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On the Determinants of Global Internet Diffusion: A Cross-Country Analysis. Hongxin Zhao, Ph.D., Saint Louis University Taewon Suh, Ph.D., Texas Tech University, St. Marcos Seung H. Kim, Ph.D., Saint Louis University Jianjun Du, Ph.D., University of Houston - Victoria A Presentation for

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on the determinants of global internet diffusion a cross country analysis
On the Determinants of Global Internet Diffusion:A Cross-Country Analysis

Hongxin Zhao, Ph.D., Saint Louis University

Taewon Suh, Ph.D., Texas Tech University, St. Marcos

Seung H. Kim, Ph.D., Saint Louis University

Jianjun Du, Ph.D., University of Houston - Victoria

A Presentation for

Temple University CIBER Workshop

March, 2004

research question
Research Question
  • If Internet revolution may marginalize those countries that are lack of education, infrastructure, and government support, then what factors may differentiate Internet development in developed and less developed economies? By addressing those factors a country may benefit from the new wave of new Internet technology.

Figure 1

Diffusion of Internet in LDCs and DCs  

(From the sample used in the analysis)

two empirical questions
Two Empirical Questions
  • This research tries to address two empirical questions:
    • What are the major determinants of Internet diffusion?
    • What explains the differences in Internet diffusion between developed and developing countries?
related literature hypotheses technological infrastructure
Related Literature & Hypotheses(Technological Infrastructure)
  • Internet provides a platform for global market place, supporting electronic commerce.
  • Goodman, et al. (1994) shows that there are three primary barriers to wider distribution of networking: 1. Government policies, law, and practices; 2. Technical impediments, and 3. Local and cultural factors.
  • Oxley and Yeung (2001) indicate that Internet diffusion is directly impacted by network infrastructure. The extent of development within telephone, computer, and communication technologies were found directly correlated with Internet diffusion.
  • Human capital is directly associated with the innovative of a nation. Nedovic-Budic et al. (1996) and Press (1992) found that lack of technical expertise and training programs for both system administration and end users in LDCs is an inhibitor to the Internet diffusions process.
    • H1a: The network infrastructure is positively related to the degree of Internet diffusion.
    • H1b: The human resource system in a nation is positively related to the degree of Internet diffusion.
related literature hypotheses policy regime
Related Literature & Hypotheses(Policy Regime)
  • The regulatory regime of a country affects the acceptance and deployment of new technology, such as the Internet. Generally, the government adopting a positive attitude towards Internet technology may engage in restructuring the domestic economy and adaptive policies to encourage diffusion. For example, in some less developed countries (LDCs), governments have played a major role in Internet development through initiating and funding the Internet and internet-related technologies. On the other hand, LDCs can also be impacted by reluctant governments that can actually impede the diffusion. (Zhao, 2002; Berkhart, et al. 1998; Petrazzini and Kibati, 1999).
    • H2a: An open policy regime tends to be positively related to the degree of Internet diffusion.
    • H2b: The impacts of policy regimes on the Internet diffusion differ between LDCs and DCs. The policy regimes in LDCs have more significant impact on Internet diffusion than the policy regimes in DCs.
related literature hypotheses national investment
Related Literature & Hypotheses(National Investment)
  • The development and deployment of technology is hindered by the lack of a strong private sector and by limited capital, as is seen in most LDCs. Therefore, for the enhancement of Internet development in LDCs, government investment is essential to create incremental improvements in national capacity. Without government support, the national network backbones established in several LDCs would not have materialized. This suggests that LDCs with a higher rate of government expenditure experience a higher speed of Internet development.
  • The velocity of Internet development may also be enhanced as the efficiency of international capital flows and foreign direct investment increase. (Quelch and Klein, 1996; Petrazzini and Kibati, 1999)
    • H3a: The level of national investment is more likely to be positively associated with the Internet diffusion in LDCs than DCs.
    • H3b: FDI is more likely to generate significant impacts on Internet diffusion in LDCs than in DCs.
related literature hypotheses cultural factors
Related Literature & Hypotheses(Cultural Factors)
  • Culture differences connote a “broad tendency to prefer certain states of affairs over others”. Cultural factors also influence how people perceive, process and interpret information [19]. To the extent that Internet use and content encounter diverse cultural expectations, human volition also plays a part in Internet diffusion. Cultural traits of countries may affect the growth of Internet hosts if permeation of the Internet is perceived by a culture either as a challenge or as an accompaniment to the mainstream social values. (Hofstede, 1980 and 2001; Kale, 1991; Fock, 2000; Schwartz, 1994; Chui, et al., 2002)
    • H4a: The degree of Internet diffusion is negatively associated with a national culture characterized by high degree of power distance.
    • H4b: The degree of Internet diffusion is positively associated with an individualistic national culture.
data and methods
Data and Methods

This study uses the reliable indicators of Internet host density and independent factors across 40 nations that have the complete data sets for both the dependent and explanatory variables. The data before 1995 were excluded because of the extremely primitive conditions of Internet development in some LDCs in the data set and the many missing values. Thus, the analysis was based on data from a cross section of countries (n = 40) over a time span of 4 years (from 1995 to 1999). This yields a sample size of 200 observations. Appendix 1 lists both LDCs (n = 20) and DCs (n = 20) included in the data analyses.

