absorptive capacity in high tech.full
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DOI: 10.1177/1558689814523677
published online 24 February 2014Journal of Mixed Methods ResearchVesna Sedoglavich, Michèle E.M. Akoorie and Kathryn Pavlovich
Quantitative MethodsMeasuring Absorptive Capacity in High-Tech Companies: Mixing Qualitative and
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Measuring AbsorptiveCapacity in High-TechCompanies: MixingQualitative and QuantitativeMethods
Vesna Sedoglavich1, Michele E.M. Akoorie2, andKathryn Pavlovich2
Abstract
The objective of this article is to show how mixed methods can be used to develop a deeperunderstanding of the construct, absorptive capacity (AC). We used qualitative data from eightcase studies to identify which types of AC knowledge firms have. Then we identified variablesfor measuring AC. We identified two types of AC: ‘‘technological’’ AC and ‘‘non-core’’ AC.Using a Karnaugh Map and a Fuzzy Logic Design, we developed a quantitative model to measureoverall AC. By focusing on interpretive integration, our results from using both qualitative andquantitative methods yielded complementary rather than contradictory findings, and the goal of‘‘knowing more’’ about AC was achieved.
Keywords
mixed methods, absorptive capacity measurement, fuzzy set theory, Karnaugh map, capabilityquantification
A firm’s ability to innovate and build on prior related knowledge has been labeled by a number
of researchers as ‘‘absorptive capacity’’ (AC). Researchers have used AC in their analysis of
diverse and complex organizational phenomena. The importance of AC has been identified by
scholars across the fields of strategic management (Lane & Lubatkin, 1998), technology man-
agement, (Schilling, 1998), international business (Kedia & Bhagat, 1988), and organizational
management (Glass & Saggi, 1998). The AC construct has been applied to a plethora of research
questions, from the decision-making process and innovation to foreign expansion and technol-
ogy transfer (Gomez-Mejia & Palich, 1997).
1Research School of Management, College of Business and Economics, Australian National University, Canberra, ACT,
Australia2Waikato Management School, University of Waikato, Hamilton, New Zealand
Corresponding Author:
Vesna Sedoglavich, Research School of Management, ANU College of Business and Economics, Canberra, ACT 0200,
Australia.
Email: [email protected]
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To understand the characteristics of AC, researchers need to think about the nature of the
phenomenon. Complex, intangible and subtle, AC has been extremely difficult to conceptualize
(Todorova & Durisin, 2006) despite there being a number of definitions in the literature. The
confusion may arise from the lack of clarity and agreement among researchers on what AC
actually means, coupled with the vagueness of prior definitions. If a firm’s AC is the joint out-
come of managerial actions and developments in the knowledge environment, researchers need
to develop a more robust understanding of how both managerial action and knowledge of the
environment contribute to the construction of a firm’s AC.
Establishing a robust measure of AC presents an even greater challenge. An apparently sim-
ple and standardized measure of AC may provide a valid and convenient method with which to
define and measure it and to bypass the complexities of the construct. Following the definition
offered in the literature (explained in the next section), the appeal of a simple and standar-
dized AC construct still masks serious problems in conceptualization and measurement. This
comes mainly from hidden assumptions and the one-dimensional methodological
approaches, which diminish the validity of the construct and weaken its utility for manage-
ment. A one-dimensional approach to studying a complex issue like AC results in an imper-
fect understanding of AC; its richness can be appreciated only if researchers conduct a
multi-method complementary study of this phenomenon (Bryman, 2007). These methodolo-
gical dilemmas, as well as their implications and solutions, are the focus of the present arti-
cle. This article describes a study that combines qualitative and quantitative methodologies
to develop a deeper understanding and application of AC in relation to the internationaliza-
tion process. Following Tashakkori and Cresswell (2008), who suggested that researchers
should utilize all possible methods to investigate the research problem, we identified the
application of a mixed method as being appropriate to this study. However, at the beginning,
mixed methods were not an obvious solution. The principal researcher at first considered a
qualitative approach only, but the initial outcomes of the qualitative research indicated that
the results would be enriched by taking different perspectives.
A discussion of the complexity of the AC construct provides a background for understanding
the process and methods of this research. Following a discussion of the reasons for using the
mixed methods, how this research evolved is described. The remainder of the article is struc-
tured as follows: First, the most widely cited definitions of AC, its assumptions, and its rela-
tionship to the internationalization process are critically reviewed. We then address issues with
the research methodology and outline the research methods used to reconceptualize and mea-
sure the AC construct. The following section summarizes and discusses the results. The final
section presents the conclusions of the research.
Absorptive Capacity Definitions, Assumptions, and Measurements
The AC construct has evolved out of prior research on internal firm performance, for example,
the role of R&D on firm performance and organizational learning (Fiol & Lyles, 1985; Levitt &
March, 1988). Van Den Bosch, Van Wijk, and Volberda (2003) claim that the term absorptive
capacity was originally used by Kedia and Bhagat (1988) in the context of technology transfers
across nations. Cohen and Levinthal (1989) associated a firm’s ‘‘learning’’ or AC with the cap-
abilities of the firm to innovate, introducing the notion of prior related knowledge as a key ante-
cedent of AC.
Researchers have used the AC construct to explain organizational phenomena that cover mul-
tiple levels of analysis from various perspectives, including the organizational learning (Huber,
1991; Kim, 1998), industrial economics (Cockburn & Henderson, 1998), resource-based view
of the firm (Lane & Lubatkin, 1998), and dynamic capabilities (Mowery, Oxley, & Silverman,
2 Journal of Mixed Methods Research
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1996) perspectives. Table 1 identifies key dimensions of AC and offers a conceptualization of
the construct.
Table 1 summarizes representative empirical studies of AC, showing that researchers have
studied the effects of AC at different levels of analysis, and adopt various means to measure the
construct.
Zahra and George (2002) reconceptualized the AC concept and raised important issues
regarding its components, antecedents, contingencies, and outcomes. The authors reviewed the
literature on knowledge absorption and incorporated into their model the significant amount of
research on learning and innovation accumulated since Cohen and Levinthal’s (1989, 1990)
seminal articles. They then presented a revised AC model, defining four distinct components of
AC (acquisition, assimilation, transformation, and exploration) and combining them into two
subsets with different value-creating potentials, namely, potential AC and realized AC (Zahra
& George, 2002).
Cognitive and structural embeddedness biases firms’ capabilities in favor of incremental
learning and strong ties (Hansen, 1999). Firms may be locked into an embedded knowledge
base, rigid capabilities, and a path-dependent managerial cognition—a firm-level form of ‘‘insti-
tutional sclerosis’’ (Gavetti & Levinthal, 2000; Olson 1984; Tripsas & Gavetti, 2000). For this
reason, they often fail to identify and absorb valuable external information. The capability to
recognize the value of new external knowledge is a critical component of AC because ‘‘the
valuing is not automatic, it is biased, and it needs to be fostered to allow the absorption to begin
at all’’ (Todorova & Durisin, 2006, p. 777).
