Exploring the potential of Generative artificial intelligence in supply chain management: A systematic literature review, STM &; TCCM approach

Exploring the potential of Generative artificial intelligence in supply chain management: A systematic literature review, STM &; TCCM approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Exploring the potential of Generative artificial intelligence in supply chain management: A systematic literature review, STM &; TCCM approach Roshan Raju This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8740637/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Generative Artificial Intelligence (Gen-AI) is the next big thing and a promising tool for the growth of organizations. It opens up new ways to improve forecasting and sustainability, and it also makes it possible for people and machines to work together. It also. We wanted to understand how Gen-AI aids organizations in managing their supply chains through a systematic literature review. We have analysed articles from 2013 to 2025 by using Structural Topic Modelling (STM) and the TCCM (Theory, Context, Characteristics, and Methodology) framework. We have identified five themes from the extant literature on the potential implementation of Gen-AI in transforming supply chain management (SCM). We have proposed six future research directions that can enhance the application of Gen-AI in managing supply chains. Generative AI Supply chain management SLR STM TCCM Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Highlights Application Generative artificial intelligence in supply chain management, Structural Topic Modelling (STM) and TCCM (Theory, Context, Characteristics, and Methodology) framework 1. Introduction With the advent of artificial intelligence (AI), many organizations are implementing it in their supply chain processes. They are also coming to leverage the power of AI in streamlining workflows and enhancing the efficiency of the last-mile delivery (Ayoub, 2024). The global supply chain market based on AI is on a sharp increase curve, and it is projected to reach 58.55 billion by the year 2031. It has a rate of 40.4% annual growth, which begins in 2024, an incredible pace at which this technology is being adopted (Sudhanshu R. et.al., 2024 ). However, a new wave of innovation has emerged in recent years. Generative artificial intelligence (Gen-AI) has radically transformed what we thought these systems were capable of and has moved the boundaries and revealed new possibilities to us that we had not previously considered. Due to the emergence of Gen-AI and Large Language Models, there has been a significant change in every industry, affecting the way businesses are run, innovate, and interact (Vaswani et al., 2017 ). According to experts, generative AI has the potential to add trillions of dollars to the world economy annually because it automates and will make more work creative and discover new efficiencies. The Gen-AI tools and services market is to skyrocket to up to 126.5 billion by 2031, which is indicative of the rapid growth in adoption and investment rates (Allied Market Research, 2023 ). Generative AI refers to a form of technology that is meant to generate original content (i.e., images, videos, text, or audio) by training on existing information. It is not merely an information analyzer or a large-scale information sorting tool but can generate entirely new results that resemble human creativity (Dwivedi et al. 2023 ; Peres et al. 2023 ). Generative AI can also assist businesses to work more efficiently and quickly by automating such activities as writing, coding, and responding to customer queries. Not only does it reduce costs of operation, but it also creates the gateway to new ideas and new business models (Eloundou et al. 2023 ). Due to the capacity to produce and generate complex material, Gen-AI is set to transform industries that lack efficiency with innovation, imagination, and comprehensive experience; these include media and design, education, and research (Feuerriegel, S., Hartmann, J., Janiesch, C., and Zschech, P. 2024 ). The history of artificial intelligence within the field of production, operations, and supply chain management research is long (Merhi and Harfouche, 2023; Ivanov, 2024 ; Elliott and Griffiths, 1990 ; Vujosevic, 1994 ). Even though it has its shortcomings, generative AI is a multiplier of what humans and technology can accomplish together to create efficient and resilient supply chains. The use of Gen-AI in the management of supply chains (SCMs) has become an imperative research topic, which requires extensive investigation because of its potential transformations. Generative Artificial Intelligence (Gen-AI) can benefit Supply Chain Management (SCM) by predicting machine maintenance, assisting man-robot collaboration, and improving responsiveness (Khlie et al., 2024 ); enhancing automation, allowing data-driven decision-making, and empowering human roles (Pal, 2025 ; Malhotra and Manzoor, 2025 ); as well as by improving customer satisfaction, reducing costs, and improving process efficiencies (Wamba et al., 2023 ; Ivanov, 2024 ). But there are also challenges when implementing Gen-AI in SCM (Ye, 2024 ). Existing literature reviews often examine AI in supply chains broadly but rarely focus specifically on Gen-AI from an SCM perspective. Given this context, the main aim of our study is to systematically review the literature on Generative artificial intelligence applications in supply chain management and examine its contribution. We will be focusing on the following research questions. RQ1: How has the literature on Gen-AI application in SCM evolved over time, and what methods and concepts have been employed? RQ2: How is Gen-AI transforming supply chain management across diverse sectors and performance outcomes associated with its adoption? RQ3: What are the research gaps and future research directions in the context of the Gen-AI application in SCM? For the purpose of answering the research questions, we have first conducted a systematic literature review (SLR) of 44 selected peer-reviewed articles. This descriptive analysis includes world cloud and a thematic map. Next, we applied structural topic modelling (STM) to understand how Gen-AI is transforming SCM in different sectors. Based on this, we have developed the five themes to address the performance outcomes associated with the adoption of Gen-AI in SCM. At the end we have applied the Theory, Context, Characteristics, and Methodology (TCCM) framework to identify the research gaps and proposed six research propositions that highlight promising directions for advancing the field of Gen-AI application in SCM. This study contributes to the field by introducing the five themes to address the performance outcomes associated with the adoption of Gen-AI in SCM. It also provides a roadmap for advancing knowledge through six propositions, including synthetic data environments, blockchain-enabled transparency, interpretability frameworks, and hybrid modelling approaches. We have structured the paper in seven sections consisting of introduction, methodology, descriptive analysis and structural topic modelling technique, research findings and analysis, TCCM framework, future research directions, and conclusion and implications of our study. 2. Methodology For our study the method of SLR suggested by Durach, Kembro, and Wieland, 2021 ; Seuring et al., 2020 ; Paul and Menzies, 2023 was adopted. For our further analysis we applied STM (Kuhn, 2018 ) and the TCCM framework (Paul and Rosado-Serrano, 2019 ; Basu et al., 2022 ; Hassan et al., 2022 ; Roy Bhattacharjee et al., 2022 ). The following sections describe every step of our SLR approach. 2.1. Phase 1 – SLR goals During the first phase, we identified literature on Gen-AI application and SCM. We aimed to compile the existing literature on the subject matter in a systematic way, that is, by analyzing the application of Generative AI in the supply chain setting. In order to direct this process, we referred to the theoretical framework that was created by Kunz and Gold ( 2017 ), which was used as a baseline to formulate our selection criteria. The thematic focus and contextual dimensions that were used in structuring our systematic literature review were also developed with the help of this framework 2.2. Phase 2 – Databases and keywords We sourced academic papers from the Scopus database, chosen for its extensive coverage of peer-reviewed journals across leading publishers. We mainly looked at academic papers in English that talked about Generative AI in the context of supply chain management. Figure 2 shows our adopted sampling process. 