Yokohama National University
Faculty of Business Administration
Working Paper No.336

KNOWLEDGE TRAJECTORY AND PERFORMANCE IN SCIENTIFICRESEARCH

Naoko KISHI ∗ABSTRACT
Knowledge trajectory in science represents what research has been conducted. This article
examines whether maintaining or changing knowledge trajectory improves subsequent
research performance. The focus is on how knowledge trajectory changes affect academic
performance when scientists choose research subject that deviates from previous research.
Publicly funded research projects of induced pluripotent stem cell (iPSC) in Japan wereanalyzed. Findings show that projects led by scientists that have stem cell research history
have published more articles in international peer-reviewed journal than those led byscientists that are otherwise. Additionally, the impact of leaders’ stem cell research history
on project performance intensified after a Japanese scientist won the Nobel Prize inPhysiology or Medicine in 2012 for discovering iPSC. These findings suggest that atargeted policy could affect academic performance if it decides scientists’ researchsubjects.
KEYWORDS: Knowledge trajectory, Public research, Stem cells

1. INTRODUCTION
Past knowledge is a foundation for future research and shapes scientificknowledge trajectory (Foster, Rzhetsky, and Evans, 2015; Nelson, 1959; Uzzi et al., 2013).
Prior research has investigated the factors that affect knowledge accumulation process to∗Professor

Graduate School of International Social Sciences
Yokohama National University
79-4 Tokiwadai Hodogaya-ku Yokohama city, JAPAN, 2408501
kishi-naoko-kn@ynu.ac.jp
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improve academic performance. For example, Furman and Stern (2011) illustrate theeffectiveness of economic institutions that promote knowledge diffusion among
researchers. Azoulay, Zivin, and Manso (2011) find that tolerance for changing researchsubjects and project evaluation timelines influence research performance. They compare
the performance of investigators funded by the Howard Hughes Medical Institute(HHMI) with those funded by the National Institutes of Health (NIH). HHMI allows
scientists to change research subjects and evaluates their performance from a long-term
perspective, whereas NIH does not allow in research subject changes and adopts arenewal system based on a short-term evaluation. The result shows that HHMI
investigators broaden their research focus after their appointment. Additionally, Sampat(2012) refers that public funding with a definite target induces research to a specificsubject. Li, Azoulay, and Sampat (2017) find that NIH-funded projects increase relevant
patents in the industry. Their findings indicate that targeted public funding affects
scientists’ research subjects choices and has a ripple effect on relevant industry growth.
By contrast, the impacts of changes in scientists’ subject choices on future researchremain unexplained.
Previous research has consistently recognized the positive impact of public
investment in Research and Development on academic performance (Arrow, 1962;
Dasgupta, 1994; Davidson and Potts, 2016). Particularly, public funding plays significant
roles in giving scientists incentives for novel sciences. For example, Packalen andBhattacharya (2020) find that NIH tends to allocate more budget to novel sciences thanto conservative subjects. Corredoira, Goldfarb, and Shi (2018) find that federal
government funding supports projects that significant impact newly developing areas. By
contrast, recent research has referred that targeted public funding directs scientists tochoose specific research. Jung and Lee (2014) describe that mission-oriented policy
promotes scientists’ myopic choices. They evaluate the effect of a U.S. government
science program launched in 2001, namely, the National Nanotechnology Initiative, on
university research. Their findings show that as industry knowledge inflow increases,
research subjects become narrower and less diverse in universities. This finding is due tothe industry’s promotion of focused research and technology commercialization.
Additionally, Blume-Kohout (2012) finds that targeted funding from NIH for specificdiseases increases the number of relevant drugs for such diseases. This article adds to theevidence of how targeted policy-driven research incentives affect scientists’ researchsubject choices and their academic performance.
This article reveals the impact of scientists’ research subject choices that are
driven by targeted policy on subsequent research. Targeted policy gives scientists
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incentives to choose specific research subjects. When grant incentives motivate them tochoose research subjects that deviate from their research history, the choices could havepositive and negative effect on their academic performance. A positive scenario pertains
to research that would not be bound by past studies. Scientists are unlikely to change theirresearch subjects to maintain their knowledge trajectory for stable academic performance(Foster, Rzhetsky, and Evans, 2015). Foster, Rzhetsky, and Evans (2015) suggest thatpolicy would significantly trigger them to choose non-traditional research. A negative
scenario occurs when a vulnerable research plan with insufficient preparation degrades
researchers’ academic performance. This article articulates the interventions of targeted
policy that change scientists’ knowledge trajectories and investigates the impact of theirresearch subject changes on academic performance.
Stem cell research in Japan is appropriate for this analysis because politicalsupport is skewed toward research of induced pluripotent stem cell (iPSC). The rationaleof skewness is that iPSC could be induced to any human body cell given its pluripotency.
iPSC couldf overcome the challenges of existing treatments of various diseases. The
skewed political support would be partly attributable to the discovery of iPSC by aJapanese scientist who won the Nobel Prize in Physiology or Medicine 2012. The
skewness provides scientists incentives to choose iPSC as a research subject. Therefore,
the context of publicly funded stem cell research fits the purpose of this article.
This analysis examines how targeted policy for iPSC and knowledge trajectorychanges caused by the targeted policy influence scientists’ research subject choices andtheir academic performance, respectively. Certain scientists with research subjects aboutstem cells are likely to be interested in iPSC. Nevertheless, other scientists whose researchinterests have been distant from stem cells may also choose iPSC research because of theperception that political support for this research increases its amount of grant. The issue
is whether these scientists could achieve the expected performance given their lack ofknowledge and research history of stem cells. Thus, this article analyzes how scientists’stem cell research history affects their academic performance in iPSC research. Results
show that projects led by scientists with no experiences of publicly funded stem cellresearch projects publish less articles in international peer-reviewed journals than those
led by scientists that are otherwise. Additionally, the data demonstrate that publicly
funded iPSC research has increased, particularly those led by scientists with no publicly
funded stem cell research. This finding suggests that when support for health mission islimited to iPSC, the narrow target excluding other stem cell options would account for
the vulnerability of cumulative knowledge in future stem cell research.
The remainder of this article is structured as follows. Section two explains the3

