Augmented analytics shifts much of the analytical work in business intelligence (BI) to artificial intelligence (AI), leaving non-technical users with one remaining safeguard: verifying AI-generated insights before acting on them. This paper examines whether trust in augmented analytics is associated with less verification and how verification relates to perceived decision quality. Survey data from 350 non-technical BI users in Vietnam were analysed with partial least squares structural equation modelling (PLS-SEM), using a newly developed ten-item verification behaviour scale (VER). The results suggest that trust is negatively associated with verification, and verification is positively associated with perceived decision quality. The indirect association of trust through verification is negative, whereas its direct association remains positive, consistent with competitive mediation. Perceived explainability is associated with higher trust, but its direct association with verification is not significant, and the hypothesized moderating role of AI literacy receives no statistical support. Because the design is cross-sectional and self-reported, the paths represent associations rather than causal effects. Automation complacency and objective decision quality were not measured, and the verification scale was supported by initial validation evidence from one sample. The findings motivate linking insights to their source data, second checks for high-value decisions and direct monitoring of verification; the effectiveness of these proposals remains untested. The study develops a survey measure of verification behaviour with initial validation evidence and shows that the positive trust–decision quality association coexists with a negative indirect association through verification.
The reliability of multi-criteria decision-making (MCDM) methods depends on the mathematical consistency of their data transformations and the stability of the rankings they produce. The additive ratio assessment (ARAS) and combinative distance-based assessment (CODAS) methods are critically examined to determine whether their computational procedures introduce unintended distortions into alternative evaluations. Particular attention is given to the treatment of cost criteria, the incorporation of an ideal alternative into the decision matrix, and the combined use of Euclidean and taxicab distances. It is shown that the nonlinear transformation used to invert cost criteria can alter the relative proportions of the original observations, potentially affecting the resulting assessments. A linear inversion procedure, implemented through the reverse sorting (ReS) algorithm, is therefore considered as an alternative for preserving proportional relationships in the transformed data. Nevertheless, this transformation alone does not resolve the other methodological concerns identified. In ARAS, incorporating an ideal alternative under sum normalization can change the normalization coefficients and may alter the scores and rankings of the existing alternatives. In CODAS, the contribution of the taxicab distance is examined across pairwise alternative comparisons. The analysis indicates that the taxicab term is activated conditionally across pairwise comparisons, so its contribution can vary across alternatives and may affect their relative scores and rankings. Consequently, the suitability of ARAS and CODAS for applications requiring transparent and stable rankings should be assessed with particular care. For applications in which additive aggregation is appropriate, the weighted sum model (WSM) is discussed as a more straightforward alternative, with an interpretable relationship between criterion weights, normalized performance values, and overall scores. The findings highlight the importance of verifying the mathematical properties of MCDM algorithms before their application and of evaluating ranking sensitivity to methodological choices to reduce the risk of misleading decision outcomes.
Selecting an appropriate antihypertensive drug class for older patients with multimorbidity requires multiple clinical considerations to be evaluated simultaneously, including comorbidity-specific suitability, treatment-related risks, therapeutic priorities and professional judgement. A transparent decision-support framework is therefore needed to structure these heterogeneous considerations without implying that a mathematical ranking constitutes a clinical recommendation. An entropy-weighted group decision-support framework was developed and evaluated using a hypothetical 72-year-old patient with multiple comorbidities. Seven antihypertensive drug classes—diuretics, beta-blockers, Angiotensin-Converting Enzyme (ACE) inhibitors, Angiotensin II Receptor Blockers (ARBs), calcium-channel blockers (CCBs), alpha-1 blockers and central alpha-2 agonists—were assessed against eight criteria: physician experience, suitability for older patients, suitability for patients with diabetes, suitability for patients with kidney disease, suitability for patients with congestive heart failure, suitability for patients with a history of myocardial infarction, medication-related complication risk and rapidity of therapeutic effect. Assessments were provided independently by an internist, a cardiologist and a urologist. Criterion weights were derived using the entropy method from transformed rank-score distributions, while rank-frequency linear assignment and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were used to aggregate expert assessments and obtain alternative rankings. ARBs were ranked first by TOPSIS and third in both optimal linear-assignment solutions. Two equally optimal linear-assignment solutions were obtained, with beta-blockers and alpha-1 blockers exchanging the first and sixth positions. Central alpha-2 agonists were ranked last by both approaches. Importantly, the complete and tie-free rankings provided by every expert resulted mathematically in identical entropy weights of 0.125 for all eight criteria, indicating that criterion differentiation was not achieved under the adopted elicitation format. The resulting rankings therefore represent methodological outputs rather than evidence of clinical superiority among antihypertensive drug classes. The framework provides a transparent means of structuring multi-criteria and multi-expert assessments in complex clinical scenarios, while its preliminary nature, limited expert panel and absence of patient-level validation preclude direct clinical application. Validation using larger and more diverse expert panels, clinically validated criteria and patient-level outcomes is warranted before the framework can be considered for clinical decision-support applications.
