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• W4285389801 abstract "The traditional global food supply chains are not just complex, but they do not support the sustainability of agriculture. The business models with the greatest growth potential are those that would allow consumers to buy more directly from producers. Before COVID-19, these alternatives were not just popular but had a relatively high market share compared to post COVID-19 era. However, due to the pandemic and the changes in consumers’ purchasing behavior, the players of short food supply chains had to adapt to the new circumstances. This is why business model innovation is nowadays a priority, which means a substantial renewal of the value delivered to customers, and a significant transformation of the processes and activities of the company/network. The study aimed to examine the dynamic innovation through applied digital marketing solutions that could open up new sales channels and increase the competitiveness of the companies involved. Since Austria is one of the “greenest” countries, its consumers are interested in purchasing sustainably, so they strive to buy directly from the producers. This motivates the authors to use its representative sample to investigate how well adapted the Austrian direct sellers are to the unsteady circumstances. Based on a previous research framework, a principal component analysis was applied and the elements defined therein were the variables of non-hierarchical clustering. The used methods highlight the lack of online distribution and marketing of the farmer shops, which could multiply the harmful effects of the pandemic. The classification of the shops demonstrated the generally low share of innovative direct sellers. We conclude that in the new era, businesses that effectively apply open business innovation models will be able to compete in the market." @default.
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• W4285389801 date "2022-09-01" @default.
• W4285389801 modified "2023-10-16" @default.
• W4285389801 title "The Potential of Digital Marketing Tools to Develop the Innovative SFSC Players’ Business Models" @default.
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• W4285389801 doi "https://doi.org/10.3390/joitmc8030122" @default.
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