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contribution of this article and discusses the factors that determine the type of knowledge
accumulated in scientific research. Section three describes the hypotheses, and Sections
four and five present the sample data and research method, respectively. Then, Section
six refers to the methodology, and Section seven presents the results. Lastly, Section eight
discusses the roles of prior knowledge in scientific research and suggests the effectivenessof scientists’ discretion to choose research subject and concludes the artilce.
2. FACTORS AFFECTING SCIENTISTS’ RESEARCH SUBJECT CHOICESScientific knowledge trajectory depends on scientists’ history of research subject
choices. Prior research has investigated the factors that affect their choices and found thatknowledge continuity in research projects depends on various factors. These factors
include public support for existing technologies (Finkelstein, 2004), the diseaseprevalence that directs medical research (Battacharaya and Packalen, 2011), state funding
for stem cell research (Alberta et al., 2012), political restrictions of research funding forspecific stem cell research (Furman, Murray, and Stern, 2012), death of star scientists(Azoulay, Fons-Rosen, and Zivin, 2019), and the advent of equipment that automates
research tasks (Furman and Teodoridis, 2020). Targeted public funding is one of thefactors on which prior research has focused because it affects how scientists advancebiomedical innovation (Alberta et al., 2012; Furman, Murray, and Stern, 2012; Packalen
and Bhattacharya, 2020; Sampat, 2012).
Knowledge continuity has mixed impacts on project performance, thoughprevious research shows that existing knowledge significantly forms the foundations ofsubsequent projects (Foster, Rzhetsky, and Evans, 2015; Nelson, 1959; Uzzi et al., 2013).
Positive impacts refer to radical innovation that depends on an extension of existingknowledge. Organizations that have relevant technical knowledge within their boundaries
successfully achieve innovation (Cohen and Levinthal, 1990; Roy and Salker, 2016). Uzzi
et al. (2013) find that high-impact research has been published through exceptional
combinations of conventional knowledge. By contrast, negative impacts refer to existingknowledge that limits the scope of search, though it improves search efficiency (March,
1991). March (1991) suggests that an organizational mechanism is crucial to promoting
distant search for radical innovation. Stuart and Podolny (1996) find that strategic
alliances provide firms with opportunities to break the constraints of local search.
Whether project leader-level knowledge continuity is significant for research
performance is noteworthy because targeted science policy should be designed to induce
scientists that have optimal attributes to engage in specific research. This article assumes
that targeted political support could promote research projects that are distant from the4

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past. It addresses the case where a narrow focus of a policy target gives scientists
incentives to pursue iPSC research. Conclusions would suggest the optimal balance ofpolitical support regarding the range of the target.
3. HYPOTHESES
To investigate how knowledge trajectory influences subsequent scientificresearch, this article focuses on publicly funded stem cell research in Japan. This provides
optimal research settings. The targeted policy for stem cell research has been skewed
toward iPSC in Japan. iPSC as a research subject attracts scientists’ interests because it
facilitates public research grant acquisition. However, whether scientists that are
incentivized by the targeted policy to start iPSC research could achieve the expected
performance remains unclear. These scientists may deteriorate project performance,
except when they have prerequisite stem cell knowledge. This article focuses on scientists’stem cell research history and explores its contribution to iPSC research project
performance.
Stem cells have several types. In constructing the variables about research history,
this article focuses on the following five types of major stem cells. First, iPSC is apluripotent stem cell, and it can be differentiated into cells that comprise multiple humanbody tissues. Embryonic stem cells (ESC) are categorized under this type of stem cells
and an alternative to iPSC for scientists. However, using ESC for research causes ethicalissues as it is made from fertilized eggs. Thus, iPSC is an attractive tool for scientists
because it does not such an issue. The production processes of iPSC and ESC haveartificial steps. Second, mesenchymal stem cell (MSC) also exhibits pluripotency, and it
can be differentiated into several cells and tissues. However, its original presence in thehuman body differentiates it from iPSC and ESC. Finally, somatic stem cells (SSC) canbe differentiated into specific human body tissues, whereas cancer stem cells (CSC) arerelevant to cancer growth and can be self-renewal.
A surrogate variable of a knowledge trajectory relevant to scientists is their stem
cell research history before iPSC research projects. The maintenance of a trajectory isdefined as the distance from existing knowledge space, following Furman and Teodoridis(2020). Stem cell research history shortens the distance between past knowledge space
and iPSC research. Scientists could find satisfactory paths to the project’s goal fromalternatives easily and cost-efficiently with relevant knowledge (Nelson, 1959).
(Nelson, 1959). Thus, researchers with relevant knowledge may achieve higher
performance than otherwise. Therefore, the analysis focus on whether scientists that starts
iPSC research despite their lack of stem cell research history could achieve high iPSC
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research performance.
Hypothesis 1 (H1) holds that scientists with stem cell research experience could
achieve higher academic performance than those that are otherwise because they could
recognize iPSC superiority regardless of targeted policy. Rich stem cell knowledge
contributes to improving academic performance. Thus, experienced scientists would
publish more articles than those who are incentivized by targeted policy to start iPSC
research without stem cell research history. H1 examines whether project leader-levelstem cell research history affects project-level academic performance. Therefore, H1holds that projects led by scientists with stem cell research history achieve higher iPSC
research performance than those led by scientists with no relevant knowledge.
H1. Projects led by scientists with stem cell research history achieve higher iPSC research
performance than those led by scientists with no relevant knowledge.
Hypotheses 2a–c (H2a–c) focus on scientists’ research history of pluripotent
stem cells and examine whether projects led by scientists that have experiences ofworking with ESC, MSC, and iPSC achieve higher performance than those led byscientists that do not. iPSC must have attracted alternative to the interest of scientists with
ESC research history given its lack of ethical issues. H2a–b hold that projects led byscientists that have ESC and MSC research history achieve higher iPSC research
performance than those led by scientists that do not. By contrast, the number of scientists
that have iPSC research history increases after its discovery in 2006 (Takahashi and
Yamanaka, 2006). Therefore, H2c holds that projects led by scientists with iPSC researchhistory achieve higher research performance than those led by scientists that are otherwise.

