The Elderly and the Internet:
A Daunting Task or a Welcome Challenge?
Master’s Thesis
Daniel Hesselbein
daniel@hesselbein.org
Student 0605263
November 2011
Supervisor: Fadi Hirzalla
University of Amsterdam
Graduate School of Communication
Abstract
Research into Internet use does not often focus on the elderly or their reasons for using – or not using – the Internet. This paper aims to provide insight into why the elderly have varying Internet habits by showing that social ties, personal motivation and digital literacy as well as physical abilities influence the intensity of their Internet use. A framework has been constructed using literature on demographics, sociability, motivation and skills. By means of a survey (n=201) consisting of questions that measure such factors as levels of optimism and the inclination to trust or distrust new technologies, as well as an experiment designed to gauge the degree of digital literacy, I will attempt to single out influences and investigate their impact on the use of the Internet by the elderly. Regression analyses have shown that the intensity of contact within an elderly person’s social circle is an important factor. Differences in personality can explain differences in the intensity of Internet use: curious or adventurous people of all ages are more likely to embrace new technologies.
Introduction
The Dutch government has invested a great deal of time and money in information and
communication technology services (ICT), with the aim of enhancing its citizens’ quality of life (De Haan & Klumper, 2004). In fact, the Netherlands was one of the first European countries to have an e-government program; the first such program was launched in 1994 (Van Dijk, Pieterson, Van Deuren & Ebbers, 2007). ICT is seen as a possible assistive technology for the elderly (Hernandez-Encuentra, Pousada & Gomez-Zuniga, 2009). According to the Dutch Ministry of Health, Welfare and Sport, an important reason for this digitization of government operations was the enhancement of “social equality” (Ministerie VWS, 1999). But does everyone benefit to the same extent? Increasingly, private and public services are becoming (exclusively) accessible via the Internet, as are many other social and cultural aspects of life (Peacock & Kunemund, 2007). The changes in the media environment are profound, with the Internet usurping the role of once separate media such as print, radio and television (Papacharissi & Rubin, 2000).
For practical reasons, I have chosen to zoom in on the aspects of social ties, individual motivation and individual skills. These facets were chosen for two reasons: 1) an overview of the relevant literature shows that these topics have not been studied exhaustively and thus merit further research; 2) these aspects might provide the key to discovering why the elderly use the Internet, and whether they possess the necessary digital literacy and physical abilities to use the Internet in a productive way.
According to Statistics Netherlands, the Dutch statistics office, the proportion of elderly people (defined here as the over-65s) in the Netherlands is on the rise (CBS, 2011a). This development is mainly a result of the ageing of the baby-boom generation, a relatively large group of people born between 1946 and 1970. This increase in the percentage of people over 65 is also discernible in the United States, where senior citizens are the fastest growing segment of the population (Eastman & Iyer, 2004). But why do the elderly appear to use the Internet far less than younger people? Millward
(2003) suggests that their general lack of Internet skills gives elderly people the impression that ICT is an activity more suited to young people. This is due, in part, to the higher incidence among the elderly of impaired vision, loss of hearing and decreased dexterity (Trocchia & Janda, 2000). Moreover, certain cognitive issues – such as the ability to locate things in a continually changing environment – also play an important role (Hanson, 2001).
There is a clear need to examine the complex issues surrounding the elderly and their use of the Internet, given the increasing implementation of e-government in the Netherlands and the fact that the elderly do not use the Internet nearly as much as other segments of the Dutch population (CBS, 2011b). This research is socially relevant because of the large increase in the percentage of elderly people, now that the baby-boomers have started to swell the ranks of the over-65s. It is also politically relevant because of the abovmentioned move of the Dutch political apparatus towards
e-government, in an attempt to reach out to its citizens more effectively. Furthermore, the “50Plus” party received 2.4% of the vote in the provincial elections held in March 2011, which is an exceptionally good result for a party participating in an election for the first time (NOS, 2011). This electoral outcome testifies to the determination of the elderly not to lose control of their environment and to exert an influence on how technologies can best serve their generation (Fox, 2004).
This paper is structured as follows. The first part presents the theories underpinning my research. I then set forth various hypotheses, after which I outline the method used to conduct the survey and analyse the data obtained by means of regression analysis. This methodological section is followed by my findings. The theoretical relevance of these findings is discussed in the last section, which presents the conclusions drawn from my empirical study, discusses the limitations of such an approach, and suggests possibilities for further research.
Theoretical background
Western society is confronted with two very noticeable trends: the ageing of its population and the growing importance of ICT. In 2010 the generation born immediately after the Second World War began to reach the age of 65. Ever-increasing numbers of over-65s are going to influence our society in various ways, but this development will have the greatest impact on health care, the economy and politics (De Jong & Van Duin, 2009; Eurostat, 2011). The increasing importance of ICT is due in part to the ageing of “the information society” and what this entails: a growing demand for information, and the development of new ways to process and transmit that information in an attempt to meet the demand. Governments certainly recognize the growing importance of ICT: local authorities, in
particular, can increase the efficiency of their organizational structures by making effective use of ICT (Ministerie VWS, 1999; De Haan & Klumper, 2004). This combination – an ageing population and rapid advances in the field of information technology – is causing a digital divide, and those who find themselves on the “wrong” side of it are largely excluded from participation in modern forms of communication and information-gathering (Becker et al., 2008). Indeed, Van Dijk and Hacker (2000) have noted a disparity between Internet users who systematically use and benefit from advanced digital technology and its more difficult applications and services, and those who use only basic digital technologies and applications. Many people in the latter group are elderly, and their exclusion from “modern life” is happening in an increasingly proto-normative way: more and more services are being moved online, and it is taken for granted that those wishing to use public and private services are computer literate (Freese, Rivas & Hargittai, 2006).
