Determination of Internet appropriation by older people through technological support services

Abstract

While social support—friends, family, and colleagues—is one of the main sources of encouragement and digital literacy for older people, there is evidence that suggests that they may prefer formal sources of support. This study aims to verify the capacity of technological support services to promote literacy and Internet use by older people in multiple contexts. The participants (N = 560) were retired adults (over 54) residing in Spain and habitual users of technological support services in four organizational contexts (nursing homes, community senior centers, university programs for seniors, and adult education programs). The results show the moderating role of organizational contexts on the capacity of formal technological supports to determine the use and appropriation of the Internet by older people. Finally, the findings are discussed in terms of their usefulness for initiatives that adapt to the needs of the users of these services.

Keywords

Digital divide digital literacy digital skills Internet access older adults

Introduction

According to Eurostat’s (2019a) data from 2005 to 2019, in the European Union, the population growth in those over 54 years of age almost quadruples that of the general population. This aging population poses opportunities and challenges for the economy, health, and social systems (Schulz et al., 2015).

Although there is evidence that supports the potential of the Internet to optimize the well-being of the elderly or compensate for their losses—physical, emotional, or social—associated with aging (Kamin et al., 2016), a high percentage of older people continue to be disconnected from the digital world (Eurostat, 2019b). This increases their likelihood of becoming isolated from societies where technology is increasingly present in public services and in private life (Friemel, 2016). To help older people benefit from the resources of the digital world, both international organizations (e.g. WHO, 2016) and the scientific community (e.g. Kamin et al., 2016; Seo et al., 2019) are taking notice of technological support services for seniors (TSSS).

For instance, recent studies (e.g. Friemel, 2016; Hunsaker et al., 2019) underline the need for research to place more emphasis on the analysis of the institutional and public supports that can assist older people in obtaining benefits and appropriating new media. Politically, “the global strategy and action plan on ageing and health 2016–2020” (WHO, 2016) recognizes the capacity of centers for older people to promote social inclusion for the older population and establishes among its objectives to promote the development of courses and actions aimed at the proper use of information and communication technologies (ICTs). On one hand, previous studies that analyzed public policies for Internet access (Millward, 2003; Morris, 2007) coincided in emphasizing that public access centers represented an advance in the digital inclusion of the elderly; however, these measures are not sufficient if they are not linked to training programs. On the other hand, empirical studies that have examined ICT adoption by older people suggest that Internet access should be situated locally, within particular organizational and community contexts, and adapted to users (Seo et al., 2017).

Considering these premises, this study tried to cover the scarcity of findings that help to understand the contributions of TSSS based on the social and organizational context from where the support is offered.

In order to identify the different types of TSSS to take advantage of the use of the Internet, this study focused on four organizational contexts; nursing homes (NH), senior community centers (SCC), university programs for seniors (UPS), and adult education programs (AEP). Also, to focus the study, the authors used the model for replication of inequalities in a digital society (RIDS) proposed by Van Deursen and Helsper (2015). From this approach, Internet access is assumed as a process of appropriation in which supports are relevant to overcome the technical and personal difficulties of older adults in the use of technology (e.g. Francis et al., 2018; Friemel, 2016; Quan-Haase et al., 2017), being able to compensate for the differences associated with aging.

Background

The RIDS model (Van Deursen and Helsper, 2015) treats Internet access as a process of appropriation (Selwyn, 2004; Van Dijk, 2005) that advances from the material—devices—and physical—autonomy—access to personal or social benefits (Van Deursen and Helsper, 2015). In this model, TSSS may mean to compensate for the differences—regarding the appropriation of the Internet—due to their sociodemographic differences. The hypotheses of the study are then argued through three aspects of the model: sociodemographic determinants, levels of Internet appropriation, and technological support systems.

Aging and Internet appropriation

Scholars have also argued that “there is a strong association between age and the so-called digital divide” (Neves and Amaro, 2012; Selwyn, 2004) and have coined the term “grey divide” (Millward, 2003) to describe the lack of “access, skills and/or knowledge that can result in older citizens being ‘information poor’” (Kania-Lundholm and Torres, 2015). However, research in this area has also been criticized for treating older adults as a homogeneous group and neglecting the diversity in older adult’s lives and how they use digital technologies (e.g. Rasi and Kilpeläinen, 2016). Currently there is sufficient evidence (e.g. Van Deursen and Van Dijk, 2018; Wagner et al., 2010) to affirm that older adults “do not uniformly conform to technology-averse stereotypes” (Barnard et al., 2013) and that “focusing on chronological age offers incomplete explanatory power” (Hardill and Olphert, 2012: 1311). In fact, although a large part is still offline or in the process of appropriation, others are users as skilled as the younger generations (Hargittai and Dobransky, 2017; Nimrod, 2016; Quan-Haase et al., 2016).

Currently, healthy aging is one of the core tasks of WHO, it is the process of promoting and maintaining functional capacity that allows older people to continue to be a resource for their families, communities, and economies (Beard et al., 2015). In this framework, the appropriation of the Internet by older people may imply the development of capacities to satisfy their basic needs, learn and make decisions, build and maintain relationships, and contribute to society (Kamin et al., 2016). Consequently, healthy aging policies should focus on the diversity of older people in terms of Internet access and use.

Levels of Internet appropriation in older people

In the RIDS model, Internet access is assumed as a three-level appropriation process (Van Dijk, 2005, 2006). The first level of access focuses on the differences of individual access to infrastructure and technical means (Van Deursen and Van Dijk, 2018), including factors such as autonomy to access (Newhagen and Bucy, 2004). In a recent study by Van Deursen and Van Dijk (2018), within a representative sample in the Netherlands, they observed an inverse correlation with age in terms of the diversity of devices used. However, although there is evidence to show that having diverse material access does not guarantee beneficial results (Van Deursen and Helsper, 2015), the autonomy of Internet access is associated with self-reported skills and the engagement in online activities that increase capital (Hargittai and Hinnant, 2008).

As the access to resources among citizens is generally overcome, a second level of access is defined in the type of use and the skills required for a productive use of ICTs (e.g. Witte and Mannon, 2010; Zillien and Hargittai, 2009). Recent studies in advanced countries such as Denmark (Van Boekel et al., 2017) and Germany (Schehl et al., 2019) have shown that older adults are a heterogeneous group, in which various categories of Internet users are identified according to frequency, type, and extent of use. However, other qualitative studies carried out in the United States (e.g. Heart and Kalderon, 2013) and Canada (Schreuers et al., 2017) showed that the range of activities conducted by older adults through technology is limited; so that their skills will be limited in the context of these activities, reducing the range of (digital) activities that could benefit their lives. This is what Schreuers et al. (2017) referred to as the digital skills bubble, where some activities and skills are very familiar, while others are not. Thus, and according to these authors, in order for these limitations to be overcome, the bubble will have to expand, so that older adults improve their skills. But this can only occur if they have more opportunities and social support to try and use ICTs (Francis et al., 2018).

