PRmatrix envisions a new, predictive paradigm for public relations research and analysis that challenges the descriptive and evaluative norms of today. The predictive paradigm derives from the use of advanced statistical techniques and methods, such as Partial Least Squares Path Modeling, and the use of predictive behavioral theories from psychology and social psychology, such as the Theory of Planned Behavior.
Predictive research is chiefly concerned with forecasting (predicting) outcomes, consequences, costs, or effects. This type of research tries to extrapolate from the analysis of existing phenomena, policies, or other entities in order to predict something that has not been tried, tested, or proposed before.
A predictive research project often asks how well something might work, or what the impact of something might be. Whereas descriptive or evaluative research makes applicable and tangible recommendations, Predictive research is often more hypothetical, theoretical, or experimental – it concerns ideas that haven’t been tried, might not be testable, or didn’t previously exist.
Predictive research has been used successfully for decades in medicine, advertising, marketing, management, finance, and the military. The ability to predict the effect of a intervention strategy on behavioral intention- vote for a candidate, donate money, drive defensively, buy a product or service – would be of great benefit to the public relations industry.
“Predictive capabilities are also an exciting new area of innovation. It isn’t enough to know what happened in the past, you need to predict what will happen next and adjust your business to take advantage of it. This is already happening in areas like advertising media planning, but the possibilities to apply this to all of marketing are immense.”
Shantanu Narayen, President and CEO, Adobe
Partial least squares path modeling (PLS) – also known as PLS structural equation modeling (PLS-SEM) – is a prediction-oriented structural equation modeling technique. It is based on the partial least squares algorithm, which was developed by Herman O. A. Wold.
PLS is widely applied in business and social sciences in order to predict endogenous latent variables and to estimate as well as test relationships between latent variables (causal analysis).
The theory of planned behavior (TPB), a modification of the theory of reasoned action, is based on the assumption that human beings are usually quite rational and make systematic use of the information available to them (Ajzen and Fishbein 1980). The theory contends that people estimate certain factors before deciding to engage or not engage in a behavior (intent). According to the theory of planned behavior, intention, devoid of unforeseen circumstances that limit individual control, will help predict future behavior.
The variance in intention is composed of three global constructs: (a) attitude toward the behavior, (b) subjective norms, and (c) control. Several studies have reported the association of the intention-behavior relationship (Ajzen 1985, 2001; Ajzen and Fishbein 1980; Conner and Armitage 1998).
It also has been found that intentions have a substantial causal effect on behavior. Webb and Sheeran (2006) conducted an extensive meta-analytic review of 37 studies that directly manipulated intention through intervention and assessed this effect on subsequent behavior. Studies using random assignment of intervention and control groups resulted in significant differences in intentions between the groups, and studies that included a follow-up measure were included in the analysis. They found that a change in intention (d = .66) directly brings about a change in behavior (d = .36), which further supports that the intention to engage in a behavior indeed affects carrying out that particular behavior.