Roman Vavrek

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.

Type of the article: Research Article

Abstract
The COVID-19 pandemic profoundly reshaped global financial markets, amplifying volatility and redefining risk perception across innovation-driven sectors. This study assesses how the COVID-19 pandemic altered volatility dynamics in the technology sector by quantifying wave-by-wave risk-return behavior using descriptive statistics, including standard deviation, coefficient of variation, and kurtosis. The analysis is based on daily stock prices and returns for the technology sector from March 8, 2019, to December 12, 2022, covering the pre-pandemic period, five pandemic waves, and a post-pandemic stabilization phase. The results reveal that market volatility surged during the first pandemic wave as digital adoption accelerated and investors sought technological assets as temporary safe havens. Subsequent waves showed alternating phases of speculative trading and market corrections, reflecting evolving investor sentiment and macroeconomic uncertainty. The coefficient of variation increased sharply during the height of the crisis, demonstrating that risk consistently exceeded returns, while kurtosis analysis indicated a higher frequency of extreme price movements compared with pre-pandemic conditions. Although volatility gradually declined during the post-pandemic period, it remained notably above pre-2020 levels, signaling a persistent re-pricing of sectoral risk. These findings confirm that the technology sector, while resilient, experienced structural changes in its volatility regime and investor behavior, with lasting implications for market stability and portfolio management in the digital economy.

Acknowledgment
This article is one of the partial outputs of the currently implemented research grant VEGA no. 1/0110/24 and research grant IGMP no. I-25-103-00.