Unraveling future research: an analysis of emergent literature in open innovation

Abstract

Breakthrough discoveries and productive collaborations are crucially important for successful open innovation, fuelling rapid generation of scientific knowledge, and making synthesis and analysis of this information difficult. Improved methods for identifying research gaps and emergent conceptual themes across open innovation research are necessary. The purposes of the study are (i) to present a novel methodological approach that utilizes centering resonance analysis (CRA) as its core text clustering engine to establish relationships and thematic patterns among diverse scientific literature pertaining to open innovation, (ii) to demonstrate this methodology in an open innovation context, (iii) to validate this approach, and (iv) to discuss the extensibility of the provided methodology to other contexts. Results show that thematic threads are consistent with recognized innovation research work, as well as highlight new threads that may be crucial in shaping future research perspectives. The authors believe that much can be gained from the proposed approach and from the use of computer-based tools to aid in the rapid development of the field of open innovation and its integration to wider-focused research on innovation.

Full text

Unraveling future research: an analysis of
emergent literature in open innovation
Behnido Calida and Patrick Hester
Department of Systems Engineering and Engineering Management, National Centers for System of Systems
Engineering, Old Dominion University, Norfolk, VA, USA
Breakthrough discoveries and productive collaborations are crucially important for successful open
innovation, fuelling rapid generation of scientific knowledge, and making synthesis and analysis of this
information difficult. Improved methods for identifying research gaps and emergent conceptual themes across
open innovation research are necessary. The purposes of the study are (i) to present a novel methodological
approach that utilizes centering resonance analysis (CRA) as its core text clustering engine to establish
relationships and thematic patterns among diverse scientific literature pertaining to open innovation, (ii) to
demonstrate this methodology in an open innovation context, (iii) to validate this approach, and (iv) to
discuss the extensibility of the provided methodology to other contexts. Results show that thematic threads
are consistent with recognized innovation research work, as well as highlight new threads that may be crucial
in shaping future research perspectives. The authors believe that much can be gained from the proposed
approach and from the use of computer-based tools to aid in the rapid development of the field of open
innovation and its integration to wider-focused research on innovation.
Keywords: open innovation;clustering analysis;literature review;future research
Received: 30 July 2010; Revised: 25 October 2010; Accepted: 18 November 2010; Published: 15 December 2010
The maturation of relevant open innovation themes
is expected to benefit the shaping of the future and
emerging direction of research and development
(R&D) enterprises (Enkel, Gassmann, & Chesbrough
2009). This poses a significant challenge as there may be
disparate sources of knowledge relevant to the open
innovation field. The main contention of this paper is to
draw out meaningful themes utilizing an integrated
methodology to help capture thematic patterns from a
highly dynamic topical domain. This is to acknowledge
that the research as well as the practice domains of open
innovation and the associated areas like entrepreneur-
ship, among others, is expected to actively advance and
continually evolve. Chesbrough (2003) outlined several
reasons behind the decreased reliance on the closed
innovation paradigm and the movement toward open
innovation. First, the mobility and availability of a highly
educated and skilled workforce has increased exponen-
tially in recent decades. To that end, significant knowl-
edge resides externally to large research enterprises.
Further, when individuals transition between organiza-
tions, their inherent knowledge transfers with them,
thereby necessitating intra-enterprise communication.
Second, increased venture capital availability translates
into enterprises, both large and small, having capital to
perform cutting edge R&D. Gone is the time when only
large R&D enterprises were able to accomplish signifi-
cant R&D breakthroughs. Further, greater availability of
funding means a shortened development window for
many R&D enterprises, thereby increasing competition
between organizations with an eye on maintaining a
leading edge research advantage. Finally, the entire
supply chain involved in an R&D enterprise has become
increasingly important to maximize collective innovation
capability, a paradigm shift that naturally encourages
open innovation between an R&D enterprise and other
members of its supply chain.
