The main aim of the study was to analyze/determine how international modern retailers’ chains can achieve a competitive advantage by introducing private labels (PLs) in the organic category and in turn stimulate consumption of food produced with respect of sustainability principles. Following a review of the relevant literature data collection process involved two steps. First, to select retailers with organic private labels (OPLs) and producers delivering products under OPLs, there were conducted in-depth semi-structured interviews with the representatives of the management board of 17 enterprises. A group of 6 enterprises was selected, including 3 retailers having OPLs, 2 producers delivering products under OPLs and one enterprise that was categorized as producer and distributor. Based on the results of the interviews, six sources of the competitive advantage of the OPLs were identified and further discussed. Second, to analyze more in depth assortment-based competitive advantage of the OPLs, 8 enterprises (5 retailers having OPLs, 2 producers delivering products under OPLs and one enterprise categorized as a producer and distributor) were approached for the interviews. To explore price-related competitive advantage three products offered under the premium PLs, the economy PLs, the organic PLs, the producer brands and the imported brands were selected for the subsequent analysis. Increasing competition in the food retailing sector pose many challenges on the retailers and stimulate them to develop the PLs that is in line with global trends. In case of the OPLs, the more popular strategy while introducing them is to differentiate the PLs with quality and image related attributes comparable to leading producer brands. Retail chains introducing the OPLs achieve the competitive advantage related to six sources: price, range of assortment, type of PLs, image of retailer, sustainability and specific process and product related attributes of organic food. Additionally the retailers use their reputation and image of retailers’ brand to guarantee the quality of new organic products introduced under the PLs. To achieve or maintain the competitive advantage, retailers tend to introduce the OPLs positioned as value added PLs. They perceive the OPLs as a way to influence the consumers' decision to buy and increase their loyalty towards store brands. The strategies of the retailers evolve and the largest retailers decided to create the premium OPLs, the others focus on introduction of the international OPLs or aim at development of new PLs based on the analysis of consumers' preferences. There are several factors behind the decision to introduce or extend the premium OPLs i.e. to expand the assortment of organic products and green products line, to increase category margins and to enhance retailer’s image among customers. Other large retailers create the OPLs with special product line design focusing on health- and environmental related attributes and high quality guaranteed by the organic regulations and the certification. The process of the introducing the premium PLs should be influenced by the inherent characteristic of organic food and its production methods that respect the principles of the sustainable development.

While there are several ways to identify customer behaviors, few extract this value from information already in a database, much less extract relevant characteristics. This paper presents the development of a prototype using the recency, frequency, and monetary attributes for customer segmentation of a retail database. For this purpose, the standard K-means, K-medoids, and MiniBatch K-means were evaluated. The standard K-means clustering algorithm was more appropriate for data clustering than other algorithms as it remained stable until solutions with 6 clusters. The evaluation of the clusters’ quality was obtained through the internal validation indexes: Silhouette, Calinski Harabasz, and Davies Bouldin. Once consensus was not obtained, three external validation indexes were applied: global stability, stability per cluster, and segment-level stability across solutions. Six customer segments were obtained, identified by their unique behavior: Lost customers, disinterested customers, recent customers, less recent customers, loyal customers, and best customers. Their behavior was evidenced and analyzed, indicating trends and preferences.

Retailers must have accurate sales forecasts to operate their businesses efficiently and effectively and to remain competitive in the marketplace. Global forecasting models like RNNs can be a powerful tool for forecasting in retail settings, where multiple time series are often interrelated and influenced by a variety of external factors. By including covariates in a forecasting model, we can better capture the various factors that can influence sales in a retail setting. This can help improve the accuracy of our forecasts and enable better decision-making for inventory management, purchasing, and other operational decisions. In this study we investigate how the accuracy of global forecasting models is affected by the inclusion of different potential demand covariates. To ensure the significance of the study's findings, we used the M5 forecasting competition's openly accessible and well-established dataset. The results obtained from the DeepAR models, which were trained on various combinations of features including time, price, events, and IDs, suggest that individually only the features corresponding to IDs improve the baseline model. However, when all the features are used together, the best performance is achieved, indicating that the individual relevance of each feature is emphasized when the information is given jointly. Comparing the model with features to the model without features, there is an improvement of 1.76\% for MRMSSE and 6.47\% for MMASE.

Spectacular shifts have been led to by The COVID-19 crisis in consumer behavior. Retailers will have to work hard to meet ever-evolving customer service experience with respect to the ways in which it may be differently affected by offline or online transactions in order to win and stay relevant. We suggest an integrative framework and construct customer service experience hypotheses, based on its antecedents and consequences that will contribute to academic study as well as managerial implications. The hypotheses are tested by a simultaneous equation model employing two data sets of the retail industry's offline and online customers. In this study, 571 samples of these businesses, 319 and 252 respondents from offline and online retail channels, respectively, were collected by means of an online web survey of consumers. The results show that the impact of consequences and antecedents of CSX differs based on the media utilized. The integrative framework of CSX in its online medium is far more effective than its explanatory power offline. The outcomes are reasonably counterintuitive in so far as they demonstrate that while most elements of CSX where a service is selected offline is the same in terms of customer loyalty and value equity, the emotional element related to the service provider is higher when the service is selected offline rather than online. These outcomes indicate that, contrary to popular fears, the online medium enables firms to develop a loyal customer foundation. These findings offer perceptivity into how an online channel could be used to better complement the offline channel, contributing towards new knowledge and understanding on CSX and how it may be utilized for managerial decision-making.

This study analyzed the levels of mental workload and the presence of burnout on a sample of fashion retailing workers from Spain and its relationship with the actual Covid-19 pandemic by exploring Covid-19 pandemic predictors of burnout and mental workload. We established a prospective cross-sectional design. Participants (n = 360) answered an online survey including questions about sociodemographic data, perception of Covid-19, CarMen-Q questionnaire (workload), and MBI (burnout syndrome). We obtained data throughout October-November 2020. The results showed that participants exhibit deep concern about the Covid-19 pandemic and its influence at the work level. Although the mental workload was near the middle point of the scale, participants showed moderate to high burnout levels, revealing that the sample is at risk of experiencing higher burnout levels over time as the pandemic and associated economic crisis continue. The multidimensional regression analysis results indicated that environmental changes, work overload, somatic symptoms, insomnia, negative job expectations, and uncertainty constituted significant mental workload predictors. Insomnia, somatic symptoms, and negative job expectations constituted significant predictors for burnout. In conclusion, the uncertainty at work derived from the Covid-19 pandemic is harming the psychological wellbeing of fashion retailing workers in Spain.