From clusters to innovation ecosystems -How technology can help us better measure their impact, GFP Ottawa 2018

1. From clusters to innovation
ecosystems - How technology can help
us better measure their impact
Catherine Beaudry
Polytechnique Montreal, Canada Research Chair in
Creation, Development and Commercialisation of Innovation
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity – Firms, workers and
disruptive technologies: Ensuring a sustainable and inclusive growth

2. Plan of the presentation
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
2
Clusters
Networks
Open
innovation
Innovation
ecosystems

3. Clusters
◉ Firms in innovation clusters are more innovative
◉ And have a greater performance (Beaudry & Swann, 2009; Delgado et al.,
2014; Maine et al., 2010)
◉ Partly because of the higher specialization and diversification externalities
therein (Beaudry & Schiffauerova, 2009)
◉ Greater cluster scientific performance
◉ Positively influence the propensity of small local firms to patent (Helmers &
Rogers, 2015)
◉ And to co-evolve with local private sector patenting (Blankenberg &
Buenstorf, 2016)
◉ Sub-national regions are the smallest spaces where the coherent and
efficient coordination of innovation can take place (Walshok et al.,
2014)
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
3

4. Demand effects to clustering
◉ Firms may cluster in a particular location to take advantage
of strong local demand
◉ Demand-side advantages emanating from the strength of
another industry in that cluster
◉ Firm stands to take market-share from its rivals
◉ Agglomerations also reduce consumer search costs
◉ Demonstration effects arising from the observation of
successful firms in a cluster incite new entrants in the
cluster
◉ In terms of innovation, customers are an important source
of new ideas within the framework of localised user-supplier
interactions (Lundvall, 1993; von Hippel, 1998)
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
4

5. Supply-side effects to clustering
◉ A wider variety of intermediate inputs at a cheaper price can be
obtained by a localised industry that supports a greater number of
specialised local suppliers
◉ A localised industry can attract and create a large pool of skilled
workers, smoothing the effects of the business cycle through
increasing numbers (David and Rosenbloom, 1990)
◉ Marshallian externalities are sector specific (Marshall, 1920), notably
the availability of labour with sector-specific skills
◉ Infrastructure benefits such as access to major motorways, railways
and airports is often cited as an attractor of firms into a cluster
◉ Informational externalities accrue to the new entrant from seeing
established firms producing successfully at a particular location
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
5

6. Geographic proximity
◉ Generally improves coordination by reducing the distance
interactions (Balland et al., 2015; Bathelt & Cohendet,
2014; Kantor & Whalley, 2014)
◉ Facilitates collaboration and tacit knowledge sharing
(Gertler, 2003) and local knowledge spillovers (Lorenzen &
Mudambi, 2013)
◉ …But it is by no means essential when knowledge is exchanged
more formally (Gertler & Levitte, 2005; Bathelt & Henn, 2014)
◉ …It is neither sufficient nor necessary to the success of
collaboration, which can very well be coordinated at long distance
through a temporary proximity (Torre, 2008).
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
6

7. Collaboration
◉ Is necessary to meet the ensuing growing complexity of
projects, technologies, products and services
◉ Acts as a catalyst, accelerating the pooling of information,
skills and resources (Tidd & Bessant, 2013)
◉ Improves the generation, valuation, and validation of ideas
(Koen et al., 2014)
◉ Increases organizations’ capacity to innovate (Beaudry, 2016;
Cropper et al., 2008; Cuijpers et al., 2011)
◉ Enables bridges to be built across and between disciplines,
industries and sectors (Dahlander & Gann, 2010; Giorgini &
Vaillant, 2016), and users (Parmentier & Mangematin, 2014)
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
7

8. Collaboration
◉Vast literature on
◉ Clusters
◉ Univ.-Ind. linkages
◉ Proximity
◉ Networks
◉ Strategic alliances
◉ Partnerships
The extended collaboration required for the challenges
brought by discontinuous technologies forces us to rethink
the innovation ecosystems and relationships between policy-
and decision-makers, experts and users imperative.
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
8

9. Cognitive proximity
◉Sharing the same knowledge (cognitive proximity)
◉ Is the principal cause of tacit knowledge spillovers from one
firm to another (Cowan et al., 2000; Breschi & Lissoni, 2001a,
2001b)
◉ Transcends a local or geographically dispersed epistemic
community that shares a common knowledge base
◉ Must be matched with a degree of social proximity (Boschma,
2005; Wink, 2008) for efficient knowledge transmission
(Agrawal et al., 2008; Broekel & Boschma, 2012)
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
9

