Ayse Ilgun-Kamanli

This study investigates the linguistic, structural, and behavioral characteristics of "brain rot" content on TikTok, an emerging phenomenon defined by cognitively shallow, hyper-stimulating videos optimized for maximum passive engagement. The study aims to operationalize and detect "brain rot" content and to examine how such content relates to user engagement and interaction quality. Drawing on a large-scale dataset of 78,452 TikTok videos, we employed a mixed-methods design combining Natural Language Processing, regression modeling, and qualitative coding. This approach enabled the identification of key textual, structural, and behavioral markers associated with low-cognitive-value media content. The findings show that videos under 15 seconds, clickbait language, high transition frequency, and emotionally triggering content are significantly associated with higher view counts but lower comment depth. The results also indicate that verified and unverified creators contribute to the diffusion of "brain rot" content across different topic areas, suggesting that platform architecture shapes both content visibility and content quality. This study contributes a novel operational framework for identifying "brain rot" content and advances theoretical understanding of attention economics and algorithmic cultivation in short-form video environments. The findings highlight the broader implications of digital content strategies for user cognition, media literacy, and platform governance.

This study examines how marketing professionals publicly articulate total rewards perceptions through LinkedIn discourse, extending natural language processing methodologies via domain-specific model development. Analyzing 35,691 comments from 2023, we identify significant shifts toward digital skill-based compensation discourse (+ 31.2%) and strong positive sentiment associations with flexible work arrangements (r = .48, p < .001), particularly among technology sector professionals. Hierarchical regression analysis confirms that digital skill discourse orientation and industry sector interact to predict sentiment variation (ΔR² = 0.04, p < .001), with digital competency emphasis demonstrating differential effects across technology, retail, and consumer goods contexts. Drawing on Skill-Based Total Rewards Theory, we provide preliminary empirical support for three propositions: Digital Skill-Reward Congruence, Market Value of Skills, and Skills-Based Flexibility principles. The sentiment analysis model achieved an F1-score of 0.89, while industry-specific analysis revealed significant variations (F(2,35688) = 27.32, p < .001) across sectors. Recognizing that LinkedIn discourse reflects publicly constructed professional identity positions rather than unfiltered latent attitudes, we interpret these patterns as expressions of signaled reward preferences within professional networking contexts. Methodologically, the study demonstrates NLP applications for large-scale professional sentiment analysis. Practically, findings inform industry-specific compensation strategies recognizing digital competencies and workplace flexibility as critical retention factors. This research contributes to compensation theory through quantitative operationalization of SBTRT propositions while establishing methodological frameworks for discourse-based organizational research.

The research investigates technical talent market segmentation through a comparison between standard recruitment methods from public job listings and the premium senior engineering recruitment model of Talent by Blind. The research uses organisational ambidexterity theory to explain how businesses achieve efficient mainstream hiring (exploitation) and innovative recruitment approaches (exploration) for their senior engineering positions. The research analyses 7,927 LinkedIn job postings to identify the main specialisations and seniority levels and work arrangements found in the accessible technical workforce. The research shows that most job postings on public job boards focus on hiring mid-level professionals working in Indian technology centres, yet director and executive positions make up only 1% of available positions. The premium senior engineering segment uses private recruitment networks and candidate acquisition methods instead of traditional job posting strategies. The research demonstrates that organisations use ambidextrous recruitment approaches to handle talent shortages and technological changes and changing workforce needs through their different recruitment systems for high-end and standard positions. The research combines market segmentation methods with organisational ambidexterity theory to explain how organisations use different recruitment approaches for modern technology workers.

As societies confront accelerating sustainability challenges, understanding the individual-level orientations that support collective action has become increasingly important. This study examines the association between educational attainment and three theoretically distinct sustainability-relevant value orientations using cross-national survey data. Drawing on the World Values Survey Wave 7, we analyze responses from 65,608 individuals across 65 countries using weighted least squares regression with country fixed effects to investigate how education relates to norm orientation, future orientation, and inclusion. The analysis reveals substantial variation in the strength of these associations across value dimensions. Education demonstrates a particularly strong relationship with future orientation, yielding a standardized effect size of 0.497, while showing considerably weaker associations with inclusion and norm orientation. Moderation analyses uncover important demographic contingencies, indicating that education gradients for norm orientation and inclusion weaken significantly with age, whereas the education-future orientation relationship remains stable across age groups. A modest gender difference emerges for future orientation, with slightly attenuated education effects among women. These findings suggest that education contributes to sustainability-relevant values primarily through cognitive pathways that enhance temporal perspective rather than through socialization into normative compliance or expansion of social tolerance. The results carry implications for education policy design and sustainable development initiatives.

