Purpose: The purpose of this paper is to provide an overview of the literature regarding the academic online learning experience under the lens of broad personality traits, in the transition from traditional to online learning due to global coronavirus pandemic (COVID-19). Design/methodology/approach: The systematic literature review is based on preferred reporting items for systematic reviews and meta-analyses (PRISMA) method and includes indexed empirical studies in academic institutes during the period of COVID-19 outbreak. Findings: Electronic sources identified 103 references; while after the elimination of duplicates and irrelevant titles, 42 papers were forwarded for abstract screening and later full-text assessment. Of these, 14 met the eligibility criteria. Finally, nine studies were included in the literature review profiling and in the qualitative analysis. Originality/value: The research insights provided in this study are useful in terms of enhancing the view that link broad personality traits and various learning outcomes, during the necessitated transition to online learning by the public health emergency of the COVID-19 pandemic.

Online conversation communities have become an influential source of consumer recommendations in recent years. We propose a set of meaningful user segments which emerge from user embedding representations, based exclusively on comments’ text input. Data were collected from three popular recommendation communities in Reddit, covering the domains of book and movie suggestions. We utilized two neural language model methods to produce user embeddings, namely Doc2Vec and Sentence-BERT. Embedding interpretation issues were addressed by examining latent factors’ associations with behavioral, sentiment, and linguistic variables, acquired using the VADER, LIWC, and LFTK libraries in Python. User clusters were identified, having different levels of engagement and linguistic characteristics. The latent features of both approaches were strongly correlated with several user behavioral and linguistic indicators. Both approaches managed to capture significant variability in writing styles and quality, such as length, readability, use of function words, and complexity. However, the Doc2Vec features better described users by varying level of contribution, while S-BERT-based features were more closely adapted to users’ varying emotional engagement. Prominent segments revealed prolific users with formal, intuitive, emotionally distant, and highly analytical styles, as well as users who were less elaborate, less consistent, but more emotionally connected. The observed patterns were largely similar across communities.

In view of the ever-increasing social media use (SMU) from brands and customers

In a world increasingly defined by systemic turbulence, the traditional governance and development models are no longer suitable. Conventionally existing methods are becoming less responsive to these new and multifaceted challenges. The current chapter focuses on social entrepreneurship as a systemic solution to development of resilience and inclusion in such uncertain environments based on knowledge. The analysis will focus on three dynamics that are interconnected, namely inclusive participation as a source of distributed intelligence, reflective learning as a crisis-to-innovation transformation and digital transformation as a social impact amplifier. The chapter also questions under what circumstances social entrepreneurship is likely to succeed. Finally, the chapter claims that a more adaptive and inclusive system should be formed through social entrepreneurship. Through this, it gives a vision of generating value that puts an emphasis over learning, equity, and long-term well-being rather than narrow economic indicators.

Performance analysis in professional basketball requires sports analytics frameworks that integrate information across players, five-player lineups, and team systems while preserving the distinct behavioral and temporal characteristics of each level. Using seven seasons of EuroLeague play-by-play data, this study develops a multilevel sports analytics framework for retrospectively diagnosing performance patterns and supporting basketball decision making. The framework constructs level-specific representations of player, lineup, and team behavior, aligns them for structural analysis, and quantifies their shared and level-specific information. Momentum is operationalized retrospectively through an empirically calibrated criterion applied to the score-difference trajectory generated using Piecewise Cubic Hermite Interpolating Polynomial (PCHIP), providing a common temporal reference across analytical levels. Clustering identifies interpretable performance archetypes that enable context-adjusted comparisons of behavioral signatures across phases of play. The results reveal limited differentiation before momentum segments, modest expression during their early stages, stronger differentiation later in the segments, sustained elevation beyond the defining interval, and subsequent attenuation. Joint and Individual Variation Explained (JIVE) analysis further identifies substantial shared and level-specific structure across players, lineups, and teams. Recurring lineups retain configuration-specific information after controlling for player, team, game, phase, and contextual effects, with an alternative reconstruction approach providing convergent evidence. Team-season prevalence patterns and historical performance profiles demonstrate additional applications of the linked representations. The study contributes to sports analytics by integrating multiple levels of basketball performance into a unified diagnostic framework that preserves level-specific information while identifying shared behavioral structure. The resulting analytics support player evaluation, lineup assessment, team-level performance diagnosis, historical benchmarking, and communication of data-driven playing identities, with additional relevance for club branding and fan engagement.