{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T14:18:56Z","timestamp":1784470736414,"version":"3.55.0"},"reference-count":61,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,18]],"date-time":"2025-05-18T00:00:00Z","timestamp":1747526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Artificial intelligence (AI) has significant potential to transform small- and medium-sized enterprises (SMEs), yet its adoption is often hindered by challenges such as limited financial and human resources. This study addresses this issue by investigating the core AI technologies adopted by SMEs, their broad range of applications across business functions, and the strategies required for successful implementation. Through a systematic literature review of 50 studies published between 2016 and 2025, we identify prominent AI technologies, including machine learning, natural language processing, and generative AI, and their applications in enhancing efficiency, decision-making, and innovation across sales and marketing, operations and logistics, finance and other business functions. The findings emphasize the importance of workforce training, robust technological infrastructure, data-driven cultures, and strategic partnerships for SMEs. Furthermore, the review highlights methods for measuring and optimizing AI\u2019s value, such as tracking key performance indicators and improving customer satisfaction. While acknowledging challenges like financial constraints and ethical considerations, this research provides practical guidance for SMEs to effectively leverage AI for sustainable growth and provides a foundation for future studies to explore customized AI strategies for diverse SME contexts.<\/jats:p>","DOI":"10.3390\/info16050415","type":"journal-article","created":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T06:31:29Z","timestamp":1747636289000},"page":"415","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Artificial Intelligence in SMEs: Enhancing Business Functions Through Technologies and Applications"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5324-2746","authenticated-orcid":false,"given":"Thang","family":"Le Dinh","sequence":"first","affiliation":[{"name":"Research Institute on SMEs, Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res, Trois-Rivi\u00e8res, QC G8Z 4M3, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9401-905X","authenticated-orcid":false,"given":"Manh-Chi\u00ean","family":"Vu","sequence":"additional","affiliation":[{"name":"Accounting Sciences Department, Universit\u00e9 du Qu\u00e9bec en Outaouais, Gatineau, QC J8X 3X7, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4205-2357","authenticated-orcid":false,"given":"Giang T.C.","family":"Tran","sequence":"additional","affiliation":[{"name":"Research Institute on SMEs, Universit\u00e9 du Qu\u00e9bec \u00e0 Trois-Rivi\u00e8res, Trois-Rivi\u00e8res, QC G8Z 4M3, Canada"},{"name":"Faculty of Statistics and Informatics, Danang University of Economics, Danang 550000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Maslak, O.I., Maslak, M.V., Grishko, N.Y., Hlazunova, O.O., Pererva, P.G., and Yakovenko, Y.Y. (2021, January 21\u201324). Artificial intelligence as a key driver of business operations transformation in the conditions of the digital economy. Proceedings of the 2021 IEEE International Conference on Modern Electrical and Energy Systems (MEES), Kremenchuk, Ukraine.","DOI":"10.1109\/MEES52427.2021.9598744"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"036","DOI":"10.53771\/ijstra.2024.7.1.0055","article-title":"Driving SME innovation with AI solutions: Overcoming adoption barriers and future growth opportunities","volume":"7","author":"Iyelolu","year":"2024","journal-title":"Int. J. Sci. Technol. Res. Arch."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lu, X., Wijayaratna, K., Huang, Y., and Qiu, A. (2022). AI-enabled opportunities and transformation challenges for SMEs in the post-pandemic era: A review and research agenda. Front. Public Health, 10.","DOI":"10.3389\/fpubh.2022.885067"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Okoli, C., and Schabram, K. (2015). A guide to conducting a systematic literature review of information systems research. Commun. Assoc. Inf. Syst., 37.","DOI":"10.17705\/1CAIS.03743"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/jisss.2009070101","article-title":"Service science, management, engineering, and design (SSMED): An emerging discipline-outline & references","volume":"1","author":"Spohrer","year":"2009","journal-title":"Int. J. Inf. Syst. Serv. Sect. (IJISSS)"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.1007\/s10796-021-10186-w","article-title":"Artificial intelligence and business value: A literature review","volume":"24","author":"Enholm","year":"2022","journal-title":"Inf. Syst. Front."