H2a. Projects led by scientists with ESC research history achieve higher iPSC research
performance than those led by scientists that are otherwise.
H2b. Projects led by scientists with MSC research history achieve higher iPSC research
performance than those led by scientists that are otherwise.
H2b. Projects led by scientists with iPSC research history achieve higher iPSC research
performance than those led by scientists that are otherwise.

Hypothesis 3 (H3) focuses on SSC to examine the impact of leader scientists’stem cell research history on the academic performance of iPSC research projects.
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Scientists aim to regenerate tissues damaged by injuries and disease through iPSCderived SSC. Their rich SSC knowledge would increase the academic performance of
iPSC research projects. Therefore, H3 holds that projects led by scientists with SSC
research history achieve higher iPSC research performance than those led by scientists
that are otherwise.

H3. Projects led by scientists with SSC research history achieve higher iPSC research
performance than those led by scientists that are otherwise.

H4 focuses on CSC to examine the impact of leader scientists’ stem cell researchhistory on the academic performance of iPSC research projects. CSC differs from otherstem cells in that it is the target to eradicate. Scientists develop cancer eradication methods
through iPSC-derived CSC. Their abundant CSC knowledge increases iPSC research
performance. Therefore, H4 holds that projects led by scientists with CSC researchhistory achieve higher iPSC research performance than those led by scientists that areotherwise.

H4. Projects led by scientists with CSC research history achieve higher iPSC research
academic performance than those led by scientists that are otherwise.

Hypothesis 5 (H5) examines how the 2012 Nobel Prize mediates the relationshipbetween project leaders’ stem cell research history and the academic performance of iPSC
research projects. High evaluations for iPSC could influence perceptions of relevant
research. The 2012 Nobel Prize would have attracted scientists’ research interest toward
iPSC and could affect reviewers’ decisions in screening the proposals for public grants
and their positive perception of iPSC research. For example, Azoulay, Stuart, and Wang(2014) find that scientists that have become HHMI investigators increase citations in theirarticles that were published before their appointment. These circumstances would
increase the number of publicly granted iPSC research projects. Thus, the proportion ofprojects led by scientists without stem cell research history would increase. Accordingly,
H5 holds that the impact of a leader’s stem cell research history on iPSC research project
academic performance intensifies after the 2012 Nobel Prize than before it was awarded
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to the Japanese scientist for discovering iPSC.

H5. The impact of a leader’s stem cell research history on the academic performance of
iPSC research projects intensifies after the 2012 Nobel Prize than before it wasawarded to the Japanese scientist for discovering iPSC.

