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Abstract

Older adults often face challenges with digital economic engagement due to concerns about cybersecurity and low levels of digital literacy, particularly a lack of confidence in their cybersecurity skills. This study examined how security behavior self-efficacy influences the digital economic behaviors of older adults. We analyzed data from 1808 South Korean participants aged 65 and older using Partial Least Squares Structural Equation Modeling (PLS-SEM). The variables included digital economic behaviors, security behavior self-efficacy, mis-and disinformation self-efficacy, and privacy self-efficacy. The findings revealed that higher cybersecurity self-efficacy positively affected the digital economic behaviors of older adults. Additionally, security behavior self-efficacy was a significant predictor of both misinformation and disinformation self-efficacy as well as privacy self-efficacy. In turn, both mis-and disinformation self-efficacy and privacy self-efficacy positively influenced digital economic behaviors, with partial mediation effects identified. Overall, cybersecurity self-efficacy is essential for older adults’ participation in the digital economy, impacting their engagement both directly and indirectly through improved awareness and privacy management. These findings highlight the need for targeted educational programs and policies aimed at enhancing digital literacy and building confidence in cybersecurity, ultimately promoting safe and effective digital participation among the elderly.

Keywords

self-efficacy cybersecurity self-efficacy digital economic behaviors older adults cross-sectional studies

Cybersecurity Self-Efficacy’s Importance

The study emphasizes that cybersecurity self-efficacy is a vital factor for older adults’ engagement in the digital economy. It influences their digital economic behaviors both directly and indirectly by enhancing awareness and privacy management.

Direct and Indirect Effects

Higher cybersecurity self-efficacy positively impacts the digital economic behaviors of older adults. Specifically, security behavior self-efficacy predicts misinformation and disinformation self-efficacy, as well as privacy self-efficacy. These, in turn, positively influence digital economic behaviors, showing partial mediation effects.

Three Dimensions of Self-Efficacy

The research identifies three key dimensions of cybersecurity self-efficacy: security behavior self-efficacy, mis-and disinformation self-efficacy, and privacy self-efficacy. These dimensions cover aspects like managing security features, discerning credible information, and controlling personal privacy.

Practical Implications

The findings underscore the need for targeted educational programs and policies to improve digital literacy and build cybersecurity confidence among older adults. This approach aims to promote safe and effective digital participation, thereby supporting digital inclusion as a vital economic facilitator for the aging population.

Introduction

The rapid digital transformation of economies worldwide has significantly changed how individuals engage in financial and commercial activities. As the global population ages, older adults have become an important demographic in the digital economy. The experience of aging is now closely linked to digital transformation,¹ with everyday tasks increasingly dependent on digital interfaces.^2,3 This shift presents considerable challenges, particularly for older adults, who encounter substantial barriers to digital engagement.⁴ This phenomenon, known as digital exclusion, results in serious social and economic consequences, limiting access to essential services, financial opportunities, and social interactions.^5
-7 The terms “digital divide” and “digital exclusion” are often used interchangeably, but they represent different aspects of the larger issue of digital inequality. The digital divide primarily refers to the gap between those who have access to digital technologies and those who do not, emphasizing economic and infrastructural factors.⁸ On the other hand, digital exclusion highlights the consequences of the technological divide, emphasizing how those lacking technological skills or resources experience cognitive marginalization, which leads to social, economic, and financial challenges.⁹ Understanding this distinction is essential for grasping the complex nature of digital inequality and for creating comprehensive strategies to address it. Research indicates that digital exclusion exacerbates feelings of isolation and depressive symptoms among older adults,¹⁰ highlighting the urgent need to promote digital inclusion. In South Korea, a leader in digital connectivity, many older adults struggle to fully integrate into the digital economy due to technological complexities, cybersecurity concerns, and varying levels of digital literacy.¹¹

Ensuring equal access and participation for older adults is essential as governments and businesses increasingly shift services online.⁷ Much of the existing research on technology and older adults has primarily focused on technology adoption^12,13 and older adults’ attitudes toward technology.^3,14,15 However, there is limited research investigating how older adults’ cybersecurity self-efficacy affects their digital economic behaviors. Cybersecurity entails protecting personal information and devices from cyber threats through various measures, such as using strong passwords, enabling 2-factor authentication, and recognizing phishing attempts.¹⁶ Cybersecurity self-efficacy refers to an individual’s belief in their own ability to perform cybersecurity-related tasks.¹⁷ This psychological concept is vital in shaping how people respond to increasing IT security challenges. Emerging studies are beginning to explore the cybersecurity vulnerabilities that older adults face, alongside their digital economic behaviors in online environments.¹⁸ In South Korea, the safety of the elderly in digital environments is improved through a combination of strong cybersecurity laws, specialized monitoring systems, and proactive data management policies. The government has implemented a comprehensive cybersecurity framework that includes national laws and a coordinated pan-government cybersecurity plan. This framework aims to protect sensitive data across various sectors.¹⁹ Launched in 2022, the MyData service enables individuals to actively manage their personal data while implementing strict security measures to protect against breaches.²⁰ While these safeguards significantly enhance online safety for the elderly, challenges still exist, particularly regarding the risk of data breaches that could compromise sensitive information. Continuous improvement and adaptation of security protocols are essential to address these evolving threats. However, many older adults lack the skills and confidence needed for digital financial transactions, e-commerce, and online banking.^6,21 They are particularly vulnerable to cyber threats such as phishing scams, identity theft, and fraudulent financial schemes.^22,23 The prevalence of cyber scams targeting the elderly in South Korea is a serious concern that mirrors global trends. A significant percentage of older adults have reported falling victim to various online scams, with estimates indicating that approximately 30% of seniors in South Korea have experienced some form of cyber fraud.²⁴ Older adults are also more susceptible to mis-and disinformation and deceptive digital content.²⁵ Concerns about security breaches deter older adults from utilizing digital financial services, even when the necessary technology is accessible.²⁶ A lack of confidence in identifying fraudulent financial schemes and misleading investment opportunities undermines digital engagement. Additionally, privacy concerns related to unauthorized data collection often deter older adults from using mobile banking, digital payments, and online shopping platforms.²⁷

Research on cybersecurity self-efficacy indicates that boosting confidence in digital security is vital for encouraging greater economic participation among older adults.²⁸ Despite the growing scholarly focus on digital inclusion and aging, several research gaps persist in understanding the digital economic behaviors of older adults, particularly concerning cybersecurity self-efficacy.²⁹ While self-efficacy has been widely studied in technology adoption,³⁰ the specific impact of cybersecurity self-efficacy on digital economic behaviors among older adults remains underexplored.³¹ Existing literature emphasizes that older adults are largely overlooked in cybersecurity research, highlighting their vulnerability to cyber threats.³² There is a lack of sufficient studies focused specifically on this demographic, which is critical given their unique cognitive and emotional changes. To support this claim, the authors propose a review and compilation of relevant prior studies that address the experiences and challenges older adults face in cyberspace. This approach aims to illustrate the gap in research and the need for targeted investigations. Addressing these gaps is crucial to ensuring that older adults can fully engage in digital economic behaviors.

