{"title":"Understanding digital engagement: factors influencing awareness and satisfaction of digital transformation","authors":"Hyeon Jo, Hyun Yong Ahn","doi":"10.1007/s10791-024-09455-4","DOIUrl":null,"url":null,"abstract":"<p>In an era marked by rapid digital transformation, understanding the factors that influence digital engagement is crucial for bridging the digital divide. This study aims to explore the impact of individual factors such as networking motive, social media use, content service usage, and economic activity on digital transformation awareness and satisfaction. Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data from 7,000 respondents of the National Information Society Agency (NIA)'s 2022 Digital Divide Survey, this research provides empirical insights into the dynamics of digital engagement. The findings reveal that networking motive significantly predicts social media use, which in turn slightly enhances digital transformation awareness but not satisfaction. Conversely, economic activity positively influences both awareness and satisfaction with digital transformation, underscoring the tangible benefits of digital economic engagement. Life service utilization emerged as a crucial factor, significantly impacting both awareness and satisfaction. These results offer critical implications for policymakers, educators, and digital platform developers, suggesting the need for targeted strategies to enhance digital literacy, promote inclusive digital services, and foster economic opportunities in the digital domain.</p>","PeriodicalId":54352,"journal":{"name":"Information Retrieval Journal","volume":"14 1","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Retrieval Journal","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10791-024-09455-4","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In an era marked by rapid digital transformation, understanding the factors that influence digital engagement is crucial for bridging the digital divide. This study aims to explore the impact of individual factors such as networking motive, social media use, content service usage, and economic activity on digital transformation awareness and satisfaction. Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data from 7,000 respondents of the National Information Society Agency (NIA)'s 2022 Digital Divide Survey, this research provides empirical insights into the dynamics of digital engagement. The findings reveal that networking motive significantly predicts social media use, which in turn slightly enhances digital transformation awareness but not satisfaction. Conversely, economic activity positively influences both awareness and satisfaction with digital transformation, underscoring the tangible benefits of digital economic engagement. Life service utilization emerged as a crucial factor, significantly impacting both awareness and satisfaction. These results offer critical implications for policymakers, educators, and digital platform developers, suggesting the need for targeted strategies to enhance digital literacy, promote inclusive digital services, and foster economic opportunities in the digital domain.
期刊介绍:
The journal provides an international forum for the publication of theory, algorithms, analysis and experiments across the broad area of information retrieval. Topics of interest include search, indexing, analysis, and evaluation for applications such as the web, social and streaming media, recommender systems, and text archives. This includes research on human factors in search, bridging artificial intelligence and information retrieval, and domain-specific search applications.