Socio-Technical Antecedents of Social Entrepreneurial Intention: The Impact of Generational Differences, Artificial Intelligence Familiarity, and Social Proximity

Abstract

This study examines the factors that influence individuals’ intentions to create socially oriented ventures, emphasizing the joint role of social and technical systems. Grounded in Socio-Technical Systems Theory, the research investigates how perceptions of social legitimacy and technological infrastructure shape social entrepreneurial intention (SEI) and how these effects are conditioned by generational cohort, familiarity and intent to use artificial intelligence (AI), and social proximity to entrepreneurial peers. Based on survey data from 388 respondents in China who expressed interest in both entrepreneurship and social problem-solving, the study applies a conditional process structural equation model to capture the complex interplay between external systems and individual-level readiness. The results show that both social and technical systems significantly and positively influence SEI, particularly among younger generations (Millennials and Generation Z). Furthermore, AI familiarity and social proximity operate as moderated mediators, differentially transmitting and shaping systemic influences on SEI. These findings advance the theoretical understanding of socio-technical determinants of social entrepreneurship and offer practical insights for fostering inclusive, generationally responsive entrepreneurial ecosystems.

1. Introduction

Social entrepreneurship has become a vital approach to addressing pressing global challenges such as social inequality, environmental sustainability, and economic exclusion [1,2]. Unlike traditional entrepreneurship, social entrepreneurship seeks not only financial return but also the creation of social value. This dual orientation has gained particular momentum in the aftermath of the COVID-19 pandemic, which intensified public interest in purpose-driven innovation and sustainable solutions [3,4,5].

Central to this movement is the concept of social entrepreneurial intention (SEI), which reflects an individual’s motivational readiness to initiate ventures aimed at generating social impact. Understanding what drives this intention is crucial, especially among younger generations such as Generation Z and Millennials. These groups increasingly prioritize ethical responsibility, social innovation, and sustainable entrepreneurship as core elements of their identity and aspirations [6,7,8,9].

However, much of the existing research on SEI has centered on individual psychological traits, such as empathy, values, or perceived behavioral control, without fully considering the role of external environments [10]. As a result, there is limited understanding of how broader contextual factors such as institutional norms, technological infrastructure, and social networks shape one’s willingness to engage in social entrepreneurship.

This research is situated in the context of China, a country experiencing rapid digital transformation alongside strong policy support for innovation and social impact. Despite these developments, empirical studies exploring how social and technical systems influence social entrepreneurial intention (SEI) in China remain scarce. Addressing this gap not only contributes to global comparative research but also offers practical insights for a uniquely dynamic and complex entrepreneurial ecosystem.

This study addresses this gap by adopting a socio-technical systems perspective [11,12,13], which highlights how individuals are embedded in both social and technical environments. Social systems (SO) include institutional legitimacy, cultural expectations, and societal support for innovation. Technical systems (TE) refer to digital infrastructure, AI tools, and technological readiness that support entrepreneurial action. These systems act as external enablers or constraints in the formation of social entrepreneurial intention [11,12].

While intrinsic motivation is important, this study argues that social entrepreneurial intention is also influenced by how individuals interpret and interact with these external systems. By doing so, it shifts the focus from purely internal traits to the interplay between the individual and their broader context [14,15].

To better understand this interaction, the study introduces two additional variables: AI familiarity and usage intention (IAI) and social proximity (SP) to entrepreneurs. These are proposed as both mediators and moderators in the pathway between socio-technical systems and EI. Generation is treated as a moderating variable that reflects fundamental differences in how individuals respond to social and technological environments. For example, younger generations may be more responsive to digital cues, while older generations may rely more on interpersonal networks [6,7]. Based on this framework, the study addresses three research questions:

(1)

Do social systems (SO) and technical systems (TE) significantly influence social entrepreneurial intention (SEI)?

(2)

Are these effects moderated by generational groups?

(3)

Do IAI and SP function as moderated mediators in the indirect effects of SO/TE on SEI?

This research contributes both theoretically and practically. Theoretically, it expands the understanding of social entrepreneurial intention by incorporating multi-level contextual and personal factors [2,14]. Empirically, it applies moderated mediation modeling to identify the direct, indirect, and conditional mechanisms underlying social entrepreneurship. Practically, the findings offer guidance for policymakers, educators, and social innovation ecosystems in designing generation-sensitive programs that encourage digital adoption, community connection, and socially responsible entrepreneurship [16].

The remainder of this paper is organized as follows: Section 2 presents the theoretical background and hypotheses. Section 3 describes the methodology. Section 4 outlines the results. Section 5 discusses conclusions, theoretical and practical implications, and future research directions.

2. Literature Review and Hypothesis Development

2.1. Literature Review

2.1.1. Socio-Technical Systems Theory

Socio-Technical Systems Theory (STST) argues that desired outcomes can only be achieved when the social system and the technical system within an organization interact and collaborate [11,17]. According to this theory, the social system consists of institutional norms, cultural values, and social networks, while the technical system includes digital infrastructure, technological tools, and platforms [18,19]. The social and technical systems do not exist independently; instead, they form a complementary and interdependent relationship. In other words, even if the technical environment is well-developed, desired results cannot be achieved without a well-prepared social system to utilize it. Conversely, a strong social system alone is not sufficient if the technical system is lacking, which limits the efficiency of business activities.

Bostrom and Sandberg [20] emphasized that the technical system plays a critical role in fostering entrepreneurial participation and that digital platforms and technological tools encourage entrepreneurs to engage in social entrepreneurship activities. Specifically, the technical system, which includes digital environments, online platforms, and automation tools, helps entrepreneurs create social value in more efficient and creative ways. This study aims to provide critical insights into how social entrepreneurial intention is formed and strengthened through the combination of social and technical systems and how these systems contribute to solving social problems. By analyzing how access to the digital environment and technological tools impacts social entrepreneurial intention, this study seeks to clarify the role of technological elements as a driving force behind entrepreneurship.

2.1.2. Generational Experiences in China

According to generational theory, economic transitions and technological changes significantly influence the values and attitudes of specific generations [2,6]. However, Twenge [21] argued that generational distinctions are inherently uncertain, and those on the boundaries of generations may have more in common with adjacent generations. Thus, generational distinctions should consider not only birth years but also common experiences such as education, exposure to technology, and entry into the labor market (

Table 1. Typical generational research and experience-based generational research.

Typical Generational Research	Generational Research Using Life Experience Model
1939~1949 (Seniors)	1939~1979 (Gen X, Boomers, Seniors)
1950~1964 (Baby Boomers)	1980~1996(Generation Y, Millennials)
1965~1979 (Generation X)	1997~2010 (Generation Z)
1980~1996 (Generation Y)
1997~2010 (Generation Z)

) [22].

The rapid digitalization and socio-economic changes in China have created different experiences across generations. For example, Generation Z is accustomed to smartphones, real-time communication, and algorithm-based personalization. Anderson and Jiang [23] noted that this generation has been greatly influenced by digital commerce and AI platforms. Generation Y, on the other hand, experienced the transition from analog to digital, with the development of Internet services and mobile ecosystems shaping their early careers [24]. Generation X and earlier generations experienced a lack of a digital environment, and many had to acquire technology in their mid-careers [21,25]. These differences affect generational digital literacy, technological self-efficacy, and openness to innovation.

