Topic:'Can'cooperation'among'competing'firms'existing'in'industry'led'open'
research'networks?'and'if'so'when?
Instructor:*Robin*Gustafsson
The*Finnish*SHOK*(Strategic*Centres*for*Science,*Technology*and*Innovation)*
organisations*are*built*on*the*idea*that*firms*operating*in*the*same*industry*–*
even*competing*firms*–*can*benefit*from*shared*research*and*development*
programs.*However,*firms*that*compete*in*the*same*markets*or*product*
segments*can*find*if*unattractive*to*cooperate*with*their*competitors.*In*this*
special*study,*the*students*will*analyze*a*rich*set*of*data*from*the*CLEEN*and*
FIMECC*to*explore*which*factors*increase*the*likelihood*of*competing*firms*
joining*together*in*an*open*research*program.*The*data*consists*of*program*
enrolment*information*between*2009R2013.*Additionally*the*students*will*
gather*data*from*company*annual*reports*for*identifying*inter*firm*rivalry.*The*
primary*empirical*challenge*in*the*study*will*be*to*identify*which*firms*are*
competitors*with*on*another*and*to*explore*using*empirical*data*the*conditions*
for*competitors*collaborating.*The*methods*for*this*study*will*be*either*
qualitative*or*a*mixture*of*qualitative*and*quantitative*(i.e*mixed*methods.).**
Cassier,*M.,*&*Foray,*D.*2002.*Public*Knowledge,*Private*Property*and*the*
Economics*of*HighRTech*Consortia.*Economics*of*Innovation*and*New*
Technology,*11(2):*123R132.
Chen,*M.*1996.*Competitor*Analysis*and*Interfirm*Rivalry:*Toward*A*Theoretical*
Integration,*Academy*ofMAnagement*Review,*21(1):*100R134*Dahlander,*L.,*&*
Gann,*D.*M.*2010.*How*Open*Is*Innovation?*Research*Policy,*39(6):*699R709.
Ritala,*P.,*&*HurmelinnaRLaukkanen,*P.*2009.*What's*in*It*for*Me?*Creating*and*
Appropriating*Value*in*InnovationRRelated*Coopetition.*Technovation,*29(12):*
819R828.*
Feedback(on(the(special(study(“EFFECT%OF%MARKET%COMMONALITY%AND%
RESOURCE%SIMILARITY%ON%PARTICIPATING%IN%OPEN%INNOVATION”(
(
We(normally(provide(about(one(page(of(feedback(for(the(final(version,(but(
because(we(use(this(study(as(an(example(in(the(class,(you(will(get(a(bit(more(
feedback.(
(
This(is(a(very(well@written(special(study.(We(particularly(appreciate(that(you(
make(a(clear(claim(and(provide(an(empirical(test(of(the(said(claim(without(too(
much(clutter(in(a(concise(form.((I.e.(we(like(that(the(report(is(short.)(
(
The(paper(starts(with(an(abstract(that(describes(the(study(well,(but(could(still(be(
improved.(The(main(purpose(of(abstract(is(to(give(a(busy(reader(a(quick(
overview(of(what(you(did(and(what(the(results(is(so(that(the(reader(can(decide(
whether(or(not(to(read(the(study.(You(open(with(a(direct(quotation(of(a(
definition,(which(is(perhaps(not(the(best(idea.(I(would(rather(rely(on(the(reader(
having(a(rough(understanding(on(what(open(innovation(is(and(then(saving(the(
definition(to(the(paper(body.(If(you(present(a(quote,(you(would(need(to(include(
the(source((citation(with(a(page(number)(here(as(well.(In(the(third(paragraph(you(
present(two(questions,(which(can(be(a(bit(confusing(as(a(study(normally(has(just(
one(research(question.(Moreover,(in(the(abstract(it(is(not(necessary(to(explain(
what(particular(statistical(model(was(used.(You(could(be(a(bit(more(descriptive(
about(the(theory,(though.(Now(the(interaction(effect(that(you(study(is(mentioned(
only(in(the(final(paragraph.(
(
(This(is(a(very(minor(comment,(but(please(refrain(adjusting(the(margins(from(the(
Word(defaults.(When(printed,(the(paper(is(now(difficult(to(keep(in(hand(without(
covering(part(of(the(text.)(
(
The(intro(is(well@structured,(and(describes(clearly(what(the(study(is(about(and(
ties(it(to(some(of(the(relevant(literature.(One(page(is(the(correct(length(for(this(
part,(and(we(like(how(you(made(use(of(that:(you(first(describe(why(open(
innovation(is(interesting(and(important,(follow(with(a(clear(research(question,(
and(the(describe(what(you(did.((
(
The(theory(and(hypotheses(section(is(well(structured(as(well.(We(like(that(you(do(
not(spend(too(much(space(on(explaining(what(the(literature(says(in(general,(but(
focus(on(your(theory(and(the(hypotheses(instead.(Nevertheless,(we(find(this(
section(of(the(paper(to(be(the(weakest(part(of(the(study.(You(could(have(
explained(and(presented(stronger(arguments(for(the(why@question.(However,(
considering(that(you(were(a(one(person(group(and(the(study(has(other(merits,(we(
allow(for(some(weaknesses.((In(other(words,(if(this(study(was(done(in(a(larger(
group,(we(would(have(suggested(this(to(be(fixed(and(if(not,(considered(reducing(
the(grade.)(
(
You(start(by(explaining(why(the(companies(that(serve(common(markets((i.e.(
potential(competitors)(might(want(to(participate(in(open(innovation.(You(explain(
“There&are&multiple&reasons&why&companies&with&common&markets&might&want&to&
participate&in&open&innovation.&For&example,&they&might&collaborate&within&certain&
segment&of&overall&market&while&at&the&same&time&competing&in&other&segments,&
such&as&Toyota&collaborating&with&PSA&Peugeot&Citroën&in&small&car&segment&in&
Europe&(PSA&Peugeot&Citroën,&2012)”.(We(like(that(you(address(the(why@question(
explicitly,(but(the(second(sentence(does(not(really(provide(an(answer.(The(press(
release(that(you(cite(provides(a(clear(rationale(for(why(the(two(companies(want(
