Building competent IT freshers: modelling digital era training frameworks

Purpose The accelerating digital transformation and artificial intelligence (AI) integration demand systematic competency-based training (CBT) for Information Technology (IT) freshers. This study aims to identify critical enablers (CEs) of effective CBT programs and examines their hierarchical relationships to inform strategic training design. Design/methodology/approach A mixed qualitative–interpretive methodology uses Interpretive Structural Modeling (ISM) to construct a hierarchical enabler framework. MICMAC analysis categorizes enablers by driving and dependence power. Expert validation ensures contextual relevance and methodological rigor. Findings A total of 15 CEs are identified, with Digital and AI Literacy, Strategic Digital Vision and Innovation Culture as foundational ISM drivers. These influence outcome-oriented factors, including soft skills development, hands-on training and performance culture. MICMAC reveals a multi-layered ecosystem emphasizing strategic sequencing for optimal training effectiveness. Research limitations/implications Expert-opinion dependency may introduce bias, and applicability varies across contexts. Future research should empirically validate the framework across diverse sectors and geographical regions. Practical implications The framework guides academic institutions, corporate training departments and policymakers in designing CBT programs prioritizing digital readiness. The hierarchical structure emphasizes foundational enabler implementation before advancing to performance outcomes. Originality/value This study pioneers ISM-MICMAC integration for analyzing IT fresher competency enablers in digital transformation contexts. It provides a structured, evidence-based framework for designing future-ready training programs addressing AI-driven workplace demands.