The Impact of Artificial Intelligence Service Competency Among Korean Citizens on Digital Utilization Outcomes in the Context of Digital Trade Expansion: The Mediating Role of E-Commerce

Abstract

This study empirically examines the impact of artificial intelligence (AI) service competency on digital economic activities, digital social participation, and daily life satisfaction. It also investigates the mediating role of e-commerce utilization in these relationships. The ultimate aim is to provide practical implications for enhancing citizens’ quality of life and promote their active participation in the digital society. This study employed data from 7001 respondents drawn from the 2023 Digital Information Gap Survey. The analysis was conducted using SPSS 29.0 and structural equation modeling (SEM) to examine the structural relationships between variables. The results revealed that perceived usefulness of AI technology had a significant positive effect on digital economic activities, while digital information competency positively influenced both digital economic activities and daily life satisfaction. In contrast, attitude toward AI technology did not have a significant effect on digital economic activities and even showed a negative association with digital social participation. Furthermore, digital information competency demonstrated a significant indirect effect on the overall digital outcomes through the mediating role of the level of e-commerce utilization. This study applies an integrated theoretical framework combining the Theory of Reasoned Action (TRA) and the Unified Theory of Acceptance and Use of Technology (UTAUT). It goes beyond theoretical interpretation by delivering practical implications that offer academic and policy value, particularly for developing policy strategies such as reducing the digital divide.

1. Introduction

Digital trade is a broad concept that encompasses the cross-border exchange of goods, services, and data enabled by digital technologies [1]. Recent international e-commerce negotiations have highlighted openness and trust-building as key agenda items in digital trade. Discussions have focused on establishing various institutional frameworks, including electronic signatures, electronic contracts, digital payments, paperless trade, data accessibility guarantees, consumer protection, and personal information security [2]. As the importance of international digital trade policy becomes increasingly prominent, future trade policies based on digital technology and the internet are expected to strengthen legal and institutional responses to new industries, including e-commerce norm improvement, data protection enhancement, and artificial intelligence-related regulations. As international digital trade policy becomes increasingly important, future trade policies enabled by digital technologies and the internet are expected to strengthen legal and institutional measures for emerging industries, improve e-commerce standards, enhance data protection, and establish artificial intelligence regulations. Such changes indicate that in addition to the reorganization of trade norms between countries, citizens’ acceptance and utilization of digital technologies within each nation are becoming key factors in determining international competitiveness. Accordingly, policy measures and institutional frameworks within international digital trade agreements are needed to reduce digital trade barriers and establish a foundation for mutually beneficial cooperation. Moreover, ICT product trade has been found to positively influence high-quality economic growth, reaffirming the importance of information and communication technologies in the advancement of digital trade [3]. Recent research on digital trade has explored multiple areas. First, a significant body of work has focused on digital trade agreements, encompassing studies on the Digital Economy Partnership Agreement (DEPA), preferential trade agreements (PTAs) addressing digital trade norms, the WTO and the utilization of digital economy technologies in PTA competition, the development process of digital trade agreements, transformations in digital trade norms, worker-centered digital trade policies, comparative analyses of digital trade agreements, blockchain-based trade finance within digital trade, Asia–Pacific regional trade agreements shaped by digital trade norms, and examination of research trends in digital trade [4,5,6,7,8,9,10,11,12,13]. These recent studies on digital trade agreements have made valuable research contributions by analyzing digital trade policies and proposing measures for institutional improvement.

Second, research has examined the importance of digital technology in trade. This includes studies on the relationship between digital trade, green technology innovation, and environmental sustainability in BRICS countries; the application of digital technology to trade transactions; the acceptance of digital technology in international trade; AI-related trade barrier technology agreements; technical barriers to trade (TBTs) in the digital domain; and case studies examining the use of digital technologies in trade practices [14,15,16,17,18,19]. These recent studies on digital technology are significant, as they explore the connections between key Fourth Industrial Revolution technologies—such as blockchain and artificial intelligence—and digital trade, as well as measures to ensure security in digital trade. However, despite these contributions, existing research on digital trade remains limited in its focus on individual users of digital technology, namely consumers. Empirical studies centered on consumer perspectives have been relatively scarce compared with theoretical approaches. In particular, although the Unified Theory of Acceptance and Use of Technology [20]. explains individuals’ intentions and behaviors regarding technology adoption, it has limitations in that it does not clearly demonstrate how attitude toward technology or perceived usefulness, as proposed in the theory, are connected to practical outcomes, such as digital economic activities, social participation, and daily life satisfaction.

Meanwhile, digital innovation is profoundly impacting various industries, including manufacturing, banking, telecommunications, and healthcare, while the strategic importance of e-commerce as a digital tool for businesses is gaining increasing emphasis [21]. Particularly in the digital age, research has shown that greater utilization of e-commerce to engage with consumers has a greater impact on corporate management performance. Moreover, electronic trust has been identified as a mediating factor in the relationship between electronic security, privacy, payment, innovation, and consumer purchase intention [22]. Although digital trade has been studied across various fields, there is a lack of empirical research examining how individual users of digital technology adopt and perceive it, as well as how these perceptions influence digital utilization outcomes. Particularly, with companies increasingly communicating with customers through mobile and internet channels, e-commerce transactions have been expanding. Therefore, this study aims to examine how individual artificial intelligence service competency influences both e-commerce utilization and digital utilization outcomes and to empirically analyze the role that e-commerce utilization plays in mediating the relationship between individuals’ AI service competency and digital utilization outcomes. This study differentiates itself from existing research by setting artificial intelligence service competency—a core technology competency of the Fourth Industrial Revolution—as an independent variable to examine its impact on digital utilization outcomes among Korean citizens and by identifying the mediating effect of e-commerce. To conduct this study, artificial intelligence service competency was defined in terms of attitude toward AI technology, perceived usefulness of AI technology, and digital information competency. Digital utilization outcomes were measured through digital economic activities, digital social participation, and daily life satisfaction. Additionally, this study seeks to offer strategic insights by identifying the structural relationships through which individual competencies in the digital trade environment lead to outcomes via platforms such as e-commerce. Through this analysis, the study is expected to provide policy-level evidence for enhancing individual citizens’ artificial intelligence and digital competencies while also making practical contributions to developing e-commerce strategies and implementing measures to reduce the digital divide.