to(cooperate(in(the(Light(Commercial(Vehicle(segment,(and(you(could(have(
explained(that(a(bit(more.(The(second(explanation(that(you(provide(is(that(for(the(
reasons(that(you(explain,(“companies(are(less(likely(to(initiate(an(attack”.(
However,(initiating(an(attack((a(competitive(move)(is(not(the(same(as(
participating(in(open(innovation;(these(are(distinct(concepts.(Same(goes(for(the(
third(explanation;(imitation(between(competitors(is(not(the(same(as(
participating(in(open(innovation.(In(sum,(while(we(like(that(you(attempt(to(
provide(multiple(explanations(for(why(competitors(might(participate(in(open(
innovation.(However,(each(of(the(three(explanations(that(you(provide(could(have(
been(developed(much(further.(
(
The(same(general(comment(applies(to(Hypothesis(2(and(Hypothesis(3(as(well;(the(
warrants(for(the(hypotheses(should(be(expanded(and(refined.(
(
The(methods(are(well(described(and(appropriately(chosen.(You(start(by(
explaining(that(you(chose(the(CLEEN(SHOK(because(the(participating(companies(
are(heterogeneous,(but(do(not(explain(why(heterogeneity(is(a(good(thing.(I(would(
prefer(a(structure(where(you(first(explain(your(sample,(then(what(you(measured(
and(how,(and(only(after(this(explain(where(the(data(comes(from.(If(you(consider(
your(data(as(an(Excel(sheet,(this(would(mean(that(you(first(explain(what(rows(
you(have,(then(what(columns(you(have,(and(only(after(this(how(you(got(the(data(
into(the(cells.(You(now(state(that(“Industry&descriptions&and&main&line&of&business&
were&obtained&from&company&data&search&engines&created&by&Kauppalehti,&Fonecta,&
and&from&Joint&business&information&system&of&the&Finnish&Patent&and&Registration&
Office&and&the&Tax&Administration”&before(explaining(why(you(needed(this(data(in(
the(first(place((this(is(explained(on(the(following(two(pages.)((
(
The(subsection(that(describes(the(sample(is(missing(the(information(of(how(
many(companies(participated(in(the(CLEEN(SHOK(and(how(many(programs(there(
were(in(the(SHOKs.(You(do(explain(this(later(in(section(3.7,(but(I(would(have(
expected(to(have(it(here.(Further,(it(is(not(clear(at(his(point(that(you(build(all(
possible(pairs(of(companies(participating(in(the(SHOWK(regardless(of(whether(
they(actually(participated(in(the(same(programs.(Without(this(information(your(
explanation(of(what(the(dependent(variable(is(would(be(difficult(to(understand.(
(
Your(description(of(the(variable(“Line(of(business(description”%is(exceptionally(
good:(it(provides(sufficient(information((for(a(reader(with(the(necessary(
background(knowledge)(of(a(complex(variable(without(being(too(long.(You(also(
address(validity(and(correctly(point(out(that(the(variable(has(some(weaknesses.(
(
The(part(about(independent(variables(could(have(been(made(more(convincing(by(
providing(evidence((i.e(citations)(that(these(measures(or(similar(measures(have(
been(used(in(the(past(and(have(been(found(useful.((I(do(not(know(if(this(is(the(
case,(but(I(would(be(surprised(if(not(at(least(some(of(your(measures(would(have(
been(used(by(someone(else(as(well.)(
(
One(thing(that(you(could(have(still(explained(in(the(independent(variables(
section(that(you(used(multiple(measures(for(market(commonality(and(resource(
similarity.(Also,(you(missed(the(opportunity(of(using(means(of(the(different(
measures(as(more(robust(composite(measures.((Similarly(to(using(multiple(items(
in(a(survey(to(measure(one(concept(and(then(aggregating(these(as(mean.)((
(
I(like(that(you(present(control(variables(in(a(separate(section.(This(section(starts(
with(the(statement(“All&control&variables&for&the&firmOdyad&are&mean&values&of&the&
variables&to&control&for&their&absolute&value&in&order&to&find&relative&differences&
between&the&companies.&“&I(do(not(understand(what(you(mean(by(this;(why(would(
mean(control(for(relative(differences?((And(also(relative(to(what?)(It(seems(that(
you(have(chosen(the(control(variables(well,(but(the(choices(are(only(partially(
justified:(A(control(variable(should(be(expected(to(correlate(with(both(the(
dependent(and(the(independent(variables,(but(you(explain(only(why(you(expect(
the(controls(to(be(correlated(with(the(dependent(variable(and(only(for(some(of(
the(variables.(The(possible(effects(or(age(and(size(are(not(explained.(
(
I(like(how(you(describe(data(preparation.(However,(all(of(this(is(really(about(how(
you(chose(the(sample(and(how(you(constructed(the(measures.(Another(option(
would(have(been(to(present(this(information(in(parts(in(the(previous(sections(
where(you(described(the(sample(and(measures.((In(my(opinion,(this(would(have(
been(a(better(choice(and(this(is(also(the(current(convention(in(high@quality(
research(journals.)(Moreover,(there(is(some(repetition(here.(In(sum,(this(section(
could(have(been(omitted(altogether.(
(
The(section(about(analysis(method(could(have(been(omitted.(Logistic(regression(
is(a(fairly(standard(tool(as(is(reporting(odds(ratios,(so(you(could(have(just(stated(
“I(used(Logistic(regression(to(analyze(the(data”(in(the(beginning(of(the(results(
section(and(be(done(with(it.(It(is(not(clear(what(you(mean(by(“Analyses&were&made&
separately&for&both&relative&values&and&mean&values&to&determine&whether&there&
was&difference&in&relative&or&absolute&values&of&variables,&e.g.&is&it&the&relative&size&
