SIMULATION AND LEARNING DYNAMICS IN BUSINESS GAMES

LACRUZ, ADONAI JOSÉ

doi:10.1590/1678-69712016/administracao.v18n2p49-79

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Human and Social Management • RAM, Rev. Adm. Mackenzie 18 (02) • Mar-Apr 2017 • https://doi.org/10.1590/1678-69712016/administracao.v18n2p49-79 linkcopy

SIMULATION AND LEARNING DYNAMICS IN BUSINESS GAMES

DINÂMICA DA SIMULAÇÃO E APRENDIZAGEM EM JOGOS DE EMPRESAS

DINÁMICA DE LA SIMULACIÓN Y DE APRENDIZAJE EN EL JUEGOS DE NEGOCIO

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Purpose: This paper studies the influence of simulation dynamics in the learning of business games participants.

Originality/gap/relevance/implications: Although many studies suggest the influence of factors linked simulation dynamics in the learning of participants of business games, it is unusual to investigate partial and simultaneous influence. Within lots of studies, the purpose is to evaluate the influence of one antecedent factor on learning of participants in business games. To explore possible inter-relationships between independent variables and effects of interaction expands the scope of analysis.

Key methodological aspects: It was examined the statements of 90 undergraduate management students of four institutions of higher education in Brazil, by analysis through multiple linear regression model in six variables linked simulation dynamics (professor, manual, team, complexity, debriefing and duration).

Summary of key results: Two variables composed the multiple linear regression model (debriefing and duration). These variables obtained a degree of association of 59% with the quantum of learning perceived by the participants of business games and explained 32% of its variation.

Key considerations/conclusions: The findings of this study may contribute to elaborate lesson plans with business games describing the influence of factors linked simulation dynamics, many of them under the professor control.

KEYWORDS
Business game; Experiential learning; Simulation dynamics; Management education; Multiple linear regression

Variables	Evidences
Simulation complexity	More complex simulations contribute to learning more than less complex simulations (p-value < 0.01) (Wolfe, 1978).
	Realism, produced through complexity, is a determining factor for learning of business games participants (Hall & Cox, 1994).
	In the main component of factorial model, the most Complex Simulation variable was associated with the factor Complexity (factorial load = 0.723 and commonality = 0.589) (Sauaia, 1995).
Duration of the simulation rounds	The simple linear regression model indicated that the duration can be predicted from the number of decisions (assumed as proxy for complexity) with significant beta weight (β = 0.829, p-value < 0.01), leading to the proposition that the duration of simulation, together with complexity, influences the learning of business games participants (Hall & Cox, 1994).
Duration of the simulation rounds	In the factorial model of main components, the variable Longest Duration was associated with Learning Climate (factorial load = 0.439), Satisfaction (factorial load = 0.420) and Complexity (factorial load = 0.381) factors. The commonality was 0.601 (Sauaia, 1995).
Professor	There was no identification of statistically significant correlation or beta weight (p-value > 0.05) between student learning and professor (Mayer, Dale, Fraccastoro, & Moss, 2011).
Professor	In the factorial model of main components, the variable Administrator of the simulation (professor) was associated to the factors of Cognitive learning (factorial load = 0.324) and Parameters of the experience (factorial load = 0.397). The commonality was 0.475 (Sauaia, 1995).
Debriefing	The quantum of perceived learning of business games participants with debriefing is superior to that of those who participated in simulations without debriefing (p-value < 0,05). The mean effect size (d = 0.45) shows an average improvement of 18% for debriefing (Lacruz, forthcoming).
Debriefing	In business games, there is a risk that the participants do not complete the experiential learning cycle due to a lack of reflective activities; therefore, it is advisable to include stages that stimulate the analysis of the results of the simulation round so that reflective observations may contribute to the thorough completion of the experiential learning cycle (Dias et al., 2013a).
Manual	There is a significant correlation between learning and the manual for business games (r = 0.32, p-value = 0.001). In addition, the results of the regression analysis indicated that learning can be predicted from the way students learned the simulation with significant beta weight for learning from the manual (β = 0.34, p-value = 0.001) (Mayer et al., 2011).
Team	There is a significant correlation between learning and the peers (r = 0.20, p-value = 0.049). In addition, the results of regression analysis indicated that transfer of learning can be predicted from the way students learned the simulation with significant beta weight for learning from the peers (β = 0.24, p-value = 0.018) (Mayer et al., 2011).
Team	In the main component factorial model, the variable Teammates was associated with the factors of Cognitive learning (factorial load = 0.305) and Team performance (factorial load = 0.508). The commonality was 0.538 (Sauaia, 1995).

