Academic entrepreneurship is defined as an activity that transcends the traditional role of academic teaching or research and has innovative and risky elements that bring economic rewards to the individual academic or the institution (technology transfer and creation of spin-off companies, etc.). These rewards can be achieved by earning reputation, prestige, and social influence directly or indirectly (Abreu and Grinevich, 2013). Many scholars believe that collaborative research, contract research, consulting, and informal relationships are the achievements of university academic entrepreneurship (Perkmann et al., 2013). The case of academic startups in Italian universities reflects how governments and academic institutions have been working to create better conditions for the successful commercialization of academic research achievements (Baldini et al., 2015). A qualitative analysis was conducted the assess the entrepreneurial behavior of 30 life sciences researchers from Australian universities. All of the respondents mentioned that capital needs and institutional policy are the main driving factors of research commercialization. Academic entrepreneurial behavior is categorized into non-entrepreneurial, semi-entrepreneurial, pre-entrepreneurial, and entrepreneurial behaviors. The prerequisites for academic entrepreneurial behaviors include commercialized research opportunities, individual as opposed to commercialized team research, and appropriate funding (Holley and Watson, 2017). The traditional definition of academic entrepreneurship centers on emerging academic entrepreneurship that generates direct financial returns and provides a wider social and economic benefit to the university ecosystem (Siegel and Wright, 2015).

Many studies have identified the connections between the specific processes of academic entrepreneurship in higher education and collaboration, including connections between individuals, organizations, and institutions. However, some scholars have argued that when investigating the entrepreneurial intent of teachers in higher education, academic entrepreneurship should be viewed from the perspectives of both economic development and individual psychology.

Technology project incubation departments in eight Brazilian universities used the development of innovative and entrepreneurial universities as a theoretical basis. They found that although project incubation departments in universities preferred to work on specific academic research projects, companies created based on academic achievements were not prioritized. Meanwhile, little effort has been made to engage the academic research audience, resulting in underutilized academic research transfer channels (Stal et al., 2016). In the academic entrepreneurial process, individuals may take on more than one role. Therefore, it is important to identify all of the stakeholders that are potentially involved. Paying more attention to how researchers are rewarded and motivated and the ways that informal collaborations (consulting arrangements, joint publications, etc.) between universities and their industry partners begin is important to achieving success in academic entrepreneurship (Wood, 2011). A bibliometric analysis of the common terms and content of 615 academic ventures on the Web of Science identified four interrelated clusters: (1) components of entrepreneurial universities; (2) university spin-off activities and commercialization; (3) characteristics, motivations, and barriers of academic entrepreneurship; (4) knowledge transfer and regional economies (Skute, 2019). Although previous research has provided important insights on academic entrepreneurship, no one as of yet has explored how organizational environmental factors motivate teachers to form positive academic entrepreneurial intents. The aim of the present study is to contribute to the existing research from the perspectives of psychological capital and role conflict. It adopts the theory of planned behavior as a research framework and provides new ideas about the psychological mechanism of how academic entrepreneurial intent is formed among university teachers.

An existing study surveyed 1,178 academics in Spanish universities with entrepreneurial attitude in academic entrepreneurship as a prior variable. It was an empirical study of entrepreneurial intent based on the theory of planned behavior, and it verified the proposed prior variable of entrepreneurial intent by constructing a structural equation model. Entrepreneurial intent was also found to be influenced by creativity, perceived utility, and entrepreneurial experience (Miranda et al., 2017a). A conceptual model was developed and validated based on the theory of planned behavior, integrating economic and psychological factors with human and social capital as economic factors and entrepreneurial attitudes, norms, and perceived control as psychological factors. It examined the entrepreneurial intents and actual innovative behaviors of academic scientists (n = 496) in Germany and found that psychological factors predicted entrepreneurial intent and that economic factors indirectly influenced entrepreneurial intent through psychological factors (Goethner et al., 2012). Similarly, a qualitative study based on grounded theory and the theory of planned behavior was conducted to construct a model for the factors of academic entrepreneurship among teachers in Chinese research universities. Human capital and role conflict were used as prior variables, and the literature on theory was used to verify the impact on academic entrepreneurial intent (Su and Zhao, 2017). The results showed that entrepreneurial spirit is a key factor in the formation of academic entrepreneurial intent among teachers in higher education institutions.

