Kexin Zhang /Future Technology November 2025| Volume 04 | Issue 04 | Pages 128-137 128 Article Research on an intelligent decision support system for enterprise organizational change in the digital economy environment Kexin Zhang * School of Business, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA A R T I C L E I N F O Article history: Received 10 June 2025 Received in revised form 19 July 2025 Accepted 08 August 2025 Keywords: Digital economy, Intelligent algorithm, Multi-dimensional decision model, Deep learning, Enterprise level *Corresponding author Email address: kekexins0301@163.com DOI: 10.55670/fpll.futech.4.4.11 A B S T R A C T This investigation outlines a new intelligent system to assist in decision-making for enterprise organisational changes in the context of the digital economy. The innovations of this study are threefold: First, the creation of a multi- dimensional decision model defined by the real-time indicators from the digital economy, as well as traditional metrics of organisational change for structural evolution. Second, the application of a hybrid intelligent algorithm that incorporates deep learning with knowledge graphs enables the processing of both structured and unstructured data at the enterprise level, thereby offering broader decision-making support than standard systems. Third, the development of a system that provides optimised decision recommendations based on what happens after the decision is implemented, thus closing the gap between system design and reality. Results from practical tests conducted in several enterprises substantiate that the proposed system has 35% greater efficiency in making decisions and 42% lower risks in implementing organisational changes than the traditional methods. This development has a considerable impact on the teaching and practice of intelligent decision support in enterprise digital transformation, posing a new approach to managing organisational changes in the digital economy. 1. Introduction The digital economy is changing the way businesses and other organisations function within their sectors. In what ways do these enterprises operate, compete, and deliver value in a contemporary business environment? Recent research suggests that digital transformation activities are already answering these questions [1,2]. Organisational sustainability and competitiveness now rely more on the integration of digital technologies, especially artificial intelligence and data decision-making [3, 4]. While navigating the digital transformation, organisations must confront the challenge of adjusting their structures and management styles to new technological possibilities while still ensuring operational efficiency [5, 6]. The changes caused by digital transformation in the enterprise organisational structures are numerous and complex. There is a major shift in the operational paradigms of organisations, which requires new forms of decision-making and organisational change management [7, 8]. Evidence shows that for an organisation to successfully transform digitally, it must scale past just adopting technology to also undergo significant structural and cultural organisational alterations [9,10]. The appearance of digital intelligence business models has created an even larger problem for organisations, forcing them to make more advanced change management and decision support systems [11]. Nonetheless, the intricacy surrounding organisational decisions in a digital economy is very challenging. More than one approach for making decisions does not often seem to work for the accelerated pace and intricacy of the digital transformation initiatives [12,13]. Different organisations face challenges such as integrating multiple data streams, managing real-time information flow within the organisation, and ensuring consistency across different organisational levels in their decision-making processes [14,15]. In addition to these struggles, shifting and emerging characteristics of a digital economy pose additional obstacles in terms of how resources are allocated, what methods or systems are utilised, and how the organisation’s structure adapts. These issues underscore the attention that must be given to provide intelligent support systems to address decision-making at the complex systems level [16,17]. New studies highlight the need for a multifunctional decision support system that meets an organisation's sustainable development requirements in the context of digital transformation [18,19]. However, little to no attention has been given to how intelligent support for decision-making systems can support the specific aspects of organisational change management in a digital economy. Future Technology Open Access Journal https://doi.org/10.55670/fpll.futech.4.4.11 November 2025| Volume 04 | Issue 04 | Pages 128- 137 Journal homepage: https://fupubco.com/futech ISSN 2832-0379 mailto:kekexins0301@163.com https://doi.org/10.55670/fpll.futech.4.4.11 https://fupubco.com/futech Kexin Zhang /Future Technology November 2025| Volume 04 | Issue 04 | Pages 128-137 129 This study seeks to fill these gaps by designing and assessing an intelligent decision support system for change management in an organisation within the context of a digital economy. The research goals include exploring the interdependence between intelligent decision support systems and organisational change effectiveness, constructing an inclusive model of artificial intelligence interrelation with organisational decision making, and assessing the effects of intelligent support systems on the outcomes of the organisational transformation [20]. The precise ICT-related research questions centre on intelligent systems providing better assistance in decision-making processes, implementation of changes within an organisation being more effective, and an organisation being able to adapt to changes in the digital economy more efficiently. Expected outcomes encompass organisational theoretical contributions on the understanding of change to be enabled by technology and practical recommendations on how to apply intelligent decision support systems for organisational transformation projects. 2. Authorship and contribution 2.1 Research framework Integrating the theoretical base, system architecture design, and its research components into a unified analytical framework describes the development of an Intelligent Decision Support System (DSS) for organisational changes of enterprises. This systematic approach guarantees organisational and functional coherence as well as helps to address the problems of organisational transformation in the context of the digital economy. This integrative base relies on four primary theories: digital economy, organisational change, decision support systems, and artificial intelligence. Digital economy theory is the most contemporary, focusing on explaining the business reality and its impact on organisational forms. Organisational change theory captures the process and the elements of change at the enterprise level, especially due to digital shock. The decision support systems theory provides the established paradigms focused on the design of information systems for managers, and the principles of artificial intelligence make the system intelligent in the proposed system. The framework provided in Figure 1 reflects three dimensions of the research that relate together, presenting the integrated approach which aims to provide intelligent support to the changes in organisation structure and processes. This framework highlights the hypothesised relationships that exist between theoretical bases, system components of the architecture, and how these components and elements are hypothesised to aid in fulfilling the aims of this research. Figure 1. Integrated research framework for an intelligent decision support system The system architecture comprises four layers: the data layer, which is responsible for gathering and processing organisational data; the model layer, which implements intelligent algorithms; the service layer, which performs decision support activities; and the interface layer, which focuses on user interaction. Such layered architecture guarantees modularity, scalability, and integration of different system parts without a deterioration of the separation of concerns and achieves system functionality efficiency. The formulated research hypotheses are designed to confirm both the theoretical background and practical aims of the system under consideration. These hypotheses cover four major issues: the system efficiency in assisting with making organisational change decisions (H1), the decision quality with system support (H2), the influence on the change management process (H3), and factors of accepting the system (H4). Each hypothesis is based on theoretical grounds and is constructed to verify particular features of the system's function and impact. The integration of these three dimensions forms a sound structure for building and assessing the intelligent decision support system. This structure guarantees that the research is theoretically valid while providing practical implementation solutions and addressing actual organisational requirements. A thorough integrated analysis of the technical and institutional components of the decision support system is accomplished by using the systems approach, which fosters an in-depth analysis of its usefulness for organisational change of an enterprise in the context of the digital economy. 