In the era of data science and big data analytics, people analytics help organizations and their human resources (HR) managers to reduce attrition by changing the way of attracting and retaining talent. In this context, employee attrition presents a critical problem and a big risk for organizations as it affects not only their productivity but also their planning continuity. In this context, the salient contributions of this research are as follows. Firstly, we propose a people analytics approach to predict employee attrition that shifts from a big data to a deep data context by focusing on data quality instead of its quantity. In fact, this deep data-driven approach is based on a mixed method to construct a relevant employee attrition model in order to identify key employee features influencing his/her attrition. In this method, we started thinking `big' by collecting most of the common features from the literature (an exploratory research) then we tried thinking `deep' by filtering and selecting the most important features using survey and feature selection algorithms (a quantitative method). Secondly, this attrition prediction approach is based on machine, deep and ensemble learning models and is experimented on a large-sized and a medium-sized simulated human resources datasets and then a real small-sized dataset from a total of 450 responses. Our approach achieves higher accuracy (0.96, 0.98 and 0.99 respectively) for the three datasets when compared previous solutions. Finally, while rewards and payments are generally considered as the most important keys to retention, our findings indicate that `business travel', which is less common in the literature, is the leading motivator for employees and must be considered within HR policies to retention.

The Internet and its increasing usage has changed informal learning in depth. This change has affected young and older adults in both the workplace and in higher education. But, in spite of this, formal and non-formal course-based approaches have not taken full advantage of these new informal learning scenarios and technologies. The Web 2.0 is a new way for people to communicate across the Internet. Communication is a means of transformation and knowledge exchange. These are the facts that cannot be obviated by the organisations in their training programmes and knowledge management. This special issue is devoted to investigating how informal learning changes or influences online information in Social Web and training strategies in institutions. In order to do so, five papers will present different approaches of informal learning in the workplace regarding Web 2.0 capabilities.

An interactive personality assessment is conducted on an industrial scale.A novel approach is proposed for data visualization.We explore the personality characteristics of software teams.This study indicates that software practitioners become more extroverted. Context: Research has shown that a significant number of software projects fail due to social issues such as team or personality conflicts. However, only a limited number of empirical studies have been undertaken to understand the impact of individuals personalities on software team configurations. These studies suffer from an important limitation as they lack a systematic and rigorous method to relate personality traits of software practitioners and software team structures.Objective: Based on an interactive personality profiling approach, the goal of this study is to reveal the personality traits of software practitioners with an aim to explore effective software team structures.Method: To explore the importance of individuals personalities on software teams, we employed a two-step empirical approach. Firstly, to assess the personality traits of software practitioners, we developed a context-specific survey instrument, which was conducted on 216 participants from a middle-sized software company. Secondly, we propose a novel team personality illustration method to visualize team structures.Results: Study results indicated that effective team structures support teams with higher emotional stability, agreeableness, extroversion, and conscientiousness personality traits.Conclusion: Furthermore, empirical results of the current study show that extroversion trait was more predominant than previously suggested in the literature, which was especially more observable among agile software development teams.