The gap between young and old as regards the intensity of computer use is narrowing as technology becomes more diffuse and user-friendly (DiMaggio, Hargittai, Neuman & Robinson, 2001). This is borne out by Dutch statistics on demographic groups: the highest rate of growth in Internet use is seen among those over the age of 65. There are two main reasons for this: 1) people now reaching retirement age have been forced in recent years to use the Internet professionally in their jobs; 2) this
group has traditionally had the smallest percentage of Internet users in the Netherlands, and so is now catching up. Between 2005 and 2010, the percentage of people over 65 who had never used the Internet before dropped noticeably, from 64% to 40% (CBS, 2011b). Even though this is a huge increase in “Internet beginners”, it still means that four out of every ten people over the age of 65 have never used the Internet. However, the regional growth shown by the CBS figures (2011b) could mean that the stimulation of social cohesion and active participation instigated by the VWS in 1999 might be working. The CBS statistics (2011b) testify to a high degree of possible access for the over-65s: in the Netherlands, 68% of the elderly have Internet access, a percentage that is very high indeed when compared with the European average of 58% and the world average of 30% (Worldinternetstats, 2011). One reason for this increase in Internet use by the over-65s is that the elderly have not yet reached a ceiling in the number of people online (as other groups seem to have done), which means that any increase is still measurable and will possibly be significant (CBS, 2011b; Eurostat, 2011).
Despite the narrowing of the gap between young and old, and the relatively high number of elderly Internet users, there is still a significant group of elderly people who are either not online or are not using the Internet as effectively as they could. My goal was to discover the possible causes of this disparity in the intensity of Internet use among the elderly. In examining this problem, I chose to explore – as stated in the introduction above – the influence exerted by social ties, individual
motivation and individual skills. After introducing the variables used in my research, I will examine the extent to which these factors affect the intensity of Internet use. This leads to the following research question:
RQ: Which of the following factors exerts the greatest influence on the intensity of Internet use among the elderly: social ties, individual motivation or individual skills?
Social ties
Because the Internet can be useful in maintaining contact with friends and family, I will focus on the usefulness of the Internet in social interaction and, conversely, the fact that social ties provide a significant stimulus to Internet use, as found by LaRose, Eastin and Gregg in 2001 and Zhao in 2006. In the late 1990s, when the Internet was not yet so widespread, increased use of the Internet was thought to mark a decline in family communication and a decrease in the size of social circles (Kraut et al., 1998). Yet a follow-up study by Kraut et al. (2002) found that these negative effects of Internet use had disappeared. In the four years between the publication of these studies, home access to the Internet had more than tripled and instant messaging had matured, making it easy to develop and/or maintain strong social ties with friends and family (Erwin, Turk, Heimberg, Fresco & Hantula, 2003).
Generally speaking, strong personal ties are intensified by physical proximity, but once strong ties have been established, they are often sustained by means of telecommunications (Wellman & Tindall, 1993). These findings are corroborated by the results of research conducted by Robinson, Kestnbaum, Neustadtl and Alvarez (2000), who state that Internet users are more likely to
communicate with friends and family face-to-face and over the phone than those who do not use the Internet. Moreover, they found that respondents who used the Internet frequently reported increases in the size of their social circles – local as well as distant. Many elderly people might have less
accessible social networks (thus having less incitement to use communications media), and this may well influence the intensity of their Internet use (Rice, 2002). The above information makes it possible to formulate the following hypothesis:
H1: The more frequent a person’s contact with his/her social circle, the more likely that person is to demonstrate a higher intensity of Internet use.
Individual motivation
Motivational factors in Internet use are determined in part by the readiness to use new technologies. This, in turn, is determined by levels of optimism, innovativeness, discomfort and insecurity – all of which affect one’s inclination to use new technologies (Parasuraman, 2000). Here we must bear in mind that – as noted by LaRose, Mastro & Eastin (2001) – the elderly often perceive themselves as lacking in skills, and are inclined to leave “modern technology” to “the youngsters”. In determining the factors that inhibit or prompt the use of technology, it is necessary to measure levels of optimism, innovativeness, discomfort and insecurity, which affect one’s willingness to use new technologies. It is to be expected that optimism and innovativeness will exert a positive effect on the intensity of Internet use and that discomfort and insecurity will exert a negative effect.
Optimism
The concept of optimism as used in this paper is defined as “a belief that technology offers people increased control, flexibility and efficiency in their life” (Parasuraman, 2000). Zielińska-Więczkowska, Muszalik, & Kędziora-Kornatowska (2011) found that optimism spurs an individual to action, such as learning how to use – and actually using – the Internet. This confirms the results of Ma and Wang (2009), who found that, as regards ICT acceptance, positive emotion promotes usage intention. This is because positive emotion increases cognitive processes by releasing more dopamine, which makes a person more efficient, flexible and innovative. This positive emotion enables ICT users to access more easily a rich and elaborately connected network such as the Internet, since it stimulates explorative behaviour and increases the motivation to try new products (Djamasbi & Strong, 2008).
Innovativeness
Innovation means change, and people often resist change before they are inclined to accept it; adapting to innovation requires changes in people’s habits, and forces them to develop new ones (Rogers, 2003). The resistance thus generated – the so-called usage barrier – is due to the incompatibility of innovations with existing habits or practices (Laukkanen, Sinkkonen, Kivijarvi & Laukkanen, 2007).
