How AI-driven stimuli trigger digital entrepreneurial hustle: a stimulus-organism-response perspective Open Access

Digital entrepreneurship is transforming contemporary business by redefining how ventures are launched and scaled (Ganefri et al., 2025). The integration of artificial intelligence (AI) into digital ventures–from automation to real-time analytics–empowers entrepreneurs with agility, data-driven decision-making and resilience under uncertainty (Uriarte, Baier-Fuentes, Espinoza-Benavides, & Inzunza-Mendoza, 2025). At the core of this adaptability lies entrepreneurial hustle: persistent, high-intensity effort to pursue opportunities. Despite widespread access to AI, effective use hinges on individual readiness–specifically, AI literacy (AIL: knowledge and skills) and AI self-efficacy (AIS: confidence in using AI). This study examines how their alignment affects digital entrepreneurial identity aspiration (EIA) and digital self-efficacy (DSE), which, in turn, drive entrepreneurial hustle (DEH). While prior work examines AIL or AIS separately, few have assessed their combined or misaligned effects. We define AIL, AIS, EIA, DSE and DEH as distinct yet connected drivers of digital entrepreneurial action.

To address this gap, the study applies the SOR framework (Mehrabian & Russell, 1974), where stimuli (AIL and AIS) influence internal states (EIA and DSE), which in turn shape behavior (DEH). This model captures both direct and mediated effects of digital readiness on entrepreneurial action. While SOR has been used in technology adoption and entrepreneurial cognition (Tran, Pham, Le, Dinh, & Pham, 2024), its application to AI-driven entrepreneurship is limited. We extend it by showing how AI competencies activate key psychological mechanisms. “Congruence” refers to AIL = AIS, while “incongruence” reflects mismatches between them (AIL > AIS vs. AIS > AIL). We predict that outcomes peak under high-high alignment and decline with divergence, with steeper drops under frustrated capability or overconfidence. The empirical focus is on Vietnamese university students–a contextually relevant group in a rapidly digitizing economy, shaped by initiatives such as “Made in Vietnam 4.0” and the National Strategy on the Fourth Industrial Revolution. Higher education plays a key role in fostering AIL and entrepreneurial capability, yet little is known about how students internalize AI as a psychological resource for identity and action. Existing research is also geographically biased toward Western and advanced Asian contexts. This study addresses these gaps by examining digital entrepreneurship in an emerging, tech-forward setting.

The research addresses three core questions:

By addressing these research questions, this study contributes by (1) extending SOR to AI-driven entrepreneurship via readiness-to-identity/efficacy-to-hustle mechanisms, (2) introducing a congruence-sensitive method (response-surface analysis) for the interaction between AIL×AIS and (3) offering evidence from an understudied context with practical implications for capability–confidence alignment. However, results are robust when we re-specify the model with AIL/AIS as stimuli proxies (via recent training/usage intensity) and institutional affordances as controls. Analyses include controls for gender, age and majors to guard against rival explanations.

AI has become a catalyst for digital entrepreneurship by enhancing decision-making, creativity and efficiency in early-stage ventures (Imjai, Nui-Suk, Usman, Somwethee, & Aujirapongpan, 2024; Upadhyay, Upadhyay, Al-Debei, Baabdullah, & Dwivedi, 2022). Its ability to process vast data and automate tasks empowers even resource-constrained entrepreneurs to act swiftly–a key feature of DEH, defined by fast, innovative action amid uncertainty (Burnell et al., 2024). While AI's functional benefits are well known, its psychological impact on entrepreneurial motivation remains underexplored. By reducing uncertainty and offering real-time insights, AI strengthens perceived control and urgency–both critical drivers of entrepreneurial behavior (Chalmers, MacKenzie, & Carter, 2021).

By automating routine tasks such as bookkeeping and customer support, AI releases cognitive and emotional capacity for strategic and creative activities. Generative AI, in particular, acts as a “force multiplier” for innovation and problem-solving (Liu & Wang, 2024)–core elements of hustling behavior. Importantly, DEH is not purely behavioral but also psychological, shaped by self-perception and agency under uncertainty. In this sense, AI serves as both a technological tool and a psychological enabler. While AI adoption enhances innovation, scalability and responsiveness (Wang & Zhang, 2024), these outcomes depend on individual readiness–specifically, AIL and AIS. Entrepreneurs high in both AIL and AIS are more likely to take initiative, persist through uncertainty and pursue unconventional strategies–traits that define DEH.

