1. Introduction
With the establishment of simulation games in the 1950s, their production has expanded dramatically, as has the usage of simulation games in formal and informal education. Higher education institutions started integrating simulation games into their courses in the mid-60s to provide an active learning experience to the students [
1]. Since then, simulation games have been widely employed to boost students’ learning, using both general and tailored simulation games [
2]. Further growth of simulation game usage in education occurred in the 21st century, additionally driven by mobile technology [
3]. According to the Global Opportunities and Industry Forecast 2020–2027 research, the simulation and virtual training market was worth
$204.41 billion in 2019 and is projected to reach
$579.44 billion by the end of 2027 [
4].
Modern generations, such as Generation Z, demand changes in learning processes suited for the new digital era. Generation Z strives for informal learning and is interested in using various new information and communication technologies in the educational process [
5,
6]. Applying game elements, such as simulation games, within the educational process is one of the major innovative ways to motivate students, which is especially important in business and management education [
7]. Business simulation games allow students to learn by experiencing different situations in a simulated environment. In addition to their usage in formal learning, business simulation games are often used in informal learning, such as business professionals, who first make business decisions in a simulated environment to improve their decision-making skills and avoid mistakes in real business settings [
7].
This change has naturally led to increased scientific research on business simulation games [
8]. Several systematic literature reviews were conducted about business simulation games. The first group of reviews focuses on a narrow group of business simulation games, focusing on a specific business function, such as decision support systems [
9], project management [
10], and business process change [
11]. The second group of reviews focus on the learning outcomes and is driven by specific research questions, such as empirical evidence of learning and effective teaching [
4] and the impact of simulation games on capabilities in decision-making and cognitive skills [
12]. The third group of reviews focus on the usage of specific technology, such as neuroscience research devices [
13,
14] and virtual reality [
15]. These groups of reviews can be referred to as domain-driven, technology-driven, and learning-driven research. However, the limitation of these reviews is that they focus on a single topic, engaging in a microlevel analysis focusing on a narrow aspect of business simulation games. Only one review could be considered a macrolevel analysis [
4], but it did not include all topics related to business simulation games.
The current literature reviews were mostly applied as systematic literature reviews (SLRs), using standard formats such as Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), which is time-consuming, causing their narrow focus. Automated literature reviews (ALRs) using natural language processing, such as word extraction, phrase extraction, and topic mining, overcome the barriers of SLRs [
16]. Using ALRs allows the unstructured analysis of research papers, allowing the broad macrolevel analysis, which leads to the extraction of a broad range of topics, thus overcoming the narrow focus of SLRs.
The current study uses a mixed approach, combining SLR and ALR to analyse the research on business simulation games to address the gaps mentioned above and deliver a wider perspective on the research trends and perspectives. This work combines computational and qualitative methods to identify important research themes, examine temporal trends of those issues over the past several decades, and suggest viable future avenues in business simulation games research. This article discusses the subsequent research questions: What are the primary research trends and topics in business simulation games research? What is the balance between learning-driven and domain-driven research topics? The answers to both questions will be used for developing the future development of business simulation games research (
Figure 1).
The paper has several contributions. First, to the best of our knowledge, this is the first study to analyse research on business simulation games using the combined SLR and ALR approach. Second, the proposed framework for data analysis is a versatile method applicable to numerous research topics. Thirdly, this work can shed light on previous and future research on business simulation games by examining the most important research trends and themes from 1973 to 2023.
To achieve the specified objective of the study, this paper is organised as follows. The first part of the paper delivers an introduction to the selected theme. After the introduction, the theoretical background is given in the second part of the paper, presenting the background for investigating simulation game perspectives in several fields. The third part of the paper refers to the methodology used in the paper. The fourth part of the paper presents the results obtained by the mixed research methods. Finally, in the fifth part of the paper, final concluding remarks, limitations of the paper, as well as recommendations for further research are provided.
5. Conclusions
Business simulation games are widely used both in high education and in business. This paper aims to identify the relevance and profoundly explore the existing literature to develop in-depth knowledge and extract patterns and the most important findings. The research goals were to provide insight into the research trends and topics in business simulation games’ research and to investigate the balance between learning-driven and domain-driven research topics. The analysis is the basis for the projections of future developments in business simulation games research.
The paper contains several contributions.
Firstly, by combining SLR and ALR, the current study analyses the research on business simulation games to solve the gaps mentioned above and provide a broader perspective on the research trends and perspectives. This paper combines computational and qualitative methodologies to identify significant study themes, examine the temporal trends of these concerns over the past several decades, and suggest possible future pathways for business simulation games research.
Secondly, the suggested data analysis framework is flexible and adaptable to various study areas. In addition to combining the SLR and ALR methods, we introduce the concept of the primary motivation in business simulation research, including learning-driven, domain-driven, and technology-driven research. Such an approach can be easily transferred to other educational, business, and management approaches.
Thirdly, this paper sheds insight into past and future research on business simulation games by analysing the most significant research trends and themes from 1973 to 2023. The following trends were identified, providing the answers to the following questions:
What is the trend in the number of research papers and citations in business simulation games? The SLR analysis revealed that the number of published papers increased after 2000 and is steadily developing. Although the number of published papers follows the linear trend, the growth of the published papers is steady, indicating stagnation. This conclusion is confirmed by the citation analysis, revealing that the most cited papers were published between 2000 and 2010, indicating that the research field of business simulation games is stagnating.
