Absorptive Capacity in High Tech.full

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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]

at COLORADO STATE UNIV LIBRARIES on June 4, 2014mmr.sagepub.comDownloaded from


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



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



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



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



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




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




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




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,




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.




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




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.




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.




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.




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.




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.


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)




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.


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.’’




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.




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.

References

Biesta, G. (2010). Pragmatism and the philosophical foundations of mixed methods research. In

A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social & behavioral research (pp.

95–117). Thousand Oaks, CA: Sage.

Boeije, H., Slagt, M., & van Wesel, F. (2013). The contribution of mixed methods research to the field of

childhood trauma: A narrative review focused on data integration. Journal of Mixed Methods Research,

7(4), 347–369.

Boynton, A., Zmud, R., & Jacobs, G. (1994). The influence of IT management practice on IT use in large

organizations. MIS Quarterly, 18(3), 299–318.

Bryman, A. (1992). Quantitative and qualitative research: Further reflections on their integration. In

J. Brannen (Ed.), Mixing methods: Qualitative and quantitative research (pp. 57–78). Aldershot,

England: Avebury.

Bryman, A. (2007). Barriers to integrating quantitative and qualitative research. Journal of Mixed Methods

Research, 1(1), 8–22.

Cockburn, I. M., & Henderson, R. M. (1998). Absorptive capacity, coauthoring behavior, and the

organization of research in drug discovery. The Journal of Industrial Economics, 46(2), 157–183.

Cohen, W., & Levinthal, D. (1990). Absorptive capacity: A new perspective on learning and innovation.

Administrative Science Quarterly, 35, 128–152.

Cohen, W. M., & Levinthal, D. A. (1989). Innovation and learning: The two faces of R&D. The Economic

Journal, 99, 569–596.

Dewdney, A. K. (1989). The turning omnibus: 61 excursions in computer science. Oxford, England:

Computer Science Press.

Eriksson, K., & Chetty, S. (2003). The effect of experience and absorptive capacity on foreign market

knowledge. International Business Review, 12(6), 673–695.

Fiol, M., & Lyles, M. (1985). Organizational learning. Academy of Management Review, 10(4), 803–813.

Freshwater, D., & Cahill, J (2013). Paradigms lost and paradigms regained. Journal of Mixed Methods

Research, 7(1), 3–5.

Gartner, W. B., & Birley, S. (2002). Introduction to the special issue on qualitative methods in

entrepreneurship research. Journal of Business Venturing, 17(5), 387–395.

Gavetti, G., & Levinthal, D. (2000). Looking forward and looking backward: Cognitive and experiential

search. Administrative Science Quarterly, 45(1), 113–137.

Glass, A. J., & Saggi, K. (1998). International technology transfer and the technology gap. Journal of

Development Economics, 55(2), 369–398.

Gomez-Mejia, L. R., & Palich, L. (1997). Cultural diversity and the performance of multinational firms.

Journal of International Business Studies, 28(2), 309–335.

Hansen, M. T. (1999). The search-transfer problem: The role of weak ties in sharing knowledge across

organizational subunits. Administrative Science Quarterly, 44(1), 82–111.

Healey, M., & Perry, C. (2000). Comprehensive criteria to judge validity and reliability of qualitative

research within the realism paradigm. Qualitative Market Research: An International Journal, 3(3),

118–126.

Hellmann, M. (2001). Fuzzy logic. Retrieved from http://www.sau.ac.in/~vivek/softcomp/fuzzyintro.pdf

Hesse-Biber, S., & Burke Johnson, R. (2013). Coming at things differently: Future directions of possible

engagement with mixed methods research. Journal of Mixed Methods Research, 7(2), 103–109.




Huber, G. P. (1991). Organizational learning: The contributing processes and literatures. Organization

Science, 2(1), 88–115.

Hurmerinta-Peltomaki, L., & Nummela, N. (2006). Mixed methods in international business research: A

value-added perspective. Management International Review, 46(4), 439–459.

IUF Dairy Analysis. (n.d.). New Zealand dairy industry. Retrieved from http://cms.iuf.org/sites/

cms.iuf.org/files/New%20Zealand%20Dairy%20Industry.pdf

Katz, R. H. (1994). Contemporary Logic Design. The Benjamin/Cummings Publishing Company, Inc.,

Redwood City, California.

Kedia, G. L., & Bhagat, R. S. (1988). Cultural constraints in transfer of technology across nations:

Implications for research in international and comparative management. Academy of Management

Review, 13(4), 559–571.

Keller, W. (1996). Absorptive capacity: On the creation and acquisition of technology in development.

Journal of Development Economics, 49(1), 199-227.

Kim, L. (1998). Crisis construction and organizational learning: Capability building in catching-up at

Hyundai Motor. Organization Science, 9(4), 506–521.

Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data.

Biometrics, 33, 159–174.

Lane, P. J., & Lubatkin, M. (1998). Relative absorptive capacity and inter-organizational learning.

Strategic Management Journal, 19(5), 461–477.

Levitt, B., & March, J. G. (1988). Organizational learning. Annual Review of Sociology, 14, 319–340.