This study conceptualizes generations not merely as behavioral determinants but as contextual lenses through which individuals interpret and respond to social and technical systems. This approach aligns with Bandura [26], whose Social Cognitive Theory explains how generational experiences and identities shape individuals’ responses to technical and social systems [25].

2.1.3. AI Familiarity and Intention to Use

AI familiarity and the intention to use AI represent an individual’s cognitive readiness and motivational disposition to engage with advanced technologies [27]. These cognitive and motivational factors act as moderated mediators influencing the relationship between social and technical systems. Previous studies have suggested that the impact of technical systems varies based on an individual’s technology acceptance [24,28]. In other words, the higher the AI familiarity and usage intention, the greater the influence of the technical system on the formation of social entrepreneurial intention (SEI). Gefen, Karahanna, and Straub [24] argued that when there is psychological distance between system signals and individuals’ behavioral outcomes, this dual role emerges. Building on these theories, this study aims to specifically explain how AI familiarity and usage intention influence how individuals internalize technical systems and how this internalization contributes to the formation of social entrepreneurial intention.

2.1.4. Social Proximity

Social proximity refers to how closely or deeply an individual is embedded within their social network of entrepreneurial actors [29]. This concept significantly impacts how signals from social and technical systems are transmitted into entrepreneurial intentions (SEI). According to the research by Granovetter [30] and Uzzi [29], strong social ties facilitate the flow of entrepreneurial resources and information, playing a crucial role in establishing social legitimacy. This study explores how social proximity mediates the interaction between social and technical systems, analyzing how alignment with social network support influences the formation of social entrepreneurial intention. Individuals with higher social proximity are more likely to access entrepreneurial resources and opportunities, which increases the likelihood of strong social entrepreneurial intention. This suggests that close relationships within entrepreneurial networks play a vital role in shaping entrepreneurial intentions.

2.1.5. Social Entrepreneurship

Social entrepreneurship is the process of identifying and pursuing opportunities that create both economic and social value [31]. Social entrepreneurial intention (SEI) is influenced by environmental factors such as social legitimacy, technical accessibility, and cultural values [2,32]. These environmental factors play a significant role in an entrepreneur’s intention to solve social problems. This study focuses on how social entrepreneurial intention is formed within social and technical systems, with a particular emphasis on how generational context and individual readiness (IAI, SP) mediate the interaction between these systems. The study explores how generational differences influence social entrepreneurship and investigates how generational experiences and identities shape individual responses to technical and social systems. Given the varying perceptions of technology and social systems across generations, this study will contribute significantly to understanding the differences in social entrepreneurial intention between generations.

2.2. Hypothesis Development

2.2.1. Theoretical Mechanism of the Study

This study investigates how social systems (SO) and technical systems (TE) influence social entrepreneurial intention (SEI) and how these relationships are moderated by generational differences. The empirical model adopts a conditional process framework [22,27], which allows for the examination of both direct and moderated effects within a unified structure. The conceptual foundation integrates several established theories that collectively illuminate the mechanism of influence. Social Cognitive Theory [26] explains how individuals develop beliefs and intentions through the observation of social cues and the internalization of behavioral norms. This perspective supports the role of perceived social belonging and interpersonal interaction, captured in the social system (SO), in motivating entrepreneurial intention. In parallel, the Technology Acceptance Model (TAM) [33] underscores the significance of perceived usefulness and ease of use in shaping technology-related attitudes and intentions. This aligns with the conceptualization of the technical system (TE), which reflects respondents’ confidence in the safety, legal safeguards, and practical utility of AI and digital infrastructures. Further, Internalization Theory [6] strengthens the argument for the influence of social systems by highlighting how individuals adopt shared values and norms through embedded social networks.

Generational differences are theorized to moderate these mechanisms. Younger cohorts (e.g., Generation Z) may be more responsive to technological affordances and more confident in engaging with emerging digital tools and infrastructures—which we refer to here as digital readiness—while older generations may rely more heavily on socially constructed norms and trust-based relationships [25]. These contrasts provide a theoretical basis for expecting variation in how social and technical systems influence social entrepreneurial intention across generational groups.

2.2.2. Environmental Influences on Social Entrepreneurial Intention

Social systems encompass environmental factors such as institutional norms, cultural values, and social expectations that influence individual behavior. These systems convey social norms and expectations regarding social entrepreneurship, which individuals internalize, leading to the development of social entrepreneurial intention (SEI).

According to Ajzen’s [31] Theory of Planned Behavior, subjective norms play a critical role in the formation of behavioral intentions as individuals internalize the norms accepted by society. In other words, when society recognizes social enterprises as positive social values and supports them with norms, individuals internalize the belief that they should act accordingly, which then leads to the intention to engage in social entrepreneurial activities [2,19].

For example, when society views social enterprises as essential social values and provides policies and programs that support them, individuals form an intention to engage in social entrepreneurship. This internalized belief leads to social entrepreneurial intention (SEI), and individuals develop the behavioral intention to act on it.

Therefore, social systems have a significant impact on the formation of an individual’s social entrepreneurial intention (SEI). The social norms, cultural values, and expectations presented within the social system transform into internalized beliefs, motivating individuals to engage in social entrepreneurial activities. Thus, the following hypothesis is proposed:

Hypothesis 1. 

Social systems will positively influence social entrepreneurial intention.

The technical system includes resources such as digital infrastructure, technological tools, and AI platforms that support entrepreneurial activity. These resources reduce operational uncertainty, enhance scalability, and increase efficiency in social entrepreneurial efforts. From the perspective of the Technology Acceptance Model (TAM) [33], technologies that are perceived as useful and easy to use increase the likelihood of adoption and usage intention. Accordingly, it may be expected that individuals who perceive technical systems as beneficial will be more inclined to develop social entrepreneurial intentions (SEI).

However, while technical systems may serve as enabling conditions, they may not directly trigger the formation of intention. In other words, technological readiness may function more as a contextual facilitator rather than a motivational driver. Without accompanying social validation or normative support, the presence of technical tools alone might be insufficient to spark meaningful entrepreneurial action [17]. In this regard, technical systems could play an indirect or complementary role rather than exerting a direct influence. Therefore, rather than proposing a directional hypothesis, the following research proposition is stated:

Hypothesis 2. 

Technical systems may not have a significant direct effect on social entrepreneurial intention.

2.2.3. Moderating and Mediating Effects of Generational Differences and Personal Characteristics: Personal Factors

Generational differences play a significant role in how individuals respond to cognitive schemes, value orientations, and institutional signals [21,33]. Specifically, Generation Z and Millennials show a higher affinity for social values, digital fluency, and strong expectations for institutional legitimacy [34]. These generational differences greatly influence how individuals interpret and respond to social and technical systems. According to Social Cognitive Theory [26], individuals interpret environmental cues and social norms differently based on prior learning and their identity.

Younger generations, particularly Generation Z, may internalize social norms more intensively and respond more favorably to signals from social systems, whereas older generations may be more resistant or neutral to such cues [25]. Conversely, Generation Z’s familiarity with technical systems may not necessarily translate to stronger positive intention, as overexposure to technology may lead to either heightened sensitivity or skepticism.

