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Keywords:

  • Internet;
  • older people;
  • social capital;
  • wellbeing

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Older Adults and Internet Use
  5. Methodology
  6. Instrumentation
  7. Results
  8. Discussion
  9. Conclusion
  10. Practical Implications
  11. References

Although it is increasingly obvious that the Internet is changing human life; the details of this change are not yet clear. A major debate in current literature involves the capacity of the Internet to enhance social capital and wellbeing in old age. In this regard, the present study attempts to investigate the relationships between Internet use and older people’s social capital and wellbeing. An online survey was conducted at the University of Sydney. 222 seniors responded to the survey. The measures used included a wide range of instruments related to the Internet use, social capital and wellbeing. Respondents used the Internet for various purposes, including seeking information, entertainment, commerce, communication, and finding new people. The main findings of the study were that the relationships between Internet use, social capital and wellbeing is a complex construct and the Internet has different effects on social capital and wellbeing resulting from different use of this technology. The study results revealed that the Internet is a 2-edged sword with the ability to both harm and help. According to the findings of this study, using the Internet can be helpful for older adults if they are aware how they use it.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Older Adults and Internet Use
  5. Methodology
  6. Instrumentation
  7. Results
  8. Discussion
  9. Conclusion
  10. Practical Implications
  11. References

The rapid growth of online communication and development of new media presents many new opportunities and challenges for social inclusion (Nations, 2005). This may support the notion that communities can be developed without regular face-to-face communication, and the Internet has positive social effects on both individuals (Hall & Havens, 2001) and communities (Solomon & Peterson 1994). It has been well documented that social interaction, particularly with family and community networks, is strongly associated with a range of positive mental health outcomes (Mulvaney-Day, Alegria, & Sribney, 2007). In Australia, as in many other countries, there is a growing interest in the role that social capital may play in determining social, economic, and health outcomes. Accordingly, the National Strategy for an Ageing Australia has accorded high priority to the enhancement of social capital in its policy framework for improving the health and quality of life of older Australians (Bishop, 2000). As the National Strategy recognises, social isolation is widespread among older Australians and has been correlated with increased risk of depression, suicide, alcoholism and institutionalisation (Bishop, 2000). People who get less emotional and social support due to social isolation are more likely to have more depression, less well being, and higher levels of disability (Hall & Havens, 2001; Prince, Harwood, Blizard, Thomas, & Mann, 1997). Groups at particular risk of having low levels of social contact include the frail housebound, people caring for a disabled relative at home, those who have relocated to supported accommodation, and elderly residents of rural and remote areas (Locher et al., 2005). Accordingly the strategy has identified a greater understanding of social capital in the context of population ageing as a priority for research (Bishop, 2000).

Generally, social capital refers to the social relationships between people that enable productive outcomes (Szreter, 2000). It can be seen as the glue that holds together social collectives such as networks of personal relationships, communities or even whole nations (Ellison, Steinfield, & Lampe, 2006). The term “social capital” was popularized by Putnam’s work Bowling Alone (2000). In this work, Putnam defined social capital as follows:

Whereas physical capital refers to physical objects and human capital refers to the properties of individuals, social capital refers to connections among individuals – social networks and the norms of reciprocity and trustworthiness that arise from them. In that sense social capital is closely related to what some have called “civic virtue.” The difference is that “social capital” calls attention to the fact that civic virtue is most powerful when embedded in a sense network of reciprocal social relations. A society of many virtuous but isolated individuals is not necessarily rich in social capital (R.D. Putnam, 2000) (P.19).

Coleman defined social capital by its function as a diversity of entities with two elements in common: presence of a social structure, and an element of action (Coleman,1988). Therefore, it has been considered as the web of supportive relationships between citizens that facilitate the resolution of co-operative action problems, and some aspects of social structure, such as levels of interpersonal trust, norms of reciprocity and mutual aid which perform as resources for such collective action (Coleman 1988 ; Putnam, Leonardi , & Nanetti 1993).

While the definition of social capital differs to some extent from researcher to researcher, there is conformity that social capital is derived from relations with other people in a social structure. Social capital is a multidimensional concept, which includes diverse parts of social structure. Each dimension involves the meaning of social capital though each alone is not able to provide fully the concept in its whole (Hean, Cowley, Forbes, Griffiths, & Maben, 2003). Scholars have identified different groups of dimensions. They are, however, ordered mainly in two categories: network structure and its content. Network in social capital identifies the structure of social relations and the content which operates within these structures. Networks are comprised of a set of relational ties across a set of actors that form a social structure (Stone, 2001). Dimensions of Social Capital in the current study are considered according to the analysis by Onyx and Bullen (2001) which suggested that there are eight distinct dimensions of social capital many are related to each other (Onyx & Bullen, 2000). The reason for using this model as dimensions of social capital is that this model is comprehensive and was used before in studies of the Australian population. This study used social capital theory to explain the concept of social capital and its structure.