that&makes&companies&prefer&coOoperating&or&do&larger&firms,&for&example,&tend&to&
coOoperate&with&each&other.&After&this,&odds&ratios&were&generated&from&each&of&the&
models.”&You(are(using(both(types(of(variables(in(the(same(models,(which(is(just(
fine.(
(
The(first(half(of(the(section(“3.7(Overview(of(the(data”(really(just(describes(what(
the(sample(is(and(would(be(better(positioned(in(the(section(that(discusses(
sample.(The(second(part(could(then(be(merged(under(the(main(section(results.(
(This(is(the(structure(that(is(commonly(used(in(published(papers.)(
(
The(tables(are(formatted(well(and(it(makes(a(lot(of(sense(to(include(two(
correlation(tables,(one(for(the(company(level(data(and(one(for(the(dyadic(data.(
You(could(have(simplified(this(a(bit(by(just(limiting(your(analyses(to(complete(
cases(so(that(you(would(not(have(to(present(different(observation(numbers((N)(
for(different(variables.(This(is(the(first(time(that(the(variable(R&D(per(revenue(is(
introduced.(I(would(have(expected(this(to(be(discussed(in(the(variables(section.(
However,(considering(that(you(do(not(use(the(variable(in(the(regressions,(it(
would(be(more(simple(to(just(not(say(anything(about(it(at(all.(
(
The(results(section(is(well@structured.(You(present(the(main(results(in(one(table(
and(then(explain(which(hypotheses(are(supported(and(which(are(not(and(then(
leave(the(discussion(of(these(results(to(later(sections(as(you(should.(The(
regression(tables(is(exceptionally(good(and(provides(all(necessary(technical(
information(as(well(as(the(purpose(of(different(models(and(variables.(One(thing(
that(you(could(have(added(is(description(of(the(effect(sizes,(i.e.(how(strong(the(
effect(of(the(significant(variables(are.(E.g.(“Compared(to(companies(that(do(not(
operate(in(the(same(industry,(companies(that(operate(in(the(same(industry(are(
roughly(two(and(a(half(times(more(likely(to(cooperate(in(open(innovation(
projects.”(
(
The(conclusions(section(well@written(as(well.(You(start(by(summarizing(the(
results(and(then(move(on(to(weaknesses,(which(you(correctly(state(relate(mostly(
to(how(the(variables(were(defined(and(are(therefore(limitations(of(the(data.(In(
terms(of(structure,(it(you(start(by(explaining(some(of(the(weaknesses(in(the(
variables(after(which(you(continue(with(a(separate(section(about(weaknesses.(
You(also(proposes(some(ways(that(these(limitations(could(be(addressed,(which(is(
a(merit.(I(also(like(the(fact(that(you(assess(not(only(the(weaknesses(of(the(
empirics,(but(also(weaknesses(in(the(theory(by(introducing(the(role(of(that(the(
type(of(research(program(can(play.(
(
You(explain(that(it(is(possible(that(relationships(are(non@linear.(This(is(actually(
something(that(can(be(tested(by(“specification(tests”(that(can(be(conducted(after(
model(estimation.(
(
EFFECT OF MARKET
COMMONALITY AND
RESOURCE SIMILARITY ON
PARTICIPATING IN OPEN
INNOVATION
Research report
TU-91.2037 Special study in Strategic management
i
Abstract
Author:
Title:
Effect of market commonality and resource similarity on participating in open
innovation
Date:
Open innovation “is the use of purposive inflows and outflows of knowledge to accelerate internal
innovation, and expand the markets for external use of innovation, respectively.” As opposed of
more traditional closed innovation where new products and services are initiated within the firm
and then selected whether they end up in the markets, open innovation paradigm states that
companies should seek ideas external sources, i.e. outside the boundaries of the company, for
insourcing ideas, and also to provide output channels for valuable ideas that originate within the
company not supported by current business model through licensing, spin-offs, etc.
In order to expand their research and development (R&D) activities and to capture their potential,
companies have become more interested in participating in R&D alliances. Despite its great
potential, open innovation has some disadvantages. In order to realize the potential in R&D
alliances, employees of the companies have to spend significant amount of time with each other
and this intensive co-operation possesses risk of increased competition in the future. Especially
for companies that are in coopetitive relationship, that is to say that companies compete and
cooperate at the same time, leakage of valuable knowledge can cause real problems.
Do competing companies find it unattractive to co-operate in open innovation programs?
Concepts market commonality and resource similarity are used to establish competitive situation.
Thus, the research question can be specified into: do companies that have similar resources
and/or common markets find it unattractive to co-operate in open innovation programs?
In this study, empirical context is Finnish energy and environment open innovation platform
called CLEEN that focuses in cleantech research. Unit of analysis is a firm-dyad, participation
data was obtained from CLEEN and data considering market commonality and resource
similarity from secondary sources. Logistic regression analysis was used to analyse participation
of firm-dyads in same research program using market commonality and resource similarity as
explanatory factors.
