Future-Ready Leadership in VUCA/BANI Conditions: An Integrative Framework and Behavioral Measure

Abstract

This dissertation develops and validates Future-Ready Leadership (FRL), a behaviorally grounded leadership framework designed for sustained disruption (VUCA/BANI)․ It addresses two gaps: the fragmentation of leadership theory and the lack of validated, behavior-specific tools that can inform leadership development and selection decisions․
FRL integrates four complementary domains: Trust-Driven Leadership, Continuous Personal Learning, Strategic Navigation, and Expansion by Value Creation․ FRL is measured with a 5-point Behaviorally Anchored Rating Scale (BARS)․ The validation process employs a staged design combined expert content review and large-sample survey testing with managers․ Analyses examined ordinal item functioning, internal structure using complementary structural methods, reliability and generalizability of scores, measurement invariance and differential item functioning across sex groups, external validity with related leadership and psychological constructs, and leader profiling via k-means clustering․
Results supported a correlated four-domain framework and did not support a substantively interpretable single general leadership factor․ The scale showed adequate composite-level precision for comparative and developmental interpretation, while domain-level dependability varied․ Fairness evidence was acceptable, with limited and small-magnitude differential item functioning․ External validity was modest yet broadly theory-consistent․ Profiling yielded five interpretable leader configurations that differed meaningfully in their domain patterns․
In summary, FRL is regarded as a robust and pragmatic tool for leadership profiling, development, coaching, and organizational capability diagnostics․ This dissertation establishes the foundational measurement of FRL with 52 items․ Future research will extend item coverage to facilitate domain-level application in high-stakes decision-making․ Future research priorities include scale expansion, replication, multi-source ratings, and predictive validation․