Innovativeness has been defined as “a tendency to be a technology pioneer and thought leader” (Parasuraman, 2000). An open attitude to continuous education and innovation enables individuals to function properly in a swiftly changing world (Zielińska-Więczkowska, 2011).
Innovativeness is therefore an attribute that prompts a cognitive response to the decision to adopt innovation – in this case, using the Internet (Lin, 2004). Those who seek novelty are more likely to adopt and master new technologies (Citrin, Sprott, Silverman & Stem, 2000), and this corroborates Bussele, Reagan, Pinkleton and Jackson (1999), who previously found that a person’s openness to innovation was a predictor of the level of Internet use. Of course there are products and services that older consumers are not interested in. As noted by Rice (2002), the elderly tend to display greater resistance to innovation in general.
The readiness of the elderly to adopt new technologies is closely related to whether they per-ceive the benefits to be derived from adopting those technologies and whether the benefits are seen to be meaningful (Black, Lockett, Winklhofer & Ennew, 2001). Considering the above-mentioned fac-tors, the question that arises is whether a person with an innovative personality is likely to use the Internet more intensively than a person who lacks innovativeness.
Discomfort
In this study discomfort is defined as “a perceived lack of control over technology and a feeling of being overwhelmed by it” (Parasuraman, 2000). Discomfort is thought to inhibit the readiness to use technology (Rose & Ogunmokun, 2010). If the Internet does not meet people’s expectations, some may experience psychological discomfort, considering it a waste of time and effort and experiencing a loss of trust. Therefore, perceived risk reduces a person’s inclination to use the Internet (Ma & Wang, 2009). Some users know little about the workings of the Internet, and doubt whether it can help them complete the task at hand. A feeling of discomfort may be caused by the perceived risk involved in the adoption of ICT (Featherman & Pavlou, 2003).
H2c: Discomfort has a negative effect on the intensity of Internet use.
Insecurity
Insecurity is defined as “distrust of technology and scepticism about its ability to work properly”
(Parasuraman, 2000). A study by Melenhorst and Bouwhuis (2004) suggests that the elderly might not be motivated to buy a new computer or learn new skills because they do not perceive it as helpful in fulfilling their aspirations. Elderly Internet users feel the need to control a technology before they adopt it for their own purposes (Karahasanovic et. al., 2008). A feeling of insecurity or ineptitude can be triggered by using technology (Mick & Fournier, 1998). This insecurity among the elderly is partly due to a lack of understanding, that is to say, they do not understand the Internet well enough to perceive its benefits or, more specifically, its ability to meet their needs (Melenhorst, Bouwhuis & Rogers, 2006).
Individual skills
The assessment of a person’s skills requires the examination of both digital literacy and physical abilities.
Digital literacy
Here I will examine the set of skills needed to access the Internet, which is also known as digital literacy. Van Dijk and Hacker (2000) discovered that one of the main reasons for not using the Internet is a lack of the digital experience necessary to acquire sufficient digital skills. This may be caused by lack of interest, fear of the computer, or the disinclination to experiment with new technologies, which are perceived as complex and therefore daunting. Only a limited amount of research has been done in this field (Shane, 2004; Van Deurzen & Van Dijk, 2009), which makes it all the more important to examine these factors. The elderly who were exposed to new technologies in their former workplaces may well possess digital skills, but might have a limited conceptualization of how new media can be used (Rice, 2002). Eastman and Iyer (2004) found that the attitude of the elderly to their past experiences and the Internet influences their willingness to acquire or enhance their ICT skills.
H3a: The higher the degree of digital literacy, the higher the intensity of Internet use.
Physical ability
Last but not least, I will examine a person's physical ability to use a computer. Impaired sight, loss of hearing or restricted mobility may limit a person’s ability to access information or fully utilize the opportunities for expression on the Internet (Rice, 2002). People respond to biological changes, to be sure: if, for instance, a person’s vision deteriorates with age, this will hinder the use of written sources, including the Internet. There are ways to compensate for this – reading large-print books, for example, or increasing the size of the font on the screen, or buying a computer keyboard specially designed for
people with impaired vision – but in each case selection and optimization will occur as the elderly readjust their skills to compensate for age-related changes in their physical capabilities (Hernandez-Encuentra et al., 2009). An attempt has been made to explain this by means of the model of selective optimization (SOC) devised by Baltes & Baltes (1990). The SOC model builds on the Uses &
Gratifications paradigm, which holds that people play an active part in their choice and use of a medium of communication (Katz, Blumler & Gurevitch, 1974), and this might explain their inclination (or disinclination) to use to the Internet. This leads to the following research question:
H3b: Physical disabilities exert a negative influence on the intensity of Internet use.
Methodology
The survey used to examine a respondent's background, social ties, individual motivation and
individual skills can be found in Appendix 2. In this section I will – for reasons of clarity – introduce the variables and discuss the method used to measure them.
Sampling procedure
The survey – which consists of 74 questions, arranged in clusters – was conducted in a single wave among 201 respondents, meaning that no longitudinal data were collected. The respondents were questioned in a variety of surroundings, including homes for the elderly, a lunchroom and various private homes. A maximum of one person per household was interviewed, in order to prevent shared values and habits from influencing the research. Because this research revolves around differences among the elderly, I tried to ensure nearly equal numbers of male and female respondents, as well as ages ranging from just 65 to considerably older (up to 90).
The respondents were given the surveys and asked to fill them in. If they asked for
explanations of the questions or needed help in answering them, assistance was provided. In the case of respondents whose eyesight was not good enough to read the survey, or whose hands trembled badly, it was read out loud to them and their answers were recorded.
