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The purpose of this article is to demonstrate how artificial intelligence contributes to the preservation of the cultural identity of native peoples as well as to reduce the gap in access to verified information in native languages of Peru.

An analysis of the context of artificial intelligence in the field of preservation of native languages was developed. Subsequently, the case of the digital tool Quispe Chequea was highlighted, which uses artificial intelligence to produce journalistic content of verification in three native languages of Peru (Quechua, Aimara and Awajún). Finally, a reflection was made on the contributions of artificial intelligence in reducing the gap in access to information, vindication and preservation of native Peruvian languages.

The findings evidenced the importance of the principles of indigenous data sovereignty to respect cultural rights. Also, it was determined that artificial intelligence contributes to the preservation of cultural values of ethnic groups. It also provides verified information in the native language in order to promote inclusion. Finally, this enabling technology proved to be an important resource in reducing social and cultural gaps.

The use of artificial intelligence will enable the preservation of the native languages of marginalized and ethnic groups. It also favors the dissemination and communication of truthful information in native languages.

This opinion article highlights a little-explored context in the field of native Peruvian languages. It highlights the contribution that artificial intelligence can bring to ethnic groups, especially in the preservation of their languages and the reduction of the information access gap.

The aim of this research is to assess the impact of the motivational dimensions of slow fashion (exclusivity, equity, authenticity, functionality and localism) on environmental and socially sustainable consumption.

The necessary information to conduct the empirical analysis was obtained through an online survey administered to consumers of slow fashion. Data processing was carried out using the partial least squares structural equation modeling (PLS-SEM) technique.

The obtained results demonstrate that exclusivity and equity significantly influence environmentally and socially sustainable consumption. However, the results indicate that authenticity and localism only have a significant influence on socially sustainable consumption and not on environmental consumption. Furthermore, the findings also reveal that functionality has a significant impact solely on environmentally sustainable consumption.

This study is based on self-reported data collected from buyers aged between 18 and 29, commonly known as Generation Z. Future research could enhance the generalizability of the findings by conducting comparative studies involving other age groups, both older and younger, who are interested in sustainable fashion. Although existing literature suggests that this generation shows a stronger preference for products and brands that promote environmentally and socially responsible practices, broadening the scope to include other demographics could provide a more comprehensive understanding of sustainable fashion consumption.

The findings suggest the importance of developing value propositions that align slow fashion with motivational factors such as exclusivity and equity. Moreover, the practical nature of these motivational dimensions is evident in maximizing their effects on environmentally friendly behaviors, care behaviors and social responsibility. It is recommended to emphasize attributes of garment scarcity, durability, degree of personalization, provenance benefits and information on the company’s social responsibility to foster environmentally and socially sustainable behaviors.

This research contributes to a better understanding of the motivational dimensions that influence sustainable consumption measures and provides valuable information for managers in the fashion industry by clarifying the attributes that act as triggers from the consumer’s perspective.

This paper aims to explore the intersection of artificial intelligence (AI) and marketing within the context of knowledge management (KM). It investigates how AI technologies facilitate data-driven decision-making, enhance business communication, improve customer personalization, optimize marketing campaigns and boost overall marketing effectiveness.

This study uses a quantitative and systematic approach, integrating citation analysis, text mining and co-citation analysis to examine foundational research areas and the evolution of AI in marketing. This comprehensive analysis addresses the current gap in empirical investigations of AI’s influence on marketing and its future developments.

This study identifies three main perspectives that have shaped the foundation of AI in marketing: proxy, tool and ensemble views. It develops a managerially relevant conceptual framework that outlines future research directions and expands the boundaries of AI and marketing literature within the KM landscape.

This research proposes a conceptual model that integrates AI and marketing within the KM context, offering new research trajectories. This study provides a holistic view of how AI can enhance knowledge sharing, strategic planning and decision-making in marketing.