Innovation adoption and exploitation in SMEs:

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Dec 4, 2013 (3 years and 8 months ago)

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1

I
nnovation

adoption and exploitation

in SMEs
:


A S
ystematic

Literature Review

L
OREDANA
V
OLPE
1

F
RANCESCO
R
ICOTTA


G
IANLUCA
V
AGNANI


M
ARCO
V
ALERI




Abstract

Innovation adoption has been
widely debated among scholars
in order to identify

variables and
mod
els
that
boost adoption
processes
with
in
firms. Recently
,

increasing

attention has been
devoted
to
understanding
effective

innovation

adoption

in

Small and Medium
-
Size
d

Enterprises (SMEs).

Taking this issue
,

we focus

our attention on potential gap
s

within
current knowledge on the
adoption of innovation in SMEs.

To this purpose, we

cond
uct

an extensive literature re
v
iew on
articles and papers dealing with innovation adoption,
published in the last
23 years, i.e.

from 1989
to 2012
. We run

content
and structural
analysis on
the

collected

data
.

Our results

contribute to the debate on
innovation
adoption

through systematization of literature
.
They also

suggest

a number of

research directions that
have not

been adequately investigated
yet
.
In
particul
ar
, scholars do not seem to have caught all the implications

of innovation adoption
,

especially for SMEs. Our results suggest that o
nly a few papers have
hitherto
addressed the main
impediment to innovation adoption, i.e. the ability to capture its benefit
s, and to enhance the
effectiveness of the technology being adopted.

The paper is organized as follows
:
Section
1
briefly
introduce
s

the topic; Section 2

discusse
s some of the literature on
innovation
adoption.

Section 3
describes the research framework and
method applied
. Section 4 presents
our

result
s.

In section 5
we discuss
some
implications

and directions for future research.


Keywords: innovation adoption
, small and medium
-
sized firms, bibliometric review,

content
analysis
.








Il presente lavoro è frutto delle riflessioni congiunte degli autori.
Tuttavia si precisa che: i paragrafi 2.1, 2.2 sono da attribuirsi a
Marco Valeri; il paragrafo 3 a Gianluca Vagnani; i paragrafi 3.1, 3.2 a Francesco Ricotta, i paragrafi 4, 5, 5.1, 5.2,

5.3 a Loredana
Volpe. L’introduzione e le conclusioni sono da attribuirsi a tutti gli autori. Lo studio, inoltre,

raccoglie alcuni risultati dell’atti it
scientifica, coordinata dal prof.
Francesco Ricotta
, che si inquadra nel progetto
“Integrazione semantica di dati e ser izi per le
imprese in rete”
co
-
finanziato dalla Regione Lazio
per il settore scientifico disciplinare SECS
-
P/08, Economia e Gestione delle
Imprese, presso il Dipartimento di Management

di Sapienza Università di Roma
. S
i ringraziano tutti i colleghi che hanno animato
il dibattito e la ricerca svolta.

1


Ricercatore
t.d.
in
Economia e gestione delle imprese

(SECS/P08)
-

Sapienza Università di Roma


e
-
mail:

loredana.volpe@uniroma1.it




Professore associato di
Economia e
gestione delle imprese

(SECS/P08)
-

Sapienza Università di Roma


e
-
mail:
francesco.ricotta
@uniroma1.it




Professore ordinario di
Economia e gestione delle imprese

(SECS/P08)
-

Sapienza Università di Roma


e
-
mail: gianluca.vagnani@uniroma1.it




Assegnista di ricerca

presso il Dipartimento di Management
-

Sapienza Università di Roma


e
-
mail:

mavaleri@libero.it


2

1

Introduction

The adoption

of technological innovation aimed at enhancing

firm competitiveness has been at the
core of the academic debate since the 1980s.
Innovation is commonly conceived as the invention
and commercialization of new
products or services based on the application of technological and/or
market knowledge
.
1

T
echnologi
cal innovations are valuable
because

they tend to increase the
access
ibility and

availability

of resources
2
,

stimulate collaboration between firms for innovatio
n
exploitation
, and

favor

access to
external financing
3

or technological expertise
.
4

Notwithstanding
scholars’ growing attention toward
innovation adoption
,
the academic literature has mostly focused
on innovation adoption processes within large firms rather than in SMEs.

Even some recent
systematic review works have not specifically considered SMEs (
5
-
12
).

Thus,
tracing the evolution
of the academic literature on
such topic

is not an easy task
, especially in the area of SMEs
.

Here comes the objective for our study.
Our aim is to explore th
e

evolution

of the academic
literature on inn
o ation adoption and inno ation exploitation, with particular reference to SME’s
adoption decisions
.

Indeed
, adoption

choices are even more
critical when it comes to Small and Medium
-
Sized
E
nterprises (SMEs). Firm size has
been associated to a number of important
implications for
the
success of
particular sta
ges of the innovation process, and

for the adoption of
particular types of
innovation.
13

Scholarly attention has mostly

focused on

the relationship between optimal
organizational size, market structure

and technological innovati
on
.
14

T
he organization of markets
and the ways in which technological innovations spread wit
hin them are rapidly changing. In an
ever more

competitive environment
,

the

adoption and exploitation of

appropriate

technological
innovations

play

a strategic role

for several reasons. One concerns challenging the firm knowledge
base and capacity to absorb competencies

that have been

created els
ewhere
.
15

A second reason
involves the
possibility to extend

technological innovation to the overall organizational structure.
T
he
adoption

o
f technological innovations
thus
strengthen
s

the firm

structure by faci
litating the
exploitation

of

environmental
opportunities
. Furthermore,

globalization has
shed new light on
the
implications of
innovation adoption
in terms of creation of

new


market
s

for technology skills

and
acceleration of

the obsolescence of technological innovation in ways t
hat often lead to the
emergence of a dominant design or the broad acceptance of certain technological

“standard”

or
product attributes.
16

More specifically,
we seek to analyze

the literature on
determinants and consequences of innovation
adopti
on and innovation exploitation,

during a period of roughly 20 years
.

Methodologically, t
o
trace

the

development
of the literature
in
a

systematic
and comprehensive
way
,

w
e perform a
bibli
ometric analysis
, which allows

obtain
ing

an overview of the intellectual structure of the

field

3

under study
,
17
,
18

and suggest
s

how such field is moving forward.
19
,
20

In our study, we partly follow
D
e Bakke
r

et al.
(2005) who have applied a similar analysis to describe the evolution of
research
and theory on corporate social responsibility (CSR) and corporate social performance (CSP).
18

Our paper is organized as follows.
Firstly, we review the theories and models that have been
dominating the academic literature on innovation adoption in the last 23 years of
research. In the
Research Framework and Method

section
s
, we describe how we buil
d

our datasets.
Here, w
e also
present
the methodology used to interpret our datasets. Finally, we discuss
our
results and provide
recommendations for future research.