Intensity of Internet use
The intensity with which the respondents used the Internet was measured by means of a battery of questions as formulated by Teo, Lim & Lai (1999). First, a person’s frequency of Internet use was measured by means of a question whose possible answers were coded on a scale of 1 to 6, with 1 indicating non-use and 6 indicating frequent use of the Internet (Appendix 2, question 6). The duration of Internet use, too, was measured by means of a question whose possible answers also ranged from 1 to 6, with 1 indicating a very short time and 6 indicating a long period (Appendix 2, question 7). After measuring the respondents’ frequency and duration of Internet use, their scores for the intensity of Internet use were calculated. This was done by adding the two scores obtained by measuring the frequency and duration, which means that a person who uses the Internet infrequently but for a long time might score the same on this scale as a person who uses the Internet more frequently but for shorter periods. It is the average intensity of Internet use that is relevant in my calculations, because the possible answers to questions 6 and 7 (Appendix 2) are measured on an ordinal level, which makes it difficult to assign a numerical value to these answers. The Cronbach’s Alpha for this constructed scale is .88.
Social ties
A person’s social ties can be measured by means of a scale constructed by Zhao (2006). Focusing on one aspect of this scale, I asked for the frequency of contact with friends and family members
people contacted close friends and family via telephone, letter, e-mail and chat software. Total scores were computed by adding up the frequency-of-contact scores for friends and family to obtain a general score for the frequency of contact with those in one’s social circle. The reason for differentiating
between friends and family is to make it possible to conduct separate regression analyses to
determine which group – friends or family – exerts a greater influence on the intensity of Internet use, and whether their combined score is influential. The Cronbach’s Alpha for this constructed scale is .79.
Individual motivation
The factors that prompt people to use the Internet are measured by means of a scale based on Parasuraman’s (2000) Technology Readiness Index. The possible answers to this battery of questions (Appendix 2, questions 21 to 56) are based on a Likert scale – a scale that offers equal numbers of positive and negative answers, as well as one neutral option – ranging from “I disagree completely” (1) to “I agree completely” (5). Separate batteries of questions were used to gauge a person’s optimism (Appendix 2, questions 21 to 30, Cronbach’s Alpha = .89), innovativeness (Appendix 2, questions 31 to 37, Cronbach’s Alpha = .88), discomfort (Appendix 2, questions 38 to 47, Cronbach’s Alpha = .77) and insecurity (Appendix 2, questions 48 to 56, Cronbach’s Alpha = .87). Combining the answers to these questions makes it possible to use regression analysis to measure the influence of a person’s overall motivation on the intensity of Internet use.
Individual skills
The scale used to measure digital literacy assesses the extent to which a person recognizes the terms most frequently associated with digital literacy, as devised by Hargittai (2005, extended in 2009). The possible answers to these questions (Appendix 2, questions 57 to 69) yield dichotomous data which are then used in regression analysis by adding them up to create a scale ranging from 0 to 13. The Cronbach’s Alpha for this scale is .93.
The respondents’ physical abilities were measured by asking them to answer various questions about their physical health and ability to use a computer (Appendix 2, questions 70 to 74). The
answers to these questions required the respondents to grade themselves on a scale of 1 to 10. It must be noted that hypothesis 3b – Physical disabilities exert a negative influence on the intensity of Internet use – refers not to abilities but to disabilities. This means that a negative effect on the intensity of Internet use is expected from low scores and a positive effect from high scores. From these scores a general score is compiled, ranging between 5 and 50, which can be used in regression analysis between physical abilities and Internet use intensity. The Cronbach’s Alpha for this scale is .83.
Internet access
The intensity of a respondent’s Internet use was measured by asking three questions, which were compiled into one score measuring Internet access. The first question asked whether the person had access to a computer with an Internet connection (Appendix 2, question 8). I asked this question first, because in the literature the factor of physical access is deemed important, and programs have been set up to provide access to those without it (Van Dijk et al., 2007; Venkatesh, Morris, Davis & Davis, 2003). Moreover, this is a way of checking statistics compiled in the Netherlands, which show that a large number of elderly people do have physical access to a computer (CBS, 2011b;
Worldinternetstats, 2011). After the general question concerning Internet access, two more questions were asked: whether the person has easy access to a computer with an Internet connection (Appendix 2, question 9), and whether that person has his/her own computer with an Internet connection
(Appendix 2, question 10). The possible answers to these questions were coded dichotomously, a “0” meaning no, and “1” meaning yes. This method of assessing different degrees of access was chosen to enable differentiation between respondents. It takes into account the fact that people can interpret “access” in different ways. They might take “access” to mean their ability to go to a library to use the Internet, which, strictly speaking, does mean that they have access. Yet this is not the same as the
access offered by one’s own computer. After ascertaining the answers to these three variables, a general score was computed, ranging from 0 to 3, indicating the level of access, which could be used in regression analysis. The Cronbach’s Alpha for this constructed scale is .82
Demographic information
We will now take a look at the general information that has been gathered and used to measure demographic – and control – variables. Demographic variables are used to determine the
characteristics of a population, and the control variables are used to test the relative influence of an independent variable.
In the context of this research, the education factor is important, because education increases the likelihood of Internet use. The higher one’s level of education, the more likely one is to use the Internet (McQuail, 1997; Peacock & Kunemund, 2007). The scale measuring the level of education was constructed in accordance with the levels of education available in the Netherlands in the past, with the names of current levels of education added so that respondents could easily select theirs. For the possible choices, see Appendix 2, question 3.
In addition to the level of education, gender also influences Internet use (Hargittai & Shafer, 2006). Gender was measured straightforwardly in my survey (Appendix 2, question 2) by using a dichotomous variable: male (1); female (0).
