Over the past decade there has been a dramatic shift in the emphasis of determinants of health and social behaviors from individuals to networks and communities. For example, in three major areas of interest for public health officials and social marketers – the prevention of HIV infection, obesity and tobacco use – the role of social networks in disease transmission and the prevalence of risk behaviors is creating new opportunities for both concepts and practices that focus on social units of analysis, change and outcome.
Concurrent sexual partnerships, that is, having two or more stable sexual partners over time is being seen as one of the previously hidden drivers of the HIV epidemic. Concurrency, especially when the partners are sexually active with others in a small world network (see below), heightens the risk of HIV transmission because these relationships are not casual or one-off sexual encounters, but are maintained over time where a level of trust develops that diminishes their perceived riskiness. Thus, when one partner becomes infected, they are highly likely to have sex with one or more other partners during the window of greatest HIV infectivity. Developing interventions to address the network effects of sexual activity are only just beginning.
Similarly, the work of Christakis & Fowler (2007, 2008) provides descriptive evidence that the likelihood of becoming obese rises as close members of one’s social network become overweight and obese and that stopping smoking is also highly susceptible to the smoking status of others. Again, the implications for interventions are only now being explored. However, it is clear that simply focusing on individual and/or environmental determinants of these conditions can no longer be a singular pursuit for social marketing or any other type of risk reduction programs.
The world of social network theory introduces us to an entirely new set of concepts and ways of thinking about human behavior and the social forces that directly influence it. Goyal, for example, posits that individual choices related to the gathering of information for making behavioral choices are shaped by the pattern of connections between people in a society. Three core network properties he discusses are (1) degrees or how many links each member in a network has with others; (2) clustering – how dense the connections between members of a network are; and (3) average distances or how far away from one another each person in a network is from another in terms of the number of links necessary to reach them (popularized as “6 degrees of separation” or “the Kevin Bacon game”). A network that is characterized with small average degrees (everyone has at least a few connections with others), high clustering (they mostly connect with others in the network), and small average distances (there are few degrees of separation between them so that most people know or are at least acquainted with most of the others) are referred to as small world networks (Watts and Strogatz, 1998). Recall that these closed networks are less likely to adopt innovations or new evidence-based recommendations (a new take on ‘the old boys network’).
One of the implications of this work with social networks is that people learn about and choose among behavioral options not only based on directly observing others in their social circle engage in behaviors and the consequences they experience, but also by whom their friends and associates connect with outside that proximal network and then bring that information or those practices back to the immediate network. Goyal (20070 concludes from his review of empirical work in economics and social networks that variations in behaviors among individuals are related to not only the connections people have within the same social group, but also from their being members of different groups as well. The implications for social marketers seem clear; who people associate with, or are connected to, must be considered and addressed by intervention efforts. These network variables, in turn, might also serve as intervention points by, for instance:
1. focusing on people with large numbers of connections within a network (connectors, influentials, or opinion leaders);
2. reducing the density of a network in which risk behaviors are concentrated by introducing more boundary spanners or increasing social connections of members of the group outside of their immediate network;
3. understanding the members of a network who are most attentive and responsive to the behaviors of others (or more easily influenced or persuadable) and providing them with protective or alternative behaviors to prevent adoption;
4. and enhancing the salience and attractiveness of the ‘out group’ [positive deviants] by positioning these practitioners of desired behaviors in a way that attracts imitation or modeling.
Social network analysis must also be sensitive to information asymmetries that will exist among individuals within a group as well as between groups. Viswanath and Kreuter point to the existence of communication inequalities as possibly underlying many of the social inequalities we see in health risks and conditions. These communication inequalities are manifested as differences among social classes in the generation, manipulation, and distribution of information at the group level and differences in access to and ability to take advantage of information at the individual level. As a consequence, communication inequalities may act as a significant deterrent to obtaining and processing information; in using the information to make prevention, treatment and survivorship-related decisions; and in establishing relationships with providers.
