalized Exchange and Social Dilemmas. Social Psychology Quarterly 56: 235–48. [ Google Scholar ] [ CrossRef ]
• Zuckerman, Harriet. 1977. Scientific Elite: Nobel Laureates in the United States . New York: The Free Press. [ Google Scholar ]

Variables (Sets)	Measurement
Productivity	Mean number of articles published by each faculty member during 2009–2010
Cooperative Scholarship	Percentage of joint articles published by each faculty member during 2009–2010
Individualistic Scholarship	Percentage of individually published articles by each faculty during 2009–2010
Tenured Dominant	Percentage of department faculty who are Full/Associate Professors
Department’s Type (Public)	Public or Private

Set	Mean	Std. Dev.	Minimum	Maximum	N Cases
Productivity	2.75	0.86	1	5	31
Cooperative Scholarship	1.92	0.91	0.5	4.5	31
Individualistic Scholarship	0.84	0.41	0.41	1.83	31
Tenured Dominant	0.83	0.07	0.65	1.00	31
Department’s Type (Public)	0.52	0.50	0	1	31

Model: Productivity = f(public, tenured dominant, cooperative, individualistic) Frequency Cutoff: 2.0; Consistency Cutoff: 0.85
	Raw Coverage	Unique Coverage	Consistency
	0.83	0.63	0.84	Miami (0.95,0.95), Pennsylvania State (0.9,0.84), U of Washington (0.89,0.62), Harvard (0.84,0.84), Duke (0.82,0.81), Iowa (0.82,0.43), Brown (0.79,0.59), Texas-Austin (0.79,0.81), UNC (0.71,0.65), Ohio State (0.71,0.59), Yale (0.68,0.53), Cornell (0.68,0.67), Princeton (0.61,0.81), Michigan (0.57,0.59)
	0.22	0.02	0.81	Pennsylvania (0.55,0.91), Northwestern (0.53,0.56)
solution coverage: 0.85; solution consistency: 0.83

	High Productive
	Public	Private
	Pennsylvania State	Harvard
	Texas-Austin	Miami
	UCLA	Duke
	UNC	Princeton
	U of Washington	Cornell
	Michigan	Brown
	Ohio State	Yale
	Rutgers	Pennsylvania
	Berkeley	Northwestern

Share and Cite

Madanipour, A.; Williams, J.; Sadri, M. Departmental Structure, Cooperative Scholarship, and Productivity: A Fuzzy Set Qualitative-Comparative Analysis of Selected Sociology Departments. Soc. Sci. 2017 , 6 , 57. https://doi.org/10.3390/socsci6020057

Madanipour A, Williams J, Sadri M. Departmental Structure, Cooperative Scholarship, and Productivity: A Fuzzy Set Qualitative-Comparative Analysis of Selected Sociology Departments. Social Sciences . 2017; 6(2):57. https://doi.org/10.3390/socsci6020057

Madanipour, Ali, James Williams, and Mahmoud Sadri. 2017. "Departmental Structure, Cooperative Scholarship, and Productivity: A Fuzzy Set Qualitative-Comparative Analysis of Selected Sociology Departments" Social Sciences 6, no. 2: 57. https://doi.org/10.3390/socsci6020057

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Research, Scholarship, and Creative Activity - Research Productivity Progress

Research productivity.

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• > Journals
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• > Faculty Research Productivity: Why Do Some of Our Colleagues...

Article contents

Descriptive statistics, faculty research productivity: why do some of our colleagues publish more than others.

Published online by Cambridge University Press:  08 April 2011

The justification for studying faculty research productivity is that it affects individual advancement and reputation within academe, as well as departmental and institutional prestige (Creamer 1998, iii). Publication records are an important factor in faculty performance evaluations, research grant awards, and promotion and salary decisions. The phrase “publish or perish” encapsulates the importance of research productivity to academic careers. In addition, questions are sometimes raised about whether an individual's status as a minority within academia (e.g., being a member of an underrepresented ethnic or racial group or being female in a male-dominated profession) affects his or her ability to publish or likelihood of publishing (Cole and Zuckerman 1984; Bellas and Toutkoushian 1999). Finally, most previous work that tackles the productivity causality puzzle comes from disciplines other than political science. Thus, one of the purposes of this report is to explore whether the existing findings about research productivity in other disciplines apply equally well to research productivity in political science.

The justification for studying faculty research productivity is that it affects individual advancement and reputation within academe, as well as departmental and institutional prestige (Creamer Reference Creamer 1998 , iii). Publication records are an important factor in faculty performance evaluations, research grant awards, and promotion and salary decisions. The phrase “publish or perish” encapsulates the importance of research productivity to academic careers. In addition, questions are sometimes raised about whether an individual's status as a minority within academia (e.g., being a member of an underrepresented ethnic or racial group or being female in a male-dominated profession) affects his or her ability to publish or likelihood of publishing (Cole and Zuckerman Reference Cole and Zuckerman 1984 ; Bellas and Toutkoushian Reference Bellas and Toutkoushian 1999 ). Finally, most previous work that tackles the productivity causality puzzle comes from disciplines other than political science. Footnote 1 Thus, one of the purposes of this report is to explore whether the existing findings about research productivity in other disciplines apply equally well to research productivity in political science. Footnote 2

The question that we wish to answer is: What factors contribute to higher or lower research output by political scientists? We base our answer on responses to a 2009 survey sponsored by the APSA. Respondents were drawn from a sample of all faculty employed in political science departments (including departments of government and public affairs) throughout the United States. ( Appendix A provides a description of the survey methodology.)

According to previous studies, several blocks of variables determine scholarly productivity. These variables include demographics and family-related factors, human capital, opportunity costs (teaching and service workload), working environment, and professional variables ( table 1 ). Among the demographic variables listed in table 1 , gender differences have received special attention. Numerous studies have revealed that women publish less than men (Fish and Gibbons Reference Fish and Gibbons 1989 ; McDowell and Smith Reference McDowell and Smith 1992 ; Broder Reference Broder 1993 , 123; Bellas and Toutkoushian Reference Bellas and Toutkoushian 1999 ; Sax et al. Reference Sax, Hagedorn, Arredondo and Dicrisi 2002 ; Maske, Durden, and Gaynor Reference Maske, Durden and Gaynor 2003 , 561; Taylor, Fender, and Burke Reference Taylor, Fender and Burke 2006 ; Evans and Bucy Reference Evans and Bucy 2010 ). This finding, however, remains controversial: Davis and Patterson ( Reference Davis and Patterson 2001 , 89) argue that women do not publish significantly less than men when source of Ph.D., type of employer, and field of specialization are held constant.

Table 1 References for Explanatory Variables for Scholarly Productivity

The second category of variables found in table 1 concern “human capital.” Footnote 3 Human capital addresses any contextual or individual attributes that could potentially influence the quality of an individual's research skills or training. The professional reputation of an academic's Ph.D.-granting department is consistently tied to differences in research productivity (Hansen, Weisbrod, and Strauss Reference Hansen, Weisbrod and Strauss 1978 ; Davis and Patterson Reference Davis and Patterson 2001 , 88; Broder Reference Broder 1993 ; Buchmueller, Dominitz, and Hansen Reference Buchmueller, Dominitz and Hansen 1999 , 71). The assumption is that top-rated schools attract the best students and then provide them with training at the frontiers of the discipline and socialization into a culture that values high-quality research (Rodgers and Neri Reference Rodgers and Neri 2007 , 76).

“Opportunity cost” variables capture the time spent teaching or doing service. Given the limited amounts of time that faculty have, teaching or administrative requirements set by the employing institution may affect faculty research productivity (Fender, Taylor, and Burke Reference Fender, Taylor and Burke 2005 ; Taylor, Fender, and Burke Reference Taylor, Fender and Burke 2006 ; Maske, Durden, and Gaynor Reference Maske, Durden and Gaynor 2003 ). Studies consistently reveal that a large teaching load significantly reduces published output (Graves, Marchand, and Thompson Reference Graves, Marchand and Thompson 1982 ). Footnote 4

The category of “current working environment”—both its culture and its availability of resources—captures primarily departmental and institutional characteristics. Broader availability of resources and incentives for publishing should influence publication rates (see table 1 ). Footnote 5 “Culture” relates to shared attitudes about not only the value of research, but also collegiality and interpersonal encouragement. Each academic's own research productivity is affected by the productivity of his or her departmental colleagues through “collaboration, academic discourse, peer expectations [and] peer pressure” or through colleagues' other attributes, such as “ability, integrity [and] professionalism” (Rodgers and Neri Reference Rodgers and Neri 2007 , 85; see also Taylor, Fender, and Burke Reference Taylor, Fender and Burke 2006 ).

As a category that is distinct from the working environment, we also consider “professional variables,” which include the achievements of an individual's academic career. For example, scholarly productivity has been associated with the ranking of the program with which an individual is affiliated (Davis and Patterson Reference Davis and Patterson 2001 , 88; Xie and Shauman Reference Xie and Shauman 1998 , 865; Garand and Graddy Reference Garand and Graddy 1999 ; McCormick and Rice Reference McCormick and Rice 2001 ; Youn Reference Youn, Breneman and Youn 1988 ). It may be that higher ranked departments select better scientists, or perhaps these departments foster greater productivity (Broder Reference Broder 1993 , 116). Footnote 6 Arguably, faculty research productivity also varies according to the researcher's specific subject matter (Fish and Gibbons Reference Fish and Gibbons 1989 , 98).

Faculty rank (instructor/lecturer, assistant professor, associate professor, full professor) is considered a professional variable. Some researchers find rank to be a predictor of productivity (Blackburn, Behymer, and Hall Reference Blackburn, Behymer and Hall 1978 ; Bellas and Toutkoushian Reference Bellas and Toutkoushian 1999 ; Dundar and Lewis Reference Dundar and Lewis 1998 ; Sax et al. Reference Sax, Hagedorn, Arredondo and Dicrisi 2002 ; Xie and Shauman Reference Xie and Shauman 1998 , 865), while others have shown that rank has no influence on faculty research productivity when other relevant variables are taken into consideration (Over Reference Over 1982 ; Wanner, Lewis, and Gregorio Reference Wanner, Lewis and Gregorio 1981 ). Footnote 7 Also categorized under professional variables is coauthorship, which is thought to “increase article production through the division of labor made necessary by increased complexity in the subject matter and by the need to saturate markets to increase the probability of getting papers accepted for publication” (Maske, Durden, and Gaynor Reference Maske, Durden and Gaynor 2003 , 555, 561; see also Hollis Reference Hollis 2001 ; Durden and Perri Reference Durden and Perri 1995 ; Davis and Patterson Reference Davis and Patterson 2001 , 90; Taylor, Fender, and Burke Reference Taylor, Fender and Burke 2006 ).

The theories behind the explanations for variation in research productivity are nearly as varied as the factors studied. Behavioral reinforcement theory views the “system of faculty ranks as a reward system as well as a schedule of reinforcement” (Tien and Blackburn Reference Tien and Blackburn 1996 , 5). A similar idea is proposed by the investment-motivated model of scientific productivity, which argues that “scientists engage in research because of the future financial rewards associated with the activity” (Levin and Stephan Reference Levin and Stephan 1991 , 115). Such a model implies a decline in research productivity over the course of an individual's career, given the finite time horizon (Diamond Reference Diamond 1984 ). Rodgers and Neri ( Reference Rodgers and Neri 2007 , 79) report that the most productive period is the first five years after the Ph.D. is conferred, and Davis and Patterson ( Reference Davis and Patterson 2001 ) report that productivity generally declines after tenure.

In contrast, a consumption-motivated model that stresses the “scientist's fascination with the research puzzle itself” (Levin and Stephan Reference Levin and Stephan 1991 , 115) does not predict a decline in research productivity over time. Likewise, selection theory (Finkelstein Reference Finkelstein 1984 ) argues that only the most productive faculty members are promoted, eliminating low producers before they reach higher ranks and thus creating a situation in which higher ranking faculty produce more. Accumulative advantage theory emphasizes the importance of resource acquisition over time (Allison and Stewart Reference Allison and Stewart 1974 ). Motivational theory draws an important distinction between intrinsic motivation (e.g., interest in research) and extrinsic motivation (e.g., desire for promotion). Intrinsic motivation may account for the continued productivity of full professors, who are no longer motivated by the possibility of promotion (Finkelstein Reference Finkelstein 1984 , 101).

Thus, theoretical approaches to the productivity question vary, as do the factors that predict faculty research productivity. The dependent variable (productivity, or research output) can also be evaluated in a variety of ways. In our analysis of scholarly productivity, we use as our dependent variable the respondent's best estimate of the total number of articles that he or she has published in refereed academic or professional journals over his or her entire career. Footnote 8 We also look at a summary measure that includes refereed journal articles, books, edited books, and book chapters. Finally, we create a model that uses books and book chapters published as controls when evaluating the total number of articles published. (See appendix A for a description of all variables included in the analyses.)

