Does learning ambidexterity affect the sense of urban integration among new-generation migrant workers in China? An empirical study based on career growth and environmental dynamism

China’s reform and opening-up spurred rapid non-agricultural development and large-scale rural-to-urban migration. New-generation migrant workers (born 1980 or later) constitute the largest segment of migrants and often aspire to settle and integrate into cities despite facing cultural, economic, and psychological barriers and risks of urban exclusion. Urban integration—capturing social identity, psychological integration, and participation in urban life—is a key benchmark of their citizenization. Traditional views emphasize learning vocational skills as a prerequisite for integration, but increasing environmental dynamism (industrial upgrading, fluctuating jobs) complicates stable employment and integration pathways. Ambidexterity theory, extended from organizations to individuals, suggests people must both exploit existing knowledge and explore new knowledge to adapt. This study investigates how learning ambidexterity (exploitative and exploratory learning) influences the sense of urban integration among new-generation migrant workers, examining career growth as a mediating mechanism and environmental dynamism as a moderator. The study addresses two questions: (1) Is it more important to strengthen exploitative learning or to develop exploratory learning? (2) How does environmental dynamism shape the pathway from learning ambidexterity to sense of urban integration?

Urban integration is a specific form of social integration relevant to rural-to-urban migrants in China, encompassing economic, cultural, regional, and social dimensions. Prior research links human capital to migrants’ willingness and ability to settle, noting many new-generation migrant workers possess limited formal training and concentrate in low-skill jobs. Scholars argue that skill acquisition, continuous upgrading, language and information skills training can facilitate urban development and retention. However, gaps remain: prior work focused on static outcomes (employment and stability) rather than dynamic goals (career growth and psychological integration), and underappreciated environmental dynamism. Ambidexterity theory distinguishes exploitative learning (refining existing skills) and exploratory learning (acquiring new knowledge) and has been applied to individuals, including migrant workers’ survival and entrepreneurship. Yet its application to sense of urban integration under dynamic environments is limited. The study posits that both learning modes may enhance integration and career growth, that career growth may mediate learning’s effects on integration, and that environmental dynamism may differentially moderate relationships by learning mode.

Design: Cross-sectional survey with stratified random sampling of enterprises in Jiangsu and Zhejiang provinces (Yangtze River Delta). Cities sampled: Wuxi and Yancheng (Jiangsu); Hangzhou and Wenzhou (Zhejiang). Participants: New-generation migrant workers (born ≥1980), recruited via company unions/HR/workshop management. Distributed 385 questionnaires (Sept–Oct 2020); 381 returned; 365 valid (Wuxi 116, Yancheng 90, Hangzhou 91, Wenzhou 68). Measures: 5-point Likert scales (1=strongly disagree to 5=strongly agree). - Learning ambidexterity (6 items) with exploitative (3) and exploratory (3) dimensions adapted from Zhu (2008) and Zheng (2018). - Sense of urban integration (15 items) from Luo & Lu (2013), covering cultural, regional, economic, and social integration; composite score computed with equal weights (0.25) for each dimension. - Career growth (15 items) from Weng et al. (2010). - Environmental dynamism (6 items) from Li & Liu (2012). Controls: Gender, age, education level. Procedures: Pre-test (clarity/readability) and pilot test (implementation issues) preceded formal survey. Informed consent obtained; ethics approved by Wuxi Institute of Technology. Analysis: SPSS 20.0. Steps: descriptive statistics and correlations; reliability (Cronbach’s alpha, CR); validity (KMO=0.915; Bartlett p<0.001; AVE discriminant validity); common method bias checks; hierarchical regression for hypothesis testing including mediation (career growth) and moderation (environmental dynamism) analyses. Reliability/validity: Cronbach’s alpha and CR >0.8 across constructs; strong construct and discriminant validity indicated.