The variety of approaches to the AC construct and the inconsistent results obtained from the
literature may be the result of the conceptual and/or methodological features of the AC con-
struct. In this section, we select features from an extensive review of the literature applying the
AC construct to the domain of the internationalization process with insights from the broader
literature on related areas, such as knowledge transfer and international strategies. We conclude
that definitions of AC have not yet been supported by empirical evidence. The construct has
remained impervious to measurement through either biases or the methodological features of
the studies, both of which undermine its utility for managerial actions in the context of the inter-
national business literature.
Furthermore, AC is a dynamic capability that influences the character and sustainability of a
firm’s competitive advantage. Viewing AC as a dynamic capability also means that it can be
influenced through appropriate managerial actions that redefine and deploy the firm’s
knowledge-based assets or that redirect the flow of information and knowledge creation in
firms, which generates change in firms’ business strategies (Sedoglavich, Akoorie, &
Pavlovich, 2009).
The extant literature relies on a single quantitative variable to measure AC. For example,
Cohen and Levinthal (1990) use research and development (R&D) expenditure to determine
AC in a firm, a rather simplistic measure that suggests that the amount of x determines the
level of y. In addition, prior literature primarily uses either qualitative or quantitative meth-
ods to examine AC from different theoretical lenses. While both quantitative and qualitative
approaches are valid ways of doing research, neither solves the complexity of the AC con-
struct nor captures a complete a picture of the subject. The objective of this article is to go
beyond standard statistical techniques (a characteristic of quantitative methods) or pure inter-
pretation of data (as in qualitative methods). We aim to achieve the stated research objectives
by using innovative mixed methodological solutions, which we explain in more detail in the
following sections.
Sedoglavich et al. 3
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Tab
le1.
Abso
rptive
Cap
acity:
Conce
ptu
aliz
atio
n.
Unit
ofA
nal
ysis
Study
Sam
ple
/Dat
aT
heo
retica
lLe
nsTr
eatm
ent/
Model
ing
Mea
sure
men
tO
utc
om
e/Effe
cts
Coun
try
Mow
ery
and
Oxle
y(1
995)
Kel
ler
(1996
)
Liu
and
White
(1997)
Conc
eptu
al(illu
stra
ted
with
stat
istica
ldat
a)
Conc
eptu
al/e
conom
ic
model
ing
145
firm
sfr
om
29
man
ufa
cturi
ng
indus
trie
sin
Chin
a
Com
pari
son
ofin
war
d
tech
nolo
gytr
ansf
er
chan
nel
san
dnat
ional
innova
tion
syst
ems
Tran
sitiona
ldyn
amic
san
d
sust
ainab
lelo
ng-
run
grow
thdep
enden
ton
rate
ofhum
anca
pital
dev
elopm
ent
Innova
tion
indev
elopin
g
econom
ies
Nat
ional
AC
AP
as
moder
ator
ofin
war
d
tech
nolo
gytr
ansf
eran
d
nat
iona
linnova
tion
syst
ems
AC
AP
allo
ws
explo
itat
ion
ofte
chnolo
gy
AC
AP
aspre
dic
tor
of
innova
tive
outp
ut
Inve
stm
ents
insc
ient
ific
and
tech
nica
ltr
ainin
gan
d
econom
icpolic
ies
that
enfo
rce
com
pet
itio
n
1.E
ngi
nee
ring
studen
tsas
per
centa
geofto
talpost
-
seco
ndar
yed
ucat
ed
popu
lation;
2.S
cien
tist
san
den
ginee
rs
per
mill
ion
ofpopu
lation;
3.S
cien
tist
san
den
ginee
rsin
R&
Dper
mill
ion
of
popu
lation
Inve
stm
ents
inR
&D
per
sonnel
Nat
ional
innova
tion
and
pro
duct
ivity
are
grea
ter
for
countr
ies
that
inve
stin
bui
ldin
g
thei
rA
CA
P.
Switch
ingo
vern
men
tpolic
y
tow
ard
anoutw
ard
ori
enta
tion
(polic
ylib
eral
ism
)gi
ves
a
countr
yonly
the
info
rmat
ion
par
tofte
chnolo
gy;
imple
men
tation,
how
ever
,
requir
esA
CA
P(o
rsk
illed
hum
anca
pital
).
Innova
tion
isdri
ven
by
syner
gy
bet
wee
nin
vest
men
tsin
AC
AP
and
inve
stm
ent
inso
urc
esof
new
know
ledge
(fore
ign
tech
nolo
gyim
port
s).
Inte
rorg
aniz
atio
nLa
ne
and
Lubat
kin
(1998)
69
R&
Dnon-
equity
allia
nce
s
bet
wee
n48
pha
rmac
eutica
l
and
22
bio
tech
nolo
gyfir
ms
Org
aniz
atio
nalle
arni
ng
theo
ry;re
sourc
e-bas
ed
theo
ry
AC
AP
aspre
dic
tor
of
org
aniz
atio
nal
lear
nin
g
inan
allia
nce
dya
d
8to
talm
easu
rebas
edon
valu
ing
new
know
ledge
(2);
assi
mila
ting
new
know
ledge
(5);
and
com
mer
cial
izin
g
new
know
ledge
(1)
AC
AP
bes
tm
easu
red
atth
e
dya
dic
unit
ofan
alys
is;re
lative
sim
ilari
ties
bet
wee
ntw
ofir
ms’
know
ledge
and
know
ledge
-
pro
cess
ing
syst
ems
are
more
import
ant
than
one
firm
’s
know
ledge
bas
e.
Org
aniz
atio
nC
ohe
nan
d
Levi
nth
al(1
990)
Boyn
ton,Z
mud,
and
Jaco
bs
(1994)
1,7
19
busi
nes
suni
tsfr
om
318
firm
sin
131
lines
of
bus
ines
sin
U.S
.
man
ufa
cturi
ng
sect
or
Org
aniz
atio
nalle
arni
ng;
econom
icth
eory
Org
aniz
atio
nalle
arni
ng
AC
AP
isuse
das
pre
dic
tor
ofin
nova
tive
activi
ty
R&
Din
tensi
ty;
resp
ons
iven
ess
ofR
&D
to
lear
ning
ince
ntive
s
(rel
evan
ce,ea
se,an
d
appr
opri
abili
ty)
R&
Dcr
eate
sa
capac
ity
to
assi
mila
tean
dex
plo
itnew
know
ledge
.
