Collaborative knowledge exchange promotes innovation Tomoya Moriab1 Jonathan Newtona and Shosei Sakaguchic

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Collaborative knowledge exchange promotes

innovation

Tomoya Moria,b,1, Jonathan Newtona, and Shosei Sakaguchic

aInstitute of Economic Research, Kyoto University. Yoshida-Honmachi, Sakyo-Ku, Kyoto, Kyoto 606-8501, Japan.; bResearch Institute of Economy, Trade and Industry, 11th

ﬂoor, Annex, Ministry of Economy, Trade and Industry 1-3-1, Kasumigaseki Chiyoda-ku, Tokyo 100-8901, Japan.;

Faculty of Economics, The University of Tokyo, 7-3-1, Hongo,

Bunkyo-ku, Tokyo 113-0033, Japan.

This manuscript was compiled on November 7, 2022

Considering collaborative patent development, we provide micro-

level evidence for innovation through exchanges of differentiated

knowledge. Knowledge embodied in a patent is proxied by word

pairs appearing in its abstract, while novelty is measured by the fre-

quency with which these word pairs have appeared in past patents.

Inventors are assumed to possess the knowledge associated with

patents in which they have previously participated. We ﬁnd that col-

laboration by inventors with more mutually differentiated knowledge

sets is likely to result in patents with higher novelty.

collaboration |knowledge |diﬀerentiation |novelty |innovation

umans are a collaborative species (

–

) and bring this

collaborative nature with them to the workplace and

to the laboratory. Collaboration within teams has been in-

creasingly important within academic research (

) and patent

development (

), and has long been an essential part of

other creative endeavors such as the production of Broadway

musicals (

). A necessary condition for successful collaboration

is successful coordination on roles and tasks (

), which can

be complicated when the characteristics of one’s collaborative

partners are imperfectly understood (

). Hence, collabora-

tion requires shared knowledge among collaborators. However,

for collaboration to be creative, it also requires diﬀerentiated

knowledge (

). There is, for example, evidence that the suc-

cessful production of Broadway musicals involves teams that

comprise both people who have previously collaborated and

people who have not previously collaborated (7,12).

Using Japanese patent data and adopting a measure of

novelty based on word combinations that appeared in the

patent (

), we ﬁnd a strong positive relationship between

collaborators’ mutual knowledge diﬀerentiation and the novelty

of their output.

Results

represents the set of distinct word pairs in patent

’s

abstract. (Hereafter, a bold capital letter expresses a set, and

the corresponding italic letter its cardinality.) The novelty

of a word pair at a given point in time is measured by the

likelihood of its appearance in patents ﬁled in the past (

Speciﬁcally, the novelty

nwt

of word pair

at time

is the

ratio of (i) the sum of

|Wp|

over all patents

ﬁled at

dates up to and including

, to (ii) the number of these patents

that include word pair

. We measure patent novelty by the

average novelty of word pairs in its abstract,

WpPw∈Wpnwtp

where tpis the patent’s ﬁling time.

We consider collaborative aspects of patent development

by focusing on the productivity per inventor pair, following

(

is the set of all inventors who participated in patent

, while

Mp≡ {

(

) :

∈Hp

is the set of pairs

of such inventors. The average pairwise-contribution to the

patent’s novelty is given by

np=1

MpWpX

w∈Wp

nwtp.

Denoting by

Git

the set of patents inventor

participated in

at time

, deﬁne

’s knowledge at

Kit

∪τ <t ∪p∈Giτ Wp

and its novelty by kit =Pw∈Kit nwt.

Inventor pair

has total knowledge

Kijt

Kit ∪Kjt

with novelty

kijt

Pw∈Kijt nwt

, and inventor

’s diﬀerenti-

ated knowledge relative to

ijt

Kit\Kjt

, with novelty

ijt =Pw∈KD

ijt

nwt.

Knowledge diﬀerentiation between

is evaluated by the

geometric mean of their respective diﬀerentiated-knowledge

shares in the union of their knowledge,

sijt =qkD

ijtkD

jit/kijt ∈[0, 0.5] .

Their average in patent p,

sp=1

MpX

(i,j)∈Mp

sijtp

measures knowledge diﬀerentiation in

. We focus on patents

with

sp>

0, since

= 0 implies no knowledge exchange as

inventors can be indexed so that K1⊆K2· · · ⊆ KHp.

We estimate the eﬀect of spon npby the model:

np=β0+β1sp+· · · +βmsm

+γ¯

Kp+δMp+fp+ϕp+τp+εp,[1]

controlling for average knowledge size

Kp≡1

HpPi∈HpKitp

inventor-pair count

(reﬂecting the costs/beneﬁts of coor-

dination and task specialization), and ﬁxed eﬀects,

ϕp

and

τp

, for ﬁrms, classes of International Patent Classiﬁcation

(IPC), and years, respectively. εpis a stochastic error.

The estimated conditional expectation and quantiles of

indicate a positive association between

and

, except for

a range of small

, while the observed

are spread over the

entire feasible range, (0, 0.5] (Fig. 1).

To see the robustness of the result, we consider citation

count of a patent as an alternative measure of output. Let

¯cp

the citation count of patent

within ﬁve years of application,

excluding self-citations, where the self-citations include those

Author contributions:Conceptualization: T.M., J.N.; Funding acquisition: T.M; Theoretical analysis:

J.N., T.M.; Empirical analysis: S.S., T.M.; Writing; J.N., T.M.

The authors declare no conﬂict of interest.

1To whom correspondence should be addressed. E-mail: mori@kier.kyoto-u.ac.jp

1–3

arXiv:2210.01392v5 [econ.GN] 4 Nov 2022

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CollaborativeknowledgeexchangepromotesinnovationTomoyaMoria,b,1,JonathanNewtona,andShoseiSakaguchicaInstituteofEconomicResearch,KyotoUniversity.Yoshida-Honmachi,Sakyo-Ku,Kyoto,Kyoto606-8501,Japan.;bResearchInstituteofEconomy,TradeandIndustry,11thoor,Annex,MinistryofEconomy,TradeandIndustry1-3-1,Kas...

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