Sample: N=365; gender 65.5% male; broad age distribution; majority monthly income 3000–6000 yuan; education mainly middle/high school; 6–15 years work experience common. Correlations: Learning ambidexterity, career growth, environmental dynamism, and sense of urban integration were significantly correlated. Regression results (selected coefficients): - H1 (Learning ambidexterity → Sense of urban integration): Supported. Exploitative learning β=0.226, p<0.01; Exploratory learning β=0.293, p<0.01 (Model 2). - H2 (Learning ambidexterity → Career growth): Supported. Exploitative learning β=0.216, p<0.01; Exploratory learning β=0.313, p<0.001 (Model 3). - H3 (Career growth → Sense of urban integration): Supported. β=0.475, p<0.001 (Model 4). - H4 (Mediation by career growth between learning ambidexterity and sense of urban integration): Partial mediation supported. With mediator, effects of exploitative (β=0.161, p<0.1) and exploratory (β=0.199, p<0.01) on integration decreased; career growth remained significant (β=0.300, p<0.001) (Model 5). - H5 (Moderation of environmental dynamism on Learning ambidexterity → Career growth): Partly supported. Exploratory×Environmental dynamism β=0.202, p<0.01 (H5b supported). Exploitative×Environmental dynamism β=−0.029, ns (H5a not supported) (Model 7). Environmental dynamism main effect on career growth: β=0.422, p<0.001 (Model 6). - H6 (Moderation of environmental dynamism on Learning ambidexterity → Sense of urban integration): Partly supported. Exploitative×Environmental dynamism β=0.166, p<0.1 (H6a supported). Exploratory×Environmental dynamism β=−0.038, ns (H6b not supported) (Model 9). Environmental dynamism main effect on sense of urban integration: β=0.238, p<0.001 (Model 8). Model fit (selected): R^2 ranged ~0.26–0.44 across models; e.g., Model 2 R^2=0.308; Model 6 R^2=0.409; Model 7 R^2=0.438; Model 8 R^2=0.354; Model 9 R^2=0.371. Overall: Both exploitative and exploratory learning positively affect career growth and sense of urban integration; exploratory learning shows stronger effects. Career growth partially mediates the learning–integration link. Environmental dynamism strengthens exploratory learning’s effect on career growth and strengthens exploitative learning’s effect on sense of urban integration.

Findings indicate that new-generation migrant workers benefit from ambidextrous learning: refining existing skills (exploitative) and acquiring new, cross-domain skills (exploratory) both enhance career development and psychological integration into urban life, with exploratory learning exerting relatively stronger effects. Career growth functions as a key mechanism translating learning inputs into integration outcomes, aligning with a learning–growth–integration pathway. Environmental dynamism operates through differentiated channels: in dynamic contexts, exploratory learning more effectively drives career growth (broadening opportunities and adaptability), while exploitative learning more strongly contributes to sense of urban integration (deepening domain-specific competence and stability that supports psychological and social embedding). These patterns clarify how learning strategies should be balanced under dynamic urban labor markets and underscore the importance of fostering both depth and breadth in skills to advance integration goals.

This study extends ambidexterity theory to the urban integration of new-generation migrant workers, demonstrating that both exploitative and exploratory learning enhance career growth and the sense of urban integration, with career growth partially mediating these relationships. Environmental dynamism differentially moderates these pathways: it amplifies exploratory learning’s contribution to career growth and exploitative learning’s contribution to urban integration. Contributions include: (1) reframing migrant integration through a learning ambidexterity lens; (2) identifying career growth as a critical transformation mechanism from learning to integration; and (3) embedding dynamic environmental factors into the analytical framework. Policy and practice implications suggest promoting a “one expertise, multiple skills” lifelong development model and supporting environments that facilitate both learning modes. Future research should test generalizability beyond the Yangtze River Delta, employ longitudinal and objective indicators to track learning-to-integration trajectories over time, and investigate how emerging technologies (e.g., AI-driven industrial change) reshape learning strategies, career growth, and integration outcomes.

Generalizability may be limited given the regional focus on the Yangtze River Delta. The study relies on subjective, cross-sectional survey data with a modest sample size (N=365), limiting causal inference and susceptibility to common method bias despite tests. Future work should expand to diverse regions, use larger samples, and incorporate longitudinal and objective measures to better capture dynamic processes and reduce self-report bias.