4. DATA AND VARIABLES
The analysis of this article focuses on publicly funded research projects toinvestigate the impact of knowledge continuity on academic performance. Certain
research has used patents to quantify and visualize a knowledge trajectory. Patent citations
quantify knowledge flow and examine the impact of accumulated knowledge on
economic growth, research productivity, and business strategy (Jaffe and Trajtenberg,
2002; Li, Azoulay, and Sampat, 2017; Roach and Cohen, 2013; Roberts et al., 2014; Stuart
and Podolny, 1996). In recent years, Azoulay et al. (2019) demonstrate that a $10 million
increase in NIH funding generates additional 2.7 relevant patents in the private sector.
Their results show that NIH’s public funding allocation affects scientists’ research subject
choices and changes a knowledge trajectory. Additionally, Sampat and Williams (2019)analyze the selection mechanism of innovation for patenting and find that patented genes
are more significant than non-patented ones. Their conclusion suggests that patented
knowledge is biased in organizational decision making. By contrast, iPSC research is still
nascent in the stem cell research field, and patents would not reflect the latest iPSC
research trend. Therefore, scientific paper analyses could appropriately articulatescientists’ behaviors for this article.
The sample data of this study comprise publicly funded iPSC research projects
in Japan. To construct the dataset, sample data are selected up from research projects thatreceive public funding from Grant-in-Aid for Scientific Research (KAKEN). The sample
includes projects whose titles contain “iPS” or “induced pluripotent stem cells” and thatstarted in the academic year 2009–Year 2017 and finished by the academic year 2018.
Japan’s academic year is from April to March of the following year. Incomplete projects
by the end of the academic year 2018 are removed from the sample because they create
poor performance bias. Publishing articles in peer-reviewed journals takes some time.
Thus, including the entire project duration is significant in determining the number ofpublished articles. The initial year of the sample period is 2009 during which the numberof publicly funded iPSC research projects began to grow after the discovery of iPSC in
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2006 (Takahashi and Yamanaka, 2006). The last year of the sample period is 2017, theyear when the number of published papers could have been counted for two years by theend of the project at the time of data collection in December 2019. The sample data
include 722 scientists and 936 projects.
Scientific research performance is measured by the number of articles that havebeen published from projects granted by the KAKEN funding. Project leaders are obliged
to report the performance of KAKEN-funded research projects. Publications in the report
include conference proceedings, articles in peer-reviewed journal, handbook chapters,
and essays in university bulletin that is published from institutions to which project
members belong and whose editorial board members are usually only researchers of
publishers. The dependent variable is Number of articles, which includes articles
published in international peer-reviewed journals only. To construct the sample data, theauthor screened manually published articles in international peer-reviewed journals fromthe performance list of the respective report.
The independent variable, Stem cell, for H1 determines whether project leaders
have stem cell research history. Stem cell is binary data that equals 1 if project leaders had
conducted KAKEN projects whose titles include “ESC,” “MSC,” “iPSC,” “SSC,” “CSC,”
or “pluripotent stem cells” and 0 otherwise.
The independent variables, ESC, MSC, iPSC, SSC, and CSC, for H2–4 determine
whether project leaders have respective stem cell research history. These variables arebinary data that equal 1 if project leaders had conducted KAKEN projects whose title had
the respective stem cell term and 0 otherwise. For example, ESC equals 1 if they had
conducted publicly funded projects whose title included ESC and 0 otherwise. Projects
whose titles include multiple stem cells are counted in respective stem cell. For example,
ESC and MSC equal 1 if the project leader had conducted a project whose title included
ESC and MSC. The sample data comprise 348 and 588 projects led by scientists with andwithout stem cell research history, respectively.
The control variables are Amount of grant (logged), Specific grant for young
scientists, Nobel Prize, and Year 2009–Year 2016. Amount of grant (logged) is the logged
total amount of grant per project and controls whether abundant financial resources allow
easier publication of articles. Specific grant for young scientists determines whether the
grant is specific for young scientists and controls the period of project leaders’ researchhistory. Senior scientists are more likely to have stem cell research history than younger
ones due to their longer careers. This specific grant is allocated to scientists under 40
years old at the start of the research project, and the sample contains 339 recipients of thisgrant. This control variable is used only to examine H1 because the number of recipients
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per stem cell is small. Specific grant for young scientists is a dummy variable that equals
1 if a type of project grant is specific for young scientists and 0 otherwise. Nobel Prizedetermines whether projects started before 2012, the year when the Japanese scientist
received the 2012 Nobel Prize for the discovery of iPSC. It controls whether the impactof scientists with stem cell research history on performance would intensify after the 2012Nobel Prize. Nobel Prize is a dummy variable that equals 1 if a project starts after 2013
and 0 otherwise. Year 2009–Year 2016 are dummy variables for the start year of respective
projects and control the influence of stem cell research history that has intensified since
the iPSC discovery. The later the projects start in the sample period, the stronger the
impact of stem cell research history on performance is. Year 2009–Year 2016 are binary
data that equal 1 if a project started in its respective year and 0 otherwise. For example,
Year 2009 equals 1 if a project started in the academic year 2009 and 0 otherwise. Year2017, which is the last year of the sample period, is not set as a dummy variable because
it has less data than the other start years. The small number of Year 2017 is due tounfinished projects in March 2019, which are excluded from the sample.
5. TREND OF iPSC RESEARCH PROJECTSGraph 1 demonstrates the number of projects with “iPSC” in their titles by start
year. It shows that the number of iPSC research projects significantly increased in 2012–2014. The decrease in 2017 is due to excluded incomplete projects by the end of theacademic year 2018. One, two, and 97 projects started in 2015, 2016, and 2017,
respectively, were incomplete by the end of the academic year 2018 and removed fromthe dataset. By contrast, the average number of articles published in international peerreviewed journals per project and start year had decreased in 2012–2014. The recovery
of average number is observed in 2015 due to a project that reported to publish 119 articles
in international peer-reviewed journals. The average value is 5.12 in 2015, excluding thisproject, and the downward trend is continuous.
Graph 2 compares the average number of articles per project led by scientists
with and without stem cell research history. Throughout the sample period, projects led
by scientists with stem cell research history have published more articles than those led
by scientists that are otherwise. The difference between the two groups is large in 2013
and 2014. Within two years after the 2012 Nobel Prize, the number of published articles
has dropped significantly in projects led by scientists without stem cell research history.
In 2015, the average value per project led by scientists without stem cell research history
is approximately 4.19, excluding a project that had published 119 articles as mentioned
in the previous paragraph. However, the difference between the two groups in 201510

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becomes less than that in 2013 and 2014. The decrease in the average value in 2017 isdue to the same reason as described in Graph 1.
Graph 3 demonstrates the proportion of projects led by scientists with respective
stem cell research history by start year. This proportion is calculated by dividing thenumber of research projects led by scientists with respective stem cell research history by
the total number of projects that start in each year. Although the proportion of projects led
by scientists with iPSC research history has demonstrated an increasing trend during thesample period, those led by scientists with ESC and SSC research history have been in adecreasing trend.
The three graphs suggest that the impact of leader scientists’ stem cell researchhistory on project performance may be strongly observed within the next two years after
the 2012 Nobel Prize. The total number of iPSC research projects increases, whereas theaverage number of published articles per project decreases. The trend of respective stem
cells in Graph 3 shows that the proportion of projects led by scientists with researchhistory of ESC, MSC, iPSC, and SSC has decreased in projects that started in 2013 during
which the total number of projects has increased significantly.

