From Opportunity to Resistance: A Structural Model of Platform-Based Startup Adoption

Abstract

This study explores the determinants of startup intention within the context of e-commerce platform-based startups in South Korea. We employ an extended technology acceptance model (TAM) that integrates individual, social, and entrepreneurial characteristics. A two-step analytical approach is applied, combining variable extraction through data mining and hypothesis testing using structural equation modeling. The results indicate that personal and social factors—such as entrepreneurial mindset and social influence—positively affect perceived usefulness, while job relevance and exposure to successful startup models enhance perceived ease of use. In contrast, security concerns and technological barriers negatively impact these relationships, posing critical obstacles to platform-based startups. This study extends the TAM framework to the platform-based startup context, offering theoretical contributions and proposing policy implications, including promoting digital literacy, developing entrepreneurial networks, and addressing security and regulatory issues. These insights offer a deeper understanding of how platform environments shape entrepreneurial behavior, providing practical guidance for startup founders, developers, and policymakers.

1. Introduction

The COVID-19 pandemic triggered rapid and profound transformations in the global economy and labor market. In particular, the unemployment rate among face-to-face service occupations surged from 3% to over 7%, with remote work environments and automation technologies rapidly expanded. These shifts have catalyzed a structural transition in the economy toward digital technologies and serve as a key driver for advancing a smart society. Individuals rapidly adapt to digital environments, navigating a transitional phase characterized by one-stop services and the convergence of online and offline interactions, commonly called the experience economy.

Despite the pandemic-related economic losses, the platform economy—centered on e-commerce—has emerged as a new growth engine. Income reductions and employment instability have led individuals to pursue economic self-sufficiency through platform-based economic activities. With the rise of the gig economy, many people have turned to platforms that enable low- or zero-capital startup opportunities. Platform-based startups have become a core component of independent and sustainable economic activity in this context. The rapid development of e-commerce platforms has created new opportunities for consumers and aspiring startup founders, fostering an environment in which co-evolution between consumers and entrepreneurs occurs within the platform ecosystem.

Research on e-commerce and platform-based startups has been active since the 1990s, with substantial research focusing on consumer behavior and platform business models [1,2,3]. Additional studies have explored the motivations behind platform-based startups [4,5], while Nambisan et al. [6] emphasize that global digital platforms and ecosystems enable entrepreneurs to integrate cross-border resources and identify new opportunities.

However, the prior literature has predominantly explored the perspective of entrepreneurs developing their own platforms rather than individuals leveraging existing platforms for startup purposes. Studies investigating the motivations and intentions of individual entrepreneurs who pursue low- or zero-capital startups within large-scale e-commerce platforms remain relatively scarce.

While a substantial body of research has explored the dynamics of platform-based entrepreneurship, most existing studies tend to focus on the supply side—namely, platform developers, digital innovators, or startup founders who create new platforms or technologies. These studies primarily examine platform design, scalability, network effects, and ecosystem orchestration. In contrast, considerably less attention has been given to the demand side of the platform economy: users engage in entrepreneurial activity by leveraging existing digital platforms without building one from scratch. This gap limits a comprehensive understanding of how ordinary users perceive, adopt, and sustain platform-based startup activities. Especially in cases of low- or zero-capital contexts, where technical expertise is not required, user motivation, behavior, and barriers remain understudied. By shifting the analytical lens from developers to users, this study aims to address this theoretical gap and offer a user-centered perspective on digital entrepreneurship.

This study examines the determinants of startup intention within the context of e-commerce platform-based entrepreneurship in South Korea, aiming to address this research gap. South Korea has a high population density, well-developed logistics infrastructure, and advanced IT and smart economy capabilities, providing an optimal environment for platform-based startups. To adapt the TAM to this context, this study incorporates variables beyond traditional technology adoption constructs. Platform entrepreneurship often involves non-expert, resource-constrained individuals utilizing third-party ecosystems for income generation or business creation. Therefore, it is essential to consider psychological traits (e.g., innovativeness, risk tolerance), contextual factors (e.g., role models, social influence), and perceived constraints (e.g., security risks, technology barriers) that are frequently cited in both the entrepreneurship and digital transformation literature. These variables were selected not only based on their theoretical grounding but also through empirical relevance identified via text mining and social network analysis, ensuring a data-informed extension of the model.

Accordingly, this study empirically analyzes the factors influencing startup intention in the context of e-commerce platforms in South Korea. A two-step approach was employed, combining text mining and structural equation modeling (SEM). The first step used data mining techniques to identify the key explanatory variables of startup intention. The second step tested the theoretical hypotheses through SEM. This integrated approach enables a comparison between theoretically grounded and empirically derived constructs, facilitating a robust assessment of the intention to start a platform-based startup (IPBS).

2. Theoretical Background and Hypotheses

2.1. Platform Business Startup

Platform businesses function as intermediaries in digital environments, connecting consumers and providers while generating value through network effects [7,8]. Following the COVID-19 pandemic, increasing economic uncertainty has driven many entrepreneurs to focus on platform startups, which offer lower initial costs and greater scalability than traditional businesses [9,10]. In particular, the expansion of e-commerce platforms has created startup opportunities, enabling entrepreneurs to implement strategies that differ from conventional business models [11]. Platform-based startups are not merely online businesses; they focus on building ecosystems involving diverse stakeholders [12,13,14,15]. This approach leverages resource sharing and network effects to enhance entrepreneurs’ competitiveness and maximize their influence in the market. Moreover, accelerating digital transformation has solidified platform-based startups as a strategic tools for business sustainability.

While traditional startups deliver a single product or service, platform-based startups grow by encouraging user interactions and network effects [12,13]. These characteristics provide advantages, such as reducing costs, improving market accessibility, and the potential for global expansion [14]. For example, as online economic activities have proliferated and digital marketplaces have expanded, traditional retail businesses have declined [9,10]. After the pandemic, with contactless consumption becoming widespread, platform startups evolved beyond survival mechanisms into symbols of innovation, opening new market opportunities [15]. These developments reflect changes in offline market structures and consumption patterns and demonstrate how platform-based entrepreneurship enables individuals to experiment with and scale new business models even during economic downturns [16].

The success of platform ventures depends on market insight and adaptability [17], as entrepreneurs must respond swiftly to consumer demands and continually revise their business models in dynamic environments [18]. The diversity of e-commerce platforms offers customizable opportunities, enhancing competitiveness [19]. Furthermore, platform entrepreneurship allows individuals to operate ventures in parallel with existing jobs, increasing income potential while mitigating risk [20]. This flexible approach to entrepreneurship enables sustainable growth even during economic instability and creates new employment across society [21].

As platform startups increasingly contribute to economic revitalization and social stability, governments and public institutions have introduced various supportive policies [22]. These policies provide financial and technical assistance to entrepreneurs and foster platform ecosystems that empower them to realize innovative ideas [23]. With ongoing technological advancements, the importance of platform ventures is expected to increase, accelerating changes in the startup environment and broader economic structures [24]. Therefore, successful platform-based entrepreneurship requires technical competence, strategic thinking, and a deep understanding of the market [25]—entrepreneurs must carefully analyze the characteristics and trends of platform businesses and develop effective startup strategies.

2.2. Impact of Personal Characteristics on Perceived Usefulness and Ease of Use

Platform-based startup awareness (PBSA) plays a critical role in digital entrepreneurship, influencing an individual’s ability to adapt to complex markets by applying their competencies [26]. Entrepreneurs who thoroughly understand platform features are more likely to recognize the value of resource integration, opportunity recognition, and operational efficiency offered by the platform. This recognition increases perceived usefulness (PU) regarding platform entrepreneurship [27]. In other words, PBSA can generate new business opportunities and drive innovation, significantly enhancing the perceived utility of platform-based startups. Moreover, a positive perception of entrepreneurship can reduce psychological resistance to platform startup activities and motivate individuals to utilize the vast array of technologies and tools embedded within the platform. Such motivation often translates into pride in sustaining or advancing one’s economic activity. Notably, platforms provide a range of technical tools that are particularly useful during the planning and launch stages of startup.