(con
tinue
d)
4
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Tab
le1.(c
ontinued
)
Unit
ofA
nal
ysis
Study
Sam
ple
/Dat
aT
heo
retica
lLe
nsTr
eatm
ent/
Model
ing
Mea
sure
men
tO
utc
om
e/Effec
ts
Cohe
nan
d
Levi
nth
al(1
990)
Boyn
ton,Z
mud
,
and
Jaco
bs
(1994)
Szula
nsk
i(1
996
)
Veu
gele
rs(1
997
)
Cock
bur
nan
d
Hen
der
son
(1998)
Kim
(1998)
1,7
19busi
nes
suni
tsfr
om
318
firm
sin
131
lines
of
busi
nes
sin
U.S
.
man
ufa
cturi
ng
sect
or
132
units
with
sim
ilar
info
rmat
ion
tech
nolo
gy(I
T)
mai
nfr
ame
syst
ems
271
resp
ond
ents
com
men
t
on
122
tran
sfer
sof38
pra
ctic
es/t
echnolo
gies
290
Flem
ish
firm
sw
ith
active
R&
Dunits
68,1
86
public
atio
ns
in
scie
ntific
journ
als
Cas
est
udy
ofa
man
ufa
cturi
ng
firm
(Hyu
ndai
Moto
rC
o.)
Org
aniz
atio
nalle
arni
ng;
econom
icth
eory
Org
aniz
atio
nalle
arnin
g
Org
aniz
atio
nalle
arnin
g/
stra
tegi
cm
anag
emen
t
Org
aniz
atio
nalle
arnin
g/
innova
tion
Indust
rial
/org
aniz
atio
n
econom
ics
Org
aniz
atio
nalle
arnin
g
theo
ry;org
aniz
atio
ns
as
lear
ning
syst
ems
AC
AP
isuse
das
pre
dic
tor
ofin
nova
tive
activi
ty
AC
AP
asa
pre
dic
tor
of
the
exte
ntof
man
ager
ialI
Tuse
AC
AP
aspre
dic
tor
of
effe
ctiv
etr
ansf
erof
bes
tpra
ctic
esw
ithin
the
firm
AC
AP
isa
moder
ator
of
leve
lofin
nova
tive
activi
ty
AC
AP
aspre
dic
tor
of
rese
arch
pro
duct
ivity
Org
aniz
atio
nal
lear
nin
gis
afu
nct
ion
ofA
CA
P;it
isth
eca
pac
ity
to
assi
mila
tekn
ow
ledge
(for
imitat
ion)
and
crea
tenew
know
ledge
(for
innova
tion)
R&
Din
tensi
ty;
resp
onsi
venes
sofR
&D
to
lear
ning
ince
ntive
s
(rel
evan
ce,ea
se,an
d
appr
opri
abili
ty)
(1)
Man
ager
ialIT
know
ledge
ofbusi
nes
spro
cess
esan
d
the
valu
eofin
form
atio
n
tech
nolo
gy;
(2)
Man
ager
ialIT
pro
cess
effe
ctiv
enes
s
9m
easu
res
that
captu
reth
e
inte
rnal
stic
kines
sof
know
ledge
AC
AP
as(1
)R
&D
dep
artm
ent
fully
staf
fed;(2
)
R&
Ddep
artm
ents
with
doct
ora
tes;
(3)
R&
D
dep
artm
ents
enga
ged
in
fund
amen
talre
sear
ch
Not
adir
ect
oper
atio
nal
izat
ion
ofA
CA
P
but
isre
flect
edby
num
ber
ofsc
ient
ific
public
atio
ns
Chan
ges
infir
mori
enta
tion
tow
ard
use
ofas
sim
ilate
d
tech
nolo
gy;tr
ansi
tion
from
tech
nolo
gyas
sim
ilation
to
imitat
eto
dev
elopm
ent
of
inte
rnal
R&
Dfu
nct
ions
to
innova
te
R&
Dcr
eate
sa
capac
ity
to
assi
mila
tean
dex
plo
itnew
know
ledge
.
Man
ager
ialIT
know
ledge
was
found
toin
fluen
cean
org
aniz
atio
n’s
exte
nt
ofIT
use
;
ITm
anag
emen
tpro
cess
effe
ctiv
enes
sdid
not
influ
ence
exte
nt
ofuse
,al
so,hig
her
leve
lsofIT
man
agem
ent
clim
ate
posi
tive
lyin
fluen
ced
both
dim
ensi
ons
ofA
CA
P.
Lack
ofA
CA
Pofth
ere
cipie
nt
is
am
ajor
sourc
eof‘‘s
tick
ines
s’’,
def
ined
asdiff
icultie
sin
imitat
ing
bes
tpra
ctic
esw
ithin
afir
m.
When
AC
AP
ispre
sent,
exte
rnal
sourc
esofR
&D
(e.g
.fr
om
allia
nce
par
tner
)st
imula
te
inte
rnal
R&
Dsp
endin
g;th
ere
is
no
sim
ilar
effe
ctw
hen
capac
ity
isnot
pre
sent.
Dev
elopin
gA
CA
Pis
not
adeq
uate
;co
nnec
tedne
ssto
scie
ntific
com
munity
isa
key
fact
or
indri
ving
afir
m’s
abili
ty
tore
cogn
ize
and
use
upst
ream
rese
arch
and
findin
gs.
AC
AP
isin
tegr
alpar
tofa
lear
nin
gsy
stem
;cre
atio
nof
cris
eske
eps
firm
on
fore
front
ofkn
ow
ledge
dev
elopm
ent
thro
ugh
inve
stm
ent
inle
arni
ng
and
incr
ease
din
tensi
tyof
effo
rts
tole
arn.
Sour
ce.A
dap
ted
from
Zah
raan
dG
eorg
e(2
002).
Not
e.A
CA
P=
abso
rptive
capac
ity;
R&
D=
rese
arch
and
dev
elopm
ent;
IT=
info
rmat
ion
tech
nolo
gy.T
he
studie
slis
ted
are
repre
sent
ativ
era
ther
than
exhau
stiv
e.
5
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Rationale for Using Mixed Methods
McGrath (1982) argued that all research methods are imperfect and incomplete and that metho-
dological pluralism has been strongly encouraged because the richness of the data/phenomenon
cannot be captured using a single method. In the field of international business, the justification
for methodological pluralism arises from the subject matter itself—international business in a
multifaceted area of research, crossing national, cultural, organizational, and personal bound-
aries and inspiring quite complicated research questions (Hurmerinta-Peltomaki & Nummela,
2006). The research of international business with its special characteristics requires innovative
methodological solutions, which necessitates the use of more multidisciplinary and multi-
method approaches (Hurmerinta-Peltomaki & Nummela, 2006).
The analysis of complex issues, such as the internationalization process and particularly AC,
requires methodological variety in order to show the complexity of the phenomenon.
Furthermore, we were interested in AC as a dynamic construct in the international business
environment. To research this changing phenomenon, we have reconsidered existing traditions
and epistemological assumptions. At the same time, international business research is experien-
cing something of a reevaluation, and established methodologies are being questioned. This has
translated into a growing interest in the use of qualitative methods for data collection and analy-
sis and in alternative epistemologies (Gartner & Birley, 2002).