Social capital theory suggests that there are some abilities and values rooted in social networks and relationships that create certain kinds of benefits for people to use, both instrumental and emotional, which depend on who one knows and how well one understands one’s social relationships. These values are achieved through investment in social relationships and translated into social and economic gain for individuals. However unlike the other forms of capital, no single individual can claim ownership of this value because it is only created through useful interactions across social networks (Coleman, 1988). Putnam et al. (1993) also proposed that the core idea of social capital theory develops around this value inserted in social networks (Putnam et al., 1993). This social connectedness produces a kind of relationship that creates potential benefits for individuals who are connected, in various forms such as valuable information acquisition, financial gain, job creation, education diffusion, or other instrumental and emotional support. By including these qualities in our social networks, future benefit for at least some individuals will be generated. According to Coleman (1990), social capital theory is a powerful framework that can be used to understand how people, and the social networks that they are a part of, interact with each other to define their wellbeing (Coleman, 1990). Components of the theory can be categorised in dimensions, levels, and types of social capital. This study uses social capital theory to explain the concept of social capital and its structure; it provides a theoretical framework to understand whether use of the Internet can generate social capital through creation of larger online ties, which defines wellbeing in older adults.

While the Internet is not very old, it has had a great effect on society in many ways (Bargh, McKenna, & Fitzsimons, 2002). The Internet has the potential to overcome time and spatial restrictions, thus providing the opportunity for isolated people to improve social networks (Mellor, Firth, & Moore, 2008).

Older Adults and Internet Use

  1. Top of page
  2. Abstract
  3. Introduction
  4. Older Adults and Internet Use
  5. Methodology
  6. Instrumentation
  7. Results
  8. Discussion
  9. Conclusion
  10. Practical Implications
  11. References

Using the Internet has significant value for elderly people. A 1999 study found that nursing home residents can use the Internet effectively for email and other access, combating the four plagues of institutionalised elders: loneliness, boredom, helplessness, and decline of mental skills (Crary, 1999). It is even estimated that confidence in learning email in elderly people is an effective way to reduce depression in them. McConatha found that using the Internet has some benefits for nursing-home residents who were taught to use this technology, including increased level of life satisfaction, mental functioning, activities of daily living, and reduced level of depression (McConatha, 2002). According to Adler’s report on SeniorNet, a U.S. site, more than half of the Internet users spent less than 2 hours a week online, 36% were online for 3 to 10 hours a week, while 5% spent more than 10 hours a week online (Adler, 1996). However, results of a survey of adults over 50 conducted on the SeniorNet during 3 months of 2002 showed that 7% of participants reported that they spent less than 5 hours a week on the Internet, 25% were online 5 – 9 hours, while 33% were online 10 – 19 hours, and 34% reported 20 hours or more. Forty-one percent of participants reported that have been using the Internet between 2 – 5 years and 46% reported over 5 years use (SeniorNet, 2002). These results show that from 1996 to 2002 there was a huge increase in the frequency of Internet use among older users.

Other research in this area (e.g., Ogozalek, 1991) has suggested that learning to use a computer can increase the self-confidence, ability to learn, and memory retention of older persons. Very recent research conducted by Xie (2007) revealed that Internet learning and use by older Chinese makes their lives after retirement more meaningful, and develops their self-evaluations and other people’s views of them (Xie, 2007).

There exists a strong discussion within the literature as to whether the Internet leads to larger communication and information exchange between people, thus encouraging social connectivity and diminishing social isolation, or whether it reduces social activity and therefore makes threatens social life. One argument raises: If people spend more time using the Internet, what other activity is given up? Conversely, it can be argued that using the Internet does not necessarily suggest that an individual is alone. In reality, it may assist social activity that replaces otherwise low-interaction or private activity (Mellor, Firth, & Moore, 2008). This study was designed to provide exploratory research and establish basic knowledge and a foundation for future research in this new field. This study seeks to answer the research questions:

Research Question 1: What is the relationship between social capital and wellbeing among older people?

Research Question 2: What is the relationship between Internet use and social capital in older people?

Research Question 3: What is the direct and indirect relationship between Internet use and wellbeing among older people?