However, excessive focus on technical complexity may cause entrepreneurs to neglect usability, lowering their perceived ease of use (PEU) [28]. Although platform openness can broaden entrepreneurial opportunities and facilitate resource integration, it can also increase complexity and evoke hesitation. Thus, while PBSA can serve as a key motivation factor, misaligned expectations may become significant barriers due to the complexity of integrating resources in platform ecosystems [6].

Job relevance (JR) refers to the alignment between entrepreneurial activities and one’s previous experience, career, and expertise. When entrepreneurs perceive that their knowledge and experience can be effectively applied in the platform environment, their perception of the suitability and utility of platform technologies increases [29]. Individuals with domain expertise are more capable of assessing market needs and developing advanced business strategies, enhancing PU [30]. Furthermore, entrepreneurs with experience in e-commerce operations are more adept at identifying how platform tools contribute to market expansion, customer relationship management, and revenue growth—factors that contribute to PU and PEU [31]. When individuals perceive high alignment between tasks and personal goals, their PU is significantly amplified [32]. Entrepreneurs who can understand and adapt to platform technologies tend to adopt platform startups more readily and can more easily learn the functions and interface of the system, resulting in greater PEU [33]. These capabilities also serve as mechanisms through which platform-based startups can transform traditional business models and enable entrepreneurs to access markets and resources more efficiently [34]. Based on the above discussion, the following research hypotheses are proposed:

H1. 

PBSA positively affects PU.

H2. 

PBSA positively affects PEU.

H3. 

Personal JR positively affects PU.

H4. 

Personal JR positively affects PEU.

2.3. Impact of Social Characteristics on PU and PEU

Social influence and pressure (SIP) play a crucial role in entrepreneurs’ decision-making processes within platform ventures. Social influence refers to the effect of information shared by peer entrepreneurs, industry experts, or social networks on an entrepreneur’s decision to pursue platform entrepreneurship [35]. Endorsements from peers and the visibility of standardized success cases encourage entrepreneurs to trust platforms’ features and benefits, thereby strengthening their PU and PEU [36]. Social pressure can influence entrepreneurial decisions by reinforcing internal motivation through peer or industry stakeholder expectations [37,38].

Entrepreneurs often adjust their strategies by observing successes and failures within their social environment; this social learning process directly shapes how entrepreneurs perceive the value of platforms [39]. In particular, observing successful entrepreneurs reinforces the feasibility and advantages of platform entrepreneurships, prompting more favorable evaluations of platform effectiveness [40]. Success models (SMs) serve as motivational cues by offering actionable strategies, reducing uncertainty, and accelerating market entry for platform entrepreneurs [41,42,43]. Moreover, word-of-mouth communication—whether through online communities, professional networks, or personal connections—can enhance individuals’ confidence in their own entrepreneurial potential [43]. Based on these theoretical considerations, the following hypotheses are proposed:

H5. 

SIP positively affects PU.

H6. 

SIP positively affects PEU.

H7. 

SM positively affects PU.

H8. 

SM positively affects PEU.

2.4. Impact of Entrepreneurial Characteristics on PU and PEU

Entrepreneurial characteristics are vital in how entrepreneurs recognize and act upon new opportunities. These characteristics are typically conceptualized as a multi-faceted construct encompassing personal innovativeness, risk tolerance, and communication skills [44]. Individual innovativeness (II) describes a tendency to adopt new technologies and ideas early, which is essential for integrating emerging tools into viable business models [45]. Entrepreneurs with high II tend to comprehend and utilize platform functionalities more effectively, enhancing both PU and PEU [46].

Risk tolerance (RT) denotes an entrepreneur’s willingness to make decisions under uncertainty. Entrepreneurs with high RT tend to approach unfamiliar technologies with confidence and take proactive step to overcome platform-related challenges [47]. As a result, they are more inclined to evaluate platform technologies as beneficial; however, the complexity encountered in learning new systems may lower evaluations of PEU [48]. In contrast, low RT is often associated with hesitancy, anxiety, or resistance to unfamiliar digital tools—factors that can negatively influence both PU and PEU [49].

Communication skill (CS) refers to an entrepreneur’s ability to exchange information and foster collaboration with customers and stakeholders [50]. Since platform-based startups primarily operate in virtual settings, the ability to engage effectively through digital channels with suppliers, platforms, and consumers becomes a strategic asset. Such communication skills facilitate information transmission and support emotionally resonant business models through empathy and interaction, contributing significantly to entrepreneurial success [51]. Furthermore, effective communication enhances collaboration, enabling entrepreneurs to engage in diverse problem-solving processes and develop novel business models [52].

Together, these three traits—II, RT, and CS—represent distinct yet complementary dimensions of entrepreneurial orientation. II captures openness to novel concepts and tools; RT reflects decision-making confidence under uncertainty; and CS underscores the importance of interpersonal dynamics in business growth. Their integration provides a holistic understanding of how entrepreneurial capacity shapes PU and PEU in platform-based contexts. Accordingly, we propose the following hypotheses:

H9. 

II positively affects PU.

H10. 

II positively affects PEU.

H11. 

RT positively affects PU.

H12. 

RT positively affects PEU.

H13. 

CS positively affects PU.

H14. 

CS positively affects PEU.

2.5. Impact of PU on IPBS

The TAM posits that PU significantly influences an individual’s intention to adopt and continuously use a particular technology—a relationship that becomes especially salient in platform-based business environments. Studies have shown that when a platform is increasingly perceived as an effective business tool, the user’s entrepreneurial intention intensifies [53,54]. In other words, as the platform’s perceived practicality and convenience increase, entrepreneurs are more likely to explore business opportunities through platform utilization.

The platform economy is key in increasing individuals’ perceptions of convenience and practicality, particularly encouraging entrepreneurial intention among university students and employees [53]. For instance, in the K-beauty industry, the PU and PEU of platform businesses are key factors that reinforce consumers’ purchase intentions. This situation enables entrepreneurs to predict and respond to consumer behavior more effectively through platform engagement [54]. Furthermore, PU has been shown to significantly influence user attitudes and behavioral intentions on digital collaboration platforms [55].

Beyond serving as transactional tools, platforms enable social interaction, allowing entrepreneurs to acquire knowledge and enhance their visibility within social and professional circles [56]. This, in turn, motivates entrepreneurs to engage with platforms and provide practical support for their startup activities. Notably, entrepreneurs with high PU are more likely to utilize social networking services (SNSs) as business tools—the more they perceive SNSs as delivering actual business value, the greater their platform engagement becomes [57].

On user-generated-content-driven platforms, the practical relevance of online reviews shapes consumer purchase decisions, increasing the likelihood of successful market entry for entrepreneurs [58]. When the platform’s information and functionalities are perceived as applicable in the entrepreneurial decision-making process, entrepreneurs are more likely to trust the platform, strengthening their entrepreneurial intention. Accordingly, PU represents not only a technological advantage but also a key motivational driver of entrepreneurial behavior [55]. Based on the discussion above, we propose the following research hypothesis:

H15. 

PU positively influences IPBS.

2.6. Impact of PEU on IPBS

PEU is critical in platform ventures, directly influencing entrepreneurs’ technology adoption and business operation strategies [59]. The success of early-stage startups is largely contingent upon how easily entrepreneurs can understand and apply platform technologies, which in turn affect business sustainability and scalability [60].

