Theory of Planned Behavior (TPB) is an important theory used to explain and predict behavioral decision-making in the field of social psychology. Since the formal proposal of Ajzen (1985), this theory has been widely used in human behavior research, and many studies have proved that this theory has a strong role in explaining and predicting the behavior intention and actual behavior of actors (Wang and Zhang, 2017). The behavior decision-making of the behavior subject is usually rational. By systematically collecting, analyzing, and using relevant information, we can fully consider whether to perform a specific behavior and the impact after the behavior occurs (Ajzen, 1991). Organizational behavior is determined by the behavior intention of core members, and behavior intention is affected by behavior attitude, subjective norms, and perceived behavior control (Ajzen, 2002). In general, the behavior of enterprises’ participation in school enterprise cooperation will be affected by many factors, e.g., enterprises’ cognition of cooperation objects, preferences of core management, public opinion pressure, their own resource conditions, corporate social reputation, government behavior, and expected risks and benefits. From the perspective of colleges and universities, to attract more high-quality enterprises to participate in school enterprise cooperation, we must clearly understand what factors influence the behavior and decision-making of enterprises. According to the actual situation of school enterprise cooperation and existing research results, the behavior attitude, behavior norms, perceived behavior control, and external policy environment of the core management of enterprises will have an impact on the decision-making of school enterprise cooperation. Therefore, a model was proposed (Figure 1).

To ensure the scientificity and effectiveness of the questionnaire, all items used for measurement are adapted from the existing literature and modified appropriately according to the research purpose and the actual situation of the research object. The project used to measure enterprises’ behavior attitude and subjective behavior norms was adapted from the study by Gu et al. (2018). The project for measuring perceived behavior control of enterprises was adapted from the study by Huang and Zheng (2015). The project for measuring behavior intention of enterprises was adapted from the study by Feng and Zhang (2020). The project for measuring enterprises’ cooperative behavior was adapted from the research by Ran (2021). Finally, three items used to measure situational factors were adapted from the study by Li et al. (2014). The final questionnaire is presented in Appendix Table 1. All items were measured using the 5-point Likert scale, ranging from 1 (very disagree) to 5 (very agree).

The survey object of this study is Suzhou enterprises in China. The reasons for choosing Suzhou enterprises are as follows: Suzhou is one of the important central cities in China’s Yangtze River Delta, one of the cities with the most complete industrial system in China, a high-tech industrial base in China with more than 160,000 industrial enterprises. A questionnaire for this study was published on sojump, China’s largest online survey platform. Core members with certain decision-making rights, mainly including the president, general manager, deputy general manager, personnel manager, chief financial officer, marketing director, and technical backbone, are invited to participate in the filling. The data of this study were collected in December 2021. In the survey sample, 78.40% of enterprises have been registered for less than 20 years, and 82.00% of enterprises have a scale of less than 1,000 people. Similar to enterprise ownership, private enterprises account for 42.40%, followed by sole proprietorship enterprises and state-owned enterprises, accounting for 17.60 and 16.00%, respectively. The types of enterprises mainly include the manufacturing industry, biological pharmaceutical industry, automobile service industry, and commerce and construction industry. In general, the survey sample comprehensively considers the years of enterprise registration, the size of employees, the nature of ownership, and the type of industry, which is well representative (Table 1).

Construct reliability and validity were further examined through CFA. As shown in Table 2, the Cronbach’s α, and composite reliability (CR) values for each construct ranged from 0.910 to 0.973, both of which were above the suggested threshold of 0.7 (Straub et al., 2004) and exhibited a satisfactory level of reliability. For construct validity, both convergent validity and discriminant validity were examined. Convergent validity was confirmed by examining both the average variance extracted (AVE) and indicator loadings. As shown in Table 2, all AVE values were higher than the recommended level of 0.5 (Fornell and Larcker, 1981). The standard loadings of all items were above the desired threshold of 0.7 and statistically significant at 0.001. This indicated good convergent validity (Chin et al., 1997).

Discriminant validity was evaluated by comparing the square root of AVE and the correlation value. The discriminant validity was assessed by comparing the square root of AVE for each construct with the correlations between that construct and other constructs (Fornell and Larcker, 1981). As shown in Table 3, the square roots of the AVEs (i.e., diagonal elements) were larger than the inter-construct correlations depicted in the off-diagonal entries, thus suggesting the discriminant validity that was adequate.

