論文 / 著書情報
Article / Book Information
Title

Accounting for the knowledge dynamics process of a science-based
innovation

Authors

Eric Jolivet, Alfonso Ávila Robinson, Hiroatsu Nohara, Shintaro
Sengoku

Citation

The Proceedings of The XXVI ISPIM Conference 2015

Pub. date2015, 6

Powered by T2R2 (Science Tokyo Research Repository)

This paper was presented at The XXVI ISPIM Conference – Shaping the Frontiers of Innovation
Management, Budapest, Hungary on 14-17 June 2015. The publication is available to ISPIM
members at www.ispim.org.

Accounting for the knowledge dynamics process of ascience-based innovation
Eric Jolivet*
Université Toulouse 1 Capitole, 2 Rue du Doyen-Gabriel-Marty, 31042
Toulouse, France.
E-mail: Eric.Jolivet@univ-tlse1.fr

Alfonso Ávila Robinson
Kyoto University, Yoshida-honmachi, Sakyo-ku, 606-8501, Kyoto,
Japan.
E-mail: aavilarobinson@icems.kyoto-u.ac.jp

Hiroatsu Nohara
Aix-Marseille university, Laboratoire d'Economie et de Sociologie duTravail (CNRS), 35 Avenue Jules
Ferry, 13626 Aix-en-Provence, France.
E-mail: hiroatsu.nohara@univ-amu.fr

Shintaro Sengoku*
Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, 1828550, Tokyo, Japan.
E-mail sengoku@mot.titech.ac.jp* Corresponding author
Abstract: Understanding the underlying waves of progress in scientific andtechnological (S&T) knowledge that allow innovative companies to generatevalue is vital for innovation managers. Established models have shown that thepathways of S&T advances are not random: a) they follow cumulative processesof coopetitive learning; b) knowledge accumulation occurs through two major
modes, namely, exploration and optimisation; c) knowledge infrastructurechannels the direction and the mode of learning. The Abernathy-Utterback model(A-U model) encapsulates these insights and their implications for management.
This model has been validated by extensive empirical studies in a number of
traditional industries. However, this model seems to be less relevant in
explaining the recent S&T dynamics observed in science-based industries suchas the biomedical industry. This calls for a critical assessment of the model anda research programme to complement the model. This study aims to address thisgap in the literature.

Keywords: This is; an example; of the style; for keywords. Please use about 10
keywords and separate them with semi-colons.

1This paper was presented at The XXVI ISPIM Conference – Shaping the Frontiers of Innovation
Management, Budapest, Hungary on 14-17 June 2015. The publication is available to ISPIM
members at www.ispim.org.

1 Introduction
The evolution, change, and transformation of scientific and technological knowledge
constitute a topic that is commonly explored in the field of innovation research. This isbecause science and technology is a major source of innovation and industrial
transformation, as Schumpeter’s seminal work established. As Bower and Christensen
stated, company strategy mainly involves ‘catching the wave’.
Because of the higher frequency and intensity of the waves of change disrupting the current
economic system, there has been a particularly pressing need for technologies of an
emerging nature. Over the years, different conceptualisations have been used to approach
the knowledge dynamics of technological innovations. A review of the literature showsthat these conceptualisations have three aspects in common (Van Lente, 2010): a
framework that clusters the heuristics and routines guiding the search processes ofinnovation; trajectories along which these search processes are channelled; and timedependent phases of development. Table 1 summarises some of these prior approaches.
Reference

Framework

Trajectory

PhasesFoster (1986),
Christensen (1992)

Technology life cycle S-curve

Birth, growth, maturity, anddemiseAbernathy-Utterback(1978)

Dominant design

Product vs. process
innovation

Fluid pattern, transition
pattern, and specific pattern

Nelson & WinterTechnology searchand selection
mechanisms

Heuristics, routines,
trajectories-

Dosi (1982)

Technologicalparadigm

Technologicaltrajectory

Pre-paradigmatic, postparadigmaticGeels (2007)