Methodology

  1. Top of page
  2. Abstract
  3. Introduction
  4. Older Adults and Internet Use
  5. Methodology
  6. Instrumentation
  7. Results
  8. Discussion
  9. Conclusion
  10. Practical Implications
  11. References

The current research has investigated Internet use by people over the age of 55 in order to ascertain the actual or potential role of the Internet in promoting the social inclusion of older people through maintenance or development of social capital and well-being. The study utilised Internet research methodology where participants were self-selected, voluntary, informed research participants on the Internet (Mann & Stewart, 2000). Data for the present study were collected online from 222 Australian Internet users aged 55 years or older. All data were gathered over a 6-month period. Participants consisted of 83 Males and 138 Females with one unknown gender. Participants’ age were categorised into six categories from 55–59 years to over 79 years of age, of which most were between 60 to 64 years-of-age (29.7%) followed by 65-69 years-of-age (23.4%) and 55-59 years (18.5%).

A website entitled “Social capital online survey” was housed on the web server for the School of Behavioural and Community Health Sciences of the University of Sydney. Participants were recruited through search engines, through advertisements, and through online community interest groups. From an invitation and information page, visitors to the site were invited to complete the questionnaire. All respondents’ information was anonymous and only a reference number (along with the date and time of questionnaire completion) and demographic information were used as identifiers. All participants had the option to withdraw from the study at any time. Participants answered a comprehensive questionnaire which took approximately 30 minutes to complete.

Benefits and limitations associated with Internet research

Web-based surveys have advantages and disadvantages like other kinds of surveys.

The practical benefits of incorporating the Internet into research designs are wide-ranging. Some of the most important gains are considered followed (derived from (Hewson, 2003; Ó Dochartaigh, 2002): decreased cost and time saving, easier handling of data, extending access to participants, validity of web-based research, anonymity, and increased accuracy through the removal of researcher’s transcription of verbal responses.

As discussed above, online surveys offer many advantages over traditional surveys. However, there are also disadvantages that should be considered. Some of the most important limitations are considered followed (derived from (AZAR, 2000; Frankel & Siang, 1999; Hewson, 2003; Ó Dochartaigh, 2002) :

  • • 
    It raises questions about sampling techniques. Samples are not representative of the general population.
  • • 
    Inappropriate for studies where large proportion of the target population may not have access to the Internet technology (e.g. racial and economic divide of the Internet).
  • • 
    Difficulty assessing representativeness of sample, the ease of the “delete” button, risks of nonsecure data assembling, processing, storage and dissemination, self-selection, and risk of multiple submissions.

Instrumentation

  1. Top of page
  2. Abstract
  3. Introduction
  4. Older Adults and Internet Use
  5. Methodology
  6. Instrumentation
  7. Results
  8. Discussion
  9. Conclusion
  10. Practical Implications
  11. References

The survey instruments used for this study consisted of existing instruments on Internet use, social capital, and wellbeing. The resulting questionnaire consisted of sections arranged in the following order:

The frequency of the Internet use was calculated based on respondent’s estimate of the time that they spent on the Internet (Shklovski, Kraut, & Raini, 2004). The measurement contained survey item including: “How many hours in the last week did you use the Internet?” on 4-point scales from less than 4 hours to more than 16 hours.

Rainie, Howards & Jones have reported that people who recently started to use the Internet are systematically different from those who have used the Internet for a long time (Howard, Rainie, & Jones, 2001). As a result, a measure of the Internet use history was included to the study instrument. History of Internet use measures the years which the participant has been using the Internet. It was applied on a 4-point scale (from less than 1 year to over 7 years).

Shklovski and Kraut’s (2004) the Breadth of Internet use scale was applied on a 7-point scale (from never to several times a day) to find different use patterns of the Internet (Shklovski et al., 2004). Respondents were asked to indicate the extent which they used 27 Internet activities. Principle Component Factor Analysis reduced these applications to 5 types of the Internet use including: Finding new people, Entertainment, Commerce, Communication, and Information. This result was the same as five dimensions of the study from which this scale was obtained (Shklovski et al., 2004). The five factors explained 64.97 percent of the variance in the data and Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy was .908 (P = 000). There was a strong positive intercorrelation between these five dimensions of the Internet applications. The published reliability coefficient for this scale was 0.79 Cronbach’s Alpha based on standardized items. For this study the reliability coefficient was .95.

Another section was social capital measurement, developed by Onyx and Bullen, which consisted of 36 questions that were answered to a 4-point scale from No, Not Much or No, Not at All to Yes, Definitely or Yes, Frequently (Onyx & Bullen, 2000). Respondents were asked to indicate the extent to which they used 36 precoded items of the social capital scale. To find the degree of general social capital, the sum of 36 items of the social capital scale was calculated. Principle component factor analysis was performed to explore the factor structure of social capital instrument. Instead of eigenvalues, the number of factors was considered eight according to Onyx and Bullen’s study (2000). Principle axis factor extractions with varimax rotation with eight factor solutions were conducted. Items loading on each factor for many factors were similar to those of the original study (Onyx & Bullen, 2000); therefore, the factor labels derived in the original study remained. The most differences were related to Pro-activity in Social context factor which may relate to the sample’s age group (older people).

