Investigating the effect of flow experience on learning performance and entrepreneurial self-efficacy in a business simulation systems context

ABSTRACT

Providing teachers with innovative teaching approaches that combine simulation-based learning systems with entrepreneurship education helps create and recognize entrepreneurial opportunities. In this study, key antecedents are identified as critical drivers of flow experience and the impact of flow experience on learning performance and entrepreneurial self-efficacy is examined in order to propose a behavior model based on flow theory. Virtual Business Retailing (VBR) software, a business simulation system for convenience store operations, is used in this study to investigate the learning behavior of students and to collect data for analysis. The results reveal that challenge-skill balance and playability play a critical role in enhancing flow experience and, consequently, in improving learning performance and entrepreneurial self-efficacy. The findings contribute to existing flow theory, the literature on simulation and game-based learning, and the literature on entrepreneurship education. Furthermore, the findings provide VBR education-system educators and developers with a better understanding of students’ expectations and needs when interacting with retail business simulation-based learning environments as well as with guidelines to effectively design VBR educational systems that are conducive to flow experience, which may help improve entrepreneurial self-efficacy in students.

KEYWORDS:

• Business simulation systems
• flow experience
• learning performance
• entrepreneurial self-efficacy
• simulation-based learning

1. Introduction

Entrepreneurship education has received abundant attention in recent years. According to a 2004 statement by the U.S. National Content Standards for Entrepreneurship Education (NCSEE), entrepreneurship education helps maximize individual and collective economic and social success at the local, national, and global levels. Universities are widely considered incubators of entrepreneurial spirit and culture because these institutions are expected to play an important role in identifying and developing the entrepreneurial traits and inclinations of students and in enabling them to start their own ventures. Many scholars and educators identify the purpose of entrepreneurship education as encouraging and stimulating student awareness of entrepreneurship as a process and as a possible career and understand how various management disciplines (e.g. accounting, finance, marketing) may be combined with new venture creation (Edelman, Brush, & Manolova, 2008; Kassean, Vanevenhoven, Liguori, & Winkel, 2015; Peterman & Kennedy, 2003). However, although conventional teaching methods such as lectures, case studies, and discussions provide students theoretical insights on entrepreneurship, these methods do not provide real-world experiences or practice (Zulfiqar, Sarwar, Aziz, Chandia, & Khan, 2018). Simulation-based learning, which repeatedly manipulates learning content and monitors manipulation results to help students better understand real-world experience and practice (Chang, Liang, Chou, & Lin, 2017; Hou & Li, 2014; Hsieh, Lin, & Hou, 2016), is currently one of the most popular approaches to entrepreneurship education (Fox, Pittaway, & Uzuegbunam, 2018).

Previous studies have emphasized the potential of digital game-based learning to improve the flow experiences and/or learning performance of students (Hsieh et al., 2016; Hung, Sun, & Yu, 2015; Hwang, Wu, & Chen, 2012; Wang & Chen, 2010). Hsieh et al. (2016) revealed that game-based learning can provide students with higher flow experiences and learning performances. Hung et al. (2015) demonstrated the ability of tablet/PC game-based learning to improve flow experience, learning performance, and satisfaction. Hwang et al. (2012) found that a web-based game approach not only significantly promoted the flow experience, learning attitudes, learning interest, and technology acceptance of students but also improved their performance on a problem-solving activity. Wang and Chen (2010) noted that integrating learning strategy (i.e. the matching challenging strategy and the challenging strategy) into game-based learning environments improves flow experiences and learning performance. However, aside from Lin, Yen, and Wang (2018), who found that a business simulation-based learning method contributed to learning performance, few studies have empirically verified this phenomenon within the context of simulation-based learning in the retail business sector.

However, student satisfaction and learning performance may be insufficient outcome measures to fully elicit the process by which business simulation-based learning transforms students from learners into entrepreneurs. Previous studies have argued that judging whether an entrepreneurial activity is successful or not can depend on the attitudes, interests and values of the individuals who are likely to form a new venture (Bird, 1988; Saeed, Yousafzai, Yani-De-Soriano, & Muffatto, 2015). Bandura (1989) emphasized the importance of perceived self-efficacy as a key factor in determining human agency and argued that people who have high perceptions of self-efficacy for a certain task are more likely to pursue and persist in that task. Chen, Greene, and Crick (1998) further defined entrepreneurial self-efficacy as the confidence of an individual in his or her ability to perform entrepreneurial roles and tasks successfully, and indicated that entrepreneurial self-efficacy was positively related to students’ intentions to start their own business. Entrepreneurial self-efficacy captures the complex and challenging knowledge, skills, and behaviors across various domains that are required to create and manage an entrepreneurial venture such as leadership (Bagheri & Abbariki, 2017; Lin & Tsai, 2008). One way to increase entrepreneurial self-efficacy in students is to provide mastery experiences or promote learning by doing such as developing business plans, utilizing business simulations and case studies, being exposed to guest speakers, and undergoing meaningful apprenticeships (Cox, Mueller, & Moss, 2002; Saeed et al., 2015). Previous studies have emphasized the importance of business simulations in constructing entrepreneurial intentions and behaviors in students (Lainema & Lainema, 2007; Newbery, Lean, Moizer, & Haddoud, 2018; Zulfiqar et al., 2018). However, although few studies have used entrepreneurial self-efficacy as an outcome measure, it may be considered as an effective measure in business simulation-based entrepreneurship education as well as a reliable predictor of individuals’ intentions to start new ventures, as it reflects beliefs regarding the likelihood of venture success.