The adoption of intelligent transportation systems (ITS) in developing urban environments is shaped by interrelated institutional, technological, infrastructural, and socioeconomic factors. Before structural relationships among these factors can be interpreted with confidence, the psychometric properties of the measurement model must be established. This study validated an integrated ITS adoption framework for Yogyakarta, Indonesia, comprising eight latent constructs: funding/budget, government policy, infrastructure, technology, social economy, social affordability, smart readiness, and the ITS adoption framework. Data from 300 respondents were analyzed using partial least squares structural equation modeling in SmartPLS 4. The measurement model was evaluated for internal consistency reliability, convergent validity, and discriminant validity. Cronbach’s alpha and composite reliability values exceeded 0.70 across all constructs, and average variance extracted (AVE) values exceeded 0.50. The Fornell–Larcker criterion and heterotrait–monotrait ratio (HTMT) further supported discriminant validity. These findings indicate that the proposed measurement model is reliable and valid, providing a methodological foundation for subsequent structural-model evaluation and hypothesis testing. The validated instrument can support rigorous investigation of ITS adoption determinants and evidence-based transportation planning in developing urban settings.
This study explored the association between sensory marketing measures and client experience in an Algerian beauty clinic. This study adopted a descriptive-analytical approach, based on a questionnaire administered to a convenience sample of 30 female clients of the clinic. Both descriptive and inferential statistical analyses were conducted to test the research hypotheses. The findings revealed that the overall application of sensory marketing in the clinic was positively associated with client experience. The pre–post comparison showed a statistically significant difference in overall client experience between the two measurement phases. Following the intervention, significant associations were observed between the sensory stimuli (sight, sound, smell, taste, and touch) and client experience. Among these five dimensions, sound showed the strongest association, followed by sight and taste. This research offered practical insights into how sensory marketing could be leveraged as a tool for enhancing client experience. Understanding changes in clients’ perceptions could help inform service improvements and sensory-marketing decisions.
With the advance of live-streaming e-commerce and the metaverse, virtual streamers as a new productive force, are becoming an emerging power in the live-streaming e-commerce industry. The characteristics of virtual streamers and their impact on consumer behavior in live-streaming rooms have gradually attracted academic attention. Although research on virtual streamers is on the rise, there is a lack of integrated synthesis of research findings, especially in the preliminary stage of virtual streamer applications in the e-commerce field. In this light, this paper conducted a holistic review and analysis of the research outcomes related to virtual streamers in the live-streaming e-commerce domain. Firstly, the conceptual connotation and categories of virtual streamers were elucidated. Subsequently, the paper traced back the relevant theories, influencing factors, and research methods concerning virtual streamer characteristics and their impact on consumer behavior. Ultimately, the paper concluded with an outlook for future investigation, in anticipation of promoting advanced application of virtual streamers in e-commerce marketing practices.