2

Literature review.
Diff
erent theoretical explanations and models on
innovation adoption


2.1

The development of theory

Rogers
21

(1995)
classical
adoption

of innovation model has provided the theoretical
foundation for
many

studies

in this domain
. According to the Author

(p. 21)
, the adoption process is ‘‘the process
through which an individual or other decision
-
making unit pass
es from first knowledge of an
innovation, to forming an attitude toward the innovation, to a decision to adopt or reject, to
implementation of the new idea, and to confirmation of this decision
.
’’ Following Rogers,
innovation scholars have mostly used inte
ntion models or behavioral decision theories to explain
the adoption and use of new technological solutions.
A
mong the most popular and well supported

models, which are
mostly
rooted in social psychology,
are

Ajzen’s (1991)
Theory of Planned
Behavior (
TPB
)
,
22

Da is’ (1989)
Technology Acceptance Model (
TAM
)
23

and the more recent
UTAUT (Unified

Theory of Acceptance and Use of Technology) developed by Venkatesh et al.
(2003)
.
24

However,

these studies have

focused on the
consumer as the primary unit of analysis,
whereas only
some more recent contributions have started to extend models of innovation adoption
from social psychology to the organizational level. In particular, it has been
suggested

that, since
innovation adopt
ion decisions are typically made by a firm top management, the consumer
-
level
models of innovation adoption may shed light even on organizational
-
level adoption processes
25
.

At the organizational level of analysis, t
he debate on the adoption of technological innovations has
focused on the role of technology
as
a means

to enable firm management
beyond the boundary

of

individual

rationality,
favor both intra
-
firm and inter
-
firm r
elationships,
26

and
increase

firm
potential to follow

new evolutionary paths.
27
-
29

Nevertheless, the
strategic management and
innovation
literature
s

still
do

not seem to
have
unanimously
grasp
ed

the strategic role of the
adoption of technological innov
ation
, especially

in SMEs
.
30

Indeed, t
he scientific debate has
mostly

4

focused
on l
arge
-
sized firms,
such that the study of

the

antecedents and consequences of innovation
adoption has broadly been referred to this type of organizations.

On one hand, i
n terms of
the
consequences

of adoption
, s
cholars have proposed many a
nd varied

justifications to

innovation
adoption. In particular, research on
organizational learning

has
distinguished between

the
notion

of

competence
-
destroying


and

competence
-
e
nhancing”

innovation

adoption.
A
compete
nce
-
destroying
innovation implies that

adopti
on leads to

a
substantial rec
onfiguration of the drivers of competition
by
redefining

capacities and
relationships
,

which are deemed

critical for firm

survival
in a
competitive environment.
31

Conversely,

competence
-
enhancing innovation

implies that adoption builds

on the organization
’s

existing
competencies

and know
-
how
, resulting in an incremental increase in the efficiency of offering a
given produc
t or service.
Furthermore
, the
academic
literature
32

has emphasized how
innovation
adoption
contribute
s

to managing
organizational
interdependencies
, and
to
solving the problems of
coordination between various organiz
ational unit
s
.
More generally
,
it has been
observed

that
innovation adoption

help
s

improve the efficiency and effectiveness of

firm
s,

and thus
positively
contribute
s

to

performance.
26
,
33
,
34

The
se

perspectives of analysis
,
mostly
developed within the

learning and

innovation literature
s
, thus

highlight the strategic role of innovation

adoption

in support
ing
firms’
decision
-
making and

operational processes
.
More specifically, w
ithi
n the
strategic management domain
,
a

further
perspective

on the implications of innovation adoption

is
rooted in the
Resources
B
ased
T
heory
.
35
,
36

By postulating that firm growth and
competitive advantage depend

on the possession of

strategic
and highly specific

resources
,
37
,
38

this perspective has
emphasized
the role of technological
innovation
in

imp
rov
ing

the way firms are managed
.
39

Bu
i
lding on
it
, a number of contributions
have been developed that start to address

the implications of innovation adoption even for SMEs.
In
particular
, researchers have

pointed at SMEs


resource shortcomings that limit their ability to
appropriate rents from
technological
innovations and associated
market niches they have entered
.
40
,
41

Insofar as
innovation involves

technologies

which

are increasingly standardized, widely used and
easily
accessible
, the
ir adoption

does not
ensure
s

SMEs

are able

to
achieve a competitive advantage

that might prove

sustainable over time
.
42
,
43

In such instances,

the contribution of adoption
to

the
competitive advantage of
SMEs
depends primarily on the ability of the
firm
to
integrate and
combine creatively

the innovation being adopted

with

the

sets of internal
human
,

technical and
financial
resources.
39

On the other hand, concerning

the antecedents of innovation adoption,
extant literature
emphasizes
how

adoption may be
the result of

specific determinants
,
especially

when referred to SMEs.
In fact
,
scholars have identified

different factors

that influence SMEs

decision to invest in

innovation.


5

One

factor

refers to the level

of SMEs
organizational development. The adoption of technological
innovations is esse
ntial to support the improvement and rationalization of business proce
sses and
infrastructure, and to

enhance the value of
extant
information and knowledge
. A second factor

concerns

the possibility to

increase

SMS’s le el of knowledge and skills. The growt
h of knowledge
and skills
requires technological innovation

to support decision
-
making processes.
44

To this end,

it
can be said that
innovation adoption is

a way to meet and support SMEs processes of structural
change and growth.

On the flip side
,
i
n adopting and using inno ations, SME’s
are likely to be
penalized by

more limited resources and technological competencies
,
45

which often affects adoption
decisions.

Furthermore, when it comes to reaping the benefits of innovatio
n once adopting,
a number of
studies have emphasized the role of organizational size in steering organizations toward either
exploration or exploitation processes
.
46

E
xploitation and exploration processes may take place
conditioned on t
heir
determinants
.
47

E
xploration is
determined by
simply desire, the wish to
discover something new.
Co
nversely, t
he
main
determinant

to exploitation is the existence of an
exploitable set of resources, assets, or capabilities under the control of the firm. Given this
distinction, conflicting findings exist concerning the impact of organizational size on th
e tendency
to explore versus exploit

the adopted innovation
.

48

In particular, prior research has argued that size
positively relates to the propensity to engage in exploitation
,

47

whilst larger organizations may have
better access to internal resources and
might

thus support exploration
.

49

The above explanations have dominated the literature on innovation adoption, in general, and
research on
SMEs’
innovation adoption and exploitation in particular. Still, none of them can be
said to domina
te the others and to have gained undisputed academic legitimacy.
Thus, f
ollowing our
review, we would expect to find a few central concepts shaping papers in our dataset.



2.2

Models on the factors influencing

the propensity to adopt technological innovations

in
SMEs

Decisions to adopt

an innovation

are influenced by

a number of variables
concerning

both
the
inherent characteristics of the
technology to be adopted
,

and

behavioral aspect
s of the decision
-
maker.
In his
pioneering
model,
Rogers (1995)

identifies

five general
factors
that influence the
widespread adoption of technological innovations. The c
haracteristics of the technological
solutions, which are able to influence the adoption decision
s,

are

the following:

1)
perceived relative advantage
,

express
i
ng

the degree of perception of a technological innovation
both before and after its adoption. The higher the level of technology adoption by firms, the greater
the perceived relative advantage;


6

2)
structural compatibility
,
express
ing

the perception of the
degree of compatibility of a
technological innovation with the structural configuration of the
organization
.
The g
reater

is

the
perception of
compatibility of
the innovation

with the

organization’s

structural undertaking, the
higher

the probability
for the

innovation to be adopted
;

3)
complexity
, i.e.
the percepti
on of the level of difficulty in

implementing a technological
innovation
both

at the beginning

of implementation and during its use;

4)
observability
, i.e.

the perceived level of performance that
an organization can

achieve

thanks to
innovation adoption
;

5)
experimentability
. It expresses the experimental possibilities of the new technology before being
adopted. Some technologies are hardly divisible in modules to be tested before
use
.