In the wake of a movement toward open innovation,
contemporary R&D enterprises have undergone signifi-
cant changes in recent years. First, a paradigm shift has
occurred at the science research policy levels (Kwa, 2006;
Waterton, 2005), resulting in reforms that have increased
collaboration between scientists and promoted research
of an interdisciplinary nature, thereby implicitly encoura-
ging open innovation. This paradigm shift has created a
movement toward Mode-2 science, as described by
Gibbons et al. (1994), away from traditional knowledge
production. Typically, the contrast is on the scale of
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Citation: Annals of Innovation & Entrepreneurship 2010, 1: 5845 - DOI: 10.3402/aie.v1i1.5845

doing research where normal research (Mode 1) is within
the confines of a single discipline while post-normal
research (Mode 2) is becoming more interdisciplinary in
nature (Gibbons et al., 1994, p. 9). These changes take
place at a time when research organizations are evolving
from the traditional structure. The second significant
change is the development of technologies that allow for
enhanced data and technology sharing computational
and organizational resource mechanisms, seamlessly in-
tegrated into the daily lives of R&D scientists’ operating
both inter- and intra-enterprise (Abramo, D’Angelo, &
Caprasecca, 2009; Committee Facilitating Interdisciplin-
ary Research, 2004, pp. 182184; Graversen, Schmidt, &
Langberg, 2005). Further, the ever-increasing need for
instant results has accelerated the pace of R&D, resulting
in a need for scientists to discover new and novel methods
for knowledge discovery and development.
Another realization that pushes further our argument
is the observed cognitive limitations that persist in
workplaces (Sparrow, 1999). Even the most well-versed
and proficient manager or scientist has practical cognitive
constraints that limit the ability to read, synthesize, and
understand relevant scientific discoveries and potentially
related research. The aforementioned cognitive capabil-
ities, while impressive, lag behind the staggering pace at
which new information is produced. Further, the organi-
zation, as a collective, assimilates this information at a
pace unlikely to be matched by an individual. This rapid
accumulation of knowledge, driven by the need for an
organization to remain at the forefront of its discipline in
order to retain its competitive advantage further exacer-
bates the problem. Through the context of open innova-
tion and discovery, surely there must be an answer to the
problem of knowledge accumulation and synthesis. What
potential approaches might be utilized to determine
potential leverage points across diverse research endea-
vors both inter- and intra-enterprise? How can the
identification of these leverage points be validated within
the research context in which they reside? Most impor-
tantly to the organization, how will the identification of
these leverage points help an organization to enhance its
competitive edge in the increasingly global R&D market?
This study presents one such approach that will assist
in highlighting potential interrelationships between
groups of functionally independent research, specifically
in the context of open innovation research. The main
hypothesis of this study is that there are cogent open
innovation themes that are otherwise undetected and,
further, can possibly be exposed and made explicit. To
support the evaluation of this hypothesis, the objectives
of this study are (1) to present an integrated framework
for conceptual data mining to be used in exploring
commonalities and identifying clusters in divergent sets
of textual data, (2) to demonstrate this methodology in
an open innovation context, (3) to validate this approach,
and (4) to discuss the extensibility of the provided
methodology in other contexts. To this end, the study is
divided into the following sections. The study first reviews
relevant research pertaining to data mining and textual
analysis that combine centering resonance analysis
(CRA), a new linguistic computational algorithm devel-
oped by Corman, Kuhn, McPhee, and Dooley (2002) and
more traditional quantitative clustering analysis techni-
ques. A generalizable methodology is then developed,
which can be applied to several areas of the research
process. This study specifically focuses on the analysis of
open innovation literature and the utility generated by the
proposed approach. The results of the study are then
validated using an expert-driven validation approach.
Finally, the study concludes with a discussion of the
implications of the proposed methodology on open
innovation and where this approach may be utilized in
other phases of the research process.
Theoretical background
This section will consider, in turn, a discussion of data
intelligence prospects for improved open innovation, a
brief overview of content analysis methods, and, finally,
some foundational background on the application of
CRA to textual data in order to provide a theoretical
foundation for the framework proposed in this study.
