Age for learning, age for teaching: the role of inter-generational, intra-household learning in Internet use by older adults in Latin America

Figures & data

Figure 1. Individuals using the Internet (% of population). Source: World Development Indicators – World Bank. Compiled by authors.

Figure 2. Percentage of US adults who use the Internet – 2012. Source: Zickuhr and Madden (2012).

Figure 3. TAM model with sociodemographic variables. Source: Adapted from Lee et al. (2014) and Niehaves and Plattfaut (2014). Compiled by authors.

Table 1. Regression variables characterization and summary statistics.

Type of variable	TAM operationalization	Indicator	Average	Std. dev.	Median	Min	Max
Dependent Variable	Use Internet	Used Internet at least once^d	0.38	0.49	0	0	1
	Days of Internet use last month	Number of days of Internet use	7.45	11.42	0	0	30
Hypothesis variables	Children presence	Number of children in household	0.23	0.63	0	0	4
	Spouse presence	Married or cohabiting^d	0.55	0.50	1	0	1
Explanatory Variables	Socio-economic status	Years of education	9.52	4.22	11	0	18
		Total average monthly spending	1202.9	1077.1	976.8	106.5	15577.4
	Perceived usefulness	Agrees: Internet is a need^d	0.62	0.49	1	0	1
	Perceived enjoyment	Agrees: Internet to be integrated^d	0.85	0.36	1	0	1
	Internet Stress	Agrees: Internet is a waste of time^d	0.23	0.42	0	0	1
	Subjective Norm	Learned to use with family or friend^d	0.17	0.38	0	0	1
	Perceived Ease of Use	Has a job^d	0.40	0.49	0	0	1
Control Variables	Geographic Location (country)	Argentina^d	0.48	0.50	0	0	1
		Peru^d	0.26	0.44	0	0	1
	Age	Age in years	65.55	4.24	65	60	91
	Gender	Male^d	0.47	0.50	0	0	1
	Devices	Devices per capita in the household	1.02	0.65	1	0	4
		Has own device^d	0.83	0.38	1	0	1

Note: The superscript d indicates the dummy variables.

Source: Survey about Internet Use: Platforms and Open Data – 2014. Compiled by authors.

Table 2. Hurdle regression.

Variables	(1)	(2)
	Probit	TNB2 REG
Number of children	0.347***	0.0001
	(0.120)	(0.052)
Presence of spouse	0.132	0.240***
	(0.175)	(0.085)
Years of education	0.107***	0.012
	(0.024)	(0.014)
Total average monthly spending	0.00007	0.00002
	(0.0001)	(0.00002)
Agree: Internet is a need	0.762***	0.256**
	(0.195)	(0.130)
Agree: Internet to be integrated	−0.375	0.307
	(0.235)	(0.199)
Agree: Internet is a waste of time	−0.539**	0.258**
	(0.228)	(0.117)
Has job	0.693***	0.045
	(0.177)	(0.085)
Country: Argentina	0.894***	0.181
	(0.242)	(0.138)
Country: Peru	1.178***	0.106
	(0.261)	(0.135)
Age	−0.225	−0.025
	(0.336)	(0.127)
Age squared	0.002	0.0002
	(0.003)	(0.001)
Gender: male	−0.173	−0.145
	(0.178)	(0.102)
Devices per capita in household	1.038***	0.041
	(0.188)	(0.057)
Has own device	0.863**	0.768
	(0.413)	(0.497)
Learned to use from family - friends		−0.060
		(0.076)
Constant	1.951	2.283
	(11.360)	(4.440)
Observations	403	148
p-Value (Wald chi^2)	0.00	0.00
Pseudo R^2	0.427	0.029
Count R^2	0.791

Notes: Robust standard errors in parentheses.

* p < 0.1.

** p < .05.

*** p < .01.

Table 3. Marginal effects, Hurdle regression.