Previous literature has thus demonstrated that university environment is a key factor in the formation of psychological capital and role conflict for teachers. Psychological capital and role conflict are also effective in motivating teachers to be more productive and satisfied with their work and resist work-related stress. Therefore, we propose the following hypotheses:

In the empirical literature on the influence of academic attitudes and perceptual behavioral control on academic entrepreneurial intent, most studies have focused on student populations. Literature on teachers’ attitudes, perceptual behavioral control, and academic entrepreneurial intent is relatively sparse (Ferreira et al., 2012; Nguyen et al., 2019; Mahfud et al., 2020). However, according to the generality of predicting human behavior in the theory of planned behavior proposed by Ajzen (2011), attitudes and perceptual control behavior influence individual intents. Positive academic attitudes and perceptual control are thus effective in predicting scholars’ entrepreneurial intent (Schlaegel and Koenig, 2014; Fernandez-Perez et al., 2015). Therefore, hypotheses H4 and H5 of this study are proposed.

According to Ajzen (1991), the prior independent variable of attitudes, perceptual control, and subjective norms is believed to be belief, which is not an innate trait but rather formed by an individual’s exposure to the social environment. Goethner et al. (2012); Huyghe and Knockaert (2015), Foo et al. (2016), and Urban (2019) found that the process of forming academic entrepreneurial intent is often influenced by an individual’s surroundings, friends, colleagues, and role models. Academic entrepreneurship is characterized by an intrinsic orientation within individuals to strive for social distinction rather than social norms. Contextual factors indirectly predict entrepreneurial intent through attitudes and perceptual control rather than social norms (Krueger et al., 2000). Therefore, hypothesis H6 of this study is proposed (see Figure 2).

H6: Subjective norms have a positive impact on academic entrepreneurial intent.

The teachers from 17 universities in southern China are selected as research objects. A total of 557 questionnaires were sent out and recovered between May 7th 2021 and June 22nd 2021. At this point, SPSS 22.0 was used to check the data, which included 32 invalid questionnaires. In the end, 525 valid questionnaires were collected with an actual recovery rate of 94%. Descriptive statistical analysis was conducted on the basic data of participants (see Table 1).

In the present study, data was collected through the questionnaire method. Participants had to respond to a series of statements using a Likert’s five-level scale (“totally agree,” “agree,” “general,” “disagree,” and “totally disagree”)The distribution is recorded as 5, 4, 3, 2, and 1, and the total score of each respondent’s attitude is the sum of the scores gained from the answers to each item in the questionnaire. The total value can be said to be the attitude of each respondent or the different states on each scale.

The scale of psychological capital reference from Luthans and Youssef-Morgan (2017) four scale items of efficiency, hope, optimism and resilience. The scale of role conflict referenced Bartunek and Rynes’s (2014) three scale items, which include time conflict, problem conflict, and knowledge conflict. The scale of academic entrepreneurial attitude and perceived behavior referenced Liñán and Chen’s (2009) three scale items. The scale of subjective norm referenced three of Bercovitz and Feldman’s (2008) seven scale items. The scale of academic entrepreneurial intention referenced Liñán and Chen (2009) and Obschonka et al. (2015).

In this study, reliability analysis, discriminant validity and convergence validity analysis, and correlation analysis were conducted on the collected valid sample data of N = 525, and a structural equation model was constructed. The results obtained are shown in Tables 2–6.

As shown in Table 2, after the factor extraction of 19 items, six factors were finally extracted. The six factors that were extracted included PC (Psychological Capital), SN (Subjective Norm), AEI (Academic Entrepreneurial Intention), RC (Role Conflict), AEA (Academic Entrepreneurial Attitude), and PBC (Perceived Behavioral Control), which were consistent with the theoretical assumption of scale structure. The factor load was above 0.6, and the factor compliance was good. The questionnaire thus had good construction validity.

As depicted in Table 3, Cronbach’s Alpha value of PC, RC, SN, AEA, PBC, and AEI is above 0.7, CR value is above 0.7, and AVE value is above 0.5. According to Hair et al. (2010), in validity evaluation, the absolute value of estimated factor load should be above 0.5 at least. The optimal index value is above 0.7, and the average variance withdrawal (AVE) index value should be above 0.5. This questionnaire thus has good convergence validity.