Sociotechnicalregimes

Transition pathways

Interactions across threelevels: regimes, niches, andlandscape

Sahal (1985)

Technological
guideposts

Innovation avenues-

Table 1. Theoretical background of the present study.
This study builds on the work of Abernathy and Utterback (1978) that was
subsequently refined by Anderson and Tushman (1990) and Benner and Tushman (2003).
In the Abernathy and Utterback model (A-U model) of technological change, the patternsof innovation are divided into three stages: fluid, transition, and specific stages. The fluidor ferment stage is characterised by the multiplication of product designs and alternativeroutes in an explorative dynamics framework. In this stage, variety is created. In thespecific stage, an established route is deepened through incremental and cumulative efforts
within the framework of exploitative dynamics. In this stage, efficiency and economic
performance dominate. Of particular interest for this study is the mechanism through which2

a formative industry enters a more mature stage (transition stage). For Abernathy and
Utterback (1978), this transition involves a process of standardisation initiated by the
introduction of a ‘dominant design’. For industry players, the ‘dominant design’ signalsthat investments should now be focused in deepening and building up from this first
product version.
The underlying knowledge dynamics proposed by the model are key to this study.
According to this model, knowledge dynamics follows two very different, successive
patterns: 1) an exploratory pattern, during which the basic potential of the technology andthe market are assessed, and the possible technological routes are discovered; and 2) a
cumulative one, during which the main players agree about the trajectory to be followed
and rush to capture the value of successive generations of innovations along the path. Thismodel is consistent with March’s observation about the patterns of organisational learning.
Managers of innovation are particularly interested in understanding the current
knowledge regime and its implications, as well as the mechanism of transition from
explorative to exploitative knowledge dynamics. Tushman et al. (1990 and 2003) argued
that these aspects greatly conditioned the nature of their investments and their returns on
investment. According to the A-U model, the transition from explorative to exploitativeknowledge dynamics relies on design freeze, i.e. the establishment of a dominant design.
This is consistent with Van Lente’s idea of irreversibility and the findings of the economics
of standards that such standardisation might take the form of a lock-in (David, 1985). The
questions of how and when an industry freezes a dominant design thus form a central
consideration while investing in new products and processes.
Given their nascent nature, emerging technologies lack such social and cognitive rulesthat guide their research and development (van Merkerk and van Lente, 2008). van
Merkerk and van Lente (2008) argue that the actors in emerging fields rely on speculationand promises. For them, this emerging phase is characterised by negotiation andexperimentation. These processes lie at the heart of standardisation.
This study explores the standardisation phase of science-based innovations. Over theyears, Abernathy and Utterback’s (1978) model (the A-U model) has been discussed andrefined at length by scholars. These research efforts have mainly focused on cases
involving traditional industries such as the automotive industry (Lee and Berente, 2013),
the float glass industry (Uusitalo and Mikkola, 2010), and the semiconductor industry(Funk, 2008), among others. Far too little attention has been paid to the use of the A-U
model in the emerging science-based industries. It is well known that the strong sectoral
specificities of innovation (Pavitt, 1984; Malerba, 2002) prevent researchers fromextrapolating across sectors. Thus, there is a need to assess the commonalities andspecificities related to standardisation of science-based innovation and those ofindustry/business-based innovation. The knowledge dynamics of science-based
innovations appear to be more networked and multi-level than the knowledge dynamics ofindustry-based innovations (Benner and Tushman, 2003; Zuker et al., 2002).
Given this context, the empirical case of induced pluripotent stem cells (iPS cells) isused in this study. iPS cells are a novel, science-intensive technology first discovered in2006 by a group of Japanese researchers at Kyoto University. The value chain of iPS cells
encompasses a complex and interrelated set of technologies (Roberts et al., 2014). Thisvalue chain runs from the upstream generation of iPS cells through the use of
reprogramming technologies to their downstream conversion into other cells and their use