Principle Component Factor Analysis reduced the 36 items from the social capital scale to eight dimensions, including: Participation in Community, Feeling of Trust, Neighbourhood Connections, Tolerance of Diversity, Value of Life, Family Connections, Pro-Activity in Social Context, and Work Connections. These eight factors explained 60.49% of the variance in the data with a Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy of .75 (P = .000). These dimensions were intercorrelated, however, Pro-activity in a social context (Social Agency) and Work Connection had the lowest correlation with other dimensions It could be possible that these two factors did not load because participants in this study were just over 55 years old (restrict age) and the majority of them were retired and less able to work and being pro-active in social context.

The Australian Wellbeing Index was the last instrument which was used in this study; it is a comprehensive measure of personal and national wellbeing. This Index is based on average levels of satisfaction with a range of personal and national life aspects. Satisfaction is stated as a percentage score, where 0% is completely dissatisfied and 100% is completely satisfied. It consists of 17 items on 10-point scale that measure the level of perceived satisfaction within seven domains: material wellbeing, health, productivity, intimacy, safety, community, and emotional wellbeing (Cummins, 1997). Principle Component Factor Analysis was done to reduce these items to two types of subjective wellbeing, including: personal and national wellbeing. The two factors explained 55.05%of the variance in the data and Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy was .888, (P = 00). Elements of the Personal Wellbeing Index were satisfaction with health, personal relationships, feeling of safety, standard of living, achieving in life, feeling part of the community, and future security. Elements of the National Wellbeing Index were satisfaction with Australian social conditions, Australian economic situation, the state of the Australian environment, Australian business, national security, and government. Reliability coefficient of this measurement ranges from 0.7 and 0.85 in Australia and overseas. The reliability coefficient for this study was .91.

The survey includes items which comprised a set of general demographic, including age, gender, marital status, education, and population density of the place of residence; personality and self-perception of health questions of which were considered as control variables. To examine whether Personality (focus on Extroverted Personality) moderated any association between Internet use and social participation, The Big Five Personality Test (Benet-Martinez & John 1998) was included (Benet-Martinez & John, 1998). In this study extroverted personality was considered as target personality type. Extroversion is related to social interest and positive affect. To observe whether health status and self-perception of health moderated any association between Internet use and social inclusion, The Psychological Self-Perception of Health Measurement (Rao et al., 2005) was included on a 2-point scale (false and true) which is an index of quality of life (Rao et al., 2005). In this study “feel healthy factor” was considered as target variables which show positive aspects of health. This factor comprises of five items such as “I don’t get sick very often.”

The data were analysed using SPSS V15.0 for Windows. Bivariate analysis was conducted to determine whether demographic and other independent variables vary by the dependent variables. Significant variable differences were identified using t-test, one-way ANOVA and Kruskal-Wallis procedure. Correlations between variables were conducted using Spearman’s rank correlation to identify relationships between independent and dependent variables. The proposed research questions were also tested using a series of Hierarchical multiple regression equations.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Older Adults and Internet Use
  5. Methodology
  6. Instrumentation
  7. Results
  8. Discussion
  9. Conclusion
  10. Practical Implications
  11. References

Overall 307 older people filled in the questionnaire from which 222 of them completed the survey, for an overall response rate of approximately 72 %, and uncompleted and multiple submissions were deleted. Nearly all (91.4%) of the participants most often used a computer at home, 5.9% at work and 1.5% at other places like school and library. The majority (66.2%) of respondents were married; 14.5% were separated/ divorced; 13.5% were widowed; and 4.5% were never married.

Regarding the nationality, participants’ were asked if they born in Australia which most (61.3%) were born in Australia with English being spoken as their first language (90.5%). Education achievement was distributed across levels, indicating that 37.8% completed high school; 50.2% having completed some higher education and 10.8% completing elementary school. Yearly income was indicated across seven groups with distribution being 15.3% in less than $15,000 level (the lowest group); 36.5% which the majority was earned $15,000 to $30,000 and with 14.9% of respondents reporting incomes higher than $61,000. Regarding population density, about half of the respondents (45.9%) lived in capital cities; 23% in regional cities and 31.1% living in country towns or rural areas. Lastly, most of the respondents (64.4%) lived with their spouse/partner; less than one-third (27.9%) lived alone and 6.8% of them lived with their family or friends.