According to the TAM, PEU also interacts with PU; the more user-friendly a technology is perceived to be, the more effectively it will be used [61,62,63]. This relationship also holds true in platform entrepreneurships. When platform functions and interfaces are intuitive, entrepreneurs are less resistant to adoption and more likely to engage with the system.

Empirical studies have confirmed that the more user-friendly a platform is perceived to be, the stronger the entrepreneurs’ intention to start a business on that platform. Ease of use reduces anxiety about technical complexity, encouraging active business engagement through the platform [62]. In this way, PEU is vital in supporting entrepreneurs in building efficient business models and establishing a stable presence in the digital economy. Based on these considerations, we propose the following hypothesis:

H16. 

PEU positively affects IPBS.

2.7. Impact of Platform Business Startup Resistance on PU and PEU

In the context of a platform startup, security risk (SR) and technology barriers (TBs) are critical factors influencing entrepreneurial behavior. SR encompasses personal data protection, information security, and intellectual property rights within the platform environment—these aspects foster user trust and encourage platform adoption [64]. When consumers or sellers engage on a platform, personal data could be shared across networks, raising concerns about privacy breaches and security risks. This anxiety is a key deterrent to adopting new technologies or services and can negatively affect an entrepreneur’s willingness to use a specific platform [65]. The role of security risk as a moderator is grounded in perceived risk theory, which posits that concerns about data safety and system reliability can inhibit technology adoption, particularly in contexts requiring trust and personal investment [66]. In platform-based entrepreneurship, where individuals often share personal and financial data, higher perceived security risk may weaken the effect of perceived ease of use or usefulness on intention. Prior research has shown that such risk perception can distort users’ cognitive evaluations and reduce behavioral consistency, thereby justifying its inclusion as a negative moderator [67].

TBs arise when new technologies and innovative services are complicated for users to understand or apply, potentially hindering platform adoption and the efficiency of entrepreneurial activities [68]. In platform environments, confidence in IT skills promotes participation and improves operational strategies [69]. In contrast, the higher the technological barrier, the more difficulty entrepreneurs may face adapting to the platform, which can ultimately weaken their entrepreneurial intention [70]. The moderating effect of technology barriers draws upon the diffusion of innovation theory, which suggests that adoption depends not only on perceived benefits but also on perceived effort and system compatibility. High perceived technological barriers (e.g., complexity, lack of support) can diminish the impact of cognitive beliefs on intention by creating friction in the decision-making process. In entrepreneurial contexts, where users often lack technical assistance, these barriers may disproportionately deter novice users. Prior studies in digital entrepreneurship confirm that system complexity moderates the ease–intention relationship [71].

Implementing security and privacy measures can enhance platform trust and promote user adoption [72]. Prior research indicates that enhanced security in electronic banking services increases customer trust and improves both PU and PEU [73]. Similarly, legal compliance and intellectual property protection help build trust and support smoother adoption of platform technologies [74]. Nevertheless, entrepreneurs often face economic, social, and regulatory challenges in using platforms—barriers that may hinder engagement even when the benefit of platform business are clear [75].

Trust is a fundamental determinant in the entrepreneurial decision to adopt technologies. Higher levels of platform security and perceived trustworthiness enhance users’ evaluations of usefulness [76]. In other words, strong technological trust leads to perceptions that platform technologies offer real value in the startup process, thereby increasing platform engagement. Conversely, large TBs will likely negatively affect PEU and PU, hindering entrepreneurs’ platform utilization. Based on this discussion, the following research hypotheses are proposed:

H17. 

SR negatively affects the relationship between PEU and IPBS.

H18. 

SR negatively affects the relationship between PU and IPBS.

H19. 

TB negatively affects the relationship between PEU and IPBS.

H20. 

TB negatively affects the relationship between PU and IPBS.

2.8. Intention to Accept Platform-Based Startups

The PEU and PU associated with digital platforms significantly influences the intention to engage in platform ventures. The TAM and the theory of planned behavior (TPB) provide theoretical frameworks for understanding these relationships, emphasizing the formation of user attitudes and perceived behavioral control as key determinants of entrepreneurial intention [77]. Platforms reduce transaction costs and alleviate the initial entry burden for entrepreneurs, thereby expanding startup opportunities [78].

In particular, digital platforms catalyze sustainable entrepreneurship across diverse markets, including emerging economies; they are increasingly recognized as vital tools that support economic, social, and environmental development [79]. Platforms are core stakeholders in entrepreneurial ecosystems, offering greater market accessibility than traditional, physically bounded business models [80]; thus, entrepreneurs can develop strategies to optimize existing resources and attract a broader consumer base without needing large-scale physical investments [81]. Furthermore, entrepreneurs with experience in e-commerce operations tend to recognize the practical benefits of platform entrepreneurship and understand how platform tools contribute to market expansion [82]. Many entrepreneurs adopt platform-based business models to implement more flexible market strategies and adapt swiftly to evolving consumer demands [83]. Against this backdrop, multiple factors—including technological convenience, economic benefits, and market adaptability—shape the intention to accept a platform-based startup, enabling entrepreneurs to pursue sustainable growth through platform utilization.

3. Materials and Methods

Before conducting the empirical analysis using SEM, this study used text mining and social network analysis (SNA) to examine the interrelationships among variables. In response to the limitations of purely theory-driven models, we adopted a hybrid approach by incorporating text mining and SNA results into construct development. Specifically, high-frequency keywords such as “risk,” “support,” “barrier,” “opportunity,” and “technology” were extracted from user-generated content related to platform-based startups. SNA further highlighted central themes like “security risk” and “system complexity” as structurally influential within the discourse network. These keywords were not used to define constructs in isolation but to triangulate with existing theoretical models (e.g., TAM and entrepreneurial intention theory), thereby enhancing construct relevance and empirical validity. This data-informed approach enabled us to refine our hypothesis development while ensuring that the model captured practical concerns and perceptions among platform users. This approach enhances the research model’s robustness and provides a more comprehensive and nuanced interpretation of the findings. The integrity of the model and the explanatory power of the results were strengthened by validating the key variables, hypotheses, and survey items—derived from the prior literature—through objective data analytics. Subsequently, SEM was applied to the final analysis of the survey data to determine how the identified variables interact and exert statistically significant effects [82].

3.1. Step One: Text Mining and SNA

SNA can process unstructured textual data to identify self-organizing patterns through refinement, classification, and clustering, allowing us to interpret embedded meanings. In particular, from textual data extracted through subject words, refined terms are treated as nodes within a network, enabling us to analyze relational structures and contextual patterns among those nodes. The emerging relationships and patterns—based on node frequency, co-occurrence, and centrality measures—provide insight into the overall network trends and their association with the focal subject terms. This analytical process helps identify key factors and the functional roles of terms surrounding the subject and allows us to recognize words that directly or indirectly influence the subject term [84].

3.1.1. Data

We used the subject terms “platform business startup” and “platform startup” and conducted web crawling across Naver (www.naver.com (accessed on 20 July 2025)) and Google, which account for over 95% of the South Korean search engine market. These platforms are considered optimal for exploring public discourse on specific topics within the Korean context. News articles and related documents published between 2020 and 2024 were collected for analysis. After removing duplicate entries, we retained 14,994 valid documents. We removed the stop words from the resulting textual data and performed morphological analysis, followed by tokenization and part-of-speech (POS) tagging. This preprocessing resulted in the extraction of 3930 unique terms. Based on the term frequency–inverse document frequency (TF–IDF) values and centrality scores, we selected the top 50 keywords for subsequent network analysis.