The SOR model explains how external stimuli (S) influence internal states (O), which drive behavioral responses (R). This study aims to examine how the alignment between AIL and AIS acts as a psychological stimulus shaping entrepreneurial cognition and behavior. Rather than treating them separately, their congruence or incongruence is considered the key trigger. AIL reflects AI knowledge and skills, while AIS denotes confidence in applying these skills. These factors are expected to influence EIA and DSE, which in turn drive DEH–persistent, creative and proactive action under uncertainty.

AIL and AIS serve as key psychological resources shaping individuals' EIAs. Those with strong AI understanding are more likely to view entrepreneurship as attainable, as AIL enhances perceived competence in tech-driven contexts (Duong, 2025). Similarly, Guan, Zhang, and Gu (2025) argue that AIS can foster entrepreneurial intentions and identity by boosting confidence in managing digital challenges. When individuals feel capable of using AI, they are more likely to perceive themselves as digital entrepreneurs, thereby reinforcing their identity and self-efficacy. The balance between AIL and AIS reflects the alignment of skill and confidence: high–high congruence strengthens cognitive coherence and entrepreneurial self-concept, while low–low congruence weakens self-perception and identity formation.

An imbalance between AI competence and confidence can undermine psychological functioning. When individuals possess high AIL but low AIS–or vice versa–they experience cognitive dissonance, reduced readiness and weakened motivation. Research suggests that optimal outcomes occur when ability and confidence align (Gottlieb, Chan, Zaver, & Ellaway, 2022)​. Overconfidence (high AIS, low AIL) often leads to frustration and reduced efficacy, while underconfidence (high AIL, low AIS) fosters anxiety and self-doubt (Choo, Peter, Paul, & Tseng, 2024). Within the SOR framework, such misalignment disrupts cognitive integration, weakening identity and self-efficacy.

Within the SOR framework, behavioral outcomes such as DEH emerge from the organismic states. This study posits that the alignment of AIL and AIS functions as a key resource influencing entrepreneurial behavior. Individuals high in both AIL and AIS are more likely to exhibit strong hustle behaviors, leveraging AI to enhance productivity, experiment with new approaches and identify opportunities (Haleem, Javaid, Asim Qadri, Pratap Singh, & Suman, 2022). Conversely, those low in both AIL and AIS lack the knowledge and confidence to utilize AI effectively, which limits efficiency and reduces engagement in proactive (Duong, 2024), tech-driven actions–thereby weakening their hustling intensity.

Imbalance effects can significantly impact the link between stimulus and response in entrepreneurship. An entrepreneur with high AI literacy (AIL) but low self-efficacy may avoid utilizing AI tools, limiting their hustling potential. For instance, they might know an effective AI strategy to grow their user base but hesitate to implement it due to low confidence, resulting in underperformance (Hamburg, O’Brien, & Vladut, 2019). Conversely, those with low AI knowledge but high confidence may work hard but inefficiently, missing out on AI enhancements and resulting in suboptimal results (Gofman & Jin, 2023). This imbalance–whether it is confidence without knowledge or knowledge without confidence–can hinder effective entrepreneurial hustle.

In the SOR framework, the organism reflects internal psychological mechanisms that shape behavior. EIA functions as a motivational driver by activating identity-based processes. It captures the degree to which individuals aspire to become entrepreneurs in the digital domain (Gregori, Holzmann, & Schwarz, 2021). A strong EIA motivates entrepreneurial action, as individuals who envision themselves as future entrepreneurs are more willing to invest effort in realizing it. High identity aspiration fosters purpose, persistence and proactive behavior, prompting engagement in entrepreneurial activities even before full identification occurs. Those with strong EIA are more likely to initiate ventures and persist through difficulties, viewing challenges as part of their identity formation journey (Fisch & Block, 2021).

