Structural Equation Modeling in Marketing Communications

Structural equation modelling (SEM) has become a cornerstone of empirical inquiry in marketing communications, offering a comprehensive framework for testing complex theoretical relationships among observed and unobserved variables. By integrating measurement models—linking manifest indicators to latent constructs—with structural models that articulate causal pathways among those constructs, SEM allows researchers to assess both the reliability of their metrics and the validity of proposed causal mechanisms in a single analytic procedure. In the domain of marketing communications, SEM has been applied to investigate the effects of advertising strategies, brand reputation, digital and social media engagement, consumer trust and motivation, and e-commerce platform adoption on purchase attitudes and loyalty. The flexibility of SEM accommodates both reflective and formative measurement specifications, enabling scholars to represent constructs such as brand equity, social currency and technological perceptions with precision. Across varied contexts—from social commerce uptake in emerging economies to the mediating role of trust in mobile app marketing—SEM facilitates the rigorous evaluation of theoretical models and the quantification of direct, indirect and total effects. Its global significance is reflected in practical applications: guiding strategic decisions in brand communication, informing platform design to enhance consumer motivation, and supporting policymakers in understanding the role of digital channels in shaping market behaviour.

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Structural Equation Modeling in Marketing Communications publication trend

The graph below shows the total number of articles in structural equation modeling in marketing communications across all publications each year (not limited to Nature Index journals).

Technical terms

Structural equation modelling (SEM): A multivariate statistical technique that combines factor analysis and multiple regression to estimate complex causal networks among latent and observed variables.

Latent variable: An unobserved construct inferred from multiple observed indicators, such as brand loyalty or consumer motivation, that cannot be measured directly.

Reflective measurement model: A specification wherein changes in a latent variable are assumed to cause variation in its indicators, implying that indicators are interchangeable manifestations of the same construct.

Formative measurement model: A specification in which indicators collectively define a latent variable, so that each indicator contributes a distinct facet of the construct—for example, different dimensions of digital trust.

Partial least squares (PLS): A variance-based SEM estimation method prioritising prediction by maximising explained variance of endogenous constructs, often used when sample sizes are modest or measurement scales are formative.

Mediation: A mechanism whereby the effect of an independent variable on a dependent variable is transmitted through one or more intervening latent constructs, enabling decomposition of direct and indirect pathways.

References

1. Causal factors influencing the use of social commerce platforms. Journal of Open Innovation: Technology, Market, and Complexity (2023).
2. PLS-based SEM Algorithms: The Good Neighbor Assumption, Collinearity, and Nonlinearity. Information Management and Business Review (2015).
3. Effect of Mobile Social Apps on Consumer’s Purchase Attitude: Role of Trust and Technological Factors in Developing Nations. SAGE Open (2021).