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Theories, Practices and Examples for Community and Social Informatics

Chapter 1



This chapter investigates whether Internet use and involvement in social networking sites are related to the unpaid help that is provided to members of the surrounding communities. Three different forms of unpaid help are studied: assistance in care, housework and technology use. Previous literature dealing with the impacts of ICT use on the social cohesion of communities and the sense of togetherness is discussed to provide a solid basis for the research. As for the empirical part of the study, the chapter analyses the Finnish Time Use Survey collected between 2009 and 2010. The results show that it is not Internet use per se that is associated with the provision of informal help to the other members of a neighbouring community. Rather, it is the social use of the Internet which is positively associated with the informal help given to others in the offline context. However, Internet use may also add to informal help, provided face-to-face, but only when the help is given in technology use. The study suggest that the social benefits of Internet use, and especially those of online social networking, for communities are strongly segmented according to the type of help provision.


For many years, sociological studies have investigated whether the adoption and use of information and communication technologies (ICTs) have weakened or reinforced people’s sociability. The development of the Internet from the platform that people use to store information and cognise what others publish (Web 1.0) to a collaborative (Web 2.0) and co-operative tool (Web 3.0, Fuchs 2008, 2010, Web squared, O’Reilly & Battelle 2009) emphasised the need to study the relationship between Internet use and sociability from new perspectives. Previous results dealing with Internet use in general have remained somewhat contradictory (e.g. DiMaggio et al. 2001, Wellman et al. 2001, Nie & Lutz 2002, Nie et al. 2002, Fortunati et al. 2012) and the reflections on whether the changes in sociability levels benefit the community in which the use of ICT takes place has not received much scientific attention. More recently, scholars have also noted that it is perhaps not the use of ICTs as such, but rather the ways and purposes of usage that matter. The social and collaborative functions of the Internet form the basis of these studies. For instance, Zhao (2006) has made a distinction between solitary online activities, such as Web surfing and news reading that do not involve direct contact with other people, and social activities, such as e-mailing and chatting, where other people engage in online interaction. In his study, the social users of the Internet had more social ties than non-users. It is against this backcloth that we are curious to investigate whether people who use the social functions of the Internet are also more likely to assist their close friends and neighbours on a voluntary basis.

In this study we will investigate whether Internet use and involvement in social networking sites are related to the unpaid help that is provided to members of the surrounding communities. This is to determine whether the use of the Internet in general or the social use of the Internet in particular, or perhaps both, is associated with the help provided to others. Furthermore, we will explore whether Internet use and engagement in social networking sites are similarly connected to three different forms of informal help: assistance in care, housework and technology use. From the perspective of community and social informatics, these questions are perennially interesting since ICT can both enhance and undermine the social cohesion of communities and affect the sense of togetherness (e.g. Putnam 2000, Turkle 2011). Considering ageing populations in Europe, there is considerable pressure to promote the use of informal care and unpaid help in order to keep the public expenditures on social services and health care under control.

The empirical results of this study are drawn from the Finnish Time Use Survey collected between 2009 and 2010. With the help of this extensive data set, consisting of both time use diaries and personal interview questionnaires, we are able to elaborate the relationship between Internet use and unpaid help provision in Finland. The data includes both household and individual level information. In terms of methodology, both descriptive and multivariate statistics are summarised. Finland is a country with one of the most rapidly ageing populations in Europe. It also has one of the highest participation rates in voluntary activities in Europe (McCloughan et al. 2011). When it is considered that Finland’s information society infrastructures are relatively advanced and citizens have good access to the Internet (78% of the entire population, Statistics Finland 2011), we believe that Finland serves as a good case for investigating the relationship between Internet usage and unpaid help.

The rest of this chapter is organised as follows. First, there is an overview of recent literature on help provision, and the main socio-demographic predictors of help are summarised. After that, the previous research on the relationship between ICT use and social interaction in general is summarised. Then the chapter proceeds to discuss the concept of community and how it has received further definitions with the rise of new ICTs. The literature review finishes with the formulation of hypotheses for this study. The data, measures and statistic tools exploited are then presented before discussing the results. The chapter concludes by summarising the empirical results and considers whether the use of the Internet and social networking sites is beneficial to informal help provision within communities.

