Business Simulation Games (BSG) and its benefits

BSG are tools that can duplicate decision-making in a real-world business context by using students' natural capacity for technology, according to the Academy of Management (AOM) and the Association for Business Simulation and Experiential Learning (ABSEL)11. BSGs are also digital environments with the aim of teaching or training through an experience that extends beyond entertainment and fun (without necessarily excluding such features), utilizing technological resources, and employing gamification techniques in daily business. Students' reactions to their simulation experiences show that they enjoyed the competitive team environment and acquired knowledge from it. In addition, frequent business decision-making may help participants make better strategic decisions12.

Previous research on “GLO-BUS” indicated that students get access to unique and rich contexts for the application of strategic management frameworks through an engaging and competitive simulation environment13. Decision-making, overviewing, target-based orientation, and a problem-solving focus are all characteristics of BSG14. Business games, in general, (1) allow students to engage with educational content in a more enjoyable and interactive way, as well as benefit from the simulation of scenarios with several factors that are difficult to represent with other methodologies; (2) allow students to gain management skills and competencies that are required in the business world15; and (3) assist participants in gaining experience without the risks and costs of putting their decisions on the line15.

Human-system interaction in the context of BSG

Regarding the systematization of research on the modeling of a game, the Taxonomy of Computer Simulations 16 and then adapted for the BSG17 considers the design elements of the user interface. Human-system interaction design, also known as interactive systems design, must take into account a number of factors, including attention and basic human capacity in task execution. In the design of interactive systems, the author emphasizes the need of working on resources that recognize recourse and devices such as assistants and automatic error checkers for probable attention aberrations. These tools are useful for simulating a student's reasoning in a certain field of knowledge, as well as receiving ideas and assistance from educators18. Given the high level of complexity and multiplicity of operational and project requirements presented, it is critical to consider the possibility of investigating new methodologies and devices to monitor and analyze the user experience of BSG. This can contribute with essential elements to guide its design and success as a learning tool.

Entrepreneurial Intention (EI)

In the literature on entrepreneurship, intention is defined as a state of mind that focuses someone's attention towards entrepreneurship, resulting in that individual prioritizing self-employment over organizational work19, 20, 21. Intentions have been proved to be a well-built predictor of planned behaviours in entrepreneurship research22, 23. Intentions define one's ability to become an entrepreneur and, more importantly, whether such ideas will be pursued effectively or not24. Thus, the Ndovela and Chinyamurindi Entrepreneurial careers: Circumstances Influencing Entrepreneurial Aspirations 149 lifespan of an entrepreneurial enterprise is influenced not just by environmental factors but also by the entrepreneur's intentions25. It has also been linked to entrepreneurship and has been seen as the heart of entrepreneurship relating to the establishment of a business. Entrepreneurial intentions have a positive effect on students’ entrepreneurial attitude, therefore posing a need for educators to reinforce this sentiment26.

Individuals, the environment, and their interactions are all involved in the phenomena of venture creation27. Previous researchers believe that entrepreneurial intentions are influenced largely by the happenings in the macro-environment 28. The influence of such environmental factors can be an interacting effect with individual actions29. Furthermore, developing an understanding between environment and behavior is essential for determining an intent towards when and how to take advantage of entrepreneurial opportunities 30, 31. By incorporating BSG into education, it is possible to modify students' intentions and build entrepreneurial aspirations in them32. After forming an entrepreneurial purpose, an individual begins to hunt for possibilities to start a new business, and if this is somewhat misleading and opportunities are not spotted or discovered, then the entrepreneurs have to imagine the future market value of their product or service33, 34. In management, BSG is seen as critical in the development of EIs among students5, 35.

Self-efficacy

Self-efficacy explains human behavior as “a product of the interplay of intrapersonal influences, the behavior individuals engage in, and the environmental forces that impinge upon them” 36. The interaction between these factors determines one's belief in one's capacity to effectively conduct a certain activity in a particular context, as well as one's expectations for the behavior's results37. Self-efficacy is the antecedent and consequence of an action choice, and it influences how people do their current task and plan for future task successes38. According to Bandura (2012)36, self-efficacy is the most influential component influencing behavior since it has an impact on other processes and factors such as goal setting, outcome expectations, and intention. The notion of self-efficacy has been applied in a variety of sectors, including entrepreneurship, due to its fundamental impact on human behavior.

