A Dual Neutrosophic Framework for Evaluating Innovation Ecosystem Quality in Digital Economy Driven Specialized and Innovative SMEs: Integrating Resistance Mapping and Probabilistic Predictive Reasoning

Abstract

The quality of an innovative ecosystem is pivotal to the survival and 
advancement of specialized and innovative small and medium-sized enterprises (SMEs) 
in the digital economy. Existing analytical models often overlook the dual complexity of 
ecosystem barriers  both visible and uncertain  and the need for adaptive forecasting in 
dynamic environments. This paper introduces a novel dual-model framework based on 
Neutrosophic Logic: the Neutrosophic Resistance Map (NRM) and the Probabilistic 
Predictive Neutrosophic Cognitive Map (PP-NCM). The NRM captures direct and 
indeterminate resistance factors that inhibit innovation across various layers of the 
ecosystem. It extends causal analysis by incorporating neutrosophic weights to model 
truth (T), indeterminacy (I), and falsity (F) of resistance. Simultaneously, the PP-NCM 
enables predictive reasoning under uncertainty, dynamically adjusting the expected 
innovation performance of SMEs based on evolving contextual inputs. Through a detailed 
mathematical formalization and a comprehensive case study involving digital 
manufacturing SMEs, the paper demonstrates how this hybrid framework identifies 
critical constraints, quantifies future innovation outcomes, and provides actionable 
intelligence for policy and strategy. 

DOI: 10.5281/zenodo.15540341