A further important model of innovation adoption stems from the
Theory of Planned Behavior
.
22

With reference
to the behavior of the decision
-
maker, the
Theory
of Planned Behaviour

(TPB)
shows that the decision to adopt

a

technological innovation depends on three variables
, namely

(1)

the positive or negative attitude of the decision maker towards adoption,

(2)

the social pressure to
adopt, intended as a sum of a
ll social pressure and expectations

to adopt
, and

(3)

the perception of
difficulty in
implementing
the

new

technology.

This perspective, and the related TA
M model

thus point at

the factors that influence individ
ual and
organizational behavior
s
.
The intention to adopt technological innovations is
a means

by which
decision makers stimulate
organizational

behavior.
Specifically, a
ccording to the
TAM model
,
such

intention is determined by both the
attitude

of the decision
-
maker to adopt t
he
innovatio
n, and by
social

pressures
to adopt. The attitude of the decision
-
maker to adopt
the innovation
is

in turn

influenced by the level of perc
eption of the benefits that the adoption
behavior will generate and

by

the consequences of this decision
for

firm

perf
ormance and growth
.

The second factor that
influences the intention to adopt is
represented

by social pressures
,
which
affect the behavior of the
decision
-
maker to comply or not with a set of
socio
-
economic rules in the firm environment. The
intention to a
dopt a
technological innovation also de
pends on the perception
of the decision
-
maker
of

the difficulty of maintaining a certain behavior once
deciding to adopt the innovation

(
so called


perceived behavioral control
”). This perception is a function of

vari
ous factors
,

such as
past
experience of the decision
-
maker and the
perceived
difficulty
in

implement
ing

the new technology.


It might therefore be said that t
he successful
adoption and subsequent exploitation

of technological
innovation

depend on a complex

interaction between

technolog
ical factors

and

SME’s

st
ructural
configuration
.
50


Given the somehow prescriptive nature of

the aforementioned

models of innovation adoption and
exploitation
,

a
nd the cumulative body of knowledge accumulated over the years from TAM

7

research, we would expect to fi
nd
scarce

divergen
c
es in

terms and concepts

within

in the literature
on innovation adoption and exploitation in SMEs
. R
ather
, we expect

a pattern of overlap of existing
concepts. Indeed, new concepts substitute for extant ones if only their explanatory power is more
effective tha
n that of established concepts in explaining why firms choose to adopt a new
technology.


3

Research framework
and method


To trace the actual evolution of academic li
terature on innovation adoption and
exploitation, we
partly
followed

Damanpour’s (1991)
51

procedure
and more
recent systematic reviews published in
management journals
.
18

Based on these works,
publications were included in our review if only they
met the following criteria: (1) the main focus of the st
udy (either conceptual or empirical) was on
i
nnovation adoption and/or innovation exploitation
;
(2) the analysis was at an organizational
level
rather than an individual or organizational population level.
On this point
,

it must be clear that

we

delimit
ed

the level of analysis in our

dataset when

only

searching for theoretical and empirical
research on innovation adoption
and exploitation in SMEs

rather than for all types of firms;

(3) the
study was published in a scholarly book or journal.


To find studies, we searched the ABI/Inform Complete and Ebsco databases for a peri
od of 23 years
from 1989 to 2012
.We also conducted an Internet sear
ch through the Google Scholar
search engine.
We searched the databases on the following categories: keywor
ds, title and abstracts.
We based our
search on terms composed of combinations of
innovation adoption
,
innovation exploitation
,

and
SMEs
.

In total, we

extracted contributions from 44

journals:
Academy of Management Journal,
Academy of Management Review, Ad
ministrative Science Quarterly, American Journal of
Sociology
,
Annual Review of Sociology
,
Cambridge Journal of Economics
,
Decision Sciences
,
Econometrica
, En
gineering Management
,
Entrepreneurship Theory & Practice
,
European Journal
of Marketing
,
Industria
l and Corporate Change
,
Industrial Marketing Management
,
Information
and Management
,
Information Systems Research
,

International Journal of Innovation Management,

International Journal
of Management Review
,
International Journal of Technology Management
,
J
ournal of Business Research
, Journal of Business Venturing,
Journal of Economic Literature
,
Journal of Engineering

and Technology,
Journal of Information Technology
,
Journal of Knowledge
Management
,
Journal of Management
,
Journal of Management Information
Systems
, Journal of
Management Research,
Journal of Management Studies
,
Journal of Marketing
,
Journal of
Marketing Research
,
Journal of Operation Management
,
Journal of Organizational Behavior
,
Journal of Politics
,
Journal of Product Innovation Management
,

Long Range Planning
,

8

Management Science,
Marketing Science
,
MIS Quarterly
,
Organization Science
,
R&D
Management
,
Research Policy
, Strategic Management Journal,
Technovation
,
The British Journal
of Social Psychology
.

I
n order to clean up the raw dataset
,

w
e manually examined all the research sources and excluded
duplicates, book reviews and articles with no apparent relationship to the topic at hand.
Furthermore, we attempted to make our dataset as comprehensive as possible by examining the
references list
ed
in a number of recent reviews
.
9
,
25
,
52

In total, t
hrough these procedures,
we
extracted
363
publications.

W
e
divided them in

three datasets
. One co
ntains 324 entries on
Innovation adoption

in general. A second dataset

I
nnovation adoption &

SMEs

focuses on
innovation adoption in SMEs, and contains 20 entries. The third

dataset

Innovation exploitation &
SMEs

addresses innovation exploitation processes in SMEs and contains

19

entries.


More generally, in
analyzing
our datasets, we based on two fundamental sets of research question
s.

Firstly,

in which journals has research on innovation adoption, innovation adoption in SMEs, and
innovation exploitation in SMEs been published?
Answer to this question provides elements to
evaluate the representativeness of our datasets, and
to
explain further res
ults.
Secondly, we tried to

understand the
development of

innovation adoption
and innovation exploitation as
field
s of
research
: what have been the most recurrent themes addressed within research on innovation
adoption, inno ation adoption in SME’s and inn
o ation exploitation in SME’s? Are the
re any
differences between the

datasets?

The methods used to address these

sets of

questions are presented

more extensively

in the following
section
.



