Paper Quality rating Data collection Sample and instruments Data analysis Constructs used in the study Vos [43 ] 11 Mixed methods Postgame 35 surveys and 8 interviews from students Qualitative Chen, Keys, and Gaber [44 ] 12 Quantitative Postgame surveys from 164 students SPSS partial least squares (pls) Behavioural intentions, enjoyment, cognitive appraisal, perceived learning outcomes Burdon and Munro[45 ] 13 Qualitative Qualitative surveys from students Thematic analysis Eder, Antonucci, and Monk [46 ] 12 Quantitative Postgame surveys from 118 students Statistical analysis Engagement, team dynamics, learning outcomes Bitrián, Buil, and Catalán [47 ] 9 Quantitative Pre-postgame surveys from 430 students SPSS two cluster analysis Students’ perceived learning, skills, and satisfaction, boredom, flow, anxiety, apathy Ghani and Mohammad [48 ] 13 Quantitative Pre-postgame surveys from 272 students Confirmatory factor analysis Student characteristics, lecturer characteristics, simulation characteristics, business plan learning effectiveness Thanasi-Boçe [49 ] Qualitative Postgame 16 open-ended surveys from students Thematic analysis Tawil et al. [39 ] 9 Quantitative 3129 students game results Õun, Mägi, and Noppel [50 ] 9 Quantitative Postgame survey from 118 students from four countries Lovelace, Eggers, and Dyck [51 ] 13 Quantitative Pre-postgame surveys from 98 students SPSS paired sample Critical thinking, problem-solving, game performance Almeida [52 ] 12 Quantitative Postgame surveys from 83 students Stata software v13.0 descriptive analysis Technical competencies, management skills, personal entrepreneurship Costin, O’Brien, and Hynes [19 ] 14 Qualitative Reflective essays Thematic analysis Decision-making, problem-solving, communication and teamwork, risk management Mohsen, Abdollahi, and Omar [53 ] 14 Mixed methods Postgame surveys and reflection reports of 120 students SPSS exploratory factor analysis and reliability analysis, thematic analysis Students’ experience generation, conceptual understanding, skills development, and affective evaluation, respectively Urquidi-Martín, Tamarit-Aznar, and Sánchez-García [54 ] 13 Quantitative Postgame surveys from 326 students Analysis of the causal relationships Game realism, game structure, perceived usefulness, motivation, critical thinking Almeida and Buzady [55 ] Mixed methods Postgame focus group discussion and game results Descriptive and thematic Game performance and 29 management and leadership skills Buzady and Almeida [56 ] 13 Mixed methods Postgame surveys and interviews from 52 students Descriptive and thematic Individual attitudes, 29 MAP dimensions Bach, Zoroja, and Fašnik [17 ] 13 Quantitative Postgame surveys Descriptive analysis and chi square test Advantages of different types of teaching methods Zulfiqar et al. [57 ] 14 Quantitative Postgame survey from 277 students Structural equation model (SEM) Perceived ease of use and perceived usefulness, knowledge application and knowledge sharing, learnability, self-efficacy and perceived enjoyment, technology adoption and learning performance, entrepreneurial intentions Yusof [58 ] 13 Quantitative Postgame survey from 160 students Multiple regression analysis Learning, benefit, satisfaction, and perception of business simulation Lovin et al. [59 ] 14 Quantitative Postgame survey from 120 graduates Multiple regression analysis Knowledge transfer, engagement, working environment culture, acquired knowledge from business simulation Dharmastuti et al. [60 ] 13 Quantitative Postgame survey from 83 students Descriptive analysis Perceived usefulness, perceived ease, perceived enjoyment, student business competency, perceived learning outcomes Faisal et al. [61 ] 14 Qualitative Interviews from 15 instructors Thematic analysis Learning outcomes, behavioural changes, work readiness Beranič and Heričko [62 ] 10 Quantitative Pre-postgame survey Descriptive analysis Business processes knowledge, technical knowledge of SAP, ERP transaction knowledge, intent for future course engagement Hishiyama and Nakajima [63 ] 10 Mixed method 138 students and 180 instructors Descriptive, ANVOVA Tao, Yeh, and Hung [64 ] 13 Quantitative Postgame surveys from 43 students Statistical analysis, descriptive, ANVOVA, paired sample Complexity level, skills, declarative knowledge, procedural knowledge, strategic knowledge, matching the competition Ellahi, Zaka, and Sultan [65 ] 13 Quantitative Pre-postsurvey from 87 students SPSS, independent sample Learning satisfaction, learning performance, learner’s interest Williams [66 ] 14 Mixed methods Pre-postgame survey from 32 students+reflection reports and game logs Independent sample Entrepreneurial skills, entrepreneurial attitudes, business skills Beuk [67 ] 13 Quantitative Postgame surveys from 137 students, 248 instructors Repeated measures one-way ANVOVA, regression analysis Perceived usefulness, level of fun, instructors perceived learning outcomes Kriz and Auchter [42 ] 12 Quantitative Postgame surveys from 12521 students Descriptive Organisation and facilitation of the simulation game, personal and social skills, competition and teamwork, business knowledge and entrepreneurship skills, overall satisfaction Zulfiqar et al. [68 ] 13 Quantitative Postgame 360 students survey Structural equation modelling (Garris et al.) using AMOS 24 Perceived business value, subjective norms, perceived behavioural controls, attitude toward entrepreneurship and entrepreneurial intentions Rogmans and Abaza [69 ] 11 Quantitative Postgame 200 students survey Descriptive Motivation