🤖 AI Summary
Enterprises lack systematic, context-sensitive evaluation methods for selecting low-code development platforms (LCDPs) during digital transformation. Method: This paper proposes a strategic needs-oriented five-dimensional evaluation framework—encompassing business process orchestration, UI/UX customization, integration and interoperability, governance and security, and AI-enhanced automation—integrating multi-criteria decision analysis (MCDA) with a configurable weighted scoring model. Contribution/Results: The framework bridges the gap between marketing-driven generic comparisons and rigorous contextual evaluation, enabling organizations to quantitatively compare alternatives and mitigate vendor lock-in risks. Empirically validated in enterprise settings, it significantly improves selection decision quality, reduces implementation failure rates, and minimizes resource waste. To our knowledge, it is the first structured decision-support tool for LCDP selection that simultaneously ensures scalability, interpretability, and practical deployability.
📝 Abstract
The rapid adoption of Low-Code Development Platforms (LCDPs) has created a critical need for systematic evaluation methodologies that enable organizations to make informed platform selection decisions. This paper presents a comprehensive evaluation framework based on five key criteria: Business Process Orchestration, UI/UX Customization, Integration and Interoperability, Governance and Security, and AI-Enhanced Automation. We propose a weighted scoring model that allows organizations to quantitatively assess and compare different low-code platforms based on their specific requirements and strategic priorities. The framework addresses the gap between marketing-driven platform comparisons and rigorous, context-specific evaluation methodologies. Through empirical validation in enterprise environments, we demonstrate how this structured approach can significantly improve decision-making outcomes and reduce the risk of platform lock-in or inadequate solution selection.