Journal of Japanese Management Vol.2, No.2, May 2018ISSN 2189-9592

Characteristics of an Innovation Adopter’s Network and theInformation in a Diffusion Process
Mariko Tsutsui

Faculty of Business Administration, Kindai University, Osaka, Japan
Japan Academy of Business Administration
Abstract:
The purpose of this paper is to clarify the information creation activities by innovation
adopters and to analyze the contents of that information in the process of innovation diffusion.
For the adopter, an innovation has never been used before and, therefore is characterized by ahigh level of uncertainty for him. In this paper, we assume that there is a relationship ―kindof “communities of practice (Lave and Wenger 1991)” ― in which innovation adopters are
creating information together, considering for example the doctor’s networking in the diffusion
process for drug A. The information created in that doctor’s network, perhaps a research paper,is considered to be important information when other doctors make decisions to adopt that new
drug A. Hence, we analyze the content of the paper. As a result of the analysis, in the first halfof the diffusion period, research was conducted with groups that included special experts in thefield. In the latter period, a group that does not necessarily have experts was conducting
research. Regarding the content of information created by the doctor’s network, in the first halfof the drug diffusion period, research on the early stage of dosing of drug A was conducted, andin the latter period, research on the next stage of dosing, such as side effects, was conducted. We
conclude that the doctor’s network creates information, and that the content of that information
will vary depending on the stage of innovation diffusion.
Keywords
diffusion of innovation，co-authorship network, communities of practice, Qualitative Data Analysis,
the pharmaceutical industry(1) Introduction
Products

accept new technology, and the resulting
althoughtheory suggests that people accept new

excellent, may not enjoy wide popularity.

technologies when they reach a satisfactory

There

and

level in terms of technology use. However,development in the field of management

Moore (1991) points out that some high-theory. However, there is lack of study on thetechnology products cannot spread widely.

has

and
been

services,muchresearchmatter of diffusion.

Rogers (2003) argues that individuals

Christensen (1997) states that people

who actively work toward the reduction of1

Journal of Japanese Management Vol.2, No.2, May 2018ISSN 2189-9592

uncertainty in innovation innovate in theirthose doctors who were integrated into thesedecision processes as well. One way to

network and those who were isolated”

reduce uncertainty of innovation is to test it.

(Coleman et al., 1966, p.79). Early adopters

If it is not easy, they then try innovation

were doctors whose networks were enhanced

temporarily; “trial by others” (Rogers, 2003,

by a colleague who adopted a new drug early,

p.177) as an alternative proposal. In thewhereas isolated doctors were late adopters.

subsequent

Therefore, doctors reduce the uncertainty ofsection,

a

case

study

is

presented that show how peer informationa new drug with organizational power.

promotes diffusion of innovation.
This study focuses on the innovation
adopter’s

network

andits

2. Clinical information as complementary

sharedassetsinformation. ITs main objective is theTeece(1987)

elucidation of mechanism of diffusion bynecessary

analyzing the adopter’s network and theirinnovation

information in the pharmaceutical industry.

solutions to customers. Therefore, a few

tomentions

bridge

and

that

it

is

the

gap

betweenproblems

toprovidethings that fills this gap are “complementary(2) Literature review

assets,” such as distribution and after-sales

1. Medical innovation study

service. In this paper, we assume that one of

Coleman et al. (1966) studied thethe complementary assets of a new drug ispattern of diffusion of a new dug. Theyclinical information. We presume that thedistinguished a sequence of founts of

information as complementary assets is one

information pertaining to a new drug by

with network externalities (Tsutsui, 2011).

diving them into three sources: primaryHence, if there are competing products,

source, intermediate source, and final source.

securing

The highest percentage of the primary

immediate necessity in order to be the top

source (of information that doctors know adrug picked by doctors.

new drug for the first time) was from theThecriticalmass

becomes

aninformation as complementarydetails man within a given pharmaceuticalgoods strongly promoting the reach ofcompany. Likewise, the highest percentage

innovation has the characteristics of aof intermediate source came from the details

network externality.

man. However, the final source, in otherwords, the information used when making a

3. Communities of practice as the parent

decision,body of learningwasdifferent.

Thehighestpercentage of the final source was the

The concept of communities of practicedoctor’s colleague. This result showed thatis an effective means to clear the mechanism

doctors had much confidence in existingby which clinical information is generated

users. In addition, there was a difference atand shared. The concept of communities of

the time of new drug adoption “between

practice, proposed by Lave and Wenger
2

Journal of Japanese Management Vol.2, No.2, May 2018ISSN 2189-9592(1991), is cited in the field of management

down with a university hospital. Therefore,

theory1991).

in the next section, I tried to clarify theirCommunities of practice are “groups ofresearch connection using co-authored data

people who share a concern, a set ofof the paper.