Theme	Variable		Theoretical Background
Age	Continuous	Complete years	–
Genre	Nominal	Female (0) and Male (1)	Apesteguia et al. (2012); Florea et al. (2003)
Learning	Ordinal (Likert)	To acquire new knowledge	Sauaia (1995); Cannon and Burns (1999); Lacruz and Villela (2006); Dias et al. (2013a); Fitó-Bertran et al. (2015)
		Integrate knowledge from various areas of administration
		Update knowledge that was already present
		Identify problems, evaluate alternatives, formulate and deploy solutions and evaluate their results
		Develop systemic (holistic)
		Adapt to new situations (flexibility)
		Stimulate Teamwork
		Develop / Improve Leadership
		Troubleshoot conflicts
Simulation dynamics	Ordinal (Likert)	Duration of the simulation rounds	Wolfe (1978); Hall and Cox(1994); Sauaia (1995); Mayer et al. (2011); Dias et al. (2013b); Lacruz (forthcoming)
		Complexity of the simulation
		Professor
		Team
		Simulation Manual
		Debriefing

HEI	Companies	Students	Respondents	Valid surveys	Answer rate
Private_1	08	32	32	31	97%
Private_2	06	24	24	24	100%
Private_3	06	24	22	21	88%
Federal	04	16	15	14	88%
Total	24	96	93	90	94%

Statistics	PLI	Professor	Manual	Team	Complexity	Debriefing	Duration
Mean	4.37	4.43	3.04	2.81	4.54	4.72	4.41
Standard deviation	0.44	0.81	1.02	1.26	0.79	0.45	0.76

Variables	PLI	Gender	Professor	Manual	Team	Complexity	Debriefing	Duration
PLI	1		0.155	0.102	0.021	0.343**	0.479**	0.424**
Gender	0.002	1	0.227*	-0.082	0.018	-0.105	0.006	-0.179
Professor			1	0.058	-0.205	-0.109	0.211*	-0.092
Manual				1	0.138	0.109	-0.120	0.092
Team					1	-0.042	-0.034	0.128
Complexity						1	0.207*	0.368**
Debriefing							1	0.205
Duration								1

Variables	Block 1 (Control)			Block 2			Block 3
Variables	B	β	T	B	β	t	B	β	T
(Constant)	4.374		74.540**	2.141		4.848**	1.597		3.684**
Gender	-0.002	-0.002	-0.023	0.000	0.000	0.003	-0.058	-0.063	-0.709
Debriefing				0.473	0.479	5.090**	0.401	0.406	4.550**
Duration							0.205	0.352	3.873**
F	0.001			12.954**			15.025**
R	0.002			0.479			0.586
R2	0.000			0.229			0.344
R2 Adjusted	-0.011			0.212			0.321

Statistics	Debriefing and Complexity		Duration and Complexity		Debriefing, Duration and Complexity
Statistics	Debriefing	Complexity	Duration	Complexity	Debriefing	Duration	Complexity
B	0.425**	0.258**	0.359**	0.221*	0.384**	0.298**	0.162
R2 Adjusted	0.268		0.201		0.336

Variables	Block 1			Block 2
Variables	B	β	t	B	β	T
(Constant)	1.597		3.684**	-2.383		0.878
Gender	-0.058	-0.063	-0.709	-0.053	-0.057	-0.647
Debriefing	0.401	0.406	4.550**	1.230	1.247	2.177*
Duration	0.205	0.352	3.873**	1.148	1.971	1.802+
Mod				-0.196	-1.977	-1.486+
F	15.025**			11.979**
R	0.586			0.600
R2	0.344			0.360
R2 Adjusted	0.321			0.330