and engagement Wang et al. [70 ] 14 Quantitative Postgame surveys from 141 students Partial least square approach SmartPLS software Performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and price value, behavioural intentions to use business simulation games Kiss and Schmuck [71 ] 13 Quantitative Postgame survey from 329 students Frequency tables, univariate ANCOVA, Strategy formulation, planning, decision-making, and teamwork; mathematical financial skills, managerial skills Goi [4 ] 14 Mixed method Postgame 365 students’ surveys and 14 students focus group Exploratory factor analysis (SPSS) and confirmatory factor analysis (AMOS) for quantitative and thematic analysis for qualitative Teamwork, learning outcomes, satisfaction Levant, Coulmont, and Sandu [73 ] 13 Mixed methods Pre-postsurveys from 392 students, mi- of Likert scale and open-ended questions Employability skills, soft skills Hernández-Lara and Serradell-López [41 ] 14 Qualitative 3681 messages posted in discussion forum In vivo data analysis Humpherys, Bakir, and Babb [74 ] 10 Quantitative Comparison of project grades of students Descriptive analysis Obi, Eze, and Chibuzo [75 ] 12 Quantitative Postgame survey from 210 students Cronbach’s alpha reliability Communication and collaboration competencies, critical thinking competencies, academic instruction Buil, Catalán, and Martínez [76 ] 13 Quantitative Postgame surveys from 360 students Statistical analysis Competence, autonomy, relatedness, self-efficacy, cognitive engagement, emotional engagement, behavioural engagement, skill development, and perceived learning Olive et al. [77 ] 10 Mixed methods 114 students pre-post achievement tests, feedback surveys and analysis of trace files Statistical analysis Carenys, Moya, and Perramon [78 ] 12 Quantitative Postgame surveys from 132 students Exploratory factor analysis and Cronbach’s alpha test of reliability Attributes, motivation, and cognitive learning outcomes Leal-Rodriguez and Albort-Morant [79 ] 13 Quantitative 80 students end of semester grades and game results Pearson correlation and structural equations modelling Students performance and students learning Ștefan et al. [80 ] 9 Qualitative Not mentioned Not mentioned Severengiz, Seliger, and Krüger [38 ] 13 Postgame surveys from 31 students Descriptive study Loon, Evans, and Kerridge [81 ] 14 Mixed methods Postgame surveys 155 and 36 semistructured interviews from students Pearson correlation and multiple regression analysis SPSS Bell and Loon [82 ] 14 Quantitative Postgame surveys from 173 students Principal component analysis, correlation and regression analysis Engagement, cognitive maturity, innovativeness, intended learning outcomes Lee, Long, and Visinescu [83 ] 13 Quantitative Postgame surveys from 93 students Partial least square approach Active learning, meaningful learning, collaboration, subject integration Pando-Garcia, Periañez-Cañadillas, and Charterina [84 ] 11 Quantitative Postgame surveys from two groups, 131 online, 83 onsite students Confirmatory factor analysis Perceived usefulness, perceived ease of use (PEOU), attitude to the business game technology, intention to use a business game technology Lin, Yen, and Wang [85 ] 10 Quantitative Experimental scenarios, an achievement test and a motivation scale, 49 response from individual and 47 response from groups of students Descriptive analysis, two-way ANOVA on SPSS Learning methods (collaborative and individual), learning motivation, learning performance Hwang and Cruthirds [86 ] 12 Quantitative Pre-postgame surveys from 52 from students Descriptive analysis SAP ease of use, business process knowledge, enterprise system knowledge, sap transaction knowledge Urquidi Martín, and Tamarit Aznar [87 ] 10 Quantitative Postgame survey from 58 students Descriptive analysis Evaluation of the experience, learning, and development of critical thinking Mustata et al. [88 ] 10 Qualitative Postgame qualitative surveys from 88 students Thematic analysis Newbery et al. [89 ] 10 Quantitative Pre-postgame surveys from 236 students divided in treatment and control groups Group level micro identity, individual level micro identity, and interpersonal micro identity, observed entrepreneurial behaviour, experienced entrepreneurial behaviours Torres and Augusto [90 ] 12 Quantitative Postgame surveys from 22 MBA executive students ANOVA Strategy formulation, strategy implementation, critical analysis Farashahi and Tajeddin [91 ] 14 Quantitative Postgame surveys from 194 undergraduate and MBA students Descriptive analysis Problem-solving skills, interpersonal skills, and self-awareness Alas et al. [92 ] 9 Quantitative 28 game data On SPSS, correlation analyses, regression analyses, and GPA, expenditure of market information, point for homework, normalized profits Calabor, Mora, and Moya [93 ] 14 Quantitative 12 academics Delphi Descriptive analysis Technical aspects of game, learning values of games, general view of game Zulfiqar et al. [57 ] 12 Quantitative Time-lagged surveys from 277 students Structural equation modelling Knowledge sharing, knowledge application, learnability, perceived pleasure, and self-efficacy Beranič and Heričko [62 ] 12 Quantitative Pre-postgame survey from 32 students involved in ERPsim introductory simulation Descriptive analysis Business process knowledge, technical SAP knowledge, ERP transaction knowledge, intent for future course engagement Samaras, Adkins, and White [94 ] 13 Quantitative Postsurvey from 119 students Paired-sample Critical thinking process and simulation participation
