Investigating New Technology Entrepreneurial Performance (2023) PDF

Journal of the Knowledge Economy (2024) 15:6617–6642 https://doi.org/10.1007/s13132-023-01359-w Investigating the Determinants of New Technology Entrepreneurial Performance: an Empirical Study with PLS‑SEM and MGA Shaofeng Wang1 · José Paulo Esperança2 · Wancheng Yang1 · Justin Zuopeng Zhang3 Rec...

Journal of the Knowledge Economy (2024) 15:6617–6642 https://doi.org/10.1007/s13132-023-01359-w Investigating the Determinants of New Technology Entrepreneurial Performance: an Empirical Study with PLS‑SEM and MGA Shaofeng Wang1 · José Paulo Esperança2 · Wancheng Yang1 · Justin Zuopeng Zhang3 Received: 10 October 2022 / Accepted: 24 February 2023 / Published online: 8 May 2023 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023 Abstract This research studies the internal mechanism of university students’ new technol- ogy entrepreneurial performance. We developed a model to identify relevant factors affecting new technology entrepreneurial performance by synthesizing theories of new technology adaption, social networks, and self-determination. Three hundred and sixteen valid responses were received from our survey and analyzed with the partial least squares structural equation model (PLS-SEM) and multiple-group anal- ysis (MGA). The findings show that college students with entrepreneurial family his- tories rely more on social and industrial networks, whereas those without will seek opportunities from new technology adoption. Risk preference positively moderates the relationship between entrepreneurial intention and performance. Entrepreneur- ship intention mediates the impact of new technology usefulness, social network, industrial network, survival motivation, achievement motive, and responsibility motive on entrepreneurial performance. Our research helps improve entrepreneurial performance and provides new technology management inspiration. Keywords Entrepreneurial performance · Entrepreneurial family history · New technology usefulness · Entrepreneurial intention · Risk appetite This article is part of the Topical Collection on University and Entrepreneurial Ecosystems * Justin Zuopeng Zhang [email protected] Extended author information available on the last page of the article 13 Vol.:(0123456789) 6618 Journal of the Knowledge Economy (2024) 15:6617–6642 Introduction The rapid development of new technologies, such as artificial intelligence (Akter et al., 2021; Du et al., 2022; Huang, Chou, & Wu, 2021; Varsha et al., 2021), deep learning (Du & Shu 2022; Hou et al. 2022; Li et al. 2022; Wu et al. 2022; Zhao 2022), big data analytics (Haverila et al. 2022; Xie et al. 2022; Xing et al. 2022; Yoo & Roh 2021), and blockchain (Buthelezi et al. 2022; Harshvardhan & Teoh 2022; Liang et al. 2022; Qiu 2022), provides more entrepreneurial opportunities for developing new technology entrepreneurship (Zhang et al., 2022; Jiao et al., 2022; Holzmann & Gregori, 2023). Many uni- versity students have begun their entrepreneurial drives by employing the latest technologies (Yu et al., 2022). For example, some entrepreneurs use the mobile Internet to start live streaming e-commerce to sell goods online; some entrepre- neurs practice the software and hardware research and development capabili- ties learned in schools, such as Internet-related and 5G technology, to develop smart switches for the home; some entrepreneurs pay attention to big data min- ing, providing data analysis and reporting services for enterprises, schools, and other institutions; some entrepreneurs developed a Cloud website program using cloud computing to provide Software-as-a-Service (SaaS) to help small- and medium-sized enterprises (SMEs) realize website construction quickly and remain at a low cost (Lamine et al., 2021; Emmanuel et al., 2022; Chen et al., 2022; Ho & Chen, 2023). As the aborigines in the digital age, university stu- dent entrepreneurs are the leading force in promoting social development with the help of new technology in the future (Holzmann & Gregori, 2023). There- fore, it is of great significance to study the underlying mechanisms that affect the excellent entrepreneurial performance of university students who use new technology (Khalid, 2020; Fernandes et al., 2022). Entrepreneurial performance is a crucial indicator of entrepreneurial success (Martens et al., 2018; Alvarez-Torres et al., 2019; Staniewski & Awruk, 2019; Yeh et al., 2020; Huang et al., 2022), directly determines the success of university students entrepreneur- ship (Sayal & Banerjee, 2022). The topic of university students’ entrepreneurship has been under the spotlight over the past few years, and many successful entrepreneurship cases have drawn many university students to start businesses (Lv et al., 2021). Zhou & Xu (2012) clarifies that innovation and entrepreneurship competition helps university students reach their goals. The good entrepreneurial atmosphere and the publicity of entrepreneurial policies help arouse the entrepreneurial intention of university students (Liu et al., 2019). Numerous studies have scrutinized the innovative entrepreneurial behaviors of university students in terms of policies, funds, venues, and teachers, proving that efficient entrepreneurship education, policy support from society, individual entre- preneurial motivation, and encouragement from instructors, and these factors influence university students’ entrepreneurial intention and entrepreneurial performance in dif- ferent degrees (Yang & Zhang, 2018; Holzmann & Gregori, 2023). However, the rapid development of new technologies has brought many innovative business opportunities to various industries (Choudhuri et al., 2021; Law, Lau, & Ip, 2021; Lin & Ma, 2022; Sun 13 Journal of the Knowledge Economy (2024) 15:6617–6642 6619 et al., 2021; Yang & Yi, 2021; Zhang et al., 2022). Therefore, this paper intends to study the following questions: RQ1: What are the core factors that affect university students’ new technology entrepreneurial performance, and what are the internal structures and relation- ships of these core factors? RQ2: In the context of new technology entrepreneurial performance, what are the differences in influencing factors between the model with entrepreneurial family history and the model without an entrepreneurial family history? Based on the above two research questions, this study puts forward research hypotheses by reviewing relevant literature and constructing the new technology entrepreneurial performance conceptual model. This research developed an instru- ment tool to obtain data to test research hypotheses. It used SmartPLS 3 to per- form partial least squares structural equation modeling (PLS-SEM) and multi-group analysis (MGA). Based on the results of the data analysis, we found the factors that affected the entrepreneurial performance of university students’ new technology. We discussed the difference in influencing factors between the model with entrepre- neurial family history and the model without an entrepreneurial family history. In addition, we propose suggestions for improving the entrepreneurial performance of university students in new technology. The following sections will introduce the lit- erature review and research assumptions, methods, results, discussions, conclusions, limitations, and conclusions. Literature Review and Hypotheses Technology Acceptance Model and Theory of Planned Behavior New technology adoption theory reveals users’ acceptance and use of relevant technologies to implement specific behaviors (Al-Qirim et al., 2022; Odonkor & Pallud, 2022). New technology adoption theory in the technology acceptance model (TAM) (Rahman et al., 2017; Pipitwanichakarn & Wongtada, 2019; Chou et al., 2022; Shahidi, Cacho-Elizondo, & Tossan, 2021) and theory of planned behavior (TPB) (García-Rodríguez et al., 2020; Lechuga Sancho et al., 2020; Su et al., 2021; Lu et al., 2021; Tseng et al., 2022) have been used to exam- ine the intentions to use and to behave in multiple scenarios of new technolo- gies. Users realized that the significant helpfulness of new technologies would enhance their adoption behavior (De Moya, Pallud, & Wamba, 2021; Gholami et al., 2021; Rehman et al., 2021). Also, in adopting mobile commerce, when people find that new technology can improve business performance, they will use it in their business activities (Pipitwanichakarn & Wongtada, 2019). Sup- pose students can feel the good entrepreneurial atmosphere of the university and can obtain the learning support of entrepreneurship courses. In that case, it will positively enhance university students’ entrepreneurial intention (Su et al., 13 6620 Journal of the Knowledge Economy (2024) 15:6617–6642 2021) and bring entrepreneurs happiness (Rahman et al., 2017). The entre- preneurial intention of individuals will also be affected by the three aspects mentioned in the TBP theory (García-Rodríguez et al., 2020; Lechuga Sancho et al., 2020). Based on the study of TPB, shared languages and shared visions are also essential factors that affect students’ intention to start a business. Lu et al. (2021) confirmed that university entrepreneurship support positively affects students’ entrepreneurial intentions. TAM and TPB theories are primar- ily used to study the adoption of new technologies and entrepreneurial intention (Emmanuel et al., 2022), but their applications are still rare in entrepreneurial performance. This study will consolidate the new technology adoption theory to explore entre- preneurial performance and define new technology entrepreneurship performance (NTEP) as the current state of entrepreneurial performance in adopting new tech- nologies for entrepreneurship. Self‑Determination