Moreover, despite the highlighting of entrepreneurial self-efficacy in the literature as an outcome measure in a business simulation-based entrepreneurship education context, the construction of entrepreneurial self-efficacy has received minimal scholarly attention. According to flow theory, flow state is defined as the phenomenon of people completely focusing on an activity in the absence of feelings of self-consciousness, allowing them to follow their intrinsic motivation to engage in the learning environment (Csikszentmihalyi, 1991). Previous studies have argued that, in game-based learning environments, flow experience is a suitable factor for exploring the learning state of learners (Kiili, 2005; Sun, Kuo, Hou, & Lin, 2017). In addition, Wang and Hsu (2014) indicated that flow experience is an important issue in education system development, helping ensure that learners are engaged in the learning process. Ibanez, Di Serio, Villaran, and Kloos (2014) noted that a main challenge in education is fostering students’ flow state. Chang et al. (2017) argued that a game-based learning environment stimulates learners’ interests and attention due to the use of simulations, sense of presence, and high interaction. They also revealed that game-based learning triggers higher learning motivation and more effectively enhances the flow experience than non-game-based learning. Hou and Li (2014) found that playing a simulation-based game that led to experiencing flow states helped students obtain more vital knowledge than obtained by students that used traditional learning methods. Although prior studies have distinguished flow theory into two approaches (i.e. flow antecedents and indicators of flow experience) and explained flow experiences in a game-based learning environment, few have applied flow theory in the context of entrepreneurship education (simulation-based learning in business and entrepreneurial education).

To understand the potential of retail business simulation-based learning systems in entrepreneurship education, this study adopted learning performance, which is frequently used in game-based learning (Hsieh et al., 2016), and entrepreneurial self-efficacy as outcome measures. Furthermore, flow theory was used in this study as a useful lens to gain sophisticated insights into the role of flow experience in the learning performance and entrepreneurial self-efficacy of students in the context of business simulation-based entrepreneurship education. Furthermore, the theoretical and practical challenges noted above are addressed by investigating the following questions: (a) What are the determinants that lead students to experience flow states when they use business simulation-based learning systems? and (b) Does flow experience improve learning performance and entrepreneurial self-efficacy?

This research contributes to the existing literature in several ways. First, research findings regarding the role of business simulation and game-based learning in entrepreneurship education are limited. This study expands scholarly knowledge in this area by utilizing a retail business simulation-based learning system for entrepreneurship education. Second, this study enriches the literature on entrepreneurship education by demonstrating the importance of the retail business simulation-based learning system in enhancing the learning performance and entrepreneurial self-efficacy of students. Finally, this study extends the application of flow theory to the context of retail business simulation-based learning and provides new insights into the field of entrepreneurship education. This theory helps explain why the simulation-based learning system is a suitable teaching and learning tool for improving flow experience in students and, subsequently, for improving learning performance and entrepreneurial self-efficacy.

The remainder of this paper is organized as follows. The next section reviews the theoretical background and develops the main hypotheses. Section 3 outlines the research methodology and data analysis. The results are shown and discussed in Sections 4 and 5. Section 6 covers the theoretical and practical implications. Finally, the study limitations and directions for further research are presented in Section 7.

2. Theoretical background and hypotheses development

2.1. Business simulation-based learning

Simulation-based learning has been shown to help students better understand certain subjects by reflecting real-world situations through the repeated manipulation of learning content and observations of the results of those manipulations (Chang et al., 2017; Hou & Li, 2014; Hsieh et al., 2016). The simulations (i.e. serious systems/games) are not necessarily “games” and support “serious” outcomes via a digital game-based learning environment (Fox et al., 2018). Thus, their function goes beyond simple entertainment to pursue an educational purpose (Pando-Garcia, Periañez-Cañadillas, & Charterina, 2016). Simulations are characterized by fun, play, rules, goals, interactivity, outcomes or feedback, win states, conflict, and problem solving (Fox et al., 2018; Prensky, 2001). Simulations offer a solution for businesses that deal with issues that are complex, ambiguous, and uncertain by imitating the real business world (Chang, Wu, Weng, & Sung, 2012). Business simulations are primarily used in business management, marketing, finance, accounting, economics, product development, and entrepreneurship (Zulfiqar et al., 2018). Business simulations simulate the decision-making processes normally undertaken when starting or managing a business and thus may strengthen the management skills of students (Pando-Garcia et al., 2016).

