In this case study we illustrate how causal models for a diabetes therapy allow market researchers to develop strong, analysis-based explanations.
Behavioral models based on comprehensive causal analysis (like the COM-B model) give market researchers a powerful tool for developing explanations with strong analytic techniques. Read the full article in Quirk’s Marketing Review.
Over time, psychologists have integrated numerous theories and frameworks to develop models to predict and influence human behavior. As better approaches to understanding the human psyche emerge, these models are adapted and improved (thus, they tend to evolve). These models can be used by market researchers to better understand consumer decision-making and thereby create programs that could influence behaviors.
At the heart of these models are causal modeling techniques. Causal modeling is now recognized as a general approach for integrating theory with measurement elements of research. Previous research suggest that causal models provide four key benefits:
1) They make the assumptions, constructs and hypothesized relationships in a researcher’s theory explicit;
2) They add a degree of precision to a researcher’s theory since they require clear definitions of constructs, operationalization and the functional relationships between constructs;
3) They permit a more complete representation of complex theories; and
4) They provide a formal framework.