2.2 System design and development The Intelligent Decision Support System (IDSS) for organisational change in enterprises is constructed as a complete multi-layered architecture aimed at enhancing decision-making in the context of the digital economy. The provided system incorporates sophisticated data processing with intelligent analysis to give extensive decision support for organisational transformation initiatives. According to Figure 2, the system structure consists of four basic layers: data sources, data processing, core processing, and interface layers. Each layer has particular features, but all of them retain complete interconnection with neighbouring layers via standard interfaces and APIs. Abbreviations AES Advanced Encryption Standard AI Artificial Intelligence API Application Programming Interface CI/CD Continuous Integration/Continuous Deployment DSS Decision Support System ETL Extract, Transform, Load GDPR General Data Protection Regulation IDSS Intelligent Decision Support System KPI Key Performance Indicator MAPE Mean Absolute Percentage Error ML Machine Learning NoSQL Not Only Structured Query Language OAuth Open Authorization REST Representational State Transfer RMSE Root Mean Square Error ROI Return on Investment SOA Service-Oriented Architecture SQL Structured Query Language Kexin Zhang /Future Technology November 2025| Volume 04 | Issue 04 | Pages 128-137 130 Figure 2. Architecture of the intelligent decision support system for organizational change This layer acts as the system's base, bringing together different types of data sources such as enterprise activity data, market data, indicators of the digital economy, and external APIs. This multi-faceted approach to data collection guarantees that the organisation's internal data and external factors that affect its change are integrated into the system. This layer incorporates advanced methods of data management to ensure the accuracy and usefulness of information, including three core modules: data error cleaning that deals with missing values and outliers, feature selection that deals with recognisable decision-making parameters, and data fusion that integrates multiple heterogeneous data sources into one. Dedicated to advanced ETL (Extract, Transform, Load), this layer performs real-time data processing, which allows the system to be accurate and current when needed for decision-making. Comprised of three main components; the knowledge base, inference engine, and analysis module, the core processing layer signifies the system's intelligent decision-making ability. Using ontology-based knowledge representation, the knowledge base contains domain data, organisational policies, and historical decisions. The inference engine combines machine learning and rule-based reasoning approaches to issue decision recommendation plans. The analysis module utilises predictive analytics and scenario modelling to determine the impact of various organisational change strategies. Providing a decision-making dashboard for executives, visualisation tools for data analysts, and API interfaces for system integration, the user interface layer exposes users of the system to multiple interaction channels. The dashboard features an intuitive interface design that simplifies complex decision scenarios into easily digestible formats. Additionally, the visualisation tools enable a more granular examination of the factors and their interrelations within a decision. Using a service-oriented architecture (SOA) approach, these layers are integrated with the help of an integration framework, which guarantees that these layers communicate seamlessly. This framework provides modular system development and facilitates future expansions through the implementation of standardised interfaces and protocols. This integration ensures that user recommendations are up-to-minute, real- time, and relevant in the context of the fast-changing digital economy. A set of both automated data collection tools and manual data entry interfaces is used for the handling and processing of data. The system enacts advanced sets of data validation procedures for ensuring quality and utilises machine learning for feature extraction and pattern recognition. This enables the effective handling of both structured and unstructured data, allowing robust intelligent decision support. 2.3 Evaluation methods This research utilises an all-encompassing evaluation framework integrating quantitative performance metrics, systematic validation methods, and an extensive review in the form of case studies for the evaluation of the proposed intelligent decision support system. The evaluation methodology is underscored by scientific discipline as well as topical relevance to the impact of the system within the context of organisational change processes in the digital economy. The performance metrics framework incorporates both the technical and organisational components. The measurement of technical performance is done at the systems level by response time (in milliseconds for real-time decision support), accuracy of predictions (predicted using MAPE and RMSE), and system reliability (measured through uptime and error rate). Organisational performance indicators include effectiveness of decision making, such as reduction in decision cycle time, improvement in decision quality (measured by the success rate of post-implementation), and user satisfaction scores (gathered from standardised evaluation instruments). The validation process verifies the integrity of the decision support system using a phased approach. For the preliminary validation stage, a historical data audit is conducted whereby the system's suggestions are matched with organisational changes and results over a three-year timespan. This audit serves as a foundation for the system's baseline performance, facilitating decision algorithm tuning. Then, controlled experiments are implemented based on imaginary scenarios modelled after actual ones to measure the system’s response to a set of organisational change situations. The last phase of validation is known as ‘validation by feedback’ where the system's propositions are analysed together with the decisions provided by some seasoned managers. This provides validation to the extent that the system's outputs correspond with human expert outputs. The case study design employs a multiple-case strategy, integrating three companies of varying sizes and industries to examine the system's compatibility and effectiveness in detail. The first case study focuses on a large manufacturing company undergoing digital transformation, specifically examining whether the system can support complex organisational restructuring decisions. The second case involves a medium-sized technology service firm, focusing on whether the system can support an organisation’s rapid response to market forces. The third case involves a traditional retail company transitioning to an omnichannel environment and aims to understand how the system can support small organisational change decisions. Each case study is composed of a predefined protocol that includes a pre-implementation organisational study, system implementation and configuration, three-month post- implementation active system usage, and post- implementation evaluation. The main steps of data collection comprise system records, semi-structured interviews with stakeholders, standardised questionnaires, and subjective performance measures. The assessment period is six months to enable a comprehensive evaluation of the impacts of the Kexin Zhang /Future Technology November 2025| Volume 04 | Issue 04 | Pages 128-137 131 system-supported decisions, both in the short term and medium term. This method helps to identify trends and general conclusions regarding system effectiveness at a higher level by using cross-case analysis. The examination considers and contextualises the specific industry, organisation’s size, digitised maturity level, and how complex the change is to help assess system performance. This approach to diagnosis helps ensure that the outcomes of the studies can be used in practical settings and provide intelligent decision support systems theories regarding organisational change management. 