Due to the dynamic nature of modern life, enhancing adaptive problem-solving (APS) skills has become essential. APS involves using cognitive, metacognitive and non-cognitive strategies to define problems, gather relevant information, and apply solutions effectively by engaging digital, social, and physical tools in evolving contexts (Greiff et al., 2021). Since one of the most crucial skills for successful integration into society is APS, researchers are interested in the factors predicting APS.

Previous research suggests that social and emotional skills (SES) — whose sub-dimensions include agreeableness, conscientiousness, emotional stability, extraversion, and openness to experience, corresponding to the Big Five framework (Kankaraš & Suarez-Alvarez, OECD, 2016; 2019) — are related to individuals’ adaptive problem-solving skills (OECD, 2021; OECD, 2024; Sullivan et al., 2010). For example, according to Greiff’s framework (2017), key elements for APS such as adaptability to uncertainty, intellectual curiosity, and stress management establish links with Social-Emotional Skills sub-dimensions like openness to experience, emotional stability, and conscientiousness.

In addition to individual-level factors, country-level indicators such as Human Development Index (HDI), Government Expenditure on Education (GEE), and ICT Development Index (IDI) are important because they reflect structural conditions that support opportunities for developing APS. Investment in education, which is a key component of HDI and GEE, enhances cognitive skills and makes society more resourceful (problem solver) (Nunes 2003; Singh et al., 2025). Additionally, IDI, which measures digital technology (ICT) through its components access, use, and skills, represents an important structural part of modern society, holds a new perspective on the measurement of information development and enhances adaptive problem solving (International Telecommunication Union, 2011; Dobrota et al., 2012). High performance across these three indicators suggests the presence of the educational and technological infrastructure that enable individuals to develop strategies and adapt to complex problems.

Furthermore, the factors related to relationships between SES and APS might differ across various age-cohorts groups and geographical regions.  For the age-cohort groups, individuals at different stages of the life course do not solve problems same way (Feraco & Meneghetti, 2023). Problem-solving strategies and skills evolve as individuals accumulate life experience and cognitive maturity. With regard to geographical regions, individuals living in different geographical regions may approach problems and solution strategies differently due to differences in levels of development, income resources, educational attainment, and access to technology, all of which shape the contexts in which problem-solving takes place (World Bank, 2024). In particular, Europe may show distinct patterns compared to other regions

However, there is no study in the literature investigating whether the relationship between APS and related factors (individual- and country-level) changes across age-cohorts groups and geographical regions. To address this gap, the current study aims to examine the extent to which individual-level (Social and Emotional Skills — SES) and country-level indicators (HDI, GEE, and IDI) predict APS, and whether these relationships vary across different age-cohorts groups and geographical regions in the second cycle of the Programme for the International Assessment of Adult Competencies (PIAAC). To address this aim, the present study formulates the following research question: 

• Research Question: To what extent do individual-level (Social and Emotional Skills — SES) and country-level indicators (HDI, GEE, IDI) predict adaptive problem-solving (APS), and how do these relationships vary across different age-cohorts groups (16-24, 25-34, 35-44, 45-54, and 55–65) and various geographical regions (Northern Europe, Western Europe, Southern Europe, Eastern Europe, Americas, Asia, and Oceania) in the second cycle of the Programme for the International Assessment of Adult Competencies (PIAAC)?

Participants The study uses data from 2nd cycle of the PIAAC. Dataset includes 143564 participants aged 16 to 65, grouped into five age-cohorts groups (16-24, 25-34, 35-44, 45-54, and 55–65) based on OECD. The majority of participants belong to the 55–65 age-cohort group (23.9%), followed by 45–54 (21.2%), and 35–44 (21.1%). The 25–34 age-cohort group accounts for 19.0% of sample, whereas 16–24 age-cohort group represents smallest proportion of sample (14.9%). Also, the participants are from 25 countries and seven geographical regions based on The Geoscheme of the United Nations. The majority of respondents are from Europe (75.6%). Within Europe, Western Europe constitutes largest population (26.1%), followed by Northern Europe (21.2%), Eastern Europe (18.0%), and Southern Europe (10.3%). Remaining participants are from non-European regions, with respondents from Americas (12.2%), Asia (8.2%), and Oceania (4.0%). Instruments The study uses two main instruments of the PIAAC 2nd cycle dataset. In PIAAC Cycle 2, SES were measured through Background Questionnaire (BQ). The most widely used and best-validated model of personality traits is called ‘Big Five’ model, in which a range of traits is subsumed under five higher order dimensions: Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Emotional Stability. For BQ, OECD working group on SES suggested implementing an established and internationally validated 30-item short version of Big Five Inventory (BFI-2-S) developed by Soto and John (2017) to measure SES. APS was measured using the Cognitive Module of Survey of Adult Skills and assessed as a new domain in the direct assessment component of PIAAC Cycle 2. Based on the framework by Greiff et al. (2017), unlike traditional tests, this module uses a computer-based and scenario-based approach where participants interact with a digital environment to solve non-routine problems. Instrument is designed to evaluate how well individuals adapt their strategies and goals when faced with unexpected changes or new information. Data Analysis In line with research question, using multilevel regression, analysis will proceed in two steps. First, main analysis will examine extent to which individual-level indicators (SES) and country-level indicators (HDI, GEE, and IDI) predict APS. Second, analysis will examine whether these relationships differ across age-cohort groups and geographical regions, focusing on differences in predictors of APS across these groups. Sample weights and plausible values will be handled using Mplus. Sample weights adjust the data to be population-representative, and plausible values provide reliable estimates of proficiency through multiple imputed scores.

It is expected that both individual- and country-level indicators will be associated with adaptive problem-solving skills in the second cycle of PIAAC. At the individual level, social and emotional skill dimensions—particularly conscientiousness, openness to experience, and emotional stability—are expected to show positive relationships with APS. At the country level, indicators such as Human Development Index (HDI), Government Expenditure on Education (GEE), and ICT Development Index (IDI) are expected to be related to APS, as they reflect national educational and technological conditions. Moreover, these relationships are expected to vary across age-cohort groups. For age-cohort groups It is expected that the relationships between social and emotional skills and adaptive problem-solving will vary across age groups, three working-age populations (16-24, 25-34, 35-44, 45-54, and 55–65), based on OECD data. Specifically, SES sub-dimensions such as conscientiousness, openness to experience, and emotional stability may be differently related to APS across age groups, as individuals may have these social-emotional characteristics to different extents at different ages when solving complex problems. For geographical regions It is expected that strength and nature of the relationship between SES and APS will vary across geographical regions due to differences in socio-economic development, educational systems, and technological contexts. In European regions, especially Northern Europe, where educational structures, labor markets, and digital infrastructures tend to be more standardized and supportive of autonomous learning and problem-solving, SES may show a stronger association with APS. These contexts may provide individuals with more opportunities to translate social-emotional resources into effective adaptive strategies when facing complex and novel problems. In contrast, other regions, where educational and occupational systems may place relatively greater emphasis on structured instruction, cognitive performance, and conformity to established norms, the contribution of SES to APS may operate differently, with adaptive problem-solving being more strongly driven by cognitive skills or contextual constraints.

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