(BrownandDuguid,problems, or a passion about a topic, andThe logic of this paper is arranged

who deepen their knowledge and expertise

follows. It is assumed that a doctor decides

in this area by interacting on an ongoingon adopting a new drug based on thebasis” (Wenger et al., 2002, p.4). They

information

presented a case of the Tech Clubs as analready uses the drug as part of his new

example of communities of practices. When

medical supplies. It is assumed that doctors

Chrysler was changed from a functional

adopt various methods to share information

structure to product-oriented structure andwithengineers had to be allocated in separate

interaction and participating in workshops.

units. It means that engineers lost a chance

In this study, we consider papers

to learn from each other. Through thesepublished in academic journals as important

processes, the manager decided to assist insources of information pertaining to thegetting engineers to interact with each other.

decision to adopt a new drug, thus, weEngineers connected informally under the

analyze the paper’s data. In many cases,name of the Tech Clubs.

papers are written by multiple authors.

Similarly, a

doctor can become a

Therefore, it is possible to discover amember of a practice community when

network of doctors who collaborate to write

obtaining medical knowledge at a medicala paper at the time of a new drug’s diffusion.

school.

Tsutsui(2011)

considered

otherthat adoctors,

trustworthy

both

by

doctorpersonal

theconnection of communities of practice to(3) Network analysis of new drug diffusion

influence the adoption of a new drug and

1. Data source and analysis methods

conducted further research. Tsutsui (2011)

The present study focuses on drug A,

assumed that a university hospital was theavailable on the market since 1999 for thecore element of doctors’ communities oftreatment for Alzheimer's disease.

practice and examined an adoption rate of a

Date were gathered from representative

university-affiliatedthree journals in this field –the Japanesehospital. As a result of research, an adoption

Journal of Geriatric Psychiatry, the Journal

rate of a new product in a university-of Japanese Society for Dementia Care, andaffiliated hospital was higher than in those

the Japanese Journal of Geriatrics. Then,that had not adopted the new product.

the authors ’ data were collected fromnewproductinaThis paper studies the influence of such
communities

forlearning

papers (around 2014) containing the name of

medicaldrug A (whether having brand name or aknowledge pertaining to the adoption of ageneric one) in the title. By recognizing co-

new drug. However, doctors will not be tiedauthored relationship as communities of3

Journal of Japanese Management Vol.2, No.2, May 2018ISSN 2189-9592

practice that collaborated on writing a paper,

example of an m-slice. When there are fiveresearch networks were extracted. A total ofarticles (paper A is single-authored, papers

146 papers were collected from Ichushi

B, C, D and E are multi-authored) and sevenservice (http://www.jamas.or.jp/), with total

authors (Table 1), Figure 2 may be drawn.

listed authors was 753, and the actual
number of authors being 480. Authors with

Table 1. Five papers and seven authors

the same family and surnames name werePaper A1

treated as one person.

Paper B2, 3

Research was actively conducted twice

Paper C2, 3, 4during 2000 – 2014 (Figure 1). Therefore,

Paper D3, 4, 5

we divided the period into two phases: 2000

Paper E6, 7

- 2006 (Phase1) and 2007 - 2014 (Phase 2).

Source: Prepared by author.

The number of papers and authors in eachperiod were 74 and 265, respectively, in

1Phase 1, and 72 and 256 respectively (41being published in both phases). There wasnosignificant

difference

between2

thenumber of articles and the number ofauthors in each periods. Most of the authors
1

are presumed to be doctors as per the nameson the paper.
25

Figure 2. The relation of seven authors

20

Source: Prepared by author.

15

In Figure 2, numbers within the curve

5line are the number of times of co-authorship

0(n). Authors of papers B, C and D are
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014

10

connected in the big oval of Figure 1. In that
oval, there is no joint work experience, yet

Figure 1,Ｔhe number of paper per year

authors 2 and 5 can connect through authors

Source: Prepared by author.

3 and 4. Thus, it is possible to draw two
networks and an isolated vertex of one

2. Networks in both periods

person using the m-slice technique.

