This study investigates how core digital technologies—artificial intelligence (AI) and blockchain—can foster green innovation and sustainable consumption through circular platform design in high-value resale markets. Using Design Science Research (DSR) methodology, including its iterative cycles, we developed and evaluated TRUCE (Trust, Resale Logic, User Centricity, Circular Infrastructure, Ecosystem Governance), a sustainability-oriented digital architecture designed to promote ethical, energy-efficient consumption. TRUCE aims to leverage AI-driven authentication, blockchain-based transparency, and consumer data analytics, aiming to embed circularity and traceability into platform governance. Aligned with the EU Green Deal’s digital agenda, it is intended to support waste reduction, lifecycle extension, and responsible consumption, contributing to Sustainable Development Goal 12 and the broader 2030 Agenda.

This study develops the Information Systems–Organizational Fit Alignment Model (ISOFAM) to evaluate information systems (IS) alignment during the pre-combination phase of mergers and acquisitions (M&A)—a critical yet underexplored stage in integration planning. Through constructivist grounded theory and the reanalysis of qualitative data from two anonymized M&A cases—one domestic (Slovenian) and one cross-border (European)—this study identifies four diagnostic dimensions: Technical Compatibility, Functional Complementarity, Cultural and Governance Fit, and Planning Maturity. ISOFAM is operationalized through visual tools, including the Risk–Opportunity Diagnostic Matrix, IS Misalignment Escalation Flowchart, and Temporal Integration Framework, which facilitate early alignment and strategic foresight. These contributions position IS as a strategic pre-combination priority, enhancing both theoretical and practical outcomes in digital M&A.

Mid-market firms now share operational judgment with machine-learning systems while remaining too small to staff enterprise-grade governance. Viability theory and recent taxonomies of AI-era organizational pathologies explain why that combination is structurally demanding; they do not yet supply a diagnostic of fit between the human activity system and the model. This article describes the diagnostic. Following a design-science process on the practice-to-theory path, the Viable-System Alignment Model (VSAM) was specified by reanalysis of a theoretically sampled mid-market archive documented between 2003 and 2025. The original archive showed recurring and subsequently stable patterns concerning observation, authority, written judgment, and coordination load, which were reanalyzed as candidate conditions of human–AI fit. The artifact comprises Observability, Decision Rights, Codification, and Metabolic Load, each with a home in the Viable System Model and a socio-technical failure mode. The central proposition is that human–AI fit is a property of the viable system rather than of the software itself. Evaluation at this stage is analytical and descriptive rather than observational; independent field evaluation is reserved for subsequent research.

This paper addresses the strategic importance of information systems (IS) compatibility and complementarity in evaluating organizational fit during the pre-combination (pre-merger) phase of mergers and acquisitions (M&As). Drawing on the author’s doctoral research and updated literature, the study develops a refined conceptual framework (underpinning the IS-OFAM model) that integrates IS alignment with strategic, cultural, and operational fit dimensions. Through grounded theory–based analysis of two case studies—one in financial services and one in food processing—the paper identifies patterns of IS oversight, cultural-technical misalingment, and underleveraged IS assets. Findings show that IS compatibility often goes unassessed, while IS complementarity is undervalued as a source of post-merger innovation. The proposed IS-Organizational Fit Alignment Model (IS-OFAM) provides this conceptual framework by embedding IS assessments into early-stage due diligence and planning. The research contributes to M&A theory and practice by linking business information systems to pre-combination diagnostics and integration strategy.