<Graph 1. Number of projects and average number of articles per project and start year><Graph 2. Average number of articles per projects led by scientists with and without stem
cell research history>< Graph 3. Proportion of projects led by scientists with respective stem cell researchhistory>

6. METHODOLOGY
The analysis is conducted at the project level to investigate the impact of project
leaders’ stem cell research history on the number of published articles in publicly funded
iPSC research projects. The dependent variable is the number of articles for H1–5.
Because it is a count data that is a non-negative integer and exhibits overdispersion whose
variance and mean are 67.05 and 5.38, respectively, a negative binomial regression
approach is appropriate (Cameron and Trivedi, 1998). The dataset comprises the numberof articles from iPSC research projects, project leaders’ stem cell research history, andfour control variables.
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H1 examines the influence of stem cell research history on academic
performance. H2–4 examine the type of stem cells in project leaders’ research history that
influences the academic performance of iPSC research projects. H5 examines whether
the impact of project leaders’ stem cell research history on academic performance haschanged around the 2012 Nobel Prize. The coefficients of independent variables arecompared between the projects that started before and after 2013.
7. RESULTS
Tables 1–3 demonstrate the descriptive statistics of all variables, theircorrelations, and negative binominal regression results, respectively.
Models 1–2 examine the effect of Stem cell on Number of articles for H1. The
two models have different control variables about grant type, before and after the 2012Nobel Prize, and start year of the projects. Specific grant for young scientists and Nobel
Prize are used for Model 1, and Year 2009–Year 2016 are used for Model 2. Specific grant
for young scientists is used only for Model 1, because the number of recipients of specific
grant for young scientists per respective start year is small. In Model 1, the coefficient ofstem cell research history is positive and statistically significant at the 5 percent level.
Projects led by scientists with stem cell research history publish more articles than those
led by scientists that are otherwise, thus supporting H1. The coefficient of Amount ofgrant (logged) is positive and statistically significant at the 1 percent level. The greater
the grant amount is, the more articles are published per project. The coefficient of Specific
grant for young scientists and Nobel Prize are negative and statistically significant at the
1 percent level. The younger the scientists are, the fewer articles are published per project.
After the Japanese scientist won the 2012 Nobel Prize for the discovery of iPSC, thenumber of published articles per iPSC research project had decreased. Model 2 examines
the effect of Stem cell on performance by controlling for Year 2009–Year 2016. The
coefficients of Stem cell and Year 2009–Year 2015 are positive and statistically significant
at the 1 percent level. The coefficients of Year 2016 are positive and statisticallysignificant at the 5 percent level. The values of the coefficients of Year 2013-2016 aresmaller than those of Year 2009–Year 2012.
Models 3–4 examine the effect of ESC on Number of articles for H2a. Although
H2a holds that project leaders’ ESC research history affects the number of articles per
project, Models 3–4 demonstrate that the coefficient of ESC is positive and statisticallyinsignificant. Models 5–6 examine the effect of MSC on Number of articles for H2b. The
coefficient of MSC is statistically significant at the 10 percent level. Project leaders’ MSC
research history affects the number of articles per project, thus supporting H2b. Models
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7–8 examine the effect of iPSC on Number of articles for H2c. Project leaders’ iPSC
research history affects the number of articles per project, thus supporting H2c. The
coefficient of iPSC is statistically significant at the 10 percent and 5 percent levels in
Model 7 and 8, respectively. Models 9–10 examine the effect of SSC on Number ofarticles for H3. The coefficient of SSC is statistically significant at the 5 percent level inModels 9–10. Project leaders’ SSC research history affects the number of articles per
project, thus supporting H3. Models 11–12 examine the effect of CSC on Number ofarticles for H4. The coefficient of CSC is statistically significant at the 10 percent levelin Models 11–12. Project leaders’ CSC research history affects the number of articles per
project, thus supporting H4.
In Models 3–12, Amount of grant (logged) and Nobel Prize are control variables
for odd-numbered models, whereas Amount of grant (logged) and Year 2009–Year 2016
are those for even-numbered models. The coefficients of Amount of grant (logged) are
positive and statistically significant at the 1 percent level in Model 3–12. The coefficientsof Nobel Prize are negative and statistically significant at the 1 percent level in the oddnumbered models. In the even-numbered models, the coefficients of Year 2009–Year 2015
and Year 2016 are positive and statistically significant at the 1 percent and 5 percent levels,
respectively.
Table 4 demonstrates the results of investigating the impact of respective stem
cell research history on the number of articles around the 2012 Nobel Prize for H5. The
independent variables in Models 13–24 are Stem cell, ESC, MSC, iPSC, SSC, CSC, andAmount of grant (logged). The coefficients of respective stem cell except CSC are larger
after than before the 2012 Nobel Prize, thus supporting H5. Stem cell, MSC, and iPSC
and SSC after the 2012 Nobel Prize are statically significant at the 1 percent, 5percent,and 10 percent levels, respectively. CSC before the 2012 Nobel Prize and Amount of grant(logged) in all models are also statically significant at the 10 percent and 1 percent levels,
respectively. The statistically significant coefficients of independent variables after the2012 Nobel Prize are larger in the order of MSC, Stem cell, SSC, and iPSC.