2.3. Phase 3 – Relevant literature In this phase we collected all the papers identified in phase two. The articles that did not meet our criteria of study were eliminated. Finally, 44 literature papers published between 2013 and 2025 were shortlisted for further analysis. Appendix A provides details of all selected literature review papers. 2.4. Phase 4 – Analysis In the fourth phase, we used Biblioshiny interface from the R package for providing descriptive analysis of the literature collected. We then used the STM and TCCM framework to find gaps in the current literature and suggest areas for future research. 3. Descriptive analysis This section outlines the details of the 44 selected literature, categorized by year of publication, country contribution, journals, world cloud and prominent themes. 3.1. Distribution of reviewed papers over time Figure 3 illustrates that our final dataset includes 44 articles published between 2013 and 2025. It's interesting to note that only two studies came out in the early years, one in 2013 and one in 2017. This shows that there wasn't much academic interest at the time. However, starting in 2021, there has been a noticeable increase in publications. This evidence shows that both industry professionals and researchers are becoming more interested in finding out what Gen-AI can do in the supply chain field. Table 1 presents a summary of the journals where the selected studies were published. Interestingly, each of the 36 papers appeared in a different journal, underscoring the wide-ranging relevance and interdisciplinary nature of generative AI research. Table 1 Number of papers per journal (included if n > 1) Journal Number of papers International Journal of Production Research 3 Technology in Society 3 Transportation Research Part E: Logistics and Transportation Review 2 Figure 4 provides the details of the Countries scientific production. a substantial portion of the reviewed studies has been conducted in the Asian region. China is leading the overall publications followed by India. To visually capture the most frequently used terms in the domain of Gen-AI within supply chain management, we created a word cloud using the Biblioshiny tool from the R package. Displayed in Fig. 5 , the word cloud highlights the most common keywords found across the literature. The prominence of each word is reflected in its size, indicating how often it appeared during the search process. Central terms such as "artificial intelligence," "generative artificial intelligence," and "supply chain management" stood out both in the visual representation and among the top-ranked keywords listed in Fig. 5 . The thematic map shown in Fig. 6 provides different themes of a research domain based on the clustering of the keywords. It is a two-dimensional plot with density (development of a theme) measured on the vertical axis and centrality (relevance of a theme) measured on the horizontal axis with themes placed in four different quadrants. Each theme is shown with a bubble and the bubble name is the word with the highest frequency of occurrence. The upper right corner has themes named ‘artificial intelligence’, ‘generative artificial intelligence’ ‘Supply chain management’, the most widely discussed topics. These are followed by ‘learning systems, ‘generative adversarial networks. The lower left quadrant indicates emerging or declining themes. ‘adversarial networks’, ‘human computer interaction’ ‘sustainable practices’ and ‘operational efficiencies’. The upper left quadrant indicates ‘niche themes’ that are well-developed but isolated themes. ‘block chains’ ‘data privacy’ and ‘generative model’ are some of the niche themes. 3.2. Text analytics using structural topic modelling Topic modeling is a computation method, which is used to reveal latent patterns in the appearance of words within a set of texts. It determines groups of words which occur more often as a pair and this constitutes a particular theme in the documents. Structural Topic Modeling (STM) was a method of Structural Topic Modeling (STM) that we used in our study by examining word frequency, and similarity to come up with meaningful topics. The steps of the procedure of STM were borrowed by Agrawal et al. ( 2022 ). In order to analyze the selected articles, we copied the text of the titles, abstracts, and keywords to prepare it. Before the model was run, we carried out a general text cleaning which included the cases of removing stop words, special characters and so on. With the inbuilt STM library of R package, we created a list of thematic topics as depicted in Table 2 . For instance, one of the dominant themes included terms like "supply," "chain," "ChatGPT," "use," "generative," "model," "benefit," "data," "management," and "challenge." These words had the highest probability of forming Theme 1. Similar processes were used to identify other themes based on keyword patterns. The Lift metrices highlights in a topic the most frequently occurring words, Frex is preferred for its ability to pinpoint words that are both frequent and exclusive to a specific theme. Table 2 STM Topic Top_Keywords FREX LIFT 1 supply, chain, chatgpt, use, generative, model, benefit, datum, management, challenge challenge, management, datum, benefit, model, generative, use, chatgpt, chain, supply generative, management, datum, use, model, challenge, chain, supply, benefit, chatgpt 2 chain, supply, generative, scm, model, capability, performance, management, study, intelligence intelligence, study, management, performance, capability, model, scm, generative, supply, chain intelligence, study, generative, management, supply, chain, model, performance, capability, scm 3 system, gan, hrv, biofeedback, hardware, network, design, use, musical, hmi hmi, musical, use, design, network, hardware, biofeedback, hrv, gan, system use, design, network, system, hardware, hmi, musical, gan, biofeedback, hrv 4 gen, learning, technology, artificial, intelligence, datum, chain, supply, retail, energy energy, retail, supply, chain, datum, intelligence, artificial, technology, learning, gen supply, chain, intelligence, artificial, datum, technology, learning, gen, energy, retail 5 optimization, technology, chain, supply, study, use, structural, problem, distribute, modeling modeling, distribute, problem, structural, use, study, supply, chain, technology, optimization supply, chain, use, study, technology, problem, structural, modeling, distribute, optimization 4. Thematic analysis The section describes the thematic topics that came out of the systematic literature review with Structural Topic Modelling (STM) applied in R. Each theme is developed with reference to the already existing literature to show the current use of Generative Artificial Intelligence (Gen- AI) in supply chain management (SCM) and research gaps that still exist. 4.1. THEME 1: Challenges and Benefits of Using Generative AI in Supply Chain Management The introduction of Generative Artificial Intelligence (Gen-AI) in the supply chain ecosystem is transforming the world of supply chain operation, decision support systems, and risk management. Generative models, such as large language models (LLMs) such as Chat GPT and Bard, have also become a part of improving communication, prediction, automation, and assurance between stages of the supply chain. Research has identified the opportunities and challenges of these tools. As an example, Fosso Wamba et al. (2024) examined how much Chat-GPT was perceived to add value to supply chains, observing such advantages as real-time decision support and a number of challenges related to trust and transparency in AI-produced output. In comparison to Chat-GPT, Raman et al. (2024) examined their roles in educational and professional training, which proved that they can improve SCM knowledge and skills. Wang et al. (2023) used generative methods to simulate software supply chain threats, whereas Wang (2024) demonstrated how predictive analytics could confirm the authenticity of a drug. Other articles, including those by Soy and Balkrishna ( 2025 ), point to the application of generative systems to detect fraud, and legal studies raise issues regarding monopolization, competition, and AI-driven supply chains’ ethical governance. Taken together, these papers demonstrate the way Gen-AI is used to augment decisions, detect anomalies, create policies, and distill knowledge, as well as highlight major issues relating to trust, transparency, and governance. 