Decision Support Process Research Articles

Performance Comparison of Hybrid Storage on OLAP Database Server Virtualization

In today's digital era, efficient database management is important for companies to support analysis and decision-making processes. The use of On Line Analytical Processing (OLAP) in databases requires optimal storage configuration, especially in virtualization environments. This study aims to evaluate the performance of OLAP databases using hybrid storage configurations, combining Solid State Drives (SSD) and Hard Disk Drives (HDD), and identify the best configuration to improve database performance. The research method was carried out using the TPC-H benchmark on four database systems: Oracle, SQL Server, MySQL, and PostgreSQL. Four storage configurations were tested by allocating SSD for database storage and HDD for the operating system and temporary files, measured with 10 and 100 virtual users. The results showed that SQL Server provided the best performance in all configurations, with faster query execution times than other databases. The configuration with SSD for database storage and HDD for the operating system and temporary files proved to be the most efficient in reducing query latency. The conclusion of this study emphasizes the importance of using hybrid storage configurations in improving OLAP database performance in virtualization environments. Configuring with SSD as database storage and HDD for operating system and temporary files is the best choice to achieve high query performance.

Supporting multi-criteria decision-making processes with unknown criteria weights

Decision support systems are crucial in today’s tech-driven world, assisting decision-makers with complex choices. Determining criteria weights is a paramount aspect, significantly influencing outcomes. Traditionally, criteria weights are derived from objective measures, subjective expert knowledge, or a combination of both, each with its own strengths and limitations. This paper presents a novel approach for addressing unknown criteria relevance by systematically generating weight vectors, thus exploring a broader decision problem space. The proposed methodology is adaptable to various multi-criteria methods, enhancing its applicability across different scenarios. Its effectiveness is empirically validated through two practical examples: Glomerular Filtration Rate (GFR) evaluation and bridge construction method selection, demonstrating its broad applicability. Comparative analysis with existing objective weighting techniques reveals the limitations of current approaches and highlights the improved decision-making capabilities enabled by the proposed method. This research addresses a critical gap in the reliability and robustness of existing methods, particularly in situations with unknown criteria weights. Key contributions include a new decision-making methodology and an innovative ranking formulation using fuzzy sets, with empirical verification strengthening the utility of the approach. This paper offers a promising solution for advancing multi-criteria decision analysis, especially in complex scenarios with uncertain criteria relevance.

Assurance of the sustainability report of municipally-owned entities

IMPACT The main implication stemming from this study is that when profit and public value logics coexist, a voluntary audited SR can be considered a component of a broad strategic vision, where reliable non-financial information can enhance relationships with stakeholders and support decision-making processes. A further implication is that the inherent limitations of limited assurance can be counter-balanced by synergic effects due to its intrinsic relationships with the outcomes of financial report audits. Finally, from a practitioner’s perspective, the term ‘sustainability report’ can be considered an umbrella term that encompasses Sustainable Development Goals.

Needs and opportunities for developing health and well-being profiles in Estonian local governments

Abstract The creation and implementation of health and wellbeing profile, an evidence-based framework for planning health promotion as holistic process, is the responsibility of public health practitioners, who tend work part-time and sometimes lack professional competences. The current study maps the level of awareness, attitudes, needs and possibilities of local government leaders and public health practitioners to develop and implement profiles effectively in Estonian local governments. Semi-structured interviews with leaders in local governments (10) and practitioners (9) were conducted in 2022-2023. Data was analysed using qualitative content analysis. Sample included representatives from all Estonian local governments (79 in total) and were categorized into subgroups based on local governments size, status of the profile, public health specialist employment, and educational background. Preliminary results indicate that leaders find as a supportive factors for enabling and encouraging profile compilation process for example: mandatory requirement; motivated implementors; well organized and effective workplan. Specialists find supportive factors to be cooperation on the national level, good communication practices and joint responsibility. Barriers for health promoters are the lack of time, motivation, financial resources, stakeholders understanding and meaningful action. The first qualitative study on these topics in Estonia highlight that creating a profile is supported by previous positive experience and have per contra an opposite negative effect to start the process. In general, the profile was considered necessary for helping identify problems and support decision-making processes for managing resources, however, assessment whether the profile should be obligatory is needed. In addition, there is a need for further support for the evidence-based decision-making process on the local level in health promotion by offering education and guidance from the national level. Key messages • Knowledge about the importance of public health, barriers and decision-making principles of local government leaders and specialists helps to develop interventions based on actual needs. • Continuously updated health and well-being profile ensures sustainability of health promotion action and is useful knowledge to policymakers, local government leaders and public health practitioners.

