Latest open access articles published in Journal of Intelligent Management Decision at https://www.acadlore.com/journals/JIMD https://www.acadlore.com/journals/JIMD Acadlore en Creative Commons Attribution(CC - BY) JIMD support@acadlore.com https://www.acadlore.com/article/JIMD/2026_5_2/jimd050204 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. 05-11-2026 <![CDATA[ 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. ]]> From Customer Experience to Purchase Intention: The Moderating Role of Trust in Hybrid Banking Decision Environments laura gundelach jonas manske doi: 10.56578/jimd050204 Journal of Intelligent Management Decision 05-11-2026 Journal of Intelligent Management Decision 05-11-2026 2026 5 2 Article 139 10.56578/jimd050204 https://www.acadlore.com/article/JIMD/2026_5_2/jimd050204 https://www.acadlore.com/article/JIMD/2026_5_2/jimd050203 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. 04-16-2026 <![CDATA[ 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. ]]> A Multi-Criteria Decision Framework for Parcel Locker Location Selection in the Area of the Visoko Distribution Center danira durmić željko stević doi: 10.56578/jimd050203 Journal of Intelligent Management Decision 04-16-2026 Journal of Intelligent Management Decision 04-16-2026 2026 5 2 Article 129 10.56578/jimd050203 https://www.acadlore.com/article/JIMD/2026_5_2/jimd050203 https://www.acadlore.com/article/JIMD/2026_5_2/jimd050202 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. 04-12-2026 <![CDATA[ <p>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.</p> ]]> An Intelligent Decision-Support Framework for Sustainable Logistics Hub Prioritization under Uncertainty: A $\boldsymbol{q}$-Rung Orthopair Fuzzy OPA Approach çağlar karamaşa basil okoth mustafa ergün doi: 10.56578/jimd050202 Journal of Intelligent Management Decision 04-12-2026 Journal of Intelligent Management Decision 04-12-2026 2026 5 2 Article 112 10.56578/jimd050202 https://www.acadlore.com/article/JIMD/2026_5_2/jimd050202 https://www.acadlore.com/article/JIMD/2026_5_2/jimd050201 Cost-schedule control in construction projects is inherently a continuous decision-making process conducted under conditions of uncertainty, rather than a purely technical or accounting activity. Conventional approaches, which rely on retrospective performance measurement and fragmented indicators, provide limited support for timely managerial intervention and often lead to delayed or suboptimal decisions. This study develops a decision-centric framework that integrates earned value analytics with organizational decision processes to enable proactive and structured cost–schedule control in small and medium-sized construction projects. The proposed framework conceptualizes cost control as a four-stage decision process—situational awareness, diagnostic analysis, predictive assessment, and intervention execution—and establishes explicit linkages between analytical signals and managerial actions. Within this structure, earned value metrics are reinterpreted as decision triggers rather than passive evaluation tools, while organizational roles are reconfigured to support timely interpretation and coordinated response. The framework is examined through an in-depth case study of a gas station construction project exposed to significant environmental and operational uncertainty. The findings indicate that cost overruns are primarily associated with delayed decision responses, fragmented information flows, and misaligned responsibility structures. By embedding real-time performance evaluation within a coherent decision architecture, the proposed approach enables earlier identification of deviations and more targeted managerial interventions. The study contributes to the literature on intelligent management decision-making by demonstrating how analytical tools can be operationalized within organizational contexts to enhance decision quality under uncertainty. It further provides a transferable framework for structuring data-informed decision processes in resource-constrained project environments. 04-02-2026 <![CDATA[ Cost-schedule control in construction projects is inherently a continuous decision-making process conducted under conditions of uncertainty, rather than a purely technical or accounting activity. Conventional approaches, which rely on retrospective performance measurement and fragmented indicators, provide limited support for timely managerial intervention and often lead to delayed or suboptimal decisions. This study develops a decision-centric framework that integrates earned value analytics with organizational decision processes to enable proactive and structured cost–schedule control in small and medium-sized construction projects. The proposed framework conceptualizes cost control as a four-stage decision process—situational awareness, diagnostic analysis, predictive assessment, and intervention execution—and establishes explicit linkages between analytical signals and managerial actions. Within this structure, earned value metrics are reinterpreted as decision triggers rather than passive evaluation tools, while organizational roles are reconfigured to support timely interpretation and coordinated response. The framework is examined through an in-depth case study of a gas station construction project exposed to significant environmental and operational uncertainty. The findings indicate that cost overruns are primarily associated with delayed decision responses, fragmented information flows, and misaligned responsibility structures. By embedding real-time performance evaluation within a coherent decision architecture, the proposed approach enables earlier identification of deviations and more targeted managerial interventions. The study contributes to the literature on intelligent management decision-making by demonstrating how analytical tools can be operationalized within organizational contexts to enhance decision quality under uncertainty. It further provides a transferable framework for structuring data-informed decision processes in resource-constrained project environments. ]]> A Decision-Centric Framework for Cost-Schedule Control under Uncertainty: Embedding Earned Value Analytics into Organizational Decision Processes xiaoling xiao qixin bo yafeng liu yajun ma doi: 10.56578/jimd050201 Journal of Intelligent Management Decision 04-02-2026 Journal of Intelligent Management Decision 04-02-2026 2026 5 2 Article 95 10.56578/jimd050201 https://www.acadlore.com/article/JIMD/2026_5_2/jimd050201 https://www.acadlore.com/article/JIMD/2026_5_1/jimd050107 This study addresses the impact of increasing environmental pressures on a global scale on the long-term strategies of businesses, with particular emphasis on the importance of environmental responsibility in port operations. Port activities are directly associated with various environmental issues, including climate change, global warming, air and water pollution, noise pollution, waste management, and energy consumption. Effectively identifying and prioritizing these issues is critical not only for protecting environmental and social well-being but also for enhancing the operational performance and competitiveness of port enterprises. In this context, the aim of the study is to evaluate the priority environmental issues faced by port operations using an analytical approach. To this end, the q-rung orthopair fuzzy step-wise weight assessment ratio analysis (q-ROF-SWARA) method, one of the multi-criteria decision-making techniques, was employed due to its ability to effectively handle uncertainty and subjective expert judgments. The findings indicate that energy consumption is the most significant environmental issue for port operations, while noise is considered the least important relative to other factors. The results provide valuable insights for decision-makers in developing sustainable port management practices and formulating effective environmental strategies. 03-19-2026 <![CDATA[ This study addresses the impact of increasing environmental pressures on a global scale on the long-term strategies of businesses, with particular emphasis on the importance of environmental responsibility in port operations. Port activities are directly associated with various environmental issues, including climate change, global warming, air and water pollution, noise pollution, waste management, and energy consumption. Effectively identifying and prioritizing these issues is critical not only for protecting environmental and social well-being but also for enhancing the operational performance and competitiveness of port enterprises. In this context, the aim of the study is to evaluate the priority environmental issues faced by port operations using an analytical approach. To this end, the q-rung orthopair fuzzy step-wise weight assessment ratio analysis (q-ROF-SWARA) method, one of the multi-criteria decision-making techniques, was employed due to its ability to effectively handle uncertainty and subjective expert judgments. The findings indicate that energy consumption is the most significant environmental issue for port operations, while noise is considered the least important relative to other factors. The results provide valuable insights for decision-makers in developing sustainable port management practices and formulating effective environmental strategies. ]]> Analyzing Prioritized Environmental Issues in Port Operations Using the q-ROF-SWARA Method selçuk korucuk ahmet aytekin ayşe güngör doi: 10.56578/jimd050107 Journal of Intelligent Management Decision 03-19-2026 Journal of Intelligent Management Decision 03-19-2026 2026 5 1 Article 87 10.56578/jimd050107 https://www.acadlore.com/article/JIMD/2026_5_1/jimd050107 https://www.acadlore.com/article/JIMD/2026_5_1/jimd050106 This study develops a structured framework for the quantitative assessment of supplier-related risk in organizational supply networks. The proposed methodology is based on the Action Priority (AP) concept from Failure Mode and Effects Analysis (FMEA), which evaluates risk using three factors: Severity (S), Occurrence (O), and Detectability (D). Based on expert assessments and AP decision matrices, individual suppliers are classified into three risk categories: Low (L), Medium (M), and High (H). To enable a more rigorous analytical representation of these qualitative assessments, the risk categories are modeled using triangular fuzzy numbers (TFNs). The fuzzy values associated with individual suppliers are aggregated using the fuzzy arithmetic mean operator and subsequently defuzzified through the centroid method. After normalization, a single quantitative indicator—the Overall Supplier Risk Index—is obtained, providing insight into the company’s overall dependence on its supplier base. The proposed framework is demonstrated through a case study of a furniture manufacturing company in the wood-processing industry involving 39 strategically important suppliers. The results indicate that the analyzed company belongs to the second risk priority level, corresponding to a low overall supply risk exposure. The developed model enables the transformation of qualitative expert evaluations into a single analytical indicator, thereby supporting managerial decision-making in supplier risk monitoring and supply strategy development. 03-15-2026 <![CDATA[ This study develops a structured framework for the quantitative assessment of supplier-related risk in organizational supply networks. The proposed methodology is based on the Action Priority (AP) concept from Failure Mode and Effects Analysis (FMEA), which evaluates risk using three factors: Severity (S), Occurrence (O), and Detectability (D). Based on expert assessments and AP decision matrices, individual suppliers are classified into three risk categories: Low (L), Medium (M), and High (H). To enable a more rigorous analytical representation of these qualitative assessments, the risk categories are modeled using triangular fuzzy numbers (TFNs). The fuzzy values associated with individual suppliers are aggregated using the fuzzy arithmetic mean operator and subsequently defuzzified through the centroid method. After normalization, a single quantitative indicator—the Overall Supplier Risk Index—is obtained, providing insight into the company’s overall dependence on its supplier base. The proposed framework is demonstrated through a case study of a furniture manufacturing company in the wood-processing industry involving 39 strategically important suppliers. The results indicate that the analyzed company belongs to the second risk priority level, corresponding to a low overall supply risk exposure. The developed model enables the transformation of qualitative expert evaluations into a single analytical indicator, thereby supporting managerial decision-making in supplier risk monitoring and supply strategy development. ]]> A Decision-Support Framework for Evaluating Supplier Portfolio Risk: Integrating the FMEA Action Priority Approach with Fuzzy Modeling nikola komatina doi: 10.56578/jimd050106 Journal of Intelligent Management Decision 03-15-2026 Journal of Intelligent Management Decision 03-15-2026 2026 5 1 Article 77 10.56578/jimd050106 https://www.acadlore.com/article/JIMD/2026_5_1/jimd050106 https://www.acadlore.com/article/JIMD/2026_5_1/jimd050105 The rapid diffusion of artificial intelligence (AI) into decision-making processes has raised critical questions about how AI reshapes human behavior, judgment, and responsibility. While existing studies often emphasize technical performance, less attention has been given to the behavioral dynamics that emerge when humans interact with AI-supported systems. This study addresses this gap by proposing an integrated Strengths, Weaknesses, Opportunities, and Threats–Analytic Hierarchy Process–Technique for Order Preference by Similarity to an Ideal Solution (SWOT–AHP–TOPSIS) framework to systematically evaluate the behavioral impact of AI-assisted decision making. First, key behavioral factors are identified using SWOT analysis, where strengths and weaknesses represent internal human behavioral traits, and opportunities and threats capture external and contextual influences related to human–AI interaction. These factors are then weighted using AHP based on expert judgments, with consistency checks ensuring methodological reliability. Finally, TOPSIS is applied to rank three AI-assisted decision scenarios—human-dominant, shared-control, and AI-dominant decision making—according to their overall behavioral performance. The results indicate that behavioral weaknesses, such as over-reliance on AI and reduced critical thinking, exert the strongest influence on decision quality. Among the evaluated scenarios, human-dominant decision making achieves the highest closeness coefficient, followed by shared-control and AI-dominant scenarios. Sensitivity analysis confirms the robustness of these rankings under reasonable variations in criterion weights. Methodologically, this study demonstrates that the SWOT–AHP–TOPSIS approach, traditionally used in strategic and operational research, can be effectively adapted to behavioral and socio-technical contexts. Substantively, the findings highlight the importance of preserving human cognitive agency in AI-assisted environments. The proposed framework offers a practical and theoretically grounded tool for researchers, designers, and policymakers to assess and guide the behavioral implications of AI-supported decision systems. 03-08-2026 <![CDATA[ The rapid diffusion of artificial intelligence (AI) into decision-making processes has raised critical questions about how AI reshapes human behavior, judgment, and responsibility. While existing studies often emphasize technical performance, less attention has been given to the behavioral dynamics that emerge when humans interact with AI-supported systems. This study addresses this gap by proposing an integrated Strengths, Weaknesses, Opportunities, and Threats–Analytic Hierarchy Process–Technique for Order Preference by Similarity to an Ideal Solution (SWOT–AHP–TOPSIS) framework to systematically evaluate the behavioral impact of AI-assisted decision making. First, key behavioral factors are identified using SWOT analysis, where strengths and weaknesses represent internal human behavioral traits, and opportunities and threats capture external and contextual influences related to human–AI interaction. These factors are then weighted using AHP based on expert judgments, with consistency checks ensuring methodological reliability. Finally, TOPSIS is applied to rank three AI-assisted decision scenarios—human-dominant, shared-control, and AI-dominant decision making—according to their overall behavioral performance. The results indicate that behavioral weaknesses, such as over-reliance on AI and reduced critical thinking, exert the strongest influence on decision quality. Among the evaluated scenarios, human-dominant decision making achieves the highest closeness coefficient, followed by shared-control and AI-dominant scenarios. Sensitivity analysis confirms the robustness of these rankings under reasonable variations in criterion weights. Methodologically, this study demonstrates that the SWOT–AHP–TOPSIS approach, traditionally used in strategic and operational research, can be effectively adapted to behavioral and socio-technical contexts. Substantively, the findings highlight the importance of preserving human cognitive agency in AI-assisted environments. The proposed framework offers a practical and theoretically grounded tool for researchers, designers, and policymakers to assess and guide the behavioral implications of AI-supported decision systems. ]]> Human Behavioral Dynamics in AI-Assisted Decision Making: An Integrated SWOT–AHP–TOPSIS Analysis vo van tuyen doi: 10.56578/jimd050105 Journal of Intelligent Management Decision 03-08-2026 Journal of Intelligent Management Decision 03-08-2026 2026 5 1 Article 64 10.56578/jimd050105 https://www.acadlore.com/article/JIMD/2026_5_1/jimd050105 https://www.acadlore.com/article/JIMD/2026_5_1/jimd050104 Digital finance has increasingly influenced the functioning and stability of industrial systems by reshaping interregional economic linkages. Based on panel data from 31 Chinese provinces spanning the period 2012–2021, this study investigates how the development of digital finance is associated with the spatial structure of industrial chain resilience. A modified gravity model is used to construct interprovincial interaction networks, and social network analysis is applied to examine their structural characteristics and temporal evolution. The empirical results show that the spatial network related to digital finance and industrial chain resilience has become progressively more connected over time, as reflected by a gradual increase in network density. However, substantial regional heterogeneity persists in network position and influence. Provinces with relatively advanced digital finance tend to occupy more central positions and exert stronger structural influence, whereas peripheral provinces remain weakly connected and play limited roles within the network. This asymmetric network configuration constrains the overall stability of the industrial chain system and highlights the importance of coordinated development in digital finance for improving systemic resilience. 01-29-2026 <![CDATA[ Digital finance has increasingly influenced the functioning and stability of industrial systems by reshaping interregional economic linkages. Based on panel data from 31 Chinese provinces spanning the period 2012–2021, this study investigates how the development of digital finance is associated with the spatial structure of industrial chain resilience. A modified gravity model is used to construct interprovincial interaction networks, and social network analysis is applied to examine their structural characteristics and temporal evolution. The empirical results show that the spatial network related to digital finance and industrial chain resilience has become progressively more connected over time, as reflected by a gradual increase in network density. However, substantial regional heterogeneity persists in network position and influence. Provinces with relatively advanced digital finance tend to occupy more central positions and exert stronger structural influence, whereas peripheral provinces remain weakly connected and play limited roles within the network. This asymmetric network configuration constrains the overall stability of the industrial chain system and highlights the importance of coordinated development in digital finance for improving systemic resilience. ]]> Digital Finance and Industrial Chain Resilience in China: A Spatial Network Perspective xiaohong dong xiangqian zhu liping li doi: 10.56578/jimd050104 Journal of Intelligent Management Decision 01-29-2026 Journal of Intelligent Management Decision 01-29-2026 2026 5 1 Article 47 10.56578/jimd050104 https://www.acadlore.com/article/JIMD/2026_5_1/jimd050104 https://www.acadlore.com/article/JIMD/2026_5_1/jimd050103 The digital transformation of commercial banks (DTCB) has altered the way financial institutions collect, process, and use information, with potential implications for firms’ investment behaviour. This study examines whether and how DTCB affects corporate investment efficiency using panel data on Chinese listed companies from 2013 to 2023. The results indicate that a higher level of DTCB is associated with a statistically significant improvement in corporate investment efficiency. Further analysis suggests that this effect operates primarily through two channels: a reduction in financing constraints and a decline in agency costs. The heterogeneity analysis shows that the positive effect of DTCB on investment efficiency is concentrated among privately owned firms, while no significant effect is observed for state-owned enterprises (SOEs). These findings provide evidence that the DTCB reshapes firms’ financing and governance environments in ways that influence investment outcomes. The study contributes to the literature on digital finance and corporate investment by offering firm-level empirical evidence on the economic consequences of banking digitalisation. 01-18-2026 <![CDATA[ The digital transformation of commercial banks (DTCB) has altered the way financial institutions collect, process, and use information, with potential implications for firms’ investment behaviour. This study examines whether and how DTCB affects corporate investment efficiency using panel data on Chinese listed companies from 2013 to 2023. The results indicate that a higher level of DTCB is associated with a statistically significant improvement in corporate investment efficiency. Further analysis suggests that this effect operates primarily through two channels: a reduction in financing constraints and a decline in agency costs. The heterogeneity analysis shows that the positive effect of DTCB on investment efficiency is concentrated among privately owned firms, while no significant effect is observed for state-owned enterprises (SOEs). These findings provide evidence that the DTCB reshapes firms’ financing and governance environments in ways that influence investment outcomes. The study contributes to the literature on digital finance and corporate investment by offering firm-level empirical evidence on the economic consequences of banking digitalisation. ]]> How Digital Transformation in Commercial Banks Shapes Corporate Investment Decisions: Evidence from Chinese Listed Firms fenfen ma liangliang dong shah fahad yuqin cui doi: 10.56578/jimd050103 Journal of Intelligent Management Decision 01-18-2026 Journal of Intelligent Management Decision 01-18-2026 2026 5 1 Article 35 10.56578/jimd050103 https://www.acadlore.com/article/JIMD/2026_5_1/jimd050103 https://www.acadlore.com/article/JIMD/2026_5_1/jimd050102 This work provides a complete methodology for adopting well-established AI methods (predictive analytics, LLM agents, forecasting) into Microsoft Dynamics 365 Customer Relationship Management (CRM) for agricultural lending. While not claiming that the algorithms are novel, this work contributes a pragmatic approach to implementing these algorithms that specifically address the regulatory, seasonal, and operational characteristics of agricultural finance, as regulated by the Farm Credit System. It focuses on the real-life constraints and constraints within the regulated financial services industry, and measurable impacts that occurred. The paper provides a domain-oriented application of specific existing AI-CRM integration, with credible statistical testing including an external validation on USDA datasets and benchmarking across peer Farm Credit institutions, as well as cross-institutional analysis. By taking a reasonably conservative duration of 18 months, the Farm Credit institutions noted a statistically significant impact (operational efficiencies of the lending institution to assess member interests) where average case resolution time reduced by 28% (67.2h to 48.4h), and lead conversions improved by 35% (25.9% to 35.0%). Each methodology of implementation also included a series of validations in compliance with regulatory oversight in financial institutions that started to build data governance, model performance compliance through a proactive risk definition, and compliance standards suitable for their institution, and within regulatory standards by regulations. Beyond statistical significance (paired tests, $p 01-13-2026 <![CDATA[ <p>This work provides a complete methodology for adopting well-established AI methods (predictive analytics, LLM agents, forecasting) into Microsoft Dynamics 365 Customer Relationship Management (CRM) for agricultural lending. While not claiming that the algorithms are novel, this work contributes a pragmatic approach to implementing these algorithms that specifically address the regulatory, seasonal, and operational characteristics of agricultural finance, as regulated by the Farm Credit System. It focuses on the real-life constraints and constraints within the regulated financial services industry, and measurable impacts that occurred. The paper provides a domain-oriented application of specific existing AI-CRM integration, with credible statistical testing including an external validation on USDA datasets and benchmarking across peer Farm Credit institutions, as well as cross-institutional analysis. By taking a reasonably conservative duration of 18 months, the Farm Credit institutions noted a statistically significant impact (operational efficiencies of the lending institution to assess member interests) where average case resolution time reduced by 28% (67.2h to 48.4h), and lead conversions improved by 35% (25.9% to 35.0%). Each methodology of implementation also included a series of validations in compliance with regulatory oversight in financial institutions that started to build data governance, model performance compliance through a proactive risk definition, and compliance standards suitable for their institution, and within regulatory standards by regulations. Beyond statistical significance (paired tests, $p <0.001$), practical impact was quantified using absolute and relative changes and bootstrap confidence intervals. The article provides the agricultural lending industry an applied methodology to adopt AI for stakeholder innovation while ensuring they are adept in their enterprise risk management requirement, and still target measurable business outcomes. Given a conservative potential implementation timetable (i.e., 18 months) and validation methodology protocols developed to ensure complete data and model validation, this approach is scalable for agricultural lending implementation and would be a useful instrument across all 72 Farm Credit System institutions.</p> ]]> Implementing AI-Driven Decision Support in Agricultural Lending Through Predictive Analytics for Customer Relationship Management karthik nallani chakravartula aravind raghu doi: 10.56578/jimd050102 Journal of Intelligent Management Decision 01-13-2026 Journal of Intelligent Management Decision 01-13-2026 2026 5 1 Article 11 10.56578/jimd050102 https://www.acadlore.com/article/JIMD/2026_5_1/jimd050102 https://www.acadlore.com/article/JIMD/2026_5_1/jimd050101 The rapid development of e-commerce has made last-mile delivery a critical bottleneck in logistics management, with its efficiency directly impacting operational costs, service quality, and environmental sustainability. To address the multi-criteria decision-making (MCDM) problem of parcel locker location selection, this study constructs an intelligent decision-support framework that integrates the Improved Fuzzy Step-wise Weight Assessment Ratio Analysis (IMF SWARA) and the Weighted Aggregated Sum Product Assessment (WASPAS) methods. Based on real-world data from the Brčko Distribution Center of a regional logistics company (X Express), the research first employs the IMF SWARA method to determine fuzzy weights for six key criteria, including availability, frequency of user requests, and accessibility. The WASPAS method is then applied to comprehensively rank twelve candidate locations. Results indicate that location A2 is the optimal choice, followed by A4 and A3. The robustness of the model is verified through sensitivity analysis, including comparisons with other MCDM methods such as ARAS, EDAS, and MARCOS, as well as systematic variation tests of the $\lambda$ parameter in WASPAS. This framework provides logistics managers with a structured and quantifiable decision-making tool, facilitating data-driven optimization of last-mile delivery networks in complex urban environments and enhancing the sustainability and operational efficiency of logistics systems. 12-25-2025 <![CDATA[ The rapid development of e-commerce has made last-mile delivery a critical bottleneck in logistics management, with its efficiency directly impacting operational costs, service quality, and environmental sustainability. To address the multi-criteria decision-making (MCDM) problem of parcel locker location selection, this study constructs an intelligent decision-support framework that integrates the Improved Fuzzy Step-wise Weight Assessment Ratio Analysis (IMF SWARA) and the Weighted Aggregated Sum Product Assessment (WASPAS) methods. Based on real-world data from the Brčko Distribution Center of a regional logistics company (X Express), the research first employs the IMF SWARA method to determine fuzzy weights for six key criteria, including availability, frequency of user requests, and accessibility. The WASPAS method is then applied to comprehensively rank twelve candidate locations. Results indicate that location A2 is the optimal choice, followed by A4 and A3. The robustness of the model is verified through sensitivity analysis, including comparisons with other MCDM methods such as ARAS, EDAS, and MARCOS, as well as systematic variation tests of the $\lambda$ parameter in WASPAS. This framework provides logistics managers with a structured and quantifiable decision-making tool, facilitating data-driven optimization of last-mile delivery networks in complex urban environments and enhancing the sustainability and operational efficiency of logistics systems. ]]> An Intelligent Decision Framework for Optimizing Sustainable Last-Mile Delivery: Parcel Locker Location with IMF SWARA-WASPAS medina fetić asad khattak stefan jovčić doi: 10.56578/jimd050101 Journal of Intelligent Management Decision 12-25-2025 Journal of Intelligent Management Decision 12-25-2025 2026 5 1 Article 1 10.56578/jimd050101 https://www.acadlore.com/article/JIMD/2026_5_1/jimd050101 https://www.acadlore.com/article/JIMD/2025_4_4/jimd040405 Assessing the performance of decision-making units (DMUs) under intuitionistic fuzzy conditions has emerged as an essential area of investigation in today’s performance evaluation studies. The framework demonstrated in Intuitionistic Fuzzy Data Envelopment Analysis (IFDEA) is a way to assess the relative performance of DMUs when the observed data are notably expressed as ambiguity or uncertainty in the inputs and outputs represented by intuitionistic fuzzy numbers (IFN). When the situations define the conditions to use models with traditional input-output distinctions, traditional models are not less applicable when the parameters are vague, thus prompting the need for a set of more flexible tools. In this work, a ranking procedure is utilized that uses the centroid of triangular intuitionistic fuzzy numbers (TIFNs) to address the IFDEA model that defined input and output variable through TIFNs, it allows to calculate the efficiency status of each unit and to differentiate the DMUs between efficient and inefficient groups. An intuitionistic super-efficiency (IFSE) model is provided to obtain a complete ranking of DMUs that identified as efficient. To help decision makers, a reference-set-oriented benchmarking strategy is created to identify relevant peer units of the DMUs identified as inefficient to assist in improving their performance. To demonstrate the strength and practical applicability of the proposed framework, two examples of application are presented, as well as discussed, the technical differences of comparing the outcomes of analysis with the ranking proposals existing in the literature. 12-17-2025 <![CDATA[ <p>Assessing the performance of decision-making units (DMUs) under intuitionistic fuzzy conditions has <span>emerged as an essential area of investigation in today’s performance evaluation studies. The framework demonstrated </span>in Intuitionistic Fuzzy Data Envelopment Analysis (IFDEA) is a way to assess the relative performance of DMUs when the observed data are notably expressed as ambiguity or uncertainty in the inputs and outputs represented<span> </span>by intuitionistic fuzzy numbers (IFN). When the situations define the conditions to use models with traditional input-output<span> </span>distinctions,<span> </span>traditional<span> </span>models<span> </span>are<span> </span>not<span> </span>less<span> </span>applicable<span> </span>when<span> </span>the<span> </span>parameters<span> </span>are<span> </span>vague,<span> </span>thus<span> </span>prompting the need for a set of more flexible tools.<span> </span>In this work, a ranking procedure is utilized that uses the centroid of triangular<span> </span>intuitionistic<span> </span>fuzzy<span> </span>numbers<span> </span>(TIFNs)<span> </span>to<span> </span>address<span> </span>the<span> </span>IFDEA<span> </span>model<span> </span>that<span> </span>defined<span> </span>input<span> </span>and<span> </span>output<span> </span>variable <span>through TIFNs, it allows to calculate the efficiency status of each unit and to differentiate the DMUs between efficient </span>and<span> </span>inefficient<span> </span>groups.<span> </span>An<span> </span>intuitionistic<span> </span>super-efficiency<span> </span>(IFSE)<span> </span>model<span> </span>is<span> </span>provided<span> </span>to<span> </span>obtain<span> </span>a<span> </span>complete<span> </span>ranking<span> </span>of <span>DMUs that identified as efficient. To help decision makers, a reference-set-oriented benchmarking strategy is created </span>to identify relevant peer units of the DMUs identified as inefficient to assist in improving their performance.<span> </span>To demonstrate the strength and practical applicability of the proposed framework, two examples of application are presented, as well as discussed, the technical differences of comparing the outcomes of analysis with the ranking proposals existing in the literature.</p> ]]> Performance Assessment of DMUs Using Intuitionistic Fuzzy DEA with Complete Ranking and Benchmarking Capabilities kshitish kumar mohanta doi: 10.56578/jimd040405 Journal of Intelligent Management Decision 12-17-2025 Journal of Intelligent Management Decision 12-17-2025 2025 4 4 Article 298 10.56578/jimd040405 https://www.acadlore.com/article/JIMD/2025_4_4/jimd040405 https://www.acadlore.com/article/JIMD/2025_4_4/jimd040404 Selecting an optimal logistics service provider is a complex multi-criteria decision-making problem that directly affects a company’s competitiveness. This paper proposes a hybrid MCDM model that integrates the Full Consistency Method (FUCOM) and Measurement Alternatives and Ranking according to Compromise Solution (MARCOS) methods. FUCOM was used to determine the weight coefficients of seven criteria, while MARCOS was applied to rank ten potential logistics providers in the market of Bosnia and Herzegovina. The case study was conducted for the company Hygiene Pro Team from Banja Luka. The results showed that provider P9 represents the most favorable solution, which was confirmed by an extensive sensitivity analysis that verified the stability of the model. The proposed FUCOM–MARCOS model provides a robust framework for strategic decision-making in logistics. 11-28-2025 <![CDATA[ <p>Selecting an optimal logistics service provider is a complex multi-criteria decision-making problem that directly affects a company’s competitiveness. This paper proposes a hybrid MCDM model that integrates the Full Consistency Method (FUCOM) and Measurement Alternatives and Ranking according to Compromise Solution (MARCOS) methods. FUCOM was used to determine the weight coefficients of seven criteria, while MARCOS was applied to rank ten potential logistics providers in the market of Bosnia and Herzegovina. The case study was conducted for the company Hygiene Pro Team from Banja Luka. The results showed that provider P9 represents the most favorable solution, which was confirmed by an extensive sensitivity analysis that verified the stability of the model. The proposed FUCOM–MARCOS model provides a robust framework for strategic decision-making in logistics.