Decoding employer value proposition preferences of digitally native job seekers: a career-oriented multi-method analytical framework

Purpose Businesses worldwide are undergoing rapid digital transformation to optimize operations and enhance talent competitiveness. Such transformations have reshaped how employees and job seekers perceive employer value in an era of technological fluidity. This study refines and contextualizes career theory to examine how digitally native job seekers evaluate employer value proposition attributes, highlighting asymmetric, developmental, and context-dependent patterns of employer attractiveness. Design/methodology/approach Survey data from digitally native job seekers were analyzed through a multi-method empirical framework. The Kano model was used to classify job attributes. Importance-performance analysis is applied to identify performance gaps, Talent value scoring is employed to attribute importance, and Latent profile analysis is deployed to segment respondents into distinct profiles. Findings The results show that salary, job security, and work–life balance function as baseline expectations, while career advancement, continuous learning, and digital infrastructure act as differentiators. Three distinct job seeker profiles emerged as security-oriented, balanced careerists and growth-oriented, each with distinct priorities in career and organizational attributes. Practical implications Employers should tailor their talent value propositions and adopt data-driven diagnostics to align EVP priorities with the diverse career orientations of digitally native talent, leveraging digital enablement and inclusive career development to enhance attractiveness and engagement. Originality/value This study contributes to employer-branding and career theory by contextually refining protean, boundaryless, and career construction perspectives through methodological integration. The integrated framework offers a replicable analytical model that bridges theoretical insights and organizational applications in emerging digital economies.