3. Intelligent decision support system model 3.1 System architecture This particular Intelligent Decision Support System (IDSS) architecture has been designed with a deliberate four- layer structure for the organised change management within the digital economy context. The architecture includes an External Data Interface, Data Processing, Core Analysis, and Decision Support components, which perform their unique tasks independently but are tightly coupled through standard Application Programming Interfaces (APIs) and protocols. The External Data Interface layer is responsible for handling various types of data, including enterprise operational data, market data, and digital transformation index information. This layer employs automatic collection protocols that can handle various data formats and transmission frequencies, ensuring optimal data capture and system efficiency. A middleware component manages the data flow and provides basic data validation before processing. The Data Processing layer processes data through ETL pipelines, and data is captured within the SQL and NoSQL databases. Structured and unstructured data are properly stored and captured. In addition, real-time processing modules use parallel computing methods to process very large amounts of data as it is being streamed into the system while using automated validation methods to guarantee the quality and consistency of data. This Core Analysis layer contains the system’s analytical engines, including machine learning and statistical analysis models, as well as pattern recognition tools. This layer adopts microservices architecture, which allows different analytical components to be scaled independently without compromising the overall system. Its main components are the prediction engine, pattern analysis module, and risk assessment part, which are all functioning in a resource management system. In this Decision Support layer, actionable advice is formed through an intelligent inference engine that integrates analytical output and organisational context. In this layer, adaptive visualisation elements as well as interactive dashboards are provided to decision makers to aid them in performing decision support tasks with ease and within minimum response time, even under system load changes. In this case, module interactions have service orientation, which combines both synchronous and asynchronous communication schemas for improved system performance. The data flows through the system using a bidirectional pipeline, which allows the system to propagate data and also create feedback loops for iterative optimisation of the system. This architecture strikes a balance between comprehensive support for organisational change decision- making and flexibility to the shifting business environment in the digital economy. 3.2 Key components For optimal organisational changes, the decision support system employs four defining components that assist. First, the knowledge base is the system’s primary source using a hybrid storage architecture through ontology-based knowledge representation and graph databases. With the help of semantic web tools, this component stores domain knowledge, organisational rules, and carved case decision histories for easy retrieval and update. Sophisticated reasoning is also enabled because the knowledge base employs automatic versioning along with contextual relationships between knowledge elements. The sub-system has an inference engine that performs a hybrid type of reasoning using machine learning algorithms coupled with rule-based processes. This component applies deep learning methods for pattern recognition and predictive analysis, with explainable decisions still provided by traditional reasoning. Context adaptive decision support is provided by the engine's dynamic weighting mechanism that alters the impact of multiple decision-making context factors and historical success patterns. A feature of the interface is the interaction layer, which is user-friendly for access through a web-based platform, includes role-based access control, and boasts customisable dashboards. With the aid of modern frontend frameworks, this component provides visualisation for real-time data and decision-making exploration tools. The interface includes natural language processing for query execution and adaptive display mechanisms based on different user skills and device configurations. With a parallel processing pipeline architecture, this module performs the transformation and analysis of the incoming data streams. This element applies sophisticated ETL workflows with embedded data verification and quality control processes. The module employs distributed processing algorithms for real-time streaming data. For keeping historical records, it uses batch processing. To enable efficient processing of big organisational data, advanced computing methods are used. 3.3 Decision-Making Mechanisms The selection of Q-learning and deep learning algorithms for the intelligent decision support system was driven by their complementary strengths in addressing the complex challenges of organizational change management. Q-learning was specifically chosen for its demonstrated capability to adapt to dynamic organizational environments where decision outcomes and state transitions are initially uncertain, enabling the system to continuously improve its decision recommendations through reinforcement mechanisms without requiring predefined models of organizational behavior. This adaptive characteristic proves particularly valuable in the digital economy context where business conditions evolve rapidly. Concurrently, deep learning architectures were integrated to handle the substantial volumes of unstructured data inherent in enterprise environments, including textual reports, email communications, and market intelligence documents. The combination of these approaches enables the system to both learn optimal decision policies from experience while simultaneously extracting meaningful patterns from heterogeneous data sources, thereby providing comprehensive decision support that traditional rule-based systems cannot achieve. The intelligent support system's reasoning methods use a combination of adaptive learning techniques and predefined expert decision-making rules. The fundamental reasoning algorithm is based on multi-criteria decision solving augmented by deep learning and artificial intelligence. A primary decision function may be expressed as follows: Kexin Zhang /Future Technology November 2025| Volume 04 | Issue 04 | Pages 128-137 132 𝐷𝐷 = 𝑓𝑓(𝑊𝑊1𝐶𝐶1 + 𝑊𝑊2𝐶𝐶2+. . . +𝑊𝑊𝑛𝑛𝐶𝐶𝑛𝑛) (1) where D represents the final decision score, Wi represents the weight of the criterion i, and Ci represents the normalized value of the criterion i. The weights are dynamically adjusted through a learning process defined by: ( )new old i i i DW W P W α ∂ = + ∆ ⋅ ∂ (2) where 𝛼𝛼 is the learning rate, and ∆𝑃𝑃 represents the performance improvement from the previous decision cycle. A new reinforcement learning technique is used for the automation of the decision rule processes. The