e Living D7.1 ICT Uptake and Usage Issue 1.0 PDF

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e-Living: Life in a Digital Europe, an EU Fifth Framework Project [IST-2000-25409] www.eurescom.de/e-living/

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e-Living D7.1 - ICT Uptake and Usage: A Cross-Sectional Analysis

Yoel Raban (ICTAF)

Tal Soffer (ICTAF)

Pencho Mihnev (Virtech Ltd.)

Kaloyan Ganev (Virtech Ltd.)


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www.eurescom.de/e-living/ Page 2 of 26

Table of Contents

1 Objectives of this report...............................................................................................................................3

2 E-Living Survey Description ........................................................................................................................3

3 Sample demographics.................................................................................................................................4

4 ICT Take-up ................................................................................................................................................5

4.1 PC & Internet take up...........................................................................................................................5

4.2 Mobile phones take up .........................................................................................................................8

4.3 Internet diffusion...................................................................................................................................9

5 ICTs use....................................................................................................................................................12

5.1 Mobile use..........................................................................................................................................12

5.2 Internet use ........................................................................................................................................15

5.3 Internet & mobile: complementary or substitutes...............................................................................18

6 ICTs impact ...............................................................................................................................................19

6.1 TV watching and Internet use ............................................................................................................19

6.2 Effects of Email use ...........................................................................................................................20

6.3 Quality of life and Internet use ...........................................................................................................22

7 Summary and conclusions ........................................................................................................................22

7.1 ICTs take up.......................................................................................................................................22

7.2 ICTs use.............................................................................................................................................24

7.3 The Impact of ICTs ............................................................................................................................25

8 Bibliography...............................................................................................................................................25


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1 Objectives of this report

The objective of the work package to which this report contributes is to describe, explain and model thechanging patterns of uptake and usage of ICT products and services across Europe. This objective relates toboth wave I and wave II of the e-Living survey (see next paragraph), which will together constitute a panelstudy. The objective of this report is to describe and analyze ICTs uptake, use and impact in the cross-sectional database of the six countries participating in the wave I survey.

Previous research has shown that demographics (education, income, etc.) play a key role in explaining thevariety of ICTs take up patterns across and within countries. Several researchers have demonstrated theimportance of demographic variables in explaining the penetration rate of ICT products and services, asdescribed in WP3 state of the art review in 2001 (OECD 1998 and 2002, NTIA 1995 and 1998, Clemente1998, and many others). The state of the art review for WP3 has also shown that explaining and predictingICTs use, and especially the impact of ICTs use, is still a formidable and challenging task.

One of the main tasks of the work package is to use sample demographics, as well as other variables, fromthe e-Living data in order to explain and predict the patterns of ICTs uptake and usage, and their impact onEuropean households. The analysis is limited at this point, since wave I provides only cross-sectional data,and panel data will be available only after wave II. Panel surveys directly measure behavioural change at thelevel of the individual sample member and thus supply information that cannot be obtained in a cross-sectional survey.

Based on cross-sectional data it could be more difficult to explain the causal links between ownership, useand impact of ICTs. We, therefore, do not attempt to explain the causality between variables at this stage.We do, however, try to estimate the impact of different variables (especially demographics) on ICTs take upand use. The words impact and predictor in the text do not necessarily infer causality.

Another estimation difficulty posed by cross-sections is the inability to include network and habit formationeffects (see Liebowitz and Margolis 1998 for explanation of network effects). Present ownership level of ICTsis strongly affected by the size of the network of users in previous periods, whereas cross-sectional data lackinformation about the past. The use of ICTs could be affected by habit formation (Dynan 2000), so thecurrent amount of use is expected to be correlated with past usage levels, which is missing in cross-sectionaldata.

We uses descriptive analysis to explain the major relationships between variables in the wave I dataset. Foreach ICT, mainly mobile phone and Internet, we attempt to find predictors of ownership, and use, guided byprevious research. After finding such predictors, multivariate analysis is used to arrive at a generallinear/logistic regression model enabling us to estimate the relative importance of each predictor.

We use the pooled sample to arrive at a general explanatory model for each ICT, and then use the sameexplanatory variables to analyze each of the six countries separately. Pooling all six countries together addvariation of the sample, and increase the sample size. However, the impact of various variables could bedifferent across countries, so separate regressions are needed for country comparisons.