Better intelligence prospects for open innovation
Technological advances and advanced computing algo-
rithms have helped to decrease the reliance on the task of
hypothesis formation historically prescribed to human
investigators. Conceptually, the concept of utilizing
computational approaches to perform a literature analy-
sis has been proven to be both useful and novel (Bray,
2001). The primary hindrance to employing a computa-
tional approach to literature analysis has been the lack of
comprehension on the part of computers to understand
the intricacies of the implications, linkages, and relevance
of literature both within a document and across docu-
ments. Initially, however, the question of what this
development may mean in the area of open innovation
should be addressed.
There are several reasons as to why improved intelli-
gence and analysis can be useful in aiding organizations
in employing an open innovation model for R&D.
A systematic approach to drawing linkages between
seemingly disparate researches will aid researchers whose
aforementioned cognitive limitations prevent them from
assimilating the entirety of literature available. Further, a
repeatable approach will help to demystify the tradition-
ally closely guarded process of knowledge discovery and
synthesis, thereby implicitly encouraging open innovation
between enterprises willing and able to share their findings
and discoveries in the interest of mutual benefit. Once a
systematic approach has been developed, many other
Behnido Calida and Patrick Hester
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applications can be explored including: (1) identifying
linkages between research projects and programs within
an R&D enterprise; (2) identifying possible linkages
between R&D enterprises, thereby presenting opportu-
nities for knowledge sharing in the spirit of open innova-
tion; and (3) improving business intelligence within an
enterprise, helping an organization to maximize its R&D
potential by identifying strengths, weaknesses, opportu-
nities, and challenges.
Overview of related content analysis methods
Content analysis, described by Krippendorff (2004, p. 18)
as ‘a research technique for making replicable and valid
inferences from texts (or other meaningful matter) to the
contexts of their use,’ has several advantages, chiefly, its
objectivity. Management research, which has a strong
grounding in values and attitudes and has social science
derivatives, must employ repeat methodologies that avoid
subjective biases (Duriau, Reger, & Pfarrer, 2007). Addi-
tionally, content analysis can be performed utilizing
unstructured input data, a feature that is very useful given
the diverse nature of scientific input likely to be employed
in a given literature review process.
There are four main approaches to content analysis, as
outlined by Neuendorff (2002): descriptive, inferential,
psychometric, and predictive. The first two, as stressed by
Neuendorff, are not empirically founded; descriptive
approaches limit conclusions to that which is under
study, while inferential content analysis is subject to
bias and, therefore, less scientifically rigorous and desir-
able. Psychometric content analysis, extends ‘beyond
simple inference in that the measures are validated
against external standards’ (Neuendorff, 2002, p. 54), is
increasing in popularity, while predictive content analysis
is used to forecast particular outcomes for the analyzed
material.
While there is recognition as to the approaches of
content analysis in use today, there is debate as to
whether or not this tool is a qualitative or quantitative
tool (see Neuendorff, 2002, p. 87). Duriau et al. (2007) see
content analysis as existing at the intersection of the
quantitative and qualitative realms. For the purposes of
this analysis, the initial software-based data analysis and
collection is taken to be quantitative analysis, whereas the
latter stages of subjective data interpretation are taken to
be qualitative and envisioned as inputs to a decision
support system.
CRA is a recent and relatively sophisticated type of
content analysis methodology. It goes beyond simple
content analysis by identifying the most crucial words in
a text document and linking these words in a network.
These linkages help to organize the words holistically by
looking at the influences of these words in the larger
document based on the word location within the docu-
ment (Corman et al., 2002). With each specific linkage
identified, useful meaning can be designated where the
CRA technique benefits from foundational linguistics
and network theories to posit accurate representation of
the textual concept. Unlike other content text analysis
methodologies that rely on frequency counts of words or
phrases, CRA considers a word to have more influence
within a text, depicting the prominence of its relationship
to other words, if it links other words together in the
network text and assists in assembling meaningful groups
of text. For these reasons, CRA appears to be the most
appropriate method for text analysis.
