Jul 2026· Journal of Soft Computing and Decision Analytics· 0 citations· 76 references
TL;DR
The results indicate that innovation and productivity incentives and institutional feasibility are the most influential evaluation criteria and a case study conducted in the Republic of Croatia shows that the mass retraining and participatory AI capital models provide the most suitable institutional responses.
Abstract
This study evaluates institutional models for adapting labour markets to the growing adoption of automation and artificial intelligence (AI). As AI continues to transform labour markets, identifying effective institutional responses has become increasingly important. Six institutional adjustment models were assessed against eight evaluation criteria using a hybrid fuzzy-rough decision-making framework that integrates the SiWeC (Simple Weight Calculation) and WASPAS (Weighted Aggregated Sum Product Assessment) methods based on expert judgments. The proposed approach explicitly incorporates uncertainty and imprecision into the evaluation process, thereby reducing subjectivity in decision-making. The results indicate that innovation and productivity incentives and institutional feasibility are the most influential evaluation criteria. A case study conducted in the Republic of Croatia further shows that the mass retraining and participatory AI capital models provide the most suitable institutional responses. The study contributes both methodologically, by proposing a robust fuzzy-rough evaluation framework, and practically, by supporting evidence-based policy decisions for labour market adaptation in the era of AI.
A new AI assisted Fuzzy Multi-Criteria Decision-Making model is presented to assess the performance of an organization and to assist in making strategic management decisions to enhance the decision consistency, transparency of decision making, strategic alignment and managerial responsiveness.
Mukul Agarwal, M. Gupta, Martina Kant et al.· Journal of Intelligent Decis...· 0 citations
A multi-criteria decision-making framework for evaluating six AI tools against eight sustainability-oriented criteria indicates that improving individual productivity and enhancing employee engagement and motivation are the most influential evaluation criteria.
A. Puška, Jurica Bosna, Darko Božanić· Journal of Operations Intell...· 0 citations
In Multi-Criteria Group Decision-Making (MCGDM), the assignment of weights to decision-makers is a crucial but methodologically delicate step, especially when the group includes both human experts and artificial experts such as intelligent agents, Artificial Intelligences (AIs) or Large Language Models (LLMs). Existing weighting strategies are often either difficult to interpret or poorly suited to heterogeneous groups of evaluators. In this paper, we investigate a fuzzy rule-based approach to expert weighting, building on a previously introduced methodological framework and focusing here on its application-oriented validation. The proposed method models expert weighting as a Fuzzy Rule-Based System (FRBS) in which the relevant properties of the experts are represented by linguistic variables and combined through interpretable IF–THEN rules. In this way, weighting policies can be expressed transparently and adapted to the requirements of the decision domain. The framework produces normalised weights in the interval [0,1], which can then be incorporated into standard MCGDM aggregation procedures. To assess the operational behaviour of the approach, we consider an application involving the weighting of four open-source LLMs (apertus:8b, gemma4:e4b, mistral-small3.2:24b, and nemotron-cascade-2:30b) over three multilingual criteria (English, Italian, Portuguese) and two resource-side criteria (VRAM, open-sourceness), each modelled by three trapezoidal fuzzy sets and combined into a five-class output partition; the underlying dataset is built from 10 independent repetitions of 100 questions per model. Under a language-focused rule base of five IF–THEN rules, the four experts receive sharply separated normalised weights (0.003,0.149,0.301,0.548)—a top-to-bottom ratio above 180—whereas a combined linguistic/resource-aware rule base of five rules flattens the distribution to (0.227,0.360,0.222,0.191) and selects a different winner, demonstrating that policy changes are encoded explicitly in the output. A 100-run Kendall’s τ perturbation analysis confirms that the induced rankings remain stable under moderate input noise, particularly for the language-focused policy, while substituting the Product t-norm with Gödel or Lukasiewicz leaves the language-focused ranking invariant but induces rank reversals in the more discriminative resource-aware policy. A comparison against three independent baselines (Markov Logic Networks, ProbLog, TOPSIS) shows that ProbLog reproduces the FRBS ordering in both case studies, MLN compresses the normalised scores under its global probabilistic interaction, and TOPSIS diverges whenever conditional IF–THEN preferences must be encoded. A worked end-to-end aggregation example with three alternatives, three criteria, and four experts further shows that the FRBS weights propagate into a clear selection of the best alternative, with aggregated scores (S1,S2,S3)=(8.88,6.78,6.21). These results confirm both the practical usability of the method and its suitability for contexts in which multiple, potentially competing, objectives must be balanced explicitly. Overall, the paper provides an application-oriented study of an FRBS-based weighting scheme for artificial experts, highlighting its interpretability, adaptability, and potential relevance for contemporary MCGDM settings.