What topics are investigated in business simulation games’ research? Our analysis revealed that business simulation games investigations have two major perspectives: (i) the Learning-driven perspective that focuses on various aspects of teaching, knowledge transfer, and training in higher education and business; and (ii) the Domain-driven perspective, which refers to the domain to which business simulation game has been focused, such as decision making, enterprise resource planning, entrepreneurship, sustainability, and other issues. The most cited papers, the most frequent phrases, and the extracted topics were analysed concerning their primary research motivations. The most cited papers were mostly learning-driven, while a smaller number were domain-driven, which is not unexpected. Authors tend to cite the most often methodological papers, while narrow-focused papers are less cited [
61]. The most striking conclusion is that none of the most cited papers are technology driven, leading to the conclusion that the stagnation in research likely results from the stagnation in the application of new technologies in business simulation games. The ALR analysis revealed that most frequent phrases are learning driven, while domain-driven phrases occur less often. However, when the topic analysis was conducted, the situation changed, indicating that the extracted topics were mostly domain driven; however, almost the same number of topics were learning driven.
What is the future trend in business simulation games’ research? The future trend is estimated based on the two conclusions. First, the SLR indicates the stagnation of the research in business simulation games. Second, the SLR and ALT reveal the balance between domain-driven and learning-driven research papers. Still, the technology-driven topics and phrases were not extracted, indicating that the technology used for business simulations is mature. Based on these two trends, the future of business simulation games does not seem to be on the path to breakthrough discoveries any near time, at least until new technologies, likely based on artificial intelligence [
62], massive gaming [
63], augmented reality [
64], e-learning [
65], or social media [
66], are introduced in the concept of business simulation games.
This paper has several practical implications. Regarding research results that revealed three main research perspectives of business simulation games, the obtained results can be a potential guideline for higher education institutions and businesses in various industries when deciding to implement simulation games in their processes. Through the results of this work, higher education institutions can become aware of the areas in which they could use simulation games to make the learning process more interesting and effective for students of Generation Z, who require a different approach to educational methods. In the same way, the results of this research can guide practitioners from the business world to consider their business perspectives in which they could incorporate and apply simulation games to establish higher quality and more efficient firm performance.
The limitations of this work are as follows. First, only papers from the Scopus database were included in the investigation, and other databases should be included in further investigations. Second, text mining has been conducted based on the paper titles, abstracts, and keywords, while the full text of the papers could be included in future work. Additionally, to obtain deeper insights into the application of simulation games in higher education, considering the results of this study, it would be interesting to conduct a systematic review of the literature with expanded keywords in the search process that would refer to the technical perspectives of simulation game usage, supported by further qualitative or quantitative research.
Author Contributions
Conceptualisation, M.P.B. and M.M.; methodology, M.P.B. and T.Ć.; software, M.P.B. and A.M.S.; validation, M.P.B., M.M., and T.Ć.; formal analysis, M.P.B.; investigation, M.M.; data curation, A.M.S.; writing—original draft preparation, T.Ć. and A.M.S.; writing—review and editing, M.P.B.; visualisation, M.P.B.; supervision, M.M.; project administration, T.Ć.; funding acquisition, M.P.B. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the Ministry of Science and Education of Croatia and the Slovenian Research Agency (ARRS), grant number BI-HR/20-21-020.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Harviainen, J.T.; Lainema, T.; Saarinen, E. Player-reported impediments to game-based learning. Trans. Digit. Games Res. Assoc. 2014, 1, 55–83. [Google Scholar] [CrossRef]