The CRA is a three step process consisting of selection,
linking, and indexing. First, selection categorizes text in
terms of patterns connecting them. Compilation of these
words and their underlying connections across all utter-
ances in the text yields a CRA network depiction of the
text. Next, the linking step converts word sequences into
networks of relationships between words. An article
found as a result of a scientific literature review is
analyzed with CRA grouping the words into noun
phrases and combining these phrases to form utterances.
Accumulating links over a set of utterances comprising a
text (or series of texts that are the result of a literature
review) yields a symmetric, valued, undirected network
whose nodes represent the center-related words. Finally,
indexing analyzes the network to determine relative node
(word) influence.
The CRA results can be interpreted in a number of
different ways. These may include: (1) investigation of a
particular author in a field to determine how the author’s
works are related to other existing research; (2) identifi-
cation of clusters of research to determine the underlying
themes of a particular field about which you little or no
knowledge, perhaps in an effort to speak the language of
a particular field or familiarize yourself with the im-
portant literature; (3) examining a seminal study in a field
in order to determine how other research in the field
relates and has furthered this early work; (4) comparing
and contrasting existing research in an effort to gain
insight; (5) observation of gaps in research, thereby
identifying opportunities for future innovation, (6)
assessment of the prevalence of a particular method or
theme in research; and (7) understanding existing
research to leverage findings to enhance efficacy of new
research initiatives within an enterprise.
Methodology
Overview of the method
Following the work presented in Calida and Hester
(2010), the methodology is shown in Fig. 1 that features
a three-stage study approach using CRA cluster analysis
as an integrated aspect of a typical literature review
research process.
Analysis of emergent literature in open innovation
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Stage 1 consists of data generation and preprocessing.
A literature review process is typically undertaken by
performing a search in a library database using research
relevant keywords, thereby identifying a set of potentially
relevant articles. A digital copy of the full text of each
article is then retrieved, excluding those that are simply a
book review or editorial and thus, irrelevant. This pool of
articles is considered the text for the literature review
process. All baseline articles must then be converted to
ASCII text files. This preformats the articles in order to
enable the next step in the clustering process. One of the
purposes of this research is to demonstrate the metho-
dology within the context of open innovation. Targeting
this open innovation context, Stage 1 proceeds with data
generation and preprocessing of scholarly research text
data that is focused on recent developments in the open
innovation topical domain. The articles that were used
for the literature search were focused on journal titles that
are considered at the research landscape forefront and,
which highlight strategic growth, long-range planning,
entrepreneurship, and e-learning technology dimensions.
Specifically, the journals that were searched included
R&D Management,Forecasting,Technology Analysis &
Strategic Management journals.
In Stage 2, each ASCII text file is subjected to cluster
analysis. Cluster text analysis is performed using two
parallel modes, one being through the use of computer
software and the other utilizing experts. Articles are
automatically processed using computer software in a
textual analysis program. The automated clustering
analysis was performed using Crawdad, an analytical
software tool developed specifically to conduct CRA.
Using the software, a network map of the words for each
article is generated and then assigned influence values
ranging from [0,1]. An influence score of 0.01 is
considered significant, while a score above 0.05 is
considered very significant (Corman & Dooley, 2006).
The software-based clustering process features thematic
analysis using an integrated exploratory factor analysis
(EFA) step. The different themes that emerged consisted
of distinctly coherent sets of words that were quantita-
tively processed within the EFA step that included a built-
in principal component analysis with varimax rotation.
These meaningful sets of words were used as a starting
point to develop a descriptive cluster name, useful in
coding a specific emergent theme. Individual words have
an influence value within each identified thematic cluster.
To better compare the influence values for each theme,
the influence values for each word were summed by
theme, given a clustering resonance value, and then
grouped by its journal title source. Beyond the level of
0.01 where a value has significance, the software produces
relative values that eventually will be used as a basis of
comparison for this specific literature review study.
