Incorporating new variables into a model of brand extension in fast fashion

  • 203 Views
  • 80 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

This study tests a brand extension in fast fashion to explore the extension’s effect on the parent brand. It investigates whether extensions to varyingly distant product classes modify customers’ attitudes toward the parent brand. University students from the Technical University of Liberec, the Faculty of Economics (Czech Republic), aged 22-25 years, participated in an online survey for this study. The number of respondents was 310. The outcomes are relevant for this segment of customers. The model with classic brand extension factors (perceived fit (FIT), attitudes toward the brand extension (ATE), parent brand attitude change (PBCH)) was constructed. Factors of fashion leaders and emotional variables (e.g., trust and loyalty) were added to the model. The model was tested using structural equation modeling (SEM) in AMOS software and was statistically significant (Chi-squared value of 6.402, p = 0.171). A positive relationship was observed between FIT and ATE (β = 0.534, p-value = 0.000), the same as trust and ATE (β = 0.693, p-value = 0.000). Equally, ATE had a significant positive impact on PBCH (β = 0.722, p-value = 0.000) and trust and loyalty (β = 0.649, p-value = 0.000). Loyalty negatively affects ATE (β = -0.126, p-value = 0.010), indicating that these customers may have problems with brand extension, similar to a fashion leader (β = -0.126, p-value = 0.010). TRUST has a negative effect on the PBCH (β = -0.338, p-value = 0.000). Insights derived from this study hold substantial relevance for marketers in fast fashion aiming to prepare brand extensions effectively.

Acknowledgment
This work is supported by the Technical University of Liberec, Faculty of Economics – internal grant.

view full abstract hide full abstract
    • Figure 1. Tested model
    • Table 1. List of items used
    • Table 2. Items, factor loadings (CFA), mean, and standard deviation
    • Table 3. Reliability and validity
    • Table 4. Results of testing the hypotheses
    • Table 5. Response rate and percentages
    • Table 6. Results of unpaired t-test
    • Conceptualization
      Jitka Burešová
    • Data curation
      Jitka Burešová
    • Investigation
      Jitka Burešová
    • Methodology
      Jitka Burešová, Roman Vavrek
    • Project administration
      Jitka Burešová
    • Supervision
      Jitka Burešová
    • Validation
      Jitka Burešová, Roman Vavrek
    • Visualization
      Jitka Burešová
    • Writing – original draft
      Jitka Burešová
    • Writing – review & editing
      Jitka Burešová, Roman Vavrek
    • Formal Analysis
      Roman Vavrek