3.1

Review process and technique adopted

A systematic review method

allows searching for assessing the field of studies
, which is

relevant to
specific research questions
53
. As Macpherson and Holt (2007:
172)
53

note, “the intent is to generate
collective insights through a meta
-
synthesis of findings thereby increasing methodological rigour
and deve
loping a reliable knowledge base from which to take policy lessons and orient future
research (Tranfield et al., 2003).”
Based on this premise, we attempted to address the
aforementioned research questions by using a variety of methods. More specifically a
nd firstly, we
tested
the
representative
ness of

our datasets by straightforward counting the number of publications
that appeared across th
e defined time period (1989
-
2012
), and of the journals in which they were
published. Second, t
he patterns we observed

from count
ing and categorizing
the papers
were
supplemented by
text analysis

to provide an additional view on our datasets.

In this way, we

9

identified

the
most recurrent t
hemes
in both the overall innovation literature and the

SME’s
innovation
adoption an
d exploitation

literature

that emerged during the review process.


3.2

The content

analysis method

Content analysis has been defined as a systematic, replicable technique for compressing many
words of text into fewer content categories based on explicit rules

o
f coding
.
54
-
56

We used text
analysis to gain further insight on the patterns observed from counting the number of publications
per year in the three datasets. As

Lasswell et al. (1952: 45)
57

obser e, “Content
analysis will not tell
us whether a given work is good literature; it will tell us whether the style is varied. It will not tell
us whether a paper is subversive; it will tell us if the contents change with party line. It will not tell
us how to convince t
he Russians; it will tell us what are the most frequent themes of Soviet
propaganda.”

In our case, we resorted to computer
-
supported text analysis as a useful technique to discover and
describe the focus of attention in three different literature streams.
56

To this purpose, fol
lowing
D
e
Bakker et al.
18
, we used titles
as text inputs
for exami
ning trends
and patterns in the literature. Titles
from papers in our datasets were extracted an
d categorized in a spreadsheet
that was then analyzed
using the content
-
an
alysis software package
WordStat

6

(Provalis

Research, 2010). This package is

specific
ally designed to process textual information such as responses to open
-
ended questions,
interviews, titles, journal articles, public speeches, and so on.
W
e used the

software to compute
word frequencies and map combinations of words across time
,

s
uch

that
to trace emerging trends
and development in the field of inquiry.

Doing a word
-
frequency count is one of the most common
assumption in content analysis.
58

The underlying premise

i
s that the words that are mentioned most
frequently are those that highlight the “hot topics” and greatest concerns.
59

We thus generated a list
of word frequencies to compare the use of

title words across several y
ear groups
.
18

In order to avoid
common problems associated with word frequencies count, such as synonyms used for st
ylistic
purposes

that may lead the researcher to underestimate the rele
vance of a concept
,
56

we performed
some pre
-
processing operations on our datasets.

As start, we did not
analyze
title
wor
ds appearing in a low frequency. We decided to take as a
cutoff
point

a frequency

of four
, which means that
we included in our analysis only those w
ords that
appeared in titles four

times or more. Then, we manually cleaned up the data by excluding
redundan
t words or words with little semantic value
,

such as prepositions, pronouns, conjunctions,
and so on.
Once determining word frequency counts, we used a Key Word In Context
(KWIC)
search to test for th
e

consistency of usage of words.

KWIC
allows the researc
her to isolate the
sentence in which that word was used so that she can see the word in some context. This procedure

10

thus helps strengthen the validity of the inferences that are being made from the data.
58

We
attempted to balance words exclusion with generaliza
tion co
n
c
erns by

also excluding all words
occurring in a number of cases less than the number of periods studied. This procedure allowed to
better pursuing our goal of making a c
omparison throughout years and between different datasets
,

whilst balancing the need of maintaining a basic enough set of words with that of results’
generalization. T
o make generalization easier, some words were combined via a categorizat
ion
process
.
56


SMEs


and

small and medium
-
sized

firms,


for instance, were recoded to SME.
Indeed, in

our quite special dataset
s
, small and medium
-
sized

firms can be regarded as one single
construct. After these pre
-
processing procedures, word frequency counts were determined for all
our datasets.

As a further extension of this content analysis, we also
analyzed word or categories that tended to
appear together through WordStat hierarchical clustering method, and related dendrogram display
of keyword co
-
occurrence.

The outcomes of word co
-
occurrencies
have
also
been graphical
represented in network form b
y applying UCINE
T network software and Netdraw
.
60

Some of these
cl
usters will be analyzed and discussed in what follows.


4

Results


This section presents an in
-
depth analysis of the data through various bibliometric procedures
. The
objective is to provide answer to
our research questions.

We discuss the number of pa
pers that
appeared across time and
the journals in wh
ich these papers were published.

Results from
content

analysis are also presented, which add

to those

based on
straightforward
papers count.


Number of papers
.
Figure 1 suggests that the innovation adoption literature in general and the
literature on innovation adoption in SMEs basically overlap until the early 2000s; only from that
period onwards, we can observe differences in the number of publications within t
he two fields,
with research in the SMEs domain hanging behind overall innovation adoption literature. The same
holds if one compares the publications counts in the innovation adoption literature with that relative
to the innovation exploitation in SMEs li
terature. This finding supports the validity of our decision
to compare research on innovation adoption and exploitation in SMEs with the more general
literature on innovation adoption. Conversely, one can observe how, in terms of number of studies,
resear
ch on innovation adoption and
that on
innovation exploitation in SMEs fundamentally
overlap, with a slight increase of scholarly interest on the topic of exploitation over innovation
adoption between 2008 and 2009.


11


----------------------------

Figure 1 he
re
----------------------------


Figure 3, figure 4 and figure 5 show variations in the number of publications in the three datasets
over time, where the time interval is distributed over different periods.

Comparison between these
figures reveals a simila
r trend in the number of publications in all the three datasets, with a decrease
in 1995
-
2000 and a considerable increase in the following period 2001
-
2006.


----------------------------

Figure 3, Figure 4, Figure 5 here
----------------------------


Journals.
Our analysis also considers the concentration of research in the field of innovation
adoption

in general

and
of
innovation

adoption and

exploitation in SMEs. To this purpose, we
compared the journals in which the articles encompassed in our three

dataset
s

have been published.
Concerning the
total

44 journals included within the analysis, it is evident
(table 1)
that the
specialist journals
Journal of Management Information Systems
,
Management Science
,
Research
Policy

and
Strategic Management Journ
al

have published the largest
percentage

of papers.

Nevertheless, there is a substantial overlap in the three datasets, and especially between those
concerning SMEs, with
Technovation

accounting for almost the same percentage of published
papers in both da
tabases.
Overall, t
he
Innovation adoption & SMEs

dataset appears more
concentrat
ed than

the
Innovation exploitation & SMEs
dataset
,

with 6 journals containing
around
60%

of all innovation adoption in SMEs papers

(table 2)
.


----------------------------

Table 1 and Table 2 here
----------------------------


Content analysis on title words.

For each of our three datasets, content analy
sis has been
performed on title

words in order to identify word frequencies and co
-
occurrencies. We also used
network
analysis software tools to graphically represent word pairs and the most central words in
the map
s
. To illustrate this procedure, results are reported
for

only the
Innovation adoption

(table 3)
dataset and the
Innovation adoption & SMEs

(table 4)
dataset. We do not
illustrate results for
the
Innovation exploitation

& SMEs

dataset because
the same patterns observed for the
Innovation
adoption & SMEs

dataset
hold
even

for

this dataset.