Once people have digital skills and are able to perform a wide repertoire of activities through the Internet, the third level of access refers to the offline benefits that can be obtained through this use. There is much literature that has analyzed the benefits of Internet and ICT use in older adults, and how these technologies can meet their needs: communicate with their families and friends (e.g. Sum et al., 2008), widens their permanent learning opportunities (e.g. Chen and Persson, 2002), allows access to health-related information (e.g. Chaffin and Maddux, 2007), and helps in exploring resources to satisfy their political needs (e.g. Gatto and Tak, 2008; Xie and Jaeger, 2008), among other benefits.

TSSS

The social environment and supports are factors that determine Internet access and appropriation (Brandtweiner et al., 2010; Van Deursen and Helsper, 2015). Among older people, social support—family, friends, and colleagues—constitute some of the main stimuli and sources of literacy for the use of the Internet (Brandtweiner et al., 2010; Friemel, 2016). However, the support given can lack immediacy, leaving older adults dependent on others’ availability to provide it (Hunsaker et al., 2019). In this sense, qualitative studies have found that older people may prefer to opt for formal sources of support (Seo et al., 2019), due to the family burden that it may entail (Peek et al., 2016), or the feeling of dependency from one’s own family or friends (Schreuers et al., 2017).

In order to compensate for differences in Internet access among older citizens, many policies and programs have been proposed to promote universal access to the Internet through TSSS (WHO, 2016). TSSS can be defined as community-based services promoted by public institutions to facilitate Internet access and literacy support for older people (Kamin et al., 2016). For example, in Spain, universities have done this through programs such as the UPS, which have facilitated Internet use by, and digital literacy for, older adults through courses. Likewise, within the formal education system in Spain, AEP also develop media and digital literacy courses. Other public policies have implemented publicly owned telecenter networks that aim to “facilitate and streamline Internet access through networked telecenters and libraries,” from senior centers and NH (Alcalá, 2014).

Previous studies on the effectiveness of TSSS have shown that physical access to the Internet is insufficient if they are not linked to digital training programs (Millward, 2003; Morris, 2007). Early initiatives have shown the benefits of literacy support for older users of community resources. For instance, Shapira et al. (2007) employing a quasi-experimental research design conducted a course in computer operation and Internet browsing with 22 older adults (mean age of 80) who resided in NH. The results of the intervention showed improvements in their interpersonal interactions, stimulated their cognitive functioning, and improved their sense of control and independence.

In line with healthy aging policies, there is evidence that the effectiveness of technological support service interventions will depend on their ability to access and adapt to the diverse needs and profiles of users. For instance, the training programs offered by Older Adults Technology Services, located primarily in SCC in New York City, supported the selective shaping of social contexts by optimizing resources and by providing means to compensate for potential risks (e.g. isolation, lack of support). Participants used more communication tools (e.g. email, texting), which allowed them to create new opportunities for enhanced contact (Kamin et al., 2016). Another recent community project in a senior center aimed at the digital literacy of African Americans (Seo et al., 2019) showed an increase in the confidence, access, and technological capacity of the participants in the program. Among the keys to success, the instructors’ focus on the learning styles, attitudes, and unique interests of the users of the center was identified. Likewise, using data from a German research project (EMN-Moves) with community-dwelling older adults living in the urban region of Nuremberg, Kamin et al. (2019) showed that technology-related support was associated with a greater number of devices used, although for older people to receive support, their awareness of the availability of support is important.

Hypotheses

From previous findings, the authors sought (a) to identify if there is a gap regarding the appropriation of the Internet among users of TSSS, (b) to verify the extent to which the literacy support available determines their appropriation of the Internet, (c) to identify the extent to which this determination is moderated by the type of TSSS, and (d) to identify whether as the age range increases, the extent to which literacy support determines Internet appropriation increases. For these purposes, the following arguments and hypotheses were formulated.

First, previous studies about TSSS have concluded that physical access to the Internet is insufficient if it is not linked to digital training programs (Millward, 2003; Morris, 2007). Therefore, given the heterogeneity of the organizational contexts in which the TSSS are located, it is reasonable to assume that the efficiency of these programs is also heterogeneous. The hypothesis is as follows:

H1. The use of TSSS does not guarantee the appropriation of the Internet (material-physical access, skills, online activities, and perceived benefits).

Second, an advanced chronological age is usually associated with a lack of use and exploitation of ICTs, but it is a weak predictor, as other variables associated with chronological age are not considered, such as gender, income, level of education, employment, or work activity (e.g. Van Deursen and Van Dijk, 2018). The hypothesis is as follows:

H2. Older men in a younger age range and high income and educational level are more likely to have a higher level of Internet appropriation than women, older seniors, and those with lower income and educational level.

Third, there is evidence that has shown the effectiveness of TSSS to increase the trust, access, and technological capacity of the elderly (e.g. Kamin et al., 2016; Seo et al., 2019). However, this effectiveness will depend on the availability of literacy supports (e.g. Kamin et al., 2019). Therefore, the hypothesis formulated is the following:

H3. Older adults who perceive a high availability of literacy support are more likely to have a higher level of Internet appropriation than those who perceive less support.

In fourth place, empirical evidence suggests that literacy support must be adapted to the social context of the users and located in a particular organizational context (e.g. Seo et al., 2017, 2019). Considering that TSSS serve a socio-demographically heterogeneous population (Tirado-Morueta et al., 2020), the following hypothesis is posited:

H4. The determination and prediction of sociodemographic characteristics and literacy support on Internet appropriation is moderated by the type of TSSS.

The availability of informal support sources can be a key driver of whether support occurs (Hunsaker et al., 2019). However, there is evidence that showed that older people may prefer to opt for formal sources of support (Peek et al., 2016; Schreuers et al., 2017). In this sense, Hunsaker et al. (2019) raised the need for studies that help characterize the timeline of technological support as well as its effects on the use of digital media. Supported by these postulates, the following hypothesis is raised:

H5. As age increases, the extent to which literacy support determines and predicts Internet appropriation increases.

Method

Participants

In order to test the hypotheses, four types of TSSS used by retired adults in Spain were considered. At the time of the study, in Spain the pre-retirement age was 55 years, and for this reason, the participants were retired adults (>55 years), who used the Internet—at least once in the previous 3 months—and were regular users of TSSS. The TSSS were located in four organizational contexts:

NH. These refer to homes or establishments in which social support activities are developed for the elderly through collective housing, temporary or permanent use that include food, health care, hygiene, and comfort, promoting the coexistence and leisure time for elderly residents. The NH that participated in the study had classrooms with Internet access, and computer workshops were held regularly.

SCC. These are non-residential Social Services facilities, intended to promote coexistence among the elderly, encouraging participation and social integration. They offer sociocultural, occupational, artistic, and recreational activities. These centers house classrooms with computers and Internet access, and computer workshops are held regularly.

UPS. These arose in the 1970s, were designed to improve the basic skills of older adults in order to promote their social, cultural participation and personal development. UPS offers digital literacy workshops.

AEP. In these centers, formal educational plans are offered to obtain the official Compulsory Secondary Education diploma. They also offer non-formal education plans. In their educational plans, subjects in the field of communication are offered.