Having admitted that single methods still have their place in the international business litera-
ture, we argue here that to understand AC in more depth, researchers need to expand their view-
point, look outside their own research traditions, and offer a fresh perspective of using mixed
methods for theory development. In going beyond the issues of methodological techniques and
by keeping the complexity of AC in mind, this study develops a link between empirical and
qualitative case study research with the philosophy of mathematical modeling, which includes,
among others, the Karnaugh map and fuzzy logic as a contemporary logic design. As interna-
tional business scholars have suggested (e.g., Michailova, 2004), from the researcher’s perspec-
tive, this potentially not only involves more than understanding the cross-border activity of
firms but also entails the contextualization of research methods.
Our approach is in alignment with arguments offered by Freshwater and Cahill (2013), who
argue for a plurality of approach in consideration of what constitutes a paradigm. This is
Table 2. The Expanded Karnaugh Map.
(Y)
(X) H M L
H A A B
M A B C
L C C C
Source: Adapted from Katz
Note. Zone A can be expressed using the Boolean algebra:
A = XH �YH + XM �YH + XH �YM
A = XH � (YH + YM) + XM �YH
Zone B can be expressed using the Boolean algebra:
B = XM �YM + XH �YL
Zone C can be expressed using the Boolean algebra:
C = XL �YH + XM �YL + XL �YL + XL �YM
C = XM �YL + XL
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contrast to Biesta’s (2010) assertion that the terms quantitative and qualitative denote kinds of
data rather than the epistemologies, designs, and ontological assumptions that are associated
with different research frameworks. However, these terms do not only apply to methods of data
collection and the choice of those methods—they have become more widely accepted as signif-
iers of methodological approach that are epistemological standpoints.
The study adopted a philosophical paradigm of realism because within a realism framework,
both qualitative and quantitative methodologies are seen as appropriate (Healy & Perry, 2000).
This was deemed appropriate as the research had three objectives that required different metho-
dological approaches. Hesse-Biber and Burke Johnson (2013, p. 2) argue for a plurality of meth-
odological approaches and philosophical perspectives but warn that it is it is critical not to lose
sight of the importance of centering the research question. We therefore defined the research
objectives very clearly.
Research Objective 1 was to explore and describe the nature of technological activities
undertaken by firms outside their home base and to understand the process of internationaliza-
tion of high-tech companies from the perspective of technology transfer, knowledge manage-
ment, and AC.
Research Objective 2 was to develop a conceptual model that integrates the underlying deter-
minants of the strategy choice and the dynamic interaction between strategy and technology, on
one hand, and AC and strategy, on the other, in order to answer the question, ‘‘How does a
firm’s superior technology and AC affect the way that international business is conducted?’’
Research Objective 3 was to measure AC to understand the significant factors that facilitate
the development and effectiveness of AC in firms. To this end, the study combines a qualitative
multiple-case study and a quantitative measurement. We used both methods to study the same
subject but by using different, specific objective-related purposes we could acquire rich empiri-
cal data as well develop a more comprehensive understanding of the subject. We strived for
complementarity because, as emphasized by Boeije, Slagt, and van Wesel (2013), we did not
want ‘‘to limit ourselves to the type of data that can be produced with a single method’’ (p.
348). We applied mixed methods as it is likely to result in unique knowledge or yields that could
not have been achieved by conducting separate qualitative and quantitative research (O’Cathain,
Murphy, & Nicholl, 2007).
The following sections explain the qualitative research first, followed by the quantitative
method utilized in this study. How qualitative and quantitative methods were combined during the
multi-method study is described. The following section describes the development and the process
of the research and outlines the data and the analysis to understand the evolution of the research.
An Illustration of How Mixed Methods Work in This Study
Qualitative Method
Reflecting on Research Objectives 1 and 2 suggests the use of a qualitative research approach.
The research context emphasizes the importance of knowledge, its impact on international activ-
ities, and the contextual nature of both the case firms and their technology developments. Using
the qualitative method in our research strategy can answer the principal research questions.
Further, the absence of a specific hypothesis suggests that research should be undertaken by
gathering qualitative data, by not making a priori assumptions about the potential outcomes. For
this reason a case study method was employed.
Research context. For this research, a cross-case study approach was adopted, with the units of
analysis being New Zealand agro technology SMEs engaged in the development and transfer of
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leading-edge technology. The single unit of analysis, a firm, was incorporated in a multiple-case
study design. The selection of a multiple-case design strengthened and broadened the capacity
to draw analytic generalizations from the study (Yin, 1998). The study was undertaken within a
specific geographical region that is the principal location of the country’s largest export earning
sector, the dairying industry, which constitutes about 25% of New Zealand’s export income
(2008–2009; IUF Dairy Analysis, 2011). The rationale for choosing these firms was quite sim-
ple. Conveniently, they were proximately located to the Ruakura Agriculture Research Centre,
in the province of Waikato, New Zealand, near (or part of) Crown (State) Research Institutes
such as AgResearch (www.agresearch.co.nz) and Plant and Food Research (www.plantand-
food.co.nz). For more than 50 years, Ruakura has been the leading agriculture and life sciences
research center in New Zealand. The areas of AgResearch’s research at Ruakura include animal
molecular biology (genomics and cloning), reproductive technologies, agricultural systems
modeling, land management, dairy science, meat science, food processing technology and
safety, and animal behavior and welfare.
The overarching objective of the state ministry responsible for the development of primary
industries, the Ministry of Primary Industries, is to promote innovation throughout the dairy
value chain to anchor not only the position of the New Zealand dairy industry, but the whole
New Zealand economy, as the dairy industry is a principal export earner (Ministry of Primary
Industries, 2013). There are two specific sub-programs. One is intended to improve ‘‘on farm’’
efficiency and capability in the supply chain; the other is related to projects ‘‘beyond the farm
gate,’’ including Food Structure Design, an emerging discipline blending food science and tech-
nology, and food engineering with the nonfood discipline of materials science. This provides a
new way for New Zealand’s food industry to develop and manufacture the increasingly com-
plex foods and ingredients required to meet the demand for healthier foods, customization, and
individualism. Within this program there are also projects to transform the dairy value chain
through robust human nutrition and health benefits. The targets are maternal and pediatric nutri-
tion and mobility. Research will provide robust scientific evidence to support claims on the
general and specific health benefits of dairy products—so-called nutraceutical products. Our
sample firms are operating in the latter sector—‘‘beyond the farm gate.’’
These case firms vary in their size, structure, and modus operandi, but all of them share an
interest in the development and ultimate transfer of new technology-based products and services
to gain competitive advantage. Empirical data-gathering techniques included in-depth personal
interviews guided by a semistructured questionnaire. We used other possible additional sources
of evidence for the study, such as documentation in the form of relevant news clippings, reports,
and articles published in the international and national business media, and archival data such
as official trade statistics.