3.1.2. Text Mining

TF-IDF and centrality analysis are widely used indicators in text mining. The former reflects the contextual relevance of a term based on its frequency, while the latter captures the strength of a term’s relational connectivity within a network. A high TF-IDF score indicates that a word holds significant semantic weight in the discourse network. Similarly, a high centrality score suggests that the term is strongly linked to the subject term and may exert direct or indirect influence within the network. Words identified through both indicators are interpreted as influential variables associated with the subject term. From this perspective, the text mining results validate and reinforce the robustness of the variables used in the SEM, particularly those derived from the prior literature [84]. Based on the TF-IDF rankings, we extracted the top 50 terms; those highly relevant to the SEM variables were selected and are presented in

Table 1. The result of the text mining of a platform business startup.

Subject Word	Platform Business Startup
Variables	Words (TF–IDF/Degree Centrality)
Platform-based startup and personal factors	Business Model (525.614/0.888), Profit (431.814/0.859), Side Hustle (428.503/0.424), Success (396.804/0.788), Necessity (350.938/0.814), Utilization (335.159/0.788), Value (301.589/0.828)
Platform-based startup and social factors	Sharing (520.395/0.679), Influence (456.012/0.374), Success (396.804/0.788), Social (359.663/0.747), Recommendation (338.002/0.586), Relationship (324.999/0.525), Participation (300.818/0.263)
Entrepreneurship	Business Model (525.614/0.888), Service (594.236/0.889), Innovation (491.110/0.859), Strategy (469.114/0.838), Market (432.738/0.869), Startup (427.649/0.717), New Industry (391.105/0.723), Competition (316.058/0.747), Value (301.589/0.828), Participation (300.818/0.263), Usefulness (289.427/0.667), Distinction (274.669/0.801)
Resistance to platform-based startups	Regulation (707.495/0.596), Policy (573.236/0.889), Business Model (525.614/0.888), Digital Barriers (418.566/0.787), Online (395.650/0.828), New Industry (391.105/0.723), Government (376.347/0.576), Technology Barriers (371.164/0.774), Patent (319.929/0.293), Law (312.643/0.626), System (288.466/0.343)
Platform business startup intention	Business Model (525.614/0.888), Service (594.236/0.889), Utilization (335.159/0.788), Intention (305.751/0.111), Value (301.589/0.828), Usefulness (289.427/0.667)

. Appendix A presents the full results of the text mining process.

3.1.3. Result of SNA

We applied convergence of iterated correlations (CONCOR)—one of the core methodologies in SNA—to cluster the network and identify the semantic structure of its subgroups. Using the UCINET 6 software, we constructed a co-occurrence matrix of the data. We subsequently compared the row vectors of each node against the CONCOR matrix (correlation- or eigenvalue-based) to reveal the underlying relational patterns. This process enabled the identification of the existence and characteristics of key clusters;

Table 2. The results of the CONCOR analysis.

Number of clusters	7
Average clustering coefficient	0.998
Ten major hub nodes (coefficient of hub node)	Policy (0.0365), Innovation (0.35), Platform (0.324), Product (0.258), Success (0.228), Company (0.21), Necessity (0.208), Regulation (0.199), Strategy (0.149), Business Model (0.136)
Significant keywords in the cluster (related SEM variables)	Business Model, Platform, Side Hustle, Social, Regulation, Innovation, Digital Barriers, Participation, Recommendation, Technology Barriers, Distinction, Value, Relationship, Intention

presents the results. The CONCOR analysis revealed 7 meaningful clusters with an average clustering coefficient of 0.998. Furthermore, 10 prominent hub nodes were identified, and several significant keywords—linked to the core SEM variables—were extracted from the clusters (Table 2, significant keywords in the cluster). Appendix B presents the detailed results of the CONCOR analysis.

By integrating the results from the text mining and CONCOR analysis, we identified a range of terms from the data on platform-based entrepreneurship that align closely with key factors emphasized in prior studies. Specifically, the text mining process yielded a set of thematically related keywords: 7 associated with personal characteristics, 7 with social factors, 12 with entrepreneurial characteristics, 11 with platform entrepreneurship resistance, and 6 with IPBS. The subsequent CONCOR analysis refined these findings by structurally clustering semantically related words, revealing which terms were important in the “platform-based startup” context based on objective data. Notably, we identified 13 significant terms closely related to the theoretical variables, offering critical insights into the motivations, drivers, attributes, barriers, and phenomena underlying platform-based entrepreneurship. These analytical results served as a valuable foundation for the subsequent SEM stage, helping to identify meaningful interactions among variables. They also offer the advantage of enhancing the empirical relevance of the model by incorporating factors that accurately reflect real-world startup contexts.

Based on the results of the CONCOR analysis, we present

Table 3. Mapping of text mining and SNA keywords to SEM constructs.

Construct SEM	Key Related Keywords	Theoretical Relevance
PBSA	business model, necessity, utilization	Indicates perception of platform startups as necessary business model innovations
JR	job, experience, utilization	Reflects connection of prior work to platform usage
SIP	influence, social, recommendation	Captures social pressure and information cues
SM	success, recommendation, relationship	Role models and success stories in networks
II	innovation, strategy, distinction	Embracing new approaches and creativity
RT	risk, opportunity	Willingness to take risks for rewards
CS	communication, participation	Skills in interacting with others
SR	regulation, policy, security	Data protection and legal concerns
TB	technology barriers, system, complexity	Difficulty in using platform systems
PU/PEU	usefulness, value, utilization	Practical benefits and ease of use
IPBS	intention, startup, business	Intention to start platform-based business

below to clearly illustrate how the core keywords identified through text mining and SNA were mapped to the constructs in the SEM model. This table demonstrates how each theoretical variable was informed by high-frequency or central keywords, enhancing the transparency and empirical validity of our construct development process.

3.2. Step Two: SEM

3.2.1. Model and Measurement Development

This study developed an SEM approach based on prior research and the results of SNA to analyze the acceptance of platform-driven ventures.

To enhance theoretical clarity, we structured our research model as a two-layer analytical framework. The first layer captures the core TAM pathway, examining how PU and PEU influence the intention to engage in IPBS. The second layer extends this core by incorporating individual, social, and entrepreneurial characteristics as external antecedents influencing PU and PEU. This layered approach ensures theoretical parsimony while capturing the psychological and contextual determinants critical to platform-based entrepreneurship. Figure 1 below visualizes this structure, illustrating the core TAM path at the center and the extended antecedents surrounding it to enrich the model’s explanatory power.

Measurement items were developed by clearly defining the conceptual dimensions of each construct based on the prior literature and selecting items that were contextually appropriate for platform startups. The measurement model used a five-point Likert scale (1 = strongly disagree; 5 = strongly agree) to assess respondents’ perceptions quantitatively.

The measurement items were adapted from previously validated studies to ensure reliability and validity; minor modifications reflect this study’s specific context and target population. Data were collected through an online survey platform in South Korea. The questionnaire consisted of 45 items, including 4 demographic variables (gender, age, educational level, and monthly income), and was designed to measure the research model’s core constructs.

Items for PU and PEU were adopted from the TAM framework, while items for social influence and entrepreneurial traits were adapted from prior studies [85]. Furthermore, moderating variables like security and technology barriers were designed to reflect technological obstacles and trust-building factors within the platform environments.

Table 4. Measurement development.