The e-Living survey is described in the next paragraph, followed by some details of the main demographicvariables used in our analysis. We then analyze the uptake of mobile phones and the diffusion of Internetusers in the sample. Paragraph five describes the patterns of mobile phones use and Internet use in the sixcountries comprising the e-Living sample. In the last paragraph we bring a preliminary analysis of some of

the impacts of ICTs use.

2 E-Living Survey Description

The e-Living survey is a household panel survey carried out in six countries: UK, Norway, Germany, Italy,Bulgaria and Israel

1. The aim of wave 1, conducted in October to December 2001 was to recruit a

representative sample of roughly 1750 households within each country by computer assisted telephoneinterviewing (CATI) in all countries except Bulgaria where telephone penetration was insufficient for thismethod to be practical. As a result face to face interviewing (CAPI) was used in Bulgaria.

In the CATI method random digit dialling was used to select households at random. One adult (aged 16+) ineach contacted household was selected using the last birthday method and was asked to respond to the

1 For full details see E-Living Deliverable D6: Wave I Documentation and Integrated Dataset. See www.eurescom.de/e-living/index.htm


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survey. The questionnaire covered standard socio-demographics, ICT ownership and use as well as detailsof work, education, skills and social interaction. The survey also contained modules on attitudes to ICTs, theenvironment, measures of quality of life and some prospective questions on likelihood of purchase of ICTs

2.

Final response rates are shown inTable 1.

Table 1: e-Living Wave 1 response rates

Final achieved sample (N) Achieved response rates (% of contacts whocompleted the survey)

UK 1760 36%

Italy 1762 42%

Norway 1753 42%

Germany 1756 35%

Israel 1750 39%

Bulgaria 1753 79%

Total 10534

This means that, for example, some 4889 people were contacted in the UK to achieve the sample of 1760whilst in Bulgaria it was only some 2219. This illustrates not only the higher response rates (and thus lessbiased sample) that face to face interviewing achieves but also, perhaps, a higher propensity amongstBulgarians to respond to surveys given that response rates for CAPI in the UK are often as low as 50-60%.

Wave 2 of the survey will go into the field in October to December 2002 and will attempt to re-interview allthose who responded at wave 1 together with their partner if present.

3 Sample demographics

Education level in the e-Living sample is based on the highest qualification attained by respondents. Thequalification scale is complicated, and goes from no qualification up to a PhD University degree. For

statistical analysis purposes we grouped the scale to 5 levels and 3 levels (primary or less, secondary andhigher education, see Table 2)

3. Education is expected to be a strong predictor of ICTs ownership and use,

especially with regard to Internet use, which still requires a high level of PC skills.

Table 2: Education level by country (sample)

Country Primary school

(Or less)Secondary school Higher education

(Including post secondary)

UK 41.5% 31.7% 26.8%

Italy 16.9% 68.9% 14.1%

Germany

38.3% 37.5% 24.3%

Norway 13.3% 45% 41.7%

Bulgaria 11.6% 54.6% 33.8%

Israel 6% 47.3% 46.7%

2

The UK translation of the questionnaire with routing codes is available at www.eurescom.de/e-living/deliverables/e-Living-D4-Wave-1-Questionnaire-FINAL.zip

3The classification scale may not be the same for every country, and a unified education level measure for all 6 countries has not been

developed yet.


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The sample distribution of monthly family incomes in Euros is described in table 3, including Bulgaria wherea different scale was used. We can see that in Norway there is a relatively large group of families withincomes above 4787 Euros. Norway is also the country with the highest per capita GDP. Family income isexpected to be an important predictor of ICTs ownership and use.

Table 3: Monthly family income by country sample (Euros)

Country >798 799-1594 1595-3190 3191-4786 4787+

UK 25.1% 17.8% 25.7% 16.7% 14.7%

Italy 28% 36.8% 26.5% 4.3% 4.4%

Germany 9.3% 26.6% 40.4% 15.4% 8.2%

Norway 1.7% 11.5% 23.8% 18% 45.1%

Israel 17.3% 29.1% 27.8% 12.9% 12.8%


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Internet access rates are similar in Israel, UK, Germany, and Italy 30% to 40%, while broadbandhouseholds take up rates are still low, with the exception of Germany.

Figure 1: PC ownership and Internet Access in e-Living households

By comparison, the eEurope Benchmarking Report for 2002 states that Internet penetration rate in the EUreached 38% in December 2001. In the US 56% of households owned a PC, and 50% had Internet accessin September 2001 (A nation online, 2002).