Participants and interviews. An interview with a key informant, being an intellectual property pro-
tection expert, was the starting point for the process of investigation. This contact provided us
with the names of firms and appropriate senior personnel. The interviewees were the most senior
persons within each company who had the primary role of being decision makers for the com-
mercialization of products developed from agri-technology.
All participants were interviewed for 1 to 2 hr by the principal researcher. All interviews
were tape-recorded and were fully transcribed. In addition, the principal researcher took field
notes during each interview, systematically wrote them up in memo form, and included them in
the analysis. Brief summaries were also prepared immediately following each interview, thereby
combining reasonable immediacy with reflective review. Annual reports were obtained from the
case companies as well. These transcriptions, notes, and summaries not only provided a basis
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for data analysis but also served a number of other purposes, including planning for the follow-
ing interviews.
We structured the case study questions to address the main elements of the research ques-
tions, and the questions were layered to prompt the interviewer to consider the implications at
several levels (Yin, 1994). Both textual and quantitative data (specifically, annual reports) were
generated as part of the field collection procedure to facilitate analysis. Transcripts of the inter-
views were sent back to participants for their comments. This provided an opportunity for parti-
cipants to identify any omissions in the transcripts and extended their involvement in the
research process. Once initial transcripts were complete, the data classification and coding
started.
Analysis. Before commencing detailed systematic analysis and without prejudice to the identi-
fied general analysis strategy or specific methods, we had a loose ‘‘play with the data’’ (Yin,
1994), which provided preliminary ideas about what to analyze and what not to analyze.
The analytic procedures of questioning and comparison in relation to the data provided the
basis for identifying and labeling concepts and categorizing these according to their attributes
and dimensions or common themes. This sorting and clustering stage centered on the search for
underlying similarities and differences in the data set. The researchers used manual data analy-
sis to enhance the depth of analysis and to start the process of comparison at an early stage of
the analytical procedure. We developed the coding for this first level of analysis from the ques-
tions in the interview guides. The analysis of the transcripts proceeded on a line-by-line basis
using a separate code for each question on the interview guide, together with remarks placed on
the margins of the transcript. The purpose of data synthesis was to move from the initial propo-
sitions generation stage to verification of the data.
Within these modes of analysis, we carried out an ongoing reduction of data to increase the
focus on key elements of the evidence. The process started with coding decisions in which less
relevant evidence was put aside and evidence considered more relevant was coded and classi-
fied. As data were continually reduced and focused, care was taken to ensure that it was not
‘‘stripped’’ of its contextual content (Miles & Huberman, 1994). In order to maintain a con-
sciousness of the relevance of data and in pursuit of the essence of their underlying phenomena,
we regularly referred to the original full-text transcripts of interviews and to field notes. We
contacted participants on several occasions to clarify the information provided in the interviews
and to give their opinion about the naming of the constructs that emerged from the analysis.
For example, the labels ‘‘technological AC’’ and ‘‘non-core AC’’ were suggested by two
respondents, and the suitability of the proposed terms was further tested with the remaining
respondents.
We compared data from one case to other cases using the process of across-case analysis.
The next stage involved identifying patterns and themes and making a note of ideas about rela-
tionships. In the following stage, we compared the relationships identified with the initial con-
ceptual framework, through comparing the data collected from the case studies with relevant
theories. First, we used a data reduction process, then displayed the data, and finally drew con-
clusions and verified the data again. Initial theorization evolved from this process. Theorization
at this stage was the systematic selection and fitting of alternative models to the data until we
obtained a best fit that explained the data most simply (Morse, 1994). This identification of pat-
tern matches within and across cases strengthened the internal validity of the study (Yin, 1994).
We then integrated the overall results from the analysis and summarized them in a frame-
work that originated from the initial provisional conceptual model. Locating the relationships
between internationalization process, on one hand, and technology transfer and AC, on the
other, within the model enabled the associated range of influencing conditions, their effects,
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and potential consequences to be systematically traced and interrelated. As shown in Figure 1,
provisionally, we expressed these as an initial conceptual framework that became explanatory
and generalizable when the provisional conditional relationships were confirmed by the data.
The purpose of this qualitative data was to address Research Objectives 1 and 2 (to explore
the process of internationalization of high-tech companies in relation to technology transfer and
AC and to develop and refine a conceptual model). We used the qualitative data analysis to
identify the factors that may have an impact on high-tech SMEs operating in the area of agri-
technology in their process of internationalization. The in-depth interviews provided a rich
account of the particular factors applying to a range of small and medium-size high-tech firms.
Yet there was still a palpable gap regarding Research Objective 3 (AC measurement). The fol-
lowing section gives the details of the quantitative data used in this study.
Quantitative Method
The requirements of Research Objective 3 were to measure AC to understand the significant
factors that facilitate development of AC in firms. For this reason and for the purpose of estab-
lishing the generalizability of the research findings, we used quantitative methodology.
This phase of the research included the development of measurements to measure AC and
the AC model development to understand the AC construct. The following steps were taken.
First, we identified seven variables from the literature that could be used to measure AC. If a
firm is able to measure its AC, this may result in improved performance. Second, we applied
Figure 1. Relationship among technological AC, non-core AC, and international strategy.
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multiple linear regression techniques to identify the fundamental relationships between each of
the dependent variables and their constituent factors. Third, these dependent variables were
implemented in the extended Karnaugh map in order to classify results into discrete zones (low,
medium, and high) to define areas of business strengths in firms. Fourth, fuzzy logic was imple-
mented to refine Karnaugh map into a more robust, non-linear AC model to allow for a smooth,
gradual transition between states (zones). The software used was the MATLAB, Fuzzy Logic
Toolbox.
In order to measure AC, we extracted demographic (objective) data from the interviews.
Given that AC is not a single-measure construct and a search of the literature was unsuccessful
in finding equivalent multiple measures to measure it, the following measures emerged from
the previous research on related topics that used multiple variables, and were designed ‘‘[so]
that we could estimate objectively’’ (Sullivan, 1994, p. 331): the ratio of Foreign Sales to Total
Sales (FSTS), Managers’ International Experience (MIE), Firm’s International Experience
(FIE), Research and Development Intensity (RDI), Research and Development to the revenue
earned from Foreign Sales (FSRD), Psychic Dispersion of International Operations (PDIO),
and Cultural Comparability (COMP); firm age was employed as a control variable.
Karnaugh maps are used to give variables a simplified means of characterizing (modeling/
viewing) and optimizing a system. Such maps provide easily inspected, two-dimensional visua-
lization. It is a tool to facilitate the management of Boolean algebraic expressions. Usually,
extensive calculations are required to obtain the minimal expression of a Boolean function, but
a Karnaugh map can be used instead. The Karnaugh map is a conventional tool (Dewdney,
1989) that may enable the presentation of all possible combinations of AC variables.