Variables	Evidences from other studies	Evidences from this study
Simulation complexity	More complex simulations contribute to more learning than less complex simulations (p-value < 0.01) (Wolfe, 1978).	The complexity, despite having a moderate statistical correlation with the perceived learning intensity of business games participants (r = 0.343, p-value < 0.01), did not compose the regression model. Redundancy was identified with the Debriefing and Duration variables.
	Realism, produced through complexity, is a determining factor for learning of business games participants (Hall & Cox, 1994).
	In the factorial model of main components, the most Complex Simulation variable was associated with the Complexity factor (factorial load = 0.723 and commonality = 0.589) (Sauaia, 1995).
Duration of the simulation rounds	The simple linear regression model indicated that the duration can be predicted from the number of decisions (assumed as proxy for complexity) with significant beta weight (β = 0.829, p-value < 0.01), leading to the proposition that the duration of the simulation, together with the complexity, influences the learning of business game participants (Hall & Cox, 1994).	The duration of the simulation rounds presented a moderate statistically significant correlation with the perceived learning intensity of business games participants (r = 0.424, p-value < 0.01) and composed the regression model (β = 0.352, p-value ≅ 0.000). In addition, it presented a statistically significant correlation with the complexity of the simulation (r = 0.368, p-value < 0.01).
Duration of the simulation rounds	In the factorial model of main components, the longest Duration variable was associated with the factors Learning Climate (factorial load = 0.439), Satisfaction (factorial load = 0.420) and Complexity (factorial load = 0.381). The commonality was 0.601 (Sauaia, 1995).
Professor	No statistically significant correlation or beta weight (p-value > 0.05) was found between student learning and professor (Mayer et al., 2011).	No statistically significant correlation or beta weight was identified between the perceived learning intensity of business games participants and the professor of the simulation. The professor presented a statistically significant correlation with the debriefing stage (r = 0.211, p-value < 0.05).
Professor	In the factorial model of main components, the variable professor was associated to the Cognitive learning (factorial load = 0.324) and Parameters of the experience (factorial load = 0.397) factors. The commonality was 0.475 (Sauaia, 1995).
Debriefing	The quantum of perceived learning of business games participants with debriefing is superior to that of those who participated in simulations without debriefing (p-value < 0.05). The mean effect size (d = 0.45) shows an average improvement of 18% because of the debriefing (Lacruz, forthcoming).	The debriefing stage presented a moderate statistically significant correlation with the perceived learning intensity of business games participants (r = 0.479, p-value < 0.01) and composed the regression model (β = 0.406, p-value ≅ 0.000). In addition, it presented a statistically significant correlation with the complexity (r = 0.207, p-value < 0.05) and professor (r = 0.211, p-value < 0.05) variables.
Debriefing	In business games, there is a risk that the participants do not complete the experiential learning cycle due to a lack of reflective activities, suggesting the need to add new stages to the process that could stimulate the analysis of the results of the simulation rounds so that reflective observations may contribute to the completion of the experiential learning cycle (Dias et al., 2013a).
Manual	There is a significant correlation between learning and the business game manual (r = 0.32, p-value = 0.001). In addition, the results of the regression analysis indicated that learning can be predicted from the way students learned the simulation with significant beta weight for learning from the manual (β = 0.34, p-value = 0.001) (Mayer et al., 2011).	No statistically significant correlation or beta weight was identified between the perceived learning intensity of business games participants and the simulation manual, nor with any of the other variables considered to evaluate the simulation dynamics.
Team	There is a significant correlation between learning and the team (r = 0.20, p-value = 0.049). In addition, the results of regression analysis indicated that the transfer of learning can be predicted from the way students learned the simulation with significant beta weight for learning from the team (β = 0.24, p-value = 0.018) (Mayer et al., 2011).	No statistically significant correlation or beta weight was identified between the perceived learning intensity of business games participants and the team, nor with any of the other variables considered to evaluate the simulation dynamics.
Team	In the main component factorial model, the variable Teammates was associated with the Cognitive learning (factorial load = 0.305) and Team performance (factorial load = 0.508) factors. The commonality was 0.538 (Sauaia, 1995).

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[1] Source: Elaborated by the authors.

[TFN1] Note: The

[TFN2] ^*
symbol indicates that the correlation is statistically significant at 5% (two-tailed) and

[TFN3] ^**
at 1%.