2.2. Entrepreneurial self-efficacy

Self-efficacy, proposed by Bandura (1997) and derived from social learning theory, refers to the subjective perception of one’s ability to accomplish a specific task or behavior. Thus, abilities matter only when individuals have the self-confidence to use them in achieving a desired outcome (Bandura, 1989). Furthermore, self-efficacy beliefs are constructed through four main processes: (1) enactive mastery experiences; (2) role modeling and vicarious experiences; (3) social persuasion; and (4) judgments of one’s own physiological and affective states such as arousal and anxiety (Bandura, 1986; St-Jean, Radu-Lefebvre, & Mathieu, 2018; Zhao, Seibert, & Hills, 2005). Entrepreneurial self-efficacy (ESE), defined as an individual’s confidence in his or her ability to successfully perform entrepreneurial roles and tasks (Chen et al., 1998), is regarded as an important antecedent to new venture intentions (Barbosa, Gerhardt, & Kickul, 2007) and is a strong predictor of entrepreneurial intentions and action (Snell, Sok, & Danaher, 2015; Zhao et al., 2005). Further, various learning opportunities are likely to be tailored to achieve positive outcomes that individuals will attribute to their own ability, effort, and performance strategies. These attributions may lead to increased self-efficacy with regard to entrepreneurial tasks (Zhao et al., 2005). Previous studies indicate that entrepreneurial self-efficacy may be enhanced through appropriate training and education and, subsequently, by leveraging the rate of entrepreneurial activities (Florin, Karri, & Rossiter, 2007; Mueller & Goic, 2003). Therefore, this study was designed to investigate the impact of business simulation-based learning methods on entrepreneurial self-efficacy beliefs within the context of entrepreneurship education.

Learning outcomes are a key concern of instructors. Young, Klemz, and Murphy (2003) suggested that learning performance evaluations address multiple student learning outcome dimensions, including (1) self-evaluation of knowledge gained, (2) understanding, (3) skills, and (4) desire to continue learning. Similarly, Piccoli, Ahmad, and Lves (2001) argued that learning outcomes refer to the development and achievement of students in terms of knowledge, skills, behaviors, and attitudes following the completion of instructional activities. In addition to increasing student awareness of their current learning state, learning outcomes also provide a basis for improving and adjusting the pedagogy and methods of delivering lessons (Lee, Hsiao, & Ho, 2014). The findings of previous studies indicate that simulating the start-up process for a real business as a training exercise contributes to the development of correct and sophisticated action knowledge with regard to entrepreneurship, which increases the entrepreneurial self-efficacy of learners (Bandura, 1989; Gielnik et al., 2015; Gist & Mitchell, 1992). In addition, the results of Snell et al. (2015) indicate that entrepreneurial self-efficacy plays a critical role in transforming marketing capabilities in order to achieve specific goals. Therefore, it is posited in this research that students perceive increased learning performance in terms of self-evaluating their knowledge, skills, understanding and desire to learn more increases when using retail business simulation-based systems. This may exert a positive influence on their confidence with regard to their ability to run a business. Thus, the following hypothesis was proposed:

H1: Learning performance has a positive influence on entrepreneurial self-efficacy.

2.3. Flow experience

The concept of flow, first pioneered by Csikszentmihalyi (1990), refers to a state of consciousness in which a person is so absorbed in an activity that he or she excels in performance without being consciously aware of his or her every movement. Flow experience refers to the state of complete absorption or engagement in an activity (Csikszentmihalyi, 1991). The flow concept has been widely applied to the context of sports, work, shopping, gaming, website use, and computer use (Tuunanen & Govindji, 2016). In the game-based learning context, the findings of previous studies indicate that games help situate students in psychological states that heighten their involvement in learning activities and focus on the tasks at hand. Thus, students may lose their sense of time and undertake activities simply for enjoyment because they may lose awareness of themselves during the flow experience (Hsieh et al., 2016; Hwang et al., 2012).

According to Csikszentmihalyi (1990), flow experiences assist individuals to become fully involved in tasks and to stretch their skills and abilities to the limit. Liu, Cheng, and Huang (2011) argued that students who reported experiencing a flow state were more likely to be aware of available solutions and to apply these solutions to new problems. Sun et al. (2017) argued that when learners achieve a flow state, they are immersed in learning activities, face challenges proactively, and thus exhibit better learning achievement. Chang et al. (2017) claimed that flow experience enables learners to concentrate and ignore unrelated thoughts, leading to superior satisfaction and learning performance. In addition, previous studies have noted a positive correlation between flow experience and learning performance, as the former enabled students to achieve higher levels of learning performance in game-based learning environments (Hsieh et al., 2016; Wang & Hsu, 2014). In order to explore the linkage between flow experience and learning performance in retail business simulation-based learning environments, the following hypothesis was proposed:

H2: Flow experience has a positive influence on learning performance.