},{"key":"ref_7","unstructured":"Laudon, K.C., and Laudon, J.P. (2017). Essentials of Management Information Systems, Pearson. Available online: https:\/\/www.chegg.com\/textbooks\/essentials-of-management-information-systems-12th-edition-9780134238241-0134238249."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Shi, B.F., Bai, C.G., and Dong, Y.Z. (2024). A big data analytics method for assessing creditworthiness of SMEs: Fuzzy equifinality relationships analysis. Ann. Oper. Res., Available online: https:\/\/www.pure.ed.ac.uk\/ws\/portalfiles\/portal\/439976429\/ShiEtalAOR2024ABigDataAnalytics.pdf.","DOI":"10.1007\/s10479-024-06054-w"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Abdullah, A., Saraswat, S., and Talib, F. (2024). A maturity model for assessing Industry 4.0 implementation using data envelopment analysis and best and worst method approaches. Int. J. Prod. Perform. Manag., Available online: https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/ijppm-12-2023-0668\/full\/html?skipTracking=true.","DOI":"10.1108\/IJPPM-12-2023-0668"},{"key":"ref_10","first-page":"100107","article-title":"Adoption of digital technologies of smart manufacturing in SMEs","volume":"16","author":"Ghobakhloo","year":"2019","journal-title":"J. Ind. Inf. Integr."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Panigrahi, R.R., Shrivastava, A.K., Qureshi, K.M., Mewada, B.G., Alghamdi, S.Y., Almakayeel, N., Almuflih, A.S., and Qureshi, M.R.N. (2023). AI Chatbot Adoption in SMEs for Sustainable Manufacturing Supply Chain Performance: A Mediational Research in an Emerging Country. Sustainability, 15.","DOI":"10.3390\/su151813743"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bettoni, A., Matteri, D., Montini, E., Gladysz, B., and Carpanzano, E. (2021, January 7\u20139). An AI adoption model for SMEs: A conceptual framework. Proceedings of the IFAC PapersOnline, Budapest, Hungary.","DOI":"10.1016\/j.ifacol.2021.08.082"},{"key":"ref_13","unstructured":"Khan, S., Hassan, M.K., Rabbani, M.R., Atif, M., Sarac, M., and Hassan, M.K. (2025, March 30). An Artificial Intelligence-Based Islamic Fintech Model on Qardh-al-Hasan for COVID 19 Affected SMEs. Available online: https:\/\/iupress.istanbul.edu.tr\/en\/book\/islamic-perspective-for-sustainable-financial-system\/chapter\/an-artificial-intelligence-based-islamic-fintech-model-on-qardh-al-hasan-for-covid-19-affected-smes."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"114195","DOI":"10.1016\/j.dss.2024.114195","article-title":"Assessing financial distress of SMEs through event propagation: An adaptive interpretable graph contrastive learning model","volume":"180","author":"Wang","year":"2024","journal-title":"Decis. Support Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"471","DOI":"10.2507\/IJSIMM23-3-694","article-title":"Attachable Iot-Based Digital Twin Framework Specialized for Sme Production Lines","volume":"23","author":"Kang","year":"2024","journal-title":"Int. J. Simul. Model."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Fuentes, J., Aguilar, J., Montoya, E., and Pinto, A. (2024). Autonomous Cycles of Data Analysis Tasks for the Automation of the Production Chain of MSMEs for the Agroindustrial Sector. Information, 15.","DOI":"10.3390\/info15020086"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"103017","DOI":"10.1016\/j.technovation.2024.103017","article-title":"Bridging the \u2018Concept-Product\u2019 gap in new product development: Emerging insights from the application of artificial intelligence in FinTech SMEs","volume":"134","author":"Cubric","year":"2024","journal-title":"Technovation"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"H\u00e4rting, R.C., and Sprengel, A. (2019, January 4\u20136). Cost-benefit considerations for Data Analytics\u2014An SME-Oriented Framework enhanced by a Management Perspective and the Process of Idea Generation. Proceedings