Social systems encompass environmental factors such as institutional norms, cultural values, and social expectations that influence individual behavioral intentions. In particular, according to Ajzen’s [31] Theory of Planned Behavior, subjective norms play a critical role in shaping behavioral intentions by internalizing the social norms accepted by society. The norms and expectations presented by the social system significantly influence the formation of social entrepreneurial intention (SEI) [2].

For example, when society recognizes social enterprises as a social value and provides norms supporting them, individuals internalize the belief that they should act accordingly. Generation Z is expected to form stronger intentions to engage in social entrepreneurship based on internalized social norms compared to older generations, who may weigh social signals less heavily in their decision-making. Thus, generational differences are expected to moderate the relationship between social systems and social entrepreneurial intention (SEI).

Hypothesis 3. 

The effect of social systems (SO) on social entrepreneurial intention (SEI) differs by generation.

The technical system consists of resources such as digital infrastructure, technological tools, and AI platforms. These systems provide opportunities for individuals to engage in social entrepreneurial activities and support the efficient execution of these activities. According to the TAM, the perceived usefulness and ease of use of technology play an important role in motivating individuals to adopt and utilize the technology, thereby forming behavioral intentions [28].

While Generation Z and Millennials are more adept with digital technologies, this does not guarantee a stronger intention to act entrepreneurially through them. Overexposure, distrust in the system, or concerns about the ethical implications of AI may instead moderate or weaken this pathway. In contrast, older generations may rely less on such systems in forming their entrepreneurial intentions. Therefore, generational differences are expected to moderate the relationship between technical systems and social entrepreneurial intention (SEI).

Hypothesis 4. 

The effect of technical systems (TE) on social entrepreneurial intention (SEI) differs by generation.

Moreover, it is important to consider the balance between social and technical systems in shaping intention. While social systems may appeal to normative alignment, technical systems support functionality and perceived feasibility. The weight placed on each system may differ across generational cohorts.

Hypothesis 5. 

The relative importance of social versus technical systems (SO vs. TE) in shaping EI varies across generations.

2.2.4. Moderated Mediation via IAI and SP Across Generations: Personalized Pathways to Social Entrepreneurial Intention

Digital familiarity (IAI) and social proximity (SP) play crucial roles in the formation of social entrepreneurial intention (SEI) by shaping how individuals process technical and social system cues. These factors not only function as mediators but also operate in generation-specific ways, providing personalized psychological and social mechanisms through which entrepreneurial motivation is activated [19].

Individuals with high AI familiarity (IAI) tend to exhibit greater confidence in utilizing technical systems. This confidence strengthens their intention to engage in social entrepreneurial activities, particularly by enabling them to overcome technical barriers. Conversely, individuals with low AI familiarity may experience anxiety or disengagement with digital tools, which weakens their motivation to initiate or pursue entrepreneurial goals.

Social proximity (SP), defined as the degree to which individuals are close to entrepreneurs through relationships such as family or friends, can also influence the path toward social entrepreneurial intention. In particular, older generations, who tend to rely more on interpersonal norms and observed experiences, may find that close social networks compensate for weaker digital engagement by fostering behavioral modeling, motivation, and perceived feasibility of social entrepreneurship [19,32].

Based on Self-Determination Theory, both IAI and SP activate internal mechanisms such as autonomy, competence, and relatedness, which in turn guide entrepreneurial intention [2,35]. However, the importance of these pathways may vary across generations. Younger individuals are more likely to respond to digital stimuli through IAI, while older individuals may be more strongly influenced by social cues represented by SP. Thus, we propose a moderated mediation structure in which IAI and SP mediate the effects of technical and social systems, respectively, and this mediation varies by generation.

Hypothesis 6. 

The mediating effect of IAI on the relationship between TE and SEI differs by generation.

Hypothesis 7. 

The mediating effect of SP on the relationship between SO and SEI differs by generation.

Hypothesis 8. 

The relative strength of IAI and SP as mediators of entrepreneurial intention varies across generations.

2.2.5. Moderating Effects of AI Familiarity and Social Proximity on Systemic Influences

AI familiarity and usage intention (IAI) operate as internal cognitive moderators that shape how individuals process external cues. Individuals with high IAI are more confident in navigating digital infrastructures and are thus more likely to activate intention based on SO and TE. In contrast, those with low IAI may experience digital inefficacy, reducing the influence of external support.

Hypothesis 9. 

The relationship between social systems (SO) and social entrepreneurial intention (SEI) is moderated by IAI.

Hypothesis 10. 

The relationship between technical systems (TE) and social entrepreneurial intention (SEI) is moderated by IAI.

In parallel, social proximity (SP) refers to an individual’s perceived closeness or embeddedness within social networks, particularly with entrepreneurial actors. It fosters entrepreneurial behavior through knowledge sharing, inspiration, and social validation [36,37]. SP plays a critical moderating role in shaping how signals from the social system (SO) and technical system (TE) influence social entrepreneurial intention (SEI). Prior work by Granovetter [38] and Uzzi [29] suggests that strong social ties facilitate resource flows and enhance social legitimacy.

Rather than acting solely as a mediator, SP may enhance or buffer the influence of SO and TE depending on the context. For instance, individuals with high SP may be more responsive to normative support (SO), thereby reinforcing their SEI. Similarly, those embedded in strong networks may better utilize technical resources (TE) via peer learning and collaboration.

Hypothesis 11. 

The relationship between social systems (SO) and social entrepreneurial intention (SEI) is moderated by social proximity (SP).

Hypothesis 12. 

The relationship between technical systems (TE) and social entrepreneurial intention (SEI) is moderated by social proximity (SP).

2.3. Research Framework

This study is based on Socio-Technical Systems Theory, which analyzes the impact of social systems (SO) and technical systems (TE) on Social Entrepreneurial Intention (SEI) [11]. The framework illustrates how various variables, such as generational differences, AI familiarity and usage intention (IAI), and social proximity (SP), moderate or mediate these relationships. In particular, each variable affects the formation of social entrepreneurial intention differently.

The research framework positions social systems and technical systems as external factors, while generational differences, IAI, and SP are conceptualized as moderating and mediating variables. This approach explores how these factors interact and influence each other, providing insights into how social entrepreneurial intention is shaped through the combination of these elements [26,35].

Figure 1, showing the research framework, presents how social systems (SO) and technical systems (TE) affect social entrepreneurial intention (SEI), both directly and indirectly through AI familiarity and intent (IAI) and social proximity (SP). It also illustrates the moderating role of generational background. For clarity, Hypothesis 8, which compares the relative strength of the two mediation paths across generations, is conceptually tested but not included in the figure.

3. Methodology

To empirically examine the proposed hypotheses, this study employed structural equation modeling (SEM) using the lavaan package in R. SEM was selected as the primary analytical approach due to its capacity to evaluate both direct and indirect effects, as well as interaction terms, within complex theoretical models. This methodological framework enabled the simultaneous testing of multiple relationships among latent variables and allowed for cross-group comparisons to identify subgroup-specific structural patterns.