Before we embark on the multivariate analyses, we first provide descriptive statistics. Across the entire set of 1,399 respondents, the average number of articles published in refereed academic or professional journals during the respondent's entire career is 10.5. In table 2 , we divide our sample into groups of men and women and compare their publication rates. On average, men publish significantly more articles than women do. We wish to note that this bivariate calculation does not control for age. The average age of women in the profession is lower than the average age for men in the profession. Thus, on average, men in the profession have more years of publishing time (based on age) than have women. Footnote 9

Table 2 Average Number of Articles Published by Subgroup

Notes . For all, p < .000.

a Percentages exclude one transgendered respondent.

b Percentages do not add to 100% because of the exclusion of respondents from Tier IV, unranked, and unknown departments.

c Percentages do not add to 100% because of the exclusion of respondents from departments within a two-year college and respondents without a program type specified.

d Percentages do not add to 100% because of the exclusion of instructors, lecturers, postdocs, fellows, and respondents without a rank specified.

Turning to human capital variables, table 2 shows that the average number of articles published during an individual's career is significantly higher among graduates from departments ranked among the top 25 (tier I schools) than among graduates from departments ranked 26–50 (tier II) and departments classified as tier III using the Schmidt and Chingos ranking ( Reference Schmidt and Chingos 2007 ). Footnote 10 Thus, the ranking of an academic's Ph.D.-granting department is bivariately tied to differences in research productivity.

Looking at the opportunity cost variables, we asked respondents to report their typical teaching load each year (for the past five years). Across all respondents, the average number of courses taught is 4.3 per year. For purposes of a simple descriptive picture, we divided respondents into three groups: faculty with low teaching loads (2.5 courses or less per year), medium teaching loads (3 to 5.5 courses per year), and high teaching loads (6 or more courses per year). On average, faculty members with the lowest teaching loads publish 14.5 articles, while individuals with heavy teaching loads publish 4.9 articles (see table 2 ). These numbers reveal a major difference in research output depending on how many courses a faculty member teaches.

The last two variables that we considered in table 2 are associated with working environment and professional achievement. Looking at the “current employment” rows, we can see that faculty who are employed by Ph.D.-granting departments publish dramatically more than faculty who are employed by MA-granting programs or departments in a four-year college. The numbers in table 2 also illustrate the effect of the professional achievement variables by highlighting the difference in the number of articles published by members of different professional ranks. On average, assistant professors have published 3.6 articles, while full professors have published 18.6 articles.

In running OLS regressions in our multivariate analysis, we employed three different versions of the dependent variable. The first simply used the respondent's raw report of the total number of articles published in refereed academic or professional journals over his or her entire career. The second approach replaced missing responses to this question with the value of zero. The third approach followed a recommendation by Fox and Milbourne ( Reference Fox and Milbourne 1999 , 256) that the number of articles published be transformed as the logarithm of one plus the original variable, with missing responses replaced with zero. This helps deal with a concentration of observations at zero and makes the distribution more closely approximate a normal distribution. Footnote 11 The tables that we publish here all use the logarithmic transformation of the number of articles produced as the dependent variable. Analyses using the other two versions of the dependent variable are consistent with the findings reported here and are available from the authors upon request.

Table 3 reports regression results using the logarithm of one plus the number of articles published (with missing responses replaced with zero) as the dependent variable. Model I of table 3 contains only demographic and family-related factors. Footnote 12 These factors explain 5% of the variation in the (log of the) number of articles produced. Two variables emerge as significant predictors: gender and number of children. According to this simple demographic model, women tend to publish less than men. Additionally, as the number of children that an individual has increases, so does the number of articles that he or she publishes.

Table 3 Log of Articles as Dependent Variable, Based on Original Responses, with Missing Responses to Predictor Variables Excluded

*** p < .001,

** p < .01,

* p < .05

Model II of table 3 incorporates the measures of human capital. Both Ph.D. program rank and number of years to complete the Ph.D. are significant. As the ranking of the program from which a faculty member received his or her Ph.D. improves, the number of articles this individual publishes increases. Footnote 13 As the number of years to complete the doctoral degree increases, the number of articles published decreases.

The problem that appears in table 3 , however, concerns Model I's loss of a large number of respondents because they neglected to report their number of children. The introduction of additional variables in Model II results in a further loss of 225 other cases. This loss occurs in part because 141 respondents did not identify the institution from which they received their Ph.D.; this prevents us from using the 2007 Schmidt and Chingos ranking variable for these respondents. The more significant missing value problem arises with the question: “In what year did you obtain your Ph.D. degree?” Within our sample, 273 respondents either did not answer or made a mistake when typing in a year. Given this missing data problem, we decided to use the multiple-imputation Amelia II program for missing data (Honaker, King, and Blackwell Reference Honaker, King and Blackwell 2010 ). Footnote 14 We used this program to impute estimates of the missing responses on each of the independent variables used in the analysis. The purpose of this approach was to increase the number of observations taken into consideration in the analysis.

Table 3.1 reports the testing of exactly the same models as are tested in table 3 , but using the imputed data and holding the number of cases in the analysis at 1,399 for all models. As in table 3 , both human capital variables are significant in Model II of table 3.1 . When using the imputed data, it is noteworthy that the significant demographics include “married.” Thus, table 3.1 reveals that women tend to publish fewer articles than men, while married and partnered persons publish more than single, divorced, or widowed academics.

Table 3.1 Log of Articles as Dependent Variable (Regressions with Imputed Data)

In Model III of table 3 , we add in the controls that we label opportunity costs: teaching load, number of new courses prepared, number of committee memberships, number of committees chaired, and amount of student advising. In this model, we jump to 29% of the variance explained in table 3 (Model III) but lose another 108 cases. According to these results, heavy teaching loads do take their toll on article production. In addition, the larger the number of new courses prepared, the lower the number of articles published. We also find, rather unexpectedly, that the more committees an individual chairs and the more advisees he or she has, the more he or she publishes.

When all respondents are considered and missing values are replaced by imputed data, the strong relationship between a higher teaching load and lower article production is confirmed, as is the negative relationship between new course preparation and article production (Model III in table 3.1 ). The overall count of advisees is again positively related to the number of articles published. To explain the findings regarding committees and advising, it may be wise to interpret both as measures of professional involvement rather than opportunity costs. It appears that we misclassified advising and committee service as opportunity costs, given that when these variables are significant, they appear to support rather than detract from article production. We return to this finding in our discussion section.

Model IV includes the working environment variables: collegial climate; count of overall resources; and current employment in a Ph.D. program, MA program, or private institution. Model IV in table 3 reveals that more positive evaluations of a department's “climate” are related to fewer articles published. Faculty members who evaluate their environment as more friendly, respectful, collegial, collaborative, and cooperative publish less, on average, than faculty who evaluate their home department as more hostile, disrespectful, contentious, individualistic, and competitive. This relationship emerges even more powerfully in the imputed data (Model IV, table 3.1 ). Footnote 15 The negative association between departmental collegiality and research productivity is affirmed in the tables that follow. We must accept the finding that faculty members who operate in the more competitive, individualistic, and hostile departments publish more on average. The defining element of this scale is the collegial versus contentious contrast, with collegiality associated with lower total publications and contention associated with a higher number of publications.

We also see from the test of Model IV (in table 3 and table 3.1 ) that more resources are associated with an increase in the number of articles published. The dummy variable for employment in a Ph.D.-granting department is also significant. Table 3 and table 3.1 show contradictory results regarding whether faculty members in private institutions tend to publish more than faculty members in public institutions. Table 3.1 's finding that faculty members employed in private rather than public institutions tend to publish more is consistent throughout the analyses using imputed values for missing responses (and therefore based on the largest possible number of respondents). Note that with Model IV, we can now explain 41% of the variance in the number of articles published (see table 3.1 ). Footnote 16

The last model that we report in tables 3 and 3.1 includes the following professional variables: current faculty rank, a series of dummy variables for current primary field of teaching and research, year the Ph.D. was granted, coauthorship, frequency of conference attendance, and the ranking of the department that currently employs the faculty member. This final model is extremely powerful; it explains 44% of the variance in the number of articles published. Footnote 17 Model V in tables 3 and 3.1 shows that as an individual moves up the academic ranks, the total number of articles published in his or her career also increases. Footnote 18 We do not find evidence that faculty members in any subfield publish significantly more than faculty members in another subfield. Footnote 19 We do find that as more time passes since the granting of the degree, the number of articles published increases. Footnote 20 Increased conference attendance is also positively related to greater article output (see tables 3 and 3.1 ).

We also tested Models III, IV, and V after adding the total number of books written or edited and book chapters published as a control. Our findings confirm the results of Maske, Durden, and Gaynor ( Reference Maske, Durden and Gaynor 2003 , 561), who report a significant positive relationship between books published and articles published. Among our respondents, the bivariate correlation between the number of articles published and the total number of books, edited books, and book chapters is .640. The correlation between the number of articles published and the number of books published is .593. A higher number of books, edited books, and book chapters is positively associated with a higher number of articles published. Thus, the activities are clearly complementary and do not detract from each other; rather, they reinforce each other.

In a related vein, we thought it valuable to report on a different approach to measuring research output. As an alternate dependent variable, we evaluate the total number of publications. To create a total publications variable, we add together responses to four questions: (1) number of articles published in refereed academic or professional journals, (2) number of monographs (books) published, (3) number of books edited, and (4) number of book chapters published. For the results presented in table 4 , missing responses to all questions are set to zero and the responses are summed (the value of one is added to the sum before the log is calculated). To save space, we do not present the results for Models I, III, and IV, and for Model II, we report only the results using the data file created by Amelia, with missing responses on the independent variables replaced with imputed values.

Table 4 Log of Total Productivity as Dependent Variable

For Model V of Table 4 , we report results using the imputed data files created by Amelia, as well as results using the log of total productivity (plus one) as the dependent variable and allowing missing cases to be deleted from the analysis. If we focus on Model V of Table 4 , which controls for human capital, opportunity costs, and important characteristics such as faculty rank, we see that women on average report lower total publications than men. Being a minority and having children are significant when using the imputed data, but when missing values are dropped from the analysis, these characteristics are not significantly related to overall total output.

According to table 4 , the longer the time an individual spends earning the Ph.D., the lower his or her number of total publications. When opportunity costs are considered, we see that the more courses that are taught, the lower a faculty member's total number of publications. The results using imputed values to replace missing responses also reveal a strong relationship between more advisees and more publications. Among the working environment variables, the total number of publications tends to be lower when the department's collegiality is high. The total number of publications is higher among those faculty members who report receiving more of the following resources: course release time, research assistance time, discretionary funds, travel funds, and summer salary. Consistently significant predictors of total productivity among the professional variables are higher faculty rank, year of degree (i.e., more time since finishing the doctoral degree), and conference attendance.

Since promotion to a higher rank and the total number of publications are inextricably combined in academia, we thought it important to divide our sample into subgroups based on academic rank and evaluate the factors that predict different levels of productivity within each rank. Acknowledging that full, associate, and assistant professor are each heterogeneous categories, in table 5 , we test Model V within ranks to identify why some faculty members are more productive than others. Footnote 21 In table 5 , we report the results of testing Model V among assistant professors only, associate professors only, and full professors only. Table 5 uses the log of one plus the number of articles published as the dependent variable. We report results based on the file created by the Amelia program, with missing responses replaced with imputed values, as well as results based on analyses with missing responses excluded from the calculations. The following findings focus only on those columns based on the data with missing responses replaced by imputed values, as we feel more confident in these results, given that they incorporate a larger number of respondents.

Table 5 Log of Articles as Dependent Variable in Subgroup Regressions

Looking first at assistant professors only ( table 5 , Model VA), we find that a higher number articles published (with the logarithmic transformation and using imputed values for missing responses) is associated with being male rather than female, having more children, taking less time to complete the doctoral degree, teaching fewer undergraduate courses, having more resources, working in a private institution, and attending more conferences. Among associate professors only ( table 5 , Model VB), a higher number of articles published is associated with being male rather than female, graduating from a higher ranked department, teaching fewer undergraduate courses, working in a competitive rather than a collegial climate, having more resources, and being employed in a department with a Ph.D. program. Among full professors only ( table 5 , model VC), a higher number of articles published is associated with being male rather than female, having more children, having more advisees, working in a competitive versus a collegial department, having more resources, being employed in a Ph.D.-granting department, working at a private institution, having a specialty other than American politics, and more years since receipt of the Ph.D.

Thus, we can see both similarities and differences in the predictors of article publication rates across academic ranks. For example, the availability of more resources is related to more publication at all ranks. A large teaching load appears to have detrimental effects on publication rates for assistant and associate professors, although not for full professors. A larger advising load is associated with more productivity among full professors, but not among associate and assistant professors. As well, when we look within these subgroups (partially controlling for age), we find that working in a private institution is associated with higher publication rates among assistant and full professors but has no significant effect on the number of articles produced by associate professors. Being employed by a Ph.D.-granting department is positively related to article production among associate and full professors, but not among assistant professors.

