Beyond Vendor Hype: An Objective IDOCRIW-MARCOS Model for Selecting AI-Based Workforce Mental Health Platforms
DOI:
https://doi.org/10.64366/ijids.v3i2.570Keywords:
IDOCRIW; MARCOS; Multi-Criteria Decision Making; Workforce Mental Health; HR Technology ProcurementAbstract
The growing adoption of artificial intelligence (AI) in workforce mental health monitoring has produced a wide range of competing platforms that differ in prediction accuracy, response time, scalability, implementation cost, and user satisfaction, so that HR and occupational-health decision-makers currently choose among them largely on the basis of vendor marketing claims rather than a structured, evidence-based comparison, making platform selection a genuine multi-criteria decision-making (MCDM) problem. This study proposes a decision support model that integrates the Integrated Determination of Objective Criteria Weights (IDOCRIW) method with the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method to give organizations an objective, reproducible alternative to subjective AHP/TOPSIS-style vendor evaluations for AI-based workforce mental health platforms. IDOCRIW combines Entropy and Criterion Impact Loss (CILOS) to derive objective criteria weights directly from vendor-reported technical specifications, removing the need for expert pairwise comparisons, while MARCOS ranks alternatives based on their utility degree relative to an ideal and an anti-ideal solution. The proposed model was demonstrated using an illustrative case study of five AI platform alternatives evaluated against five criteria: prediction accuracy, response time, scalability, implementation cost, and user satisfaction. The IDOCRIW results indicate that scalability (weight 0.260) is the most influential criterion, followed by response time (0.220). The MARCOS ranking identifies SmartWell as the top-ranked alternative with a final utility function value of 0.981, ahead of MentalCare AI (0.973) and WorkSense AI (0.969); a follow-up TOPSIS comparison computed on the same weighted data confirms SmartWell's top position but reverses the third- and fourth-ranked alternatives, showing that the ranking method itself, and not only the criteria weights, materially affects the recommendation. A sensitivity analysis, including combined-criterion perturbation scenarios, confirms that the ranking of the top alternative remains stable under moderate weight changes. For HR practitioners, the model offers a transparent and reproducible basis for AI-vendor procurement decisions that reduces reliance on unverified accuracy claims in vendor marketing materials.
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Copyright (c) 2026 Asyahri Hadi Nasyuha, Ananda Hadi Elyas, Muhammad Khoiruddin Harahap
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