- Perez-Colado, I.J.; Perez-Colado, V.M.; Martínez-Ortiz, I.; Freire-Moran, M.; Fernández-Manjón, B. UAdventure: The eAdventure reboot: Combining the experience of commercial gaming tools and tailored educational tools. In Proceedings of the IEEE Global Engineering Education Conference (EDUCON), Athens, Greece, 25–28 April 2017; pp. 1755–1762. [Google Scholar] [CrossRef] [Green Version]
- Chang, C.Y.; Hwang, G.J. Trends in digital game-based learning in the mobile era: A systematic review of journal publications from 2007 to 2016. Int. J. Mob. Learn. Organ. 2019, 13, 68–90. [Google Scholar] [CrossRef]
- Faisal, N.; Chadhar, M.; Goriss-Hunter, A.; Stranieri, A. Business Simulation Games in Higher Education: A Systematic Review of Empirical Research. Hum. Behav. Emerg. Technol. 2022, 2022, 1578791. [Google Scholar] [CrossRef]
- Postolov, K.; Magdinceva Sopova, M.; Janeska Iliev, A. E-learning in the hands of generation Y and Z. Posl. Izvr. 2017, 11, 107–119. [Google Scholar] [CrossRef] [Green Version]
- Nguyen Ngoc, T.; Viet Dung, M.; Rowley, C.; Pejić Bach, M. Generation Z job seekers’ expectations and their job pursuit intention: Evidence from transition and emerging economy. Int. J. Eng. Bus. Manag. 2022, 14, 1–13. [Google Scholar] [CrossRef]
- Greco, M.; Baldissin, N.; Nonino, E. An Exploratory Taxonomy of Business Games. Simul. Gaming 2013, 44, 645–682. [Google Scholar] [CrossRef] [Green Version]
- Adobor, H.; Daneshfar, A. Management simulations: Determining their effectiveness. J. Manag. Dev. 2006, 25, 151–168. [Google Scholar] [CrossRef]
- Ben-Zvi, T. The efficacy of business simulation games in creating Decision Support Systems: An experimental investigation. Decis. Support Syst. 2010, 49, 61–69. [Google Scholar] [CrossRef]
- Hussein, B.A. On using simulation games as a research tool in project management. In Organising and Learning through Gaming and Simulation; International Simulation and Gaming Association: Delft, The Netherlands, 2007; pp. 1–8. [Google Scholar]
- Löffler, A.; Jacoby, D.; Faizan, N.; Utesch, M.; Kienegger, H.; Krcmar, H. Teaching methods for simulation games: The example of learning business process change. In Proceedings of the 2019 IEEE Global Engineering Education Conference, Dubai, United Arab Emirates, 8–11 April 2019; pp. 1336–1344. [Google Scholar]
- Reynaldo, C.; Christian, R.; Hosea, H.; Gunawan, A.A. Using video games to improve capabilities in decision making and cognitive skill: A literature review. Procedia Comput. Sci. 2021, 179, 211–221. [Google Scholar] [CrossRef]
- Ferreira, C.P.; González-González, C.S.; Adamatti, D.F. Business simulation games analysis supported by human-computer interfaces: A systematic review. Sensors 2021, 21, 4810. [Google Scholar] [CrossRef]
- Pakdaman-Savoji, A.; Nesbit, J.; Gajdamaschko, N. The conceptualisation of cognitive tools in learning and technology: A review. Australas. J. Educ. Technol. 2019, 35, 2. [Google Scholar] [CrossRef]
- Zheng, J.; Wu, C.Z.; Li, F.; Li, J. Research Status of the Application of Virtual Reality Technology on Self-efficacy. In Proceedings of the 2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, China, 3–5 December 2021; pp. 608–611. [Google Scholar]
- Felizardo, K.R.; Carver, J.C. Automating systematic literature review. In Contemporary Empirical Methods in Software Engineering; Springer: Berlin/Heidelberg, Germany, 2020; pp. 327–355. [Google Scholar]
- Deshpande, A.A.; Huang, S.H. Simulation games in engineering education: A state-of-the-art review. Comput. Appl. Eng. Educ. 2009, 19, 399–410. [Google Scholar] [CrossRef]
- Ochoa, A. Simulation and gaming: Simile or synonym? Peabody J. Educ. 1969, 47, 104–107. [Google Scholar] [CrossRef]
- Quinn, C.N. Engaging Learning: Designing E-Learning Simulation Games; John Wiley & Sons: San Franscisco, CA, USA, 2005; pp. 1–213. [Google Scholar]
- Havola, S.; Koivisto, J.-V.; Mäkinen, H.; Haavisto, E. Game Elements and Instruments for Assessing Nursing Students’ Experiences in Learning Clinical Reasoning by Using Simulation Games: An Integrative Review. Clin. Simul. Nurs. 2020, 46, 1–14. [Google Scholar] [CrossRef]
- Sanina, A.; Kutergina, E.; Balashov, A. The Co-Creative approach to digital simulation games in social science education. Comput. Educ. 2020, 149, 103813. [Google Scholar] [CrossRef]
- Martin, A. The design and evolution of a simulation/game for teaching information systems development. Simul. Gaming 2000, 31, 445–463. [Google Scholar] [CrossRef] [Green Version]
- Ruohomäki, V. Viewpoints on learning and education with simulation games. In Simulation Games and Learning in Production Management; Springer: Boston, MA, USA, 1994; pp. 13–25. [Google Scholar]
- Léger, P.-M. Using a Simulation Game Approach to Teach ERP Concepts. In HEC Montréal Groupe de Recherche en Systèmes D’Information; HEC Montréal: Montréal, QC, Canada, 2006; pp. 1–15. [Google Scholar]