Buying intentions of home PC among non-PC households are within the range of 5% to 10%, although thismeasure is known to be an over estimation of eventual real actions. Buying intentions of Internet accessamong non-Internet households are similar in magnitude 2% to 8%.

Figure 2: PC ownership and Internet Access by income level(excluding Bulgaria)

PC ownership and Internet access, including broadband access, are strongly dependent on family monthlyincome levels, as shown in Figure 2. PC ownership increases from 17% in households with monthly incomeof 800 Euros, to 83% among households with monthly income of 4800 Euros and above. The same trend isalso apparent in Bulgaria, where PC ownership and Internet access climb from zero for low incomes (lessthan 51 Euros) to 26% and 13% respectively for higher family incomes (more than 254 Euros).

A similar pattern can be found with respect to the education level of respondents (Figure 3). PC ownershipand Internet access increases from 29% and 18% (respectively) for householders with primary schooleducation to 72% and 58% for householders with higher education degree. This pattern is also found in theBulgarian sample, where PC ownership increases from 3% (Internet access 2%) for householders withprimary education to 18% (Internet access 10%) for householders with higher education.


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Similar patterns are found in the US by recent studies, A Nation Online 2002, and The UCLA InternetReport 2001.

Figure 3: PC ownership and Internet Access by education level

Figure 4describes the main location where householders access the Internet. The general pattern is similarwith the exception of Bulgaria. In most countries between 52% and 60% of Internet users are home users,except for Bulgaria (15%). The workplace (or college) is used to access the Internet by 29% to 42% of usersin all 6 countries. Accessing the Internet in public libraries and cyber cafes is not a common practice in mostcountries with the exception of Bulgaria. In Bulgaria these public places are used to access the Internet by43% of all Internet users. This pattern of behavior in Bulgaria could be attributed to several factors, amongwhich are:

The high costs of PC & Internet equipment and access (in relation to the incomes) The technological infrastructure of the public telephone network is relatively unreliable for Internet

use (usually the Cyber Cafes maintain dedicated high-speed leased lines vs. the slow dial-upconnections which regular user can afford at home)

The Internet service is rather new, attracting at the moment only a fairly small percentage of thepopulation, mainly early adopters groups of young persons with limited financial means.


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Figure 4: Place where Internet is used the most

4.2 Mobile phones take up

Mobile ownership rates in the e-Living country samples range from 77% (Norway) to 9% (Bulgaria). Israel,UK and Italy follow Norway with 68% to 71%, and Germany with 61%. Mobile ownership is also strongly

related to demographics, such as income and age (Figure 5, Table 4). In Bulgaria mobile ownership in the55+ age groups is negligible, mainly due to relatively low family incomes and high ownership and use prices.

The coefficients in Table 4 measure the effect of a unit increase in the independent variable on the log oddsof the dependent variable. The negative impact of age on mobile ownership is very significant in allcountries, as also observed in Figure 5. Households income

6has a positive impact on mobile ownership in

all countries, except in Italy and in Norway. Gender is significant in five countries, where woman ownershiprate is smaller than men (the UK is the exception). The amount of Interest in new technology (scale) is apositive predictor of mobile ownership in five countries (except in Israel). Mobile phones are a necessity inthe workplace, and in many cases are simply given to employees by their employers (work status is a partialmeasure of this effect). Work status is a significant predictor of mobile ownership in four countries (Germanyis the exception). Education level has a positive significant effect only in Italy, Germany and Israel.Household size has a negative significant effect only in Israel. This may be an indication for the presence of

young children, or older persons, who may not own a mobile phone. Individuals in larger families could alsoshare mobile phones for economical reasons. The number of close friends is positively related to mobileownership only in Israel.

Figure 5: Mobile ownership by age for each country

We can see, that in the pooled sample most of the explanatory variables have significant effects on mobileownership. Pooling the 6 countries together results in increased range of variation, especially indifferentiating variables, such as households size and the number of close friends. The relatively lowexplanatory power could be partially attributed to the lack of network effect (large number of users increaseprobability of purchase), which could only be measured in time series.

Table 4: Mobile ownership (0,1) logistic regression results (Statistically significant beta values, p

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e Living D7.1 ICT Uptake and Usage Issue 1.0 PDF