As table 3 shows, the words associated to
innovation adoption inc
rease over time
,

thus

signa
l
ling that the literature on the topic is
progressively broadening. In particular, it might be interesting to
underline

the growing frequency
of the word “network effects
,


which seems to suggest
the raising

attention of the lite
rature toward

12

the
adoption
implications of collaboration between firms, and between SMEs and large firms in
particular.
A similar increase in theory
-
related terminology across time emerges from table 4 and
table 5.
Interestingly, t
hese tables highlight the

higher

frequency of the word “performance.” Yet,
such increase could be interpreted as a broadening of the literature toward a more comprehensive
analysis of the implications for firm performance of innovation adoption and exploitation,
especially when re
ferred to SMEs.



---------------------------

Table 3, Table 4, Table 5 here
----------------------------


Analysis of word co
-
occurrencies.
To further illustrate the development of central concepts in the
literature on innovation adoption in SMEs
, fig
ure
5 and figure 8 allow comparing, based on
Freeman’s (1979) degree
central
ity,
61

the most frequent
words in the
Innovation adoption

and
Innovation adoption & SMEs

datasets. Again, we mainly focus on the latter dataset since for the
Innovation exploitation & SMEs

the same patterns
apply
. From the
se

figures, one can infer the
highest centrality of the two concepts “inno ation” and “knowledge” in both fields of litera
ture in
the period 1989
-
2012. Yet, figure 8 seems to validate our previous observation that the innovation
adoption literature in SMEs is more concerned with performance implications and the determinants
of orientation toward adoption. This pattern appears

even more ev
ident if one looks at word co
-
o
ccur
r
encies
.
Some variety emerges
in

the different word pairs
contained
in the two datasets.
F
or
the
Innovation adoption

dataset

we find,

among the clusters having clear mutual relationships
,


innovation
-
adoption
-
technology
-
knowledge


and

in
novation
-
adoption
-
SME
-
knowledge.


F
or the
Innovation adoption & SMEs
dataset, instead,
these clusters refer to

innovation
-
SME
-
performance


and

innovation
-
orientation
-
performance.


Furthermore, in the general
Innovation
adoption

network
,

it can be seen that the number of word pairs is expanding over time. New
emerging themes
, in th
e time period considered
,

seem to

especially relate

innovation adoption to
globalization issues (“global”), and

again

to
network effects.


5

Disc
ussion


Where do we go from here?
At the beginning of this article, we

made our expectations on a twofold
level
:

(1) to find a few central concepts shaping papers in
our dataset
s

and (2)
to find
a pattern of
overlap of existing concepts
,

rather than
divergent terms and concepts in the literature on innovation

13

ado
ption and exploitation in SMEs.
Our results confirm both these expectations.
Still, a number of
interesting observations can be made out of our current analysis.

5.1

Implications for innovation ad
option theory

Although earlier studies already
accounted for

the state of the art in the
innovation adoption

literature, our analysis extends and complements these findings by using bibliometric methods and
by visualizing
the

areas/topics
on which
scholars

have tended to focus their attention
.

In particular,
some significant implications for theory can be emphasized.

Firstly,
we observe a decrease in the number of publications on innovation adoption in general for
the period 1995
-
2000, whereas a considerabl
e increase in the following period 2001
-
2006. To
explain such inc
rease one should consider that
the highlighted trend might
signal

a real shift in the
interest on the topics of innovation adoption and innovation exploitation.


Secondly,
the fact that all the three datasets overlap until 2000
is also meani
ngful. Indeed
,
considering the different sizes of the
Innovation adoption & SMEs

and
Innovation exploitation &
SMEs

datasets, our result
s mean

that, especially between 1997 and 2000, the
two datasets might
hide a nested relationship, with the
Innovation exploitation & SMEs

dataset being part of the
Innovation adoption & SMEs

dataset
. Such nested relationship in turn suggests that, at least until
2000, research tends to be dominated by a st
rong focus on innovation adoption itself

e.g. on its
determinants
62

rather than on innovation expl
oitation.

Thirdly
, our result
s stimulate

further understanding of why the number of publications in the three
datasets diverges after 2000. Although inquiring into the causes of this divergence is beyond the
scope of the present study, one might tentative
ly hypothesize that such a shift occurred in the last
ten years (2000
-
2012) due to a rising attention toward both the pursuit of innovation as something
vital to achieve competitive advantage and growth
in dynamic, technology
-
based industry,
63

and to
the relationship between firm size and the innovation adoption process.
13

In terms of extant

innovation adoption
literature, this
shift

has led to a lower interest in traditional models of
innovation adoption

e.g. Da is et al.’s
23

TAM model

substituted by

a growing focus on the
consequences of innovation adoption, such as its effects on firm growth, market value
64
, and
performance
65
. In terms of innovation exploitation, instead, scholars appear more and more willing
to provide evidence to conventional wisdom
15
,
66

that innovative activities in a previous period c
an
influence a firm’s future inno ation acti ities by pro iding a knowledge base
,

which is able to
encompass an “absorpti e capacity” toward technological competence from external sources.
13

However, future research should seek further explanations for the changes we observed in our
datasets.


14

5.2

Implications
for
managerial practice

Our results have direct managerial
implications.

First, our research delineates some factors that can be expected to foster or discourage innovation
adoption. These factors are largely controllable by managers and therefore can be altered to orient
innovation adoption decisions for

their firms. In particular, w
e register the progressive emergence
of some concepts, which are related to the relationship between network “externalities” and firm
decisions to adopt an innovation and later exploit it. On the one hand, such increasing focu
s
of
literature
on network effects may signal the importance
for managers to consider

the advantages of
alliances or cooperative arrangements
with partner

firms, such as complementary resources and
expertise (
eg.
67
-
70
) or enhanced legitimacy of products and services (
eg.
71
). On the other hand,
bei
ng aware of

network effects

may help

managers understand

how
their
firms

are likely to

react to
early adopters imitative pressures (e.g.
72
,
73
) or face

technology compliance needs (e
g.
74
).

Secon
d, our study warns managers to look at

firm size
as

a critical determinant not only when
choosing to adopt an innovation, but also in deciding the innovation mode
13

which is likely to affect
future
prospect
s

for firm growth. For example
,

motivated by the
search for complement
ary
resources to successfully adopt and exploit an innova
tion, acquisitions might take p
lace through
which small growing firms are acquired by large
r

firms
,

which aim at

increas
ing

their growth rate.
However, it has been shown that this kind of innovation
adoption
-
related acquisitions impose a
limit on further growth of the resulting firm.
75

Therefore,

m
anagers

should
be aware of

these issues
related to firm size if they want to make appropriate innovation adoption decisions for their firm
s.

5.3

Limitations

W
e acknowledge the
presence of some
limitation
s in our study, which however
provide

t
he
opportunity
to advance the field beyond the confines of our exploration.

To begin with, one might notice that we
main
ly use

titles in our analysis.

