STEEPLEM Strategic Foresight for Industrial Metaverse Technologies in Maintenance Support and Technician Skill Development

Abstract

An industrial metaverse that fuses artificial intelligence, robotics, digital twins and extended reality could change how maintenance is performed and how technicians are trained, but its development depends on interacting social, technological, economic, environmental, political, legal, ethical and media forces. This conceptual paper applies a STEEPLEM foresight analysis to the use of industrial metaverse technologies for maintenance support and skill development. The force scores are the author's structured judgments, informed by the literature, and are reported transparently so that readers can substitute their own. On these scores, technology adoption and the intensity of regulation emerge as critical uncertainties, a selection supported in 98.5 percent of 10,000 perturbed draws when correlated forces are grouped into adoption and regulation clusters. Four scenarios are constructed on these axes, and six emergent patterns and nine future logics are traced. A minimax regret analysis of six strategic moves, repeated under four value scales, ranks tacit knowledge capture as the only move without regret in any scenario within the assigned payoff matrix, while proprietary platform lock-in carries the highest regret. Because the payoffs are ordinal judgments, these rankings indicate robustness under stated assumptions rather than universal superiority. The paper offers a transparent, reproducible approach to strategy under deep uncertainty and five propositions for testing how industrial technologies are adopted