Measurement Items Construct	Item	Sources
PBSA	[PBSA1] I believe that platform-based startups can lead to the creation and accumulation of wealth. [PBSA2] I believe that platform-based startups contribute to social job creation. [PBSA3] I believe that platform-based startups help expand the economically active population. [PBSA4] I consider platform-based startups to be an innovative means of conducting business. [PBSA5] I believe that platform-based startups reflect a transformation in business models in response to the changes of the times.	[26]
JR	[JR1] My current work is directly related to e-commerce platforms. [JR2] My current work involves the frequent use of IT technologies. [JR3] My current work is broadly related to the field of e-commerce.	[30]
SIP	[SIP1] People around me or in my social group are engaged in buying and selling products through platform businesses. [SIP2] Buying and selling products through platform-based startups reflects a social image of me as perceived by others. [SIP3] People around me or in my social group say that I am well-suited for a platform-based startup business.	[43]
SM	[SM1] There are people around me who are successfully running a platform-based startup or business. [SM2] I have relatives or acquaintances who have succeeded through a platform-based startup business. [SM3] Someone close to me—such as a relative or acquaintance—has succeeded through a platform-based startup business. [SM4] I have a family member who serves as a role model for successful platform-based startup businesses.	[44]
II	[II1] I pursue personal business success and development. [II2] I strive to incorporate the latest techniques into my work processes. [II3] I make efforts to apply creative approaches to my work. [II4] I actively embrace creative and original ideas. [II5] My friends or family seek my advice before adopting new technologies.	[46,47]
RT	[RT1] I actively respond to environmental changes, even if they involve taking risks. [RT2] I prefer business opportunities with high returns, even if they involve high risks. [RT3] When opportunities arise, I take on challenges—even if they are somewhat risky. [RT4] When I have confidence in a business idea, I am willing to invest boldly.	[50]
CS	[CS1] I tend to understand problems by considering others’ perspectives. [CS2] I listen attentively to others and respond appropriately. [CS3] I actively express my opinions. [CS4] I try to mediate conflicts through various approaches. [CS5] I tend to pay attention to others’ arguments and behaviors.	[52]
SR	[SR1] I am concerned about potential copyright infringement when engaging in a platform-based startup business. [SR2] I believe there is a risk that personal or consumer information could be leaked during the platform-based startup business. [SR3] I believe that platform-based startup systems are vulnerable to external hacking threats.	[73]
TB	[TB1] The platform-based startup system itself is difficult to use. [TB2] It is difficult to understand how to generate revenue through a platform-based startup. [TB3] The training programs for platform-based startups are difficult to follow. [TB4] Building networks and marketing strategies for platform-based startups and revenue generation is difficult and complex.	[67]
PU	[PU1] Platform-based startup business is more effective than other startup methods. [PU2] Generating revenue through a platform-based startup is more effective than through other types of businesses. [PU3] Platform-based startups lead to higher work efficiency compared to other types of businesses.	[59]
PEU	[PEU1] The process of a platform-based startup feels easier compared to other startup methods. [PEU2] Marketing and selling after launching a platform-based startup are easy to carry out. [PEU3] The process of launching and generating revenue through a platform-based startup is easy to understand and learn.	[61]
IPBS	[IPBS1] I am willing to pursue a new business, such as a platform-based startup. [IPBS2] I am willing to sell products and generate income through a platform-based startup. [IPBS3] I am willing to expand my business opportunities through a platform-based startup.	[79,81]

presents a detailed list of the measurement items and their sources.

3.2.2. Data Collection and Sampling

This study selected a sample of individuals with an awareness of platform-based entrepreneurship for the survey. The data were collected through a professional online survey agency (ezsurvey.co.kr). A pilot test was conducted before the main survey to ensure respondents’ comprehension and enhance the validity of the questionnaire items. In the pilot phase, 50 questionnaires were distributed, and feedback was collected to refine and optimize the final version. A total of 500 responses were collected during the primary survey. Insincere or incomplete responses were excluded after reviewing the responses for consistency and reliability, leaving 478 valid cases for the final analysis. Demographic characteristics were assessed using four variables: gender, age, educational attainment, and monthly income. Frequency analysis was conducted to evaluate the sample’s representativeness.

Table 5. Demographics of respondents.

Classification	Indicators	Frequency	%
Gender	Male	287	60.0
	Female	191	40.0
Age	20–29	61	12.8
	30–39	100	20.9
	40–49	80	16.7
	50–59	104	21.8
	Over 60 (including 60 years old)	133	27.8
Education level	Elementary school graduate	2	0.4
	Middle school graduate	5	1
	High school graduate	63	13.2
	University graduate/dropout (including junior college)	360	75.3
	Graduate school graduate	48	10
Monthly income	Less than KRW 1,000,000 (excluding KRW 1,000,000)	35	7.3
	KRW 1,000,000–1,999,999 (excluding KRW 1,999,999)	61	12.8
	KRW 2,000,000–2,999,999 (excluding KRW 2,999,999)	109	22.8
	KRW 3,000,000–3,999,999 (excluding KRW 3,999,999)	82	17.2
	KRW 4,000,000–4,999,999 (excluding KRW 4,999,999)	89	18.6
	KRW 5,000,000–5,999,999 (excluding KRW 5,999,999)	43	9.0
	More than KRW 6,000,000 (excluding KRW 6,000,000)	59	12.3

presents the demographic profile and descriptive statistics of the respondents.

Demographic analysis of the survey respondents indicates a slightly higher proportion of male participants (60.0% male and 40.0% female). Regarding age distribution, respondents aged 60 and above accounted for the highest percentage (27.8%), followed by those in their 50s (21.8%), 30s (20.9%), 40s (16.7%), and 20s (12.8%). This age distribution suggests a relatively high level of interest in platform-based entrepreneurship among middle-aged and older adults. Concerning educational background, individuals who had completed or partially completed undergraduate education represented the largest group (75.3%). Those with a graduate-level education accounted for 10.0%; respondents with only a high school diploma made up 13.2%, and only 1.4% had completed middle school or less, indicating that platform-based entrepreneurs tend to have relatively high levels of education. Regarding monthly income distribution, the most significant proportion fell within the KRW 2,000,000–2,999,999 range (22.8%), followed by KRW 4,000,000–4,999,999 (18.6%) and KRW 3,000,000–3,999,999 (17.2%). High-income earners (over KRW 6,000,000) comprised 12.3%, while low-income earners (below KRW 1,000,000) accounted for 7.3%. These results indicate that platform-based entrepreneurship is accepted across diverse income brackets, with a strong tendency among middle-income individuals to leverage platforms as entrepreneurial opportunities.

3.2.3. Data Analysis

After data collection, statistical analyses were conducted using SPSS 29.0 and AMOS 26.0 software. Confirmatory factor analysis (CFA) was performed on the final measurement model, which consisted of 45 items, to verify the reliability and validity of the latent constructs. A two-step analytical procedure was then applied to test the structural model. In the first step, we examined the hypothesized relationships among the key variables to evaluate the validity of the proposed research hypotheses. In the second step, interaction term analysis was conducted to assess how resistance factors moderate platform business startups. This analytical process allowed us to evaluate the direct and moderating effects among the variables within the model.

3.3. Results

3.3.1. Confirmatory Factor Analysis

The CFA results indicate that the measurement model demonstrates satisfactory levels of reliability and validity—all factor loadings exceeded 0.70, confirming a strong association between each item and its corresponding construct. This outcome suggests that the observed variables effectively represent their theoretical constructs, supporting the model’s construct validity. The composite reliability (CR) values ranged from 0.825 to 0.887, exceeding the recommended threshold of 0.70 and indicating high internal consistency across all constructs. Additionally, the average variance extracted (AVE) values ranged from 0.562 to 0.674, surpassing the standard threshold of 0.50, confirming convergent validity. Cronbach’s alpha coefficients also exceeded 0.80 for all constructs, further supporting the reliability of the measurement items.

Table 6. The reliability and results of the CFA.