Impact of pathways navigation status on approval rates and outcomes of oral lung cancer treatments.

415 Background: Oral targeted therapies in lung cancer represent a costly but critical component of care. Currently, the approval process for these treatments can sometimes take several weeks. Assessing the approval rates and efficacy of on-pathway treatment recommendations is an important step towards bringing clinicians and payors together to streamline the decision support and authorization process. Methods: We retrospectively analyzed authorization requests for oral anti-cancer agents for lung cancer. Eligible requests were filed between August 1, 2022, to March 1, 2024, and represented new treatment starts for that agent. In addition to collecting information about final authorization decisions, we determined whether the use case was consistent with Dana-Farber Pathways treatment recommendations in place at the time of the order. Furthermore, we measured survival from the start of oral therapy. Median survivals were calculated using the Kaplan-Meier method. These were assessed for the whole cohort and for each class of genomic aberrations. Results: There were a total of 214 eligible oral therapy claims filed between August 1, 2022, to March 1, 2024. 147 (69%) of these were considered on pathway, while 67 (31%) were off pathway. In total, 100% of the on-pathway navigations received payor approval, compared to 93% of the off-pathway navigations. The estimated 12-month survival was 86.5% for the on-pathway group, compared to 56.5% for the off-pathway group. Median overall survival has not yet been reached for either group. EGFR and ALK targeted therapies accounted for the majority of authorization requests, with 114 and 28, respectively. Conclusions: With the use of Dana-Farber Lung Cancer Pathways, the majority of requests for targeted therapies were consistent with pathway recommendations. This on-pathway cohort not only achieved a 100% payor authorization rate, but the 12-month survival was superior to that of off-pathway requests. These data provide a meaningful starting point for pathways vendors and healthcare payors to develop mutually agreed upon recommendations for use of these costly but impactful therapies.

Clinical Decision Support and Natural Language Processing in Medicine: Systematic Literature Review.

Ensuring access to accurate and verified information is essential for effective patient treatment and diagnosis. Although health workers rely on the internet for clinical data, there is a need for a more streamlined approach. This systematic review aims to assess the current state of artificial intelligence (AI) and natural language processing (NLP) techniques in health care to identify their potential use in electronic health records and automated information searches. A search was conducted in the PubMed, Embase, ScienceDirect, Scopus, and Web of Science online databases for articles published between January 2000 and April 2023. The only inclusion criteria were (1) original research articles and studies on the application of AI-based medical clinical decision support using NLP techniques and (2) publications in English. A Critical Appraisal Skills Programme tool was used to assess the quality of the studies. The search yielded 707 articles, from which 26 studies were included (24 original articles and 2 systematic reviews). Of the evaluated articles, 21 (81%) explained the use of NLP as a source of data collection, 18 (69%) used electronic health records as a data source, and a further 8 (31%) were based on clinical data. Only 5 (19%) of the articles showed the use of combined strategies for NLP to obtain clinical data. In total, 16 (62%) articles presented stand-alone data review algorithms. Other studies (n=9, 35%) showed that the clinical decision support system alternative was also a way of displaying the information obtained for immediate clinical use. The use of NLP engines can effectively improve clinical decision systems' accuracy, while biphasic tools combining AI algorithms and human criteria may optimize clinical diagnosis and treatment flows. PROSPERO CRD42022373386; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=373386.

Energy system optimization based on fuzzy decision support system and unstructured data

To address the complex challenges in energy systems, this study proposes a novel optimization framework that integrates fuzzy decision support and unstructured data processing technologies. This framework aims to improve efficiency, reduce costs, decrease environmental impact, increase system flexibility, and enhance user satisfaction, thereby promoting sustainable development in the energy industry. The framework combines the innovative Energy Semantic Mapping Model (ESMM) and the advanced deep learning architecture ResNet to process textual and visual data effectively. ESMM enables accurate prediction of energy demand, while ResNet significantly reduces equipment maintenance costs and improves energy distribution efficiency. These advancements are critical as they address the limitations of existing approaches in handling large-scale unstructured data and making informed decisions under uncertainty. The Environmental Impact Assessment (EIA) confirms the model's effectiveness in reducing carbon emissions. A comprehensive economic analysis demonstrates substantial cost savings in energy procurement and operations and maintenance, with overall savings exceeding 25%. Enhanced user satisfaction and reduced system response times further validate the practical utility of the proposed approach. Additionally, a genetic algorithm is used to optimize the fuzzy rule base, enhancing the robustness and adaptability of the model. Experimental results show superior performance compared to traditional systems, providing strong empirical evidence for the intelligent transformation of energy systems. This research contributes to the field by offering a more sophisticated and flexible solution for managing energy systems, particularly in terms of leveraging unstructured data and improving decision-making processes.