The rapid integration of artificial intelligence (AI) into mobile banking applications has considerably transformed digital financial services, shifting the primary challenge from user adoption to sustaining long-term usage. In emerging digital banking markets such as Indonesia, continuance intention has become critical to the development of mobile banking. The purpose of this study is to examine, from a post-adoption perspective, the effects of artificial intelligence capabilities and trust on continuance intention in mobile banking. A quantitative research design was employed to conduct a cross-sectional survey of 150 mobile banking users in Indonesia. The results obtained from Partial Least Squares Structural Equation Modeling (PLS-SEM) showed that both artificial intelligence capabilities and trust had significant positive effects on continuance intention in mobile banking. More specifically, users’ perceptions of artificial intelligence capabilities, such as personalization, responsiveness, automation, and learning ability, all played a crucial role in reinforcing continued usage. In addition, trust, as a core psychological determinant, directly affected users’ willingness to rely on AI-enabled mobile banking and to be loyal to such services. Simply put, technological competence alone was not sufficient to sustain long-term usage without corresponding levels of user trust. Therefore, the development of advanced AI functionality and trust-building strategies should be aligned. This study contributes to the literature on mobile banking and information systems by conducting post-adoption research through the integration of artificial intelligence capabilities and trust within a parsimonious research model. With a focus on continuance intention rather than initial adoption, the study provided a more relevant explanation for user behavior in a competitive digital banking environment. The findings offered convincing and practical insights for banks and fintech providers to ensure long-term sustainability of mobile banking services.
Evaluating technological innovation performance in regional public hospitals requires balancing multiple policy objectives, including operational efficiency, distributive equity, and innovation value creation. Conventional evaluation methods often rely on fixed indicator weights, which inadequately capture trade-offs among competing objectives and limit their usefulness for strategic resource allocation. To address this limitation, this study develops a multi-objective decision optimization framework that reformulates innovation performance evaluation as a constrained decision-making problem under fiscal, institutional, and policy conditions. A multi-objective linear programming model is constructed to jointly optimize efficiency, fairness, and innovation value. Using three-year panel data from regional public hospitals, the framework is validated through comparative evaluation, sensitivity analysis, and statistical testing. The results show that the optimized weighting structure improves institutional performance balance, reduces inter-regional disparities in innovation capacity, and strengthens the contribution of research investment to technological output and knowledge transformation. Human capital composition, research funding intensity, and technology commercialization capability are identified as key variables shaping the innovation performance frontier. Scenario analysis further shows that institutional performance varies under different policy preferences, highlighting the need for adaptive weighting mechanisms. The findings provide a practical and interpretable framework for evidence-based innovation performance evaluation and public hospital governance.
The reliability and interpretability of the Complex Proportional Assessment (COPRAS) method in multi-criteria decision-making (MCDM) have been critically re-evaluated. Although COPRAS has frequently been promoted as a method capable of separately assessing the influence of benefit and cost criteria without requiring explicit inversion of cost attributes, it is demonstrated that these claims are not fully supported by the mathematical structure of the method. A theoretical analysis reveals that COPRAS inherently relies on Sum normalization, through which hidden attribute prioritization and rating distortion may be introduced. Furthermore, it is shown that, in the presence of a single cost criterion, COPRAS becomes mathematically equivalent to the Weighted Sum Model (WSM) implemented with Sum normalization and a nonlinear inverse-sum transformation of cost criteria. Consequently, the purported methodological distinction between COPRAS and conventional additive aggregation approaches is substantially reduced. Particular attention is drawn to the nonlinear inversion embedded in the COPRAS formulation for cost criteria aggregation. Because the inverse transformation is applied to the total contribution of cost criteria rather than to individual criterion values, the resulting influence of cost attributes on the final utility score is shown to be only indirectly represented. Under certain conditions, significant discrepancies are produced between the nominal and actual contribution of cost criteria, thereby affecting both rating stability and ranking consistency. Through numerical demonstrations and comparative analyses, distortions in alternative ratings and rank reversals are identified when COPRAS results are compared with those obtained from the conventional WSM framework. The analysis further indicates that the observed inconsistencies are primarily associated with the combined effects of Sum normalization and nonlinear cost treatment. To address these limitations, the WSM integrated with a linear cost-transformation procedure based on the Reverse Sorting (ReS) algorithm is proposed as a more transparent and mathematically consistent alternative. The findings suggest that the application of COPRAS in practical MCDM problems should be approached with caution, particularly in decision environments where ranking sensitivity and interpretability are of critical importance.
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