Variable	Constructs	Measurement Items	Factor Loadings	CR	AVE	Cronbach’s Alpha
Personal Factors	PBSA	PBSA1	0.804	0.872	0.578	0.872
		PBSA2	0.798
		PBSA3	0.825
		PBSA4	0.746
		PBSA5	0.795
	JR	JR1	0.744	0.838	0.635	0.835
		JR2	0.818
		JR3	0.812
Social Factors	SIP	SIP1	0.798	0.829	0.619	0.826
		SIP2	0.804
		SIP3	0.751
	SM	SM1	0.749	0.836	0.562	0.835
		SM2	0.736
		SM3	0.769
		SM4	0.746
Entrepreneurship Factors	II	II1	0.798	0.884	0.605	0.884
		II2	0.773
		II3	0.775
		II4	0.803
		II5	0.702
	RT	RT1	0.795	0.877	0.640	0.876
		RT2	0.806
		RT3	0.821
		RT4	0.809
	CS	CS1	0.708	0.887	0.612	0.884
		CS2	0.837
		CS3	0.81
		CS4	0.856
		CS5	0.784
Resistance to Platform Business Startups	SR	SR1	0.717	0.825	0.615	0.818
		SR2	0.859
		SR3	0.86
	TB	TB1	0.836	0.881	0.649	0.880
		TB2	0.812
		TB3	0.849
		TB4	0.815
Perceived Usefulness	PU	PU1	0.755	0.854	0.663	0.851
		PU2	0.778
		PU3	0.774
Perceived Ease of Use	PEU	PEU1	0.724	0.838	0.632	0.837
		PEU2	0.758
		PEU3	0.784
Intention to Start a Platform-Based Startup	IPBS	IPBS1	0.785	0.860	0.674	0.855
		IPBS2	0.773
		IPBS3	0.771

KMO = 0.898; Bartlett = 11,911.101; <0.001. GFI: 0.909; AGFI: 0.893; CFI: 0.979; NFI: 0.909; RMSEA: 0.024; PCMIN/DF: 1.271.

summarizes these CFA results.

Examining each construct reveals that all personal (IPBS and JR) and social factors (SIP and SMs) demonstrated high reliability and validity. Additionally, entrepreneurial qualities, including II, RT, and CS, showed robust measurement properties. These results suggest that the variables identified as determinants of platform-based entrepreneurship are empirically well-supported. The resistance factors—SR and TBs—also exhibited strong CR and AVE values, indicating that security and technological complexity concerns may be prominent in shaping startup intentions within platform business environments. The core TAM variables—PU and PEU—also demonstrate strong reliability, suggesting they are valid constructs for analyzing their relationship with IPBS.

Regarding the model fit, the Kaiser–Meyer–Olkin (KMO) value was 0.898, indicating excellent sampling adequacy. Bartlett’s test of sphericity showed statistically significant correlations among variables (p < 0.001). The major fit indices also met or exceeded the recommended thresholds (GFI = 0.909, AGFI = 0.893, CFI = 0.979, and NFI = 0.909), indicating a well-fitting model. In particular, the RMSEA value was as low as 0.024, suggesting minimal model error, while the PCMIN/DF ratio was 1.271, supporting the model’s goodness of fit to the data. These results confirm that the measurement model demonstrates high reliability and validity, with strong theoretical coherence in explaining the relationships among the variables.

A correlation matrix analysis was conducted to assess discriminant validity; the results confirm that the constructs used in this study maintain conceptual distinctiveness. Discriminant validity is established when the square root of the AVE for each construct exceeds its correlations with other constructs—all variables satisfy this criterion [86]. The results in

Table 7. Discriminant validity assessment criteria and correlation matrix.

	IPBS	PEU	PU	TB	SR	CS	RT	II	SM	SIP	JR	PBSA
IPBS	1
PEU	0.43	1
PU	0.535	0.391	1
TB	−0.38	−0.23	−0.23	1
SR	−0.42	−0.23	−0.14	0.367	1
CS	0.203	0.575	0.191	−0.15	−0.16	1
RT	0.397	0.191	0.495	−0.30	−0.24	0.175	1
II	0.433	0.447	0.453	−0.31	−0.28	0.316	0.376	1
SM	0.452	0.462	0.554	−0.25	−0.26	0.296	0.321	0.462	1
SIP	0.399	0.47	0.473	−0.18	−0.09	0.292	0.331	0.439	0.531	1
JR	0.43	0.402	0.462	−0.26	−0.22	0.295	0.375	0.415	0.464	0.434	1
PBSA	0.339	0.124	0.33	−0.18	−0.14	0.118	0.26	0.231	0.24	0.28	0.313	1
$\sqrt{AVE}$	0.821	0.795	0.814	0.806	0.784	0.782	0.800	0.778	0.750	0.787	0.797	0.760

indicate that the measurement items accurately represent their respective constructs, and the theoretical distinctions among variables are maintained within the research model.

The analysis of intervariable correlations revealed that IPBS exhibited strong positive correlations with both PU (r = 0.535) and PEU (r = 0.430), supporting the theoretical framework of the TAM. Furthermore, II (r = 0.433) and JR (r = 0.430) showed significant positive associations with IPBS. This outcome suggests that the stronger the perceived relevance between one’s job and platform-based startups, the greater the intention to pursue such ventures. SIP (r = 0.399) and SMs (r = 0.452) were also positively correlated with IPBS, indicating that reference to successful cases or perceived social expectations can enhance the intention to engage in platform-based entrepreneurship.

Conversely, TBs (r = −0.380) and SR (r = −0.420) negatively correlated with entrepreneurial intention. These findings suggest that increased concerns about technological complexity or security issues may suppress entrepreneurs’ willingness to utilize platform systems. TBs were also negatively associated with PU (r = −0.230) and PEU (r = −0.230), implying that when users perceive platform technologies as difficult to use, they may undervalue their usefulness. Similarly, IS was negatively correlated with PU (r = −0.140) and PEU (r = −0.230), suggesting that security concerns could undermine user trust and reduce entrepreneurial motivation.

Overall, these findings confirm that the constructs in the research model are conceptually distinct, and the interrelationships among variables are consistent with the underlying theoretical framework. PU and PEU emerged as key predictors of IPBS, while SIP and II also played significant roles. In contrast, TBs and SR act as inhibitors of entrepreneurial intention, highlighting the need for policy interventions. These results imply that efforts to mitigate technological and security barriers could facilitate platform-based entrepreneurship. Moreover, policy support considering entrepreneurs’ innovativeness and job relevance may enhance platform acceptance and promote sustainable engagement.

3.3.2. Hypothesis Testing

The results of hypothesis testing from H1 to H10 (personal characteristics) revealed that PBSA (β = 0.114, p = 0.011), JR (β = 0.118, p = 0.028), SIP (β = 0.121, p = 0.031), and SMs (β = 0.298, p < 0.001) all had significant positive effects on PU; thus, H1, H3, H5, and H7 were supported. However, PBSA had no statistically significant effect on PEU (β = −0.058, p = 0.208); thus, H2 was not supported. Moreover, JR (β = 0.111, p = 0.045), SIP (β = 0.193, p = 0.001), and SMs (β = 0.160, p = 0.007) significantly influenced PEU, supporting H4, H6, and H8. Furthermore, II (β = 0.164, p = 0.002) significantly affected PEU, supporting H10. These results align with theoretical expectations, suggesting that job relevance, social influence, success models, and innovativeness positively shape users’ perceptions of platform-based entrepreneurship’s usefulness and ease of use.

Hypotheses H9 through H14 examine the effects of various elements of entrepreneurial traits on PU and PEU. II showed significant positive effects on PU (β = 0.120, p = 0.021) and PEU (β = 0.164, p = 0.002), thus supporting H9 and H10. RT had a significant effect on PU (β = 0.263, p < 0.001), supporting H11; however, it did not significantly influence PEU (β = −0.073, p = 0.135), leading to the rejection of H12. CS did not significantly affect PU (β = −0.064, p = 0.146); thus, H13 was not supported. Nonetheless, CS significantly positively affected PEU (β = 0.404, p < 0.001), thereby supporting H14. These findings suggest that RT enhances the PU of a platform but does not directly impact PEU. Conversely, CS plays a more critical role in shaping PEU than PU.