Only 9.9% of the respondents used the Internet less than 4 hours per week, with 36.5% reporting Internet use between 4-10 hours per week, 23.4% reporting 10-16 hours per week, and 29.3% reporting than 16 hours per week. Regarding history of Internet use, nearly half of the respondents (44.1%) had used the Internet for more than 7 years; 28.4% between 5 to 7 years, 24.3% between 2 to 4 years, and only 2.3% had used the Internet for less than 1 year. Participants most commonly used the Internet for communication (M = 2.43 & SD = 1.48), seeking information (M = 2.23 & SD = 1.23), and commercial purposes (M = 1.81 & SD = 1.26) following with entertainment purposes (M= 1.62 & SD= 1.32). The smallest Internet application in seniors was using the Internet to communicate with unknown people or finding new people (M = 1.22 & SD = 1.45).

The Kruskal-Wallis procedure, a nonparametric test, was used to find the relationships between general social capital and demographic variables. the test was significant for nationality (χ2=−10.04, P = .002), being non-English speaking background (χ2=−6.19, P = .013), education (χ2= 14.01, P = .003) and income (χ2= 14.71, P = .012). Which means those who was born in Australia with English speaking background, and higher education and income were more likely to have a higher level of general social capital. Graph 1 shows the relationship between general social capital and Internet application. As can be seen, greater use of the Internet for all different applications was related to the higher level of general social capital. However, the changes were very small after scale 2 of social capital. This means that up to a level of usage, social capital was likely to be increased, but remain constant with little changes except for using the Internet for communication.

image

Figure 1. The relationship between General social capital and the Breadth of Internet use.

Download figure to PowerPoint

Wellbeing’s results revealed that life in Australia had the highest percentage (83.9%); indicating participants had the highest life satisfaction for life in Australia. Higher percentages of life satisfaction followed by satisfaction with standard of living (78.6%); satisfaction with life as a whole and how safe they feel (78.5% and 77.9% respectively). The lowest percentage of component of life satisfaction was satisfaction with business in Australia (54.6%) followed by national security in Australia and natural environment in Australia (58.4% and 58.5% respectively). Principle Component Factor Analysis reduced The Australian Well-Being Index to 2 types of subjective wellbeing: personal and national wellbeing. Spearman’s Bivariate correlation showed that subscales of wellbeing were correlated (r = .549). Descriptive statistics of wellbeing shows a higher level of personal wellbeing (M = 7.38, SD = 1.48) than the national wellbeing (M = 6.40, SD = 1.71).

In order to determine whether there were significant differences in wellbeing between those with different demographic characteristics, a set of t-tests and ANOVA were conducted. The t-test results revealed that males, and those with non-English backgrounds, were more likely to have a higher degree of national wellbeing (t =−.569 & P = .038 and t = .871 & P = .043 respectively). The ANOVA test results revealed that there were significant differences in personal wellbeing between different population densities. Those who were from high population density areas were more likely to have a higher degree of personal wellbeing (F = 1.55 & df = 3, P = .013). Regarding national wellbeing, results showed that older participants with a higher income were more likely to have a higher degree of national wellbeing (F = 1.43 & df =7, P = .044 and F = 1.67 & df = 6, P = .008 respectively).

Research Question 1: What are the associations between social capital and wellbeing in elder people? Hierarchical multiple regression analyses were conducted to evaluate which dimension of social capital predicted wellbeing (Table 1).

Table 1.  Multiple regression analysis of Social capital dimensions and Wellbeing subscales
CriterionSignificant predictorβ- regression coefficientFUnique variance due to predictor
  1. Notice: PW = Personal Wellbeing and NW = National Wellbeing, TRUS= Feeling of trust; VAL = Value of life; FAM= Family connections; WOR= Work connections.

PWTRUS.14123.7310.1%
 VAL.34455.1420.7%
 [F = 9.48, P = .00, R2= 27.1%]
NWFAM.17316.337.2%
 WOR.3135.52.5%
 [F = 5.68, P = .00, R2= 18.3%]

Results indicate that from different dimensions of social capital only a combination of two dimensions were predictors of personal wellbeing: higher degree of feelings of trust (β= .141) and value of life (β= .344). It indicates that those with higher trust and more validated life involvement are more likely to have a higher sense of personal wellbeing. The two variables accounted for 27.1% of the variance. A combination of two variables predicted national wellbeing: a higher degree of family (β= .173) and work connections (β= .313). It is likely that those with a higher level of family and work connections have a higher level of national wellbeing. The two variables accounted for 18.3% of the variance.