Previous literature

The different forms of help

Prior literature makes distinctions regarding the provision of help. First, formal help provided through institutionalised actors, both public and private, is typically separated from informal help – alternatively concep­tu­alised as unpaid labour – provided by family members, friends and neighbours (e.g. van Groenou et al. 2006, p. 747, Kahn et al. 2011, p. 78). However, the conceptual boundary between the two has been questioned by arguing that formal caregivers may also become personally attached to the recipients of care (Kröger 2009, pp. 400–401). Second, empirical studies make a distinction between the recipients of assistance: care help can be targeted at both relatives and non-relatives (Kahn et al. 2011, p. 78). Some prior accounts have noted that different forms of help tend to be shared with either kin or non-kin contacts, but rarely with both (Gallagher & Gerstel 2001, Salminen 2012). In practice, informal assistance may work as an alternative to formal care or can, as often is the case, be provided and received to supplement inadequacies in formal help. In this study, we focus on informal help provided to neighbours, friends and co-workers (i.e. non-relatives), who are viewed as comprising the surrounding community of a help provider.

At the operational level, informal help is typically divided into parts according to the type of help activity. In this study we divide informal help into three subcategories: housework, care work and help in technology use. Housework comprises cooking, housekeeping, maintenance, shopping and errands and related travels (Shelton & John 1996, Robinson & Godbey 1999, Gershuny 2000). We examine care work separately from housework as research literature shows that care work is considered more rewarding than housework and maintenance tasks, and this is reflected in the differences in the perceived pleasantness of these tasks (Sullivan 1996, Robinson & Godbey 1999). Despite these differences, housework and caregiving are also linked to each other. For instance, cooking and washing up often take place simultaneously with the child care. It is also interesting to note that help in housework and care have been found to be the most common types of help within social support networks. Furthermore, they are exchanged more typically between close relatives than acquaintances such as community members (van der Gaag 2005, p. 159). As the third subcategory, we analyse help in technology use separately from other forms of informal help. This is because literature reveals the importance of social support networks for the take up and use of new communication technology. Arguably, people are more likely to give up a new technology if they have no friends or neighbours from whom to request support in use (Murdock et al. 1992, Hargittai 2003).

Previous literature is also illustrative of the predictors of informal help. As regards gender, men provide less assistance than women, which partly derives from men’s higher labour market participation rates and more secure employment positions (Bracke et al. 2008) as well as from culturally-produced views of what tasks are proper for men and women (Shelton & John 1996). The impact of age seems to differ. Some studies indicate that older people help fewer non-relatives than younger ones (Gallagher 1994), whereas others have found that older adults (50–64 year-olds) are the most helpful group in this respect (Bracke et al. 2008). Education has proved to be a rather consistent predictor and a stronger one than income. Higher levels of education are linked to more help supplied to non-relatives (Gallagher 1994, Bracke et al. 2008). Marital status is also found to condition the provision of help to non-kin. Married people provide less support to non-kin than unmarried people, which is probably related to their time-consuming responsibilities at home (e.g. child-rearing) (Kahn et al. 2011). In addition to marital status, the presence of dependent children also decreases the provision of care to non-relatives, although the effects have been considerably small and gendered (dependent children affect more the help given by women than that of men) (Bracke et al. 2008, Kahn et al. 2011). Moreover, employment status has been found to be weakly associated with the informal help to non-relatives. Employed people provide help to others slightly more than those who are currently not employed (e.g. unemployed, students and housewives or husbands) (Kahn et al. 2011).