All measurement items were adapted from previously validated scales and assessed using a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). The constructs measured included social systems (SO), technical systems (TE), social entrepreneurial intention (SEI), AI familiarity and intent to use (IAI), and social proximity (SP). Prior to conducting structural analyses, the measurement model was evaluated for reliability and validity. Cronbach’s alpha coefficients exceeded 0.90 for all constructs, ensuring internal consistency. Convergent validity was confirmed using composite reliability (CR) and average variance extracted (AVE), while discriminant validity was verified using the Fornell–Larcker criterion and the heterotrait-monotrait (HTMT) ratio [39]. Multicollinearity diagnostics were also performed, with all variance inflation factor (VIF) values falling within acceptable thresholds.

The analytical process was structured into four sequential SEM models. Model 1 tested the direct effects of SO and TE on SEI, providing a baseline analysis of structural influences without considering interactions or mediating variables. Model 2 examined generational differences through multi-group SEM, comparing Generation Z with all other cohorts to assess whether the structural effects of SO and TE on SEI differ across age groups. Model 3 introduced moderated mediation mechanisms by evaluating whether IAI and SP mediate the effects of SO and TE on SEI and whether these mediation effects vary depending on generational group membership. Model 4 tested moderation effects by including interaction terms between the system-level predictors (SO, TE) and individual-level characteristics (IAI, SP), assessing whether these factors conditionally influence the path to SEI.

All structural paths and indirect effects were estimated using maximum likelihood estimation with bootstrapping (5000 replications) to ensure robustness. Moderation was assessed through the significance of interaction terms, and moderated mediation was tested via conditional indirect effects using index-based approaches [27]. Where appropriate, measurement and structural invariance tests were applied to evaluate the stability of model parameters across generational groups.

By progressing through this four-stage SEM strategy, the study offers a comprehensive analysis of how system-level influences and individual characteristics interact in shaping social entrepreneurial intention, providing insight into both universal mechanisms and generationally contingent pathways.

3.1. Data Collection

This study targeted individuals who simultaneously exhibit entrepreneurial orientation and social mindset, specifically those who are likely to start a business and are interested in contributing to social well-being. The inclusion criteria required participants to answer “Yes” to both of the following screening questions: “Have you ever started a new business?” and “Are you interested in ideas that contribute to society?” Only respondents who met both conditions were included, ensuring the relevance of the sample to the study’s objective.

Data collection was conducted through an online survey platform in March 2025 using non-probability convenience sampling. Participants were recruited through entrepreneurship-related online communities, university mailing lists, and business-oriented social media channels in China, primarily in urban centers including Beijing, Shanghai, and Guangzhou. While the exact proportions by province were not controlled, efforts were made to diversify the participant pool geographically within practical limits.

To ensure response quality and prevent duplication, IP address tracking was employed, and anonymity was strictly maintained in accordance with institutional ethics guidelines. A total of 422 responses were collected, and after removing incomplete or inconsistent responses, 418 were retained for analysis. This sample size satisfies the minimum power requirements for structural equation modeling (SEM), including complex models involving interaction and mediation terms.

Although convenience sampling limits generalizability, the sample composition aligns with the study’s theoretical interest in early-stage social entrepreneurs and digitally connected respondents who are most likely to engage with both technical and social systems in the context of entrepreneurship.

3.2. Survey Instrument

This study draws on Socio-Technical Systems Theory (STST), which emphasizes that individual outcomes such as social entrepreneurial intention (SEI) are shaped through the interaction between social systems (SO) and technical systems (TE) [11]. To ensure construct validity, all multi-item scales were adapted from previously validated measures and adjusted to fit the context of social entrepreneurship in digital environments.

The social system (SO) was operationally defined as the institutional, relational, and normative context that supports or constrains socially oriented entrepreneurship. It was measured using items related to trust in institutions and social belonging, adapted from Chai and Kim [40], McKnight, Choudhury and Kacmar [37], Teo et al. [41], and Lin et al. [42].

The technical system (TE) was defined as the perceived digital infrastructure, technological safety, and reliability supporting entrepreneurial activity. Items reflected engagement with online platforms, perceived digital security, and technological readiness, adapted from Chai and Kim [40] and Gefen, Karahanna, and Straub [24].

AI familiarity and usage intention (IAI) captured individuals’ prior exposure to, and intent to use, artificial intelligence tools in work and decision-making. This construct included items adapted from recent technology adoption research and tools such as ChatGPT-4o or DeepSeek V2.

Social proximity (SP) was defined as the extent of personal and social network ties with entrepreneurs. Items were adapted from literature on entrepreneurial ecosystems and social capital [43], measuring whether family members, friends, or neighbors had entrepreneurial experience.

The dependent variable, social entrepreneurial intention (SEI), was defined as the self-reported commitment and motivation to create ventures aimed at addressing societal challenges. The scale was adapted from established measures of social entrepreneurship motivation and opportunity recognition [14].

All items were rated on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). Demographic variables—including gender, education, age cohort, marital status, employment status, and residential tier—were also collected and are reported descriptively but excluded from structural modeling. A summary of the operational definitions and measurement sources for all constructs is provided in

Table 2. Operational Definitions of Key Constructs.

Construct	Operational Definition	Measurement Source(s)
Social System (SO)	Institutional, relational, and normative context that supports or constrains socially oriented entrepreneurship. Includes perceptions of trust in institutions and feelings of social belonging.	McKnight, Choudhury & Kacmar [37]; Chai & Kim [40]; Teo et al. [41]; Lin et al. [42]
Technical System (TE)	Perceived digital infrastructure, technological safety, and reliability that enable or hinder entrepreneurial activity. Includes digital access, platform engagement, and perceived digital security.	Gefen, Karahanna & Straub [24]; Chai & Kim [40]
AI Familiarity and Intent to Use (IAI)	Individuals’ prior exposure to, and intention to use, artificial intelligence tools (e.g., ChatGPT, DeepSeek) in work and decision-making contexts.	Venkatesh & Davis [44]
Social Proximity (SP)	Extent of direct or indirect personal network ties with entrepreneurs (e.g., family, friends, neighbors with entrepreneurial experience).	Entrepreneurial ecosystem and social capital literature [30,43]
Social Entrepreneurial Intention (SEI)	Self-reported commitment and motivation to launch ventures aimed at solving societal problems through entrepreneurial means.	Established SEI motivation and opportunity recognition scales [14]

.

The full list of survey items is provided in Appendix A, which includes the English-translated versions of the original Chinese-language survey items. Only the translated English items are presented to ensure brevity and clarity.

3.3. Sampling

The final dataset consists of responses from 388 qualified participants, collected through an online self-administered questionnaire conducted in March 2025. To ensure conceptual relevance, eligibility was determined through two screening questions assessing respondents’ interest in both entrepreneurship and social impact. Only individuals who expressed a clear interest in both domains were allowed to proceed with the full survey.

Participants were recruited using non-probability convenience sampling, primarily through entrepreneurship-related online communities, university mailing lists, and social media platforms focused on innovation and business. While not using random selection, this approach was suitable for reaching digitally active, early-stage entrepreneurial individuals likely to engage with both social and technical systems [20].

Table 3. Demographic characteristics of the respondents.

