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Journal of Intelligent Management Decision
Journal of Intelligent Management Decision (JIMD)
ISSN (print): 2958-0072
ISSN (online): 2958-0080
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2024: Vol. 3

Journal of Intelligent Management Decision (JIMD) serves as a specialized platform in the burgeoning field of intelligent management and decision-making. Renowned for its unique approach, JIMD combines peer-reviewed, open-access content, focusing on both the theoretical advancements and practical implementations in intelligent decision-making processes. This journal is dedicated to contributing to the academic discourse on how intelligence and analytics influence managerial decisions, playing a critical role in evolving business and organizational strategies. By emphasizing the real-world applications and impacts of intelligent management, JIMD sets itself apart from other journals in its category. Committed to a steady dissemination of knowledge, JIMD is published quarterly by Acadlore, with its issues typically released in March, June, September, and December each year.

  • Professional Service - Every article submitted undergoes an intensive yet swift peer review and editing process, adhering to the highest publication standards.

  • Prompt Publication - Thanks to our proficiency in orchestrating the peer-review, editing, and production processes, all accepted articles see rapid publication.

  • Open Access - Every published article is instantly accessible to a global readership, allowing for uninhibited sharing across various platforms at any time.

željko stević
University of East Sarajevo, Bosnia and Herzegovina | website
Research interests: Logistics; Supply Chain Management; Transport; Traffic Engineering; Soft Computing; Multi-Criteria Decision-Making Problems; Rough Set Theory; Sustainability; Fuzzy Set Theory; Neutrosophic Set Theory; Circular Economy
jiafu su
Chongqing Technology and Business University, China | website
Research interests: Innovation Management; Knowledge Management; Supply Chain Management

Aims & Scope


The Journal of Intelligent Management Decision (JIMD) (ISSN 2958-0072) is a pioneering international open-access journal that focuses on the cutting-edge research in intelligent management within various organizational contexts using information systems. Its mission is to push the boundaries of intelligent management and decision-making, providing impactful and insightful content for researchers, business leaders, and senior managers worldwide. JIMD welcomes diverse submissions, including reviews, research papers, short communications, and special issues on specific topics. The journal stands out for its broad spectrum of scholarship that deepens understanding of the application of intelligent information systems in organizations.

JIMD encourages detailed theoretical and experimental research publications, imposing no restrictions on paper length to ensure comprehensive detail and reproducibility. The journal also offers:

  • Every publication benefits from prominent indexing, ensuring widespread recognition.

  • A distinguished editorial team upholds unparalleled quality and broad appeal.

  • Seamless online discoverability of each article maximizes its global reach.

  • An author-centric and transparent publication process enhances submission experience.


JIMD’s scope is comprehensive, covering a diverse range of topics:

  • Innovative Technology in Management: Explores cutting-edge technologies like AI, machine learning, and blockchain in managerial decision-making.

  • Strategic Enterprise Architecture: Examines the role of enterprise architecture in aligning IT infrastructure with business goals.

  • Business Policy and Strategic Management: Investigates the development of business policies and strategies in the era of digital transformation.

  • Data Mining and Analytics: Focuses on leveraging data mining and analytics for marketing insights and value creation.

  • E-Business Models and Strategies: Analyses emerging e-business models and digital strategies for competitive advantage.

  • Digital Learning and Training: Explores the impact of digital technologies on training and e-learning in corporate settings.

  • Digital Marketing: Investigates new trends and strategies in e-marketing.

  • Entrepreneurship and Social Enterprise: Studies the role of technology in driving entrepreneurship and social enterprise.

  • Information Systems Strategy: Focuses on strategic planning and analysis of information systems.

  • Competitive Advantage through IT: Examines how information technology can be leveraged for competitive advantage.

  • Industry-specific Information Systems: Looks at the application of information systems in different industries.

  • Intelligent Decision-making Theories and Applications: Explores new theories and practical applications in intelligent decision-making.

  • Alignment of IT and Organizational Strategy: Studies the alignment between IT systems and organizational strategies.

  • Organizational Behavior and HRM: Investigates how intelligent systems affect organizational behavior and human resource management.

  • Knowledge Management Systems: Focuses on the development and implementation of knowledge management systems.

  • Logistics and Operations Management: Examines the role of intelligent systems in logistics and operations.

  • Fuzzy Systems in Decision-making: Studies the application of fuzzy logic in organizational decision-making.

  • Cybersecurity and Information Security Strategies: Addresses strategies for managing cybersecurity risks in organizations.

  • Supply Chain Management: Explores the role of intelligence in optimizing supply chain management.

  • Customer Relationship Management (CRM) Systems: Looks at the development and use of CRM systems in managing customer relations.

  • Ethical Implications and Social Responsibility: Examines the ethical considerations and social responsibilities in the use of intelligent systems in management.

  • Sustainable Business Practices: Investigates how intelligent management contributes to sustainable business practices.

Recent Articles
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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.


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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.


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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.


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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.


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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.


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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.


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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.


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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.


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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.


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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.
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