Subjective Norms

Subjective norms is key indicators of intentions 39, 40. Subjective norms is influenced by perceived expectation levels from significant people, such as family, colleagues, and role models, according to Peng (2012)41. The importance of social connection in this environment, as the presence of others influences people's thoughts, feelings, and behaviors. Others, both worldwide (e.g., 42) and within the South African career research literature, agree with this viewpoint (e.g., 43). Furthermore, subjective norms can also play a role in determining entrepreneurship intent as a characteristic that influences experiences 44.

The direct effect of Human-system interaction on Self-efficacy

Miller (2010) 50 noted that the simulation is the “kind of learning tool that can be very effective in moving students from the lower rungs of learning to the upper rungs where true critical analysis and understanding takes place” (p. 161). Indeed, BSG is considered a successful experience learning tool “in which the learner was directly in touch with the realities being studied” 51, 52. BSGs, which provide a more realistic view of the entrepreneurial experience than theoretical teachings and allow experiential learning with no real-world consequences53, 54. This approach can help students observe the relationship between decisions and their outcomes55. Furthermore, student's game performance is frequently linked to their grades, implying genuine repercussions and urging them to improve their behavior.

Studies highlight the combination of education in systems thinking and team skills training through game simulation, leading to more sustainable systems management. As a result, students' self-efficacy especially increases during their own game design phase. The training program for self-efficacy through gaming simulation demonstrates that the interactive design of simulation games supports change processes in educational organizations56. Accordingly, we hypothesize:

H1: Human-system interaction has a positive effect on Self-efficacy.

Human-system interaction and EI

Human-system interaction can be defined as “the degree to which learners believe that they can easily take and study the learning contents via interacting with the learning function of the e-learning platform” 57. Business game human-system interaction “can develop student’s entrepreneurial skills and encourage them to undertake entrepreneurial activities. The simulation experience allowed students to face challenges, overcome limitations, improve their analytical skills, and enhance their business knowledge58. Provided that learners find it interesting while interacting with BSG in some aspects (game design, game challenge...), they will go through high perceived playfulness59 and they will be more likely to experience flow 60, which straightforwardly lead to the learners' engagement in BSG. Buil (2019)61 hypothesized that engagement has a positive impact on skill development and perceived learning, which can not only meet the target of the courses but gradually form students' entrepreneurial intention as well.

In examining the relationship between EI and human-system interaction, a number of studies have yielded significant results focusing on two different aspects of human-system interaction (website quality). Students generally express a positive attitude toward BSG and the perceived learning from BSG. This positive feeling continues, as well, years after students have finished their simulation exercises and moved into the business world62. Accordingly, we hypothesize:

H2: Human-System Interaction has a positive effect on EI.

Self-efficacy and EI

Self-efficacy is instrumental in producing the intended or desired results of their efforts. Bandura 63, 64 explains self-efficacy as “one's belief in one's ability to succeed in particular situations or to accomplish a task "and is "the confidence in one’s own ability to achieve intended results”. Human performance is affected by external issues such as the nature of the task, the tools being used, and the situation. So, self-efficacy is the belief in one's ability to act resulting from those actions. Merhi65, 66 noted that self-efficacy is a personal assessment of one's ability to carry out a variety of tasks and actions. The higher the confidence level is, the higher their intention to choose entrepreneurship as their future career enhances. Several researches have shown that self-efficacy strongly influences individuals' ability to become entrepreneurs, their efforts to create a new business, their persistence in the face of change, their resilience facing challenges of creating new businesses, and their success in operating the business role 67, 68, 69. Accordingly, we hypothesize:

H3: Self-efficacy has a positive effect on EI.

