In 1957, Professor Schreiber of University of Washington developed a simulation game named “Top Management Decision Game,” which was introduced into the curriculum (Watson, 1981). Since then, the teaching method has been extensively adopted in business majors, especially after the 1980s. Amongst the background of computer popularization, computer simulation games have been rapidly popularized and applied in management courses, and a large number of scholars have conducted relevant studies. Over 1200 related papers were published between 1960 and 2019 alone (Hallinger and Wang, 2020). Virtual simulation games have been based on the experiential learning theory (Kolb, 1983), combined with the organization theory and the game theory, to design rules and algorithms (Sterman, 1994; Geurts et al., 2007), which is widely used in business education (Keys and Wolfe, 1990; Wolfe, 1993; Anderson and Lawton, 2009; Faria et al., 2009). The teaching method of virtual simulation game has also been recently applied in entrepreneurship education in many colleges and universities, and has been demonstrated to effectively improve the investment and learning performance of college students in entrepreneurship learning (Kriz and Auchter, 2016; Charrouf and Janan, 2019; Isabelle, 2020; Zulfiqar et al., 2021).

In the present study, a virtual simulation game was used in the course of entrepreneurship education. Students participated in the course in teams of 3–5 people. Each team needed to register and set up a company, study the market demand, clarify the priority market segments for the team to enter, position the style and characteristics of the product, and decide the research and development costs. Each virtual company needed to decide whether to build its own factory and the size of the factory, as well as the financing method. The required funds could be obtained through bank loans, issuing bonds and selling shares. On a quarterly basis, each company needed to make financial budgets and decisions on 62 projects, covering research and development, marketing, production, logistics, human resources and other aspects of business operations. Sixteen companies were allowed to compete in each industry, and at the end of each quarter, the system’s mainframe ran calculations based on the decisions submitted by each company and reported the results of the game back to each company. At least eight quarters of simulation were conducted in each round.

Biggs (1993) proposed the presage-process-product (3P) model of college learning. In said model, the early variables include students’ individual characteristics and learning experience, the process variables mainly refer to students’ learning methods, and the outcome variables refer to students’ performance and gains. Prophase variables determine how students deal with a certain task, which further affects their learning results. That is, learning style is a mediating factor between learning experiences and learning results. Simultaneously, learning experiences directly predict learning outcomes (Trigwell et al., 2013). The interaction between prophase variables, process variables and outcome variables forms a dynamic system (Biggs et al., 2001).

The learning result of college students is a key indicator in evaluating the quality of university education (Douglass et al., 2012). According to existing studies, factors affecting the learning result can be essentially classified into two categories: student engagement (Fredricks et al., 2004) and learning experience (Richardson, 2005). Numerous studies have confirmed that student engagement of college students is a significant predictor of learning outcomes. Academic achievement and satisfaction will be higher with the investment of more time, energy and emotion from students (Wang and Eccles, 2013; Zepke, 2014; Zusho, 2017). College students’ learning experience refers to their perception, opinion and understanding of the learning environment (Entwistle, 1991). A large number of studies have shown that college students’ perception of the learning environment can affect their learning behavior and learning results (Diseth, 2007; Trigwell et al., 2013; Guo et al., 2017). However, there is a scarcity of existing studies in which the relationship between learning experience, learning engagement and learning outcomes is explored (Yin and Ke, 2017), especially in the field of entrepreneurship education. As such, in the present study, the 3P model proposed by Biggs (1993) was taken as the research framework, students’ learning experience in virtual simulation game courses was taken as the independent variable, students’ engagement was taken as the intermediary variable, and entrepreneurial intention was taken as the dependent variable to construct the research model.

Learning experience is the main factor affecting student engagement (Astin, 1984; Biggs, 1993; Coates and McCormick, 2014), in order to improve the level of students’ learning experience, the teaching content needs to be designed and the teaching process needs to be managed with students as the center of the concept (Gibb, 2002; Murah and Abdullah, 2012; Robinson et al., 2016).