2. Theoretical Background

2.1. Examination of Artificial Intelligence Competency Using the Theory of Reasoned Action and the Unified Technology Acceptance Model

The Theory of Reasoned Action, proposed by [23]. posits that human behavior is determined by behavioral intention, which is shaped by attitude and subjective norms. According to this theory, behavioral intention serves as a direct antecedent of actual behavior and is influenced by two key factors: individual attitude and subjective norms. Here, attitude refers to an individual’s positive or negative evaluation of a specific behavior, whereas subjective norms represent perceived social expectations or pressures; essentially, the motivation to conform to the opinions of significant others. At the heart of this theory is a sequential process in which individuals develop beliefs about behavioral outcomes; these beliefs shape attitude, which then drives behavioral intentions and eventually results in actual behavior. In other words, humans engage in behavior only after sufficiently anticipating the potential consequences of a specific action and logically and systematically evaluating the related information. This perspective provides the foundation for explaining how attitude toward artificial intelligence technology may translate into actual digital utilization behavior [23].

As illustrated in Figure 1, the core of the TRA conceptual model is that a specific behavior is determined by an individual’s behavioral intention [23] (p. 15). The Theory of Reasoned Action can be viewed as providing the theoretical basis for understanding how attitude toward artificial intelligence technology, a core component of artificial intelligence service competency in this study, influences digital utilization outcomes such as individual digital economic activities, social participation, and daily life satisfaction. That is, when individuals hold positive attitudes toward artificial intelligence technology, it can lead to intentions to utilize related technologies (e.g., behavior X), which, in turn, promote actual digital utilization behavior. In particular, beliefs about the positive consequences of using artificial intelligence technology (i.e., beliefs about the outcomes of behavior X) are expected to play a key role in shaping individual attitudes toward the technology.

Meanwhile, the Unified Theory of Acceptance and Use of Technology (UTAUT), a representative theoretical model integrating research on information technology (IT) user acceptance, explains the factors that influence individuals’ intentions and behaviors related to adopting new technologies. This theory was developed by integrating existing technology acceptance theories and identifies the key factors that influence users’ decisions to adopt and use technology. In other words, it is a comprehensive theory that explains the core factors influencing individuals’ adoption and use of new technologies, as well as the interactions between these factors [20]. The research model underlying this theory is illustrated in Figure 2 [20] (p. 447).

UTAUT identifies four key factors that influence technology acceptance. The first factor, performance expectancy, refers to the belief that using a particular technology will enhance an individual’s job performance. The second factor, effort expectancy, represents the degree to which a technology is perceived as easy to use, directly reflecting its usability. The third factor, social influence, denotes the extent to which individuals perceive that important others, such as colleagues, supervisors, or family members, believe they should use the technology. The fourth factor, facilitating conditions, refers to whether organizational and technical infrastructures are adequately in place to support users in utilizing the technology. The influence of these four factors can vary depending on moderating variables such as gender, age, experience, and voluntariness. UTAUT aims to identify how these factors impact behavioral intention, i.e., the intention to use the technology in the future, and use behavior, which reflects the actual frequency of technology usage [20]. In the process of selecting and performing specific behaviors, individual attitudes and perceptions play a crucial role in guiding actions toward more positive directions. Meanwhile, UTAUT offers a useful theoretical framework for explaining the concepts of perceived usefulness of artificial intelligence technology and digital information competency addressed in this study. In this theory, performance expectancy refers to the belief that individuals anticipate practical improvements in performance, such as enhanced digital economic activities or increased social participation, through the utilization of artificial intelligence technology. This concept is closely aligned with the notion of perceived usefulness as defined in this study. Additionally, effort expectancy describes the degree to which individuals perceive the technology as easy to access and use. When focusing on the direct relationship between individual attitudes and behavioral intention, UTAUT offers a more multifaceted and integrated perspective on the factors influencing technology acceptance. Therefore, this study seeks to comprehensively analyze how artificial intelligence service competency influences digital utilization outcomes by integrating the strengths of both theories. Particularly, artificial intelligence technology possesses unique characteristics, such as learning capability, autonomy, and unpredictability, that distinguish it from conventional information technology. These characteristics can shape individuals’ attitudes toward artificial intelligence technology and their perception of its usefulness. Therefore, beyond merely delivering technology, the ability to foster positive perceptions of artificial intelligence and actively utilize information can be regarded as a core competency in the digital age. The discussion of the Theory of Reasoned Action and the Unified Theory of Acceptance and Use of Technology is expected to provide a theoretical foundation, suggesting that artificial intelligence service competency comprising attitude toward artificial intelligence technology, perceived usefulness, and digital information competency can significantly influence the extent of e-commerce platform utilization and digital utilization outcomes. While the existing Unified Theory of Acceptance and Use of Technology (UTAUT) has mainly addressed users’ behavioral intention toward technology adoption, this study integrates the attitude–behavior linkage from the Theory of Reasoned Action (TRA) with the expectancy–utility constructs of UTAUT to propose an extended pathway illustrating how the adoption of AI technology translates into tangible digital outcomes.