Social networks have also played a major role in the development of the so-called Web 2.0 in which collaborative and dynamic models of communication underlie the philosophy, software development and user behaviors. The ubiquity and popularity of blogs and microblogs, social network sites, social sharing sites, wikis and virtual worlds have made the network connections among people more obvious. And, more importantly, these social media have unleashed a set of tools and resources that allow the people formerly known as the audience to create content for themselves and to tap into pools of collective wisdom. Social marketing programs must adapt to be both relevant to people’s (new) lives and to harness this collective wisdom and power for social change.
Similarly, the explosion in the adoption of wireless communication technologies, notably mobile telephones and smart phones, are enabling communications that transcend place-based methods (‘we call to individuals, not places’; Ling, 2008) and are further driving home the idea that people are communities, not individuals (Ahonen & Moore, 2005). As I have said elsewhere, while we have always been aware that there are social influences for many of the individual behaviors we seek to influence for environmental protection and the improvement of public health and social conditions, social technologies are changing the weights we use in our models of determinants of behavior and the ways in which we approach changing them. Given all of this, in the future let’s consider the consequences and implications of adopting a social networking perspective into our work as social marketers:
• How can we enhance linkages that already exist among people, organizations, and communities to allow them to access, exchange, utilize, and leverage the knowledge and resources of the others?
• How do we help develop, nurture, and sustain new types of linkages that bring together like-minded people, mission-focused organizations, and communities that share interests to address common problems and achieve positive health and social change?• How do we identify, encourage and enable the many different types of indigenous helpers that are found in social networks so that they can be more effective in promoting positive health, environmental and social behaviors and policies?
• What do we do to better engage communities in monitoring, problem analysis, and problem solving; striving to health and social equity; and increasing social capital?• How do we go about weaving together existing social networks of individuals, organizations and communities to create new sources of power and inspiration to address health and social issues?
• How does a networked view of the world disrupt our usual ways of thinking about and engaging the people, organizations, and communities with which we usually work? What are the insights we can gain from this perspective?Clearly we need to dig much deeper into social netwrok theory to understand the dynamics of diffusion and change of various types of health behaviors. More importantly, we need to develop and test programs for which change among social networks and connections are a primary focus of our efforts. Where information and communication technologies may evolve to in the next few years is an open question. But blending social media tools and mobile technologies with social marketing to capitalize on and impact social networks is also an area for further exploration in achieving scalable results for networks of healthy people.
References
Adimora, A.A., Schoenbach, V.J. and Doherty I.A. (2007). Concurrent sexual partnerships among men in the United States. American Journal of Public Health, 97: 2230-2237.
Ahonen, T.T. and Moore, A. (2005). Communities Dominate Brands. London, UK: futuretext.
Christakis, N.A. and Fowler, J.H. (2007). The spread of obesity in a large social network over 32 years. The New England Journal of Medicine, 357: 370-379.
Christakis, N.A. and Fowler, J.H. (2008). The collective dynamics of smoking in a large social network. The New England Journal of Medicine, 358: 2249-2258.
Goyal, S. (2007) Connections: An Introduction to the Economics of Networks. Princeton, NJ: Princeton University Press.
Helleringer, S., Kohler, H-P. (2007). Sexual network structure and the spread of HIV in
Africa: evidence from Likoma Island, Malawi,. AIDS, 21: 2323–2332.
Lefebvre, R.C. (2007). The new technology: The consumer as participant rather than target audience. Social Marketing Quarterly, 13: 31-42.
Ling, R. (2008). New Tech, New Ties: How Mobile Communication is Reshaping Social Cohesion. Cambridge, MA: The MIT Press.
Mah, T.L. and Halperin, D.T. (2008). Concurrent sexual partnerships and the HIV epidemics in Africa: evidence to move forward. AIDS and Behavior. Published online: 22 July 2008. [Cited 5 March 2009].
Viswanath, K. and Kreuter, M.W. (2007). Health disparities, communication inequalities, and e-Health: A commentary. American Journal of Preventive Medicine, 32(5 Suppl): S131–S133.
Watts, D.J. and Strogatz, S.H. (1998). Collective dynamics of ‘small-world’ networks,. Nature, 393 (June): 440-442.
Watts, D.J. (2003). Six Degrees: The Science of a Connected Age. New York: W.W. Norton & Company.
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