{"title":"Evaluation of an artificial intelligence project in the software industry based on fuzzy analytic hierarchy process and complex adaptive systems","authors":"Tsung-Sheng Chang","doi":"10.1108/jeim-02-2022-0056","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>Artificial intelligence (AI) is the most progressive commodity among current information system applications. In-house development and sales of beneficial products are difficult for many software development and service companies (SDSCs). SDSCs have some implicit concerns about implementing AI software development due to the complexity of AI technology; they require an evaluation framework to avoid development failure. To fill the void, this study identified the factors influencing SDSCs when developing AI software development.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>Based on complex adaptive systems theory, three aspects were developed as the main factors of hierarchy, namely, employees' capabilities, environmental resources and team capabilities. Fuzzy analytic hierarchy process (FAHP) was used to assess the SDSCs' attitude. Based on SDSCs, attitudes toward implementing AI software projects were collected to calculate the hierarchy of factors.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>The outcome of FAHP is used as understanding the key factors of SDSCs for selecting an AI software project, toward the improvement of overall project planning. Employees' stress resistance was considered as a priority for the project, although professional AI skills and resources were also important.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>This study suggested three variables developed using complex adaptive systems. This study contributes to a better understanding of the critical aspects of developing AI software projects in SDSCs. The study's findings have practical and academic implications for SDSCs and subsequent academic development, broadening the scope of AI software development research.</p><!--/ Abstract__block -->","PeriodicalId":47889,"journal":{"name":"Journal of Enterprise Information Management","volume":"5 2","pages":""},"PeriodicalIF":7.4000,"publicationDate":"2023-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Enterprise Information Management","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1108/jeim-02-2022-0056","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 2
Abstract
Purpose
Artificial intelligence (AI) is the most progressive commodity among current information system applications. In-house development and sales of beneficial products are difficult for many software development and service companies (SDSCs). SDSCs have some implicit concerns about implementing AI software development due to the complexity of AI technology; they require an evaluation framework to avoid development failure. To fill the void, this study identified the factors influencing SDSCs when developing AI software development.
Design/methodology/approach
Based on complex adaptive systems theory, three aspects were developed as the main factors of hierarchy, namely, employees' capabilities, environmental resources and team capabilities. Fuzzy analytic hierarchy process (FAHP) was used to assess the SDSCs' attitude. Based on SDSCs, attitudes toward implementing AI software projects were collected to calculate the hierarchy of factors.
Findings
The outcome of FAHP is used as understanding the key factors of SDSCs for selecting an AI software project, toward the improvement of overall project planning. Employees' stress resistance was considered as a priority for the project, although professional AI skills and resources were also important.
Originality/value
This study suggested three variables developed using complex adaptive systems. This study contributes to a better understanding of the critical aspects of developing AI software projects in SDSCs. The study's findings have practical and academic implications for SDSCs and subsequent academic development, broadening the scope of AI software development research.
期刊介绍:
The Journal of Enterprise Information Management (JEIM) is a significant contributor to the normative literature, offering both conceptual and practical insights supported by innovative discoveries that enrich the existing body of knowledge.
Within its pages, JEIM presents research findings sourced from globally renowned experts. These contributions encompass scholarly examinations of cutting-edge theories and practices originating from leading research institutions. Additionally, the journal features inputs from senior business executives and consultants, who share their insights gleaned from specific enterprise case studies. Through these reports, readers benefit from a comparative analysis of different environmental contexts, facilitating valuable learning experiences.
JEIM's distinctive blend of theoretical analysis and practical application fosters comprehensive discussions on commercial discoveries. This approach enhances the audience's comprehension of contemporary, applied, and rigorous information management practices, which extend across entire enterprises and their intricate supply chains.