As a result, there is indication that, at least in cleantech collaboration in public-private cluster,
market commonality and resource similarity have positive effect on participation in open
innovation. Interaction between these factors regarding participation was not observed. However,
these results can be only thought of as indicative and in the future these results should be
reinforced by conducting a study based on primary data obtained directly from the companies.
Keywords: Collaboration, co-operation, open innovation, market commonality, resource similarity
ii
iii
Contents
Abstract .................................................................................................................................................... i!
1.! Introduction ...................................................................................................................................... 1!
2.! Theory and hypotheses ................................................................................................................... 2!
3.! Methods ........................................................................................................................................... 3!
3.1.!
Data collection .......................................................................................................................... 3!
3.2.!
Dependent variables ................................................................................................................ 4!
3.3.!
Independent variables .............................................................................................................. 4!
3.3.1!
Market commonality .......................................................................................................... 4!
3.3.2!
Resource similarity ............................................................................................................ 5!
3.4.!
Control variables ...................................................................................................................... 5!
3.5.!
Data preparation ....................................................................................................................... 6!
3.6.!
Analysis method ....................................................................................................................... 7!
3.7.!
Overview of the data ................................................................................................................ 7!
4.! Results ............................................................................................................................................. 9!
4.1.!
Logistic regression models ....................................................................................................... 9!
5.! Discussion ..................................................................................................................................... 10!
5.1.!
Limitations and further research ............................................................................................. 11!
6.! Conclusion ..................................................................................................................................... 13!
References ........................................................................................................................................... 14!
Appendix 1. Histograms of variables .................................................................................................... 16!
1
1. Introduction
Open innovation, as defined by Chesbrough (2006, p. 1), “is the use of purposive inflows and outflows
of knowledge to accelerate internal innovation, and expand the markets for external use of innovation,
respectively.” As opposed of more traditional closed innovation where new products and services are
initiated within the firm and then selected whether they end up in the markets, open innovation
paradigm states that companies should seek ideas external sources, i.e. outside the boundaries of the
company, for insourcing ideas, and also to provide output channels for valuable ideas that originate
within the company not supported by current business model through licensing, spin-offs, etc
(Chesbrough et al., 2006).
Summing up previous research, Faems et al (2010, p. 3) wrote that in order to expand their research
and development (R&D) activities and to capture their potential, companies have become more
interested in participating in R&D alliances, namely “formal agreements between otherwise
independent companies“. Despite its great potential, open innovation has some disadvantages. In
order to realize the potential in R&D alliances, employees of the companies have to spend significant
amount of time with each other to be able to transfer meaningful codified and tacit knowledge, and this
intensive co-operation possesses risk of increased competition in the future, resulted by unintended
transfer of knowledge (Faems et al., 2010). Especially for companies that are in coopetitive
relationship, that is to say that companies compete and cooperate at the same time (Bengtsson &
Kock, 2000), this leakage of valuable knowledge can cause real problems.
This paper asks the question: do competing companies find it unattractive to co-operate in open
innovation programs? Concepts market commonality and resource similarity from framework
presented by Chen (1996) are used to establish competitive situation. Thus, the research question can
be specified into: do companies that have similar resources and/or common markets find it
unattractive to co-operate in open innovation programs?
Empirical context for the study is Finnish energy and environment open innovation platform called
CLEEN that focuses in cleantech research. In short, CLEEN is one of six Strategic Centres for
Science, Technology and Innovation (SHOKs) that are aimed to be “a unique cooperation platform for
innovative companies and spearheading research” in Finland. These public-private partnerships are
limited to selected few fields that have significance to society as well as businesses. (CLEEN, 2014) In
SHOKs, there are various research programs that companies are able to participate in. For example
CLEEN has seven different research programs focusing different topics, with content such as “Carbon
Capture and Storage Program (CCSP)” (CLEEN, 2014).
2
2. Theory and hypotheses
In order to study whether competition affects the companies’ willingness to participate in open
innovation programs, competition between companies has to be established and thereafter it can be
evaluated whether it affects participation in open innovation programs. In this research competitive
situation between a firm-dyad, i.e. between a pair of firms, is analysed with framework proposed by
Chen (1996). The framework consists of two dimensions between a firm dyad: market commonality
and resource similarity.
Market commonality is the main component used in competitor analysis and it is thought of as driver
of rivalry (Chen, 1996). In his article, Chen (1996, p. 106) defined market commonality as “degree of
presence that a competitor manifests in the markets it overlaps with the focal firm”. There are multiple
reasons why companies with common markets might want to participate in open innovation. For
example, they might collaborate within certain segment of overall market while at the same time
competing in other segments, such as Toyota collaborating with PSA Peugeot Citroën in small car
segment in Europe (PSA Peugeot Citroën, 2012). As an another example, Chen (1996) argued that in
order to avoid retaliation, if market commonality is high, companies are less likely to initiate an attack,
but will respond to an attack initiated by a competitor to defend for potential threat. It is also known
that companies have tendency to imitate companies they compete with (Lieberman & Asaba, 2006).
Thus, when company A participates in open innovation platform, competing company B would
respond, in defending its position or just to imitate competition, by also joining to the same program to
avoid getting behind.