With regard to demographics, our results appear to reveal that women employed in political science departments in the United States publish less on average than their male counterparts. When we divide respondents by rank and conduct our analysis within these ranks, using imputed data, the relationship between articles published and gender is significant at all ranks. Several explanations for the existence of this gender difference have been offered: Xie and Shauman ( Reference Xie and Shauman 1998 ), for example, argue that female scientists are less likely to hold the positions and have access to the facilitating resources that are conducive to higher rates of publication performance. This finding may be relevant for political scientists at the associate professor level, at which women are less likely than men to be employed by a top-ranked department. At the assistant professor level, however, women are more likely than men to be employed by a top-ranked department. In addition, women and men on average report equal access overall to resources. Footnote 22

Another explanation that has been offered in the literature is that women spend more time “mentoring” than do male faculty. Collins ( Reference Collins, Collins, Chrisler and Quina 1998 ) finds that women are more likely than men to devote time to teaching and advising, serve in part-time positions, and teach in fields unlike the ones in which they were trained. Among our respondents, we do not see significant differences between men's and women's teaching loads, either for graduate or undergraduate courses, nor do we see significant differences in committee membership, committee chairing, or overall levels of advising. We note, however, that our questions count the number of these activities but do not ask respondents to report on the amount of time spent on these activities. Female political scientists are also no more likely than male political scientists to work in part-time positions and no more likely to teach or do research in a field that differs from their major field as a graduate student.

Another explanation is that women spend more time than their male colleagues on household and childcare responsibilities (Gmelch, Wilke, and Lovrich Reference Gmelch, Wilke and Lovrich 1986 ; Suitor, Mecom, and Feld Reference Suitor, Mecom and Feld 2001 ). Footnote 23 This explanation seems valid if traditional divisions of labor between men and women exist within the household. We did not, however, include a question in our survey about time devoted to domestic or child-rearing chores, so we cannot check this hypothesis. Looking at our sample of political science faculty members, we see that at the level of assistant professor, men are more likely than women to have children. At the associate and full professor levels, men and women are equally likely to have children. Footnote 24

It is also important to note that the men in our sample have, on average, been in the profession longer than the women in the sample. On average, female respondents received their doctoral degree in 1994, while male respondents received their degrees in 1990. However, we do control for year that a degree was awarded in Model V, and we still find in much of our analysis that women publish less than men.

The findings regarding whether members of a racial or ethnic minority publish more or less when opportunity costs, working environment, and professional characteristics are taken into consideration are inconsistent. We do not find any relationship between self-identification as a minority and number of article publications when the sample is divided by rank. Footnote 25 We note that among the political science faculty who responded to our survey, racial minorities are no more or less likely to be employed by a department that offers a doctoral or an MA degree. Political science faculty who are members of a racial or ethnic minority group are also no more or less likely to be married or have children, and they have, on average, the same number of children as do nonminorities. We find no differences on average in age or year that a degree was awarded when we compare minority group respondents with nonminorities. The average teaching load at the graduate level is the same for minorities and nonminorities, while at the undergraduate level, minorities have a slightly lower teaching load.

A finding that we think particularly important to a profession that places a great deal of emphasis on publications when evaluating faculty performance is the negative effect of a heavy teaching load on research output. The opportunity costs of teaching a large number of courses and preparing new courses are significant indeed. Thus, our findings correspond to the findings of many other scholars—that time spent teaching takes away from time spent doing research (Maske, Durden, and Gaynor Reference Maske, Durden and Gaynor 2003 , 561; Bellas and Toutkoushian Reference Bellas and Toutkoushian 1999 ; Xie and Shauman Reference Xie and Shauman 1998 , 865; Hamovitch and Morgenstern Reference Hamovitch and Morgenstern 1977 , 636; Porter and Umbach Reference Porter and Umbach 2001 ; Taylor, Fender, and Burke Reference Taylor, Fender and Burke 2006 , 858).

Our findings do diverge from previous findings regarding what is generally classified as “service.” A heavy teaching burden generally has a negative effect on publishing, but advising does not. We speculate that the positive relationship between student advising and higher article production is related to the constructive effects on intellectual activity (including the possibility of coauthorship) that are associated with frequent one-on-one interaction with advisees. We believe that advising represents a measure of professional involvement and should be considered a bonus rather than a cost. A particularly strong relationship exists between the number of Ph.D. students an individual advises and the total number of articles that he or she publishes in refereed academic or professional journals. Having a publishing research group and advising appear to go hand in hand.

Finally, we would like to highlight one more finding. The presence of a collegial climate within the department tends to be associated with less productivity. In other words, a degree of competiveness, even hostility, does not detract from productivity. Other attitudinal measures, such as one's evaluation of the research climate within the department, are positively associated with publications. We will address this evaluative dimension of the professional environment further in a follow-up report.

Appendix A: Survey Methodology

Questionnaire design.

In 2005, the APSA Committee on the Status of Women in the Profession (CSWP) proposed to the president of APSA that the association conduct research associated with the recommendations that emerged from the March 2004 Workshop on Women's Advancement in Political Science organized by Michael Brintnall and Linda Lopez (APSA), Susan Clarke (University of Colorado, Boulder), and Leonie Huddy (Stony Brook University). Once the research proposal was approved, the CSWP used questionnaires that had been employed in research published by Hesli and Burrell ( Reference Hesli and Burrell 1995 ); Hesli, Fink, and Duffy ( Reference Hesli, Fink and Duffy 2003 ); and Hesli et al. ( Reference Hesli, DeLaat, Youde, Mendez and Lee 2006 ) to develop a new survey instrument. Additional questions were added from questionnaires developed by the National Research Council and the University of Michigan's Fall 2001 Survey of Academic Climate and Activities, which was created for an NSF ADVANCE project. The following reports were also used to help generate questions:

• Blau, F. 2002. “Report of the Committee on the Status of Women in the Economics Profession.” American Economic Review 92: 516–20.

• Commission on Professionals in Science and Technology (CPST). 2000. Professional Women and Minorities: A Total Human Resource Data Compendium . 13th ed. Washington, DC: CPST.

• Creamer, Elizabeth. 1998. Assessing Faculty Publication Productivity: Issues of Equity . ASHE-ERIC Higher Education Report, Vol. 26, No. 2. Washington, DC: George Washington University.

• Fox, Mary Frank. 1995. “Women and Scientific Careers.” In Handbook of Science and Technology Studies , ed. S. Jasanoff, J. Markle, J. Petersen, and T. Pinch, 205–23. Newbury Park, CA: Sage.

• Fox, Mary Frank. 1998. “Women in Science and Engineering: Theory, Practice, and Policy in Programs.” Signs: Journal of Women in Culture and Society 24: 201–23.

• Sarkees, Meredith Reid, and Nancy E. McGlen. 1992. “ Confronting Barriers: The Status of Women in Political Science.” Journal of Women, Politics & Policy 12 (4): 43–86.

A draft copy of the questionnaire was circulated to the members of the APSA status committees. The questionnaire was revised and expanded to address the concerns of the members of the status committees. The instrument was pilot-tested by distributing it to all political science faculty members at one research university and one private four-year college. The feedback from the pilot test was used to make further revisions to the questionnaire.

Sample Selection

We used as our target population the names contained within the APSA “faculty” file. We used this file of 11,559 names to create a sample population file of size 5,179 names. The original “faculty” file was stratified by department size. To ensure the adequate representation of faculty members from medium- and small-size schools, we oversampled from these groups. Names were selected randomly from the “faculty” file for the “sample” file.

Survey Procedure

Using e-mail addresses, all persons in the sample file were sent a letter of invitation to participate in the study from the executive director and the president of the APSA. Bad e-mail addresses (addresses that bounced back) were replaced with random selections from the “faculty” file. These persons were also mailed an invitation letter. The cleaned “survey” file was sent to the Survey Research Center at the Pennsylvania State University (SRC).

Individuals in the sample were sent an e-mail from SRC inviting them to participate in the survey. This invitation included a link to the web-based survey containing a unique identifier for each potential participant. Only one completed survey was allowed for each identifier. The initial invitation was e-mailed to respondents on August 27, 2009. Follow-up reminders were sent to nonresponders on September 10, 2009; September 24, 2009: October 8, 2009; and October 29, 2009. From among the 5,179 original addresses, 1,399 completed the survey (252 invalid addresses, 105 refusals, and 3,423 nonrespondents).

The distributions of the variables reported in table 2 provide an opportunity to compare the average characteristics of survey respondents to the population as whole (from which the sample was drawn). As indicated in table 2 , among the total set of respondents, 68% are male and 32% are female. With regard to faculty rank, 30% are assistant professors, 27% are associate professors, 35% are full professors, and the remaining fall into smaller categories such as instructors or administrators. Among assistant professor respondents, 44% are female; among associate professors, 29% are female; and among full professors, 24% are female. With regard to department type, 34% of respondents work in a Ph.D.-granting program, 20% work in an MA-granting program, 41% work in a department within a four-year college, and the rest are employed in some other type of academic unit.

According to APSA data, the percentage of females in the population from which we drew the sample (all political science faculty members in the United States) was 28% in 2009. Breaking this down by rank and institution type, we get the following distributions:

Appendix B: Variables Included

Dependent variables, article productivity.

Survey question: For your entire career, please give your best estimate of the number you have produced or have been awarded for each of the following:

______ number of articles published in refereed academic or professional journals

In one version of this variable, all missing values were set to zero. In another version, we take the logarithmic transformation of the number of articles plus 1.

Total Productivity

______ number of monographs (books) published

______ number of books edited

______ number of book chapters published

All missing values of articles, monographs, edited books, and book chapters were set to zero, and we then took a logarithmic transformation of the sum of these items plus one.

Independent Variables

Survey question: What is your gender?

c. Transgender

The dummy variable equals 1 if the response is b.

Survey question: Do you identify yourself as a member of an ethnic and racial minority group?

c. Don't know

The dummy variable equals 1 if the response is a.

Survey question: What is your personal status?

a. Never married

b. Married (first time)

c. Married (second or third time)

d. Member of an unmarried opposite or same-sex partnership

e. Separated/divorced

The dummy variable equals 1 if the response is b, c, or d.

Number of Children

Survey question: Do you or a spouse/partner of yours have any children?

a. Yes (If yes, how many?)

An interaction variable between a dummy for having children (response a) and the number of children specified.

Number of Years to Complete Ph.D.

Survey questions:

(1) In what year did you begin work on your Ph.D.?

(2) In what year did you obtain your degree?

The reported variable is the year of getting the Ph.D. degree minus the year of beginning the degree program.

Ph.D. Program Rank

Survey question: From which university did you obtain your degree?

The program is ranked based on Schmidt and Chingos' ( Reference Schmidt and Chingos 2007 ) rankings, classifying 25 as tier 1, 26–50 as tier 2, 51–75 as tier 3, 76–86 as tier 4, and unranked as tier 5. Foreign degrees and degrees from majors other than political science were set as missing. The score is then reversed so that higher numbers represent higher ranked programs.

Teaching Load

Survey question: During the past five years, what is your typical teaching load each year? (If in your current position for less than five years, base this on the period since your appointment.)

Number of New Courses Prepared

Survey question: In the past 5 years, how many new courses (courses that you have not taught previously—do not include even major revisions of courses you have taught before) have you prepared for your department or college (if you have a joint appointment, refer to your primary unit)?

Number of Committee Memberships

Survey question: In a typical year during the past five years, on how many committees do you serve?

Number of Committees Chaired

Survey question: In a typical year during the past five years, how many committees do you chair?

Amount of Student Advising

Survey question: For how many of each of the following types of individuals do you currently serve as official advisor? Undergraduates, MA students, PhD students, postdocs

The variable was generated by following steps. First, dummy variables were created to represent higher-than-average advising for each student group. For example, the respondent would receive a “1” on undergraduate advising if his or her reported number of undergraduate students advised was higher than the overall mean for that question. The same coding rule was applied to other student groups such as MA students, doctoral students, and postdocs. Next, we counted the overall number of 1s from those four dummies.

Collegial Climate

Survey question: Please rate the climate of your unit(s)/department(s) on the following continuum by selecting the appropriate number (check the appropriate box). For example, in the first row, the value 1 indicates hostile, while the value 5 indicates friendly, and the numbers in between represent relative combinations of each.

A principal component analysis, with the Varimax rotation method revealed two separate components. The Collegial Climate Scale is composed of hostile–friendly, disrespectful–respectful, contentious–collegial, individualistic–collaborative and competitive–cooperative. We calculated the mean score for those five dimensions, with higher numbers indicating a collegial climate.

Count of Overall Resources

Survey question: Have you received any of the following resources as a result of your own negotiations, the terms of an award, or as part of an offer by the university, since your initial contract at your current position? If so, please check all that apply.

Using the count command, we added up the total number of checks for all rows and all columns.

Ph.D. Program

Survey question: What is the type of department where you are employed?

a. Ph.D.-granting program

b. MA-granting program

c. Department within a four-year college

d. Department within a two-year college

e. Other academic unit (specify)

Same question as above, with the dummy variable equals to 1 if the response is b.