10. Networks
◉ External networks of inventors (Crescenzi et al., 2016) that rely
on prior social proximities (see Bercovitz & Feldman, 2011)
◉ Are necessary to innovation teams
◉ Lead to the successful commercialization of university research
◉ National and international links are required to generate
innovations (Benneworth & Dassen, 2011; Walshok et al., 2014)
◉ “Moving to a ‘network ontology’ of innovation allows a better
understanding of the relationship between regional and global
innovation” (Walshok et al., 2014:347-348) than does the
framework of national or regional systems of innovation
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
10

11. Knowledge networks
◉ Organizations, units within organizations, and individuals
occupying key network positions
◉ (more central, with higher clustering or higher reach, for instance)
◉ … are more productive and innovative (Beaudry & Kananian,
2013; Gilsing et al. 2008; Grigoriou & Rothaermel, 2014;
Schiffauerova & Beaudry, 2012; Schilling & Phelps, 2007;
Tahmooresnejad & Beaudry, 2015, 2017; Tsai, 2001)
Central and star inventors have a positive impact on the quality of patents
Repeated collaboration have a decreasing impact
Foreign collaboration fosters more commercially valuable innovation
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
11

12. Knowledge networks
◉ But which of Coleman’s (1988) network closure
◉ (dense local structures with strong ties between nodes)
◉ or Burt’s (1992, 1997) structural holes
◉ (sparser network with weaker network links)
◉ …are more conducive to innovation
◉ Rost (2011) suggested that both approaches are
complementary (rather than substitutes) in that in the
presence of a weak network structure (structural holes),
strong ties (Coleman) are beneficial to innovation
Firms are
increasing the
density of these
networks
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
12

13. University-industry linkages
◉ University-industry linkages (Perkmann et al., 2013)
◉ Bridge the gap between knowledge and innovation (Baycan &
Stough, 2013)
◉ Lead to more impactful research (Lebeau et al., 2008)
◉ Mediating role of students and industry employees (Bodas
Freitas et al., 2013) are crucial in emergent industries
A university researcher is more likely to be listed as an inventor of a
patented innovation, regardless of the assignee, if he receives
private funding, has a fairly high level of cliquishness in the scientific
network and has shown a prior capacity to successfully collaborate
with industry, a concept that we named innovation loops
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
13

14. Open and co-innovation
◉ Innovation and business models have evolved towards more open and
interactive structures (Cohendet & Simon, 2017)
◉ where informal links adjoin formal relationships (Autio & Thomas, 2014)
◉ Open innovation research (Chesbrough, 2003) has moved towards
“collaborations with external networks, ecosystems and
communities” (West et al., 2014:809)
◉ We need a new model of “co-innovation” (Lee et al., 2012) both at
the technological and at the organizational level, backed by business
models that are
◉ Innovative (Gambardella & McGahan, 2010)
◉ Open (Chesbrough, 2007)
◉ Complementary
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
14

15. Ecosystems
◉ It is now necessary to “go beyond the traditional framework and
boundaries of industry and technological innovation, renewing the
approach of local systems, towards issues of business systems or
industrial ecology” (Torre & Zimmerman, 2015:30).
◉ Innovation clusters and knowledge networks have been shown to have
a positive influence on innovation
◉ A natural extension postulates that organizations involved in strong innovation
ecosystems should also be more innovative (Adner, 2006; Carayannis &
Campbell, 2009; Rohrbeck et al., 2009
◉ Ecosystems also encompass the broader network literature that
includes user participation, explicitly covering both
◉ Upstream (production-driven)
◉ Downstream (user-driven) activities (Autio & Thomas, 2014)
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
15

16. Ecosystems
◉ Moore (1993) conceptualized business ecosystems (Mira-
Bonnardel et al., 2012)
◉ Peltoniemi & Vuori (2004) described their attributes
◉ Iansiti & Levien (2002, 2004) described their constituents
◉ Industrial ecosystems literature (Frosch & Gallopoulos,
1989; Galateanu & Avasilcai, 2013) encompasses:
◉ Digital business ecosystems (Dini & Nachira, 2007; Stanley &
Briscoe, 2010)
◉ Entrepreneurial ecosystems (Fetters et al., 2010; Isenberg, 2010)
◉ Innovation ecosystems (Adner, 2006; Rohrbeck et al., 2009)
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
16