Research Question 2: What is the relationship between Internet use and social capital in older people? Hierarchical multiple regression was performed to seek the relationships between frequency of Internet use, history of Internet use and Internet usage patterns with social capital dimensions controlling for control variables (Table 2).

Table 2.  Multiple regression analysis of Social capital dimensions and Internet use
CriterionSignificant predictorβ- regression coefficientFUnique variance due to predictor
  1. Notice: PAR= Participation in Community; FEEL= Feeling of trust; NEI= NeighbourhoodConnections; FAM= Family connections; TOLE= Tolerance of diversity; VAL = Value of life; PRO=Pro-activity in Social context; WOR= Work connections.

PAREducation.1374.111.9%
 New people.22910.965.1%
 [F = 7.61, P = .001, R2 = 7%]
FEELMarital status−.1746.292.8%
 Education.2506.483.1%
 Population density.1795.802.6%
 Subjective health.1786.833.2%
 Communication.1344.061.8%
 [F = 6.18, P = .00, R2 = 13.4%]
NEIAge.1666.292.4%
 Marital status−.1535.12.3%
 Communication.1745.942.8%
 [F = 5.48, P = .001, R2 = 7.5%]
FAMSubjective health.1545.832.8%
 History of Internet use.1424.242%
 [F = 5.08, P = .007, R2 = 4.8%]
TOLEHistory of Internet us.1587.263.4%
 [F = 7.26, P = .008, R2 = 3.4%]
VALAge.1898.153.5%
 Subjective health.19510.064.7%
 Frequency of Internet−.1545.482.3%
 Communication.24910.944.9%
 [F = 9.14, P = .00, R2 = 15.4%]
PROAge−.315229.7%
 Marital status.18310.364.1%
 Education.1475.242%
 Entertainment−.21513.685.7%
 [F = 13.85, P = .00, R2 = 21.6%]
WORAge−.41636.6715.2%
 Population density−.1364.771.8%
 Marital status.1585.922.3%
 Entertainment−.1597.43%
 [F = 14.49, P = .00, R2 = 22.4%]

A combination of two variables predicted participation in community including: a higher educational level (β= .137) and using the Internet to communicate with unknown people (β= .229). These two variables explained 7% of the variance. The results for feeling of trust show that an arrangement of five variables predicted this dimension of social capital: being married (β=−.174), a higher level of education (β= .250), living in a low population density area (β= .179), subjective health (β= .178), and using the Internet for communication (β= .134) which accounted for 13.4% of the variance.

Neighbourhood connection was predicted by three variables: being older (β= .166), married (β=−.153), and using the Internet for communication (β= .174). These combinations of variables accounted for 7.5% of the variance. For family connections subjective health (β= .154) and history of Internet use (β= .142) were predictors which explained 4.8% of the variance. Tolerance of diversity was predicted by a longer period of time use of the Internet (β= .158) which explained 3.4% of the variance.

Value of life was predicted with four variables: being older (β= .189), subjective health (β= .195), using the Internet more for communication (β= .249) and a lower degree of frequency of Internet use (β=−.154). 15.4% of the variance was explained by these four variables. Regarding pro-activity in social context, results revealed that being younger (β=−.315), widowed or separated (β= .183), being more educated (β= .147), and a lower degree of using the Internet for entertainment (β=−.215) predicted this dimension of social capital. These four variables accounted for 21.6% of the variance. Finally, work connection was predicted with a combination of four variables: being younger (β=−.416), living in low population density areas (β=−.136), being widowed or separated (β= .158) and a lower degree of using the Internet for entertainment (β=−.159) which explained 22.4% of the variance.

Research Question 3: What is the direct relationship between Internet use and wellbeing among older people? Results of Hierarchical multiple regressions for the relationships between personal and national well-being and Internet use are shown in table 3.

Table 3.  Summary of Multiple regression analysis of Internet usage and Well-being
CriterionSignificant predictorβ- regression coefficientFUnique variance due to predictor
Personal Well-beingFinding new people−.1403.851.8%
 Entertainment−.1582.292.9%
 [F = 1.86, P = .12, R2= 4.3%]
National Well-beingFrequency of Internet use−.1182.421.1%
 [F = 1.77, P = .172, R2=1.7%]