2.2. Artificial Intelligence Service Competency, E-Commerce Utilization Level, and Digital Utilization Outcomes

The emergence and advancement of artificial intelligence have ushered in a new digital revolution, profoundly transforming individual lives and society as a whole. In particular, artificial intelligence service competency, which is the ability of individuals to effectively understand and utilize artificial intelligence technology in the digital age, has emerged as essential for thriving in today’s digital environment. The importance of artificial intelligence competency is similarly highlighted from an organizational perspective. Prior studies have conceptualized a company’s ability to effectively leverage artificial intelligence resources as AI competency or have defined it as the capability to select and utilize all AI-specialized resources. Such AI utilization competency has been recognized as a core driver of performance generation [24,25]. Such comprehensive competency in effectively understanding AI is also essential at the individual level. Accordingly, individuals in a digital society are expected to develop key elements of artificial intelligence service competency, including attitudes toward AI technology, perceived usefulness of AI, and digital information competency. First, according to [26], who proposed the Technology Acceptance Model, defined one’s attitude toward using as users’ overall evaluation of utilizing the technology. Such an attitude shapes users’ cognitive and emotional responses during the early stages of technology adoption and directly influences their subsequent behavioral intentions. Another key component of this competency is the perceived usefulness of artificial intelligence technology. Perceived usefulness refers to the degree to which individuals expect or believe that a specific technology or system will enhance their work performance, and it has been established as a core explanatory variable within the Technology Acceptance Model [26]. This serves as one of the most practical factors users consider when deciding whether to adopt a technology and represents a core component of the artificial intelligence service competency defined in this study. Third, another key competency is digital information competency, which refers to the ability to effectively search for, analyze, and practically utilize information within a digital environment. This competency extends beyond the technical skills required to operate digital devices, encompassing comprehensive thinking and judgment abilities that enable individuals to identify reliable information from diverse online sources and effectively apply it to real-world decision-making and problem-solving processes. In particular, digital information competency is considered a fundamental ability that is essential for understanding and independently utilizing complex, advanced technologies, such as artificial intelligence-based services. Factors influencing digital information competency have been analyzed across different generations. The findings indicate that young, middle-aged, and elderly groups alike tend to demonstrate higher levels of digital information competency when their digital social capital, digital self-efficacy, and digital usage motivation are elevated [27]. Meanwhile, research on enhancing e-commerce utilization has shown that AI technology positively influences utilization levels and enables sellers to predict purchasing patterns. Therefore, AI-driven analysis plays a crucial role in e-commerce success strategies, including inventory management, marketing, and customer relationship management. Thus, AI should be utilized not merely as an automation tool, but as a strategic management resource, with data-driven decision-making recognized as important even for small- and medium-sized enterprises [28]. As such, the level of e-commerce utilization is considered to play a crucial mediating role in transforming information acquired through AI technology in the digital environment into tangible economic and social activities, extending beyond mere consumption behaviors. Next, an examination of the impact of digital transformation technology adoption on corporate performance revealed that larger corporations exhibit higher adoption and utilization rates of digital technologies, while companies with longer histories show a lower likelihood of adopting new technologies. Nevertheless, as the adoption of digital technologies has a positive impact on corporate sales, both central and local governments should provide policy support to facilitate digital transformation. Through this, the digitalization of small- and medium-sized enterprises is expected to create opportunities for improving business structures [16]. An analysis of the moderating role of green innovation in the relationship between corporate digital transformation and sustainability performance revealed that greater levels of green innovation amplify the positive effects of digital transformation. The study further emphasized that governments should expand policy support to enable companies to enhance sustainable performance through initiatives such as building digital infrastructure, providing subsidies, and offering tax incentives for green innovation [29]. As highlighted in the preceding studies, artificial intelligence service competency is expected to play a significant role in enhancing the utilization of e-commerce platforms by shaping individuals’ digital acceptance attitudes and technology utilization capabilities. As such, the level of e-commerce utilization is highly likely to serve as a key mediating variable influencing individual digital utilization outcomes, such as digital economic activities, social participation, and daily life satisfaction, and is directly linked to enhancing quality of life and promoting active participation in the digital age. Therefore, this study aims to empirically examine the impact of artificial intelligence service competency on digital utilization outcomes, with the e-commerce utilization level serving as a mediating factor, and to develop a comprehensive understanding of the structural relationships between these variables. In particular, artificial intelligence (AI) exhibits distinctive attributes—such as autonomy, algorithmic opacity, and adaptive learning—that may result in adoption mechanisms and user behaviors different from those associated with conventional information technologies. Accordingly, this study seeks to examine how the inherent uncertainty and autonomy of AI influence digital utilization outcomes.

3. Research Model and Hypotheses

3.1. Research Model

This study aims to examine how artificial intelligence service competency influences digital utilization outcomes among Korean citizens and to explore the mediating role of e-commerce utilization level in these relationships. The research model developed for this purpose comprises independent, mediating, and dependent variables, defined as follows. First, the independent variable, artificial intelligence service competency, is defined by three sub-components: attitude toward artificial intelligence technology, perceived usefulness, and digital information competency. Next, the dependent variable, digital utilization outcomes, was measured across three areas: digital economic activities, social participation, and daily life satisfaction. Additionally, the e-commerce utilization level was established as a core mediating variable in the relationship between artificial intelligence service competency and digital utilization outcomes; that is, this study aims to empirically examine how attitude toward artificial intelligence technology acceptance and information utilization capabilities translate into actual digital outcomes. This study, grounded in the Technology Acceptance Model [26]. and the Unified Theory of Acceptance and Use of Technology (UTAUT) [20]. establishes a structural framework in which attitude toward AI technology (A1), perceived usefulness of AI technology (A2), and digital information competency (A3) influence the digital performance outcomes digital economic activities (C1), digital social participation (C2), and daily life satisfaction (C3) through the mediating role of the level of e-commerce utilization (B1).

The purpose of this study is to elucidate how attitudinal and perceptual factors toward AI technology are transformed into actual digital behaviors, emphasizing the pivotal role of competency-based factors (digital information competency) and mediating mechanisms associated with e-commerce utilization within this process.

Through this analysis, the study aims to derive practical and policy implications for enhancing individual technology acceptance competency, where the proposed research model is presented in Figure 3 below. Meanwhile, in this study, attitude toward AI technology (A1), perceived usefulness of AI technology (A2), and digital information competency (A3) are conceptually integrated under the broader construct of AI service competency. However, in the analysis, each factor was treated as an independent variable to examine its individual effects. Similarly, digital economic activities (C1), digital social participation (C2), and daily life satisfaction (C3) are collectively conceptualized as digital utilization outcomes. Yet, during the analysis, each was treated as a separate dependent variable to identify its specific effects.

3.2. Research Hypotheses

3.2.1. Relationship Between Artificial Intelligence Service Competency and Digital Economic Activities

This study suggests that attitudinal factors toward technology, particularly self-efficacy, can influence not only the intention to use a system but also participation in economic activities that leverage digital technology. Particularly, as the level of self-efficacy increases, a greater number of individuals are likely to voluntarily participate in various digital economic domains, such as e-commerce and digital finance. The accumulation of positive user experiences may, in turn, foster a virtuous cycle that further promotes and expands digital economic activities. Additionally, the perceived usefulness of digital technology serves as a key factor in determining whether individuals choose to engage in economic activities using that technology. Particularly in the digital economic environment, practical benefits offered by technology, such as time savings, ease of access, and economic efficiency, act as driving forces that directly shape users’ behavioral decisions. Therefore, when digital technology is perceived as providing practical benefits, individuals are more likely to participate more voluntarily and consistently in digital economic activities [30]. Next, with the rapid evolution of the mobile environment, digital transformation is accelerating, and the importance of the digital economy is increasingly being emphasized. This study examines the impact of digital competency on digital economic activities, with a focus on smartphone use. As a result, the study confirms that digital competency directly influences the use of life services. Moreover, its impact on economic activities was found to be mediated by individuals’ experiences with life services [31]. These findings suggest that individual attitudes toward artificial intelligence (AI) technology, perceived usefulness, and digital information competency within the digital economic environment can serve as key antecedent factors influencing participation in digital economic activities. Particularly, a positive perception of AI technology, recognition of its usefulness, and the capability to search for and utilize information are considered practical factors that contribute to the activation of economic activities through digital means. Accordingly, this study aims to empirically examine the impact of artificial intelligence service competency on digital economic activities, and, for this purpose, the following research hypotheses were formulated:

H1. 