Virtual simulation games are first and foremost games, so the experience of the game itself is a significant factor that constitutes students’ course experience. Choi and Kim (2004) divided the influencing factors of the online game player experience into human-computer interaction factors and social interaction factors. Human-computer interaction includes goals, feedback and operability, while social interaction includes interaction place and interaction way. Such factors have been confirmed to have a significant positive impact on player experience. Yen and Lin (2020) introduced a retail simulation game into a marketing course to build a research model based on Flow theory. The antecedents of flow experience were divided into challenge-skill balance, playability, goals, feedback and control, and the influence of such variables on flow experience and learning performance was investigated.

In the course of virtual simulation games, students usually participate in a competition in the form of a team. As such, the communication, trust and cooperation between team members will certainly affect students’ investment and learning performance. Thanasi-Boçe (2020) adopted a marketing simulation system in a course. Sixteen students were divided into four teams to conduct a simulated competition. Through qualitative investigation and research, students gave feedback that team cooperation was crucial, and the interaction and even conflict among team members was found to have improved their ability to manage the team. Lohmann et al. (2018) conducted a survey of 365 students in Australia and Hong Kong and confirmed that teamwork can directly and significantly positively affect students’ results and satisfaction with business simulation learning.

Obviously, the personal characteristics of participants in virtual simulation games are also significant factors that can affect learning engagement and performance. Buil et al. (2020) constructed the self-system model of motivational development (Skinner et al., 2008). Based on the survey results of 360 students, the satisfaction degree of competency, independent choice, membership and self-efficacy was found to affect their cognitive, affective and behavioral engagement in business simulation games, and the cognitive and affective engagement would significantly positively affect students’ skill development and knowledge acquisition. Hernández-Lara et al. (2019) surveyed 115 undergraduate and master’s students and found that, in business simulation game courses, students’ generic skills (such as teamwork, decision-making ability, information processing ability, entrepreneurial ability, and new technology application ability) could have a significant positive impact on learning outcomes.

To summarize, the following hypothesis is proposed in the present study:

Student engagement is a multidimensional concept, including students’ time, energy and investment in cognition, emotion and behavior. Cognitive investment refers to the use of deep learning methods and strategies, with intrinsic learning motivation, emotional investment refers to the interest and satisfaction of learning and the relationship with teachers and peers, and behavioral investment refers to the participation in in-class and out-of-class learning activities related to behavior (Fredricks et al., 2004). In general, learning results include increases in knowledge, improvements in ability and changes in attitude (Anderson and Lawton, 2009). Since the focus of the virtual simulation game adopted in the present study was on the application of knowledge and improvement of analytical and decision-making ability, entrepreneurial skill development was measured as an indicator of learning results.

First, good education game design has clear education objectives and performance evaluation standards, and gives feedback and rewards according to students’ performance in the game. Students will strive to achieve performance objectives or gain an advantageous position in the competition. The sense of achievement of breaking through customs and the sense of honor of winning the competition urge students to be willing to invest a lot of time and energy in independent learning. Students will carefully analyze the competitive situation, evaluate the decisions of competitors, seek differentiated products and marketing innovation, reduce operating costs, and ensure sound financial operation. Second, all decisions are made by the student team. Therefore, students’ ability to innovate, analyze and solve problems, and make decisions can be trained and improved. In the course of virtual simulation games, team members trust each other and cooperate closely, which can make participants feel comfortable. Team members enhance each other’s knowledge in interaction. Division of labor and cooperation can improve the efficiency and accuracy of decision-making. Therefore, a good team atmosphere can make students more willing to participate in simulation games, and their ability can be better developed. Third, students’ sense of self-efficacy is also considerably important. Students who are more confident in virtual simulation games are more willing to invest time, energy and emotion to study the rules of the game and competitors, and can gain more. A study by Charrouf and Janan (2019) confirmed that, compared with students who have no experience playing video games, students who like to play video games at ordinary times would find start-up simulation games easier to operate, and would be more willing to devote themselves to learning, with stronger entrepreneurial intention.