The data are drawn from reports published in World Telecommunication Yearbook [180], the World Development Indicators (Various years), The World Competitiveness Yearbook (various years), and Hofstede’s cultural indices.

data and methods1
Data and Methods

Log (INET)it = 0 + 1(NETWORK)it + 2(HUMAN)it + 3(RESTRUCT)it +

4(ADAPT)it + 5(OPEN)it + 6(GEXPEND)it + 7(FDI)it +

8(PD)it + 8(INDV)it + it


Country Name

LDCs (n = 20)

Brazil, Chile, China, Columbia, Czech, Greece, Hungary, India, Indonesia, Korea, Malaysia, Mexico, Philippine, Poland, Portugal, Russia, South Africa, Thailand, Turkey, Venezuela

DCs (n = 20)

Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Israel, Italy, Japan, Netherlands, New Zealand, Norway, Spain, Sweden, Switzerland, United Kingdom, United States

Appendix 1

Countries included in the data


Variable Name












Internet Host Density: The number of active Internet Protocol addresses per 10,000 people. (Source: World Telecommunication Report)

Network Infrastructure: Composite variable of the standardized scores of personal computers, mobile phones, and telephone mainlines per 10,000 people. (Source: World Development Indicators)

Human Resource System: Composite variable of Scientists and engineers in R&D per million people and technicians in R&D per million people. (Source: World Development Indicators)

Restructuring of Economy: Scores from a survey item asking “restructuring the domestic economy is well-adapted for long term competitiveness” with 1-9 scale, one-year lagged. (Source: World Competitiveness Yearbook)

Government’s Adaptable Policy: Scores from a survey item asking “the government adapts its policies to changes in the economic environment” with 1-9 scale, one-year lagged. (Source: World Competitiveness Yearbook)

Market Openness Indicator: The sum of exports and imports of goods and services, measured as a share of GDP. (Source: World Development Indicators)

Government’s Expenditure: Central government’s total expenditure including nonrepayable current and capital expenditure, % of GDP. (Source: World Development Indicators)

Gross FDI: The sum of the absolute values of inflows and outflows of foreign direct investment recorded in the balance of payments financial account, % of PPP GDP. (Source: World Development Indicators)

Power Distance: An index from Hofstede (2001)

Individualism: An index from Hofstede (2001)

Table 1

Definitions and Sources of Variables

statistical results and findings
Statistical Results and Findings
  • Because of the cross-country and time-series nature, we employed the first-order autocorrelation procedure to correct the potential problem of serial correlations. Table 3 presents the estimates of regression adjusted for autocorrelation along the model statistics. The highly adjusted R2 based on the first-order autocorrelation indicates the models fit the data. The Durbin-Watson statistics showed no severer existence of serial correlation.

Table 2

Means, Deviations, and Correlations between Independent Variables


Table 3

Regression Estimates of Explanatory Variables on Internet Diffusion

Dependent variable: Log(INET), t-statistics in parentheses * p < .05; ** p < .01; *** p < .001


Implications and Conclusion

  • Our findings suggest that network infrastructure is a necessary condition required to ensure a high rate of Internet diffusion. Without this propitious condition, Internet diffusion cannot occur.
  • Our findings indicate that the thrust of policies of developing countries should be focused on infrastructure building and creating a national environment that promotes innovation. The obstacles comprising lack of competition, high costs of providing Internet services, and transferred costs to end users need to be removed .
  • The transparent economic and trade regime will provide a favorable environment for Internet growth. In addition, ‘digital divide’ between nations could not be narrowed without cultivating competence in the private sectors within LDCs regarding a digital economy.

Implications and Conclusion

  • Our findings also give an implication for the firms in emerging market economies. While the use of the Internet in DCs to date has been to link value-adding commercial supply chains into seamless, logistical communication networks that are vastly more efficient [8], the firms intending to invest in Internet-related sectors in LDCs should make the efforts to understand the policy and trade environments before heavily committing resources in this new sector. The accord between firms’ strategic decisions on foreign investment and the business environments of target national markets should be identified and assessed. To take advantage of the fast Internet diffusion in foreign markets, foreign investments in the Internet-based technologies and businesses have to be synchronized with the national trade and economic policies. A promising strategic decision always abides by a proper environmental scanning.