Understanding digital engagement: factors influencing awareness and satisfaction of digital transformation

IF 1.7 3区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Information Retrieval Journal Pub Date : 2024-07-25 DOI:10.1007/s10791-024-09455-4
Hyeon Jo, Hyun Yong Ahn
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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.

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了解数字化参与:影响对数字化转型的认识和满意度的因素
在以快速数字化转型为标志的时代,了解影响数字化参与的因素对于消除数字鸿沟至关重要。本研究旨在探讨网络动机、社交媒体使用、内容服务使用和经济活动等个体因素对数字化转型意识和满意度的影响。本研究利用偏最小二乘法结构方程模型(PLS-SEM)分析了来自国家信息中心(NIA)2022 年数字鸿沟调查的 7000 名受访者的数据,为数字参与的动态变化提供了实证见解。研究结果表明,建立联系的动机会极大地影响社交媒体的使用,而社交媒体的使用又会略微提高人们对数字化转型的认识,但却不会提高人们的满意度。相反,经济活动对数字化转型的认知度和满意度都有积极影响,凸显了数字经济参与的切实益处。生活服务利用率是一个关键因素,对认知度和满意度都有显著影响。这些结果为政策制定者、教育者和数字平台开发者提供了重要启示,表明有必要制定有针对性的战略,以提高数字素养,促进包容性数字服务,并在数字领域创造经济机会。
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来源期刊
Information Retrieval Journal
Information Retrieval Journal 工程技术-计算机:信息系统
CiteScore
6.20
自引率
0.00%
发文量
17
审稿时长
13.5 months
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.
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