</p> ]]> Application of the FUCOM and MARCOS Methods for Selecting Logistics Service Providers marko blagojević dimitrije blagojević algimantas danilevičius evelin krmac salvatore antonio biancardo doi: 10.56578/jimd040404 Journal of Intelligent Management Decision 11-28-2025 Journal of Intelligent Management Decision 11-28-2025 2025 4 4 Article 288 10.56578/jimd040404 https://www.acadlore.com/article/JIMD/2025_4_4/jimd040404 https://www.acadlore.com/article/JIMD/2025_4_4/jimd040403 It could be argued that the competitive resources possessed by organisations today are similar. One of the most important factors that differentiates businesses, provides a competitive advantage, and enables them to stay one step ahead of their competitors is human capital. Organisations' ability to act in line with their mission, vision, and goals depends on the effective and efficient management of this capital. Selecting the right personnel is one of the most important stages in managing human resources effectively and efficiently. If the selected personnel do not perform as expected, it can indeed harm the organisation. The purpose of this study is therefore to identify the selection criteria prioritised by human resources managers in a call centre, a hospital, a bank, a public economic enterprise, and two companies operating in an organized industrial zone in personnel selection. The criteria prioritised in personnel selection were first collected during initial interviews with relevant managers to create a pool of criteria. Ten of these criteria were then presented to the managers in a second interview, and they were asked to rank them in order of importance. Data obtained from each manager was analysed using the IMF-SWARA method. According to the results, the most important criterion for managers was “Position and competency alignment (PCA)”, while the least important criterion was “solving problems promptly and effectively (SPP)”. These findings demonstrate that managers prioritise compatibility between the qualities of the job and those of the personnel. It is believed that these results can guide managers in organisations operating in the relevant sector, as well as individuals considering working in this sector. 11-11-2025 <![CDATA[ It could be argued that the competitive resources possessed by organisations today are similar. One of the most important factors that differentiates businesses, provides a competitive advantage, and enables them to stay one step ahead of their competitors is human capital. Organisations' ability to act in line with their mission, vision, and goals depends on the effective and efficient management of this capital. Selecting the right personnel is one of the most important stages in managing human resources effectively and efficiently. If the selected personnel do not perform as expected, it can indeed harm the organisation. The purpose of this study is therefore to identify the selection criteria prioritised by human resources managers in a call centre, a hospital, a bank, a public economic enterprise, and two companies operating in an organized industrial zone in personnel selection. The criteria prioritised in personnel selection were first collected during initial interviews with relevant managers to create a pool of criteria. Ten of these criteria were then presented to the managers in a second interview, and they were asked to rank them in order of importance. Data obtained from each manager was analysed using the IMF-SWARA method. According to the results, the most important criterion for managers was “Position and competency alignment (PCA)”, while the least important criterion was “solving problems promptly and effectively (SPP)”. These findings demonstrate that managers prioritise compatibility between the qualities of the job and those of the personnel. It is believed that these results can guide managers in organisations operating in the relevant sector, as well as individuals considering working in this sector. ]]> Use of the IMF SWARA Method in Personnel Selection and its Solution nuri karaca alptekin ulutaş ali oğuz bayrakçıl dillip kumar das sarfaraz hashemkhani zolfani cipriana sava doi: 10.56578/jimd040403 Journal of Intelligent Management Decision 11-11-2025 Journal of Intelligent Management Decision 11-11-2025 2025 4 4 Article 278 10.56578/jimd040403 https://www.acadlore.com/article/JIMD/2025_4_4/jimd040403 https://www.acadlore.com/article/JIMD/2025_4_4/jimd040402 The optimization of project schedules in the presence of uncertainty remains a critical challenge in project management. This study proposes a hybrid methodology that combines Monte Carlo Simulation (MCS) with Integer Linear Programming (ILP) to optimize project crashing strategies under conditions of schedule risk. The approach was applied to a real-world telecommunications infrastructure project, which involved the construction of 50 towers within a stringent contractual deadline. MCS was employed to model the uncertainty in activity durations and assess the likelihood of on-time project completion, while ILP was used to determine the most cost-effective crashing strategy. The findings indicate that, without any mitigation measures, the probability of completing the project within the planned 68-day schedule was a mere 3%. However, upon implementing risk response measures, this probability increased to 21%. A comparative analysis demonstrated that delay penalties increase at a much higher rate than crashing costs, highlighting the significant financial benefits of early intervention. This study illustrates that the integration of probabilistic risk analysis with optimization techniques not only enhances schedule reliability but also minimizes cost overruns, providing a robust decision-making framework for complex projects. By leveraging the combination of MCS and ILP, the proposed methodology supports the development of more resilient and economically efficient project plans, particularly in projects characterized by high uncertainty and time-sensitive constraints. 10-12-2025 <![CDATA[ <p>The optimization of project schedules in the presence of uncertainty remains a critical challenge in project management. This study proposes a hybrid methodology that combines Monte Carlo Simulation (MCS) with Integer Linear Programming (ILP) to optimize project crashing strategies under conditions of schedule risk. The approach was applied to a real-world telecommunications infrastructure project, which involved the construction of 50 towers within a stringent contractual deadline. MCS was employed to model the uncertainty in activity durations and assess the likelihood of on-time project completion, while ILP was used to determine the most cost-effective crashing strategy. The findings indicate that, without any mitigation measures, the probability of completing the project within the planned 68-day schedule was a mere 3%. However, upon implementing risk response measures, this probability increased to 21%. A comparative analysis demonstrated that delay penalties increase at a much higher rate than crashing costs, highlighting the significant financial benefits of early intervention. This study illustrates that the integration of probabilistic risk analysis with optimization techniques not only enhances schedule reliability but also minimizes cost overruns, providing a robust decision-making framework for complex projects. By leveraging the combination of MCS and ILP, the proposed methodology supports the development of more resilient and economically efficient project plans, particularly in projects characterized by high uncertainty and time-sensitive constraints.</p> ]]> Optimizing Project Schedules under Uncertainty: A Hybrid Approach to Crashing and Risk Evaluation Using Monte Carlo Simulation and Integer Linear Programming abdalla ehnaish ibrahim badi doi: 10.56578/jimd040402 Journal of Intelligent Management Decision 10-12-2025 Journal of Intelligent Management Decision 10-12-2025 2025 4 4 Article 268 10.56578/jimd040402 https://www.acadlore.com/article/JIMD/2025_4_4/jimd040402 https://www.acadlore.com/article/JIMD/2025_4_4/jimd040401 The accelerating growth of urban populations, rapid city expansion, and inadequacies in transportation infrastructure have exacerbated traffic congestion and environmental burdens in metropolitan areas. These challenges have intensified the demand for sustainable mobility strategies, with electric vehicles emerging as a central component of urban decarbonization and efficiency initiatives. In this study, a structured multi-criteria decision-making framework was established to determine the most suitable electric vehicle for courier services. The framework was developed using the analytic hierarchy process (AHP), which enables the systematic evaluation of both criteria and sub-criteria and provides a robust mechanism for prioritizing alternatives. To enhance reliability, the model was implemented and validated using Expert Choice software, allowing for consistency testing and sensitivity analysis. Three categories of electric vehicles—electric cars, electric scooters, and electric bicycles—were assessed against a comprehensive set of decision factors encompassing economic, operational, environmental, and infrastructural dimensions. The resulting preference weights indicated that electric cars (0.387) represent the most suitable option for courier services under the evaluated conditions, followed closely by electric scooters (0.316) and electric bicycles (0.297). The ranking highlights the relative advantages of electric cars in balancing load capacity, operational flexibility, and environmental impact, while also reflecting the growing feasibility of scooters and bicycles for last-mile delivery. By offering a transparent and replicable approach to alternative vehicle selection, this research contributes to the optimization of courier logistics and the promotion of environmentally responsible transportation systems in congested urban environments. The methodological framework developed in this study may be adapted for broader applications in sustainable transport planning and fleet management, supporting policy-makers and practitioners in achieving urban sustainability objectives. 09-18-2025 <![CDATA[ The accelerating growth of urban populations, rapid city expansion, and inadequacies in transportation infrastructure have exacerbated traffic congestion and environmental burdens in metropolitan areas. These challenges have intensified the demand for sustainable mobility strategies, with electric vehicles emerging as a central component of urban decarbonization and efficiency initiatives. In this study, a structured multi-criteria decision-making framework was established to determine the most suitable electric vehicle for courier services. The framework was developed using the analytic hierarchy process (AHP), which enables the systematic evaluation of both criteria and sub-criteria and provides a robust mechanism for prioritizing alternatives. To enhance reliability, the model was implemented and validated using Expert Choice software, allowing for consistency testing and sensitivity analysis. Three categories of electric vehicles—electric cars, electric scooters, and electric bicycles—were assessed against a comprehensive set of decision factors encompassing economic, operational, environmental, and infrastructural dimensions. The resulting preference weights indicated that electric cars (0.387) represent the most suitable option for courier services under the evaluated conditions, followed closely by electric scooters (0.316) and electric bicycles (0.297). The ranking highlights the relative advantages of electric cars in balancing load capacity, operational flexibility, and environmental impact, while also reflecting the growing feasibility of scooters and bicycles for last-mile delivery. By offering a transparent and replicable approach to alternative vehicle selection, this research contributes to the optimization of courier logistics and the promotion of environmentally responsible transportation systems in congested urban environments. The methodological framework developed in this study may be adapted for broader applications in sustainable transport planning and fleet management, supporting policy-makers and practitioners in achieving urban sustainability objectives. ]]> Application of the Analytic Hierarchy Process for Optimizing the Selection of Electric Vehicles in Urban Courier Services sreten simović jelena šaković-jovanović tijana ivanišević aleksandar trifunović doi: 10.56578/jimd040401 Journal of Intelligent Management Decision 09-18-2025 Journal of Intelligent Management Decision 09-18-2025 2025 4 4 Article 256 10.56578/jimd040401 https://www.acadlore.com/article/JIMD/2025_4_4/jimd040401 https://www.acadlore.com/article/JIMD/2025_4_3/jimd040305 The rapid proliferation of mobile banking has transformed the delivery of financial services, necessitating a comprehensive understanding of service quality and its impact on customer satisfaction. In this study, bibliometric and content analyses were employed to examine the evolution of research on mobile banking service quality and customer satisfaction, with emphasis placed on research trends, influential contributors, thematic structures, and emerging gaps. Data retrieved from the Scopus database spanning 2003–2025 were analyzed using VOSviewer and Biblioshiny software to conduct co-word analysis, citation analysis, co-authorship mapping, and bibliographic coupling. Findings indicate a marked acceleration of research activity after 2015, with significant contributions originating from India, Indonesia, and Saudi Arabia, while University Tun Hussein Onn Malaysia emerged as one of the most active institutions. The International Journal of Bank Marketing was identified as the leading publication outlet, and scholars such as Lee and Chung were recognized as influential authors. Network analysis revealed that customer satisfaction, trust, security, service quality, and usability constitute the dominant themes in this research domain. Co-authorship analysis demonstrated robust collaborations among Saudi Arabia, China, the United Kingdom, and the United States, whereas bibliographic coupling confirmed that trust and service quality are central drivers of mobile banking adoption. The originality of this study lies in the provision of a structured synthesis of the intellectual landscape of mobile banking research and in the articulation of critical knowledge gaps. Limitations include reliance on Scopus-indexed studies and the exclusion of non-English publications, which may restrict global comprehensiveness. Future research should prioritize the integration of artificial intelligence in mobile banking, the role of mobile financial services in advancing financial inclusion, and the implications of evolving regulatory frameworks for customer trust and satisfaction. By consolidating existing evidence and highlighting strategic research directions, this study offers a foundation for advancing theoretical, methodological, and practical understanding of mobile banking services. 09-14-2025 <![CDATA[ The rapid proliferation of mobile banking has transformed the delivery of financial services, necessitating a comprehensive understanding of service quality and its impact on customer satisfaction. In this study, bibliometric and content analyses were employed to examine the evolution of research on mobile banking service quality and customer satisfaction, with emphasis placed on research trends, influential contributors, thematic structures, and emerging gaps. Data retrieved from the Scopus database spanning 2003–2025 were analyzed using VOSviewer and Biblioshiny software to conduct co-word analysis, citation analysis, co-authorship mapping, and bibliographic coupling. Findings indicate a marked acceleration of research activity after 2015, with significant contributions originating from India, Indonesia, and Saudi Arabia, while University Tun Hussein Onn Malaysia emerged as one of the most active institutions. The International Journal of Bank Marketing was identified as the leading publication outlet, and scholars such as Lee and Chung were recognized as influential authors. Network analysis revealed that customer satisfaction, trust, security, service quality, and usability constitute the dominant themes in this research domain. Co-authorship analysis demonstrated robust collaborations among Saudi Arabia, China, the United Kingdom, and the United States, whereas bibliographic coupling confirmed that trust and service quality are central drivers of mobile banking adoption. The originality of this study lies in the provision of a structured synthesis of the intellectual landscape of mobile banking research and in the articulation of critical knowledge gaps. Limitations include reliance on Scopus-indexed studies and the exclusion of non-English publications, which may restrict global comprehensiveness. Future research should prioritize the integration of artificial intelligence in mobile banking, the role of mobile financial services in advancing financial inclusion, and the implications of evolving regulatory frameworks for customer trust and satisfaction. By consolidating existing evidence and highlighting strategic research directions, this study offers a foundation for advancing theoretical, methodological, and practical understanding of mobile banking services. ]]> Mobile Banking Service Quality and Customer Satisfaction: A Bibliometric and Content Analysis with Future Research Directions rahmanwali sahar abdullah ziarmal ismail labib samiullah hotak doi: 10.56578/jimd040305 Journal of Intelligent Management Decision 09-14-2025 Journal of Intelligent Management Decision 09-14-2025 2025 4 3 Article 235 10.56578/jimd040305 https://www.acadlore.com/article/JIMD/2025_4_3/jimd040305 https://www.acadlore.com/article/JIMD/2025_4_3/jimd040304 In an era defined by digital transformation and systemic volatility, conventional approaches to strategic risk management have been increasingly challenged by the complexity and unpredictability of modern operational environments. To address these limitations, a novel artificial intelligence (AI)–driven framework has been developed to enhance organizational resilience and optimize strategic decision-making. Constructed through a systematic review conducted in accordance with PRISMA 2020 guidelines, this study synthesizes current academic literature and industry publications to identify critical enablers, practical gaps, and methodological advancements in AI-enabled risk governance. The proposed framework integrates real-time analytics, predictive modelling, and adaptive governance mechanisms, aligning them with enterprise-wide strategic objectives to support decision-making under volatile, uncertain, complex, and ambiguous (VUCA) conditions. Anchored in dynamic capabilities theory and decision support systems (DSS) literature, the framework is designed to facilitate proactive risk anticipation, reduce cognitive and algorithmic biases in decision-making, and foster strategic alignment in rapidly evolving contexts. Its adaptability to small and medium-sized enterprises (SMEs), as well as its cross-sectoral relevance, underscores its scalability and practical utility. Nonetheless, the effectiveness of the framework is contingent upon the availability of high-quality data, the level of digital maturity within organizations, and the implementation of responsible AI principles. By bridging the gap between theoretical innovation and real-world applicability, this study contributes a robust foundation for future empirical validation and sector-specific customization. The framework is expected to inform governance and technology leaders aiming to institutionalize AI-based resilience capabilities, thereby supporting sustainable strategic outcomes in both developed and emerging markets. 08-11-2025 <![CDATA[ In an era defined by digital transformation and systemic volatility, conventional approaches to strategic risk management have been increasingly challenged by the complexity and unpredictability of modern operational environments. To address these limitations, a novel artificial intelligence (AI)–driven framework has been developed to enhance organizational resilience and optimize strategic decision-making. Constructed through a systematic review conducted in accordance with PRISMA 2020 guidelines, this study synthesizes current academic literature and industry publications to identify critical enablers, practical gaps, and methodological advancements in AI-enabled risk governance. The proposed framework integrates real-time analytics, predictive modelling, and adaptive governance mechanisms, aligning them with enterprise-wide strategic objectives to support decision-making under volatile, uncertain, complex, and ambiguous (VUCA) conditions. Anchored in dynamic capabilities theory and decision support systems (DSS) literature, the framework is designed to facilitate proactive risk anticipation, reduce cognitive and algorithmic biases in decision-making, and foster strategic alignment in rapidly evolving contexts. Its adaptability to small and medium-sized enterprises (SMEs), as well as its cross-sectoral relevance, underscores its scalability and practical utility. Nonetheless, the effectiveness of the framework is contingent upon the availability of high-quality data, the level of digital maturity within organizations, and the implementation of responsible AI principles. By bridging the gap between theoretical innovation and real-world applicability, this study contributes a robust foundation for future empirical validation and sector-specific customization. The framework is expected to inform governance and technology leaders aiming to institutionalize AI-based resilience capabilities, thereby supporting sustainable strategic outcomes in both developed and emerging markets. ]]> AI–Driven Frameworks for Strategic Risk Management: A Systematic Review and Model for Organizational Resilience and Decision Support khalid zeriouh mehdi amara doi: 10.56578/jimd040304 Journal of Intelligent Management Decision 08-11-2025 Journal of Intelligent Management Decision 08-11-2025 2025 4 3 Article 224 10.56578/jimd040304 https://www.acadlore.com/article/JIMD/2025_4_3/jimd040304 https://www.acadlore.com/article/JIMD/2025_4_3/jimd040303 Green supplier selection (GSS) as a critical strategic element is placed in the limelight in contemporary supply chain management (SCM), owing to the growing emphasis on environmental responsibility and sustainability. This study presents a fuzzy multi-criteria decision-making (FMCDM) framework, employing Fuzzy Logarithmic Percentage Change-Driven Objective Weighting (FLOPCOW) method to determine the relative importance of sustainability criteria under uncertainty. A panel of five academic and industry experts was selected to identify 21 criteria, which were categorized into three main dimensions including environmental performance (C1), resource efficiency (C2), and corporate sustainability policies (C3). Triangular fuzzy numbers (TFNs) were adopted to model linguistic ambiguities in expert judgments whereas fuzzy normalization was applied to ascertain the weights of criteria. Key findings indicated that corporate sustainability policies (C3) were prioritized as the most influential dimension, followed by environmental performance (C1) and resource efficiency (C2). This suggested the centrality of institutional governance in advancing long-term sustainability objectives. Sub-criteria analysis further revealed ecological training programs, air emissions control, and sustainability reporting as the most critical indicators in the interplay of operational practices and transparent governance. FLOPCOW has effectively processed expert opinions with the use of fuzzy normalization, hence advocating a clear and repeatable approach for the evaluation of green suppliers. Furthermore, it highlighted the importance of policy-based criteria in supplier assessment and organizations could then align their purchasing decisions with sustainability goals by considering more on governance-related factors like compliance and stakeholder engagement. 07-28-2025 <![CDATA[ <p>Green supplier selection (GSS) as a critical strategic element is placed in the limelight in contemporary supply chain management (SCM), owing to the growing emphasis on environmental responsibility and sustainability. This study presents a fuzzy multi-criteria decision-making (FMCDM) framework, employing Fuzzy Logarithmic Percentage Change-Driven Objective Weighting (FLOPCOW) method to determine the relative importance of sustainability criteria under uncertainty. A panel of five academic and industry experts was selected to identify 21 criteria, which were categorized into three main dimensions including environmental performance (C1), resource efficiency (C2), and corporate sustainability policies (C3). Triangular fuzzy numbers (TFNs) were adopted to model linguistic ambiguities in expert judgments whereas fuzzy normalization was applied to ascertain the weights of criteria. Key findings indicated that corporate sustainability policies (C3) were prioritized as the most influential dimension, followed by environmental performance (C1) and resource efficiency (C2). This suggested the centrality of institutional governance in advancing long-term sustainability objectives. Sub-criteria analysis further revealed ecological training programs, air emissions control, and sustainability reporting as the most critical indicators in the interplay of operational practices and transparent governance. FLOPCOW has effectively processed expert opinions with the use of fuzzy normalization, hence advocating a clear and repeatable approach for the evaluation of green suppliers. Furthermore, it highlighted the importance of policy-based criteria in supplier assessment and organizations could then align their purchasing decisions with sustainability goals by considering more on governance-related factors like compliance and stakeholder engagement.</p> ]]> Prioritizing Sustainability Criteria in Green Supplier Selection Using Fuzzy Logarithmic Percentage Change-Driven Objective Weighting (FLOPCOW) Method gülay demir prasenjit chatterjee doi: 10.56578/jimd040303 Journal of Intelligent Management Decision 07-28-2025 Journal of Intelligent Management Decision 07-28-2025 2025 4 3 Article 212 10.56578/jimd040303 https://www.acadlore.com/article/JIMD/2025_4_3/jimd040303 https://www.acadlore.com/article/JIMD/2025_4_3/jimd040302 It is crucial to ensure system reliability in changing situations where systems are required to operate in uncertainty or against disturbances. Human-in-the-Loop (HITL) simulations have in recent times emerged as an important key in ensuring the robustness and resilience of the system via evaluation and testing. The method introduces human decision-making and adaptability as well as providing insight into zones of possible weak points of the system and failure modes which are not even captured by computer-based systems. By incorporating HITL simulations into the system, designers and engineers could simulate operational challenges in real life, identify unforeseen defects, and implement mitigative strategies to enhance both robustness-ensuring consistent performance in the nominal operation and resilience-maintaining functionality in and after disruptions. This article looks at the effectiveness of HITL simulations in various domains, and particularly at the role that these contribute to system robustness and resilience. Among the most significant issues will be the nature of building the simulation environments, the test for the range of scenarios, and the roles for humans to be simulated within the loop. We investigate the behavior of humans during stress and uncertainty, then provide valuable feedback to the system to help it learn. By revealing the vulnerabilities of the system design and acknowledging human effects on recovery and decision-making operations, HITL simulations finalize the development of more adaptable, stable systems that could recover rapidly from interruptions. To conclude, HITL simulations are a critical tool for improving the reliability of systems, hence providing a comprehensive framework to address either expected or unexpected challenges in complex operating environments. 07-27-2025 <![CDATA[ It is crucial to ensure system reliability in changing situations where systems are required to operate in uncertainty or against disturbances. Human-in-the-Loop (HITL) simulations have in recent times emerged as an important key in ensuring the robustness and resilience of the system via evaluation and testing. The method introduces human decision-making and adaptability as well as providing insight into zones of possible weak points of the system and failure modes which are not even captured by computer-based systems. By incorporating HITL simulations into the system, designers and engineers could simulate operational challenges in real life, identify unforeseen defects, and implement mitigative strategies to enhance both robustness-ensuring consistent performance in the nominal operation and resilience-maintaining functionality in and after disruptions. This article looks at the effectiveness of HITL simulations in various domains, and particularly at the role that these contribute to system robustness and resilience. Among the most significant issues will be the nature of building the simulation environments, the test for the range of scenarios, and the roles for humans to be simulated within the loop. We investigate the behavior of humans during stress and uncertainty, then provide valuable feedback to the system to help it learn. By revealing the vulnerabilities of the system design and acknowledging human effects on recovery and decision-making operations, HITL simulations finalize the development of more adaptable, stable systems that could recover rapidly from interruptions. To conclude, HITL simulations are a critical tool for improving the reliability of systems, hence providing a comprehensive framework to address either expected or unexpected challenges in complex operating environments. ]]> Ensuring System Reliability Through Human-in-the-Loop (HITL) Simulations: A Robustness and Resilience Approach to Disaster Management safiye turgay muhammed serkan şahin doi: 10.56578/jimd040302 Journal of Intelligent Management Decision 07-27-2025 Journal of Intelligent Management Decision 07-27-2025 2025 4 3 Article 187 10.56578/jimd040302 https://www.acadlore.com/article/JIMD/2025_4_3/jimd040302 https://www.acadlore.com/article/JIMD/2025_4_3/jimd040301 An artificial intelligence (AI)-powered agricultural advisory system, termed NDEMRI (Nurturing Digital Extension via Mobile and Responsive Intelligence), has been developed to provide evidence-based farming guidance to rural communities across sub-Saharan Africa through short message service (SMS). Designed for compatibility with basic GSM-enabled mobile phones and independent of internet access for end-users, the system integrates large language models (LLMs) via the ChatGPT API to generate contextually relevant, linguistically localized responses to a wide array of agricultural queries. A quasi-experimental evaluation was conducted in the northern regions of Cameroon over a four-month period, employing a matched control group methodology involving 831 treatment farmers and 400 controls. Statistically significant improvements were observed among participants using NDEMRI, with mean crop yields increasing by 16.6% and agricultural incomes rising by 23%, relative to the control group. Adoption of improved agronomic practices was notably higher among users of the system. A total of 2,487 unique messages were exchanged, covering themes such as pest management, planting schedules, soil health, and post-harvest storage, with 78% of users reporting that system responses were context-sensitive and adapted to local climatic and cultural conditions. The technical architecture is characterized by modular natural language understanding pipelines, embedded guardrails to minimize model hallucinations, and a reproducible framework for contextualization based on regional agricultural datasets. A detailed economic analysis demonstrated the financial sustainability of the intervention, with favorable cost-benefit ratios and scalability potential. These findings offer robust empirical evidence that the integration of accessible communication technologies with state-of-the-art AI can overcome infrastructural limitations, enhance decision-making in low-resource farming environments, and serve as a viable model for transforming agricultural extension services across the African continent. 07-06-2025 <![CDATA[ <p>An artificial intelligence (AI)-powered agricultural advisory system, termed NDEMRI (Nurturing Digital Extension via Mobile and Responsive Intelligence), has been developed to provide evidence-based farming guidance to rural communities across sub-Saharan Africa through short message service (SMS). Designed for compatibility with basic GSM-enabled mobile phones and independent of internet access for end-users, the system integrates large language models (LLMs) via the ChatGPT API to generate contextually relevant, linguistically localized responses to a wide array of agricultural queries. A quasi-experimental evaluation was conducted in the northern regions of Cameroon over a four-month period, employing a matched control group methodology involving 831 treatment farmers and 400 controls. Statistically significant improvements were observed among participants using NDEMRI, with mean crop yields increasing by 16.6% and agricultural incomes rising by 23%, relative to the control group. Adoption of improved agronomic practices was notably higher among users of the system. A total of 2,487 unique messages were exchanged, covering themes such as pest management, planting schedules, soil health, and post-harvest storage, with 78% of users reporting that system responses were context-sensitive and adapted to local climatic and cultural conditions. The technical architecture is characterized by modular natural language understanding pipelines, embedded guardrails to minimize model hallucinations, and a reproducible framework for contextualization based on regional agricultural datasets. A detailed economic analysis demonstrated the financial sustainability of the intervention, with favorable cost-benefit ratios and scalability potential. These findings offer robust empirical evidence that the integration of accessible communication technologies with state-of-the-art AI can overcome infrastructural limitations, enhance decision-making in low-resource farming environments, and serve as a viable model for transforming agricultural extension services across the African continent.</p> ]]> NDEMRI: An AI-Driven SMS Platform for Equitable Agricultural Extension in Rural Africa isaac touza sali emmanuel mana tchindebe etienne adawal urbain guidedi kaladzavi kolyang doi: 10.56578/jimd040301 Journal of Intelligent Management Decision 07-06-2025 Journal of Intelligent Management Decision 07-06-2025 2025 4 3 Article 173 10.56578/jimd040301 https://www.acadlore.com/article/JIMD/2025_4_3/jimd040301 https://www.acadlore.com/article/JIMD/2025_4_2/jimd040205 In response to escalating urban traffic congestion, environmental degradation, and mobility inefficiencies, intelligent transportation systems (ITS) and sustainable mobility strategies have been increasingly recognised as vital components of smart city development. In this study, the city of Trabzon, Türkiye, was examined as a representative urban environment facing such challenges. Six major intersections exhibiting persistent traffic congestion were selected for conversion from conventional fixed-time signal control to adaptive, traffic-actuated signalisation systems. Detailed performance evaluations were conducted, incorporating microsimulation modelling and real-time traffic flow analysis. The implementation of adaptive signalisation was found to significantly reduce vehicular delay, queue lengths, and intersection-level emissions, while enhancing operational efficiency and traffic safety. A complementary analysis assessed the economic and environmental impacts of this intervention, revealing considerable annual savings in fuel consumption and marked reductions in carbon dioxide (CO$_2$) emissions, thereby underscoring the long-term sustainability and cost-effectiveness of the proposed system. In parallel, the integration of electric vehicles (EVs) and micromobility solutions—including electric buses, minibuses, passenger cars, bicycles, and scooters—was proposed to further promote sustainable urban mobility. Strategic placement of EV charging infrastructure was suggested, with spatial planning informed by expected demand distribution and intermodal connectivity. Economic modelling demonstrated a reduction in operational fuel expenditure, while environmental projections indicated a substantial decrease in transport-related greenhouse gas emissions. Furthermore, micromobility modes were proposed as critical for addressing first- and last-mile connectivity gaps, mitigating short-distance vehicular traffic, and alleviating urban parking demand. Policy recommendations emphasised the necessity of strong municipal leadership in facilitating infrastructure deployment, public adoption, and behavioural shifts towards low-emission transport alternatives. The findings position Trabzon as a viable model for medium-sized urban centres seeking to implement scalable and replicable smart mobility frameworks. By integrating adaptive traffic control with zero-emission mobility, this study provides actionable insights into the design of efficient, economically viable, and environmentally sustainable urban transportation ecosystems. 