A combination of two variables predicted personal wellbeing: a lower degree of usage of the Internet to find new people (β=−.140) and for entertainment (β=−.158). This means that those who use the Internet to meet new people for social purposes or communicate with people for the first time online and use the Internet for entertainment are more likely to have a lower degree of personal wellbeing. The variables accounted for 4.3% of the variance. Regarding national wellbeing, data revealed that frequency of Internet use was a negative predictor for this dimension of wellbeing (β=−.118); indicating those with more hours spent on the Internet are more likely to have less national wellbeing. The variables accounted for 1.7% of the variance.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Older Adults and Internet Use
  5. Methodology
  6. Instrumentation
  7. Results
  8. Discussion
  9. Conclusion
  10. Practical Implications
  11. References

The main findings of the study were that the relationship between Internet use, social capital and wellbeing is a complex construct and the Internet has different effects on social capital and wellbeing resulting from different uses of this technology. This study’s result shows that social capital was higher in those who were born in Australia, English speakers, and those with higher education and income level. Research shows that similar to the other kinds of capital, there is an uneven distribution of social capital in society in terms of social class, gender, age, race, marital status, income, education, ethnicity, and locality (Edwards & Foley, 1998; R.D Putnam 2000; Stoltz, 2003). However, Onyx and Bullen in their study in five Australian communities concluded that generally social capital was not correlated with demographic variables such as age, gender, work, salary, or qualification levels (Onyx & Bullen, 2000). Privileged groups possess higher levels of social capital than more marginalized ones. Groups like immigrants, poor people, the poorly educated, those with disabilities and elderly people, may feel that they are socially excluded due to little or no social capital (R. D. Putnam; Lewis 2003).

As can be seen in the graph 1, greater use of the Internet for all different applications was related to the higher level of general social capital; however, the changes were very small after scale 2 of social capital. This means that up to a level of usage, social capital was likely to be increased, but remained constant with little change except for using the Internet for communication. This finding supports Kraut’s (1998) point of view that optimum usage level of the Internet affected social capital most positively (Kraut et al., 1998). However, for using the Internet as a communication tool to keep in contact with relatives and friends, increased use instantly had a positive effect. LaRose and colleagues found that among certain populations, depression was relieved through interpersonal communication facilitated by the Internet. (LaRose, Eastin, & Gregg, 2001).

Research Question 1: What are the associations between social capital and wellbeing in elder people? Results of this study show that higher feelings of trust and values of life might make a higher level of personal wellbeing followed by higher family and work connections which might make higher level of national wellbeing. Therefore, direct relationships were found between social capital and wellbeing. This means that with increasing social capital in older adults there is a chance that their wellbeing will increase. The findings support Putnam et al. (1993) and Coleman’s (1990) social capital theory, that social capital is a predefining indicator of wellbeing. other instrumental support and emotional support (Putnam et al., 1993).

Research Question 2: What is the relationship between Internet use and social capital in older people? The amount of the Internet experience might affect users’ social capital. Those who were long-term users were more likely to have a higher family connections and tolerance of diversity. Time is an important variable in creating satisfying relationships over the Internet (Park & Floyd, 1996; Walther, Anderson, & Park, 1994). New users may be less competent at using the medium to obtain social support resulting in less ability to compensate the lack of social cues available in e-mail (Walther, 1996). Matei (2004) reported that ethnically homogenous states tended more to create online groups which demonstrates that bonding social translated to the Internet as well (Matei, 2004). Those who are Internet users for a long period of time are more likely to be connected with the world by being informed of local and international news; and more likely to be familiar with different cultures which reduces their prejudice and racism and increases their tolerance of diversity.

Results show that using the Internet to communicate with unknown people predicted participation in community. Lin stated that social participation via the Internet is a new form of civic association that, while performed in isolation, is one of the new forms of civic participation (N. Lin, 2001). In our study, using the Internet for communication predicted a feeling of trust, neighbourhood connection and value of life. In contrast to Putnam’s time displacement hypothesis (R. D Putnam 1995), type of content, not length of exposure, influences levels of social capital. In research conducted with Cody et al., it was pointed out that older adults who were trained to use the Internet reported having high levels of social connectivity, high levels of perceived social support, and generally more positive attitude towards ageing (Cody, 1999).

Regarding the use of the Internet for entertainment, pro-activity in social context and work connection were negatively predicted by this application of the Internet. Shah, Kwak and Holbert’s (2001) findings indicated while using the Internet for entertainment was negatively related to social capital, information exchange usage was positively related to social capital (D. V. Shah, Kwak, & Holbert, 2001).

Research Question 3: What is the direct relationship between Internet use and wellbeing among older people? A lower degree of usage of the Internet for entertainment and communicating with unknown people predicted personal wellbeing and a lower frequency of Internet predicted national wellbeing. The reason might be because of the effect that using the Internet for entertainment, like using for killing time, has on pro-activity in social context and work connections. The other reason might be increased time spent on their own which makes them lonely and reduces the level of family and community connections. The entertainment capability of the Internet keeps people away from family and friends, furthermore, it leads to a decline in awareness of the local community and its politics by facilitating global communication and involvement (N.H. Nie et al., 2002).