As the data were self-reported from a single source, we made a statistical analysis of the data to assess common method bias. First, we further assessed the method factor according to the steps suggested by Liang et al. (2007). The results demonstrated that the loadings of the principal variables were all significant at the p < 0.001 level, whereas none of the common method factor loadings was significant. These results further indicated that common method bias was unlikely to be a concern in this study.

Second, we conducted a multicollinearity test to examine the correlations between the independent variables. A variance inflation factor (VIF) value above 10 indicates a multicollinearity problem. As shown in Table 4, the VIF values for the variables in this study were all below 10, indicating the absence of multicollinearity.

Figure 2 indicates that behavior attitude and perceived behavior control have a positive impact on behavior intention, which supports hypotheses H1 and H3. Behavior intention and perceived behavior control have a positive impact on participation behavior, thus supporting hypotheses H4 and H5. Subjective norms have no significant impact on behavior intention, so they reject hypothesis H2. Corporate behavior intention and participation in school enterprise cooperation are positively regulated by the policy environment (β = 0.171, p < 0.001) (Table 5), thus supporting H6. Among the two antecedents of behavior intention, perceived behavior control has a greater impact on behavior intention (β = 0.670, p < 0.001), followed by behavior attitude (β = 0.299, p < 0.001). Among the two antecedents of participation behavior, behavior intention has a greater impact on participation behavior (β = 0.545, p < 0.001), followed by perceived behavior control (β = 0.379, p < 0.001) (Table 6).

Next, we examined trust in platform and satisfaction mediation effects using the bootstrapping approach provided by Preacher and Hayes (2008). The use and test of mediating effect is the main trend in management studies. Table 7 shows that behavior intention plays a complete intermediary role between behavior attitude and participation behavior, and behavior intention plays a partial intermediary role between subjective perception norms and participation behavior, with a 95% bootstrap confidence interval, excluding 0. This finding suggests that behavior intention mediates the effect of behavior attitude, perceived behavioral control on the enterprise cooperative behavior.

This study yielded interesting findings. As the results indicate that behavior attitude, subjective norms, and perceived behavior control have different degrees of impact on enterprises’ intention to participate in school enterprise cooperation and actual participation behavior. First, behavior attitude and perceived behavior control have a significant positive impact on enterprises’ intention to participate in school enterprise cooperation. Behavior attitude have a stronger effect on behavior intention (β = 0.299, p < 0.001), and perceived behavioral control have stronger effects on behavior intention (β = 0.379, p < 0.001). Moreover, subjective norms do not significantly affect the enterprises’ behavior intention to participate in school enterprise cooperation. The possible reason is that enterprises, as profit-making social organizations, think more about their own interests and less about the subjective normative pressure from similar enterprises, industry associations, and news media. This also confirms Li’s (2021) view that expected income is the primary driving force to stimulate enterprises to participate in school enterprise cooperation. In particular, small and medium-sized enterprises will further consider social norms only when their reasonable demands are met. In addition, the findings are consistent with those of the previous studies (Ran, 2021), indicating that the enterprises’ behavior intention to participate in school enterprise cooperation is mainly affected by behavior attitude and perceived behavior control.

Second, behavior intention and perceived behavior control have a significant positive impact on the actual behavior of enterprises’ participation in school enterprise cooperation. Regarding the effects of behavior intention and perceived behavioral control on cooperative behavior, our results indicate that behavior intention and perceived behavioral control can predict cooperative behavior and that behavior intention (β = 0.545, p < 0.001) plays a greater role in determining cooperative behavior than do perceive behavioral control (β = 0.379, p < 0.01). This suggests that behavior intention is the leading factor affecting enterprises’ participation in school enterprise cooperation. Our findings extend those of previous studies (Li et al., 2014), suggesting that behavior intention and perceived behavior control have the same effect on enterprises’ participation in school enterprise cooperation. Finally, this study confirms that the policy environment has a positive regulatory effect on the enterprises’ behavior intention to participate in school enterprise cooperation and the actual participation behavior. The interaction between policy environment and behavior intention has a significant positive impact on the actual behavior of enterprises’ participation in school enterprise cooperation (β = 0.171, p < 0.01). The hypothesis is verified to be in line with the literature, highlighting regulating effect of policy environment on behavior intention and cooperative behavior (Jing, 2019; He and Wu, 2020).