- Chua, A.Y.K. The design and implementation of a simulation game for teaching knowledge management. J. Am. Soc. Inf. Sci. Technol. 2005, 56, 1207–1216. [Google Scholar] [CrossRef]
- Geithner, S.; Menzel, D. Effectiveness of Learning Through Experience and Reflection in a Project Management Simulation. Simul. Gaming 2016, 47, 228–256. [Google Scholar] [CrossRef]
- Whiteley, T.R.; Faria, A.J. A study of the relationship between student final exam performance and simulation game participation. Simul. Gaming 1989, 20, 44–64. [Google Scholar] [CrossRef]
- Peters, V.A.M.; Vissers, G.A.N. A simple classification model for debriefing simulation games. Simul. Gaming 2004, 35, 70–84. [Google Scholar] [CrossRef]
- Tao, Y.-H.; Cheng, C.-J.; Sun, S.-Y. What influences college students to continue using business simulation games? The Taiwan experience. Comput. Educ. 2009, 53, 929–939. [Google Scholar] [CrossRef]
- Faria, A.J.; Nulsen, R. Business simulation games: Current usage levels a ten year update. Dev. Bus. Simul. Exp. Exerc. 1996, 11, 1–7. [Google Scholar]
- Faria, A.J.; Hutchinson, D.; Wellington, W.J.; Gold, S. Developments in business gaming: A review of the past 40 years. Simul. Gaming Interdiscip. J. 2009, 40, 464–487. [Google Scholar] [CrossRef]
- Hofstede, G.J.; De Caluwé, L.; Vincent Peters, V. Why simulation games work-in search of the active substance: A synthesis. Simul. Gaming 2010, 41, 824–843. [Google Scholar] [CrossRef]
- Saastamoinen, T.; Härkänen, M.; Vehviläinen-Julkunen, K.; Näslindh-Ylispangar, A. Impact of 3D Simulation Game as a Method to Learn Medication Administration Process: Intervention Research for Nursing Students. Clin. Simul. Nurs. 2022, 66, 25–43. [Google Scholar] [CrossRef]
- Pejić Bach, M.; Miloloza, I.; Zoroja, J. Teaching health care management with simulation games. In Proceedings of the 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, 21–25 May 2018; pp. 546–551. [Google Scholar]
- Pejic Bach, M.; Tustanovski, E.; Ip, A.W.; Yung, K.L.; Roblek, V. System dynamics models for the simulation of sustainable urban development: A review and analysis and the stakeholder perspective. Kybernetes Int. J. Syst. Cybern. 2019, 49, 460–504. [Google Scholar] [CrossRef]
- Bosilj Vukšić, V.; Pejić Bach, M.; Tomičić-Pupek, K. Utilization of discrete event simulation in business processes management projects: A literature review. J. Inf. Organ. Sci. 2017, 41, 137–159. [Google Scholar] [CrossRef] [Green Version]
- Balić, A.; Turulja, L.; Kuloglija, E.; Pejić-Bach, M. ERP Quality and the Organisational Performance: Technical Characteristics vs. Information and Service. Information 2022, 13, 474. [Google Scholar] [CrossRef]
- Georgiou, A. A Case Study of Investor R&D Evaluation using Game Theory. Entren. Enterp. Res. Innov. 2022, 8, 91–98. [Google Scholar]
- Turulja, L.; Bajgorić, N. Knowing means existing: Organisational learning dimensions and knowledge management capability. Bus. Syst. Res. Int. J. Soc. Adv. Innov. Res. Econ. 2018, 9, 1–18. [Google Scholar] [CrossRef] [Green Version]
- Pejic Bach, M.; Zoroja, J.; Strugar, I. Obstacles to using business simulation games in Croatian bussiness education institutions. Ann. DAAAM Proc. Int. DAAAM Symp. 2010, 1, 615–616. [Google Scholar]
- Pejić Bach, M.; Meško, M.; Zoroja, J.; Godnov, U.; Ćurlin, T. Usage of simulation games in higher educational institutions teaching economics and business. Entren. Enterp. Res. Innov. 2020, 6, 27–36. [Google Scholar]
- Blazic, A.J.; Ribeiro, C.; Fernandes, J.; Pereira, J.; Arh, T. Analysing the required properties of business simulation games to be used in e-learning and education. Intell. Inf. Manag. 2012, 4, 348–356. [Google Scholar]
- Zapalska, A.; Brozik, D.; Rudd, D. Development of active learning with simulations and games. US-China Educ. Rev. A 2 2012, 2, 164–169. [Google Scholar]
- Morin, J.; Tamberelli, F.; Buhagiar, T. Educating business integrators with a computer-based simulation game in the flipped classroom. J. Educ. Bus. 2020, 95, 121–128. [Google Scholar] [CrossRef]
- Pejić-Bach, M.; Cerpa, N. Planning, Conducting and Communicating Systematic Literature Reviews. J. Theor. Appl. Electron. Commer. Res. 2019, 14, 1–4. [Google Scholar] [CrossRef] [Green Version]
- Pasko, O.; Chen, F.; Oriekhova, A.; Brychko, A.; Shalyhina, I. Mapping the literature on sustainability reporting: A Bibliometric analysis grounded in Scopus and Web of science core collection. Eur. J. Sustain. Dev. 2021, 10, 303. [Google Scholar] [CrossRef]
- Simsek, Z.; Fox, B.; Heavey, C. Systematicity in organisational research literature reviews: A framework and assessment. Organ. Res. Methods 2021, 1. [Google Scholar] [CrossRef]
- Davi, A.; Haughton, D.; Nasr, N.; Shah, G.; Skaletsky, M.; Spack, R. A review of two text-mining packages: SAS TextMining and WordStat. Am. Stat. 2005, 59, 89–103. [Google Scholar] [CrossRef]