In the absence of reliable data concerning the population of older adults attending these TSSS, non-probability quota sampling was conducted, in order to obtain a similar number of subjects in each institutional support system. Quotas of 140 adults over 54 and Internet users were established in the four types of TSSS (N = 560). The selection of the centers and participants was random among the users of the four TSSS in three cities of autonomous communities which have a low, medium, and high per capita income and aging index (INEbase, 2019a, 2019b): Huelva, Asturias, and Madrid. Although the TSSS are not mutually exclusive (one user can be a user of multiple services) the participants were exclusive users of one of them.

In order to obtain an unbiased sample within the same type of institutional support, a maximum of five questionnaires were distributed with the prior authorization of the entity’s administrator. Questionnaires were administered during the third week of October 2017.

The mean age of the respondents was 67.78 years. Broken down by type of context, the mean age of the respondents in NH was 73.28, in SCC 69.74, in UPS 63.98, and in AEP 63.91. Regarding gender, 56.7% were female, 56.4% being female in NH, 55.6% in SCC, 64.0% in UPS, and 50.4% in AEP.

Measurements

To test the hypotheses, variables relating to the demographic characteristics of the sample (gender and age) and socioeconomic status (educational level and monthly income) were controlled. Many studies of digital inequality have found that age, gender, education, and income are associated with Internet use (e.g. Van Deursen and Van Dijk, 2018) and with capital-enhancing activities and digital skills (Hargittai and Dobransky, 2017). Covariates included chronological age re-codified into a categorical variable with three categories based on the retirement age (from 55 to 64, from 65 to 74, and over 74 years), gender (female and male), educational level (primary, secondary, and tertiary studies), and monthly income taking as a reference the pension bracket in Spain (less than 600 Euros, from 601 to 1200 Euros, from 1201 to 1800 Euros, and more than 1800 Euros).

In order to measure the literacy support received from the TSSS, the Eurostat scale (2018c) was used, with values from 1 (never) to 4 (very frequently). This scale measured the degree of digital literacy provided by the institution’s teachers for older people to learn to use the Internet safely, find information, create materials, and connect with other people (α = .95). The variable literacy support was the mean value of the responses to all the items on the scale.

To operationalize the dimensions of appropriation of the Internet, the following measures were used:

Technical means refer to the availability of adequate equipment to carry out a given online activity (Van Deursen and Van Dijk, 2018): a dichotomous scale (No = 0; Yes = 1) was used to ask the respondents for the number of Internet access devices (smartphone, tablet, laptop, desktop computer, others). The variable technical means was the sum of the responses (min = 1 and max = 5).

Autonomy to access the Internet refers to freedom to use the technology when and where one wants without constraint from others such as lines of library patrons or employer supervision (Hagirttai, 2008; Hargittai and Dobransky, 2017). A dichotomous scale (No = 0; Yes = 1) was used to assess the number of places from where the respondent accessed the Internet at least once a week (home, the street, social centers, educational centers, and community services). Finally, the variable autonomy of access was the sum of the places of access reported by the respondents (min = 1 and max = 5).

Digital skills refer to skills that enable users to derive the full benefits that access can provide (Hargittai and Hinnant, 2008). To measure the digital skills a synthesis of the scale by Goldhammer et al. (2013)—skills for access, management and diffusion of information—related to basic skills, was used, with values from 1 (not true) to 4 (completely true) (α = .95). The digital skills variable was the mean value of the responses to each item.

To operationalize online activities and perceived benefits (or purposes of use) by older people, the organizational scheme suggested by Schulz et al. (2015) was used. This framework distinguishes (a) social connectedness and (b) activities of daily life and leisure as domains of life in which the development of technology at the service of aging must advance. It also differentiates (a) online activities—personal environment—and (b) the benefits—utility—that technologies can provide to prevent or compensate for deficits associated with aging or improve their well-being:

To measure the online activities, 21 activities—relating to social connectivity, social life, and leisure—were considered, using items from scales proposed by Van Deursen and Helsper (2015), with values from 1 (never) to 7 (continuously). Following an exploratory factor analysis, the following categories were obtained—social interactions (e.g. I talk with my family/friends, share information) (α = .90), learning (e.g. I do my academic tasks, I look for information about . . ., I do online courses) (α = .67), civil life (e.g. I participate in associations, read e-books, shop online, make travel reservations) (α = .88), and entertainment (e.g. I play online, listen to music, read the press) (α = .86).

To measure the perceived benefits, 19 benefits—relating to social connectivity, social life, and leisure—were considered. A selection of items was used from the scales used by Papachariss and Rubin (2000)—α values greater than .78—and by Ku et al. (2013)—α values greater than .85—on the gratifications obtained through the use of the Internet, with values of 1 (disagree) to 4 (totally agree). An exploratory factor analysis was performed and three categories were obtained. Social contact refers to the need for psychological connection with others and to create positive human interaction (e.g. I use Internet to connect with people, to belong to a group, to interact with my friends easily, to feel closer to my friends/family) (α = .92). And social enhancement refers to values an individual derives from gaining acceptance and the approval from others as well as enhancing his or her social status within the online network (e.g. I use Internet to let others know I care for them, to look friendly, to look intelligent, to express myself freely) (α = .93). Evasion refers to the fun and enjoyment an individual derives from interacting with others in an online network (e.g. I use Internet to escape, to be glad, to entertain myself, to relax) (α = .94).

Supplemental Appendix 1 includes more details about each scale’s wording and descriptive results as well as the convergent validity of variables analyzed—average variance (AVE), factorial loading, composite reliability (CR), and Cronbach’s alpha of each scale.

Data analysis

To test H1 the K-means algorithm (MacQueen, 1967) was used with the Statistical Package for the Social Sciences (SPSS, version 22.0) software. Cluster analysis is the most commonly used technique in studies identifying media user types (Brandtzæg, 2010). In K-means cluster analysis, K indicates the number of clusters and it is an input parameter. In addition, all variables must be within the same range when they are entered into the analysis; in the dataset used, all variable values were 0 or 4. The quartiles of each variable were obtained by assigning a value of 1–4, with 1 constituting 25% of those surveyed with the lowest score, and 4 constituting 25% with the highest score. Supplemental Appendix 2 shows the data characteristics and their transformation into quartiles.

One of its main weaknesses is that the K number must be determined in advance (Wu et al., 2018), so in order to determine the optimal number of these, the algorithm was executed repeatedly with different K values, comparing the results obtained, considering the number of iterations necessary for convergence (convergence factor), the size of F, the Euclidean distance (similarity measure), and the number of subjects (Muñoz Baena, 2013). In this case, the optimal solution was K = 2 because it was obtained with the least number of iterations (6th), it showed the greatest Euclidean distance between the initial centers (9487), and the size of F was greater than that of the rest of solutions.