Hypothesis 1: The greater the market commonality is between the firm-dyad, the more willing
they are to co-operate in open innovation
Chen (1996, p. 107) defined resource similarity as “the extent to which a given competitor
possesses strategic endowments comparable […] to those of focal firm”. In their research for
management strategies of R&D alliances, Faems et al (2010) summed up risks that participating in
R&D alliance poses for the company: as knowledge between companies become more similar,
company risks more intensive competition in the future. This means that rational companies should
participate in programs they are able to utilize the results of the research program when the time
comes and not be defeated by competition. To clarify the point further, there has to be a way of
utilizing the research results in a way that company can use the results to its own advantage.
In this case, new information is created by collaborative effort, R&D capabilities of individual
companies are especially important to effectively utilize the research results. This is because having
3
internal R&D is thought to increase absorptive capacity, i.e. being able to understand new external
ideas and to co-develop ideas further (Dahlander & Gann, 2010). An example of this might be a large
company understanding the value of cutting edge research done by the smaller company and by
collaboration the smaller company also benefits by getting access to other resources of a larger
company such as large supply chain network, therefore crating a possible win-win situation. Thus,
having similar R&D resources is necessary to be able to compete for external ideas.
Hypothesis 2: The greater the resource similarity between the firm-dyad, the more willing they
are to co-operate in open innovation
If by collaboration companies increase the risk of competition in the future because of unwanted
knowledge transfer, companies might be hesitant to collaborate with companies that are also
participating in similar markets with them. Accordingly, when companies share similar markets and
resources, the risk of unwanted knowledge transfer could be too high if the other company is a direct
competitor, making the option of co-operation in open innovation unattractive.
Hypothesis 3: The greater the overlap between market commonality and resource similarity
between the firm-dyad, the less willing they are to co-operate in open innovation
3. Methods
3.1. Data collection
Out of possible SHOKs, CLEEN was chosen as source of data because the companies participating in
that cluster are quite heterogeneous group, as opposed to, for example FIMECC, where most of the
companies are quite similar.
Data for this study were collected from various secondary sources. Information about participation of
various companies to SHOK programs and companies’ shareholder stakes were obtained from
CLEEN. Industry descriptions and main line of business were obtained from company data search
engines created by Kauppalehti, Fonecta, and from Joint business information system of the Finnish
Patent and Registration Office and the Tax Administration (Fonecta, 2014; Kauppalehti, 2014; PRH,
2014). Most of the financial data were collected from Bureau van Dijk’s Orbis database, supplemented
by Fonecta’s database when available (Bureau van Dijk, 2014).
Before defining variables, it must be understood that because unit of analysis is a firm-dyad, i.e. a firm
pair, all values of the variables are in relation to the other firm. For example, regarding the dependent
variable, that is introduced in next, as company either participates in the same research program or
doesn’t, values for variable that is formed for the firm-dyad are whether the firm-dyad does participate
4
in the same research program, thus is a pair, or don’t participate in the same research program, thus
is not a pair.
3.2. Dependent variables
Participation in open innovation within the same research program can be seen from data
obtained from CLEEN. CLEEN has various research programs that companies can participate in, and
there is data in which program the company has participated in. Thus, for the firm-dyad, the
dependent variable is a binary variable and has two values: 1 for “is a pair” or 0 for “is not a pair”.
3.3. Independent variables
All continuous independent variables consist of variables that contain values of relative difference
between the firm-dyad to be able to determine if the two companies are similar or different from each
other. This means, for example, that for continuous variable revenue per employee, nominator is the
firm from the dyad with larger value and denominator the firm with smaller value, thus relative
difference between the two firms for variable revenue per employee is obtained.
Also, logarithmic transformation was applied to base variables revenue, number of employees, age,
total fixed assets, and intangible fixed assets to better describe the nature of the variables, i.e. the
proportional increase of matters more than absolute increase. For example 10 000€ more revenue is
more significant when current revenue is 50 000€ than one million euros. From these transformed
variables, proportional variables such as revenue per employee were calculated.
3.3.1
Market commonality
Main line of business is classified by Finnish Tax Administration and it is based on Standard
industrial classification created by Finnish Statistics (Finnish Patent and Registration Office, 2014).
Only one main line of business classification is given to one firm based on how most of the value
added is created by the company (Tilastokeskus, 2008). The classification has five levels of
granularity, first describing the broad category, such as “Professional, scientific and technical
activities”, and the last more specific category, such as “Research and experimental development on
biotechnology”. In this study, two-digit industry code was used. As the maker of the statistics is
concerned about classifying the company most accurate way possible, overlap of main line of
business classification is most likely the best readily available secondary source indicator for market
commonality between the firm-dyad. The variable has two values: the classification is the same for
firm-dyad or it is not the same.
Line of business description is a description about what company does and is provided to Finnish
Patent and Registration Office by the company and therefore it should reflect in which markets
5
company operates. From those descriptions keywords are mined using text mining software to obtain
similarities between the descriptions of the firm-dyad, where more similar descriptions would indicate
that companies have common markets (Meyer, Hornik, & Feinerer, 2008).
Text mining was performed using tm-package in R (Meyer et al., 2008). First, whitespace was
removed, content was transformed into lower case, punctuation was removed, Finnish stop words
were removed, and the words were stemmed. Second, words that didn’t describe the line of business
were removed, such as word “company”. Third, synonymous words were processed so that only one
word describing one thing remained. Finally, using the remaining words, the Euclidian distances
were calculated and merged to the firm-dyad dataset. Smaller the value is for Euclidian distance
between the companies, the more similar company description is and thus should indicate larger
market commonality between the firm-dyad and vice versa.