17. Informal relationships
◉ Ties’ relational character (Baum et al., 2012), informal
relationships and collaborations play a crucial role in open
innovation (West et al., 2014) and in catalyzing knowledge
within innovation ecosystems,
◉ Social ties
◉ Participation in associations and their events
◉ Trade fairs and international community gatherings
◉ Knowledge trading (Bathelt & Henn, 2015; Fichter, 2009; Henkel et
al., 2014; Laursen & Salter, 2014; Mina et al., 2014).
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
17

18. Innovation ecosystems…
◉… are at the confluence of innovation networks and
knowledge clusters, where individuals, organizations,
technology are interacting, formally and informally, to
“catalyse creativity, trigger invention, and accelerate
innovation across scientific and technological
disciplines, public and private sectors […] in a top-
down, policy-driven as well as bottom-up,
entrepreneurship-empowered fashion” (Carayannis &
Campbell, 2009:202-203)
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
18

19. Innovation ecosystems…
◉ … include participants from outside the traditional value chain,
such as customers, universities, regulators, innovation
coordinators/intermediaries, and firms that co-evolve with the
ecosystem often in symbiotic relationships (Iansiti & Levien,
2004; Mazzucato & Robinson, 2017; Ritala et al., 2013)
◉ In practice?
◉ The Innovation Supercluster Initiative is more akin to the
creation of innovation ecosystems (Rothschild, 1990) than to
clusters per se
◉ Involve a multitude of organisations from both supply and demand sides
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
19

20. 3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
Challenges of innovation ecosystems
Governance
RegulationIP
◉Sailing between
◉ Governance
◉ IP
◉ Regulation
◉Is the most
challenging task
in managing
innovation
ecosystems
28-29 June 2018
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21. Way forward?
◉Research must now equip
organizational actors with
◉ Frameworks for thinking
◉ Appropriate indicators
◉ Evidence-based decision-making
tools
◉… to build new innovation
policies
◉… to bring about the
necessary transformations in
ecosystem innovation
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
21

22. Measuring innovation is tricky at best
Measuring innovation within ecosystems and attributing impact to
various stakeholders is less than trivial…
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
22

23. Innovation measures (I/II)
◉ Main measures (Becheikh, Landry & Amara, 2006)
◉ Number of innovations (Oslo manual)
◉ Percentage of revenues from innovations
◉ R&D intensity
◉ Patent metrics
◉ Indices
◉ Indicators depend on the studies’ objectives and the nature of
the data available
◉ National statistics databases (Statistic Canada, USPTO)
◉ Difficult/restricted access
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
23
PROXY LAND…

24. “
”
A technological product innovation is the implementation /
commercialisation of a product with improved performance
characteristics such as to deliver objectively new or improved
services to the consumer, a technological process innovation is
the implementation/adoption of new or significantly improved
production or delivery methods
OCDE et Eurostat, 2005 p.54
Similar definitions for process, organisational, marketing and social innovations.
But are the subtleties truly understood by respondents to innovation
questionnaires? Proxy measures often include R&D, patents and the likes. Only
hint at a possible link with wealth creation and society’s wellbeing
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
24

25. Innovation measures (II/II)
◉Questionnaire-based surveys
◉ Precise information vs low response rate (Sauermann, 2013)
◉ Several methodological biases
◉ Acquiescence bias (Watson, 1992)
◉ Demand characteristics bias (Orne, 1962)
◉ Extreme responding (Furnham, 1986)
◉ Self-reporting bias also called social desirability bias (Nederhof, 1985)
◉ Selection bias (Heckman, 1979)
◉ Non-response bias (Deming, 1990)
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
25

26. Web content analysis of tech enterprises’ websites
Web-mining indicators may provide new information that is not
captured through classical methodologies and may possibly be
complementary to traditional data sources (Gök et al., 2014 )
Alternative measures
Advantages
◉ Access for everyone
◉ Lots of information available
◉ Frequent update
◉ Available at any time
Drawbacks
◉ Unstructured data
◉ Significant differences between
sites
◉ Self-reporting bias
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
26