As confirmed by a large number of studies, simulated game experience can effectively improve students’ participation and learning outcomes (Anderson and Lawton, 2009; Beltrão and Barçante, 2015; Kriz and Auchter, 2016; Liberona and Rojas, 2017; Charrouf and Janan, 2019; Isabelle, 2020; Mirjana et al., 2020; Kauppinen and Choudhary, 2021; Zulfiqar et al., 2021). Thus, the following hypotheses are proposed in the present study:

In the field of university learning, Astin (1984) paid early attention to the relationship between student engagement and learning results, proposing that students’ learning gains and development are directly related to the quantity and quality of students’ learning input. Numerous subsequent studies have confirmed that student engagement is a significant predictor of learning outcomes, and the more students engage in learning, the better their academic performance will be (Wang and Eccles, 2013; Zepke, 2014; Zusho, 2017). In the field of entrepreneurship education, there is a scarcity of research on the relationship between entrepreneurial learning input and learning outcomes. Several scholars have confirmed that entrepreneurial learning input can improve entrepreneurial self-efficacy (Minniti and Bygrave, 2001; Isabelle, 2020) and entrepreneurial confidence (Rae and Carswell, 2001), thereby influencing entrepreneurial intention. In a recent study, Zulfiqar et al. (2021) introduced business simulation into entrepreneurship courses and confirmed that such teaching method significantly increased student engagement, which in turn significantly improved entrepreneurial intentions and learning performance. As such, the following hypotheses are proposed in the present study:

In the present study, students from the University of Electronic Science and Technology of China, Zhongshan Institute, were taken as the survey object. Students used virtual simulation games in the course, and students formed teams of 3–5 people. The startup teams had to create a new company within the system, develop new products to market, and compete with other teams. After the entrepreneurship course, the research team conducted an electronic questionnaire survey among 192 students who participated in the course. From business major students, 177 valid samples were obtained, including 44% male students and 56% female students.

For the course learning experience, system design, team experience and self-efficacy were comprehensively considered, and the research results of Choi and Kim (2004), Lohmann et al. (2018), and Buil et al. (2020) were comprehensively drawn on to design 13 questions. See Supplementary Appendix QUESTIONNAIRE Q1–Q13 for details. Four questions were designed for student engagement based on the measurement scale of Fredricks et al. (2004) and Buil et al. (2020). See questionnaire Q14–Q17 in the Supplementary Appendix. The research results of Isabelle (2020) was used to form an entrepreneurial skills development scale and four questions were designed. See Supplementary Appendix Q18–Q21 for details. All question types were measured by the 7-level Likert scale, with 1 meaning strongly disagree and 7 meaning strongly agree.

Exploratory factor analysis was conducted on the learning experience scale using SPSS 23.0 software. The KMO value was 0.861, and the significance level of Bartlett’s sphericity test was lower than 0.001. The number of factors was not limited, and the component matrix after rotation is shown in Table 1. Factor 1 was “goal and feedback” (GF), Factor 4 was “selectivity” (SL), Factor 2 was “teamwork” (TW), and Factor 3 was “general self-efficacy” (GSE). The loads of all items were between 0.628 and 0.904, and the cumulative variance interpretation of all factors reached 79.725%. The overall variance interpretation rate of the first factor was 48.244%, less than 50%. Therefore, the present study was seemingly not affected by the common method deviation. GF and SL were game experiences, TW was team experience and GSE was personal efficacy, and thus, H1 was verified.

On the basis of 177 pieces of valid sample data, the reliability of the scale was tested by SPSS 23.0 software. The Cronbach’s α coefficient of the overall scale was 0.95, and the Cronbach’s α coefficient of each subscale ranged from 0.849 to 0.913. The results are shown in Table 2. All values exceeded the standard of 0.7 (Nunnally and Bernstein, 1994), indicating good reliability of the scale.