Validity of the calculated distances for main line of business description were qualitatively evaluated
by randomly selecting 24 firm-dyads by name and then evaluating on scale from 1 to 3 whether they
are competitors, might be competitors, or are not competitors, respectively. From this evaluation
correlation of 0.31 was obtained between the subjective evaluation and calculated Euclidian distance.
It is to say that there is some correlation, but the indicator could be made better by fine-tuning text
mining further.
3.3.2
Resource similarity
Non-physical assets, such as patents and brands, are called intangible assets. These are assets that
are generated within firm and have significant impact on companies’ market value in today’s economy
(Austin, 2007), thus should give some indication about R&D capabilities of the company. Ratio of
intangible fixed assets to total fixed assets is used to describe how dependent company is of its
intangible assets. If this ratio is high, company is more likely to produce more complex products and to
have more R&D capabilities, thus would gain more utility from participating in open innovation. For the
firm-dyad the overlap of these ratios between firm-dyad should indicate similar resources.
Revenue per employee is obtained by dividing logarithmic transformation of company’s revenue by
the logarithmic transformation of number of people it employs. The higher the ratio, the more profitable
the company is and it might indicate that company also spends more money on R&D spending. Based
on this logic, the more the ratios overlap between the firm-dyad, the more similar their resources are.
3.4. Control variables
All control variables for the firm-dyad are mean values of the variables to control for their absolute
value in order to find relative differences between the companies.
6
Logarithmic transformations of firm size (number of employees) and age are used as controlling
variables to rule out effects of company maturity. Number of employees are used as indicator of size
because it should resemble value added quite well and coordination costs of the company are
dependent on the number of employees rather than, say, revenue (Kumar, Rajan, & Zingales, 1999).
Also, some of the companies are shareholders of CLEEN that is the company organizing the open
innovation projects. Owning shares of the open innovation platform might indicate that company feels
more positively about open innovation, which as result might affect willingness to participate in
research programs. Thus, whether company is a shareholder of CLEEN is used as control variable
that had three values for the firm-dyad: “both”, “other” and “neither”, to indicate if both firms are
shareholder, just the other one, or neither, respectively.
Participation frequency to the research programs was calculated, indicating in how many programs
company participated in. This is important because as company participates in more and more
programs, the more companies it collaborates with. Finally, as opposed to relative values calculated of
variables intangible fixed assets to total fixed assets and revenue per employee as independent
variables, mean values of those same variables between the firm-dyad were used as controls.
3.5. Data preparation
To prepare the data for analysis, data handling was performed in six steps. First, institutions such as
universities were filtered from the initial data to gain dataset that contained only companies. Second,
only company line of business descriptions in Finnish were used, thus two descriptions written in
Swedish had to be discarded. Third, companies that participated in a certain research program were
identified and labelled. Fourth, all possible firm-dyad combinations were made and this firm dyad data
was merged with the data containing variables such as size, age, and revenue. Fifth, to prepare the
data for analysis, all of the variables for the firm dyad were treated so that the values were in relation
to the other company of the dyad. Continuous variables such as age were treated so that age of the
older company was divided by age of the younger company, thus the relative age difference was
obtained. In addition, mean values of the variables of the firm dyad were calculated. Categorical
variables were evaluated on the basis if the values were the same or not, or for example, in the case
of whether company is a shareholder, the labelling was “both”, “one”, and “neither”. Obtaining the data
of the line of business description was a bit trickier. For this variable, Euclidian distances of the main
line of business descriptions of the firm-dyad were calculated according to previous explanation.
Finally, missing data was replaced with mean values of each variable.
7
3.6. Analysis method
Since the dataset contained categorical variables as well as continuous variables data was analysed
using logistic regression. After preparing the data, there were both relative difference of the variable
between the firm dyad and mean values of the firm dyad for continuous variables. For example, the
dataset contained both relative difference of revenue and the mean value of revenue between the firm
dyad. Analyses were made separately for both relative values and mean values to determine whether
there was difference in relative or absolute values of variables, e.g. is it the relative size that makes
companies prefer co-operating or do larger firms, for example, tend to co-operate with each other.
After this, odds ratios were generated from each of the models.
3.7. Overview of the data
Data consisted of cross-sectional data of companies and institutions that participated in different
research programs in SHOK co-operation platform CLEEN that has been set up for cleantech
research. All in all there were seven research programs that had the participation data from 130
different institutions and companies. After removing all of the institutions and bankrupt companies, 102
different companies remained. Table 1 summarizes participation in different research programs before
and after processing of data. One company can participate in more than one research program which
explains the difference between total participants in table 1 and overall number of companies.
Table 1. Participation data to different research programs
Program
name
All
participants
Participants after
processing
% of
companies
SGEM
28
19
60 %
FCEP
17
8
68 %
MMEA
49
33
60 %
CCSP
27
15
64 %
EFEU
17
12
59 %
BIOENERGIA
37
26
59 %
REMA
20
19
51 %
Total:
195
132
60 %
The companies participating in the research cluster cannot be considered as a random sample of
Finnish companies. According to Statistics Finland most of the companies in Finland are SMEs
(Statistics Finland, 2014), but in CLEEN research cluster, large companies represent disproportionally
large portion of overall companies. This is quite understandable, because research conducted in
8
CLEEN has long-term focus, which might make it impossible for smaller firms to participate actively
because of smaller amount of resources.
Means, standard deviations and correlations of the non-dyadic transformed variables between
continuous variables are presented in table 2. It can also be seen from the table 2 that the data for
continuous variables was quite complete, except for amount of R&D budget to revenue, which was
discarded from analysis due to missing data. For categorical variables data was fully complete except
for variable main line of business description where two descriptions were in Swedish and had to be
discarded.