Private Institution

Survey question: Is this a public or a private institution?

The dummy variable equals to 1 if the response is b.

Faculty Rank

Survey question: What is the title of your primary current appointment?

We created an ordinal variable using the following coding: 1 (instructors, lecturers, postdocs and fellows), 2 (assistant professors), 3 (associate professors), and 4 (full professors, emeritus, and administrative positions)

Subfield Dummies

Survey question: Which of the following best describes your current primary field of teaching and research?

a. American

b. Comparative

c. International relations

f. Other (please specify)

American subfield equals 1 if the response is a. Comparative subfield equals 1 if the response is b. IR subfield equals 1 if the response is c. Theory subfield equals 1 if the response is d. Methods subfield equals 1 if the response is e.

Year of Degree

Survey question: In what year did you obtain your degree?

Coauthorship

Survey question: Which of following most accurately describes the majority of your publications?

a. Most are sole-authored

b. Most are coauthored with colleagues in my department

c. Most are coauthored with scholars from other departments in my institution

d. Most are coauthored with colleagues from outside my institution

e. Most are coauthored with students

The dummy variable equals 1 if the response is b, c, d, or e.

Frequency of Conference Attendance

Survey question: How often have you attended political science conferences in the past three years?

Current Program Ranking

A ranking of the department for which the respondent is currently working. Programs are ranked based on Schmidt and Chingos' ( Reference Schmidt and Chingos 2007 ) ranking, classifying top 25 as tier 1, 26–50 as tier 2, 51–75 as tier 3, 76–86 as tier 4, and unranked as tier 5. The score is then reversed so that higher numbers represent higher ranked departments.

1 The list of prior studies of faculty research productivity is lengthy, and we present only a cursory overview here. An extensive bibliography on the topic is available from the authors upon request.

2 Prior political science studies generally address the effect of research productivity on departmental or program-level performance (e.g., Ballard and Mitchell Reference Ballard and Mitchell 1998 ; Garand and Graddy Reference Garand and Graddy 1999 ; McCormick and Bernick Reference McCormick and Bernick 1982 ; McCormick and Rice Reference McCormick and Rice 2001 ; Miller, Tien, and Peebler Reference Miller, Tien and Peebler 1996 ).

3 Fox and Milbourne ( Reference Fox and Milbourne 1999 , 257) use a similar classification for determinants of research output.

4 Teaching load may be measured by self-reported percentages of time spent on teaching (Bellas and Toutkoushian Reference Bellas and Toutkoushian 1999 ), the number of courses a faculty member teaches (Fox Reference Fox 1992 ; Maske, Durden, and Gaynor Reference Maske, Durden and Gaynor 2003 ), or the number of credit hours he or she teaches (Taylor, Fender, and Burke Reference Taylor, Fender and Burke 2006 ). Because the amount of effort required to teach a course can vary depending on whether the course is at an undergraduate or graduate level, a few studies have distinguished between undergraduate and graduate teaching (Fox Reference Fox 1992 ; Porter and Umbach Reference Porter and Umbach 2001 , 174).

5 Studies concerning many of the factors listed in the table 1 have produced mixed results. Jordan, Meador, and Walters ( Reference Jordan, Meador and Walters 1988 ; Reference Jordan, Meador and Walters 1989 ) have reported greater academic research productivity in private than public institutions, but Golden and Carstensen ( Reference Golden and Carstensen 1992 ) have argued that this effect declines after controlling for both research support and the department's reputational ranking (see Dundar and Lewis Reference Dundar and Lewis 1998 , 613, for this review).

6 Davis and Patterson report that after tenure is granted, the publication advantage of being employed at a top-tier institution disappears, although “faculty in all ranks of departments offering the Ph.D. publish more than … economists employed in departments not granting the Ph.D” ( Reference Davis and Patterson 2001 , 87).

7 Because academic rank is also partly determined by research productivity, Broder employs two-stage least squares and suggests a model in which rank and productivity are simultaneously determined ( Reference Broder 1993 , 117).

8 This accounting is the most common measure used in studies of scholarly productivity (Dundar and Lewis Reference Dundar and Lewis 1998 , 616; Broder Reference Broder 1993 , 119; Maske, Durden, and Gaynor Reference Maske, Durden and Gaynor 2003 ; Rodgers and Neri Reference Rodgers and Neri 2007 , 73), although Buchmueller, Dominitz, and Hansen ( Reference Buchmueller, Dominitz and Hansen 1999 , 68) count both all publications and the number of articles published in “top” journals. Fox and Milbourne ( Reference Fox and Milbourne 1999 , 259) count papers and notes in refereed, internationally recognized journals and authored research books (adjusting for coauthored papers by dividing the number of pages by the number of authors). Some studies limit the time frame during which a scholar's work is published (Tien and Blackburn Reference Tien and Blackburn 1996 ; Sax et al. Reference Sax, Hagedorn, Arredondo and Dicrisi 2002 ).

9 The average age of male respondents was 50 years, while the average age of female respondents was 45. Thus, on average, the male respondents had 5 more years of publishing time (based on age) than did the female respondents.

10 Tier IV and unranked departments are excluded from table 2 .

11 All three versions of the dependent variable are highly correlated: the correlation coefficient for the original responses (answering the question of how many articles had been published) and the measure that replaces missing responses with zero is 1.0, while the correlation coefficient between the original responses and the log of one plus the measure that replaces missing responses with zero is 0.78.

12 Because some of the factors that contribute to faculty research productivity involve individual-level attributes while other variables are related to departmental or institutional environments, multi-level statistical models have been suggested to account for the nested structure of data (Porter and Umbach Reference Porter and Umbach 2001 ).

13 In ranking departments, we used Schmidt and Chingos' ( Reference Schmidt and Chingos 2007 ) rankings. We also tested models using the 1995 National Research Council rankings and the U.S. News and World Report rankings of graduate programs. Similar results emerge regardless of the source of the ranking.

14 Specifically, we used the standalone version of AmeliaView in the Windows environment, downloadable from the developer's website at http://gking.harvard.edu/amelia/ . We did not use the multiple imputation procedures to replace missing values in our dependent variables but instead only imputed a group of independent variables. We transformed positively skewed variables by using a natural logarithm. Slight changes were made to two of the imputed variables to have them make practical sense. For the “year of Ph.D. degree” variable, we assigned the year (value) of 2009 if the imputed values were more than 2009.3. For the “climate collegiality” variable, we assigned a value of five if the imputed values were greater than the scale value maximum of five from original dataset. For the rest of the variables, we used the imputed values' unaltered form as drawn from an Amelia-created output dataset.

15 Other attitudinal variables are also related to article output, but in the interest of parsimony, we did not include these other scales in our model. More information on the different attitudinal indicators included in the survey is available from the authors on request.

16 When Model IV is tested on the original version of the question with missing responses removed, the significant professional environment variables are the count of overall resources and current employment in a department that offers the Ph.D. degree.

17 Other variables were also tested prior to settling on Model V as our full model. For example, we checked whether holding a joint appointment, being part-time, being in a tenure track position, or having received a postdoctoral fellowship affected article production, but we found each to be consistently insignificant. These results may partly be a reflection of the highly skewed distribution on these variables, given that relatively few respondents hold a joint appointment, are part-time, hold a non-tenure track position, or have received a postdoctoral fellowship. We also looked at characteristics of the undergraduate institution and major but found these to be unrelated to article output. We checked the geographic location of the Ph.D.-granting institution but found this variable as well to be unrelated to the number of articles published. Finally, we checked whether the number of graduate courses taught (in addition to the number of undergraduate courses taught) affected article production. This number is highly correlated (inversely) with the number of undergraduate courses taught and highly correlated with employment in a Ph.D.-granting department, so we dropped the number of graduate courses taught from our analysis.

18 On average, American faculty members spend six years in a rank before promotion (Bayer and Smart Reference Bayer and Smart 1991 ).

19 As a measure of subfield specialization, we also asked respondents about their major field when working on their doctoral degree. Needless to say, answers to this question are highly correlated with answers to the question about current primary field of teaching and research. We decided to use current specialization in our models, as it is both more proximate and a better predictor—although in general, field of specialization is only weakly or not at all related to publication rates.

20 Age is important to productivity as both a measure of years of experience and an indicator of cohort effects, which may include the state of knowledge in a field at the time an individual is undergoing his or her education or the state of the job market at the time that the doctorate is received (Levin and Stephan Reference Levin and Stephan 1991 , 118). We tested the relationship between age and the number of articles published and found the relationship to be strong and significant. Needless to say, age and year of degree are highly correlated. We selected to use the latter as our predictor variable in the tables reported here. Additional analyses using age are available from the authors upon request. Although the general finding is that “scientists become less productive as they age” (Levin and Stephan Reference Levin and Stephan 1991 , 126; Oster and Hamermesh Reference Oster and Hamermesh 1998 ), the total number of years in academe is correlated with the total number of articles produced (Maske, Durden, and Gaynor Reference Maske, Durden and Gaynor 2003 , 561).

21 In hypothesizing differences between older and younger cohorts, Broder ( Reference Broder 1993 , 121) estimates predictive models for three different samples: the full sample, individuals more than six years from obtaining their Ph.D., and assistant professors only.

22 Sax et al. conclude that “women publish less in part because they are less driven by a desire to produce numerous publications and receive professional accolades…. Women are more likely than men to view an academic career as an ‘opportunity to influence social change’” ( Reference Sax, Hagedorn, Arredondo and Dicrisi 2002 , 436). Harris and Kaine ( Reference Harris and Kaine 1994 ) find that, in general, individual motivation is as important for productivity as resource support.

23 Several other explanations for observed differences between men and women have been offered, such as the ideas that women generally do not have enough professional connections and collegial networks that can facilitate publishing (Mathew and Andersen Reference Mathew and Andersen 2001 ), that women specialize less than men (Leahey Reference Leahey 2006 ), and that discriminatory practices exist in the publication process (Ferber and Teiman Reference Ferber and Teiman 1980 ). We do not have the data required to adequately test these arguments.

24 Sax et al. determine that “family factors, such as having children … have virtually no effect [on faculty research productivity]” ( Reference Sax, Hagedorn, Arredondo and Dicrisi 2002 , 436). Similarly, Hamovitch and Morgenstern ( Reference Hamovitch and Morgenstern 1977 ) assert that child-rearing does not interfere with women's research productivity.

25 Race, as measured by Maske, Durden, and Gaynor ( Reference Maske, Durden and Gaynor 2003 , 562), was found to have no effect on the total number of articles produced (see also Sax et al. Reference Sax, Hagedorn, Arredondo and Dicrisi 2002 , 438).

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• Volume 44, Issue 2
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• DOI: https://doi.org/10.1017/S1049096511000242

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Scholarly productivity is grounded, in part, in strong research skills and writing habits. The Office of Faculty Affairs partners with the UGA Libraries, Write@UGA , the Department of English, and faculty colleagues to provide programs and workshops in support of faculty writing productivity.

Many offices provide resources and support for scholarly productivity. For example, if you are interested in the Scholarship of Teaching and Learning, see the SoTL page on the Center for teaching and Learning’s website. Each fall, you can enjoy the Office of Research orientation that showcases its extensive support for faculty. Public Service and Outreach also supports scholarly endeavors. You may also want to connect with one of UGA’s centers and institutes .

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Select Books and Resources

• Belcher, W. L. (2009b). Writing your journal article in twelve weeks: A guide to academic publishing success. Sage.
• Boice, R, & Jones, F. (1984). Why academicians don’t write. The Journal of Higher Education, 55(5), 567–582.
• Boice, R. (1990). Professors as writers: A self-help guide to productive writing. New Forums Press.
• Geller, A. E., & Eodice, M. (2013). Working with faculty writers . Utah State University Press.
• Germano, W. (2001). Getting it published: A guide for scholars and anyone else serious about serious books (1st ed.). University of Chicago Press.
• Jensen, J. (2017). Write no matter what: Advice for academics. University of Chicago Press.
• Johnson, W., & Mullen, C. (2007). Write to the top!: How to become a prolific academic (1st ed.). Palgrave Macmillan.
• Moxley, J., & Taylor, T. (Eds.). (1996). Writing and publishing for academic authors (2nd ed.). Rowman & Littlefield.
• Murray, R. (2004). Writing for academic journals.
• Murray, R. (2014). Writing in social spaces: A social processes approach to academic writing.
• Silvia, P. J. (2007). How to write a lot.
• Stevens, D. D. (2019). Write more, publish more, stress less! : Five key principles for a creative and sustainable scholarly practice.
• Sword, H. (2012). Stylish academic writing.
• Sword, H. (2016). The writer’s diet: A guide to fit prose.
• Sword, H. (2017). Air & light & time & space: How successful academics write.
• Sword, H. (2023). Writing with pleasure.
• Tulley, C. (2018). How writing faculty write: Strategies for process, product, and productivity.