The income factor will now be discussed because, tellingly, statistics compiled by De Haan, Klumper, and Steyaert (2004) reveal that household income is the most important factor in the acquisition of ICT skills. Research has shown that more highly educated people and those in higher income groups are more likely to use the Internet (Fox, 2004; CBS, 2011b). In general, the digital divide parallels economic inequality (DiMaggio et al. 2001; Korupp & Szydlik, 2005). On the other hand, the research undertaken by Porter and Donthu (2006) shows that people with low incomes tend to pursue online hobbies and entertainment. This seeming contradiction makes the income factor all
the more relevant as an object of study. In my research, income was measured and included in the demographic information used to make a base calculation by examining the Dutch average given by the CBS (2010) and then devising three categories: one consisting of the average income for this age group, one that is lower and one that is higher. Most of those who filled in my survey had below-average incomes (Appendix 2, question 4).
The last demographic factor used here was the respondents' living situation. According to Peacock and Kunemund (2007), when there are at least two people living together, the exchange of information on Internet technology may become more interesting, but at the same time less necessary. Confirmation of this type of social exchange is found in Korupp and Szydlik (2005), whose findings reveal that Internet laggards often live alone. The respondents’ living situation was measured by asking if they lived without help (0) or if they were assisted (1) – living in a home for the elderly, for instance (Appendix 2, question 5).
Analyses
Before computing the scores for access, motivation and recognition, the respective scales were analysed by PCA to determine whether all the variables used to measure the latent constructs were actually measuring what they were meant to measure. After the necessary recoding of the variables, the final analyses could be done. The questions in the survey aimed at obtaining demographic data were incorporated into the base score used to answer the main research question. The base cluster comprised the following variables: education, gender, income, living situation and Internet access as the independent variables (x), and intensity of Internet use as the dependent variable (y). After this, the independent variables (x) that measure social ties (the contact frequency with friends and family), individual motivation (optimism, innovativeness, discomfort and insecurity) and individual skills (digital literacy and physical abilities) were added cluster-wise to the successive regression analyses.
Results
Before answering the main research question, I will give the results of the regression analyses conducted to test hypotheses 1 through 5. The hypotheses stated here were expanded upon whenever it proved necessary to differentiate between results combined earlier to create the scales needed for the main regression analyses. Also, the hypotheses will be discussed in relation to the models used to answer the main research question (Appendix 1). Before examining the individual hypotheses and the main research question, it is necessary to discuss the base model, to which variables were added successively (see Appendix 1, models 1– 4), in order to determine their relative influence on the intensity of Internet use and thus test my hypotheses.
Model 1
This base cluster consists of demographic and control variables, as shown in Appendix 1, model 1. The variables education (β=.10, SE=.05, sig<.01), income (β=.12, SE=.19, sig<.05) and Internet access (β=2.25, SE=.13, sig<.01) showed a significant influence on the intensity of Internet use. The factors gender (β=.53, SE=.26, sig>.05) and living situation (β=.96, SE=.29, sig>.05) showed no significant influence on the intensity of Internet use.
Model 2
In model 2, the variable contact frequency was added to the variables used in model 1, which yielded the following results:
Hypothesis 1 – The more frequent a person’s contact with his/her social circle, the more likely that person is to demonstrate a higher intensity of Internet use – was confirmed. As shown in Appendix 1,
model 2, a significant positive relationship was found between the frequency of contact with one’s social circle and the intensity of Internet use (β=.01, SE=.00, sig<.05).
The variables gender (β=.67, SE=.27, sig<.05), living situation (β=.84, SE=.29, sig<.01), Internet access (β=2.18, SE=.13, sig<.01) and contact frequency (β=.01, SE=.00, sig<.05) show a significant positive relationship with the intensity of Internet use. In contrast, the variables education (β=.06, SE=.05, sig>.05) and income (β=.13, SE=.19, sig>.05) now show insignificant results. As seen in Appendix 1, model 2, when determining the influence exerted by contact frequency within one's social circle, the variables education and income no longer show significant results in model 2, whereas gender and living situation do. Apparently gender, living situation, Internet access and contact frequency exert a greater influence than education and income on the intensity of Internet use in this model.
Model 3
In model 3, the variables measuring motivation were added to model 2, which yielded the following results:
Hypothesis 2a – Optimism has a positive effect on the intensity of Internet use – was rejected. As shown in Appendix 1, model 3, the motivational factor optimism (β=.58, SE=1.11, sig>.05) does not show a significant influence on the intensity of Internet use.
Hypothesis 2b – Innovativeness has a positive effect on the intensity of Internet use –
was confirmed. As shown in Appendix 1, model 3, the motivational factor innovativeness (β=2.16, SE=.98, sig<.05) shows a significant positive influence on the intensity of Internet use.
Hypothesis 2c – Discomfort has a negative effect on the intensity of Internet use – was rejected. As shown in Appendix 1, model 3, the motivational factor discomfort (β=.-1.90, SE=1.29, sig>.05) does not show a significant influence on the intensity of Internet use.
Hypothesis 2d – Insecurity has a negative effect on the intensity of Internet use – was rejected. As shown in Appendix 1, model 3, the motivational factor insecurity (β=.10, SE=1.08, sig>.05) does not show a significant influence on the intensity of Internet use.