O
ur
choice to use titles is

motivated by the fact of being readily available in electronic form. However, in order to corroborate
our findings and reveal additional patterns, we encourage future research to extend content analysis
to also enco
mpass abstracts a
nd full papers
.

Furthermore, it cannot be excluded that the observed patter
n
s, i.e. the
decrease in the number of
publications on innovation adoption
between

1995
-
2000,
followed by an

increase between

2001
-
2006
, be the

result
of

our data selection procedur
e in the event that the ABI/Inform Complete and
Ebsco databases are more comprehensive in the 2000s than in the 1990s.
C
onsideration of longer
time periods

might therefore represent a valuable extension of our analysis.




15

Conclusions

T
he dom
inance of
knowledge, orientation, performance, network effects

as central concepts
,

and the
proliferation

of the number of links with
new still minority words signal

that the field of innovation
adoption is vibrant and developing.
Although it might be

difficult to d
raw any strong conclusions
without

extension

of
our analysis

beyond its limitations, we can see the contribution of this paper

as
being

in further exploring and complementing the findings of earlier studies on innovation adoption,
innovation exploitation a
nd SMEs by using bibliometric methods
. Thanks

to

these methods we have
tried to uncover relevant features
in the evolution of the literature on innovation adoption and
therefore
in

the

intellectual structure of
this

research domain
.

We have shown how the latter have
become solidly integrated within the m
anagement science
.

However, o
n the other hand
,

research on
innovation adoption and exploitation in SMEs still needs to be expanded. Ou
r results thus
eventually emphasize

the potential

of such a field of research to contribute to understanding SMEs


behavior, performance and growth substantially more than it currently does.




16

Table
1
. Number of articles published on
Innovation adoption

between 1989
-
2012 per publication source


Innovation adoption

Journal

No publications

% of total



Journal

No
publications

% of
total




Academy of Management Journal

5

1,5%



Journal of
Marketing
Research

16

4,9%




Academy of Management Review

8

2,5%



Journal of
Operation
Management

1

0,3%




Administrative Science Quarterly

6

1,9%



Journal of
Organizational
Behavior

2

0,6%




American Journal of Sociology

1

0,3%



Journal of Politics

1

0,3%




Annual Review of Sociology

1

0,3%



Journal of Product
Innovation
Management

16

4,9%




Decision Sciences

7

2,2%



Long Range
Planning

4

1,2%




Econometrica

2

0,6%



Management
Science

30

9,3%




Engineering Management

2

0,6%



Marketing Science

3

0,9%




Entrepreneurship Theory and Practice

2

0,6%



MIS Quarterly

3

0,9%




Industrial and Corporate Change

23

7,1%



Organization
Science

6

1,9%




Information and
Management

15

4,6%



R&D Management

5

1,5%




Information Systems Research

8

2,5%



Research Policy

32

9,9%




International Journal of Management Review

1

0,3%



Strategic
Management
Journal

32

9,9%




International Journal of Technology Management

9

2,8%



Technovation

21

6,5%




Journal of Business Venturing

4

1,2%



The Journal of
Marketing

1

0,3%




Journal of Economic Literature

1

0,3%









Journal of Engineering and Technology

2

0,6%









Journal of Information Technology

1

0,3%









Journal of Management

1

0,3%









Journal of Management Information Systems

30

9,3%









Journal of Management Studies

18

5,6%









Journal of Marketing

5

1,5%
























17

Table
2
. Number of articles published on
Innovation adoption & SMEs
, and
Innovation exploitation & SMEs

between
1989
-
2012 per publication source


Innovation adoption &
SMEs



Innovation exploitation &
SMEs

Journal

No
publications

% of
total


Journal

No
publications

% of
total

Cambridge Journal of Economics

1

5,0%


Decision Sciences

1

5,3%

Entrepreneurship Theory &
Practice

1

5,0%


Entrepreneurship Theory and Practice

1

5,3%

European Journal of Marketing

2

10,0%


European Journal of Marketing

1

5,3%

Industrial Marketing Management

1

5,0%


Industrial Marketing Management

1

5,3%

Information Systems Research

1

5,0%


International Journal of Innovation
Management

2

10,5%

Journal of Business Research

2

10,0%


Journal of Business Venturing

1

5,3%

Journal

of Business Venturing

2

10,0%


Journal of Information Technology

1

5,3%

Journal of Knowledge
Management

1

5,0%


Journal of Management Research

1

5,3%

Journal of Management Studies

1

5,0%


Journal of Management Studies

1

5,3%

R&D Management

1

5,0%


Journal of Marketing

1

5,3%

Research Policy

1

5,0%


Journal of Marketing Management

1

5,3%

Strategic Management Journal

2

10,0%


MIS Quarterly

1

5,3%

Technovation

2

10,0%


R&D Management

1

5,3%

The British Journal of Social
Psychology

2

10,0%


Research

Policy

1

5,3%





Strategic Management Journal

1

5,3%





Technovation

2

10,5%





The British Journal of Social Psychology

1

5,3%



















18

Table
3
. Word Frequencies
Innovation adoption

dataset per Year Group



1989
-
1994

1995
-
2000

2001
-
2006

2007
-
2012

INNOVATION

10

7

54

48

TECHNOLOGY

5

4

51

38

KNOWLEDGE

7

5

43

28

ADOPTION

4

2

39

36

RESEARCH

7

4

35

34

NETWORK_EFFECTS

4

4

27

24

DIFFUSION

12

4

22

20

FIRM

1

2

18

24

ORGANIZATION

3

3

12

14

THEORY

7

2

9

10

GLOBAL

1

1

14

10

PRODUCT



13

11

MANAGEMENT


1

10

12

PERFORMANCE



8

14

DETERMINANTS

2


10

7

INDUSTRY


1

9

9

STRATEGY

1

1

11

6

CAPABILITIES

2


11

4

MARKET

1


12

3

ROLE

1


10

4

DYNAMICS



10

4

BUSINESS

1


6

4

SME



6

5

IMPLEMENTATION

3


5

2

SYSTEMS

1


6

2

ELECTRONIC

2


3

3

SOFTWARE

1

1

5

1

HETEROGENEITY



2

5

INDIVIDUAL

1

1

4

1

CHANGE

1


2

3


Table
4
. Word Frequencies
Innovation adoption & SMEs

dataset per Year Group



1989
-
1994

1995
-
2000

2001
-
2006

2007
-
2012

INNOVATION

2

1

8

6

PERFORMANCE

1


6

6

FIRM

1

1

3

2

KNOWLEDGE

1


4

1

ORIENTATION



4

2

RELATIONSHIP

1


2

3

SME


1

1

4


Table
5
. Word Frequencies
Innovation exploitation & SMEs

dataset per Year Group



1989
-
1994

1995
-
2000

2001
-
2006

2007
-
2012

INNOVATION

1

1

7

6

SME



1

5

5

FIRM

1

1

3

3

PERFORMANCE

1



3

3

KNOWLEDGE





2

2



19


Figure
1
. Number of publications Per Year,
Innovation adoption
,
Innovation adoption &

SMEs, Innovation exploitation
& SMEs








Figure
2
. Number of publications Per Year Group,
Innovation adoption

dataset













0
5
10
15
20
25
30
35
40
45
Innovation adoption
Innovation adoption & SMEs
Innovation exploitation & SMEs
0
50
100
150
200
1989-1994
1995-2000
2001-2006
2007-2012
No of studies

20



Figure
3
.