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With $1.22 billion in research expenditures, one of the 15 largest academic research libraries, top ranked graduate fields, and some of the best facilities in academia, Cornell University is consistently ranked as one of the most comprehensive universities in the world.

Cornell has more than 150 interdisciplinary research centers, institutes, laboratories, and programs where research is conducted on everything from nano-particles to labor migration in Nepal. The Graduate Linkage program links graduate students at Cornell University’s Ithaca campus with the Weill Cornell Medical College (WCMC) and Graduate School of Medical Sciences (WCGSMS).

Graduate education receives strong financial support with nearly $50 million in tuition, stipend, and organized research expenditures. Cornell is ranked among the nation’s top five academic institutions in National Science Foundation research funding. ( Source: Cornell Research .)

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• Cornell scientists are building and testing a prototype of a linear accelerator-driven, ultrabright x-ray source – the Energy Recovery Linac (ERL) – an essential tool for investigating all types of materials, physical phenomena, biological molecules, and chemical processes.
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How to be a productive scholar.

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Not every academic wants to be as productive as the late  G. Michael Pressley . A professor of education and psychology at Michigan State University, Pressley had published more than 350 articles and books by the time he died in 2006. But most scholars — emerging and seasoned alike — do look for ways to boost their productivity. What can we learn from the super prolific? How do they do it? [...]

Productivity starts with a good mentor."

For the full article, visit: https://www.chronicle.com/article/how-to-be-a-productive-scholar

Institutional Research, Assessment and Planning

Strategic plan for research, scholarship, and creative activities.

In Appalachian State University's 2022-2027 Strategic Plan , one of the six strategic priorities is advancing research, innovation and creativity. The university has developed a strategic plan to accomplish this priority by building a roadmap to facilitate research, scholarship and creative activities (RSCA) at App State. This is the first such plan in the history of the university. 

The new Strategic Plan for RSCA has four goals, each of which is supported by multiple strategies and action items:

• Goal 1: Increase Awareness and Understanding of the Vital Role RSCA Plays in Fulfilling the University’s Mission.
• Goal 2: Enhance the Overall Productivity and Efficiency of App State’s RSCA.
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• Goal 4: Increase External Engagement and Investment in RSCA.

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The Strategic Plan for RSCA was developed through an inclusive, iterative process that reflects the diverse perspectives of the App State Community. A steering committee and working group led the development effort, with the support of faculty and staff members who contributed their ideas through a series of meetings held in person and on Zoom, as well as through an anonymous online survey.

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Interim Vice Provost for Research and Innovation Christine Hendren, Ph.D. is leading implementation of the Strategic Plan for RSCA through a systematic process that engages members of the App State Community in a range of activities to help bring the new plan to life. The implementation plan will enable our community to collectively articulate and enhance our shared vision for excellence in research, scholarship and creative activities at our university.  Learn more about the detailed implementation plan, including its five themes, specific actions for each theme and the timeline for those actions. 

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Insights from the Top-Ranking Scholars on their Research Productivity

American Marketing Association Doctoral Student Special Interest Group

The academic world has long wondered about the factors that lead some research scholars to publish prolifically in the top journals in marketing. Especially intriguing is the consistency of a select few research scholars who rank at the top of the most productive researchers in marketing, year after year. Is there a secret formula? If yes, what is it? What does it take to follow in their footsteps and build a strong publication record over the years? AMA DocSIG reached out to the top 5 researchers in the Author Research Productivity List for Premier AMA and Premier Marketing Journals (2011 – 2020) to learn more about their productivity and consistent presence in the Most Published lists. We reached out to Dr. Pradeep Chintagunt a, Dr. Darren Dahl , Dr. Dhruv Grewal , Dr. Rajdeep Grewal , Dr. Christian Homburg , Dr. V. Kumar, and Dr. Robert Palmatier who collectively represent the top 5 researchers in the Author Research Productivity List for Premier AMA and Premier Marketing Journals to gain insights on researching and publishing that can benefit doctoral students and young researchers. Unfortunately, at time of the publication of this article, we were unable to receive responses from Dr. Dhruv Grewal and Dr. Palmatier. The insights from the other top researchers shed light on the question of whether research is driven by “nature” or “nurture,” or a combination of both. The top 5 researchers who shared their insights all agreed that that a lot of factors have contributed to their consistent research productivity. Some of the most important factors in consistently publishing in top marketing journals are intrinsic qualities, or the “nature” of individual researchers—for instance, hard work, resilience, passion for research, and willingness to learn . Some other factors are extrinsic and depend on the individual researcher’s environment that “nurtures” them, for instance, collaborations with research teams or coauthors, discussions with the marketing academic community, and interactions with managers . We delve deeper into these intrinsic and extrinsic factors in this article.

Some of the most important factors in consistently publishing in top marketing journals are intrinsic qualities, or the “nature” of individual researchers—for instance, hard work, resilience, passion for research, and willingness to learn.

            According to Dr. Rajdeep Grewal , hard work is a key intrinsic factor for researchers to consistently publish in top journals. Most of the top researchers we spoke to also emphasize the importance of persistence to go through the tough process of publishing quality research. Dealing with the review process and manuscript rejection is difficult for all researchers, even the most productive researchers. Their success lies in their ability to stay motivated even in the face of the rejection and learn from reviewers’ feedback. Dr. Kumar reveals, “For me, getting articles accepted is very difficult, and it wears me out. It is tough to keep the motivation high when manuscripts keep getting rejected. But when I work with coauthors and doctoral students, we all need to build the confidence so that we do not give up but keep working with hope. Therefore, persistence, and learning to survive the rejection are two necessary traits.” The importance of being resilient in dealing with manuscript rejection is echoed by Dr. Chintagunta , who stresses, “We need to be tenacious without viewing the review process as adversarial”. He says, “We get rejected all the time; the question is what do we do after that so hanging in there and taking the comments seriously is important.” Dr. Dahl believes that the secret to success in publishing lies in learning from the review process and feedback. He discloses, “Like everyone, I have a lot of rejection decisions! I think those that are successful at publishing are able to take learnings from the rejection decisions or tough reviews and reapply themselves to the research challenge. That is often easier said than done, since rejections are so tough, but I think this is a big factor in success.” While acknowledging that dealing with manuscript rejection can be potentially demotivating and create disharmony, Dr. Kumar notes that, “I always believe that better times are ahead, and I keep working.”

At the same time, the dynamic nature of marketing and the evolutions in the field foster a conducive environment for researchers to work on timely, relevant, and new and interesting topics. As Dr. Dahl mentions, “Marketing is continually evolving in terms of topics of interest, approaches to data collection, statistical tools utilized, and expectations/norms in the research exposition.” The top journals look for new, interesting topics and value innovative submissions. As a result, Dr. Kumar believes that the newness and timeliness of the topic are important, but the research must follow “the principle of rigor and relevance as the foundation” . The identification of new and interesting topics for research implies that researchers need to stay updated on emerging topics of interest. Dr. Rajdeep Grewal notes, “Staying current with methodological advances, working on important topics – either emerging ones or topics that managers consider to be important – has helped me consistently publish in top journals.”

In order to identify truly interesting topics and publish with impact, Dr. Homburg relies on his interactions with managers, saying, “I regularly talk with managers about their struggles and successes. Even though their tactics with immediate returns are intriguing, it is mostly their strategic choices that make for substantive topics.” He also advises researchers and doctoral students to base their research on managerially relevant phenomena rather than on data availability. Dr. Homburg cautions, “A significant share of research appears to be driven by data availability instead of starting from phenomena relevant to managers. Research from the ivory tower is rarely impactful. Marketing research that can change the way managers think and act has an impact. Sadly, many PhD programs put too little emphasis on marketing strategy issues.”

Dr. Dahl suggests a different approach to ensure that the research is focused and directed toward publication from the start. He takes his cue from the top journals by being aware and mindful of what they are looking for, identifying the journal that best fits the research, and bringing innovative ideas and methods to the research. According to him, “I watch the signals sent by the editors of these journals and I also look to see how I can personally improve my own approach to research in the methods and topics I look to pursue. At the outset of research projects, I look to the journal that would best fit for the project. Importantly, the conceptualization and empirical work are built out to fit the journal – being purposeful about what you target as outlet is important. I think I try to put a high value point on being innovative and creative in the questions I tackle, the experimental designs I utilize, and/or the variables I work with.”

Regardless of the approach that researchers choose to develop their ideas, Dr. Chintagunta urges researchers to “stay passionate about research and be open to learning about new ideas and methods” as they keep publishing in top journals. The collaborations of the top researchers with their research team and coauthors contribute immensely to their consistent productivity, motivation, and learning over the years. Dr. Kumar recognizes the vital role of his research team, saying, “I am fortunate to have great coauthors and the opportunity to learn from them constantly.” Collaborations with other researchers and doctoral students provide opportunities for learning and skill development and create encouraging and motivating work environments that promote quality research. According to Dr. Homburg , “ I think the research team I work with is a big part of the consistency. I have been blessed with amazing coauthors – people that contribute so much and are fun to work with. I look to work with people that augment my skill set and make the research better.” The support of the wider marketing community in developing research ideas is also critical. Dr. Chintagunta identifies this as a key factor in his research productivity, narrating, “Being surrounded by the wonderful marketing community has helped me consistently publish in top journals. Beginning with my colleagues, students (current and former) and everyone else in the profession who is willing to have a research conversation that may (or in many cases, may not) go anywhere.”

            The top researchers also have words of help and encouragement for researchers and doctoral students who struggle with the publication hurdle. According to them, any research that makes it to publication needs to be well developed, relevant in a broader context, and have a clearly defined contribution. Dr. Homburg believes in prioritizing quality over quantity and underscores the significance of publishing impactful research, saying, “Substantive issues require much research, thought, and time to develop. One thoroughly developed paper easily outweighs the long-term impact of several quick-win pieces. Aiming for interesting and high-quality projects helps me consistently publish in top journals.” In his experience of reading and reviewing manuscripts over the years, Dr. Homburg believes that researchers need to consider the relevance and implications of their research to managers in the real world. He explains, “Papers that did not pass the publication hurdle commonly suffered from being too narrow. Research typically focuses on very specific issues. For example, whether some abstract environmental variable Z moderates firm capability X’s relationship on performance measure Y. In reality, however, many marketing challenges appear in a broader context. Therefore, I recommend young scholars, especially, to take a step back and consider the broader implications of their research.” Dr. Dahl identifies the need to be very clear about the contribution of the research (theoretical, substantive, and/or methodological) as one of the biggest lessons that he has learned over time. According to Dr. Dahl, the contribution hurdle is the greatest barrier that needs to be overcome to publish in the top journals and researchers need to define the contribution of their research clearly. He urges, “Keeping the contribution as the central goal and being able to clearly express what the contribution of the work is, is paramount to landing your work.”

            While these top researchers acknowledge that multiple different factors contribute to their consistent research productivity, their insights suggest that research is driven by a combination of “nature” and “nurture”. The environment around a researcher (collaborations, managerial interactions, and networking in the marketing community) can help in the identification and development of a research idea for publication. However, there is no substitute for hard work and passion in converting a research idea to an impactful publication. The researcher will need to draw on their inner strength and resilience to handle rejection from reviewers and stay motivated through the review process. Undoubtedly, the advice of the top researchers in the field will be valuable in enabling young researchers and doctoral students in building a strong and impactful publication record.

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Evaluation of the Heart and Stroke Foundation of Canada Research Scholarship Program: research productivity and impact

Affiliation.

• 1 Department of Medicine, University of Alberta, Edmonton, Canada. [email protected]
• PMID: 9179090

Objective: To compare the research productivity, and its impact, of individuals awarded research scholarships from the Heart and Stroke Foundation of Canada (HSFC) with that of a parallel group of unsuccessful applicants during the funding years 1980/81 to 1989/90 inclusive. Research productivity was defined as the number of peer reviewed publications, and impact was evaluated from the number of publications cited; the number of citations per publication; the number of citations per individual; and the impact score.

Study selection: Data were collected on 192 individuals. Cohorts were defined as successful and unsuccessful individuals entering the system in the same year. The study comprised 10 separate cohorts. Data were collected on yearly publications and citation counts for each individual. These data, along with journal impact factors, were obtained from the Institute for Scientific Information.

Conclusions: During the 10 years of the study, individuals funded by the HSFC published more papers, more of their papers were cited, and they received more citations per individual than the unfunded comparison group. This consistency in multiple indicators provides strong evidence that funded individuals are more productive and that their work has a greater impact on the body of knowledge in this area. Although this study cannot unequivocally show a direct causal relation between funding and research success, the trend as shown by the indicators studied suggests a beneficial effect.

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Academic Writing Productivity: Evidence Based Strategies

• School of Behavioral Sciences

Research output : Contribution to journal › Article › peer-review

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T1 - Academic Writing Productivity: Evidence Based Strategies

AU - Sosin, Lisa S.