This paper was presented at The XXVI ISPIM Conference – Shaping the Frontiers of Innovation
Management, Budapest, Hungary on 14-17 June 2015. The publication is available to ISPIM
members at www.ispim.org.

in application domains as different as drug screening, toxicity testing, cell therapies, etc.
(Sengoku et al., 2011). Within this formative bio-manufacturing system, the production ofhigh-quality iPS cells at a high volume and low cost is a crucial requirement for thecommercialisation of iPS cell-derived products (Silva et al., 2015). By focusing on thealternative methods for the generation of iPS cells, this study addresses the following
questions: What are the dynamics of knowledge evolution that are followed by sciencebased innovations such as iPS cell generation technologies? In particular, how are thetrajectories and paradigms of the dominant technological approaches selected? Moreover,what are the pathways of the structure formation and evolution of the networks surrounding
this knowledge? Is the structure conducive to domination? The reasons for choosing aliving cell-based process for the present study are: i) this is a novel case of a science-based
innovation; ii) this technology is expected to give rise to a wide range of innovations,
including those related to medical and pharmaceutical uses; and iii) few extant studies havesystematically explored the dynamics of knowledge transfer and innovation.
The rest of this paper is structured as follows. Section 2 provides a description of thecase study and the materials and methods used in this research. Section 3 presents theresults of this study. Section 4 concludes the paper with discussions and conclusions.

2 Research design
Case study and scope of research
In order to test the research questions in an empirical manner, we selected stem cell
technology, specifically, induced pluripotent stem (iPS) cells, as a case of innovative
biomedical technology (Sengoku et al., 2011). As a pluripotent stem cell species, iPS cells
have the ability to proliferate indefinitely and to differentiate into almost any other cell
type in the body. iPS cells were first discovered by Takahashi and Yamanaka (2006) at
Kyoto University. Since their discovery, iPS cells have been regarded as a promising,potentially disruptive technology for drug discovery and development and regenerative
medicine (Sengoku et al., 2011; Barfoot et al., 2013). Given these potentials and thepossibility to circumvent the ethical and political debates surrounding embryonic stem (ES)cells, the field of iPS has experienced exponential growth in the recent years (Barfoot etal., 2013).
Different approaches have been proposed by prior researchers to generate pluripotent
stem cells from mature cells (Table 2). These approaches are generally called nuclear
reprogramming as they attempt to reverse mature cells into a state of pluripotency. In
particular, iPS cells are generated by the introduction of defined genetic or chemical factors,
also known as transcription or inducing factors. Over the years, different methods ofgenerating iPS cells have been reported. These iPS cell (iPSC) reprogramming methods
mainly differ in terms of the ‘vehicle’ used to deliver these pluripotency-relatedtranscription factors into the cell nuclei. These ‘reprogramming vehicles’ could be as
different as DNA or RNA molecules, protein, nanomaterials, and bacteria. The different
iPS cell reprogramming methods are discussed in detail later.
Methods

Experimental approach

4

Machanistic insights

Nuclear transfer

Reproducible cloning: functional test Allows epigenetic changes to be
for reprogramming to totipotency
distinguished from genetic changes
Somatic cell nuclear transfer: efficientdeviation of genetically matched ES
cells with normal potency

Cell fusion

Nuclear reprogramming of somatic
genome in hybrids generated withpluripotent cells

Allows study of the genetics ofreprogramming

Cell extraction

Exposure of somatic nuclei orpermeabilised cells to extracts fromoocytes or pluripotent cells

Allows biochemical and kinetic
analysis of reprogramming

Cell explanation

Explanation in nuclear selects for
pluripotent, reprogrammed cells

Allows study of the genetics ofreprogramming

Directreprogramming bydefined factors

Generation of pluripotent cells by the
introduction of defined genetic or
chemical factors

Generated cells are autologous to
donorTechnically straightforward

Table 2. Representative approaches for nuclear reprogramming (Hochedlinger andJaenisch, 2006; Qi et al., 2014).