This study aims to examine how cybersecurity self-efficacy impacts the digital economic behaviors of older adults in South Korea. By analyzing security behavior self-efficacy, mis-and disinformation self-efficacy, and privacy self-efficacy, this research seeks to uncover the mechanisms that drive older adults’ engagement in digital economic behaviors. This study will provide empirical evidence from South Korea and contribute to important discussions on cybersecurity self-efficacy and digital economic behaviors among older populations.

Literature Review and Hypothesis Development

Digital Economic Behaviors

As digital services become more integrated into everyday life, older adults who do not engage with these technologies are at a greater risk of digital exclusion. Digital exclusion refers to the inability to access or effectively use digital technologies and services due to various barriers such as lack of access to devices (often referred to as device poverty), unaffordable data plans (data poverty), insufficient digital skills or knowledge, and restricted internet usage.⁶ This exclusion can prevent older adults from improving their cognitive abilities and self-efficacy through technology.³³ Additionally, it can limit their access to essential services and economic opportunities⁷ while increasing their risk of developing depressive symptoms.¹⁰

Meanwhile, digital transformation and demographic change are 2 key processes that have significantly influenced the experience of aging.¹ Everyday digital services, such as ticket machines, self-checkout kiosks, cash machines, and online reservations, have become essential aspects of daily life.² These digital technologies and services are transforming the ways older adults (ages 65 and older) interact socially and economically, affecting how they receive and access information and content.³⁴ These technologies can be particularly beneficial for older adults (ages 65 and older) by helping them maintain social connections and mental health, ensuring their independence, and enabling them to engage in important life goals.³⁵

Thus, improving the inclusion and engagement of older adults in digital technology is becoming increasingly important.^5,36 Digital inclusion for older adults, especially regarding digital economic behavior, involves ensuring that they have the necessary access, skills, and confidence to participate effectively in the digital economy. This includes activities such as online banking, e-commerce, digital payment systems, and accessing financial services. By prioritizing digital inclusion, we can ensure that older adults are not left behind, but instead can actively contribute to and benefit from the opportunities of the digital world.

The narrative around aging and digital economic behaviors must change. In the context of older adults, digital economic behavior refers to the degree to which individuals use digital devices to engage in economic activities and to access economic opportunities, including digital marketing, financial services, and e-commerce engagement. To encourage the engagement of older adults in the digital economy, recent research shows that it offers new opportunities for older adults to manage their finances, find jobs, and access health-related information.³⁷ For example, digital learning platforms have become important tools for promoting workforce inclusion. Furthermore, training in digital literacy—covering topics like email security and financial management—is essential for protecting older adults and increasing their awareness of potential cyber scams, thereby helping to close the technological gap.³⁸ Additionally, such training can enhance cognitive function, reduce feelings of isolation, boost self-confidence, and promote social integration among the elderly.³⁹ Digital inclusion programs for the elderly are essential for ensuring their access to technology and online opportunities. These initiatives focus on providing digital literacy training tailored for older adults and often collaborate with local agencies to recruit participants and deliver relevant training.⁴⁰ It’s important for these programs to consider the diverse needs of older adults to make the training effective.⁴¹ However, significant gaps remain in reaching all elderly individuals, especially those facing financial barriers or lacking access to technology. Addressing these challenges is crucial for achieving digital inclusion. As the digital economy continues to evolve, it is vital to address these aspects to inspire older adults to participate in digital economic behavior, ultimately empowering them to make the most of these opportunities. Despite their growing importance, there is limited knowledge about the elderly digital economic behavior and its facilitating factors among individuals aged 65 years and older. This study aims to fill this gap by providing representative data from South Korea.

Cybersecurity Self-Efficacy

Self-efficacy is a key concept in social cognitive theory (SCT), as proposed by Bandura.⁴² It refers to an individual’s belief in their ability to perform the behaviors necessary to achieve specific goals. Essentially, self-efficacy represents how people evaluate their own capacity to accomplish tasks or succeed within a given context.⁴³ Individuals with a high level of self-efficacy tend to possess a strong conviction(or confidence) in their ability to harness the motivation, cognitive resources, and strategies needed to complete tasks successfully.⁴⁴ Furthermore, SCT highlights the importance of self-efficacy in controlling behavior during potentially threatening situations. Those with a robust sense of self-efficacy are more likely to focus on analyzing problems and developing effective solutions.⁴⁵ This belief in one’s abilities is a key determinant of behavior.⁴³ For older adults, having cybersecurity self-efficacy can be a crucial predictor of their engagement in and persistence with digital economic behaviors.

Researchers in the information systems field developed computer self-efficacy (CSE) based on the general concept of self-efficacy, and it was first introduced by Davis et al.⁴⁶ CSE is defined as an individual’s judgment of one’s ability to use a computer.⁴⁷ It has been associated with various end-user computing behaviors, including the adoption of information systems,⁴⁸ the use of ICT by older adults,³⁰ digital literacy training for older adults learning.^49,50 Meanwhile, Bandura⁵¹ highlights the significance of domain specificity and cautions against using self-efficacy measures that are not linked to specific contexts. Supporting this notion, Marakas et al⁵² clarify the concept of CSE by distinguishing between general self-efficacy and task-specific self-efficacy. General CSE captures a person’s overall confidence across various computer applications. Meanwhile, task-specific CSE is vital as it reflects an individual’s confidence to perform specific computer-related tasks within the vast realm of general computing. It is crucial to recognize the importance of domain specificity, a point strongly made by Agarwal et al⁵³ and more recently by Gupta and Bostrom.⁵⁴ They highlight that the concept of “specificity” has often been neglected and emphasize the need for researchers to define study parameters with greater clarity. Moreover, they advocate for a revised understanding of CSE that directly ties to the tasks that users perform, as these tasks are the basis for their self-efficacy assessments and should consider the context and objectives of the research.

