The current business environment characterized by volatility, uncertainty, complexity, and ambiguity (VUCA) advances numerous challenges for organizations. To respond effectively to these changing demands, traditional approaches to solving problems often prove inadequate in this dynamic context. A new approach, the ProCESS methodology, was developed and tested in the last three years within an Erasmus+ consortium in four European countries. This approach stimulates unconventional thinking and the finding of creative solutions for real-world business challenges. The aim of this perspective paper is to present the research data collected in two Romanian companies by testing ProCESS methodology. In the discussion section, the paper highlights the potential of this methodology that uses various artistic tools like drawing, music, modeling, or meditation to encourage participants to tap into their sensory, emotional, and spiritual sides for finding new and unexpected solutions. The paper also discusses potential influences on organizational culture and employee well-being.

This study aims to investigate the phenomenon of non-disclosure of personal information among male individuals, employing the Communication Privacy Management Theory as a guiding framework. The objectives of the study encompass identifying the specific types of personal information male students refrain from disclosing, examining the underlying reasons for their non-disclosure practices, and assessing the impact of non-disclosure on their interpersonal relationships. Qualitative research methods, primarily in-depth interviews, were employed to gather insights, with six male students from Sultan Idris Education University (UPSI) participating in the interviews. The findings reveal that male students at UPSI do engage in non-disclosure of personal information, albeit to a certain extent. Specifically, the findings discovered four types of personal information—secrets, traumas, dark history, and family matters—that these students commonly choose not to disclose. Notably, there are four categories of personal information they tend to withhold, namely secrets, traumas, dark history, and family matters. The reluctance to disclose stems from factors such as insecure attachment, a reluctance to worry about their parents, and strained relationships with their family members. Furthermore, the study highlights that non-disclosure of personal information has both negative and positive repercussions on the participants’ relationships with others. Moreover, the study underscores that non-disclosure of personal information can have both negative and positive effects on the participants’ relationships, shedding light on the complexities of navigating personal privacy choices in the university and job-seeking context. The study contributes valuable insights into the challenges of employability dilemmas faced by male university students concerning the management of personal information.

The telecommunications services market faces essential challenges in an increasingly flexible and customer-adaptable environment. Research has highlighted that the monopolization of the spectrum by one operator reduces competition and negatively impacts users and the general dynamics of the sector. This article aims to present a proposal to predict the number of users, the level of traffic, and the operators’ income in the telecommunications market using artificial intelligence. Deep Learning (DL) is implemented through a Long-Short Term Memory (LSTM) as a prediction technique. The database used corresponds to the users, revenues, and traffic of 15 network operators obtained from the Communications Regulation Commission of the Republic of Colombia. The ability of LSTMs to handle temporal sequences, long-term dependencies, adaptability to changes, and complex data management makes them an excellent strategy for predicting and forecasting the telecom market. Various works involve LSTM and telecommunications. However, many questions remain in prediction. Various strategies can be proposed, and continued research should focus on providing cognitive engines to address further challenges. MATLAB is used for the design and subsequent implementation. The low Root Mean Squared Error (RMSE) values and the acceptable levels of Mean Absolute Percentage Error (MAPE), especially in an environment characterized by high variability in the number of users, support the conclusion that the implemented model exhibits excellent performance in terms of precision in the prediction process in both open-loop and closed-loop.