Finally, H15 and H16 test the influence of PU and PEU on IPBS. The results showed that PU had a strong positive effect on IPBS (β = 0.468, p < 0.001), and PEU also significantly influenced IPBS (β = 0.263, p < 0.001), supporting both H16 and H16. These findings suggest that users’ intentions to engage in platform entrepreneurship are primarily driven by their perceptions of the platform’s usefulness and ease of use. The more users perceive a platform as useful, the more likely they will adopt it. Likewise, the easier the platform is perceived to be to use, the higher the likelihood of engagement.

Table 8. Results of structural equation model.

Hypotheses	β	t-Value	p-Value	Hypothesis
H1: PBSA⟶PU	0.114	2.554	0.011	Supported
H2: PBSA⟶PEU	−0.058	−1.259	0.208	Unsupported
H3: JR⟶PU	0.118	2.2	0.028	Supported
H4: JR⟶PEU	0.111	2.003	0.045	Supported
H5: SIP⟶PU	0.121	2.153	0.031	Supported
H6: SIP⟶PEU	0.193	3.278	0.001	Supported
H7: SM⟶PU	0.298	5.112	0.000	Supported
H8: SM⟶PEU	0.16	2.704	0.007	Supported
H9: II⟶PU	0.12	2.308	0.021	Supported
H10: II⟶PEU	0.164	3.035	0.002	Supported
H11: RT⟶PU	0.263	5.419	0.000	Supported
H12: RT⟶PEU	−0.073	−1.496	0.135	Unsupported
H13: CS⟶PU	−0.064	−1.453	0.146	Unsupported
H14: CS⟶PEU	0.404	7.996	0.000	Supported
H15: PU⟶IPBS	0.468	8.622	0.000	Supported
H16: PEU⟶IPBS	0.263	5.177	0.000	Supported

GFI: 0.921; AGFI: 0.907; CFI: 0.982; NFI: 0.922; RMSEA: 0.024; PCMIN/DF: 1.274.

summarizes the detailed results of the hypothesis testing.

3.3.3. Moderation Analysis

A regression model was constructed to examine the moderating effects of SR and TBs, including the independent variables, dependent variables, and their interaction terms. The analysis used the SPSS Process Macro, and 95% confidence intervals were estimated via the bootstrap method.

Table 9. Results of moderating effect test.

Path	β	p-Value	Boot 95%CI		Hypothesis
			LLCI	ULCI
The moderating effect of SR between PU and IPBS.	−0.1446	0.0006	−0.2269	−0.0622	supported
The moderating effect of SR between PEU and IPBS.	−0.1487	0.0005	−0.2318	−0.0655	supported
The moderating effect of TBs between PU and IPBS.	0.0030	0.9430	−0.0802	0.0862	unsupported
The moderating effect of TBs between PEU and IPBS.	−0.1052	0.0082	−0.1831	−0.0273	supported

shows that the interaction between SR and PU significantly and negatively affected IPBS (β = −0.1446, p = 0.0006). This interaction effect is in the opposite direction of the direct effect of PU on IPBS; thus, the moderating effect is considered valid. Similarly, the interaction between SR and PEU significantly negatively affected IPBS (β = −0.1487, p = 0.0005). Although the moderation effect of SR on the PEU⟶IPBS path is statistically significant, the effect size is relatively small (β = −0.1487). This suggests that while concerns about security may attenuate the influence of ease of use on startup intention, the practical impact may be context-dependent and warrants further longitudinal or segmented analysis. In this case, the interaction effect aligns with the direction of the main effect of PEU on IPBS, supporting a meaningful moderation effect. In addition, the interaction between TBs and PEU had a significant adverse effect on IPBS (β = −0.1052, p = 0.0082), indicating a valid moderating effect; however, the interaction between TBs and PU was not statistically significant (β = 0.0030, p = 0.9430), suggesting that this moderating effect is not supported.

To better illustrate the moderating effects of SR and TBs on the relationships among PU, PEU, and IPBS, visual analyses are presented in Figure 2, Figure 3, Figure 4 and Figure 5. Figure 2 and Figure 3 show that both PU and PEU positively affect IPBS; however, when security risk is low, the strength of the positive influence of PU and PEU on IPBS is attenuated. These results suggest that higher security risk concerns can diminish the impact of perceived value and usability on entrepreneurial intention. Figure 4 and Figure 5 present the results related to TBs. When technology barriers are low, the positive effect of PEU on IPBS is more substantial. In contrast, when technology barriers are high, the effect of PEU on IPBS is weakened, indicating that perceptions of usability play a more significant role in enhancing startup intention when technical complexity is minimal.

Conversely, Figure 4 shows that PU maintains a relatively parallel trajectory across high and low levels of technology barriers, suggesting no moderating effect. This outcome confirms that SR and TBs exert differential moderating effects on the relationship between platform-related perceptions (PU and PEU) and IPBS.

4. Discussion

4.1. Theoretical Implications

This study established a theoretical framework for explaining platform-based startup intention. We empirically analyzed how entrepreneurs’ perceptions and external influences affect their decisions to pursue platform-based entrepreneurship. The platform economy has proliferated and is critical in lowering entry barriers and expanding entrepreneurial opportunities; however, research has been limited in exploring the decision-making factors that influence individuals’ adoption of platform-based startups. Prior studies have primarily focused on the TAM and the TPB to explain platform-related entrepreneurial acceptance [87,88]—this study extends these frameworks by integrating personal, social, and environmental factors to more comprehensively assess their effects on startup intention.

Our findings reveal that the perception of platform-based entrepreneurship positively influences PU but does not significantly affect PEU. This result aligns with earlier studies suggesting that the opportunities and benefits of platform-based startups are highly regarded. However, the insignificant relationship in H2 (PBSA ⟶ PEU) may be attributed to the perceived technical complexity of platform systems. This is supported by text mining results, which revealed frequent keywords such as “technology barriers” and “system complexity,” suggesting that startup awareness does not automatically translate into perceived ease of use if technical concerns dominate user perceptions. The perceived complexity of operational tasks and technical requirements may hinder entrepreneurs’ perceptions of ease of use [89]. JR significantly enhanced both PU and PEU, indicating that entrepreneurs who relate platform usage to their existing skills and experiences are more likely to perceive it as valuable and accessible. This outcome supports prior research suggesting that industry-specific knowledge and experience often drive entrepreneurial engagement [90]. These findings also align with the motivational theories of entrepreneurship, which propose that entrepreneurs can formulate business models and market strategies in the most familiar domains.

SIP and SMs were also found to be important in shaping startup intention. Previous research highlights the role of social support in influencing entrepreneurial beliefs and attitudes [91]. Similarly, this study found that external encouragement and social pressure significantly affect entrepreneurial decision-making. Entrepreneurs who learned from successful platform-based startup cases were more confident and more likely to take action. Furthermore, individuals with higher II were more open to new business models and adapted more effectively to digital platforms, reinforcing their entrepreneurial intentions. This finding aligns with prior research suggesting that highly innovative entrepreneurs prefer digital platform-based startups over traditional forms of entrepreneurship [92].

Conversely, RT showed a dual effect in the context of platform-based entrepreneurship. While risk-tolerant individuals were more inclined to experiment with new platforms, risk-averse individuals tended to approach platform entrepreneurship more cautiously. This finding suggests that RT includes both opportunities and constraints dimensions, highlighting the need for strategies to mitigate perceived risks and reduce uncertainty in the platform startup environment [93].