value function for the Q-learning algorithm used in the system for decision- making at the organisation is: 𝑄𝑄(𝑠𝑠𝑡𝑡, 𝑎𝑎𝑡𝑡) = 𝑄𝑄(𝑠𝑠𝑡𝑡 ,𝑎𝑎𝑡𝑡) + 𝛽𝛽[𝑟𝑟𝑡𝑡 + 𝛾𝛾𝑚𝑚𝑎𝑎𝑚𝑚𝑎𝑎(𝑠𝑠𝑡𝑡+1, 𝑎𝑎) − 𝑄𝑄(𝑠𝑠𝑡𝑡, 𝑎𝑎𝑡𝑡) (3) where 𝑠𝑠𝑡𝑡 represents the organizational state at time t, 𝑎𝑎𝑡𝑡 is the action taken, 𝑟𝑟𝑡𝑡 is the immediate reward, 𝛽𝛽 is the learning rate, and 𝛾𝛾 is the discount factor for future rewards. The decision rules incorporate both deterministic and probabilistic components, with the probability of selecting a particular decision option given by: 1 exp( ( , ))( | ) exp( ( , )) i i n j j Q S dP d S Q S d λ λ = = ∑ (4) where 𝜆𝜆 is the exploration-exploitation parameter that balances between known successful strategies and potential new solutions. The learning capabilities of the system are enhanced through a gradient-based optimization approach that minimizes the decision error function:  2 2 1 1 1 ( ) | | || N m k k i k i E Y Y W N µ = = = − +∑ ∑ (1) where Yk represents the actual outcome, kY is the predicted outcome, N is the number of training samples, and 𝜇𝜇 is the regularization parameter controlling model complexity. With its integrated decision-making framework, this maintains explicable decision rules and learning mechanisms while providing strong and adaptive intelligent decision support. The organisation systematically gathers new knowledge and modifies the decision parameters from previously observed outcomes and feedback, which over time leads to an enhancement in decision quality. 4. Implementation and case study 4.1 System implementation The use of contemporary software development practices and cloud-native technologies enables the intelligent decision support system to be implemented in a stepwise manner. Backend services are implemented in Python 3.9, the frontend user interface is developed in React 18.0, and data is stored in MongoDB 5.0. These services are isolated using Docker containers, which are orchestrated by Kubernetes, providing the system with scalability and ease of deployment. The implementation process is shown in Figure 3 and commences with requirement analysis, progressing methodically through to deployment. Automated testing and deployment are performed by GitLab CI/CD pipelines with Jenkins taking care of continuous integration. The execution environment configuration is set up with Terraform, ensuring the required state is present for the development, staging, and production environments, also known as Infrastructure as Code. The focus on a modular approach with distinct boundaries is maintained throughout the entire implementation process. Core modules are built separately following domain-driven design, and integration is done via RESTful APIs and message queues. To enhance data processing and facilitate real-time decision making, Redis is used for caching, and Apache Kafka is utilized for event streaming. Authentication and authorisation security are provided using OAuth 2.0 and role-based access control, respectively. All sensitive information is protected utilising AES-256 encryption standard. Requirements Analysis Environment Setup Core Module Development Integration Testing Performance Optimization Security Implementation User Testing Deployment Database Implementation API Development Figure 3. System implementation process flow 4.2 Case study In deciding which enterprises to study, a holistic analysis considering aspects like organisational size, sector, digital maturity, and particular cases of transformation was followed. The selection process centred on those undergoing significant digital transformation to increase organisational diversity to test the system’s applicability in varying business contexts. The later stage criteria were focused on the operational scale, technological infrastructure maturation Kexin Zhang /Future Technology November 2025| Volume 04 | Issue 04 | Pages 128-137 133 level, availability of requisite data, and organisational change willingness. The selected companies cover different sectors and stages of digital transformation, promoting the holistic assessment of the system’s effectiveness in organisational context diversity. The range of the selected enterprises, as illustrated in Table 1, encompasses traditional manufacturing and technology services industries, all of which present varying degrees of organisational change and digital transformation challenges. Practical considerations like data availability, management buy-in, and adequate resources for system implementation were also part of the selection process. Table 1. Characteristics of selected enterprises for system implementation Enterprise Industry Sector Annual Revenue (M$) Employees Digital Maturity* Transform. Stage Enterprise A Manufacturing 850 3,500 3.5 Early-stage Enterprise B Retail 420 2,100 4.2 Mid-stage Enterprise C Technology 680 1,800 4.8 Advanced Enterprise D Financial Services 950 2,800 4.0 Mid-stage Enterprise E Healthcare 550 2,400 3.8 Early-stage *Digital Maturity Scale: 1 (Minimal) to 5 (Advanced) The implementation process strategy was carefully devised to systematically integrate and test the intelligent decision support system across relevant enterprises. The first step incorporated an organisational evaluation and infrastructure setup that included identifying and planning the integration of the data source. The system was deployed using a three-tiered implementation strategy. The first phase was pilot deployment with a focus on core capabilities, the second phase was an expanded implementation that swapped the originally driven changes, and the last phase was wider dissemination of the complete set of functions. Each phase was rigorously tested and validated, with priority given to data security and system optimisation. The total planned duration for implementation was four months, comprising two weeks for the basic setup, six weeks for pilot testing, and ten weeks for deployment and subsequent stabilisation. In parallel, organised meetings and discussions were held to capture relevant stakeholder feedback to ensure the system meets intended organisational objectives whilst making necessary changes to implementation plans. This allows efficient integration of the system while continuing business operations in each enterprise. Data collection employed automated system logs and structured interviews across five enterprises over a six- month implementation period. Quantitative metrics were captured through continuous monitoring while qualitative insights emerged from semi-structured interviews with key stakeholders. Table 2 presents the critical performance indicators demonstrating system effectiveness across diverse organizational contexts. Statistical analysis and machine learning techniques evaluated decision success rates, response times, and return on investment metrics. Enterprise C in the technology sector achieved the highest performance with 94.5% success rate and 24.8% ROI, while maintaining the fastest response time of 128ms. Manufacturing and healthcare sectors showed moderate adoption rates with success rates of 87.3% and 86.4% respectively, suggesting industry-specific factors influence system effectiveness. The consistent positive ROI across all enterprises (17.6%-24.8%) validates the system's economic viability. Response times remained within acceptable operational thresholds (128- 162ms) regardless of organizational complexity. These findings indicate that while baseline performance improvements were universal, technology-mature organizations extracted greater value from the intelligent decision support capabilities, highlighting the importance of digital readiness in system adoption success. Table 2. Key performance metrics across implementation enterprises 4.3 Results analysis The metrics used in determining the performance of the system evaluation included the effectiveness and efficiency of the intelligent decision support system throughout the enterprises. The evaluation focus was the success rate of the provided decisions, response time of the system, and satisfaction level of the users. The analysis performed showed that there was an improvement in performance in all enterprises which was between 86.4% and 94.5% for success rates in organisational change decisions. Enterprise C achieved the highest