The Karnaugh map is an array of cells, with each cell representing a specific product of vari-
ables and their complements. Each row and column of the map corresponds to a value of one of
the two logic variables. These values are assigned in such a way that they correspond to 1 when
they are substituted into the product lying at the intersection of their respective row and column.
In this research, the basic Karnaugh map was expanded into three discrete states (zones) to
define areas of business strength (AC) more accurately. The new Karnaugh map allows us to
identify whether a variable, in this case, the strength of a firm’s AC, is low, medium, or high.
The expanded design incorporates three dimensions (high, medium, and low), thus differing
from the standard Karnaugh map, which utilizes only two possibilities (true or false). Therefore,
the basic Boolean algebra still applies but with expanded arithmetical principles.
Then, we established a quantitative AC model and used the modified Karnaugh map to iden-
tify the areas of business strength in the firms and, by using fuzzy logic design, to facilitate tran-
sition between zones (high, medium, and low). There were two reasons for using fuzzy logic in
this research. The first reason is that one cannot objectively allocate a value to a firm’s AC due
to its core complexity and ambiguity. Second, when the areas of business strength in firms had
been identified, fuzzy logic design facilitated the transition between zones such as high, medium,
and low, allowing for a gradual transition between them. This illustrates real-world situations
more accurately. The value of fuzzy logic was illustrated in this research using the two different
approaches to the same problem: linear and fuzzy. The Karnaugh map is a linear system, while
fuzzy logic introduces a gradual transition between the states of the Karnaugh map.
The notion principal to fuzzy systems is that truth values or states (in fuzzy logic) or mem-
bership values (in fuzzy sets) are indicated by a value on the range [0.0, 1.0], with 0.0 repre-
senting absolute Falseness and 1.0 representing absolute Truth. Fuzzy logic allows for set
membership values between and including 0 and 1 and, in its linguistic form, imprecise con-
cepts such as ‘‘slightly,’’‘‘quite,’’ and ‘‘very’’ (Hellmann, 2001). Importantly, it allows partial
membership in a set (Hellmann, 2001). It is an organized and mathematical method of handling
inherently imprecise concepts such as AC. Due to its flexibility and the imprecise nature of the
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information available for interpretation, fuzzy logic was found to be the most appropriate
method in this research where emphasis was not on precision but on significance.
Results. As suggested by Sedoglavich et al. (2009), AC in high-tech firms consists of two sub-
sets, technological AC and non-core AC, which are the characteristics of a particular industry.
Technological AC comprises the accumulated knowledge acquired in relation to R&D activities,
while non-core AC centers on knowledge acquired outside the R&D activities and the area of
expertise in high-tech firms, such as marketing and sales operations. After applying the seven
variables that emerged from the literature review (FSTS, MIE, FIE, RDI, FSRD, PDIO, and
COMP) to the firms, the results were loaded into the modified Karnaugh map in order to classify
the results into discrete zones (high, medium, low) and to identify the areas of business strength
in which firms should invest and grow and the areas in which firms should give serious thought
of divesting. The AC for each firm is calculated by multiplying each variable with its corre-
sponding significance value (weighting).
Furthermore, each variable was allocated a different weighting for technological AC and
non-core AC depending on their importance for a total AC. The term relative was used for this
purpose (i.e., relative technological AC and relative non-core AC). These were used for model-
ing the individual and total AC responses (see Table 3).
The variable contribution weightings shown in Table 3 were allocated based on a consensus
of findings through the extended literature review. The work of scholars such Simonin (1999);
Fiol and Lyles (1985); Levitt and March (1988); Miller and Chen (1996); Zahra, Ireland, and
Hitt (2000); Eriksson and Chetty (2003); Cohen and Levinthal (1990); and Zahra and George
(2002) was helpful in this regard. Interrater reliability was used as an estimated method to corre-
late these authors’ observations to obtain the consensus of each variable weighting relative to
each of the ACs. Interrater reliability measures the homogeneity of agreement, and administers
the same form to the same people by two or more raters/interviewers, to establish the extent of
consensus on the use of the instrument by those who administer it (Shrout & Fleiss, 1979). The
major interest in this research was not to determine how the variables differ from each other but
how they may have an impact on each individual AC. Interrater reliability helped in establish-
ing a consensus in the previous literature about the factors that influence AC.
More specifically, we used Fleiss’ kappa, a statistical measure for assessing the reliability of
agreement between a fixed number of raters when assigning categorical ratings to a number of
items (Landis & Koch, 1977). Agreement can be thought of as follows: If a fixed number of
Table 3. Variable Contribution Weighting.
VariablesNon-Core
ACAbs
Error 6Relative
Non-Core ACRel
Error 6TechAC
AbsError 6
RelativeTech AC
RelError 6
TotalAC
FSTS 0.9 0.1 0.23 11% 0.8 0.1 0.20 13% 0.43MIE 0.8 0.1 0.20 13% 0.6 0.1 0.15 17% 0.35FIE 0.9 0.1 0.23 11% 0.6 0.1 0.15 17% 0.38RDI 0.1 0.1 0.03 100% 0.9 0.1 0.23 11% 0.25FSRD/age 0.1 0.1 0.03 100% 0.9 0.1 0.23 11% 0.25PDIO 0.6 0.1 0.15 17% 0.1 0.1 0.03 100% 0.18COMP 0.6 0.1 0.15 17% 0.1 0.1 0.03 100% 0.18TOTAL 1 1
Note. FSTS = Foreign Sales to Total Sales; MIE = Managers’ International Experience; FIE = Firm’s International
Experience; RDI = Research and Development Intensity; FSRD = Research and Development to the revenue earned
from Foreign Sales; PDIO = Psychic Dispersion of International Operations; COMP: Cultural Comparability.
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people assign numerical ratings to a number of items, the kappa will give a measure for how
consistent the ratings are. The measure used in this research calculated the degree of agreement
and was scored as a number between 0 and 1 for each absorptive capacity variable.
In addition, the research was not looking for the precision of a variable weighting, but its signif-
icance, and the importance of that weighting to the non-core and technological AC. For this reason,
it was allowed that true value lies between the boundaries of variation 6 0.1 (see Table 3, columns
3 and 7). These weightings were further normalized (relative non-core AC and relative technologi-
cal AC) prior to being implemented by the AC equation (Table 3, columns 4 and 8).
This is then followed by the summation of all the significance-adjusted terms. See the
Equation 1, which led to the firms’ AC results, presented in Figure 2:
AC = FSTS � XFSTS + MIE � XMIE + FIE � XFIE + RDI � XRDI
+ FSRDage � XFSRD, age + PDIO � XPDIO + COMP � XCOMP: ð1Þ
Fuzzy logic was implemented to refine the modified Karnaugh map into a more robust, non-
linear model as it allows for a smoother, gradual transition between states of the Karnaugh map.
The refined model is based on the implementation of the modified Karnaugh map for zone clas-
sification, while the transition between zones was facilitated through fuzzy logic design.
