AI concepts relevant for AI and e-commerce/retail secors

INAIR (Increasing the Uptake of AI in Retail) is a Coordination and Support Action funded by the European Union'’s Horizon Europe Research and Innovation programme - Grant Agreement No. 101133847. The project aims to contribute to reducing the AI skills gap of European MSMEs in Retail, to let them exploit the potential of AI for greening their businesses, support their competitiveness in the global market and ultimately contribute to reaching the digital decade target of 75%+ European companies adopting AI technologies by 2030. 

These datasets contain AI concepts identified from scientific literature as part of the INAIR Horizon Europe project. It includes keywords for skills and knowledge relevant to AI in the retail and e-commerce sectors. The data supports qualitative and quantitative analyses by providing detailed references to articles discussing each concept. This resource aims to facilitate further research and understanding of the AI-related skills and technologies impacting the retail and e-commerce industries.

In our data retrieval process, we used the Scopus API to systematically extract the relevant publications. We collected a diverse range of publications from the database, including conference proceedings, book chapters, and other materials. To ensure the reliability of our analysis, we refined our focus exclusively to peer-reviewed articles, specifically limiting our scope to the categories of articles and reviews. 

The keywords combinations we used were:  

TITLE-ABS-KEY ( X AND Y ) AND PUBYEAR > 2017 AND PUBYEAR < 2025

Where: X=['sales', 'retail', 'e-commerce'], Y=['AI', 'artificial intelligence']. These keywords were scrutinized within titles, abstracts, author keywords, and ‘topics’ as delineated by the platform.

`concepts_unique.xlsx` lists unique concepts

`concepts_master.xlsx` is a master file referencing all articles mentioning each given concept.

The former file allows for browsing skills and knowledge concepts, while the latter serves as a reference to works addressing each concept and enables quantitative analysis, such as identifying the most frequently mentioned concepts.