Presentation on theme: "Harokopeio University Athens. Innovative model of software development, where software is open for inspection, modification and exchange without restrictions."— Presentation transcript:
Harokopeio University Athens
Innovative model of software development, where software is open for inspection, modification and exchange without restrictions or discrimination. High diffusion of OSS in different market segments: Linux, Android, Apache, Mozilla Firefox, Open Office, PhP, Java, MySQL, Moodle, Joomla, Apache OfBiz...
Limited Knowledge regarding: OSS diffusion curves Factors that shape OSS diffusion among countries
How is OSS diffused among countries? What theoretical foundation can be used to identify country level factors that affect OSS diffusion? What are the factors that impact the diffusion process? What is the impact of these factors at different stages of the diffusion process?
Diffusion of Innovations Theory + Socioeconomic Theories Endogenous Growth Theory Exogenous Growth Theory Institutional Theory
A country is conceptualized as a socio- economic system within which OSS growth occurs. The model is based on the idea that the forces of growth to an economic system comprise of institutional, endogenous and exogenous factors : OSS it = F(X endog, X exog, X inst )
Economic growth is generated from within a system as a direct result of internal processes (Romer, 1986, 1994).
Hypothesis H1: Telecommunications infrastructure has a positive impact on OSS growth. Infrastructure such as the Internet and broadband connections are key elements not only for the diffusion but also for the existence OSS, as the OSS development model is totally based on virtual teams and remote management and collaboration.
Hypothesis H2: Human capital skills and education can impact OSS diffusion. Human capital is a key input to the development of knowledge, new ideas and products associated with technological progress. Prior studies demonstrate that the average level of education and the quality of human capital are influential drivers for individual technology adoption. Inexperienced and unskilled users would be very reluctant to migrate to an OSS system, due to the fear of lack of technological support and long term.
Hypothesis H3: Innovation can be a ‘pushing’ factor for OSS diffusion. The growth of OSS is mainly due to the large number of skilled and qualified developers with willingness to create and innovate. Thus, OSS is not only an innovative technology, but also is a continuous source of innovation. Prior studies have identified the close relationship between OSS and innovation.
Hypothesis H4. Economic growth is an impacting factor for OSS diffusion. Previous research has showed that economic growth is directly related to technological growth. Although OSS can be acquired at low or zero price, the study tests the possibility that economic development creates demand for more technologically advanced products such as OSS.
Growth is primarily determined by external factors, such as the flow of goods, ideas, capital and technology innovations, rather than internal factors (Solow, 1965).
Hypothesis H5: Technological openness can leverage OSS adoption among countries. A country’s openness can be defined as the degree to which a country is open to business and economic influences through trade activities. It can be perceived as the external force that captures knowledge spillovers among countries. The study considers ICT trade as the channel for achieving such spillovers.
It attends to the deeper understanding of the processes by which social structures are maintained and reproduced (Scott, 2004).
The quality and operation of institutions may have a severe impact on the way that social systems behave towards a technology. Governments have the opportunity to intervene and take actions affecting the markets and the dissemination of new technologies. Assuming that trade and innovation can affect OSS diffusion process (H3 and H5), the effects of government’s regulations and policies in these sectors are further examined.
Hypothesis H6: regulation in trade could have a negative impact on OSS adoption among countries. Hypothesis H7: regulation in IPR could have a negative impact on OSS adoption among countries.
HypothesisMeasureVariable name Endogenous growth theory factors H1:Telecommunications infrastructure measured by the number of broadband subscribers. (Source: World Bank Indicators) Natural log BBS H2: Education and Human capital measured by the Human Capital Index. (Source: United Nations Development Program ) Ranged in [0, 1] HCI H3: Innovation measured by “charges for the use of intellectual property” indicators. (Source: World Bank Indicators) Natural log INNOV H4: Economic growth measured by GDP per capital. (Source: World Bank Indicators) Natural log GDP Exogenous growth theory factors H5: Technological openness measured by ICT trade. (Source: World Bank Indicators) Natural log ICTtrd Institutional theory factors H6: Policy regarding trade is measured by the regulatory trade barriers index. (Source: Economic Freedom Network -Fraser Institute) Ranged in [1,10] RTB H7: Policy for the protection of property rights is measured by the Protection of property rights index. (Source: Economic Freedom Network -Fraser Institute) Ranged in [1,10] IPR
Describes the process of the diffusion of innovations through a social system, seeking explanations in terms of “how” innovations diffuse through it, by the means of appropriate mathematical models, the diffusion models.
The Bass diffusion model suggests that a new product adoption decision is driven by two factors: coefficient of external influence, the influence that is independent of the existing number of adopters coefficient of internal influence, the social influence of the existing number of adopters.