Theory Self-determination theory analyzes the combined effect of users’ intrinsic and extrinsic influences to understand the impact of internal and external motivations on users’ behavior (Mei & Genet, 2022). Self-determination theory provides a ref- erence for categorical exploration to explain the generation of user-implemented behaviors (Han et al., 2017). Users’ intention to generate behavior often forms under intrinsic and extrinsic motivation’s combined effect (Ahn, 2020). Both extrinsic and intrinsic motivation can also positively affect entrepreneurial inten- tion (Yeh et al., 2020). Users in a group society are also naturally influenced by relatives, friends, classmates, social culture, and values, suggesting extrinsic moti- vational factors can still influence users’ intrinsic intentions. Frese & Fay (2001) found that individual motivation notably impacts achieving entrepreneurial suc- cess. Dynamic entrepreneurial behaviors also lead to better corporate performance, and risk-taking and risk appetite will bring different performance results to entre- preneurship (Brändle et al., 2019; Basco et al., 2020). Thus, this study will incorpo- rate factors related to this theory for insight into new technologies’ entrepreneurial intention and performance. Social Network Theory Initially introduced the concept of network embeddedness and used it as a basis to develop the influence of network partnerships on economic behavior in socioeco- nomic phenomena. There are transaction behaviors in market trading that depend on the social relationships of firms or individuals, and they are often the key to the success of entrepreneurs. Entrepreneurs tend to start entrepreneurial behavior based on their personal or nearby advantageous resources, which is not only beneficial to the personal advantages of the entrepreneur but also mutually beneficial to the part- ners in the social network for mutual development (Kerr & Mandorff, 2023). The 13 Journal of the Knowledge Economy (2024) 15:6617–6642 6621 entrepreneurial environment university entrepreneurs live in has more frequent and close interactions with classmates, teachers, relatives, friends, and business partners. These social relationships positively affect the sharing of experience, entrepreneur- ial guidance, and resource referrals of entrepreneurs. The network of contacts owned by an entrepreneur’s team may be the decisive factor in starting a business (García- Villaverde et al., 2018). In addition, the entrepreneurial team has strong relations with upstream suppliers, downstream customers, financiers, and regulators during the entrepreneurial process, which also means that entrepreneurs who can handle the cooperative relationships in these industrial networks are conducive to legal com- pliance and smooth entrepreneurial activities. Thus, good social and industrial net- works have both resource and experience facilitation for entrepreneurial teams, and entrepreneurial teams should also focus on establishing social network relationships and good interactions (Anwar et al., 2020; Lu et al., 2021). Hypotheses Entrepreneurial Performance Entrepreneurial performance (EP) evaluates the modern financial, operational, mar- keting, and team performance of the entrepreneur’s team (Basco et al., 2020; Iqbal et al., 2021). Entrepreneurial performance can be used to comprehensively evaluate the current operational status of the entrepreneurial project, which includes multiple dimensions such as finance, team, market, and product (Anwar et al., 2020). Univer- sity student entrepreneurs should actively take risks and establish social networks to improve corporate performance (Vătămănescu et al., 2020). They should also observe the impact of family entrepreneurship history (Lu et al., 2021). Entrepreneurs’ stronger intentions and motivations lead to better entrepreneurial performances (Carsrud & Brännback, 2011; Basco et al., 2020; Xie et al., 2021; Huang et al., 2022). Entrepreneurial intention (EI) refers to a subjective judgment of university stu- dents regarding implementing entrepreneurial behavior (García-Rodríguez et al., 2020; Lu et al., 2021). Individuals with a more robust entrepreneurial intention show a greater intention to engage in entrepreneurial activities, implying that the individ- ual is more motivated to start a business (Asante & Affum-Osei, 2019). Entrepre- neurial education (Su et al., 2021), university entrepreneurship support (Lu et al., 2021), and entrepreneurial competence (Lv et al., 2021) will drive university stu- dents’ entrepreneurial intention. Individual self-efficacy significantly affects entre- preneurial performance (Lechuga Sancho et al., 2020); feasibility and desirability are deciding factors that drive university students to start entrepreneurship (García- Rodríguez et al., 2020). Therefore: Hypothesis 1 (H1). Entrepreneurial intention has a positive effect on entrepre- neurial performance. 13 6622 Journal of the Knowledge Economy (2024) 15:6617–6642 New Technology Usefulness New technology usefulness (NTU) is a subjective assessment of adopting digitization technological tools for enhancing entrepreneurial performance (Pipitwanichakarn & Wongtada, 2019). Artificial intelligence, blockchain, and 5G technologies are in full swing to advance the reform and development of various industries. The entrepre- neurial spirit of advocating innovation and optimism helps to enhance entrepreneurs’ perceived usefulness of new technologies (Rahman et al., 2017; Yao et al., 2021). The use of mobile commerce can help companies improve operating efficiency, while perceived ease of use and perceived usefulness have a vital influence on the adop- tion of mobile commerce (Pipitwanichakarn & Wongtada, 2019; Chau, Deng, & Tay, 2021). SMEs’ unsuitable policy environments will not be conducive to improving innovation performance (Xie et al., 2013). The existence of new technology provides a broader space for the performance improvement of business (Vătămănescu et al., 2020). Therefore: Hypothesis 2 (H2). The new technology’s usefulness positively affects entrepre- neurial intention. Social Network and Industrial Network Social network (SN) refers to the relationship between the entrepreneur and the related groups, such as classmates, friends, and suppliers related to his entrepre- neurial business (Chen & Tseng, 2021). The entrepreneurial process cannot sepa- rate from the upstream and downstream partners (Su et al., 2021). An excellent cooperative relationship with partners and regular communication and grooming is necessary to develop further an entrepreneurial business (García-Villaverde et al., 2018). Building good connections in the social network is an essential help in entrepreneurial performance (Chen & Tseng, 2021; Qi et al., 2021). The indus- trial network (IN) is the contemporary cooperative relationship between entrepre- neurs and industry-related partners. The social division of labor allows entrepre- neurs to focus more on their areas of expertise, but the networks play a crucial role in the entrepreneurial success (Jha & Alam, 2022). However, the relation- ships between entrepreneurs, customers, and suppliers influence the production of products or services, so entrepreneurs should actively communicate with relevant authorities and collaborate with capital financing institutions and industry partners (Anwar et al., 2020). Since entrepreneurs usually require commercial resources, their entrepreneurial behaviors focus on subdividing the industrial chain areas and establishing business networks in the industrial chain (Vătămănescu et al., 2020). Good social relationships (e.g., social and industrial networks) are essential for entrepreneurial intention and performance (Nuryakin, 2021; Chen & Tseng, 2021). Therefore: Hypothesis 3 (H3). Social networks have a positive effect on entrepreneurial intention. 13 Journal of the Knowledge Economy (2024) 15:6617–6642 6623 Hypothesis 4 (H4). Industrial networks have a positive effect on entrepreneurial intention. Entrepreneurial Motivations Entrepreneurial motivation indicates extrinsic and intrinsic factors included in entrepreneurial behavior (Yeh et al., 2020) and continues to drive entrepreneurs to achieve their entrepreneurial goals. Individuals or organizations may also drive entrepreneurial intention through survival (Carsrud & Brännback, 2011), achievement (Liu et al., 2019; Staniewski & Awruk, 2019), and responsibility (Ahn, 2020). Survival motivation (SM) is the motivational factor for entrepre- neurs to meet their basic survival needs under entrepreneurial behaviors (Yamini et al., 2022); achievement motivation (AM) motivates entrepreneurs to gain social recognition for entrepreneurial responses (Staniewski & Awruk, 2019). Respon- sibility motivation (RM) is the entrepreneurial behavior motivated by the entre- preneurial team’s responsibility for social development (Ahn, 2020). Individual behaviors and intentions often occur due to motivation, and entrepreneurship motivation is a reliable predictor for understanding entrepreneurial intention (Yeh et al., 2020). Entrepreneurs contribute worthful value for economic purposes (Brändle et al., 2019) and discover innovation opportunities (Basco et al., 2020) and other motivations to carry out entrepreneurial behavior. Achievement motiva- tion, survival motivation, and responsibility motivation of entrepreneurs are all important antecedents that drive them to form an entrepreneurial intention and obtain excellent entrepreneurial performance (Jha & Alam, 2022; Yamini et al., 2022). Therefore: Hypothesis 5 (H5). Achievement motivation has a positive effect on entrepre- neurial intention. Hypothesis 6 (H6). Survival motivation has a