Variable	Subcategory	Frequency	Percent
Gender	Male	197	50.8%
	Female	191	49.2%
Education Level	High School	81	20.9%
	Technical and Vocational School	114	29.4%
	Undergraduate	159	41.0%
	Master	34	8.8%
Age	1939–1979	95	24.5%
	1980–1996	134	34.5%
	1997–2010	159	41.0%
Marital Status	Single	142	36.6%
	Married—no children	131	33.8%
	Married—one or more children	115	29.6%
Career Status	Employed	174	44.8%
	Unemployed	69	17.8%
	Self-employed	130	33.5%
	Retired	15	3.9%
City	First-tier city	138	35.6%
	Second-tier city	175	45.1%
	Third- and fourth-tier city	41	10.6%
	Township/Rural area	34	8.8%

presents the demographic profile of the respondents. The gender distribution was nearly balanced, with 50.8% male and 49.2% female. In terms of educational attainment, the majority held undergraduate degrees (41.0%), followed by technical or vocational school graduates (29.4%), high school graduates (20.9%), and those with postgraduate education (8.8%). Generational groups were defined by birth year: 24.5% were born between 1939 and 1979 (Generation X and older), 34.5% between 1980 and 1996 (Generation Y), and 41.0% between 1997 and 2010 (Generation Z).

Regarding marital status, 36.6% were single, 33.8% were married without children, and 29.6% were married with one or more children. For career status, 44.8% were employed, 17.8% unemployed, 33.5% self-employed, and 3.9% retired. Respondents were also geographically diverse: 35.6% lived in first-tier cities (e.g., Beijing, Shanghai, Guangzhou), 45.1% in second-tier cities, 10.6% in third- or fourth-tier cities, and 8.8% in township or rural areas. Although the use of convenience sampling limits the generalizability of the findings, the sample reflects the study’s theoretical focus on individuals with entrepreneurial interest and digital engagement within a variety of socio-technical contexts in China.

3.4. Reliability Analysis

To assess the internal consistency of each latent construct used in the study, Cronbach’s alpha reliability coefficients were calculated. Following Pallant’s [39] guideline, alpha values above 0.70 indicate acceptable reliability, and values above 0.80 reflect strong internal consistency.

As summarized in

Table 4. Summary of reliability and convergent validity results.

Construct	Cronbach’s α	CR	AVE
Social System (SO)	0.934	0.798	0.627
Technical System (TE)	0.982	0.969	0.863
AI Familiarity and Intent to Use (IAI)	0.990	0.990	0.923
Social Proximity (SP)	0.981	0.981	0.895
Social Entrepreneurial Intention (SEI)	0.982	0.983	0.903

, all constructs demonstrated excellent internal reliability. The Social System (SO), Technical System (TE), and AI Familiarity and Intent to Use (IAI) constructs, each consisting of eight items, yielded Cronbach’s alpha values of 0.934, 0.982, and 0.990, respectively. The six-item scales for Social Proximity (SP) and Social Entrepreneurial Intention (SEI) also showed strong internal consistency, with alpha values of 0.981 and 0.982.

To further assess reliability, composite reliability (CR) and average variance extracted (AVE) were calculated. As shown in Table 4, all CR values exceeded the commonly accepted threshold of 0.70, and all AVE values surpassed the recommended threshold of 0.50, confirming both internal consistency and convergent validity [45]. Notably, the constructs for TE (0.969 for CR, 0.863 for AVE), IAI (0.990; 0.923), SP (0.981; 0.895), and SEI (0.983; 0.903) exhibited particularly strong values. Although the CR (0.798) and AVE (0.627) for SO were slightly lower, both remained above acceptable levels. These findings collectively indicate that the measurement model is reliable and that the indicators consistently represent their respective latent constructs.

To assess discriminant validity, heterotrait-monotrait (HTMT) ratios were calculated among all latent constructs. As shown in

Table 5. HTMT values for discriminant validity.

	Social System (SO)	Technical System (TE)	Information Accessibility and Interpretation (IAI)	Social Proximity (SP)	Social Entrepreneurial Intention (SEI)
Social System (SO)	—	0.277	0.399	0.195	0.277
Technical System (TE)	0.277	—	0.341	0.073	0.073
IAI	0.399	0.341	—	0.497	0.721
Social Proximity (SP)	0.195	0.073	0.497	—	0.634
SEI	0.277	0.073	0.721	0.634	—

, all HTMT values fall well below the recommended threshold of 0.90 [45], indicating adequate discriminant validity. The highest observed HTMT value was 0.721 between IAI and SEI, which is still within acceptable limits. These results confirm that each construct is empirically distinct from the others in the measurement model.

To assess potential multicollinearity among predictors, variance inflation factors (VIFs) were calculated based on composite scores for social system (SO), technical system (TE), information accessibility and interpretation (IAI), and social proximity (SP). As reported in

Table 6. VIF values for predictor variables.

Predictor	VIF
Social System (SO)	1.42
Technical System (TE)	1.26
Information Accessibility and Interpretation (IAI)	1.63
Social Proximity (SP)	1.34

, all VIF values fall well below the conservative threshold of 5.0 and the more lenient threshold of 10.0, with the highest value being 1.63. These results indicate an absence of problematic multicollinearity among the predictors. Therefore, the explanatory variables can be considered sufficiently independent, supporting the stability and interpretability of the structural path coefficients in the SEM analysis [46,47].

4. Empirical Results

The results of the structural equation modeling are presented in

Table 7. Structural equation model results for the effects of social and technical systems on social entrepreneurial intention.

Path	Estimate	Std. Error	z-Value	p-Value	Std. Coefficient
SEI~SO	0.465	0.063	7.427	<0.001	0.385
SEI~TE	−0.064	0.049	−1.291	0.197	−0.067
Model Fit Summary: CFI = 1.000, TLI = 1.000, RMSEA = 0.000, SRMR = 0.000, N = 388

Note: SEI = Social Entrepreneurial Intention; SO = Social System; TE = Technical System. Standardized coefficients are shown in the last column.

. The path from social system (SO) to social entrepreneurial intention (SEI) was positive and statistically significant (β = 0.385, p < 0.001), supporting Hypothesis 1 (H1). This finding indicates that individuals who perceive stronger social belonging and engagement in socio-technical contexts are more likely to express an intention to pursue socially motivated entrepreneurial activity.

In contrast, the path from technical system (TE) to SEI was not statistically significant (β = −0.067, p = 0.197), supporting Hypothesis 2 (H2). This result is consistent with the theoretical proposition that technical systems, while essential for facilitating digital operations, may not independently generate the motivational impetus required for social entrepreneurship. Although prior research has emphasized the utility and efficiency of technical infrastructure in entrepreneurial processes [24,40,41,48], the findings of this study suggest that such systems alone may not inspire the kind of prosocial orientation embedded in SEI.

These contrasting effects underscore the distinct psychological mechanisms that underlie social entrepreneurial behavior [49]. While technical systems may play an indirect enabling role—enhancing operational ease and digital access—they appear insufficient to predict SEI without complementary support from socially embedded systems. In sum, the results confirm that a robust social system, characterized by shared identity and normative belonging, plays a more pivotal role than technical readiness in fostering the intention to launch socially oriented ventures.

To test whether the effects of social systems (SO) and technical systems (TE) on social entrepreneurial intention (SEI) differ by generation, a multi-group SEM analysis was conducted comparing Generation Z with all other generational cohorts. As shown in

Table 8. Multi-group SEM results: generational moderation effects.