In line with these suggestions, we adapt the general definition of CSE to make it more relevant to the field of information security. Information security is defined as the protection of information and the systems that use, store, and transmit that information.⁵⁵ Self-efficacy in information security is defined as “a belief in one’s capability to protect information and information systems from unauthorized disclosure, modification, loss, destruction, and lack of availability.”²⁹ Meanwhile, research indicates that older adults may have heightened concerns regarding their ability to use digital technology, assess information on digital devices, and safeguard personal information while using these devices.⁴

Therefore, based on these characteristics, we define cybersecurity self-efficacy among older adults as the belief in one’s ability to control digital devices and the information shared, enabling them to use specific software applications for engaging in digital economic behaviors. Cybersecurity encompasses a wide range of knowledge, skills, and behaviors. The 3-dimensional model of cybersecurity self-efficacy is often supported by established self-efficacy theory, as well as its capacity to capture the complex nature of cybersecurity behaviors and its practical usefulness in developing targeted interventions. The cybersecurity lifecycle includes 3 key phases: prevention, detection, and response.⁵⁶ The selected 3 dimensions typically strike a balance between cognitive understanding, proactive protective behaviors, and reactive problem-solving, creating a robust and streamlined model for research and practice. The 3 dimensions—security behavior self-efficacy, misinformation and disinformation self-efficacy, and privacy self-efficacy—specifically address important aspects of cybersecurity engagement for older adults. This concept can be better understood through 3 key dimensions. First, security behavior self-efficacy reflects an individual’s beliefs in his/her own ability to manage and implement security features and settings of their personal digital devices, such as smartphones, computers, and tablets²⁸ A person with high security behavior self-efficacy feels confident in setting up security features like strong passwords, biometric authentication, and 2-factor authentication. They can also update software regularly, recognize potential vulnerabilities, and troubleshoot security issues when necessary. This skillset ensures that users maintain control over their devices, reducing the risk of unauthorized access and malware attacks.²³

Second, in the digital landscape, mis-and disinformation and deceptive content are widespread, posing security risks such as phishing scams, fake websites, and fraudulent messages. Misinformation is false or inaccurate information—getting the facts wrong, while disinformation is false information which is deliberately intended to mislead—intentionally misstating the facts.⁵⁷ Mis-and disinformation self-efficacy refers to an individual’s beliefs in his/her own ability to differentiate between legitimate information and factually incorrect, misleading, and/or hyper-partisan information when using digital services.²⁷ Older adults may be more susceptible to mis-and disinformation due to limited familiarity with digital cues that indicate credibility.²⁵ A person with strong mis-and disinformation self-efficacy can differentiate between credible and unreliable sources, identify suspicious emails or links, and verify claims before taking action. This dimension is essential in preventing older adults from falling victim to cyber threats that exploit mis-and disinformation.

Third, privacy self-efficacy encompasses an individual’s beliefs in his/her own ability to manage privacy in a given communication context.⁵⁸ It involves recognizing the importance of safeguarding personal information, such as social media profiles, banking details, and location data.⁵⁹ A person with strong privacy self-efficacy takes proactive measures to control their digital footprint by adjusting privacy settings, using encrypted communication channels, and limiting the amount of personal data shared online. This knowledge empowers older adults to make informed decisions about their digital interactions while reducing their exposure to identity theft and data breaches.

The Effects of Security Behavior Self-Efficacy

Security behavior self-efficacy reflects an individual’s beliefs in his/her own ability to effectively complete tasks related to cybersecurity on digital devices.²⁸ Security behavior self-efficacy has been linked to technology adoption.^28,60 Previous research has indicated that higher levels of security self-efficacy are associated with a stronger intention to adopt and use contact tracing applications⁶¹ and mobile commerce applications.³¹ This concept may also be relevant for engaging in digital economic behaviors.

Meanwhile, according to the Unified Theory of Acceptance and Use of Technology (UTAUT), 4 key factors influence the likelihood of technology adoption: performance expectancy, effort expectancy, social influence, and facilitating conditions.⁴⁸ While security behavior self-efficacy is not explicitly defined as an independent factor in the UTAUT framework, it can be understood as part of the facilitating conditions since it is a crucial resource for engaging in digital economic behaviors.⁴⁸ Therefore, it is expected that individuals with higher levels of security behavior self-efficacy are more likely to participate in digital economic behaviors. Thus, we propose the following hypothesis.

H1: Security behavior self-efficacy positively influences digital economic behaviors.

In the context of online information assessment within digital economic behaviors, previous studies have shown a significant correlation between sensemaking outcomes and levels of self-efficacy.^62,63 Security behavior self-efficacy enhances an individual’s ability to implement and execute security measures, such as creating strong passwords, performing timely software updates, and effectively detecting threats.²⁸ These skills contribute to a greater awareness of digital threats, including deceptive mis-and disinformation. Additionally, knowledge of cybersecurity often overlaps with digital literacy skills, which include verifying URLs, ensuring secure connections (HTTPS), and recognizing signs of manipulation.⁶⁴ For instance, an individual proficient at identifying phishing attempts or fraudulent websites is more likely to engage in critical thinking when evaluating the credibility of online news, emails, or social media communications.²⁷

Individuals who actively engage in protective security behaviors tend to fact-check information, identify deceptive patterns, and avoid interacting with unreliable sources. Empirical evidence suggests that older adults who take proactive security measures are generally more skeptical of dubious digital content, making them less susceptible to misinformation and disinformation.²⁵ This ability boosts their confidence in distinguishing between authentic and misleading information, thereby enhancing their self-efficacy regarding mis-and disinformation. Consequently, we propose the following hypothesis.

H2: Security behavior self-efficacy positively influences mis-and disinformation self-efficacy.