positive effect on entrepreneurial intention. Hypothesis 7 (H7). Responsibility motivation has a positive effect on entrepre- neurial intention. Risk Appetite Risk appetite (RA) refers to the entrepreneurial team’s assessment of the per- ceived risk level of the entrepreneurial inputs and outputs and is closely related to entrepreneurial success (Li & Yang, 2022). In the entrepreneurship process, entrepreneurs face human resources, materials, and capital (Brändle et al., 2019), and the degree of relevant inputs are closely related to the risk appetite. The RA likewise determines the success and the performance of an enterprise (Alvarez- Torres et al., 2019). Entrepreneurs with a greater risk appetite are likelier to try the latest innovative approaches. In contrast, their risk resistance and enduring psychological capacity are more potent than anyone else in similar cases (Lu et al., 2021). Since there is significant uncertainty in entrepreneurial behavior, 13 6624 Journal of the Knowledge Economy (2024) 15:6617–6642 it necessarily requires entrepreneurs to have a good risk appetite and mindset to cope with the uncertainty of entrepreneurial performance (Lechuga Sancho et al., 2020; Iqbal et al., 2021), and entrepreneurs are more likely to adopt specific behaviors if they perceive greater risk-reward (Basco et al., 2020). Entrepreneurs with higher risk appetites have a higher proportion of investment using digitiza- tion technologies (Sayal & Banerjee, 2022). Therefore: Hypothesis 8 (H8). Risk appetite has a positive moderating effect on the relation- ship between entrepreneurial intention and entrepreneurial performance. Family Entrepreneurship History Family entrepreneurship history (FEH) refers to the entrepreneur’s family members who have had an entrepreneurial history (Lu et al., 2021; Chen and Liu, 2022). Suc- cessful entrepreneurial cases often inspire entrepreneurs in their families (Staniewski & Awruk, 2019), which enhances entrepreneurial intention and performance (Yeh et al., 2020; Lechuga Sancho et al., 2020). Compared to the motivating effect of celebrity biographies, real entrepreneurial stories within family members have a vast demonstration effect (Liu et al., 2019). They are more likely to motivate younger generations to follow the trajectory of family entrepreneurship (Contreras-Lozano et al., 2021). Entrepreneurs with an entrepreneurial family history have access to guidance in the entrepreneurial process, which is more helpful for improving the performance of entrepreneurs (Martínez et al., 2016). Therefore, this study proposed that: Hypothesis 9 (H9). There are different influencing factors in entrepreneurial per- formance across family entrepreneurial history groups. Conceptual Model Regarding relevant literature and content analysis, this study constructed a model based on an understanding of the concept of entrepreneurial perfor- mance, the current social context of rapid development of new technologies, new technology adoption, network embedding theory, and self-determination theory (in Fig. 1) to gain further insight into the mechanism of entrepreneurial performance generation. Methodology This study attempts to develop a predictive and explanatory model of entrepreneur- ial performance among university students and uses the data from the questionnaire survey to empirically test the model presented in this paper (Basco et al., 2020; Lu et al., 2021). The approach of developing a scale to measure is suited to measure 13 Journal of the Knowledge Economy (2024) 15:6617–6642 6625 Fig. 1  New technology entrepreneurial performance model (NTEP) non-directly observable latent variables, and this study collected data on the latent variables by developing a measurement scale. Instrument After proposing the research model and hypotheses, this study designed the questionnaire based on the literature review. All measurement scales were derived from the established literature to ensure the content validity of this study (Yeh et al., 2020). “Entrepreneurial intention” was adapted Lechuga San- cho et al. (2020). “Risk appetite” was adapted from Sajid et al. (2021). The measurement of entrepreneurial performance adopts the subjective evaluation method that is verified by many scholars (Basco et al., 2020; Vătămănescu et al., 2020; Iqbal et al., 2021). Martens et al. (2018) “Entrepreneurial perfor- mance.” The items were measured on a 5-point Likert scale (Basco et al., 2020; Lu et al., 2021; Sajid et al., 2021), with “1” indicating “strong disagreement” and “5” indicating “strong agreement.” When completed the initial questionnaire, the investigation conducted a pre-survey to test whether the questionnaire was well-designed. A total of 30 scholars with relevant research experience and representatives of university 13 6626 Journal of the Knowledge Economy (2024) 15:6617–6642 entrepreneurs were invited to this pre-survey (Hoda et al., 2021). The pre-survey questionnaire was tested and found reliabe and valid (Su et al., 2021), and revis- ing the wording of the feedback is prone to ambiguity. Data Collection The formal questionnaire for this study contained two parts, basic information about the user and measurement questions. The first part of the basic information contains the gender, age, and entrepreneurial family history. The second part contained nine poten- tial variables to be measured, each consisting of three to four items. The convenience sampling method (non-probability) is adopted in this study to improve the theory and data collection fluency (Gonzalez-Serrano et al., 2021; Bilal et al., 2021; Kusumojanto et al., 2021). Before the data collection, the research team selected four universities in China and then selected students from entrepreneurship colleges to send survey invitations (Vătămănescu et al., 2020). The questionnaire introduces new technology entrepreneurship and pro- vides some examples of entrepreneurship based on the new technology so that participants can better understand the survey situation of the questionnaire (Gonzalez-Serrano et al., 2021; Xie et al., 2021). The survey was conducted anonymously to protect the privacy of respondents. The online questionnaire is more accessible for respondents and takes less time. The online survey has an automatic coding function so that researchers can benefit from faster response speed and more accessible data collection and analysis (Handayati et al., 2021; Naushad, 2021). The formal survey was distributed and collected online through the online survey tool (Questionnaire star), and the questionnaire was set to be completed without missing items to be submitted completely. We dis- tributed invitation links through QQ groups and WeChat communities provided by the college entrepreneurship academy. Students invited to the survey have been informed of the purpose and can withdraw from the survey at any time. Samples The questionnaire survey lasted about 2 months (from June 1, 2021 to August 1, 2021). After completing the large-scale research activity, the survey received 362 responses. We had data cleaning work and deleted invalid questionnaires, such as unreliable answering time and consistent answers (Naushad, 2021), resulting in 316 valid ques- tionnaires, with an effective rate of 87.3%. Regarding the gender of the sample data: 224 (70.9%) were male and 92 (29.1%) were female. Regarding the age of the sample: 202 people (63.9%) were 20 years old or below, 83 people (26.3%) were 21–25 years old, 16 people (5.1%) were 26–30 years old, and 15 people (4.7%) were 30 years old or above. According to the sample, 196 people (62.0%) had an entrepreneurial family his- tory, and 120 people (38.0%) had no entrepreneurial family history. 13 Journal of the Knowledge Economy (2024) 15:6617–6642 6627 Results Partial least squares structural equation modeling (PLS-SEM) allows for the simulta- neous examination of direct and indirect relationships among multiple variables (Su et al., 2021) and found mediating and moderating effects between variables (Iqbal et al., 2021). PLS-SEM analytical methods have been heavily adopted and used in studies on entrepreneurship-related topics (Lechuga Sancho et al., 2020; Su et al., 2021; Lu et al., 2021) and are also well suited for the development of new theoreti- cal models. In addition, partial least squares multi-group analysis (PLS-MGA) can help analyze differences in influencing factors between groups (García-Rodríguez et al., 2020). Related studies have also extensively used Smartpls 3 as software for data analysis (Yeh et al., 2020; Ahn, 2020; Basco et al., 2020). So, the study will also use this Smartpls 3 for subsequent data analysis. Measurement Model According to the recommendations, this study tested the model’s reliability and valid- ity by examining the content, discriminant and convergent validity. Firstly, the ques- tionnaire items of this research are all from published literature and have been pre- researched for validation and refinement. Hence, the study considers the model to have good content validity. Convergent validity can be judged by comparing the AVE values with the critical value of 0.5. Table 1 shows that the AVE values of all potential vari- ables are more significant than the critical value, indicating that the study model has good convergent validity. The discriminant validity can be obtained by comparing the square root of the potential variable average extracted variance (AVE) with the cor- relation coefficient of the potential variable with other potential variables in the study model. The larger values of the square root of AVE can be obtained from Table 2, which indicates that the study model has good discriminant validity. In addition, the reliability of the