Ioannis Koliousis , Abdulrahman Al-Surmi , Mahdi Bashiri
{"title":"人工智能与政策制定;小城市能否实现数字化转型?","authors":"Ioannis Koliousis , Abdulrahman Al-Surmi , Mahdi Bashiri","doi":"10.1016/j.ijpe.2024.109324","DOIUrl":null,"url":null,"abstract":"<div><p>This study investigates digital transformation and the usability of emerging technologies in policymaking. Prior studies categorised digital transformation into three distinct phases of digitisation, digitalisation, and digital transformation. They mainly focus on the operational or functional levels, however, this study considers digital transformation at the strategic level. Previous studies confirmed that using new emerging AI-based technologies will enable organisations to use digital transformation to achieve higher efficiency. A novel methodological AI-based approach for policymaking was constructed into three phases through the lens of organisational learning theory. The proposed framework was validated using a case study in the transportation industry of a small municipality. In the selected case study, a confirmatory model was developed and tested utilising the Structural Equation Modelling with data collected from a survey of 494 local stakeholders. Artificial Neural Network was utilised to predict and then to identify the most appropriate policy according to cost, feasibility, and impact criteria amongst six policies extracted from the literature. The results from this research confirm that utilisation of the AI-based strategic decision-making through the proposed generative AI platform at strategic level outperforms human decision-making in terms of applicability, efficiency, and accuracy.</p></div>","PeriodicalId":14287,"journal":{"name":"International Journal of Production Economics","volume":null,"pages":null},"PeriodicalIF":9.8000,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0925527324001816/pdfft?md5=d5a6f78050e7154306444c9ba6213fd1&pid=1-s2.0-S0925527324001816-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence and policy making; can small municipalities enable digital transformation?\",\"authors\":\"Ioannis Koliousis , Abdulrahman Al-Surmi , Mahdi Bashiri\",\"doi\":\"10.1016/j.ijpe.2024.109324\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study investigates digital transformation and the usability of emerging technologies in policymaking. Prior studies categorised digital transformation into three distinct phases of digitisation, digitalisation, and digital transformation. They mainly focus on the operational or functional levels, however, this study considers digital transformation at the strategic level. Previous studies confirmed that using new emerging AI-based technologies will enable organisations to use digital transformation to achieve higher efficiency. A novel methodological AI-based approach for policymaking was constructed into three phases through the lens of organisational learning theory. The proposed framework was validated using a case study in the transportation industry of a small municipality. In the selected case study, a confirmatory model was developed and tested utilising the Structural Equation Modelling with data collected from a survey of 494 local stakeholders. Artificial Neural Network was utilised to predict and then to identify the most appropriate policy according to cost, feasibility, and impact criteria amongst six policies extracted from the literature. The results from this research confirm that utilisation of the AI-based strategic decision-making through the proposed generative AI platform at strategic level outperforms human decision-making in terms of applicability, efficiency, and accuracy.</p></div>\",\"PeriodicalId\":14287,\"journal\":{\"name\":\"International Journal of Production Economics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":9.8000,\"publicationDate\":\"2024-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0925527324001816/pdfft?md5=d5a6f78050e7154306444c9ba6213fd1&pid=1-s2.0-S0925527324001816-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Production Economics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0925527324001816\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Production Economics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0925527324001816","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
Artificial intelligence and policy making; can small municipalities enable digital transformation?
This study investigates digital transformation and the usability of emerging technologies in policymaking. Prior studies categorised digital transformation into three distinct phases of digitisation, digitalisation, and digital transformation. They mainly focus on the operational or functional levels, however, this study considers digital transformation at the strategic level. Previous studies confirmed that using new emerging AI-based technologies will enable organisations to use digital transformation to achieve higher efficiency. A novel methodological AI-based approach for policymaking was constructed into three phases through the lens of organisational learning theory. The proposed framework was validated using a case study in the transportation industry of a small municipality. In the selected case study, a confirmatory model was developed and tested utilising the Structural Equation Modelling with data collected from a survey of 494 local stakeholders. Artificial Neural Network was utilised to predict and then to identify the most appropriate policy according to cost, feasibility, and impact criteria amongst six policies extracted from the literature. The results from this research confirm that utilisation of the AI-based strategic decision-making through the proposed generative AI platform at strategic level outperforms human decision-making in terms of applicability, efficiency, and accuracy.
期刊介绍:
The International Journal of Production Economics focuses on the interface between engineering and management. It covers all aspects of manufacturing and process industries, as well as production in general. The journal is interdisciplinary, considering activities throughout the product life cycle and material flow cycle. It aims to disseminate knowledge for improving industrial practice and strengthening the theoretical base for decision making. The journal serves as a forum for exchanging ideas and presenting new developments in theory and application, combining academic standards with practical value for industrial applications.