With the recent proliferation of ICT-based digital technologies and the widespread use of devices such as smartphones and tablets, digital transformation has rapidly progressed across various areas of daily life. From basic services such as shopping a...
With the recent proliferation of ICT-based digital technologies and the widespread use of devices such as smartphones and tablets, digital transformation has rapidly progressed across various areas of daily life. From basic services such as shopping and ticket reservations to banking, stock trading, and insurance, service channels have shifted from offline to online, and access to and use of information has become a necessity rather than a choice. In particular, e-commerce and digital finance have emerged as the most actively digitized domains, and unlike services that serve leisure functions, they have become essential economic activities that directly impact consumers’ daily lives (KPMG, 2020; World Bank, 2021).
Digital transformation offers consumers various benefits such as time savings and monetary advantages. However, marginalized groups who face difficulties in utilizing digital tools are at risk of being excluded from these potential benefits, leading to inequalities in opportunity. This has become a pressing social issue known as the “digital divide,” which manifests not only in terms of access but also in the level of digital usage (Gonzales, 2016). In particular, socially disadvantaged groups who struggle with fear of technology and low self-confidence often lack the willingness and capacity for self-directed learning, leading to limited use of digital technologies. To address this, social support and educational assistance are essential. The government has responded by implementing programs such as the “Digital Learning Center” initiative and is working to bridge the gap through partnerships with local communities and the private sector (Ko & Park, 2020; Ministry of Science and ICT, 2022).
Meanwhile, as interest in the digital divide has increased, numerous studies have investigated its influencing factors. Prior research has highlighted demographic factors such as income, age, and education level, psychological factors such as digital literacy, motivations, and attitudes, and social factors such as social support (Lythreatis et al., 2022). In particular, social support, defined as the emotional, informational, and material assistance provided within an individual’s social environment, has recently gained attention in the field of consumer studies as a key facilitator of digital technology adoption and utilization (Kappeler et al., 2020; Gu & Sa, 2024; Oh, 2017; Hwang & Hwang, 2017). However, levels of social support vary among individuals, and these differences may stem from regional disparities. This is because digital utilization is influenced not only by individual characteristics but also by environmental characteristics of the communities to which individuals belong.
Nevertheless, most studies that have examined the factors influencing digital information utilization have focused only on individual-level factors. Recently, however, there has been a growing body of research advocating for the inclusion of community-level approaches (Domenech-Abella et al., 2020; Kemperman et al., 2019), and such perspectives are increasingly being reflected in research on digital utilization. Therefore, this study aims to examine the level of digital service utilization and investigate differences based on individual and regional characteristics, using multilevel data that combines the 2022 Intelligent Information Society User Panel Survey with public datasets. Furthermore, the study seeks to analyze the influence of social support on consumers’ use of digital services. The analysis focuses particularly on e-commerce and digital financial services, which are the most actively digitized sectors, to explore the concrete patterns of digital transactional service use and the role of social support within these domains.
In this study, social support is defined as “all forms of assistance and resources received from or believed to be available from people and environments surrounding the individual.” It is classified into formal and informal social support based on the type of provider. At the individual level, the number of household members is used as a proxy variable for informal social support. At the regional level, the number of digital learning centers is used as a proxy for formal social support, and the accessibility of support networks is used as a proxy for informal regional-level support.
After excluding cases with missing values and responses from teenagers, the final dataset consists of individual-level data from 4,976 respondents and macro-level data from 17 metropolitan areas and 228 local governments. Analyses were conducted using Stata 18 SE and SPSS 26.0. To answer [Research Question 1], independent samples t-tests, ANOVA, and post hoc tests were used to determine whether there were group differences in consumers’ digital service utilization by individual and regional characteristics. For [Research Question 2], a multilevel modeling approach was applied to examine the effect of social support on digital service use.
The analysis results are as follows. First, examining consumers’ use of digital transactional services revealed that digital financial services were more frequently used than e-commerce services overall. Among the four types of digital services, the rate of non-use was particularly high for mobile payment services.
Second, differences in digital service utilization were observed based on individual and regional characteristics. At the individual level, utilization varied by gender, age, income, marital status, presence of children, educational attainment, occupation, and place of residence. Women showed higher levels of online shopping use than men. Older individuals demonstrated lower utilization levels, while higher income and higher education were associated with higher usage levels. Unmarried individuals and those without children had higher utilization rates, and professionals and office workers showed the highest use levels among occupational groups. Urban residents used all types of digital services more than those in rural areas.
Regarding regional characteristics, digital learning center accessibility was negatively associated with e-commerce and mobile payment use—regions with lower accessibility showed higher usage. This suggests that these regions may already possess higher digital competencies. In contrast, for financial services, higher support network accessibility was associated with higher service utilization. Gross Regional Domestic Product (GRDP) showed a more pronounced relationship in the digital financial domain than in e-commerce, with residents of higher-GRDP regions using financial services more than those in middle- and low-GRDP regions. Furthermore, access to offline financial branches was positively associated with digital financial service utilization, suggesting that even with physical access, consumers continue to prefer the advantages offered by digital services.
Third, the multilevel model revealed that the influence of social support on digital service use varied by service type. At the individual level, the number of household members had a significantly positive effect on online shopping and financial service use, indicating that informal support plays a role in specific contexts. At the regional level, accessibility to digital learning centers significantly influenced the use of reservation and booking services. This may be due to the focus of these educational programs on such practical applications and the relatively low learning burden associated with them. Support network accessibility had a significant positive effect on both financial and mobile payment service use. Given the financial and trust-sensitive nature of these services, environments with accessible and reliable support networks are more conducive to active use.
Moreover, expectations regarding technology and digital competency—used as control variables—had significant positive effects on all service types, which aligns with prior studies suggesting that favorable perceptions of technology and high digital proficiency promote digital service use. Lastly, GRDP was found to significantly influence online shopping only, possibly due to better logistics and delivery infrastructure and higher consumer purchasing power in economically developed regions.