In this 38–1 2025 issue, the journal publishes eight research articles from authors and universities in six countries: Australia, Brazil, Chile, Colombia, Spain and the United Kingdom. Below is a brief description of each article in this issue:
Calderon-Téllez et al. (2025) explore the integration of rework cycle system dynamics (SD) models within the Project Management Institute (PMI) process groups, highlighting its potential to enhance project management. The findings suggest that SD models can better capture the extended project life cycle, including front-ending, delivery and back-ending phases. This approach aids in understanding project delays and improving long-term decision-making. The study emphasizes the importance of SD modeling in addressing the complexity and uncertainty inherent in projects, particularly in developing countries. It also identifies the need for future research to incorporate agile and hybrid methodologies into SD models. The practical implications include using SD models to support project managers in planning and executing projects more effectively, ensuring sustainability and innovation. The study concludes that linking rework cycles with PMI process groups offers a dynamic and adaptive perspective, enabling more anticipation and better management of rework during project execution. This integration is crucial for advancing project management practices and achieving sustainable project success.
Jerônimo Soares and de Melo (2025) developed a systematic literature review of entrepreneurship research in Latin America based on the Global University Entrepreneurial Spirit Student’s Survey (GUESSS) data, mapping its use in 23 empirical studies from 2015 to 2023. They used the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) protocol. Findings reveal that the theory of planned behavior is the most used framework and that factors related to the individual, the university environment and the family influence entrepreneurial intention. Key gaps include limited comparative studies between countries. The study highlights the need for more structured research using GUESSS data to improve academic programs and entrepreneurship initiatives oriented toward students.
Valdés-Elizalde et al. (2025) investigate how age, gender, educational level and access barriers, such as economic limitations and lack of time, influence cultural consumption (such as books, films, theater, music, visual arts, festivals, etc.). This sector goes beyond entertainment: cultural industries generate $2.3 trillion annually worldwide, representing 3.1% of the global gross domestic product and employing 6.2% of the global workforce. Data from the National Survey of Cultural Participation (Chile) covering 12,151 selected participants were analyzed using advanced machine learning techniques (the LightGBM and SHAP analysis). They find differences to be considered in cultural inclusion policies; childhood cultural participation has a more pronounced positive impact on individuals with lower educational levels, supporting the importance of promoting access to culture from an early age to balance long-term differences in cultural consumption. The consumption of cultural goods not only contributes to social well-being and individual happiness but also serves as an indicator of human development and social cohesion.
Castillo-Vergara et al. (2025) developed a theoretical model to test the relationship between digital capability and Industry 4.0 and the impact of this relationship on innovation performance in Chilean SMEs. Using partial least squares structural equation modeling and fuzzy-set qualitative comparative analysis, data from 536 SMEs were analyzed. The results reveal two dimensions of digital capabilities: management and information and communication technologies (ICTs). Management models composed of enterprise resource planning and customer relationship management systems are essential for optimizing organizational management. ICTs facilitate the smooth flow of information within an organization, improving the efficiency of production processes. Their results confirm that Industry 4.0 influences innovation performance.
Sottolichio et al. (2025) investigate how negative emotions influence consumer behavior in the financial services sector. It highlights that consumer dissatisfaction is primarily driven by affective factors, particularly pleasure rather than cognitive evaluations like disconfirmation. The research involved a sample of 735 valid surveys from customers who experienced service failures. Initially, the model included 14 negative emotions, later refined to focus on three key emotions following statistical validation. The findings indicate that loyalty is influenced solely by affective elements (pleasure and activation) and not by cognitive factors. This suggests that emotional responses dominate satisfaction and loyalty outcomes, even after service failures. The study contributes to understanding consumer behavior by emphasizing the significance of emotional dimensions over cognitive assessments, offering practical implications for service recovery strategies in the financial sector. This research is pioneering in its exclusive focus on negative emotions and their impact on consumer satisfaction and loyalty.
Martin-Melero et al. (2025) predict corporate insolvency in Spain by comparing machine learning and analytical formulas applied to eight financial ratios. Employing a dataset that includes 388,145 solvent and 842 insolvent companies, the study conducted 27 simulations using different sampling approaches. Their results show that dataset imbalance significantly affects prediction performance, with machine learning models outperforming analytical formulas when the balance is achieved via downsampling or upsampling. The best results were obtained using Amat’s model ratios, which were adapted for Spanish companies. This work is novel in testing multiple sampling approaches and using a minimal set of financial ratios for insolvency prediction, contrasting with the broader datasets often found in the literature.
Liébana-Cabanillas et al. (2025) aim to understand the factors influencing the intention to use virtual voice assistants (VVAs). It employs a modified UTAUT2 framework alongside privacy calculus theory. An online survey was conducted with 232 participants to gather data. The findings reveal that hedonic motivation is a stronger predictor of continued use intention than price value. Additionally, the perceived privacy risk moderates the relationship between various determinants and the intention to continue using VVAs. The research contributes to a comprehensive understanding of VVA adoption and highlights new business opportunities for companies leveraging this technology. The study emphasizes the importance of addressing psychological and social dimensions in technology acceptance models, suggesting that future research could further explore these aspects. Overall, the paper provides valuable insights into user behavior regarding VVAs and underscores the significance of privacy considerations in technology adoption.
Veneziani and Soares (2025) examine how the Associação Kalunga Comunitária do Engenho II (AKCE) leveraged organizational ambidexterity to mitigate the impacts of COVID-19. Focusing on the balance between operational alignment and adaptability, the study identifies innovative practices such as online management, mechanized farming and the development of a medicinal plant project. These strategies highlight the community’s resilience, fostering short-term survival and long-term cultural preservation. Using qualitative methods, including interviews and field observations, the findings reveal the effective application of contextual ambidexterity. This approach integrates collective decision-making, community trust and resource adaptation to sustain the Kalunga community’s socio-economic activities during the pandemic. The study underscores the potential of non-profit organizations to achieve ambidexterity, offering practical and social insights for preserving traditional cultures while adapting to external challenges. It provides a framework for future research on innovation and ambidexterity in marginalized communities.
This journal prepares three special issues on qualitative accounting (submissions closed), artificial intelligence (deadline 30-06-2025), and advances in management using partial least squares structural equations modelling (PLS/SEM)) (deadline 21-03-2025). You can find information about these issues on the website. Another special issue in preparation is about the best papers presented at the last CLADEA conference (2024), focusing on six tracks of the conference: innovation, value chains, organizational behavior, entrepreneurship, marketing and social responsibility. Wishing you have a stimulating reading of the articles of this issue.
