Erratum

Figures & data

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	Socioeconomic 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 (Waldchi^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 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.

^acorr(y,ŷ).

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.

.

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.