Material and method
To explore the dynamics of knowledge evolution related to iPS cells, this study retrievedscientific publications from Thomson Reuters’ Web of Science (WoS) database. In orderto obtain iPS cell-related publications, a broad search query was used: TS = ((induc*
NEAR/25 pluripoten* NEAR/25 stem) OR ((IPS OR IPSC) AND (stem NEAR/5 cell*))),
where * is a wildcard. A total of 2,283 publications were obtained from the search; thesewere manually evaluated in order to identify the documents relevant to the field of iPS
cells. A total of 1,535 articles and conference proceedings published from 2006 to 2012were retained after this procedure. Subsequently, the set of publications relevant to iPS cellwere further evaluated to select those publications directly related to the reprogramming of
iPS cells. Thus, only those publications focusing on the development or enhancement ofan iPS cell generation technology were selected for this study. Finally, 581 publications
were found to be directly related to this subfield of iPS cell research. Each of thesepublications was assessed to define the specific reprogramming method(s) involved in the
research. This was done by evaluating the titles, abstracts, and/or experimental/materials
sections of these publications. For this purpose, a taxonomy of iPS reprogramming
methods was developed in this study (see Table 3).
No.

Delivery system

Brief description

I

Viral vectorbased methods

Use of viral vectors to reprogram mature cells into iPS cells. The viral
vectors used include retroviral, lentiviral, Sendai viral, adenoviral, andbaculoviral vectors.

II

Transposonbased methods

Non-viral approach involving the delivery of genes through transposons,such as piggyBac (PB) and Sleeping Beauty (SB) transposons.

III

Plasmid-based
methods

Non-viral delivery approach involving episomal plasmids, minicircle
DNA, and conventional transient plasmids.

This paper was presented at The XXVI ISPIM Conference – Shaping the Frontiers of Innovation
Management, Budapest, Hungary on 14-17 June 2015. The publication is available to ISPIM
members at www.ispim.org.

IV

Protein-based
methods

Non-viral, non-integrative delivery approach based on proteins.

V

Chemical-based
methods

Non-viral, non-integrative delivery approach conducted through chemical
methods, especially using small molecules. It also involves the
improvement of reprogramming methods through chemicals.

VI

RNA-based
methods

Non-viral, non-integrative approach based on the delivery of
reprogramming genes through different RNA types: RNA virus (Sendai
virus), synthetic mRNA, miRNA, and RNA replicon. It also involves the
improvement of reprogramming performance through RNA.

VII

Other methods

Non-viral reprogramming approaches using nanoparticles, bacteria, andhuman artificial chromosomes (HAC), among others.

Table 3. Taxonomy of delivery systems for iPS cell generation/reprogramming.

The construction of the taxonomy of reprogramming methods relied on an exhaustive
review of the technical literature and additional sources of information, such as online
resources and expert advice. There appears to be consensus among researchers about theclassification of iPS cell reprogramming approaches. Two trajectories that are crucial for
distinguishing the different reprogramming methods are the nature of the reprogramming
vehicle and its integration/excisability. The former relates to whether viruses are used as
the carriers for the delivery of the iPS cell-related genes. The latter refers to the deleterious
integration of material into the genome of the host cell. There are different problems
associated with the integration of genetic materials: cell death, residual expression andreactivation of reprogramming factors, immunogenicity, uncontrolled silencing oftransgenes, and insertational mutagenesis (Hu, 2014). In the case of excisable vectors, theintegrated materials can be removed from the genome using specific enzymes.
The 581 publications that were collected from the WoS database together with theirrespective allocated reprogramming approach(es) formed the basis for the results that arepresented in the following section. To test the research questions proposed in this study, aseries of bibliometric methods and social network approaches, supported by a series ofinterviews with domain experts, were devised (Table 4). For the dynamics of knowledge
creation, a series of longitudinal bibliometric analyses were conducted to trace thetrajectories of knowledge accumulation for each iPS cell generation/reprogramming
method. Additionally, citation networks were built using the software CitNetExplorer (vanEck and Waltman, 2014) in order to evaluate the evolution of this subfield of research andthe reprogramming paradigms used in the studies (de Nooy et al., 2011). To obtain a clearer
visualisation of the citation networks, this study set a threshold of eight or more citations
for the network nodes. Because of the restrictions of the software, up to 100 nodes could
be displayed on the network.
Topic
Knowledge
creation dynamics

Research questions
What are the dynamics of knowledge
evolution for science-based innovations?
How are the trajectories and paradigms ofthe dominant theories selected?