This study helps to better explain the mechanism of enterprises’ participation in school enterprise cooperation through TPB model. Using the TPB model, many studies have explored the influences of subjective cognition of behavior subjects on inter-organizational cooperative behavior (Li et al., 2014). These studies show that the behavior attitude, subjective norms, and perceived behavior control of behavior subjects have a positive impact on the intention and behavior of school enterprise cooperation. However, most of these studies focus on the cooperative behavior of enterprises and its influencing factors from the perspective of “rational people” (Ran, 2017; Xiao and Chen, 2019), lack of exploration on the impact of situational factors on organizational behavior. Different from previous research conclusions, in addition to individual subjective cognitive factors, the policy environment also affects the decision-making behavior of enterprises to a great extent.

Based on the TPB model, four main factors that affect enterprises’ participation in school enterprise cooperation are determined, namely behavior attitude, perceived behavior control, behavior intention, and policy environment. Behavior attitude has a significant positive impact on enterprises’ participation in school enterprise cooperation through behavior intention. Perceived behavior control not only has a significant positive impact on enterprise behavior directly but also has a significant positive impact on enterprise behavior indirectly through behavior intention. The policy environment has a significant positive regulatory effect on the intention and behavior of enterprises to participate in school enterprise cooperation. This study not only supplements the academic literature on the generation mechanism of enterprises’ participation in school enterprise cooperation but also provides a new perspective for colleges and universities to evaluate the potential cooperation behavior of enterprises.

This study offers useful managerial implications from two aspects. For the first time, with the transformation of knowledge production mode, school enterprise cooperation has increasingly become an important way for colleges and universities to implement applied talents’ cultivation. How to attract more high-quality enterprises to participate in school enterprise cooperation has become the focus of higher education in the world. This study found that enterprises’ behavior attitude, perceived behavior control, behavior intention, and policy environment have a significant positive impact on enterprises’ participation in school enterprise cooperation and then revealed the internal mechanism of school enterprise cooperation. For university managers, on the one hand, they should establish the cooperation concept of complementary advantages, resource sharing and mutual benefit and win-win, meet the reasonable interest demands of enterprises in school enterprise cooperation in order to improve the behavior attitude and behavior intention of enterprises, and then promote the achievement of marching cooperation. On the other hand, we should build an information service platform for school enterprise cooperation with the help of industry associations, unblock information communication channels, and eliminate information islands between enterprises and universities.

Second, the relationship among enterprises, universities, and the government is essentially a “three spiral” relationship in school enterprise cooperation. With the rapid expansion of the scale of higher education and the continuous dilution of school running resources, the effect of the government in the process of school enterprise cooperation is becoming more and more obvious. This study found that the policy environment has a significant regulatory effect on the intention and behavior of enterprises to participate in school enterprise cooperation. Therefore, optimizing the policy environment of industry education integration is another key point to comprehensively deepen school enterprise cooperation. For government managers, on the one hand, they should strengthen the upper and lower connection of school enterprise cooperation policies to ensure the systematic nature and unity between upper and lower policies of school enterprise cooperation. On the other hand, they should strengthen the policy support between the education department and the industrial department, as well as the coordination between the policy implementation subjects in order to form the internal and external resultant force of the school enterprise cooperation policy.

Based on the TPB model, this study constructs a decision-making behavior model of enterprises’ participation in school enterprise cooperation and tests the model through the questionnaire data of enterprise core management. The results show that the constructed model is highly competent in explaining the problem, but this research still has the following limitations: first, this article selects Suzhou enterprises in economically developed areas as representatives, but enterprises in other economically relatively backward areas may have certain differences; second, this study does not consider the impact of cost, income, and risk on enterprises’ participation in school enterprise cooperation decision-making. In future research, income, cost, risk, and economic factors can be used as regulatory variables to compare the participation of enterprises in different regions in school enterprise cooperation.