A positive attitude toward artificial intelligence technology will have a positive effect on digital economic activities.

H2. 

Perceived usefulness of artificial intelligence technology will have a positive effect on digital economic activities.

H3. 

Digital information competency will have a positive effect on digital economic activities.

3.2.2. Relationship Between Artificial Intelligence Service Competency and Digital Social Participation

This study investigated the impact of IT innovation and cognitive needs among individual characteristics on online social participation while also examining the effect of digital literacy on online civic engagement. As a result, positive attitudes toward artificial intelligence technology were found to be significant factors in promoting digital social participation. Particularly, as the perception that artificial intelligence technology enhances social communication and information access efficiency strengthens, individuals are more likely to engage in active and sustained social participation through AI-based digital platforms. Therefore, the more individuals perceive artificial intelligence technology as useful for social communication and information utilization, the more likely they are to actively participate in online social activities through AI-based digital platforms [32]. Additionally, older adults represent a group with particularly low digital competency, and their quality of life has been declining recently due to digital exclusion. Particularly, this study examined the impact of digital literacy among older adults on their participation in social activities. To strengthen the validity of the analysis, age, education, health, satisfaction with economic status, perceived recognition of the elderly, and economic status were included as control variables [33]. Building on the preceding studies, this study highlights that attitude toward artificial intelligence technology, perceived usefulness, and information utilization competency are key factors influencing digital social participation. Accordingly, this study aims to empirically examine the impact of artificial intelligence service competency on digital social participation, and, for this purpose, the following hypotheses were derived:

H4. 

A positive attitude toward artificial intelligence technology will have a positive effect on digital social participation.

H5. 

The perceived usefulness of artificial intelligence technology will have a positive effect on digital social participation.

H6. 

Digital information competency will have a positive effect on digital social participation.

3.2.3. Relationship Between Artificial Intelligence Service Competency and Daily Life Satisfaction

Ref. [34] analyzed the impact of software, digital devices, and hardware utilization capabilities on life satisfaction among middle-aged and elderly individuals. Similarly, Kim Hyeong-min [35] conducted an empirical study targeting farmers and fishermen, examining the effects of attitude toward digital technology and device efficacy on life satisfaction. The results show that both factors had a positive impact on life satisfaction. Building on these preceding studies, this study aims to empirically examine the impact of attitude toward artificial intelligence technology, perceived usefulness, and digital information competency on individuals’ daily life satisfaction. Artificial intelligence services offer various forms of utility, such as psychological satisfaction, convenience in daily life, and enhanced efficiency that can serve as key factors contributing to improvements in quality of life. Particularly, the more positively individuals view AI technology and perceive its usefulness, the more likely they are to actively utilize it in various aspects of daily life, such as leisure, health, social activities, and interpersonal relationships, ultimately leading to enhanced life satisfaction. Additionally, digital information competency, i.e., the ability to effectively search for and utilize digital devices and information, plays a key role in solving everyday problems and seizing new opportunities in a digital society, thereby contributing to improved life satisfaction. Accordingly, this study aims to empirically examine the impact of artificial intelligence service competency on daily life satisfaction; for this purpose, the following hypotheses were derived:

H7. 

A positive attitude toward artificial intelligence technology will have a positive effect on daily life satisfaction.

H8. 

The perceived usefulness of artificial intelligence will have a positive effect on daily life satisfaction.

H9. 

Digital information competency will have a positive effect on daily life satisfaction.

3.2.4. The Mediating Effect of E-Commerce Utilization Level

Choi Soon-hwa [36] found in a study on e-commerce service use among elderly individuals that higher levels of self-efficacy and perceived usefulness have a more significant impact on e-commerce use. In particular, e-commerce utilization was identified as a key activity that extends beyond simple consumption behavior, contributing to active engagement in the digital environment and enhancing quality of life. This context indirectly suggests that the level of e-commerce utilization may serve as a mediating variable between digital competency and individual life outcomes. In addition, Kwon Hyuk [37] analyzed the effects of the e-commerce utilization level, digital social capital, and digital usage attitude on digital economic activities, digital utilization outcomes, and life satisfaction using SPSS. The results indicate that the e-commerce utilization level is a key variable that significantly influences all dependent variables. In particular, this study aimed to examine the independent effect of e-commerce utilization level on individuals’ economic and social digital activities and overall quality of life while controlling for gender, age, and household income. Thus, e-commerce utilization represents practical digital utilization capability that goes beyond simple online purchasing, encompassing information access, service use, and active participation in digital platforms. It is expected that higher levels of digital competency and positive attitudes toward technology can further enhance these digital outcomes. Accordingly, this study seeks to investigate whether the level of e-commerce utilization mediates the relationships between the attitude toward artificial intelligence technology, perceived usefulness, and digital information competency, as well as their effects on digital economic activities, digital social participation, and daily life satisfaction. Additionally, previous research analyzing factors influencing the level of e-commerce utilization has shown that both digital information competency and e-commerce utilization are structurally shaped by individual psychological and social factors, including digital self-efficacy, social capital, and usage motivation. In particular, digital self-efficacy consistently showed a significant impact on digital information competency across young, middle-aged, and elderly groups, and in some cases, it also positively influenced the level of e-commerce utilization [27]. These findings suggest that higher levels of positive attitudes toward digital technology, perceived usefulness, and information competency lead to greater e-commerce utilization, which, in turn, can significantly influence digital utilization outcomes, such as digital social participation, digital economic activities, and daily life satisfaction [38]. examined the effects of attitude toward digital technology, digital competency, and digital transformation intention on e-commerce utilization and digital social participation among digitally vulnerable groups and empirically validated the mediating role of digital social capital in this process. In particular, by setting mobile device ownership as a control variable, the study aimed to eliminate the potential impact of differences in digital access environments on the relationships between the key variables. Building on these prior studies, this research anticipates that the level of e-commerce utilization may serve as a mediating factor in the relationships where attitude toward artificial intelligence technology, perceived usefulness of AI services, and digital information competency influence digital economic activities, digital social participation, and daily life satisfaction. Sun and Tu (2025) [39]. conducted a multi-level analysis of AI investment and supply chain coordination strategies among e-commerce platforms, manufacturers, and logistics providers within the e-commerce ecosystem. Their findings provide a complementary foundation for the present study’s discussion on the strategic utilization of AI-based digital platforms. Accordingly, this study aims to examine how artificial intelligence service competency influences digital utilization outcomes and to investigate the mediating role of e-commerce utilization in this relationship. Based on this objective, the following research hypotheses were proposed:

H10. 