6

Research Methods
Longitudinal methods,
citation networks,interviews

Social dynamics

How do individuals or groups of actors
define the objectives for the efficientcollective action of a community? What is
the structure of networks on which
dominant designs are grounded, and how it
has evolved?

Two-mode networks(delivery systems andorganisations),
interviews

Table 4. Summary of research method.
Further, two-mode networks were constructed to evaluate the dynamics of the groups
engaged in scientific development in the field of iPS cell reprogramming/generation. These
networks relate the different delivery systems with the co-authoring organisation of therelevant publication subset. A threshold equal to or greater than three was set for the
network edges. The analysis spanned over three periods: 2006–2009, 2010–2012, and2006–2012. The results of the two research methods were validated through a series ofinterviews with experts in the field of iPS cell generation/reprogramming.

3 Results
This section presents the results of the two analyses of this study related to the dynamics
of knowledge creation and social dynamics. As discussed in the previous sections, 1,537
publications relevant to the field of iPS cells published between 2006 and 2012 wereidentified from the WoS database. Of these publications, 581 (around 38%) were found to
be directly related to the reprogramming of iPS cells.

Dynamics of knowledge creation
Figure 1 presents the general longitudinal trends for the total number of publications
collected related to the field of iPS cells as a whole (Figure 1a) and for those related to iPS
cell (iPSC) reprogramming approaches in particular (Figure 1b).
Figure 1a shows that the year 2010 marked a transition in the field of iPS cells froman upstream- to a downstream-dominated path of knowledge accumulation. Downstreamdominated knowledge paths involve activities such as the differentiation of iPS cells into
other types of body cells or the use of these differentiated iPS cells in any of the potential
application domains. This does not imply that the immanent problems related to the search
processes in the field of iPS cell reprogramming have already been solved. These problems
remain highly relevant. However, in terms of the number of publications, the iPS cell
reprogramming publications appear to be outweighed by those on downstream activities.
Figure 1b shows that the accumulated knowledge in this subfield is mainly related to viral
vector-based reprogramming approaches. This is not surprising, since these approaches
were used in the pioneering studies on iPS cells. Nevertheless, as shown in Figure 1b, aseries of alternative reprogramming approaches have been demonstrated in recent years.

This paper was presented at The XXVI ISPIM Conference – Shaping the Frontiers of Innovation
Management, Budapest, Hungary on 14-17 June 2015. The publication is available to ISPIM
members at www.ispim.org.

This is directly related to the on-going efforts aimed at developing more suitable andefficient methods of iPS cell generation (Roberts et al., 2014).

Figure 1. Cumulative number of publications related to iPSC reprogramming: a) by total
number of publications, and b) by type of delivery system.
Figure 2 assesses the proportion of publications across the different viral-based
reprogramming methods over the years. The pioneering retroviral approaches used by
Takahashi and Yamanaka (2006) appear to be gradually giving way to alternative viralbased methods. The most common alternative viral vector-based methods are thelentivirus-, Sendai virus-, and adenovirus-based reprogramming methods. This shift issignificantly related to the potentially non-integrating nature of these viral approaches, i.e.
the viral vectors do not integrate into the genome. Thus, these approaches appear to be
attracting the interest of researchers as potential processes for the generation of clinicalgrade iPS cells.

