The objective of this study is to identify and analyze the scientific literature with a bibliometric analysis to find the main topics, authors, sources, most cited articles, and countries in the literature on virtual reality in education. Another aim is to understand the conceptual, intellectual, and social structure of the literature on the subject and identify the knowledge base of the use of VR in education and whether it is commonly used and integrated into teaching–learning processes. To do this, articles indexed in the Main Collections of the Web of Science, Scopus and Lens were analyzed for the period 2010 to 2021. The research results are presented in two parts: the first is a quantitative analysis that provides an overview of virtual reality (VR) technology used in the educational field, with tables, graphs, and maps, highlighting the main performance indicators for the production of articles and their citation. The results obtained found a total of 718 articles of which the following were analyzed 273 published articles. The second stage consisted of an inductive type of analysis that found six major groups in the cited articles, which are instruction and learning using VR, VR learning environments, use of VR in different fields of knowledge, learning processes using VR applications or games, learning processes employing simulation, and topics published during the Covid-19 pandemic. Another important aspect to mention is that VR is used in many different areas of education, but until the beginning of the pandemic the use of this so-called “disruptive process” came mainly from students, Institutions were reluctant and slow to accept and include VR in the teaching–learning processes.

In the practice of Digital Marketing (DM), Web Analytics (WA) and Key Performance Indicators (KPIs) can and should play an important role in marketing strategy formulation. It is the aim of this article to survey the various DM metrics to determine and address the following question: What are the most relevant metrics and KPIs that companies need to understand and manage in order to increase the effectiveness of their DM strategies? Therefore, to achieve these objectives, a Systematic Literature Review has been carried out based on two main themes (i) Digital Marketing and (ii) Web Analytics. The search terms consulted in the databases have been (i) DM and (ii) WA obtaining a result total of n = 378 investigations. The databases that have been consulted for the extraction of data were Scopus, PubMed, PsyINFO, ScienceDirect and Web of Science. In this study, we define and identify the main KPIs in measuring why, how and for what purpose users interact with web pages and ads. The main contribution of the study is to lay out and clarify quantitative and qualitative KPIs and indicators for DM performance in order to achieve a consensus on the use and measurement of these indicators.

Artificial Intelligence (AI) is increasingly present in organizations. In the specific case of Human Resource Management (HRM), AI has become increasingly relevant in recent years. This article aims to perform a bibliometric analysis of the scientific literature that addresses in a connected way the application and impact of AI in the field of HRM. The scientific databases consulted were Web of Science and Scopus, yielding an initial number of 156 articles, of which 73 were selected for subsequent analysis. The information was processed using the Bibliometrix tool, which provided information on annual production, analysis of journals, authors, documents, keywords, etc. The results obtained show that AI applied to HRM is a developing field of study with constant growth and a positive future vision, although it should also be noted that it has a very specific character as a result of the fact that most of the research is focused on the application of AI in recruitment and selection actions, leaving aside other sub-areas with a great potential for application.

In the digital age, the global software development sector has been a forerunner in implementing new ways and configurations for remote teamwork using information and communication technologies on a widespread basis. Crises and technological advances have influenced each other to bring about changes in the ways of working. In the 70’s of the last century, in the middle of the so-called oil crisis, the concept of teleworking was defined using remote computer equipment to access office equipment and thus avoid moving around using traditional vehicles. Then from the 90s, with the advent of communications and the widespread use of the Internet, the first virtual work teams were implemented in software development companies that already had some of the important characteristics needed to work in this way, such as, cultural diversity, characterized tasks, geographical distribution of members, communication, interdependence of tasks, leadership, cohesion, empowerment, confidence, virtuality. This manuscript groups the main factors into different models proposed by the literature and also analyzes the results of a study conducted in the midst of the Covid-19 crisis on 317 software development teams that had to work in virtual teams (VT). The results of the quantitative methodology with structural equation modeling based on variance using the partial least squares route method are analyzed. The results of the research focus on some determinants that can directly affect the performance of the virtual team. A first determinant is communication in relation to the tasks. The second is trust in relation to leadership, empowerment and cohesion. The results of virtual teams provide information that can serve as a basis for future research lines for the implementation of virtual work strategies in post-pandemic work.

The main aim of this study is to identify the key factors in User Generated Content (UGC) on the Twitter social network for the creation of successful startups, as well as to identify factors for sustainable startups and business models. New technologies were used in the proposed research methodology to identify the key factors for the success of startup projects. First, a Latent Dirichlet Allocation (LDA) model was used, which is a state-of-the-art thematic modeling tool that works in Python and determines the database topic by analyzing tweets for the #Startups hashtag on Twitter (n = 35.401 tweets). Secondly, a Sentiment Analysis was performed with a Supervised Vector Machine (SVM) algorithm that works with Machine Learning in Python. This was applied to the LDA results to divide the identified startup topics into negative, positive, and neutral sentiments. Thirdly, a Textual Analysis was carried out on the topics in each sentiment with Text Data Mining techniques using Nvivo software. This research has detected that the topics with positive feelings for the identification of key factors for the startup business success are startup tools, technology-based startup, the attitude of the founders, and the startup methodology development. The negative topics are the frameworks and programming languages, type of job offers, and the business angels’ requirements. The identified neutral topics are the development of the business plan, the type of startup project, and the incubator’s and startup’s geolocation. The limitations of the investigation are the number of tweets in the analyzed sample and the limited time horizon. Future lines of research could improve the methodology used to determine key factors for the creation of successful startups and could also study sustainable issues.