The following section illustrates the implementation of the refined model onto the research
data and the matching of the quantitative results with the qualitative interpretation of the data.
Figures 3 and 4 show the eight case firms based on the results of AC measurements plotted onto
the refined model.
Figures 3 and 4 clearly lay out the eight firms’ locations on the model. However, the three-
dimensional surface plot (Figure 4) provides a visual perception of the research cases, in which
one can almost immediately recognize the firms’ zones.
Interpretation of the Results
In this section, the firms’ results on the refined model (see Figure 3 and 4), which we obtained
by using a quantitative approach, are interpreted. We then interpreted this approach in the rela-
tion to the qualitative case analysis.
Company AC
0
0.2
0.4
0.6
0.8
1
C1 C2 C3 C4 C5 C6 C7 C8Company
AC
val
ueNon-Core ACTech ACTotal AC
High AC [0.41-0.63]Med AC [0.2-0.41]Low AC [0-0.2]
High
Low
Med
Figure 2. Firms’ AC results.
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Of the eight case firms, four had high overall AC, three had medium overall AC, and one
had low overall AC. When we deconstructed the AC concept into technological and non-core
AC, we found that five firms needed to increase their technological AC and three firms needed
to increase their non-core AC (that is, their marketing orientation, market knowledge, and
organizational skills to reflect a business rather than a science orientation). Case Firm 8, for
example, used ‘‘technology push’’ rather than ‘‘market pull’’ as its strategy. Another firm (Case
Firm 1) with medium total AC admitted that ‘‘scientists were running the firm,’’ although the
business development team works closely with scientists to identify the needs in established
markets, which meant that the firm had a medium rather than a low level of AC.
Figure 3. Companies mapped onto the AC model, top view.
Figure 4. Companies mapped onto the AC model, side view.
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Discussion
This article illustrated how mixed methods were combined and integrated to achieve the same
goal. We wanted to overcome the vagueness of the existing AC construct and to measure the
effectiveness of AC. This research found that if a firm is able to measure its AC, this may
enhance their international business activities and help them gain an enhanced presence in the
world market. The study expanded the existing literature on internationalization processes by
developing variables for and evaluating AC in firms. Further, the findings of the study helped
develop a refined AC model that can be used as a valuable tool for firm self-assessment to
facilitate gaining insight toward further growth and development.
To facilitate the generation of the reliable findings, we took particular care in selecting the
methodology, research method and research tools for the study, focusing on the research
objectives.
We deployed the case study method within a phenomenological framework to provide the
high level of insight that the research questions demanded. The pre-structured framework iden-
tified in the study’s design process and incorporated in the case study protocol afforded consis-
tency in the analysis process and provided a framework for reporting the findings.
In this article, we have explained the research procedure by defining the research setting and
methods of examination, and supported this using methodological theory. We have also clarified
the various methodological aspects with reference to the extant literature and previous relevant
research.
In the course of this study, we developed a discussion on the assumptions surrounding AC.
We found that there were numerous interpretations and propositions and a complete absence of
hypothesizing relationships, a process that hardly fits the positivist ideal of objective collection
of neutral or purely descriptive facts (Van Maanen, 1983). However, this disorderliness and
complexity did not discourage us from proceeding with the research. On the contrary, these
apparent obstacles triggered deeper reflection on the AC phenomenon itself and on the actions
needed to overcome ambiguity. This spurred our curiosity to continue with the research, to
understand the ambiguity inherent in the AC construct, to provide a measure of AC, and, on a
more practical note, to help firms find the shortest path to achieve better performance. As has
happened in other projects (Trend, 1979), we tried to reconcile apparently conflicting research
approaches and find an interpretation that synthesized the evidence.
At the beginning, mixed methods were not an obvious solution in this study. Quite the oppo-
site, the principal researcher initially considered starting with a qualitative approach only.
However, the outcome of the qualitative research suggested that the results would be improved
by taking different perspectives. After the initial qualitative case analysis, we concluded that pur-
suing further research using a different methodological approach strengthened our view that the
qualitative data patterns initially identified were worthy of further investigation. The final out-
comes of the research proved our assumptions, which justified the purpose of this research
experiment by confirming that the choice of mixed methods should be based on the fact that the
research problem would remain unsolved if only one method were used (Bryman, 1992, 2007).
We took particular care to overcome ‘‘the weakest link in most mixed methods designs’’ (Boeije
et al., 2013, p. 348), which is actual integration of the quantitative and qualitative component
(Bryman, 2007; O’Cathain et al., 2007). Moran-Ellis et al. (2006) offer the following definition
of integration in mixed method research: integration is the ‘‘specific relationship between two or
more methods where the different methods retain their paradigmatic nature but are intermeshed
with each other in pursuit of the goal of knowing more’’ (p. 51). In this research, integration
took place when we interpreted the qualitative and quantitative data and compared them (see
Table 4). Moran-Ellis et al. refer to this type of integration as interpretive integration.
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Table 4. Comparing Qualitative and Qualitative Research Results.
Quantitative research results Qualitative research results
C1 has medium ACtotal.
To achieve high ACtotal it needs a small
increase in ACtech, while maintaining
the present level of ACnon-core.
This firm produces a range of diverse products, from developing new
products to saving the environment. The firm does not have a sharp focus,
except for producing fruit-based food and food components. Its focus not
only is still divided between products it produces and projects it wants to
pursue, but also is shown in its trying to balance business and science, as
the respondent put it, ‘‘Scientists are running the firm.’’
C2 has a moderately high ACtotal.
To achieve even higher ACtotal it needs a
small increase in
ACnon-core, while maintaining
the present level of ACtech.
This is a young ‘‘born global’’ company. From the very beginning this firm’s
intention has been to internationalize, but only to the extent of finding the
distributors and agents interested in this sort of technology. The firm’s
business strategy revolves around developing innovative, on-demand media
solutions. Although, the firm’s understanding of its environment is drawn
directly from its markets, specific research and reliance on networking
provides a basis for partner/agent selection and initial operational
strategies. A key resource, apart from the shared knowledge and innovative
spirit, is the firm’s marketing partners located in other countries.
C3 has a medium ACtotal.
To achieve a high ACtotal it needs to
choose one of two shortest paths: a
small increase in ACnon-core while
significantly increasing the level of
ACtech; or a small increase in ACtech,
while significantly increasing the level
of ACnon-core. Choosing to equally
invest in both directions leads to a
longer path.
This organization is formed to manage a cluster that is the New Zealand’s
growth hub for Ag-Biotech businesses. It was established to boost
economic growth within the region by encouraging the further
development of its existing agri-tech firms. This is where leading primary
sector science and technology and research meet a catalyst for
entrepreneurship and the growth accelerants of resourcing, collaboration,
and a global channel of opportunities.
C4 has a low ACtotal.
To achieve a high ACtotal it needs
equally invest in ACnon-core and ACtech.