According to SCT, effectively leveraging digital services hinges on possessing the right knowledge and skills. Studies reveal that individuals with higher self-efficacy exhibit greater confidence in their ability to achieve various goals tied to digital services, especially when navigating privacy concerns.⁶⁵ Those with expertise in cybersecurity are far more likely to embrace proactive privacy-enhancing strategies. This may include utilizing privacy-focused browsers, disabling location tracking, and scrupulously evaluating data-sharing agreements before giving consent.⁶⁶

The idea of security behavior self-efficacy plays a pivotal role in helping individuals grasp digital vulnerabilities, thus heightening their awareness of potential privacy threats, including data surveillance, social engineering tactics, and unauthorized data exposure. This understanding is vital for empowering people, equipping them with the technical knowledge and strategic capabilities they need to take control of their online privacy settings. For instance, older adults who master 2-factor authentication, manage app permissions, and employ encrypted communication tools are significantly more inclined to adopt proactive measures to protect their personal data.²²

Since security behavior self-efficacy positively influences one’s confidence in managing privacy matters, it is reasonable to assert that past experiences with security-related behaviors on digital devices further bolster an individual’s belief in their capacity to safeguard privacy.^51,67 Moreover, research indicates that individuals who are highly confident in their digital security are more proactive in safeguarding their personal information, which in turn strengthens their privacy self-efficacy.²⁶ Therefore, we suggest the following hypothesis.

H3: Security behavior self-efficacy positively influences privacy self-efficacy.

The Effects of Mis-and Disinformation Self-Efficacy and Privacy Self-Efficacy

As digital economies continue to evolve, enhancing individuals’ ability to critically assess information is essential for fostering informed and secure behavior in the digital economic landscape. Consumers frequently encounter deceptive advertising, fraudulent reviews, and illegitimate financial schemes in this context.²⁷ Individuals with high levels of self-efficacy in recognizing and addressing mis-and disinformation are more likely to make informed decisions. Those who are confident in their ability to identify mis-and disinformation tend to make wiser choices regarding data privacy, subscription services, and financial agreements.²⁶ Additionally, the ability to distinguish between credible and misleading information significantly affects how individuals engage with digital financial products, such as online banking, digital wallets, and financial applications. Empirical evidence indicates that when users trust their abilities to differentiate credible information from inaccuracies, they are more likely to exhibit healthier online purchasing behaviors and investment strategies.⁶⁸

In conclusion, individuals with increased self-efficacy regarding mis-and disinformation are better equipped to navigate the digital landscape without falling into excessive skepticism or withdrawing from online economic activities.⁶⁹ Therefore, self-efficacy in relation to mis-and disinformation plays a crucial role in shaping behaviors within the digital economy. As a result, we posit the following hypothesis.

H4: Mis-and disinformation self-efficacy positively influences digital economic behaviors.

In the realm of digital economic behaviors, the relationship between consumers and service providers goes beyond simple transactions involving goods or services. In this context, consumers expect to receive accurate information from digital platforms, while companies aim to collect personal data about consumers to help predict their future purchasing behaviors. As a result, the information shared by consumers is primarily controlled by the companies, although consumers retain a secondary level of control over their data. This means that companies govern how data is collected and used, either for their own purposes or for sharing with external parties.

When considering digital economic behaviors as dependent variables, existing literature indicates that privacy self-efficacy significantly influences the likelihood of accepting digital services, such as Internet banking.⁷⁰ This finding supports earlier research conducted by Tan and Teo⁷¹ and Shih and Fang.⁷² Furthermore, individuals who are more confident in managing their privacy tend to believe they can effectively reduce the negative impacts of privacy violations. This belief often leads them to disclose more information and expand their privacy boundaries. The positive connection between privacy self-efficacy and privacy management has been confirmed by previous studies in both social media contexts^73,74 and e-commerce contexts.⁷⁵ We hypothesize that older adults with high privacy self-efficacy are more likely to fully utilize digital services, showing greater engagement in digital economic behaviors.⁷⁶ Thus, we propose the following hypothesis.

H5: Privacy self-efficacy positively influences digital economic behaviors.

Meanwhile, this study includes the perceived value of digital services as a control variable. Perceived value refers to the degree to which individuals believe digital technology will improve their daily lives. According to Zeithaml,⁷⁷ perceived value is a crucial factor in user engagement. The value-based adoption model suggests that perceived value is linked to higher satisfaction and a stronger intention to continue engaging with digital economic behaviors.⁷⁸ Previous research indicates that perceived value positively affects the intention to continue using digital services among older adults, including mobile health services⁷⁹ and the digital delivery of public services.⁸⁰ Recent research emphasizes the importance of the relationship between education and digital economic behavior. Education plays a significant role in shaping how individuals engage with the digital economy.⁸¹ Additionally, some studies point out that high-income individuals are better equipped to navigate this economy, which influences their choices in the labor market and workplace. These behaviors can be improved through nudging strategies to enhance outcomes in the digital economy.⁸²

All hypotheses are presented in Figure 1.

Figure 1.

Research model.

Methods

Data Collection

This investigation employed a secondary data from the “Report on Digital Divide 2021,” published by the Ministry of Science and ICT of the Republic of Korea.⁸³ Consistent with the OECD’s definition, older adults are defined as individuals aged 65 and above, recognizing that many countries begin offering pension and social security support at this age. Accordingly, data from participants in this age group were carefully extracted, processed, and analyzed. The survey was conducted through structured in-person interviews by trained specialists from a reputable survey organization. Data collection occurred between October and December 2021. In accordance with the Personal Information Protection Act, all data were anonymized to ensure compliance with regulatory standards. The survey used a proportional stratified probability sampling method to ensure accurate representation of various demographic groups, with a particular focus on vulnerable populations. This method aims to reflect the actual distribution of key groups within the national population, including the elderly, people with disabilities, low-income individuals, farmers and fishermen, North Korean defectors, and marriage immigrants. The total sample size consisted of 15 000 individuals. The sample size for each demographic group was determined in proportion to its estimated share of the national population or its relevance to policy. Among the general citizen group, the sample included 2300 elderly individuals to ensure adequate representation for age-based analysis. From a total sample of 2300 subjects aged 65 and older, the analysis focused on 1808 participants by excluding responses from 492 individuals identified as non-users of digital services.

This research was conducted following the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines to ensure thorough and transparent reporting.⁸⁴ The STROBE checklist was completed and is included as a Supplemental File.

Sample Demographics

Table 1 delineates the demographic characteristics of the sample. The distribution of participant genders is nearly balanced, with males constituting 50.2% and females 49.8%. The educational attainment levels are predominantly low, with 53.7% of participants being high school graduates or fewer. A mere 12.6% of individuals possess a college degree or higher. Income data indicates a reduction in participant numbers as income rises, particularly with a minimal proportion earning 9 million won or above, suggesting a predominance of individuals from moderate to low-income backgrounds. The age analysis highlights a focus on the older adult demographic, with an average age of 64.5 years and a median age of 64, thus validating the sample’s representation of the senior population.

Table 1.

