Transcript Dimitrios Pontikakis - National University of Ireland, Galway

Dimitrios Pontikakis
The determinants of technology
adoption: Evidence from SMEs in
Greece
Technology: A definition
In the context of economics technology
encompasses not just technical change
(as for example in engineering), but also
expertise, revolutionary methods (as for
example in management) and innovative
ideas in general.
Technology and Economics
 Classical economists saw technology as the
consequence of structural change rather than its
cause.
 Early economics research on technology
focused on R&D.
 Attempts were made to identify the motives for
R&D (i.e. profit seeking) but largely ignored its
consequences.
 It was only in the late 1950s and early 1960s
that the wider implications of new technology in
the economy are systematically analysed (Solow,
1956; Griliches, 1957; Mansfield, 1961; Arrow,
1962).
Technology and Economics: The key
relationships
 Directly affecting the development process
through rises in productivity (Solow, 1956).
 Some argue that continuous innovation is a
prerequisite of sustainable growth (Romer,
1990).
 Parente and Prescott (1994) have emphasized
barriers to technology adoption as a key
determinant of differences in per capita income
across countries.
 At the firm level: product/service differentiation
(temporary monopoly), cost cutting, productivity
increases  competitive advantage
General Sources of Technology for Firms
Firm
Internal Sources
Internal R&D
Department
External Sources
Chance
Innovation
Acquiring
existing
technologies
Spillovers
Spillovers Previously
trained staff
Market
transaction
spillovers
Subcontracting
R&D to specialised
centres, universities
etc.
Diffusion
Mansfield (1961) argued that too much
emphasis had been placed on the
creation of new technology often
ignoring the fact that existing technologies
may pose an alternative if adopted.
For the majority of firms and certainly for
SMEs, acquiring existing technologies
(through diffusion) is perhaps the only
viable source of technological capital.
Technology and Economics
 The creation of new technology by itself bears
little relationship to economic matters.
 The contribution of technical change to the
economy at large will have to be established
through the study of diffusion.
 Diffusion is:
“the process by which an innovation is
communicated through certain channels over
time among the members of a social system”
(Rogers, 1983: 5)
 The present study focused on diffusion across
firms (inter-firm diffusion)
Diffusion
Numerous empirical studies have shown
that the diffusion of a technology in
industry is far from uniform.
Some firms adopt early (early adopters),
some when everybody else does (majority
adopters) and some very late or never
(laggards).
Diffusion Curve (sigmoid)
Diffusion
Not all technologies diffuse, even when
they are technically superior.
The Dvorak keyboard
IBM’s OS/2
Diffusion
  What determines whether a technology
diffuses?
Categories of Diffusion Determinants
Adopters’
Characteristics
Technology’s
Attributes
DIFFUSION
Environment
Determinants of Diffusion
The technology’s relative advantage of
particular importance; indicative of a
NEED for the technology
No need = No adoption
Empirical Study
The diffusion of modern, internet-enabled
personal computers (IEPCs) in Greek
SMEs, 1990-2004.
Selection of technology: arguably all firms
can benefit from the adoption of IEPCs
 high relative advantage
Selection of adopter set: SMEs have
constrained access to capital
Case Study
 Case study of particular relevance to policy
makers in the light of the EU-sponsored
‘Information Society’ framework.
 Various government sponsored schemes (“GoOnline”, “Technomesiteia”, “Adapt”, “Human
Networks of Knowledge Promotion”
acknowledge that the diffusion of computers in
Greek SMEs is low and seek to address the
problem.
 One programme (“Go-Online”) indicated that the
decision to adopt IEPCs is particularly inelastic
to financial incentives.
Empirical Study
 A representative sample of 100 companies was
been chosen based on data on the make up of
the Greek SME sector (data from National Office
of Statistics, EOMMEX, Ministry of Development,
and Eurostat).
 Competition issues are taken into consideration.
 Data was collected by means of questionnaire.
 Attempts to investigate the relative weights of
different diffusion determinants in the context of
SMEs in Greece.
Data Collected - Adopters
Cumulative number of adopters (Yi=1) across time
Cumulative
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
80
70
60
50
40
30
20
10
0
Econometric Estimation
 Aim: model the relationship between the
determinants of adoption Xi and the decision to
adopt Yi
 Estimation Problem: Relationship between Xi
and Yi is non-linear; precludes the application of
traditional regression methods
 Logistic regression attempts to transform a nonlinear relationship into a linear one, using
logarithmic expression  “LOGIT MODEL”
Econometric Estimation
Pi
1
Xi
-∞
0
+∞
Econometric Estimation
 A ‘logit’ model was chosen where the dependant variable
is dichotomous (can either take a value of 0=nonadoption, 1=adoption)
 The model is well-established in economic diffusion
research and used before by: Karshenas and Stoneman
(1995) in the diffusion of manufacturing processes in the
US; Courchance, Nickerson and Sullivan (2002) in the
diffusion of internet banking; Gourlay (1998) in the
diffusion of ATMs in the UK; Kauffmann (1998) for
environmental technologies and others.
 To the established model I have also added the
independent variables of ‘previous experiences’ and ‘life
expectancy’.
Econometric Estimation
Equation form:
Yi = β1 + β2iΧ2i + β3iΧ3i + … Χ14i + ui
Estimated using Eviews 4.0
Estimation Results
 Estimated model (best fit for data) with most
significant variables:
Yi = β1 + β2 dct5i + β3 lifexpi + β4 prevxpi + β5
dm1i + β6 capavaili + ui
 Coefficient Exponentiation:
Hypotheses Accepted
 capavail : The availability of financial capital facilitates
adoption while the lack of financial capital discourages it
(odd 2).
 dm1 : SMEs that engage in any co-operative
relationship with multinational enterprises are more likely
to adopt the technology (odd 0.20).
 prevxp : Firms that adopted an earlier generation of the
technology and considered the experience as beneficial
are more likely to adopt (odd 10.6).
 dct5 : Firms that perceive their industry as ‘competitive’
are more likely to adopt while firms that perceive little
competition in their industry are less likely to adopt
(odd 6.6).
 lifexp : Technologies with a low life expectancy are less
likely to be adopted (odd 0.18):.
Questions