In order to ensure experimental control and subse-
quent validation, experts manually cluster articles from
the baseline set using his/her subjective judgment to
identify common thematic elements present in the
articles. Comparison of the two approaches is undertaken
in Stage 3. Validation should typically be compared with
an expert-based study of similar information. The themes
that were extracted from the literature using CRA can be
compared to the themes (and their descriptions) that were
summarized in a recent work by Fredberg, Elmquist, and
Ollila (2008). The baseline work cited featured a com-
prehensive literature review of open innovation and
mentioned the following open innovation themes in
literature, namely, as follows:
1. The notion of open innovation
2. Business models
3. Organizational design and boundaries of the firm
4. Leadership and culture
5. Tools and technologies
6. IP, patenting, and appropriation
7. Industrial dynamics and manufacturing
Using a ‘Finder’ built-in function in the text analysis
software, a similar CRA map was generated for the above
listed themes and their associated descriptions. The
‘Finder’ command treats each one of the above generated
CRA networks as a full-text query and ranks other CRA
networks produced in the previous cluster analysis for
closeness to that query, enabling the user to perform a
full-text search. Finder produces an output window
showing the files most resonant with the query, ranked
from highest to lowest.
Requirement considerations for data preconditioning
and cluster analysis procedure
Word resonance is important to consider when analyzing
CRA network structures. It provides a general measure of
the mutual relevance of two texts. The more frequently
two texts use the same words, especially in similarly
influential positions, the higher the underlying resonance
of those words and, thus, the more prominent are those
words in the underlying text’s structure and message.
Computer software helps to make this process repeatable
Fig. 1. Simplified three-stage method overview.
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and objective, as individual biases influence the reliability
and repeatability of the process.
The CRA analyzes the raw information present in text,
determining how literature sources are interrelated and
grouping them into clusters based on topical similarities.
All assessment of relationships between literature sources
are not predetermined by the user or the software,
thereby ensuring that the software mimics the natural
process undertaken by researchers, scientists, and pro-
gram managers in subjectively identifying relationships
between multiple sources of information. Relationships
may exist based on the analyst’s perspective or experi-
ence, the underlying method in the work, historical
context of the work, results, or language used.
The resulting textual network clustering establishes
conceptual linkages between different literature sources
using textual frequency, location, and relevance within
the text. Clustering results may be verified and validated
by comparing the two modes of clustering undertaken in
Stage 2. Clustering results may be compared in terms of
the themes identified and membership of the themes.
Validation should be undertaken to compare the results
both across experts and between the experts and the
software. It should be noted that, once the method has
been validated for a significant number of literature
reviews, the expert-intensive steps can be removed from
the proposed process. This enables the advantages
described earlier in the study to be fully realized, namely,
the analysis and synthesis of large amounts of informa-
tion unable to be analyzed by a human-only system.
Results and discussion
Stage 1: preclustering step
The search, using open innovation as the input search
query and limited to all content published from 2009 to
July 2010, yielded 94 articles. The complete reference list
of the source samples used is listed in Appendix 1. From
these articles, 91 literature articles remained after dis-
carding 3 articles that were editorial in nature. The
breakdown of each article used in this literature review
is shown in Table 1. The contents of each identified article
are then converted to an ASCII text file, digitally stored
in a specified file folder. The converted files are then
subjected to clustering analysis in Stage 2.
Stage 2: clustering step and theme development
As an example, one of the network maps for one of the
eventual themes identified and its most influential words
are shown Fig. 2 (only very significant words are shown).
Corresponding network maps for each of the themes
identified were generated and analyzed in the same way.
The settings in the software were configured to identify
the 250 most influential words that commonly appeared
in at least two or more of the articles in the literature
pool. The goal was to detect which words appear to be
the most important words across the specified literature.
These influential words will be utilized in the later
analysis when thematic factors are formulated.
Each of the emergent themes and their corresponding
resonance values are shown in Table 2. Note that all the
combined influence values (or resonance) per thematic
cluster show values more than 0.01, indicating that the
suggested clustering solution is significant. Noteworthy is
Table 1
.Summary of stage 1: data generation and preprocessing
Journal title Article count Search Input query
Technology analysis and
strategic management
47 (‘open’ AND
‘innovation’
Foresight 14 Inclusive years:
R&D management 30 2009July 2010)
Article pool total 91
Words Influence
Value
technology 0.25576
foresight 0.13939
analysis 0.08938
aviation 0.0757
research 0.07045
future 0.06464
paper 0.05715
potential 0.05687
stakeholder 0.05211
ResearchAviation
Analysis
Paper Potential
Stakeholder
Extensive
Project Challenge
Innovation
Complexity
Development
Centre
Impact
Future
Scare Policy
Foresight
Technology
Fig. 2. Example of a CRA network map and word-influence value table.