The method proposed by Fornell and Larcker (1981) was also adopted to determine whether the convergence validity existed if the square root of AVE was higher than the correlation coefficient between the two variables. As shown in Table 4, the AVE square roots of PC, RC, SN, AEA, PBC and AEI were 0.767, 0.807, 0.833, 0.796, 0.747, and 0.862, respectively, which were all greater than their corresponding correlation coefficients, indicating that the questionnaire had good convergence validity.

As illustrated in Table 4, AEA was significantly positively correlated with PC (r = 0.290, P < 0.01). PBC was positively correlated with PC (R = 0.418, P < 0.01) and negatively correlated with RC (r = −0.354, P < 0.01). AEI was significantly positively correlated with SN (r = 0.366, P < 0.01), AEA (R = 0.435, P < 0.01), PBC (r = 0.401, P < 0.01). Therefore, the hypothesis is tentatively supported.

According to the test method of Bagozzi and Yi (1988), from Table 5 that x2/DF value is 2.184, RMSEA value is 0.048, GFI value is 0.939, AGIF value is 0.919, NFI value is 0.939 and CFI value is 0.966 (see Figure 3). The value of TLI was 0.959, and the value of IFI was 0.966. The fitting indexes of the model both reached ideal values, indicating that the fit of the structural equation model was good.

As shown in Table 6, PC had a significant positive effect on AEA (β = 0.386, P < 0.001). PC also had a significant positive effect on PBC (β = 0.467, P < 0.001), RC had a significant negative effect on PBC (β = −0.334, P < 0.001), and PC had a significant positive effect on AEA (β = 0.386, P < 0.001). AEA had a significant positive effect on AEI (β = 0.312, P < 0.001), PBC had a significant positive effect on AEI (β = 0.242, P < 0.001), and SN had a significant positive effect on AEI (β = 0.261, P < 0.001). Therefore, all six of the hypotheses in this study were supported.

Literature on academic entrepreneurship has neglected to investigate the entrepreneurial intent of teachers in higher education from a psychological perspective. Studies that use qualitative analysis are sparse, and most were conducted on university students (Baluku et al., 2020; Mahfud et al., 2020; Shi et al., 2020; Zhao et al., 2020). Used a qualitative method and discussed the link between the lack of psychological capital and teacher motivation. They argued that superior psychological capital will increase motivation, and that more research should be conducted that focuses on psychological capital and teachers (Viseu et al., 2016). Others have also argued that the roles of academic researcher and entrepreneur should be conceptually distinct in the process of academic entrepreneurship to avoid the negative effects of role conflict on academic entrepreneurship (Wood, 2011).

Based on these findings, the research model adopted in this study was constructed based on the theory of planned behavior. As shown in Table 6, the influence of academic attitudes, perceptual behavioral control, and subjective norms on academic entrepreneurial intent (H4, H5, and H6) were all supported. The results about the impact of academic attitude and perceptual behavioral control are in agreement with the findings of Goethner et al. (2012) and Miranda et al. (2017a). However, the effects of subjective norms on academic entrepreneurial intent did not hold in previous studies. Our results concerning the hypothesis of subjective norms are not the same as those of western scholars, but there are some relevant studies showing that the influence of subjective norms on academic entrepreneurial intent is supported when the sample is drawn from Chinese teachers in higher education (Su and Zhao, 2017; Shi et al., 2020). Social identity theory holds that subjective norms on academic entrepreneurial intent are established because they are shaped by the moderating effect of group identity (Terry et al., 1999). Therefore, we believe that this difference is due to variations in cultural environments. In a university context, in the absence of sociocultural and traditional entrepreneurship, academic entrepreneurial intent depends more on individual personalities than on subjective norms (de Janasz et al., 2007). The academic entrepreneurial intent of teachers in higher education is strongly supported by surrounding groups and environmental policies. When higher education administrators have participated in academic entrepreneurship, in particular, the possibility of academic entrepreneurship of the teachers in higher education is improved on the whole (Brennan et al., 2005; Su and Zhao, 2017).

