TOWARDS RELIABLE E-READINESS MEASUREMENT: A STRUCTURAL EQUATION MODELING APPROACH

1. TOWARDS RELIABLE E-READINESS MEASUREMENT:A STRUCTURAL EQUATION MODELING APPROACH Dan M. Grigorovici Corina Constantin Krishna Jayakar Richard Taylor Jorge Schement The Institute for Information Policy The Pennsylvania State University

2. Introduction • The present state of affairs in the measurement of Information Society and “e-readiness” still shows signs of a young discipline: “conceptual incoherence”, “operational fragmentation”, “conceptual fit disjuncture” (Arquette 2001, p. 3) • Attempts to create “information indicators” date back to the 1960’s with Machlup’s attempt to measure the part of gross national product (GNP) represented by “information goods and services” (Shifflett & Schement 1996, 3), while consistent work in this area took off with Borko and Menou’s work on the “Information Utilization Potential” (IUP) (Shifflett & Schement 1996, 17) • Current e-readiness measures are descriptive, rankings-based, and do no dissociate between underlying factors. No reliable and valid assessment of factor structure is offered when considering various indexes proposed

3. Introduction • Recent research proposes that connectivity is not the only factor underlying differences in e-readiness: Digital Divide (Grigorovici et al., 2004) • Our current 4C model re-designs the information metrics issue based on connectivity, capability, content and context. There, we acknowledged the need to move beyond an understanding of Information Society (and hence access) issues, solely in terms of connectivity

4. Research Questions and Hypotheses H1:There are four factors underlying e-readiness of a country: capability, connectivity, content, and context H2: The effects of connectivity on e-readiness are moderated by capability, content and context

5. Method • Secondary data provided by World Bank’s World Development Indicators Database 2003 and ITU World • Data were analyzed at the country level and above. • The final dataset contained variables related to population demographics (i.e., population, rural & urban socio-economic indicators, literacy level, level of completion for primary and secondary education, etc.), economic indicators such as GDP, unemployment, wages, etc., media outlets (i.e., number of daily newspapers, radio stations, etc.), and ICT- related variables (either economic variables such as tariffs, ICT revenues and investments, or use variables such as number of cell phone subscriptions, cable subscriptions, telephone main lines, etc.). • A number of 237 cases (countries and regions) were included in the analysis.

6. Data Analysis Three-step approach: • Confirmatory Factor Analysis for all sub-indices when required • simple sum composites formed for the sub-indices found, composite reliabilities calculated, and introduced into a simplified model, thus reducing the number of unknown parameters • Fit of the simplified model assessed

7. Results (1) • Connectivity = spending + availability • availability - total number of cable, telephone, and cell phone subscriptions, number of personal computers available at home, total number of telephone main lines, number of telephone main lines per 1000 inhabitants (Chi-Square = 6.91, df = 5, p = 0.22729, RMSEA = 0.037; composite error variance = 11681.6797). • spending - cell phone monthly subscription and connection fees, dial-up PSTN monthly subscription and charge, dial-up ISP monthly subscription and charge, overall dial-up charge per hour of use (Chi-Square = 3.30, df = 3, p = 0.34739, RMSEA = 0.021; composite error variance = 14867.8877). • acceptable fit (Chi-Square=5.019, df=2, P-value=0.081, RMSEA=0.081, NFI = .99, CFI = .99) (Fig. 1)

8. 0, d3 1 0 mlicap00 0, 14867.8876 1 1 spendc d1 spend 1 0, 0 0, 11681.6797 1 1 avail95 d2 availc connectiv Results (1) Figure 1: Path diagram for the Connectivity Index Chi-Square=5.019, df=2, P-value=0.081, RMSEA=0.081, NFI = .99, CFI = .99 • adding Internet usage as a predicted variable to the model – increased fit (Chi-square = 5.206, df = 4, p = .267, NFI = .993, IFI = .998, CFI = .99, RMSEA = .036) (Fig. 2) • regression weight of connectivity on Internet usage = only .02 (standardized estimate), and not statistically significant.

