Search Results (4)

Growing awareness of the fashion industry’s negative impact on people and the environment has led to considerable growth of the sustainable fashion market. At the same time, Black Friday purchases increase annually as the sales event develops into a global phenomenon. As sustainable fashion brands are choosing to participate in the event, many communicate their offers via the social media platform Instagram. To gain a competitive advantage and maintain their sustainable corporate images, some brands use greenwashing and/or bluewashing strategies. The first part of this study explores which strategies were employed in Instagram content posted by sustainable brands, using quantitative and qualitative content analysis. We propose a research-based model of nine greenwashing/bluewashing strategies. The second part of the study examines predictive factors for consumer evaluations of Black Friday ads by sustainable brands, using an online survey and a stepwise multiple regression analysis. Findings show that consumers’ critical attitude towards Black Friday and high ad skepticism predict positive evaluations while sustainable purchase behavior predicts negative evaluations. These insights suggest that ‘sustainable’ Black Friday campaigns may appeal to consumers who show a general concern for the environment and issues of social sustainability, but not to those who exhibit actual sustainable behavior. Full article

Partial least squares structural equations modeling (PLS-SEM) uses sampling bootstrapping to calculate the significance of the model parameter estimates (e.g., path coefficients and outer loadings). However, when data are time series, as in marketing mix modeling, sampling bootstrapping shows inconsistencies that arise because the series has an autocorrelation structure and contains seasonal events, such as Christmas or Black Friday, especially in multichannel retailing, making the significance analysis of the PLS-SEM model unreliable. The alternative proposed in this research uses maximum entropy bootstrapping (meboot), a technique specifically designed for time series, which maintains the autocorrelation structure and preserves the occurrence over time of seasonal events or structural changes that occurred in the original series in the bootstrapped series. The results showed that meboot had superior performance than sampling bootstrapping in terms of the coherence of the bootstrapped data and the quality of the significance analysis. Full article

Panic buying and hoarding behavior is a significant component of crisis- and disaster-related consumption displacement that has received considerable attention during the COVID-19 pandemic. Understanding such purchasing and stockpiling behavior provides critical information for government, disaster managers and the retail sector, as well as policy makers to adjust crisis response strategies and to better understand disaster management, including preparedness and response strategies. This study examines consumer purchasing behavior, retail spending and transactional data for different retail sectors between January 2017 and December 2020 using data for the greater Christchurch region in New Zealand. Once COVID-19-related panic buying began, overall spending increased sharply in anticipation of lockdowns. Transactional spending increased and subsided only slowly to a level higher than pre lockdown. The magnitude of the panic buying event far exceeded historical seasonal patterns of consumer spending outside of Christmas, Easter and Black Friday, although daily spending levels were comparable to such consumption events. The results of the study highlight the importance of comparing panic buying to other events in terms of purchasing motivations and also considering that so-called panic buying may contribute to greater individual and household resilience. The volume of sales alone is not adequate to define panic buying. Instead, the extent of divergence from the normal daily spending value per retail transaction of a given population provides a much more accurate characteristic of panic buying. Full article

The Black Friday event has become a global opportunity for marketing and companies’ strategies aimed at increasing sales. The present study aims to understand consumer behavior through the analysis of user-generated content (UGC) on social media with respect to the Black Friday 2018 offers published by the 23 largest technology companies in Spain. To this end, we analyzed Twitter-based UGC about companies’ offers using a three-step data text mining process. First, a Latent Dirichlet Allocation Model (LDA) was used to divide the sample into topics related to Black Friday. In the next step, sentiment analysis (SA) using Python was carried out to determine the feelings towards the identified topics and offers published by the companies on Twitter. Thirdly and finally, a data-text mining process called textual analysis (TA) was performed to identify insights that could help companies to improve their promotion and marketing strategies as well as to better understand the customer behavior on social media. The results show that consumers had positive perceptions of such topics as exclusive promotions (EP) and smartphones (SM); by contrast, topics such as fraud (FA), insults and noise (IN), and customer support (CS) were negatively perceived by customers. Based on these results, we offer guidelines to practitioners to improve their social media communication. Our results also have theoretical implications that can promote further research in this area. Full article