- Feng, L.; Chiam, Y.K.; Lo, S.K. Text-mining techniques and tools for systematic literature reviews: A systematic literature review. In Proceedings of the 2017 24th Asia-Pacific Software Engineering Conference (APSEC), Nanjing, China, 4–8 December 2017; pp. 41–50. [Google Scholar]
- Saraçli, S.; Doğan, N.; Doğan, İ. Comparison of hierarchical cluster analysis methods by cophenetic correlation. J. Inequalities Appl. 2013, 2013, 1–8. [Google Scholar] [CrossRef]
- Lin, J.; Keogh, E.; Wei, L.; Lonardi, S. Experiencing SAX: A novel symbolic representation of time series. Data Min. Knowl. Discov. 2007, 15, 107–144. [Google Scholar] [CrossRef] [Green Version]
- Mergel, G.D.; Silveira, M.S.; da Silva, T.S. A method to support search string building in systematic literature reviews through visual text mining. In Proceedings of the 30th Annual ACM Symposium on Applied Computing, Salamanca, Spain, 13–17 April 2015; pp. 1594–1601. [Google Scholar]
- Odone, A.; Salvati, S.; Bellini, L.; Bucci, D.; Capraro, M.; Gaetti, G.; Amerio, A.; Signorelli, C. The runaway science: A bibliometric analysis of the COVID-19 scientific literature. Acta Biomed. 2020, 91, 34–39. [Google Scholar]
- Kanawattanachai, P.; Yoo, Y. The impact of knowledge coordination on virtual team performance over time. MIS Q. 2007, 31, 783–808. [Google Scholar] [CrossRef] [Green Version]
- Kanawattanachai, P.; Yoo, Y. Dynamic nature of trust in virtual teams. J. Strateg. Inf. Syst. 2002, 11, 187–213. [Google Scholar] [CrossRef]
- Rulke, D.L.; Galaskiewicz, J. Distribution of knowledge, group network structure, and group performance. Manag. Sci. 2000, 46, 612–625. [Google Scholar] [CrossRef]
- Barzilai, S.; Blau, I. Scaffolding game-based learning: Impact on learning achievements, perceived learning, and game experiences. Comput. Educ. 2014, 70, 65–79. [Google Scholar] [CrossRef]
- Kiili, K. Foundation for problem-based gaming. Br. J. Educ. Technol. 2007, 38, 394–404. [Google Scholar] [CrossRef]
- Van den Bossche, P.; Gijselaers, W.; Segers, M.; Woltjer, G.; Kirschner, P. Team learning: Building shared mental models. Instr. Sci. 2011, 39, 283–301. [Google Scholar] [CrossRef] [Green Version]
- Avolio, B.J.; Waldman, D.A.; Einstein, W.O. Transformational leadership in a management game simulation: Impacting the bottom line. Group Organ. Stud. 2007, 13, 59–80. [Google Scholar] [CrossRef]
- Peritz, B. Are methodological papers more cited than theoretical or empirical ones? The case of sociology. Scientometrics 1983, 5, 211–218. [Google Scholar] [CrossRef]
- Westera, W.; Prada, R.; Mascarenhas, S.; Santos, P.A.; Dias, J.; Guimarães, M.; Georgiadis, K.; Nyamsuren, E.; Bahreini, K.; Yumak, Z.; et al. Artificial intelligence moving serious gaming: Presenting reusable game AI components. Educ. Inf. Technol. 2020, 25, 351–380. [Google Scholar] [CrossRef] [Green Version]
- Seay, A.F.; Jerome, W.J.; Lee, K.S.; Kraut, R.E. Project Massive: A study of online gaming communities. In Proceedings of the CHI’04 Extended Abstracts on Human Factors in Computing Systems, Vienna, Austria, 24–29 April 2004; pp. 1421–1424. [Google Scholar]
- Jajic, I.; Khawaja, S.; Hussain Qureshi, F.; Pejić Bach, M. Augmented Reality in Business and Economics: Bibliometric and Topics Analysis. Interdiscip. Descr. Complex Syst. INDECS 2022, 20, 723–744. [Google Scholar] [CrossRef]
- Głodowska, A.; Wach, K.; Knežević, B. Pros and Cons of e-Learning in Economics and Business in Central and Eastern Europe: Cross-country Empirical Investigation. Bus. Syst. Res. Int. J. Soc. Adv. Innov. Res. Econ. 2022, 13, 28–44. [Google Scholar] [CrossRef]
- Vizcaya-Moreno, M.F.; Pérez-Cañaveras, R.M. Social media used and teaching methods preferred by generation z students in the nursing clinical learning environment: A cross-sectional research study. Int. J. Environ. Res. Public Health 2020, 17, 8267. [Google Scholar] [CrossRef]
Figure 1.
Research questions; Source: Authors’ work.
Figure 2.
Stages of SLR; Source: Authors’ work.
Figure 3.
Stages of ALR; Source: Authors’ work.
Figure 4.
Number of papers per publication year (2000−2021); Source: Authors’ work based on Scopus.
Figure 5.
Paper’s access type in the period 1973–1999 vs. 2000–2023; Source: Authors’ work based on Scopus.
Figure 6.
The research paper’s countries; Source: Authors’ work based on Scopus.
Figure 7.
Number of papers and citations from 2000 to 2023; Source: Authors’ work based on Scopus.
Figure 8.
Scatter plot of the number of papers and citations from 2000 to 2023; Source: Authors’ work based on Scopus.
Figure 9.
Word cloud word occurrence; Source: Authors’ work based on Scopus.
Figure 10.
Word cloud phrase occurrence (10+ occurrence); Source: Authors’ work based on Scopus.