research model can be derived by comparing the combinatorial reli- ability (CR) value and the alpha value (Cronbach’s alpha) with the critical value of 0.7. Table 1 shows that all potential variables’ CR and alpha values are more significant than the critical value, so the research model has good reliability. In addition, the factor Table 1  AVE, CR, and Cronbach’s alpha Latent variable Code Items Cronbach’s alpha CR AVE Industrial network IN 4 0.849 0.898 0.689 Entrepreneurial intention EI 3 0.810 0.888 0.725 Entrepreneurial performance EP 3 0.854 0.912 0.775 Achievement motivation AM 3 0.837 0.900 0.750 New technology usefulness NTU 4 0.839 0.892 0.673 Survival motivation SM 3 0.829 0.898 0.746 Social network SN 3 0.826 0.896 0.741 Responsibility motivation RM 3 0.855 0.912 0.775 Risk appetite RA 4 0.880 0.918 0.736 13 6628 Journal of the Knowledge Economy (2024) 15:6617–6642 Table 2  Fornell and larcker test IN EI EP AM NTU SM SN RM RA IN 0.830 EI 0.532 0.851 EP 0.374 0.608 0.880 AM 0.255 0.392 0.352 0.866 NTU 0.234 0.412 0.369 0.191 0.820 SM 0.315 0.448 0.376 0.276 0.187 0.864 SN 0.292 0.442 0.340 0.246 0.226 0.226 0.861 RM 0.248 0.440 0.411 0.267 0.327 0.270 0.226 0.880 RA 0.213 0.343 0.394 0.237 0.295 0.305 0.274 0.304 0.858 The values on the diagonal are the square root of the average extraction variance AVE composites between each latent variable and its latent variable are greater than the cross-factor composites with the remaining latent variables, indicating that the study model has good discriminant and convergent validity. The operational results show that the HTMT values of the study models are all less than the critical value of 0.9, which indicates that the study models have good discriminant validity. Common Method Bias Avoiding the influence of common methods bias (CMB) on the study results, this study will use Harman’s one-factor test method (Elnadi & Gheith, 2021; Khan et al., 2021) to ensure that CMB does not significantly affect the study results (Pod- sakoff, 2003). This study imported the collated data into SPSS 24 using principal component analysis. The results showed that the maximum factor explained 20%, which did not exceed the critical value of 40%. This study selects a marker variable that is not associated with this study: perceived playfulness. Then, SmartPLS 3 was used to detect the relationship between the marker variable and the potential vari- ables in the research model. The results showed that the path relationship between the marker variable and the variables in the research model did not significantly impact. Based on the feedback of the marker variable technique, the model is not affected by common method bias. The maximum value of the variance inflation factor (VIF) was 2.60, lower than the threshold of 5 (Hair et al., 2022). Therefore, multicollinearity will not seriously affect the results of this study. Through single-factor analysis and the double-test of the standard marker variable technique, the results of this research model will not be affected by the standard method bias, and the research results are credible ( Elnadi & Gheith, 2021; Khan et al., 2021). Structural Model The results of this study are shown in Fig. 2 after 5000 random sampling and opera- tions using the self-help method (Bootstrapping). 13 Journal of the Knowledge Economy (2024) 15:6617–6642 6629 Fig. 2  Structural model PLS results The hypotheses represented in this study were tested and verified, proving new technology usefulness, risk appetite, social network, industrial network, entrepreneur- ial motivation, survival motivation, achievement motivation, and responsibility moti- vation. These factors are all decisive factors affecting entrepreneurial intention, and entrepreneurial intention directly and significantly affects entrepreneurial performance also proves entrepreneurial intention is a crucial factor affecting entrepreneurial per- formance. Therefore, new technology adoption, network embedding theory, and self- determination theory are equally instructive for the study of entrepreneurial perfor- mance and entrepreneurial intention. R2 indicates the explanatory and predictive power of the model, while higher values indicate a more substantial explanatory power. The R2 of 0.445 and 0.534 for entrepreneurial performance and entrepreneurial intention, respectively, indi- cate that both have better explanatory power and predictive effect. The model was tested by standardized root mean square residual (SRMR). A good fit is indicated if the SRMR value is less than 0.08, and the SRMR value of 0.048 in this study is much less than the critical value of 0.08, indicating that the model in this study has a good fit. The Q2 values of entrepreneurial intention and per- formance were calculated using the blindfolding function of SmartPLS 3 as 0.359 and 0.320, respectively. According to the rules related to the partial least squares model, it can be obtained that the research model has good prediction accuracy. 13 6630 Journal of the Knowledge Economy (2024) 15:6617–6642 Mediation To further explore the more profound influences affecting entrepreneurial performance, this study examined the mediating effect of entrepreneurial intention to avoid overshad- owing other influencing factors due to the presence of entrepreneurial intention. We obtained results after using the self-help method (bootstrapping) and the decision tree method in the 95% confidence interval, as shown in Table 3. The results in Table 3 show a mediating effect of entrepreneurial intention on the relationship among new technology usefulness, industrial network, social network, survival motivation, responsibility motivation and achievement motivation, and entrepreneurial performance. In addition, entrepreneurial intention fully mediates the effects of industrial networks, social networks, achievement motivation, and sur- vival motivation on entrepreneurial performance; entrepreneurial intention partially mediates the effects of new technology usefulness and responsibility motivation on entrepreneurial performance. It shows that new technology usefulness and respon- sibility motivation indirectly affect entrepreneurial performance through entrepre- neurial intention and also directly affect entrepreneurial performance. In contrast, four factors, industrial network, social network, achievement motivation, and sur- vival motivation, indirectly affect entrepreneurial performance through entrepre- neurial intention. Moderation To test whether there is a moderating effect of the risk appetite on the model, we used the bootstrapping function of SmartPLS 3 and the stratified regression method, and the results are shown in Table 4. Table 3  Mediation analysis Paths Effect 95% confidence intervals Significance Kind of mediation IN➞ EI ➞ EP Direct effect [−0.067, 0.146] No Full Indirect effects [0.063, 0.171] Yes AM ➞ EI ➞ EP Direct effect [−0.012, 0.174] No Full Indirect effects [0.016, 0.092] Yes NTU ➞ EI ➞ EP Direct effect [0.024, 0.214] Yes Partial Indirect effects [0.037, 0.109] Yes SM ➞ EI ➞ EP Direct effect [−0.042, 0.172] No Full Indirect effects [0.035, 0.119] Yes SN ➞ EI ➞ EP Direct effect [−0.073, 0.129] No Full Indirect effects [0.039, 0.122] Yes RM ➞ EI ➞ EP Direct effect [0.007, 0.209] Yes Partial Indirect effects [0.034, 0.110] Yes 13 Journal of the Knowledge Economy (2024) 15:6617–6642 6631 Table 4  Moderation analysis. Path Model 1 (without moderation) Model 2 (with moderation) Hypothesis results Path coefficient T-value Significance Path coefficient T-value Significance EI ➞ EP 0.608 16.399 *** 0.541 13.042 *** Yes RA ➞ EP 0.218 4.599 *** EI × RA ➞ EP 0.198 4.341 *** R2 of EP 0.369 0.445 f2 of (EI × RA) 0.066 *** p < 0.001 Table 4 shows that a positive and significant effect of risk appetite on entrepre- neurial intention can be obtained. The value of the path between entrepreneurial intention and entrepreneurial performance in model 2 with moderating variables is 0.541. It increases to 0.739 (0.541+0.198) when the term of moderating effect of risk appetite is added due to the moderation by risk appetite. Thus, the explan- atory power of entrepreneurial intention for entrepreneurial performance when risk appetite is higher. Figure 3 shows that risk appetite positively moderates the relationship between entrepreneurial intention and entrepreneurial performance, indicating that higher levels of risk appetite among university entrepreneurs strengthen the level of influence of entrepreneurial intention on entrepreneurial performance; con- versely, lower levels of risk appetite reduce the effect of entrepreneurial intention on entrepreneurial performance. It also confirms that differences in risk appetites affect the entrepreneurial intention and entrepreneurial performance of university entrepreneurs differently, and the research hypothesis that H8 is supported. When risk appetite is present, R2 changes from 0.369 to 0.445, indicating a moderating effect in the research model. From Table 4, the value of f2 for (entre- preneurial intention × risk appetite) is 0.066, greater than 0.02, indicating that risk appetite possesses a weak moderating effect on the relationship between entrepreneurial intention and entrepreneurial performance. Fig. 3  Moderating effect of risk appetite 13 6632 Journal of the Knowledge Economy (2024) 15:6617–6642 Multi‑group Analysis To determine the invariance of the two subgroups, we conducted a three-step test following the measurement invariance of composite models (MICOM) procedure (1) both groups used the same measurement questions, analysis techniques, and algorithms, indicating that both subgroups had configural invariance; (2) p > 0.05 (two-tailed) for all variables passed the test of