E-commerce utilization will mediate the effect of attitude toward artificial intelligence technology on digital economic activities.

H11. 

E-commerce utilization will mediate the effect of attitude toward artificial intelligence technology on digital social participation.

H12. 

E-commerce utilization will mediate the effect of attitude toward artificial intelligence technology on daily life satisfaction.

H13. 

E-commerce utilization will mediate the effect of perceived usefulness of artificial intelligence technology on digital economic activities.

H14. 

E-commerce utilization will mediate perceived usefulness of artificial intelligence technology on digital social participation.

H15. 

E-commerce utilization will mediate perceived usefulness of artificial intelligence technology on daily life satisfaction.

H16. 

E-commerce utilization will mediate digital information competency on digital economic activities.

H17. 

E-commerce utilization will mediate digital information competency on digital social participation.

H18. 

E-commerce utilization will mediate digital information competency on daily life satisfaction.

4. Research Methods

4.1. Sampling Design

This study utilized raw data from 7001 general citizens collected through the 2023 Digital Information Gap Survey. To focus the analysis on the relationship between artificial intelligence service competency and digital utilization outcomes, individuals from vulnerable groups (persons with disabilities and low-income populations), as well as farmers, fishermen, marriage migrants, and North Korean defectors, were excluded. In particular, this study focused on the general population and secured over 7000 valid responses covering variables such as gender, age, education level, and residential area size. After excluding one case with a missing response for the occupation item, the final effective sample comprised 6999 individuals [40]. This study was based on the questionnaire from the Statistics Korea Digital Information Gap Survey (2023), which defines digital technology as “technologies applied to information devices such as the Internet, computers, and smartphones, as well as all methods of collecting, processing, and utilizing information through these technologies.” Within this definitional scope, the study conceptualizes artificial intelligence (AI) as an advanced form of digital technology and reinterprets the relevant survey items as the variable attitude toward AI technology. In other words, although the original survey items were designed to measure perceptions of “digital technology,” this study recontextualized them to focus on AI service capabilities, thereby adapting the constructs to examine the influence of attitude toward AI technology on digital economic activities, digital social participation, and daily life satisfaction. This study reconceptualized artificial intelligence (AI) as the most advanced and integrative form of digital technology, encompassing existing technologies such as the Internet and computers. This conceptual adaptation was grounded in the recognition that AI extends beyond basic information processing by incorporating autonomous learning and decision-making capabilities. The detailed survey items used for this conceptual adaptation are presented in the Appendix A. The survey, supervised by the Ministry of Science and ICT, was conducted from October to December 2023 over a three-month period to monitor the annual progress of policies that aimed to reduce the digital information gap. Targeting 15,000 general citizens nationwide, proportional stratified sampling based on demographic characteristics, such as gender and age, was employed to ensure the representativeness and generalizability of the sample. This survey was conducted by Gallup Korea, a professional research organization, under the supervision of the Ministry of Science and ICT and the oversight of the National Information Society Agency. The demographic characteristics of the sample used in this study, prior to further analysis, are presented in

Table 1. Demographic characteristics analysis results.

Division	Item	Frequency (n)	Proportion (%)
Gender	Male	3492	49.9
	Female	3508	50.1
Age	Under 19	832	11.9
	20 s	917	13.1
	30 s	961	13.7
	40 s	1127	16.1
	50 s	1216	17.4
	Over 60	1947	27.8
Education Level	Elementary graduate and below	626	8.9
	Middle school graduate	1033	14.8
	High school graduate	2666	38.1
	College graduate or higher	2675	38.2
Occupation	Agriculture/fisheries	208	3.0
	Service/sales job	2041	29.2
	Production-related job	727	10.4
	Professional management/office job	1541	22.0
	Homemaker	988	14.1
	Student	1078	15.4
	Unemployed/others	416	5.9
Size of Residential Area	Urban area	6444	92.1
	Rural area	556	7.9

[40].

4.2. Operational Definitions

The operational definitions of the variables used to verify the research model are as follows. The independent variables include four items measuring attitude toward artificial intelligence technology, four items assessing the perceived usefulness of artificial intelligence technology, and seven items for digital information competency. The dependent variables consist of four items for digital economic activities, four items for digital social participation, and four items for daily life satisfaction. As mediating variables, the level of e-commerce utilization was measured with three items. Gender, age, and education were included as control variables. A detailed description of the operational definitions for each variable is provided in

Table 2. Operational definitions.

Variable	Definition	Questionnaire Items	Number of Questions
Attitude toward AI Technology	Expectation and perception that AI technology provides convenience and opportunities in everyday life and brings about positive change	I believe AI technology will create more economic opportunities.	4
Perceived Usefulness of AI Technology	The degree to which AI technology or services are perceived to practically improve quality of life and convenience	AI services will help improve my daily life.	7
Digital Information Competency	The capability to effectively understand and use digital devices and information	I am able to perform environment settings and create documents on smart devices.	7
Level of E-commerce Utilization	Broadly defines e-commerce as including product purchases as well as digital transaction services in the financial and public sectors	How frequently have you used e-commerce, financial, and public services (e.g., tax payments)?	3
Digital Economic Activities	Experience level in online activities involving employment, entrepreneurship, marketing, and income generation	I have used the internet to search for information related to income growth and financial planning.	4
Digital Social Participation	Experience level in active participation in social or community activities via digital media	I have submitted policy suggestions to the government via the internet.	4
Daily Life Satisfaction	Overall satisfaction with the psychological, social, and cultural aspects of daily life	How satisfied are you with your leisure time, health, social activities, and relationships in daily life?	4

below.