9. Results (2) • capability = previous experience with computers • CFA model for experience with computers - good fit (Chi-Square=0.69, df=2, P-value=0.70893, RMSEA=0.000; composite error variance = 473377.8727). • financial capability (average wages in 2001 per country), level of completion for secondary education added to the previous experience with computers to form overall index. • overall fit for capability (chi-square = .858, df = 1, p = .358, NFI = .995, CFI = 1.000, RMSEA = 0.000). • Internet usage added as a predicted variable - overall good fit maintained (Chi-square = 3.843, df = 3, p=.279, NFI = .987, CFI = .997, RMSEA = .034). • regression weight of capability on Internet usage = .78

10. 0, d3 1 0, 14867.8876 0 mlicap00 1 1 spendc d1 spend 1 0, 0 1 1 avail95 d2 availc connectiv 0, 11681.6797 netusi01 1 0, d4 Results (2) Figure 2: Path diagram for the Connectivity Index, after Internet Usage(“netusi01”) was added Chi-square = 5.206, df = 4, p = .267, NFI = .993, IFI = .998, CFI = .99, RMSEA = .036

11. Results (3) • content - overall number of secure servers reported in 2001, total number of Internet hosts and number of hosts per 100 inhabitants in 2001, number of Internet hosts, overall and per 100 inhabitants, added in 2001 compared to 2000. • reliable overall index (chi-square = .357, df = 3, p = .949, NFI = 1.000, CFI = 1.000, RMSEA = 0.000) • not enough measured indicators to predicting Internet usage.

12. 0 0, 473377.8727 1 1 experpc d1 expercomp 0, 0, 1 d2 wage01 1 capabil 0, 1 d3 scnded00 netusi01 1 0, d4 Results (3) Figure 3: Path analysis of the Capability Index Chi-square = 3.843, df = 3, p=.279, NFI = .987, CFI = .997, RMSEA = .034

13. Results (4) • context - media outlets (number of daily newspapers, radios, and TVs; chi-square = .771, df = 2, p = .856, NFI = .998 IFI = 1.000, RMSEA = 0.000), ICT (teledensity, telecom revenues & investments; chi-square = 0.180, df = 1, p = 0.671, NFI = 1.000, CFI = 1.000, RMSEA = 0.000), demographics (total population in 2001, population density, rural vs. urban population in percent of total population; chi-square = 1.718, df = 5, p = .887, NFI = .999, CFI = 1.000, RMSEA = 0.000) • not enough data to form an overall CONTEXT index. Literacy level, GDP variables, and additional demographics needed to test an overall model for CONTEXT.

14. 0, 1 d1 secsrv01 1 0, 1 d2 hostt01 0, 0, 1 content d3 hosinh01 0, 1 d4 adhost01 0, 1 d5 adhosp01 Results (4) Figure 4: Path diagram for the Content Index Chi-square = .357, df = 3, p = .949, NFI = 1.000, CFI = 1.000, RMSEA = 0.000

15. Discussion • Based on our results, the idea that connectivity or infrastructure is not the most important predictor of ICT development and there are other factors that count (GSEIS 2003) is now empirically tested: capability seems to be a better predictor of actual usage than availability • Limitation: the available data that we used are actually gathered from developed and some developing countries, and this might skew the distributions since it is well known that the lack of data for the less and least developed countries are a source of systematic variation thus skewness, which could explain the low coefficient that we have obtained and reported above.

16. Discussion • In individual models, the capability index seems to be a better predictor of Internet usage, explaining roughly 55% of the variance in e-usage. • Consistent with a few current conceptualizations, the InfoMetrics 4C model can prove to be the direction research should go further. The findings reported here support the factor independence of the 4C’s. • This further step is necessary to test whether a single index is to sought, and also the relationships between all the C factors in the model.