Model	χ2	df	AIC	BIC	RMSEA	Δχ2	Δdf	p-Value
Unconstrained Model	2970.3	412	25,327	25,873	—	—	—	—
Constrained Model	3002.4	414	25,355	25,894	0.279	32.13	2	<0.001 ***

Note: Δχ² = chi-square difference between unconstrained and constrained models. A significant p-value (<0.001) indicates that the structural path coefficients differ significantly across generational groups. *** p < 0.001.

, the chi-square difference between the unconstrained model (which allowed path coefficients to vary across groups) and the constrained model (which imposed equality constraints on regression paths) was statistically significant (Δχ² = 32.13, Δdf = 2, p < 0.001). This result indicates that the structural effects of SO and TE on SEI differ meaningfully by generation.

The findings provide empirical support for Hypotheses 3 and 4, which predicted that the influence of both social and technical systems on SEI would vary depending on generational identity. Furthermore, since both paths contributed to the significant model difference, the analysis also supports Hypothesis 5, suggesting that the relative importance of social versus technical systems differs across generational groups.

These results highlight generation as a key boundary condition in the socio-technical architecture of social entrepreneurship. For younger individuals, particularly those in Generation Z, social systems may exert a stronger influence due to heightened responsiveness to peer norms, prosocial narratives, and digital belonging [6,21,50]. In contrast, older generations may interpret social and technical cues differently, placing greater weight on prior experience or perceived feasibility [35]. The presence of significant moderation underscores the need for tailored strategies in entrepreneurship education and policy—strategies that recognize generational differences in how social purpose and technological means are integrated into entrepreneurial intentions.

Table 9. Moderated mediation effects of IAI and SP by generation.

Mediation Pathway	Generation Group	Indirect Effect	Significance (p-Value)
TE → IAI → SEI (H6)	Gen Z	−0.072	0.057 *
	Others	−0.006	ns
SO → SP → SEI (H7)	Gen Z	0.394	<0.001 ***
	Others	0.152	<0.01 **

Note: * p < 0.10; ** p < 0.01; *** p < 0.001; SEI = Social Entrepreneurial Intention; TE = Technical System; IAI = AI Familiarity and Intent; SO = Social System; SP = Social Proximity. Indirect effects estimated via = in lavaan with bootstrapping (5000 samples).

presents the generation-specific indirect effects of technical and social systems on social entrepreneurial intention (SEI), mediated by AI familiarity (IAI) and social proximity (SP), respectively. The results provide empirical support for Hypotheses 4a, 4b, and 4c.

Hypothesis 6 is partially supported: the indirect effect of technical systems on SEI through IAI was negative for both groups and marginally significant for Generation Z (β = −0.072, p = 0.057). This finding suggests that among younger individuals, greater reliance on technical systems—when mediated through AI familiarity—may slightly reduce social entrepreneurial intention. One possible explanation is that overexposure to or critical engagement with AI technologies may attenuate motivation, especially when such tools are perceived as lacking social or ethical grounding [35,51].

Hypothesis 7 is strongly supported: the indirect effect of social systems on SEI through SP was statistically significant for both Generation Z and older cohorts but notably stronger for Generation Z (β = 0.394 vs. 0.152). This result underscores the importance of relational closeness to entrepreneurial figures—such as family members, peers, or mentors—as a powerful socializing mechanism that activates entrepreneurial motivation more effectively in younger individuals [32,43].

Hypothesis 8 is also supported, as the comparative strength of these two mediation mechanisms differed meaningfully by generation. For Generation Z, SP emerged as the dominant psychological pathway toward SEI, whereas IAI played a comparatively weaker or even negative role. In contrast, for older generations, SP remained a positive mediator, but its magnitude was lower, and IAI was largely negligible. These generational variations reveal that the dominant motivational pathway toward social entrepreneurship is contingent upon age-related factors.

Overall, these findings emphasize that generational differences moderate not only the direct effects of social and technical systems on SEI, but also the psychological mechanisms through which these systems exert their influence. For Generation Z, social connectedness and peer validation via SP appear to be particularly salient [6,21]. Conversely, older cohorts may require greater emphasis on facilitating conditions and network development to translate systemic support into intention.

From a practical perspective, these results suggest that interventions to foster social entrepreneurship should be generationally tailored. For younger individuals, strategies should highlight social modeling, relational trust, and shared purpose. For older individuals, initiatives may need to focus on building digital confidence and offering access to meaningful entrepreneurial networks. Such personalized approaches may improve the effectiveness of programs aiming to cultivate socially motivated entrepreneurial behavior.

Table 10. Moderation effects of IAI and SP on the relationship between systemic factors and SEI.

Interaction Term	Estimate	Std. Error	p-Value	Std. Coefficient
SO × IAI	0.281	0.040	<0.001	1.815
TE × IAI	−0.061	0.028	0.027	−0.465
SO × SP	−0.281	0.042	<0.001	−1.670
TE × SP	0.032	0.030	0.289	0.215

Note. SEI = Social Entrepreneurial Intention; IAI = AI Familiarity and Intent to Use; SP = Social Proximity. Standardized coefficients are shown for interpretability.

presents the results of the moderation analysis examining how AI familiarity and usage intention (IAI) and social proximity (SP) influence the relationships between systemic factors—social system (SO) and technical system (TE)—and social entrepreneurial intention (SEI). These moderation effects were tested to evaluate Hypotheses 5 and 6.

The interaction term between SO and IAI was positive and statistically significant (estimate = 0.281, p < 0.001), supporting Hypothesis 9. This suggests that individuals with higher levels of AI familiarity are more responsive to social systems when forming their SEI. In other words, digital confidence strengthens the motivational impact of social norms, belonging, and identity on the intention to engage in social entrepreneurship.

Conversely, the interaction between TE and IAI was negative and statistically significant (estimate = −0.061, p = 0.027), providing support for Hypothesis 10. Interestingly, this result implies that for individuals with higher AI familiarity and increased exposure to technical systems may slightly reduce their intention to engage in social entrepreneurship. This counterintuitive finding could be attributed to overexposure to technology or skepticism about its authenticity and ethical implications, especially among digitally native individuals who may value purpose over efficiency.

In line with Hypothesis 11, the interaction between SO and SP was also significant (estimate = −0.281, p < 0.001). However, the negative direction of the coefficient indicates a diminishing marginal effect: for individuals who are already embedded in strong social networks (i.e., high SP), the added influence of formal social systems (SO) may be reduced or even redundant. This suggests that personal relationships with entrepreneurial role models may substitute for institutional signals when forming SEI.

In contrast, the interaction between TE and SP was not statistically significant (p = 0.289), leading to the rejection of Hypothesis 12. This result implies that the influence of technical systems on SEI is not contingent on an individual’s level of social embeddedness. That is, having strong social proximity does not enhance or buffer the effect of technical systems on entrepreneurial intention.

Taken together, these results confirm that personal characteristics such as digital familiarity and social connectedness moderate the way individuals interpret and respond to systemic influences. They support Hypotheses 9, 10, and 11, while Hypothesis 12 is not supported. The findings emphasize the importance of personalization in fostering SEI. For example, interventions targeting digitally confident individuals may benefit more from purpose-driven narratives than from technical support alone, while those embedded in strong social networks may respond best to peer influence and community engagement rather than institutional promotion.