Sample Profiles.

Items	Category	Frequency	Ratio (%)
Gender	Male	908	50.2
Gender	Female	900	49.8
Education	Elementary school graduate or below	185	10.2
	Middle school graduate or below	424	23.5
	High school graduate or below	971	53.7
	College graduate or above	228	12.6
Average monthly income (million won)	Less than 1	104	5.8
	1-2	346	19.1
	2-3	383	21.2
	3-4	359	19.9
	4-5	196	10.8
	5-6	195	10.7
	6-7	129	7.1
	7-8	52	2.9
	8-9	33	1.8
	9-10	1	0.1
	10 or more	10	0.6
Age	Mean	64.5
	Min	55.0
	Max	99.0
	Median	64.0
	Percentile	25th percentile: 59.0 50th percentile: 64.0 75th percentile: 70.0

Measurements

Table 2 illustrates that all variables have been refined for precision and operational alignment with the research model. Digital economic behaviors as a dependent variable refer to the degree to which individuals use digital devices to engage in economic activities and to access economic opportunities. This is measured by 3 questionnaire items. Among 3 independent variables, security behavior self-efficacy refers to an individual’s beliefs in his/her own ability to manage and implement security features and settings of their personal digital devices,²⁸ also measured by 3 questionnaire items. Mis-and disinformation self-efficacy refers to an individual’s beliefs in his/her own ability to critically evaluate the credibility and accuracy of information encountered in digital environments, measured by 3 questionnaire items as well.²⁷ Privacy self-efficacy refers to an individual’s beliefs in his/her own ability to manage privacy in a given communication context and to make informed choices about their privacy, and this is also evaluated through 3 questionnaire items.⁵⁸ Meanwhile, perceived value as a control variable, refers to the degree to which individuals believe digital devices will improve their daily lives, measured by 3 questionnaire items.⁸⁵

Table 2.

Operational Definitions of the Variables.

Variables	Definition of variables
Security behavior self-efficacy	An individual’s beliefs in his/her own ability to manage and implement security features and settings of their personal digital devices
Mis-and disinformation self-efficacy	An individual’s beliefs in his/her own ability to differentiate between legitimate information and factually incorrect, misleading, and/or hyper-partisan information when using digital services
Privacy self-efficacy	An individual’s beliefs in his/her own ability to manage privacy in a given communication context
Digital economic behaviors	The degree to which individuals use digital devices to engage in economic activities and to access economic opportunities
Perceived value	The degree to which individuals believe digital technology will improve their daily lives

Table 3 outlines the specific measurement items for each variable in the study. The dependent variable, digital economic behaviors, is assessed using a 4-point Likert type scale (Never, Seldom, Sometimes, Often). Three independent variables—security behavior self-efficacy, mis-and disinformation self-efficacy, and privacy self-efficacy—are evaluated with a 5-point Likert type scale (Strongly Disagree, Disagree, Neutral, Agree, Strongly Agree). Additionally, the control variable, perceived value, is measured using a 4-point Likert type scale (Strongly Disagree, Disagree, Agree, Strongly Agree). The study analyzed categorical control variables, such as gender and education, in our Partial Least Squares (PLS) analysis. Males are coded as 1 and females as 0 for the gender variable. In terms of education, those with an elementary school education or below serve as the reference group. Individuals who are middle school graduates or below are coded as 1, while others are coded as 0 (Edu Middle). High school graduates or below are also coded as 1, with others coded as 0 (Edu High). Finally, college graduates and those with higher degrees are coded as 1, while all others are coded as 0 (Edu College).

Table 3.

Reliability of Each Construct.

Items	Factor loading	Mean (Std.)	Cronbach’s alpha	CR	AVE
<Security behavior self-efficacy> “For the following questions, security measures are individual actions such as running and updating antivirus software, keeping passwords secure, running a firewall, when necessary, etc.” I feel comfortable taking measures to secure my smart personal devices. I have the resources and the knowledge to take the necessary security measures. I regularly take the necessary security measures to secure my smart personal devices.	0.75 0.91 0.91	2.52 (1.04)	.82	0.89	0.73
<Mis-and disinformation self-efficacy> I can effectively determine credible information by comparing search results with additional sources. I use reference materials and reputable websites to distinguish between authentic information and fake news. I adjust settings like filtering functions to minimize exposure to harmful information online.	0.93 0.94 0.95	2.02 (1.02)	.93	0.96	0.88
<Privacy self-efficacy> I understand how to manage my online communications to safeguard personal information on social media. I know the procedures to follow if I encounter defaming or insulting comments about myself online. I am familiar with the steps to report violations of my rights on social media platforms.	0.93 0.95 0.94	2.00 (1.04)	.94	0.96	0.90
<Digital economic behaviors> I have been involved in marketing activities, such as promotion or advertising, to help establish a business online. I actively seek out information related to financial technologies to sustain or boost my income through online activities. I participate in online shopping activities, such as group purchases or price comparisons, to reduce my expenses.	0.93 0.96 0.96	1.68 (0.79)	.94	0.96	0.89
<Perceived value> I believe that digital technology significantly improves my quality of life. I believe that digital technology has positive benefits for my daily life. I aim to further utilize digital technology in various areas of my life.	0.82 0.87 0.87	2.85 (0.60)	.81	0.89	0.73

Partial Least Squares Structural Equation Modeling (PLS-SEM), used within the SmartPLS platform, is designed to handle datasets that may not fully meet the assumptions required for traditional covariance-based SEM.⁸⁶ A key feature of PLS-SEM is its ability to standardize the dataset. This standardization process helps reduce the negative effects of differences in scale ranges. This robustness is an important reason why varying Likert type scales are often not seen as a significant obstacle in PLS-SEM.⁸⁷ Consequently, the fact that one construct uses a 4-point scale and another uses a 5-point scale is less of a critical issue to this study.

Results

Measurement Model

We employed PLS-SEM to assess both the measurement and structural aspects of our model using SmartPLS 4 software.^88,89 The PLS-SEM methodology is particularly beneficial when the research goal is to explore theoretical expansions of existing theories.⁹⁰ This study has enhanced the understanding and execution of digital economic behaviors, a concept that has not been thoroughly examined in relation to older adults, highlighting the empirical investigation of causal relationships among constructs from a predictive perspective. As a result, we utilized PLS-SEM for our data analysis.