Hence, to test H2 to H4 a logistic regression was used, where K is the dependent variable (number of clusters; that is, typology of users according to the degree of appropriation of the Internet). Since K < 3, a binary logistic regression (BLR) was used. The primary objective of this technique is to model how it influences the probability of occurrence of a dichotomous event. To avoid the problem derived from having categorical predictors with limited cases in each category, descriptive statistics were run on each of their predictors and categories were contracted. A diagnosis of multicollinearity was also carried out, which verified that sociodemographic variables and literacy support were not strongly related, showing tolerance values higher than 1 and Variance Inflation Factor (VIF) lower than 10. Finally, outliers or cases that are not well explained by their model were verified. For this, the residuals were specified and outliers were eliminated. Likewise, to test H5, a BRL was applied to the fragmented sample in 55–64, 65–74, and over 74 years old.

Results

Table 1 specifies the sociodemographic characteristics of the sample. In general terms, UPS users tend to be younger and of medium-high socioeconomic status. NH and AEP users tend to have a medium-low socioeconomic level. SCC users are usually older and belong to an upper-middle level. In addition, it should be noted that the older users are found in NH and SCC. Regarding the provision of literacy support, it should be noted that TSSS users in contexts with a lower socioeconomic status (i.e. NH and AEP) reported that they had more support available than in other contexts (i.e. UPS and SCC).

Table 1.

Descriptive analysis of the sample (frequencies and percentages).

		Total sample	NH	SCC	UPS	AEP
Age	55–64	194 (35.6)	8 (5.8)	33 (24.1)	78 (56.5)	75 (56.8)
	65–74	235 (42.0)	72 (52.2)	70 (51.1)	46 (33.3)	47 (35.6)
	>74	116 (20.7)	58 (42.0)	34 (24.8)	14 (10.1)	10 (7.6)
Gender	Female	304 (54.3)	75 (56.4)	75 (55.6)	89 (64.0)	65 (50.4)
Gender	Male	232 (41.4)	58 (43.6)	60 (44.4)	50 (36.0)	64 (49.6)
Educational level	Primary	183 (32.7)	69 (50.0)	33 (24.1)	20 (14.4)	61 (44.9)
	Secondary	195 (34.8)	38 (27.5)	56 (40.9)	62 (44.6)	39 (28.7)
	Tertiary or higher	172 (30.7)	31 (22.5)	48 (35.0)	57 (41.0)	36 (26.5)
Monthly incomes	Up to 1200 €	348 (62.1)	101 (72.1)	81 (57.9)	47 (33.6)	119 (85.0)
Monthly incomes	Over 1200 €	212 (37.9)	39 (27.9)	59 (42.1)	93 (66.4)	21 (15.0)
Literacy support	Tertile 1	175 (31.3)	29 (23.6)	63 (49.2)	19 (15.4)	19 (15.4)
	Tertile 2	140 (25.0)	37 (30.1)	29 (22.7)	28 (22.2)	46 (37.4)
	Tertile 3	185 (33.0)	57 (46.3)	38 (28.1)	34 (27.0)	58 (47.2)

NH: nursing homes; SCC: senior community centers; UPS: university programs for seniors; AEP: adult education programs.

The cluster analysis identified two clusters, which demonstrate the heterogeneity in Internet appropriation among users of TSSS. The clusters obtained were the following (Table 2):

Table 2.

Results of the K-means cluster analysis.

		Sporadic users (n = 287)	More advanced users (n = 256)	F/χ²
Material access		1.57	2.33	137.02***
Autonomy of access		1.54	2.77	199.82***
Digital skills		1.60	3.27	382.07***
Online activities	Learning	1.64	3.07	374.64***
	Social interactions	1.66	3.22	438.02***
	Civic life	1.84	3.29	342.68***
	Entertainment	1.64	3.22	460.33***
Benefits perceived	Social connection	1.67	3.27	523.87***
	Social enhancement	1.87	3.16	328.16***
	Evasion	1.76	3.23	411.88***
Nursing homes		58.1	41.9	37.25***
Senior community centers		65.7	34.3
University programs for seniors		31.4	68.8
Adult education programs		57.9	42.1

Seventeen cases were lost. Convergence was achieved in the sixth iteration. The minimum distance between the initial centers was 9.487.

***

p < .001.

Cluster 1 (n = 287): Sporadic users. This group represents users who have less than two devices and have few places to access the Internet. Likewise, they sporadically carry out online activities and do not usually perceive benefits when using the Internet.

Cluster 2 (n = 256): More advanced users. This group represents users who have more than two devices and access the Internet from various points. Likewise, they use the Internet more frequently in a wide range of activities and have a greater perception of the benefits regarding social connectivity, daily routines, and leisure activities.

The analysis of the contingency between the type of TSSS and the cluster shows similarities and divergences in the profiles of the users of these services. For example, the proportion of sporadic and more advanced users is very similar between NH and AEP. Also, the proportions of sporadic and more advanced users between SCC and UPS are inverse. In this sense, it can be seen that more than two thirds of the UPS users belong to the more advanced users cluster.

To test H2 to H4, a BLR was used (Table 3). First, it was verified that the chi-square significance in the models was less than .05 (p < .001), which indicated that the independent variables explained whether or not they belonged to the cluster of more advanced users. Second, the Nagelkerke R² values showed the part of the cluster variance explained by the models. Third, the predicted overall percentage showed that the independent variables were good predictors of the cluster.

Table 3.

Results of the BRL.

		Exp(B)^p-values
		Total		Nursing homes		Senior community centers		University programs for seniors		Adult education programs
Age group (ref. 55–64)	65–74	.65*	.62*	4.26	2.90	.75	1.04	.75	1.15	.26**	.18**
Age group (ref. 55–64)	>74	.43**	.41**	1.70	1.13	.29*	.42	6.13	3.88	.26	.37
Gender (ref. Female)	Male	1.32	1.31	.77	.68	1.81	1.41	2.90*	3.22*	1.07	1.08
Educational level (ref. Primary)	Secondary	1.58	1.79*	3.75**	4.39*	1.08	1.19	3.73	6.85*	.86	.90
Educational level (ref. Primary)	Tertiary or higher	3.19***	3.83***	4.61**	5.87**	2.92	3.52	5.22*	10.69**	3.84*	5.58*
Income (ref. <1200 €)	>1200 €	1.26	1.44	.46	.37	2.11	1.45	.67	.92	3.02	4.81*
Literacy support (ref. Tertile 1)	Tertile 2		1.44		2.55		1.59		1.24		2.12
Literacy support (ref. Tertile 1)	Tertile 3		5.67***		15.85***		10.81***		15.08		2.96
Nagelkerke R²		.13	.27	.20	.42	.21	.41	.16	.31	.25	30
Δ Nagelkerke R²			.14		.22		.20		.15		.05
Hosmer—Lemeshow Test (p)		2.62 (.95)	10.35 (.24)	9.17 (.24)	9.76 (.28)	8.15 (.41)	7.11 (.52)	4.48 (.81)	3.57 (.89)	2.87 (.94)	7.86 (.44)
Chi-square		51.01***	101.49***	20.48**	63.48***	21.23**	40.17***	16.58*	31.26***	24.35***	26.41**
Percentage of classification		62.8	69.0	66.7	75.2	68.0	78.4	67.7	73.2	67.8	69.9

BLR: binary logistic regression.