2.4. Flow antecedents

Kiili (2005, 2006) distinguished flow theory into the two different approaches of flow antecedent and indicators of flow experience in order to explain flow experiences. Kiili (2005, 2006) conceptualized flow antecedents as the characteristics of the game itself, stating that simulation game-based learning can enhance students’ balance of challenges and skills, clear goals, feedback, sense of control and playability, which are important antecedents of flow experience. The indicators of flow experience, related to the flow state, include concentration, time distortion, the autotelic experience, and the loss of self-consciousness (Kiili, 2005, 2006). This study investigated flow experience in the context of retail business simulation-based learning based on the work of Kiili (2005, 2006) because the flow experience framework proposed in that research is well suited to addressing educational game design characteristic requirements (Hsieh et al., 2016).

Previous studies have argued that students are more likely to experience flow when game-based learning environments challenge them in ways that match their skills (Csikszentmihalyi, 1991; Liu et al., 2011). Wang and Hsu (2014) revealed that, compared to learners who perceived a challenge-skill imbalance, learners who perceived a challenge-skill balance achieved higher flow experience. Hwang et al. (2012) argued that a well-developed game provides clear goals, unambiguous feedback, and a good sense of control, assisting students to overcome challenges and appreciate the playability, enjoyment and attraction of the game. Furthermore, Lin and Joe (2012) argued that when individuals perceive that their working processes are totally beyond their control, they are unlikely to concentrate on work activities, leading to weak flow experience. In addition, the findings of previous research indicate that a high level of self-control over learning content promotes a more pleasing learning experience (Park, Parsons, & Ryu, 2010; Rogers & Muller, 2006). Hou and Li (2014) suggested that the challenges and goals of a game are two key design elements that promote flow experience. Moreover, Ghani (1995) suggested that perceived tasks and challenges, skills, and playfulness can be considered antecedents of flow based on a human–computer interaction perspective. This study, following the theoretical lens of these prior studies, applied flow theory and evaluated the influence of flow antecedents on flow experience in a retail business simulation-based learning context. Thus, the following hypotheses were proposed:

H3a: Challenge-skill balance has a positive influence on flow experience.

H3b: Playability has a positive influence on flow experience.

H3c: Goals of an activity have a positive influence on flow experience.

H3d: Feedback has a positive influence on flow experience.

H3e: Sense of control has a positive influence on flow experience.

The proposed research model for this study is shown in Figure 1.

Figure 1. Research model.

3. Methodology

3.1. Virtual business-retailing (VBR) software and data collection

This study focuses on developing and empirically examining a model of entrepreneurial experience for a retail business simulation system. The participants were all business administration department students who were currently enrolled in marketing courses at a university in Taiwan. The marketing course was taught by an experienced professor who was seeking innovative learning and teaching methods. Retail business simulation-based learning system VBR software was adopted. VBR software has become popular in Taiwan education, especially in the context of business-related learning, as it allows students to engage in the virtual world and interact with peers during marketing-related decision making. In this simulation environment, students made decisions about store location, store opening and closing times, product lines to carry, physical inventory levels, shelf space arrangement and allocation, pricing and promotion, market research, staffing levels, purchasing, borrowing, and other issues. This study utilized VBR software to help students experience how to develop and run their own business. The participants were informed of the key learning tasks and goals and assumed the roles of virtual entrepreneurs exposed to intensive business competition. The participants were expected to learn how to make sensible decisions, and, thereby, acquire the necessary skills necessary to become an entrepreneur. After a period of instruction and experience using the VBR system, participants were asked to answer a questionnaire related to their learning experience and outcomes.

3.2. Sample

Participants in this study were recruited using a convenience sampling method. The authors advertised a survey in the marketing course. Among the 97 respondents who voluntarily participated in the survey, 94 provided complete and valid responses, yielding a response rate of 96.9%. The participants were sophomores and juniors majoring in marketing. All shared similar backgrounds. The valid responses had the following characteristics: 77.7% were female, 59.6% were 16–20 years of age, and 74.5% had an average monthly income of less than US$330. Although convenience sampling may lead to representativeness problems, Calder, Phillips, and Tybout (1981) pointed out that homogenous samples were more appropriate when the purpose of the research involved the application of theory. In addition, Calder et al. (1981) pointed out that homogenous samples reduce the probability of errors of inference. Therefore, using a convenience sample in this study seems appropriate.