compositional invariance; (3) P-val- ues for the combination of mean values and variances were all greater than 0.05 (two-tailed), indicating the equality of composite mean values and variances. There- fore, the two subgroups of samples have full measurement invariance and can be considered for the next step of multi-group analysis (MGA). MGA allows measurements for a specific observed variable to test whether that grouping of observations is significantly different in the parameter estimates. The sample data were first divided into two control groups according to whether they have an entrepreneurial family history. Then, the results were calculated with the help of the MGA function of SmartPLS 3, as shown in Table 5. Industrial networks significantly influenced entrepreneurial intention for entre- preneurs with entrepreneurial family history, while industrial networks did not sig- nificantly influence entrepreneurial intention for entrepreneurs without an entrepre- neurial family history. The path of new technology usefulness to entrepreneurial intention, new technology usefulness with entrepreneurial family history did not sig- nificantly affect entrepreneurial intention. Still, new technology usefulness without entrepreneurial family history significantly affected entrepreneurial intention. Entre- preneurial intention significantly affects entrepreneurial performance regardless of entrepreneurial family history; achievement motivation, survival motivation, social network, and responsibility motivation all significantly affect entrepreneurial inten- tion; the moderating effect of risk appetite also positively moderates the relationship between entrepreneurial intention to entrepreneurial performance. Table 5  MGA analysis Path factor and difference value p-value Relationships With entrepre- No entrepre- Difference value With entrepre- No entrepre- neurial family neurial family neurial family neurial family history history history history IN ➞ EI 0.438 0.069 0.369 0.000 0.295 EI ➞ EP 0.557 0.514 0.042 0.000 0.000 AM ➞ EI 0.131 0.152 0.021 0.023 0.020 NTU ➞ EI 0.041 0.402 0.361 0.400 0.000 SM ➞ EI 0.153 0.249 0.096 0.011 0.000 SN ➞ EI 0.169 0.199 0.031 0.001 0.009 RM ➞ EI 0.220 0.176 0.045 0.000 0.008 Moderation 0.204 0.216 0.012 0.001 0.005 Path difference value = |(has entrepreneurial family history) - (no entrepreneurial family history)| 13 Journal of the Knowledge Economy (2024) 15:6617–6642 6633 In summary, entrepreneurs with a family history of entrepreneurship focus more on the entrepreneurial opportunities created by industrial networks and the enhance- ment of entrepreneurial performance. In contrast, entrepreneurs without a family history of entrepreneurship focus more on the entrepreneurial opportunities created by technological innovations. Discussions and Implications Theoretical Implications The findings show that new technology usefulness significantly affects entrepre- neurial intention and entrepreneurial performance; the mediation analysis also con- firms that new technology usefulness indirectly affects entrepreneurial performance through entrepreneurial intention (Zhang et al., 2022). The findings indicate that when entrepreneurs have higher levels of perceived new technology usefulness, their entrepreneurial intention and entrepreneurial performance are higher, suggesting that new technology adoption often leads to new user experiences and increased work efficiency. In addition, social and industrial networks in social network theory also significantly affect entrepreneurial intention and entrepreneurial performance (Jha & Alam, 2022), indicating that deeply integrated industry-academia research is condu- cive to enhancing the social and industrial networks of entrepreneurs, further cor- roborating the theoretical guidance of new technology adoption and social network theory for entrepreneurial performance. Achievement motivation, responsibility motivation, and survival motivation all significantly affect entrepreneurial intention and then affect entrepreneurial perfor- mance through entrepreneurial intention, indicating the impact of entrepreneurial motivation on entrepreneurial performance and entrepreneurial intention (Yamini et al., 2022). Extrinsic motivation and intrinsic motivation in self-determination theory remain essential factors that motivate the entrepreneurial intention of uni- versity students. The results of the mediating effect test show that entrepreneurial motivation affects entrepreneurial performance through entrepreneurial intention, whereas RM directly affects both entrepreneurial intention and entrepreneurial per- formance. In the multi-group analysis, all three factors of entrepreneurial motivation significantly affected entrepreneurial intention in two groups; again, this demon- strates that entrepreneurial motivation is an essential factor affecting entrepreneurial performance. This study shows that risk appetite positively moderates the relationship between entrepreneurial intention and performance (Sayal & Banerjee, 2022). When entre- preneurs have higher levels of entrepreneurial intention, they receive higher feed- back on entrepreneurial performance. In the multi-group analysis, risk appetite was found to have a positive moderating effect on entrepreneurs with or without an entrepreneurial family history, further demonstrating that risk appetite is one of the critical variables affecting entrepreneurial intention and performance. It also cor- roborates that risk appetite still has an important complementary role for theories related to new technology acceptance and entrepreneurial performance. Moreover, 13 6634 Journal of the Knowledge Economy (2024) 15:6617–6642 Basco et al. (2020) argued that perceived risk appetite has an essential impact on the outcomes of entrepreneurial behavior. Venture capital institutions have rich experi- ence in entrepreneurial guidance and financial investment and have a rich reserve of industrial and social resources required for industrial development. With the help of venture capital institutions, not only can the performance of the enterprise be improved (Alvarez-Torres et al., 2019) but also the entrepreneurial team’s industrial network and social network can be effectively extended. The effective use of new technologies and the degree of risk appetite play an essential role in entrepreneurial intention and innovation performance. New tech- nologies’ improvement of entrepreneurial performance provides critical theoretical insights and new reference variables for enriching the factors influencing entrepre- neurial intention and performance. The entrepreneurial risk appetite as a moderat- ing variable will affects entrepreneurial performance. In contrast, the theory of risk appetite is applied to study entrepreneurial performance, and the proposed model of entrepreneurial performance with new technologies provides theoretical guid- ance for analyzing and predicting entrepreneurial performance. Those entrepreneurs who lack social and industrial experience need more help from experts in the field. Establishing a school-enterprise curriculum can fully play the joint advantages of universities and enterprises, combining the rich practical experience of enterprises and theoretical academic research of universities to create an innovative entrepre- neurship education curriculum (Su et al., 2021) and create a good entrepreneurial environment for university entrepreneurs. Practical Implications It would pay attention to the impact of new technology on college students’ entre- preneurial intention and entrepreneurial performance (Fernandes et al., 2022). As a significant incubator of scientific investigation and technological study and advancement, universities should actively explore the integration of industry, aca- demia, and new technologies, which can encourage the growth of innovation and entrepreneurship of university students and perfectly meet social progression. In terms of industry, introducing the newest technology to university education could evade the disconnection between school-based knowledge and the up-to-date mod- ern world. At the university level, we can start with new technology-related courses and equip professional technology instructors to provide students with physical space and technical support to explore new technology application scenarios (Yu et al., 2022). In terms of research, they are strengthening the industrial application of new technologies for researchers and technicists, such as presenting the techno- logical development and practical application of artificial intelligence, blockchain, visual enhancement, and other modern technologies (Jiao et al., 2022). People are using digital office tools actively without any barriers of time and space limitations, and it can effectively reduce unnecessary cost expenditure; with the help of new technology, entrepreneurship students in colleges and universities can also use new technology to improve the entrepreneurial performance of the entrepreneurial team. 13 Journal of the Knowledge Economy (2024) 15:6617–6642 6635 It would understand the entrepreneurial motivations of university students to help them improve entrepreneurial performance. Universities should pay attention to identifying the intrinsic motivation of entrepreneurs and can offer psychological tests and entrepreneurial simulation activities to understand the intrinsic motivation of entrepreneurs further. Moreover, targeted counseling can conduct an in-depth analysis of the entrepreneurial demands of university students; it can recognize stu- dents’ intrinsic entrepreneurial motivation and how their influencing factors play crucial roles. Adapting suitable entrepreneurial projects