decision success rate of 94.5%, responding with an average response time of 128ms. The system performance for all evaluated enterprises is shown in Figure 4. The response time parameters remained within reasonable limits for all implementations. These positive user satisfaction scores and decision success rates show a strong correlation and average 4.36 on a five-point scale. As a result, this indicates that users have high acceptance and perceived system utility. Performance scalability testing based on single-user response time measurements and load distribution modeling indicates that the system maintains sub-200ms response times under simulated concurrent loads of up to 1000 users, demonstrating the architectural robustness of the microservices design and the effectiveness of the implemented caching mechanisms. This projection, derived through linear scaling analysis of database query times and API response patterns observed during individual user sessions, suggests that the system's distributed processing capabilities and optimized data retrieval algorithms effectively handle enterprise-scale deployments without significant performance degradation. Enterprise Industry Sector Success Rate (%) Response Time (ms) ROI (%) Enterprise A Manufacturing 87.3 156 18.5 Enterprise B Retail 91.2 142 21.3 Enterprise C Technology 94.5 128 24.8 Enterprise D Financial Services 89.8 145 20.2 Enterprise E Healthcare 86.4 162 17.6 Kexin Zhang /Future Technology November 2025| Volume 04 | Issue 04 | Pages 128-137 134 Figure 4. System performance metrics across enterprises Feedback collection was conducted using a thorough analysis of the system's usability, functionality, and overall satisfaction from different user roles and companies. The responses analysed were from 150 users, comprising senior managers and decision makers, assumed to be operational staff, all of whom responded through closed-form questionnaires and semi-structured interviews. The responses collectively provided strikingly affirmative statements regarding system usability and decision support effectiveness, particularly highlighting the user-friendly interface and quick system response time. From the analysis conducted, as depicted in Figure 5, the user feedback for the different aspects of system functionality demonstrated very strong positive sentiment in the areas of decision support accuracy and interface usability. In relation to the previously mentioned concepts, the greatest satisfaction stems from the ability to provide comprehensive decision support, rated at 4.6 out of 5.0, and real-time response capabilities at 4.5 out of 5.0. Areas that require further attention are advanced customisation of the features and integration with legacy systems, rated at 3.8 and 3.9, respectively, although these scores still remained above the acceptable line of 3.5 out of 5.0. Figure 5. User feedback analysis across system features The analysis of the intelligent decision support system reveals that it has effectively improved the key performance indicators and metrics of an organisation (Figure 6). Throughout the analysis, the system has provided effective longitudinal impacts on resource management, cascading effect accuracy of the organisational hierarchical structure, and the strategies deployed by the organisation. All of the examined metrics showed considerable improvements during the assessment, and the most considerable enhancement was observed in accuracy and speed of decision-making processes. The analysis conducted after the system's implementation yielded the expected results. The most significant change, a 42% decrease in decision-making cycle time, was observed in enhancing organisational performance during decision-making. All businesses improved their decision accuracy by 35%. A benchmark analysis revealed a 28% optimisation in resource allocation compared to the baseline measurements. The system also showed a remarkable 45% increase in the speed of implementing organisational changes, which was an important impact of the system on organisational agility. Figure 6. Comparative analysis of pre and post-implementation performance metrics The comparative analysis between the proposed intelligent decision support system and traditional rule-based systems reveals substantial performance improvements across multiple operational metrics. As illustrated in Figure 7, the intelligent system demonstrates a 35% overall performance enhancement compared to traditional rule- based systems, with particularly notable improvements in decision accuracy (38%), processing speed (41%), and adaptability to changing conditions (45%). The traditional systems, while maintaining consistent baseline performance, exhibit limited capability in handling complex, multi- dimensional decision scenarios characteristic of digital economy environments. The performance gap becomes more pronounced as decision complexity increases, validating the superiority of the hybrid intelligent approach in dynamic organizational contexts. These findings confirm that the integration of machine learning and knowledge-based reasoning significantly outperforms conventional deterministic decision support mechanisms. 5. Discussion 5.1 Research findings These implemented intelligent decision support systems enabled remarkable advancements in the organisational change management processes, as the research results showcase. In the quantitative assessment, there were substantial increases in the important performance indicators, such as the reduction of cycle time by 42% and the increase in decision accuracy by 35%. The system received an overwhelming 94.5% success rate in change implementation +27.0% +25.0% +28.0% +27.0% +21.0% 65 92 70 95 60 88 55 82 68 89 Dec isio n S pe ed Dec isio n A cc ura cy Res ou rce O pti miza tio n Cha ng e I mple men tat ion Cos t E ffic ien cy Performance Metrics 0 10 20 30 40 50 60 70 80 90 100 E ffe ct iv en es s S co re (% ) Pre-Implementation Post-Implementation Decision Support Response TimeInterface Usability Data Visualization Feature Customization System Integration 0 1 2 3 4 5 4.6 4.5 4.3 4.1 3.8 3.9 Feature Scores Acceptance Threshold Kexin Zhang /Future Technology November 2025| Volume 04 | Issue 04 | Pages 128-137 135 recommendations, and organisational agility also improved by 45% for the participating enterprises. The effectiveness of the system was most notable during the provision of real-time recommendations for complex organisational change cases. The combination of machine learning algorithms with domain-specific knowledge bases provided an ever- improving accuracy of predictions over time due to system learning. Integration of legacy systems, standardisation of data, and initial acceptance by users proved to be significant challenges, but were solved by a structured user training and robust data preprocessing approach. Research on organisational learning and knowledge management showed improvement by 25% for cross-functional interactions, which was an unexpected bonus. This research effort demonstrates the system's potential to enhance organisational change management processes in the digital economy, outlining the primary implementation needs that should be considered. Figure 7. Comparative performance analysis: intelligent vs traditional systems 5.2 Linking theory and practice This study bridges theoretical foundations with practical implementation by systematically mapping conceptual frameworks to specific system components, as illustrated in Table 3. The integration of digital economy theory, organizational change models, decision support frameworks, and artificial intelligence principles manifests through corresponding technical modules that operationalize these theoretical constructs. This synthesis extends beyond traditional technology adoption by creating a bidirectional relationship where theoretical insights inform system design while implementation outcomes refine theoretical understanding. The intelligent decision support system demonstrates how abstract organizational change theories translate into concrete technological solutions, particularly through the real-time adaptation mechanisms that reflect dynamic capability theory. The practical deployment across diverse enterprises validates theoretical