The moderating variable on the relationship between Human-system interaction and Self-efficacy

In business simulation games, opinion of peers is very important for a number of reasons: (a) students interact with each other and share information and thoughts which strongly affect their positive intention; (b) Visser and Krosnick (1998)70 also found that young students learn more efficiently and effectively from the people who are close and important to them such as teachers and parents. Knowledge, information, and resources acquired through social and external networks can help students identify, realize opportunities and obtain external resources, advice, and information from their networks71. As a result, social support which is defined as subjective norms that students get from family and friends is crucial to translating business knowledge, skills, information of acquired to their confidence in abilities 72.

Additionally, it has also been suggested that entrepreneurial self-efficacy may be enhanced through appropriate training and education and, subsequently, by leveraging the rate of entrepreneurial activities73, 74. this study was designed to investigate the impact of BSG’s interactive system with students on entrepreneurial self-efficacy within the context of entrepreneurship education. The support obtained from the subjective norms is essential to change the BSGs interaction with students into a his/her belief in the ability to perform a number of tasks and to increase the motivation and desire to start a business72, 75. Taking into account the previous arguments, there are significant differences between individuals with different subjective norms regarding human-system interaction influence on self-efficacy, we propose our first hypothesis:

H4: Human-system Interaction has a positive effect on Self-efficacy and will be moderated by Subjective Norms.

Data collection

The data was collected via a survey using Google Forms from business universities throughout Vietnam. Data collection in Vietnam can be difficult as there are a few business universities applying BSG in their courses. Therefore, we have to investigate the number of schools throughout Vietnam that use BSG as a teaching method to develop a list of potential respondents who show willingness for survey participation. The purpose of the study was explained to the students before they were asked to fill out the questionnaire. Students who participated in the study were majoring in Marketing, International Business, Commercial Business, etc. Five participants have had experience with the BSG in courses and have played it before in several courses. A total of 238 students were interviewed to take part in the survey. Of them, 43 surveys were removed from the sample because they indicated that they had no experience in business simulation games. In the end, a total of 195 valid surveys were used for data analysis, resulting in an 81.9% usable response rate.

Measurement

To establish a rigorous measurement of the manifest variables, the instrument development process followed the prescriptions recommended in the seminal articles focused on enhancing the validity of measurements in positivist studies 76. Measurement items were adapted from previous literature with little modifications of words and sentences in accordance with this study. The measures for subjective norms are adapted from Pender (1986)77. An example of the subjective norms is “The school you are studying creates many conditions to encourage you to pursue your EI”. The measures for human-system interaction are adapted from Ajzen (1991)78. An example of the simulation design is “The business simulation game offers full detailed instructions online”. Additionally, to measure intrinsic motivation, the well-known Situational Motivation Scale79 was employed. This includes statements such as “I am confident in my ability to start my own business” (self-efficacy). The measures for entrepreneurial intention are adapted from Albert Shapero (1982)80. An example of the EI is “I have very serious thoughts about starting a business, setting up a company”. In all cases, the 5-point Likert scale was used for almost all questions to measure the responses with 1 - indicating strongly disagree, and 5 - indicating strongly agree.

Contextual Qualitative Data

In terms of Human-System Interaction construct, beside the item that was collected from Cheng, Y. M. (2020)’s57 questionnaire, we added two more items based on comments of interviewees. For the opinion “The participant’s guide is very helpful not only in the game but appropriate for real-life economy as well”, we added “Business simulation games allow me to learn a lot of real business knowledge” and “The business simulation game offers complete online instructions” for this construct.

With regards to Subjective Norms construct, beside the items that were collected from Frarrukh et al (2019)’s81 questionnaire, we added three more items based on the viewpoints of our interviewees. For the comment “Family has a significant impact on student's self-efficacy because they are provided mental and physical advice to be an entrepreneur”, the item “Family influences my entrepreneurial intentions” was added. For the opinion “The comments of friends can affect his mindset that he has the ability to startup”, we added “If friends around me think I am good for doing business, then I would think I am appropriate for being an entrepreneur”. For the comment “The others’ advice makes me think of starting a business”, then the item “From the subjective effects of society, my business intentions are rekindled” was added for the construct.

Referring to Self-efficacy construct, beside the items that were collected from Yen, W. C. (2020)’s82 questionnaire, we just added one more item “I believe I can overcome my financial limitations to gain start-up opportunities” due to the opinion “The financial status is crucial when a person considers whether he/she should startup”.