4.2. Theme 2: Enhancing Supply Chain Performance through Generative Intelligence and SCM Modelling A second theme is the ways and means through which Gen-AI is being incorporated with the sophisticated modelling techniques to promote decision-making, responsiveness, and overall supply chain performance. It has been proven that AI-based predictive analytics and system modelling can enhance both the real-time operations and the long-term strategic planning. Khlie et al. ( 2024 ) found their use in predictive maintenance, human-robot collaboration, and supply chain responsiveness. Li et al. (2024) investigated the role of Gen-AI in sustainable operations based on the practice-grounded approach, whereas Jackson et al. (2024) associated the motivation of Gen-AI adoption with the SCM performance using the dynamic capability theory. In the same way, Wael Al-Khatib and Khattab (2024) demonstrated that GenAI enhances the work of digital supply chains through innovation ambidexterity, and Nuerk and Dařena (2025) suggested a system engineering framework to optimize SCM innovation. These studies combined imply that GenAI is getting integrated on strategic and tactical levels, enhancing supply chains with flexibility, resilience, and effectiveness. 4.3. Theme 3: Designing Human-Machine Interfaces with GANs and Biofeedback Systems The third theme is that generative adversarial networks (GANs) and biofeedback systems can be used to create adaptive and secure human-machine interfaces (HMIs). Examples of areas where these approaches are being considered include healthcare, hardware assurance, and human performance monitoring. Idrobo-Avila et al. (2022) have shown the possibility of controlling and optimizing musical biofeedback systems by using GANs to regulate heart rate variability, indicating the potential of Gen-AI in personalized medicine and human-computer interfaces. Al Hasan et al. (2023) applied explainable AI vision systems for hardware testing, addressing issues of trust and anomaly detection in hardware supply chains. Together, these studies show how generative systems and biofeedback technologies can improve transparency, explainability, and user interaction in critical supply chain environments where human decision-making must be augmented by intelligent systems. 4.4. Theme 4: Applications of Generative AI and Data Intelligence in Retail and Energy Supply Chains Another emerging theme concerns the application of Gen- AI in retail and energy supply chains, where intelligent interfaces and data-driven systems are being used to make things more open, safe, and efficient. For instance, Soy and Balkrishna ( 2025 ) demonstrated the use of predictive analytics to verify drug authenticity and combat counterfeiting. Their work shows how AI-driven interfaces can connect digital sensing with user-facing verification. These developments reflect a growing convergence between generative models, cognitive systems, and retail- or energy-specific supply chains, emphasizing the important function of explainability and trust in consumer-facing applications. 4.5. Theme 5: Optimization and Structural Modelling in a Distributed Supply Chain Systems This thematic area focuses on the application of optimization algorithms and structural modeling to enhance decision-making in distributed supply chain systems. As supply chains become increasingly global and decentralized, robust models are required. With supply chains becoming more decentralized and global, there is a need to have strong models that are able to deal with complexity, uncertainty, and sustainability objectives. Jannach and Zanker ( 2011 ) used the constraint satisfaction problem (CSP) frameworks to model the challenges of distributed configuration and provided automated solutions to multifaceted product design and logistic problems. Yavan et al. (2024) combined evolutionary algorithms with structural optimization in Building Information Modelling (BIM) projects with an example that AI-based optimization can be utilized in the field of sustainable infrastructure planning. In the meantime, Twaissi and Al-Khatib ( 2024 ) examined the way Jordanian SMEs implement Gen-AI to promote the resilience of supply chains in the conditions of technological and organizational limitations. Taken together, these studies support the significance of AI-based optimization when it comes to enhancing the efficiency, flexibility, and sustainability of a distributed SCM setting. 5. TCCM approach To synthesize insights from the reviewed studies and identify research gaps, the TCCM (Theory, Context, Characteristics, and Methodology) framework was applied. 5.1. Theoretical Foundations The analysis of the SLR reveals several theories that underpin the research in the field of Generative Artificial Intelligence (Gen- AI) and supply chain management (SCM). Key theories identified include: Practice-Based View (PBV): This theory emphasizes the importance of practices in understanding how organizations can effectively utilize Gen- AI to enhance sustainable supply chain performance. Theory of Resource Orchestration: This theory focuses on how organizations can manage and coordinate resources to achieve competitive advantages, particularly in the context of AI adoption. Dynamic Capabilities Theory: This theory highlights the ability of organizations to integrate, build, and reconfigure internal and external competencies to address rapidly changing environments. These theories provide a solid foundation for understanding the implications of Gen- AI in SCM, but there are gaps in their application across different contexts and industries. 5.2. Context and Characteristics of the Studies The context and characteristics of the studies indicate a strong focus on technological adoption, particularly regarding Gen- AI and its applications in SCM. Key characteristics include: Emphasis on Sustainability: Many studies explore the integration of green practices and circular economy principles within supply chains, highlighting the need for sustainable solutions. Technological Integration: The studies often discuss the integration of AI technologies with supply chain processes, emphasizing the potential for improved efficiency and decision-making. Ethical Considerations: There is a growing concern regarding the ethical implications of AI deployment, including issues of trust, security, and data privacy. While these characteristics are well-represented, there is a need for more comprehensive studies that address the ethical and regulatory challenges associated with AI in SCM. 5.3. Methodologies Employed The methodologies used in the studies vary widely, reflecting the diverse approaches to researching Gen- AI in SCM. Common methodologies include: Quantitative Surveys: Many studies utilize quantitative methods, such as PLS-SEM and t-tests, to analyse survey data and draw conclusions about the relationships between Gen- AI usage and supply chain performance. Systematic Literature Reviews (SLR): SLRs are employed to synthesize existing research and identify trends and gaps in the literature. Case Studies: In-depth case studies provide insights into real-world applications of Gen- AI in SCM, offering practical implications for practitioners. Despite the variety of methodologies, there is a lack of longitudinal studies that assess the long-term impacts of AI adoption on supply chain performance 6. Future research directions We have suggested the following six future research propositions based on the thematic analysis and the TCCM framework Proposition 1 Supply chains today are facing a number of disruptions, which are creating issues for organizations. Future studies in supply chain can utilise Gen-AI to create synthetic data environments of these natural disruptions. This environment can be used to train and understand how the Gen-AI models are reacting to data that are not collected historically. The Gen-AI based model with iterations can give more formidable results, which can help supply chain mangers to take better decisions. Proposition 2 The Gen-AI outputs need to be trustworthy for supply chains to perform better. For this purpose, Gen-AI can also be combined with other technology like block chain for better decision-making. With the transparency and verifiability factor that block chain provides, it could result in better accountability of Gen-AI-driven recommendations. Future studies should concentrate on this integration will create more trust and faith in the system for better management of supply chains. Proposition 3 With regards to supply chains of micro-, small- and medium-sized enterprises (MSME) and small- and medium-sized enterprises (SMEs), the restrictions come with regards to budget to run the complex Gen-AI models. They are interested in Gen-AI models that are lightweight and can be in line with a resource-limited settings. For this purpose, future studies should consider developing Gen-AI that is accessible to these companies, which form a major backbone of global supply chains. Proposition 4 Future research should also focus on creating toolkits that make it easier to understand how Gen-AI works in SCM. When using AI, there are many problems with trust and