Figure 11.
Bubble plot of the publication’s total frequencies; Source: Authors’ work based on Scopus.
Figure 12.
Cluster results of the phrases; Source: Authors’ work based on Scopus.
Figure 13.
Mapping of clusters; Source: Authors’ work based on Scopus.
Figure 14.
Business simulation games research perspectives; Source: Authors’ work.
Table 1.
Research areas.
Subject Area | Documents |
---|
Computer Science | 54% |
Social Sciences | 37% |
Business, Management, and Accounting | 31% |
Engineering | 22% |
Mathematics | 12% |
Economics, Econometrics, and Finance | 6% |
Decision Sciences | 6% |
Psychology | 6% |
Environmental Science | 5% |
Other research areas | 17% |
Table 2.
The research papers publication journals, conference proceedings, or book series.
Source | # of Papers | % of Papers |
---|
Simulation and Gaming | 19 | 6% |
Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence, Lecture Notes in Bioinformatics | 10 | 3% |
International Journal of Management Education | 8 | 2% |
Communications in Computer and Information Science | 7 | 2% |
Computers and Education | 7 | 2% |
ACM International Conference Proceeding Series | 5 | 2% |
Advances in Intelligent Systems And Computing | 5 | 2% |
Journal of Educational Computing Research | 5 | 2% |
Procedia Computer Science | 5 | 2% |
Proceedings Winter Simulation Conference | 4 | 1% |
Journal of Business Research | 3 | 1% |
Journal of Marketing Education | 3 | 1% |
Lecture Notes in Informatics Proceedings Series of The Gesellschaft Fur Informatik | 3 | 1% |
Simulation Gaming | 3 | 1% |
Sustainability Switzerland | 3 | 1% |
Table 3.
Citation statistics.
# of papers (1973–2022) | 332 |
# of cited papers (1973–2022) | 241 |
# of citations | 5570 |
# of self-citations | 407 |
Average citation per paper | 17.38 |
Average citation per paper without self-citations | 16.15 |
h-index | 34 |
Table 4.
Number of citations of the top 10 most cited publications.
Paper | Publication Year | # of Citations | Primary Research Motivation |
---|
<2019 | 2019 | 2020 | 2021 | 2022 | Learning-Driven | Domain-Driven |
---|
[54] | 2007 | 334 | 28 | 30 | 37 | 25 | ∅ | ✓ |
[55] | 2002 | 275 | 25 | 19 | 15 | 20 | ∅ | ✓ |
[56] | 2000 | 202 | 7 | 6 | 12 | 8 | ∅ | ✓ |
[31] | 2009 | 141 | 13 | 14 | 22 | 15 | ∅ | ✓ |
[57] | 2014 | 77 | 26 | 34 | 27 | 33 | ✓ | ∅ |
[58] | 2007 | 150 | 11 | 12 | 9 | 14 | ✓ | ∅ |
[59] | 2011 | 86 | 15 | 19 | 34 | 28 | ✓ | ∅ |
[60] | 1988 | 142 | 10 | 7 | 3 | 6 | ✓ | ∅ |
[30] | 1998 | 133 | 8 | 7 | 7 | 1 | ∅ | ✓ |
[29] | 2009 | 107 | 12 | 6 | 12 | 15 | ✓ | ∅ |
Table 5.
Extracted words (100+ frequency).
WORDS | FREQ. | NO. CASES | % CASES | TF • IDF |
---|
LEARNING | 720 | 175 | 52.87% | 199.3 |
STUDENTS | 466 | 161 | 48.64% | 145.9 |
RESEARCH | 229 | 120 | 36.25% | 100.9 |
EDUCATION | 213 | 109 | 32.93% | 102.8 |
RESULTS | 208 | 141 | 42.60% | 77.1 |
DECISION | 180 | 72 | 21.75% | 119.2 |
KNOWLEDGE | 179 | 77 | 23.26% | 113.4 |
TEACHING | 179 | 79 | 23.87% | 111.4 |
SKILLS | 173 | 76 | 22.96% | 110.5 |
PERFORMANCE | 163 | 66 | 19.94% | 114.1 |
DEVELOPMENT | 150 | 83 | 25.08% | 90.1 |
DESIGN | 148 | 84 | 25.38% | 88.1 |
ANALYSIS | 145 | 82 | 24.77% | 87.9 |
EXPERIENCE | 142 | 72 | 21.75% | 94.1 |
SYSTEMS | 141 | 59 | 17.82% | 105.6 |
MODEL | 139 | 66 | 19.94% | 97.3 |
TEAM | 137 | 32 | 9.67% | 139.0 |
DATA | 121 | 68 | 20.54% | 83.2 |
ENVIRONMENT | 119 | 67 | 20.24% | 82.6 |
MAKING | 117 | 61 | 18.43% | 85.9 |
PROCESS | 117 | 71 | 21.45% | 78.2 |
STUDENT | 117 | 50 | 15.11% | 96.0 |
PROJECT | 108 | 29 | 8.76% | 114.2 |
TRAINING | 108 | 56 | 16.92% | 83.3 |
SYSTEM | 106 | 45 | 13.60% | 91.9 |
Table 6.
Extracted phrases (10+ frequency).