The relationship between ICT use and social interaction

At the turn of the 21st century, sociologists began to contemplate the possible impacts of technology use on physically co-present social intercourse. Robert D. Putnam’s (2000) analysis of the decline of the American community was one of the main promoters of this debate. He considered that “individualising” technologies, such as television and probably also the Internet, whose possible impact on social interaction was difficult to foresee at that time, would diminish people’s civic engagement and activity. A few years earlier, Graham and Marvin (1996, p. 207) had already anticipated a shift towards home-centredness which, according to them, resulted from advances in telecommunications and more individualised services targeted at households. In fact, some early accounts on the use of the Internet seemed to provide support for these arguments. Internet use was considered to have a “displacement effect” on physically proximate interactions and, hence, time online was even seen as an asocial activity (e.g. Nie & Lutz 2002, Nie et al. 2002).

Later, many studies showed that Internet users actually have larger social networks than non-users (e.g. DiMaggio et al. 2001, Wellman et al. 2001). In some more sophisticated analyses, possible modifying factors have been controlled. For instance, Zhao (2006, p. 13) showed that the type of online activities and the amount of time people spend on these activities are connected to offline social connection. People who use the Internet for interpersonal contact (e.g. email and chat) are more likely to engage in larger social circles offline than those who use it for solitary purposes (e.g. Web surfing). Also Petrič’s (2006) study stresses the importance of the differences between social uses of the Internet and instrumental uses, where the Internet appears as a mean of achieving personal goals. Fortunati et al. (2013) show that in the five largest European Union countries the overall volume of people’s sociability actually remained quite stable between 1996 and 2009. While mobile phones and PCs were associated with the increase in sociability in 1996, the Internet was an ICT that particularly added to people’s sociability in 2009. Taken all together, these results demonstrate that there is no single, but many, Internet effects (Wellman et al. 2001, p. 451). It might well be that, in particular, the social uses of the Internet add to social intercourse, and hence are also positively associated with the provision of informal help. This puzzling controversy between previous findings partly results from different research designs and indicators employed in the studies (cf. Zhao 2006).1

Community reconsidered

New ICTs that make possible social interaction and the sense of community at distance have forced social scientists to think over the sufficiency of the classical definitions of “community”. The concept of community has been large­ly defined by stressing either social relations within a certain geo­graph­ical area or belonging to a group which is included in the com­mu­nity (Stacey 1969, 135). In their definition of community as a totality, Cnaan et al. (2008) named three elements: shared ecology (with place and location de­fining and restricting social life), social organisation and shared cultural and symbolic meanings. Of these three elements of community, the second, social organisation, consists of mobilisation, the formation of new social networks, and the possession of social capital.

Thus, community does have an element comprising social ties as such with no geo­graphical or territorial limitations. In other words, social ties and net­works are more or less essential for a local community to develop, but social networks can be autonomous from a certain area or location. A good example of this has resulted from the rise of Internet technology. The con­cept of com­munity is stretched to cover the idea of virtual com­munities where people can gather together online regardless of their phys­ical lo­ca­tions. Bearing this in mind, Hampton (2003) has proposed new dimensions to the con­cept of community. He puts forward the idea that “community” could be seen as consisting of concatenating social ties that make up networks of sociability, help and support.

Taking Hampton’s proposal into consideration, this study builds on a data-driven conceptualisation of community. The community is viewed as consisting of neighbours, friends and co-workers to whom assistance in house­work, care and technology use is provided. As defined in relation to these three groups of people, community emerges as a relatively broad concept. It does not include the idea of strong shared values, which would produce social co­hesion, but it is not based on pure self-interest either as the provision of help must be gratuitous by nature. However, our application of com­mun­ity relates to the idea of network-based social capital, which considers network contacts as channels mediating different kinds of re­sources and thus takes into account the potential assets embedded in the net­work contacts (Lin 2007). As regards the character of social contacts, we concentrate in this study on informal contacts. Contrary to voluntary work channelled through formal organisations, informal help given to a near community is personal in character and does not necessarily involve any mediating organisations.

Research hypotheses

By focusing on the help provided to members of a surrounding community, this study makes inroads into a less studied field of social and community informatics. Due to a lack of previous research results dealing with the relationship between Internet use and informal help (other than civic participation), hypotheses are based on the above-reviewed literature. As the most recent and convincing research results argue that Internet use – the social uses of the Internet in particular – enhances social interaction in physically proximate relations, we expect that:

H1) The frequency of Internet use increases the provision of informal help for surrounding communities.