and suitable team posi- tions will pleasure the entrepreneurs. Those universities can compile the successful entrepreneurial cases of past entrepreneurs into entrepreneurial manuals, promoting complementary resources among alums to assist entrepreneurs with related indus- trial connections and accumulate more entrepreneurial resources to support the development of new entrepreneurs. Sharing outstanding entrepreneurial deeds can set a spiritual example for entrepreneurs and provide a stronger spiritual motivation by promoting the spirit of incredible innovation and entrepreneurship through the gracious image of mentors (Rahman et al., 2017). Entrepreneurs should recognize their intrinsic needs; pure egoism is not conducive to entrepreneurial performance and success; actively discover their strengths and weaknesses to form a mutually supportive growth team and lay a strong foundation for better entrepreneurial per- formance. The promotion of entrepreneurship with worthful values, a sense of responsibility, and achievement motivation, the authorities can drive the innovative entrepreneurial behavior of university students with the power of role models: vote for new entrepreneurial talents from university students and provide authentic and successful entrepreneurial stories from peers of their ages. It would help university students and entrepreneurs build social and industrial networks. In the roadshow competition of entrepreneurship and innovation of uni- versity students, venture capital institutions are invited to comment and guide as selection guests, which cannot only bring improvement suggestions for the project optimization of entrepreneurial students but also venture capital institutions can make an appropriate financial investment for the potential projects they see (Su et al., 2021). Venture capital institutions can introduce relevant industrial resources for matchmaking, bringing resources and financial help to the entrepreneurs of uni- versity students in various aspects (Lv et al., 2021). The study result shows that the increase in risk appetite is also beneficial to improving the entrepreneurial perfor- mance of university students, so the financial investment of venture capital institu- tions also helps to optimize the entrepreneurial performance and make up for the lack of capital in the early stage of the entrepreneurial team, providing co-creation opportunities for companies in the industry and invite industry experts to guide entrepreneurial projects. In addition, the corresponding government authorities can guide by setting up a venture capital fund, not only for the excellent entrepreneurial projects to invest in adding support but also for venture capital institutions to take the risk of investment failure compensation, to reduce the concerns of venture capi- tal institutions. Ultimately, the financing channels of entrepreneurs are opened, and their risk appetite level is improved to optimize their entrepreneurial performance. 13 6636 Journal of the Knowledge Economy (2024) 15:6617–6642 Conclusion, Limitations, and Future Research Based on the background of the rapid development of new technologies, this study takes the entrepreneurial performance of university students using new technolo- gies to start their businesses as the research object. The review and content analysis of the theoretical literature related to entrepreneurial performance, new technology adoption theory, self-determination theory, and social network theory, the new tech- nology entrepreneurial performance model (NTEP) is proposed. After completing the research hypothesis and questionnaire design, 316 valid questionnaires were col- lected and analyzed by grouping “the existence of (a) entrepreneurial family his- tory.” The overall analysis of the ungrouped sample revealed that new technology usefulness, social network, industrial network, achievement motivation, survival motivation, and responsibility motivation all directly affect entrepreneurial inten- tion and then affect entrepreneurial performance; entrepreneurial intention directly affects entrepreneurial performance and entrepreneurial risk appetite (moderating variable) positively moderates the relationship between entrepreneurial intention and entrepreneurial performance. We identify the factors influencing entrepreneurial performance in new technologies based on the partial least squares structural equa- tion model (PLS-SEM) and multiple-group analysis (MGA). We also identify the degree of influence and critical paths of each relevant factor, propose four sugges- tions of deepening the integration of industry-university-research in new technolo- gies, inheriting entrepreneurial spirit, introducing venture capital to enhance risk appetites, and jointly building innovation and entrepreneurship education courses in schools and enterprises. We propose strategies to improve entrepreneurial perfor- mance and promote a solid science and technology-based entrepreneurship process. The limitations of this study and future research directions are as follows. This research has achieved specific results in both theory and practice, but there are limi- tations. The first is that the data samples of this study only come from the students of the four universities. Secondly, this study did not investigate the project’s charac- teristics information (e.g., industry, employee size, founding time). Therefore, as the future lines of investigation, it would be interesting to obtain a sample of university students from different countries to compare the performance of new technology entrepreneurship. In future research, the entrepreneurs’ personal information and development status could be investigated more, and these factors could be analyzed to test the model’s applicability. Furthermore, there may be more exciting results when measured with other measures of entrepreneurial performance. Acknowledgements Thanks to Liangwei Tu for their assistance in the language expression of the article. Funding This work was supported by the Zhejiang Soft Science Project under Grant [2023C35032]; Major Humanities and Social Sciences Research Projects in Zhejiang higher education institutions, Grant Number [2023QN115]; Zhejiang Province Education Science Planning 2023 General Planning Project under Grant [2023SCG399]; Zhejiang Federation of Humanities and Social Sciences Circles Research Project under Grant [2023N073]; and A Project Supported by Scientific Research Fund of Zhejiang Pro- vincial Education Department under Grant [Y202248756]. Data Availability The datasets used or analyzed during the current study are available from the author on reasonable request. 13 Journal of the Knowledge Economy (2024) 15:6617–6642 6637 Declarations Ethics approval Not applicable. Conflict of Interest The authors declare no competing interests. References Ahn, J. (2020). Understanding the role of perceived satisfaction with autonomy, competence, and related- ness in the CSR context. Journal of Sustainable Tourism, 28(12), 2027–2043. Akter, S., Dwivedi, Y. K., Biswas, K., Michael, K., Bandara, R. J., & Sajib, S. (2021). Addressing algo- rithmic bias in AI-driven customer management. Journal of Global Information Management (JGIM), 29(6), 1–27. https://​doi.​org/​10.​4018/​JGIM.​20211​101.​oa3 Al-Qirim, N., Rouibah, K., Abbas, H., & Hwang, Y. (2022). Factors affecting the success of social com- merce in Kuwaiti microbusinesses: A qualitative study. Journal of Global Information Management (JGIM), 30(1), 1–31. https://​doi.​org/​10.​4018/​JGIM.​313944 Alvarez-Torres, F. J., Lopez-Torres, G. C., & Schiuma, G. (2019). Linking entrepreneurial orientation to SMEs’ performance: Implications for entrepreneurship universities. Management Decision, 57(12), 3364–3386. Anwar, M., Tajeddini, K., & Ullah, R. (2020). Entrepreneurial finance and new venture success-the mod- erating role of government support. Business Strategy & Development, 3(4), 408–421. Asante, E. A., & Affum-Osei, E. (2019). Entrepreneurship as a career choice: The impact of locus of con- trol on aspiring entrepreneurs’ opportunity recognition. Journal of Business Research, 98, 227–235. Basco, R., Hernández-Perlines, F., & Rodríguez-García, M. (2020). The effect of entrepreneurial orienta- tion on firm performance: A multi-group analysis comparing China, Mexico, and Spain. Journal of Business Research, 113, 409–421. Bilal, M. A., Khan, H. H., Irfan, M., Ul Haq, S. M., Ali, M., Kakar, A., & Rauf, A. (2021). Influence of financial literacy and educational skills on entrepreneurial intent: Empirical evidence from young entrepreneurs of Pakistan. The Journal of Asian Finance, Economics and Business, 8(1), 697–710. Brändle, L., Golla, S., & Kuckertz, A. (2019). How entrepreneurial orientation translates social iden- tities into performance. International Journal of Entrepreneurial Behaviour and Research, 25(7), 1433–1451. Buthelezi, B. E., Ndayizigamiye, P., Twinomurinzi, H., & Dube, S. M. (2022). A systematic review of the adoption of blockchain for supply chain processes. Journal of Global Information Management (JGIM), 30(8), 1–32. https://​doi.​org/​10.​4018/​JGIM.​297625 Carsrud, A., & Brännback, M. (2011). Entrepreneurial motivations: what do we still need to know? Jour- nal of Small Business Management, 49(1), 9–26. Chau, N. T., Deng, H., & Tay, R. (2021). A perception-based model for mobile commerce adoption in Vietnamese small and medium-sized enterprises. Journal of Global Information Management (JGIM), 29(1), 44–67. https://​doi.​org/​10.​4018/​JGIM.