DSE enhances cognitive self-regulation, enabling individuals to translate intentions into sustained effort. High entrepreneurial self-efficacy predicts greater goal setting, persistence and resilience (Staniewski, Awruk, Leonardi, & Słomski, 2025). In complex digital settings, confidence reduces fear of failure and promotes problem-solving–both of which are key to hustle behavior. Strong DSE drives proactive, unconventional actions that advance ventures (Burnell et al., 2024), making individuals more likely to engage in persistent, skill-aligned entrepreneurial effort.

The SOR framework suggests that stimuli influence behavior indirectly through internal states (Wu & Wang, 2025). In this model, EIA and DSE mediate the relationship between AI-related stimuli (AIL and AIS) and DEH. AIL and AIS enhance identity aspiration and confidence, which in turn drive hustle, consistent with SOR's focus on internal mechanisms (Duong, 2024). In entrepreneurship, AI stimuli alone do not trigger hustle–it is their internalization that motivates action. The alignment of AIL and AIS further shapes this process: high–high congruence strengthens identity and hustle, while imbalance weakens them. Thus, EIA and DSE are expected to partially mediate the AIL–AIS–DEH link, with balance influencing hustle both indirectly through cognition and directly through readiness for sustained effort.

Figure 1 illustrates our SOR framework, highlighting stimuli (AI affordances), organismic readiness (AIL, AIS) and shaping EIA/DSE, as well as response (DEH), with congruence and incongruence explicitly modeled.

Undergraduate students were chosen for their relevance to early-stage entrepreneurial development and identity formation (Chadha, Upadhaya, & Devi, 2025). Though not all intend to start ventures, variations in their entrepreneurial cognition reveal key behavioral antecedents. With over 90% of users employing generative AI, they are well-suited for studying AI-related readiness. Using stratified random sampling across 224 Vietnamese universities (123 in the North, 101 in the South), five institutions per region were randomly selected based on Webometrics impact tiers to ensure diversity and representativeness. Data were collected over a ten-week period (October 5–December 15, 2024). With university approval, printed surveys were distributed with faculty assistance to ensure clarity and voluntary participation. Of 2,000 questionnaires, 1,061 valid responses were retained after screening. The final sample (45.5% female, 54.5% male) comprised students aged 18–23, with the majority aged 20–21 (42.1%) and 22–23 (47.0%). About 52.5% majored in economics and business, and 47.5% in engineering or other fields. Nearly half (47.0%) used generative AI tools (e.g. ChatGPT, Gemini and Deepseek) frequently, 36.1% used them occasionally and 16.9% used them rarely. Family business backgrounds were nearly balanced (52.2% yes and 47.8% no).

All constructs were measured using established scales from prior research, with items rated on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). AIS was assessed using four items adapted from Latikka, Turja and Oksanen (2019), while AIL was measured with twelve items based on Wang, Rau and Yuan (2022). DSE was evaluated through four items adopted from Ashraf, Alam and Alexa (2021), and EIA was captured using six items derived from Gregori et al. (2021). Finally, DEH was assessed using twelve items adapted from Burnell et al. (2024). Detailed item descriptions, sources and factor loadings are presented in Appendix A.

CFA (confirmatory factor analysis) results indicated a satisfactory model fit (χ² = 3273.681, df = 655, GFI = 0.822, AGFI = 0.799, CFI = 0.850, TLI = 0.839, NFI = 0.819, RMSEA = 0.061). All constructs demonstrated strong reliability, with Cronbach's alpha values ranging from 0.769 to 0.897 and CR values exceeding 0.70. Although AVE ranged from 0.34 to 0.59–slightly below 0.50–the high composite reliability supports adequate convergent validity (Fornell & Larcker, 1981). Factor loadings exceeded 0.50, confirming construct validity. HTMT ratios (see Appendix B) were below 0.85, indicating clear discriminant validity among constructs (see Table 1).

To assess common method bias (CMB), two diagnostic techniques were used. First, Harman's single-factor test revealed that the first factor accounted for 31.291% of the total variance, which is below the 50% threshold, indicating CMB is unlikely to be a major issue (Harman, 1976). Second, a common latent factor (CLF) test demonstrated poor model fit: χ² = 6964.524, df = 665, CFI = 0.638 and RMSEA = 0.095. Additionally, comparisons of standardized factor loadings between the five-factor model and the one-factor model revealed differences of less than 0.20, suggesting no substantial inflation due to shared method variance. Overall, these results confirm that CMB is unlikely to threaten the validity of the findings.