When observing the influence of the motivational factors optimism, innovativeness, discomfort and insecurity on the intensity of Internet use, it was found that the variables living situation (β=.93, SE=.28, sig<.01), Internet access (β=1.99, SE=.14, sig<.01), contact frequency (β=.01, SE=.00, sig<.05) and innovativeness (β=2.16, SE=.98, sig<.05) exert a significant positive influence. The variables education (β=.07, SE=.05, sig>.05), gender (β=.32, SE=.27, sig>.05), income (β=.07, SE=.19, sig>.05), optimism (β=.58, SE=1.11, sig>.05), discomfort (β=-1.90, SE=1.29, sig>.05) and insecurity (β=.10, SE=1.08, sig>.05) do not significantly influence the intensity of Internet use. As seen in Appendix 1, model 3, the factor gender has now become insignificant in comparison with model 2. Apparently, in this model, innovativeness exerts a greater influence than gender on the intensity of Internet use.
Model 4
In model 4, the variables measuring digital literacy and physical abilities were added to model 3, which yielded the following results:
Hypothesis 3a – The higher the degree of digital literacy, the higher the intensity of Internet use – was confirmed. As shown in Appendix 1, model 4, it was found that the ability to recognize the terms associated with digital literacy (β=.27, SE=.04, sig<.01) exerts a significant positive influence on the
intensity of Internet use. These results are not wholly reliable, however, because there is still the question of causality. Do some elderly people use the Internet more than others because they have the necessary skills, or did they acquire those skills because they use the Internet more frequently?
Hypothesis 3b – Physical disabilities exert a negative influence on the intensity of Internet use – was rejected. As shown in Appendix 1, model 4, it was found that physical abilities (β=.11, SE=.12, sig>.05) do not exert a significant influence on the intensity of Internet use.
As seen in Appendix 1, model 4, when examining the result of adding recognition and physical abilities to the variables constituting model 3, the variables living situation (β=.57, SE=.27, sig<.05), Internet access (β=1.45, SE=.15, sig<.01), innovativeness (β=2.04, SE=.88, sig<.05) and recognition (β=.27, SE=.04, sig<.01) can be seen to exert a significant positive influence on the intensity of
Internet use. The factors education (β=.02, SE=.05, sig>.05), gender (β=.28, SE=.25, sig>.05), income (β=-.07, SE=.17, sig>.05), contact frequency (β=.01, SE=.00, sig>.05), optimism (β=.-1.29, SE=1.05, sig>.05), discomfort (β=-1.58, SE=1.17, sig>.05), insecurity (β=1.84, SE=.10, sig>.05) and physical abilities (β=.01, SE=.00, sig<.05) do not show significant results. In comparison to model 3, the variable contact frequency now shows insignificant results. Apparently recognition exerts a greater influence than contact frequency in this model.
Conclusions and discussion
When examining the individual hypotheses, it appears that more frequent contact with one’s social circle will lead to a higher intensity of Internet use, in accordance with the findings of LaRose, Eastin and Gregg (2001) and Zhao (2006). This could be because people who are more sociable and already have frequent contact with their social circle use the Internet to remain in contact with that social circle and to expand it (Birnie & Horvath, 2002). This agrees with Nie’s (2001) findings, which suggest that,
although Internet users show a higher degree of social connectivity and participation, it is not the use of the Internet that makes a person more sociable.
My results concerning the motivational factors show that innovativeness is the only factor that exerts a significant positive influence on the intensity of Internet use, which means that in my
research, innovativeness is the only motivational personality trait that has a significant effect. This shows that Citrin et al. (2000) and Zielińska-Więczkowska et al. (2011) were correct in stating that those who seek novelty are more likely to adopt and master new technologies. The last cluster of variables (measuring digital literacy and physical abilities) added to the regression analysis (see Appendix 1, model 4) shows that recognition exerts a positive influence on the intensity of Internet use, as was to be expected, having used the terms associated with digital literacy that were measured by Hargittai in 2009. This result could mean, as found by Rice (2002) and Eastman and Iyer (2004), that higher exposure of the elderly to new technologies will influence their digital literacy.
It was interesting to discover the insignificance of physical abilities on the intensity of Internet use. Indeed, people with bad eyesight or limited mobility often welcome the opportunity to use a computer. This was commented on by several elderly people, who said that they liked being able to use a computer, because their hands had become too shaky to write legibly. The zoom function, too – although many people were unaware of its existence – was seen as very useful in compensating for bad eyesight, at least by those who could recognize this variable.
In answering the main research question – Which of the following factors exerts the greatest influence on the intensity of Internet use among the elderly: social ties, individual motivation or individual skills? – four regression analyses were conducted. As shown in Appendix 1, models 1 to 4, the first regression analysis was carried out between the demographic and control variables
(education, gender, income, living situation and Internet access) and the intensity of Internet use. This resulted in a base score, of which the adjusted R² equals .71. After this, the frequency of contact with one’s social circle (adjusted R²=.72), individual motivation (adjusted R²=.73) and individual skills
(adjusted R²=.78) were added as clusters to the successive regressions, meaning that all the factors of which a cluster exists were added simultaneously. The skills cluster exerted the greatest positive influence on the variance (5%) in the intensity of Internet use when compared with the influence exerted by motivation (1%) and social ties (1%), as seen in Appendix 1. The outcome of the
regression analyses conducted to test the main research question explains 78% of the variance in the intensity of Internet use.
When examining the results of the regression analyses used to test the hypotheses and when observing the differences in R² among the various models (Appendix 1, models 1-4), it appears that recognition exerts the strongest influence of all the variables used in this research. As mentioned earlier, however, this part of my research is subject to causality.
An important implication of this research is that governments should realize that their use of the Internet to stimulate social equality cannot have an optimal effect on those who find themselves on the “wrong” side of the digital divide, as many of the elderly do, even though there is a trend to promote social equality by stimulating Internet use (Ministerie VWS, 1999). Although many people find it useful to deal with government bodies and companies online, there is still a need for non-digital ways of getting things done. It should not be taken for granted that those wishing to use public and private services are computer literate (Freese, Rivas & Hargittai, 2006). It is interesting to note the insignificance of the variable income on the intensity of Internet use in combination with the other variables, since this is contrary to the expectations raised by Fox (2004).
