Behavior change challenge: Change your way of thinking
In his editorial in the latest issue of The Pop Reporter, Arvind Singhal, the Samuel Shirley and Edna Holt Marston Endowed Professor at the University of Texas El Paso, challenges behavior change scholars and practitioners to change their way of thinking about how behavior change actually occurs.
The purpose of this editorial is to provoke scholars and practitioners engaged in social change, including those involved in health promotion and education, to think differently about how (behavior) change happens… This problematic prevailing mind-set of behavior change stated as “if we do this to individuals, they will behave in this way”–is a result of the overwhelming dominance of cause-effect Newtonian thinking that spilled over to social science and was reified over decades without much questioning.
He suggests that many of us are wedded to thinking about complex health problems and behavior change goals in linear, individualistic, cognitive-processing frameworks rather than in ways in which outcomes can be thought of as dynamic and emergent, and where serendipity, self organizing and surprise is valued.
What do you think? If his arguments are true, what changes would need to be made to Behavior Change Communication programming based on them?
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Ruwaida Salem said,
March 13, 2008 @ 4:41 pm
Arvind Singhal argues that behavior change scholars and practitioners should move away from behavioral theories and models that focus on the individual and that assume that individuals make rational and predicatable choices. Instead, he supports a different set of theories that recognize that individuals are complex beings who are part of complex social systems. I think the essence of his editorial is that the interaction between a social network of connected individuals plays a critical role in people’s behaviors and behavior changes.
I came across similar arguments while I was writing the recently published Population Reports issue, “Communication for Better Health.” I think there are a lot of people out there who see the so-called “individual behavior change” theories and models and the “social change” theories and models as being “black” and “white”–as belonging to two different spheres. But I think, more and more, we’re seeing a convergence between the two types of theories and models. For example, many of the “individual behavior change” theories have a much greater emphasis on social networks, similar to what Dr. Singhal supports. For example, Everett Rogers’ diffusion of innovations theory has evolved to include more emphasis on social networks, recognizing their importance to behavior change.
I think behavior change communication programming should be based on a broad range of behavioral theories. It shouldn’t be that you subscribe to only one type or another type of theory. Different theories make different contributions to the field, and different situations will require different theories and approaches.
Douglas Storey said,
March 14, 2008 @ 10:39 am
Dr. Singhal is right to challenge narrow short term, individual-level, behaviorist views of communication effects. The problem is that the field of communication moved past this perspective a long time ago. Short term deterministic cause and effect models have not been the dominant paradigm for over 30 years, if they ever were at all. That practitioners continue to HOPE for the magic bullet that will solve the world’s problems is another matter. It could be argued that the call for a focus on networked interdependent individuals and communities is one more example of the search for a magic bullet.
Communication scientists have long recognized that some attitudes, norms and behaviors change slowly, others change rapidly, some are sustained and others aren’t. In every case, however, the change is attributable to MULTIPLE factors, some exerting direct and others exerting indirect influence, some internal to the actor while others are external social or structural factors. The effect of any combination of factors on a behavioral outcome—whether it is a personal decision, a collective decision, a policy decision or a change in norms at the societal level–is not deterministic, but probabilistic. In non-statistical terms, this means that a certain outcome is more likely to happen if certain factors are present (including, perhaps, serendipity, self-organizing and surprise) and less likely to happen if they are absent (or if the combination of factors is different).
Dr Singhal argues that individual behavior change models “subscribe implicitly to questionable assumptions” such as that “…individuals are capable of controlling their context, operate in a level playing field, make decisions of their free will, and mainly through a rational cognitive processing framework.” Some models MIGHT assume such things–although I challenge him to find a commonly used one that actually does—but as a whole, behavior change theories most certainly do NOT make these assumptions. It may be a useful rhetorical strategy to lump a diverse set of models into one category, but to do so grossly oversimplifies them. In fact, behavior change theories range across a wide spectrum from those with a stronger individual cognitive focus to those with a stronger collective and structural focus; few, if any, are purely one or the other. In any event, degree of control over one’s context, equity of opportunity, autonomy and the relative importance of rationality versus emotion and other factors are all VARIABLES, not assumed constants.
No health communication practitioners that I know believe in a “machine” view of social systems or think that a perfectly deterministic approach to communication for health and development is possible. For Dr Singhal to suggest that the “machine” view is the “prevailing mindset of behavior change” is patently wrong. What communication scientists DO seek is to understand how individual and social change occurs and then to take those processes into account when developing communication programs. Inescapably, those processes are complex and communication programs must be similarly complex if they hope to make a difference.
Dr Singhal’s own example of Taru, one could argue, illustrates a perfectly clear multivariate causal effect: exposure to a fictional model of village celebration caused other villages (not all of them, but some of them—probabilistically as a function of multiple contextual and individual variables at play in those villages) to replicate (imperfectly, I’m sure) a similar celebration in their own village. He clearly believes himself that Taru caused the celebration of girls’ birthdays to spread (or at least start to spread) and he hopes that telling the story of Taru (i.e., the use of a narrative communication strategy) will encourage other communities to treat women better in other ways, as well. If he doesn’t believe that Taru contributed to change, why is it offered as an example of an effective approach to development communication?
Douglas Storey
Center for Communication Programs
Johns Hopkins Bloomberg School of Public Health