The determinants of technology adoption: Evidence from SMEs in

Greece

Dimitrios Pontikakis

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

Firm

Internal Sources External Sources

Internal R&DDepartment

Chance Innovation

Acquiring existing

technologies

Subcontracting R&D to

specialised centres,

universities etc.Spillovers

Spillovers - Previously

trained staff

Market transaction spillovers

General Sources of Technology for Firms

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 keyboardIBM’s OS/2

Diffusion

What determines whether a technology diffuses?

Categories of Diffusion Determinants

Adopters’Characteristics

DIFFUSION

Technology’sAttributes

Environment

Determinants of Diffusion

The technology’s relative advantage of particular importance; indicative of a NEED for the technology

No need = No adoption

Empirical StudyThe 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 advantageSelection 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 (“Go-Online”, “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

0

1020

3040

50

6070

80

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 non-linear relationship into a linear one, using logarithmic expression “LOGIT MODEL”

Econometric Estimation

0

Pi

Xi

+ ∞- ∞

1

Econometric Estimation

A ‘logit’ model was chosen where the dependant variable is dichotomous (can either take a value of 0=non-adoption, 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