06-10-2025 <![CDATA[ In response to escalating urban traffic congestion, environmental degradation, and mobility inefficiencies, intelligent transportation systems (ITS) and sustainable mobility strategies have been increasingly recognised as vital components of smart city development. In this study, the city of Trabzon, Türkiye, was examined as a representative urban environment facing such challenges. Six major intersections exhibiting persistent traffic congestion were selected for conversion from conventional fixed-time signal control to adaptive, traffic-actuated signalisation systems. Detailed performance evaluations were conducted, incorporating microsimulation modelling and real-time traffic flow analysis. The implementation of adaptive signalisation was found to significantly reduce vehicular delay, queue lengths, and intersection-level emissions, while enhancing operational efficiency and traffic safety. A complementary analysis assessed the economic and environmental impacts of this intervention, revealing considerable annual savings in fuel consumption and marked reductions in carbon dioxide (CO$_2$) emissions, thereby underscoring the long-term sustainability and cost-effectiveness of the proposed system. In parallel, the integration of electric vehicles (EVs) and micromobility solutions—including electric buses, minibuses, passenger cars, bicycles, and scooters—was proposed to further promote sustainable urban mobility. Strategic placement of EV charging infrastructure was suggested, with spatial planning informed by expected demand distribution and intermodal connectivity. Economic modelling demonstrated a reduction in operational fuel expenditure, while environmental projections indicated a substantial decrease in transport-related greenhouse gas emissions. Furthermore, micromobility modes were proposed as critical for addressing first- and last-mile connectivity gaps, mitigating short-distance vehicular traffic, and alleviating urban parking demand. Policy recommendations emphasised the necessity of strong municipal leadership in facilitating infrastructure deployment, public adoption, and behavioural shifts towards low-emission transport alternatives. The findings position Trabzon as a viable model for medium-sized urban centres seeking to implement scalable and replicable smart mobility frameworks. By integrating adaptive traffic control with zero-emission mobility, this study provides actionable insights into the design of efficient, economically viable, and environmentally sustainable urban transportation ecosystems. ]]> Intelligent Urban Mobility and Traffic Management: A Case Study of Smart Intersection Implementation and E-Mobility Integration in Trabzon, Türkiye metin mutlu aydın eren dağlı boris gitolendia doi: 10.56578/jimd040205 Journal of Intelligent Management Decision 06-10-2025 Journal of Intelligent Management Decision 06-10-2025 2025 4 2 Article 148 10.56578/jimd040205 https://www.acadlore.com/article/JIMD/2025_4_2/jimd040205 https://www.acadlore.com/article/JIMD/2025_4_2/jimd040204 To enhance cost-efficiency and streamline logistics operations in industrial manufacturing, centralised warehouse systems have increasingly been adopted as a strategic alternative to decentralised storage structures. In this study, the storage framework of a lubricating oil production facility has been examined to assess the operational implications of decentralised warehousing currently in use. It has been identified that the existing system incurs excessive operational costs, prolongs handling times, and demands a disproportionately high labour force, thereby constraining the overall efficiency of the supply chain. In response to projected increases in production output, the feasibility of constructing a centralised, gravity-fed warehouse equipped with automated and robotic technologies for the handling of palletised goods has been investigated. This proposed facility would be strategically integrated with the product packaging unit to form a unified logistical hub within the manufacturing site. A comprehensive analysis was conducted to determine the optimal location for the central warehouse, with key criteria including material flow, space availability, connectivity to production lines, and scalability. The results indicate that the implementation of a centralised automated storage and retrieval system (AS/RS) would significantly improve warehouse throughput, reduce operational expenditures, and align closely with long-term production expansion plans. Additionally, the integration of advanced storage technologies is expected to enhance inventory visibility, minimise human error, and support real-time production coordination. It is concluded that the establishment of a central warehouse facility, functioning as a core node in the internal logistics network, is essential for achieving sustainable operational efficiency and future-proofing the lubricating oil manufacturing process. 05-27-2025 <![CDATA[ To enhance cost-efficiency and streamline logistics operations in industrial manufacturing, centralised warehouse systems have increasingly been adopted as a strategic alternative to decentralised storage structures. In this study, the storage framework of a lubricating oil production facility has been examined to assess the operational implications of decentralised warehousing currently in use. It has been identified that the existing system incurs excessive operational costs, prolongs handling times, and demands a disproportionately high labour force, thereby constraining the overall efficiency of the supply chain. In response to projected increases in production output, the feasibility of constructing a centralised, gravity-fed warehouse equipped with automated and robotic technologies for the handling of palletised goods has been investigated. This proposed facility would be strategically integrated with the product packaging unit to form a unified logistical hub within the manufacturing site. A comprehensive analysis was conducted to determine the optimal location for the central warehouse, with key criteria including material flow, space availability, connectivity to production lines, and scalability. The results indicate that the implementation of a centralised automated storage and retrieval system (AS/RS) would significantly improve warehouse throughput, reduce operational expenditures, and align closely with long-term production expansion plans. Additionally, the integration of advanced storage technologies is expected to enhance inventory visibility, minimise human error, and support real-time production coordination. It is concluded that the establishment of a central warehouse facility, functioning as a core node in the internal logistics network, is essential for achieving sustainable operational efficiency and future-proofing the lubricating oil manufacturing process. ]]> Optimizing Warehouse Capacity in Industrial Manufacturing Through Centralised Storage System Integration žarko jovanović željko stević stojan simić doi: 10.56578/jimd040204 Journal of Intelligent Management Decision 05-27-2025 Journal of Intelligent Management Decision 05-27-2025 2025 4 2 Article 137 10.56578/jimd040204 https://www.acadlore.com/article/JIMD/2025_4_2/jimd040204 https://www.acadlore.com/article/JIMD/2025_4_2/jimd040203 The reliability and precision of stock market forecasting are of paramount importance to investors, regulatory authorities, and financial institutions. Traditional centralized systems for data processing and model deployment have been found to suffer from critical vulnerabilities, including susceptibility to tampering, single points of failure, and a lack of verifiability. To address these limitations, a novel hybrid framework has been developed that integrates advanced deep learning models with decentralized blockchain infrastructure to ensure both predictive accuracy and data integrity in financial time series forecasting. Temporal dependencies in market dynamics are captured through the use of recurrent neural networks (RNNs) and long short-term memory (LSTM) architectures, which have been extensively trained to model non-linear and non-stationary behaviors in high-frequency financial data. In parallel, a private Ethereum-based blockchain has been deployed to record cryptographic hashes of input datasets, model parameters, and forecasting outputs, thereby ensuring transparency, auditability, and immutability across the data lifecycle. To enable computational scalability, deep learning operations have been executed off-chain, while on-chain mechanisms are utilized for secure checkpointing and traceability. Empirical validation has been conducted using real-time data from the Borsa İstanbul (BIST), demonstrating significant improvements in forecasting accuracy when compared with baseline statistical and machine learning (ML) models. Moreover, the integration of blockchain technology has enabled a verifiable audit trail for all predictive operations, enhancing trust in the data pipeline without compromising computational efficiency. The proposed framework represents a significant advancement towards secure, transparent, and trustworthy artificial intelligence (AI) in financial forecasting, with potential implications for the broader decentralized finance (DeFi) ecosystem and regulatory-compliant AI deployments in capital markets. 05-21-2025 <![CDATA[ The reliability and precision of stock market forecasting are of paramount importance to investors, regulatory authorities, and financial institutions. Traditional centralized systems for data processing and model deployment have been found to suffer from critical vulnerabilities, including susceptibility to tampering, single points of failure, and a lack of verifiability. To address these limitations, a novel hybrid framework has been developed that integrates advanced deep learning models with decentralized blockchain infrastructure to ensure both predictive accuracy and data integrity in financial time series forecasting. Temporal dependencies in market dynamics are captured through the use of recurrent neural networks (RNNs) and long short-term memory (LSTM) architectures, which have been extensively trained to model non-linear and non-stationary behaviors in high-frequency financial data. In parallel, a private Ethereum-based blockchain has been deployed to record cryptographic hashes of input datasets, model parameters, and forecasting outputs, thereby ensuring transparency, auditability, and immutability across the data lifecycle. To enable computational scalability, deep learning operations have been executed off-chain, while on-chain mechanisms are utilized for secure checkpointing and traceability. Empirical validation has been conducted using real-time data from the Borsa İstanbul (BIST), demonstrating significant improvements in forecasting accuracy when compared with baseline statistical and machine learning (ML) models. Moreover, the integration of blockchain technology has enabled a verifiable audit trail for all predictive operations, enhancing trust in the data pipeline without compromising computational efficiency. The proposed framework represents a significant advancement towards secure, transparent, and trustworthy artificial intelligence (AI) in financial forecasting, with potential implications for the broader decentralized finance (DeFi) ecosystem and regulatory-compliant AI deployments in capital markets. ]]> Enhancing Stock Market Forecasting Through Deep Learning and Decentralized Data Integrity: A Blockchain-Integrated Framework safiye turgay abdulkadir aydin suat erdoğan metin yıldırım mustafa kavacık doi: 10.56578/jimd040203 Journal of Intelligent Management Decision 05-21-2025 Journal of Intelligent Management Decision 05-21-2025 2025 4 2 Article 118 10.56578/jimd040203 https://www.acadlore.com/article/JIMD/2025_4_2/jimd040203 https://www.acadlore.com/article/JIMD/2025_4_2/jimd040202 To address the evolving preferences of residents in smart community development and the uncertainty inherent in expert-driven technology adoption decisions, an integrated Quality Function Deployment (QFD) framework has been proposed. This framework combines Interval Type-2 Fuzzy Sets (IT2 FSs), a modified Kano Model, Regret Theory, and the Grey-Entropy Technique for Order Preference by Similarity to Ideal Solution (GETOPSIS). IT2 FSs were employed to accommodate the semantic ambiguity of user demands, enabling more precise interpretation of linguistic input. A refined Kano classification was used to categorise 15 demand indicators, from which 5 Customer Requirements (CRs) and 10 Design Requirements (DRs) were derived. Regret Theory was incorporated to model behavioural biases commonly observed in expert evaluations, particularly the tendency to avoid perceived short-term losses. Additionally, a dynamic weight adjustment mechanism was introduced based on corporate life cycle theory, revealing strategic divergences between early-stage enterprises, which prioritise basic security infrastructure, and mature firms, which emphasise sustainable, energy-efficient technologies. The GETOPSIS method was further enhanced to improve the robustness of technology prioritisation under uncertainty. The principal contributions of this study are threefold: (1) the provision of a QFD framework capable of modelling high-order uncertainty through linguistic variables, (2) the integration of behavioural decision theory to better reflect real-world expert judgement, and (3) the development of an improved GETOPSIS approach for more reliable multi-criteria decision-making. The proposed framework provides theoretical and methodological foundations for advancing adaptive technology adoption strategies in smart communities and may serve as a decision-support tool for policymakers and developers in rapidly evolving urban environments. 05-14-2025 <![CDATA[ To address the evolving preferences of residents in smart community development and the uncertainty inherent in expert-driven technology adoption decisions, an integrated Quality Function Deployment (QFD) framework has been proposed. This framework combines Interval Type-2 Fuzzy Sets (IT2 FSs), a modified Kano Model, Regret Theory, and the Grey-Entropy Technique for Order Preference by Similarity to Ideal Solution (GETOPSIS). IT2 FSs were employed to accommodate the semantic ambiguity of user demands, enabling more precise interpretation of linguistic input. A refined Kano classification was used to categorise 15 demand indicators, from which 5 Customer Requirements (CRs) and 10 Design Requirements (DRs) were derived. Regret Theory was incorporated to model behavioural biases commonly observed in expert evaluations, particularly the tendency to avoid perceived short-term losses. Additionally, a dynamic weight adjustment mechanism was introduced based on corporate life cycle theory, revealing strategic divergences between early-stage enterprises, which prioritise basic security infrastructure, and mature firms, which emphasise sustainable, energy-efficient technologies. The GETOPSIS method was further enhanced to improve the robustness of technology prioritisation under uncertainty. The principal contributions of this study are threefold: (1) the provision of a QFD framework capable of modelling high-order uncertainty through linguistic variables, (2) the integration of behavioural decision theory to better reflect real-world expert judgement, and (3) the development of an improved GETOPSIS approach for more reliable multi-criteria decision-making. The proposed framework provides theoretical and methodological foundations for advancing adaptive technology adoption strategies in smart communities and may serve as a decision-support tool for policymakers and developers in rapidly evolving urban environments. ]]> An Integrated Quality Function Deployment Framework Incorporating Interval Type-2 Fuzzy Sets and Behavioural Decision Theory for Optimising Smart Community Technology Adoption xianchuang du zaohong zhou qizhang chen yanlin mao doi: 10.56578/jimd040202 Journal of Intelligent Management Decision 05-14-2025 Journal of Intelligent Management Decision 05-14-2025 2025 4 2 Article 92 10.56578/jimd040202 https://www.acadlore.com/article/JIMD/2025_4_2/jimd040202 https://www.acadlore.com/article/JIMD/2025_4_2/jimd040201 Effective business system management necessitates strategic planning, efficient resource monitoring, and consistent team coordination. In practice, decision-making (DM) processes are frequently challenged by uncertainty, imprecision, and the need to aggregate diverse information sources. To address these complexities, a confidence-based algebraic aggregation framework incorporating the $p, q, r$-Fraction Fuzzy model has been proposed to enhance decision accuracy under uncertain environments. Within this framework, four novel aggregation operators are introduced: the Confidence $p, q, r$-Fraction Fuzzy Weighted Averaging Aggregation ($Cpqr$-FFWAA) operator, the Confidence $p, q, r$-Fraction Fuzzy Ordered Weighted Averaging Aggregation ($Cpqr$-FFOWAA) operator, the Confidence $p, q, r$-Fraction Fuzzy Weighted Geometric Aggregation ($Cpqr$-FFWGA) operator, and the Confidence $p, q, r$-Fraction Fuzzy Ordered Weighted Geometric Aggregation ($Cpqr$-FFOWGA) operator. These operators are designed to capture the inherent vagueness and subjectivity in business-related decision inputs, thereby facilitating robust assessments. The theoretical properties of the proposed operators—such as idempotency, boundedness, and monotonicity—are rigorously analyzed to ensure mathematical soundness and operational reliability. To illustrate the practical applicability of the model, a detailed case study is provided, demonstrating its effectiveness in maintaining resource sufficiency, preventing financial disruptions, and ensuring organizational coherence. The use of these aggregation mechanisms allows for systematic integration of expert confidence levels with varying degrees of fuzzy information, resulting in optimized decisions that are both data-informed and uncertainty-resilient. The methodological contributions are positioned to support real-world business contexts where dynamic inputs, incomplete data, and human judgment intersect. Consequently, the proposed approach offers a substantial advancement in intelligent decision-support systems, providing a scalable and interpretable tool for business performance enhancement. 04-21-2025 <![CDATA[ Effective business system management necessitates strategic planning, efficient resource monitoring, and consistent team coordination. In practice, decision-making (DM) processes are frequently challenged by uncertainty, imprecision, and the need to aggregate diverse information sources. To address these complexities, a confidence-based algebraic aggregation framework incorporating the $p, q, r$-Fraction Fuzzy model has been proposed to enhance decision accuracy under uncertain environments. Within this framework, four novel aggregation operators are introduced: the Confidence $p, q, r$-Fraction Fuzzy Weighted Averaging Aggregation ($Cpqr$-FFWAA) operator, the Confidence $p, q, r$-Fraction Fuzzy Ordered Weighted Averaging Aggregation ($Cpqr$-FFOWAA) operator, the Confidence $p, q, r$-Fraction Fuzzy Weighted Geometric Aggregation ($Cpqr$-FFWGA) operator, and the Confidence $p, q, r$-Fraction Fuzzy Ordered Weighted Geometric Aggregation ($Cpqr$-FFOWGA) operator. These operators are designed to capture the inherent vagueness and subjectivity in business-related decision inputs, thereby facilitating robust assessments. The theoretical properties of the proposed operators—such as idempotency, boundedness, and monotonicity—are rigorously analyzed to ensure mathematical soundness and operational reliability. To illustrate the practical applicability of the model, a detailed case study is provided, demonstrating its effectiveness in maintaining resource sufficiency, preventing financial disruptions, and ensuring organizational coherence. The use of these aggregation mechanisms allows for systematic integration of expert confidence levels with varying degrees of fuzzy information, resulting in optimized decisions that are both data-informed and uncertainty-resilient. The methodological contributions are positioned to support real-world business contexts where dynamic inputs, incomplete data, and human judgment intersect. Consequently, the proposed approach offers a substantial advancement in intelligent decision-support systems, providing a scalable and interpretable tool for business performance enhancement. ]]> Enhancing Business System Performance Through Confidence-Based Algebraic Aggregation and the <i>p, q, r</i>-Fraction Fuzzy Model for Robust Decision-Making abdul samad jan muhammad rifaqat ali doi: 10.56578/jimd040201 Journal of Intelligent Management Decision 04-21-2025 Journal of Intelligent Management Decision 04-21-2025 2025 4 2 Article 78 10.56578/jimd040201 https://www.acadlore.com/article/JIMD/2025_4_2/jimd040201 https://www.acadlore.com/article/JIMD/2025_4_1/jimd040105 The optimization of railway train selection in Pakistan has become increasingly critical due to rapid population growth and rising travel demands. Despite efforts by the Railway Transport (RT) Department to enhance efficiency, productivity, and safety through policy reforms and infrastructure advancements, persistent challenges such as outdated technology, infrastructure bottlenecks, frequent delays, and inadequate maintenance continue to hinder progress. Addressing these issues is imperative to ensuring sustainable, efficient, and resilient railway operations. Given the multifaceted and uncertain nature of railway system modeling and management, decision-making (DM) processes necessitate robust methodologies capable of handling imprecise and ambiguous data. In this study, an innovative DM framework is introduced, leveraging intuitionistic fuzzy sets (IFSs) as an advanced extension of fuzzy sets (FSs) to manage uncertainty and hesitation in complex scenarios. By employing Einstein t-norm and t-conorm-based operators, novel operational laws for intuitionistic fuzzy credibility numbers (IFCNs) are proposed. Three key aggregation techniques—Confidence Intuitionistic Fuzzy Credibility Einstein Weighted Averaging (CIFCEWA), Confidence Intuitionistic Fuzzy Credibility Einstein Ordered Weighted Averaging (CIFCEOWA), and Confidence Intuitionistic Fuzzy Credibility Einstein Hybrid Weighted Averaging (CIFCEHWA) operators—are developed to provide a structured approach for processing and analyzing intuitionistic fuzzy data. To evaluate the practical applicability and reliability of the proposed methodology, a structured DM algorithm is formulated and validated using a real-world railway train selection case study. The incorporation of confidence levels within the IFCN framework enhances DM precision by quantifying the degree of certainty, thereby reducing risk and improving reliability. The findings demonstrate that the proposed approach effectively addresses the inherent uncertainties in railway selection processes, leading to more informed and strategic planning. Furthermore, the applicability of IFCNs extends beyond railway systems, offering valuable insights for domains such as artificial intelligence, financial DM, management science, and engineering, where uncertainty plays a pivotal role. 03-13-2025 <![CDATA[ The optimization of railway train selection in Pakistan has become increasingly critical due to rapid population growth and rising travel demands. Despite efforts by the Railway Transport (RT) Department to enhance efficiency, productivity, and safety through policy reforms and infrastructure advancements, persistent challenges such as outdated technology, infrastructure bottlenecks, frequent delays, and inadequate maintenance continue to hinder progress. Addressing these issues is imperative to ensuring sustainable, efficient, and resilient railway operations. Given the multifaceted and uncertain nature of railway system modeling and management, decision-making (DM) processes necessitate robust methodologies capable of handling imprecise and ambiguous data. In this study, an innovative DM framework is introduced, leveraging intuitionistic fuzzy sets (IFSs) as an advanced extension of fuzzy sets (FSs) to manage uncertainty and hesitation in complex scenarios. By employing Einstein t-norm and t-conorm-based operators, novel operational laws for intuitionistic fuzzy credibility numbers (IFCNs) are proposed. Three key aggregation techniques—Confidence Intuitionistic Fuzzy Credibility Einstein Weighted Averaging (CIFCEWA), Confidence Intuitionistic Fuzzy Credibility Einstein Ordered Weighted Averaging (CIFCEOWA), and Confidence Intuitionistic Fuzzy Credibility Einstein Hybrid Weighted Averaging (CIFCEHWA) operators—are developed to provide a structured approach for processing and analyzing intuitionistic fuzzy data. To evaluate the practical applicability and reliability of the proposed methodology, a structured DM algorithm is formulated and validated using a real-world railway train selection case study. The incorporation of confidence levels within the IFCN framework enhances DM precision by quantifying the degree of certainty, thereby reducing risk and improving reliability. The findings demonstrate that the proposed approach effectively addresses the inherent uncertainties in railway selection processes, leading to more informed and strategic planning. Furthermore, the applicability of IFCNs extends beyond railway systems, offering valuable insights for domains such as artificial intelligence, financial DM, management science, and engineering, where uncertainty plays a pivotal role. ]]> Optimizing Railway Train Selection in Pakistan Using Confidence-Driven Intuitionistic Fuzzy Methods with Einstein-Based Operators khaista rahman quaid iqbal doi: 10.56578/jimd040105 Journal of Intelligent Management Decision 03-13-2025 Journal of Intelligent Management Decision 03-13-2025 2025 4 1 Article 66 10.56578/jimd040105 https://www.acadlore.com/article/JIMD/2025_4_1/jimd040105 https://www.acadlore.com/article/JIMD/2025_4_1/jimd040104 The retail sector is increasingly confronted with challenges arising from digital disruption and shifts in consumer behaviour. Amidst this transformation, the integration of augmented reality (AR) has been identified as a promising avenue to revitalise the in-store shopping experience, offering a means to engage customers more effectively and enhance competitiveness. This study investigates the extent to which AR applications can improve the shopping experience in physical retail settings, with particular emphasis on their capacity to foster customer flow states. A survey of 239 participants, comprising both general consumers and retail professionals, was conducted to explore the impact of AR on the shopping process. The findings suggest that AR significantly enhances the shopping experience, contributing to heightened customer engagement and immersion. However, while AR is found to influence flow states, the flow experience itself does not mediate the relationship between AR use and the shopping experience. These results offer important insights into the application of AR in brick-and-mortar retail environments, providing a management-oriented perspective on how its strategic implementation can generate sustainable competitive advantages. Moreover, the study contributes to existing AR literature by extending the understanding of its role in traditional retail, highlighting practical considerations for retailers aiming to adopt such technologies. The evidence also underscores the potential of AR in fostering behaviours and experiences that are essential for maintaining the competitiveness of physical stores in the digital age. Therefore, the adoption of AR technologies is not only recommended for enhancing the customer experience but also for driving innovation within the retail industry. 03-06-2025 <![CDATA[ The retail sector is increasingly confronted with challenges arising from digital disruption and shifts in consumer behaviour. Amidst this transformation, the integration of augmented reality (AR) has been identified as a promising avenue to revitalise the in-store shopping experience, offering a means to engage customers more effectively and enhance competitiveness. This study investigates the extent to which AR applications can improve the shopping experience in physical retail settings, with particular emphasis on their capacity to foster customer flow states. A survey of 239 participants, comprising both general consumers and retail professionals, was conducted to explore the impact of AR on the shopping process. The findings suggest that AR significantly enhances the shopping experience, contributing to heightened customer engagement and immersion. However, while AR is found to influence flow states, the flow experience itself does not mediate the relationship between AR use and the shopping experience. These results offer important insights into the application of AR in brick-and-mortar retail environments, providing a management-oriented perspective on how its strategic implementation can generate sustainable competitive advantages. Moreover, the study contributes to existing AR literature by extending the understanding of its role in traditional retail, highlighting practical considerations for retailers aiming to adopt such technologies. The evidence also underscores the potential of AR in fostering behaviours and experiences that are essential for maintaining the competitiveness of physical stores in the digital age. Therefore, the adoption of AR technologies is not only recommended for enhancing the customer experience but also for driving innovation within the retail industry. ]]> Enhancing the Retail Experience Through Augmented Reality: The Role of Flow in Brick-and-Mortar Stores louisa kürvers jonas manske doi: 10.56578/jimd040104 Journal of Intelligent Management Decision 03-06-2025 Journal of Intelligent Management Decision 03-06-2025 2025 4 1 Article 53 10.56578/jimd040104 https://www.acadlore.com/article/JIMD/2025_4_1/jimd040104 https://www.acadlore.com/article/JIMD/2025_4_1/jimd040103 The strategic positioning of distribution, sales, and service facilities plays a critical role in ensuring the efficiency, reliability, and cost-effectiveness of supply chains. In particular, the location of such facilities within the transshipment network significantly influences both operational costs and consumer satisfaction by affecting delivery times and service quality. This study introduces a mixed-integer linear programming (MILP) model designed to optimize the layout of a postal supply chain network. The model aims to minimize the key cost components, including transportation, facility location, and holding costs, within a four-echelon supply chain consisting of suppliers, warehouses, retailers, and recipients. Parcels are initially collected by suppliers and delivered to regional warehouses, which then allocate them to selected retail locations. The selection of optimal retail locations is based on a cost minimization criterion, after which parcels are transported to the final delivery points—post offices situated in various cities. A distinctive feature of the proposed model is the assumption that demand at the recipient level is determined at the supplier level, thereby facilitating more centralized demand management and reducing uncertainties in the planning process. The model incorporates several constraints, such as flow balance, capacity limitations, and retailer selection. The optimization problem is solved using LINGO 16 software, and a comprehensive analysis is conducted to identify the optimal configuration of retailer locations and parcel flow distribution. A numerical example is provided to demonstrate the practical application of the model, and sensitivity analysis is performed to assess the impact of key parameters—such as retailer capacity and initial inventory levels—on the overall cost. The results indicate that increasing retailer capacity leads to a reduction in total supply chain costs, highlighting the benefits of economies of scale and parcel consolidation. However, an increase in the initial quantity of parcels results in higher costs due to elevated transportation and holding expenses. These findings offer valuable insights for decision-makers seeking to optimize postal supply chains, balancing the need for cost efficiency with the provision of high-quality service. 02-26-2025 <![CDATA[ The strategic positioning of distribution, sales, and service facilities plays a critical role in ensuring the efficiency, reliability, and cost-effectiveness of supply chains. In particular, the location of such facilities within the transshipment network significantly influences both operational costs and consumer satisfaction by affecting delivery times and service quality. This study introduces a mixed-integer linear programming (MILP) model designed to optimize the layout of a postal supply chain network. The model aims to minimize the key cost components, including transportation, facility location, and holding costs, within a four-echelon supply chain consisting of suppliers, warehouses, retailers, and recipients. Parcels are initially collected by suppliers and delivered to regional warehouses, which then allocate them to selected retail locations. The selection of optimal retail locations is based on a cost minimization criterion, after which parcels are transported to the final delivery points—post offices situated in various cities. A distinctive feature of the proposed model is the assumption that demand at the recipient level is determined at the supplier level, thereby facilitating more centralized demand management and reducing uncertainties in the planning process. The model incorporates several constraints, such as flow balance, capacity limitations, and retailer selection. The optimization problem is solved using LINGO 16 software, and a comprehensive analysis is conducted to identify the optimal configuration of retailer locations and parcel flow distribution. A numerical example is provided to demonstrate the practical application of the model, and sensitivity analysis is performed to assess the impact of key parameters—such as retailer capacity and initial inventory levels—on the overall cost. The results indicate that increasing retailer capacity leads to a reduction in total supply chain costs, highlighting the benefits of economies of scale and parcel consolidation. However, an increase in the initial quantity of parcels results in higher costs due to elevated transportation and holding expenses. These findings offer valuable insights for decision-makers seeking to optimize postal supply chains, balancing the need for cost efficiency with the provision of high-quality service. ]]> A Mathematical Model for Optimizing Postal Supply Chain Networks and Facility Location hamed fazlollahtabar doi: 10.56578/jimd040103 Journal of Intelligent Management Decision 02-26-2025 Journal of Intelligent Management Decision 02-26-2025 2025 4 1 Article 44 10.56578/jimd040103 https://www.acadlore.com/article/JIMD/2025_4_1/jimd040103 https://www.acadlore.com/article/JIMD/2025_4_1/jimd040102 This systematic review seeks to synthesize the existing literature on the integration of blockchain technology into sustainable finance, with a particular focus on its role in enhancing transparency and accountability. A bibliometric analysis was conducted using the PRISMA methodology, incorporating a meta-analysis of scholarly articles published between 2018 and 2023. The analysis was based on data extracted from databases such as Springer Link, Dimensions, and Google Scholar, using the search terms "blockchain," "sustainable," "finance," "transparency," and "accountability." Open-access articles from reputable, peer-reviewed journals were selected to ensure the reliability of the data. Research questions were framed following the PICo method, addressing the specific impacts of blockchain technology on sustainable finance systems. The review highlights that blockchain has the potential to significantly enhance transparency and accountability in sustainable finance by providing robust mechanisms for transaction traceability and verification. Notably, blockchain technology has been applied to improve carbon market management, facilitate green bond issuance, and support the disclosure of Environmental, Social, and Governance (ESG) data. Despite these promising applications, several challenges remain, including regulatory uncertainties, technological limitations, and integration complexities, which could hinder its widespread adoption. To facilitate the global integration of blockchain in sustainable finance, it is recommended that financial institutions invest in technological infrastructure and training. Furthermore, policymakers should work towards harmonizing regulatory frameworks, while researchers are urged to pursue interdisciplinary, empirical studies to address the potential and limitations of blockchain technology. A shift in academic curricula to include blockchain’s implications in finance and sustainability is also recommended to better prepare future professionals. In conclusion, while blockchain holds significant promise for improving transparency and accountability, its broader adoption will require addressing technological, regulatory, and socio-economic barriers. 