Aug 2026· Urbanization, Sustainability and Society· 0 citations· 68 references
TL;DR
This study synthesises technical and stakeholder dimensions of AI in property valuation using a structured qualitative approach via SLR and proposes a novel hybrid framework that integrates stakeholder trust factors with model precision to enhance both reliability and acceptance of AI tools.
Abstract
This study aims to address the growing concerns surrounding the use of artificial intelligence (AI) in property valuation, particularly issues of transparency, trust and accuracy. This study focuses on aligning AI models with expectations to foster responsible adoption in real estate decision-making.
A systematic literature review (SLR) of 44 peer-reviewed studies published between 2018 and 2025 was conducted. NVivo software was used for qualitative coding, and the political, economic, social, technological, legal and environmental framework guided the analysis of external factors influencing AI adoption. The study examined both technical model performance and stakeholder concerns.
Random forest and support vector machines were most frequently applied in structured valuation tasks, while artificial neural networks were reported in contexts involving non-linear modelling and complex data patterns. Despite demonstrated predictive capabilities, policymakers and professional stakeholders placed greater emphasis on transparency, explainability and legal accountability. Identified trust-related challenges included algorithmic bias, limited model interpretability, regulatory ambiguity and insufficient integration of contextual factors. These findings informed the development of a hybrid AI valuation framework that integrates technological performance with governance mechanisms, contextual calibration and professional judgement to strengthen property valuation quality.
This study is limited by the absence of primary data from stakeholder interviews. The findings are based solely on published literature, which may not fully capture real-time industry perspectives or emerging on-the-ground challenges.
The proposed framework offers a transparent, data-driven solution for valuers, investors and regulators, supporting better-informed decisions and encouraging ethical AI adoption in real estate.
This study synthesises technical and stakeholder dimensions of AI in property valuation using a structured qualitative approach via SLR. It proposes a novel hybrid framework that integrates stakeholder trust factors with model precision to enhance both reliability and acceptance of AI tools.
It is mapped how the literature addresses fairness in finance, the metrics employed, the financial contexts considered, and the effectiveness of mitigation techniques, to synthesize existing knowledge, identify methodological gaps, and provide guidance for future research and policy development.
Marcelo Wecchi, Lilian Berton· AI and Ethics· 0 citations
This study contributes a unified research model for stakeholder-specific AI adoption, demonstrates a mixed quantitative-qualitative approach, and establishes a scalable foundation beginning with engineers and extending to other stakeholders through future empirical validation.
Trust in artificial intelligence (AI) has received growing attention in academic research, ethical debates, and societal discourse. However, limited research has examined how laypeople conceptualize trust in AI assistants. This study aims to identify recurring conceptual dimensions that underlie lay definitions of trust in AI. Open-ended survey responses were collected from English-speaking participants (N = 204) recruited via two online platforms. Responses were analyzed using inductive qualitative content analysis within a postpositivist framework. Three core themes emerged. The most prevalent dimension mentioned was performance (85%), with participants defining trust primarily in terms of accuracy, source quality, and competence. The second dimension emphasized safety (38%), reflecting participants’ concerns about harm prevention, protection and responsible system behavior. The third dimension involved perceived moral integrity of AI (26%), including references to unbiasedness, honesty, and fairness. The findings indicate that lay conceptualizations of AI trust are predominantly anchored in performance expectations, but with significant and noteworthy secondary emphasis on moral attributes related to in safety and integrity. These results offer an empirically grounded framework for understanding public trust in AI and may inform future measurement and design initiatives.
Sandrine Toudjui, Jay Tang, Hamna Akhter et al.· Discover Artificial Intellig...· 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
This study analyzes the transition toward anticipatory public administration through the use of artificial intelligence (AI). Despite the growing deployment of predictive models, a clear research gap remains regarding the socio-technical prerequisites—such as institutional trust, data infrastructure, and ethical–legal frameworks—that condition their success. To address this, a Systematic Literature Review (SLR) was conducted following the PRISMA 2020 guidelines, retrieving 68 open-access articles published between 2020 and 2025 from the Scopus and Web of Science databases. Data synthesis was performed using descriptive bibliometric mapping and qualitative thematic analysis. The results show that scientific interest has experienced accelerated growth since 2024, highlighting a preference for machine learning systems that automate service delivery. While AI enables significant benefits, including a 40% reduction in budgetary errors and early fraud detection, progress is hindered by algorithmic opacity (“black box” models), the risk of structural bias, and civil servants’ confirmation bias. The study concludes that technological complexity must be subordinated to democratic safeguards. To this end, a research agenda is proposed alongside practical implications, recommending that public institutions implement mandatory independent algorithmic audits, enforce strict interpretability standards in public procurement, and adopt hybrid regulatory frameworks to ensure fair and accountable governance.
G. L. Peña Pazos, M. Fernández Miranda, E. E. García Panta et al.· Administrative Sciences· 1 citation
Robo-advisory services offer automated, algorithm-based investment advice and portfolio management, enabling cost efficiency and wider access to financial services. Despite these advantages, adoption in emerging economies remains limited due to psychological, cognitive, and social barriers. Trust is particularly critical in financial decision-making, as it reduces perceived risk and uncertainty associated with algorithm-driven systems. Trust is an important component, since adoption depends on people's faith in technological advances and the safety of their investments. The study purposes to contribute to the larger in the field of financial technology analysing the factors which impact on the adoption of Robo-advisory services and provide new insights into the psychological and social dynamics. Using a quantitative research design, primary data were collected from 285 investors through a structured questionnaire. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was employed to test the proposed relationships among financial awareness, behavioral biases, innovativeness, subjective norms, trust, and intention to use robo-advisory services. The results indicate that trust is the strongest predictor of adoption intention and significantly mediates the effects of key antecedent variables. The findings could help policy-makers and financial institutions to enhance the features of robo-advisory services with targeted interventions and marketing strategies.
Neenu Bala, Saloni Raheja· Journal of Intelligent Decis...· 0 citations
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