This study examines the relationship between national open educational resource (OER) policies and Sustainable Development Goal 4 (SDG4) outcomes across 187 countries between 2015 and 2024, with particular attention to the moderating role of artificial intelligence (AI) readiness. Despite widespread optimism about digital technologies as catalysts for universal education, systematic evidence linking formal OER policy frameworks to measurable improvements in educational access and completion remains limited. The analysis employs fixed effects and difference-in-differences estimation strategies using an unbalanced panel dataset comprising 435 country-year observations. The research investigates how OER policies associate with primary completion rates and out-of-school rates while testing whether these relationships depend on countries’ technological and institutional capacity for advanced technology deployment. The findings reveal that AI readiness demonstrates consistent positive associations with educational outcomes, with a ten-point increase in the readiness index corresponding to approximately 0.46 percentage point improvements in primary completion rates and 0.31 percentage point reductions in out-of-school rates across fixed effects specifications. The difference-in-differences analysis indicates that OER-adopting countries experienced completion rate increases averaging 0.52 percentage points relative to non-adopting countries in the post-2020 period, though this estimate remains statistically imprecise (p equals 0.440), preventing definitive causal conclusions. Interaction effects between policies and readiness yield consistently positive coefficients across specifications, but these associations similarly fail to achieve conventional significance thresholds given sample size constraints and limited within-country variation. While the directional patterns align with theoretical expectations that policy effectiveness depends on digital capacity, the evidence should be characterized as suggestive rather than conclusive. These findings represent preliminary assessment of policies in early implementation stages. Most frameworks were adopted between 2019 and 2022, providing observation windows of two to five years before data collection ended in 2024. This timeline proves insufficient for educational system transformations to fully materialize in aggregate indicators, as primary education cycles span six to eight years and implementation processes operate gradually through sequential stages of content development, teacher training, and institutional adaptation. The analysis captures policy impacts during formation rather than at equilibrium, establishing baseline patterns that require extended longitudinal observation for definitive evaluation. High-income countries demonstrate interaction coefficients between policies and readiness that approach marginal statistical significance (p less than 0.10), while low-income subsamples show coefficients near zero with wide confidence intervals. These patterns suggest that OER frameworks function as complementary interventions whose effectiveness depends critically on enabling infrastructure including digital connectivity, governance quality, technical workforce capacity, and innovation ecosystems. The results carry important implications for how countries sequence educational technology reforms and how international development organizations design technical assistance programs. The evidence cautions against uniform policy recommendations across diverse contexts, indicating that countries at different stages of digital development require fundamentally different strategies that coordinate policy adoption with foundational capacity building. However, the modest short-term effects and statistical imprecision observed here should not be interpreted as evidence of policy ineffectiveness, but rather as confirmation that immediate transformation is unlikely given implementation complexities and temporal constraints. The study contributes systematic cross-national evidence on aggregate policy associations while highlighting the conditional nature of educational technology effectiveness and establishing the need for continued longitudinal research as policies mature beyond the early implementation phase captured in this analysis.

This study examines how personality traits shape leadership communication on social media outlets by comparing the Twitter/X/X discourse of Donald Trump and Elon Musk. Employing a mixed-methods content analysis of 2,391 original tweets collected between December 2021 to December 2023, natural language processing techniques are integrated—lexical diversity measures, part-of-speech distributions, sentiment analysis, topic modeling—and both closed- and open-vocabulary approaches for Big Five trait inference. Also conduct time-series and crisis-specific analyses to capture temporal evolution and response strategies. Analysis of normalized engagement metrics reveals that Trump achieves superior per-follower interaction rates, generating 1,101 retweets and 4,119 likes per million followers compared to Musk's 494 retweets and 2,201 likes per million followers, indicating distinct audience engagement strategies optimized for different communication objectives. By bridging trait-based leadership theories with computational linguistics, this research demonstrates that social media platforms facilitate diverse, personality-congruent leadership expressions rather than enforcing uniformity. Practically, findings suggest that effective digital leadership requires aligning social media strategies with authentic identity and contextual objectives. Crisis communication should extend established patterns rather than relying on generic templates.