Digital inequalities in the Internet of Things: differences in attitudes, material access, skills, and usage

Figures & data

Figure 1. Resources and appropriation theory (simplified), Van Dijk (2005).

Table 1. Demographic profiles.

	Overall		IoT users
	N	%	N	%
Gender
Male	705	52.0	297	52.8
Female	651	48.0	266	47.2
Age
18–35	277	20.4	162	28.8
36–50	267	19.7	137	24.3
51–65	381	28.1	154	27.4
66+	431	31.8	110	19.5
Education
Low	956	70.5	380	67.5
High	400	29.5	183	32.5

Table 2. Items used for measuring IoT attitude and skills.

	M	SD
IoT attitude (α = .76) Using Internet-of-Things …
makes life less social (R)	2.41	1.05
makes people servants of technology (R)	2.35	.97
will control our lives (R)	2.42	1.02
makes people too dependent (R)	2.50	.99
is inflicted on us (R)	2.60	1.05
dehumanizes society (R)	2.69	1.01
makes it difficult to protect my privacy (R)	2.34	.98
causes mental instability (R)	3.02	.93
IoT skills (α = .93)
I know how to connect smart devices to the Internet	3.39	1.31
I know how to share information from smart devices on the Internet	3.26	1.30
I know how to operate smart devices by using applications	3.46	1.34
I know how to interpret data from smart devices	3.32	1.30
I know how to connect smart devices to my WiFi-network	3.39	1.35
I feel confident operating smart devices	3.15	1.22
I know how to change on a smart device with whom I share data	3.00	1.27
I know how to read data from smart devices	3.30	1.26
I know how to change how often data is gathered by smart devices	3.01	1.27

Note: R = reversed.

Figure 2. IoT devices (smart) used for health purposes (% of Dutch adult population).

Figure 3. IoT devices (smart) used for home purposes (% of Dutch adult population).

Figure 4. IoT devices (smart) used for home purposes (% of Dutch adult population).

Table 3. Regression analysis to predict attitudinal (OLS) and material IoT access (Logistic).

	IoT attitude (β)	Material IoT access (odd ratio)
		Any	Health	Home	Security
Gender (M/F)	−.03	0.94	0.93	1.03	0.57**
Age (ref. 18–35)
36–50	−.07	0.59*	0.67	0.62*	0.70
51–65	−.07	0.38***	0.39***	0.44***	0.59
66+	−.14	0.20***	0.33**	0.13***	0.16**
Employment (ref. employed)
Unemployed	−.03	0.75	0.75	1.15	0.47
Retired	.04	0.77	0.61	1.48	1.31
Disabled	−.01	0.86	0.89	0.77	0.55
Homemaker	.03	0.60	0.66	0.52	0.67
Student	−.01	0.83	1.37	0.39*	0.57
Education (ref. low)
Middle	−.03	1.09	1.08	1.21	0.99
High	.01	1.05	1.38	1.17	0.87
Marital status (ref. married)
Single	.00	0.56***	0.69*	0.54**	0.96
Widow	.03	0.84	0.98	0.63	1.21
Divorced	−.03	0.88	1.05	1.12	1.63
Income (ref. low)
Middle	.08**	1.31*	1.30	1.57**	1.69*
High	.10**	1.74**	1.51	2.15***	2.30**
Social support	.05	1.02	1.00	0.94	0.83*
IoT attitude		2.03***	1.96***	2.29***	1.56**
Constant		0.20**	0.09**	0.04***	0.19*
Nagelkerke R^2		.19	.16	.18	.11
Chi-square		199.94***	158.39***	171.21***	78.27***
R^2	.03
F	2.33**

*p < .05, **p < .01, ***p < .001.

Figure 5. Path model for IoT access (among IoT users).

Note: n = 563 IoT users. Paths significant at .05 level; path from attitudinal IoT access to usage IoT access – Home was added to improve model fit. R²’s in italic.

Table 4. Significant direct, indirect, and total effects of IoT access determinants.

	Direct effects	Indirect effects	Total effects	Hypothesis*
H6a.Income→IoTattitude	.12		.12	A
H7a.Income→IoTskills	x	.04	.04	PA
H8a.Income→IoTusage–Health/Home/Security	x/x/x	.01/.01/x	.01/.01/x	PA
H6b.Socialsupport→IoTattitude	x	x	x	R
H7b.Socialsupport→IoTskills	x	−.03	−.03	PA
H8b.Socialsupport→IoTusage–Health/Home/Security	x/x/−.10	−.01/−.00/x	−.01/−.00/−.10	PA
H6c.Education→IoTattitude	.09	x	.09	A
H7c.Education→IoTskills	x	.03	.03	PA
H8c.Education→IoTusage–Health/Home/Security	.11/x/x	.01/.01/x	.12/.01/x	PA
H6d.Employment→IoTattitude	x	x	x	R
H7d.Employment→IoTskills	x	x	x	R
H8d.Employment→IoTusage–Health/Home/Security	x	x	x	R
H6e.Maritalstatus→IoTattitude	x	x	x	R
H7e.Maritalstatus→IoTskills	x	x	x	R
H8e.Maritalstatus→IoTusage–Health/Home/Security	x/.12/x	x	x/.12/x	PA
H6f.Gender→IoTattitude	x	x	x	R
H7f.Gender→IoTskills	−.14	x	−.14	A
H8f.Gender→IoTusage–Health/Home/Security	x/x/−.10	−.03/−.01/−	−.03/−.01/−.10	PA
H6g.Age→IoTattitude	x	x	x	R
H7g.Age→IoTskills	−.18	−	−.18	A
H8g.Age→IoTusage–Health/Home/Security	x/x/x	−.03/−.02/x	−.03/−.02/x	PA

*A: accepted; PA: partly accepted (indirect contribution only); R: rejected.

Van Dijk, J. A. G. M. (2005). The deepening divide: Inequality in the information society. London: SAGE.

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