<Table 1. Descriptive statistics><Table 2. Correlations>13

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Working Paper No.336<Table 3. Results of negative binomial regression><Table 4. Comparison of the impact of stem cell research history before and after the2012 Nobel Prize>

8. DISCUSSION AND CONCLUSIONSThis article concludes that project leaders with stem cell research history
positively affect the academic performance of iPSC research. This effect intensified after
the 2012 Nobel Prize. Since 2013, the proportion of projects led by scientists with iPSC
research history has demonstrated the increasing trend, and the proportion of projects led
by scientists with SSC research history has demonstrated a decreasing trend. Although
iPSC research history is positively associated with research performance, its coefficientsare less than those of MSC, SSC, and CSC research history. Additionally, the average
number of published articles per iPSC research project had decreased after the 2012 Nobel
Prize. Therefore, projects led by scientists with insufficient stem cell research backgroundbefore the start of iPSC research may have poor performance.
The analysis examines how scientists’ stem cell research history affects project
performance. Results partly support five hypotheses. Although project leaders’ stem cellresearch history affects the number of articles in projects, the effect varies depending on
the type of stem cells in research history and timing of the start of projects. The impact ofscientists’ iPSC research history is not as strong as that of MSC and SSC research history.
The coefficient of iPSC in Model 22 is smaller than those of MSC and SSC in Models 18
and 22, respectively. ESC research history does not affect the performance of subsequent
iPSC research projects.
The magnitude of the impact of research history changes around the 2012 Nobel
Prize. H5 results demonstrate that the research history of MSC, SSC, and iPSC isstatistically significant only after the 2012 Nobel Prize. A comparison of the impact ofstem cell research history reveals that the case where project leaders have MSC researchhistory has the largest coefficient after the 2012 Nobel Prize. Therefore, the decreasing
proportion of projects led by scientists with MSC and SSC research history would partly
explain the decline of the average number of articles per project in 2013–2014.
Additionally, the increasing number of projects led by scientists with iPSC researchhistory would not significantly offset the poor performance of projects led by scientists
with no stem cell research history.
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The hypothesis that scientists’ ESC research history affects the number ofarticles per project is not supported, which might be due to the legal limit of ESC use inJapan. While the number of ESC projects in research history is modest, ESC researchneeds procedures for obtaining research permission from an ethical viewpoint. This
laborious process might have hindered its further development. Additionally, H5 results
demonstrate that project leaders’ CSC research history contributes only to performance
before the 2012 Nobel Prize. This finding may be explained by approaches other than
CSC that may have appeared in research on cancer treatment using iPSC. These two
unproven hypotheses would need more in-depth investigation based on a field research.
Targeted public funding could change the scientific knowledge trajectory, theimpact of which would not be limited to scientific research. An increasing number of
iPSC research projects caused by targeted public funding were further stimulated by the2012 Nobel Prize. This event has a ripple effect on business development in surrounding
areas, because private innovation is often rooted in publicly funded research (Azoulay etal., 2019). Public funding faces the tradeoff between short-term political agenda and longterm scientific progress. Packalen and Bhattacharya (2020) suggest that NIH should
pursue both and that potential fields should be provided with ample funding to promote
rapidly advancing fields. Accordingly, a policy focus on stem cell research would beappropriate. By contrast, Mazzucato and Semieniuk (2017) argue that public researchfunding has an objective of shaping and creating markets. Policymakers that develop
public support skewed toward iPSC in Japan should have aimed at creating a market forregenerative medicine, because it included the Ministry of Economy, Trade, and Industry.
Pursuing this economic objective may exclude diverse inefficient choices and lead to thesimple skew toward iPSC.
Challenges associated with targeted policy include whether choosing iPSC as a
research subject would be the optimal solution for scientists. For example, in the case ofusing stem cells to treat cardiac diseases, the candidate options are ESC, MSC, iPSC, andcardiac stem cells (Segers and Lee, 2008; Garbern and Lee, 2013). In addition to stem
cells, the efficacy of treatment using skeletal myoblasts has been clarified, and aconsensus on the best option for transplantation has not been reached (Garbern and Lee,2013). Without political solicitation, the choice of iPSC to treat cardiac disease is left tothe scientists’ discretion. Political support for iPSC may have deprived scientists in Japanof choosing their optimal research subjects. Nelson (1959) describes that setting loose
goals of basic research is rational, which could adapt to great uncertainty involved. Thisphenomenon would expect more payoff than closely defined goals (Nelson, 1959).
Additionally, Comroe and Dripps (1967) investigate the accumulating process of essential
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Working Paper No.336

clinical knowledge in the field of cardiovascular and pulmonary diseases. Their results
demonstrate that 41 percent of essential knowledge that later contributed to clinicaladvances were not studies conducted for clinical application. They conclude that clinicaladvances depend on research diversity. Murray (2002) investigates the co-evolution ofscience and technology regarding tissue engineering and finds that co-mingling exists infocal patent and paper networks. No star scientists have become the center in the networks.
Their findings demonstrate that targeted public funding for science would face challengesof optimal distribution for promoting progress. Thus, allowing scientists to choose
optimal stem cell may be rational, because the clinical application of stem cells is in its
uncertain stage. Policies that target specific tools would motivate scientists to choose thelatest research tools in their study. However, skewed public funding toward iPSC researchin Japan differs from other targeted policies, such as for gene therapy, in that the range oftarget is narrow. Under such a policy, scientists may choose iPSC over other stem cells,
regardless of whether the choices are optimal for their research remains unknown. The
impact of excluding other candidate stem cells from research on academic performanceand industry should be examined in future research.