Dependent Variable: Cluster (0 = sporadic users; 1 = more advanced users).

p < .05, **p < .01, ***p < .001.

Likewise, results of the logistic regression analysis did partially confirm H2. Younger older adults (55–64) and with tertiary education, Exp (B) = 3.19, p > .001, are more likely to belong to the cluster of advanced users than people with primary-level studies.

The data confirmed H3, that is, a frequent literacy support in TSSS increases the probability of belonging to the more advanced user cluster, Tertile 3: Exp (B) = 5.67, p < .001. Likewise, the data showed that literacy supports are a stronger determinant (Δ R² = .14) than sociodemographic factors (R² = .13).

Regarding H4, the data showed that the type of TSSS moderates the determination of Internet appropriation through literacy supports (Figure 1). On one hand, the determination of sociodemographic characteristics was greater in NH (R² = .20) and AEP (R² = .25). While in NH, the probability of Internet appropriation was high among the elderly with medium or higher educational level, Secondary: Exp (B) = 3.75, p > .01; Tertiary: Exp (B) = 4.61, p < .01, in AEP the probability of appropriation was high among the younger older adults with the highest educational level, Tertiary: Exp (B) = 3.84, p > .05. On the other hand, the determination of Internet appropriation through literacy support is greater in NH, Δ R² = .20; Tertile 3: Exp (B) = 15.85, p < .001, and in SCC, Δ R² = .21; Tertile 3: Exp (B) = 10.81, p < .001. In contrast, the determination of appropriation through literacy support in UPS (Δ R² = .15) and in AEP (Δ R² = .05) was lower.

Figure 1.

Graphic representation of odd ratios, Exp (B), of literacy supports.

Regarding H5, BLR in the three age groups showed the following results (Table 4, Figure 2). On one hand, the values of chi-square significance were less than .001 in 65–74 and over 74 models. In contrast, the 55–64 model did not have such a good fit to the data. Also, the overall percentage predicted was higher in 65–74 (67.9%) and over 74 (79.8%) models. On the other hand, in the group over 74 years, it was more likely than younger groups for those who frequently received literacy support to belong to the cluster of advanced users, Exp (B) = 27.00, p < .001, than those who barely received support. Finally, the data also showed that in the group over 74 years, the frequency of literacy support explained the variance (Δ R² = .26) much better than in the younger age groups.

Table 4.

Results of the BLR.

		55–64 years	65–74 years	Over 74 years
Literacy support (ref. Tertile 1)	Tertile 2	1.14	.80	3.77
Literacy support (ref. Tertile 1)	Tertile 3	2.44*	3.68***	27.00***
Nagelkerke R²		.05	.14	.40
Δ R²			.09	.26
Chi-square		6.71*	21.54***	33.86***
Percentage of classification		59.7	67.0	79.8

BLR: binary logistic regression.

Dependent Variable: Cluster (0 = sporadic users; 1 = advanced users).

Figure 2.

Graphical representation of odd ratios, Exp (B), of literacy supports across age groups.

Discussion

This study sought to understand whether the different types of TSSS sponsored by government agencies in Spain are helping older people to appropriate the Internet. The results showed that determination of literacy supports is moderated by the social and organizational context in which the technology support service operates, and also showed that as age progresses, the relevance of formal literacy supports increases. In the next lines, the main findings are described.

Regarding whether the use of TSSS does not guarantee the appropriation of the Internet (material-physical access, skills, online activities, and perceived benefits), the data showed that the users of TSSS (over 54 years of age) is a heterogeneous group both for their sociodemographic characteristics and for their ability to use and take advantage of the use of the Internet. The cluster analysis served to identify two patterns of Internet use, sporadic and more advanced, similar to the two patterns most frequently identified in studies on digital inequality, although they showed a less heterogeneous reality than studies covering a broader life cycle (see Brandtzæg, 2010). These patterns refer to a certain polarization with respect to the three levels of access and use of the Internet: the material access (Van Deursen and Van Dijk, 2018) and autonomy to access (Hassani, 2006), in the amplitude of online activities (Heart and Kalderon, 2013; Schreuers et al., 2017), and in the benefits perceived when using the Internet (Van Deursen and Helsper, 2015). Therefore, the data reinforced the idea that providing access to the Internet is not enough to reduce digital inequality among older adults if it is not linked to appropriate training programs (Millward, 2003; Morris, 2007) and older users perceive that literacy support is available (Kamin et al., 2019).

Regarding whether older men in a younger age range and high income and educational level are more likely to have a higher level of Internet appropriation than women, older seniors, and those with lower income and educational level, the data showed a clear association between sociodemographic status and levels of Internet appropriation, similar to the findings of other studies that attested to the influence of age (e.g. Neves and Amaro, 2012), and the educational level (e.g. Smith, 2014) on the frequency and use of the Internet. That is, the data suggest that aging and low educational level are significant obstacles to appropriating the use of the Internet in an individual’s daily life. Therefore, a high educational level can be considered a protective factor of digital exclusion and facilitator of appropriation. Regarding the influence of gender, the results showed that among older participants being male was not a protective factor of exclusion and facilitator of digital appropriation.

Regarding whether older adults who perceive a high availability of literacy support are more likely to have a higher level of Internet appropriation than those who perceive less support, the results of the total sample showed that the literacy supports better explained the variance of the ability to use the Internet than the demographic characteristics and socioeconomic status of the older adults. In this sense, the frequency of literacy supports acted as the main facilitating factor for Internet appropriation (Schreuers et al., 2017). These findings confirm the findings of other studies such as that of Kamin et al. (2016) who showed that the availability of formal supports was associated with an increase in the use of communication tools and opportunities to enhance social contact. Likewise, the findings also coincide with the results of community projects (Seo et al., 2019) that showed an increase in the confidence, access, and technological capacity of the participants in the program. However, the ability to determine the availability of literacy support was conditioned by the social and organizational context in which the support service operates.

Regarding whether the determination and prediction of sociodemographic characteristics and literacy support on Internet appropriation is moderated by the type of TSSS, the data showed that social and organizational context moderated the determination of literacy support on Internet appropriation. That is, heterogeneity was found between the different types of support services in terms of the determination of literacy support depending on the social and organizational context in which it operates (Kamin et al., 2016). The findings of empirical studies suggest that literacy supports for seniors should be situated locally, within particular organizational and community contexts (Seo et al., 2017, 2019). As in previous studies carried out in Spain (Tirado-Morueta et al., 2020), it was found that technological support services serve a population with heterogeneous and unique socio-emotional characteristics, which condition the use of technology in their daily life. For example, it could be observed that in organizational contexts whose users are younger (i.e. university programs and adult education), socioeconomic status turned out to be less decisive. According to other findings (Francis et al., 2018), it would be expected for users closer to their previous work stage maintain relationships that provide informal support (e.g. family, friends, ex-colleagues) which compensates for differences due to socioeconomic status. On the other hand, it was observed that in the contexts in which the users are older (i.e. NH and community centers for the elderly), the availability of literacy support was more decisive.