Analysis of emergent literature in open innovation
Citation: Annals of Innovation & Entrepreneurship 2010, 1: 5845 - DOI: 10.3402/aie.v1i1.5845 5
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Tabl e 2
.Ten emergent thematic clusters in recent open innovation literature
Cluster description (number) Reference article (by author name, year) Within-cluster resonance
Directionality of open innovation technology
transactions (1)
Lichtenthaler (2009); Lichtenthaler and Ernst (2009) 0.3200
Notional trends in open innovation research (2) Enkel and Gassmann (2010); Enkel, Gassmann, and Chesbrough (2009); Gassmann, Enkel, and
Chesbrough (2010)
0.2121
Role of tools and technologies in open innovation (3) Richardson (2009a, 2009b) 0.1632
State of Play in the Futures Field (SoPiFF) project:
metascanning methodology (4)
May (2009); Slaughter (2009); Slaughter and Riedy (2009) 0.1423
Knowledge and technology transfer indicators: is-
sues and considerations (5)
Barbolla and Corredera (2009); Bosch-Sijtsema and Postma (2010); Bouncken and Winkler (2010);
Chatenier, Verstegen, Biemans, Mulder, and Omta (2010); Glod, Duprel and Keenan (2009); Quinta´s,
Va´ zquez, Garcı
´a, and Caballero (2009); Tijssen (2009)
0.0578
Empirical accounts of futures and foresight planning
in firms, businesses, and industries (6)
Bows, Anderson, and Mander (2009); Cozzens et al. (2010); Elliot (2009); Forge, Blackman and Lindmark
(2009); Havas (2009); Jenssen (2009); Jørgensen and Jørgensen (2009); Kim and Wilemon (2009);
Ko¨ nno¨la¨, Ahlqvist, Eerola, Kivisaari, and Koivisto (2009); Lawrence (2009); Misuraca (2009); Salo,
Brummer, and Ko¨nno¨la¨ (2009); Vecchiato and Roveda (2010)
0.0442
Institutional and societal theories, frameworks, and
models of open innovation (7)
Allarakhia, Kilgour and Fuller (2010); Grimaldi (2009); Harty (2010); Knie and Ha
˚rd (2010); Knockaert,
Spithoven, and Clarysse (2010); Nahuis (2009); Papenhausen (2009); Savory (2009)
0.0416
Enhancing technology-based competitive advantage
in multinational industries through open
innovation (8)
Bradfield and El-Sayed (2009); Ebner, Leimeister, and Krcmar (2009); Hemphill (2010); Ili, Albers, and Miller
(2010); Lamastra (2009); Pantzar and Shove (2010); Rohrbeck (2010); Rohrbeck, Ho¨ lzle, and Gemu
¨nden
(2009); Rohrbeck, Steinhoff, and Perder (2010); Sieg, Wallin, and Krogh (2010); Stuermer, Spaeth, and
Krogh (2009)
0.0410
Interorganizational knowledge flows, collaboration,
and organizational learning networks (9)
Asakawa, Nakamura, and Sawada (2010); Aylen (2010); Chiang and Hung (2010); Chiaroni, Chiesa, and
Frattini (2010); Ha¨ ussler (2010); Holmes and Smart (2009); Keupp and Gassmann (2009); Lee, Ohta, and
Kakehi (2010); Neyer, Bullinger, and Moeslein (2009); Paananen (2009); Siedlok, Smart, and Gupta (2010);
Sofka and Grimpe (2010); Wincent, Anokhin, and Boter (2009)
0.0409
Conceptual challenges and applicability of emergent
open innovation mechanisms and tools:
outsourcing, technology roadmapping, weak
signal analysis (10)
Anthony (2009); Daim, Basoglu, Dursun, Saritas, and Gerdsri (2009); Dasgupta and Sanyal (2009); van der
Duin and den Hartigh (2009); Ekins and Hughes (2009); Fichter (2009); Gerdsri, Assakul, and Vatananan
(2010); Glassey (2009); Hanna and Daim (2009); Heiskanen, Hyysalo, Kotro, and Repo (2010); Ho and
Chen (2009); Hughes and Wareham (2010); Karlsen, Øverland, and Karlsen (2010); Leung (2010);
