{"title":"Critical Obstacles Affecting Adoption of Industrial Big Data Solutions in Smart Factories","authors":"Fei Xing, G. Peng, Jia Wang, Daifeng Li","doi":"10.4018/jgim.314789","DOIUrl":null,"url":null,"abstract":"Industrial big data is the key to realize the vision of smart factories. This research aims to identify and explore potential barriers that prevent organizations from deploying industrial big data solutions in the development of smart factories through a socio-technical perspective. The research follows an inductive qualitative approach. Twenty-seven semi-structured interviews were conducted with the CEO, smart factory manager, IT managers, departmental heads, and IS consultants in the selected case company. The interview data were analyzed using a thematic analysis method. Derived from a thematic analysis, six sets of barriers including technical, data, technical support, organization, individual, and social issues were identified, as well as the relationships between them. An empirical framework was developed to highlight the relationship between these barriers. This study contributes to the knowledge of industrial big data in general and provides constructive insight into industrial big data implementation in smart factory development particularly.","PeriodicalId":46306,"journal":{"name":"Journal of Global Information Management","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Global Information Management","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.4018/jgim.314789","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: 6
Abstract
Industrial big data is the key to realize the vision of smart factories. This research aims to identify and explore potential barriers that prevent organizations from deploying industrial big data solutions in the development of smart factories through a socio-technical perspective. The research follows an inductive qualitative approach. Twenty-seven semi-structured interviews were conducted with the CEO, smart factory manager, IT managers, departmental heads, and IS consultants in the selected case company. The interview data were analyzed using a thematic analysis method. Derived from a thematic analysis, six sets of barriers including technical, data, technical support, organization, individual, and social issues were identified, as well as the relationships between them. An empirical framework was developed to highlight the relationship between these barriers. This study contributes to the knowledge of industrial big data in general and provides constructive insight into industrial big data implementation in smart factory development particularly.
期刊介绍：
Authors are encouraged to submit manuscripts that are consistent to the following submission themes: (a) Cross-National Studies. These need not be cross-culture per se. These studies lead to understanding of IT as it leaves one nation and is built/bought/used in another. Generally, these studies bring to light transferability issues and they challenge if practices in one nation transfer. (b) Cross-Cultural Studies. These need not be cross-nation. Cultures could be across regions that share a similar culture. They can also be within nations. These studies lead to understanding of IT as it leaves one culture and is built/bought/used in another. Generally, these studies bring to light transferability issues and they challenge if practices in one culture transfer.
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