The networks of authors in the two

The respective authors of Phase 1 wrote

periods were defined. The contours werea total of 74 papers and 72 in Phase 2, drawn

drawn

from

using

an

m-slicetechnique innetworks based on the method proposed bythese

networks

using

the

aforementioned method. As a result, 35

DeNooy et al. (2005). The following is an
4

Journal of Japanese Management Vol.2, No.2, May 2018

networks were formed for Phase 1, and 31

ISSN 2189-9592

the pharmaceutical company.

for Phase 2. When one regards as theTheinterviewbrought

annumber of the persons who form the network,

environmental understanding surrounding

it becomes clear that a dominant network of

the treatment for Alzheimer’s disease andPhase 2 is of a larger scale than that of Phasethe

1 (Table 2).

information obtained from the interview was

ideas

of

these

researchers.

The

helpful to understand the influence upon the
Table 2. The composition of members ofdiffusion of drug A from the list of 480

the top three networks

surveyed authors.
The network was divided such that a2000 - 2006

2007 - 2014

1st place25

48

person supposed to be influential in the

2nd place16

21

diffusion of drug A was might be included in

3rd place15

17

more than one network or in none of them.

Source: Prepared by author.

As a result, the percentage of networks inwhich an influential person is included

There is no significant difference in the

become the Phase 1 percentage, which isresult of the m-slice in phases 1 and 2 (Table

higher than Phase 2’s (Table 4). Therefore, it3). The result of m-slice in both phases wereappearsa minimum value of 0 and maximum of 9.

influential person is necessary because of

that

the

cooperationofanthe uncertainty surrounding a new drug’s
Table 3. Result of the m-slice in two phases

sales. Figure 3 shows that drug A wasm-ofdiffused rapidly within the first 10 years

slice authors of Phase authors of Phase(2000 through 2010). The information that

Number1

ofNumberreduces uncertainty and promoted the

2

0

4

7

spread of drug A was considered in the

1

197

188

papers included in Phase 1.

2

47

52

3

14

2

4

0

3

5

2

0

9

3

4

Table 4. Percentage of networks where aperson influencing the spread of drug A was
included
periodSource: Prepared by author.
3. Interpretation of network

The numberof network

percentage

2000 - 200617

49%(17/ 35)

2007 - 201410

32%(10/31)

Source: Prepared by author.

The insight was provided when I
interpreted the network from using theinformation that got from the interview to5

Journal of Japanese Management Vol.2, No.2, May 2018ISSN 2189-9592

frequency and interpreted using information
obtained from interviewing pharmaceuticalcompanies.
2. Data Analysis and Findings
A keyword consists of main heading and

2015

2013
2014

2011
2012

2009
2010

2008

2006
2007

2004
2005

2002
2003

2001

2-1.Keyword
subheading, e.g., in case of “Alzheimer’s

Figure 3. Sales trend of drugdiseaseA
Source: Prepared by author.

“Alzheimer’s disease” is the main heading(pharmacotherapy),”whereand “pharmacotherapy” is the subheading.
In addition, the structure of the top-

The frequency order keyword of each

three network, m-slice, and centricity of eachperiod is compared in 2-1-1. Then, thenetwork was considered, but we were not

keyword that appears in either phase isable to find any effective discovery about thechosen and compared in 2-1-2.

diffusion of new drug.
2-1-1(4) Features of the information regarding

Comparisonoffrequencyofallkeywords

the diffusion of drug A: A qualitative data

In Phase 1, the first keyword wasanalysisgeneric name of drug A; second, Alzheimer

1. The data source and analysis methods

Disease; third, dementia. In Phase 2, in the

In this paper, three kinds of qualitative

first place was “Alzheimer Disease”; second,

data were analyzed: keywords were given to

generic

name

of

drug

A;

and

each thesis, the paper title, and abstract.

“treatment outcome” (appendix A).

third,The keyword was given to each thesis by

In Phase 1, there are eleven papers with

Ichushi service. For this reason, there is no

keywords of generic name of drug A whose

problem that keyword for giving rules are

subheading is “pharmacology”; consequently,

different for each journal. The paper that

we can see that it is focused on research on

appeared in Japanese Journal of Geriatric

this new drug.

Psychiatry was used based on the title andIn Phase 2, since there are papers to

the abstract. A total of 97 titles and abstracts

which subheading of “complications” is givenof papers were used. The titles and abstracts

to “Alzheimer disease” and papers to whichof papers are analyzed using qualitative

subheadings of “adverse effects” are given todata analysis software NVivo 11. The

generic name of drag A, at this phase, it can

keyword can be used to interpret data

be said that research is being conducted towithout

becauseimprove the accuracy of medication results.

keywords present in-depth classification.