5. Results Analysis

5.1. Research Model Test

This study tested the research model using SPSS 29.0 and structural equation modeling (SEM). The analysis was conducted on survey data from 7001 respondents. However, due to missing responses in certain variables (e-commerce utilization level, digital economic activities, and digital social participation), the final analysis was based on a total of 6563 valid samples. All analyses were conducted using valid respondents, with missing values excluded automatically. In addition, the proportion of missing responses across all variables was below 1%, satisfying the assumption of Missing Completely at Random (MCAR). To address the minimal missing data, the Full Information Maximum Likelihood (FIML) estimation method in AMOS 29.0 was applied, thereby minimizing the potential information loss and bias that could arise from traditional listwise deletion procedures. Subsequently, the overall fit of the structural equation model was evaluated. The model yielded χ² = 7753.257 (df = 477, p < 0.001) and χ²/df = 16.254. Although the χ²/df ratio exceeded the conventional threshold, this was attributed to the large sample size (N = 6563), which tends to inflate the chi-square statistic. Other fit indices demonstrated satisfactory model performance, with NFI = 0.941, IFI = 0.945, TLI = 0.945, and CFI = 0.953, with all exceeding the recommended cutoff of 0.90. Furthermore, the RMSEA value of 0.047 indicates an excellent model fit below the 0.05 criterion. Therefore, the structural model in this study can be considered to have achieved an overall good fit.

Table 3. Model fit indices of the structural equation model.

Fit Indices	Recommended Criteria	Results	Evaluations
χ2(df), p	Non-significant (p > 0.05)	7753.257 (df = 477), p < 0.001	Sensitive to large sample
χ2/df (CMIN/DF)	≤3.00 (acceptable ≤ 5.00)	16.254	Exceeds criterion (due to large N)
NFI	≥0.90	0.941	Good
IFI	≥0.90	0.945	Good
TLI	≥0.90	0.945	Good
CFI	≥0.90	0.953	Excellent
RMSEA	≤0.08 (good), ≤0.05	0.047	Excellent

presents a summary of the major model fit indices.

Before the main analysis, the results of the validity and reliability tests for the measurement variables were as follows. First, the factor loadings (λ) for all latent variables exceeded 0.6, confirming satisfactory convergent validity. The composite reliability (CR) values ranged from 0.68 to 0.96, surpassing the recommended threshold of 0.70, while the average variance extracted (AVE) values ranged from 0.35 to 0.85, with most exceeding 0.50, thereby establishing the reliability and validity of the measurement model. In addition, according to the Fornell–Larcker criterion, the square root of each construct’s AVE was greater than the inter-construct correlation coefficients, demonstrating adequate discriminant validity. Although the AVE values for perceived usefulness of AI technology and daily life satisfaction were slightly below the conventional threshold of 0.50, their corresponding CR values were close to or above 0.70, and all item loadings were above 0.6. These results indicate that the measurement items exhibit sufficient internal consistency and stability to compensate for the marginal AVE values. Moreover, the square roots of the AVE values for all constructs exceeded their inter-correlations, reaffirming discriminant validity across the measurement model. Next, the analysis of the internal consistency of the variables revealed the following Cronbach’s α values: attitude toward artificial intelligence technology (0.848), perceived usefulness of artificial intelligence services (0.866), digital information competency (0.970), e-commerce utilization level (0.878), digital economic activities (0.844), digital social participation (0.921), and daily life satisfaction (0.684). These results indicate that the internal consistency of all constructs was adequately secured. Detailed information is provided in

Table 4. Confirmatory factor analysis and reliability assessment.

Measurement Items		Factor Loading (λ)	CR	AVE	Cronbach’s ɑ
Attitude toward AI Technology	Positive perception	0.712	0.850	0.586	0.848
	Perceived convenience of life	0.752
	Favorable attitude	0.786
	Willingness to use	0.809
Perceived Usefulness of AI Technology	Perceived usefulness of AI information services	0.734	0.870	0.480	0.866
	Perceived usefulness of AI financial services	0.737
	Perceived usefulness of AI communication services	0.699
	Perceived usefulness of AI healthcare services	0.708
	Perceived usefulness of AI housing and living convenience services	0.610
	Perceived usefulness of AI transportation services	0.654
	Perceived usefulness of AI education services	0.701
Measurement Items	Program installation capability	0.925	0.960	0.860	0.970
	Ability to connect wired or wireless internet	0.924
	Ability to configure internet browser settings	0.919
	Ability to connect external devices (e.g., PC)	0.929
	Ability to transfer files via the internet	0.922
	Ability to detect and remove malware or viruses on a PC	0.854
	Ability to create and edit documents using office software (e.g., Hangul, Excel)	0.872
Level of E-commerce Utilization	Degree of online shopping transactions	0.859	0.880	0.720	0.878
	Degree of online financial transactions	0.880
	Extent of e-government or public service transactions	0.797
Digital Economic Activities	Participation in digital job-seeking or career development activities	0.822	0.850	0.590	0.844
	Engagement in digital startup or business activities	0.825
	Participation in digital income-generating or investment activities	0.749
	Engagement in cost-saving activities such as cross-border online purchasing	0.649
Digital Social Participation	Participation in online opinion expression activities	0.869	0.920	0.750	0.921
	Submission of digital policy suggestions	0.878
	Participation in digital volunteer activities	0.865
	Engagement in online surveys or digital signature campaigns	0.859
Daily Life Satisfaction	Satisfaction with interpersonal relationships	0.584	0.680	0.350	0.684
	Satisfaction with family relationships	0.537
	Satisfaction with work or occupational activities	0.619
	Satisfaction with physical and mental health	0.625

below.

5.2. Assessment of Discriminant Validity

The discriminant validity was assessed to ensure that each latent construct represented a conceptually distinct dimension.

Table 5. Discriminant validity test (Fornell–Larcker criterion).

Constructs	A	B	C
A	0.765	0.499	0.564
B	0.499	0.693	0.415
C	0.564	0.415	0.925

A: AI technology attitude, B: perceived usefulness of AI technology, C: digital information competency.

shows that all inter-construct correlation coefficients were below the threshold value of 0.85, confirming the absence of multicollinearity between the constructs. Moreover, the square root of the average variance extracted (√AVE) for each construct exceeded its corresponding inter-construct correlations, indicating that each latent variable was empirically distinct. Therefore, the measurement model in this study satisfied the Fornell–Larcker criterion and demonstrated adequate discriminant validity.

5.3. Structural Equation Model Analysis Results

The structural equation modeling (SEM) results revealed that attitude toward AI technology (A1), one of the subdimensions of AI service competency, did not exert a significant effect on the level of e-commerce utilization (B1) (β = −0.026, p = 0.130).