H2) Engagement in social networking sites increases the provision of informal help more than the overall frequency of Internet use.

To elaborate further, the connection between Internet use and the provision of help for neighbouring communities, we divide the measure of overall help into three parts: help in care, housework and technology use. As the frequency of Internet use and engagement in social networking sites are likely to be more strongly connected to the assistance in technological use than to the help in care and homework, we expect to see that:

H3) Internet use and involvement in social networking sites increase all three forms of help, yet the connection is stronger as regards help in technology use.

Data and methods


The analysis is based on the latest Finnish Time Use Survey (FTUS) collected in 2009–2010 by Statistics Finland. The FTUS is an extensive interview survey in which all members (over 10 years old) of participating households keep accurate diaries on their time use during one weekday and one weekend day. The FTUS was collected by using a two-phase, single stage cluster sample, in which households served as clusters and individuals were elementary units. The data includes 7,480 recorded diary days and 3,795 interviewed respondents from 2,614 different households. The FTUS is part of the Harmonised European Time Use Survey (HETUS), coordinated by Eurostat and the University of Essex. The interview data includes information about the main activity (employed, unemployed, studying, etc.), working hours (length, pattern), voluntary work, hobbies and health (altogether 111 questions). In the interview data, each person is also asked to report for what purposes and how often they use a computer or the Internet and if they are using social media, that is if they are members of social networks such as Facebook or MySpace. In this study, the sub-sample of over 15-year-old respondents is analysed by utilising the information from the interview questions (n=3444).2 Younger respondents are excluded from the analysis as they are clearly nett recipients of unpaid help. Table 1.1 presents descriptive statistics on the sample.

Table 1.1. Sociodemographic characteristics of respondents


In the interview data each respondent was asked to report how frequently he or she uses the Internet. The response scale for Internet use ranged from 1=“Never” to 6=“Daily”. In addition, respondents were asked about their social media usage: “Are you are a registered user of social networking sites (e.g. Facebook or MySpace)?” (see Table 1.2). This question was asked only of those who reported having used the Internet at least once or twice a year. The social networking measure was recoded afterwards so that those who reported not having used the Internet received the value zero.

In the interviews, respondents were also asked whether they had pro­vided unpaid help to other households during the last four weeks in the following activities: childcare, caring for sick or elderly, cooking, cleaning, gardening, shopping and services, repairs and construction, car and bicycle maintenance, caring for pets, transportation and removals, and technology use. For each category of unpaid help, respondents were also asked to report to whom they provided help: relatives (broken down into the following categories: parents, adult children, children, siblings, grand children, other relatives); neighbours, friends or colleagues; and other people. From these questions, dichotomy indices for three different forms of informal help given to members of the surrounding community (i.e. neighbours, friends or colleagues) were constructed: assistance in care (including childcare and caring for sick or elderly), housework (including cooking, cleaning, gardening, shopping and services, repairs and construction, car and bicycle main­te­nance, caring for pets, transportation and removals) and technology use (including computer use and digital television). In addition, an aggregate measure of whether any informal help was provided to the surrounding community was created by combining the three indices.

Respondents’ gender, age, educational level, marital status, presence of dependent children and employment status were used as controls in the statistical models. In previous studies, these factors were identified as im­portant predictors of the provided informal help, as was presented above.

Statistical procedures

This study employs both descriptive and multivariate statistics. Three logistic regression models are conducted separately for each measure of informal help: the first model includes only Internet use, in the second model engagement in social networking sites and in the third model controls for background factors (gender, age, education, marital status, presence of dependent children and employment status) were added as covariates. This strategy allows comparison between the effects of Internet use and involvement in social networking sites before and after accounting for other factors.