WORDS | FREQ. | NO. CASES | % CASES | TF • IDF | Primary Research Motivation |
---|
Learning-Driven | Domain-Driven |
---|
DECISION MAKING | 96 | 48 | 14.50% | 2 | ✓ | ∅ |
SUPPLY CHAIN | 66 | 22 | 6.65% | 2 | ∅ | ✓ |
LEARNING OUTCOMES | 60 | 29 | 8.76% | 2 | ✓ | ∅ |
HIGHER EDUCATION | 44 | 30 | 9.06% | 2 | ✓ | ∅ |
EXPERIENTIAL LEARNING | 34 | 21 | 6.34% | 2 | ✓ | ∅ |
LEARNING ENVIRONMENT | 31 | 16 | 4.83% | 2 | ✓ | ∅ |
DECISION SUPPORT SYSTEMS | 31 | 13 | 3.93% | 3 | ∅ | ✓ |
REAL WORLD | 26 | 20 | 6.04% | 2 | ✓ | ∅ |
TEACHING AND LEARNING | 26 | 19 | 5.74% | 3 | ✓ | ∅ |
TEACHING METHODS | 26 | 11 | 3.32% | 2 | ✓ | ∅ |
INTELLIGENT TUTORING | 24 | 5 | 1.51% | 2 | ✓ | ∅ |
FLOW EXPERIENCE | 23 | 7 | 2.11% | 2 | ✓ | ∅ |
TEAM COHESION | 23 | 4 | 1.21% | 2 | ✓ | ∅ |
PROBLEM SOLVING | 22 | 16 | 4.83% | 2 | ✓ | ∅ |
LEARNING PROCESS | 20 | 15 | 4.53% | 2 | ✓ | ∅ |
ENTREPRENEURSHIP EDUCATION | 20 | 7 | 2.11% | 2 | ∅ | ✓ |
TEAM PERFORMANCE | 19 | 7 | 2.11% | 2 | ✓ | ∅ |
SUSTAINABLE DEVELOPMENT | 19 | 4 | 1.21% | 2 | ∅ | ✓ |
PERCEIVED LEARNING | 17 | 10 | 3.02% | 2 | ✓ | ∅ |
EMOTIONAL INTELLIGENCE | 17 | 2 | 0.60% | 2 | ✓ | ∅ |
LEARNING EXPERIENCE | 15 | 12 | 3.63% | 2 | ✓ | ∅ |
ENTERPRISE RESOURCE PLANNING | 15 | 11 | 3.32% | 3 | ∅ | ✓ |
LEARNING PERFORMANCE | 15 | 7 | 2.11% | 2 | ✓ | ∅ |
STUDENT ENGAGEMENT | 15 | 4 | 1.21% | 2 | ✓ | ∅ |
HUMAN FACTORS | 14 | 6 | 1.81% | 2 | ✓ | ∅ |
UNDERGRADUATE STUDENTS | 13 | 13 | 3.93% | 2 | ✓ | ∅ |
SKILLS DEVELOPMENT | 13 | 11 | 3.32% | 2 | ✓ | ∅ |
DESIGN METHODOLOGY | 12 | 12 | 3.63% | 2 | ✓ | ∅ |
INFORMATION SYSTEMS | 12 | 10 | 3.02% | 2 | ∅ | ✓ |
LEARNING EFFECTIVENESS | 12 | 7 | 2.11% | 2 | ✓ | ∅ |
ACTIVE LEARNING | 12 | 7 | 2.11% | 2 | ✓ | ∅ |
INTRINSIC MOTIVATION | 11 | 5 | 1.51% | 2 | ✓ | ∅ |
ARTIFICIAL INTELLIGENCE | 11 | 5 | 1.51% | 2 | ∅ | ✓ |
ENTREPRENEURIAL ATTITUDE | 11 | 4 | 1.21% | 2 | ∅ | ✓ |
TRANSFORMATIONAL LEADERSHIP | 11 | 3 | 0.91% | 2 | ∅ | ✓ |
DATA COLLECTED | 10 | 10 | 3.02% | 2 | ✓ | ∅ |
KNOWLEDGE AND SKILLS | 10 | 9 | 2.72% | 3 | ✓ | ∅ |
QUASI EXPERIMENTAL | 10 | 9 | 2.72% | 2 | ✓ | ∅ |
TECHNOLOGY ACCEPTANCE MODEL | 10 | 8 | 2.42% | 3 | ✓ | ∅ |
STUDENTS PERCEPTIONS | 10 | 7 | 2.11% | 2 | ✓ | ∅ |
THINKING SKILLS | 10 | 5 | 1.51% | 2 | ✓ | ∅ |
HIGHER ORDER THINKING | 10 | 4 | 1.21% | 3 | ✓ | ∅ |
VIRTUAL TEAMS | 10 | 4 | 1.21% | 2 | ✓ | ∅ |
TEAM LEARNING | 10 | 3 | 0.91% | 2 | ✓ | ∅ |
ERP CHALLENGE | 10 | 2 | 0.60% | 2 | ∅ | ✓ |
FOOTBALL MANAGER | 10 | 2 | 0.60% | 2 | ∅ | ✓ |
REMEDIAL TUTORING | 10 | 1 | 0.30% | 2 | ✓ | ∅ |
Table 7.