To evaluate the measurement model, we examined all reflectively measured first-order constructs by analyzing both convergent and discriminant validity. As shown in Table 3, all individual items exhibited loading values that exceeded the recommended threshold, with factor loadings ranging from 0.75 to 0.96. For construct validity, composite reliability (CR) must be greater than 0.7, and the average variance extracted (AVE) should surpass 0.5.⁹¹ As presented in Table 3, the CR of all constructs varied between 0.89 and 0.96, while the AVE ranged from 0.73 to 0.90. Therefore, the reliability of all constructs significantly exceeded the recommended threshold.

To evaluate discriminant validity, the square root of AVE must exceed the correlations between constructs. This means that the mean variance shared by the construct and its indicators should surpass the variance shared with other constructs.⁹⁰ As shown in Table 4, the inter-construct correlations (presented in the off-diagonal elements) and the square root of the AVE (shown in the diagonal elements) demonstrate that all constructs have a higher variance with their respective indicators than with the variances shared with other constructs. Additionally, the Heterotrait-Monotrait Ratio (HTMT) is calculated to assess discriminant validity. The HTMT measure evaluates the average correlations among indicators across different constructs, with an acceptable threshold for discriminant validity suggested to be less than 0.90.⁹² As seen in Table 5, this research provides strong evidence supporting discriminant validity. In summary, our findings indicate that the measurement instruments for all constructs are reliable and exhibit adequate levels of both convergent and discriminant validity.

Table 4.

Squared Pairwise Correlations and Assessment of the Discriminant Validity.

No	Construct	1	2	3	4	5	6	7	8	9	10
1	Security behavior self-efficacy	0.86 *
2	Mis-and disinformation self-efficacy	0.76	0.94
3	Privacy self-efficacy	0.80	0.80	0.95
4	Digital economic behaviors	0.44	0.45	0.44	0.94
5	Perceived value	0.39	0.35	0.34	0.25	0.85
6	Gender	0.16	0.16	0.17	0.13	0.09	1.00
7	Average monthly income	0.42	0.35	0.32	0.29	0.29	0.06	1.00
8	Edu Middle	−0.30	−0.26	−0.22	−0.22	−0.14	−0.06	−0.29	1.00
9	Edu High	0.21	0.15	0.13	0.09	0.12	0.00	0.18	−0.60	1.00
10	Edu College	0.35	0.35	0.31	0.30	0.22	0.15	0.33	−0.21	−0.41	1.00

Figures along the diagonal in bold are values of the square root of the AVE.

Table 5.

Heterotrait–Monotrait Ratio (HTMT).

No	Construct	1	2	3	4	5	6	7	8	9
1	Security behavior self-efficacy
2	Mis-and disinformation self-efficacy	0.86
3	Privacy self-efficacy	0.88	0.86
4	Digital economic behaviors	0.51	0.49	0.46
5	Perceived value	0.48	0.41	0.38	0.29
6	Gender	0.18	0.17	0.18	0.13	0.10
7	Average monthly income	0.47	0.36	0.33	0.30	0.32	0.06
8	Edu Middle	0.33	0.27	0.23	0.23	0.16	0.06	0.29
9	Edu High	0.24	0.15	0.13	0.10	0.14	0.00	0.18	0.60
10	Edu College	0.39	0.36	0.32	0.31	0.25	0.15	0.33	0.21	0.41

In our study, all constructs were evaluated using the same self-reported methodology, raising the possibility of common method bias (CMB) as a relevant issue. To assess the implications of CMB, we implemented statistical remedies based on the recommendations of Podsakoff et al.⁹³ This research applied Harman’s single-factor test to determine the potentially harmful consequences of CMB on our findings.⁹⁴ An exploratory factor analysis was performed, encompassing all constructs under investigation. The findings revealed that the unrotated principal components factor analysis elucidated 75.6% of the overall variance, with a singular factor constituting less than 32.9% of the variability within the dataset. This is significantly below the recommended threshold of 50%.⁹⁴ These findings suggest that CMB is not a significant concern in this study.

Testing Hypotheses

The analysis of the structural model was executed utilizing the PLS bootstrapping technique.⁹⁰ Prior to the evaluation of the structural model, we examined the Variance Inflation Factor (VIF) and the explained variance (R²) value. When the VIF values are approximately 5 or lower,⁹⁵ collinearity distort the regression results. The VIF values for all constructs were observed to range from 1.48 to 2.47. Figure 2 provides a comprehensive summary of the path coefficients and the explained variances within the model. The R² value depicted in Figure 2 demonstrates that the structural model of this investigation elucidated 57.6% of the variance in mis-and disinformation self-efficacy, 64.0% of the variance in privacy self-efficacy, and 26.5% of the variance in digital economic behaviors.⁹⁶

Figure 2.

Results of testing the hypotheses.

As shown in Figure 2, the results of our study show that security behavior self-efficacy affects digital economic behaviors positively (H1, β = .09, P < .05). Also, security behavior self-efficacy significantly influences both mis-and disinformation self-efficacy (H2, β = .76, P < .001) and privacy self-efficacy (H3, β = .80, P < .001). This study also found that both mis-and disinformation self-efficacy (H4, β = .18, P < .001) and privacy self-efficacy (H5, β = .12, P < .05) were significant antecedents of digital economic behaviors. The study, as detailed in Table 6, validated all 5 hypotheses.

Table 6.

Summary of Testing Hypotheses.

No	Hypothesis	Path coefficient	t-Value	P-Value	Outcome
H1	Security behavior self-efficacy → Digital economic behaviors	0.09	2.12	.034	Supported
H2	Security behavior self-efficacy → Mis-and disinformation self-efficacy	0.76	72.78	.000	Supported
H3	Security behavior self-efficacy → Privacy self-efficacy	0.80	97.40	.000	Supported
H4	Mis-and disinformation self-efficacy → Digital economic behaviors	0.18	4.32	.000	Supported
H5	Privacy self-efficacy → Digital economic behaviors	0.12	2.45	.014	Supported
Control 1	Perceived value → Digital economic behaviors	0.05	2.25	.024	Supported
Control 2	Gender → Digital economic behaviors	0.07	1.63	.103	Not significant
Control 3	Average monthly income → Digital economic behaviors	0.07	2.99	.003	Supported
Control 4	Edu Middle → Digital economic behaviors	−0.04	1.14	.256	Not significant
Control 5	Edu High → Digital economic behaviors	0.14	2.91	.004	Supported
Control 6	Edu College → Digital economic behaviors	0.47	4.81	.000	Supported

Additionally, we found that perceived value significantly positively influenced digital economic behaviors (Control 1: β = .05, P < .05). Among the demographic control variables, gender did not have a significant effect on digital economic behaviors (Control 2: β = .07, n.s.). However, average monthly income showed a significant positive influence on digital economic behaviors (control 3: β = .07, P < .01). Education also had a notable positive effect, with both the Edu High group (Control 5: β = .14, P < .01) and the Edu College group (Control 6: β = .47, P < .001) exhibiting significant results, while the Edu Middle group did not show a significant effect (Control 4: β = −.04, n.s.).