Since there were no additional comments for the Entrepreneurial Intention, our items for this construct were all collected from Doanh, D. C. (2021)’s 83 questionnaire and no more items were added.

Measurement model assessment

Table 1 reveals that all the reflective constructs have high levels of internal reliability and consistency, as demonstrated by the above composite reliability values. All the constructs’ reliability coefficients ranged from 0.838 to 0.893, with all of them above 0.70, indicating that the items are reliable measures for their perspective constructs 84, 85. To test the reliability of EI, human-system interaction, self-efficacy, and subjective norms, Cronbach’s alpha coefficient was computed. The Cronbach’s alpha values varied from 0.718 to 0.82, which exceeded the minimum acceptable values and proved good internal consistency reliability for each latent construct. For exploratory studies, values 0.70 are considered acceptable 84.

To assess the convergent validity for each construct, the standardized factor loadings were used to determine the validity of the four latent constructs 86, 87. The findings indicated that each factor loading of the constructs ranged from 0.629 to 0.892 and exceeded the recommended level of 0.50. As each factor loading on each construct was greater than 0.50, the convergent validity for each construct was established, thereby providing evidence of construct validity for all the constructs in this study86.

Additionally, the AVE was calculated to assess the discriminant validity for the four constructs88 for which the AVE ranged from 0.510 to 0.735. All values of all constructs were likewise found to be higher than the threshold of 0.5, demonstrating adequate convergent validity89. The items utilized in this study also have strong convergent validity, since they loaded highly (more than 0.50) on their respective components, according to the results. Table 1 summarizes the findings.

The discriminant validity of the construct is shown in Table 2. Its discriminant validity is supported by the fact that the square root of the AVE between each pair of factors is greater than the estimated correlation between factors85, 88. Table 2 compares cross-loadings and indicates that an indicator's loadings are higher than other loadings in the same column and row for its own construct. Furthermore, the results indicate that there is discriminant validity between all the constructs based on the loadings and cross-loadings criterion depicted in Table 1.

Structural model assessment and hypotheses testing

We used bootstrapping technique to analyze the significance of indicators 90. The use of a bootstrapping technique to analyze the significance of the loadings obtained on the observed variables is based on the model's estimates and calculates the estimates of the parameters and their confidence intervals based on multiple estimated 91, 92. Table 3 presents the values of coefficients of the Structural Model – Between Constructs. The values were estimated by a bootstrapping technique. All T-values higher than 1.96 (significance level = 5%) and p-values lower than 0.05, except human-system interaction with EI90, 92.

Thus, 2 hypotheses proposed by the authors are accepted. Specifically:

• Self-efficacy has the strongest and positive impact on EI (H3) with the coefficients β= 0.576 and p= 0.000.

• Human-system interaction has the lowest and positive impact on Self-efficacy (H1) with the coefficients β= 0.140 and p= 0.023.

Mediating Effect: Self-efficacy is a mediator between Human-system interaction and EI (H2b).

Table 4 and Table 5 presents an indirect effect. T-values higher than 1.96 (significance level = 5%) and P Value less than 0.05, so indirect effect is significant. Thus, there exists an indirect relationship from HSI to EI. It can be concluded that Self-efficacy plays a mediating role between Human-system interaction and EI, hypothesis H2 is accepted.

Moderating effect:Human-system interaction has a positive effect on Self-efficacy and will be moderated by Subjective norms (H4).

From Table 6, the P-values of the relationship Subjective norm affects Self-efficacy is 0.000 < 0.05, showing that Subjective norm impacts on Self-efficacy. Regression coefficient Original Sample (O) = 0.573 > 0 shows that Subjective norm has a positive effect on Self-efficacy. The P-values of the relationship Human-system interaction affects Self-efficacy is 0.023 < 0.05, showing that Human-system interaction impacts on Self-efficacy. Regression coefficient Original Sample (O) = 0.140 > 0 shows that Human-system interaction has a positive effect on Self-efficacy. Moreover, P-values of the moderating relationship Subjective norms*Human-system interaction affects Self-efficacy is 0.015 < 0.05, showing that Subjective norm*Human-system interaction has an impact on Self-efficacy. Thus, the Subjective norm has the moderating role from Human-system interaction to Self-efficacy. Original Sample regression coefficient (O) = 0.158 > 0 shows that increasing Subjective norm will increase the impact of Human-system interaction on Self-efficacy. Therefore, hypothesis H4 is accepted.