PY - 2019/10/1

Y1 - 2019/10/1

KW - academic writing

UR - https://digitalcommons.liberty.edu/ccfs_fac_pubs/206

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SUNY Old Westbury Accounting Faculty Ranked Nationally for Research, Scholarship Productivity

The State University of New York at Old Westbury has been ranked 14th in the nation in a recent study of the productivity of academia in the accounting profession. Additionally, two members of the faculty of the College’s School of Business were named among the top 20 in a review of “Leading Academic Authors.”   

“These are tremendous accomplishments for the School of Business and the faculty of the Accounting, Taxation and Business Law Department ,” said Dr. Teresa A. Miller, SUNY Old Westbury officer-in-charge. “Professional distinction at this high level shows that our faculty truly fulfills the mission of the School of Business by providing high-quality, relevant scholarship and teaching. For an accounting department at a four-year liberal arts college to achieve this demonstrates that it is punching far above its weight class.”   

Published in the  May 2020 edition of Issues in Accounting Education , the study reviewed all contributions to the top 5 peer-reviewed practice Journals for the period between 2013-2017 to develop a list of top academic institutions contributing to accounting practice. 

The scholarly productivity of SUNY Old Westbury’s accounting faculty exceeded that of a host of accounting departments at highly regarded research universities, including North Carolina State University, Rice University, St. John’s University and Rutgers University-Newark.  Dr. James Fornaro and Dr. Carey Lang tied for 16th in the ranking of authors and SUNY Old Westbury was the only SUNY institution in the rankings.   

"Knowledge generation and knowledge dissemination are integral parts of a teacher's arsenal when they enter a classroom,” said Dr. Raj Devasagayam, dean of the School of Business. “Our mission requires us to excel in our classrooms and deliver innovative education. This honor underscores our commitment to our students." 

“In recent years, a number of important bodies in the accounting profession have publicly called for greater ‘practice-relevant’ research in our discipline,” said Accounting, Taxation and Business Law Department Chair Kenneth Winkelman.  “Specifically, AACSB International has itemized ‘publications in practitioner journals’ as an important measure demonstrating ‘impact,’ a key component in obtaining and maintaining accreditation under their world-leading standards.” 

SUNY Old Westbury is a member of AACSB International , The Association to Advance Collegiate Schools of Business.  AACSB International provides quality assurance, business education intelligence, and professional development services to over 1,700 member organizations and more than 840 accredited business schools worldwide.  

Issues in Accounting Education is a publication of the American Accounting Association , the largest community of accountants in academia. The journal publishes research, commentaries, instructional resources, and book reviews to assist accounting faculty in teaching and to address important issues in accounting education. 

SUNY Old Westbury has been preparing students for careers in accounting, finance, management, and marketing for more than 40 years. Through the instruction and research of its faculty, the School of Business pursues its mission of empowering students with the knowledge, skills, and values to think critically, communicate effectively, and act responsibly. Serving more than 800 students in its undergraduate and graduate programs, the School of Business offers rigorous academic training in a small class atmosphere. 

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Digital Scholarship Series at Kelvin Smith Library

Join the Kelvin Smith Library for Digital Scholarship workshops, where you will learn how open-source and digital tools can transform your research and enhance the creation and communication of your scholarship. 

Throughout the fall, the workshops will take place in Kelvin Smith Library, classroom 215 at 1 p.m. The series begins on Monday, Sept. 16, with “Open Source: What does it mean? Why is it important?” This workshop will be hosted by David Beales, digital scholarship partner.

To see examples of research projects by faculty and students using digital scholarship tools, take a look at the past Freedman Student and Faculty Fellows.  

Learn more about each workshop and register.

Propel AI initiative aims to weave AI into scholarship, research

This fall, roughly 60 faculty members will take part in a new artificial intelligence initiative from the Office of the Vice President for Research. Called Propel AI, the effort aims to empower faculty to incorporate artificial intelligence into their work — regardless of their academic area or previous experience with AI.

Through a series of workshops throughout the 2024-2025 academic year, faculty will gain insights into the rapidly evolving landscape of AI research and work with each other to foster collaborative partnerships across disciplines.

We spoke with Bryant Walker Smith, associate professor of law and an expert on emerging technologies, about the new initiative. Smith is one of three faculty members tapped by Vice President for Research Julius Fridriksson to guide Propel AI as it launches this semester.

My understanding is that you’re part of a group that’s been formed by the vice president for research to increase the adoption of AI tools on campus. Is that kind of the goal here?

Bryant Walker Smith: What we are trying to do is foster a community within the university of people who are really interested in AI in whatever way that that speaks to them. And so that includes some people who have no real experience with AI and some people who have been using some of these machine learning tools for a decade.

I think that’s really important, because if a university goes out and just tries to compete with every other university for the established AI superstar in each field, we’re never going to get all of them. I think the better approach is to realize how much is already happening, how we can support what’s happening, and even more than that, how many people have yet to pick the low-hanging fruit applicable to their work?

That’s what I’m really interested in — somebody who has an amazing idea, and it’s just never occurred to them that they could use some AI tools to do the work 10 times as fast, or analyze 10 times as much data or reach people 10 times more effectively.

So, one of our challenges is, how do we take this diverse group of participants and make the internal connections in a way that is helpful to them and also try to bring in other expertise, whether that’s expertise from our own faculty, our own AI Institute or expertise from outside the university.

"This is an impressive group, and they are bringing a lot of expectations to this initiative. I really want to give them an experience that delivers what they’re looking for, whether that’s information or knowledge or skills or community or connections."

You had an application process for faculty. Did you see a lot of interest?

I was surprised by the level of interest, and I was even more impressed with the application statements. There were so many times when I added a note like, ‘We need to get them involved as a teacher here,’ ‘we need to partner with them,’ ‘they need to present’ or ‘I had no idea this was happening.’

For example, I have for years been obsessed with the idea of animal communication and the ways that machine learning tools might provide us insight or give us the impression that we’re able to communicate with animals. And I saw in the applications that one of my colleagues is doing that. I was like, ‘Oh my goodness, I need to meet them.’ That was really exciting — seeing how much cool stuff is happening at our university, and how much thought is already going into the potential for AI tools.

What are some other examples that crossed your desk?

One person wrote, ‘I don’t have any experience with AI, but I work with large data sets and complex engineering and social science topics.’ And immediately that just shouted, ‘Oh my goodness, you have access to data. And if we give you this little boost — an introduction to these tools — you could potentially do so much more with those data.’ And then there was someone who works in film history and the history of technology. So, again, they have access to incredible data — and this person actually has some experience with machine learning. So, they’ve been thinking about, ‘How can I play with these vast data sets that previously would have literally taken somebody watching every movie, every film?’ Now there’s the possibility to do in hours what previously would have taken years.

Did you get the kind of breadth that you were hoping for — from arts and humanities, as opposed to the fields that we might expect to see more applications from?

I have not analyzed the proportion of our applicants and how they correspond to their numbers at the university as a whole. But, for example, we have a fair number of people who are doing medical or public health research, and I think that was to be expected. We also have people in English, in criminology, in law, in journalism, and in social work, African American studies, environmental science, business. Also nursing, exercise science and several from education.

I think the other thing that we’ve emphasized is people from a range of points in their career. We have some instructors, some assistant professors, some associate professors, some full professors. That’s another kind of diversity we really want.

Along with the excitement about adopting AI and its potential, there’s also some people who have fear or hesitation around it. Are limits and potential misuses going to be sort of baked into the workshops that you’re doing?

Certainly, one of our goals is to provide a very holistic assessment of AI, including the various and varied limits, misconceptions, benefits, opportunities and risks — the ways that it can be used and misused in society and in research and in teaching.

This is the story of progress or policy or research or anything where you replace an old set of problems with a new set of problems. And you really hope that the new set of problems, in aggregate, are less than in your old set. I think we’re going to see that here as well.

It sounds like a very complicated but worthwhile endeavor.

Our colleagues are incredible, and that is both with regard to what they are thinking about generally, and the way that many of them have already integrated AI into their work. This is an impressive group, and they are bringing a lot of expectations to this initiative. I really want to give them an experience that delivers what they’re looking for, whether that’s information or knowledge or skills or community or connections.

Duke School of Medicine Names Recipients of 2024-2025 Dean’s Scholarships

Duke University Executive Vice President for Health Affairs and School of Medicine Dean Mary E. K lotman , MD, and the Office of Biomedical Graduate Education have announced the recipients of the 2024-2025 Dean’s Scholarships. 

The scholarships, awarded to first-year PhD students, recognize exceptional scientific potential and the unique perspectives and experiences the recipients bring to their scientific fields. Each scholar will receive a $2,000 award. 

The 2024 Dean’s Scholarship recipients are: 

• Melissa Aldana, University Program in Genetics and Genomics 
• Connor Herbst, Biostatistics 
• Jillian Saunders, Neurobiology 

The International Dean’s Scholarship recognizes second-year international PhD students who show exceptional academic and scientific promise and their PhD advisors who exemplify excellence in mentoring. These scholars will receive one year of tuition and fees, and stipend to support their research efforts. 

The 2024 International Dean’s Scholarship awardees are:    

• Yaxin (Zoe) Liu, Immunology, mentor: Ed Miao, MD/PhD 
• Han (Medy) Mu, Neurobiology, mentor: Josh Huang, PhD 
• Dennis Owusu, Computational Biology and Bioinformatics, mentor: Sandeep Dave, MD  
• Sooyeon (Soo) Park, Cell and Molecular Biology, mentor: Sheng Yang He, PhD 
• Brenda Pardo Loredo, Developmental and Stem Cell Biology, mentor: Stefano diTalia, PhD 
• Kijun (Ki) Song, Biochemistry, mentor: Priyamvada Acharya PhD 
• Jialin (Charlie) Wu, Cell and Molecular Biology, mentor: Jorg Grandl, PhD 

All 2024-2025 Dean’s Scholars will take part in yearlong programming designed to foster community-building and enhance professional development. 

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NSF Scholarships in Science, Technology, Engineering, and Mathematics Program (S-STEM)

View guidelines, important information about nsf’s implementation of the revised 2 cfr.

NSF Financial Assistance awards (grants and cooperative agreements) made on or after October 1, 2024, will be subject to the applicable set of award conditions, dated October 1, 2024, available on the NSF website . These terms and conditions are consistent with the revised guidance specified in the OMB Guidance for Federal Financial Assistance published in the Federal Register on April 22, 2024.

Important information for proposers

All proposals must be submitted in accordance with the requirements specified in this funding opportunity and in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) that is in effect for the relevant due date to which the proposal is being submitted. It is the responsibility of the proposer to ensure that the proposal meets these requirements. Submitting a proposal prior to a specified deadline does not negate this requirement.

Supports institutions of higher education to fund scholarships for academically talented low-income students and to study and implement a program of activities that support their recruitment, retention and graduation in STEM.

In 1998 Congress enacted the American Competitiveness in the Twenty-First Century Act which provided funds to the National Science Foundation (NSF) to create a mechanism whereby the hiring of foreign workers in technology-intensive sectors on H-1B visas would help address the long-term workforce needs of the United States. Initially, scholarships were only provided for students in mathematics, engineering, and computer science. Later legislation authorized NSF to expand the eligible disciplines at the discretion of the NSF director. Undergraduate and graduate degrees in most disciplinary fields in which NSF provides research funding (with some exclusions described elsewhere in this document) are eligible as long as there is a national or regional demand for professionals with those degrees to address the long-term workforce needs of the United States.

The main goal of the S-STEM program is to enable low-income students with academic ability, talent or potential to pursue successful careers in promising STEM fields. Ultimately, the S-STEM program seeks to increase the number of academically promising low-income students who graduate with a S-STEM eligible degree and contribute to the American innovation economy with their STEM knowledge. Recognizing that financial aid alone cannot increase retention and graduation in STEM, the program provides awards to institutions of higher education (IHEs) not only to fund scholarships, but also to adapt, implement, and study evidence-based curricular and co-curricular [1] activities that have been shown to be effective supporting recruitment, retention, transfer (if appropriate), student success, academic/career pathways, and graduation in STEM.

Social mobility for low-income students with academic potential is even more crucial than for students that enjoy other economic support structures. Hence, social mobility cannot be guaranteed unless the scholarship funds the pursuit of degrees in areas where rewarding jobs are available after graduation with an undergraduate or graduate degree.

The S-STEM program encourages collaborations, including but not limited to partnerships among different types of institutions; collaborations of S-STEM eligible faculty, researchers, and academic administrators focused on investigating the factors that affect low-income student success (e.g., institutional, educational, behavioral and social science researchers); and partnerships among institutions of higher education and business, industry, local community organizations, national labs, or other federal or state government organizations, as appropriate.

To be eligible, scholars must be domestic low-income students, with academic ability, talent or potential and with demonstrated unmet financial need who are enrolled in an associate, baccalaureate, or graduate degree program in an S-STEM eligible discipline. Proposers must provide an analysis that articulates the characteristics and academic needs of the population of students they are trying to serve. NSF is particularly interested in supporting the attainment of degrees in fields identified as critical needs for the Nation. Many of these fields have high demand for training professionals that can operate at the convergence of disciplines and include but are not limited to quantum computing and quantum science, robotics, artificial intelligence and machine learning, computer science and computer engineering, data science and computational science applied to other frontier STEM areas, and other STEM or technology fields in urgent need of domestic professionals. It is up to the proposer to make a compelling case that a field is a critical need field in the United States.