When controlling for the literacy support variable, some changes in the differences due to age and socio-educational status were found. These results suggest that literacy support can reduce differences due to educational level in contexts in which there is usually a greater imbalance in educational level (i.e. NH and university programs). These results, in line with the model for RIDS proposed by Van Deursen and Helsper (2015), suggest that literacy support has the capacity to compensate for deficits due to the scarcity of economic resources, a low educational level or limitations due to change in social or generational status. Therefore, the results reinforce the idea that technological support services for the elderly have the capacity to adapt their literacy programs to the unique characteristics of their users (e.g. Seo et al., 2019). The results also suggest that the relevance of literacy support is conditioned by the ability to adapt the service to the needs of users. In this sense, in organizational contexts where training is integrated into a formal curriculum (i.e. AEP), the determination of support was lower than in non-formal contexts (i.e. NH, senior centers, and university programs) where support is usually tailored (Kamin et al., 2016, 2019; Seo et al., 2019; Shapira et al., 2007).

Finally, this study addressed the timeline of technological support in the adoption of the Internet by older people (Hunsaker et al., 2019). There is sufficient evidence that has shown that the lack of availability of social support makes older people prefer to opt for more formal sources of support (Schreuers et al., 2017; Seo et al., 2019) such as technological support services. In this sense, the cross-age analysis showed that as the age of the group progresses, the relationship between the type of institutional support and the level of Internet appropriation increased. In other words, the data supported the idea that as people age, the relevance of institutional support and digital literacy programs for the appropriation of technology increases. Specifically, starting at the age of 74, institutional supports were especially relevant for older adults.

Practical implications

Life expectancy increases worldwide, although this increase is associated with the country’s income level, in a range of 63 years (low income) to 81 years (high income) (The World Bank, 2018). This study was carried out in Spain, one of the countries that together with Switzerland and Japan lead a group of 26 countries in which life expectancy at birth exceeds 80 years ; therefore the transfer of these results will be especially relevant for similar contexts.

Also, although Internet services around the world are becoming more and more affordable, this trend cannot be generalized to all countries (e.g. underdeveloped countries) or to all population groups. These data demonstrate that affordability may not be the only barrier to Internet appropriation for older adults (ITU, 2020). The findings of this study carried out in a country with an advanced economy and ranked 35 in the Networked Readiness Index (Baller et al., 2016)—with affordable Internet services and a high presence of online services in the administration, business, and social life— support that in addition to the Internet access material, older people need to have accessible services that a high availability of supports for their digital literacy. Likewise, the results of this study provide empirical evidence that supports one of the objectives of the “decade of healthy aging” (WHO, 2020: 11–12), such as “ensuring that communities promote the capacities of older people.” For this, it will be necessary for technology support services to be located in accessible and friendly communities where older people tend to hang out and engage in activities (e.g. community centers, NH, senior clubs, public services, universities, and libraries).

Limitations and future studies

In order to guide future research, some of the study limitations should be considered. One of the limitations of the study is the use of self-administered questionnaires. In this sense, it is advisable to delve into this topic using qualitative data collection and analysis techniques (e.g. Hunsaker et al., 2019).

Likewise, it would be interesting to consider an assessment of the needs (e.g. functional limitations or social isolation) of older adults and their social supports. Furthermore, further studies could focus on other types of organizational contexts and disadvantaged social contexts associated with aging (e.g. Seo et al., 2019), taking into account the various didactic and organizational aspects and including the perspective of teachers and social workers. Finally, future experimental and longitudinal studies that control previous skills, uses, and benefits will make it possible to measure the effect of technological support services on Internet appropriation.

Conclusion

The aim of the study was to understand how the use of different types of technological support services affects older people’s appropriation of the Internet. The sample was obtained from four organizational contexts (NH, SCC, UPS, and AEP) habitually used by older people in Spain, which offer TSSS.

Through cluster analysis and using different dimensions associated with the use and exploitation of the Internet (material access, autonomy to access, digital skills, and the diversity of online activities and benefits perceived from using the Internet), a polarization—regarding the appropriation of Internet—among users of technological support services was found, which confirm that material access to the Internet is not sufficient for older people to know how to take advantage of the potential of the Internet. And in this sense, the effectiveness of support services is very heterogeneous.

The data of the study have shown, in the first place, the capacity of technological supports as a source of appropriation of the Internet by older people. Second, the capacity of these services to adapt to the social context in which they operate and compensate for the deficits of users associated with age, their limited economic resources, or their educational deficiencies was also evidenced. Third, the results suggest that the ability of technology support services to enable older people to appropriate the Internet will depend on the adaptability of their programs to their users.

Finally, regarding the technological support timeline, the analysis of the data by age range (55–64, 65–74, and over 74 years) showed that as the age increases, the determination of the appropriation of the Internet through technological support services increases. These data suggest a greater dependence on the technological support as age increases and decreases the support of family, friends, and colleagues.

Supplemental Material

sj-docx-1-nms-10.1177_14614448211019155 – Supplemental material for Determination of Internet appropriation by older people through technological support services

Supplemental material, sj-docx-1-nms-10.1177_14614448211019155 for Determination of Internet appropriation by older people through technological support services by Ramón Tirado-Morueta, Alejandro Rodríguez-Martín, Emilio Álvarez-Arregui, Miguel Ángel Ortíz-Sobrino and José Ignacio Aguaded-Gómez in New Media & Society

Supplemental Material

sj-pdf-2-nms-10.1177_14614448211019155 – Supplemental material for Determination of Internet appropriation by older people through technological support services

Supplemental material, sj-pdf-2-nms-10.1177_14614448211019155 for Determination of Internet appropriation by older people through technological support services by Ramón Tirado-Morueta, Alejandro Rodríguez-Martín, Emilio Álvarez-Arregui, Miguel Ángel Ortíz-Sobrino and José Ignacio Aguaded-Gómez in New Media & Society

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is conducted within the framework of “Alfamed” (Euro-American Network of Researchers), with the support of the R+D Project “Youtubers and Instagrammers: media competence in emerging prosumers” (RTI2018-093303-B-I00), financed by the State Research Agency of the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund (ERDF).

ORCID iD

Alejandro Rodríguez-Martín

Supplemental material

Supplemental material for this article is available online.

Author biographies

Ramón Tirado-Morueta, PhD, is a full professor at the Department of Pedagogy of the University of Huelva. He has extensive experience as a teacher in the area of Educational Technology. He has participated and directed several research projects in different areas, such as Blended Learning and the integration of information and communication technology (ICT) in educational centers.

Alejandro Rodríguez-Martín, PhD, is a professor at the Department of Education Sciences of the University of Oviedo. He has extensive experience as a teacher in the area of Inclusive education. He is a member of the European Network on Inclusive Education & Disability (incluD-ed). His research interests include Entrepreneurial Training, Inclusive Education, ICT, and innovation.

Emilio Álvarez-Arregui, PhD, is a professor at the Department of Education Sciences of the University of Oviedo.

Miguel Ángel Ortíz-Sobrino, PhD, is a professor in the area of Informative Enterprise at the Complutense University of Madrid and is a Delegate for Medialab/Inforadio of the Faculty of Information Sciences of the same University. His lines of research are focused on the information company, radio and television production, and the relationship of minors with the media. He is Director of the Master in Institutional Communication, Marketing and Events Production in the Complutense University of Madrid.