The design of the scale was based on the mature scale of existing studies, which has good content validity. AMOS 23.0 was used for confirmatory factor analysis, and the goodness of fit index of the six-factor model was good (χ^2/df = 2.085, CFI = 0.936, GFI = 0.838, IFI = 0.937, TLI = 0.922, and RMSEA = 0.079) (Arbuckle, 2003). The CR values of all variables were greater than 0.7, and the AVE values were greater than 0.5, indicating that the scale had good convergence validity (Fornell and Larcker, 1981). The square root of AVE of each variable was greater than the correlation coefficient between this variable and all other variables, indicating that the scale had good discriminative validity (Fornell and Larcker, 1981).

The mean value, standard deviation and correlation coefficients of all variables are shown in Table 3. The overall level of the virtual simulation game course learning experience was higher. The average values of system goal and feedback, selectivity and team cooperation experience exceeded 6 points. The general self-efficacy level was relatively low at only 5.375. The average value was the lowest of all variables, and the standard deviation was the largest of all variables, indicating that students perceived the game as challenging. The perception difference between students was also large. In the course, students’ overall investment was high, and the students also agreed with the effectiveness of the course in improving entrepreneurial skills. As shown in Table 3, all variables were significantly correlated (P < 0.001). The largest correlation coefficient with entrepreneurial skill development was students’ experience of “goals and feedback” in the game, followed by students’ general self-efficacy.

The independent sample T-test for gender shows that there were no significant differences between boys and girls in the level of learning experience, learning engagement and perceived entrepreneurial skill development.

AMOS 23.0 was used to test the hypotheses, and the results are shown in Figure 1 and Table 4. The model fitting index was good (χ^2/df = 2.085, CFI = 0.936, GFI = 0.838, IFI = 0.937, TLI = 0.922, and RMSEA = 0.079) (Arbuckle, 2003). As shown in Table 4, GF had no significant effect on SE (β = 0.118, P > 0.05) and SL on SE (β = 0.072, P < 0.01), and H2a was not supported. TW had a significant positive effect on SE (β = 0.223, P < 0.001), and H2b was verified. GSE had a significant positive effect on SE (β = 0.510, P < 0.001), and H2c was verified. GF had a significant positive effect on ESD (β = 0.294, P < 0.001) and SL had a significant positive effect on ESD (β = 0.175, P < 0.05), and H3a was verified. TW had a significant positive effect on ESD (β = 0.146, P < 0.05), H3b was verified. GSE had a significant positive effect on ESD (β = 0.260, P < 0.001), and H3c was verified. SE had a significant positive effect on ESD (β = 0.199, P < 0.05), and H4 was verified.

Based on the 3P learning process theoretical model of Biggs (1993), for the virtual simulation game in entrepreneurship education, college students’ learning experience was taken as the pre learning variable, students’ engagement (SE) was taken as the learning process variable and entrepreneurial skill development (ESD) was taken as the learning outcome variable to explore the action mechanism of entrepreneurial learning experience on college students’ entrepreneurial skill development. The following conclusions were drawn:

Firstly, focusing on the student experience in virtual simulation game courses, multiple dimensions were comprehensively considered in the present study, such as game design, team cooperation and student self-efficacy, and a structural equation model was constructed to test their relationship with student engagement and entrepreneurial skill development, which is a significant innovation. Several existing studies have only focused on games themselves (Almeida and Buzády, 2019; Yen and Lin, 2020; Capelo et al., 2021; Zulfiqar et al., 2021), teamwork (Arias-Aranda and Bustinza-Sánchez, 2009; Lohmann et al., 2018; Buil et al., 2020; Thanasi-Boçe, 2020), and students’ personal traits (Eggers et al., 2017; Alstete and Beutell, 2019; Charrouf and Janan, 2019; Hernández-Lara et al., 2019; Buil et al., 2020), lacking a comprehensive study design.