27. First crude measures
◉Keywords frequency analysis of innovation concepts
and factors
◉ Recurrent themes related to innovation concepts such as
R&D, collaboration, intellectual property, and external
financing can be observed in websites through keywords
frequency analysis
◉Proposition: A greater number of keywords related to
a concept in a company's website indicates that the
company considers this concept important
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
27

28. Examples – Perception of innovation
INNOV_PERCEPTION (1) (2) (3) (4)
ln(RD_EXPENSE_PROP) 0.195*** 0.206*** 0.207*** 0.213***
ln(INVESTMENT) 0.065*** 0.060*** 0.061*** 0.059***
dWEB_IP 0.057*** 0.682*** 0.760***
ln(WEB_RD) -0.032 -0.282***
[ln(WEB_RD)]2 0.035**
Constant 2.209*** 2.070*** 2.106*** 2.131***
Number of observations 204 204 204 204
F 31.16*** 25.417*** 19.433*** 16.873***
R2 0.237 0.276 0.281 0.299
Adjusted R2 0.229 0.265 0.266 0.281
One-tailed t-test applied for the main variables
*p<0.10; **p<0.05; ***p<0.01
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
Significant web
based indicators
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29. Examples – Reduced time to market
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
Significant web
based indicators
TIME_MARKET (1) (2) (3) (4)
dFINLAND -0.977*** -0.918*** -0.917*** -0.947***
ln(RD_EXPENSE_PROP) 0.064** 0.065** 0.069**
dEXTERNE_FIN 0.494*** 0.493*** 0.490***
ln(WEB_FIN) -0.014 -0.259**
[ln(WEB_FIN)]2 0.044**
Constant 1.758*** 1.159*** 1.160*** 1.187**
Number of observations 345 345 345 345
F 107.21*** 52.48*** 39.29*** 32.282***
R2 0.238 0.316 0.316 0.323
Adjusted R2 0.236 0.310 0.308 0.313
One-tailed t-test applied for the main variables
*p<0.10; **p<0.05; ***p<0.01
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30. Comment
◉ Low correlations between our web-based indicators and their
corresponding questionnaire-based indicators, consistent with
the findings of Gök et al. (2015)
◉ Valid models with web-based indicators and their corresponding
questionnaire-based indicators show their complementary
nature
◉ Method paves the way for building more complex web-
based/text-based indicators from websites and other sources of
data for content analysis
◉ Use machine and deep learning techniques such as Natural
Language Processing/Understanding to minimize false positives
and attempt at ‘understanding’ the nature of the text
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
30

31. What’s next?
Clusters
Networks
Open
innovation
Innovation
ecosystems
SSHRC-funded partnership doubled with a CFI text
mining/analysis infrastructure to study six
innovation ecosystems: AI, Sustainable mobility,
Personalised medicine, Advanced materials, Digital
aerospace, Smart manufacturing
Multiply the richness of detailed qualitative
analysis using Big Data analytics to build the next
generation of innovation metrics 28-29 June 2018

32. Digressions
◉ Technology adoption has multiple benefits
◉ Productivity increase
◉ Higher quality of products (Baldwin & Lin, 2002)
◉ Improved product flexibility (Young et al., 1993; Spina et al., 1996)
◉ Production cost reduction (Beaumont & Schroder, 1997; Rischel & Burns, 1997; Small,
1998)
◉ Adoption of advanced and ICT technologies can have an impact on
collaboration and open innovation practices
◉ Digital technology can play an important role in facilitating collaboration between
different organisations internally and externally
◉ Collaborative ICT technologies is breaking organisational barriers and facilitating
collaboration between different units of the same firm, but more importantly, between
different firms and organisations
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
32

33. Adv. design and information control tech.
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◉ SAT 2014 data
◉ Extranet and electronic data interchange
(EDI) is the most popular technology to be
combined
◉ Smaller sample of firms have adopted these
more advanced design and production (next
family) technologies
◉ These have not been around for long enough
to have been adopted
◉ With the exception of CAD/CAM, ERP,
MRP, Wireless communication for
production
a.CAE, CAM, Virtual Product development
b.Virtual manufacturing
c.Enterprise Resource Planning (ERP)
d.Manufacturing Execution System
e.Software Integration of quality results
f. Manufacturing Resource Planning (MRP)
g.Extranet and EDI
h.Wireless communications for production
i. Sensor network and integration
j. Computer integrated manufacturing
k.Automated systems for inspection
l. Unmanned aerial system (drone)
0
100
200
300
400
500
a g c h gh cg ag ac acg
Most adopted tech.
(exclusive)