Table 2. Means, standard deviations, and correlations
N
Mean
s.d.
1.
2.
3.
1. Revenue per employee
97
16528
26696
2. R&D budget to revenue
12
0.02
0.03
-0.12
3. Intangible fixed assets to total fixed assets
87
0.88
0.10
0.11
0.54
4. Number of employees
99
34790
19391
-0.75***
-0.21 0.05
5. Age
102 26330
0.91
-0.34***
0.10
6. Participation frequency
102 46023
0.64
-0.25*
-0.09 0.01
4.
5.
-0.06 0.46***
0.44***
0.30**
* p < 0.05, ** p <0.01, *** p < 0.001
In tables 3 means, standard deviations and correlations of transformed dyadic data is shown, which is
the data that was used in the logistic regression. Distribution of variables is shown in the appendix 1.
Table 3. Means, standard deviations, and correlations between variables of firm dyads
N
1. Number of employees
5148
(mean)
2. Age (mean)
5151
3. Participation frequency
5151
(mean)
4. Revenue per employee
5044
(mean)
5. Intangible fixed assets to
4437
total fixed assets (mean)
6. Euclidean distance
5151
7. Revenue per employee
4656
(difference)
8. Intangible fixed assets to
total fixed assets
4350
(difference)
* p < 0.05, ** p <0.01, *** p < 0.001
Mean
s.d.
1.
2.
3.
4.
5.
6.
4.95
1.82
2.72
0.64
0.46***
1.26
0.45
0.44***
0.30***
4.45
1.98
-0.73***
-0.33***
-0.24***
0.88
0.08
0.05**
-0.05***
0.01
0.08***
6.55
1.69
-0.04**
-0.03*
-0.05***
0.06***
-0.07***
1.77
0.98
-0.26***
-0.15***
-0.05**
0.69***
0.13***
0.03*
0.99
0.42
0.09***
0.03*
0.09***
-0.09***
-0.51***
-0.04**
7.
-0.04*
9
4. Results
4.1. Logistic regression models
102 different companies can be paired in 5151 different ways. Six different logistic regression models
were calculated from the data, after which odds ratios were calculated from the models. Table 4
presents six models where model 1 contains only the control variables. Model 2 includes both control
variables and independent variables, and models 3 to 6 present effect of the different moderator
variables.
Table 4. Odds ratios of logistic regression models and their significance levels
Variable
Control variables
(Intercept)
Number of employees (mean)
Age (mean)
Shareholder - Neither
Shareholder - One
Participation frequency (mean)
Revenue per employee (mean)
Intangible fixed assets to total fixed assets
(mean)
Control
____________Control & Independent variables___________
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
0.01***
0.89**
1.20**
0.94
0.78
4.18***
1.04
0.03**
1.00
1.14
0.79
0.68*
4.17***
1.20**
0.03**
0.99
1.15
0.78
0.67*
4.15***
1.19**
0.03**
1.00
1.15
0.80
0.68*
4.17***
1.20**
0.03**
1.00
1.15
0.80
0.68*
4.16***
1.20**
0.01**
1.00
1.15
0.78
0.67*
4.15***
1.20**
4.60**
1.86
1.92
1.88
1.82
1.90
2.56***
4.75***
0.76***
2.56***
2.56***
1.01
1.01
1.01
0.97
1.24
0.83*
0.84*
0.83*
0.73*
0.83*
0.61
0.62
0.55
0.61
2.05
Market commonality
Main line of business classification - Same
Euclidean distance
Resource similarity
Revenue per Employee (difference)
Intangible fixed assets to total fixed assets
(difference)
Moderator variables
Business classification * Revenue per
employee (difference)
Business classification * Intangible fixed assets
to total fixed assets (difference)
Euclidean distance * Revenue per employee
(difference)
Euclidean distance * Intangible fixed assets to
total fixed assets (difference)
Number of observations
2
Pseudo-R (Nagelkerke)
Deviance
0.67
2.87
1.02
0.83
4342
3486
3486
3486
3486
3486
0.101
0.125
0.126
0.128
0.125
0.125
4575.76
3597.54
3595.19
3596.55
3597.04
3596.72
* p < 0.05, ** p <0.01, *** p < 0.001
Hypothesis 1 stated that the greater the market commonality is between the firm-dyad, the more
willing they are to co-operate in open innovation. As it can be seen from table 4, variable main line of
10
business classification, which represented market commonality, has odds ratio greater than one and is
highly significant (p < 0.001) in models 2, 3, 4, 5, and 6. Thus, hypothesis 1 was supported.
Hypotheses 2 stated that willingness of companies to co-operate would increase when resource
similarity increases. According to table 2, odds ratios for difference in revenue per employee is below
one and significant (p < 0.05) in models 2, 3, 4, 5, and 6, meaning that as difference between
resources gets larger, companies are less willing to co-operate. Thus, hypothesis 2b gets weak
support from the data. Although not directly comparable because of different measurement scales, it
seems that according to the larger odds ratios, market commonality seems to play larger role than
resource similarity.
Hypothesis 3 predicted interaction between resource similarity and market commonality. It stated that
the greater the overlap between market commonality and resource similarity between the firm-dyad,
the less willing they are to co-operate in open innovation. Hypothesis 3 doesn’t get any support from
the data since all of the odds ratios for moderator variables are nonsignificant.
5. Discussion
By collaborating in R&D alliances, companies can supplement their own research efforts. SHOKs are
designed to provide public-private collaboration platform for research that has longer-term goals than
companies usually tend to have. Participation in open innovation is not risk-free, since usually
intensive collaboration is required, therefore at the same time companies bear the risk of exposing
their critical skills and experience to their competitors. In this study, collaboration of firm-dyads were
studied based on secondary data and program participation data.