José Ignacio Aguaded-Gómez, PhD, is a Full professor at the Department of Pedagogy of the University of Huelva, Spain. He leads the fields of Technology and Innovation and is the Chairman of Group “Comunicar,” in Andalusia, a veteran in Spain in Education through Mass Media. He is the Editor of “Comunicar,” Media Education Research Journal, which is distributed in Europe and America. He is also scientific advisor to several national and international scientific journals.

References

Alcalá

(2014) Diseño de programas de e-inclusión para alfabetización mediática de personas mayors. Comunicar: Revista científica iberoamericana de comunicación y educación 42: 173–180. DOI: 10.3916/C42-2014-17.

Baller

Dutta

Lanvin

(2016) Global Information Technology Report 2016. Geneva: Ouranos.

Barnard

Bradley

Hodgson

, et al. (2013) Learning to use new technologies by older adults: perceived difficulties, experimentation behaviour and usability. Computers in Human Behavior 29(4): 1715–1724. DOI: 10.1016/j.chb.2013.02.006.

Beard

Officer

Cassels

, et al. (2015) World Report on Aging and Health. Geneva: WHO.

Brandtweiner

Donat

Kerschbaum

(2010) How to become a sophisticated user: a two-dimensional approach to e-literacy. New Media & Society 12(5): 813–833. DOI: 10.1177/1461444809349577.

Brandtzæg

(2010) Towards a unified Media-User Typology (MUT): a meta-analysis and review of the research literature on media-user typologies. Computers in Human Behavior 26(5): 940–956. DOI: 10.1016/j.chb.2010.02.008.

Chaffin

Maddux

(2007) Accessibility accommodations for older adults seeking e-health information. Journal of Gerontological Nursing 33(3): 6–12. Available at: https://goo.gl/QEk4LA (accessed 1 March 2020).

Chen

Persson

(2002) Internet use among young and older adults: relation to psychological well-being. Educational Gerontology 28(9): 731–744. DOI: 10.1080/03601270290099921.

Eurostat (2019a) Individuals-frequency of Internet Use. Brussels: European Commission. Available at: https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_ci_ifp_fu&lang=en (accessed 8 February 2020).

10.

Eurostat (2019b) Individuals-Internet Activities. Brussels: European Commission. Available at: https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_ci_ac_i&lang=en (accessed 8 February 2020).

11.

Eurostat (2019c) Individuals’ Level of Internet Skills. Brussels: European Commission. Available at: https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_sk_iskl_i&lang=en (accessed 8 February 2020).

12.

Francis

Kadylak

Makki

, et al. (2018) Catalyst to connection: when technical difficulties lead to social support for older adults. American Behavioral Scientist 62(9): 1167–1185. DOI: 10.1177/0002764218773829.

13.

Friemel

(2016) The digital divide has grown old: determinants of a digital divide among seniors. New Media & Society 18(2): 313–331. DOI: 10.1177/1461444814538648.

14.

Gatto

Tak

(2008) Computer, Internet, and e-mail use among older adults: benefits and barriers. Educational Gerontology 34(9): 800–811. DOI: 10.1080/03601270802243697.

15.

Goldhammer

Naumann

Keßel

(2013) Assessing individual differences in basic computer skills. European Journal of Psychological Assessment 29(4): 263–275. DOI: 10.1027/1015-5759/a000153.

16.

Hardill

Olphert

(2012) Staying connected: exploring mobile phone use amongst older adults in the UK. Geoforum 43(6): 1306–1312. DOI: 10.1016/j.geoforum.2012.03.016.

17.

Hargittai

Dobransky

(2017) Old dogs, new clicks: digital inequality in skills and uses among older adults. Canadian Journal of Communication 42(2). DOI: 10.22230/cjc.2017v42n2a3176.

18.

Hargittai

Hinnant

(2008) Digital inequality. Communication Research 35(5): 602–621. DOI: 10.1177/0093650208321782.

19.

Hassani

(2006) Locating digital divides at home, work, and everywhere else. Poetics 34(4–5): 250–272. DOI: 10.1016/j.poetic.2006.05.007.

20.

Heart

Kalderon

(2013) Older adults: are they ready to adopt health-related ICT? International Journal of Medical Informatics 82(11): e209–e231. DOI: 10.1016/j.ijmedinf.2011.03.002.

21.

Hunsaker

Nguyen

Fuchs

, et al. (2019) “He explained it to me and I also did it myself”: how older adults get support with their technology uses. Socius: Sociological Research for a Dynamic World. Epub ahead of print 4 December. DOI: 10.1177/2378023119887866.

22.

INEbase (2019a) Renta media por persona. Instituto Nacional de Estadística. Available at: https://www.ine.es/jaxiT3/Datos.htm?t=9947 (accessed 10 February 2020).

23.

INEbase (2019b) Índice de envejecimiento por provincias. Instituto Nacional de Estadística. Available at: https://www.ine.es/jaxiT3/Datos.htm?t=1489 (accessed 10 February 2020).

24.

ITU (2020) Measuring Digital Development. ICT Price Trends 2019. Available at: http://handle.itu.int/11.1002/pub/8155ab0c-en (accessed 15 January 2020).

25.

Kamin

Beyer

Lang

(2019) Social support is associated with technology use in old age. Zeitschrift für Gerontologie und Geriatrie 53: 256–262. DOI: 10.1007/s00391-019-01529-z.

26.

Kamin

Lang

Kamber

(2016) Social contexts of technology use in old age. In: Knon

(ed.) Gerontechnology. Research, Practice, and Principles in the Field of Technology and Aging. New York: Springer, pp. 35–56.

27.

Kania-Lundholm

Torres

(2015) The divide within: older active ICT users position themselves against different “others.” Journal of Aging Studies 35: 26–36. DOI: 10.1016/j.jaging.2015.07.008.

28.

Chu

Tseng

(2013) Gratifications for using CMC technologies: a comparison among SNS, IM, and e-mail. Computers in Human Behavior 29(1): 226–234. DOI: 10.1016/j.chb.2012.08.009.

29.

MacQueen

(1967) Some Methods for Classification and Analysis of Multivariate Observations. In: Proceedings of the 5th Berkeley symposium on mathematical statistics and probability, pp. 281–297. Available at: https://projecteuclid.org/euclid.bsmsp/120051299 (accessed 10 February 2020).

30.

Millward

(2003) The ‘grey digital divide’: perception, exclusion and barriers of access to the Internet for older people. First Monday 8(7). DOI: 10.5210/fm.v8i7.1066.

31.

Morris

(2007) E-literacy and the grey digital divide: a review with recommendations. Journal of Information Literacy 1(3): 13–18. DOI: 10.11645/1.3.14.

32.

Muñoz Baena

(2013) Problemática en la elección del número de cluster en el algoritmo K-Means. MoleQla: Revista de Ciencias de la Universidad Pablo de Olavide 10: 15.

33.