02-17-2025 <![CDATA[ This systematic review seeks to synthesize the existing literature on the integration of blockchain technology into sustainable finance, with a particular focus on its role in enhancing transparency and accountability. A bibliometric analysis was conducted using the PRISMA methodology, incorporating a meta-analysis of scholarly articles published between 2018 and 2023. The analysis was based on data extracted from databases such as Springer Link, Dimensions, and Google Scholar, using the search terms "blockchain," "sustainable," "finance," "transparency," and "accountability." Open-access articles from reputable, peer-reviewed journals were selected to ensure the reliability of the data. Research questions were framed following the PICo method, addressing the specific impacts of blockchain technology on sustainable finance systems. The review highlights that blockchain has the potential to significantly enhance transparency and accountability in sustainable finance by providing robust mechanisms for transaction traceability and verification. Notably, blockchain technology has been applied to improve carbon market management, facilitate green bond issuance, and support the disclosure of Environmental, Social, and Governance (ESG) data. Despite these promising applications, several challenges remain, including regulatory uncertainties, technological limitations, and integration complexities, which could hinder its widespread adoption. To facilitate the global integration of blockchain in sustainable finance, it is recommended that financial institutions invest in technological infrastructure and training. Furthermore, policymakers should work towards harmonizing regulatory frameworks, while researchers are urged to pursue interdisciplinary, empirical studies to address the potential and limitations of blockchain technology. A shift in academic curricula to include blockchain’s implications in finance and sustainability is also recommended to better prepare future professionals. In conclusion, while blockchain holds significant promise for improving transparency and accountability, its broader adoption will require addressing technological, regulatory, and socio-economic barriers. ]]> Enhancing Transparency and Accountability in Sustainable Finance Through Blockchain Technology: A Systematic Review of the Literature mohan bhandari ghanashyam tiwari maheshwor dhakal doi: 10.56578/jimd040102 Journal of Intelligent Management Decision 02-17-2025 Journal of Intelligent Management Decision 02-17-2025 2025 4 1 Article 23 10.56578/jimd040102 https://www.acadlore.com/article/JIMD/2025_4_1/jimd040102 https://www.acadlore.com/article/JIMD/2025_4_1/jimd040101 The transformation of public services into electronic formats (e-services) has gained significant momentum with the advancement of information and communication technologies, particularly due to the widespread use of the Internet and increasing citizen expectations. This transition has not only enhanced the efficiency of traditional public services but also facilitated new forms of e-governance that promote greater interaction, transparency, accessibility, and accountability between citizens and the state. Within this context, this study seeks to address the question: What are the key factors influencing citizens' satisfaction with e-services? The case of student satisfaction with the e-services provided by Anadolu University in Eskişehir, Turkey, serves as the focal point for the investigation. A survey conducted among 1,000 students from eight faculties and one graduate school at Anadolu University assessed their satisfaction with a variety of e-services, including Anasis, Mergen, Anadolu Mobil, E-Mail, library services, cafeteria services, and others. The collected data were analyzed using a combined methodology that integrated the E-GovQual model and the Importance-Performance Analysis (IPA) method. The E-GovQual model provided a comprehensive framework for evaluating the quality of e-services, allowing for an in-depth understanding of students' perceptions. The IPA method, on the other hand, facilitated the identification of performance gaps in e-service delivery and highlighted areas in need of improvement, based on students' expectations. The findings of the analysis were used to formulate strategic recommendations for decision-makers, students, and researchers. This research contributes to the growing body of knowledge on e-governance and user satisfaction in educational institutions, offering practical insights for optimizing online platforms to better meet student needs and expectations. 02-13-2025 <![CDATA[ <p>The transformation of public services into electronic formats (e-services) has gained significant momentum with the advancement of information and communication technologies, particularly due to the widespread use of the Internet and increasing citizen expectations. This transition has not only enhanced the efficiency of traditional public services but also facilitated new forms of e-governance that promote greater interaction, transparency, accessibility, and accountability between citizens and the state. Within this context, this study seeks to address the question: <em>What are the key factors influencing citizens' satisfaction with e-services</em>? The case of student satisfaction with the e-services provided by Anadolu University in Eskişehir, Turkey, serves as the focal point for the investigation. A survey conducted among 1,000 students from eight faculties and one graduate school at Anadolu University assessed their satisfaction with a variety of e-services, including Anasis, Mergen, Anadolu Mobil, E-Mail, library services, cafeteria services, and others. The collected data were analyzed using a combined methodology that integrated the E-GovQual model and the Importance-Performance Analysis (IPA) method. The E-GovQual model provided a comprehensive framework for evaluating the quality of e-services, allowing for an in-depth understanding of students' perceptions. The IPA method, on the other hand, facilitated the identification of performance gaps in e-service delivery and highlighted areas in need of improvement, based on students' expectations. The findings of the analysis were used to formulate strategic recommendations for decision-makers, students, and researchers. This research contributes to the growing body of knowledge on e-governance and user satisfaction in educational institutions, offering practical insights for optimizing online platforms to better meet student needs and expectations.</p> ]]> Evaluation of Citizen Satisfaction with E-Services: An Analysis Using the E-GovQual Model and Importance-Performance Analysis emrah ayhan puspa saanantha irfani wna dilek al doi: 10.56578/jimd040101 Journal of Intelligent Management Decision 02-13-2025 Journal of Intelligent Management Decision 02-13-2025 2025 4 1 Article 1 10.56578/jimd040101 https://www.acadlore.com/article/JIMD/2025_4_1/jimd040101 https://www.acadlore.com/article/JIMD/2024_3_4/jimd030405 This paper addresses the issues of incomplete safety management systems and the challenge of optimizing multiple safety objectives concurrently in wind power project construction. An approach for solving Multi-objective Optimization Problem (MOP) based on the Non-Dominated Sorting Genetic Algorithm (NSGA) is proposed. First, key safety risk factors in the construction process of wind power projects are systematically analyzed and identified. A multi-dimensional evaluation index system, including personnel safety, equipment safety, environmental safety, and management safety, is established. Next, a mathematical model is developed with safety, cost, and construction period as the optimization objectives. The NSGA-II and NSGA-III algorithms are applied to solve the model. Case study results show that: (1) the proposed MOP model effectively balances the multiple objectives in wind power project construction; (2) compared with traditional methods, the NSGA demonstrates significant advantages in solution efficiency and diversity; (3) the obtained Pareto optimal solution set provides multiple feasible options for engineering decision-making. The research results provide theoretical foundations and practical guidance for safety management in wind power project construction. 12-19-2024 <![CDATA[ This paper addresses the issues of incomplete safety management systems and the challenge of optimizing multiple safety objectives concurrently in wind power project construction. An approach for solving Multi-objective Optimization Problem (MOP) based on the Non-Dominated Sorting Genetic Algorithm (NSGA) is proposed. First, key safety risk factors in the construction process of wind power projects are systematically analyzed and identified. A multi-dimensional evaluation index system, including personnel safety, equipment safety, environmental safety, and management safety, is established. Next, a mathematical model is developed with safety, cost, and construction period as the optimization objectives. The NSGA-II and NSGA-III algorithms are applied to solve the model. Case study results show that: (1) the proposed MOP model effectively balances the multiple objectives in wind power project construction; (2) compared with traditional methods, the NSGA demonstrates significant advantages in solution efficiency and diversity; (3) the obtained Pareto optimal solution set provides multiple feasible options for engineering decision-making. The research results provide theoretical foundations and practical guidance for safety management in wind power project construction. ]]> Optimization of Multi-Objective Safety Management System for Wind Power Projects Based on Non-Dominated Sorting Genetic Algorithm qianhan zhang tao ma qinghai xie doi: 10.56578/jimd030405 Journal of Intelligent Management Decision 12-19-2024 Journal of Intelligent Management Decision 12-19-2024 2024 3 4 Article 248 10.56578/jimd030405 https://www.acadlore.com/article/JIMD/2024_3_4/jimd030405 https://www.acadlore.com/article/JIMD/2024_3_4/jimd030404 The evaluation of supply chain (SC) efficiency in the presence of uncertainty presents significant challenges due to the multi-criteria nature of SC performance and the inherent ambiguities in both input and output data. This study proposes an innovative framework that combines Rough Set Theory (RST) with Data Envelopment Analysis (DEA) to address these challenges. By employing rough variables, the framework captures uncertainty in the measurement of inputs and outputs, defining efficiency intervals that reflect the imprecision of real-world data. In this approach, rough sets are used to model the vagueness and granularity of the data, while DEA is applied to assess the relative efficiency of decision-making units (DMUs) within the SC. The effectiveness of the proposed model is demonstrated through case studies that highlight its capacity to handle ambiguous and incomplete data. The results reveal the model’s superiority in providing actionable insights for identifying inefficiencies and areas for improvement within the SC, thus offering a more robust and flexible evaluation framework compared to traditional methods. Moreover, this integrated approach allows decision-makers to assess the efficiency of SC more effectively, taking into account the uncertainty and complexity inherent in the data. These findings contribute significantly to the field of supply chain management (SCM) by offering an enhanced tool for performance assessment that is both comprehensive and adaptable to varying operational contexts. 12-19-2024 <![CDATA[ The evaluation of supply chain (SC) efficiency in the presence of uncertainty presents significant challenges due to the multi-criteria nature of SC performance and the inherent ambiguities in both input and output data. This study proposes an innovative framework that combines Rough Set Theory (RST) with Data Envelopment Analysis (DEA) to address these challenges. By employing rough variables, the framework captures uncertainty in the measurement of inputs and outputs, defining efficiency intervals that reflect the imprecision of real-world data. In this approach, rough sets are used to model the vagueness and granularity of the data, while DEA is applied to assess the relative efficiency of decision-making units (DMUs) within the SC. The effectiveness of the proposed model is demonstrated through case studies that highlight its capacity to handle ambiguous and incomplete data. The results reveal the model’s superiority in providing actionable insights for identifying inefficiencies and areas for improvement within the SC, thus offering a more robust and flexible evaluation framework compared to traditional methods. Moreover, this integrated approach allows decision-makers to assess the efficiency of SC more effectively, taking into account the uncertainty and complexity inherent in the data. These findings contribute significantly to the field of supply chain management (SCM) by offering an enhanced tool for performance assessment that is both comprehensive and adaptable to varying operational contexts. ]]> Evaluating Supply Chain Efficiency Under Uncertainty: An Integration of Rough Set Theory and Data Envelopment Analysis lorenzo cevallos-torres fatemeh zahra montazeri fatemeh rasoulpour doi: 10.56578/jimd030404 Journal of Intelligent Management Decision 12-19-2024 Journal of Intelligent Management Decision 12-19-2024 2024 3 4 Article 231 10.56578/jimd030404 https://www.acadlore.com/article/JIMD/2024_3_4/jimd030404 https://www.acadlore.com/article/JIMD/2024_3_4/jimd030403 Manufacturing firms face increasing pressure to enhance their competitiveness, penetrate new markets, and prioritise customer satisfaction in an increasingly dynamic global business environment. To remain competitive, these firms must adopt innovative strategies that address the evolving demands of customers. In this context, a firm’s capacity to innovate is critical, as it directly influences both the development and implementation of strategic initiatives. Innovation capacity in manufacturing companies is shaped by numerous interrelated factors, each contributing to a firm's ability to respond to technological advancements, market shifts, and changing consumer expectations. This study aims to identify the key determinants of innovation capacity in manufacturing firms based in Ordu Province, Turkey, with a focus on the role of corporate identity. A multi-criteria decision-making (MCDM) approach, specifically the Criteria Importance Assessment (CIMAS) technique, is employed to determine the relative importance of these factors. The findings suggest that “clustering and international networking activities” emerge as the most significant factor influencing innovation capacity, while the “level of entrepreneurship” is found to have the least impact. These results underscore the importance of collaboration, international connections, and strategic partnerships in driving innovation, while highlighting the comparatively limited role of entrepreneurship in fostering innovation within the studied region. The findings have significant implications for manufacturing firms, particularly in terms of strategy development, resource allocation, and the identification of key areas for improvement in innovation processes. Additionally, the research provides valuable insights for policymakers seeking to enhance the innovation capacity of manufacturing sectors in emerging markets. 12-11-2024 <![CDATA[ Manufacturing firms face increasing pressure to enhance their competitiveness, penetrate new markets, and prioritise customer satisfaction in an increasingly dynamic global business environment. To remain competitive, these firms must adopt innovative strategies that address the evolving demands of customers. In this context, a firm’s capacity to innovate is critical, as it directly influences both the development and implementation of strategic initiatives. Innovation capacity in manufacturing companies is shaped by numerous interrelated factors, each contributing to a firm's ability to respond to technological advancements, market shifts, and changing consumer expectations. This study aims to identify the key determinants of innovation capacity in manufacturing firms based in Ordu Province, Turkey, with a focus on the role of corporate identity. A multi-criteria decision-making (MCDM) approach, specifically the Criteria Importance Assessment (CIMAS) technique, is employed to determine the relative importance of these factors. The findings suggest that “clustering and international networking activities” emerge as the most significant factor influencing innovation capacity, while the “level of entrepreneurship” is found to have the least impact. These results underscore the importance of collaboration, international connections, and strategic partnerships in driving innovation, while highlighting the comparatively limited role of entrepreneurship in fostering innovation within the studied region. The findings have significant implications for manufacturing firms, particularly in terms of strategy development, resource allocation, and the identification of key areas for improvement in innovation processes. Additionally, the research provides valuable insights for policymakers seeking to enhance the innovation capacity of manufacturing sectors in emerging markets. ]]> Assessing the Innovation Capacity of Manufacturing Firms in Ordu Province: A Multi-Criteria Evaluation Using CIMAS ahmet aytekin selçuk korucuk doi: 10.56578/jimd030403 Journal of Intelligent Management Decision 12-11-2024 Journal of Intelligent Management Decision 12-11-2024 2024 3 4 Article 224 10.56578/jimd030403 https://www.acadlore.com/article/JIMD/2024_3_4/jimd030403 https://www.acadlore.com/article/JIMD/2024_3_4/jimd030402 Evaluating renewable energy policies is crucial for fostering sustainable development, particularly within the European Union (EU), where energy management must account for economic, environmental, and social criteria. A stable framework is proposed that integrates multiple perspectives by synthesizing the rankings derived from four widely recognized Multi-Criteria Decision Analysis (MCDA) methods—Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Stable Preference Ordering Towards Ideal Solution (SPOTIS), and Multi-Objective Optimization by Ratio Analysis (MOORA). This approach addresses the inherent variability in individual MCDA techniques by applying Copeland’s compromise method, ensuring a consensus ranking that reflects the balanced performance of renewable energy systems across 16 EU countries. To further enhance the reliability of the framework, the Stochastic Identification of Weights (SITW) approach is employed, optimizing the criteria weights and strengthening the consistency of the evaluation process. The results reveal a strong alignment between the rankings generated by individual MCDA methods and the compromise rankings, particularly among the highest-performing alternatives. This alignment highlights the stability of the framework, enabling the identification of critical drivers of renewable energy policy performance—most notably energy efficiency and environmental sustainability. The compromise approach proves effective in balancing multiple, sometimes conflicting perspectives, offering policymakers a structured tool for informed decision-making in the complex domain of energy management. The findings contribute to the development of advanced frameworks for decision-making by demonstrating that compromise rankings can offer robust solutions while maintaining methodological consistency. Furthermore, this framework provides valuable insights into the complex dynamics of renewable energy performance evaluation. Future research should explore the applicability of this methodology beyond the EU context, incorporating additional dimensions such as social, technological, and institutional factors, and addressing the dynamic evolution of energy policies. This framework offers a solid foundation for refining policy evaluation strategies, supporting sustainable energy management efforts in diverse geographic regions. 11-04-2024 <![CDATA[ Evaluating renewable energy policies is crucial for fostering sustainable development, particularly within the European Union (EU), where energy management must account for economic, environmental, and social criteria. A stable framework is proposed that integrates multiple perspectives by synthesizing the rankings derived from four widely recognized Multi-Criteria Decision Analysis (MCDA) methods—Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Stable Preference Ordering Towards Ideal Solution (SPOTIS), and Multi-Objective Optimization by Ratio Analysis (MOORA). This approach addresses the inherent variability in individual MCDA techniques by applying Copeland’s compromise method, ensuring a consensus ranking that reflects the balanced performance of renewable energy systems across 16 EU countries. To further enhance the reliability of the framework, the Stochastic Identification of Weights (SITW) approach is employed, optimizing the criteria weights and strengthening the consistency of the evaluation process. The results reveal a strong alignment between the rankings generated by individual MCDA methods and the compromise rankings, particularly among the highest-performing alternatives. This alignment highlights the stability of the framework, enabling the identification of critical drivers of renewable energy policy performance—most notably energy efficiency and environmental sustainability. The compromise approach proves effective in balancing multiple, sometimes conflicting perspectives, offering policymakers a structured tool for informed decision-making in the complex domain of energy management. The findings contribute to the development of advanced frameworks for decision-making by demonstrating that compromise rankings can offer robust solutions while maintaining methodological consistency. Furthermore, this framework provides valuable insights into the complex dynamics of renewable energy performance evaluation. Future research should explore the applicability of this methodology beyond the EU context, incorporating additional dimensions such as social, technological, and institutional factors, and addressing the dynamic evolution of energy policies. This framework offers a solid foundation for refining policy evaluation strategies, supporting sustainable energy management efforts in diverse geographic regions. ]]> A Robust Framework for Renewable Energy Policy Evaluation Using MCDA and Compromise Ranking with Stochastic Weight Identification bartłomiej kizielewicz wojciech sałabun doi: 10.56578/jimd030402 Journal of Intelligent Management Decision 11-04-2024 Journal of Intelligent Management Decision 11-04-2024 2024 3 4 Article 213 10.56578/jimd030402 https://www.acadlore.com/article/JIMD/2024_3_4/jimd030402 https://www.acadlore.com/article/JIMD/2024_3_4/jimd030401 The integration of Electric Vehicles (EVs) into modern power grids presents both challenges and opportunities. This study investigates the influence of slack bus compensation on the stability of voltage levels within these grids, particularly as EV penetration increases. A comprehensive simulation framework is developed to model various grid configurations, accounting for different scenarios of EV load integration. Historical charging data is meticulously analysed to predict future load patterns, indicating that heightened levels of EV integration lead to a notable decrease in voltage stability. Specifically, voltage levels were observed to decline from 230 V to 210 V under conditions of 100% EV penetration, necessitating an increase in slack bus compensation from 0 MW to 140 MW to sustain system balance. Advanced machine learning techniques are employed to forecast real-time load demands, significantly reducing both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), thereby optimising slack bus performance. The results underscore the critical role of real-time load forecasting and automated control strategies in addressing the challenges posed by EV integration into power grids. Furthermore, the study demonstrates that intelligent systems, coupled with machine learning, can enhance power flow management and bolster grid stability, ultimately improving operational efficiency in the distribution of energy. Future research will focus on refining machine learning models through the utilisation of more granular data sets and exploring decentralized control methodologies, such as federated learning, thereby providing valuable insights for grid operators as the adoption of EVs continues to expand. 10-30-2024 <![CDATA[ <p>The integration of Electric Vehicles (EVs) into modern power grids presents both challenges and opportunities. This study investigates the influence of slack bus compensation on the stability of voltage levels within these grids, particularly as EV penetration increases. A comprehensive simulation framework is developed to model various grid configurations, accounting for different scenarios of EV load integration. Historical charging data is meticulously analysed to predict future load patterns, indicating that heightened levels of EV integration lead to a notable decrease in voltage stability. Specifically, voltage levels were observed to decline from 230 V to 210 V under conditions of 100% EV penetration, necessitating an increase in slack bus compensation from 0 MW to 140 MW to sustain system balance. Advanced machine learning techniques are employed to forecast real-time load demands, significantly reducing both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), thereby optimising slack bus performance. The results underscore the critical role of real-time load forecasting and automated control strategies in addressing the challenges posed by EV integration into power grids. Furthermore, the study demonstrates that intelligent systems, coupled with machine learning, can enhance power flow management and bolster grid stability, ultimately improving operational efficiency in the distribution of energy. Future research will focus on refining machine learning models through the utilisation of more granular data sets and exploring decentralized control methodologies, such as federated learning, thereby providing valuable insights for grid operators as the adoption of EVs continues to expand.</p> ]]> Impact of Slack Bus Compensation on Voltage Stability in Power Grids Integrated with Electric Vehicles: A Machine Learning Approach for Intelligent Management harpreet kaur channi khushi sehgal swapandeep kaur doi: 10.56578/jimd030401 Journal of Intelligent Management Decision 10-30-2024 Journal of Intelligent Management Decision 10-30-2024 2024 3 4 Article 202 10.56578/jimd030401 https://www.acadlore.com/article/JIMD/2024_3_4/jimd030401 https://www.acadlore.com/article/JIMD/2024_3_3/jimd030305 Bibliometric analysis is a quantitative research method employed to measure and assess the impact, structure, and trends within academic publications. It aims to uncover patterns, connections, and research gaps either within a specific field or across interdisciplinary domains. This study utilizes bibliometric methods to investigate research gaps within the digital business domain, focusing on qualitative insights identified in existing literature. A systematic literature review (SLR) approach is adopted to ensure a rigorous synthesis of relevant studies. The analysis follows three key phases: data collection, bibliometric evaluation, and data visualization. Through these phases, trends, thematic gaps, and areas for future exploration are identified, offering a clearer understanding of the evolution and direction of digital business research. The insights derived are intended to inform sustainable business practices, with implications for environmentally conscious business models, value-driven marketing strategies, and the integration of sustainable operations. Moreover, the findings highlight potential avenues for enhanced technological innovation and interdisciplinary collaboration in digital business. This study provides a robust framework for scholars seeking to explore uncharted areas within digital business and offers actionable guidance on key research themes requiring further investigation. The use of bibliometric tools ensures comprehensive coverage of existing literature and fosters the development of a coherent research agenda aligned with emerging trends in the field. 09-29-2024 <![CDATA[ Bibliometric analysis is a quantitative research method employed to measure and assess the impact, structure, and trends within academic publications. It aims to uncover patterns, connections, and research gaps either within a specific field or across interdisciplinary domains. This study utilizes bibliometric methods to investigate research gaps within the digital business domain, focusing on qualitative insights identified in existing literature. A systematic literature review (SLR) approach is adopted to ensure a rigorous synthesis of relevant studies. The analysis follows three key phases: data collection, bibliometric evaluation, and data visualization. Through these phases, trends, thematic gaps, and areas for future exploration are identified, offering a clearer understanding of the evolution and direction of digital business research. The insights derived are intended to inform sustainable business practices, with implications for environmentally conscious business models, value-driven marketing strategies, and the integration of sustainable operations. Moreover, the findings highlight potential avenues for enhanced technological innovation and interdisciplinary collaboration in digital business. This study provides a robust framework for scholars seeking to explore uncharted areas within digital business and offers actionable guidance on key research themes requiring further investigation. The use of bibliometric tools ensures comprehensive coverage of existing literature and fosters the development of a coherent research agenda aligned with emerging trends in the field. ]]> A Comprehensive Guide to Bibliometric Analysis for Advancing Research in Digital Business asti marlina damara tri fazriansyah widhi ariyo bimo hanif zaidan sinaga hendri maulana ritzkal doi: 10.56578/jimd030305 Journal of Intelligent Management Decision 09-29-2024 Journal of Intelligent Management Decision 09-29-2024 2024 3 3 Article 187 10.56578/jimd030305 https://www.acadlore.com/article/JIMD/2024_3_3/jimd030305 https://www.acadlore.com/article/JIMD/2024_3_3/jimd030304 Container-based virtualization has emerged as a leading alternative to traditional cloud-based architectures due to its lower overhead, enhanced scalability, and adaptability. Kubernetes, one of the most widely adopted open-source container orchestration platforms, facilitates dynamic resource allocation through the Horizontal Pod Autoscaler (HPA). This auto-scaling mechanism enables efficient deployment and management of microservices, allowing for rapid development of complex SaaS applications. However, recent studies have identified several vulnerabilities in auto-scaling systems, including brute force attacks, Denial-of-Service (DoS) attacks, and YOYO attacks, which have led to significant performance degradation and unexpected downtimes. In response to these challenges, a novel approach is proposed to ensure uninterrupted deployment and enhanced resilience against such attacks. By leveraging Helm for deployment automation, Prometheus for metrics collection, and Grafana for real-time monitoring and visualisation, this framework improves the Quality of Service (QoS) in Kubernetes clusters. A primary focus is placed on achieving optimal resource utilisation while meeting Service Level Objectives (SLOs). The proposed architecture dynamically scales workloads in response to fluctuating demands and strengthens security against autoscaling-specific attacks. An on-premises implementation using Kubernetes and Docker containers demonstrates the feasibility of this approach by mitigating performance bottlenecks and preventing downtime. The contribution of this research lies in the ability to enhance system robustness and maintain service reliability under malicious conditions without compromising resource efficiency. This methodology ensures seamless scalability and secure operations, making it suitable for enterprise-level microservices and cloud-native applications. 09-24-2024 <![CDATA[ Container-based virtualization has emerged as a leading alternative to traditional cloud-based architectures due to its lower overhead, enhanced scalability, and adaptability. Kubernetes, one of the most widely adopted open-source container orchestration platforms, facilitates dynamic resource allocation through the Horizontal Pod Autoscaler (HPA). This auto-scaling mechanism enables efficient deployment and management of microservices, allowing for rapid development of complex SaaS applications. However, recent studies have identified several vulnerabilities in auto-scaling systems, including brute force attacks, Denial-of-Service (DoS) attacks, and YOYO attacks, which have led to significant performance degradation and unexpected downtimes. In response to these challenges, a novel approach is proposed to ensure uninterrupted deployment and enhanced resilience against such attacks. By leveraging Helm for deployment automation, Prometheus for metrics collection, and Grafana for real-time monitoring and visualisation, this framework improves the Quality of Service (QoS) in Kubernetes clusters. A primary focus is placed on achieving optimal resource utilisation while meeting Service Level Objectives (SLOs). The proposed architecture dynamically scales workloads in response to fluctuating demands and strengthens security against autoscaling-specific attacks. An on-premises implementation using Kubernetes and Docker containers demonstrates the feasibility of this approach by mitigating performance bottlenecks and preventing downtime. The contribution of this research lies in the ability to enhance system robustness and maintain service reliability under malicious conditions without compromising resource efficiency. This methodology ensures seamless scalability and secure operations, making it suitable for enterprise-level microservices and cloud-native applications. ]]> Security-Enhanced QoS-Aware Autoscaling of Kubernetes Pods Using Horizontal Pod Autoscaler (HPA) vani rajasekar muzafer saračević darjan karabašević dragiša stanujkić amor hasić melisa azizović srivarshan thirumalai doi: 10.56578/jimd030304 Journal of Intelligent Management Decision 09-24-2024 Journal of Intelligent Management Decision 09-24-2024 2024 3 3 Article 175 10.56578/jimd030304 https://www.acadlore.com/article/JIMD/2024_3_3/jimd030304 https://www.acadlore.com/article/JIMD/2024_3_3/jimd030303 The traditional manufacturing sector in China is increasingly challenged by rising labour costs and the diminishing demographic advantage. These issues exacerbate existing inefficiencies, such as limited value addition, high resource consumption, prolonged production cycles, inconsistent product quality, and inadequate automation. To address these challenges, a production scheduling framework is proposed, guided by three key objectives: the prioritisation of high-value orders, the reduction of total processing time, and the earliest possible completion of all orders. This study introduces a multi-objective constrained greedy model designed to optimise scheduling by balancing these objectives through maximum weight allocation, shortest processing time selection, and adherence to the earliest deadlines. The proposed approach incorporates comprehensive reward and penalty factors to account for deviations in performance, thus fostering a balance between operational efficiency and product quality. By implementing the optimised scheduling strategy, it is anticipated that significant improvements will be achieved in production efficiency, workforce motivation, product quality, and organisational reputation. The enhanced operational outcomes are expected to strengthen the core competitiveness of enterprises, particularly within the increasingly complex landscape of pull production systems. This research offers valuable insights for manufacturers seeking to transition towards more efficient, automated, and customer-centric production models, addressing both short-term operational challenges and long-term strategic objectives. 