Additional Analysis of Mediating Effects

According to the systematic mediator analysis conducted using PLS-SEM, as explained by,⁸⁷ we assess the significance of 1 direct ( $c^{'}$ ) and 2 indirect effects ( $a_{1}$ × $b_{1}$ and $a_{2}$ × $b_{2}$ ). Given that the direct effect is statistically significant and both indirect effects are also significant, we can support the case for partial mediation.^87,97 Additionally, we compute the Variance Accounted For (VAF), which indicates the proportion of the indirect effect relative to the total effect, to evaluate the magnitude of each mediation effect. For this study, the formulas used for direct effect (DE), indirect effect (IE), total effect (TE), and VAF are defined as follows:

D E + I E = T E a n d V A F = \frac{| I E |}{| T E |} \times 100 %

In the established criteria, a VAF value under 20% indicates no mediation effect, a VAF value between 20% and 80% signifies partial mediation, and a VAF exceeding 80% denotes full mediation.⁹⁸ As shown in Table 7, the VAF for each model is below the 80% threshold, which further supports the conclusion of partial mediation.

Table 7.

Summary of Mediating Effects.

No		Direct effect	Path coefficient	t-Value	P-value	VAF	Outcome
Model 1	$a_{1}$	Security behavior self-efficacy→ Mis-and disinformation self-efficacy	.76	72.78	.000	0.60	Partial mediation
	$b_{1}$	Mis-and disinformation self-efficacy→ Digital economic behaviors	.18	4.32	.000
	$c^{'}$	Security behavior self-efficacy→ Digital economic behaviors	.09	2.12	.034
Model 2	$a_{2}$	Security behavior self-efficacy→ Privacy self-efficacy	.80	97.40	.000	0.52	Partial mediation
	$b_{2}$	Privacy self-efficacy→ Digital economic behaviors	.12	2.45	.014
	$c^{'}$	Security behavior self-efficacy→ Digital economic behaviors	.09	2.12	.034

Security behavior self-efficacy has both direct and indirect effects on digital economic behaviors. The direct effect (H1 = 0.09) is statistically significant, though it is less robust compared to the indirect effects. These indirect effects are mediated by mis-and disinformation self-efficacy and privacy self-efficacy, suggesting that the influence of security control on digital economic behavior primarily occurs by first enhancing risk awareness. Notably, the strong path coefficients from security behavior self-efficacy to mis-and disinformation self-efficacy (H2 = 0.76) and privacy self-efficacy (H3 = 0.80) indicate a significant relationship. This finding highlights that better security behavior self-efficacy is associated with increased self-efficacy of mis-and disinformation and privacy threats. Therefore, improving individuals’ security behavior self-efficacy could positively influence their overall cybersecurity self-efficacy. Overall, the findings reveal a complex relationship between cybersecurity self-efficacy and digital economic behaviors.

Discussion

Summary of the Research

This study builds on SCT to explore the concept of cybersecurity self-efficacy among older adults and examines how it influences their digital economic behaviors. It identifies 3 dimensions of cybersecurity self-efficacy: security behavior self-efficacy, mis-and disinformation self-efficacy, and privacy self-efficacy.

The study tested 5 hypotheses regarding the relationship between cybersecurity self-efficacy and digital economic behaviors in older adults in South Korea, confirming all hypotheses. First, it found that higher security behavior self-efficacy leads to greater digital economic behaviors (H1: β = .09, P < .05). Older adults who have greater confidence in managing cybersecurity risks are more likely to engage in activities such as online banking, e-commerce, and digital payments. This suggests that confidence in handling cybersecurity concerns reduces fear and hesitation when participating in online financial transactions. Second, security behavior self-efficacy significantly predicts both mis-and disinformation self-efficacy (H2: β = .76, P < .001) and privacy self-efficacy (H3: β = .80, P < .001). Older adults who feel confident in their ability to establish security features, recognize cyber threats, and manage their personal digital security are also more adept at critically assessing online information and protecting their privacy. This indicates that those with strong security practices are better equipped to evaluate online content and safeguard their privacy. Third, mis-and disinformation self-efficacy positively influences digital economic behaviors (H4: β = .18, P < .001), whereas privacy self-efficacy has a weaker effect (H5: β = .12, P < .05). This indicates that older adults who are skilled at recognizing false information and managing privacy settings are more likely to engage in digital economic behaviors. Furthermore, post-hoc mediation analysis revealed that both mis-and disinformation self-efficacy and privacy self-efficacy mediate the relationship between security behavior self-efficacy and digital economic behaviors. Consequently, security behavior self-efficacy influences digital economic behaviors both directly and indirectly through mis-and disinformation self-efficacy and privacy self-efficacy.

Research Implications

The findings of this study provide several important implications for research. This study provides an initial step toward understanding the applicability of cybersecurity self-efficacy in a new domain of older adults’ digital inclusion. Our results confirm that the cybersecurity self-efficacy of older adults is a meaningful construct in explaining their digital economic behaviors and digital inclusion. First, prior research has primarily focused on general technology self-efficacy and its influence on digital adoption among older adults.³⁰ This study builds upon that understanding by highlighting the importance of cybersecurity self-efficacy.²⁹ The results indicate that older adults with higher levels of cybersecurity self-efficacy are more likely to engage in digital economic behaviors. This study extends existing research on the digital inclusion of older adults by demonstrating the critical role of cybersecurity self-efficacy in facilitating their participation in the digital realm. Second, the study emphasizes the connection between digital inclusion and economic behaviors, illustrating that confidence in cybersecurity is not merely a technological issue but also an economic enabler. Older adults who feel secure in their ability to protect their cybersecurity concerns are more inclined to engage in digital economic behaviors.³¹ Our findings may assist researchers seeking a relevant theoretical framework to promote digital economic participation among aging populations. Third, a notable finding of the study is that self-efficacy in evaluating mis-and disinformation self-efficacy significantly impacts digital economic behaviors. Older adults who can critically assess online information are less vulnerable to fraudulent schemes, deceptive advertising, and financial misinformation.²⁷ Furthermore, the study reinforces the idea that privacy self-efficacy influences digital economic behaviors by affecting consumer trust. Older adults who are confident in their ability to manage online privacy settings are more likely to participate in digital transactions.²⁶ We contribute to new evidence-based discussions about the digital inclusion of older adults from a cybersecurity perspective, which opens avenues for initiatives aimed at improving digital literacy and encouraging more older adults to adopt digital economic behaviors.

