This post is part of the continuing mini-series on RQ–RH–D–M across fields. Its purpose is to provide a compact, practical toolkit showing how research questions, research hypotheses or working propositions, data, and methodology can be aligned in one specific discipline.

Marketing is especially suitable for this kind of exercise because it combines naturally consumer behavior, branding, pricing, digital engagement, advertising, loyalty, segmentation and market performance. It also supports quantitative, qualitative and mixed methods designs, making it ideal for demonstrating how the same substantive issue can be studied through behavioral metrics, survey data, platform data, interviews and integrated designs.

In marketing, many studies begin from frameworks such as consumer behavior theory, theory of planned behavior, brand equity models, customer satisfaction and loyalty models, service quality frameworks, diffusion of innovations, relationship marketing, market orientation theory and digital marketing and technology-adoption frameworks. These models define constructs such as attitude, purchase intention, trust, brand loyalty, satisfaction, perceived quality, adoption, engagement or customer value, which are measured through multi-item scales, choice behavior, experimental responses, transaction data, digital interaction metrics, interviews or integrated consumer datasets.

Note: The entries in the Methodology are intentionally general and indicative. They are meant to illustrate plausible methodological directions, not to exhaust the full range of possible methods, model variants or analytic choices available to the researcher. Researchers are not expected to apply all of the methodological tools listed in column Methodology in a single study. The entries are intended to indicate suitable methodological options or families of approaches from which the researcher selects those that best fit the research question, hypothesis, data, and design.