09-16-2024 <![CDATA[ The traditional manufacturing sector in China is increasingly challenged by rising labour costs and the diminishing demographic advantage. These issues exacerbate existing inefficiencies, such as limited value addition, high resource consumption, prolonged production cycles, inconsistent product quality, and inadequate automation. To address these challenges, a production scheduling framework is proposed, guided by three key objectives: the prioritisation of high-value orders, the reduction of total processing time, and the earliest possible completion of all orders. This study introduces a multi-objective constrained greedy model designed to optimise scheduling by balancing these objectives through maximum weight allocation, shortest processing time selection, and adherence to the earliest deadlines. The proposed approach incorporates comprehensive reward and penalty factors to account for deviations in performance, thus fostering a balance between operational efficiency and product quality. By implementing the optimised scheduling strategy, it is anticipated that significant improvements will be achieved in production efficiency, workforce motivation, product quality, and organisational reputation. The enhanced operational outcomes are expected to strengthen the core competitiveness of enterprises, particularly within the increasingly complex landscape of pull production systems. This research offers valuable insights for manufacturers seeking to transition towards more efficient, automated, and customer-centric production models, addressing both short-term operational challenges and long-term strategic objectives. ]]> Optimization of Production Scheduling Through a Multi-Objective Constrained Greedy Model jing gao gaoxiang sun tianhe qian doi: 10.56578/jimd030303 Journal of Intelligent Management Decision 09-16-2024 Journal of Intelligent Management Decision 09-16-2024 2024 3 3 Article 159 10.56578/jimd030303 https://www.acadlore.com/article/JIMD/2024_3_3/jimd030303 https://www.acadlore.com/article/JIMD/2024_3_3/jimd030302 Logistics performance plays a pivotal role in fostering economic growth and enhancing global competitiveness. This study aims to evaluate the logistics performance of G8 nations through multi-criteria decision-making (MCDM) models. Standard Deviation (SD) has been applied to determine the weights of evaluation criteria, while the Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN) has been employed to rank the countries based on their performance. The findings indicate that Timeliness emerges as the most critical factor influencing logistics efficiency. Among the G8 nations, Germany achieves the highest logistics performance, reflecting the robustness of its logistical infrastructure and operational efficiency. The results reinforce the premise that logistics performance is instrumental to both international trade and economic competitiveness. Nations demonstrating strong logistical capabilities are better positioned to excel in global markets, while those with underdeveloped logistics systems may face increased economic vulnerabilities. Enhancing logistical frameworks, including infrastructure and systems, is therefore essential for nations striving to improve their global standing. The insights presented underscore the importance of strategic investment in logistics infrastructure as a key policy instrument for enhancing economic resilience and international trade potential. 09-14-2024 <![CDATA[ Logistics performance plays a pivotal role in fostering economic growth and enhancing global competitiveness. This study aims to evaluate the logistics performance of G8 nations through multi-criteria decision-making (MCDM) models. Standard Deviation (SD) has been applied to determine the weights of evaluation criteria, while the Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN) has been employed to rank the countries based on their performance. The findings indicate that Timeliness emerges as the most critical factor influencing logistics efficiency. Among the G8 nations, Germany achieves the highest logistics performance, reflecting the robustness of its logistical infrastructure and operational efficiency. The results reinforce the premise that logistics performance is instrumental to both international trade and economic competitiveness. Nations demonstrating strong logistical capabilities are better positioned to excel in global markets, while those with underdeveloped logistics systems may face increased economic vulnerabilities. Enhancing logistical frameworks, including infrastructure and systems, is therefore essential for nations striving to improve their global standing. The insights presented underscore the importance of strategic investment in logistics infrastructure as a key policy instrument for enhancing economic resilience and international trade potential. ]]> Evaluating the Logistics Performance of G8 Nations Using Multi-Criteria Decision-Making Models ayse topal alptekin ulutaş doi: 10.56578/jimd030302 Journal of Intelligent Management Decision 09-14-2024 Journal of Intelligent Management Decision 09-14-2024 2024 3 3 Article 150 10.56578/jimd030302 https://www.acadlore.com/article/JIMD/2024_3_3/jimd030302 https://www.acadlore.com/article/JIMD/2024_3_3/jimd030301 The efficiency of utility vehicle fleets in municipal waste management plays a crucial role in enhancing the sustainability and effectiveness of non-hazardous waste disposal systems. This research investigates the operational performance of a local utility company's vehicle fleet, with a specific focus on waste separation at the source and its implications for meeting environmental standards in Europe and beyond. The study aims to identify the most efficient vehicle within the fleet, contributing to broader goals of environmental preservation and waste reduction, with a long-term vision of achieving "zero waste". Efficiency was evaluated using Data Envelopment Analysis (DEA), where key input parameters included fuel costs, regular maintenance expenses, emergency repair costs, and the number of minor accidents or damages. The output parameter was defined as the vehicle's working hours. Following the DEA results, the Criteria Importance Through Intercriteria Correlation (CRITIC) method was employed to assign weightings to the criteria, ensuring an accurate reflection of their relative importance. The Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method was then applied to rank the vehicles based on their overall efficiency. The analysis, conducted over a five-year period (2019-2023), demonstrated that Vehicle 3 (MAN T32-J-339) achieved the highest operational efficiency, particularly in 2020. These findings underscore the potential for optimising fleet performance in waste management systems, contributing to a cleaner urban environment and aligning with global sustainability objectives. The proposed model provides a robust framework for future applications in similar municipal settings, supporting the transition towards more eco-friendly waste management practices. 08-09-2024 <![CDATA[ The efficiency of utility vehicle fleets in municipal waste management plays a crucial role in enhancing the sustainability and effectiveness of non-hazardous waste disposal systems. This research investigates the operational performance of a local utility company's vehicle fleet, with a specific focus on waste separation at the source and its implications for meeting environmental standards in Europe and beyond. The study aims to identify the most efficient vehicle within the fleet, contributing to broader goals of environmental preservation and waste reduction, with a long-term vision of achieving "zero waste". Efficiency was evaluated using Data Envelopment Analysis (DEA), where key input parameters included fuel costs, regular maintenance expenses, emergency repair costs, and the number of minor accidents or damages. The output parameter was defined as the vehicle's working hours. Following the DEA results, the Criteria Importance Through Intercriteria Correlation (CRITIC) method was employed to assign weightings to the criteria, ensuring an accurate reflection of their relative importance. The Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method was then applied to rank the vehicles based on their overall efficiency. The analysis, conducted over a five-year period (2019-2023), demonstrated that Vehicle 3 (MAN T32-J-339) achieved the highest operational efficiency, particularly in 2020. These findings underscore the potential for optimising fleet performance in waste management systems, contributing to a cleaner urban environment and aligning with global sustainability objectives. The proposed model provides a robust framework for future applications in similar municipal settings, supporting the transition towards more eco-friendly waste management practices. ]]> Optimising the Efficiency of Municipal Utility Vehicle Fleets Using DEA-CRITIC-MARCOS: A Sustainable Waste Management Approach eldina huskanović draženko bjelić boris novarlić doi: 10.56578/jimd030301 Journal of Intelligent Management Decision 08-09-2024 Journal of Intelligent Management Decision 08-09-2024 2024 3 3 Article 139 10.56578/jimd030301 https://www.acadlore.com/article/JIMD/2024_3_3/jimd030301 https://www.acadlore.com/article/JIMD/2024_3_2/jimd030205 This study investigates the application of Multi-Criteria Decision Analysis (MCDA) methods to the classification of research papers within a Systematic Literature Review (SLR). Distinctions are drawn between compensatory and non-compensatory MCDA approaches, which, despite their distinctiveness, have often been applied interchangeably, leading to a need for clarification in their usage. To address this, the methods of Entropy Weight Method (EWM), Analytic Hierarchy Process (AHP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were utilized to determine the parameters for ranking papers within an SLR portfolio. The source of this ranking comprised publications from three major databases: Scopus, ScienceDirect, and Web of Science. From an initial yield of 267 articles, a final portfolio of 90 articles was established, highlighting not only the compensatory and non-compensatory classifications but also identifying methods that incorporate features of both. This nuanced categorization reveals the complexity and necessity of selecting an appropriate MCDA method based on the dataset characteristics, which may exhibit attributes of both approaches. The analysis further illuminated the geographical distribution of publications, leading contributors, thematic areas, and the prevalence of specific MCDA methods. This study underscores the importance of methodological precision in the application of MCDA to systematic reviews, providing a refined framework for evaluating academic literature. 05-22-2024 <![CDATA[ This study investigates the application of Multi-Criteria Decision Analysis (MCDA) methods to the classification of research papers within a Systematic Literature Review (SLR). Distinctions are drawn between compensatory and non-compensatory MCDA approaches, which, despite their distinctiveness, have often been applied interchangeably, leading to a need for clarification in their usage. To address this, the methods of Entropy Weight Method (EWM), Analytic Hierarchy Process (AHP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were utilized to determine the parameters for ranking papers within an SLR portfolio. The source of this ranking comprised publications from three major databases: Scopus, ScienceDirect, and Web of Science. From an initial yield of 267 articles, a final portfolio of 90 articles was established, highlighting not only the compensatory and non-compensatory classifications but also identifying methods that incorporate features of both. This nuanced categorization reveals the complexity and necessity of selecting an appropriate MCDA method based on the dataset characteristics, which may exhibit attributes of both approaches. The analysis further illuminated the geographical distribution of publications, leading contributors, thematic areas, and the prevalence of specific MCDA methods. This study underscores the importance of methodological precision in the application of MCDA to systematic reviews, providing a refined framework for evaluating academic literature. ]]> A Novel Approach for Systematic Literature Reviews Using Multi-Criteria Decision Analysis vilmar steffen maiquiel schmidt de oliveira flavio trojan doi: 10.56578/jimd030205 Journal of Intelligent Management Decision 05-22-2024 Journal of Intelligent Management Decision 05-22-2024 2024 3 2 Article 116 10.56578/jimd030205 https://www.acadlore.com/article/JIMD/2024_3_2/jimd030205 https://www.acadlore.com/article/JIMD/2024_3_2/jimd030204 In the dynamic landscape of mobile technology, where a myriad of options burgeons, compounded by fluctuating features, diverse price points, and a plethora of specifications, the task of selecting the optimum mobile phone becomes formidable for consumers. This complexity is further exacerbated by the intrinsic ambiguity and uncertainty characterizing consumer preferences. Addressed herein is the deployment of fuzzy hypersoft sets (FHSS) in conjunction with machine learning techniques to forge a decision support system (DSS) that refines the mobile phone selection process. The proposed framework harnesses the synergy between FHSS and machine learning to navigate the multifaceted nature of consumer choices and the attributes of the available alternatives, thereby offering a structured approach aimed at maximizing consumer satisfaction while accommodating various determinants. The integration of FHSS is pivotal in managing the inherent ambiguity and uncertainty of consumer preferences, providing a comprehensive decision-making apparatus amidst a plethora of choices. The elucidation of this study encompasses an easy-to-navigate framework, buttressed by sophisticated Python codes and algorithms, to ameliorate the selection process. This methodology engenders a personalized and engaging avenue for mobile phone selection in an ever-evolving technological epoch. The fidelity to professional terminologies and their consistent application throughout this discourse, as well as in subsequent sections of the study, underscores the meticulous approach adopted to ensure clarity and precision. This study contributes to the extant literature by offering a novel framework that melds the principles of fuzzy set (FS) theory with advanced computational techniques, thereby facilitating a nuanced decision-making process in the realm of mobile phone selection. 04-11-2024 <![CDATA[ <p>In the dynamic landscape of mobile technology, where a myriad of options burgeons, compounded by fluctuating features, diverse price points, and a plethora of specifications, the task of selecting the optimum mobile phone becomes formidable for consumers. This complexity is further exacerbated by the intrinsic ambiguity and uncertainty characterizing consumer preferences. Addressed herein is the deployment of fuzzy hypersoft sets (FHSS) in conjunction with machine learning techniques to forge a decision support system (DSS) that refines the mobile phone selection process. The proposed framework harnesses the synergy between FHSS and machine learning to navigate the multifaceted nature of consumer choices and the attributes of the available alternatives, thereby offering a structured approach aimed at maximizing consumer satisfaction while accommodating various determinants. The integration of FHSS is pivotal in managing the inherent ambiguity and uncertainty of consumer preferences, providing a comprehensive decision-making apparatus amidst a plethora of choices. The elucidation of this study encompasses an easy-to-navigate framework, buttressed by sophisticated Python codes and algorithms, to ameliorate the selection process. This methodology engenders a personalized and engaging avenue for mobile phone selection in an ever-evolving technological epoch. The fidelity to professional terminologies and their consistent application throughout this discourse, as well as in subsequent sections of the study, underscores the meticulous approach adopted to ensure clarity and precision. This study contributes to the extant literature by offering a novel framework that melds the principles of fuzzy set (FS) theory with advanced computational techniques, thereby facilitating a nuanced decision-making process in the realm of mobile phone selection.</p> ]]> Decision Support System for Mobile Phone Selection Utilizing Fuzzy Hypersoft Sets and Machine Learning muhammad tahir hamid muhammad abid doi: 10.56578/jimd030204 Journal of Intelligent Management Decision 04-11-2024 Journal of Intelligent Management Decision 04-11-2024 2024 3 2 Article 104 10.56578/jimd030204 https://www.acadlore.com/article/JIMD/2024_3_2/jimd030204 https://www.acadlore.com/article/JIMD/2024_3_2/jimd030203 In general, a stable and strong system shouldn't have an overly sensitive/dependent response to inputs (unless consciously and planned desired), as this would reduce efficiency. As in other techniques, approaches, and methodologies, if the results are excessively affected when the input parameters change in MCDM methods, this situation is identified with sensitivity analyses. Oversensitivity is generally accepted as a problem in the MCDM (Multi-Criteria Decision Making) methodology family, which has more than 200 members according to the current literature. The MCDM family is not just a weight coefficient-sensitive methodology. MCDM types can also be sensitive to many different calculation parameters such as data type, normalization, fundamental equation, threshold value, preference function, etc. Many studies to understand the degree of sensitivity simply monitor whether the ranking position of the best alternative changes. However, this is incomplete for understanding the nature of sensitivity, and more evidence is undoubtedly needed to gain insight into this matter. Observing the holistic change of all alternatives compared to a single alternative provides the researcher with more reliable and generalizing evidence, information, or assumptions about the degree of sensitivity of the system. In this study, we assigned a fixed reference point to measure sensitivity with a more robust approach. Thus, we took the distance to the fixed point as a base reference while observing the changeable MCDM results. We calculated sensitivity to normalization, not just sensitivity to weight coefficients. In addition, past MCDM studies accept existing data as the only criterion in sensitivity analysis and make generalizations easily. To show that the model proposed in this study is not a coincidence, in addition to the graphics card selection problem, an exploratory validation was performed for another problem with a different set of data, alternatives, and criteria. We comparatively measured sensitivity using the relationship between MCDM-based performance and the static reference point. We statistically measured the sensitivity with four types of weighting methods and 7 types of normalization techniques with the PROBID method. The striking result, confirmed by 56 different MCDM ranking findings, was this: In general, if the sensitivity of an MCDM method is high, the relationship of that MCDM method to a fixed reference point is low. On the other hand, if the sensitivity is low, a high correlation with the reference point is produced. In short, uncontrolled hypersensitivity disrupts not only the ranking but also external relations, as expected. 04-09-2024 <![CDATA[ In general, a stable and strong system shouldn't have an overly sensitive/dependent response to inputs (unless consciously and planned desired), as this would reduce efficiency. As in other techniques, approaches, and methodologies, if the results are excessively affected when the input parameters change in MCDM methods, this situation is identified with sensitivity analyses. Oversensitivity is generally accepted as a problem in the MCDM (Multi-Criteria Decision Making) methodology family, which has more than 200 members according to the current literature. The MCDM family is not just a weight coefficient-sensitive methodology. MCDM types can also be sensitive to many different calculation parameters such as data type, normalization, fundamental equation, threshold value, preference function, etc. Many studies to understand the degree of sensitivity simply monitor whether the ranking position of the best alternative changes. However, this is incomplete for understanding the nature of sensitivity, and more evidence is undoubtedly needed to gain insight into this matter. Observing the holistic change of all alternatives compared to a single alternative provides the researcher with more reliable and generalizing evidence, information, or assumptions about the degree of sensitivity of the system. In this study, we assigned a fixed reference point to measure sensitivity with a more robust approach. Thus, we took the distance to the fixed point as a base reference while observing the changeable MCDM results. We calculated sensitivity to normalization, not just sensitivity to weight coefficients. In addition, past MCDM studies accept existing data as the only criterion in sensitivity analysis and make generalizations easily. To show that the model proposed in this study is not a coincidence, in addition to the graphics card selection problem, an exploratory validation was performed for another problem with a different set of data, alternatives, and criteria. We comparatively measured sensitivity using the relationship between MCDM-based performance and the static reference point. We statistically measured the sensitivity with four types of weighting methods and 7 types of normalization techniques with the PROBID method. The striking result, confirmed by 56 different MCDM ranking findings, was this: In general, if the sensitivity of an MCDM method is high, the relationship of that MCDM method to a fixed reference point is low. On the other hand, if the sensitivity is low, a high correlation with the reference point is produced. In short, uncontrolled hypersensitivity disrupts not only the ranking but also external relations, as expected. ]]> Does the Performance of MCDM Rankings Increase as Sensitivity Decreases? Graphics Card Selection and Pattern Discovery Using the PROBID Method mahmut baydaş mustafa kavacık zhiyuan wang doi: 10.56578/jimd030203 Journal of Intelligent Management Decision 04-09-2024 Journal of Intelligent Management Decision 04-09-2024 2024 3 2 Article 91 10.56578/jimd030203 https://www.acadlore.com/article/JIMD/2024_3_2/jimd030203 https://www.acadlore.com/article/JIMD/2024_3_2/jimd030202 In the evolution of blockchain technology, the traditional single-chain structure has faced significant challenges, including low throughput, high latency, and limited scalability. This paper focuses on leveraging multichain sharding technology to overcome these constraints and introduces a high-performance carbon cycle supply data sharing method based on a blockchain multichain framework. The aim is to address the difficulties encountered in traditional carbon data processing. The proposed method involves partitioning a consortium chain into multiple subchains and constructing a unique “child/parent” chain architecture, enabling cross-chain data access and significantly increasing throughput. Furthermore, the scheme enhances the security and processing capacity of subchains by dynamically increasing the number of validator broadcasting nodes and implementing parallel node operations within subchains. This approach effectively solves the problems of low throughput in single-chain blockchain networks and the challenges of cross-chain data sharing, realizing more efficient and scalable blockchain applications. 04-06-2024 <![CDATA[ <p>In the evolution of blockchain technology, the traditional single-chain structure has faced significant challenges, including low throughput, high latency, and limited scalability. This paper focuses on leveraging multichain sharding technology to overcome these constraints and introduces a high-performance carbon cycle supply data sharing method based on a blockchain multichain framework. The aim is to address the difficulties encountered in traditional carbon data processing. The proposed method involves partitioning a consortium chain into multiple subchains and constructing a unique “child/parent” chain architecture, enabling cross-chain data access and significantly increasing throughput. Furthermore, the scheme enhances the security and processing capacity of subchains by dynamically increasing the number of validator broadcasting nodes and implementing parallel node operations within subchains. This approach effectively solves the problems of low throughput in single-chain blockchain networks and the challenges of cross-chain data sharing, realizing more efficient and scalable blockchain applications.</p> ]]> High-Performance Carbon Cycle Supply Data Sharing Method Based on Blockchain Multichain Technology yuanjun liu lin zhang ashim khadka doi: 10.56578/jimd030202 Journal of Intelligent Management Decision 04-06-2024 Journal of Intelligent Management Decision 04-06-2024 2024 3 2 Article 77 10.56578/jimd030202 https://www.acadlore.com/article/JIMD/2024_3_2/jimd030202 https://www.acadlore.com/article/JIMD/2024_3_2/jimd030201 The cold chain industry plays a pivotal role in ensuring the quality and safety of temperature-sensitive products throughout their journey from production to consumption. Central to this process is the effective monitoring of temperature fluctuations, which directly impacts product integrity. With an array of temperature monitoring devices available in the market, selecting the most suitable option becomes a critical task for organizations operating within the cold chain. This paper presents a comprehensive analysis of seven prominent temperature monitoring devices utilized in the cold chain industry. Through a systematic evaluation process, each device is rigorously assessed across six key criteria groups: price, accuracy, usability, monitoring and reporting capabilities, flexibility, and capability. A total of 23 independent metrics are considered within these criteria, providing a holistic view of each device's performance. Building upon this analysis, a robust decision support model is proposed to facilitate the selection process for organizations. The model integrates the findings from the evaluation, allowing stakeholders to make informed decisions based on their specific requirements and priorities. Notably, the Chemical Time Temperature Integrators (CTTI) emerge as the top-ranked device, demonstrating superior performance across multiple criteria. The implications of this research extend beyond device selection, offering valuable insights for enhancing cold chain efficiency and product quality. By leveraging the decision support model presented in this study, organizations can streamline their temperature monitoring processes, mitigate risks associated with temperature excursions, and ultimately optimize their cold chain operations. This study serves as a foundation for further research in the field of cold chain management, paving the way for advancements in temperature monitoring technology and strategies. Future studies may explore additional criteria or expand the analysis to include a broader range of devices, contributing to ongoing efforts aimed at improving cold chain sustainability and reliability. 04-02-2024 <![CDATA[ The cold chain industry plays a pivotal role in ensuring the quality and safety of temperature-sensitive products throughout their journey from production to consumption. Central to this process is the effective monitoring of temperature fluctuations, which directly impacts product integrity. With an array of temperature monitoring devices available in the market, selecting the most suitable option becomes a critical task for organizations operating within the cold chain. This paper presents a comprehensive analysis of seven prominent temperature monitoring devices utilized in the cold chain industry. Through a systematic evaluation process, each device is rigorously assessed across six key criteria groups: price, accuracy, usability, monitoring and reporting capabilities, flexibility, and capability. A total of 23 independent metrics are considered within these criteria, providing a holistic view of each device's performance. Building upon this analysis, a robust decision support model is proposed to facilitate the selection process for organizations. The model integrates the findings from the evaluation, allowing stakeholders to make informed decisions based on their specific requirements and priorities. Notably, the Chemical Time Temperature Integrators (CTTI) emerge as the top-ranked device, demonstrating superior performance across multiple criteria. The implications of this research extend beyond device selection, offering valuable insights for enhancing cold chain efficiency and product quality. By leveraging the decision support model presented in this study, organizations can streamline their temperature monitoring processes, mitigate risks associated with temperature excursions, and ultimately optimize their cold chain operations. This study serves as a foundation for further research in the field of cold chain management, paving the way for advancements in temperature monitoring technology and strategies. Future studies may explore additional criteria or expand the analysis to include a broader range of devices, contributing to ongoing efforts aimed at improving cold chain sustainability and reliability. ]]> A Novel Approach Based on CRITIC-MOOSRA Methods for Evaluation and Selection of Cold Chain Monitoring Devices vukašin pajić milan andrejić mia poledica doi: 10.56578/jimd030201 Journal of Intelligent Management Decision 04-02-2024 Journal of Intelligent Management Decision 04-02-2024 2024 3 2 Article 68 10.56578/jimd030201 https://www.acadlore.com/article/JIMD/2024_3_2/jimd030201 https://www.acadlore.com/article/JIMD/2024_3_1/jimd030105 In multi-criteria decision-making (MCDM), accurately quantifying qualitative data and simulating real-world scenarios remains a significant challenge, particularly in the presence of inherent imprecision and incompleteness of information. Fuzzy logic, recognized for its capacity to model uncertainty and ambiguity, emerges as a pivotal theory in decision-making processes. This study introduces an enhancement to the Defining Interrelationships Between Ranked Criteria II (DIBR II) method, employing triangular fuzzy numbers with variable confidence intervals for the determination of criteria weight coefficients-essential for assessing their significance and impact on final decisions. The enhanced method, hereafter referred to as the Fuzzy-DIBR II (F-DIBR II), is elaborated upon through a comprehensive description of its algorithmic steps, underscored by a numerical example that highlights its potential. Validation of F-DIBR II is undertaken via a comparative analysis against the traditional DIBR II approach, placing particular emphasis on its application within the Fuzzy Complex Proportional Assessment (COPRAS) framework, geared towards evaluating sustainable mobility measures. This focal point not only reaffirms the necessity of integrating fuzzy logic into the DIBR II methodology but also validates its practical applicability in addressing real-world issues. Contributions of this research extend beyond the theoretical enhancements of fuzzy theory within the MCDM landscape, offering tangible implications for the application of F-DIBR II in sustainable mobility analyses. The consistency in professional terminology throughout the study ensures clarity and coherence, aligning with the stringent standards of top-tier academic journals. 03-14-2024 <![CDATA[ <p>In multi-criteria decision-making (MCDM), accurately quantifying qualitative data and simulating real-world scenarios remains a significant challenge, particularly in the presence of inherent imprecision and incompleteness of information. Fuzzy logic, recognized for its capacity to model uncertainty and ambiguity, emerges as a pivotal theory in decision-making processes. This study introduces an enhancement to the Defining Interrelationships Between Ranked Criteria II (DIBR II) method, employing triangular fuzzy numbers with variable confidence intervals for the determination of criteria weight coefficients-essential for assessing their significance and impact on final decisions. The enhanced method, hereafter referred to as the Fuzzy-DIBR II (F-DIBR II), is elaborated upon through a comprehensive description of its algorithmic steps, underscored by a numerical example that highlights its potential. Validation of F-DIBR II is undertaken via a comparative analysis against the traditional DIBR II approach, placing particular emphasis on its application within the Fuzzy Complex Proportional Assessment (COPRAS) framework, geared towards evaluating sustainable mobility measures. This focal point not only reaffirms the necessity of integrating fuzzy logic into the DIBR II methodology but also validates its practical applicability in addressing real-world issues. Contributions of this research extend beyond the theoretical enhancements of fuzzy theory within the MCDM landscape, offering tangible implications for the application of F-DIBR II in sustainable mobility analyses. The consistency in professional terminology throughout the study ensures clarity and coherence, aligning with the stringent standards of top-tier academic journals.</p> ]]> Enhancing Multi-Criteria Decision-Making with Fuzzy Logic: An Advanced Defining Interrelationships Between Ranked II Method Incorporating Triangular Fuzzy Numbers duško tešić darko božanić mohammad khalilzadeh doi: 10.56578/jimd030105 Journal of Intelligent Management Decision 03-14-2024 Journal of Intelligent Management Decision 03-14-2024 2024 3 1 Article 56 10.56578/jimd030105 https://www.acadlore.com/article/JIMD/2024_3_1/jimd030105 https://www.acadlore.com/article/JIMD/2024_3_1/jimd030104 This study aims to elucidate the role of established public utility logistics in facilitating the integration of transition economies into the modern, developed world, with a particular focus on comprehensive waste management and the implementation of green logistics schemes. The research highlights the “green islands” waste collection system—networks of collection points that serve as separators for secondary raw materials from generated waste. It is demonstrated that such systems not only contribute to the optimization of public utility company costs and the selection of optimal transport routes but also play a crucial role in elevating public awareness regarding the importance of the 3R principle (Reduce, Reuse, Recycle). A significant contribution of this study lies in its demonstration of how academic knowledge can be transferred to the business sector through spin-offs, evidencing a theoretical model of green logistics schemes that can increase the total amount of secondary raw materials recovered from waste by 20% by 2030 in the City of Doboj. The research underscores the role of citizens, students, and businesses as primary waste producers in transition economies, emphasizing the effectiveness of a rewards system for conscientious waste selection at the source. Moreover, the establishment of an optimal transport route designed to support these green islands is shown to enhance the collection and recycling of valuable secondary raw materials, thereby preventing their disposal in landfills without value recovery. This innovative approach not only raises public awareness towards a more sustainable environment but also establishes a foundation for long-term environmental health. Through the lens of green logistics, this study presents a compelling model for comprehensive waste management in transition economies, advocating for practices that ensure the sustainable management of resources and contribute to environmental protection and public health. 03-13-2024 <![CDATA[ <p>This study aims to elucidate the role of established public utility logistics in facilitating the integration of transition economies into the modern, developed world, with a particular focus on comprehensive waste management and the implementation of green logistics schemes. The research highlights the “green islands” waste collection system—networks of collection points that serve as separators for secondary raw materials from generated waste. It is demonstrated that such systems not only contribute to the optimization of public utility company costs and the selection of optimal transport routes but also play a crucial role in elevating public awareness regarding the importance of the 3R principle (Reduce, Reuse, Recycle). A significant contribution of this study lies in its demonstration of how academic knowledge can be transferred to the business sector through spin-offs, evidencing a theoretical model of green logistics schemes that can increase the total amount of secondary raw materials recovered from waste by 20% by 2030 in the City of Doboj. The research underscores the role of citizens, students, and businesses as primary waste producers in transition economies, emphasizing the effectiveness of a rewards system for conscientious waste selection at the source. Moreover, the establishment of an optimal transport route designed to support these green islands is shown to enhance the collection and recycling of valuable secondary raw materials, thereby preventing their disposal in landfills without value recovery. This innovative approach not only raises public awareness towards a more sustainable environment but also establishes a foundation for long-term environmental health. Through the lens of green logistics, this study presents a compelling model for comprehensive waste management in transition economies, advocating for practices that ensure the sustainable management of resources and contribute to environmental protection and public health.