8Figure 2. Number of publications over time according to the type of viral vectors.
Social dynamics
This section describes the dynamics of change among the scientific communities involvedin the development of iPS cell reprogramming approaches. A series of two-mode networks
was built to relate the different iPS cell reprogramming approaches with the affiliated coauthoring organisations of the collected publications (Figure 3). Three time periods are
displayed in Figure 3: 2006–2009, 2010–2012, and 2006–2012.
In these networks, the location of a particular organisation denotes the patterns ofusage of iPS cell reprogramming methods. The analysis of the spread of the nodes in termsof their geographical region shows that Asian organisations tend to focus on either
retroviral or lentiviral approaches. In contrast, American organisations and some European
organisations tend to use both approaches. Moreover, American organisations appear to be
more willing to use alternative iPS cell reprogramming methods, such as plasmid-,transposon-, chemical-, RNA-, or protein-based approaches. Some Japanese organisations,such as Riken University or Kyoto University, are exceptions to this trend. This findingreflects the strong downstream competences of American and European organisations
compared to those of their Asian counterparts.

This paper was presented at The XXVI ISPIM Conference – Shaping the Frontiers of Innovation
Management, Budapest, Hungary on 14-17 June 2015. The publication is available to ISPIM
members at www.ispim.org.

Figure 3. Two-mode networks of organisations and delivery methods over time.

10An alternative way of tracing the dynamics of knowledge accumulation is throughthe construction and analysis of citation networks. As described earlier, the publications
related to iPS cell reprogramming obtained from the WoS database together with their listof references were used to construct the citation network shown in Figure 4. The software
CitNetExplorer was used to construct the citation network. In this network, the nodes arearranged by the year of publication on the y-axis. Moreover, the network nodes arecoloured according to the type of reprogramming approach used in each study. As these
networks include the entire list of cited references, those nodes that are not included in thelist of iPS cell reprogramming-related documents are coloured in light blue in Figure 4. Inline with the previous findings, the pathways of knowledge accumulation are dominatedby viral-based reprogramming approaches. While alternative reprogramming approaches
were used during 2009–2010, their influence was not significant enough for them to build
their own specific paths of knowledge evolution.

Figure 4. Citation network of publications related to iPS cell reprogramming.

This paper was presented at The XXVI ISPIM Conference – Shaping the Frontiers of Innovation
Management, Budapest, Hungary on 14-17 June 2015. The publication is available to ISPIM
members at www.ispim.org.

4 Discussion
The question of knowledge creation dynamics
Our observation related to Figure 3 confirmed the differences among research institutions
according to the different delivery methods used; this tendency is particularly evident in
the case of the methods located at the periphery of the network. For instance, while there
were a few overlaps in the methods of the research institutions using Sendai virus,
transposons, chemicals, and proteins, most of these institutions were interrelated in termsof viral-based methods. Further, we observed that the emergence of these peripheral
methods does not mean a takeover from the central ones, although those at the periphery(such as transposons or chemicals) are substantially superior in clinical usage (Sengoku, inpress). These results strongly suggest that the agreement to form a dominant design has
never been a general trend but is limited to each representative academic group.
These results are similar to the findings reported in prior studies, which showed a clear
difference among organisations according to the innovation process used, even in
categories using the same methodology, especially in the context of the three groups that
had succeeded in the generation of human iPS cells (Roberts 2014). For each of these celllines, the trajectories were protected by separate sets of patents that were produced at acommercial base by an individual biotech firm or affiliated biotech firms and weretranslated into clinic use by a part of the individual academic group.

The question of knowledge content codification and incorporation: Technology
Development was found to take place individually, with very few cooperative interactions
for integration. However, there were scientific conditions that motivated each of these
actors to participate in a coding and institutionalisation process within the institutional
realm.
Figure 4 presents the knowledge interrelations in the course of the development ofreprogramming methods as citing-cited networks, where prior additional references were
integrated in Takahashi’s study on the discovery of the iPS cells in 2006 (Takahashi and
Yamanaka, 2006). This study was extended across regions by subsequent studies; notably,
the intensive interrelations of knowledge were developed through citing-cited compilinginteractions. This result implies that the conditions related to intensive knowledge transfer
at the scientific research level led to the compartmentalisation of the groups, which thenconcentrated on a specific methodology, resulting in the existence of multiple designs for
iPSC reprogramming.