Liu, X., & White, S. (2001). Comparing innovation systems: A framework and application to China’s

transitional context. Research Policy, 30(7), 1091–1114.

McGrath, J. (1982). The study of research choices and dilemmas. In J. McGrath, J. Martin, & R. Kulka

(Eds.), Judgment calls in research (pp. 69–102). Beverly Hills, CA: Sage.

Michailova, S. (2004). Reflections on conducting research in Eastern Europe. In R. Marschan-Piekkari &

C. Welch (Eds.), Handbook of qualitative research methods for international business (pp. 365–383).

Cheltenham, England: Elgar.

Miles, M. B., & Huberman, M. A. (1994). Qualitative data analysis: An expanded source book (2nd ed.).

Thousand Oaks, CA: Sage.

Miller, D., & Chen, M. J. (1994). The simplicity of competitive repertoires: An empirical analsysi.

Strategic Management Journal, 17, 419–439.

Ministry for Primary Industries. (2013). Transforming the dairy value chain. Retrieved from http://

www.mpi.govt.nz/agriculture/funding-programmes/primary-growth-partnership/transforming-the-dairy-

value-chain

Moran-Ellis, J., Alexander, V. D., Cronin, A., Dickinskon, M., Fielding, J., Sleney, J., & Thomas, H.

(2006). Triangulation and integration: Processes, claims and implications. Qualitative Research, 6(1),

45–59.

Morse, J. M. (1994). Critical issues in qualitative research methods. Newbury Park, CA: Sage.

Mowery, D. D., Oxley, J. E., & Silverman, B. S. (1996). Strategic alliances and interfirm knowledge

transfer. Strategic Management Journal, 17(Winter Special Issue), 77–91.

O’Cathain, A., Murphy, E., & Nicholl, J. (2007). Integration and publications as indicators of ‘‘yield’’

from mixed methods studies. Journal of Mixed Methods Research, 1(2), 147–163.

Olson, M. (1984). The rise and decline of nations. New Haven, CT: Yale University Press.

Sedoglavich, V., Akoorie, M., & Pavlovich, K. (2009). Broken links: Absorptive capacity and the

internationalization process of high-tech companies. World Review of Entrepreneurship, Management

and Sustainable Development, 5(2), 149–165.

Schilling, M. (1998). Technological lockout: An integrative model of the economic and strategic factors

driving technology success and failure. Academy of Management Review, 23(2), 267–284.

Shrout, P. E., & Fleiss, J. L. (1979). Intraclass orrelations: Uses in assessing rater reliability. Psychological

Bulletin, 86(2), 420–428.

Simonin, B. L. (1999). Ambiguity and the process of knowledge transfer in strategic alliances. Strategic

Management Journal, 20, 595-623.




Sullivan, D. (1994). Measuring the degree of internationalization of a firm. Journal of International

Business Studies, 25(2), 325–342.

Szulanski, G. (1996). Exploring internal stickiness: Impediments to the transfer of best practice within the

firm. Strategic Management Journal, 17(Winter Special Issue), 27–43.

Tashakkori, A., & Creswell, J. (2008). Editorial: Mixed methodology across disciplines. Journal of Mixed

Methods Research, 2(1), 3–6.

Todorova, G., & Durisin, B. (2006). Absorptive capacity: Valuing a reconceptualization. Academy of

Management Review, 32(3), 774–786.

Trend, M. G. (1979). On the reconciliation of qualitative and quantitative analysis: A case study. In T. D.

Cook & C. S. Reichardt (Eds.), Qualitative and quantitative methods in evaluation research (pp. 68–

86). Beverly Hills, CA: Sage.

Tripsas, M., & Gavetti, G. (2000). Capabilities, cognition and inertia: Evidence from digital imaging.

Strategic Management Journal, 21(10/11), 1147–1162.

Van Den Bosch, F. A. J., Van Wijk, R., & Volberda, H. W. (2003). Absorptive capacity: Antecedents,

models and outcomes (Erasmus Research Institute of Management [ERIM] Report ERS-2003-035-

STR). Rotterdam, the Netherlands: ERIM. Retrieved from http://papers.ssrn.com/sol3/

papers.cfm?abstract_id=411675

Van Maanen, J. (1983). Epilog: Qualitative methods reclaimed. In J. Van Maanen (Ed.), Qualitative

Methodology (pp. 247–268). Beverly Hills, CA: Sage.

Veugelers, R. (1997). Internal R & D expenditures and external technology sourcing. Research Policy,

26(3), 303–315.

Yin, R. (1998). The abridged version of case study research: Design and method. In L. Bickman & D. Rog

(Eds.), Handbook of applied social research methods (pp. 229–259). Thousand Oaks, CA: Sage.

Yin, R. (1994). Case study research: Design and methods. (2nd ed.). Thousand Oaks: Sage.

Zahra, S., & George, G. (2002). Absorptive capacity: A review, reconceptualization, and extension.

Academy of Management Review, 27(2), 185–203.

Zahra, S. A., Ireland, D. R., & Hitt, M. A. (2000). International expansion by new venture firms:

International diversity, mode of market entry, technological learning and performance. Academy of

Management Journal, 43(5), 925–950.




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