Practical Implications

This research provides valuable insights for companies offering digital services aimed at older adults, as well as a deeper understanding for policymakers and organizations that support this demographic. First, the importance of security self-efficacy in influencing the level of engagement in digital economic behaviors among older adults highlights the need for customized educational programs. The fundamental aim of these initiatives ought to be to enhance the security self-efficacy of older individuals in engaging with digital services, thereby alleviating concerns regarding digital challenges.⁹⁹ Second, a lack of awareness or interest in managing security self-efficacy can hinder digital literacy among older adults, potentially making them hesitant or reluctant to use digital technologies. Therefore, encouraging older adults to engage with and feel confident about digital security is crucial for policymakers who aim to support this demographic.⁴ Policymakers can use these insights to create digital initiatives that improve motivation and provide practical guidance for the safe use of digital devices. Third, addressing the challenges related to the comprehension of mis-and disinformation self-efficacy among older adults requires a multidisciplinary approach that involves collaboration between academics, digital service providers, policymakers, technology experts, and older adults themselves. By working together, these stakeholders can develop effective strategies aimed at reducing the threats posed by mis-and disinformation and ensuring that older adults possess the necessary knowledge and skills to protect themselves in an increasingly AI-driven landscape.¹⁰⁰ Additionally, policymakers must proactively safeguard the rights and interests of older adults, ensuring access to essential resources and support for safe and competent engagement in digital economic behaviors.¹⁰¹ Fourth, the study highlights education as a crucial factor that positively influences older adults’ engagement in digital economic behaviors. Those with higher education levels typically have stronger cognitive skills, better adaptability to new information, and more exposure to technology, which boosts their confidence in using digital services. Demographic data indicated that 53.7% of participants had a high school education or less, while only 12.6% held a college degree or higher. This disparity suggests that lower educational attainment may exacerbate digital exclusion among the elderly. The link between education and digital engagement provides important insights for policymakers and organizations. Targeted educational programs and digital literacy initiatives are essential for older adults with lower educational backgrounds to bridge the technological gap and enhance their confidence in using digital services. Fifth, the research highlights that perceived value significantly influences digital economic behaviors among older adults. This aligns with the value-based adoption model, which links higher perceived value to increased satisfaction and engagement in digital activities. Therefore, initiatives to enhance digital engagement should not only focus on usability and security but also clearly showcase the benefits that digital technologies can provide. Additionally, education and average monthly income positively impact these behaviors. Higher education levels contribute to a better understanding of digital services, leading to greater confidence, while financial stability may reduce concerns about online transactions. Together, education and income act as key enablers of digital economic participation, underscoring the need for policies that enhance both digital literacy and economic accessibility for the elderly.

Limitations and Future Research

Although this study offers valuable insights, additional research is necessary to enhance our understanding of older adults’ digital experiences. First, this research presents a cross-sectional analysis of digital economic behaviors exhibited by older adults. However, longitudinal studies are necessary to track how these behaviors change over time and to develop hypotheses about the comparative influences of temporal, spatial, and social factors.¹⁰² Second, the significance of all hypotheses suggests that the model is robust; however, its applicability in different cultural and socioeconomic contexts is still unclear. Therefore, future research should include comparative outcome analyses among diverse aging cohorts from various countries and conduct cross-national investigations.¹⁰³ Third, with the growing importance of artificial intelligence, virtual reality, and assistive technologies, future research should explore how these advancements affect the digital engagement of older adults and, in turn, their overall well-being.¹⁰⁴ As digital devices and services continue to evolve rapidly, it is essential to conduct more empirical studies to enhance the experiences of older adults in digital environments. Fourth, this study utilizes a quantitative approach, employing secondary data to assess the relationships between variables without directly intervening with or manipulating the study subjects. A formal sample size calculation or power analysis was not conducted prior to data collection. Although a large sample size typically enhances statistical power and the reliability of findings, the lack of a pre-hoc power analysis is a common limitation in studies that use secondary data. This limitation should be acknowledged, as it prevents us from determining whether the sample size was adequately powered to detect all potential effects of interest at a predefined confidence level.

Conclusion

This study highlights the essential role of security behavior self-efficacy in encouraging older adults to engage in digital economic behaviors. By examining aspects of security behavior, mis-and disinformation, and privacy self-efficacy, the research demonstrates that security behavior self-efficacy has both direct and indirect effects on participation in online financial activities. Higher security behavior self-efficacy in these areas decreases hesitation and promotes greater digital inclusion among the elderly. The findings emphasize the need for targeted educational programs and policy interventions aimed at improving older adults’ digital literacy and security behavior self-efficacy. This will empower them to navigate the evolving digital economy safely and effectively. Overall, the study contributes significantly to our understanding of digital inclusion from a cybersecurity standpoint, underscoring its importance as a vital economic facilitator for the aging population.

Supplemental Material

sj-docx-1-inq-10.1177_00469580251370933 – Supplemental material for Impact of Cybersecurity Self-Efficacy on Digital Economic Behaviors Among Older Adults

Supplemental material, sj-docx-1-inq-10.1177_00469580251370933 for Impact of Cybersecurity Self-Efficacy on Digital Economic Behaviors Among Older Adults by Sohee Kim in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

Footnotes

ORCID iD

Sohee Kim

Ethical Considerations

After receiving ethical approval, the Ministry of Science and ICT of the Republic of Korea launched the Digital Divide Survey in 2021. This project serves as a national representative continuous survey. We utilized the public data from the Digital Divide Survey, and no further ethics approval was required.

Author Contributions

Dr. Kim is the sole author of the manuscript and is responsible for 100% of the writing in the paper.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The datasets generated and analyzed for this study are accessible on the Open Government Data (OGD) portal of the Republic of Korea. For more details, please visit . Any further inquiries may be directed to the corresponding author.

Supplemental Material

Supplemental material for this article is available online.

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