Due to the use of time series, a small and a large country that have exactly the same number of adopters over time will have the same diffusion parameters. The use of fixed time periods for all countries in a data set creates higher risk of left-hand truncation bias, where the estimates of the intercept of the diffusion curve are inflated for those countries that start their adoption process earlier than the time frame represented by the data.
n i,t is the number of adopters at time t, N i,t-1 is the cumulative number of adopters up to time t-1. S i is the social system size, C i is the long-run penetration ceiling. B i is the diffusion growth rate. A i1 = n i,1 /C i S i is the diffusion curve intercept.
Logistic models test covariates that affect the first year adoption (A i1 ) and diffusion growth rates (B i ): d 1 and d 2 are vectors of parameters and X i is the vector of the covariates for country i. X i is the vector of the covariates for country i.
Vector of covariates X i Xi = (GDP, BBS, HCI, INNOV, ICTtrd, RTB, IPR) Social system size S i is assumed each country’s population. Long-term penetration ceiling C i is defined as the proportion of the population aged between 15 and 65, as this age range should include the majority of OSS users. Alignment of introduction timing of OSS : The model sets time t=1, the first year of adoption of each country, so that all countries’ diffusion will set off from a common point of the time axis.
The analysis is based in data from 25 countries over the period OSS penetration: cumulative number of subscribed users/developers per country in the SourceForge.net. Data queried out of the University of Notre Dame (UND) database which keeps SourceForge.net data for academic and scholarly research purposes. C i parameter: derived by the World Bank Indicators (population ages 15-64, % of total). S i, parameter: extracted by the United Nations database
Country listings according to: Number of per country registered users in the SourceForge.net in 2009 (I) Number of per country registered users, taking into account the parameter C i S i of each country in 2009 (II) User rates the first year of adoption, A i1 (III) Diffusion Growth rate, B i (IV)
(i) 2009 (ii) According to C i S i. (2009) (iii) According to Α i,1 (1999) (iv) According to Β i 1ΗΠΑΟλλανδίαΝορβηγίαΟλλανδία 2ΓερμανίαΦιλανδία Γαλλία 3ΙνδίαΝορβηγίαΑυστραλία Ιταλία 4ΚαναδάςΑυστραλίαΗΠΑ Νορβηγία 5ΓαλλίαΚαναδάς Ελλάδα 6ΚίναΗΠΑΟλλανδία Φιλανδία 7ΒραζιλίαΒέλγιοΓερμανία Κορέα (Δημ.) 8ΑυστραλίαΓερμανίαΒέλγιο Γερμανία 9ΙταλίαΓαλλίαΒρετανία ΗΠΑ 10ΒρετανίαΙταλίαΓαλλία Βραζιλία 11ΟλλανδίαΕλλάδαΙσπανία Αυστραλία 12ΡωσίαΒρετανίαΙταλία Καναδάς 13Ισπανία Ρουμανία Βρετανία 14ΙαπωνίαΡουμανίαΕλλάδα Βέλγιο 15ΒέλγιοΑργεντινή 16ΜεξικόΒραζιλίαΡωσία Ρουμανία 17ΤουρκίαΚορέα (Δημ.)Βραζιλία Ν. Αφρική 18ΑργεντινήΡωσίαΜεξικό Ρωσία 19ΡουμανίαΤουρκίαΙαπωνία Τουρκία 20ΦιλανδίαΝ. ΑφρικήΚίνα Ισπανία 21Κορέα (Δημ.)ΤυνησίαΙνδία Τυνησία 22ΝορβηγίαΙαπωνίαΝ. Αφρική Ιαπωνία 23Ν. ΑφρικήΜεξικόΚορέα (Δημ.) Ινδία 24ΕλλάδαΙνδίαΤυνησία Μεξικό 25ΤυνησίαΚίναΤουρκία Κίνα
First year penetration (Α i,1 ) R 2 = 0.92 Diffusion rate (Β i ) R 2 =0.87 Constructs coefficientstand errorcoefficientstand error BBS 0.23*** *** HCI 0.03*** ***0.035 INNOV 0.11*** ***0.051 GDP 0.01*** ***0.025 ICTtrd 0.09*** ***0.029 RTB 0.012*** ***0.015 IPR 0.12*** ***0.011 Significance level * = p < 0.10, ** = p < 0.05, *** = p<0.01
Factors that determine OSS diffusion: Innovation Quality of human capital Telecommunications infrastructure ICT trade Regulation protecting IPR
OSS evolves in social systems where knowledge, education and creativity is highly developed. Economic growth and less regulation barriers in trade affect OSS only in the beginning of the diffusion process. Differences in the weight of impact at the two diffusion stages. Factors related to the quality of human capital and innovation are more critical in the growth phase of the diffusion. Close relationship between OSS and innovation.