Polynomial regression and response surface analysis were employed to test hypotheses H1 through H6, capturing both linear and non-linear relationships between AIL and AIS, as well as their effects on EIA, DSE and DEH. Table 2 reports the regression coefficients and surface parameters, and the response surfaces are illustrated in Figures 2–4.

For EIA, Model 1 shows that both AIL (β = 0.421, p < 0.001) and AIS (β = 0.376, p < 0.001) have significant positive effects, suggesting that increases in either factor enhance EIA. The inclusion of quadratic terms significantly improves model fit (R² = 0.315, ΔR² = 0.310), justifying the use of the quadratic model for response surface analysis. Figure 2 illustrates the response surface of the (in)congruence between AIL and AIS on EIA. The congruence slope (ɕ₁ = 0.800, p < 0.001) is significantly positive, indicating that EIA increases when AIL and AIS are jointly high–demonstrating that the high-high congruence condition leads to greater identity aspiration than the low-low condition, which supports H1a. The surface plot confirms this interpretation, as the highest EIA values are observed at the rear corner of the graph, where both AIL and AIS are at their highest. In contrast, the congruence curvature (ɕ₂ = 0.040, p = 0.277) is not significant, suggesting a linear rather than curvilinear relationship along the line of congruence. Both the incongruence slope (ɕ₃ = 0.050, p = 0.541) and curvature (ɕ₄ = 0.050, p = 0.697) are non-significant, indicating that deviation between AIL and AIS does not relate to a meaningful decline in EIA, providing no support for H2a.

For DSE, model 2 reveals that both AIL (β = 0.521, p < 0.001) and AIS (β = 0.214, p < 0.001) are significant predictors. The addition of quadratic terms results in a significant improvement in explained variance (R² = 0.224, ΔR² = 0.218). Figure 3 plots the response surface of the (in)congruence between AIL and AIS on DSE. The congruence slope (ɕ₁ = 0.740, p < 0.001) is significantly positive, confirming that DSE is enhanced when both AIL and AIS are high, supporting H1b. This effect is clearly visualized in the surface plot, where the highest DSE levels appear at the rear corner of the graph. The congruence curvature (ɕ₂ = 0.090, p = 0.238) is not significant, suggesting a linear congruence relationship. However, the incongruence slope (ɕ₃ = 0.310, p < 0.001) is significantly positive, and the incongruence curvature (ɕ₄ = −0.230, p = 0.052) is marginally significant. These findings suggest an asymmetrical incongruence effect, where DSE declines more steeply when AIS is lower than AIL compared to the reverse. This result supports H2b, indicating that misalignment has a negative impact on DSE.

For DEH, Model 3 indicates that AIL (β = 0.550, p < 0.001) and AIS (β = 0.188, p < 0.001) are both significant contributors. The model explains a large proportion of variance (R² = 0.489, ΔR² = 0.485). Figure 4 presents the response surface of the (in)congruence between AIL and AIS on DEH. The congruence slope (ɕ₁ = 0.740, p < 0.001) is positive and significant, indicating that DEH is maximized when both AIL and AIS are high, supporting H3. The surface plot confirms this, as the rear corner–representing high-high congruence–displays the highest levels of DEH. The incongruence slope (ɕ₃ = 0.360, p < 0.001) and incongruence curvature (ɕ₄ = 0.280, p < 0.001) are also significant, confirming that misalignment between AIL and AIS leads to a notable reduction in DEH. These findings support H4, as both congruence and asymmetrical incongruence contribute to variance in entrepreneurial hustle.

To test H5 and H6, Model 4 in Table 3 shows that both EIA (β = 0.237, p < 0.001) and DSE (β = 0.220, p < 0.001) are significantly and positively associated with DEH, indicating that both identity aspiration and self-efficacy perceptions make meaningful contributions to entrepreneurial hustle. These results provide strong support for H7 and H8. Mediation effects were evaluated using bootstrapped indirect effects with 95% confidence intervals (see Table 4). All indirect effects are statistically significant. AIL impacts DEH through EIA (β = 0.070, CI [0.033, 0.108]) and DSE (β = 0.089, CI [0.061, 0.121]); AIS likewise influences DEH through both EIA (β = 0.077, CI [0.040, 0.112]) and DSE (β = 0.086, CI [0.061, 0.114]). Moreover, the block variable, which represents the combined effect of AIL-AIS congruence and incongruence, significantly predicts DEH via EIA (β = 0.024, CI [0.013, 0.035]) and DSE (β = 0.022, CI [0.012, 0.030]). These mediation results support H7 and H8.