This finding suggests that, even when individuals hold a positive attitude toward AI technology, such an attitude alone is insufficient to translate into actual behavioral engagement. Rather, specific capability-related factors such as perceived usefulness of AI technology and digital information competency must precede behavioral adoption. Furthermore, the negative direct effects of attitude toward AI technology on certain outcome variables (digital economic activities [C1] and digital social participation [C2]) indicate that a favorable perception of technology does not necessarily yield behavioral or social outcomes. This phenomenon may be explained by the cognitive–behavioral gap, technology fatigue, or instrumental-utility-driven decision tendencies. In other words, even when individuals evaluate technology positively, concrete execution factors such as utility perception and competency play a more decisive role in shaping actual behavior. In contrast, both perceived usefulness of AI technology (A2) and digital information competency (A3) showed significant positive effects on the level of e-commerce utilization (B1). Notably, digital information competency exhibited the strongest effect, confirming that information-searching and utilization skills serve as a key driver in transforming individual competencies into digital economic activities within AI-enabled service environments. Moreover, perceived usefulness of AI technology also demonstrated a significant positive influence, indicating that individuals who perceive AI technologies as practically useful are more likely to engage actively in e-commerce platforms.

Next, perceived usefulness of AI technology (A2) had a significant positive effect on digital economic activities (C1) and daily life satisfaction (C3), but its effect on digital social participation (C2) was not statistically significant (β = −0.017, p = 0.202). In contrast, digital information competency (A3) exerted significant positive effects on both digital economic activities (C1) and daily life satisfaction (C3), whereas its effect on digital social participation (C2) was statistically insignificant (β = 0.029, p = 0.057). These findings imply that, while the ability to search, analyze, and utilize information directly contributes to economic performance and overall life satisfaction, community-oriented behaviors such as social participation may require additional mediating or interactional mechanisms, such as digital collaboration or civic motivation, to manifest effectively. Furthermore, the mediating variable, level of e-commerce utilization (B1), had a significant positive effect on all three digital outcome variables—digital economic activities (C1), digital social participation (C2), and daily life satisfaction (C3)—with the strongest influence observed for digital social participation (C2). This result indicates that the utilization of e-commerce platforms extends beyond simple transactional functions, fostering participation in online communities, opinion expression, and digital civic engagement. In this sense, e-commerce serves as a critical enabler of digital inclusion and social connectedness in the AI-driven digital economy. The results of the path analysis for the structural equation model, focusing on the direct effects among the variables, are presented in

Table 6. Path analysis results from the structural equation model (direct effects).

Path	Standardized Coefficient (β)	p-Value
A1 → B1	−0.026	0.130
A2 → B1	0.048	<0.001
A3 → B1	0.526	<0.001
A1 → C1	−0.043	0.013
A2 → C1	0.069	<0.001
A3 → C1	0.214	<0.001
A1 → C2	−0.117	<0.001
A2 → C2	−0.017	0.202
A3 → C2	0.029	0.057
A1 → C3	0.519	<0.001
A2 → C3	0.061	<0.001
A3 → C3	0.056	0.003
B1 → C1	0.408	<0.001
B1 → C2	0.676	<0.001
B1 → C3	0.094	<0.001

A1: attitude toward AI technology, A2: perceived usefulness of AI technology, A3: digital information competency. B1: level of e-commerce utilization, C1: digital economic activities, C2: digital social participation, C3: daily life satisfaction.

below. These findings provide detailed insights into the structural relationships examined in this study.

Next, to address the limitations of relying solely on a single statistical approach based on bootstrapping confidence intervals (BC CIs) for mediation testing, this study additionally employed the theoretical procedure proposed by Baron and Kenny [41]. Specifically, the presence of indirect effects was assessed by examining the significance of the A→B and B→C paths, thereby enabling a theoretical and interpretive understanding of the mediating mechanism. Moreover, since the standardized coefficients (β) for all indirect effects were derived from a single integrated model using AMOS structural equation modeling (SEM), the analysis achieved both statistical validity and model consistency, offering methodological advantages over stepwise multiple regression approaches. The mediation analysis revealed that digital information competency (A3) exerted significant indirect effects on digital economic activities (C1), digital social participation (C2), and daily life satisfaction (C3) through the level of e-commerce utilization (B1). Notably, a full mediation effect was confirmed for digital social participation (C2), indicating that e-commerce utilization functions as a critical pathway linking information competency to digital performance outcomes. Conversely, the indirect effects of attitude toward AI technology (A1) and perceived usefulness of AI technology (A2) were not statistically significant. This finding suggests that individuals’ positive attitudes or perceived usefulness toward AI technology do not directly translate into digital engagement or performance outcomes; rather, they influence such outcomes indirectly through execution-based factors such as digital information competency (A3). In summary, even without applying bootstrapping, this study ensured theoretical validity and methodological robustness in verifying the mediation effects by integrating inferential consistency based on path significance with the holistic SEM framework. The significant indirect pathways of digital information competency (A3) underscore that within the process of AI technology adoption, actual competency—not merely attitude or perceived usefulness—plays a pivotal mediating role in driving digital economic and social performance. The results of the mediation analysis (indirect effects) for the structural equation model are presented in

Table 7. Mediation analysis results of the structural equation model (indirect effects).

Route	Standardized Indirect Effect (β)	Significance of Indirect Effect	Mediation Type
A1 → B1 → C1	0.000	Not Significant	No Mediation
A1 → B1 → C2	0.000	Not Significant	No Mediation
A1 → B1 → C3	0.000	Not Significant	No Mediation
A2 → B1 → C1	0.020	Not Significant	Partial Mediation
A2 → B1 → C2	0.032	Not Significant	Full Mediation
A2 → B1 → C3	0.005	Not Significant	Partial Mediation
A3 → B1 → C1	0.288	Significant	Partial Mediation
A3 → B1 → C2	0.586	Significant	Full Mediation
A3 → B1 → C3	0.183	Significant	Partial Mediation

below.

These findings indicate that enhancing digital information competency and leveraging e-commerce utilization, rather than relying solely on attitudes toward AI technology or perceived usefulness of AI technology, serve as the primary mechanisms that foster individual digital inclusion and improvements in digital performance outcomes. The final structural model of this study is illustrated in Figure 4 below.

