Abstract
PurposeFresh food products remain one of the most challenging product categories for e-commerce managers. The literature emphasizes the importance of perceived freshness in explaining their purchase behavior. However, studies on online purchases of fresh food products are scarce, especially regarding repurchase intentions, and the role of perceived freshness in online settings has so far been disregarded. This research addresses this gap by examining the role of perceived freshness in the intention to repurchase fresh food products online.Design/methodology/approachGuided by the expectation confirmation theory (ECT) and the perceived risk theory, this study defined a set of hypotheses tested through structural equation modeling. Participants were consumers with previous experience in purchasing fresh food products online.FindingsThe findings indicate that the importance of sensory attributes negatively affected the perceived freshness of fresh food products purchased online, while the importance of non-sensory attributes had a non-significant impact. Expectations of freshness positively affected perceived freshness and confirmation of freshness, as suggested by ECT. The hypothesized positive effects of confirmation on satisfaction and of satisfaction on intention to repurchase fresh food products online were also supported. Finally, it was found that repurchase intention was negatively affected by perceived performance risk and financial risk.Originality/valueThis article contributes to the limited literature on online purchase of fresh food by focusing on perceived freshness as a determinant of repurchase intention.

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This article aims to explore menopausal women's views on empowerment in menopause-related femvertising on social media and to examine its outcomes for both women and brands. It includes a qualitative study comprising in-depth interviews with menopausal women who were active social media users (n = 15). The data were subject to content analysis using NVIVO software. The results reveal that menopause empowerment strategies on social media are perceived by women as a source of knowledge, facilitating social support, focusing on self-worth enhancement, and deconstructing stereotypes and taboos. Despite positive impacts such as self-esteem and self-confidence, these messages can also induce discomfort and feelings of segregation. Although the study highlights potential benefits for brands, including improved image and engagement, it also identifies risks such as skepticism, distrust, and customer loss. This research contributes to the femvertising and branding literature by addressing the largely overlooked segment of menopausal women. It highlights knowledge dissemination as a critical and previously underexplored dimension of femvertising and demonstrates that menopause empowerment carries distinct dynamics and consequences for both women and advertising brands, shedding light on the complexity of femvertising strategies. The findings can assist brands and social organizations aiming to develop more effective strategies for engaging menopausal audiences.

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Accurate power forecasting from renewable energy sources (RES) is crucial for integrating additional RES capacity into the power system and realizing sustainability goals. This work emphasizes the importance of integrating decentralized spatio-temporal data into forecasting models. However, decentralized data ownership presents a critical obstacle to the success of such spatio-temporal models, and incentive mechanisms to foster data-sharing need to be considered. The main contributions are a) a comparative analysis of the forecasting models, advocating for efficient and interpretable spline LASSO regression models, and b) a bidding mechanism within the data/analytics market to ensure fair compensation for data providers and enable both buyers and sellers to express their data price requirements. Furthermore, an incentive mechanism for time series forecasting is proposed, effectively incorporating price constraints and preventing redundant feature allocation. Results show significant accuracy improvements and potential monetary gains for data sellers. For wind power data, an average root mean squared error improvement of over 10% was achieved by comparing forecasts generated by the proposal with locally generated ones.

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