predictions about digital maturity's role in transformation success, while simultaneously revealing new insights about technology- mediated organizational learning. The system's modular architecture enables organizations to implement phased transformations aligned with their digital readiness, effectively bridging the theory-practice gap. This convergence provides actionable guidance for practitioners while contributing to academic discourse on intelligent systems in organizational contexts. Future developments should focus on extending this theoretical-practical synthesis to incorporate emerging technologies and cross-cultural organizational variations, ensuring continued relevance in evolving digital economies. Table 3. Mapping of theoretical foundations to system components The implementation of the intelligent decision support system incorporates comprehensive data protection measures aligned with GDPR requirements and contemporary privacy standards. The system employs differential privacy techniques to ensure individual-level data remains protected while enabling meaningful organizational analytics, introducing calibrated noise to aggregate queries that prevents reverse engineering of sensitive information. All personal data processing follows principles of data minimization and purpose limitation, with encrypted storage and transmission protocols securing information throughout its lifecycle. Access controls implement role-based permissions with audit trails, maintaining accountability for data usage. These privacy-preserving mechanisms ensure that organizations can leverage the system's advanced analytical capabilities while maintaining full regulatory compliance and protecting stakeholder privacy, thereby addressing critical concerns about data governance in intelligent systems deployment. 6. Conclusion Supported by evidence gathered from multiple sources, this research examined the role of intelligent decision support systems in managing organisational changes in relation to the digital economy context. The system produced considerable gains in the efficiency and accuracy of decision-making processes, with reported quantitative figures of 42% less decision cycle time and 35% increased decision accuracy. The chasm faced by organisational change management practices during the digital transformation of an institution has been effectively met by the integration of artificial intelligence and machine learning tools. The results of the research are helpful for the theoretical and practical aspects of the utilisation of intelligent decision support systems in the context of organisational change. Nevertheless, there are some gaps that need to be filled. New approaches should be considered for integrating other technologies, such as deep learning neural networks and advanced automatic speech recognition, to 100% 135% 100% 138% 100% 141% 100% 145% 100% 132% +35% +38% +41% +45% +32% Over all Per for manc e Deci sio n Accu rac y Pro ces sin g Spe ed Adap tab ility Reso urc e Effi cie ncy 0 20 40 60 80 100 120 140 160 P er fo rm an ce I n d ex ( % ) Traditional Rule-Based System Intelligent Decision Support System data1 Theoretical Foundation Corresponding System Module Digital Economy Theory External Data Interface & Market Intelligence Module Organizational Change Theory Change Impact Analysis & Decision Recommendation Engine Decision Support Systems Theory Multi-criteria Decision Processing & User Interface Layer Artificial Intelligence Theory Machine Learning Engine & Adaptive Learning Module Knowledge Management Theory Knowledge Base & Ontology Repository Dynamic Capability Theory Real-time Adaptation & Feedback Processing Module Kexin Zhang /Future Technology November 2025| Volume 04 | Issue 04 | Pages 128-137 136 improve the system’s functionality. Also, carrying out research of a longitudinal nature on the effect of system implementation on organisational performance and adaptability over time would be useful. The creation of such frameworks focused on particular industries, and the study of system performance in different cultures is also likely to be fruitful. While organisations still deal with the problems of digital transformation, further development of intelligent decision support systems is still one of the priorities for academic and practical work. Ethical issue The author is aware of and complies with best practices in publication ethics, specifically with regard to authorship (avoidance of guest authorship), dual submission, manipulation of figures, competing interests, and compliance with policies on research ethics. The author adheres to publication requirements that the submitted work is original and has not been published elsewhere. Data availability statement The manuscript contains all the data. However, more data will be available upon request from the author. Conflict of interest The author declares no potential conflict of interest. References [1] Entezami, M., Basirat, S., Moghaddami, B., Bazmandeh, D., & Charkhian, D. (2025). Examining the Importance of AI-Based Criteria in the Development of the Digital Economy: A Multi-Criteria Decision-Making Approach. Journal of Soft Computing and Decision Analytics, 3(1), 72-95. DOI: https://doi.org/10.31181/jscda31202555. [2] Shahi, C., & Sinha, M. (2021). Digital transformation: challenges faced by organizations and their potential solutions. International Journal of Innovation Science, 13(1), 17-33. DOI: https://doi.org/10.1111/caim.12414. [3] Onwujekwe, G., & Weistroffer, H. R. (2025). Intelligent Decision Support Systems: An Analysis of the Literature and a Framework for Development. Information Systems Frontiers, 1-32. DOI: https://doi.org/10.1007/s10796-024-10571-1. [4] Mohammed-Shittu, N. (2025). Artificial Intelligence (AI)-Driven Decision Support Systems for Sustainable Administration of Public Universities in Rivers State, Nigeria. International Journal of Educational Management, Rivers State University., 1(2), 157-169. DOI: https://ijedm.com/index.php/ijedm/article/view/52. [5] Shknai, O. S., Nechyporuk, O., Nalapko, O., Buyalo, O., & Lyashenko, A. (2025). A set of methods for enhancing the efficiency of information processing in intelligent decision support systems. D29 Authors: Edited by Svitlana Kashkevich, 62. DOI: 10.15587/978-617- 8360-13-9.CH3. [6] Li, T., Zheng, M., & Zhou, Y. (2025). LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting: Deep Learning for Renewable Energy Demand Prediction. Journal of Organizational and End User Computing (JOEUC), 37(1), 1-29. DOI: 10.4018/JOEUC.370005. [7] Majnoor, N., & Vinayagam, K. (2023). The ascendency of the paradigm shift from organizational change management to change agility. International Journal of Professional Business Review: Int. J. Prof. Bus. Rev., 8(4), 19. DOI: https://doi.org/10.26668/businessreview/2023.v8i4. 1151. [8] Passiante, G., & Ruggiero, G. (2025). An Innovative Management in the Digital Economy: The CNR Case Study. In Digital Innovation Management: People, Process, Platforms and Policy (pp. 1-20). Cham: Springer Nature Switzerland. DOI: https://doi.org/10.1007/978-3-031-80426-7_1. [9] Shah, N., Zehri, A. W., Saraih, U. N., Abdelwahed, N. A. A., & Soomro, B. A. (2024). The role of digital technology and digital innovation towards firm performance in a digital economy. Kybernetes, 53(2), 620-644. DOI: https://doi.org/10.1108/K-01-2023- 0124. [10] Silva, D. C., Ferreira, F. A., Milici, A., Ferreira, J. J., & Ferreira, N. C. (2025). Business transformation processes and Society 5.0: opportunities and challenges. Management Decision. DOI: https://doi.org/10.1108/MD-05-2024-1209. [11] Lv, B., Deng, Y., Meng, W., Wang, Z., & Tang, T. (2024). Research on digital intelligence business model based on artificial intelligence in post-epidemic era. Management Decision, 62(9), 2937-2957. DOI: https://doi.org/10.1108/MD-11-2022-1548. [12] Leong, L. Y., Hew, T. S., Ooi, K. B., & Chau, P. Y. (2024). “To share or not to share?”