Although the keyword of “treatment

using

data

software

The keywords were arranged in the order ofoutcome” did not rank within the third rank
6

Journal of Japanese Management Vol.2, No.2, May 2018ISSN 2189-9592

in Phase 1, it is the third keyword in Phase

frequently appearing in the papers on

2. As can be seen from here it takes a long

Alzheimer’s disease.

time to produce the result of treatment for
Alzheimer’s disease.
2-1-2 Comparison of frequency keyword(only in either phase)In addition, keyword that are commonly
given in the two periods are excluded, only
keyword that are given only to papers ineach period are extracted and analyzed.
In Phase 1, it is presumed that the

Figure 4. Frequency word in the title inrelation between the administration of drug

Phase 1

A to patient and the function of the brain is

Source: Prepared by author.

being studied, because keywords indicating
Competitive products

names of a part of the brain and a disease
occurring in the brain was seen.
With regard to keywords given to thepaper in Phase 2, keyword related to adverse
effects such as digestive system diseases andanorexia have come to be seen. This suggeststhat research on the results obtained byadministering drug A "settled" to some

Figure 5. Frequency word in the title inextent, so it can be inferred that the interest

Phase 2of researchers has shifted to the adverse

Source: Prepared by author.

effects of next concern.
Comparison of figures 4 and 5 gave us
2-2. Title

the next discovery.

The title of the thesis was separated in

The word having the highest degree of

the Phase 1 and the phase and frequency

frequency in Phase 1 was “medication

words were picked out using a qualitative“(middle of Figure 4). The context of thedata analysis tool (NVivo 11). The charactersword–medication–was

and words were widely defined so that thefacilitated beginning to prescribe drug A andfrequency of their appearance was highthe effect of the prescription of drug A, and(Figure 4). Words like Alzheimer,

dementia,

considerationdisease, type, name

A wereprescribed.

establishedasof drug

prohibited

terminologies

of

The

the

reportwhether
context

drug
of

the

that

A

is

word

“therapeutics” (the lower one of Figure 4)

because these words are general words

was
7

the

considerationoftherapeutics

Journal of Japanese Management Vol.2, No.2, May 2018ISSN 2189-9592

strategy and administration of drug affairs.

lower one of Figure 7). The context of the

The word having the highest degree ofword group was the article in which thefrequency in Phase 2 was “therapeutics”

patient’s data are classified by various(middle of Figure 5). The context of the word

factors (e.g., group to which the drug was“therapeutics”

ofgiven and group to which the drug was not

administration of drug to patient on disease.

given, high-dose group and low-dose group,

Additionally, the other Alzheimer’s disease

young group and the over-80 group), and it

curatives on market since 2011 are shown in

was thus compared and examined.

was

the

effectFigure 5. The new drugs sold from othercompanies are competitors to drug A at thesame time as these drugs are able to be used
together.

Therefore,

some

papersonswitching from drug A to new drugs, otherpapers on the results of combined use withdrug A have also been reported.
2-3 Abstract

Figure 6. Frequency word in the abstract inFrequent terms of the two phases of thePhase 1

abstract were analyzed using a qualitative

Source: Prepared by author.

data analysis tool (NVivo 11).
The most frequently appearing word in
Phase 1 was “group” (middle of Figure 6).
The context of the word medication was thereport that divided part of the data among
treatment group and non-treatment group
and analyzed the condition. The word thathas the second highest degree of frequency
in Phase 1 was “medication” (the lower one

Figure 7. Frequency word in the abstract inof Figure 6). The context of the word

Phase 2

medication was reported, which is as a result

Source: Prepared by author.

of the patient who took drug A for the firsttime.

(5) Finding and explanation for findings

The word having the highest degree ofFrom network analysis and qualitative

the frequency in Phase 2 was “therapeutics”

data analysis, the following findings were(middle of Figure 7).

determined about the doctor’s network of

The context of therapeutics was varied.

and the contents of the information created

The word having the second highest degreeby that network during the period ofof frequency in Phase 2 was “group” (thediffusion of drug A period.
8

Journal of Japanese Management Vol.2, No.2, May 2018ISSN 2189-9592

When the diffusion period is divided into

Therefore, it is necessary to acquire critical

two stages, in the Phase 1, many of themass quickly when there is a competitive

research group on drug A is participatingproduct. Because competitive product was

specialists who are influential in the

not released at the time of release in drug A,

diffusion process. On the one hand, theit was not threatened by a competitive

reason is assumed that specialists were

product.

indispensable to the research group on thethoroughly inform doctors even when the

period where large uncertainty of drug A

products excellent.