March-Chorda` , Yagu
¨e-Perales, and Seoane-Trigo (2009); Maqueira-Marı
´n, Bruque-Ca´ mara, and Moyano-
Fuentes (2009); McIvor, Humphreys, and McKittrick (2010); Mu
¨ller-Seitz and Reger (2009); Piirainen and
Lindqvist (2010); Raasch, Herstatt, and Balka (2009); Reiner, Natter, and Drechsler (2009); Rossel (2009,
2010); Schiele (2010); Thal and Bedingfield (2010); Thal and Heuck (2010); Treyer (2009); Weber,
Kubeczko, Kaufmann, and Grunewald (2009); Yoon (2010)
0.0282
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the observation that the clusters with the largest within-
cluster resonance are authored by the same set of authors
and appearing in a single journal. Understandably, there
is matching resonance between words having higher
influence values since specific authors use specific texts
or groups of texts under a specific and consistent mean-
ingful context in each instance. The rest of the thematic
clusters, however, despite having a smaller within-cluster
resonance value, are distributed across different authors
and between at least two different journal titles. It is
interesting to highlight that despite heterogeneity in
authorship and publication source, each of the identified
articles demonstrates how widespread and diverse the
notion, application, and ideas related to open innovation
have become.
As shown in Table 2, the different emergent themes
identified from a narrowed down pool of recent open
innovation articles may be clustered according to the
following themes:
1. Directionality of open innovation technology trans-
actions
2. Notional trends in open innovation research
3. Role of tools and technologies in open innovation
4. State of Play in the Futures Field (SoPiFF) project:
metascanning methodology
5. Knowledge and technology transfer indicators:
issues and considerations
6. Empirical accounts of futures and foresight plan-
ning in firms, businesses, and industries
7. Institutional and societal theories, frameworks, and
models of open innovation
8. Enhancing technology-based competitive advantage
through open innovation
9. Interorganizational knowledge flows, collaboration,
and organizational learning networks
10. Conceptual challenges and applicability of emergent
open innovation mechanisms and tools: outsour-
cing, technology roadmapping, weak signal analysis
Stage 3: validation
To analyze the validity of each suggested thematic cluster,
the key themes identified in the baseline literature is
analyzed with the ones produced using the methodology
in this study. Table 3 presents the themes identified here
in contrast with the corresponding theme from Fredberg
et al. (2008) that is most resonant with each and their
resonance value. These thematic comparisons can be
grouped according to the following cases:
Case 1: A high resonance value (0.05) implies
a some strong coherence in thematic meanings
between two clusters
Case 2: A resonance value of between 0.01 and
0.05 indicates a significant coherence in thematic
meanings between two clusters
Case 3: Clusters may not match significantly with
another cluster (no resonance value above 0.01)
An example of Case 1 is the strong resonance between
Cluster 3 and the ‘tools and technologies’ cluster
identified by Fredberg et al. (2008). In this instance, the
specific clusters share a thematic similarity, indicating a
strong alignment in their overall discourse and meaning.