</p> ]]> Enhancing Comprehensive Waste Management in Transition Economies Through Green Logistics: A Case Study of Bosnia and Herzegovina boris novarlić predrag ðurić doi: 10.56578/jimd030104 Journal of Intelligent Management Decision 03-13-2024 Journal of Intelligent Management Decision 03-13-2024 2024 3 1 Article 42 10.56578/jimd030104 https://www.acadlore.com/article/JIMD/2024_3_1/jimd030104 https://www.acadlore.com/article/JIMD/2024_3_1/jimd030103 The paradigm shift towards sustainable transportation underscores the burgeoning focus on electric vehicles (EVs) as a viable alternative to combustion-powered counterparts. Concurrently, the corpus of scholarly publications exploring this domain has expanded, endeavoring to address multifaceted challenges across various disciplines. Among the methodologies enlisted, Multi-criteria Decision Analysis (MCDM) emerges as a pivotal tool for decision-makers, facilitating the resolution of complex problems characterized by multiple criteria and alternatives. This research employs a modified Systematic Literature Review (SLR) methodology, integrating the Analytical Hierarchical Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for article ranking. This novel approach not only enhances the precision of the ranking process but also earmarks the articles exerting substantial influence on the scholarly landscape. The exhaustive review culminates in a curated portfolio of 73 seminal articles, with a pronounced emphasis on Charging Stations applications, accounting for approximately 42.46% of the collective focus. This study's findings illuminate the prevailing trends within the nexus of EV research and MCDM, delineating a trajectory for future inquiries and applications. In doing so, it underscores the indispensable role of MCDM in navigating the complexities inherent in the transition to electrified mobility solutions. The meticulous application of the SLR methodology, augmented by AHP and TOPSIS, not only refines the academic discourse but also paves the way for a more structured and impactful exploration of EVs within the realm of sustainable transportation. 03-11-2024 <![CDATA[ <p>The paradigm shift towards sustainable transportation underscores the burgeoning focus on electric vehicles (EVs) as a viable alternative to combustion-powered counterparts. Concurrently, the corpus of scholarly publications exploring this domain has expanded, endeavoring to address multifaceted challenges across various disciplines. Among the methodologies enlisted, Multi-criteria Decision Analysis (MCDM) emerges as a pivotal tool for decision-makers, facilitating the resolution of complex problems characterized by multiple criteria and alternatives. This research employs a modified Systematic Literature Review (SLR) methodology, integrating the Analytical Hierarchical Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for article ranking. This novel approach not only enhances the precision of the ranking process but also earmarks the articles exerting substantial influence on the scholarly landscape. The exhaustive review culminates in a curated portfolio of 73 seminal articles, with a pronounced emphasis on Charging Stations applications, accounting for approximately 42.46% of the collective focus. This study's findings illuminate the prevailing trends within the nexus of EV research and MCDM, delineating a trajectory for future inquiries and applications. In doing so, it underscores the indispensable role of MCDM in navigating the complexities inherent in the transition to electrified mobility solutions. The meticulous application of the SLR methodology, augmented by AHP and TOPSIS, not only refines the academic discourse but also paves the way for a more structured and impactful exploration of EVs within the realm of sustainable transportation.</p> ]]> Systematic Literature Review on Electric Vehicles and Multicriteria Decision Making: Trends, Rankings, and Future Perspectives maiquiel schmidt de oliveira vilmar steffen flávio trojan doi: 10.56578/jimd030103 Journal of Intelligent Management Decision 03-11-2024 Journal of Intelligent Management Decision 03-11-2024 2024 3 1 Article 22 10.56578/jimd030103 https://www.acadlore.com/article/JIMD/2024_3_1/jimd030103 https://www.acadlore.com/article/JIMD/2024_3_1/jimd030102 In geographically isolated regions, where infrastructure limitations and remote locations pose significant challenges, a mobile application has been developed to facilitate an efficient emergency response system. This system, designed to bridge the gap in emergency support, employs a multi-faceted strategy that combines human expertise with advanced machine learning (ML) technologies. Upon activation through the application, a coordinated mechanism is triggered, dispatching local mechanics equipped with the necessary tools, resources, vehicles, and spare parts to the site of the emergency. This immediate on-site assistance is essential for addressing mechanical failures and ensuring timely support for individuals in remote areas.At the heart of the application lies a sophisticated ML model, trained on an extensive dataset comprising a wide array of emergencies likely to occur in rural settings. This model, characterized by its convolutional neural network (CNN) architecture and optimized for mobile deployment through TensorFlow Lite (TFLite), demonstrates an impressive diagnostic accuracy rate of 98%. Such precision significantly enhances the application’s capacity to diagnose issues accurately, prioritize response efforts, and optimize resource allocation.Moreover, the application leverages data-driven insights not only to streamline the emergency response process but also to facilitate predictive maintenance. By continuously learning from incoming data, the ML model can predict potential problems and suggest preventative measures to users, thereby minimizing the likelihood of future breakdowns. This predictive capability underscores the application’s role in promoting resilience within rural communities.Community engagement is further encouraged through the inclusion of local mechanics in the emergency response network. This initiative not only expands the pool of available skilled professionals but also fosters a sense of community solidarity, crucial for enhancing the system’s overall effectiveness.In summary, the development of this mobile application represents a significant advancement in emergency assistance for rural communities. By integrating real-time response capabilities with sophisticated ML models, the system not only addresses the immediate challenges of emergency support in remote areas but also contributes to the creation of a more resilient and interconnected community fabric. 03-04-2024 <![CDATA[ <p>In geographically isolated regions, where infrastructure limitations and remote locations pose significant challenges, a mobile application has been developed to facilitate an efficient emergency response system. This system, designed to bridge the gap in emergency support, employs a multi-faceted strategy that combines human expertise with advanced machine learning (ML) technologies. Upon activation through the application, a coordinated mechanism is triggered, dispatching local mechanics equipped with the necessary tools, resources, vehicles, and spare parts to the site of the emergency. This immediate on-site assistance is essential for addressing mechanical failures and ensuring timely support for individuals in remote areas.At the heart of the application lies a sophisticated ML model, trained on an extensive dataset comprising a wide array of emergencies likely to occur in rural settings. This model, characterized by its convolutional neural network (CNN) architecture and optimized for mobile deployment through TensorFlow Lite (TFLite), demonstrates an impressive diagnostic accuracy rate of 98%. Such precision significantly enhances the application’s capacity to diagnose issues accurately, prioritize response efforts, and optimize resource allocation.Moreover, the application leverages data-driven insights not only to streamline the emergency response process but also to facilitate predictive maintenance. By continuously learning from incoming data, the ML model can predict potential problems and suggest preventative measures to users, thereby minimizing the likelihood of future breakdowns. This predictive capability underscores the application’s role in promoting resilience within rural communities.Community engagement is further encouraged through the inclusion of local mechanics in the emergency response network. This initiative not only expands the pool of available skilled professionals but also fosters a sense of community solidarity, crucial for enhancing the system’s overall effectiveness.In summary, the development of this mobile application represents a significant advancement in emergency assistance for rural communities. By integrating real-time response capabilities with sophisticated ML models, the system not only addresses the immediate challenges of emergency support in remote areas but also contributes to the creation of a more resilient and interconnected community fabric.</p> ]]> Deploying Mobile Applications for Emergency Flood Response in Geographically Isolated Areas: A Data-Driven Approach hoang ha nguyen ha huy cuong nguyen chiranjibe jana doi: 10.56578/jimd030102 Journal of Intelligent Management Decision 03-04-2024 Journal of Intelligent Management Decision 03-04-2024 2024 3 1 Article 15 10.56578/jimd030102 https://www.acadlore.com/article/JIMD/2024_3_1/jimd030102 https://www.acadlore.com/article/JIMD/2024_3_1/jimd030101 In an era characterized by intense labor market competition for skilled and motivated personnel, the adoption of innovative strategies, such as gamification, has emerged as a critical factor for cultivating an engaging workplace environment. This investigation explores the impact of gameful experiences on employee behavior within the context of credit institutions, focusing on three primary behaviors: knowledge sharing, team identity development, and affective commitment to the organization. An empirical analysis, conducted through the collection of 382 employee responses, reveals that gameful experiences exert a significant positive influence on these behaviors. Specifically, it is demonstrated that such experiences enhance the propensity for knowledge sharing among colleagues, foster the development of a stronger team identity, and increase affective commitment towards the company. These findings contribute to the expansion of the nomological network of gameful experience in the professional setting, highlighting the individual team behaviors that are pivotal for organizational success. Furthermore, the results advocate for the integration of gamification strategies within workplace design, underscoring the potential of gameful experiences to promote behaviors that are beneficial to organizational objectives. By delving into the relatively unexplored domain of gamification within workplace design, this research not only enriches the academic discourse on gamification but also provides practical insights for the application of gameful experiences to enhance employee engagement and behavior. In doing so, it underscores the transformative potential of gamification in shaping workplace dynamics and fostering an environment conducive to collaborative and committed work practices. 02-01-2024 <![CDATA[ <p>In an era characterized by intense labor market competition for skilled and motivated personnel, the adoption of innovative strategies, such as gamification, has emerged as a critical factor for cultivating an engaging workplace environment. This investigation explores the impact of gameful experiences on employee behavior within the context of credit institutions, focusing on three primary behaviors: knowledge sharing, team identity development, and affective commitment to the organization. An empirical analysis, conducted through the collection of 382 employee responses, reveals that gameful experiences exert a significant positive influence on these behaviors. Specifically, it is demonstrated that such experiences enhance the propensity for knowledge sharing among colleagues, foster the development of a stronger team identity, and increase affective commitment towards the company. These findings contribute to the expansion of the nomological network of gameful experience in the professional setting, highlighting the individual team behaviors that are pivotal for organizational success. Furthermore, the results advocate for the integration of gamification strategies within workplace design, underscoring the potential of gameful experiences to promote behaviors that are beneficial to organizational objectives. By delving into the relatively unexplored domain of gamification within workplace design, this research not only enriches the academic discourse on gamification but also provides practical insights for the application of gameful experiences to enhance employee engagement and behavior. In doing so, it underscores the transformative potential of gamification in shaping workplace dynamics and fostering an environment conducive to collaborative and committed work practices.</p> ]]> Gamification in the Workplace: Enhancing Employee Engagement Through Gameful Experiences jonas manske doi: 10.56578/jimd030101 Journal of Intelligent Management Decision 02-01-2024 Journal of Intelligent Management Decision 02-01-2024 2024 3 1 Article 1 10.56578/jimd030101 https://www.acadlore.com/article/JIMD/2024_3_1/jimd030101 https://www.acadlore.com/article/JIMD/2023_2_4/jimd020405 In light of both domestic and international research on blockchain and supply chains, coupled with the blockchain development in the Hainan Free Trade Port of China, a supply chain model sensitive to demand and consumer behavior has been established. This study selects Hainan Free Trade Port and the established Hong Kong Free Trade Port for comparison. Integrating diverse tax policies of these ports, the research employs a Stackelberg game led by market pioneers to analyze fluctuations in relevant factors. The findings indicate that the incorporation of blockchain technology impacts the sales prices within supply chains. Furthermore, the utilization of blockchain significantly mitigates the influence of other variables on supply chain profits. Compared to supply chains without blockchain integration, those utilizing this technology establish substantial profit advantages. 12-22-2023 <![CDATA[ In light of both domestic and international research on blockchain and supply chains, coupled with the blockchain development in the Hainan Free Trade Port of China, a supply chain model sensitive to demand and consumer behavior has been established. This study selects Hainan Free Trade Port and the established Hong Kong Free Trade Port for comparison. Integrating diverse tax policies of these ports, the research employs a Stackelberg game led by market pioneers to analyze fluctuations in relevant factors. The findings indicate that the incorporation of blockchain technology impacts the sales prices within supply chains. Furthermore, the utilization of blockchain significantly mitigates the influence of other variables on supply chain profits. Compared to supply chains without blockchain integration, those utilizing this technology establish substantial profit advantages. ]]> Analyzing Price and Profit Dynamics in Free Trade Port Supply Chains: A Blockchain-Centric Approach Under Consumer Sensitivity fan jiang ilija tanackov qiushuang zhang shaoqing tian doi: 10.56578/jimd020405 Journal of Intelligent Management Decision 12-22-2023 Journal of Intelligent Management Decision 12-22-2023 2023 2 4 Article 202 10.56578/jimd020405 https://www.acadlore.com/article/JIMD/2023_2_4/jimd020405 https://www.acadlore.com/article/JIMD/2023_2_4/jimd020404 In the dynamic and unpredictable landscape of modern logistics, the capability to swiftly and effectively adapt to market and consumer fluctuations is imperative for service quality enhancement and competitive positioning. This research delves into the pivotal role of logistics flexibility as a mechanism for logistics firms, particularly those with a corporate identity, to navigate rapid market changes, customer demands, and service differentiation. The primary focus is the appraisal of logistical flexibility, utilizing the Interval-Valued Fermatean Fuzzy (IVFF) Stepwise Weight Assessment Ratio Analysis (SWARA) method to meticulously weigh identified criteria crucial for assessing this flexibility. The methodology's rigor lies in its comprehensive analysis and structured approach, which prioritizes criteria based on their relevance and impact. The findings underscore the paramount importance of 'Logistics Information Integration' as a critical factor in assessing logistics flexibility, highlighting its role in the seamless execution of logistics operations. Conversely, 'Asset Efficiency', while significant, ranks lower in the hierarchy of criteria, suggesting a lesser impact on overall logistics flexibility. These insights offer a strategic roadmap for logistics firms aiming to enhance their adaptive capabilities and provide a foundational framework for stakeholders and model developers seeking to optimize logistics operations. This study contributes to the logistics field by offering a nuanced understanding of flexibility parameters and their implications for service excellence and market differentiation. 12-12-2023 <![CDATA[ In the dynamic and unpredictable landscape of modern logistics, the capability to swiftly and effectively adapt to market and consumer fluctuations is imperative for service quality enhancement and competitive positioning. This research delves into the pivotal role of logistics flexibility as a mechanism for logistics firms, particularly those with a corporate identity, to navigate rapid market changes, customer demands, and service differentiation. The primary focus is the appraisal of logistical flexibility, utilizing the Interval-Valued Fermatean Fuzzy (IVFF) Stepwise Weight Assessment Ratio Analysis (SWARA) method to meticulously weigh identified criteria crucial for assessing this flexibility. The methodology's rigor lies in its comprehensive analysis and structured approach, which prioritizes criteria based on their relevance and impact. The findings underscore the paramount importance of 'Logistics Information Integration' as a critical factor in assessing logistics flexibility, highlighting its role in the seamless execution of logistics operations. Conversely, 'Asset Efficiency', while significant, ranks lower in the hierarchy of criteria, suggesting a lesser impact on overall logistics flexibility. These insights offer a strategic roadmap for logistics firms aiming to enhance their adaptive capabilities and provide a foundational framework for stakeholders and model developers seeking to optimize logistics operations. This study contributes to the logistics field by offering a nuanced understanding of flexibility parameters and their implications for service excellence and market differentiation. ]]> Evaluating Logistics Flexibility in Istanbul-Based Companies Using Interval-Valued Fermatean Fuzzy SWARA selçuk korucuk ahmet aytekin doi: 10.56578/jimd020404 Journal of Intelligent Management Decision 12-12-2023 Journal of Intelligent Management Decision 12-12-2023 2023 2 4 Article 192 10.56578/jimd020404 https://www.acadlore.com/article/JIMD/2023_2_4/jimd020404 https://www.acadlore.com/article/JIMD/2023_2_4/jimd020403 Intermodal transportation, crucial for contemporary logistics, enhances supply chain efficiency through integrated multimodal coordination. Central to this ecosystem, intermodal terminals act as pivotal points for seamless mode transitions, significantly influencing cost reduction and environmental sustainability. This research delves into the complex dynamics of intermodal terminal governance, striving to discern the most effective models while establishing a robust evaluative framework. A meticulous examination of seven distinct governance models is conducted against nine criteria, encompassing aspects such as efficiency, cost-effectiveness, regulatory compliance, and socio-economic impact. Employing a novel hybrid Multiple Criteria Decision-Making (MCDM) model, which amalgamates the Best-Worst Method (BWM) and Comprehensive Distance-based Ranking (COBRA) within a grey analytical context, the study facilitates a nuanced, uncertainty-accommodating assessment. Findings highlight the Public-Private Partnership, Concession Agreement, and Cooperative Governance models as exemplary, underscoring the benefits of synergistic public-private cooperation and community engagement. The research contributes significantly by identifying key governance models, providing a comprehensive evaluation framework, and introducing the hybrid MCDM model as an instrumental tool for decision-making within the transportation sector. Structured into five sections, the analysis progresses from an extensive literature review to a detailed methodology of the hybrid model, followed by the presentation of evaluative results, a discussion on the broader implications, and a conclusion synthesizing the principal insights. This investigation offers vital contributions to academic discourse and practical decision-making, laying groundwork for future exploration in this vital field. 12-11-2023 <![CDATA[ Intermodal transportation, crucial for contemporary logistics, enhances supply chain efficiency through integrated multimodal coordination. Central to this ecosystem, intermodal terminals act as pivotal points for seamless mode transitions, significantly influencing cost reduction and environmental sustainability. This research delves into the complex dynamics of intermodal terminal governance, striving to discern the most effective models while establishing a robust evaluative framework. A meticulous examination of seven distinct governance models is conducted against nine criteria, encompassing aspects such as efficiency, cost-effectiveness, regulatory compliance, and socio-economic impact. Employing a novel hybrid Multiple Criteria Decision-Making (MCDM) model, which amalgamates the Best-Worst Method (BWM) and Comprehensive Distance-based Ranking (COBRA) within a grey analytical context, the study facilitates a nuanced, uncertainty-accommodating assessment. Findings highlight the Public-Private Partnership, Concession Agreement, and Cooperative Governance models as exemplary, underscoring the benefits of synergistic public-private cooperation and community engagement. The research contributes significantly by identifying key governance models, providing a comprehensive evaluation framework, and introducing the hybrid MCDM model as an instrumental tool for decision-making within the transportation sector. Structured into five sections, the analysis progresses from an extensive literature review to a detailed methodology of the hybrid model, followed by the presentation of evaluative results, a discussion on the broader implications, and a conclusion synthesizing the principal insights. This investigation offers vital contributions to academic discourse and practical decision-making, laying groundwork for future exploration in this vital field. ]]> Evaluating Governance Models in Intermodal Terminal Operations: A Hybrid Grey MCDM Approach mladen krstić snežana tadić leonardo agnusdei doi: 10.56578/jimd020403 Journal of Intelligent Management Decision 12-11-2023 Journal of Intelligent Management Decision 12-11-2023 2023 2 4 Article 179 10.56578/jimd020403 https://www.acadlore.com/article/JIMD/2023_2_4/jimd020403 https://www.acadlore.com/article/JIMD/2023_2_4/jimd020402 An analytical approach was adopted to ascertain the optimal distribution channel for Bingo LLC's final products, deploying a multifaceted decision-making framework that incorporated the Full Consistency Method (FUCOM) and Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) methodologies. Weighting coefficients essential for distribution channel selection were derived using FUCOM, informed by responses to a meticulously designed questionnaire administered to experts from distinct Bingo LLC branches in Maglaj and Kraševo. The gathered data, reflecting a range of pertinent criteria, facilitated the computation of weighting coefficients via the FUCOM technique within a Microsoft Excel environment. These coefficients were subsequently employed in the execution of the MARCOS method to determine the hierarchical positioning of the potential alternatives. This process culminated in the identification of the most advantageous distribution channel alternative for the company. The overarching aim of this analysis was to elucidate the most efficacious distribution channel strategy to enhance Bingo LLC's business operations, underpinned by the hypothesis that proficient management of distribution channels is a critical determinant of commercial success. The implications of this research extend to the broader field of trade, highlighting the significance of strategic distribution channel management. This study stands as a testament to the application of decision-making models in operational enhancements and contributes to the existing body of knowledge with empirical evidence from the case of Bingo LLC. 11-08-2023 <![CDATA[ <p>An analytical approach was adopted to ascertain the optimal distribution channel for Bingo LLC's final products, deploying a multifaceted decision-making framework that incorporated the Full Consistency Method (FUCOM) and Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) methodologies. Weighting coefficients essential for distribution channel selection were derived using FUCOM, informed by responses to a meticulously designed questionnaire administered to experts from distinct Bingo LLC branches in Maglaj and Kraševo. The gathered data, reflecting a range of pertinent criteria, facilitated the computation of weighting coefficients via the FUCOM technique within a Microsoft Excel environment. These coefficients were subsequently employed in the execution of the MARCOS method to determine the hierarchical positioning of the potential alternatives. This process culminated in the identification of the most advantageous distribution channel alternative for the company. The overarching aim of this analysis was to elucidate the most efficacious distribution channel strategy to enhance Bingo LLC's business operations, underpinned by the hypothesis that proficient management of distribution channels is a critical determinant of commercial success. The implications of this research extend to the broader field of trade, highlighting the significance of strategic distribution channel management. This study stands as a testament to the application of decision-making models in operational enhancements and contributes to the existing body of knowledge with empirical evidence from the case of Bingo LLC.</p> ]]> Selection of Logistics Distribution Channels for Final Product Delivery: FUCOM-MARCOS Model željko stević nedžada mujaković alireza goli sarbast moslem doi: 10.56578/jimd020402 Journal of Intelligent Management Decision 11-08-2023 Journal of Intelligent Management Decision 11-08-2023 2023 2 4 Article 172 10.56578/jimd020402 https://www.acadlore.com/article/JIMD/2023_2_4/jimd020402 https://www.acadlore.com/article/JIMD/2023_2_4/jimd020401 In the pivotal task of selecting an assault boat conducive for military operations, especially amidst the challenges posed by water obstacles, the utilisation of Multi-Criteria Decision-Making (MCDM) methods surfaces as vital. In this investigation, a meticulous application of the DIBR II (Defining Interrelationships Between Ranked criteria II) - BM (Bonfferoni Mean) – CoCoSo (COmbined COmpromise SOlution) multi-criteria decision-making model is performed. Initially, the weight coefficients of the criteria were determined via the DIBR II method, with expert opinions being cohesively aggregated using BM operators. Subsequently, the CoCoSo method was employed to discern the optimal alternative among various assault boats. A comprehensive analysis, entailing the examination of the sensitivity of the output results to alterations in the weight coefficients of the criteria, was conducted post-final ranking of alternatives. Noteworthy is the finding that negligible deviations in defining the weight coefficients by experts do not impose a significant impact on the ultimate selection of the optimal alternative. Furthermore, a comparative analysis alongside other MCDM methods corroborated not only the efficacy but also the superiority of the implemented model. The insights derived underscore the practical applicability, stability, and accuracy of the proposed model in choosing assault boats for military operations. This exploration fortifies the decision-making process in military contexts related to overcoming water obstacles and portends potential applicability in domains necessitating intricate multi-criteria decision-making. 10-12-2023 <![CDATA[ <p>In the pivotal task of selecting an assault boat conducive for military operations, especially amidst the challenges posed by water obstacles, the utilisation of Multi-Criteria Decision-Making (MCDM) methods surfaces as vital. In this investigation, a meticulous application of the DIBR II (Defining Interrelationships Between Ranked criteria II) - BM (Bonfferoni Mean) – CoCoSo (COmbined COmpromise SOlution) multi-criteria decision-making model is performed. Initially, the weight coefficients of the criteria were determined via the DIBR II method, with expert opinions being cohesively aggregated using BM operators. Subsequently, the CoCoSo method was employed to discern the optimal alternative among various assault boats. A comprehensive analysis, entailing the examination of the sensitivity of the output results to alterations in the weight coefficients of the criteria, was conducted post-final ranking of alternatives. Noteworthy is the finding that negligible deviations in defining the weight coefficients by experts do not impose a significant impact on the ultimate selection of the optimal alternative. Furthermore, a comparative analysis alongside other MCDM methods corroborated not only the efficacy but also the superiority of the implemented model. The insights derived underscore the practical applicability, stability, and accuracy of the proposed model in choosing assault boats for military operations. This exploration fortifies the decision-making process in military contexts related to overcoming water obstacles and portends potential applicability in domains necessitating intricate multi-criteria decision-making.</p> ]]> Optimising Assault Boat Selection for Military Operations: An Application of the DIBR II-BM-CoCoSo MCDM Model duško tešić darko božanić marko radovanović aleksandar petrovski doi: 10.56578/jimd020401 Journal of Intelligent Management Decision 10-12-2023 Journal of Intelligent Management Decision 10-12-2023 2023 2 4 Article 160 10.56578/jimd020401 https://www.acadlore.com/article/JIMD/2023_2_4/jimd020401 https://www.acadlore.com/article/JIMD/2023_2_3/jimd020305 Advancements in technology have revolutionized communication, socialization, and work paradigms. The surges in globalization, the permeation of digital culture, and the expansion of online communication tools have prompted organizations globally to adopt virtual teams. These virtual environments, while beneficial, present a myriad of challenges that necessitate the application of system dynamics to optimize performance. A systematic review was conducted to analyze previous studies focusing on the leadership of virtual teams within the context of systems thinking. Seven databases, including Sage Online, Springer, JSTOR, Taylor and Wiley Online Library, Francis Online, Google Scholar, and Semantic Scholar, were utilized. From an initial pool of 5,070 studies, 30 were meticulously screened, summarized, and synthesized based on pre-established inclusion and exclusion criteria. The review highlighted the recurrent emphasis on factors such as communication technology, trust, intra-team relationships, and leadership strategies as pivotal for enhancing virtual team performance. This synthesis aims to present a comprehensive overview of current research trajectories in the field, delineating existing research gaps, limitations, and challenges. 09-27-2023 <![CDATA[ <p>Advancements in technology have revolutionized communication, socialization, and work paradigms. The surges in globalization, the permeation of digital culture, and the expansion of online communication tools have prompted organizations globally to adopt virtual teams. These virtual environments, while beneficial, present a myriad of challenges that necessitate the application of system dynamics to optimize performance. A systematic review was conducted to analyze previous studies focusing on the leadership of virtual teams within the context of systems thinking. Seven databases, including Sage Online, Springer, JSTOR, Taylor and Wiley Online Library, Francis Online, Google Scholar, and Semantic Scholar, were utilized. From an initial pool of 5,070 studies, 30 were meticulously screened, summarized, and synthesized based on pre-established inclusion and exclusion criteria. The review highlighted the recurrent emphasis on factors such as communication technology, trust, intra-team relationships, and leadership strategies as pivotal for enhancing virtual team performance. This synthesis aims to present a comprehensive overview of current research trajectories in the field, delineating existing research gaps, limitations, and challenges.</p> ]]> Managing Virtual Teams and System Thinking: A Systematic Review addisalem tadesse zerihun ayenew doi: 10.56578/jimd020305 Journal of Intelligent Management Decision 09-27-2023 Journal of Intelligent Management Decision 09-27-2023 2023 2 3 Article 151 10.56578/jimd020305 https://www.acadlore.com/article/JIMD/2023_2_3/jimd020305 https://www.acadlore.com/article/JIMD/2023_2_3/jimd020304 Disruptive technologies such as the big data analytics, blockchain, Internet of Things, and artificial intelligence have each impacted how businesses operate. The most recent example of disruptive technology is artificial intelligence (AI), which has the most potential to revolutionize marketing completely. Practitioners worldwide are searching for artificial intelligence (AI) solutions most suited for their marketing functions. Artificial intelligence can provide marketers with assistance in a variety of ways to boost client satisfaction. This article looks at the exciting new developments in artificial intelligence (AI) and marketing that have been occurring recently, it examines the latest developments in marketing using artificial intelligence (AI). These breakthroughs encompass predictive analytics for analyzing customer behaviour, integrating chatbots to enhance customer support, and implementing AI-driven content personalization tactics. This article also covers the horizons and problems of artificial intelligence and marketing, the precise applications of AI in a range of marketing segments, and their impact on marketing sectors. Additionally, this article examines the particular applications of AI in marketing. 09-06-2023 <![CDATA[ <p>Disruptive technologies such as the big data analytics, blockchain, Internet of Things, and artificial intelligence have each impacted how businesses operate. The most recent example of disruptive technology is artificial intelligence (AI), which has the most potential to revolutionize marketing completely. Practitioners worldwide are searching for artificial intelligence (AI) solutions most suited for their marketing functions. Artificial intelligence can provide marketers with assistance in a variety of ways to boost client satisfaction. This article looks at the exciting new developments in artificial intelligence (AI) and marketing that have been occurring recently, it examines the latest developments in marketing using artificial intelligence (AI). These breakthroughs encompass predictive analytics for analyzing customer behaviour, integrating chatbots to enhance customer support, and implementing AI-driven content personalization tactics. This article also covers the horizons and problems of artificial intelligence and marketing, the precise applications of AI in a range of marketing segments, and their impact on marketing sectors. Additionally, this article examines the particular applications of AI in marketing.</p> ]]> Strategic Framework for Leveraging Artificial Intelligence in Future Marketing Decision-Making nouri hicham habbat nassera sabri karim doi: 10.56578/jimd020304 Journal of Intelligent Management Decision 09-06-2023 Journal of Intelligent Management Decision 09-06-2023 2023 2 3 Article 139 10.56578/jimd020304 https://www.acadlore.com/article/JIMD/2023_2_3/jimd020304 https://www.acadlore.com/article/JIMD/2023_2_3/jimd020303 In decision-making scenarios, challenges often arise from closely knitted criteria or inherent uncertainties. Such uncertainties prominently pervade the realm of sustainable energy, particularly concerning hydrogen generation systems. A critical need is identified to elucidate the efficiency, costs, and environmental implications of these technologies as a shift towards a low-carbon economy is pursued. In this study, the interdependencies among decision-making variables were examined, revealing their collective influence and correlations. By utilizing the framework of Intuitionistic Hypersoft Sets (IHSSs), uncertainties were addressed, multi-criteria decision-making (MCDM) was harnessed, technological selection was facilitated, resource allocation was optimized, and environmental ramifications were assessed. The primary objective of this research was to decipher the conundrum of choosing among multiple hydrogen production methodologies. Such an approach fosters the adoption of environmentally conducive hydrogen production methods, heralding a shift towards a greener energy future. Notably, further research could probe into methodologies like AHP and TOPSIS in a neutrosophic context, offering tantalizing avenues for exploration. 