openness. These issues arise because these generative AI models are like "black boxes." Researchers can pinpoint and formulate the interpretability frameworks of AI concerning human decision-making. People can check and improve AI-generated decisions about supply chain management. So, the researchers can connect machine intelligence with management judgment. Proposition 5 Another promising area of research is the design of generative human-machine systems that respond to emotional and physiological signals in real time. Adaptive interfaces can be made in important areas like health monitoring, logistics assurance, and safety-critical hardware management using techniques like generative adversarial networks (GANs) and reinforcement learning. The systems could significantly enhance human performance and decision-making in high-stakes supply chain environments, provided that ethical standards concerning privacy, consent, and psychological well-being are concurrently established. Proposition 6 Finally, research should continue to develop hybrid modeling techniques that incorporate simulation, machine learning, and optimization to tackle the complexity of modern supply chains. These models would combine real-time operational data with predictive analytics to create decision-making frameworks that are modular and adaptable. Adding human-in-the-loop features to these systems could make them more flexible, stronger, and easy to understand. Such hybrid approaches have the potential to redefine supply chain operations by balancing automation with managerial oversight. 7.Conclusion Gen-AI is transforming organizations and their supply chains drastically. Our study focused on understanding the application of Gen-AI in SCM by conducting a systematic literature review along with Structural Topic Modelling (STM) and the TCCM framework. To answer our research question one, we conducted a systematic literature review of 44 articles; we found that studies in the conjunction of Gen-AI and SCM are in the nascent stages. But since 2021, the growth of publications has increased in this field. For our research question two, we applied the STM approach and identified five themes, which are changing the ways SCM is being managed through Gen-AI. To answer our research question three, we applied the TCCM framework to identify the research gaps and proposed six research propositions. Our study's results have important implications for supply chain professionals who want to use Gen-AI to improve performance, manage risk, and make their operations more sustainable. The study also adds to existing literature by significantly highlighting the major themes, identified research gaps, and research propositions for future studies to address the performance outcomes associated with the adoption of Gen-AI in SCM. The study is confined by two limitations. First, we have taken only journals listed in Scopus database. Future studies can add more databases such as Google Scholar, and Web of Science. Second, our research is confined to the application of Gen-AI in supply chain management; subsequent studies may explore additional facets of human, technological, and production interfaces. Our study has shown that Gen-AI holds immense potential to manage and improve sustainable supply chain ecosystems. But this is not as easy as it sounds, organizations have to find answers to the bigger questions of trust, faith, transparency, and ethics while adopting Gen-AI for managing their supply chains. 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SHS Web of Conferences , 207 , 03015. https://doi.org/10.1051/shsconf/202420703015 Additional Declarations The authors declare potential competing interests as follows: The authors report there are no competing interests to declare Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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11:48:49","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8740637/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8740637/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101629286,"identity":"6957e855-7cd5-4cc1-a253-abd6df346bf1","added_by":"auto","created_at":"2026-02-02 05:10:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":9166,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSystematic literature review process\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8740637/v1/f52ab538c0031b6cd7e6737b.png"},{"id":101629337,"identity":"a30c5b35-de15-4551-bc91-9749f1b1b147","added_by":"auto","created_at":"2026-02-02 05:10:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19230,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSampling process\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8740637/v1/c0f6820e231ebd6ce5ec50d0.png"},{"id":101629334,"identity":"84e1afc2-367a-42de-ba67-4f5b02a6c1de","added_by":"auto","created_at":"2026-02-02 05:10:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":20262,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of papers\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8740637/v1/20c899dd441851ad7351a6c8.png"},{"id":101629391,"identity":"fdbc3e91-9c85-4bbf-a036-982f374efced","added_by":"auto","created_at":"2026-02-02 05:11:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":129800,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCountries scientific production\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8740637/v1/33a82a7916082d3d64f4c7ac.png"},{"id":101629306,"identity":"b5e24018-fe0d-4c91-bb58-674021b9e50c","added_by":"auto","created_at":"2026-02-02 05:10:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":61639,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWord cloud\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8740637/v1/cb0f24cfe27c227db4987a15.png"},{"id":101629443,"identity":"f2ab507a-3d0a-4915-bf51-1930ae9cddb1","added_by":"auto","created_at":"2026-02-02 05:11:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":62220,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThematic map\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8740637/v1/4e9707d78b7c445c9cc460f1.png"},{"id":101629473,"identity":"8cac3401-30c3-40c7-885a-55cdd8207dd2","added_by":"auto","created_at":"2026-02-02 05:11:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1111491,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8740637/v1/acd26f3c-bf74-4d3f-8743-314c9b0a4eae.pdf"}],"financialInterests":"The authors declare potential competing interests as follows: The authors report there are no competing interests to declare","formattedTitle":"\u003cp\u003eExploring the potential of Generative artificial intelligence in supply chain management: A systematic literature review, STM \u0026amp;; TCCM approach\u003c/p\u003e","fulltext":[{"header":"Highlights","content":"\u003cp\u003eApplication Generative artificial intelligence in supply chain management, Structural Topic Modelling (STM) and TCCM (Theory, Context, Characteristics, and Methodology) framework\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eWith the advent of artificial intelligence (AI), many organizations are implementing it in their supply chain processes. They are also coming to leverage the power of AI in streamlining workflows and enhancing the efficiency of the last-mile delivery (Ayoub, 2024). The global supply chain market based on AI is on a sharp increase curve, and it is projected to reach 58.55\u0026nbsp;billion by the year 2031. It has a rate of 40.4% annual growth, which begins in 2024, an incredible pace at which this technology is being adopted (Sudhanshu R. et.al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, a new wave of innovation has emerged in recent years. Generative artificial intelligence (Gen-AI) has radically transformed what we thought these systems were capable of and has moved the boundaries and revealed new possibilities to us that we had not previously considered. Due to the emergence of Gen-AI and Large Language Models, there has been a significant change in every industry, affecting the way businesses are run, innovate, and interact (Vaswani et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to experts, generative AI has the potential to add trillions of dollars to the world economy annually because it automates and will make more work creative and discover new efficiencies. The Gen-AI tools and services market is to skyrocket to up to 126.5\u0026nbsp;billion by 2031, which is indicative of the rapid growth in adoption and investment rates (Allied Market Research, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenerative AI refers to a form of technology that is meant to generate original content (i.e., images, videos, text, or audio) by training on existing information. It is not merely an information analyzer or a large-scale information sorting tool but can generate entirely new results that resemble human creativity (Dwivedi et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Peres et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Generative AI can also assist businesses to work more efficiently and quickly by automating such activities as writing, coding, and responding to customer queries. Not only does it reduce costs of operation, but it also creates the gateway to new ideas and new business models (Eloundou et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Due to the capacity to produce and generate complex material, Gen-AI is set to transform industries that lack efficiency with innovation, imagination, and comprehensive experience; these include media and design, education, and research (Feuerriegel, S., Hartmann, J., Janiesch, C., and Zschech, P. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe history of artificial intelligence within the field of production, operations, and supply chain management research is long (Merhi and Harfouche, 2023; Ivanov, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Elliott and Griffiths, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Vujosevic, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Even though it has its shortcomings, generative AI is a multiplier of what humans and technology can accomplish together to create efficient and resilient supply chains. The use of Gen-AI in the management of supply chains (SCMs) has become an imperative research topic, which requires extensive investigation because of its potential transformations.\u003c/p\u003e \u003cp\u003eGenerative Artificial Intelligence (Gen-AI) can benefit Supply Chain Management (SCM) by predicting machine maintenance, assisting man-robot collaboration, and improving responsiveness (Khlie et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); enhancing automation, allowing data-driven decision-making, and empowering human roles (Pal, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Malhotra and Manzoor, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); as well as by improving customer satisfaction, reducing costs, and improving process efficiencies (Wamba et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ivanov, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). But there are also challenges when implementing Gen-AI in SCM (Ye, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Existing literature reviews often examine AI in supply chains broadly but rarely focus specifically on Gen-AI from an SCM perspective.\u003c/p\u003e \u003cp\u003eGiven this context, the main aim of our study is to systematically review the literature on Generative artificial intelligence applications in supply chain management and examine its contribution. We will be focusing on the following research questions.\u003c/p\u003e \u003cp\u003eRQ1: How has the literature on Gen-AI application in SCM evolved over time, and what methods and concepts have been employed?\u003c/p\u003e \u003cp\u003eRQ2: How is Gen-AI transforming supply chain management across diverse sectors and performance outcomes associated with its adoption?\u003c/p\u003e \u003cp\u003eRQ3: What are the research gaps and future research directions in the context of the Gen-AI application in SCM?\u003c/p\u003e \u003cp\u003eFor the purpose of answering the research questions, we have first conducted a systematic literature review (SLR) of 44 selected peer-reviewed articles. This descriptive analysis includes world cloud and a thematic map. Next, we applied structural topic modelling (STM) to understand how Gen-AI is transforming SCM in different sectors. Based on this, we have developed the five themes to address the performance outcomes associated with the adoption of Gen-AI in SCM. At the end we have applied the Theory, Context, Characteristics, and Methodology (TCCM) framework to identify the research gaps and proposed six research propositions that highlight promising directions for advancing the field of Gen-AI application in SCM.\u003c/p\u003e \u003cp\u003eThis study contributes to the field by introducing the five themes to address the performance outcomes associated with the adoption of Gen-AI in SCM. It also provides a roadmap for advancing knowledge through six propositions, including synthetic data environments, blockchain-enabled transparency, interpretability frameworks, and hybrid modelling approaches.\u003c/p\u003e \u003cp\u003eWe have structured the paper in seven sections consisting of introduction, methodology, descriptive analysis and structural topic modelling technique, research findings and analysis, TCCM framework, future research directions, and conclusion and implications of our study.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003eFor our study the method of SLR suggested by Durach, Kembro, and Wieland, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Seuring et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Paul and Menzies, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e was adopted. For our further analysis we applied STM (Kuhn, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and the TCCM framework (Paul and Rosado-Serrano, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Basu et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hassan et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Roy Bhattacharjee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The following sections describe every step of our SLR approach.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Phase 1 \u0026ndash; SLR goals\u003c/h2\u003e \u003cp\u003eDuring the first phase, we identified literature on Gen-AI application and SCM. We aimed to compile the existing literature on the subject matter in a systematic way, that is, by analyzing the application of Generative AI in the supply chain setting. In order to direct this process, we referred to the theoretical framework that was created by Kunz and Gold (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which was used as a baseline to formulate our selection criteria. The thematic focus and contextual dimensions that were used in structuring our systematic literature review were also developed with the help of this framework\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Phase 2 \u0026ndash; Databases and keywords\u003c/h2\u003e \u003cp\u003eWe sourced academic papers from the Scopus database, chosen for its extensive coverage of peer-reviewed journals across leading publishers. We mainly looked at academic papers in English that talked about Generative AI in the context of supply chain management. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows our adopted sampling process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Phase 3 \u0026ndash; Relevant literature\u003c/h2\u003e \u003cp\u003eIn this phase we collected all the papers identified in phase two. The articles that did not meet our criteria of study were eliminated. Finally, 44 literature papers published between 2013 and 2025 were shortlisted for further analysis. Appendix A provides details of all selected literature review papers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Phase 4 \u0026ndash; Analysis\u003c/h2\u003e \u003cp\u003eIn the fourth phase, we used Biblioshiny interface from the R package for providing descriptive analysis of the literature collected. We then used the STM and TCCM framework to find gaps in the current literature and suggest areas for future research.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Descriptive analysis","content":"\u003cp\u003eThis section outlines the details of the 44 selected literature, categorized by year of publication, country contribution, journals, world cloud and prominent themes.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Distribution of reviewed papers over time\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates that our final dataset includes 44 articles published between 2013 and 2025. It's interesting to note that only two studies came out in the early years, one in 2013 and one in 2017. This shows that there wasn't much academic interest at the time. However, starting in 2021, there has been a noticeable increase in publications. This evidence shows that both industry professionals and researchers are becoming more interested in finding out what Gen-AI can do in the supply chain field.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a summary of the journals where the selected studies were published. Interestingly, each of the 36 papers appeared in a different journal, underscoring the wide-ranging relevance and interdisciplinary nature of generative AI research.