L. Castronovo, Giuseppe Filippone, G. Giacopelli et al.· Electronics· 0 citations
Purpose – This systematic review examines how artificial intelligence (AI) can support strategic decision-making in private universities, the organizational conditions shaping its value, and the governance and implementation risks that constrain responsible use.Methodology – Searches were conducted in the Web of Science Core Collection, Scopus, and Google Scholar between March and May 2026, with the final update on May 31, 2026. After duplicate removal, screening, full-text assessment, and evidence appraisal, 46 substantive sources published between 1955 and 2025 were included in this review. Four additional methodological references supported the review reporting and synthesis. Because the evidence base was heterogeneous, narrative thematic synthesis was applied while distinguishing direct private university evidence from evidence transferred from general higher education, organizational decision research, and AI governance.Findings – The synthesis identifies four interconnected roles of AI: environmental intelligence, decision augmentation, strategic execution, and governance infrastructure. AI can strengthen institutional sensing, the comparison of strategic alternatives, implementation coordination, and decision traceability. However, direct empirical evidence specific to private universities is limited. Strategic value depends on data quality, organizational learning, analytical capability, decision ownership, auditability, governance capacity, strategic fit, and alignment with the institutional mission. Therefore, AI is best understood as a human-led decision-support capability rather than a substitute for institutional judgment.Research limitations – The heterogeneous corpus prevents statistical estimation of a common institutional effect, while the review is restricted to English-language sources from three search platforms. Therefore, the four-part architecture should be treated as an evidence-organizing framework rather than a validated causal model.Originality – This review integrates higher education, organizational decision-making, strategic management, and AI governance evidence into an institution-level capability architecture for responsible AI-supported strategic decision-making.
Yun-Dong Wu, Wei-Jian Kong· Artificial Intelligence in E...· 0 citations
: Artificial Intelligence (AI) has increasingly emerged as an important technology for transforming public-sector operations and improving organizations' capacity to make timely, accurate, and evidence-based decisions. Despite its growing adoption, concerns about technological readiness, skills, data availability, automation, and effective integration continue to influence the extent to which public institutions realize the benefits of AI. This study examined the effect of Artificial Intelligence adoption on decision-making effectiveness in the public sector. The study was guided by the Technology Acceptance Model (TAM) and adopted a positivist research philosophy and a descriptive correlational research design. The target population comprised employees in ministries and extra-budgetary institutions in Kenya, estimated at 236,700 employees in 2024. Using Yamane's formula at a 5% level of precision, the study determined a sample size of 399 respondents. Of the 399 questionnaires considered issued, 327 were completed and returned, representing an illustrative response rate of 82.0%. Data were collected using a structured questionnaire and analyzed using descriptive statistics, Pearson correlation, and simple linear regression. Artificial Intelligence adoption was assessed using AI data analysis, predictive analytics, process automation, and AI decision-support systems, while decision-making effectiveness was assessed in terms of accuracy, timeliness, quality, and efficiency. Descriptive findings indicated a relatively high level of Artificial Intelligence adoption, with an overall mean of 3.79 and standard deviation of 1.16. AI-generated data analysis providing useful information for organizational decisions recorded the highest mean (4.22), while the use of AI to automate routine administrative tasks recorded the lowest mean (3.25). Inferential findings established a strong positive relationship between Artificial Intelligence adoption and decision-making effectiveness (r = 0.746, p < 0.01). Regression analysis indicated that Artificial Intelligence adoption explained 55.7% of the variation in decision-making effectiveness (R² = 0.557). The regression model was statistically significant, F (1, 325) = 408.65, p < 0.001. Further, Artificial Intelligence adoption had a positive and statistically significant effect on decision-making effectiveness (β = 0.746, t = 20.195, p < 0.001), leading to rejection of the null hypothesis. The study concluded that Artificial Intelligence adoption significantly enhanced decision-making effectiveness in the public sector. It recommended increased investment in AI technologies, digital infrastructure, employee training, and responsible AI governance, while maintaining appropriate human oversight. The study contributed empirical evidence on the importance of AI adoption in strengthening decision-making effectiveness within public-sector organizations.
Belvin Keitany, Stephene Oloo Magadi· International Journal of Sci...· 0 citations
Labor market efficiency represents a multidimensional public policy problem that requires the simultaneous consideration of institutional, structural, as well as technological and dynamic factors. The aim of this paper is to identify and rank the strategies that, according to expert evaluation, contribute most to the long-term efficiency of the labor market of the Republic of Croatia. For this purpose, a multi-criteria evaluation model was developed, encompassing eight criteria and eight strategies identified on the basis of the relevant theoretical and empirical literature. The evaluation was conducted based on the assessments of nine experts from the academic community who deal with labor market issues. The fuzzy Modified Simple Weight Calculation (fuzzy M-SiWeC) method was applied to determine the relative importance of the criteria, while the ranking of the strategies was carried out using the fuzzy COmpromise Ranking from Alternative Solutions (fuzzy CORASO) method. The results show that technological adaptability and innovation incentives represent the most important evaluation criterion, whereas the reform of education and lifelong learning is the highest-ranked strategy for increasing the efficiency of the Croatian labor market. This is followed by active employment policies based on data analytics. The obtained results point to the need to direct Croatian labor market policies towards the systematic alignment of education and lifelong learning with labor market needs, as well as the development of active employment policies based on data analytics.
Unknown authors· Science Engineering and Tech...· 0 citations
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