Table 3
.Comparison of present themes to reference (Fredberg et al., 2008)
Themes presented in this study Highest resonance reference theme Resonance
Directionality of open innovation technology transactions (1) Industrial dynamics and manufacturing 0.0235
Notional trends in open innovation research (2) The notion of open innovation 0.0185
Role of tools and technologies in open innovation (3) Tools and technologies 0.0644
State of Play in the Futures Field (SoPiFF) project:
metascanning methodology (4)
Leadership and culture 0.0239
Knowledge and technology transfer indicators: issues and
considerations (5)
Tools and technologies 0.0129
Empirical accounts of futures and foresight planning in firms,
businesses, and industries (6)
The notion of open innovation 0.0362
Institutional and societal theories, frameworks, and models
of open innovation (7)
Business models 0.0004
Enhancing technology-based competitive advantage in
multinational industries through open innovation (8)
Industrial dynamics and manufacturing 0.0359
Interorganizational knowledge flows, collaboration, and
organizational learning networks (9)
Organizational design and boundaries of the firm 0.0276
Conceptual challenges and applicability of emergent open
innovation mechanisms and tools: outsourcing, technology
roadmapping, weak signal analysis (10)
Business models 0.0363
Analysis of emergent literature in open innovation
Citation: Annals of Innovation & Entrepreneurship 2010, 1: 5845 - DOI: 10.3402/aie.v1i1.5845 7
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This indicates that, given that the focus of this study’s
literature review is on more recent literature, the theme of
tools and technologies is still a relevant area of literature
with respect to open innovation. An example of Case 2 is
indicated by the resonance indicating a significant
coherence between Cluster 4 and the ‘leadership and
culture’ cluster in the reference study. This indicates that
there is likely similarity between the two themes and their
associated literature sources. Case 3 is indicated by
Cluster 7, which only has a very weak resonance
(0.0004) with the ‘business models’ case. This may be
interpreted as a new application area, hence research is
still too narrow and focused and older literature has little
commonality with this emerging field. Note that in four
instances, themes identified in this reference study
matched significantly with two themes presented in this
study (e.g. ‘industrial dynamics and manufacturing’ had a
significant resonance with Clusters 1 and 8). This may
indicate a thematic overlap in the themes presented in this
study. Additionally, the ‘IP, patenting, and appropriation’
cluster from the reference literature did not resonate with
any of the newly identified clusters. This may be due to a
paucity of studies on prevailing conceptual research
threads, indicating a departure from this area as a
possibly emergent and crucial theme in the continually
evolving open innovation literature. Overall, the compar-
isons cited above show many strong linkages between the
baseline conducted by acknowledged experts in the field
and the CRA-driven literature review performed in this
study. This suggests that the integrated methodology
developed in this study, employing the described CRA-
based technique, is valid and useful for literature-based
analysis in the context of open innovation.
Conclusions
At the rate that new knowledge is produced, correspond-
ing scientific literature is created at a staggering pace. The
analysis and synthesis of this information can be aided by
using an integrated literature review methodology featur-
ing CRA as its core text clustering engine. Resultant
research themes were compared with a review of open
innovation literature performed by Fredberg et al. in
2008. While the usefulness of new clustering techniques
such as CRA extends beyond what was presented in this
paper, one of the contributions and the main focus of this
work is to draw out clear emergent research streams (i.e.
such as the case of open innovation as a distinct, yet
widely speculated new paradigm) that will most likely
redefine the future research perspectives on open innova-
tion and entrepreneurship research. Though tangential,
another unique contribution, in any case, is the demon-
stration of how the methodology/technique was able to
identify meaningful similar/related themes on disparately
different sources of innovation and entrepreneurship
literature. As open innovation is still an emerging field,
new literature can be compared with the literature themes
proposed by Fredberg et al. (or those presented in this
study) to determine commonalities with existing thematic
constructs. Further, in the event that little resonance is
discovered with existing literature themes, the methodol-
ogy assists in identifying novel research that represents a
departure from existing thematic concentrations of open
innovation. Lastly, any prospective concurrence of the
results was validated against literature reviews and
analyses undertaken by experts. While thorough valida-
tion remains to be undertaken, the authors believe much
can be gained from the use of computer-based tools to
aid in the rapid development of the field of open
innovation and its integration to wider-focused research
on innovation.
Conflict of interest and funding
The authors have not received any funding or benefits
from industry or elsewhere to conduct this study.
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Analysis of emergent literature in open innovation
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