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of papers per journal (included if n\u0026thinsp;\u0026gt;\u0026thinsp;1)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJournal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of papers\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternational Journal of Production Research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnology in Society\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransportation Research Part E: Logistics and Transportation Review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides the details of the Countries scientific production. a substantial portion of the reviewed studies has been conducted in the Asian region. China is leading the overall publications followed by India.\u003c/p\u003e \u003cp\u003eTo visually capture the most frequently used terms in the domain of Gen-AI within supply chain management, we created a word cloud using the Biblioshiny tool from the R package. Displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the word cloud highlights the most common keywords found across the literature. The prominence of each word is reflected in its size, indicating how often it appeared during the search process. Central terms such as \"artificial intelligence,\" \"generative artificial intelligence,\" and \"supply chain management\" stood out both in the visual representation and among the top-ranked keywords listed in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe thematic map shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e provides different themes of a research domain based on the clustering of the keywords. It is a two-dimensional plot with density (development of a theme) measured on the vertical axis and centrality (relevance of a theme) measured on the horizontal axis with themes placed in four different quadrants. Each theme is shown with a bubble and the bubble name is the word with the highest frequency of occurrence. The upper right corner has themes named \u0026lsquo;artificial intelligence\u0026rsquo;, \u0026lsquo;generative artificial intelligence\u0026rsquo; \u0026lsquo;Supply chain management\u0026rsquo;, the most widely discussed topics. These are followed by \u0026lsquo;learning systems, \u0026lsquo;generative adversarial networks. The lower left quadrant indicates emerging or declining themes. \u0026lsquo;adversarial networks\u0026rsquo;, \u0026lsquo;human computer interaction\u0026rsquo; \u0026lsquo;sustainable practices\u0026rsquo; and \u0026lsquo;operational efficiencies\u0026rsquo;. The upper left quadrant indicates \u0026lsquo;niche themes\u0026rsquo; that are well-developed but isolated themes. \u0026lsquo;block chains\u0026rsquo; \u0026lsquo;data privacy\u0026rsquo; and \u0026lsquo;generative model\u0026rsquo; are some of the niche themes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Text analytics using structural topic modelling\u003c/h2\u003e \u003cp\u003eTopic modeling is a computation method, which is used to reveal latent patterns in the appearance of words within a set of texts. It determines groups of words which occur more often as a pair and this constitutes a particular theme in the documents. Structural Topic Modeling (STM) was a method of Structural Topic Modeling (STM) that we used in our study by examining word frequency, and similarity to come up with meaningful topics. The steps of the procedure of STM were borrowed by Agrawal et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn order to analyze the selected articles, we copied the text of the titles, abstracts, and keywords to prepare it. Before the model was run, we carried out a general text cleaning which included the cases of removing stop words, special characters and so on. With the inbuilt STM library of R package, we created a list of thematic topics as depicted in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFor instance, one of the dominant themes included terms like \"supply,\" \"chain,\" \"ChatGPT,\" \"use,\" \"generative,\" \"model,\" \"benefit,\" \"data,\" \"management,\" and \"challenge.\" These words had the highest probability of forming Theme 1. Similar processes were used to identify other themes based on keyword patterns. The Lift metrices highlights in a topic the most frequently occurring words, Frex is preferred for its ability to pinpoint words that are both frequent and exclusive to a specific theme.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSTM\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTop_Keywords\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFREX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLIFT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esupply, chain, chatgpt, use, generative, model, benefit, datum, management, challenge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echallenge, management, datum, benefit, model, generative, use, chatgpt, chain, supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003egenerative, management, datum, use, model, challenge, chain, supply, benefit, chatgpt\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echain, supply, generative, scm, model, capability, performance, management, study, intelligence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eintelligence, study, management, performance, capability, model, scm, generative, supply, chain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eintelligence, study, generative, management, supply, chain, model, performance, capability, scm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esystem, gan, hrv, biofeedback, hardware, network, design, use, musical, hmi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehmi, musical, use, design, network, hardware, biofeedback, hrv, gan, system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003euse, design, network, system, hardware, hmi, musical, gan, biofeedback, hrv\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egen, learning, technology, artificial, intelligence, datum, chain, supply, retail, energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eenergy, retail, supply, chain, datum, intelligence, artificial, technology, learning, gen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003esupply, chain, intelligence, artificial, datum, technology, learning, gen, energy, retail\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eoptimization, technology, chain, supply, study, use, structural, problem, distribute, modeling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emodeling, distribute, problem, structural, use, study, supply, chain, technology, optimization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003esupply, chain, use, study, technology, problem, structural, modeling, distribute, optimization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Thematic analysis","content":"\u003cp\u003eThe section describes the thematic topics that came out of the systematic literature review with Structural Topic Modelling (STM) applied in R. Each theme is developed with reference to the already existing literature to show the current use of Generative Artificial Intelligence (Gen- AI) in supply chain management (SCM) and research gaps that still exist.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1. THEME 1: Challenges and Benefits of Using Generative AI in Supply Chain Management\u003c/h2\u003e \u003cp\u003eThe introduction of Generative Artificial Intelligence (Gen-AI) in the supply chain ecosystem is transforming the world of supply chain operation, decision support systems, and risk management. Generative models, such as large language models (LLMs) such as Chat GPT and Bard, have also become a part of improving communication, prediction, automation, and assurance between stages of the supply chain.