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In this paper, I explore what shapes the identities of digital nomads (DNs), a class of remote workers who travel and work concurrently. Through extensive fieldwork and interviews with 50 digital nomads conducted in seven coworking hostels in Mexico in 2022, I construct a theory of DN identity. I base this upon the frequent transformations they undergo in their Circumstances, which regularly change their worker identity.

DNs relinquish traditional social determinants of identity, such as nationality and religion. They define their personal identities by their passions and interests, which are influenced by the people they meet. DNs exist in inherently transitive social spaces and, without rigid social roles to fulfil, they represent themselves authentically. They form close relationships with other long-term travellers to combat loneliness and homesickness. Digital nomads define their worker identities around their location independence. This study shows that DNs value their nomadic lifestyle above promotions and financial gain. They define themselves by productivity and professionalism to ensure the sustainability of their lifestyle. Furthermore, digital nomad coworking hubs serve focused, individual work, leaving workplace politics and strict ‘office image’ norms behind. Without fixed social and professional roles to play, digital nomads define themselves personally according to their ever-evolving passions and the sustainability of their nomadic life. Based on these findings, I present a cyclical framework for DN identity evolution which demonstrates how relational, logistical, and socio-personal flux evolves DN’s worker identities.

This article details findings on how leaders of deeper learning schools establish, maintain, and propel unique teaching and learning environments. In this case study, the authors present findings from data collected through interviews with 30 leaders of self-proclaimed deeper learning initiatives and site visits to those elementary and secondary schools.

Using a case study approach, the authors collected data from interviews and observations of 30 school leaders.

The study's findings indicate how leaders of schools that engage in deeper learning tend to adhere to three core practices. First, the leaders of deeper learning schools in this study intently listened to the community to ascertain needs and desires; this drove the vision. Second, leaders of deeper learning schools created learning spaces that empowered students and gave them voice, agency, and choice. Third, leaders of deeper learning schools sought to humanize the schooling experience.

This study provides actionable examples of what leaders currently do to engage kids and teachers in deeper learning. These leaders offer insights into specific actions and practices that they espoused to make the schooling experience markedly different.

Previous studies focused on the deeper learning of schools and students. This is one of the first studies to focus on the inteplay between deeper learning and school leaders.

The stock market generates massive databases of various financial companies that are highly volatile and complex. To forecast daily stock values of these companies, investors frequently use technical analysis or fundamental analysis. Data mining techniques coupled with fundamental and technical analysis types have the potential to give satisfactory results for stock market prediction. In the current paper, an effort is made to investigate the accuracy of stock market predictions by using the combined approach of variables from technical and fundamental analysis for the creation of a data mining predictive model.

We chose 381 companies from the National Stock Exchange of India's CNX 500 index and conducted a two-stage data analysis. The first stage is identifying key fundamental variables and constructing a portfolio based on that study. Artificial neural network (ANN), support vector machines (SVM) and decision tree J48 were used to build the models. The second stage entails applying technical analysis to forecast price movements in the companies included in the portfolios. ANN and SVM techniques were used to create predictive models for all companies in the portfolios. We also estimated returns using trading decisions based on the model's output and then compared them to buy-and-hold returns and the return of the NIFTY 50 index, which served as a benchmark.

The results show that the returns of both the portfolios are higher than the benchmark buy-and-hold strategy return. It can be concluded that data mining techniques give better results, irrespective of the type of stock, and have the ability to make up for poor stocks. The comparison of returns of portfolios with the return of NIFTY as a benchmark also indicates that both the portfolios are generating higher returns as compared to the return generated by NIFTY.

As stock prices are influenced by both technical and fundamental indicators, the current paper explored the combined effect of technical analysis and fundamental analysis variables for Indian stock market prediction. Further, the results obtained by individual analysis have also been compared. The proposed method under study can also be utilized to determine whether to hold stocks for the long or short term using trend-based research.

The under-representation of women working in Science, Technology, Engineering and Mathematics (STEM) careers is a persistent problem worldwide. This dilemma is exacerbated by the fact that an insufficient number of women enroll in STEM studies, and a significant proportion of those who do join then opt out of their STEM careers at different points in their lives. The protean attitude emphasizes agentic individual control over one’s career, and thus offers women substantial potential for developing and enhancing career outcomes. Therefore, this study aims to investigate coping self-efficacy as an antecedent and career identity as a consequent of a protean attitude for women working in STEM.

Using a questionnaire survey, data were collected from 482 women working in STEM in the Middle East region. Multiple regression and bootstrapping methods were used in the analysis of the data.

The findings indicate that coping self-efficacy positively affects both protean attitude and career identity. The results also show that a protean attitude mediates the relationship between coping self-efficacy and career identity.

This research presents organizational management and government policy recommendations aimed at increasing the recruitment and retention of women in STEM careers.

The study addresses some of the main challenges related to identifying antecedents and outcomes of protean attitude.