Variables	(1)	(2)
	AME Probit	AME TNB2 REG
Number of children	0.073***	0.002
	(0.025)	(1.063)
Presence of spouse	0.028	4.766***
	(0.037)	(1.616)
Years of education	0.023***	0.238
	(0.005)	(0.292)
Total average monthly spending	0.00002	0.0003
	(0.00002)	(0.0003)
Agree: Internet is a necessity	0.163***	4.799**
	(0.041)	(2.245)
Agree: Internet to be integrated	−0.079	5.515*
	(0.050)	(3.121)
Agree: Internet is a waste of time	−0.114**	5.830**
	(0.048)	(2.911)
Has job	0.152***	0.900
	(0.040)	(1.705)
Country: Argentina	0.180***	3.697
	(0.044)	(2.863)
Country: Peru	0.265***	2.186
	(0.056)	(2.827)
Age	0.005	−0.090
	(0.005)	(0.262)
Gender: male	−0.036	−2.940
	(0.037)	(2.076)
Devices per capita in household	0.218***	0.825
	(0.035)	(1.150)
Has own device	0.171**	10.943**
	(0.071)	(4.767)
Learned to use from family – friend		−1.212
		(1.541)
Observations	403	148

Notes: Robust standard errors in parentheses. dy/dx for dichotomous variables calculated as the discrete change.

* p < .1.

** p < .05.

*** p < .01.

Table A1. Goodness-of-fit statistics – discrete choice models.

Goodness of fit	Discrete choice models
	Probit	Logit
Log verosim (null)	−264.9618	−264.9618
Log verosim (model)	−151.7035	−152.1946
AIC	335.407	336.3893
BIC	399.39	400.3723
Pseudo R^2	0.4275	0.4256

Note: Compiled by authors.

Table A2. Regression – overdispersion test.

	Coefficient	Standard error	t-Test	p-Value	Confidence interval
	0.50971	0.11097	4.59	0	0.29157–0.72786

Note: Compiled by authors.

Table A3. Goodness-of-fit statistics – count models.

Goodness of fit	Count models
	Hurdle Poisson	Hurdle NB
AIC	1510.341	1284.309
BIC	1658.302	1436.269
Squared correlation^a	0.421	0.422

Note: Compiled by authors.

^a .

Table A4. Prediction table – probit.

y			Accuracy rate
	0	1^a
0	231	24	91%
1	62	96	61%
Total	293	120	79%

Note: Compiled by authors.

^a .

Table A5. Correlations matrix – regression’s variables.

days_inter	1
Child	0.039	1
Spouse	0.094	−0.035	1
Yearseduc	0.366*	−0.152	0.118	1
Expendit_pm	0.172*	0.130	0.053	0.137	1
inter_need	0.298*	0.078	0.097	0.159	0.190*	1
inter_integ	0.135	0.053	−0.022	0.063	0.0889	0.299*	1
inter_waster	−0.188*	0.103	0.054	−0.204*	−0.044	−0.103	−0.074	1
Learn_fam_fri	0.501*	0.054	0.010	0.178*	0.096	0.159	0.053	−0.123	1
Job	0.250*	−0.004	0.064	0.078	0.065	0.095	0.115	−0.01	0.168*	1
Country_arg	0.007	−0.182*	−0.086	−0.166*	0.073	−0.203*	−0.016	−0.067	−0.028	−0.010	1
Country_per	0.1660*	0.148	0.033	0.092	−0.128	0.031	−0.022	−0.160	0.170*	0.107	−0.570*	1
Age	−0.0585	−0.0549	0.0013	−0.054	−0.059	0.020	0.026	−0.003	−0.043	−0.231*	−0.027	0.155	1
Male	0.0073	0.023	0.222*	0.063	0.037	0.025	0.005	−0.002	0.055	0.220*	−0.189*	0.202*	0.127	1
Device_pc	0.4729*	−0.092	−0.030	0.410*	0.165*	0.224*	0.156	−0.144	0.340*	0.152	−0.027	0.006	−0.091	0.001	1
Device_own	0.2942*	0.029	0.048	0.286*	0.094	0.232*	0.050	−0.058	0.193*	0.164*	−0.221*	0.112	−0.083	0.053	0.499*	1

Note: Values adjusted for the Sidak correction. Symmetrical matrix. Compiled by authors.

*Correlation significant at 10% or less.

Zickuhr, K., & Madden, M. (2012). Older adults and internet use. Pew Research Center’s Internet & American Life Project.

 Google Scholar

Lee, E., Han, S., & Chung, Y. (2014). Internet use of consumers aged 40 and over: Factors that influence full adoption. Social Behavior and Personality: An International Journal, 42(9), 1563–1574. doi: 10.2224/sbp.2014.42.9.1563

 Web of Science ®Google Scholar

Niehaves, B., & Plattfaut, R. (2014). Internet adoption by the elderly: Employing IS technology acceptance theories for understanding the age-related digital divide. European Journal of Information Systems, 23(6), 708–726. doi: 10.1057/ejis.2013.19

 Web of Science ®Google Scholar