\u003c/p\u003e \u003cp\u003eResearch has identified the opportunities and challenges of these tools. As an example, Fosso Wamba et al. (2024) examined how much Chat-GPT was perceived to add value to supply chains, observing such advantages as real-time decision support and a number of challenges related to trust and transparency in AI-produced output. In comparison to Chat-GPT, Raman et al. (2024) examined their roles in educational and professional training, which proved that they can improve SCM knowledge and skills. Wang et al. (2023) used generative methods to simulate software supply chain threats, whereas Wang (2024) demonstrated how predictive analytics could confirm the authenticity of a drug. Other articles, including those by Soy and Balkrishna (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), point to the application of generative systems to detect fraud, and legal studies raise issues regarding monopolization, competition, and AI-driven supply chains\u0026rsquo; ethical governance. Taken together, these papers demonstrate the way Gen-AI is used to augment decisions, detect anomalies, create policies, and distill knowledge, as well as highlight major issues relating to trust, transparency, and governance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Theme 2: Enhancing Supply Chain Performance through Generative Intelligence and SCM Modelling\u003c/h2\u003e \u003cp\u003eA second theme is the ways and means through which Gen-AI is being incorporated with the sophisticated modelling techniques to promote decision-making, responsiveness, and overall supply chain performance. It has been proven that AI-based predictive analytics and system modelling can enhance both the real-time operations and the long-term strategic planning. Khlie et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found their use in predictive maintenance, human-robot collaboration, and supply chain responsiveness. Li et al. (2024) investigated the role of Gen-AI in sustainable operations based on the practice-grounded approach, whereas Jackson et al. (2024) associated the motivation of Gen-AI adoption with the SCM performance using the dynamic capability theory. In the same way, Wael Al-Khatib and Khattab (2024) demonstrated that GenAI enhances the work of digital supply chains through innovation ambidexterity, and Nuerk and Dařena (2025) suggested a system engineering framework to optimize SCM innovation. These studies combined imply that GenAI is getting integrated on strategic and tactical levels, enhancing supply chains with flexibility, resilience, and effectiveness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Theme 3: Designing Human-Machine Interfaces with GANs and Biofeedback Systems\u003c/h2\u003e \u003cp\u003eThe third theme is that generative adversarial networks (GANs) and biofeedback systems can be used to create adaptive and secure human-machine interfaces (HMIs). Examples of areas where these approaches are being considered include healthcare, hardware assurance, and human performance monitoring. Idrobo-Avila et al. (2022) have shown the possibility of controlling and optimizing musical biofeedback systems by using GANs to regulate heart rate variability, indicating the potential of Gen-AI in personalized medicine and human-computer interfaces. Al Hasan et al. (2023) applied explainable AI vision systems for hardware testing, addressing issues of trust and anomaly detection in hardware supply chains. Together, these studies show how generative systems and biofeedback technologies can improve transparency, explainability, and user interaction in critical supply chain environments where human decision-making must be augmented by intelligent systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Theme 4: Applications of Generative AI and Data Intelligence in Retail and Energy Supply Chains\u003c/h2\u003e \u003cp\u003eAnother emerging theme concerns the application of Gen- AI in retail and energy supply chains, where intelligent interfaces and data-driven systems are being used to make things more open, safe, and\u003c/p\u003e \u003cp\u003eefficient. For instance, Soy and Balkrishna (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) demonstrated the use of predictive analytics to verify drug authenticity and combat counterfeiting. Their work shows how AI-driven interfaces can connect digital sensing with user-facing verification. These developments reflect a growing convergence between generative models, cognitive systems, and retail- or energy-specific supply chains, emphasizing the important function of explainability and trust in consumer-facing applications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Theme 5: Optimization and Structural Modelling in a Distributed Supply Chain\u003c/h2\u003e \u003cp\u003e \u003cb\u003eSystems\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis thematic area focuses on the application of optimization algorithms and structural modeling to enhance decision-making in distributed supply chain systems. As supply chains become increasingly global and decentralized, robust models are required. With supply chains becoming more decentralized and global, there is a need to have strong models that are able to deal with complexity, uncertainty, and sustainability objectives. Jannach and Zanker (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) used the constraint satisfaction problem (CSP) frameworks to model the challenges of distributed configuration and provided automated solutions to multifaceted product design and logistic problems. Yavan et al. (2024) combined evolutionary algorithms with structural optimization in Building Information Modelling (BIM) projects with an example that AI-based optimization can be utilized in the field of sustainable infrastructure planning. In the meantime, Twaissi and Al-Khatib (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) examined the way Jordanian SMEs implement Gen-AI to promote the resilience of supply chains in the conditions of technological and organizational limitations. Taken together, these studies support the significance of AI-based optimization when it comes to enhancing the efficiency, flexibility, and sustainability of a distributed SCM setting.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. TCCM approach","content":"\u003cp\u003eTo synthesize insights from the reviewed studies and identify research gaps, the TCCM (Theory, Context, Characteristics, and Methodology) framework was applied.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.1. \u003cb\u003eTheoretical Foundations\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe analysis of the SLR reveals several theories that underpin the research in the field of Generative Artificial Intelligence (Gen- AI) and supply chain management (SCM). Key theories identified include:\u003c/p\u003e \u003cp\u003ePractice-Based View (PBV): This theory emphasizes the importance of practices in understanding how organizations can effectively utilize Gen- AI to enhance sustainable supply chain performance.\u003c/p\u003e \u003cp\u003eTheory of Resource Orchestration: This theory focuses on how organizations can manage and coordinate resources to achieve competitive advantages, particularly in the context of AI adoption.\u003c/p\u003e \u003cp\u003eDynamic Capabilities Theory: This theory highlights the ability of organizations to integrate, build, and reconfigure internal and external competencies to address rapidly changing environments.\u003c/p\u003e \u003cp\u003eThese theories provide a solid foundation for understanding the implications of Gen- AI in SCM, but there are gaps in their application across different contexts and industries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Context and Characteristics of the Studies\u003c/h2\u003e \u003cp\u003eThe context and characteristics of the studies indicate a strong focus on technological adoption, particularly regarding Gen- AI and its applications in SCM. Key characteristics include:\u003c/p\u003e \u003cp\u003eEmphasis on Sustainability: Many studies explore the integration of green practices and circular economy principles within supply chains, highlighting the need for sustainable solutions.\u003c/p\u003e \u003cp\u003eTechnological Integration: The studies often discuss the integration of AI technologies with supply chain processes, emphasizing the potential for improved efficiency and decision-making.\u003c/p\u003e \u003cp\u003eEthical Considerations: There is a growing concern regarding the ethical implications of AI deployment, including issues of trust, security, and data privacy.\u003c/p\u003e \u003cp\u003eWhile these characteristics are well-represented, there is a need for more comprehensive studies that address the ethical and regulatory challenges associated with AI in SCM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Methodologies Employed\u003c/h2\u003e \u003cp\u003eThe methodologies used in the studies vary widely, reflecting the diverse approaches to researching Gen- AI in SCM. Common methodologies include:\u003c/p\u003e \u003cp\u003eQuantitative Surveys: Many studies utilize quantitative methods, such as PLS-SEM and t-tests, to analyse survey data and draw conclusions about the relationships between Gen- AI usage and supply chain performance.\u003c/p\u003e \u003cp\u003eSystematic Literature Reviews (SLR): SLRs are employed to synthesize existing research and identify trends and gaps in the literature.\u003c/p\u003e \u003cp\u003eCase Studies: In-depth case studies provide insights into real-world applications of Gen- AI in SCM, offering practical implications for practitioners.