Table 11. Hypothesis testing summary.

Hypothesis	Reformulated Hypothesis Statement	Status
H1	Social systems (SO) positively influence social entrepreneurial intention (SEI).	Supported
H2	Technical systems (TE) do not have a significant direct effect on SEI, acting more as enabling conditions.	Supported
H3	The effect of social systems on SEI is moderated by generation.	Supported
H4	The effect of technical systems on SEI is moderated by generation.	Supported
H5	The relative importance of SO versus TE in influencing SEI varies across generations.	Supported
H6	The mediating effect of IAI on the relationship between TE and SEI differs by generation.	Partially Supported
H7	The mediating effect of SP on the relationship between SO and SEI differs by generation.	Supported
H8	The relative strength of IAI and SP as mediators of SEI varies by generation.	Supported
H9	IAI moderates the relationship between SO and SEI: Higher IAI strengthens the positive effect of SO.	Supported
H10	IAI moderates the relationship between TE and SEI: Higher IAI weakens the positive effect of TE.	Supported
H11	SP moderates the relationship between SO and SEI: High SP reduces the marginal impact of SO.	Supported
H12	SP moderates the relationship between TE and SEI: No significant moderation observed.	Not Supported

summarizes the hypothesis testing results, offering empirical support for the proposed socio-technical model of social entrepreneurial intention (SEI). Hypothesis H1 was supported, confirming that social systems (SO)—including institutional norms and cultural values—positively influence SEI, in line with the Theory of Planned Behavior. Hypothesis H2 was also supported, though the effect of technical systems (TE) on SEI was not statistically significant. This suggests that TE serves more as an enabling condition rather than a direct motivational driver of SEI. Hypotheses H3–H5 were validated via multi-group SEM, showing that generational differences moderate the influence of SO and TE on SEI. Generation Z showed stronger responsiveness to systemic inputs, with social systems having relatively greater influence on intention formation than technical systems. Hypotheses H6–H8 were also supported. IAI mediated the relationship between TE and SEI negatively for Gen Z, while SP significantly mediated the SO–SEI path, especially for younger individuals. These findings highlight that the dominant psychological pathways differ across generations, with SP being more influential for Gen Z. Moderation analysis further supported H9 and H10. IAI strengthened the SO–SEI relationship but weakened the TE–SEI link, possibly due to critical attitudes toward digital systems among AI-familiar individuals. H11 was supported, indicating that strong SP can reduce reliance on formal social systems. H12, however, was not supported, suggesting no moderating effect of SP on TE.

Overall, the results underscore the importance of integrating systemic and personal factors in explaining SEI. Generation-specific strategies emphasizing social modeling for younger individuals and digital confidence-building for older adults are recommended to promote social entrepreneurship more effectively.

5. Conclusions

This study was motivated by the increasing societal need for entrepreneurial initiatives that prioritize social value creation, especially in a context marked by digital transformation and generational shifts. While social entrepreneurship offers critical pathways for addressing systemic issues such as inequality, climate risk, and institutional distrust, the drivers behind individuals’ intentions to pursue such ventures remain under-theorized. Anchored in Socio-Technical Systems Theory (STST) [11] and informed by Generational Theory [6] and Conditional Process Modeling [27], this study examined how perceptions of social (SO) and technical (TE) environments influence social entrepreneurial intention (SEI) and how these effects are shaped by generational identity and personal characteristics such as AI familiarity (IAI) and social proximity (SP).

The findings indicate that social systems (SO) play a significant and robust role in shaping SEI, whereas technical systems (TE), though conceptually important, do not exert a direct motivational effect. This highlights their function as contextual enablers rather than primary drivers. Generation Z and Y participants exhibited stronger responsiveness to both social and technical cues, confirming the relevance of generational differences in shaping intention. Furthermore, the study reveals that IAI and SP act as moderated mediators, but their influence diverges across groups. SP strongly mediates the relationship between SO and SEI for younger generations, whereas IAI showed a marginally negative mediating effect in the TE-to-SEI pathway for Generation Z. These results suggest that digital familiarity does not universally enhance entrepreneurial motivation and in some cases may even weaken it, especially among digitally saturated individuals [21,51].

The moderation analysis further supports the nuanced role of personal characteristics. IAI positively moderates the SO–SEI relationship but negatively moderates the TE–SEI path. This suggests that digital confidence may reinforce normative cues while generating critical distance from purely technical solutions. In contrast, SP moderates the SO–SEI relationship negatively, possibly indicating a substitution effect between informal networks and formal social institutions. The non-significant moderation of SP in the TE–SEI path suggests that technical engagement may not depend heavily on relational context.

Taken together, these findings offer important theoretical and practical contributions. They enrich current models of social entrepreneurship by showing that intentions are shaped not only by external systems but also by personalized, generation-sensitive mechanisms of mediation and moderation. Practically, the results suggest the need for tailored policy and educational interventions. Younger generations may benefit more from socially grounded mentorship and peer modeling, while older cohorts may require enhanced support for building digital confidence and trust. A one-size-fits-all approach is unlikely to suffice in fostering the next wave of social entrepreneurs.

5.1. Theoretical Contributions

This study contributes to the theoretical advancement of social entrepreneurship research by integrating socio-technical systems theory with generational and conditional process perspectives to explain the formation of social entrepreneurial intention (SEI). By conceptualizing social (SO) and technical systems (TE) as distinct but interrelated contextual factors, the study advances understanding of how environmental structures influence entrepreneurial cognition and motivation. The findings confirm that social systems exert a robust direct effect on SEI, whereas technical systems function more as enabling conditions rather than direct motivational drivers. This distinction enriches the literature by demonstrating that systemic support varies in its psychological salience and should not be treated as uniformly influential.

A further theoretical contribution lies in the generational differentiation of systemic effects. The results show that Generation Z is more responsive to both social and technical systems but particularly to social system cues, supporting the application of generational theory in entrepreneurship studies. By validating hypotheses H3–H5 through multi-group SEM, the study reveals that generational identity significantly conditions the pathways through which system-level cues translate into intention.

Moreover, the study adds conceptual clarity by disentangling the roles of AI familiarity and intent (IAI) and social proximity (SP) as both moderators and moderated mediators. While IAI and SP mediate the effects of TE and SO, respectively, their mediating strength also varies by generation, confirming a moderated mediation structure [27]. For instance, SP strongly mediated the SO–SEI relationship for Generation Z, while IAI showed a marginally negative indirect effect in the TE–SEI pathway for the same group. These findings highlight that mediators are not passive conduits but dynamic filters shaped by generational and contextual positioning.

The moderation analysis further refines this perspective. IAI enhances the motivational impact of social systems but weakens the effect of technical systems, suggesting a dual-edged role of digital familiarity. SP, on the other hand, reduces the marginal utility of formal social systems when informal networks are strong, illustrating a potential substitution effect. These conditional patterns underscore the importance of personalized pathways in the formation of SEI.

By empirically validating a complex interplay of direct, mediated, and moderated relationships, this study extends conditional process theory within the context of social entrepreneurship. It provides a more nuanced understanding of how structure, cognition, and generational identity interact to shape entrepreneurial motivation, offering a multi-layered view of agency that is contextually embedded and psychologically dynamic.

