Abstract
The main aim of this chapter is to explore the role of social media in the internationalization of business-to-business (B2B) companies, addressing a remaining gap in the literature. It includes a qualitative study, where data from semi-structured interviews was subject to thematic content analysis. The study found that social media acts as a facilitator of internationalization in the B2B setting. While social media alone does not determine the success of internationalization efforts, it has become an essential tool for firms seeking to engage with global markets and maintain a competitive edge. The study provides relevant insights for managers, recommending that businesses should track social media performance to understand its impact on internationalization efforts and adjust strategies accordingly. © 2025 Elsevier B.V., All rights reserved.

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Abstract
The development of artificial intelligence (AI) in the last decade has reshaped government operations and raised privacy concerns as automated processes become commonplace. This study aims to identify the main privacy issues associated with government use of AI in public services. Using a bibliometric analysis that includes co-citation of references and authors, bibliographic coupling, and keyword co-occurrence approaches, the study analyzed the literature on this topic through VOSViewer and the Web of Science database. Findings highlight significant privacy concerns: (i) opaque data-driven decisions, (ii) bias in predictive algorithms, (iii) difficulty obtaining explanations for decisions, (iv) mistrust in AI systems, (v) ethical lapses in AI execution, and (vi) trust deficit in government AI use. Additionally, 18 research questions are defined, addressing ethical limits of privacy in AI government use. A consensus in the literature urges governments to enact laws ensuring data privacy "by default" in AI decision-making and data management/transfer to third parties. © The Editor and Contributing Authors Severally 2025. All rights reserved.

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.

Abstract
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.

Abstract
As many automated algorithms find their way into the IT systems of the banking sector, having a way to validate and interpret the results from these algorithms can lead to a substantial reduction in the risks associated with automation. Usually, validating these pricing mechanisms requires human resources to manually analyze and validate large quantities of data. There is a lack of effective methods that analyze the time series and understand if what is currently happening is plausible based on previous data, without information about the variables used to calculate the price of the asset. This paper describes an implementation of a process that allows us to validate many data points automatically. We explore the K-Nearest Neighbors algorithm to find coincident patterns in financial time series, allowing us to detect anomalies, outliers, and data points that do not follow normal behavior. This system allows quicker detection of defective calculations that would otherwise result in the incorrect pricing of financial assets. Furthermore, our method does not require knowledge about the variables used to calculate the time series being analyzed. Our proposal uses pattern matching and can validate more than 58% of instances, substantially improving human risk analysts' efficiency. The proposal is completely transparent, allowing analysts to understand how the algorithm made its decision, increasing the trustworthiness of the method.