\u003c/p\u003e \u003cp\u003eDespite the variety of methodologies, there is a lack of longitudinal studies that assess the long-term impacts of AI adoption on supply chain performance\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Future research directions","content":"\u003cp\u003eWe have suggested the following six future research propositions based on the thematic analysis and the TCCM framework\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eProposition 1\u003c/strong\u003e \u003cp\u003eSupply chains today are facing a number of disruptions, which are creating issues for organizations. Future studies in supply chain can utilise Gen-AI to create synthetic data environments of these natural disruptions. This environment can be used to train and understand how the Gen-AI models are reacting to data that are not collected historically. The Gen-AI based model with iterations can give more formidable results, which can help supply chain mangers to take better decisions.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eProposition 2\u003c/strong\u003e \u003cp\u003eThe Gen-AI outputs need to be trustworthy for supply chains to perform better. For this purpose, Gen-AI can also be combined with other technology like block chain for better decision-making. With the transparency and verifiability factor that block chain provides, it could result in better accountability of Gen-AI-driven recommendations. Future studies should concentrate on this integration will create more trust and faith in the system for better management of supply chains.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eProposition 3\u003c/strong\u003e \u003cp\u003eWith regards to supply chains of micro-, small- and medium-sized enterprises (MSME) and small- and medium-sized enterprises (SMEs), the restrictions come with regards to budget to run the complex Gen-AI models. They are interested in Gen-AI models that are lightweight and can be in line with a resource-limited settings. For this purpose, future studies should consider developing Gen-AI that is accessible to these companies, which form a major backbone of global supply chains.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eProposition 4\u003c/strong\u003e \u003cp\u003eFuture research should also focus on creating toolkits that make it easier to understand how Gen-AI works in SCM. When using AI, there are many problems with trust and openness. These issues arise because these generative AI models are like \"black boxes.\" Researchers can pinpoint and formulate the interpretability frameworks of AI concerning human decision-making. People can check and improve AI-generated decisions about supply chain management. So, the researchers can connect machine intelligence with management judgment.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eProposition 5\u003c/strong\u003e \u003cp\u003eAnother promising area of research is the design of generative human-machine systems that respond to emotional and physiological signals in real time. Adaptive interfaces can be made in important areas like health monitoring, logistics assurance, and safety-critical hardware management using techniques like generative adversarial networks (GANs) and reinforcement learning. The systems could significantly enhance human performance and decision-making in high-stakes supply chain environments, provided that ethical standards concerning privacy, consent, and psychological well-being are concurrently established.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eProposition 6\u003c/strong\u003e \u003cp\u003eFinally, research should continue to develop hybrid modeling techniques that incorporate simulation, machine learning, and optimization to tackle the complexity of modern supply chains. These models would combine real-time operational data with predictive analytics to create decision-making frameworks that are modular and adaptable. Adding human-in-the-loop features to these systems could make them more flexible, stronger, and easy to understand. Such hybrid approaches have the potential to redefine supply chain operations by balancing automation with managerial oversight.\u003c/p\u003e \u003c/p\u003e"},{"header":"7.Conclusion","content":"\u003cp\u003eGen-AI is transforming organizations and their supply chains drastically. Our study focused on understanding the application of Gen-AI in SCM by conducting a systematic literature review along with Structural Topic Modelling (STM) and the TCCM framework. To answer our research question one, we conducted a systematic literature review of 44 articles; we found that studies in the conjunction of Gen-AI and SCM are in the nascent stages. But since 2021, the growth of publications has increased in this field. For our research question two, we applied the STM approach and identified five themes, which are changing the ways SCM is being managed through Gen-AI. To answer our research question three, we applied the TCCM framework to identify the research gaps and proposed six research propositions.\u003c/p\u003e \u003cp\u003eOur study's results have important implications for supply chain professionals who want to use Gen-AI to improve performance, manage risk, and make their operations more sustainable. The study also adds to existing literature by significantly highlighting the major themes, identified research gaps, and research propositions for future studies to address the performance outcomes associated with the adoption of Gen-AI in SCM.\u003c/p\u003e \u003cp\u003eThe study is confined by two limitations. First, we have taken only journals listed in Scopus database. Future studies can add more databases such as Google Scholar, and Web of Science. Second, our research is confined to the application of Gen-AI in supply chain management; subsequent studies may explore additional facets of human, technological, and production interfaces.\u003c/p\u003e \u003cp\u003eOur study has shown that Gen-AI holds immense potential to manage and improve sustainable supply chain ecosystems. But this is not as easy as it sounds, organizations have to find answers to the bigger questions of trust, faith, transparency, and ethics while adopting Gen-AI for managing their supply chains.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe authors report there are no competing interests to declare\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgrawal R, Wankhede VA, Kumar A, Upadhyay A, Garza-Reyes JA (2022) Nexus of Circular Economy and Sustainable Business Performance in the Era of Digitalization. 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Int J Prod Res. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00207543.2023.2294116\u003c/span\u003e\u003cspan address=\"10.1080/00207543.2023.2294116\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe R (2024) Revolutionizing industrial efficiency through generative AI: Case studies and impacts on supply chain operations. \u003cem\u003eSHS Web of Conferences\u003c/em\u003e, \u003cem\u003e207\u003c/em\u003e, 03015. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1051/shsconf/202420703015\u003c/span\u003e\u003cspan address=\"10.1051/shsconf/202420703015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"NICMAR University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Generative AI, Supply chain management, SLR, STM, TCCM","lastPublishedDoi":"10.21203/rs.3.rs-8740637/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8740637/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGenerative Artificial Intelligence (Gen-AI) is the next big thing and a promising tool for the growth of organizations. It opens up new ways to improve forecasting and sustainability, and it also makes it possible for people and machines to work together. It also. We wanted to understand how Gen-AI aids organizations in managing their supply chains through a systematic literature review. We have analysed articles from 2013 to 2025 by using Structural Topic Modelling (STM) and the TCCM (Theory, Context, Characteristics, and Methodology) framework. We have identified five themes from the extant literature on the potential implementation of Gen-AI in transforming supply chain management (SCM). We have proposed six future research directions that can enhance the application of Gen-AI in managing supply chains.\u003c/p\u003e","manuscriptTitle":"Exploring the potential of Generative artificial intelligence in supply chain management: A systematic literature review, STM \u0026amp;; TCCM approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 05:08:24","doi":"10.21203/rs.3.rs-8740637/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb12ed71-0075-4d4c-be6d-9cc376e4da50","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T05:08:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-02 05:08:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8740637","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8740637","identity":"rs-8740637","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}