ABSTRACT
Purpose: There is a paucity of evidence on how small new ventures cope with shifts from physical space to cyberspace imposed by external crises, such as pandemics. Further, even though the concept of space is highly relevant to understanding entrepreneurship, the concept has been underutilised in entrepreneurship research. In particular, the potential of understanding entrepreneurship in terms of the interplay between physical space and cyberspace is yet to be explored. The authors address these research gaps by pursuing the following research question: How did micro new ventures experience the shift from physical space to cyberspace (technology adaptation) imposed by the coronavirus disease 2019 (COVID-19) crisis? Design/methodology/approach: Data were collected via semi-structured interviews with founders of start-ups associated with two incubators, in Spain and Monaco. Thematic analysis of interview transcripts was conducted, approaching the data with the focus on firm positioning in "real” space and in cyberspace and on possible transformations of business models. Findings: The pandemic opened new opportunities for small new ventures, as many start-ups were successful in shifting into cyberspace by undergoing a radical digital transformation and ended up with more scalable business models and in many cases transformed themselves into micro-multinationals. Research limitations/implications: Overall, firms tended to shift from physical space to cyberspace, following the firms' customers to cyberspace, finding new and more international, customers in cyberspace or guiding the firms' existing customer base into cyberspace. Firms that maintained the pre-pandemic position were either already fully digital or had sufficient resources to hold position in the anticipation of the post-pandemic future. Originality/value: The authors introduce the concept of cyberspace in the context of entrepreneurship studies and explore the trajectories of firms in a crisis. © 2023, Emerald Publishing Limited.
ABSTRACT
This research focuses on the detection of false claims in Spanish through the use of machine learning techniques. Most of the current work related to automated, or semi-automated, fake news detections are carried out for the English language, however, there is still a large room for improvement in other languages such as Spanish. The detection of fake news content and its dissemination (spread) in online platforms is an open and hard problem, this work is focused on the detection of false and misleading information spreading during the election campaign in the Spanish Parliament and Catalonia crisis in 2019, migration crisis, COVID-19 pandemic, and hate speech against minorities. We propose the use of a machine learning model adapted for dealing with human language understanding tasks, called BERT, which has been trained and experimentally tested. We have collected a corpus of different types of false information and claims such as articles, posts on Facebook, WhatsApp's messages, tweets, and others. The results evidence how usage of machine learning techniques can help in the identification of false statements with more than 88% accuracy, and in collecting samples of false information. The experiments, with a comparison between different machine learning methods, have also been carried out using previous datasets, providing a comparison between different approaches. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.