What makes consumers happy? The role of FoMO, brand-person congruence and emotional marketing in a multi-gender model

Description

This dataset contains the de-identified respondent-level data used in the study “What makes consumers happy? The role of FoMO, brand–person congruence and emotional marketing in a multi-gender model”, published in Management Letters / Cuadernos de Gestión in 2026 (DOI: 10.5295/cdg.252548rr). Data were collected through a structured online questionnaire administered to 360 young consumers in Mexico using a quantitative, non-experimental, and cross-sectional research design. The dataset includes respondent identification codes, gender, age, and item-level responses for four constructs: Emotional Marketing (ME1–ME9), Consumer Happiness (FC1–FC14), Brand–Person Congruence (MP1–MP9), and Fear of Missing Out (FOMO1–FOMO8). In the published article, these constructs are reported as EM, CH, BPC, and FoMO, respectively. All construct indicators were measured using a five-point Likert scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). Blank cells are retained as missing responses. The dataset supports the covariance-based structural equation modelling and gender-based multigroup analyses reported in the associated article. It contains no names, email addresses, contact information, or other direct personal identifiers. The wording and theoretical sources of the measurement indicators are reported in Appendix Table A.1 of the associated article. The data may be used for replication, secondary statistical analysis, methodological training, and further research on consumer happiness, emotional marketing, brand–person congruence, FoMO, and gender-related differences in consumer behaviour.

Steps to reproduce

Open the deposited Excel dataset in Jamovi version 2.3.28 or compatible statistical software. Each row represents one participant, and each column represents an identification, sociodemographic, or measurement variable. Verify the variable structure. The dataset contains respondent identification, gender, age, and item-level responses for Emotional Marketing (ME items), Consumer Happiness (FC items), Brand–Person Congruence (MP items), and Fear of Missing Out (FOMO items). All construct indicators were measured on a five-point Likert scale ranging from 1 = strongly disagree to 5 = strongly agree. Inspect the data for missing values, out-of-range responses, duplicate records, and coding inconsistencies. Blank cells should be treated as missing values. Apply the same missing-data treatment throughout all analyses and document any excluded observations. Calculate descriptive statistics for all items, including mean, standard deviation, skewness, and kurtosis. Assess common method variance through Harman’s single-factor test using an unrotated exploratory factor analysis. Specify a four-construct measurement model comprising Brand–Person Congruence, FoMO, Emotional Marketing, and Consumer Happiness. Evaluate internal consistency and convergent validity using Cronbach’s alpha, composite reliability, and average variance extracted. Assess discriminant validity using the Fornell–Larcker and HTMT criteria. Estimate the covariance-based structural equation model with the following direct relationships: Brand–Person Congruence → Emotional Marketing; Brand–Person Congruence → Consumer Happiness; FoMO → Emotional Marketing; FoMO → Consumer Happiness; and Emotional Marketing → Consumer Happiness. Estimate the indirect effects of Brand–Person Congruence and FoMO on Consumer Happiness through Emotional Marketing. Report standardised coefficients, significance levels, and explained variance (R²). Evaluate model fit using chi-square, CMIN/DF, CFI, TLI, IFI, RMSEA, SRMR, and PGFI. Conduct a gender-based multigroup SEM analysis. First test configural, metric, and scalar measurement invariance. Compare changes in CFI and RMSEA across invariance models before interpreting differences in structural paths between groups. Compare the reproduced estimates with Tables 1–4 and Figure 2 of the associated article. Minor numerical differences may occur depending on the software version, estimator, and missing-data treatment used.

Institutions

• Autonomous University of Tamaulipas

  Tamaulipas, Ciudad Victoria
• Universidad de Cádiz

  Andalusia, Cadiz

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