The question of social dynamics: Which dominant designs are grounded?
Our findings strongly support the projection of scientific research networks of cooperation
as well as the associated constraints, which are understood to be the nature of the sciencebased industry. As shown in Figures 3 and 4, we configured a multi-layer situation ofcooperation and competition: differences in methodological development (Figure 3) withintensive interrelation through the citation of scientific publications used in knowledge
creation (Figure 4).

12This finding conforms to what was reported in prior studies. One representative case is
the study of the conflict between collaboration and competition conducted by Bubela et al.
(2010). Their bibliometric research showed that technological development at the patenting
level negatively affects collaboration patterns in scientific research, suggesting the coexistence of collaboration and commercialisation in the science-driven industry.

Contribution
Science-based innovations have increasingly contributed to the renewal and creation of
innovative businesses and industries recently. However, classical models of technologicaldynamics and evolution were mainly designed based on industry-based innovations. This
leads to the questions: How do these classical tools apply to science-based innovations?
How can we adapt these models to turn them into more accurate instruments for managers?
In this study, we applied the classical Abernathy-Utterback model that was later refinedby Tushman to the case of the innovative science-based biomedical industry, specificallyto iPS cells. We found the distinction between the explorative and exploitative dynamics
of knowledge to be accurate. However, the classical model needed to be adapted to suit amore multi-level and networked environment along three dimensions of source,mechanism, and the effects of standardisation.
Based on these findings and the differences between the studied case of a science-basedindustry and the classical industry cases, we extended the Abernathy-Utterback model toaccount for the development of science-based as well as traditional industries.

Practical implications, limitations, and future researchThis present presents the following practical implications.


The overall framework proposed by the Abernathy-Utterback model holds for the
analysis of the development of the science-based industry; we were able to confirm
the exploratory versus exploitative staging in the innovation journey of the iPS cells.
However, the model was much less accurate in the crucial phase of standardisationand transition in the science-based case. Thus, ‘classical industries’ and contemporary‘science-based industries’ differ along three dimensions: technological sourcing, themechanism of standardisation, and the effect of standardisation.



In science-based cases, the horizontal and inter-organisational emergence of standards
and technological trajectories is further complicated by a vertical and interinstitutional imperative of bridging the two worlds of science and industry.

This study has some limitations. First, the bibliometric analysis was limited to
publications; it was not fully expanded to other indicators. Patent issues in particular appear
to be more closely related to the present discussion. It could be argued that the analysis
based on the scientific articles constitutes a rough indicator of model competition or
evolution, as the patent-related issues in the life sciences are tightly linked to newknowledge in the scientific articles. However, this claim has to be verified in future
research.

This paper was presented at The XXVI ISPIM Conference – Shaping the Frontiers of Innovation
Management, Budapest, Hungary on 14-17 June 2015. The publication is available to ISPIM
members at www.ispim.org.

Second, in the present study, we do not distinguish the intended uses of iPS cells. As
mentioned earlier, the iPS cell technology can be applied not only for therapeutic uses but
also for basic research and drug discovery purposes. Based on the characteristics and thepros/cons of each reprogramming method, the choice of methods may be affected by theorientation of the usages, in which the quality standards and the production process could
vary. These aspects should be integrated in future research.

Acknowledgement
This research was supported by the Japanese Ministry of Education, Culture, Sports,
Science and Technology and the Japan Society for the Promotion of Science (a grant-inaid for Scientific Research B, grant no. 26301022). In creating this manuscript, we received
valuable assistance from Dr Yoshimi Yashiro, Associate Professor at the Center for iPS
Cell Research (CiRA). We express our deepest gratitude and appreciation for all theassistance we received.

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This paper was presented at The XXVI ISPIM Conference – Shaping the Frontiers of Innovation
Management, Budapest, Hungary on 14-17 June 2015. The publication is available to ISPIM
members at www.ispim.org.

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