Optimizing hierarchical classifiers with parameter tuning and confidence scoring

Hierarchical classifiers play a crucial role in addressing complex classification tasks by breaking them down into smaller, more manageable sub-tasks. This paper continues a series of works, focused on the technical Ukrainian texts hierarchical classification, specifically the classification of repair works and spare parts used in automobile maintenance and servicing. We tackle the challenges posed by multilingual data inputs – specifically Ukrainian, Russian, and their hybrid – and the lack of standard data cleaning models for the Ukrainian language. We developed a novel classification algorithm, which employs TF-IDF victimization with unigrams and bigrams, keyword selection, and cosine similarity for classification. This paper describes a method for training and evaluating a hierarchical classification model using parameter tuning for each node in a tree structure. The training process involves initializing weights for tokens in the class tree nodes and input strings, followed by iterative parameter tuning to optimize classification accuracy. Initial weights are assigned based on predefined rules, and the iterative process adjusts these weights to achieve optimal performance. The paper also addresses the challenge of interpreting multiple confidence scores from the classification process, proposing a machine learning approach using Scikit-learn's GradientBoostingClassifier to calculate a unified confidence score. This score helps assess the classification reliability, particularly for unlabeled data, by transforming input values, generating polynomial parameters, and using logarithmic transformations and scaling. The classifier is fine-tuned using hyper parameter optimization techniques, and the final model provides a robust confidence score for classification tasks, enabling the verification and classification results optimization across large datasets. Our experimental results demonstrate significant improvements in classification performance. Overall classification accuracy nearly doubled after training, reaching 92.38 %. This research not only advances the theoretical framework of hierarchical classifiers but also provides practical solutions for processing large-scale, unlabeled datasets in the automotive industry. The developed methodology can enhance various applications, including automated customer support systems, predictive maintenance, and decision-making processes for stakeholders like insurance companies and service centers. Future work will extend this approach to more complex tasks, such as extracting and classifying information from extensive text sources like telephone call transcriptions.

Demonstrating a decision support process for landscape conservation design

AbstractDespite the recent increase in landscape conservation and the design processes agencies are undertaking, there remains an implementation gap due to an inability to evaluate general strategies and account for uncertainties faced by managers. We demonstrated how a decision support process (DSP), recently developed to inform landscape conservation design, can address uncertainties and complexities inherent in landscape conservation to facilitate long‐term, large‐scale conservation planning. We applied the DSP to landscape conservation efforts within conservation opportunity areas (COA) of states in the Gulf Coastal Plains and Ozarks region. We engaged state planners within the region to identify important landscape conservation uncertainties they face in planning. We developed, simulated, and modeled the impacts of conservation addressing 3 uncertainties identified by state wildlife managers and evaluated the impacts by examining the responses of state and local populations of 2 bird species of conservation concern, prairie warbler (Setophaga discolor) and wood thrush (Hylocichla mustelina). The responses of prairie warbler populations to conservation strategies indicated that both approaches of protecting quality habitat from land‐use change and restoring and enhancing lower quality and nonhabitat improved their viability at regional, statewide, and COA scales. However, we noted that the relative effectiveness of strategies varied among states in ways that suggest a state's process for delineating COAs and what threats they face could influence which conservation approach to consider. Our findings highlight the need for regional coordination and the use of decision‐support processes to guide effective conservation at landscape scales.