3.3. Measures

To ensure the content validity of the constructs, all measures were adapted from existing scales, with minor word modifications made to increase their applicability to the context of this study. Each item was measured using a 7-point Likert scale (1: “strongly disagree”; 7:“strongly agree”). All measures are shown in Appendix A.

3.3.1. Entrepreneurial self-efficacy

Based on Chen et al. (1998), entrepreneurial self-efficacy was defined in this study as a student’s confidence in his or her ability to perform entrepreneurial roles and tasks successfully while using the retail business simulation-based learning system (i.e. VBR software). The items used to measure entrepreneurial self-efficacy were adopted from McGee, Peterson, Mueller, and Sequeira (2009).

3.3.2. Learning performance

Based on Piccoli et al. (2001), learning performance was defined as developments and achievements in student knowledge, skills, behaviors, and attitudes after completion of the retail business simulation-based learning activities. The items used to measure learning performance were adopted and modified from Premkumar and Bhattacherjee (2008).

3.3.3. Flow experience

Based on Csikszentmihalyi (1991) and Kiili (2006), flow experience was defined in this study as the state of complete absorption or engagement in the retail business simulation-based learning system in terms of concentration level, time distortion, autotelic experience, and loss of self-consciousness. The items used to measure flow experience were adopted from Kiili (2006).

3.3.4. Challenge-skill balance

Based on Csikszentmihalyi (1990) and Aube, Brunelle, and Rousseau (2014), we defined challenge-skill balance as the correspondence between the challenges associated with skills required to successfully complete the retail business simulation-based learning activity. The items used to measure challenge-skill balance were also adopted from Kiili (2006).

3.3.5. Playability

Based on Csikszentmihalyi (1990) and Aube et al. (2014), playability was defined as the degree to which a student is automatically and spontaneously involved in the retail business simulation-based learning activity without the need to reflect on his or her behavior. The items used to measure playability were also adopted from Kiili (2006).

3.3.6. Goals

Based on Csikszentmihalyi (1990) and Aube et al. (2014), goals were defined as the degree to which a student has a clear understanding of the needs associated with completing the retail business simulation-based learning tasks. The items used to measure goals were also adopted from Kiili (2006).

3.3.7. Feedback

Based on Csikszentmihalyi (1990) and Aube et al. (2014), feedback was defined as the degree to which the retail business simulation-based learning activity provides a student with clear and immediate feedback on his or her performance. The items used to measure feedback were also adopted from Kiili (2006).

3.3.8. Control

Based on Csikszentmihalyi (1990) and Aube et al. (2014), control was defined as a feeling of quasi-invulnerability, in which the possibility of failing is not present in the mind of the individual. The items used to measure control were also adopted from Kiili (2006).

4. Data analysis and results

This study followed the two-step procedure suggested by Anderson and Gerbing (1988). First, a measurement model was created to establish the validity and reliability of the proposed constructs. Second, a structural model was used to assess the structural relationships of the proposed constructs. The Partial Least Squares (PLS) method was utilized to empirically test the proposed constructs related to the retailer simulation-based learning system in the research model. The PLS, a form of component-based structural equation modeling, examines the psychometric properties of measurement models and the variable relationships of structural models. This study used SmartPLS 3.0 software for the PLS analysis because it supports both exploratory and confirmatory research and is efficient with regard to small sample sizes (Chin & Newsted, 1999; Reinartz, Haenlein, & Henseler, 2009).

4.1. Measurement models

Reliability, convergent validity, and discriminant validity were all evaluated in order to ensure the adequacy of the measurement model. The results show that Cronbach’s α and CR for all constructs were higher than the recommended threshold of 0.7 (Hair, Anderson, Tatham, & Black, 1998; Nunnally & Bernstein, 1994; see Table 1), indicating good reliability. As shown in Table 1, the results show that the AVE for each construct exceeded 0.5 (Fornell & Larcker, 1981), establishing convergent validity. Moreover, the results for discriminant validity show that the correlation between the construct pairs was lower than the square root of the AVE for each construct (Fornell & Larcker, 1981). In addition, the HTMT value, which is evaluated using the average correlation among the indicators across constructs relative to the average correlations among the indicators within the same construct, was below 0.85 (see Table 2; Henseler, Ringle, & Sarstedt, 2015), indicating good discriminant validity.

Table 1. Measurement model results.

Construct	Cronbach's alpha	CR	AVE
Entrepreneurial self-efficacy	0.95	0.96	0.5
Learning performance	0.92	0.94	0.7
Flow experience	0.90	0.92	0.5
Challenge-skill balance	0.86	0.93	0.9
Playability	0.87	0.94	0.9
Goal	0.82	0.92	0.9
Feedback	0.83	0.92	0.9
Control	0.92	0.96	0.9

Table 2. HTMT values.