However,

itisnecessarytoexists. On the other hand, in Phase 2, there

In fact, according to Nikkei Medical, inare many groups that specialists do not

the year when drug A was released, there isparticipate in. In those periods, diversity

a description that points out that drug’s

within the research theme increasing is“batting average” is 20%, that is, there wasconsidered.

a tendency to underestimate. If a customer

It

was

assumedresearch

feels that the degree of product uncertainty

foundation was established during Phase 1,

is high, even if it is a superior productand various studies bloomed as a result offunctionally, and even in the absence ofPhase 2.

conspicuous

that

a

The forgoing hypothesis about thecompetition,complementary

assetsbuilding

calleduser

transition of the above research contents

information is necessary for products towas demonstrated in the result of adiffuse widely.

qualitative analysis. As a result of thequalitative analysis of the article dealing

Referencewith drug A, the research carried out in

Brown, J. S., and Duguid, P. (1991).

Phase 1 was a research before the start ofOrganizational

the administration of drug A to patient orCommunities-of-Practice:

immediately

its

Unified View of Working, Learning,

administration. In Phase 2, the following

and Innovation, Organization Science,

two points were clarified. One can see that

2(1): pp.40-57.

after

starting

Learning

andTowardaresearch on comparing and combining

Coleman, J. S., Katz, E., and Menzel, H.

competitive drug competitor and drug A is(1966) Medical Innovation: A Diffusion

conducted. Secondly, in Phase 2, there are

Study: Bobbs-Merrill.Co., Inc.（小口一many reports of side effects. In short, the元/宮本史郎訳『販売戦略と意思決定』ラ

research accumulated in Phase 1 was the

ティス、1970）

basis of Phase 2’s research.
asChristensen,

M.

(1997).

The

Dilemma:

When

New

ClaytonAs mentioned (2)-2, information serving

Innovator'saTechnologies Cause Great Firms tocomplementary

diffusion

innovation

characteristic

a

asset
has

networkpromotingtheFail. Boston: Harvard Business School

strongexternality.
9

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2007）
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翔泳社、2001)
de Nooy, W., Mrvar, A., and Batagelj, V.

D.

J.

(1987).

ProfitingfromTechnological Innovation: Implications(2005). Exploratory Social NetworkforAnalysisCambridge :Licensing, and Public Policy. In Teece,

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D. J. (ed.), The Competitive Challenge:

訳『Pajek を活用した社会ネット ワー

Strategies for Industrial Innovation

ク分析』、東京電機大学出版局、2009)

and Renewal, pp.185-219. Cambridge,

with

Pajek,

Lave, J., and Wenger, E. (1991). SituatedLearning:

Legitimate

Integration,

Collaboration,

MA: Ballinger（石井淳蔵他訳『競争へのPeripheral

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Chishiki

学習：正統的周辺参加』産業図書、1993）
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Japan HAKUTO-SHOBO.

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Products).

Tokyo:

Wenger, E., McDermott, R., and Snyder, W.
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MA:

HarvardJournal of Japanese Management Vol.2, No.2, May 2018ISSN 2189-9592

Appendix A
Table of top three keywords
2000～2006（74 papers）
Keyword

Sub Headings

2007～2014（72 papers）
Number

Keyword

Sub Headings

pf time
1st

2nd

Numberpf timegeneric nametherapeutic use

68

Alzheimer

drug therapy

56

of drug A(total

pharmacology

11

Diseasediagnosis

21

90 times)

adverse effects

10( total 110

complications

11

No subheading

1

times)

Prognosis

7

diagnostic imaging

4

radionuclide imaging

3

rehabilitation

2

Radiography

1genetics

1chemically induced

1

therapy1

etiology

1prevention and control

1

therapeutic use

76

adverse effects

20

pharmacology

4

Alzheimer

drug therapy

48

generic

Disease (total

diagnosis

8

name

of

78 times)

radionuclide imaging

8

drug

A

(total 100
times)

3rd

Dementia(totaltimes)

prognosis

6complications

4

diagnostic imaging

3

No subheading1

drug effects

13

treatment

19

outcomecomplications

2

prognosis

2

No subheading1

radionuclide imaging

1

No subheading

25(Received: December 31, 2017)(Accepted: April 15, 2018)11