This might change in the future, when a greater percentage of the elderly have acquired the necessary digital skills. Moreover, software is now becoming available that can be used by people with relatively low levels of digital literacy, as interfaces are increasingly designed for intuitive use. Of course it is also possible that Internet skills will change over time, and that skills learned only recently will soon be outdated. The younger generation may one day find itself in a position similar to that of the elderly now, for no matter how hard we try to keep up, our minds will inevitably deteriorate. One of
my respondents – who was actually very knowledgeable about the Internet, despite her advanced age – regretted her loss of memory and decreasing ability to learn computer skills. She was using the Internet as well as she could, but she realized that her memory wasn’t good enough to learn about its more complex facets. Taking the long-term view, this research has another implication for policy-makers, since it points out the importance of remaining aware that the assessment of the population’s digital literacy and the instruction of all age groups is an ongoing process which – like any form of education – is never-ending.
Possible criticism of this thesis might concern the drawbacks of the test for digital literacy. It was set up to prompt recognition of certain terms, but in some cases the terms were self-explanatory (see Appendix 2, questions 57 to 69). It might be prudent to conduct future research on digital literacy on a computer, which is the medium corresponding with the need for online skills. Moreover,
measuring people’s physical abilities by means of self-grading leaves the door wide open to
exaggeration and underestimation. This suggests the interesting possibility of using a computer to test people in a controlled experiment, thus measuring their real digital literacy. This same experiment could be used to test elderly people’s real ability – as opposed to their self-perceived ability – to use a computer and the Internet.
“The Elderly and the Internet” may well remain an interesting topic of research for generations to come, especially when the elderly being studied have been exposed to the Internet from the day they were born.
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Appendix 1
Table 1
Results of the regression analyses of the variables.
Model 1 Model 2 Model 3 Model 4
β SE sig. β SE sig. β SE sig. β SE sig.
Base Cluster Education .10 .05 **.00 .06 .05 .25 .07 .05 .16 .02 .05 .71
Gender .53 .26 .06 .67 .27 *.01 .32 .27 .24 .28 .25 .26
Income .12 .19 *.05 .13 .19 .51 .07 .19 .71 -.07 .17 .68
Living situation .96 .29 .54 .84 .29 **.00 .93 .28 **.00 .57 .27 *.03
Internet access 2.25 .13 **.00 2.18 .13 **.00 1.99 .14 **.00 1.45 .15 **.00
Social Contact Freq. Contact frequency .01 .00 *.02 .01 .00 *.04 .01 .00 .18
Motivation Cluster Optimism .58 1.11 .61 -1.29 1.05 .22
Innovativeness 2.16 .98 *.03 2.04 .88 *.02
Discomfort -1.90 1.29 .14 -1.58 1.17 .18
Insecurity .10 1.08 .36 1.84 .10 .06
Skills Cluster Recognition .27 .04 **.00
Physical abilities .11 .12 .37
Adjusted R² .71 .72 .73 .78
Appendix 2
Survey, translated from Dutch, with Mean and Standard Deviation for all questions. Clarification of the mean values: The dichotomous variables in this research are coded using 0 and 1. The questions with more than two possible answers range from 1 upwards.
1. What is your age? (M=73.56, SD=6.70)
2. What is your gender? (M=.50, SD=.50)
3. What is your level of education? (M=4.34, SD=1.89)
O No education (never completed elementary school)
O Elementary education
O LTS, LEAO, LHNO, VMBO
O MAVO, (M)ULO, MBO-kort, VMBO-t
O MBO-lang, MTS, MEAO, BOL, BBL, INAS
O HAVO, VWO, HBS, MMS
O HBO, HTS, HEAO, HBO-V
O University
O Other, namely …...
4. What is your monthly income? (M=1.69, SD=.80)
O Less than €1500
O Between €1500 and €1650
O More than €1650
5. What is your living situation? (M=.68, SD=.47)
O Assisted
O Independent
6. How often, on average, do you use the Internet? (M=3.36, SD=1.92)
O Never
O Less than once a month
O A couple of times a month
O A couple of times a week
O Approximately once a day
7. How long do you use the Internet at an average sitting? (M=2.96, SD=1.13)
O Several minutes
O Less than half an hour
O Between one-half and one hour
O Between one and two hours
O Two to three hours
O Three hours or longer
8. Do you have access to a computer with an Internet connection? (M=.85, SD=.36)
O Yes O No
9. Can you easily arrange to use a computer with an Internet connection? (M=.70, SD=.46)
O Yes O No
10. Do you have your own computer with Internet access? (M=.59, SD=.49)
O Yes O No
11. How many of your friends have you seen in person this year? (M=6.43, SD=6.89)
12. How many friends have you spoken to on the telephone this year? (M=6.43, SD=6.89)
13. How many friends have you written to this year? (M=2.48, SD=5.77)
14. How many friends have you contacted by e-mail this year? (M=4.27, SD=8.76)
15. How many friends have you ‘chatted’ to on the computer this year? (M=.35, SD=1.09)
16. How many members of your family have you seen in person this year? (M=10.32, SD=7.04)
17. How many members of your family have to spoken to on the telephone this year? (M=6.95, SD=4.84)
18. How many members of your family have you written to this year? (M=2.08, SD=3.22)
19. How many members of your family have you contacted by e-mail this year? (M=3.64, SD=6.21)
Please indicate on a scale of 1 (disagree completely) to 5 (agree completely) to what extent you agree with the following statements.