This study also supported the hypotheses that psychological capital on the control of attitudes and perceived behaviors have an effect on academic entrepreneurship (see Table 6). We found that although there are few directly relevant prior studies, there is indirect evidence that psychological capital exists as a significant predictor of academic entrepreneurial intents. As the first aim of the study demonstrated, the more significant academic attitudes and perceptual behavioral controls are, the greater the predictability of academic entrepreneurial intents will be. Because academic attitude and perceptual behavioral control are significantly influenced by psychological capital, a study conducted by Contreras et al. (2017) on the relationship between psychological capital and entrepreneurial intents found that entrepreneurial intents are correlated with the dimensions of psychological capital. It has been shown that a positive effect exists in the interaction between psychological capital and entrepreneurial intent (Yalap et al., 2020) and that psychological capital has a significant impact on entrepreneurial intents through traditional financial, human, and social capital (Zhao et al., 2020). Both startup capital and psychological capital are important predictors of entrepreneurial success, but psychological capital is a better predictor (Baluku et al., 2016). Therefore, we argue that when psychological capital controls for academic entrepreneurial attitudes and perceived behaviors are supported by hypothesis testing, the latter (PC) exerts a stronger influence than the former (AEA and PBC) in predicting the indirect role of academic entrepreneurial intents. Concurrently, based on the premise that the research object is Chinese teachers in higher education, we also found that the psychological capital of Chinese teachers in higher education has an important effect on job performance, scientific research, and innovation behavior and job engagement (Juan, 2021). Academic entrepreneurship is one of the ways of scientific research innovation, but there is still a scarcity of empirical literature studies on this topic in China. This study used empirical research methods to prove that the psychological capital of Chinese teachers in higher education is one of the important factors that predict academic entrepreneurship.

We also found that our hypothesis about the effect of role conflict on the control of perceptual behavior (H3) is supported. This conclusion is the same as the one obtained by Su and Zhao (2017), who performed a qualitative analysis based on a model of teacher academic entrepreneurship constructed in a Chinese context.

With respect to spin-off business creation (a form of academic entrepreneurship), the conflict between entrepreneurship and social identity remains common (O’kane et al., 2015; Wang et al., 2021). Research shown that role conflict is negatively related to the performance of academic entrepreneurship, as explained by social identity theory (Zou et al., 2019). To this end, based on the properties of the theory of planned behavior (where human control over will and behavior is considered as a continuum), we explain how enhancing the academic entrepreneurial intent of teachers in higher education can be achieved by increasing their perceived behavioral control (behavior under non-volitional control), thereby reducing the negative impact of role conflict. Based on social learning theory, it has been demonstrated that the more experience academic entrepreneurs have, the weaker the negative impact of role conflict will be (Zhang et al., 2021). As this study confirms, perceptual behavioral control effectively reduces the negative effect of role conflict on the academic entrepreneurial intention of teachers in higher education.

In summary, this study showed that psychological capital is an important leading indicator that can predict academic entrepreneurial intents, and in the context of colleges and universities, managers can reduce the negative impact of role conflict caused by teachers who serve as entrepreneurs and academics through perceptual behavioral control. As Peng (2018) has said, “PASSAGE.” Dealing with the relationship between difference and legacy is thus key to achieving academic entrepreneurship. We hope that in comparing our findings with research that has been conducted in other countries, the universal implications of our research have been made clear and can provide useful suggestions about academic entrepreneurship for administrators in universities in China and beyond.

The limitations of this study are twofold. First, our sample was selected from a group of university teachers living in the southern region of China, and the geographical differences may cause the findings of the study to differ. In order to address this issue, future research could take the form of a comparative study of university teachers in China and Korea. Second, designing survey questions to assess teachers’ entrepreneurial intent is a challenging task. Open-ended questions can be problematic in terms of quality data recovery and the analysis of results. Future research could use textual information mining and data analysis to obtain key terms from teacher postings and comments about academic entrepreneurship on the Internet. Third, the influencing factors of the academic entrepreneurial intents of teachers in higher education are complex and diverse. In the future, we can introduce regulatory variables or intermediary variables to further explore academic entrepreneurial intent.

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