Neves

Amaro

(2012) Too old for technology? How the elderly of Lisbon use and perceive ICT. The Journal of Community Informatics 8(1): 1–12. Available at: http://ci-journal.net/index.php/ciej/article/view/800 (accessed 21 February 2020).

34.

Newhagen

Bucy

(2004) Routes to media access. In: Bucy

Newhagen

(Eds) Media Access: Social and Psychological Dimensions of New Technology Use. Mahwah. NJ: Lawrence Erlbaum Associates, pp. 3–23.

35.

Nimrod

(2016) Older audiences in the digital media environment. Information, Communication & Society 20(2): 233–249. DOI: 10.1080/1369118x.2016.1164740.

36.

OECD (2019) Health at a Glance 2019: OECD Indicators. Paris: OECD Publishing. Available at: https://doi.org/10.1787/4dd50c09-en (accessed 15 January 2020).

37.

Papacharissi

Rubin

(2000) Predictors of Internet use. Journal of Broadcasting & Electronic Media 44(2): 175–196. DOI: 10.1207/s15506878jobem4402_2.

38.

Peek

Luijkx

Rijnaard

, et al. (2016) Older adults’ reasons for using technology while aging in place. Gerontology 62(2): 226–237. DOI: 10.1159/000430949

39.

Quan-Haase

Martin

Schreuers

(2016) Interviews with digital seniors: ICT use in the context of everyday life. Information, Communication & Society 19(5): 691–707. DOI: 10.1080/1369118X.2016.1140217.

40.

Quan-Haase

Wellman

(2017) Connected seniors: how older adults in East York exchange social support online and offline. Information, Communication & Society 20(7): 967–983. DOI: 10.1080/1369118X.2017.1305428.

41.

Rasi

Kilpeläinen

(2016) Older people’s use and learning of new media: a case study on remote rural villages in Finnish Lapland. In: International Conference on Human Aspects of IT for the Aged Population. Cham: Springer, pp. 239–247. DOI: 10.1007/978-3-319-39949-2_23.

42.

Schehl

Leukel

Sugumaran

(2019) Understanding differentiated internet use in older adults: a study of informational, social, and instrumental online activities. Computers in Human Behavior 97: 222–230. DOI: 10.1016/j.chb.2019.03.031.

43.

Schreuers

Quan-Haase

Martin

(2017) Problematizing the digital literacy paradox in the context of older adults’ ICT use: aging, media discourse, and self-determination. Canadian Journal of Communication 42(2): 359–377. DOI: 10.22230/cjc.2017v42n2a3130.

44.

Schulz

Wahl

Matthews

, et al. (2015) Advancing the aging and technology agenda in gerontology. The Gerontologist 55(5): 724–734. DOI: 10.1093/geront/gnu071.

45.

Selwyn

(2004) Reconsidering political and popular understandings of the digital divide. New Media & Society 6(3): 341–362. DOI: 10.1177/1461444804042519.

46.

Seo

Erb

Altschwager

, et al. (2019) Evidence-based digital literacy class for older, low-income African-American adults. Journal of Applied Communication Research 47(2): 130–152. DOI: 10.1080/00909882.2019.1587176.

47.

Seo

Erba

Geana

, et al. (2017) Calling Doctor Google? Technology adoption and health information seeking among low-income African-American older adults. The Journal of Public Interest Communications 1(2): 153. DOI: 10.32473/jpic.v1.i2.p153

48.

Shapira

Barak

Gal

(2007) Promoting older adults’ well-being through Internet training and use. Aging & Mental Health 11(5): 477–484. DOI: 10.1080/13607860601086546

49.

Smith

(2014) Older Adults and Technology Use. Pew Research Center. Internet & American Life Project. Available at: http://www.pewinternet.org/2014/04/03/older-adults-and-technology-use/ (accessed 14 February 2020).

50.

Sum

Mathews

Hughes

, et al. (2008) Internet use and loneliness in older adults. Cyberpsychology & Behavior 11(2): 208–211. DOI: 10.1089/cpb.2007.0010.

51.

The World Bank (2018) Life expectancy at birth, total (years). Available at: https://data.worldbank.org/indicator/SP.DYN.LE00.IN (accessed 15 January 2020).

52.

Tirado-Morueta

Aguaded-Gómez

Ortíz-Sobrino

MÁ

, et al. (2020) Determinants of social gratifications obtained by older adults moderated by public supports for Internet access in Spain. Telematics and Informatics 49: 101363. DOI: 10.1016/j.tele.2020.101363.

53.

Van Boekel

Peek

Luijkx

(2017) Diversity in older adults’ use of the internet: identifying subgroups through latent class analysis. Journal of Medical Internet Research 19(5): e180. DOI: 10.2196/jmir.6853.

54.

Van Deursen

Helsper

(2015) The third-level digital divide: who benefits most from being online? In: Communication and Information Technologies Annual. Emerald Group Publishing Limited, pp. 29–52. DOI: 10.1108/S2050-206020150000010002.

55.

Van Deursen

Van Dijk

(2018) The first-level digital divide shifts from inequalities in physical Access to inequalities in material access. New Media & Society 21(2): 354–375. DOI: 10.1177/1461444818797082.

56.

Van Dijk

(2005) The Deepening Divide: Inequality in the Information Society. Thousand Oaks, CA: SAGE.

57.

Van Dijk

(2006) Digital divide research, achievements and shortcomings. Poetics 34: 221–235. DOI: 10.1016/j.poetic.2006.05.004.

58.

Wagner

Hassanein

Head

(2010) Computer use by older adults: a multi-disciplinary review. Computers in Human Behavior 26(5): 870–882. DOI: 10.1016/j.chb.2010.03.029.

59.

World Health Organization (WHO) (2016) The Global Strategy and action plan on ageing and health 2016-2020: towards a world in which everyone can live a long and healthy life: Resolution (No. A69/A/CONF./8). WHO. Available at: https://www.who.int/ageing/GSAP-Summary-EN.pdf?ua=1 (accessed 23 February 2020)

60.

World Health Organization (WHO) (2020) Decade of Healthy Ageing 2020-2030. A final proposal at the 146th WHO executive board meeting in February 2020. WHO, Geneva. Available at: https://www.who.int/docs/default-source/decade-of-healthy-ageing/final-decade-proposal/decade-proposal-final-apr2020-en.pdf?sfvrsn=b4b75ebc_3 (accessed 15 January 2020).

61.

Witte

Mannon

(2010) The Internet and Social Inequalities. New York: Routledge.

62.

Wang

Zou

(2018) Research on Internet information mining based on agent algorithm. Future Generation Computer Systems 86: 598–602. DOI: 10.1016/j.future.2018.04.040.

63.

Xie

Jaeger

(2008) Older adults and political participation on the Internet: a cross-cultural comparison of the USA and China. Journal of Cross-Cultural Gerontology 23(1): 1–15. DOI: 10.1007/s10823-007-9050-6.

64.

Zillien

Hargittai

(2009) Digital distinction: status specific types of Internet usage. Social Science Quarterly 90(2): 274–291. DOI: 10.1111/j.1540-6237.2009.00617.x.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB

0.36 MB