This study explored how AI-related capacities–AIL and AIS–shape digital entrepreneurial behavior through psychological mechanisms within the SOR framework. Supporting H1a and H1b, results showed that high-high congruence between AIL and AIS positively influences EIA and DSE by enhancing perceived control, motivation and cognitive coherence between skills and self-beliefs. However, while incongruence did not significantly diminish EIA (H2a), it negatively affected DSE (H2b), suggesting that identity aspiration endures despite imbalance, whereas self-efficacy is more sensitive to misalignment. Thus, EIA reflects long-term identity goals, while DSE depends more on immediate perceptions of competence.

The findings support H3 and H4, showing that high congruence between AIL and AIS significantly increases DEH, while imbalance reduces hustle intensity. This highlights that alignment between cognitive readiness and motivational confidence enhances productivity and creativity, whereas mismatches hinder action through hesitation or cognitive strain. H5 and H6 further confirm that EIA and DSE positively predict DEH–individuals who identify as digital entrepreneurs and feel capable of success display greater persistence and engagement, consistent with prior research. Mediation results (H7 and H8) indicate that EIA and DSE partially mediate the AIL–AIS–DEH relationship, suggesting that AI-related capacities influence hustle primarily through their impact on entrepreneurial identity and self-belief, thereby reinforcing the psychological mechanisms proposed by the SOR framework.

This study advances technology and entrepreneurship research by identifying AIL and AIS as key drivers of entrepreneurial outcomes. Moving beyond the general role of digital technologies, it distinguishes between AI-related knowledge and confidence and examines their joint effects on identity, efficacy and hustle–deepening the understanding of AI readiness. The findings emphasize the importance of balancing cognitive and psychological resources to enhance entrepreneurial cognition and action. The study also extends the SOR framework by incorporating a dual-stimulus configuration (AIL and AIS) and testing its behavioral pathways through internal states such as identity aspiration. Using advanced analytical techniques, it is shown that cognitive congruence significantly influences motivation and behavior in digital entrepreneurship. Thus, this research integrates AI capability development, psychological modeling and entrepreneurial motivation into a unified framework, offering actionable insights for education and policy in an AI-driven economy.

This study provides practical implications for key stakeholders in the digital entrepreneurship ecosystem.

For educators and accelerators, the results highlight that building AIL alone is insufficient–training must also strengthen AIS. Programs should combine hands-on AI learning with feedback and mentoring to enhance both capability and confidence. Accelerators can further support balance by designing tasks that simultaneously develop technical skills and self-belief. For AI tool designers, enhancing usability, incorporating embedded guidance and adopting an application-oriented design can foster greater entrepreneurial engagement. Tools that enable intuitive learning and showcase practical use cases help to build both competence and confidence among emerging entrepreneurs.

Investors and policymakers should consider entrepreneurs' psychological readiness alongside technical skills when assessing potential. Programs that pair AI upskilling with confidence-building interventions–such as mentoring, peer learning and identity development–can foster more persistent and proactive entrepreneurial behavior. More broadly, the findings underscore that digital entrepreneurship relies not only on access to technology but also on the psychological internalization of digital tools. Stakeholders should therefore aim to cultivate AI-competent entrepreneurs who are both capable and confident to act.

While this study advances entrepreneurship theory and offers practical insights into AI skill development, several limitations remain. Its cross-sectional design and the student sample in Vietnam constrain generalizability (Ngo, Tran, & Hoang, 2024); future research should employ longitudinal and cross-cultural approaches. The findings apply primarily to individuals developing entrepreneurial identities in educational contexts and replication among practicing founders is recommended. Moreover, the absence of significant negative effects of AIL on self-efficacy warrants further exploration. Future studies using observational or mixed methods and revisiting these constructs as AI evolves to provide deeper insights.

The supplementary material for this article can be found online.