According to the results of this study, the co-operation of firm-dyads can be explained with market
commonality and resource similarity. It seems that main line of business classification made by
Statistics Finland is quite good predictor and can be thought as describing well the industry where the
company operates. However, the descriptions are quite broad, especially in this study where the
second level categories were used. For example the description of category 71000, where largest
portion of companies in this study were classified (appendix 1), is “Architectural and engineering
activities; technical testing and analysis”. Therefore it is quite obvious that under that label there are a
lot of different kinds of companies that probably are not direct competitors, but rather companies that
have complementary skills, and thus provide fertile grounds for collaboration. Consequently, it is not
entirely clear whether this actually describes market commonality or rather resource similarity, but
because of the limitation of this study mainly to secondary data, it was probably the best pick to
describe market commonality. On the other hand, the main line of business description was not very
11
good indicator. This is probably because the descriptions are made by companies themselves and
there is a bias in trying to fit in everything that company might do business in, whereas the
classifications are given by statisticians who are concerned with creating representative statistics.
Resource similarity was predicted by two variables: revenue per number of employees and by ratio of
intangible fixed assets to total fixed assets. Of the two variables, revenue per number of employees is
better indicator, giving some indication that resource structure of the company matters. One
explanation could be that high revenue per employee indicates that company is more profitable and
thus has more resources to participate in R&D projects than less profitable companies.
Concerning the hypotheses, hypotheses 1 and 2 assumed linear relationship, which might not be
necessarily true as it might bet that it might be that relationship is non-linear. For example, having
totally different resources, or in the other end, identical resources might make collaboration
unattractive, but having somewhat similar resources could turn the situation more attractive. However,
given the scope of this study these relationships were not investigated.
5.1. Limitations and further research
This study is limited in various ways: the data is gathered from secondary sources, variables capture
the companies in very rough cut manner, and so forth. There are at least four things to test the theory
more accurately.
First, unfortunately not enough data could be obtained for R&D budget compared to revenue that
might have been more accurate measure of company’s current R&D capabilities rather than assets
that have been accumulated over the years. This probably isn’t that big of a problem since age was
controlled for. Also, comparing the companies might not be the best way to evaluate the situation,
since even if resources of the companies might differ, they still might input same amount of resources
into the open research programme.
Second, it would be important to tap into how companies themselves perceive the situation, i.e. do
they feel they are in competition with the companies participating in the open innovation and if so, with
whom. Dataset used in this study wasn’t fine-grained enough to really know about the competitive
situation between single firm-dyad.
Third, based on the dataset used in this study we cannot identify all potential participants that might
have been able to participate but decided not to. We only know the companies that participated and
which programs they cooperated in. Thus, to gain more proper understanding, the potential
participants that didn’t participate would need to be identified, and the reasons behind not participating
12
would need to be investigated. For example, currently there is huge bias toward large companies, so
why didn’t larger number of small companies participate?
Fourth, market commonality could be perhaps taken better into account if some kind of scale is
devised to take also one-digit classification into account. For example, code 25000 and code 24000
are now classified as equally different at codes 25000 and 89000. It could be taken into account that
the first two codes have more common markets than the latter two codes.
There are also factors that might cause the need to revise the theory. The content of the research
programs that companies are participating in might be very critical, which this study doesn’t account at
all. Content of the research program might be potentially very important factor when companies decide
whether to participate in the program or not. It doesn’t seem too unrealistic to think that program that is
directly in the core of the company’s business raises more concern about potential competition than a
program that is outside company’s main focus. For example, CLEEN program Future Combustion
Engine Power Plant surely is more important than Measurement, Monitoring and Environmental
Efficiency Assessment - another CLEEN program - for a company that manufactures combustion
engines. Assessing the content of the program might also make it possible to classify programs in
terms of motivation behind participating in the programme. For example, one kind of program might be
interesting for the company as a means to alleviate carbon credit expenses, and some other program
might be able to provide solution to lower risk in certain market segment of the company.
Also, it might be worthwhile taking into account other kind of relationship between the companies than
just competition. For example, it might be that steel manufacturer has collaborated with a software
company previously that also participates in the research program.
Finally, CLEEN and other SHOKs are public-private collaboration platforms and as such one cannot
generalize the results to only collaboration between companies. Even though institutions have been
filtered out from the analysed data, it might be that two companies that otherwise wouldn’t want to
work together are in same research program only because they want to have access to the resources
of the institutional participant. Also, the programs are not completely funded by private sector which
might also make it easier for companies to participate, thus generalizability of the results decreases.
Based on the results it seems that firm-dyad level analysis provides good grounds for further research.
But in order to address the limitations mentioned above, researchers would probably have to ask
companies directly more specific questions rather than to rely on secondary data. Also, different ways
of measuring market commonality and resource similarity should be devised. For example, as a
13
measure of market commonality, news published about the company could be mined from
newspapers and see which companies are mentioned alongside that company.
6. Conclusion
There is some evidence that in public-private cleantech research, similar resources and common
markets have a positive impact on participating in collaborative research between firm-dyads.
Because these results are based mostly on secondary data, they should be taken only an aid to
designing more accurate study. In the future, researchers should obtain primary data from the
companies themselves to get more accurate knowledge about how company employees actually
perceive the competitive situation and the motivations behind participating in collaborative research
programs.
14
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16
Appendix 1. Histograms of variables
Figure 1. Continuous variables
17
Figure 2. Categorical variables