Descriptive results

Table 1.2 presents descriptive statistics on Internet use, social networking site memberships and the provision of informal help. The measure of frequency of Internet use shows a rather bipolar distribution: almost one-fourth never uses the Internet and almost half use it daily. However, using the Internet only occasionally is very rare. It seems that people either use the Internet quite frequently or not at all. Less than a third of respondents are registered as a member in social networking sites. This proportion is rather high as the sample is representative of the whole population. However, being a registered user does not yet indicate how actively these sites are used in practice. Based on previous studies (Smith & Rainie 2010) we can assume that there are also many respondents who have registered with these sites, but have never started to use them. Table 1.2 also shows that one-fourth of respondents have provided unpaid help to the members of the surrounding community. Most of this help seems to be in the form of housework activities. By contrast, helping the members of the surrounding communities in care duties and technology use are quite rare activities.

All covariates were significantly associated with both frequency of Internet and social networking site use, especially age and employment situation (not presented in the tables). For instance, as regards to age, the above mentioned bipolar distribution of Internet use is chiefly a product of age distribution. The share of Internet non-users increases and that of daily users decreases steadily with age; while about 80 per cent of 16 to 24 year-olds use the Internet daily, over 80 per cent of those aged 64 years or more did not use the Internet at all. In addition, of all covariates, age was most strongly associated with both social networking and informal help. As to employment situation, retired people differ from all other groups as 60 per cent of them never use the Internet. Similarly, over 96 per cent of retirees are not members of social networking sites.

Table 1.2. Descriptive statistics on Internet use, social networking and informal help

Internet use and social networking site involvement as the predictors of informal help

The results of the logistic regression models predicting the provision of informal help in general are presented in Table 1.3. It shows that Internet use has a strong positive effect on informal help, but this effect is reduced when introducing the measure of social networking sites to the model. More interestingly, the effect of Internet use completely disappears when background factors are controlled in the model. However, the positive coefficient of social networking on informal help remains significant, al­though its level of significance decreases. Age, gender and marital status also have significant effects on overall informal help. Older people, females and married persons were less likely to provide unpaid help to members of their community. After examining the model in more detail, it was found that age was the single most important factor reducing the effects of both Internet use and social networking. As was noted earlier, the age of respondent was strongly connected to Internet use, social networking and informal help. This result clearly indicates the importance of controlling for age differences when studying effects of Internet or other ICT use on informal help. Older people are less likely to provide informal help to others, use the Internet and to be registered members of social networking sites. These results clearly oppose H1, which has to be rejected. However, these results provide support for H2; it is not Internet use per se but the social uses of the Internet that increase the provision of informal, unpaid help to other people.

Table 1.3. The effects of Internet use and involvement in social networking sites on informal help

Next, the study examines informal help in more detail by conducting the same analysis separately for the three sub-categories of informal help: help in care, housework and technology use. The third hypothesis stated that Internet use and social networking is supposed to have a stronger connection with help provided in technology use. The results presented in Table 1.4 are partly supportive of this claim. In fact, after controlling for background factors, the frequency of Internet use is connected only to informal help given in technology use. Also, the social use of the Internet (i.e. involvement in social networking sites) has a clearly stronger effect on help given in technology use than on any other forms of informal help. Gender and marital status also play a major role in predicting help provided in technology use, education to a lesser extent. Not surprisingly, men and singles are clearly more likely to give help in this domain. Those with higher education are also more inclined to provide help in technology use than respondents with primary education only.

In addition to technology use, membership in social networking sites also increases slightly the probability of providing informal help in housework. However, age and gender seem to be more important factors for help in housework. Again, older people and females are less likely to provide help in this domain. Also, married people probably give less help in housework duties. The direction of gender differences in housework help may seem at first glance rather surprising. However, it should be noted that many of the duties belonging to this category are in fact repairs and other maintenance work, which men are more often responsible for than women (Robinson & Godbey 1999, Sayer 2010).

Neither Internet use in general nor the social use of it increases the prob­ability of giving help in caring for children or older people. The sig­ni­ficant positive effects of both Internet use and social networking are again reduced mainly due to the introduction of age, and to a lesser extent gender, to the model. Age, gender, and presence of dependent children are the most important factors explaining help in care work. As was the case with technology and housework, older people are less likely, and women and those who have their own children more likely to help others in care work.