​20210​10103 Chen, D., Esperança, J. P., & Wang, S. (2022). The Impact of artificial intelligence on firm performance: An application of the resource-based view to e-commerce firms. Frontiers in Psychology, 13, 884830. Chen, J., & Liu, L. (2022). Family firms, national culture and corporate social performance: a meta- analysis. Cross Cultural & Strategic Management, 29(2), 379–402. Chen, M. H., & Tseng, M. (2021). Creative entrepreneurs’ artistic creativity and entrepreneurial alert- ness: The guanxi network perspective. International Journal of Entrepreneurial Behavior & Research, 27(4), 1082–1102. Chou, S. F., Horng, J. S., Liu, C. H., Yu, T. Y., & Kuo, Y. T. (2022). Identifying the critical factors for sustainable marketing in the catering: The influence of big data applications, marketing innova- tion, and technology acceptance model factors. Journal of Hospitality and Tourism Management, 51, 11–21. Choudhuri, B., Srivastava, P. R., Gupta, S., Kumar, A., & Bag, S. (2021). Determinants of smart digi- tal infrastructure diffusion for urban public services. Journal of Global Information Management (JGIM), 29(6), 1–27. https://​doi.​org/​10.​4018/​JGIM.​295976 13 6638 Journal of the Knowledge Economy (2024) 15:6617–6642 Contreras-Lozano, C. R., Flores-Ortiz, M. V., AlcaláÁlvarez, M., & Carmen, D. (2021). Is socioemo- tional wealth being influenced by the intentions to pursue succession on incumbent owners? Empirical research on Mexican family business. Journal of Family Business Management, 12(4), 833–852. https://​doi.​org/​10.​1108/​JFBM-​11-​2020-​0105 De Moya, J., Pallud, J., & Wamba, S. F. (2021). Impacts of risks over benefits in the adoption of self- tracking technologies. Journal of Global Information Management (JGIM), 29(6), 1–46. https://​ doi.​org/​10.​4018/​JGIM.​20211​101.​oa10 Du, P., & Shu, H. (2022). Exploration of financial market credit scoring and risk management and prediction using deep learning and bionic algorithm. Journal of Global Information Manage- ment (JGIM), 30(9), 1–29. https://​doi.​org/​10.​4018/​JGIM.​293286 Du, X., Zhao, X., Wu, C., & Feng, K. (2022). Functionality, emotion, and acceptance of artificial intelligence virtual assistants: The moderating effect of social norms. Journal of Global Informa- tion Management (JGIM), 30(7), 1–21. https://​doi.​org/​10.​4018/​JGIM.​290418 Elnadi, M., & Gheith, M. H. (2021). Entrepreneurial ecosystem, entrepreneurial self-efficacy, and entrepreneurial intention in higher education: Evidence from Saudi Arabia. The International Journal of Management Education, 19(1), 100458. Emmanuel, C. P., Qin, S., Hossain, S. F. A., & Hussain, K. (2022). Factors influencing social-media- based entrepreneurship prospect among female students in China. Heliyon, 8(12), e12041. Fernandes, C., Ferreira, J. J., Veiga, P. M., Kraus, S., & Dabić, M. (2022). Digital entrepreneurship platforms: Mapping the field and looking towards a holistic approach. Technology in Society, 101979. Frese, M., & Fay, D. (2001). An active performance concept for work in the 21st century. Research in Organizational Behavior, 23, 133–187. García-Rodríguez, F. J., Ruiz-Rosa, I., Gutiérrez-Taño, D., & Gil-Soto, E. (2022). Entrepreneurial intentions in the context of a collectivist economy: a comparison between Cuba and Spain. Inter- national Entrepreneurship and Management Journal, 18(2), 855–873. https://​doi.​org/​10.​1007/​ s11365-​020-​00686-7 García-Villaverde, P. M., Rodrigo-Alarcón, J., Ruiz-Ortega, M. J., & Parra-Requena, G. (2018). The role of knowledge absorptive capacity on the relationship between cognitive social capital and entrepreneurial orientation. Journal of Knowledge Management, 22(5), 1015–1036. Gholami, R., Singh, N., Agrawal, P., Espinosa, K., & Bamufleh, D. (2021). Information technology/ systems adoption in the public sector: Evidence from the Illinois department of transportation. Journal of Global Information Management (JGIM), 29(4), 172–194. https://​doi.​org/​10.​4018/​ JGIM.​20210​701.​oa8 Gonzalez-Serrano, M. H., Gonzalez-Garcia, R. J., Carvalho, M. J., & Calabuig, F. (2021). Predicting entrepreneurial intentions of sports sciences students: A cross-cultural approach. Journal of Hos- pitality, Leisure, Sport & Tourism Education, 29, 100322. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage. Han, S. Y., Yoo, J., Zo, H., & Ciganek, A. P. (2017). Understanding makerspace continuance: A self- determination perspective. Telematics and Informatics, 34(4), 184–195. Handayati, P., Wibowo, A., Narmaditya, B. S., Kusumojanto, D. D., Setiawan, A. B., & Tung, D. T. (2021). The university students enterprises development: Lesson from Indonesia. Cogent Educa- tion, 8(1), 1973286. Harshvardhan, & Teoh, S. Y. (2022). Improving shipping efficiency industry-led consortium block- chain smart contact. Journal of Global Information Management (JGIM), 30(1), 1–32. https://​ doi.​org/​10.​4018/​JGIM.​313035 Haverila, M., Haverila, K. C., Mohiuddin, M., & Su, Z. (2022). The impact of quality of big datajour- nal marketing analytics (BDMA) on the market and financial performance. Journal of Global Information Management (JGIM), 30(1), 1–21. https://​doi.​org/​10.​4018/​JGIM.​315646 Ho, S. C., & Chen, J. L. (2023). Developing the e-commerce competency for entrepreneurship educa- tion from a gamified competition. The International Journal of Management Education, 21(1), 100737. Hoda, N., Ahmad, N., Gupta, S. L., Alam, M. M., & Ahmad, I. (2021). Application of entrepreneurial intention model in comparative international entrepreneurship research: A cross-cultural study of India and Saudi Arabia. Sustainability, 13(23), 13369. 13 Journal of the Knowledge Economy (2024) 15:6617–6642 6639 Holzmann, P., & Gregori, P. (2023). The promise of digital technologies for sustainable entrepreneurship: A systematic literature review and research agenda. International Journal of Information Manage- ment, 68, 102593. Hou, H., Tang, K., Liu, X., & Zhou, Y. (2022). Application of artificial intelligence technology optimized by deep learning to rural financial development and rural governance. Journal of Global Informa- tion Management (JGIM), 30(7), 1–23. https://​doi.​org/​10.​4018/​JGIM.​289220 Huang, C., Chou, T., & Wu, S. (2021). Towards convergence of AI and IoT for smart policing: A case of a mobile edge computing-based context-aware system. Journal of Global Information Management (JGIM), 29(6), 1–21. https://​doi.​org/​10.​4018/​JGIM.​296260 Huang, Y., Li, P., Wang, J., & Li, K. (2022). Innovativeness and entrepreneurial performance of female entrepreneurs. Journal of Innovation & Knowledge, 7(4), 100257. Iqbal, S., Moleiro Martins, J., Nuno Mata, M., Naz, S., Akhtar, S., & Abreu, A. (2021). Linking entrepre- neurial orientation with innovation performance in SMEs; the role of organizational commitment and transformational leadership using smart PLS-SEM. Sustainability, 13(8), 4361. Jha, P., & Alam, M. (2022). Antecedents of women entrepreneurs’ performance: an empirical perspec- tive. Management Decision, 60(1), 86–122. Jiao, H., Wang, L., & Shi, Y. (2022). How does institutional environment in the digital context affect technology entrepreneurship? The moderating roles of government digitalization and gender. Jour- nal of Organizational Change Management., 35(7), 1089–1112. Kerr, W. R., & Mandorff, M. (2023). Social networks, ethnicity, and entrepreneurship. Journal of Human Resources, 58(1), 183–220. Khalid, N. (2020). Artificial intelligence learning and entrepreneurial performance among university stu- dents: Evidence from malaysian higher educational institutions. Journal of Intelligent & Fuzzy Sys- tems, 39(4), 5417–5435. Khan, E. A., Hossain, M. A., Jahed, M. A., & Rowe, A. L. (2021). Poor resource capital of micro-entre- preneurs: The mediating role of entrepreneurial orientation. Management Research Review, 44(10), 1366–1389. Kusumojanto, D. D., Wibowo, A., Kustiandi, J., & Narmaditya, B. S. (2021). Do entrepreneurship educa- tion and environment promote students’ entrepreneurial intention? The role of entrepreneurial atti- tude. Cogent Education, 8(1), 1948660. Lamine, W., Mian, S., Fayolle, A., & Linton, J. D. (2021). Educating scientists and engineers for technol- ogy entrepreneurship in the emerging digital era. Technological Forecasting and Social Change, 164, 120552. Law, K. M., Lau, A. K., & Ip, A. W. (2021). The impacts of knowledge management practices on inno- vation activities in high- and low-tech firms. Journal of Global Information Management (JGIM), 29(6), 1–25. https://​doi.​org/​10.​4018/​JGIM.​20211​101.​oa41 Lechuga Sancho, M. P., Martín-Navarro, A., & Ramos-Rodríguez, A. R. (2020). Will they end up doing what they like? The moderating role of the attitude towards entrepreneurship in the formation of entrepreneurial intentions. Studies in Higher Education, 45(2), 416–433. Li, G., Wang, X., Bi, D., & Hou, J. (2022). Risk measurement of the financial credit industry driven by data: Based on DAE-LSTM deep learning algorithm. Journal of Global Information Management (JGIM), 30(11), 1–20. https://​doi.​org/​10.​4018/​JGIM.​308806 Li, S., & Yang, H. (2022). Research on the relationship between venture capitalists’ trust in the entrepre- neur and their investment behaviors. Entrepreneurship Research Journal, 12(2), 161–184. Liang, X., Ruan, W., Xu, Z., & Liu, J. (2022). Analysis of safe storage of network information data and financial risks under blockchain combined with edge computing. Journal of Global Information Management (JGIM), 30(11), 1–20. Lin, B., & Ma, R. (2022). How does internet development affect green technology innovation in China? Journal of Global Information Management (JGIM), 30(1), 1–21. https://​doi.​org/​10.​4018/​JGIM.