Construct	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) Entrepreneurial self-efficacy
(2) Learning performance	0.67
(3) Flow experience	0.50	0.63
(4) Challenge-skill balance	0.68	0.61	0.63
(5) Playability	0.50	0.67	0.56	0.63
(6) Goal	0.61	0.50	0.46	0.70	0.43
(7) Feedback	0.53	0.61	0.52	0.72	0.58	0.84
(8) Control	0.41	0.51	0.30	0.63	0.62	0.67	0.83

4.2. Hypotheses tests

The results of the structural model are presented in Table 3. The first hypothesis was supported, as learning performance positively influenced entrepreneurial self-efficacy (β = 0.64, t = 12.15, p < 0.00). This result explained a substantial proportion of the variance in entrepreneurial self-efficacy (R²=0.41). The results further identified that flow experience had a highly significant influence (β = 0.59, t = 8.22, p < 0.00) on learning performance, thus supporting H2. The variance explained in terms of R² was 0.34 for learning performance. In H3, it was proposed that five factors would be positively associated with flow experience. Out of these five factors, the challenge-skill balance (β = 0.34, t = 3.27, p < 0.00) and playability (β = 0.33, t = 3.60, p < 0.00) had significant and positive influences on flow experience, which supported H3a and H3b, respectively. However, control (β = −0.32, t = 1.81, p < 0.1) had a significant negative influence on flow experience, and neither goals (β = 0.10, t = 0.82, p > 0.1) nor feedback (β = 0.23, t = 1.53, p > 0.1) had a significant effect on flow experience. Therefore, the results of this study failed to support H3e, H3c, and H3d. The variance explained in terms of R² was 0.40 for flow experience.

Table 3. Results of the Hypotheses Testing.

Hypothesis	Coefficient	Conclusion
H1	Learning performance→Entrepreneurial self-efficacy	0.64***	Supported
H2	Flow experience→Learning performance	0.59***	Supported
H3a	Challenge-skill balance→Flow experience	0.34***	Supported
H3b	Playability→Flow experience	0.33***	Supported
H3c	Goal→Flow experience	0.10	Not supported
H3d	Feedback→Flow experience	0.23	Not supported
H3e	Control→Flow experience	−0.32*	Not supported

*p<0.1; **p<0.05; ***p<0.01.

5. Discussion

This study provided several meaningful findings, which are described in the following subsections.

5.1. Learning performance and entrepreneurial self-efficacy

The results of this study show that learning performance significantly and positively impacted entrepreneurial self-efficacy. Thus, students with higher learning performances tend to believe strongly that they are able to run their own retail business successfully. This finding is similar to Snell et al. (2015), who found that marketing capabilities were significantly and positively related to entrepreneurial self-efficacy, indicating that an individual’s ability to practice various marketing activities may be self-monitored and assessed to in order formulate and make adjustments to his/her sense of entrepreneurial self-efficacy.

5.2. Flow experience and learning performance

The results of this study show that flow experience had a significant and positive impact on learning performance. This finding implies that superior learning performance can be achieved when students concentrate exclusively on retail business simulation-based learning tasks. This finding is consistent with the works of Chang et al. (2017), Hsieh et al. (2016), Sun et al. (2017) and Wang and Hsu (2014), all of whom associated flow experience with superior learning performance in game-based learning environments.

In addition, this study provides guidelines for building entrepreneurial self-efficacy by identifying five key design features in the retail business simulation-based learning system. This study suggests that these systems must be supported by flow experience (i.e. concentration, time distortion, the autotelic experience and loss of self-consciousness) and that, in the absence of this support, learning performance may not occur.

5.3. Flow antecedents and flow experience

The results show that the challenge-skill balance had a significant and positive impact on flow experience. This finding implies that flow experience is created when students perceive greater balance between challenge and skill within the retail business simulation-based learning environment. This result is consistent with the arguments of Csikszentmihalyi (1991), Kiili (2005, 2006), and Liu et al. (2011), who argued that if game-based learning environments offer students challenges that match their skills, students will be more likely to experience flow. In other words, the challenge-skill balance is a driver of flow experience. In addition, this result is consistent with the work of Wang and Hsu (2014), who revealed that, compared to learners who perceive the challenge-skill imbalance, those who perceive challenge-skill balance achieve higher flow experience.

In addition, the results of this study found that playability had a significant and positive impact on flow experience. This finding implies that the retail business simulation-based learning system enhanced play ability, and further led the participants to become immersed in their learning activities. Therefore, when students sufficiently accept a simulation-based learning system, they will obtain adequate flow experience. This result is consistent with the arguments of Kiili (2005, 2006), who identified play ability as an antecedent of flow experience. Moreover, the results of this study suggest that challenge-skill balance and playability are two critical roles for promoting the flow experience of students in retail business simulation-based learning.