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Graph 1. Number of projects and average number of articles per project and start year

Average number of articles per project

2

50

3

4

100

5

6

7

150

Number of projects

2009

2011

2013
Start year

2015

2017

Number of projects
Average number of articles per project20

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Working Paper No.336

Graph 2. Average number of articles per projects led by scientists with and without stem
cell research history

Average number of articles per project21

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Working Paper No.336

Graph 3. Proportion of projects led by scientists with respective stem cell research history

Proportion of projects led by scientists with stem cell research history

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Table 1.

Descriptive statistics
Variables

N

Mean

S.D.

Min.

Max.

Number of articles

936

5.38

8.19

0

119

Stem Cell

936

0.37

0.48

0

1

iPSC

936

0.23

0.42

0

1

ESC

936

0.12

0.33

0

1

MSC

936

0.03

0.16

0

1

SSC

936

0.13

0.33

0

1

CSC

936

0.03

0.17

0

1

Amount of grant (logged)

936

6.71

0.27

6.16

8.31

Specific grant for young scientists

936

0.36

0.48

0

1

Nobel Prize

936

0.64

0.48

0

1

Year 2009

936

0.07

0.25

0

1

Year 2010

936

0.10

0.29

0

1

Year 2011

936

0.10

0.31

0

1

Year 2012

936

0.10

0.29

0

1

Year 2013

936

0.14

0.34

0

1

Year 2014

936

0.16

0.37

0

1

Year 2015

936

0.16

0.37

0

1

Year 2016

936

0.13

0.34

0

1

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Table 2. Correlations
Variables
1. Number of articles

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

-

2. Stem Cell

0.13

3. iPSC

0.05

0.70

4. ESC

0.07

0.48

0.18

5. MSC

0.04

0.22

0.03

-0.00

6. SSC

0.14

0.49

0.19

0.29

0.07

7. CSC

0.05

0.23

0.07

0.09

0.20

0.01

8. Amount of grant (logged)

0.34

0.18

0.08

0.14

-0.01

0.20

0.01

9. Specific grant for young scientists

-0.26

-0.27

-0.15

-0.16

-0.11

-0.24

-0.08

-0.32

10. Nobel Prize

-0.09

0.06

0.17

-0.11

0.02

-0.06

0.03

0.02

0.01

11. Year 2009

0.05

-0.02

-0.13

0.08

-0.05

0.04

-0.02

-0.00

-0.01

-0.36

12. Year 2010

0.04

-0.08

-0.12

0.03

-0.01

0.01

0.01

-0.00

0.06

-0.43

-0.09

13. Year 2011

0.02

-0.00

-0.07

0.06

-0.02

0.02

-0.02

-0.04

-0.03

-0.45

-0.09

-0.11

14. Year 2012

0.06

-0.01

0.03

0.03

0.03

0.03

-0.01

0.01

-0.03

-0.43

-0.09

-0.10

-0.11

15. Year 2013

-0.01

-0.02

0.01

-0.00

-0.01

-0.01

-0.02

0.05

-0.02

0.30

-0.11

-0.13

-0.14

-0.13

16. Year 2014

-0.02

0.04

0.06

0.02

-0.02

-0.01

0.06

0.02

0.01

0.33

-0.12

-0.14

-0.15

-0.14

-0.17
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Working Paper No.336
17. Year 2015

0.03

-0.00

0.03

-0.07

0.05

-0.02

-0.01

0.02

-0.05

0.33

-0.12

-0.14

-0.15

-0.14

-0.17

-0.19

18. Year 2016

-0.07

0.05

0.07

-0.07

0.03

-0.03

0.01

-0.04

-0.01

0.30

-0.11

-0.13

-0.13

-0.13

-0.16

-0.17

-0.17

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Table 3. Results of negative binominal regression(1) Results for Stem cell and ESC
Independent variables
Stem Cell

Dependent variable: Number of articles
Model 1Model 20.19 (0.09)*

0.35 (0.08)**

ESC

Model 3Model 40.14 (0.13)

0.14 (0.13)

1.36 (0.15)**

1.34 (0.15)**

MSC
iPSC
SSC
CSC
Amount of grant (logged)

1.01 (0.15)**

Specific grant for young scientists-0.76 (0.09)**

Nobel Prize-0.30 (0.08)**

1.26 (0.15)**-0.27 (0.09)**

Year 2009

1.00 (0.25)**0.95 (0.25)**

Year 2010

1.06 (0.24)**

1.00 (0.24)**

Year 20111.02 (0.23)**

1.00 (0.24)**

Year 2012

1.09 (0.24)**

1.09 (0.24)**

Year 20130.77 (0.23)**0.79 (0.23)**

Year 20140.70 (0.23)**0.72 (0.23)**

Year 2015

0.97 (0.22)**0.98 (0.22)**

Year 2016

0.55 (0.23) *0.59 (0.23)*

Constant-4.87 (1.00)**-7.86 (1.04)**-7.35 (1.04)**-8.29 (1.05)**

N

936

936

936

936

Log likelihood-2462.71-2484.87-2502.93-2492.81

LR 𝜒𝜒 2185.70**141.38**105.25**125.50****P<0.01, *P<0.5
S.D. is in parenthesis.

