How to measure purchase intention in marketing theses: Likert scales and empirical validation

Purchase intention is the central construct of most marketing theses, but measuring it rigorously demands more than a questionnaire: it requires a theoretical framework, a replicated scale and psychometric validation that holds up before the committee.

What is purchase intention and how is it modeled?

Purchase intention is defined as the disposition or subjective probability that a consumer will buy a product or service in the future. In marketing research it is conceived as a latent variable, not directly observable, that must be inferred from indicators. The most-used theoretical framework remains Ajzen’s (1991) Theory of Planned Behavior, which posits three antecedents: attitude toward the behavior, subjective norm and perceived behavioral control. Derived models integrate additional constructs such as trust, perceived value and perceived quality (Echchad and Ghaith, 2022), and have been applied to both offline and online settings (Peña-García et al., 2020).

Key antecedents: country-of-origin image, ethnocentrism and xenocentrism

In globalized markets, purchase intention is shaped by product origin. The country-of-origin image influences how consumers evaluate foreign goods, as documented among Mexican consumers during the pandemic (Morán-Huertas, 2021). In parallel, consumer ethnocentrism favors domestic over imported products (Morán-Huertas and López Lira, 2022), whereas xenocentrism and cosmopolitanism work the opposite way, driving preference for the foreign (Morán-Huertas, Guerra-Rodríguez and López-Botello, 2020). Including at least one of these constructs strengthens the empirical contribution of a thesis.

Building the instrument with Likert scales

The standard practice is to adapt previously validated scales rather than writing items from scratch. The 5- or 7-point Likert format is the most widespread, as it allows quasi-interval treatment and eases multivariate analysis. A typical item would be: “I intend to buy this brand in the coming months,” with responses from 1 (strongly disagree) to 7 (strongly agree). For purchase intention, use at least three items covering the cognitive, affective and behavioral dimensions. The recommendation is to translate and back-translate the items, keep theoretical unidimensionality and avoid double-barreled wording.

Empirical validation, step by step

Defending the instrument requires a sequential protocol. First, content validity via expert judgment, ideally computing Aiken’s V coefficient. Second, a pilot with 30 to 100 cases to refine items. Third, reliability via Cronbach’s alpha (≥ 0.70) and, preferably, McDonald’s omega. Fourth, exploratory factor analysis (EFA) to examine structure and, with a sample ≥ 200, confirmatory factor analysis (CFA) verifying standardized loadings above 0.60, alongside convergent validity (AVE > 0.50) and discriminant validity (Fornell-Larcker criterion or HTMT < 0.85). The work of Morán-Huertas, Guerra-Rodríguez and López-Botello (2020) illustrates this procedure applied to consumer behavior.

Analysis with PLS-SEM and SPSS

Once the instrument is validated, the next step is testing the structural model. SPSS handles descriptive statistics, EFA and reliability analyses; SmartPLS, based on PLS-SEM, is the preferred tool when the model includes several latent constructs and the goal is prediction. PLS-SEM suits moderate samples and does not require multivariate normality. Nekmahmud et al. (2022) use precisely this approach to model purchase intention for green products, assessing composite reliability, AVE and path coefficients with bootstrapping-based significance.

Trends 2020-2026 and common mistakes

The recent agenda shifts toward three fronts: post-COVID digital and omnichannel purchase behavior, sustainable or green purchase intention, and the role of electronic reviews and social-media marketing (Nekmahmud et al., 2022; Peña-García et al., 2020). Among the most frequent mistakes: using scales without cross-cultural adaptation, confusing face validity with content validity, skipping the pilot, reporting only Cronbach’s alpha without AVE or discriminant validity, and interpreting loadings below 0.50. Avoiding them markedly raises the study’s defensibility.

References

• Echchad, M., & Ghaith, A. (2022). Purchasing intention of green cosmetics using the Theory of Planned Behavior: The role of perceived quality and environmental consciousness. Expert Journal of Marketing, 10(1), 62-71.
• Morán-Huertas, A. J. (2021). Impact of the Country-of-Origin Image on the Purchase Intention of Foreign Products in Mexican Consumers During the COVID-19 Pandemic. Proceedings of the International Conference on Industrial Engineering and Operations Management. https://ieomsociety.org/proceedings/2021monterrey/510.pdf
• Morán-Huertas, A. J., & López Lira, A. (2022). El etnocentrismo como antecedente de la intención de compra de productos nacionales. VinculaTégica EFAN, 6(1). https://vinculategica.uanl.mx/index.php/v/article/view/57
• Morán-Huertas, A. J., Guerra-Rodríguez, P., & López-Botello, C. K. (2020). Factores psicosociológicos que influyen en la intención de compra de productos extranjeros en los consumidores mexicanos. VinculaTégica EFAN, 6(1). https://vinculategica.uanl.mx/index.php/v/article/view/587
• Nekmahmud, M., Naz, F., Ramkissoon, H., & Fekete-Farkas, M. (2022). Transforming consumers’ intention to purchase green products: Role of social media. Technological Forecasting and Social Change, 185, 121923. https://doi.org/10.1016/j.techfore.2022.121923
• Peña-García, N., Gil-Saura, I., Rodríguez-Orejuela, A., & Siqueira-Junior, J. R. (2020). Purchase intention and purchase behavior online: A cross-cultural approach. Heliyon, 6(10), e05128. https://doi.org/10.1016/j.heliyon.2020.e05128