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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&D Department 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 • Not all technologies diffuse, even when they are technically superior. • The Dvorak keyboard • IBM’s OS/2
Diffusion • What determines whether a technology diffuses?
Adopters’ Characteristics Technology’s Attributes DIFFUSION Environment Categories of Diffusion Determinants
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 (“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
Econometric Estimation • Aim: model the relationship between the determinants of adoption Xiand 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”
Pi 1 Xi - ∞ 0 + ∞ Econometric Estimation
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 + β2dct5i + β3lifexpi + β4prevxpi + β5dm1i + β6capavaili + 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):.