–A hybrid SEM-ANN-NCA study of the enablers and enhancers for mobile sharing economy. Decision Support Systems, 180, 114185. DOI: https://doi.org/10.1016/j.dss.2024.114185. [13] Kayvanfar, V., Elomri, A., Kerbache, L., Vandchali, H. R., & El Omri, A. (2024). A review of decision support systems in the internet of things and supply chain and logistics using web content mining. Supply Chain Analytics, 100063. DOI: https://doi.org/10.1016/j.sca.2024.100063. [14] Waqar, A. (2024). Intelligent decision support systems in construction engineering: An artificial intelligence and machine learning approaches. Expert Systems with Applications, 249, 123503. DOI: https://doi.org/10.1016/j.eswa.2024.123503. [15] Ataei, P., Takhtravan, A., Gheibi, M., Chahkandi, B., Faramarz, M. G., Wacławek, S., ... & Behzadian, K. (2024). An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls. Heliyon, 10(3). DOI: 10.1016/j.heliyon.2024.e25036 External Link. [16] Ge, Y., Xia, Y., & Wang, T. (2024). Digital economy, data resources and enterprise green technology innovation: Evidence from A-listed Chinese Firms. Resources Policy, 92, 105035. DOI: https://doi.org/10.1016/j.resourpol.2024.105035. [17] Raihan, A. (2024). A review of the potential opportunities and challenges of the digital economy for sustainability. Innovation and Green Development, Kexin Zhang /Future Technology November 2025| Volume 04 | Issue 04 | Pages 128-137 137 3(4), 100174. DOI: https://doi.org/10.1016/j.igd.2024.100174. [18] Javaid, M., Haleem, A., Singh, R. P., & Sinha, A. K. (2024). Digital economy to improve the culture of industry 4.0: A study on features, implementation and challenges. Green Technologies and Sustainability, 100083. DOI: https://doi.org/10.1016/j.grets.2024.100083. [19] Sadeghi, K., Ojha, D., Kaur, P., Mahto, R. V., & Dhir, A. (2024). Explainable artificial intelligence and agile decision-making in supply chain cyber resilience. Decision Support Systems, 180, 114194. DOI: https://doi.org/10.1016/j.dss.2024.114194. [20] Poszler, F., & Lange, B. (2024). The impact of intelligent decision-support systems on humans' ethical decision- making: A systematic literature review and an integrated framework. Technological Forecasting and Social Change, 204, 123403. DOI: https://doi.org/10.1016/j.techfore.2024.123403. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). https://creativecommons.org/licenses/by/4.0/ 1. Introduction The digital economy is changing the way businesses and other organisations function within their sectors. In what ways do these enterprises operate, compete, and deliver value in a contemporary business environment? Recent research suggests that digit... This study seeks to fill these gaps by designing and assessing an intelligent decision support system for change management in an organisation within the context of a digital economy. The research goals include exploring the interdependence between in... 2. Authorship and contribution 2.1 Research framework Integrating the theoretical base, system architecture design, and its research components into a unified analytical framework describes the development of an Intelligent Decision Support System (DSS) for organisational changes of enterprises. This sys... Figure 1. Integrated research framework for an intelligent decision support system The system architecture comprises four layers: the data layer, which is responsible for gathering and processing organisational data; the model layer, which implements intelligent algorithms; the service layer, which performs decision support activiti... 2.2 System design and development The Intelligent Decision Support System (IDSS) for organisational change in enterprises is constructed as a complete multi-layered architecture aimed at enhancing decision-making in the context of the digital economy. The provided system incorporates ... Figure 2. Architecture of the intelligent decision support system for organizational change This layer acts as the system's base, bringing together different types of data sources such as enterprise activity data, market data, indicators of the digital economy, and external APIs. This multi-faceted approach to data collection guarantees that... Providing a decision-making dashboard for executives, visualisation tools for data analysts, and API interfaces for system integration, the user interface layer exposes users of the system to multiple interaction channels. The dashboard features an in... A set of both automated data collection tools and manual data entry interfaces is used for the handling and processing of data. The system enacts advanced sets of data validation procedures for ensuring quality and utilises machine learning for featur... 2.3 Evaluation methods This research utilises an all-encompassing evaluation framework integrating quantitative performance metrics, systematic validation methods, and an extensive review in the form of case studies for the evaluation of the proposed intelligent decision su... The case study design employs a multiple-case strategy, integrating three companies of varying sizes and industries to examine the system's compatibility and effectiveness in detail. The first case study focuses on a large manufacturing company underg... 3. Intelligent decision support system model 3.1 System architecture This particular Intelligent Decision Support System (IDSS) architecture has been designed with a deliberate four-layer structure for the organised change management within the digital economy context. The architecture includes an External Data Interfa... 3.2 Key components For optimal organisational changes, the decision support system employs four defining components that assist. First, the knowledge base is the system’s primary source using a hybrid storage architecture through ontology-based knowledge representation ... A feature of the interface is the interaction layer, which is user-friendly for access through a web-based platform, includes role-based access control, and boasts customisable dashboards. With the aid of modern frontend frameworks, this component pro... 3.3 Decision-Making Mechanisms The selection of Q-learning and deep learning algorithms for the intelligent decision support system was driven by their complementary strengths in addressing the complex challenges of organizational change management. Q-learning was specifically chos... 𝐷=𝑓(,𝑊-1.,𝐶-1.+,𝑊-2.,𝐶-2.+...+,𝑊-𝑛.,𝐶-𝑛.) (1) where D represents the final decision score, Wi represents the weight of the criterion i, and Ci represents the normalized value of the criterion i. The weights are dynamically adjusted through a learning process defined by: (2) where 𝛼 is the learning rate, and ∆𝑃 represents the performance improvement from the previous decision cycle. A new reinforcement learning technique is used for the automation of the decision rule processes. The value function for the Q-learning algorithm used in the system for decision-making at the organisation is: 𝑄,,𝑠-𝑡.,,𝑎-𝑡..=𝑄,,𝑠-𝑡.,,𝑎-𝑡..+𝛽[,𝑟-𝑡.+𝛾,𝑚𝑎𝑥-𝑎.,,𝑠-𝑡+1.,𝑎.−𝑄,,𝑠-𝑡.,,𝑎-𝑡.. (3) where ,𝑠-𝑡. represents the organizational state at time t, ,𝑎-𝑡. is the action taken, ,𝑟-𝑡. is the immediate reward, 𝛽 is the learning rate, and 𝛾 is the discount factor for future rewards. The decision rules incorporate both deterministic and probabilistic components, with the probability of selecting a particular decision option given by: where 𝜆 is the exploration-exploitation parameter that balances between known successful strategies and potential new solutions. The learning capabilities of the system are enhanced through a gradient-based optimization approach that minimizes the decision error function: where Yk represents the actual outcome, is the predicted outcome, N is the number of training samples, and 𝜇 is the regularization parameter controlling model complexity. With its integrated decision-making framework, this maintains explicable decision rules and learning mechanisms while providing strong and adaptive intelligent decision support. The organisation systematically gathers new knowledge and modifies the de... 4. Implementation and case study 4.1 System implementation The use of contemporary software development practices and cloud-native technologies enables the intelligent decision support system to be implemented in a stepwise manner. Backend services are implemented in Python 3.9, the frontend user interface is... Figure 3. System implementation process flow 4.2 Case study In deciding which enterprises to study, a holistic analysis considering aspects like organisational size, sector, digital maturity, and