The studies of Cohen (1988) and Faul et al., (2007) were used to evaluate the coefficient of determination (r²), determining that the f² values ​​were equal to 0.02, 0.15, and 0.35 are considered small, medium, and large effects.

Table 7 presents the effect levels of relationships:

• Subjective norms on Self-efficacy (f² = 0.443): has a large effect.

• Self-efficacy on EI (f² = 0.440): has a large effect.

• Human-system interaction*Subjective norms on Self-efficacy (f²e = 0.060): has a small effect.

• Human-system interaction on Self-efficacy (f² = 0.025): has a small effect.

• Human-system interaction on EI (f² = 0.010): has a very small or no effect.

According to the responses, Table 8, the construct Self-efficacy presented an R² of 0.430, the construct EI presented an R² of 0.369, both of which were accepted.

Figure 2 shows the model that was developed as a result of this research. The path coefficients and significance levels for each hypothesis are indicated by the numbers in the arrows. The standard error and f square generated by the bootstrapping procedure were used to determine the importance of the paths.

For the PLS-SEM and linear regression models, Table 9 compares the root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and Q Predict values (LM). For RMSE, MAE, and MAPE, the results demonstrate that PLS-SEM has higher error values than LM, indicating that PLS-SEM has low out-of-sample predictive power, with the exception of SE2. It was further confirmed in Table 9, where the Q Predict values for PLS-SEM and LM show that PLS-SEM values are slightly lower than LM values except for SE2, but those were positive and greater than zero. There are a few explanations for this. PLS Predict produces case-specific predictions on the composite model level 93. The PLS-SEM model of this study is complex as this includes both single-order and second-order formative constructs and used reflective indicators. Besides, the number of observations used (1950) was large in this study. Large sample size in PLS-SEM has the likelihood to detect some misspecifications94. Literature suggested that PLS-SEM offers a better solution with a small number of observations used with many constructs and many items95, and the PLS Predict approach is useful when the dataset is very small partitioning is problematic93.

Limitations and future research

Despite the fact that this paper has a number of significant contributions, the acknowledgment of its shortcomings must not be taken for granted. These limitations also suggest the directions for further research.

Firstly, due to the lack of universities in Vietnam applying BSG as a teaching method, the range of respondents who participate in the survey is exceedingly undiversified, with the majority from UEH University and Southern universities. Validating this model in other locations, such as extending to North and Central Vietnam, will augment the body of knowledge and enrich our understanding about elements of business-based gamification appreciably influencing students' EI. Consequently, future research may authenticate the model and hypotheses presented above in other locations.

Secondly, owing to the fact that the number of students who have been playing BSG before is scant, data analysis is conducted from a small size of the sample. Accordingly, future research could collect data in a larger size, especially respondents with entrepreneurial experiences.

Thirdly, this study only focuses on investigating the relationship between proficiency in BSG and EI which is based on one factor of SDT (namely, competence). Thus, future research might magnify the research model to comprise other factors of SDT such as autonomy or/and relatedness to completely understand the power of intrinsic motivation in the case of BSG and its functions to create EI. Furthermore, BSG mentioned in this study in general definition, means that the research does not thoroughly point out the name of the game that is being investigated and the related domain of business. Hence, future research could scrutinize the research model and hypotheses presented above with regard to more specific games which simulate the risk-free economy, remarkably in more exclusive fields and departments such as Logistics, Marketing, etc. to have deeper expertise about users’ EIs in terms of distinctive business-based gamification.

Finally, in this study, we have focused on only two types of external conditions (namely, subjective norms and simulation design). However, other types of conditions exist. Therefore, future research could focus on examining other types of exterior stipulation such as human-human interaction (peer-to-peer interaction, tutor-student interaction), reward, punishment... to fully comprehend the relationship between independent and dependent variables from all aspects.