In response to H3, the results presented here confirm that involvement in social networking sites increases the provision of unpaid help more than the frequency of Internet use. However, the effect of social networking site involvement was statistically significant only in the case of providing help in technology use and housework. Respectively, the effect of Internet use was significant only in the case of help given in technology use. Furthermore, it is worth noting that the predictive capacity (Nagelkerke’s pseudo R2) of the full models ranged from 4.7 per cent to 26.4 per cent of total variance. The highest figure was received by the model where the provision of help in technology use was explained.

Table 1.4. The effects of Internet use and social networking on informal help in care, housework and technology use

Concluding remarks

This chapter aimed to determine whether Internet use and involvement in social networking sites are related to the unpaid help that is provided to the members of surrounding communities. The rationale of this study relates to the fact that today, people in affluent Western countries have relatively easy access to the Internet and social media. Additionally, the time people spend by using these new ICTs can either end up “stealing” time from face-to-face interactions with neighbours and friends or people’s online sociality may have positive spill-over effects on offline sociability, such as the provision of informal help. Furthermore, the study explored whether Internet use and engagement in social networking sites are similarly connected to help given in care, housework and technology use.

The results of the study show that it is not Internet use per se that is as­so­ciated with the provision of informal help to the other members of a neigh­bouring community. Rather, it is the social use of the Internet which is positively associated with the informal help given to others in the offline context. This result, which supports the simple fact that so­cial people are social both online and offline, corroborates what Zhao (2006) and others have argued earlier; social activities preformed online are connected to people’s sociability in physically proximate relations. Instead, the results presented in this chapter question the relevance of the perennial debate on whether the Internet use increases or decreases people’s sociability (e.g. DiMaggio et al. 2001, Wellman et al. 2001, Nie & Lutz 2002, Nie et al. 2002). In the light of this study, Internet use may add to informal help provided face-to-face, but only when the help is given in technology use.

This study refines previous research results by disaggregating the applied indicator of sociability, namely the provision of informal help, in three sub-categories. The results, controlled for socio-demographic variables, clearly indicated that it is solely help in technology use that is affected by Internet use. As regards help in care and housework, the connection disappeared when socio-demographic factors were controlled. In contrast, the membership of social networking sites does slightly increase the informal help provided in house­work. If we had only looked at the overall provision of help, results would have been skewed by the fact that the increase in overall help is largely accounted for by help in technology use. This result is in line with common sense as well; those who can use the Internet and social networking sites are also competent at providing assistance with technological problems to others.

All in all, the results of this study suggest that the social benefits of In­ternet use, and especially those of online social networking for communities, are strongly segmented according to the type of help provision. The results also reveal that Internet use and involvement in social networking sites may indicate user sociability. They are tools for interaction rather than sources or generators of any substantial increase in help provided to the surrounding communities. In this study, the community was defined broadly by referring to neighbours, friends and co-workers who make up the networks of sociability, help and support. The results are also likely to depend on a country context, which makes us call for more analyses from cross-national perspectives. In the future, more detailed analysis of the social consequences of Internet use, which clearly takes into account to whom the help is provided, is required.


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1 For instance, Nie et al. (2002) studied diary-based time use with a sample targeted at approximately 6,000 Americans between the ages of 18 and 64. In comparison, many findings presented by Wellman and his colleagues are based on a single specific residential community called “Netville”. The number of respondents included in their analyses has often been considerably small; 109 homes with 52 participants who were “connected” and 21 who were “not connected” (Wellman et al. 2001, Hampton & Wellman 2003, Hampton 2007). In addition, Nie et al. utilised time-use indicators (e.g. time online and activity time with family, friends and so on), but Wellman et al. accounted for the number of people who were at least recognised, talked with and visited.

2 We use specific methods designed for analysing complex samples (PASW 18.0 Complex Samples package) in order to account for the sampling design in FTUS. If estimation is done by assuming a simple random sample, the obtained estimates are likely to be biased (Landis et al. 1982, Lehtonen & Pahkinen 2004).

Theories, Practices and Examples for Community and Social Informatics

   by Tom Denison, Mauro Sarrica and Larry Stillman