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, 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.
The banking sector is experiencing a substantial transformation driven by digitalization, evolving customer expectations, and increasing competitive pressure. In hybrid banking environments, where customers interact through both digital and in-branch channels, customer experience and trust have become critical factors shaping managerial and customer decision processes. Although prior research has extensively examined the relationship between customer experience and behavioral intention, trust has predominantly been conceptualized as a mediating mechanism, while its moderating role in hybrid banking contexts remains insufficiently explored. This study investigates the influence of customer experience on trust and purchase intention, with particular emphasis on the moderating effect of trust in hybrid banking decision environments. A quantitative online survey was conducted among 371 bank customers in Germany. The collected data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results showed that customer experience exerted a strong positive effect on trust ($\beta$ = 0.858) and a significant direct effect on purchase intention ($\beta$ = 0.369). Trust also demonstrated a significant positive influence on purchase intention ($\beta$ = 0.370) and significantly strengthened the relationship between customer experience and purchase intention through its moderating effect ($\beta$ = 0.097). The model explained a substantial proportion of variance in trust ($R^2$ = 0.737) and a moderate proportion in purchase intention ($R^2$ = 0.454). The findings indicate that trust functions not only as a direct relational mechanism but also as a contextual condition influencing how customer experience translates into behavioral intention in hybrid banking settings. This study provides a more differentiated understanding of customer decision behavior in digitally integrated banking environments and offers practical implications for customer experience management and trust-oriented decision strategies in the financial services sector.
The rapid expansion of e-commerce has intensified the complexity of last-mile delivery, where increasing parcel volumes and urban constraints continue to challenge traditional distribution models. Among emerging solutions, parcel lockers have gained attention for their potential to improve delivery efficiency while reducing operational and environmental pressures. However, their effectiveness largely depends on appropriate location planning, which requires the simultaneous consideration of multiple and often conflicting criteria. This study develops a multi-criteria decision framework for parcel locker location selection by integrating the Opinion Weight Criteria Method (OWCM) and the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method. The proposed framework enables the systematic evaluation of alternative locations by combining structured expert judgment with compromise-based ranking. Criteria weights are derived through OWCM to ensure consistency in preference representation, while MARCOS is employed to assess alternatives based on their relative distance from ideal and anti-ideal solutions. The model is applied within a last-mile delivery context to examine its practical applicability. The results identify the most suitable location among a set of feasible alternatives and demonstrate stable performance under varying weighting scenarios. Sensitivity and comparative analyses confirm that the ranking outcomes remain consistent across different conditions and methodological configurations. The findings provide a structured approach to location planning in urban logistics and offer practical support for decision-makers seeking to deploy parcel locker systems under complex operational environments. The proposed framework can be extended to similar decision problems involving infrastructure placement and multi-criteria evaluation.
Sustainable logistics hub planning in emerging economies is often challenged by high levels of uncertainty, limited data availability, and the need to balance economic, environmental, and social objectives. Supporting consistent and transparent decision-making under such conditions remains a key issue in infrastructure planning. To address this, the present study develops an intelligent decision-support framework for prioritizing logistics hubs in complex and uncertain environments. The proposed framework combines $q$-rung orthopair fuzzy sets with the ordinal priority approach, enabling the representation of imprecise expert judgments alongside ordinal preference information within a unified multi-criteria structure. The approach is applied to the case of Kenya, where logistics development involves multiple and often conflicting criteria. A comprehensive evaluation system is established, and expert assessments are incorporated to derive priority rankings. The results show that operational efficiency and economic considerations play a dominant role in the decision process, while environmental and social factors receive comparatively lower weights. Sensitivity and comparative analyses confirm the stability and reliability of the findings. The study provides a structured and uncertainty-aware decision-support tool that can assist infrastructure planning and offers practical insights for policy and managerial decision-making in logistics systems.
Submit to JIMD
Review for JIMD
Propose a Special Issue 
Open Access
News 
Journal Metrics
Journal Overview
Associated Journals