Furthermore, the results of this study found that goals and feedback did not significantly impact flow experience. This finding contradicts that of Hou and Li (2014), who suggested that the goals of a game area key design element for promoting flow experience, and that of Kiili (2005, 2006), who suggested that goals and feedback are antecedents of flow experience.

One possible explanation for the non-significant relationship found between feedback and flow experience is that, although prior research showed that game-based learners tend to learn from their mistakes (Liu et al., 2011) and that the retail business simulation-based system provide students with an opportunity to instantly try different options when external feedback enabled them to go beyond their perceived abilities, the cognitive load associated with the feedback burdened students, making it difficult to achieve flow experience. One possible explanation for the non-significant relationship between goals and flow experience is that students are generally familiar with the traditional instructional curriculum and that many require constant external reassurance and support. As a result, students with high-level goals may be waiting for specific teacher instructions to improve their knowledge and skills, making it difficult to become immersed in this innovative learning and teaching method.

Furthermore, the results show that control had a significant and negative impact on flow experience in this study. The lack of support for H3e is surprising, as it is inconsistent with Lin and Joe (2012), who found a significant and positive relationship. They indicated that satisfying individuals’ instinctual sense of control and autonomy is critical to enhancing their flow experience. Moreover, this finding contradicts Kiili (2005, 2006), who suggested that sense of control is an important antecedent of flow experience. Possible reasons for this result include student attention being narrowly focused on a limited stimulus field. Specifically, the participants were given only one chance to play the game, and many used their limited available time to explore or to manipulate game operations, activities which distracted the participants from the learning tasks and inhibited their flow experience.

6. Practical implications

In addition to exploring the antecedents of flow experience in the context of retail business simulation-based learning, the model of behavior developed in this study may be used to explore the influence of flow experience on learning performance and, subsequently, on entrepreneurial self-efficacy. The findings of this study provide several meaningful insights for educators and developers who utilize and develop retail business simulation-based learning systems (VBR software).

First, the findings suggest that flow experience is an important driver of learning performance and entrepreneurial self-efficacy. When designing experiential activities, simulation system developers should take four elements (i.e. concentration, time distortion, the autotelic experience, and the loss of self-consciousness) into account in order to create an optimal flow experience. Students who are able to engage in the optimal flow experience become absorbed in the learning activities and thus enhance their relevant mental activities (i.e. remembering, thinking, feeling, and making decisions), which helps them effectively acquire knowledge and skills and enhance learning performance. In addition, when students participate in retail business simulation-based learning activities and use the many flexible learning strategies to enhance their performance, they experience a more realistic picture of what entrepreneurship requires and become more confident in their entrepreneurial abilities based on their formation of a more realistic perspective of their talents and abilities.

Second, among all the influential antecedents of flow experience, challenge-skill balance and playability are known to have the strongest impacts on flow experience. Simulation system developers should incorporate appropriate, challenging elements into the system design and simulate manipulation scenarios that allow students to make choices based on their abilities. This may be expected to increase student enjoyment of the learning materials while still challenging them, as the level of difficulty is adjusted based on need in order to maintain student enthusiasm for learning and to achieve flow experiences. In addition, the user interface design of the system should be as simple to use as possible (e.g. progressing through different scenes freely) to enable students to be more explorative and flexible and, thereby, become more immersed in the learning tasks at hand. In addition, in the future, incorporating touchscreens into these types of simulation-based learning systems may not only enhance student interest but also facilitate their more-active engagement in learning activities.

Third, developers of retail business simulation systems should design an appropriate and adequate feedback mechanism after each successful action that helps users reflect on their gaming strategies and improve through trial and error in order to maintain a high level of flow experience.

Fourth, teachers who adopt an innovative teaching and learning approach should play a steady supporting role in the process in order to help reduce feelings of loneliness and stress in novice entrepreneurs (students) and thus facilitate their desire to better understand the goals of the game. In addition, developers should set clear goals to help students identify their strategies and objectives, which may further enhance the flow experience of these students in the learning activities.

Finally, teachers should train students in advance to familiarize them with system operations, helping students to feel fully in control of their playing actions and to explore the nature of the problem-solving tasks. As a result, these students will be better able to concentrate on using the system and avoid being distracted by efforts to master system operations.

7. Limitations and directions for future research

Although this study offers several important contributions to theory and practice, there are several limitations that suggest avenues for further study. First, this study used a cross-sectional research design. Future research should use a longitudinal design in order to provide a richer and deeper understanding of how the retail business simulation-based learning system (VBR software) impacts students’ entrepreneurial self-efficacy. In addition, a longitudinal research design offers the potential to identify students who went on to establish business ventures and/or to succeed as entrepreneurs.

