Constructing a Multi-Objective Decision-Making Model for Investment Valuation of Technological Innovation Projects: a Blockchain–Big Data Fusion Perspective
DOI:
https://doi.org/10.70393/6a696574.333835ARK:
https://n2t.net/ark:/40704/JIET.v1n1a01Disciplines:
Computer ScienceSubjects:
Big DataReferences:
27Keywords:
Technological Innovation Projects, Investment Valuation, Multi-Objective Decision-Making, Blockchain, Big Data, Fusion AlgorithmsAbstract
Technological innovation projects are notoriously difficult to value due to their high uncertainty and the coupling of multiple value dimensions. Traditional valuation approaches suffer from several critical limitations, including insufficient data credibility (tampering risk rate exceeding 30%), overly single-dimensional objective design (financial orientation accounting for 75%), and subjective weight assignment (reliance on expert judgment above 60%). To address these issues, this study develops a five-dimensional, blockchain–big data fusion-driven multi-objective decision-making valuation model integrating technology, finance, market, risk, and ecosystem dimensions. Blockchain-based decentralized evidence preservation enables trusted provenance and traceability of multi-source data, while big data analytics—using an integrated LSTM + Random Forest framework—supports objective quantification and dynamic optimization. An improved CRITIC–TOPSIS method is further employed to solve the multi-objective collaborative decision-making problem. Empirical validation based on 426 global innovation projects (covering six domains such as AI and medical devices, including 158 cross-border projects) demonstrates that the proposed model controls valuation deviation to 7.2% ± 1.3%, representing reductions of 74.8% and 68.4% compared with the traditional DCF method (28.6% ± 4.2%) and relative valuation (23.1% ± 3.8%), respectively. Improvements are more pronounced for technology-intensive projects (deviation 5.9%), and the risk quantification error for cross-border technology transfer projects decreases by 62.7%. Overall, the model overcomes the conventional “single-objective, static, experience-driven” limitations and provides a rigorous yet practical methodological framework for investment decision-making in innovation projects [1–3].
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