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Bachelor of Arts, Bachelor of Science, Bachelor of Engineering and Bachelor of Applied Science

Master of Arts, Master of Science and Master of Engineering

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S-STEM Eligible Disciplines

Disciplinary fields in which research is funded by NSF, including technology fields associated with the S-STEM-eligible disciplines (e.g., biotechnology, chemical technology, engineering technology, information technology, etc.).

The following degrees and disciplines are excluded :

• Clinical degree programs, including medical degrees, nursing, veterinary medicine, pharmacy, physical therapy, and others not funded by NSF, are ineligible degrees.
• Business school programs that lead to Bachelor of Arts or Science in Business Administration degrees (BABA/BSBA/BBA) are not eligible for S-STEM funding.
• Masters and Doctoral degrees in Business Administration are also excluded.

Proposers are strongly encouraged to contact Program Officers before submitting a proposal if they have questions concerning degree or disciplinary eligibility.

The S-STEM program particularly encourages proposals from 2-year institutions, Minority Serving Institutions (MSIs), predominately undergraduate institutions, and urban, suburban and rural public institutions.

[1] an activity at a school or college pursued in addition to the normal course of study.

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S-stem: reviewer survey 2024, s-stem: overview webinar, program contacts.

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New Research Documents Substantial Financial  and Safety Impacts from Truck Driver Detention

FOR IMMEDIATE RELEASE

September 10, 2024

Contact: Alex Leslie

Phone: 651-641-6162 ext.2

Email: [email protected]

Washington, D.C. – The American Transportation Research Institute (ATRI) today released a new report that quantifies the major consequences that truck driver detention at customer facilities has on industry productivity and safety. The research quantifies the direct costs for fleets, truck drivers and supply chains in general. It also corroborates previous research that detained trucks drive faster both after, and before, a detained trip occurs.

While driver detention has decreased slightly in the last few years, the overall costs of being detained at customer facilities for more than two hours is substantial. In 2023, drivers reported being detained in 39.3 percent of all stops. The frequency of detention was even higher among women drivers (49.1%), refrigerated trailer drivers (56.2%), and among fleets that operate in the spot market (42.5%).

Based on industry-reported data, truck drivers were detained between 117 and 209 hours per year, depending on the sector. In for-hire trucking alone, the total time lost to truck driver detention exceeded 135 million hours in 2023.

While 94.5 percent of fleets charge detention fees, they are paid for fewer than 50 percent of those invoices. As a result, the trucking industry lost $3.6 billion in direct expenses and $11.5 billion in lost productivity from driver detention in 2023. Additional ATRI impact assessments quantified supply chain inefficiencies, lost driver pay and driver turnover resulting from detention.

Finally, an analysis of ATRI’s large truck GPS data at different customer facility types found that detention contributes to higher truck speeds. Trucks that were detained drove 14.6 percent faster on average than trucks that were not detained. Interestingly, trucks also drove faster on trips to facilities where they were detained, indicating that truck drivers know which firms and facilities will likely detain them.

“Detention is so common that many industry professionals have accepted it as inevitable without realizing the true extent of its costs,” said Chad England, C.R. England CEO . “ATRI’s report puts real-world numbers to the true impact that truck driver detention has on trucking and the broader economy.” A full copy of the report is available through ATRI’s website here .

The American Transportation Research Institute (ATRI) has been engaged in critical transportation studies and operational tests since 1954.  ATRI is a 501(c)(3)  not-for-profit research organization headquartered in Washington, D.C., with offices in Atlanta, Minneapolis, and  Sacramento.

ATRI’s primary mission is to conduct transportation research, with an emphasis on the trucking industry’s essential role in a safe, efficient and viable transportation system.  ATRI’s research focus areas include: Congestion and Mobility; Economic Analysis; Safety and Security; Technology and Operations; Environment; and Transportation Infrastructure.

ATRI’s extensive experience covers a broad range of commercial vehicle operations including leadership and/or participation in numerous national freight analyses, technology research initiatives and field operational tests.

ATRI presently manages the U.S. DOT’s Freight Mobility Program, and has provided freight mobility and performance measures technical assistance to 31 state DOTs and 11 of the 15 largest MPOs in the U.S. ATRI has received top research awards from ITS America, TIDA, University of Minnesota and the Institute of Transportation Engineers.

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• v.111(5); Sep-Oct 2014

Measuring Academic Productivity and Changing Definitions of Scientific Impact

This manuscript provides a brief overview of the history of communication of scientific research and reporting of scientific research impact outcomes. Current day practices are outlined along with examples of how organizations and libraries are providing tools to evaluate and document the impact of scientific research to provide a meaningful narrative suitable for a variety of purposes and audiences.

Introduction

As a measure of scientific impact, publication counts alone are an insufficiently descriptive reporting metric of scientific research activities for the public, physicians, scientists, academic institutions and funding agencies. Shrinking biomedical research funding, along with a growing emphasis by key stakeholders to demonstrate tangible and meaningful outcomes, have motivated stakeholders to devise alternative methods that more concretely quantify the impact of scientific research on knowledge diffusion, healthcare professional uptake, and public health outcomes. In order to establish strategic directions, university administrators simultaneously face increased pressure to analyze research productivity and impact, as well as a return on investment from research. Concurrently, funding agencies face public demand via lawmakers to ensure judicious use of taxpayer supported research while promoting the transparency and availability of research findings. The objective of this manuscript is to briefly review existing and emerging means of reporting on and quantifying scientific research impact, ongoing trends towards harmonization of evaluation for reporting of impact, and to present some examples of how academic libraries provide support.

The Historical Context of Scientific Research Communication

Formal reporting of scientific research and the peer review process dates to 1665 with Henry Oldenburg, (See Figure 1 ), the Secretary of the Royal Society of London, publisher of the Philosophical Transactions , the earliest known scientific journal in continuous publication. [See: Philosophical Transactions, Royal Society Publishing: ( http://rstl.royalsocietypublishing.org/ ).]

Henry Oldenburg in a portrait by Johannes van Cleef.

Image courtesy of the National Library of Medicine.

Scientists during the Renaissance were reluctant to share their scientific discoveries out of concern that others would claim their work. To address this concern, Oldenburg implemented a series of practices that established the process of modern day peer review. He appointed members of the Royal Society as independent experts to review manuscripts before approving for publication. By registering authors and manuscripts, i.e., “time stamping” of new scientific findings, Oldenburg’s methods obligated others to “cite” findings in subsequent manuscripts and ensured a regular schedule for publication of the accepted manuscripts in Philosophical Transactions. 1 The Royal Society of London’s practices were the precursors to modern-day principles of scientific communication and peer review, of which scholarly journals operate in their role to communicate scientific research findings.

The capacity for physicians and scientists to find applicable scientific literature from the 17 th to early 20 th century was rudimentary at best. To keep abreast of contemporary scientific discoveries or clinical findings physicians and scientists relied on journal subscriptions, catalogs containing a list of items held by a library, case histories, medical society memberships or correspondence with others in their field. 2 In the United States (U.S.), print bibliographic indexes or bibliographies on a specific subject to locate scientific literature were not available until the latter part of the 19 th century. Bibliographic indices are collections of references to the published literature generally arranged by subject and/or by author names. In 1879 Index Medicus (the print precursor to MEDLINE®/PubMed®) was introduced and allowed for an additional means of discovery of scientific research findings. 3

Bibliometrics: One Step Forward

During the 20 th century peer review was a proxy for impact, with the quantity of peer-reviewed journal articles or monographs serving as leading indicators of one’s research penetration and professional community recognition for evaluation purposes as exemplified by the “publish or perish” philosophy coined by Logan Wilson in 1942. 4 The large influx of U.S. governmental funding for health research following World War II led to an increase in the number of scientific journals to meet the demand of scientific research reporting. The proliferation of journals and articles spurred the development of bibliographic tools to manage and index peer-reviewed scientific publications. Eugene Garfield in his seminal work from 1955 suggested the possibility of a citation index based on mechanical means to control and track the scientific literature. 5 This led to the development of an index of scientific literature in the early 1960s based not only on indexing of the literature on a specific subject, but also indexing citations to the literature, the Science Citation Index, precursor to the Thomson Reuters Web of Science database. 6 Journal Citation Reports (JCR) followed in 1976, which introduced the Journal Impact Factor score for peer-reviewed journals.

Eugene Garfield and Irving Sher, founders of the Institute for Scientific Information (later absorbed by Thomson Reuters) proposed the JCR Impact Factor score in 1963 as a means of comparing peer-reviewed journals regardless of size. This metric was also used as a journal selection tool for inclusion in the Science Citation Index and later as an acquisitions tool by libraries. 7 The Science Citation Index and Journal Citation Reports were ground-breaking resources that provided new means of quantifying the scientific literature and paved the way for new proxies for impact: citation counts and the JCR Impact Factor score. Tenure and securing external funding gradually became associated with publishing in “high impact” peer-reviewed journals and how frequently an investigator’s publications are cited. 8

The development of automated systems for management of publication data and methods of analysis fostered new areas of study, in particular bibliometrics, a term introduced by Alan Pritchard in 1969. 9 Studies in bibliometrics that outlined the applications of publication and citation data for measuring scientific impact spurred the U.S. government and the National Science Foundation (NSF) to adopt metrics available from Science Citation Reports and Journal Citation Reports for reporting purposes. 10 , 11 Academic institutions soon followed suit.

For varied reasons, the JCR Impact Factor score evolved into a proxy for individual author’s impact or influence of their published works, however unintentional. 12 The higher the JCR Impact Factor score of a journal, the more prestigious any manuscript in that journal was deemed to be. Garfield stressed that the JCR Impact Factor score was designed as a metric for journal performance and warned against its use to evaluate scientific articles and authors. 13 One reason for wide-spread use of the JCR Impact Factor score is that it was, and still is, an easy-to-find single numeric score, and does not require extensive knowledge of database searching. Another reason favoring use of the JCR Impact Factor score as a universal metric for author impact is that the interfaces of the early citation databases were crude. Many physicians and journal editors lack any bibliometric database training or familiarity. To construct a search query for citation analysis required third-party mediation from experts (usually medical librarians) who were familiar with formulating queries, reconciling author name variants, data idiosyncrasies, and capable of interpreting the results. 14

Moving Beyond Citations and the JCR Impact Factor Score

Although citations and the JCR Impact Factor score have been used as an indicator of influence and impact for decades, the landscape is changing. 15 , 16 Advances in computer and digital technology along with the general availability of the Internet spurred the development of additional resources. In 2004, two new citation data resources were introduced: Google Scholar ( http://scholar.google.com/ ), and Elsevier Scopus ( http://scopus.com ). In 2005, Hirsch introduced the h index which is derived from a formula using publications and citations to provide “an estimate of the importance, significance, and broad impact of a scientist’s cumulative research contributions.” 17 The h index is based on a formula that includes the “X” number of an author’s publications and the number of citations that have been cited at least “X” times. For example, an author with an h index of ten has ten publications that have been cited at least ten times. Although the h index is increasingly recognized as a viable and even preferable alternative to the JCR Impact Factor score and raw citation counts to quantify academic productivity, it is not a perfect measure of one’s academic portfolio. First, the h index ignores bedside clinical instruction, journal editing, mentoring, and textbook authorship without which academic medicine would cease to exist. Second, the h index is simply a construct based upon citations, which does not necessarily measure clinical relevance. 18

The attempt to develop one-size-fits all metrics to measure productivity and impact for all disciplines and authors remains elusive despite the numerous attempts to do so. Among the many derivatives are: the v index 19 which includes the proportion of time devoted to research to normalize for clinical academicians who may devote only 40 to 50% of their time to research; the Absolute index (Ab index) 20 which takes into account the impact of research findings while weighting the physical and intellectual contributions of the researcher; and the hi-5 index! 21 which is the h index over a five year period, to name a few.

Article-Level Metrics

Sophisticated publisher platforms and social media applications have resulted in a new set of metrics beyond citation counts that provide for tracking of a work (journal articles, books, slides, software, conference papers, data sets, figures, etc.) based on usage at the document level unit of analysis. Article-level metrics represent “tallies” based on usage and the social or public engagement of a work that can be captured in order to determine how a work is shared among others, commented upon, recommended, viewed, downloaded, cited in bibliographic databases, or saved in online reference managers. 22 , 23 Some article-level metrics are more scholarly in nature, and perhaps more meaningful within the context of end-user uptake since they are documented in the literature (i.e., citations) or either tied with specified technology parameters (i.e., downloads and views). Other article-level metrics remain to a point, anonymous and transient, but nonetheless, can serve as an early harbinger of the potential influence of a work (i.e., comments, mentions, favorites, bookmarks, recommendations). See Table 1 for examples of Article-Level Metrics.