Second, the design of the methodology was conducted by a convenience sampling method to find volunteer participants who were business students taking marketing courses from a university in Taiwan. Future research should recruit larger and more diverse samples that include students in different academic disciplines, of different education levels, and different regions or countries in order to expand the generalizability of the research findings.

Third, to better understand the flow experience of students, future research should undertake a qualitative study consisting of student interviews addressing their system-playing experience. In addition, this study used self-report questionnaires to measure flow experience, which relied on participants’ recollections of their VBR-software-based flow experience. Future research should use a psychophysiological approach to examine flow experience in order to provide a deeper understand of the VBR software design.

Finally, future research may consider exploring other factors that affect the motivation of students to develop interest in and engage with retail business simulation-based learning and that affect their beliefs regarding self-efficacy. These include distinct desires and needs related to different academic majors (e.g. students who self-select an entrepreneurship major versus those who do not), the innate personal traits and characteristics of students, and the personality of the teachers.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Ministry of Science and Technology, Taiwan: [grant number MOST 105-2511-S-025-003-MY2].

Notes on contributors

Wan-Chu Yen

Wan-Chu Yen is an Assistant Professor in the College of Management and Design at Ming Chi University of Technology, Taiwan. She received her Ph.D. in Business Administration from National Chengchi University, Taiwan. Her current research interests include educational use of business simulation systems, social networking, and online user behavior. She has published papers in Computers & Education, Internet Research, and Service Industries Journal.

Hsin-Hui Lin

Hsin-Hui Lin is a Professor in the Department of Distribution Management at National Taichung University of Science and Technology, Taiwan. She received her Ph.D. in Business Administration from National Taiwan University of Science and Technology. Her current research interests include electronic commerce, educational use of business simulation systems, internet entrepreneurship education, service marketing, and customer relationship management. Her work has been published in academic journals such as Academy of Management Learning & Education, Computers & Education, British Journal of Educational Technology, Information & Management, Information Systems Journal, International Journal of Information Management, Internet Research, Managing Service Quality, Service Industries Journal, Computers in Human Behavior, Journal of Global Information Management, Information Systems and e-Business Management, and Journal of Educational Computing Research.

Appendix A: Constructs and measures

Table

Construct	Measure
Entrepreneurial self-efficacy
Searching	How much confidence do you have in your ability to  …  ? Brainstorm (come up with) a new idea for a product or service. Identify the need for a new product or service. Design a product or service that will satisfy customer needs and wants.
Planning	How much confidence do you have in your ability to  …  ? Estimate customer demand for a new product or service. Determine a competitive price for a new product or service. Estimate the amount of start-up funds and working capital necessary to start my business. Design an effective marketing/advertising campaign for a new product or service.
Marshaling	How much confidence do you have in your ability to  …  ? Get others to identify with and believe in my vision and plans for a new business. Network – i.e. make contact with and exchange information with others. Clearly and concisely explain verbally/in writing my business idea in everyday terms.
Implementing-people	How much confidence do you have in your ability to  …  ? Supervise employees. Recruit and hire employees. Delegate tasks and responsibilities to employees in my business. Deal effectively with day-to-day problems and crises. Inspire, encourage, and motivate my employees. Train employees.
Implementing-financial	How much confidence do you have in your ability to  …  ? Organize and maintain the financial records of my business. Manage the financial assets of my business. Read and interpret financial statements.
Attitude toward venturing	In general, starting a business is... Worthless/worthwhile Disappointing/rewarding Negative/positive
Learning performance	I can learn new skills if I use business simulation games. Business simulation games help me improve my learning I can gain knowledge when I use business simulation games. I have the ability to apply the material. I want to learn more about this subject (course/learning goals). I have enough understanding of this subject (course/learning goals).
Flow experience
Concentration	My attention was focused entirely on playing the game. It was no effort to keep my mind on game events. I had total concentration while playing the game. I was totally immersed in playing the game.
Time distortion	My sense of time altered (either sped up or slowed down). The way time passed seemed to be different from normal.
Autotelic experience	I really enjoyed the playing experience. I loved the feeling of playing and want to capture it again. The playing experience left me feeling great. I found the experience extremely rewarding.
Loss of self-consciousness	I was not concerned with what others may have been thinking about my playing performance. I was not worried about my performance during playing.
Challenge-skill balance	I was challenged, but I believed my skills would allow me to meet the challenge. The challenge that the game provided and my skills were at an equally high level.
Playability	I could use the user interface of the game spontaneously and automatically without having to think. Using the user interface was easy.
Goal	I knew clearly what I wanted to do and achieve. The goals of the game were clearly defined.
Feedback	I was aware how I was performing in the game. I could tell by the way I was performing how well I was doing.
Control	I felt in total control of my playing actions. I had a feeling of control of my actions.