of the Knowledge-Based and Intelligent Information & Engineering Systems (KES 2019), Budapest, Hungary.","DOI":"10.1016\/j.procs.2019.09.324"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2919","DOI":"10.1007\/s00500-024-09640-z","article-title":"Coupling of SME innovation and innovation in regional economic prosperity with machine learning and IoT technologies using XGBoost algorithm","volume":"28","author":"Wang","year":"2024","journal-title":"Soft Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e12910","DOI":"10.1002\/eng2.12910","article-title":"Demonstrating computer vision to small- and medium-sized enterprises in manufacturing: Toward overcoming costs and implementation challenges","volume":"6","author":"Werheid","year":"2024","journal-title":"Eng. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.matpr.2019.08.248","article-title":"Design and development of intelligent handling system for components in small and medium scale industries","volume":"27","author":"Soundattikar","year":"2020","journal-title":"Mater. Today Proc."},{"key":"ref_22","first-page":"7619","article-title":"E-commerce utilization analysis and growth strategy for smes using an artificial intelligence","volume":"45","author":"Zhong","year":"2023","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_23","first-page":"100666","article-title":"Enhancing SMEs digital transformation through machine learning: A framework for adaptive quality prediction","volume":"41","author":"Chiu","year":"2024","journal-title":"J. Ind. Inf. Integr."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lee, K.J., Hwangbo, Y., Jeong, B., Yoo, J., and Park, K.Y. (2021). Extrapolative Collaborative Filtering Recommendation System with Word2Vec for Purchased Product for SMEs. Sustainability, 13.","DOI":"10.3390\/su13137156"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"115124","DOI":"10.1109\/ACCESS.2024.3445499","article-title":"Feature Enhanced Ensemble Modeling With Voting Optimization for Credit Risk Assessment","volume":"12","author":"Yang","year":"2024","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rawindaran, N., Jayal, A., and Prakash, E. (2021). Machine Learning Cybersecurity Adoption in Small and Medium Enterprises in Developed Countries. Computers, 10.","DOI":"10.3390\/computers10110150"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Willenbacher, M., Scholten, J., and Wohlgemuth, V. (2021). Machine Learning for Optimization of Energy and Plastic Consumption in the Production of Thermoplastic Parts in SME. Sustainability, 13.","DOI":"10.3390\/su13126800"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.bushor.2024.04.013","article-title":"The paradoxes of generative AI-enabled customer service: A guide for managers","volume":"67","author":"Ferraro","year":"2024","journal-title":"Bus. Horiz."},{"key":"ref_29","unstructured":"Bauer, M., van Dinther, C., Kiefer, D., and Assoc Informat, S. (2020, January 15\u201317). Machine Learning in SME: An Empirical Study on Enablers and Success Factors. Proceedings of the AMCIS 2020, Virtual."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"122492","DOI":"10.1016\/j.techfore.2023.122492","article-title":"Machine-learning-enabled intelligence computing for crisis management in small and medium-sized enterprises (SMEs)","volume":"191","author":"Zhao","year":"2023","journal-title":"Technol. Forecast. Soc. Change"},{"key":"ref_31","unstructured":"Mohanta, P.R., and Mahanty, B. Modelling Critical Success Factors for the Implementation of Industry 4.0 in Indian Manufacturing MSMEs. Proceedings of the IFIP Advances in Information and Communication Technology."},{"key":"ref_32","first-page":"869","article-title":"Multi-Step Clustering of Smart Meters Time Series: Application to Demand Flexibility Characterization of SME Customers","volume":"142","author":"Dormido","year":"2024","journal-title":"CMES-Comput. Model. Eng. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xia, Y., Xu, T., Wei, M.X., Wei, Z.K., and Tang, L.J. (2023). Predicting Chain\u2019s Manufacturing SME Credit Risk in Supply Chain Finance Based on Machine Learning Methods. Sustainability, 15.","DOI":"10.3390\/su15021087"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yoo, H.S., Jung, Y.L., and Jun, S.P. (2023). Prediction of SMEs\u2019 R&D performances by machine learning for project selection. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-34684-w"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"529","DOI":"10.15388\/24-INFOR559","article-title":"Pricing Powered by Artificial Intelligence: An Assessment Model for the Sustainable Implementation of AI Supported Price Functions","volume":"35","author":"Erdmann","year":"2024","journal-title":"Informatica"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Vargas, M., Mosquera, R., Fuertes, G., Alfaro, M., and Perez Vergara, I.G. (2024). Process Optimization in a Condiment SME through Improved Lean Six Sigma with a Surface Tension Neural Network. Processes, 12.","DOI":"10.3390\/pr12092001"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/j.bushor.2024.05.008","article-title":"Generative artificial intelligence in small and medium enterprises: Navigating its promises and challenges","volume":"67","author":"Rajaram","year":"2024","journal-title":"Bus. Horiz."},{"key":"ref_38","unstructured":"Villa, A., Taurino, T., Perrone, G.P., Villa, A., and Borgo, E. Promoting SME Innovation Through Collaboration and Collective-Intelligence Network in SMEs: The PMInnova Program. Proceedings of the IFIP Advances in Information and Communication Technology."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wen, Y. (2019, January 1\u20133). Research and Implementation of Intelligent ERP Platform for SMEs Based on Cloud Computing. Proceedings of the 2019 3RD International Conference on Artificial Intelligence Applications and Technologies (AIAAT 2019), Beijing, China.","DOI":"10.1088\/1757-899X\/646\/1\/012014"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"103649","DOI":"10.1016\/j.tre.2024.103649","article-title":"Roles of AI: Financing selection for regretful SMEs in e-commerce supply chains","volume":"189","author":"Yao","year":"2024","journal-title":"Transp. Res. Part E-Logist. Transp. Rev."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/JOEUC.333472","article-title":"The Optimization of Supply Chain Financing for Bank Green Credit Using Stackelberg Game Theory in Digital Economy Under Internet of Things","volume":"35","author":"Zhang","year":"2023","journal-title":"J. Organ. End User Comput."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Goga, A.S., Toth, Z., Meclea, M.A., Puiu, I.R., and Boscoianu, M. (2024). The Proliferation of Artificial Intelligence in the Forklift Industry-An Analysis for the Case of Romania. Sustainability, 16.","DOI":"10.20944\/preprints202410.0395.v1"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Tawil, A.R.H., Mohamed, M., Schmoor, X., Vlachos, K., and Haidar, D. (2024). Trends and Challenges towards Effective Data-Driven Decision Making in UK Small and Medium-Sized Enterprises: Case Studies and Lessons Learnt from the Analysis of 85 Small and Medium-Sized Enterprises. Big Data Cogn. Comput., 8.","DOI":"10.3390\/bdcc8070079"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Mohamed, M., and Weber, P. (2020, January 15\u201317). Trends of digitalization and adoption of big data analytics among UK SMEs: Analysis and lessons drawn from a case study of 53 SMEs. Proceedings of the 2020 IEEE International Conference on Engineering, Technology and Innovation, ICE\/ITMC 2020, Cardiff, UK.","DOI":"10.1109\/ICE\/ITMC49519.2020.9198545"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e33516","DOI":"10.1016\/j.heliyon.2024.e33516","article-title":"Using a genetic backpropagation neural network model for credit risk assessment in the micro, small and medium-sized enterprises","volume":"10","author":"Chen","year":"2024","journal-title":"Heliyon"},{"key":"ref_46","first-page":"611","article-title":"Using artificial intelligence to analyze SME e-commerce utilization and growth strategies","volume":"24","author":"Wang","year":"2024","journal-title":"J. Comput. Methods Sci. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Basar, M.S., Christiansen, L., Nannerup, P.D., and Antonsen, M.G. (2022, January 6\u20139). Identification of Barriers to and Opportunities for Adoption of Machine Vision for Small and Medium-sized Enterprises. Proceedings of the 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), Stuttgart, Germany.","DOI":"10.1109\/ETFA52439.2022.9921607"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"91","DOI":"10.5267\/j.dsl.2024.10.011","article-title":"The impact of ChatGPT integration and customer relationship management on MSME sales performance with operational efficiency as a mediating variable","volume":"14","author":"Sutrisno","year":"2025","journal-title":"Decis. Sci. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s10479-023-05816-2","article-title":"Natural language processing analysis of online reviews for small business: Extracting insight from small corpora","volume":"341","author":"McCloskey","year":"2024","journal-title":"Ann. Oper. Res."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Mathieu, B., Anas, N., Thomas, P., Robert, P., and Samir, L. (2024). Exploring the applications of natural language processing and language models for production, planning, and control activities of SMEs in industry 4.0: A systematic literature review. J. Intell. Manuf., 1\u201321.","DOI":"10.1007\/s10845-024-02509-w"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1109\/TEM.2022.3203469","article-title":"Why Do SMEs Adopt Artificial Intelligence-Based Chatbots?","volume":"71","author":"Sharma","year":"2024","journal-title":"IEEE Trans. Eng. Manag."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Cordero, J., Barba-Guaman, L., and Guam\u00e1n, F. (2022). Use of chatbots for customer service in MSMEs. Appl. Comput. Inform., ahead-of-print.","DOI":"10.1108\/ACI-06-2022-0148"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"101685","DOI":"10.1016\/j.techsoc.2021.101685","article-title":"Chatbot for SMEs: Integrating customer and business owner perspectives","volume":"66","author":"Selamat","year":"2021","journal-title":"Technol. Soc."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.procs.2023.10.018","article-title":"Implementing Robotic Process Automation in small and medium-sized enterprises-implications for organisations","volume":"225","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_55","first-page":"84","article-title":"Robotic Process Automation in Small Enterprises: An Investigation into Application Potential","volume":"30","author":"Sven","year":"2023","journal-title":"Complex Syst. Inform. Model. Q."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Han, T.A., Pandit, D., Joneidy, S., Hasan, M.M., Hossain, J., Hoque Tania, M., Hossain, M.A., and Nourmohammadi, N. (2023, January 8\u201310). An Explainable AI Tool for Operational Risks Evaluation of AI Systems for SMEs. Proceedings of the 2023 15th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), Kuala Lumpur, Malaysia.","DOI":"10.1109\/SKIMA59232.2023.10387301"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1016\/j.ejor.2024.03.008","article-title":"Towards the development of an explainable e-commerce fake review index: An attribute analytics approach","volume":"317","author":"Das","year":"2024","journal-title":"Eur. J. Oper. Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1007\/s11301-024-00405-4","article-title":"Investigation of artificial intelligence in SMEs: A systematic review of the state of the art and the main implementation challenges","volume":"75","author":"Oldemeyer","year":"2024","journal-title":"Manag. Rev. Q."},{"key":"ref_59","unstructured":"Fillion, G., and Le Dinh, T. (2007, January 11\u201314). An Extended Model of Adoption of Technology in Households: A Model Test on People Using a Mobile Phone. Proceedings of the Allied Academies International Conference. Academy of Management Information and Decision Sciences, Jacksonville, FL, USA."},{"key":"ref_60","first-page":"141","article-title":"A knowledge-based model for context-aware smart service systems","volume":"6","author":"Thi","year":"2022","journal-title":"J. Inf."},{"key":"ref_61","unstructured":"Le Dinh, T., Phan, T.-C., and Bui, T. (2016, January 11\u201314). Towards an architecture for big data-driven knowledge management systems. Proceedings of the 22nd Americas Conference on Information Systems, AMCIS 2016, San Diego, CA, USA."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/5\/415\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:34:46Z","timestamp":1760031286000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/5\/415"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,18]]},"references-count":61,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["info16050415"],"URL":"https:\/\/doi.org\/10.3390\/info16050415","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,18]]}}}