Results also revealed that using the Internet to find new people may decrease personal wellbeing in older people. One of the reasons for this decrease might be spending time with unknown people detaches older adults from friends and makes them socially isolated. Over a long period, gradually they feel that they do not have any friends who understand them and share their views. This feeling might produce anxiety and stress for them especially when they are pressured (Gross et al., 2002). The other reason might be that participants who were using the Internet to find new people were those who felt alienated and lonely and because of their loneliness tended to find new people in the Internet to compensate for their loneliness.

Conclusion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Older Adults and Internet Use
  5. Methodology
  6. Instrumentation
  7. Results
  8. Discussion
  9. Conclusion
  10. Practical Implications
  11. References

The use of the Internet has become more common for seniors. The proliferation of Internet use has transformed the concept of place, consistent with the change of notions of time and distance. New media enabled virtual interpersonal interaction among individuals and introduced diverse new ways of sharing information and communicating with others. Internet use allows older adults to socialize regardless of boundaries with mobility and physical impairment. People now meet one another online, maintain diverse social relationships, and create another form of community called the online community. What is more, people not only create new relationships online, but they also import preexisting relationships into the online area. Such relationships encourage the exchange of information and create reciprocal trust among members. While seniors are located in social networks, they find a certain position in this structure, which assists interaction among people and may produce social capital. However, it remains important how the Internet builds social capital.

There are though too many unknowns to provide a complete summary of older adults’ Internet use, social capital, and wellbeing. There is debate about the impact of this technology on older adults’ social capital, loneliness, and wellbeing. Based on the preceding results, several conclusions were drawn from this study. The study results revealed that how people use the Internet is as important as how much time they spend on the Internet. Furthermore, the Internet is a two-edged sword with the ability to both harm and help.

Different applications of the Internet also might have a range of effects on an older population, some beneficial and others harmful.. The results of this research revealed that from five different applications of the Internet, using the Internet for communication and information seeking were more likely to have positive effects on participants’ social capital, loneliness and wellbeing, although the effects of the these applications of the Internet on wellbeing were mediated by and social capital. It means that these applications were indirectly positive predictors of wellbeing with their direct effects on social capital. In contrast using the Internet to find new people and for entertainment were direct predictors of less wellbeing. All this suggests that it is appropriate to make older adults aware of the different effects of the Internet and encourage them to the more beneficial usages to make the Internet an effective way to reduce their loneliness, and increase their social capital and wellbeing.

Practical Implications

  1. Top of page
  2. Abstract
  3. Introduction
  4. Older Adults and Internet Use
  5. Methodology
  6. Instrumentation
  7. Results
  8. Discussion
  9. Conclusion
  10. Practical Implications
  11. References

Although the Internet has a range of benefits for older people and Internet service providers encourage them to use this technology, there are a number of significant, but not insoluble, obstacles. Many could be addressed by creative marketing and education that addresses older adults’ more inflexible ideas about computers. This study presents the following suggestions to increase usability of the Internet for the older population.

  • • 
    Assist older adults to learn all the ways that the Internet might be put to use in improving their lives. The most effective way is describing functions in terms of the needs that older adults have in a simple language.
  • • 
    Another significant concern is sufficient training that is patient, paced, accessible, and long-term (Brian & Buday, 2007).
  • • 
    Accessibility of the Internet is another matter, for example placing computers in public spaces like community centres, senior centres, and libraries.
  • • 
    It is recommended that service providers take time to recognise what each individual user may want to use the Internet for, their interests, and their history of computer use, which can be helpful when determining what features and functions an older adult is ready to adopt next in their computer and Internet education.
  • • 
    The Internet provides much information to increase older adults’ level of knowledge and attitudes regarding health issues. Therefore, it is suggested that the Internet program developers provide information appropriate to older adults’ needs to improve their quality of life.
  • • 
    It is recommended that purchasing products online be made accessible for elderly people, particularly for those over 80, those with mobility problems, and those who are living in rural areas and areas with low access.
  • • 
    For those older adults living in a community setting such as a retirement community, assisted living or a nursing home, the use of the Internet provides a resource to remain connected to the outside world. Therefore, it is suggested that computers be located in these places and adequate training made available.

References

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  2. Abstract
  3. Introduction
  4. Older Adults and Internet Use
  5. Methodology
  6. Instrumentation
  7. Results
  8. Discussion
  9. Conclusion
  10. Practical Implications
  11. References
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