The Role of Artificial Intelligence in Criminal Justice

This study explores the transformative role of artificial intelligence (AI) in the criminal justice system, examining its applications, benefits, and potential challenges. AI technologies, including machine learning, predictive analytics, and natural language processing, are increasingly integrated into various facets of criminal justice, from law enforcement and legal processes to corrections and rehabilitation. Through a comprehensive literature review, this research analyzes how AI enhances the efficiency and effectiveness of criminal justice operations. Key findings reveal that AI can significantly improve crime prediction and prevention, aid in evidence analysis, streamline administrative tasks, and support decision-making processes. For instance, predictive policing models using AI can identify crime hotspots and allocate resources more effectively, while AI-driven tools can assist in analyzing large volumes of legal documents and evidence. However, the study also highlights critical concerns related to bias, fairness, transparency, and ethical implications. There is a growing need for frameworks that ensure AI applications in criminal justice are transparent, accountable, and aligned with ethical standards to prevent discrimination and protect civil liberties. The role of AI in criminal justice presents a dual-edged sword, offering significant advancements while posing substantial risks if not properly managed. This study provides a balanced perspective, offering insights for policymakers, practitioners, and researchers on leveraging AI for a more just and efficient criminal justice system. Future research directions are proposed to address the ethical challenges and to develop robust regulatory frameworks for AI in criminal justice.

Supporting decision-making processes in the assessment of water-related risks: a review of models and methods

Supporting decision-making processes in the assessment of water-related risks: a review of models and methods

Patient-centered clinical decision support challenges and opportunities identified from workflow execution models.

To use workflow execution models to highlight new considerations for patient-centered clinical decision support policies (PC CDS), processes, procedures, technology, and expertise required to support new workflows. To generate and refine models, we used (1) targeted literature reviews; (2) key informant interviews with 6 external PC CDS experts; (3) model refinement based on authors' experience; and (4) validation of the models by a 26-member steering committee. We identified 7 major issues that provide significant challenges and opportunities for healthcare systems, researchers, administrators, and health IT and app developers. Overcoming these challenges presents opportunities for new or modified policies, processes, procedures, technology, and expertise to: (1) Ensure patient-generated health data (PGHD), including patient-reported outcomes (PROs), are documented, reviewed, and managed by appropriately trained clinicians, between visits and after regular working hours. (2) Educate patients to use connected medical devices and handle technical issues. (3) Facilitate collection and incorporation of PGHD, PROs, patient preferences, and social determinants of health into existing electronic health records. (4) Troubleshoot erroneous data received from devices. (5) Develop dashboards to display longitudinal patient-reported data. (6) Provide reimbursement to support new models of care. (7) Support patient engagement with remote devices. Several new policies, processes, technologies, and expertise are required to ensure safe and effective implementation and use of PC CDS. As we gain more experience implementing and working with PC CDS, we should be able to begin realizing the long-term positive impact on patient health that the patient-centered movement in healthcare promises.

Carbon footprint assessment of maintenance and rehabilitation techniques for sewer systems

ABSTRACT The research study presented in this paper sets out to improve our understanding of the environmental impacts associated with the maintenance and rehabilitation of conventional gravity sewer systems and, through this, support decision-making processes in sewer system management. The objects assessed are open trench replacement, trenchless renovation, open trench and trenchless spot repair, inspection, and hydraulic cleaning of sewer pipes. Carbon footprint assessment was carried out according to the ISO14067:2018 standard. The life cycle stages considered included raw material extraction, manufacturing processes, maintenance/rehabilitation, and the transportation of materials. The functional unit was defined as: ‘A gravity sewer section with a length of 45 metres and a diameter of 200 to 1500 mm operating in normal conditions over a period of 100 years in the Netherlands’. A sensitivity analysis was included to assess how the results vary as a consequence of changes in the input dimensions. The findings of this study suggest that the main opportunities for reducing the environmental impacts of conventional, open trench pipe replacement lie in the choice of the pipe material and the design of the asphalt pavement that requires reinstatement. Adopting trenchless sewer rehabilitation technologies can significantly reduce the environmental burdens of sewer system rehabilitation.