Number of publications Per Year Group,
Innovation adoption & SMEs

dataset









Figure
4
. Number of publications Per Year Group,
Innovation exploitation

& SMEs

dataset













0
5
10
15
1989-1994
1995-2000
2001-2006
2007-2012
No of studies
0
5
10
15
1989-1994
1995-2000
2001-2006
2007-2012
No of studies

21

Figure
5
. Title word degree centrality,
Innovation adoption,
1989
-
2012






Figure
6
. Word pairs frequencies,
Innovation adoption,
1989
-
2012


Note: Jaccard's

coefficient
-

This coefficient is computed from a fourfold table as a/(a+b+c) where a represents cases where both
items occur, and b and c represent cases where one item is found but not the other. In this coefficient equal weight is given

to
matches and
non matches.


22


Figure
7
. Word pairs grouped by closeness,
Innovation adoption,
1989
-
2012






Figure
8
. Title word degree centrality,
Innovation adoption & SMEs,
1989
-
2012





23

Figure
9
. Word pairs frequencies,
Innovation adoption & SMEs,
1989
-
2012



Note: Jaccard's coefficient
-

This coefficient is computed from a fourfold table as a/(a+b+c
) where a represents cases where both
items occur, and b and c represent cases where one item is found but not the other. In this coefficient equal weight is given

to
matches and non matches.




Figure
10
. Word pairs grouped by clo
seness,
Innovation adoption & SMEs,
1989
-
2012













24

References

1. M. A. Hitt, R. D. Ireland, S. M. Camp and D. L. Sexton, Strategic entrepreneurship: Entrepreneurial strategies
for wealth creation,
Strategic Management Journal

22

(6

7), 479
-
491 (2001).

2. J. H. Dyer and H. Singh, The relational view: Cooperative strategy and sources of interorganizational
competitive advantage,
Academy of Management Review

23

(4), 660
-
679 (1998).

3. S. A. Zahra, Technology strategy and new venture pe
rformance: a study of corporate
-
sponsored and
independent biotechnology ventures,
Journal of Business Venturing

11

(4), 289
-
321 (1996).

4. M. A. Hitt, R. D. Ireland and J. S. Harrison, Mergers and acquisitions: A value creating or value destroying
strategy,
Blackwell Handbook of Strategic Management
, 384
-
408 (2001).

5. J. Anand, J. W. Rottman and M. C. Lacity, A review of the predictors, l
inkages, and biases in IT innovation
adoption research,
Journal of Information Technology

21

(1), 1
-
23 (2006).

6. N. Anderson, C. K. De Dreu and B. A. Nijstad, The routinization of innovation research: A

constructively
critical review of the state

of

the

s
cience,
Journal of Organizational Behavior

25

(2), 147
-
173
(2004).

7. C. Camisón
-
Zornoza, R. Lapiedra
-
Alcamí, M. Segarra
-
Ciprés and M. Boronat
-
Navarro, A meta
-
analysis of
innovation and organizational size,
Organization Studies

25

(3), 331
-
361 (2004).

8. M
. M. Crossan and M. Apaydin, A multi

dimensional framework of organizational innovation: A
systematic review of the literature,
Journal of Management Studies

47

(6), 1154
-
1191 (2010).

9. R. T. Frambach and N. Schillewaert, Organizational innovation adoption: A multi
-
level framework of
determinants and opportunities for future research,
Journal of Business Research

55

(2), 163
-
176
(2002).

10. R. Garcia and R. Calantone, A critical look a
t technological innovation typology and innovativeness
terminology: a literature review,
Journal of Product Innovation Management

19

(2), 110
-
132 (2002).

11. S. Gopalakrishnan, Damanpour, F. , A review of Innovation research in economics, sociology, and
t
echnology management,
Omega

25

(1), 15
-
28 (1982).

12. M. D. Williams, Y. K. Dwivedi, B. Lal and A. Schwarz, Contemporary trends and issues in IT adoption and
diffusion research,
Journal of Information Technology

24

(1), 1
-
10 (2009).

13. D. R. King, J. G. C
ovin and W. H. Hegarty, Complementary resources and the exploitation of technological
innovations,
Journal of Management

29

(4), 589
-
606 (2003).

14. B. Kogut, The network as knowledge: generative rules and the emergence of structure,
Strategic
Management J
ournal

21

(3), 405
-
425 (2000).

15. W. M. Cohen and D. A. Levinthal, Absorptive capacity: a new perspective on learning and innovation,
Administrative Science Quarterly
, 128
-
152 (1990).

16. J. M. Utterback and W. J. Abernathy, A dynamic model of process and product innovation,
Omega

3

(6),
639
-
656 (1975).

17. V. Hill and K. M. Carley, An approach to identifying consensus in a subfield: The case of organizational
culture,
Poetics

27

(1), 1
-
30 (1999).

18. F. G. De Bakker, P. Groenewegen and F. Den Hond, A bibliometric analysis of 30 years of research and
theory on corporate social responsibility and corporate social performance,
Business & Society

44

(3),
283
-
317 (2005).

19. K. M. Eis
enhardt, Agency theory: An assessment and review,
Academy of Management Review
, 57
-
74
(1989).

20. J. D. Margolis and J. P. Walsh, Misery loves companies: Rethinking social initiatives by business,
Administrative Science Quarterly

48

(2), 268
-
305 (2003).

21
. E. M. Rogers,
Diffusion of innovations
. (Simon and Schuster, 1995).

22. I. Ajzen, The theory of planned behavior,
Organizational behavior and human decision processes

50

(2),
179
-
211 (1991).

23. F. D. Davis, Perceived usefulness, perceived ease of use, a
nd user acceptance of information technology,
MIS Quarterly

13

(4), 319
-
340 (1989).

24. V. Venkatesh, M. G. Morris, G. B. Davis and F. D. Davis, User acceptance of information technology:
Toward a unified view,
MIS Quarterly
, 425
-
478 (2003).

25. C. K. Riem
enschneider, D. A. Harrison and P. P. Mykytyn, Understanding IT adoption decisions in small
business: integrating current theories,
Information & Management

40

(4), 269
-
285 (2003).

26. M. E. Porter and V. E. Millar, (Harvard Business Review, Reprint Servi
ce, 1985).

27. D. F. Abell,
Defining the business: The starting point of strategic planning
. (Prentice
-
Hall Englewood
Cliffs, 1980).

28. C. W. Hofer and D. Schendel,
Strategy formulation: Analytical concepts
. (West Publishing Company St.
Paul, MN, 1978).


25

2
9. J. C. Henderson and N. Venkatraman, Strategic alignment: Leveraging information technology for
transforming organizations,
IBM systems journal

32

(1), 4
-
16 (1993).