34. Adv. design and information control tech.
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◉ Technologies i, j and h are completely separated
from the rest of the network and has the highest lift
◉ Virtual technologies and Resource planning usually
go hand in hand (rule 2)
◉ Support(S): Proportion of firms that have adopted
technologies A and B regardless of other
technologies adopted
◉ Confidence (C): Probability that if a company has
adopted technology A, it has also adopted
technology B
◉ Lift(L): Measures the interestingness of a rule (rules
that have a lift greater than 1 imply that the
confidence of this rule is greater than what’s
expected)
h. Wireless
communications
for production
j. Computer
integrated
manufacturing
i. Sensor network and integration
b. Virtual
mnfg
a.CAE/CAM
c. ERP
d. Manufacturing
Execution
System
e. Software
Integration of
quality results
f. MRP
g. Extranet
and EDI
Rules Description S C L
8 af => c 0.095 0.77 1.78
2 bc => a 0.053 0.90 1.82
6 ij => h 0.064 0.77 2.13

35. Adv. design and info. control tech.
Type of innovation: All Tech. Non tech. Product
Nb Adv. design and information control tech. (natural log) +*** +*** +*** +***
Use of concurrent engineering (dummy) + + - +*
Use of cross-functional teams (dummy) - + - +***
Collaboration with public organisations (dummy) + + + -
Collaboration with other businesses (dummy) - + - +
Use of competitive technological intelligence (dummy) - + + +
Use of sustainable development strategy (dummy) - - -*** -**
Use of product data mngt (PDM) or lifecycle mngt (LMC) (dummy) + + + +
Outsource activities (dummy) + +* + +**
Nb employees (natural log) -*** -*** -*** -***
Age (natural log) + - + -
Industry dummies yes yes yes yes
First stage regression (only the instruments are shown)
Nb obstacles to adoption of Adv. design & info. control tech. (natural log) - - - -
Nb measures taken to overcome these obstacles (natural log) +*** +*** +*** +***
Capital expenditures on Adv. Business intelligence tech. (natural log) +*** +*** +*** +***
Number of observations (firms that have adopted Adv. des. info. tech.) 1412 1412 1412 1412
28-29 June 2018
-
+
-
+
+

36. What we can do with this data
◉Identify which bundle of technologies has the most
impact on innovation, productivity, growth, etc.
◉Geolocalise these firms and the technology
◉ Study whether there are local patterns of adoption
◉ Identify where the technology comes from
◉ Determine whether learning within clusters or communities of
practice are taking place
◉But this data needs to be complemented with more
data from a variety of sources…
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
36

37. Understanding innovation within ecosystems
require a Statistics Act 2.0
◉ Remote access to protected data
◉ Access to high powered computing for data analysis
◉ Big data facilitated data collection (e.g. web-based)
◉ Mainly rapidly changing and unstructured
◉ Obstacles to data sharing/merging need to be
eliminated/drastically reduced
◉ While protecting privacy/confidentiality of data
◉ Co-development of data gathering mechanisms and analysis
with stakeholders (industry, government, academia)
◉ Including qualitative data gathering methods and analysis
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
37

38. Thank you
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
38

39. Methodology|Web content analysis strategy
28-29 June 2018
3rd annual Conference of the Global Forum on Productivity –
Firms, workers and disruptive technologies: Ensuring a
sustainable and inclusive growth
Subject Concepts Keywords
High tech
innovation
(Lee et al,
2013)
R&D
(Geroski et al.
1993;
Klette et Griliches,
2000)
research and development, r&d, laboratories, researcher, scientist, product development,
technology development, development phase, technical development, development
program, development process, development project, development cent, development
facility, technological development, development effort, development cycle,
development research, research & development, development activity, fundamental
research, basic research (Gök et al. 2014)
Intellectual property
(Pavitt, 1985)
Patent, intellectual property, trade secret, industrial design
Collaboration
(McNeil et al. 2007)
affiliation, collaboration, cooperation, partners, partnership (Ramdani et al. 2014)
External financing
(Kalil, 2005; McNeil
et al., 2007)
atlantic canada opportunities agency, business development bank of canada,
sustainable development technology,
venture capital, atlantic innovation fund, nrc-irap, fednor,
Industrial research assistance program, grants, private investment
Indicators: Keywords frequency/pages number * 1000
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