Data Communication as a key to integrated river basin management

This article discusses data communication as a key to implement integrated water resource management (IWRM) in river basin management. The performance of a river basin is determined by many aspects/sectors which are managed by different level of governments and various agencies within them. Implementing IWRM requires integrated data information system provided by those various government agencies. Although it has been regulated in the Law No 17 Year 2019, integrated information system reliable to support decision-making process has not existed yet. This article reviews what is the condition of water related data in Bengawan Solo River Basin Management – the type of water related data, the data frequencies, and the data sharing mechanism. It aims to examine data communication among actors and supports the decision-making process in Bengawan Solo River Basin Management. There are three stages were undertaken to achieve this overall aim, that are 1) identifying the type of water-related data need be shared in sharing water resource from the literature; 2) identifying the availability of water-related data identified in the stage 1 in the existing data provided by government agencies; 3) conducting a series of focused group discussion (FGD) to elaborate the use of the available data to understand its limitations to support decisionmaking process. The research found that the implementation of IWRM in Bengawan Solo River Basin is difficult to be achieved because there is a lack of data communication among data providers. It points that the absence of data communication occurred because there is no definite data sharing framework specify what data need to be shared, the frequency of data collection and updating, and the mechanism in sharing data legitimated by all agencies involved in Bengawan Solo River Basin Management. Thus, it reveals that data cannot be used maximally to support effective river basin management not because of technology drawbacks or a lack of data, but its uncertainty on how data needs to be communicated. In addition, there no certainty of who is the responsible agency or authority to fulfill five aspects of data communication. This is the final homework of IWRM implementation in managing river basin in Indonesia.

Eco-efficiency of power supply systems for offshore platforms

This study compared from an Eco-efficiency point of view three electricity supply alternatives for oil and gas extraction, preliminary treatment, and distribution activities that occur on floating production, storage, and offloading platforms (FPSO) operated off the coast of Brazil. The performance of autonomous systems (AS), in which independent open-cycle gas turbines generate electricity, was confronted with those achieved, respectively, by a Power Hub (PH) system and by an arrangement that associates the Power Hub system with an offshore wind farm (PHWE). The environmental dimension of the Eco-efficiency indicator was specified based on applying the Life Cycle Assessment technique for the impact categories of Global Warming Potential, Primary Energy Demand, and Fine Particulate Matter Formation. The economic dimension followed a similar approach based on Life Cycle costing (LCC) when considering the same units' construction, operation, maintenance, and dismantling costs. Given the characteristics of Brazilian oil fields, a comparison was made for three different stages of the operation cycle: C1: maximum extraction of oil and gas; C2: equity of volumes extracted from fuels and water + CO2; and C3: minimal extraction, but economically viable, of fuels. The results indicated that the PH and PHWE systems accumulate better environmental performance than the AS system, particularly regarding Global Warming Potential (35 % and 55 %, respectively) and Fine Particle Matter Formation (47 % and 59 % reduction). However, more than these gains are needed to compensate for the same systems' construction, operation, and maintenance investments, which were increased by up to 76 % in the case of PHWE. The study also concluded that the PH technology and its variation involving wind electrification are promising but that research efforts should be made to reduce costs. These results provide elements that can both subsidize designers from O&G companies in developing new extraction technologies and support decision processes conducted by other stakeholders and policy formulators linked to this segment.

Analytic network process in traditional market solid waste management in Malang Regency, Indonesia

One type of decision support process that assists in decision-making during Multi-Criteria Decision Making (MCDM) is the Analytic Network Process (ANP). ANP can determine the best pattern of sustainable traditional market solid waste management in Malang Regency. The six categories used in ANP analysis consist of environmental impacts (DL), technical operations (TO), regulations (PH), institutions and organizations (KO), financing (P), and community participation (PSM). The weighting results of the criteria that had the largest weight was the sub-criterion of Strengthening the Active Role of the Community (PSM1) with a limiting weight of 0.058271. The largest alternative weighting result was Scenario 3 (Integrated Solid Waste Management). Scenario 3 had a priority with the highest weight of 0.213951. The results of the sensitivity test when all criteria values were changed to 0.999 also changed the value of the alternatives. The changes in alternative weights when the sensitivity test was performed consisted of 3 sub-criteria that were sensitive to the changes, namely the sub-criteria of soil pollution (DL2), direct benefits (P4), and ease of operation (TO2). This research shows that the use of ANP provides a more efficient decision-making process in traditional market solid waste management.

Tracheostomy and long-term invasive ventilation decision-making in children: A scoping review.

An increasing number of children are surviving critical illnesses requiring tracheostomy/long-term ventilation (LTV). This scoping review seeks to collate the available evidence on decision-making for tracheostomy/LTV in children. Systematic searches of electronic databases and websites were conducted for articles and reports. Inclusion criteria included: (1) children 0-18 years old; (2) described use of tracheostomy or tracheostomy/LTV; and (3) information on recommendations for tracheostomy decision-making or decision-making experiences of family-caregivers or health care providers. Articles not written in English were excluded. Of the 4463 records identified through database search and other methods, a total of 84 articles, 2 dissertations, 1 book chapter, 3 consensus statement/society guidelines, and 8 pieces of grey literature were included. Main thematic domains identified were: (1) legal and moral standards for decision-making; (2) decision-making models, roles of decision-makers, and decisional aids towards a shared decision-making model; (3) experiences and perspectives of decision-makers; (4) health system and society considerations; and (5) conflict resolution and legal considerations. A high degree of uncertainty and complexity is involved in tracheostomy/LTV decision-making. There is a need for a standardized decision-support process that is consistent with a child's best interests and shared decision-making. Strategies for optimizing communication and mechanism for managing disputes are needed.