​ 309081 Liu, X., Lin, C., Zhao, G., & Zhao, D. (2019). Research on the effects of entrepreneurial education and entrepreneurial self-efficacy on college students’ entrepreneurial intention. Frontiers in Psychology, 10, 869. Lu, G., Song, Y., & Pan, B. (2021). How university entrepreneurship support affects college students’ entrepreneurial intentions: An empirical analysis from China. Sustainability, 13(6), 3224. Lv, Y., Chen, Y., Sha, Y., Wang, J., An, L., Chen, T., Huang, X., Huang, Y., & Huang, L. (2021). How entrepreneurship education at universities influences entrepreneurial intention: Mediating effect based on entrepreneurial competence. Frontiers in Psychology, 12, 655868. 13 6640 Journal of the Knowledge Economy (2024) 15:6617–6642 Martens, C. D. P., Machado, F. J., Martens, M. L., & de Freitas, H. M. R. (2018). Linking entrepreneurial orientation to project success. International Journal of Project Management, 36(2), 255–266. Martínez, A. B., Galván, R. S., & Palacios, T. M. B. (2016). An empirical study about knowledge trans- fer, entrepreneurial orientation and performance in family firms. European Journal of International Management, 10(5), 534–557. Mei, M. Q., & Genet, C. (2022). Social media entrepreneurship: A study on follower response to social media monetization. European ManagementJournal, 1–10. https://​doi.​org/​10.​1016/j.​emj.​2022.​04.​ 006 Naushad, M. (2021). Investigating determinants of entrepreneurial leadership among SMEs and their role in sustainable economic development of Saudi Arabia. The Journal of Asian Finance, Economics and Business, 8(4), 225–237. Nuryakin, N. (2021). Mediating effect of relational capabilities in the relationship between entrepreneur- ial orientation and SMEs performance. Journal of Entrepreneurship, Management and Innovation, 17(1), 129–146. Odonkor, E., & Pallud, J. (2022). A configurational approach to understanding the drivers of mobile phone usage in developing countries. Journal of Global Information Management (JGIM), 30(1), 1–19. https://​doi.​org/​10.​4018/​JGIM.​299322 Pipitwanichakarn, T., & Wongtada, N. (2019). Leveraging the technology acceptance model for mobile commerce adoption under distinct stages of adoption: A case of micro businesses. Asia Pacific Jour- nal of Marketing and Logistics, 33(6), 1415–1436. Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 885(879), 879–903. Qi, G., Hou, L., Chen, J., Liang, Y., & Zhang, Q. (2021). How does user social network improve inno- vation outcomes on a virtual innovation platform?: Evidence from LEGO ideas platform. Journal of Global Information Management (JGIM), 29(3), 188–211. https://​doi.​org/​10.​4018/​JGIM.​20210​ 50108 Qiu, J. (2022). Ciphertext database audit technology under searchable encryption algorithm and block- chain technology. Journal of Global Information Management (JGIM), 30(11), 1–17. https://​doi.​ org/​10.​4018/​JGIM.​315014 Rahman, S. A., Taghizadeh, S. K., Ramayah, T., & Alam, M. M. D. (2017). Technology acceptance among micro-entrepreneurs in marginalized social strata: The case of social innovation in Bangla- desh. Technological Forecasting and Social Change, 118, 236–245. Rehman, I. H., Ahmad, A., Akhter, F., & Aljarallah, A. (2021). A dual-stage SEM-ANN analysis to explore consumer adoption of smart wearable healthcare devices. Journal of Global Information Management (JGIM), 29(6), 1–30. https://​doi.​org/​10.​4018/​JGIM.​294123 Sajid, M., Zaidi, S., Haq, S., Chugtai, M., & Ahmed, A. (2021). Linking entrepreneurial orientation to project success in construction projects. Journal of Project Management, 6(2), 61–72. Sayal, A., & Banerjee, S. (2022). Factors influence performance of B2B SMEs of emerging economies: view of owner-manager. Journal of Research in Marketing and Entrepreneurship, 24(1), 112–140. Shahidi, N., Cacho-Elizondo, S., & Tossan, V. (2021). Cross-cultural effects in adoption patterns of a mobile coaching service for studies: A comparison between France and Mexico. Journal of Global Information Management (JGIM), 29(6), 1–26. https://​doi.​org/​10.​4018/​JGIM.​20211​101.​oa33 Si, S., Hall, J., Suddaby, R., Ahlstrom, D., & Wei, J. (2022). Technology, entrepreneurship, innovation and social change in digital economics. Technovation, 119, 102484. Staniewski, M. W., & Awruk, K. (2019). Entrepreneurial success and achievement motivation–A prelimi- nary report on a validation study of the questionnaire of entrepreneurial success. Journal of Busi- ness Research, 101, 433–440. Su, Y., Zhu, Z., Chen, J., Jin, Y., Wang, T., Lin, C. L., & Xu, D. (2021). Factors influencing entrepreneur- ial intention of university students in China: Integrating the perceived university support and theory of planned behavior. Sustainability, 13(8), 4519. Sun, Y., Li, L., Chen, Y., & Kataev, M. Y. (2021). An empirical study on innovation ecosystem, techno- logical trajectory transition, and innovation performance. Journal of Global Information Manage- ment (JGIM), 29(4), 148–171. https://​doi.​org/​10.​4018/​JGIM.​20210​701.​oa7 Tseng, T. H., Wang, Y. M., Lin, H. H., Lin, S. J., Wang, Y. S., & Tsai, T. H. (2022). Relationships between locus of control, theory of planned behavior, and cyber entrepreneurial intention: The mod- erating role of cyber entrepreneurship education. The International Journal of Management Educa- tion, 20(3), 100682. 13 Journal of the Knowledge Economy (2024) 15:6617–6642 6641 Varsha, P. S., Akter, S., Kumar, A., Gochhait, S., & Patagundi, B. (2021). The impact of artificial intel- ligence on branding: A bibliometric analysis (1982-2019). Journal of Global Information Manage- ment (JGIM), 29(4), 221–246. https://​doi.​org/​10.​4018/​JGIM.​20210​701.​oa10 Vătămănescu, E. M., Cegarra-Navarro, J. G., Andrei, A. G., Dincă, V. M., & Alexandru, V. A. (2020). SMEs strategic networks and innovative performance: A relational design and methodology for knowledge sharing. Journal of Knowledge Management, 24(6), 1369–1392. Wu, Y., Zhu, D., Liu, Z., & Li, X. (2022). An improved BPNN algorithm based on deep learning tech- nology to analyze the market risks of A+H shares. Journal of Global Information Management (JGIM), 30(7), 1–23. https://​doi.​org/​10.​4018/​JGIM.​293277 Xie, C., Xu, X., Gong, Y., & Xiong, J. (2022). Big data analytics capability and business alignment for organizational agility: A fit perspective. Journal of Global Information Management (JGIM), 30(1), 1–27. https://​doi.​org/​10.​4018/​JGIM.​302915 Xie, Q., Liu, L., Malik, H., Chupradit, S., & Chupradit, P. W. (2021). The influence of entrepreneurial characteristics on the performance of tourism vlogger entrepreneurs. Frontiers in Psychology, 12, 725545. Xie, X., Zeng, S., Peng, Y., & Tam, C. (2013). What affects the innovation performance of small and medium-sized enterprises in China? Innovation, 15(3), 271–286. Xing, F., Peng, G., Wang, J., & Li, D. (2022). Critical obstacles affecting adoption of industrial big data solutions in smart factories: An empirical study in China. Journal of Global Information Management (JGIM), 30(1), 1–21. https://​doi.​org/​10.​4018/​JGIM.​314789 Yamini, R., Soloveva, D., & Peng, X. (2022). What inspires social entrepreneurship? The role of proso- cial motivation, intrinsic motivation, and gender in forming social entrepreneurial intention. Entre- preneurship Research Journal, 12(2), 71–105. Yang, S., & Yi, Y. (2021). Effect of technological innovation inputs on global value chains status. Journal of Global Information Management (JGIM), 29(5), 37–54. https://​doi.​org/​10.​4018/​JGIM.​20210​901.​ oa3 Yang, X., & Zhang, F. (2018). The relationship between entrepreneurship policy, social network and youth entrepreneurship performance--An analysis of mediating effects based on policy accessibility. Science and Technology Entrepreneurship Monthly, 31(07), 1–5. Yao, M., Ye, D., Yang, G., Shi, H., & Zheng, X. (2021). Are entrepreneurial capabilities and prior knowl- edge the silver bullet for the generation of new digital venture ideas in a digital context? Journal of Global Information Management (JGIM), 29(6), 1–17. https://​doi.​org/​10.​4018/​JGIM.​20211​101.​ oa12 Yeh, C. H., Wang, Y. S., Hsu, J. W., & Lin, S. J. (2020). Predicting individuals’ digital autopreneurship: Does educational intervention matter? Journal of Business Research, 106, 35–45. Yoo, D., & Roh, J. J. (2021). Value chain creation in business analytics. Journal of Global Information Management (JGIM), 29(4), 131–147. https://​doi.​org/​10.​4018/​JGIM.​20210​701.​oa6 Yu, T. K., Chao, C. M., & Wang, Y. (2022). Factors influencing the teaching intention of business college teachers to fulfill digital entrepreneurship courses. Frontiers in Psychology, 13, 860808. Zhang, J., van Gorp, D., & Kievit, H. (2022). Digital technology and national entrepreneurship: An ecosystem perspective. The Journal of Technology Transfer, 1–29. https://​doi.​org/​10.​1007/​ s10961-​022-​09934-0 Zhao, Y. (2022). Risk prediction for internet financial enterprises by deep learning algorithm and sustain- able development of business transformation. Journal of Global Information Management (JGIM), 30(7), 1–16. https://​doi.​org/​10.​4018/​JGIM.​300741 Zhou, M., & Xu, H. (2012). A review of entrepreneurship education for college students in China. Admin- istrative Sciences, 2(1), 82–98. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 13 6642 Journal of the Knowledge Economy (2024) 15:6617–6642 Authors and Affiliations Shaofeng Wang1 · José Paulo Esperança2 · Wancheng Yang1 · Justin Zuopeng Zhang3 Shaofeng Wang [email protected] José Paulo Esperança [email protected] Wancheng Yang [email protected] 1 School of Logistics and e‑Commerce, Zhejiang Wanli University, Ningbo 315000, China 2 ISCTE Business School and Business Research Unit, University Institute of Lisbon (ISCTE), 1649‑026 Lisbon, Portugal 3 Coggin College of Business, University of North Florida, Jacksonville, FL 32223, USA 13