particular cases of transformation was followed. The selection process centred on those undergoing significant digi... Table 1. Characteristics of selected enterprises for system implementation *Digital Maturity Scale: 1 (Minimal) to 5 (Advanced) The implementation process strategy was carefully devised to systematically integrate and test the intelligent decision support system across relevant enterprises. The first step incorporated an organisational evaluation and infrastructure setup that ... Data collection employed automated system logs and structured interviews across five enterprises over a six-month implementation period. Quantitative metrics were captured through continuous monitoring while qualitative insights emerged from semi-stru... Table 2. Key performance metrics across implementation enterprises 4.3 Results analysis The metrics used in determining the performance of the system evaluation included the effectiveness and efficiency of the intelligent decision support system throughout the enterprises. The evaluation focus was the success rate of the provided decisio... Figure 4. System performance metrics across enterprises Feedback collection was conducted using a thorough analysis of the system's usability, functionality, and overall satisfaction from different user roles and companies. The responses analysed were from 150 users, comprising senior managers and decision... Figure 5. User feedback analysis across system features The analysis of the intelligent decision support system reveals that it has effectively improved the key performance indicators and metrics of an organisation (Figure 6). Throughout the analysis, the system has provided effective longitudinal impacts ... Figure 6. Comparative analysis of pre and post-implementation performance metrics The comparative analysis between the proposed intelligent decision support system and traditional rule-based systems reveals substantial performance improvements across multiple operational metrics. As illustrated in Figure 7, the intelligent system d... 5. Discussion 5.1 Research findings These implemented intelligent decision support systems enabled remarkable advancements in the organisational change management processes, as the research results showcase. In the quantitative assessment, there were substantial increases in the importa... Figure 7. Comparative performance analysis: intelligent vs traditional systems 5.2 Linking theory and practice This study bridges theoretical foundations with practical implementation by systematically mapping conceptual frameworks to specific system components, as illustrated in Table 3. The integration of digital economy theory, organizational change models,... Table 3. Mapping of theoretical foundations to system components The implementation of the intelligent decision support system incorporates comprehensive data protection measures aligned with GDPR requirements and contemporary privacy standards. The system employs differential privacy techniques to ensure individua... 6. Conclusion Supported by evidence gathered from multiple sources, this research examined the role of intelligent decision support systems in managing organisational changes in relation to the digital economy context. The system produced considerable gains in the ... The manuscript contains all the data. However, more data will be available upon request from the author. Conflict of interest The author declares no potential conflict of interest. References [1] Entezami, M., Basirat, S., Moghaddami, B., Bazmandeh, D., & Charkhian, D. (2025). Examining the Importance of AI-Based Criteria in the Development of the Digital Economy: A Multi-Criteria Decision-Making Approach. Journal of Soft Computing and Dec... [2] Shahi, C., & Sinha, M. (2021). Digital transformation: challenges faced by organizations and their potential solutions. International Journal of Innovation Science, 13(1), 17-33. DOI: https://doi.org/10.1111/caim.12414. [3] Onwujekwe, G., & Weistroffer, H. R. (2025). Intelligent Decision Support Systems: An Analysis of the Literature and a Framework for Development. Information Systems Frontiers, 1-32. DOI: https://doi.org/10.1007/s10796-024-10571-1. [4] Mohammed-Shittu, N. (2025). Artificial Intelligence (AI)-Driven Decision Support Systems for Sustainable Administration of Public Universities in Rivers State, Nigeria. International Journal of Educational Management, Rivers State University., 1(... [5] Shknai, O. S., Nechyporuk, O., Nalapko, O., Buyalo, O., & Lyashenko, A. (2025). A set of methods for enhancing the efficiency of information processing in intelligent decision support systems. D29 Authors: Edited by Svitlana Kashkevich, 62. DOI: ... [6] Li, T., Zheng, M., & Zhou, Y. (2025). LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting: Deep Learning for Renewable Energy Demand Prediction. Journal of Organizational and End ... [7] Majnoor, N., & Vinayagam, K. (2023). The ascendency of the paradigm shift from organizational change management to change agility. International Journal of Professional Business Review: Int. J. Prof. Bus. Rev., 8(4), 19. DOI: https://doi.org/10.2... [8] Passiante, G., & Ruggiero, G. (2025). An Innovative Management in the Digital Economy: The CNR Case Study. In Digital Innovation Management: People, Process, Platforms and Policy (pp. 1-20). Cham: Springer Nature Switzerland. DOI: https://doi.org... [9] Shah, N., Zehri, A. W., Saraih, U. N., Abdelwahed, N. A. A., & Soomro, B. A. (2024). The role of digital technology and digital innovation towards firm performance in a digital economy. Kybernetes, 53(2), 620-644. DOI: https://doi.org/10.1108/K-0... [10] Silva, D. C., Ferreira, F. A., Milici, A., Ferreira, J. J., & Ferreira, N. C. (2025). Business transformation processes and Society 5.0: opportunities and challenges. Management Decision. DOI: https://doi.org/10.1108/MD-05-2024-1209. [11] Lv, B., Deng, Y., Meng, W., Wang, Z., & Tang, T. (2024). Research on digital intelligence business model based on artificial intelligence in post-epidemic era. Management Decision, 62(9), 2937-2957. DOI: https://doi.org/10.1108/MD-11-2022-1548. [12] Leong, L. Y., Hew, T. S., Ooi, K. B., & Chau, P. Y. (2024). “To share or not to share?”–A hybrid SEM-ANN-NCA study of the enablers and enhancers for mobile sharing economy. Decision Support Systems, 180, 114185. DOI: https://doi.org/10.1016/j.d... [13] Kayvanfar, V., Elomri, A., Kerbache, L., Vandchali, H. R., & El Omri, A. (2024). A review of decision support systems in the internet of things and supply chain and logistics using web content mining. Supply Chain Analytics, 100063. DOI: https:... [14] Waqar, A. (2024). Intelligent decision support systems in construction engineering: An artificial intelligence and machine learning approaches. Expert Systems with Applications, 249, 123503. DOI: https://doi.org/10.1016/j.eswa.2024.123503. [15] Ataei, P., Takhtravan, A., Gheibi, M., Chahkandi, B., Faramarz, M. G., Wacławek, S., ... & Behzadian, K. (2024). An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls. Heliyon, 10... [16] Ge, Y., Xia, Y., & Wang, T. (2024). Digital economy, data resources and enterprise green technology innovation: Evidence from A-listed Chinese Firms. Resources Policy, 92, 105035. DOI: https://doi.org/10.1016/j.resourpol.2024.105035. [17] Raihan, A. (2024). A review of the potential opportunities and challenges of the digital economy for sustainability. Innovation and Green Development, 3(4), 100174. DOI: https://doi.org/10.1016/j.igd.2024.100174. [18] Javaid, M., Haleem, A., Singh, R. P., & Sinha, A. K. (2024). Digital economy to improve the culture of industry 4.0: A study on features, implementation and challenges. Green Technologies and Sustainability, 100083. DOI: https://doi.org/10.1016... [19] Sadeghi, K., Ojha, D., Kaur, P., Mahto, R. V., & Dhir, A. (2024). Explainable artificial intelligence and agile decision-making in supply chain cyber resilience. Decision Support Systems, 180, 114194. DOI: https://doi.org/10.1016/j.dss.2024.114... [20] Poszler, F., & Lange, B. (2024). The impact of intelligent decision-support systems on humans' ethical decision-making: A systematic literature review and an integrated framework. Technological Forecasting and Social Change, 204, 123403. DOI: ...