Social Impact Theory - Original Research

Original Research

Social Impact theory (SIT) is a theory that uses mathematical equations to predict the level of social impact created by specific social situations. The theory was developed in 1981 by Bibb Latané, a psychologist who was working at Ohio State. Latané described social impact as a phenomenon in which people affect one another in social situations. Through daily experiences such as embarrassment, persuasion, humor, and a plethora of other experiences, one can see the number of situations that are governed by the presence and actions of others. The impact can not only be observed visually, it also alters forces within the target such as thoughts, attitudes, incentives and physiological state. Latané noticed that social impact was governed by three laws that can be translated into mathematical equations. These rules are social forces, psychosocial law and multiplication/division of impact.

The first rule, that of social forces, claims that when social forces function within a social structure, the result is social impact. Latané explained this interaction as I = f(SIN). That is, social impact is affected by strength (S), immediacy (I) and number of people (N). This theory postulates that the greater the number of sources of social impact in a social situation, the greater the impact would be. Thus, the equation I = f(SIN) describes social impact as a multiplicative function based on the number of people acting on the target. Within this equation, the strength (S) is a measure of how much influence, power, or intensity the target perceives the source to possess. The amount of influence, power, or intensity is often determined through factors such as age, social class, whether or not a previous relationship had existed, or anticipation of a future relationship existing. Immediacy (I) takes into account how recent the event occurred and whether or not there were other intervening factors. The number of people (N) is the number of sources exerting social influence on the target. The I = f(SIN) equation illustrates that there is more social impact when higher status individuals are the source, when the action is more immediate, and when there are a greater number of sources.

The second rule that governs social impact is psychosocial law. The psychosocial law states that the most significant difference in social impact will occur in the transition from 0 to 1 source and as the number of sources increases, this difference will level out. The equation Latané uses for this law is I = sN^t . That is, some power (t) of the number of people (N) multiplied by the scaling constant (s) determines social impact. Latané applied this theory to previous studies done on imitation and conformity as well as on embarrassment. Asch’s study of conformity in college students contradicts the psychosocial law, showing that one or two sources of social impact make little difference. However, Gerard, Wilhelmy, and Conolley conducted a similar study on conformity sampling from high school students. High school students were deemed less likely to be resistant to conformity than college students, and thus may be more generalizable, in this regard, than Asch’s study. Gerard, Wilhelmy, and Conolley’s study supported the psychosocial law, showing that the first few confederates had the greatest impact on conformity. Latané applied his law to imitation as well, using Milgram’s gawking experiment. In this experiment various numbers of confederates stood on a street corner in New York craning and gawking at the sky. The results showed that more confederates meant more gawkers, and the change became increasingly insignificant as more confederates were present. In a study Latané and Harkins conducted on stage fright and embarrassment, the results also followed the psychosocial law showing that more audience members meant greater anxiety and that the greatest difference existed between 0 and 1 audience members.

The third rule of social impact is multiplication/divisions of impact. This rule states that the strength, immediacy, and number of targets play a role in social impact. That is, the more strength and immediacy and the greater number of targets in a social situation causes the social impact to be divided amongst all of the targets. The equation that represents this division is I = f(1/SIN). This rule relates to diffusion of responsibility, in which individuals feel less accountable as the number of people present increases. In emergency situations, the impact of the emergency is reduced when more people are present.

The social impact theory is both a generalizable and a specific theory. It uses one set of equations, which are applicable to many social situations. For example, the psychosocial law can be used to predict instances of conformity, imitation and embarrassment. Yet, it is also specific because the predictions that it makes are specific and can be applied to and observed in the world. The theory is falsibiable as well. It makes predictions through the use of equations; however, the equations may not be able to accurately predict the outcome of social situations. Social impact theory is also useful. It can be used to understand which social situations result in the greatest impact and which situations present exceptions to the rules.

While Social Impact theory explores social situations and can help predict the outcomes of social situations, it also has some shortcomings and questions that are left unresolved. The rules guiding the theory depict people as recipients that passively accept social impact and do not take into account the social impact that people may actively seek out. The model is also static, and does not fully compensate for the dynamics involved in social interactions. The theory is relatively new and fails to address some pertinent issues. These issues include finding more accurate ways to measure social outcomes, understanding the “t” exponent in psychosocial law, taking susceptibility into account, understanding how short-term consequences can develop into chronic consequences, application to group interactions, understanding the model’s nature (descriptive vs. explanatory, generalization vs. theory).