6. Conclusions

This study empirically examined the effects of artificial intelligence service competency among Korean citizens on digital utilization outcomes, including digital economic activities, social participation, and daily life satisfaction, within the rapidly evolving digital trade environment. To this end, data from the 2023 Digital Divide Survey were utilized, and statistical analyses were conducted using structural equation modeling (SEM) with AMOS.

The analysis results indicate that perceived usefulness of AI technology (A2) had a significant positive effect on both digital economic activities (C1) and daily life satisfaction (C3). This finding suggests that individuals who perceive AI technology as practically useful are more likely to experience improvements in their economic performance and overall quality of life. However, the effect on digital social participation (C2) was not statistically significant, implying that collective behaviors such as social engagement may require additional mediating factors beyond mere perception. Furthermore, digital information competency (A3) was found to have a significant positive effect on digital economic activities (C1) and daily life satisfaction (C3), while its direct effect on digital social participation (C2) was statistically insignificant. This indicates that information search, analysis, and utilization skills directly contribute to enhancing individuals’ economic activities and life satisfaction, but extending these effects to social participation may necessitate practical mediating mechanisms, such as the level of e-commerce utilization (B1). In contrast, attitude toward AI technology (A1) did not have a significant effect on digital economic activities (C1) and exhibited a negative effect on digital social participation (C2). This indicates that a merely favorable attitude toward AI technology does not necessarily translate into actual digital engagement, suggesting the possible influence of psychological factors such as the cognitive–behavioral gap or technology fatigue. These results highlight the gap between behavioral intention and actual behavior, as described in the Theory of Reasoned Action [23]. Next, the mediation analysis revealed the following results: Digital information competency (A3) demonstrated significant indirect effects on all three digital outcomes—digital economic activities (C1), digital social participation (C2), and daily life satisfaction (C3)—through the mediating role of the level of e-commerce utilization (B1). In particular, full mediation was confirmed for digital social participation (C2) and daily life satisfaction (C3), indicating that e-commerce utilization serves as a key pathway through which information competency translates into tangible digital outcomes. Conversely, the indirect effects of attitude toward AI technology (A1) and perceived usefulness of AI technology (A2) via e-commerce utilization were not statistically significant, suggesting that practical, competency-based mediating mechanisms exert a greater influence on digital outcomes than mere attitudes or perceptions. Overall, these findings confirm that digital information competency (A3) and level of e-commerce utilization (B1)—rather than positive attitudes toward or perceived usefulness of AI technology—are decisive factors in improving socio-economic performance. This implies that digital competence and practical utilization experience function as core mechanisms of digital inclusion, surpassing the influence of psychological factors related to technology adoption. Accordingly, future policy efforts should move beyond improving basic accessibility and instead focus on practice-oriented educational programs that enhance digital information competency (A3) and promote active e-commerce utilization (B1). Specifically, developing training modules that facilitate the practical use of AI technologies, designing user-centered interfaces, and improving accessibility for digitally vulnerable groups can simultaneously strengthen the overall population’s digital literacy and capacity for social participation. These findings suggest that the ability to search, analyze, and utilize digital information can lead to practical engagement through e-commerce platforms, thereby serving as a crucial link to enhancing quality of life. Consequently, policy interventions that aim to strengthen digital information competency should extend beyond theoretical instruction to include hands-on programs integrated with e-commerce platform utilization.

Finally, this study offers the following policy implications: given that AI acceptance and digital information competency can be translated into digital outcomes through e-commerce platforms, public policy efforts to bridge the digital divide should shift from simply improving access to providing practical training in platform utilization.

From a practical standpoint, it is important to recognize that private platforms (e.g., Coupang, 11st, Naver Shopping) serve not only as consumer channels but also as intermediaries that facilitate both social participation and economic activity. Accordingly, to enhance the social value generated by these platforms, it is essential to improve user-centric interfaces, design UI/UX that considers digitally marginalized groups, and strengthen community-based features. Furthermore, these platform-based ecosystems extend beyond domestic transactions to function as essential infrastructure for AI-driven digital trade. As e-commerce platforms accumulate consumer–producer transaction data and integrate more deeply with global networks, they play a pivotal role in facilitating cross-border e-commerce and enabling small- and medium-sized enterprises (SMEs) to expand into international markets. Consequently, advancing the sophistication and reliability of AI-powered services on private platforms, alongside strengthening accessibility through digital inclusion, can serve as a strategic foundation for enhancing a nation’s overall digital trade competitiveness.

Theoretically, this study builds upon technology acceptance theories by empirically examining the structural relationships between AI acceptance and socio-economic outcomes, with digital commerce practices serving as a mediating mechanism. Specifically, by examining how perceived usefulness and digital information competency translate into actual digital behaviors, this study presents a refined explanatory model that incorporates practical engagement elements into the Unified Theory of Acceptance and Use of Technology (UTAUT). Furthermore, this study extends theoretical discussions by demonstrating how the characteristics of AI (e.g., autonomy, predictive uncertainty) can expand the interpretative framework of existing technology acceptance theories. This study does not merely apply the existing Theory of Reasoned Action (TRA) and the Unified Theory of Acceptance and Use of Technology (UTAUT). Rather, it extends these frameworks beyond the intention-to-adopt stage to the performance-outcome stage by empirically examining how AI service competency translates into tangible digital economic and social outcomes. In doing so, the study proposes an expanded analytical framework that incorporates practical behavioral performance into conventional technology acceptance theory.

Finally, the research is significant in that it empirically proves that individuals’ digital commerce practices, facilitated by private e-commerce platforms, have a positive impact on their socio-economic well-being. This finding demonstrates that the impact extends beyond mere technology acceptance, highlighting that actual platform utilization experiences can contribute to enhanced participation in economic activities, social connections, and life satisfaction.

However, it is important to note that this study, based on a cross-sectional survey of the general population, has limitations in that it does not fully account for the diversity of platform usage patterns based on demographic characteristics such as age, income level, and occupation. Furthermore, this study has a limitation in that it does not fully capture the structural dimensions of the digital divide, as individuals from vulnerable groups, such as persons with disabilities, low-income populations, farmers and fishers, marriage migrants, and North Korean defectors, were excluded from the sample. Therefore, future research should examine how e-commerce experiences vary across demographic groups and the social implications of these differences. Additionally, it is essential to examine trust and ethical considerations in AI-based e-commerce environments and explore the relationship between digital consumption behaviors and social participation using in-depth qualitative methods.

This research is expected to contribute to the development of more sophisticated and practical digital policy frameworks while also enriching and advancing existing theories of technology acceptance.