The firm was established to help dairy farmers create profitable and
sustainable dairy farm businesses. It sees its environment principally in
terms of needs of the New Zealand dairy farmers. It monitors the changes
in the external/international environment that may have potential impact on
the farmers’ productivity.
Although R&D is highly emphasized, the firm produces exclusively for the
domestic market demands. There is still no international policy in the
company. This is a good example of a firm that does not fully utilize its
resources and does not completely benefit from technology and knowledge
it develops.
C5 has a moderately high ACtotal.
To achieve even higher ACtotal it needs
an increase in ACnon-core, while
maintaining the present level of
ACtech.
It is a young and enthusiastic New Zealand–based research and technology
firm with main capabilities in science and innovation. The firm sees its
environment principally in terms of research, development, science, and
innovation. In understanding its environment, the firm does not distinguish
between its domestic and international markets. Strategic direction is set
by an overall global strategy.
This unique strategic architecture gives the firm a significant competitive
advantage by creating a specialist knowledge base that enables it to devise
customized products for a broad range of problems. The technology
development process is a part of their future internationalization strategy
because they continuously look to find areas where their expertise can be
applied.
(continued)
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Conclusion
This study is positioned at the intersection of qualitative and quantitative research. By integrat-
ing qualitative and quantitative methods, it is possible to develop constructs and measures that
are robust, validated, reliable, and clearly positioned within the domain of international business
research.
This article described how qualitative and quantitative approaches were combined in a
research about AC and the internationalization process in high-tech firms. The article has
explained and justified the motivation for the various decisions associated with the research
design, selection of the research instruments, data collection procedures, and analytical tech-
niques used for the investigation. The fundamental research objective of building a theory that
explains factors associated with AC development and its influence on the internationalization
process formed the basis upon which the methodological decisions were made.
Using mixed methods helped to solve a research problem that would have been impervious
to resolution if only one method were used. It facilitates our understanding of the research prob-
lem, which in turn can lead to unexpected qualitative interpretations of reality. Similar to
Bryman (2007), the rationale for conducting mixed methods research facilitates integration,
‘‘which is thought to be essential for generating the knowledge yield’’ (Boeije et al., 2013,
p. 348) of the AC concept, which could have a consequence on theory, practice and policy.
Table 4. (continued)
Quantitative research results Qualitative research results
C6 has a moderately high ACtotal. This
company is in a unique position,
where to achieve an even higher
ACtotal it can choose to invest in either
ACnon-core, ACtech or both.
This firm fits into a category ‘‘traditional’’ firm, which has developed its
domestic market first and then explored and expanded into the foreign
markets. This is an internationally active firm. Product and manufacturing
flexibility allows the firm to highly customize product in recognition to the
customer’s individual requirements as well as individual markets. Research
and development is vital to the firm’s success. The firm deals with large
pharmaceutical companies.
The firm’s sales team travels extensively to markets to gain firsthand
experience and gather ideas from customers incorporating these into the
developing programs and with its international network of agents provides
customers with seamless response to any enquiry, linking local knowledge
with its specific expertise.
C7 has a moderately high ACtotal.
To achieve even higher ACtotal it should
consider investing in ACtech, while
maintaining the present level of
ACnon-core.
This well-established firm has a long business history in the New Zealand and
foreign markets. The firm strives at building its world-class product
innovations, supported by practical ‘how to’ customer advice. The firm’s
success is attributed to its expertise in product innovation and strong
brand management, which is a core role of marketing in the firm.
C8 has upper medium ACtotal.
To achieve high ACtotal it should consider
investing in ACtech, while maintaining
the present level of ACnon-core.
This firm puts its emphasis to help improve the effectiveness and efficiency of
farming in New Zealand by providing innovative solutions for on-farm
problems. The firm is focused on the discovery and development of new
and leading-edge technologies for the benefit of New Zealand’s pastoral
sector.
The majority of its projects are funded by government agencies, leaving
commercialization as a secondary priority. ‘‘We are moving forward on a
wave of knowledge. Much of this is science, ‘‘which focuses on ‘why’
(science), rather than ‘how to’ (technology) which concerns the practical
implementation of science.’’
Sedoglavich et al. 17
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Contribution to Theory, Practice, and Policy
This research has implications for international business theory and AC. First, we developed
measurements for AC that showed that AC could be empirically tested and measured. Second,
we developed a refined AC model. Third, the research attempted to import the idea of the sig-
nificance of AC for the strategic development in firms into the international business literature.
Fourth, we demonstrated how the application of mixed methods generated better understanding
of the concept.
In terms of contributing to practice, first, we concluded that a firm operating in diverse
foreign markets accumulates a wide range of knowledge, which provides it with the AC to
recognize which new opportunities to exploit. However, a firm needs to have the capability
to successfully integrate this knowledge on an ongoing basis. The focus should be on identi-
fying the type of AC knowledge/capability necessary to facilitate the exploitation of new
emerging information. Second, firms need to measure AC to determine which AC factor
should be developed and reinforced in order to improve the firm’s performance. Third, being
able to measure AC may enable a firm to improve its overall performance. Fourth, the
research view of AC as a dynamic capability suggests that it can be influenced through
appropriate managerial actions that redefine and deploy the firm’s knowledge-based assets.
Fifth, the findings of the study helped develop an AC model, which can be used as a valu-
able tool for firm self-assessment to facilitate gaining insight toward further growth and
development.
Finally, in terms of policy, we refer to the research context and the overarching objective of
New Zealand government policy ‘‘to develop innovation in the primary industry sector.’’ What
is lacking here is an understanding that ‘‘more science’’ is not the only answer. What is missing
here is an understanding of how technological AC and non-core AC need to be developed in
tandem. In a global environment where competition is ever increasing and competitive advan-
tage is often based on slight differences, this lack of understanding of the importance of AC is
a cause for concern.
Limitations and Suggestions for Further Research
These findings fill a gap in past thinking by making a contribution to our understanding of the
impact of AC on the internationalization process of a firm. However, it became apparent that
there were some limitations in the research design: (a) single industry data collection, (b) self-
reported data, (c) lack of durational data, and (d) limitation of findings to small and medium-
size enterprises.
The data to investigate the conceptual model were collected from a single industry, rather
than across industries and on a longitudinal basis. However, it was anticipated that single-
industry data would be satisfactory to examine the conceptual model, which proved to be cor-
rect. Furthermore, the data collected on each case relied on self-reported information, which is
not thought to be an issue because only individual participants can report reliably on these fac-
tors. Furthermore, to enhance the reliability of the findings, mixed methods have been used,
combining quantitative and qualitative research.
While the measures of AC captured perhaps the most important dimensions, durational data
would have provided greater depth in terms of understanding the relationships between the areas
of investigation. Rather than being inconclusive, the findings of this study open new opportuni-
ties for further research.
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Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or pub-
lication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
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