## 2 Materials and Methods  
Techniques for bibliometric analysis can be classified into two categories, performance analysis and scientific mapping [16]. While the first of these studies the contributions of the constituents of the research, scientific mapping focuses on the relationships between them. Techniques used for scientific mapping include citation analysis, co-citation analysis, bibliographic coupling, co-word analysis, and co-authorship analysis. In this work, co-word analysis was used. This studies the actual content of the publication from the keywords, defined by the authors themselves, or by their frequent occurrence in titles, abstracts, or full texts. In this analysis, it is assumed that the words that frequently appear together have a thematic relationship and have been used in different works to preview the future of a scientific field [17; 18; 19; 20]. Co-word analysis is the study of co-occurrences, that is, the joint appearances of two terms in a given text with the purpose of identifying the conceptual and thematic structure of a scientific domain [21]. This analysis allows for the exploration of existing or future relationships among topics in a research field by focusing on the written content of the publication itself [16]. The quality of a co-word analysis depends on several factors such as the data sources used, the appropriate selection of search terms, and the mathematical methods of subsequent analysis of the results [22]. The source used in this study was Scopus, an abstract and citation database launched in 2004 by Elsevier [23] and one of the main bibliographic reference databases [24; 25]. In addition to the source used, another fundamental aspect in a bibliometric analysis is the quality of the search terms used. One of the main problems with co-word analysis is the so-called \"indexer effect\" [22; 26], derived from the search terms used in data acquisition. In this study, author keywords were used as search terms in the database, as opposed to automatically indexed keywords, because they better describe the content of the papers [27]. Three areas overlap in this work: project management, innovation, and artificial intelligence. To select the most appropriate search terms in each field, other bibliometric studies carried out in those fields were reviewed:  
* The application of artificial intelligence to different scientific fields has been the aim of numerous bibliometric studies [28; 29; 30; 31; 32; 33]. Some of these studies use only \"artificial intelligence\" as the search term [32; 33], but this generic name would not include in the results valid publications for the purpose of this work, in which the authors have been more specific when defining the part of artificial intelligence that defines their study. On the opposite side, other papers incorporate many other keywords in trying to include all the related results [29; 30; 34]. Delving into this same line of research, several studies analyze, among other aspects, the temporal evolution of the most frequently used keywords in this field of artificial intelligence [28; 31; 35; 36]. As these studies show, the number of terms and their variants is very high, given the multitude of techniques (neural networks, fuzzy logic, expert systems, support vector machine, random forest, etc.) and applications (classification, prediction, optimization, pattern recognition, decision aid, etc.) included in this field. In this work, we chose to select only those keywords used most frequently and with a more general character, that is, those that, by themselves, encompass several techniques and applications. As a result, the search terms used were: \"artificial intelligence\", \"knowledge based\", \"machine learning\", and \"data mining\". * In the field of innovation, different bibliometric studies have also been carried out with derivative terms such as \"open innovation\", \"innovation management\", \"innovation system\", \"innovation policy\", \"radical innovation\", and \"innovation model\" [37].