Another noteworthy finding concerns the role of CS in platform-based entrepreneurship. Strong communication skills help entrepreneurs interact effectively with partners, customers, and other stakeholders, increasing the likelihood of business success [93]. However, the results indicated that CS did not significantly affect PU but had a significant positive impact on PEU. This result suggests that communication is essential for entrepreneurial engagement, but its influence may be more relevant in the post-launch phase, particularly during ongoing platform operations and management. The lack of significant influence of CS on PU contradicts findings in the social commerce literature. This discrepancy may arise from the early-stage focus of platform startups, where technical usability is prioritized over interpersonal communication. From a technical stance, this also implies that communication with platform system operators is critical for successful implementation and use.

The asymmetrical moderation results may reflect the nature of cognitive processing. While PEU addresses immediate user interaction and technical concerns—thus being more vulnerable to SR and TBs—PU captures long-term benefit perceptions, which may remain stable despite external barriers. This could explain why TBs did not significantly moderate the PU ⟶ IPBS relationship. Interestingly, the moderating effect of TBs on the relationship between PU and IPBS was statistically non-significant. This null finding suggests that perceived technology barriers may not significantly alter how users evaluate the usefulness of platform-based startups. One possible explanation is that PU represents a user’s long-term cognitive evaluation of potential benefits, which remains relatively stable despite perceived technical complexity. In contrast, TBs may more directly influence PEU, which is associated with immediate operational difficulties rather than benefit assessment.

Finally, SR and TBs were found to hinder platform-based startup intention. Greater concerns regarding data protection and system complexity were associated with an increased reluctance to adopt platforms, presenting significant obstacles to the widespread expansion of platform entrepreneurship. These findings support the existing literature on the negative effects of security and technological challenges in e-commerce adoption [94]. Promoting platform-based entrepreneurship thus requires improving information security systems and enhancing technical accessibility. Proactive measures must be taken to address concerns regarding the use of personal data, consumer information, log data, and media content by platform business. Such efforts can attract more prospective entrepreneurs and increase their willingness to engage in platform-based startups.

4.2. Practical Implications

Based on the key findings of this study, the following practical implications are prioritized in alignment with the most influential variables. In particular, the discrepancy between PU and PEU, the negative moderating effects of SR and TBs, and the strong effects of job relevance and social influence are highlighted as strategic leverage points for enhancing platform-based entrepreneurship.

First, the results indicate that although entrepreneurs perceive platform-based startups as useful, they may still experience difficulties in actual usage due to low PEU. This suggests a need for more hands-on and task-specific training programs that go beyond general platform education. Training should include interactive practice such as product registration, digital payment integration, and customer communication simulations. By improving operational familiarity, these programs can lower entry barriers and build confidence in platform use. Additionally, incorporating content related to AI-based business tools, data analytics for market decision-making, and digital marketing strategies will help entrepreneurs apply advanced platform functions effectively.

Second, the strong relationship between job relevance and both PU and PEU suggests that entrepreneurs with work experience aligned with platform businesses are more likely to perceive them as valuable and easy to use. Therefore, tailoring startup support policies to leverage entrepreneurs’ existing expertise can increase success rates. For example, promoting platform-based business models in sectors such as healthcare, education, logistics, and creative industries can create opportunities for professionals to transition into entrepreneurial roles with minimal skill adaptation barriers.

Third, the study emphasizes the importance of social influence and successful role models in shaping startup intentions. Positive feedback and peer success stories can reduce uncertainty and enhance motivation. Thus, building structured networks and mentorship programs that connect novice entrepreneurs with experienced platform founders could encourage participation. Activities such as sector-focused seminars, interactive workshops, and practical case-sharing sessions would further strengthen these social effects.

Fourth, addressing barriers such as security concerns and technological complexity is crucial. Entrepreneurs often hesitate to engage with platforms due to fears over data privacy breaches, cybersecurity vulnerabilities, and the complexity of platform systems. Effective solutions include implementing clear and user-friendly security guidelines, transparent data protection policies, and simple consent management processes to build trust. At the same time, improving the intuitiveness of platform interfaces and offering accessible technical support can lower perceived complexity and enhance PEU.

Finally, regulatory and institutional support play an essential role in sustaining platform-based entrepreneurship. As the platform economy evolves rapidly, existing regulations may lag behind, creating legal uncertainties for entrepreneurs. Therefore, policies that modernize regulations to accommodate emerging business models, simplify licensing processes, and expand startup tax incentives are needed. Additionally, providing legal advisory services specific to platform entrepreneurs can help navigate compliance requirements efficiently. It is also important to note the demographic limitation of this study. With 27.8% of respondents aged 60 and above, age-related differences in risk perceptions and technology usage confidence may have influenced the findings. Older entrepreneurs, in particular, may prioritize data security and system stability over ease of use. This suggests that age-inclusive design and differentiated support programs should be considered when developing policies and platforms to ensure broader participation.

In summary, enhancing platform-based entrepreneurship requires a multi-dimensional approach that combines training, policy design, technological improvements, and social support systems. Efforts to improve perceived ease of use and address security concerns stand out as the most actionable strategies for increasing entrepreneurial engagement. By focusing on these factors, stakeholders can promote a sustainable and inclusive platform startup ecosystem that adapts to diverse entrepreneurial needs.

Ultimately, among the various factors examined, improving ease of use and mitigating perceived risks stand out as the most actionable strategies for enhancing platform-based entrepreneurial participation. Focusing resources on training, risk communication, and technological accessibility will yield the highest return on policy- and platform-level interventions.

5. Conclusions and Limitations

This study analyzed the factors influencing entrepreneurial intention within e-commerce platforms by extending the TAM to empirically examine the effects of personal, social, entrepreneurial, and resistance factors. The findings indicate that PU and PEU are primary determinants of platform-based startup intention, and factors such as II, JR, SIP, and SMs serve as enablers. In contrast, resistance factors such as SR and TBs significantly inhibited platform-based entrepreneurial intention. These results suggest that platform-based entrepreneurship is a multi-dimensional phenomenon shaped by technological, social, and economic environments. In particular, entrepreneurial intention will likely increase when individuals can relate their prior work experience and competencies to the platform business model—a feature that distinguishes platform-based entrepreneurship from traditional forms. Social influence and visible success stories were also emphasized, indicating that social networks and exposure to entrepreneurial role models strongly impact decision-making.

Conversely, technical complexity and security concerns remain significant barriers to platform-based entrepreneurship and may discourage potential entrepreneurs from adopting digital platforms. Therefore, strengthening cybersecurity measures and improving technological accessibility is essential for supporting a sustainable entrepreneurial ecosystem. Governments and private sector actors should implement targeted support programs to alleviate technical burdens and provide structured educating and mentoring to enhance their digital capabilities. Such strategic support can equip entrepreneurs to innovate actively within platform environments and build sustainable business models.

This study provides theoretical insights and practical implications regarding the determinants of platform-based entrepreneurial intention; however, it also has several limitations. First, we relied on cross-sectional data, which limits the ability to observe changes in entrepreneurial perceptions and behavior over time. Future research should adopt longitudinal approaches to track such changes more effectively. Second, our sample was restricted to platform entrepreneurs in South Korea, which limits the generalizability of the findings to broader, global contexts. Comparative studies across countries with differing entrepreneurial ecosystems are recommended. Third, the analysis was based solely on survey data and did not include experimental or qualitative methods. Future studies should incorporate experimental designs or in-depth case studies to better understand entrepreneurs’ actual platform usage patterns and decision-making dynamics. Additionally, future studies should consider segmenting participants based on age, digital literacy, or platform type to capture more nuanced insights. From a practical standpoint, e-commerce platforms should improve digital onboarding through simplified user interfaces and tailored support services—especially for older or first-time entrepreneurs.

Finally, beyond the factors investigated in this study, elements such as regulatory frameworks and competitive dynamics within platform ecosystems may also shape startup intention. Future research would benefit from integrating a wider set of contextual and structural variables to broaden our understanding of the drivers and constraints of platform-based entrepreneurship.