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Yokohama National University
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Working Paper No.336(2) Results for MSC and iPSC

Independent variables

Dependent variable: Number of articles
Model 5Model 60.48 (0.25)+

0.42 (0.25)+

Model 7

Model 80.18 (0.10)+

0.20 (0.10)*

1.35 (0.15)**

1.33 (0.15)**

Stem Cell
ESC
MSC
iPSC
SSC
CSC
Amount of grant (logged)

1.38 (0.15)**

1.37 (0.15)**

Specific grant for young scientists
Nobel Prize-0.29 (0.09)**-0.31 (0.09)**

Year 2009

0.98 (0.25)**

1.04 (0.25)**

Year 2010

1.01 (0.24)**1.08 (0.24)**

Year 20111.02 (0.24)**

1.07 (0.24)**

Year 20121.08 (0.24)**

1.11 (0.24)**

Year 20130.77 (0.23)**0.82 (0.23)**

Year 20140.73 (0.22)**0.74 (0.23)**

Year 2015

0.96 (0.22)**1.01 (0.22)**

Year 2016

0.57 (0.23)*

0.60 (0.23)*

Constant-7.52 (1.04)**-8.45 (1.05)**-7.31 (1.03)**-8.29 (1.04)**

N

936

936

936

936

Log likelihood-2501.39-2491.77-2501.97-2491.38

LR 𝜒𝜒 2

108.33***127.58**107.19**128.36****P<0.01, *P<0.5, +P<0.1
S.D. is in parenthesis.

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Yokohama National University
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Working Paper No.336(3) Results for SSC and CSC
Dependent variable: Number of articles

Independent variables

Model 9Model 100.29 (0.12)*

0.30 (0.12)*

Model 11

Model 120.41 (0.24)+

0.46 (0.24)+

1.37 (0.15) **1.36 (0.15)**

Stem Cell
ESC
MSC
iPSC
SSC
CSC
Amount of grant (logged)

1.30 (0.16)**

1.28 (0.15)**

Specific grant for young
scientists
Nobel Prize-0.27 (0.09)**-0.28 (0.09)**

Year 2009

0.98 (0.25)**

0.99 (0.25)**

Year 2010

1.01 (0.24)**1.02 (0.24)**

Year 20111.01 (0.24)**1.01 (0.24)**

Year 20121.11 (0.24)**

1.10 (0.24)**

Year 20130.82 (0.23)**0.81 (0.23)**

Year 20140.75 (0.23)**0.73 (0.23)**

Year 2015

0.99 (0.22)**1.00 (0.22)**

Year 2016

0.59 (0.23)*

0.58 (0.23)*

Constant-7.00 (1.04)**-7.89 (1.05)**-7.44 (1.03)**-8.40 (1.04)**

N

936

936

936

936

Log likelihood-2500.66-2490.29-2501.87-2491.28

LR 𝜒𝜒 2109.80**130.55**107.37**128.56****P<0.01, *P<0.5, +P<0.1
S.D. is in parenthesis.

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Yokohama National University
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Table 4. Comparison of the impact of stem cell research history before and after the 2012 Nobel Prize
Dependent variable: Number of articles
Independent variables

Stem Cell

Model 13Model 14Model 15

Stem cell
beforeafter

0.11(0.13)

0.42(0.11)**

ESC

Model 16

Model 17

ESC

Model 18

Model 19

MSC

beforeafter

0.09(0.17)

0.18(0.18)MSC

Model 20

Model 21

iPSC

before

after

-0.01
(0.42)

0.63(0.31)*iPSC

Model 22

Model 23

SSC

beforeafter

0.02(0.19)

0.22(0.12)+

SSC

Model 24

CSC

beforeafter

0.24(0,17)

0.33(0.17)+

beforeafter

0.28(0.30)
1.35(0.21)**-7.58(1.42)**
603

N

1.36
(0.22)**-7.39(1.48)**
333

1.22(0.21)**-6.86(1.41)**
603

1.36(0.22)**-7.40(1.48)**
333

1.35(0.21)**-7.54(1.42)**
603

1.38(0.22)**-7.48(1.48)**
333

1.37(0.21)**-7.72(1.42)**
603

1.38(0.22)**-7.48(1.47)**
333

1.31(0.21)**-7.37(1.40)**
603

1.34(0.22)**-7.28(1.47)**
333

1.26(0.22)**-6.96(1.45)**
603

0.67(0.40)+
1.38(0.22)**-7.56(1.46)**
333

Log likelihood-940.80-1553.64-941.01-1560.38-941.17-1558.39-941.16-1559.11-940.15-1558.98-939.47-1560.41

LR 𝜒𝜒 248.09**

62.59**

47.69**

49.12**

47.36**

53.09**

47.37**

51.64**

49.39**

51.91**

50.76**

49.04**

CSC
Amount of grant (logged)
Constant**P<0.01, *P<0.5, +P<0.1
S.D. is in parenthesis.
29