09-04-2023 <![CDATA[ <p>In decision-making scenarios, challenges often arise from closely knitted criteria or inherent uncertainties. Such uncertainties prominently pervade the realm of sustainable energy, particularly concerning hydrogen generation systems. A critical need is identified to elucidate the efficiency, costs, and environmental implications of these technologies as a shift towards a low-carbon economy is pursued. In this study, the interdependencies among decision-making variables were examined, revealing their collective influence and correlations. By utilizing the framework of Intuitionistic Hypersoft Sets (IHSSs), uncertainties were addressed, multi-criteria decision-making (MCDM) was harnessed, technological selection was facilitated, resource allocation was optimized, and environmental ramifications were assessed. The primary objective of this research was to decipher the conundrum of choosing among multiple hydrogen production methodologies. Such an approach fosters the adoption of environmentally conducive hydrogen production methods, heralding a shift towards a greener energy future. Notably, further research could probe into methodologies like AHP and TOPSIS in a neutrosophic context, offering tantalizing avenues for exploration.</p> ]]> Sustainable Hydrogen Production: A Decision-Making Approach Using VIKOR and Intuitionistic Hypersoft Sets muhammad saqlain doi: 10.56578/jimd020303 Journal of Intelligent Management Decision 09-04-2023 Journal of Intelligent Management Decision 09-04-2023 2023 2 3 Article 130 10.56578/jimd020303 https://www.acadlore.com/article/JIMD/2023_2_3/jimd020303 https://www.acadlore.com/article/JIMD/2023_2_3/jimd020302 This study addresses the challenge of selecting appropriate electric vehicles for urban logistics, with a specific focus on the impact of various multi-criteria analysis methods on this complex decision-making process. The investigation utilizes a mixed methodology, combining objective weight determination methods, such as Entropy, CRITIC (Criteria through the Inter-Criteria Correlation), and MEREC (Method Based on the Removal Effects of Criteria), alongside standard deviation and a modified version of the standard deviation method. The Simple Additive Weighting (SAW) method was further employed for alternative ranking. Application of these methods across nine diverse Small Van vehicles, assessed according to 12 criteria, highlighted the paramountcy of Charge Time and Cargo Volume as factors bearing the most significant weight in decision-making. The Toyota Proace City Verso Electric L2 emerged as a superior choice under most conditions. Yet, the results varied when applying weights deduced through the MEREC method, leading to the ascendency of the Renault Kangoo E-Tech. The study underscores that the objective determination of criteria weights plays an influential role in the ranking of alternatives, hence, the requirement for decision-makers' subjectivity in the final choice, factoring in the unique attributes of individual companies. This research contributes to the understanding of how multi-criteria analysis can facilitate electric vehicle selection for urban logistics, playing a crucial part in reducing harmful urban emissions. 08-09-2023 <![CDATA[ This study addresses the challenge of selecting appropriate electric vehicles for urban logistics, with a specific focus on the impact of various multi-criteria analysis methods on this complex decision-making process. The investigation utilizes a mixed methodology, combining objective weight determination methods, such as Entropy, CRITIC (Criteria through the Inter-Criteria Correlation), and MEREC (Method Based on the Removal Effects of Criteria), alongside standard deviation and a modified version of the standard deviation method. The Simple Additive Weighting (SAW) method was further employed for alternative ranking. Application of these methods across nine diverse Small Van vehicles, assessed according to 12 criteria, highlighted the paramountcy of Charge Time and Cargo Volume as factors bearing the most significant weight in decision-making. The Toyota Proace City Verso Electric L2 emerged as a superior choice under most conditions. Yet, the results varied when applying weights deduced through the MEREC method, leading to the ascendency of the Renault Kangoo E-Tech. The study underscores that the objective determination of criteria weights plays an influential role in the ranking of alternatives, hence, the requirement for decision-makers' subjectivity in the final choice, factoring in the unique attributes of individual companies. This research contributes to the understanding of how multi-criteria analysis can facilitate electric vehicle selection for urban logistics, playing a crucial part in reducing harmful urban emissions. ]]> The Influence of Objective Weight Determination Methods on Electric Vehicle Selection in Urban Logistics adis puška ilija stojanović anđelka štilić doi: 10.56578/jimd020302 Journal of Intelligent Management Decision 08-09-2023 Journal of Intelligent Management Decision 08-09-2023 2023 2 3 Article 117 10.56578/jimd020302 https://www.acadlore.com/article/JIMD/2023_2_3/jimd020302 https://www.acadlore.com/article/JIMD/2023_2_3/jimd020301 This study presents an in-depth investigation of watermelon cultivation in Bangladesh, focusing on the assessment of production levels, costs, influential factors, and the application of Fuzzy Cognitive Map (FCM) technology for precision agriculture. Utilizing degree centrality and closeness centrality measures, the FCM model is employed to systematically examine the interplay among various elements involved in watermelon cultivation in Bangladesh and to elucidate the impacts of these factors on production yield. The findings contribute to the advancement of precision agriculture practices and provide valuable insights for optimizing watermelon production management in Bangladesh. 06-25-2023 <![CDATA[ This study presents an in-depth investigation of watermelon cultivation in Bangladesh, focusing on the assessment of production levels, costs, influential factors, and the application of Fuzzy Cognitive Map (FCM) technology for precision agriculture. Utilizing degree centrality and closeness centrality measures, the FCM model is employed to systematically examine the interplay among various elements involved in watermelon cultivation in Bangladesh and to elucidate the impacts of these factors on production yield. The findings contribute to the advancement of precision agriculture practices and provide valuable insights for optimizing watermelon production management in Bangladesh. ]]> Fuzzy Cognitive Map-Based Analysis for Optimizing Watermelon Production Management sahidul islam ashraful alam muhammad gulzar doi: 10.56578/jimd020301 Journal of Intelligent Management Decision 06-25-2023 Journal of Intelligent Management Decision 06-25-2023 2023 2 3 Article 105 10.56578/jimd020301 https://www.acadlore.com/article/JIMD/2023_2_3/jimd020301 https://www.acadlore.com/article/JIMD/2023_2_2/jimd020205 With the growing need for digital business transformation, corporate venture capital (CVC) investors have been faced with the challenge of how to deal with this trend. Although digital business transformation and CVC are highly relevant, previous studies have investigated them separately instead of their relationships. Therefore, this research aimed to study the impact of CVC on digital business transformation to fill this research gap. Based on an exploratory research design, eleven experts from different industries were interviewed. The following results were found in this study: (1) after the CVC unit collaborated with an Open Innovation (OI) unit, the CVC activities were integrated into the decentralized OI activities, and a dedicated team in the CVC unit was responsible for OI and venture client-based OI activities, thus achieving digital OI; (2) CVC was used to pursue ambidexterity, digital exploration or exploitation; (3) CVC supported digital business transformation at the organizational, social, and technical levels, which provided an answer to the overarching research question of how CVC supported innovation processes. Theoretical implications of this study lied in enhancing the understanding between CVC and digital business transformation, thus extending the understanding of CVC organization and impact. Furthermore, this study provided practical implications and recommendations on organizing CVC and using it to achieve digital business transformation according to strategic objectives. 05-31-2023 <![CDATA[ With the growing need for digital business transformation, corporate venture capital (CVC) investors have been faced with the challenge of how to deal with this trend. Although digital business transformation and CVC are highly relevant, previous studies have investigated them separately instead of their relationships. Therefore, this research aimed to study the impact of CVC on digital business transformation to fill this research gap. Based on an exploratory research design, eleven experts from different industries were interviewed. The following results were found in this study: (1) after the CVC unit collaborated with an Open Innovation (OI) unit, the CVC activities were integrated into the decentralized OI activities, and a dedicated team in the CVC unit was responsible for OI and venture client-based OI activities, thus achieving digital OI; (2) CVC was used to pursue ambidexterity, digital exploration or exploitation; (3) CVC supported digital business transformation at the organizational, social, and technical levels, which provided an answer to the overarching research question of how CVC supported innovation processes. Theoretical implications of this study lied in enhancing the understanding between CVC and digital business transformation, thus extending the understanding of CVC organization and impact. Furthermore, this study provided practical implications and recommendations on organizing CVC and using it to achieve digital business transformation according to strategic objectives. ]]> Impact of Corporate Venture Capital on Digital Business Transformation: A Case Study in Germany nadine ladnar daniel harder ricardo palomo alexander zureck doi: 10.56578/jimd020205 Journal of Intelligent Management Decision 05-31-2023 Journal of Intelligent Management Decision 05-31-2023 2023 2 2 Article 85 10.56578/jimd020205 https://www.acadlore.com/article/JIMD/2023_2_2/jimd020205 https://www.acadlore.com/article/JIMD/2023_2_2/jimd020204 The interrelation between logistics and international trade is crucial for understanding a country's ability to increase its share in global trade. An adequate and well-integrated logistics sector and infrastructure are required for this purpose. This study employs the novel Multi-Criteria Decision Analysis (MCDA) approach known as REF-III and two distinct models to investigate the activities of countries in terms of infrastructure, logistics, international trade, and economic growth. The results from both models indicate that China and Russia are leading the rankings. However, when focusing on the efficiency of trade and economic growth, the United States occupies the first place. Notably, several Caucasian and Balkan countries rank poorly in both models, possibly due to the multiple crises, wars, and turmoil they have experienced over the past forty years. The investments and improvements made in infrastructure and logistics by the countries excelling in global trade and logistics should serve as a model for other nations to emulate. 05-31-2023 <![CDATA[ The interrelation between logistics and international trade is crucial for understanding a country's ability to increase its share in global trade. An adequate and well-integrated logistics sector and infrastructure are required for this purpose. This study employs the novel Multi-Criteria Decision Analysis (MCDA) approach known as REF-III and two distinct models to investigate the activities of countries in terms of infrastructure, logistics, international trade, and economic growth. The results from both models indicate that China and Russia are leading the rankings. However, when focusing on the efficiency of trade and economic growth, the United States occupies the first place. Notably, several Caucasian and Balkan countries rank poorly in both models, possibly due to the multiple crises, wars, and turmoil they have experienced over the past forty years. The investments and improvements made in infrastructure and logistics by the countries excelling in global trade and logistics should serve as a model for other nations to emulate. ]]> Ranking Countries According to Logistics and International Trade Efficiencies via REF-III ahmet aytekin selçuk korucuk çağlar karamaşa doi: 10.56578/jimd020204 Journal of Intelligent Management Decision 05-31-2023 Journal of Intelligent Management Decision 05-31-2023 2023 2 2 Article 74 10.56578/jimd020204 https://www.acadlore.com/article/JIMD/2023_2_2/jimd020204 https://www.acadlore.com/article/JIMD/2023_2_2/jimd020203 Responsible use of social media requires a level of culture and awareness on the part of the user themselves that allows them to understand and absorb the enormous amount of information they receive through these mediums, verify it, and then share it with their friends and users. This leads to numerous problems related to public health and safety. This research aims to identify the most significant effects of the dissemination of fake news in times of crisis on public health and safety, as well as to propose strategies to overcome this phenomenon. A hybrid Grey-ARAS (Additive Ratio Assessment) model was used to rank the potential impacts and propose strategies to overcome them. Four experts in the field of public health, data analysis, and contagious diseases participated in this study to determine the weights. Eight factors affecting public health and safety were proposed, along with seven strategies to mitigate these impacts. The results showed that the most important factors are creating panic and anxiety among people along with the contribution to the misleading public policy decisions. The results also showed that the most appropriate strategies to overcome the impact of fake news are to encourage people to check facts and monitor social media. A sensitivity analysis of the results obtained was also performed, proposing 20 different scenarios to adjust the relative weights of the criteria. The results showed a certain stability when using different scenarios. 05-11-2023 <![CDATA[ Responsible use of social media requires a level of culture and awareness on the part of the user themselves that allows them to understand and absorb the enormous amount of information they receive through these mediums, verify it, and then share it with their friends and users. This leads to numerous problems related to public health and safety. This research aims to identify the most significant effects of the dissemination of fake news in times of crisis on public health and safety, as well as to propose strategies to overcome this phenomenon. A hybrid Grey-ARAS (Additive Ratio Assessment) model was used to rank the potential impacts and propose strategies to overcome them. Four experts in the field of public health, data analysis, and contagious diseases participated in this study to determine the weights. Eight factors affecting public health and safety were proposed, along with seven strategies to mitigate these impacts. The results showed that the most important factors are creating panic and anxiety among people along with the contribution to the misleading public policy decisions. The results also showed that the most appropriate strategies to overcome the impact of fake news are to encourage people to check facts and monitor social media. A sensitivity analysis of the results obtained was also performed, proposing 20 different scenarios to adjust the relative weights of the criteria. The results showed a certain stability when using different scenarios. ]]> Using Grey-ARAS Approach to Investigate the Role of Social Media Platforms in Spreading Fake News During COVID-19 Pandemic ibrahim badi eltohami m. elghoul doi: 10.56578/jimd020203 Journal of Intelligent Management Decision 05-11-2023 Journal of Intelligent Management Decision 05-11-2023 2023 2 2 Article 66 10.56578/jimd020203 https://www.acadlore.com/article/JIMD/2023_2_2/jimd020203 https://www.acadlore.com/article/JIMD/2023_2_2/jimd020202 As environmental awareness grows, consumers' green and low-carbon preferences have become essential factors for market enterprises to consider in decision-making. This paper conducts a literature review of dual-channel supply chain pricing decisions under the influence of consumers' low-carbon preference. The analysis is carried out from two aspects: dual-channel supply chain types and consumers' low-carbon preference. By combining psychological games and analyzing relevant literature, this paper provides insights into the factors that affect consumers' low-carbon preference and explores the synergies among various factors, including government policies. Moreover, this paper suggests future research directions, such as conducting empirical research on relevant models, to support the diversified development of the dual-channel field. 05-08-2023 <![CDATA[ As environmental awareness grows, consumers' green and low-carbon preferences have become essential factors for market enterprises to consider in decision-making. This paper conducts a literature review of dual-channel supply chain pricing decisions under the influence of consumers' low-carbon preference. The analysis is carried out from two aspects: dual-channel supply chain types and consumers' low-carbon preference. By combining psychological games and analyzing relevant literature, this paper provides insights into the factors that affect consumers' low-carbon preference and explores the synergies among various factors, including government policies. Moreover, this paper suggests future research directions, such as conducting empirical research on relevant models, to support the diversified development of the dual-channel field. ]]> Dual-Channel Supply Chain Pricing Decisions for Low-Carbon Consumers: A Review chenjin song baojian xu ling xu doi: 10.56578/jimd020202 Journal of Intelligent Management Decision 05-08-2023 Journal of Intelligent Management Decision 05-08-2023 2023 2 2 Article 57 10.56578/jimd020202 https://www.acadlore.com/article/JIMD/2023_2_2/jimd020202 https://www.acadlore.com/article/JIMD/2023_2_2/jimd020201 This study applies the FMEA-QFD approach to assess risks in the distribution process, with a focus on warehouse and transport processes, which are commonly associated with user dissatisfaction and customer loss. The methodology identifies the forms, effects, and causes of failures and determines priorities for each category. In that manner, for the warehousing process, long reception time, additional costs, and lack of experience have the highest priority. In the transportation process, time losses, generating additional costs, and longer vehicle retention time are the three failure effects with the highest priorities. Corrective and preventive measures are also defined. The proposed approach is highly applicable in practice and can be modified for use in other industries. This paper contributes both theoretically and practically to the field of logistics. 04-27-2023 <![CDATA[ This study applies the FMEA-QFD approach to assess risks in the distribution process, with a focus on warehouse and transport processes, which are commonly associated with user dissatisfaction and customer loss. The methodology identifies the forms, effects, and causes of failures and determines priorities for each category. In that manner, for the warehousing process, long reception time, additional costs, and lack of experience have the highest priority. In the transportation process, time losses, generating additional costs, and longer vehicle retention time are the three failure effects with the highest priorities. Corrective and preventive measures are also defined. The proposed approach is highly applicable in practice and can be modified for use in other industries. This paper contributes both theoretically and practically to the field of logistics. ]]> FMEA-QFD Approach for Effective Risk Assessment in Distribution Processes vukašin pajić milan andrejić marjan sternad doi: 10.56578/jimd020201 Journal of Intelligent Management Decision 04-27-2023 Journal of Intelligent Management Decision 04-27-2023 2023 2 2 Article 46 10.56578/jimd020201 https://www.acadlore.com/article/JIMD/2023_2_2/jimd020201 https://www.acadlore.com/article/JIMD/2023_2_1/jimd020105 With the rapid development of industrial technology, the application fields of AGV are constantly expanding. In this article, a differential vehicle is selected to construct a dynamic model of differential vehicle and establish a co-simulation platform of MATLAB/Simulink and ADAMS, which fully considers the nonlinear friction between wheels and the ground, the body mass and its own moment of inertia during steering, simulates the actual motion trajectory of the vehicle under different paths, and compares the ideal trajectory with the actual ADAMS output, which is generally consistent with the theory, and the basic path trend tends to be consistent. The deviation between them also reflects that the differential vehicle is a multi-degree-of-freedom strong nonlinear system, so the platform can better simulate the actual motion process of the vehicle. 03-28-2023 <![CDATA[ <p>With the rapid development of industrial technology, the application fields of AGV are constantly expanding. In this article, a differential vehicle is selected to construct a dynamic model of differential vehicle and establish a co-simulation platform of MATLAB/Simulink and ADAMS, which fully considers the nonlinear friction between wheels and the ground, the body mass and its own moment of inertia during steering, simulates the actual motion trajectory of the vehicle under different paths, and compares the ideal trajectory with the actual ADAMS output, which is generally consistent with the theory, and the basic path trend tends to be consistent. The deviation between them also reflects that the differential vehicle is a multi-degree-of-freedom strong nonlinear system, so the platform can better simulate the actual motion process of the vehicle.</p> ]]> Study on Dynamic Simulation of Differential Driving Cargo Transport Vehicle senlin liu yu wang doi: 10.56578/jimd020105 Journal of Intelligent Management Decision 03-28-2023 Journal of Intelligent Management Decision 03-28-2023 2023 2 1 Article 38 10.56578/jimd020105 https://www.acadlore.com/article/JIMD/2023_2_1/jimd020105 https://www.acadlore.com/article/JIMD/2023_2_1/jimd020104 Although some of the resources required to meet human needs can be provided by a country, the rest must be supplied from other countries because not every country has all resources. Therefore, the demand for international trade emerges. A country earns incomes by selling its surplus and obtains scarce resources from other countries with these incomes. In this context, some political and economic initiatives have been established by countries, which work in harmony to facilitate and regulate international trade and create a common market. Two of them are Shanghai Pact and the Belt and Road Initiative (BRI). However, even if the initiatives meet certain common needs, it is very important to carry out logistics activities correctly in order to ensure effective and efficient foreign trade. If planned and correctly carried out, logistics activities are expected to make both import and export processes efficient and reduce resource usage. In this study, it is aimed to examine the effects of logistics performance in international trade in countries of Shanghai Pact and the BRI. In order to measure logistics performance in the research, two kinds of data are used, namely, the Logistics Performance Index (LPI) data, published by the World Bank every two years, and the import and export data, also published by the World Bank. With six sub-criteria of LPI modeled as independent variables and import and export as dependent variables, Tobit analysis is made by using EViews 10 software package. According to the analysis results, customs clearance, logistics quality and traceability have effects on export, and infrastructure, customs clearance and logistics quality have effects on import. 03-26-2023 <![CDATA[ <p><span style="color: black; font-family: Times New Roman, serif">Although some of the resources required to meet human needs can be provided by a country, the rest must be supplied from other countries because not every country has all resources. Therefore, the demand for international trade emerges. A country earns incomes by selling its surplus and obtains scarce resources from other countries with these incomes. In this context, some political and economic initiatives have been established by countries, which work in harmony to facilitate and regulate international trade and create a common market. Two of them are Shanghai Pact and the Belt and Road Initiative (BRI). However, even if the initiatives meet certain common needs, it is very important to carry out logistics activities correctly in order to ensure effective and efficient foreign trade. If planned and correctly carried out, logistics activities are expected to make both import and export processes efficient and reduce resource usage. In this study, it is aimed to examine the effects of logistics performance in international trade in countries of Shanghai Pact and the BRI. In order to measure logistics performance in the research, two kinds of data are used, namely, the Logistics Performance Index (LPI) data, published by the World Bank every two years, and the import and export data, also published by the World Bank. With six sub-criteria of LPI modeled as independent variables and import and export as dependent variables, Tobit analysis is made by using EViews 10 software package. According to the analysis results, customs clearance, logistics quality and traceability have effects on export, and infrastructure, customs clearance and logistics quality have effects on import.</span></p> ]]> Relationship Between International Trade and Logistics: An Evaluation on Countries of Shanghai Pact and the Belt and Road Initiative salim üre oğuzhan demir çağatay karaköy alptekin ulutaş doi: 10.56578/jimd020104 Journal of Intelligent Management Decision 03-26-2023 Journal of Intelligent Management Decision 03-26-2023 2023 2 1 Article 30 10.56578/jimd020104 https://www.acadlore.com/article/JIMD/2023_2_1/jimd020104 https://www.acadlore.com/article/JIMD/2023_2_1/jimd020103 Due to COVID-19, the trade and circulation of second-hand goods grew rapidly in the past three years, bringing new opportunities to online second-hand Consumer to Consumer (C2C) trading platform. This paper aimed to study the influencing factors of the platform by constructing a questionnaire based on willingness degree influencing factors in probit model. This paper designed analysis framework of influencing factors and variables, including information acquisition before commodity transaction, communication and consultation during transaction, service after transaction, and user's personal characteristics. Then this paper constructed a binary discrete probit model, with consumers on the platform considered as survey objects. Finally, this paper summarized key influencing factors of the C2C platform based on analysis results of the questionnaire, and put forward measures and suggestions to improve the operating efficiency and performance of the platform. 03-26-2023 <![CDATA[ <p>Due to COVID-19, the trade and circulation of second-hand goods grew rapidly in the past three years, bringing new opportunities to online second-hand Consumer to Consumer (C2C) trading platform. This paper aimed to study the influencing factors of the platform by constructing a questionnaire based on willingness degree influencing factors in probit model. This paper designed analysis framework of influencing factors and variables, including information acquisition before commodity transaction, communication and consultation during transaction, service after transaction, and user's personal characteristics. Then this paper constructed a binary discrete probit model, with consumers on the platform considered as survey objects. Finally, this paper summarized key influencing factors of the C2C platform based on analysis results of the questionnaire, and put forward measures and suggestions to improve the operating efficiency and performance of the platform.</p> ]]> Influencing Factors of Second-Hand Platform Trading in C2C E-commerce lele wang hao sun doi: 10.56578/jimd020103 Journal of Intelligent Management Decision 03-26-2023 Journal of Intelligent Management Decision 03-26-2023 2023 2 1 Article 21 10.56578/jimd020103 https://www.acadlore.com/article/JIMD/2023_2_1/jimd020103 https://www.acadlore.com/article/JIMD/2023_2_1/jimd020102 The road mode of transportation holds a leading position in the world and its upward trend is still noticeable. The aim of this research is to determine the efficiency of the observed transportation company, i.e. efficiency of its business throughout the analyzed period of years, which in this case is from 2018 to 2022. Specifically, in this paper, it has been observed a transportation company located in Bosnia and Herzegovina, which is engaged in road internal and international transportation. An integrated PCA-DEA model was used for efficiency analysis. Six input and four output parameters were defined. Due to the small number of decision-making units, PCA (Principal component analysis) based on 70% of the information of all data collected was first applied, and the number of input-output parameters was reduced to 2 to 1. After that, DEA (Data Envelopment Analysis) was applied to obtain final efficiency values. The results show that the previous year (2022) is the most efficient under the considered conditions and circumstances, and that the company is on the right track, as it increases its efficiency. 03-26-2023 <![CDATA[ <p>The road mode of transportation holds a leading position in the world and its upward trend is still noticeable. The aim of this research is to determine the efficiency of the observed transportation company, i.e. efficiency of its business throughout the analyzed period of years, which in this case is from 2018 to 2022. Specifically, in this paper, it has been observed a transportation company located in Bosnia and Herzegovina, which is engaged in road internal and international transportation. An integrated PCA-DEA model was used for efficiency analysis. Six input and four output parameters were defined. Due to the small number of decision-making units, PCA (Principal component analysis) based on 70% of the information of all data collected was first applied, and the number of input-output parameters was reduced to 2 to 1. After that, DEA (Data Envelopment Analysis) was applied to obtain final efficiency values. The results show that the previous year (2022) is the most efficient under the considered conditions and circumstances, and that the company is on the right track, as it increases its efficiency.</p> ]]> PCA-DEA Model for Efficiency Assessment of Transportation Company medina taletović siniša sremac doi: 10.56578/jimd020102 Journal of Intelligent Management Decision 03-26-2023 Journal of Intelligent Management Decision 03-26-2023 2023 2 1 Article 11 10.56578/jimd020102 https://www.acadlore.com/article/JIMD/2023_2_1/jimd020102 https://www.acadlore.com/article/JIMD/2023_2_1/jimd020101 Quality is a key success factor in the market. For the successful performance of a company, it is very important to have high quality and a quality system, and strive for its development and improvement. In this paper, it has been measured the quality of logistics service using the SERVQUAL model in the TC company as one of the most well-known and most used models in the field of quality measurement. The users of the transport service, the respondents, provided certain information on their expectations, as well as their perceptions of the quality of the transport service from the aspect of all five dimensions: reliability, assurance, empathy, tangibles and responsiveness. The SERVQUAL model was chosen to obtain the final results of the quality of service provided to users. The FUCOM method was applied to obtain the final weights of dimensions. The main goal of this paper is to assess the quality of the transport service in the TC company, so that the company has an insight into its current state, and based on it, takes further steps to improve its quality. Increasing service quality results in greater satisfaction of service users, greater satisfaction results in increased loyalty, which further guarantees the positive performance of the company itself and its safe existence and work on the market. The results have shown that the company has negative final values from the aspect of all five dimensions, which tells us that users are not satisfied with the service provided, and the greatest dissatisfaction was expressed from the aspect of the dimension of tangibles. 03-23-2023 <![CDATA[ <p>Quality is a key success factor in the market. For the successful performance of a company, it is very important to have high quality and a quality system, and strive for its development and improvement. In this paper, it has been measured the quality of logistics service using the SERVQUAL model in the TC company as one of the most well-known and most used models in the field of quality measurement. The users of the transport service, the respondents, provided certain information on their expectations, as well as their perceptions of the quality of the transport service from the aspect of all five dimensions: reliability, assurance, empathy, tangibles and responsiveness. The SERVQUAL model was chosen to obtain the final results of the quality of service provided to users. The FUCOM method was applied to obtain the final weights of dimensions. The main goal of this paper is to assess the quality of the transport service in the TC company, so that the company has an insight into its current state, and based on it, takes further steps to improve its quality. Increasing service quality results in greater satisfaction of service users, greater satisfaction results in increased loyalty, which further guarantees the positive performance of the company itself and its safe existence and work on the market. The results have shown that the company has negative final values from the aspect of all five dimensions, which tells us that users are not satisfied with the service provided, and the greatest dissatisfaction was expressed from the aspect of the dimension of tangibles.</p> ]]> Measuring Logistics Service Quality Using the SERVQUAL Model alma jusufbašić željko stević doi: 10.56578/jimd020101 Journal of Intelligent Management Decision 03-23-2023 Journal of Intelligent Management Decision 03-23-2023 2023 2 1 Article 1 10.56578/jimd020101 https://www.acadlore.com/article/JIMD/2023_2_1/jimd020101 https://www.acadlore.com/article/JIMD/2022_1_2/jimd010206 Artificial intelligence, in a larger sense, refers to computers that have human intelligence-specific capabilities such as obtaining information, perceiving, seeing, thinking, and making decisions. At first glance, artificial intelligence, often known as "Artificial Intelligence" (AI) in the literature, causes everyone to associate something distinct. According to researches, the concept of artificial intelligence evokes an electro-mechanical robot replacing human beings, but everyone involved in this field is aware that there is a definite difference between human beings and machines. The aim of this article is to show the importance of using AI in today’s HR practices. In this context, one of the qualitative research designs, phenomenological research, was deemed 1appropriate for the thesis study. Because phenomenology establishes a framework for exploring subjects that aren't utterly unfamiliar but whose meaning isn't quite clear.AI-based HR apps have the ability to boost employee productivity while also assisting HR personnel in becoming educated advisers who can boost employee performance. AI-enabled HR solutions are capable of evaluating, predicting, diagnosing, and locating more powerful and capable employees. 12-29-2022 <![CDATA[ <p>Artificial intelligence, in a larger sense, refers to computers that have human intelligence-specific capabilities such as obtaining information, perceiving, seeing, thinking, and making decisions. At first glance, artificial intelligence, often known as "Artificial Intelligence" (AI) in the literature, causes everyone to associate something distinct. According to researches, the concept of artificial intelligence evokes an electro-mechanical robot replacing human beings, but everyone involved in this field is aware that there is a definite difference between human beings and machines. The aim of this article is to show the importance of using AI in today’s HR practices. In this context, one of the qualitative research designs, phenomenological research, was deemed 1appropriate for the thesis study. Because phenomenology establishes a framework for exploring subjects that aren't utterly unfamiliar but whose meaning isn't quite clear.AI-based HR apps have the ability to boost employee productivity while also assisting HR personnel in becoming educated advisers who can boost employee performance. AI-enabled HR solutions are capable of evaluating, predicting, diagnosing, and locating more powerful and capable employees.