5.2. Practical and Policy Implications

The findings of this study offer actionable implications for both practice and policy in promoting social entrepreneurship, especially in socio-technological contexts marked by generational diversity and digital transformation. First, the strong and consistent influence of social systems (SO) on social entrepreneurial intention (SEI) highlights the importance of fostering supportive cultural norms, institutional narratives, and public discourse that validate and encourage prosocial ventures [34,52]. Stakeholders in entrepreneurship education, incubators, and civic platforms should embed values of social purpose into their programming while building visible pathways for socially meaningful innovation.

Second, the limited and non-significant direct effect of technical systems (TE) suggests that access to digital infrastructure alone may be insufficient to spark intention. Instead, technological tools must be contextualized within purpose-driven frameworks. Policies should not merely promote digital platforms or AI tools but actively link them to community needs, ethical narratives, and social impact outcomes to enhance motivational salience [2].

The significant moderation by generation (H3–H5) underscores the need for age-specific intervention strategies. Generation Z, for example, showed heightened responsiveness to both SO and TE, especially when mediated by social proximity (SP). This suggests that entrepreneurship programs targeting younger cohorts should emphasize peer-led initiatives, social belonging, and network-based learning. Conversely, for older generations, interventions may benefit from strengthening institutional trust and facilitating intergenerational mentorship models that bridge digital and experiential gaps.

The dual roles of AI familiarity and usage intention (IAI) and social proximity (SP) as moderated mediators offer further granularity. The finding that high IAI can both facilitate and attenuate entrepreneurial motivation—depending on its interaction with systemic factors—calls for a more nuanced approach to digital skill-building. AI training programs should go beyond technical competence to incorporate ethical reflection, collaborative applications, and alignment with social values [4]. Similarly, enhancing SP through community-based initiatives and role model exposure may prove more effective in environments where formal system support is lacking or unclear.

Overall, these results advocate for a multi-level, personalized approach to policy and program design. Successful promotion of social entrepreneurship requires the integration of structural support, digital literacy, generational attunement, and relationship-based motivation. Policies that recognize this complexity and adapt to diverse motivational pathways will be better positioned to cultivate socially driven innovators across demographic boundaries.

5.3. Discussion

The findings of this study provide novel insights into the formation of social entrepreneurial intention (SEI) within digitally mediated and generationally segmented contexts. Consistent with socio-technical systems theory (STST) [11,17,40,49], our results confirm that social systems (SO)—comprising institutional trust, social belonging, and normative cues—exert a stronger motivational influence on SEI than technical systems (TE), which appear to act more as contextual enablers. This supports the claim that social embeddedness remains central in entrepreneurship, even amidst rapid technological advancement [18,30,43].

These results align with past research showing the importance of relational and cultural supports in shaping social venture creation [1,10,31,32,52]. For example, Vedula et al. [3] emphasized that prosocial entrepreneurship often emerges from socially embedded values rather than from purely instrumental, technology-driven motivations. Similarly, Park and Bae [9] argued that legitimacy in social enterprises arises from alignment with communal expectations. Our study extends this understanding by showing that these mechanisms are contingent on generational identity and digitally mediated dispositions such as AI familiarity.

Interestingly, while the Technology Acceptance Model (TAM) has long emphasized perceived ease of use and usefulness as drivers of behavioral intention [28,33,44], our results suggest that in the context of social entrepreneurship, these technical perceptions (captured in TE) are not sufficient to drive intention directly. Instead, their influence is indirect and highly dependent on mediators like AI familiarity and moderators like generation. This finding supports recent critiques that call for a more nuanced understanding of digital readiness in entrepreneurial contexts [17,53,54].

The generational differences observed—particularly the heightened sensitivity of Generation Z to social cues and network proximity—are consistent with prior work on generational cognition and digital behavior [21,23,25,55,56]. For instance, Twenge [21] and Lyons et al. [25] observed that younger generations are more attuned to social purpose and peer influence, which aligns with our finding that social proximity (SP) mediates the SO–SEI path more strongly for Generation Z.

Furthermore, the moderated mediation effects of AI familiarity (IAI) raise critical questions about the presumed linear benefits of digital competence. Contrary to assumptions that digital fluency necessarily enhances motivation, our results suggest that high IAI may attenuate entrepreneurial intention when not anchored in social values—a dynamic aligned with Prensky’s distinction between “digital natives” and deeper digital literacy [51]. This echoes Bacq, Geoghegan, Josefy, Stevenson, and Williams [1] and Benavides et al. [4], who stressed that digital solutions must be contextualized within social missions to generate authentic engagement.

Taken together, this study contributes to a richer understanding of SEI by integrating structural systems, personal disposition, and generational identity into a unified model. While prior literature has treated these components in isolation, our findings reinforce the importance of conditional processes [22,27] and embedded contexts [18,30,38,43] in motivating social entrepreneurship. In doing so, the study not only supports but extends the socio-technical paradigm and its relevance to next-generation social innovators.

5.4. Limitations and Future Research

This study advances understanding of the socio-technical mechanisms underlying social entrepreneurial intention (SEI), yet several limitations should be acknowledged to guide future inquiry. The use of a cross-sectional design constrains the ability to make definitive causal claims. While moderated mediation analysis provided insight into the psychological and contextual pathways shaping SEI, the directionality of these relationships remains inferential. Future longitudinal or experimental designs could offer stronger evidence of temporal sequencing and causal dynamics, particularly as individuals’ perceptions of social and technical systems evolve [27].

The study relied on self-reported measures for all constructs, which may introduce bias due to common method variance or social desirability. Although validated items and attention filters were employed to mitigate this risk, incorporating behavioral indicators—such as participation in social ventures or engagement in entrepreneurial programs—would enhance the ecological validity of future research.

Moreover, the sample, while demographically diverse within the national context, was limited to respondents in China. The socio-technical environment in China is uniquely shaped by rapid digitalization, collectivist social values, and strong institutional scaffolding, which may not generalize to entrepreneurial ecosystems in other countries. Comparative studies across regions or institutional regimes could reveal important contextual contingencies in how social and technical systems shape intention.

While the study integrated multiple layers of influence, including systemic inputs (SO and TE), individual mediators (IAI and SP), and generational moderators, the model does not exhaust all potentially relevant variables. Factors such as entrepreneurial self-efficacy, perceived feasibility, or institutional trust may further explain variance in SEI, and future work should test these extensions. In particular, SP and IAI were modeled as moderated mediators, a complex form requiring substantial statistical power. Although bootstrapping and multi-group analysis were applied, further testing using simulation-based or Bayesian methods could enhance robustness and extend conditional process modeling approaches within entrepreneurship research.

In addition, although the study employed structural equation modeling within a deductive and quantitative framework, it did not include control variables (e.g., gender, education, or income) in the model. While this decision aligns with the study’s conceptual focus, it may limit the precision of causal inferences. Future research should consider including such controls to enhance the robustness and generalizability of the findings.

Finally, the findings emphasize generation-specific motivational patterns yet did not deeply differentiate between Generation Z and Millennials. Future research could further stratify generational cohorts or explore intra-generational heterogeneity to refine the understanding of how age-related identity and digital fluency shape responses to systemic cues.