EXTENDING TASK-TECHNOLOGY FIT WITH DUAL INFORMATION PROCESSING MODES IN DISASTER DECISION SUPPORT

ABSTRACT Leveraging the analytic and heuristic modes of information processing (i.e. the dual information processing modes) equips disaster professionals with valuable insights into decision support processes during a disaster. Drawing from task-technology fit (TTF), we explore the fit between the dual information processing modes and the influence of different task conditions on perceived disaster decision support by conducting an empirical study involving 136 disaster professionals. The findings of this study extend the theory of TTF by incorporating the dual information processing modes and examine how disaster professionals evaluate these modes in disaster decision support processes.

On the capabilities of technologies for multidimensional representation of data in decision support processes

The authors show the objectivity of the problem of expanding the sphere of influence and increasing the complex power of anthropogenic impacts on natural ecosystems with the absolute and relative exhaustion of resource support for anthropogenic activity in the biosphere. The article proposes a solution based on a multidimensional representation of information (multidimensional data model), which allows analyzing multivariate information about the subject area in the decision-making process.

Leveraging green innovation from big data analytics: Examining the role of resource orchestration and green dynamic capabilities

PURPOSE: The notion of big data analytics (BDA) has received increased attention from both researchers and managers. Keeping in view the significance of BDA, the current research aims to examine the role of BDA capability to leverage firm green innovation (GI). Drawing from the dynamic capability view, current study suggests that BDA capability prompts green dynamic capabilities (DCs), enabling organizations to attain GI successfully. Particularly, present study proposes that BDA analytics prompt GI directly as well as through green DCs. Moreover, this study also draws from complementarity perspective and proposes that resource orchestration capability (ROC) is likely to enhance the effectiveness of green DCs in eliciting GI. Thus, the objectives of the current study are threefold; first, it aims to unveil the role of BDA capability in prompting GI; second, it examines the mediating role of green DCs for the relationship between BDA capability and GI; and third, this research examines the moderating effect of ROC to examine if it strengthens the effects of green DCs. METHODOLOGY: This study involves testing hypotheses using primary data collected by using the method of survey questionnaire. The data were collected from 291 Pakistani organizations. Pakistan is an emerging economy where businesses are responsible for substantial amounts of carbon di-oxide and greenhouse gasses (GHG) emissions. Therefore, Pakistani organizations serve as a suitable context for the study. The respondent organizations were from both the manufacturing and service sectors. PLS-SEM was employed as an analytical approach for testing the hypotheses. Construct validity and reliability were confirmed prior to hypotheses testing. FINDINGS: Results demonstrate that BDA capability positively affects GI (β=0.33, p<0.01), indicating that organizations with strong BDA capabilities involve in GI activities. Likewise, results indicate a positive relationship between BDA capability and green DCs (β=0.35, p<0.01) and between green DCs and GI (β=0.50, p<0.01). Results also indicate that green DCs play a mediating role between BDA capability and firm GI (β=0.18, p<0.01). This indicates that BDA capability is an imperative capability of organization that promotes green DCs and fosters GI. Finally, findings indicate that ROC strengthens the effectiveness of green DCs in prompting GI (β=0.14, p<0.01). IMPLICATIONS: Findings imply that organizations that prioritizing green innovations (GI) should invest more in developing BDA capabilities. These actions may involve acquiring and analyzing large volumes of data associated with sustainability, which can provide insights and support decision-making processes. By leveraging BDA capability, managers can uncover insights and patterns that can help them make informed decisions, recognize areas for improvement, and devise innovative solutions to align organizational strategic objectives with sustainability goals. ORIGINALITY AND VALUE: This study contributes to the literature by offering an integrated framework based on BDA and DCs to seek solutions to economic concerns while ensuring the sustainability value of the business activities. The findings also imply that businesses should focus on developing ROC, and integrating them with green DCs to further enhance GI initiatives.