30. G. Premkumar, A meta
-
analysis of research on information technology implementation in small business,
Journal of Organizational Computing and Electronic Commerce

13

(2), 91
-
121 (2003).

31. M. L. Tushman and P. Anderson, Technological discontinuities an
d organizational environments,
Administrative science quarterly
, 439
-
465 (1986).

32. J. F. Rockart and J. E. Short, IT and the networked organization: towards more effective management of
interdependence, (1989).

33. N. Rackoff, C. Wiseman and W. A. Ullri
ch, Information systems for competitive advantage: implementation
of a planning process,
MIS Quarterly

9

(4), 285
-
294 (1985).

34. E. K. Clemons, Information systems for sustainable competitive advantage,
Information & Management

11

(3), 131
-
136 (1986).

35.

E. T. Penrose, The Theory of the Growth of the Firm. 1959,
Resources

(1995).

36. J. Barney, Firm Resources and Sustained Competitive Advantage,
Journal of Management

17

(1), 99
-
120
(1991).

37. B. Wernerfelt, A resource

based view of the firm,
Strategic Ma
nagement Journal

5

(2), 171
-
180 (1984).

38. I. Dierickx and K. Cool, Asset stock accumulation and sustainability of competitive advantage,
Management
Science

35

(12), 1504
-
1511 (1989).

39. E. K. Clemons and M. C. Row, Sustaining IT advantage: the role of structural differences,
MIS Quarterly
,
275
-
292 (1991).

40. R. Grant, The resource
-
based theory of competitive advantage: implications for strategy formulation,
California Management Revi
ew

33

(3), 114
-
135 (1991).

41. C. Boschetti and M. Sobrero, Risorse e vantaggio competitivo: ricorsi storici o nuove prospettive di analisi,
Economia e politica industriale

91

(1996).

42. E. K. Clemons, presented at the System Sciences, 1989. Vol. IV: Emer
ging Technologies and Applications
Track, Proceedings of the Twenty
-
Second Annual Hawaii International Conference on, 1989
(unpublished).

43. W. J. Kettinger, V. Grover, S. Guha and A. H. Segars, Strategic information systems revisited: a study in
sustaina
bility and performance,
MIS Quarterly
, 31
-
58 (1994).

44. T. H. Davenport, Innovazione dei processi,
Franco Angeli

1194

(1994).

45. J. Tidd, J. Bessant and K. Pavitt, (Wiley (Chichester, West Sussex, England and New York), 1997).

46. J. G. March, Exploration and exploitation in organizational learning,
Organization Science

2
, 71
-
87 (1991).

47. F. T. Rothaermel and D. L. Deeds, Exploration and exploitation alliances in biotechnology: A system of new
product development,
Strategic Ma
nagement Journal

25

(3), 201
-
221 (2004).

48. D. Lavie, U. Stettner and M. L. Tushman, Exploration and Exploitation Within and Across Organizations,
The Academy of Management Annals

4

(1), 109
-
155 (2010).

49. C. M. Beckman, P. R. Haunschild and D. J. Phillips, Friends or strangers? Firm
-
specific uncertainty, market
uncertainty, and network partner selection,
Organization Science
, 259
-
275 (2004).

50. E. Turban and J. R. Meredith,
Fundamentals of management s
cience
. (Irwin Homewood (Il), 1991).

51. F. Damanpour, Organizational innovation: A meta
-
analysis of effects of determinants and moderators,
Academy of Management Journal
, 555
-
590 (1991).

52. P. Verdegem and L. De Marez, Rethinking determinants of ICT acce
ptance: Towards an integrated and
comprehensive overview,
Technovation

31

(8), 411
-
423 (2011).

53. A. Macpherson and R. Holt, Knowledge, learning and small firm growth: a systematic review of the
evidence,
Research Policy

36

(2), 172
-
192 (2007).

54. B. Ber
elson,
Content Analysis in Communication Research
. (Free Press, 1952).

55. K. Krippendorff, Content analysis: an introduction to its methodology,
The Sage commtext series (5)

(1980).

56. R. P. Weber,
Basic content analysis
. (Sage Publications, Incorporated
, 1990).

57. H. Lasswell, D. Lerner and I. De Sola Pool,
The Comparative Study of Symbols
. (Stanford, Calif., 1952).

58. S. Stemler, An overview of content analysis,
Practical assessment, research & evaluation

7

(17), 137
-
146
(2001).

59. K. Krippendorff, R
eliability in content analysis,
Human Communication Research

30

(3), 411
-
433 (2004).

60. S. P. Borgatti, M. G. Everett and L. C. Freeman, Ucinet for Windows: Software for social network analysis,
(2002).

61. L. C. Freeman, Centrality in social networks co
nceptual clarification,
Social networks

1

(3), 215
-
239 (1979).

62. K. Hoffman, M. Parejo, J. Bessant and L. Perren, Small firms, R&D, technology and innovation in the UK:
a literature review,
Technovation

18

(1), 39
-
55 (1998).

63. K. M. Eisenhardt and J. A. Martin, Dynamic capabilities: what are they?,
Strategic Management Journal

21

(10
-
11), 1105
-
1121 (2000).


26

64. H. J. Cho and V. Pucik, Relationship between innovativeness, quality, growth, profitability, and market
value,
Strat
egic Management Journal

26

(6), 555
-
575 (2005).

65. N. Rosenbusch, J. Brinckmann and A. Bausch, Is innovation always beneficial? A meta
-
analysis of the
relationship between innovation and performance in SMEs,
Journal of Business venturing

26

(4), 441
-
457 (
2011).

66. B. Levitt and J. G. March, Organizational learning,
Annual review of sociology
, 319
-
340 (1988).

67. B. Kogut, Joint ventures and the option to expand and acquire,
Management Science

37

(19
-
33) (1991).

68. G. Bittlingmayer, Merger as a form of in
vestment,
KYKLOS

49

(127
-
153) (1996).

69. T. J. Gerpott, Successful integration of R&D functions after acquisitions: An exploratory empirical study,
R&D Management

28

(161
-
178) (1995).

70. J. Hagedoorn, Understanding the rationale of strategic technology p
artnering: Interorganizational modes

of cooperation and sectoral differences,
Strategic Management Journal

14

(371
-
385) (1993).

71. J. Baum and C. Oliver, Institutional linkages and organizational mortality,
Administrative Science Quarterly

36

(187
-
218) (1991).

72. Y. A. Au, Y. A. AU and R. J. KAUFFMAN, What do you know? Rational expectations in information
technology adoption and investment,
Journal of Management Information Systems

20

(2), 49
-
76
(2003).

73. Y. A. Au and R. J. Kauffman, Should

we wait? Network externalities, compatibility, and electronic billing
adoption,
Journal of Management Information Systems
, 47
-
63 (2001).

74. R. Lange, S. R. McDade and T. A. Oliva, The Estimation of a Cusp Model to Describe the Adoption of
Word for Window
s*,
Journal of Product Innovation Management

21

(1), 15
-
32 (2004).

75. K. M. Davidson, Why acquisitions may not be the best route to innovation,
Journal of Business Strategy

12

(3), 50
-
52 (1991).