</p> ]]> Analysis of the Impact of Artificial Intelligence in Enhancing the Human Resource Practices valeriia biliavska rui alexandre castanho ana vulevic doi: 10.56578/jimd010206 Journal of Intelligent Management Decision 12-29-2022 Journal of Intelligent Management Decision 12-29-2022 2022 1 2 Article 128 10.56578/jimd010206 https://www.acadlore.com/article/JIMD/2022_1_2/jimd010206 https://www.acadlore.com/article/JIMD/2022_1_2/jimd010205 The term "classification" refers to a supervised learning technique in which samples are given class labels based on predetermined classes. Fuzzy classifiers are renowned for their ability to address the issue of outliers and deliver the performance resilience that is much needed. The major goal of this study is to provide a classification algorithm that is effective and accurate. In this work, we address Archimedean-Dombi aggregation operator by extending the similarity classifier. Earlier, Dombi operators were used to study the similarity classifier. We focus on the application of Archimedean-Dombi operators during the classifier's aggregate similarity calculation. Since Archimedean and Dombi operators are well-known for offering appropriate generalization and flexibility respectively in aggregating data, so a different version of the similarity classifier is created. One real-world medical dataset, namely Parkinson disease data set is used to test the proposed approaches. When compared to older existing operators, the new classifiers have better classification accuracy. 12-29-2022 <![CDATA[ <p>The term "classification" refers to a supervised learning technique in which samples are given class labels based on predetermined classes. Fuzzy classifiers are renowned for their ability to address the issue of outliers and deliver the performance resilience that is much needed. The major goal of this study is to provide a classification algorithm that is effective and accurate. In this work, we address Archimedean-Dombi aggregation operator by extending the similarity classifier. Earlier, Dombi operators were used to study the similarity classifier. We focus on the application of Archimedean-Dombi operators during the classifier's aggregate similarity calculation. Since Archimedean and Dombi operators are well-known for offering appropriate generalization and flexibility respectively in aggregating data, so a different version of the similarity classifier is created. One real-world medical dataset, namely Parkinson disease data set is used to test the proposed approaches. When compared to older existing operators, the new classifiers have better classification accuracy.</p> ]]> A Fuzzy Similarity Based Classification with Archimedean-Dombi Aggregation Operator abhijit saha jayasri reddy rishikesh kumar doi: 10.56578/jimd010205 Journal of Intelligent Management Decision 12-29-2022 Journal of Intelligent Management Decision 12-29-2022 2022 1 2 Article 118 10.56578/jimd010205 https://www.acadlore.com/article/JIMD/2022_1_2/jimd010205 https://www.acadlore.com/article/JIMD/2022_1_2/jimd010204 Risk monitoring and risk prediction are of great significance to improve the safety of urban rail operation. Existing studies often analyze the topological characteristics of accident networks from the perspective of network theory, in order to point out the role of specific influencing factors in urban rail accidents. This article proposes a risk analysis method of urban rail operation accidents, which takes risk factors, risk points and risk events as nodes to form a network, and combines the interaction between risk points to evaluate the safety of the whole system. The existing system safety analysis methods all build models based on the accidents that have occurred. Based on the analysis of the existing urban rail transit infrastructure and operating environment, this article puts forward the risk factors and risk points that may cause risk events, and combines the mechanical connection, electrical connection and signal connection among risk points to deeply explore the interaction between risks so as to find the key risk points that cause accidents and evaluate the safety of the whole system. The results show that the proposed risk analysis method can provide effective theoretical support for risk monitoring. 12-29-2022 <![CDATA[ <p> Risk monitoring and risk prediction are of great significance to improve the safety of urban rail operation. Existing studies often analyze the topological characteristics of accident networks from the perspective of network theory, in order to point out the role of specific influencing factors in urban rail accidents. This article proposes a risk analysis method of urban rail operation accidents, which takes risk factors, risk points and risk events as nodes to form a network, and combines the interaction between risk points to evaluate the safety of the whole system. The existing system safety analysis methods all build models based on the accidents that have occurred. Based on the analysis of the existing urban rail transit infrastructure and operating environment, this article puts forward the risk factors and risk points that may cause risk events, and combines the mechanical connection, electrical connection and signal connection among risk points to deeply explore the interaction between risks so as to find the key risk points that cause accidents and evaluate the safety of the whole system. The results show that the proposed risk analysis method can provide effective theoretical support for risk monitoring.</p> ]]> Topological Modeling and Analysis of Urban Rail Transit Safety Risk Relationship man li xinyi zhou jinxin liu weikai ma xiwei li doi: 10.56578/jimd010204 Journal of Intelligent Management Decision 12-29-2022 Journal of Intelligent Management Decision 12-29-2022 2022 1 2 Article 108 10.56578/jimd010204 https://www.acadlore.com/article/JIMD/2022_1_2/jimd010204 https://www.acadlore.com/article/JIMD/2022_1_2/jimd010203 Market changes last years have led to an additional understanding of people importance as the main resources of companies. Truck drivers are one of the occupations with the greatest shortage. More attention is being paid to ways of retaining employees. One of the most important measures is bonus or reward. There is a lack of models in the literature and it is exactly the main motive of this research. Proposed models create a basis for future theoretical research, but also for practical applications. The main assumption is that models must provide a fair way to earn bonuses in a "healthy environment". Two models are proposed. The first model for distribution company with a heterogeneous fleet of vehicles with less capacity. The second model refers to homogenous heavy truck fleet. In the first case, several criteria are used: distance (kilometers) driven, number of tours/rides, number of unloading stops and number of pallets. The second model is based on fuel consumption, distance driven, vehicle maintenance, driver experience (years in the company) and overall dispatcher score. The results show the convenience of applying the proposed models. Certain differences were also identified in the observed models. It can be concluded that there is no universal model for performance appraisal and bonus calculation. Ideas for overcoming and improving models are also proposed. Described models in original or adapted form can be applied to evaluate the performance of drivers in a wide variety of transport systems. 12-29-2022 <![CDATA[ <p>Market changes last years have led to an additional understanding of people importance as the main resources of companies. Truck drivers are one of the occupations with the greatest shortage. More attention is being paid to ways of retaining employees. One of the most important measures is bonus or reward. There is a lack of models in the literature and it is exactly the main motive of this research. Proposed models create a basis for future theoretical research, but also for practical applications. The main assumption is that models must provide a fair way to earn bonuses in a "healthy environment". Two models are proposed. The first model for distribution company with a heterogeneous fleet of vehicles with less capacity. The second model refers to homogenous heavy truck fleet. In the first case, several criteria are used: distance (kilometers) driven, number of tours/rides, number of unloading stops and number of pallets. The second model is based on fuel consumption, distance driven, vehicle maintenance, driver experience (years in the company) and overall dispatcher score. The results show the convenience of applying the proposed models. Certain differences were also identified in the observed models. It can be concluded that there is no universal model for performance appraisal and bonus calculation. Ideas for overcoming and improving models are also proposed. Described models in original or adapted form can be applied to evaluate the performance of drivers in a wide variety of transport systems.</p> ]]> Different Approaches for Performance Appraisal and Bonus Calculation: The Case of Truck Drivers milan andrejić doi: 10.56578/jimd010203 Journal of Intelligent Management Decision 12-29-2022 Journal of Intelligent Management Decision 12-29-2022 2022 1 2 Article 97 10.56578/jimd010203 https://www.acadlore.com/article/JIMD/2022_1_2/jimd010203 https://www.acadlore.com/article/JIMD/2022_1_2/jimd010202 E-commerce is referred as any transaction in which the sale and purchase of goods or services takes place via the Internet and leads to the import or export of goods or services. Supply always needs demand. We need marketing to expand the demand in order to sell more services or products. Organizations need to have a good understanding of their customers and their desires in order to become succeed in business, and to get this understanding, they must use tools and techniques to measure the customer's interest. With using of data mining techniques and with the discovery of hidden and valuable knowledge of data, organizations don’t miss the opportunity to sell more and provide better customer satisfaction. The customer segmentation is one of the methods of customer recognition. This method is used when we look for groups of similar data. Segmenting is one of the most important topics in reaching modern marketing and managing successful customer relationship management. The purpose of this paper is to design an electronic marketing model using the k-mean algorithm. First, customer`s data is collected and after preparing and pre-processing data, using the k-mean algorithm, segmentation customers and future marketing strategies and recommendations are discussed and eventually using the theory of the possibility, the possibility and requirement for the proposal to be considered, and each of the recommendations or strategies are given numbers with the name of the possibility and necessity of the system output, and a more favorable proposal is obtained. 12-29-2022 <![CDATA[ <p>E-commerce is referred as any transaction in which the sale and purchase of goods or services takes place via the Internet and leads to the import or export of goods or services. Supply always needs demand. We need marketing to expand the demand in order to sell more services or products. Organizations need to have a good understanding of their customers and their desires in order to become succeed in business, and to get this understanding, they must use tools and techniques to measure the customer's interest. With using of data mining techniques and with the discovery of hidden and valuable knowledge of data, organizations don’t miss the opportunity to sell more and provide better customer satisfaction. The customer segmentation is one of the methods of customer recognition. This method is used when we look for groups of similar data. Segmenting is one of the most important topics in reaching modern marketing and managing successful customer relationship management. The purpose of this paper is to design an electronic marketing model using the k-mean algorithm. First, customer`s data is collected and after preparing and pre-processing data, using the k-mean algorithm, segmentation customers and future marketing strategies and recommendations are discussed and eventually using the theory of the possibility, the possibility and requirement for the proposal to be considered, and each of the recommendations or strategies are given numbers with the name of the possibility and necessity of the system output, and a more favorable proposal is obtained.</p> ]]> Intelligent Marketing Decision Model Based on Customer Behavior Using Integrated Possibility Theory and K-Means Algorithm hamed fazlollahtabar doi: 10.56578/jimd010202 Journal of Intelligent Management Decision 12-29-2022 Journal of Intelligent Management Decision 12-29-2022 2022 1 2 Article 88 10.56578/jimd010202 https://www.acadlore.com/article/JIMD/2022_1_2/jimd010202 https://www.acadlore.com/article/JIMD/2022_1_2/jimd010201 Road capacity utilization is causally connected with an appropriate level of efficiency and an optimal level of traffic safety. Therefore, in this paper, it is considered the issue of maximum utilization of road capacity through the maximization of the input parameter AADT (Annual Average Daily Traffic), and the minimization of output parameters related to the categories of traffic accidents. It was defined six main road sections, which were evaluated based on seven techno-operational criteria using an integrated Multi-criteria decision-making (MCDM) model. The data refer to buses as a vehicle category. The Improved Fuzzy Step-Wise Weight Assessment Ratio Analysis (IMF SWARA) method was chosen to determine the weights of criteria, while the road sections were ranked using the Evaluation based on distance from average solution (EDAS). In addition, in one of the stages of applying the model when it comes to AADT, the Bonferroni operator (BFO) is used. The results show that the highest level of safety refers to a main road section with the following characteristics: average AADT, minimal deviation from the speed limit, an ascent of 7% and the lowest number of traffic accidents by all categories. In the paper, it was performed a multi-phase sensitivity analysis in order to identify possible differences in results when determining new circumstances. 12-29-2022 <![CDATA[ <p>Road capacity utilization is causally connected with an appropriate level of efficiency and an optimal level of traffic safety. Therefore, in this paper, it is considered the issue of maximum utilization of road capacity through the maximization of the input parameter AADT (Annual Average Daily Traffic), and the minimization of output parameters related to the categories of traffic accidents. It was defined six main road sections, which were evaluated based on seven techno-operational criteria using an integrated Multi-criteria decision-making (MCDM) model. The data refer to buses as a vehicle category. The Improved Fuzzy Step-Wise Weight Assessment Ratio Analysis (IMF SWARA) method was chosen to determine the weights of criteria, while the road sections were ranked using the Evaluation based on distance from average solution (EDAS). In addition, in one of the stages of applying the model when it comes to AADT, the Bonferroni operator (BFO) is used. The results show that the highest level of safety refers to a main road section with the following characteristics: average AADT, minimal deviation from the speed limit, an ascent of 7% and the lowest number of traffic accidents by all categories. In the paper, it was performed a multi-phase sensitivity analysis in order to identify possible differences in results when determining new circumstances.</p> ]]> Multi-Criteria Decision-Making Model for Evaluating Safety of Road Sections željko stević marko subotić edis softić branko božić doi: 10.56578/jimd010201 Journal of Intelligent Management Decision 12-29-2022 Journal of Intelligent Management Decision 12-29-2022 2022 1 2 Article 78 10.56578/jimd010201 https://www.acadlore.com/article/JIMD/2022_1_2/jimd010201 https://www.acadlore.com/article/JIMD/2022_1_1/jimd010108 For the integrated optimization of job-shop production scheduling and predictive maintenance, this paper fully considers such constraints as product delivery time and changing machine failure rate, and establishes a multi-objective optimization model aiming to minimize the processing cost and the product processing time. The model includes the changing machine failure rate into the integrated optimization of job-shop production scheduling and predictive maintenance, and enables the prediction of the machine state according to the processing time of the current job, laying the basis for the decision-making of the machine activity and the reasonable and effective production planning. In addition, the non-dominated sorting genetic algorithm (NSGA)-II was designed to solve the proposed model. The algorithm performance was improved through the operator crossover and mutation by the simulated binary crossover algorithm (SBX). The proposed strategy was verified through a case study. 09-29-2022 <![CDATA[ <p>For the integrated optimization of job-shop production scheduling and predictive maintenance, this paper fully considers such constraints as product delivery time and changing machine failure rate, and establishes a multi-objective optimization model aiming to minimize the processing cost and the product processing time. The model includes the changing machine failure rate into the integrated optimization of job-shop production scheduling and predictive maintenance, and enables the prediction of the machine state according to the processing time of the current job, laying the basis for the decision-making of the machine activity and the reasonable and effective production planning. In addition, the non-dominated sorting genetic algorithm (NSGA)-II was designed to solve the proposed model. The algorithm performance was improved through the operator crossover and mutation by the simulated binary crossover algorithm (SBX). The proposed strategy was verified through a case study.</p> ]]> Integrated Multi-objective Optimization of Predictive Maintenance and Production Scheduling: Perspective from Lead Time Constraints zhiyuan zhao qilong yuan doi: 10.56578/jimd010108 Journal of Intelligent Management Decision 09-29-2022 Journal of Intelligent Management Decision 09-29-2022 2022 1 1 Article 67 10.56578/jimd010108 https://www.acadlore.com/article/JIMD/2022_1_1/jimd010108 https://www.acadlore.com/article/JIMD/2022_1_1/jimd010107 The purpose of the article is to study the peculiarities of managing the development of human capital in the conditions of digitalization. In the course of the study, the method of measuring the human potential index, developed by the UN - the Human Development Index, including taking into account socio-economic inequality, the Gender Inequality Index and the multidimensional poverty index, was applied as basic indicators that reflect the level of development of human capital, which is especially important in the conditions of digitalization of society. The article discusses the concept of human potential, its components, methods of determination. The dynamics of changes in the human development index of Ukraine and its components during 1990-2020 were analyzed. A comparative analysis of the values of the human development index, the human development index taking into account socio-economic inequality, the gender inequality index of Ukraine and other countries was carried out. The importance of the development of human potential in the context of the development of the information society and the digital economy is proven, the specifics of working conditions, requirements for the workforce are given. The factors affecting the development of human potential in the conditions of the digital economy are considered, and ways of solving the identified problems are proposed like creating conditions for development of the population, to ensure a positive balance of reproduction of the population and migration, development of social infrastructure, access of the population to quality medical, educational, and social services, etc. 09-29-2022 <![CDATA[ <ul><li>The purpose of the article is to study the peculiarities of managing the development of human capital in the conditions of digitalization. In the course of the study, the method of measuring the human potential index, developed by the UN - the Human Development Index, including taking into account socio-economic inequality, the Gender Inequality Index and the multidimensional poverty index, was applied as basic indicators that reflect the level of development of human capital, which is especially important in the conditions of digitalization of society. The article discusses the concept of human potential, its components, methods of determination. The dynamics of changes in the human development index of Ukraine and its components during 1990-2020 were analyzed. A comparative analysis of the values of the human development index, the human development index taking into account socio-economic inequality, the gender inequality index of Ukraine and other countries was carried out. The importance of the development of human potential in the context of the development of the information society and the digital economy is proven, the specifics of working conditions, requirements for the workforce are given. The factors affecting the development of human potential in the conditions of the digital economy are considered, and ways of solving the identified problems are proposed like creating conditions for development of the population, to ensure a positive balance of reproduction of the population and migration, development of social infrastructure, access of the population to quality medical, educational, and social services, etc.</li></ul> ]]> Management of Human Capital Development in the Era of the Digital Economy anastasiia samoilovych olha popelo iryna kychko oleksandr samoilovych ivan olyfirenko doi: 10.56578/jimd010107 Journal of Intelligent Management Decision 09-29-2022 Journal of Intelligent Management Decision 09-29-2022 2022 1 1 Article 56 10.56578/jimd010107 https://www.acadlore.com/article/JIMD/2022_1_1/jimd010107 https://www.acadlore.com/article/JIMD/2022_1_1/jimd010106 The lethal coronavirus illness (COVID-19) has evoked worldwide discussion. This contagious, sometimes fatal illness, is caused by the severe acute respiratory syndrome coronavirus 2. So far, COVID-19 has quickly spread to other countries, sickening millions across the globe. To predict the future occurrences of the disease, it is important to develop mathematical models with the fewest errors. In this study, classification and regression tree (CART) models and autoregressive integrated moving averages (ARIMAs) are employed to model and forecast the one-month confirmed COVID-19 cases in Nigeria, using the data on daily confirmed cases. To validate the predictions, these models were compared through data tests. The test results show that the CART regression model outperformed the ARIMA model in terms of accuracy, leading to a fast growth in the number of confirmed COVID-19 cases. The research findings help governments to make proper decisions on how the prepare for the outbreak. Besides, our analysis reveals the lack of quarantine wards in Nigeria, in addition to the insufficiency of medications, medical staff, lockdown decisions, volunteer training, and economic preparation. 09-29-2022 <![CDATA[ <p>The lethal coronavirus illness (COVID-19) has evoked worldwide discussion. This contagious, sometimes fatal illness, is caused by the severe acute respiratory syndrome coronavirus 2. So far, COVID-19 has quickly spread to other countries, sickening millions across the globe. To predict the future occurrences of the disease, it is important to develop mathematical models with the fewest errors. In this study, classification and regression tree (CART) models and autoregressive integrated moving averages (ARIMAs) are employed to model and forecast the one-month confirmed COVID-19 cases in Nigeria, using the data on daily confirmed cases. To validate the predictions, these models were compared through data tests. The test results show that the CART regression model outperformed the ARIMA model in terms of accuracy, leading to a fast growth in the number of confirmed COVID-19 cases. The research findings help governments to make proper decisions on how the prepare for the outbreak. Besides, our analysis reveals the lack of quarantine wards in Nigeria, in addition to the insufficiency of medications, medical staff, lockdown decisions, volunteer training, and economic preparation.</p> ]]> Hybrid Neural Network Prediction for Time Series Analysis of COVID-19 Cases in Nigeria adedayo f. adedotun doi: 10.56578/jimd010106 Journal of Intelligent Management Decision 09-29-2022 Journal of Intelligent Management Decision 09-29-2022 2022 1 1 Article 46 10.56578/jimd010106 https://www.acadlore.com/article/JIMD/2022_1_1/jimd010106 https://www.acadlore.com/article/JIMD/2022_1_1/jimd010105 Over the past 15 years, online travel agencies (OTAs), such as Booking.com and Expedia.com, have grown significantly. This growth is correlated with their spending on R&D and marketing initiatives. Digital marketing promotes services and products by using an electronic platform. This study aims to ascertain the role of digital marketing in perceived quality and consumer confidence in hotel performance in the age of digital technology. This quantitative study employs statistical testing, namely the partial least squares (PLS) design, in order to understand the link between the two aforementioned variables. In the meantime, the authors conducted a data search by going through a Google form questionnaire on hotel visitors in Tangerang, specifically between October 2020 and October 2021. The survey questionnaires were distributed to 145 respondents, and Smart PLS 3.0 was used in the analytic procedure. The results of digital marketing, including consumer perceptions of quality and trust, play a key role in hotel success. This conclusion is a succinct summary of the research findings in the hope that it will be useful for future research on a comparable topic by academics and other hotel managers. 09-29-2022 <![CDATA[ <p>Over the past 15 years, online travel agencies (OTAs), such as Booking.com and Expedia.com, have grown significantly. This growth is correlated with their spending on R&D and marketing initiatives. Digital marketing promotes services and products by using an electronic platform. This study aims to ascertain the role of digital marketing in perceived quality and consumer confidence in hotel performance in the age of digital technology. This quantitative study employs statistical testing, namely the partial least squares (PLS) design, in order to understand the link between the two aforementioned variables. In the meantime, the authors conducted a data search by going through a Google form questionnaire on hotel visitors in Tangerang, specifically between October 2020 and October 2021. The survey questionnaires were distributed to 145 respondents, and Smart PLS 3.0 was used in the analytic procedure. The results of digital marketing, including consumer perceptions of quality and trust, play a key role in hotel success. This conclusion is a succinct summary of the research findings in the hope that it will be useful for future research on a comparable topic by academics and other hotel managers.</p> ]]> Hotel Performance in the Digital Era: Roles of Digital Marketing, Perceived Quality and Trust juliana amelda pramezwary diena m. lemy rudy pramono arifin djakasaputra agus purwanto doi: 10.56578/jimd010105 Journal of Intelligent Management Decision 09-29-2022 Journal of Intelligent Management Decision 09-29-2022 2022 1 1 Article 36 10.56578/jimd010105 https://www.acadlore.com/article/JIMD/2022_1_1/jimd010105 https://www.acadlore.com/article/JIMD/2022_1_1/jimd010104 The number of fire incidences has increased as a result of Libya's rapid and continuous growth. The growing population in Misurata, the third largest city in Libya and the expansion of industrial facilities both have a hand in the rise in fire incidents. The capabilities available to handle these accidents are limited. One of the biggest challenges is probably the dispersed location of fire stations, which slows down reaction times because it can take an hour for rescue personnel to reach the scene. This study intends to offer decision-makers a model for determining the ideal location of fire stations, using a hybrid FUCOM-COCOSO approach, and apply the model to optimize the location of a fire station in Misurata. The alternatives were compared using six criteria, which were established based on previous research and expert opinions. High population density had a weight of 0.348, making it the most significant factor, and distance from current fire stations had a weight of 0.217. As these sites are dispersed throughout the city, four prospective locations were analyzed to implement a new station. The results indicate that the ideal station must be close to the city's industrial region. This is as a result of its proximity to the city's industrial complex and to densely populated areas. Similar results were obtained by the proposed method, and five other methods. 09-29-2022 <![CDATA[ <p>The number of fire incidences has increased as a result of Libya's rapid and continuous growth. The growing population in Misurata, the third largest city in Libya and the expansion of industrial facilities both have a hand in the rise in fire incidents. The capabilities available to handle these accidents are limited. One of the biggest challenges is probably the dispersed location of fire stations, which slows down reaction times because it can take an hour for rescue personnel to reach the scene. This study intends to offer decision-makers a model for determining the ideal location of fire stations, using a hybrid FUCOM-COCOSO approach, and apply the model to optimize the location of a fire station in Misurata. The alternatives were compared using six criteria, which were established based on previous research and expert opinions. High population density had a weight of 0.348, making it the most significant factor, and distance from current fire stations had a weight of 0.217. As these sites are dispersed throughout the city, four prospective locations were analyzed to implement a new station. The results indicate that the ideal station must be close to the city's industrial region. This is as a result of its proximity to the city's industrial complex and to densely populated areas. Similar results were obtained by the proposed method, and five other methods.</p> ]]> A Composite Approach for Site Optimization of Fire Stations ibrahim badi muhammad lawan jibril mahmut bakır doi: 10.56578/jimd010104 Journal of Intelligent Management Decision 09-29-2022 Journal of Intelligent Management Decision 09-29-2022 2022 1 1 Article 28 10.56578/jimd010104 https://www.acadlore.com/article/JIMD/2022_1_1/jimd010104 https://www.acadlore.com/article/JIMD/2022_1_1/jimd010103 The decentralization of blockchain technology greatly improves the trust relationship in the supply chain network. In view of the lack of trust, uncertainty, and asymmetry in the supply chain network, this paper integrates the blockchain technology to build a network dynamics model of trust representation, calculation, and propagation, and explores how the blockchain influences the supply chain network. The result indicates that the network scale increased by 115.89%, the network connectivity increased by 60.31%, and the average shortest path decreased by 4.95%, after the blockchain trust framework had been deployed in the agricultural supply chain. Meanwhile, the network topology performance such as degree distribution and average clustering coefficient were optimized to varying degrees. Taking agricultural supply chain as an example, the practical significance of topological change was explained. Overall, the blockchain trust mechanism improves the topology of the supply chain network by affecting the trust relationship between nodes. 09-29-2022 <![CDATA[ <p>The decentralization of blockchain technology greatly improves the trust relationship in the supply chain network. In view of the lack of trust, uncertainty, and asymmetry in the supply chain network, this paper integrates the blockchain technology to build a network dynamics model of trust representation, calculation, and propagation, and explores how the blockchain influences the supply chain network. The result indicates that the network scale increased by 115.89%, the network connectivity increased by 60.31%, and the average shortest path decreased by 4.95%, after the blockchain trust framework had been deployed in the agricultural supply chain. Meanwhile, the network topology performance such as degree distribution and average clustering coefficient were optimized to varying degrees. Taking agricultural supply chain as an example, the practical significance of topological change was explained. Overall, the blockchain trust mechanism improves the topology of the supply chain network by affecting the trust relationship between nodes.</p> ]]> Optimization of the Trust Propagation on Supply Chain Network Based on Blockchain Plus ling chen shan su doi: 10.56578/jimd010103 Journal of Intelligent Management Decision 09-29-2022 Journal of Intelligent Management Decision 09-29-2022 2022 1 1 Article 17 10.56578/jimd010103 https://www.acadlore.com/article/JIMD/2022_1_1/jimd010103 https://www.acadlore.com/article/JIMD/2022_1_1/jimd010102 The way agri-food companies conduct business has changed as a result of changes in the market. These companies must start working in a more environmentally friendly manner. This study aims to examine, assess, and compare how various fuzzy methodologies are applied in green supplier selection (GSS), using an agri-food industry as an example. The company Biljana Brko, which engages in GSS, was observed in this study. The selection aids in the acquisition of raw materials and materials whose environmental impact will be minimized. Ecological and economic factors were taken into consideration when choosing green suppliers. Experts who assessed the weight of the criteria and the suppliers with linguistic values were chosen to carry out this selection. In order to do this, a fuzzy set that effectively applies these linguistic values was employed. The fuzzy SWARA (FSWARA) approach was utilized to calculate the weights of the criteria, revealing that the criterion of Environmental Management System has the highest weight. Drawing on the opinions of experts, suppliers were ranked using the fuzzy MABAC, MARCOS, and CRADIS techniques. The results show that supplier S2 receives the highest ratings. Along with this provider, supplier S3 is noteworthy because it excelled in the sensitivity analysis across a variety of scenarios. In light of this, Biljana Brko should give preference to these suppliers. Further, the results of the three adopted techniques were compared. The comparison reveals that the ranking order produced by all three techniques is remarkably similar. This supplier order differed slightly from the FMABAC method just in one scenario. Hence, this work demonstrates that the three fuzzy techniques can solve the GSS problem and other problems by ranking alternatives. 09-29-2022 <![CDATA[ <p style="text-align: justify;">The way agri-food companies conduct business has changed as a result of changes in the market. These companies must start working in a more environmentally friendly manner. This study aims to examine, assess, and compare how various fuzzy methodologies are applied in green supplier selection (GSS), using an agri-food industry as an example. The company Biljana Brko, which engages in GSS, was observed in this study. The selection aids in the acquisition of raw materials and materials whose environmental impact will be minimized. Ecological and economic factors were taken into consideration when choosing green suppliers. Experts who assessed the weight of the criteria and the suppliers with linguistic values were chosen to carry out this selection. In order to do this, a fuzzy set that effectively applies these linguistic values was employed. The fuzzy SWARA (FSWARA) approach was utilized to calculate the weights of the criteria, revealing that the criterion of Environmental Management System has the highest weight. Drawing on the opinions of experts, suppliers were ranked using the fuzzy MABAC, MARCOS, and CRADIS techniques. The results show that supplier S2 receives the highest ratings. Along with this provider, supplier S3 is noteworthy because it excelled in the sensitivity analysis across a variety of scenarios. In light of this, Biljana Brko should give preference to these suppliers. Further, the results of the three adopted techniques were compared. The comparison reveals that the ranking order produced by all three techniques is remarkably similar. This supplier order differed slightly from the FMABAC method just in one scenario. Hence, this work demonstrates that the three fuzzy techniques can solve the GSS problem and other problems by ranking alternatives.</p> ]]> Fuzzy Multi-Criteria Analyses on Green Supplier Selection in an Agri-Food Company adis puška ilija stojanović doi: 10.56578/jimd010102 Journal of Intelligent Management Decision 09-29-2022 Journal of Intelligent Management Decision 09-29-2022 2022 1 1 Article 2 10.56578/jimd010102 https://www.acadlore.com/article/JIMD/2022_1_1/jimd010102 https://www.acadlore.com/article/JIMD/2022_1_1/jimd010101 09-29-2022 <![CDATA[ <p><br></p> ]]> Editorial to the Inaugural Issue željko stević doi: 10.56578/jimd010101 Journal of Intelligent Management Decision 09-29-2022 Journal of Intelligent Management Decision 09-29-2022 2022 1 1 Article 1 10.56578/jimd010101 https://www.acadlore.com/article/JIMD/2022_1_1/jimd010101