Examples of Article-Level Metrics

Article-level metrics are available from various publisher sources and platforms, software applications, and databases. These metrics can serve as complementary measures of impact to citations, empowering authors to highlight multiple examples of scholarly output and reach beyond the traditional peer-reviewed journal article.

Recent Trends for Reporting of Scientific Impact by the Government, Funding Organizations and Publishers

The trend from bibliometric-based measures to quantify the overall value of research outputs is slowly shifting towards more meaningful outcomes of measurable impact. The U.S. Government and funding bodies are taking notice of performance and impact measures with an emphasis on outcomes that transcend bibliometrics. The National Institutes of Health (NIH) ( http://grants.nih.gov/grants/glossary.htm ) currently defines ‘impact’ as “the likelihood for the project to exert a sustained, powerful influence on the research field(s) involved.” In 2012, the NIH began implementation of a new standardized research performance progress report, (RPPR), ( http://grants.nih.gov/grants/rppr/ ), as a means of harmonizing the reporting of federally-funded research across all governmental agencies that disburse extramural funding. One section of the RPPR is “Products” and includes not only publications, but also other products such as websites that disseminate the results of research activities, inventions, technologies, patents, software, databases, etc. Another section is “Impact” and grantees are instructed to report on ways that their research has had an impact. The National Science Foundation’s Biographical Sketch includes a section titled “Synergistic Activities” to allow for listing of examples that demonstrate the broader impact of an individual’s professional and scholarly activities ( http://www.nsf.gov/pubs/gpg/nsf04_23/2.jsp ).

Agencies such as the National Institute of Environmental Health Sciences (NIEHS) have implemented strong evaluation programs that emphasize reporting of qualitative-based outcomes and produced a manual: Partnerships for Environmental Public Health: Evaluation Metrics Manual, ( http://www.niehs.nih.gov/research/supported/assets/docs/a_c/complete_peph_evaluation_metrics_manual.pdf ). The Centers for Disease Control (CDC) has developed the Science Impact Framework, ( http://www.cdc.gov/od/science/impact/ ) which utilizes a combination of quantitative and qualitative indicators to measure impact towards health outcomes, through five levels of influence: disseminating science, creating awareness, catalyzing action, effecting change, and shaping the future. Of particular interest is the inclusion of metrics that reflect indicators of “internal” impact such as new collaborations or partnerships that reflect on the organization and investigators themselves as opposed to external impact indicators such as public health outcomes.

Research organizations and universities also face increased pressure to report on research outcomes and to demonstrate a return on investments. Research organizations and universities have joined with funding agencies to develop methods that enhance the transparency of research findings and to document tangible outcomes for the public. One effort is the Science and Technology for America’s Reinvestment: Measuring the Effect of Research on Innovation, Competitiveness and Science, or STAR METRICS project, ( https://www.starmetrics.nih.gov/ ), launched in 2010. STAR METRICS is an effort led by the NIH and the NSF under the auspices of Office of Science and Technology Policy (OSTP), in collaboration with research organizations and universities. The objectives for STAR METRICS are to establish uniform and auditable measures of the impact of science spending and to develop measures of impact on scientific knowledge, social outcomes, workforce outcomes and economic growth. Specific metrics and testing of the metrics are still in development as of this writing.

Publishers are also stressing the need for improving the methods of evaluating and reporting on impact from scientific research. The San Francisco Declaration on Research Assessment (DORA), ( http://am.ascb.org/dora/ ), recently issued a set of recommendations urging funding bodies, publishers and institutions to avoid use of the JCR Impact Factor score as a means of assessing research impact or scientific quality. DORA also stressed the use of other metrics to shift the focus towards the scientific content of an article rather than the publication metrics of a journal. Among other metrics suggested by DORA are article-level metrics, the scientific content of a publication, the influence of a work on policy and practice, and the h index. DORA also emphasizes the recognition of research outputs beyond the peer-reviewed journal article.

The Role of Libraries

Evaluation of scientific research findings and activities is an increasingly important effort by academic medical libraries. New resources and evolving recognition by funding agencies allow medical libraries to demonstrate transformative service models as essential consultants by leveraging expertise of literature searching (published and unpublished) to retrieve information that quantifies scientific impact based on bibliometrics and other measures. Evaluation and consultation to assess productivity and impact can occur at the individual author level; the department level; the research group level, including physical or virtual research groups; the institutional/university level; or for a transient population such as scholars/trainees in which longitudinal tracking is required for reporting purposes. Some libraries are going beyond use of traditional bibliometric evaluation methods by using social network tools to illustrate impact in the translational environment of the millennial generation. 24

Note to Readers

If you are not affiliated with an institution that has a subscription to a citation database such as Elsevier Scopus or Thomson Reuters Web of Science, consider using Google Scholar ( http://scholar.google.com/ ), a freely available resource. Google Scholar allows for searching of scholarly literature and citations, and includes a feature for authors to create a personalized profile that contains a listing of publications and citations, affiliation information, and contact information to allow for discovery of your works. By creating a profile, your profile appears when a search of your name is executed in Google Scholar. After a profile is created, publications and citations will automatically be added to your profile and metrics such as the h index and the i10-index are available. Privacy settings for the Google Scholar profile are controlled by the individual.

Medical librarians possess skill sets that are well-suited for conducting evaluation of research findings: familiarity with various database and resources, knowledge of the scholarly processes for dissemination of scientific research, formulating search queries, reconciling author variant names, capturing data from databases, and providing reports based on publication data. Librarians can also provide consultation and reports for specific purposes such as benchmarking, tenure/promotion, recruiting, performance, and funding applications and renewals as well as recommending bibliographic resources or other databases. 25 Some libraries are creating frameworks for scientists to identify qualitative outcomes beyond publication data for documenting and quantifying meaningful health outcomes. One example of a framework developed by a library is the Becker Model, a framework for assessment of research impact that includes a list of over 300 examples of biomedical-based outcomes including bibliometric measures. 26 The outcomes are grouped under five pathways represented by the research cycle with multiple examples noted for some outcomes. Evaluation services provided by libraries have parlayed into invaluable partnerships with campus units with some librarians serving as official members on tracking and evaluation teams affiliated with Clinical and Translational Sciences Awards (CTSA). 27 The Becker Model is currently being used by the Washington University Institute of Clinical and Translational Sciences ( http://icts.wustl.edu/ ) for evaluation purposes. See the Assessing the Impact of Research website: ( https://becker.wustl.edu/impact-assessment/ ) for more information.

Crafting a narrative of scientific research impact is a daunting task. Strides have been made in recognizing that impact transcends publication counts. Impact includes both improvement in public health outcomes and other outcomes correlated with the diffusion of knowledge such as new research collaborations focused on a specific area of study, synthesis into clinical applications, or influence on public policy. These advances in the quantification of “impact” are occurring in tandem with efforts to harmonize reporting of research activities and outputs. The future holds great promise for a more complete and illuminating narrative of the multilevel impact of scientific research. Advances in digital technology afford numerous avenues to disseminate research findings and to document the diffusion of innovations. The capacity to measure and report tangible outcomes can be used for a variety of purposes and tailored for various audiences ranging from the layperson, physicians, investigators, organizations, and funding agencies.

Cathy C. Sarli, MLS, AHIP, is the Scholarly Publishing and Evaluation Coordinator, Bernard Becker Medical Library and Christopher R. Carpenter, MD, MSc, FACEP, FAAEM, AGSF, is Associate Professor, Emergency Medicine and Director, Evidence Based Medicine, at Washington University School of Medicine in St. Louis.

Contact: ude.ltsuw@cilras

None reported.

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• September 10, 2024 Issue
• Deadline Is Next Week for...

Deadline Is Next Week for Resident Research Scholarships

September 10, 2024

The ACS offers 2-year  resident research scholarships  for residents in general surgery or a surgical specialty training program. These scholarships are supported by the generosity of Fellows, Chapters, and friends of the College, to encourage residents in pursuing careers in academic surgery anywhere on the research continuum. The deadline to submit an application is Monday, September 16 .

The awardee must be a Resident Member of the ACS who has completed at least 2 postdoctoral years in an accredited surgical training program (any specialty) in the US or Canada at the time the scholarship starts (July of the award year).

The scholarship is for $60,000 over 2 years and may be used for direct costs related to research only, such resident salary, personnel, equipment, supplies, and/or travel. Scholarship acceptance of it requires commitment for the 2-year period in full-time research.

View additional details about the Resident Research Scholarship and a link to apply.

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Register for Free September 24 Webinar on Importance of Surgery Champions

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Clinical Risk Prediction Score Helps ID Patients Who May Develop LVAD-VSI

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Attend Webinar on Complying with New Age Friendly Hospital Measure

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New MyATLS App Offers Interactive Trauma Information, Course Prep, and More

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Chapter Meeting Offers Valuable Content, Unique Opportunities to Network

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Trauma Surgeons Look Forward to Annual TQIP Conference

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Center for Environmental Research and Technology

We Engineer Excellence

CE-CERT Graduate Students Awarded ITS California Scholarships

Two graduate students from the Center for Environmental Research and Technology (CE-CERT) at the University of California, Riverside —Saswat Priyadarshi Nayak and Dongbo Peng—have been recognized for their innovative contributions to the field of Intelligent Transportation Systems (ITS). Both have received the prestigious Intelligent Transportation Society of California (ITSCA) and California Transportation Foundation (CTF) scholarships, celebrating significant achievements in their academic careers.

Innovative Research Shaping the Future of Mobility

Dongbo Peng, a third-year Ph.D. student, is leading efforts in sustainable freight dispatching methods within the ITS domain. His research focuses on developing solutions that significantly reduce the environmental impact of freight operations and addressing key challenges in urban planning and environmental sustainability. 

"I am greatly honored to be awarded the ITSCA and CTF scholarship. It is a significant milestone in my academic journey,” says Dongbo. “It represents not only recognition of my past work but also a powerful motivator encouraging me to continue pursuing innovative research in sustainable freight transportation.” 

Saswat Priyadarshi Nayak, a fourth-year Ph.D. student, is focused on enhancing the accuracy and reliability of Connected and Automated Vehicle (CAV) technologies through his work on vehicle positioning and multi-sensor fusion.

 "Being awarded the ITSCA and CTF scholarship is a significant honor,” says Saswat. “It validates my hard work and commitment to contributing to intelligent transportation systems and underscores the importance of this research area in shaping the future of mobility." 

Mentorship and Support at CE-CERT

Both students credit the dynamic and supportive academic environment at CE-CERT as instrumental to their success. 

“I am incredibly grateful to my advisors, Dr. Barth and Dr. Boriboonsomsin, for their exceptional guidance and supervision throughout my research journey,” says Dongbo. He also attributes his success to Dr. Wu and his peers for their steadfast support and enthusiasm. 

Saswat also shares his gratitude for the support from his advisors, "My experience at CE-CERT over the past five years has been incredible. Dr. Barth and Dr. Wu have been exceptionally supportive, providing key guidance at every stage of my Ph.D. journey."

Endorsements from Advisors

Dr. Matthew Barth, Associate Dean for Research and Graduate Education at the Bourns College of Engineering, commended both students, "Saswat and Dongbo exemplify the type of scholarly dedication and innovative thinking we nurture at CE-CERT. Their work not only contributes to academic knowledge but also has the potential to significantly impact real-world transportation systems."

Dr. Kanok Boriboonsomsin, Research Engineer and Associate Director at CE-CERT, praised their contributions, noting, "Both students have a unique ability to translate complex theoretical concepts into practical solutions that address real-world problems. It’s this practical application of their research that has already begun to influence our approach to projects at CE-CERT, paving the way for more sustainable transportation solutions."

Dr. Guoyuan Wu, Adjunct Professor and Research Engineer, also expressed his admiration for the progress and dedication of the students. "Both Saswat and Dongbo are not just focused on results but are truly passionate about understanding and solving the underlying challenges in transportation systems. Their innovative approaches and diligent work ethic are impressive," said Dr. Wu.

Professional Development at the ITSCA Annual Meeting

The scholarships also include invitations to the ITSCA Annual Meeting—a key event for professional development in the ITS field. This opportunity allows Saswat and Dongbo to engage with industry leaders, share their research, and expand their academic and professional horizons.

Looking Forward

Both scholars are excited about the potential for their research to make a lasting impact. 

"It’s an exciting time in ITS research. The technological advancements are opening new doors for innovation,” says Saswat. “It’s never too late to contribute to this evolving field, be it in policymaking, planning, or research." 

Dongbo encourages his peers to embrace new challenges:"embrace challenges as opportunities to innovate and explore new research ideas and career opportunities."

As representatives of the Transportation Systems Research (TSR) group at CE-CERT, Saswat and Dongbo are prime examples of how dedicated research and innovative thinking can profoundly influence the future of ITS. We celebrate their achievements and the continued impact of their work in pushing the boundaries of sustainable transportation technologies and services.