Highest frequency keywords per year.
Year | Highest Frequency Keywords per Year |
---|
1973 | DECISION_MAKING |
1975 | TEAM_PERFORMANCE |
1980 | DECISION_MAKING |
1988 | TRANSFORMATIONAL_LEADERSHIP; DATA_COLLECTED |
1990 | UNDERGRADUATE_STUDENTS |
1993 | INTELLIGENT_TUTORING; TEACHING_AND_LEARNING |
1994 | INTELLIGENT_TUTORING |
1995 | DATA_COLLECTED |
1996 | LEARNING_PROCESS; PROBLEM_SOLVING |
1997 | INTELLIGENT_TUTORING |
1998 | INTELLIGENT_TUTORING |
2001 | SUSTAINABLE_DEVELOPMENT |
2002 | VIRTUAL_TEAMS |
2005 | REAL_WORLD; LEARNING_ENVIRONMENT |
2006 | REAL_WORLD; LEARNING_ENVIRONMENT |
2007 | VIRTUAL_TEAMS; TEAM_PERFORMANCE; DECISION_MAKING; REAL_WORLD |
2008 | SUPPLY_CHAIN; FLOW_EXPERIENCE |
2009 | EXPERIENTIAL_LEARNING; ARTIFICIAL_INTELLIGENCE; DESIGN_METHODOLOGY |
2010 | TECHNOLOGY_ACCEPTANCE_MODEL; DECISION_SUPPORT_SYSTEMS; INFORMATION_SYSTEMS |
2011 | TEAM_LEARNING; TEAM_PERFORMANCE; ENTREPRENEURSHIP_EDUCATION; LEARNING_EFFECTIVENESS; TRANSFORMATIONAL_LEADERSHIP |
2012 | TEACHING_METHODS; LEARNING_ENVIRONMENT |
2013 | LEARNING_EFFECTIVENESS; ENTERPRISE_RESOURCE_PLANNING |
2014 | PERCEIVED_LEARNING; PROBLEM_SOLVING |
2015 | KNOWLEDGE_AND_SKILLS; ENTREPRENEURSHIP_EDUCATION; UNDERGRADUATE_STUDENTS; PERCEIVED_LEARNING |
2016 | HUMAN_FACTORS; ENTERPRISE_RESOURCE_PLANNING; LEARNING_EXPERIENCE; DECISION_SUPPORT_SYSTEMS; ARTIFICIAL_INTELLIGENCE |
2017 | SUSTAINABLE_DEVELOPMENT; DECISION_MAKING; HUMAN_FACTORS; PROBLEM_SOLVING |
2018 | TRANSFORMATIONAL_LEADERSHIP; HUMAN_FACTORS; UNDERGRADUATE_STUDENTS |
2019 | TEAM_COHESION; INTRINSIC_MOTIVATION; TEAM_PERFORMANCE; LEARNING_OUTCOMES; STUDENT_ENGAGEMENT |
2020 | ACTIVE_LEARNING; DATA_COLLECTED; LEARNING_OUTCOMES; SKILLS_DEVELOPMENT |
2021 | STUDENTS_PERCEPTIONS; HIGHER_ORDER_THINKING; TEAM_COHESION; LEARNING_PERFORMANCE; ACTIVE_LEARNING |
2022 | ENTREPRENEURIAL_ATTITUDE; ENTREPRENEURSHIP_EDUCATION; QUASI_EXPERIMENTAL; LEARNING_OUTCOMES; LEARNING_PERFORMANCE |
2023 | STUDENT_ENGAGEMENT; HIGHER_ORDER_THINKING; LEARNING_OUTCOMES; EXPERIENTIAL_LEARNING; LEARNING_EXPERIENCE |
Table 8.
Extracted clusters.
Topic Theme | Topic Keywords | Primary Research Motivation |
---|
Knowledge Driven | Domain Driven |
---|
Active learning | Active learning, Virtual teams | ✓ | ∅ |
Information systems | Data collected, Perceived learning, Skills development, Information systems, Learning Effectiveness | ∅ | ✓ |
Learning process | Decision-making, Learning process, Teaching and learning, Teaching methods | ✓ | ∅ |
ERP challenge | ERP challenge, Learning environment, Real world, Problem-solving, Knowledge and skills, Undergraduate students | ∅ | ✓ |
Experiential learning | Experiential learning, Learning outcomes, Higher education, Learning experience, Thinking skills, Sustainable development | ✓ | ∅ |
Entrepreneurial attitude | Artificial intelligence, Entrepreneurial attitude, Entrepreneurship education, Flow experience | ∅ | ✓ |
Design methodology | Design methodology, Quasi-experimental, Higher_order_thinking, Student engagement | ✓ | ∅ |
Technology acceptance model | Learning performance, Technology acceptance model | ∅ | ✓ |
Emotional intelligence | Emotional intelligence, Team cohesion, Team performance, Intrinsic motivation | ∅ | ✓ |
Decision support systems | Decision support systems, Human factors, Supply chain, Enterprise resource planning | ∅ | ✓ |
Intelligent tutoring | Intelligent tutoring, Remedial tutoring | ✓ | ∅ |
| Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).