Disagree Agree
1 2 3 4 5
21. Technology gives people more control over their daily lives. O O O O O (M=3.29, SD=1.27)
22. Products and services that rely on the newest technologies are much more
convenient to use. O O O O O (M=2.72, SD=1.19)
23. You like the idea of doing business via the computer because you are not
limited to regular business hours. O O O O O (M=3.08, SD=1.49)
24. You prefer to use the most advanced technology available. O O O O O (M=2.47, SD=1.26)
25. You like computer programs that allow you to tailor things to fit your own needs. O O O O O (M=3.20, SD=1.35)
26. Technology makes you more efficient at your job. O O O O O (M=3.33, SD=1.28)
27. You find new technologies mentally stimulating. O O O O O (M=2.84, SD=1.26)
28. Technology gives you more freedom of movement. O O O O O (M=3.25, SD=1.34)
29. Learning about a technology can be as rewarding as using that technology. O O O O O (M=2.81, SD=1.27)
30. You feel confident that machines will follow through with what you
instructed them to do. O O O O O (M=3.27, SD=1.18)
Disagree Agree
1 2 3 4 5
31. Other people come to you for advice on new technologies. O O O O O (M=2.68, SD=1.36)
32. Your friends seem to be learning more about the newest technologies than O O O O O (M=3.01, SD=1.40)
you are. [reverse scored]
33. In general, you are among the first in your circle of friends to acquire a new
technology when it appears. O O O O O (M=2.70, SD=1.31)
34. You can usually figure out new high-tech products and services without
help from others. O O O O O (M=2.99, SD=1.40)
35. You keep up with the latest technological developments in your areas of interest. O O O O O (M=2.66, SD=1.39)
36. You enjoy the challenge of figuring out high-tech gadgets. O O O O O (M=2.61, SD=1.27)
37. You find you have fewer problems than other people in making
Please indicate on a scale of 1 (disagree completely) to 5 (agree completely) to what extent you agree with the following statements.
Disagree Agree
1 2 3 4 5
38. Technical support lines are not helpful because they don’t explain things in O O O O O (M=3.50, SD=1.29
terms you understand.
39. Sometimes you think that technology systems are not designed for use by O O O O O (M=3.32, SD=1.26)
ordinary people.
40. There is no such thing as a manual for a high-tech product or service that’s
written in plain language. O O O O O (M=3.22, SD=1.26)
41. When you get technical support from a provider of a high-tech product or service, you sometimes feel as if you are being taken advantage of by someone who
knows more than you do. O O O O O (M=2.88, SD=1.38)
42. If you buy a high-tech product or service, you prefer the basic model to
one with a lot of extra features. O O O O O (M=3.36, SD=1.22)
43. It is embarrassing when you have trouble with a high-tech gadget
while people are watching. O O O O O (M=2.42, SD=1.25)
44. Caution is required before replacing important people-tasks with technology,
because new technology can break down or be disconnected. O O O O O (M=3.66, SD=1.08)
45. Many new technologies have health or safety risks that are not discovered until
after people have used them. O O O O O (M=3.27, SD=.1.08)
46. New technology makes it too easy for governments and companies to spy on people. O O O O O (M=3.47, SD=1.03)
47. Technology always seems to fail at the worst possible time. O O O O O (M=3.15, SD=1.29)
Disagree Agree
1 2 3 4 5
48. You do not consider it safe to give out a credit card number via the computer. O O O O O (M=3.13, SD=1.36)
49. You do not consider it safe to do any kind of financial business online. O O O O O (M=3.05, SD=1.43)
50. You worry that information you send over the Internet will be seen by other people. O O O O O (M=2.94, SD=1.30)
51. You do not feel confident doing business with a place that can only be reached online. O O O O O (M=2.84, SD=1.26)
52. Any business transaction you do electronically should be confirmed later in writing. O O O O O (M=2.77, SD=1.43)
53. Whenever something gets automated, you need to check carefully that the machine
or computer is not making mistakes. O O O O O (M=3.16, SD=1.28)
54. The human touch is very important when doing business with a company. O O O O O (M=4.03, SD=.95)
55. When you call a business, you prefer to talk to a person rather than a machine. O O O O O (M=4.14, SD=1.05)
56. If you provide information to a machine or over the Internet, you can never be
Please state whether you recognize the following terms that have to do with the Internet and its use.
57. Back to the previous page O Yes O No (M=.65, SD=.48)
58. Home O Yes O No (M=.65, SD=.48)
59. Favourites O Yes O No (M=.60, SD=.49)
60. Zoom O Yes O No (M=.46, SD=.50)
61. Modem O Yes O No (M=.52, SD=.50)
62. Internet banking O Yes O No (M=.71, SD=.45)
63. Explorer O Yes O No (M=.39, SD=.58)
64. Search engine O Yes O No (M=.58, SD=.49)
65. HTML O Yes O No (M=.28, SD=.55)
66. Download O Yes O No (M=.60, SD=.49)
67. Cookie O Yes O No (M=.32, SD=.47)
68. Web log O Yes O No (M=.39, SD=.49)
69. Mouse O Yes O No (M=.77, SD=.42)
Please indicate on a scale of 1 to 10
70. How good your eyes are (M=6.71, SD=.1.60)
71. How good your hearing is (M=6.68, SD=.1.63)
72. Your physical ability to use a mouse (M=7.24, SD=.1.79)
73. Your physical ability to use a keyboard (M=7.09, SD=.1.79)
74. Your physical ability to sit in a chair at the computer (M=7.17, SD=.1.76)