Secondly, based on the theoretical framework of learning process, the influencing factors of college students’ entrepreneurial skills development was investigated in the present study, which is innovative. Existing studies on the effect of virtual simulation game courses on college students’ entrepreneurial learning results have generally been based on the planned behavior theory (Newbery et al., 2016; Zulfiqar et al., 2018, 2021), the technology acceptance model (Zulfiqar et al., 2018, 2021) and Flow theory (Choi and Kim, 2004; Yen and Lin, 2020), which tend to limit the perspective to the game system itself. As such, there is a failure to fully present the complete chain of “input-process-result” and its mechanism of entrepreneurial simulation learning. In the present study, Biggs (1993) 3P learning process theoretical model was applied to the field of entrepreneurship education, the relationship between entrepreneurial learning experience, student engagement and entrepreneurial skill development was explored, and the path mechanism of entrepreneurship education influencing the outcome of entrepreneurial learning was deepened and expanded from the perspective of student learning. Such factors are crucial for advocating student-centered entrepreneurship education (Robinson et al., 2016).

Thirdly, the present study has considerable theoretical significance for clarifying the mechanism through which game design influences learning outcomes. Previous studies on game design has mostly focused on Flow theory (Csikszentmihalyi, 1990), and most of the results support that game design goals, feedback, and selectivity can significantly positively influence students’ flow experience (Choi and Kim, 2004). Notably, there are also unsupported results (Yen and Lin, 2020). However, existing studies have not further explored the direct impact of game design experience on learning results. Empirical research in the present study confirms that although students’ game design experience does not significantly affect their learning engagement, there is a direct and significant positive impact on learning results.

In the context of the global COVID-19 pandemic, the present study has considerable practical significance for entrepreneurship education. By introducing online virtual simulation games into entrepreneurship classes, students can learn entrepreneurship through the Internet and develop their entrepreneurial skills. The results of the present study have significant practical implications for teachers in terms of how to better conduct virtual simulation game courses.

First, when introducing virtual simulation games into entrepreneurship courses, teachers should pay attention to whether the design of the game itself meets the teaching objectives, whether the game establishes clear performance evaluation standards, provides rich and clear learning materials, and whether it can provide clear and clear feedback to students after each decision-making operation. Attention should also be paid to whether the game provides different levels of difficulty and different environmental parameters for teachers to adjust settings, so as to provide students with an “optional” competition environment. Such factors can directly affect students’ sense of learning.

Secondly, teachers should pay special attention to the atmosphere of students’ team in the teaching of virtual simulation games. Because students’ experience of the team atmosphere will not only affect students’ engagement, but also directly affect the learning result. Teachers can establish an assessment mechanism and take team participation as an indicator of students’ individual assessment to avoid free-riding. At the same time, for teams with conflicts, teachers need to actively intervene, adjust and encourage the CEO to create a harmonious, democratic and mutual trust team atmosphere.

Thirdly, teachers should pay attention to improving students’ general self-efficacy in the course of virtual simulation games. The results of the present study show that students’ general self-efficacy has the greatest impact on students’ engagement, and is also a significant factor affecting the development of entrepreneurial skills. Teachers need to review relevant professional knowledge before the game, explain the game rules in detail, and test to understand students’ mastery of relevant knowledge and rules. After 1–2 rounds of competition, teachers can also allow students to choose the difficulty level of the competition independently, so as to enhance students’ sense of self-efficacy, and then improve students’ investment and ability, which is consistent with the research conclusions of Alstete and Beutell (2019).

There were several limitations in the present study. First, the focus was on the three factors of game, team and individual in the course of virtual simulation games, but the factor of teacher may be ignored, which needs to be supplemented in future research. Second, entrepreneurial skill development is used as an evaluation index of learning results in the present study, and future research can be further extended to entrepreneurial intention, entrepreneurial behavior and other indicators. Third, the method of questionnaire survey was adopted to collect cross-sectional data. In the future, qualitative research and experimental research can be used comprehensively to ensure more rigorous research results.