2020
Kallio, Henrik
Essays on small group decision-making in a dynamic business simulation game Väitöskirja
Tieto- ja palvelujohtaminen, Aalto-yliopisto, 2020, ISBN: 978-952-60-3965-7.
Abstract | Links | BibTeX | Avainsanat: business simulation, englanninkieliset väitöskirjat, LASSO selection, logistic regression, multinomial logistic regression, real-time dynamic decision-making, regression analysis, regression tree, sequence analysis
@phdthesis{Kallio2020,
title = {Essays on small group decision-making in a dynamic business simulation game},
author = {Henrik Kallio},
url = {http://urn.fi/URN:ISBN:978-952-60-3965-7},
isbn = {978-952-60-3965-7},
year = {2020},
date = {2020-01-01},
school = {Tieto- ja palvelujohtaminen, Aalto-yliopisto},
abstract = {This dissertation brings new insights to the analysis of decision-making. It studies the behavior of teams in a real-time business simulation context. The study uses multiple perspectives to reveal new information about patterns in decision-making.
The research project was conducted in "RealGame", which is a real-time business simulation. While much research has been done in the analysis of decision-making in static situations, little is known about how decision-making happens in dynamic contexts where time plays a crucial role. Therefore, it is important to produce new information about the behavior of decision-makers in such environments.
The research uses concepts of real-time and dynamic decision-making theory and is based on the framework of prescriptive decision-making. The research questions in this study are related to the formation of decision-making teams, the structure of successful decisions and the different decision-making strategies in a real-time context. It contributes to previously published studies of decision-making in simulated business environments. It is one of the few studies which considers small teams' decision-making behavior in a real-time simulated business environment.
The use of multiple evaluations brings new and complementary insights into the behavior of decision-making teams in business simulation environments. The results give preliminary information about real-time behavior. Thus, they contribute to the existing literature of decision-making theory. The results also give practical advice for organizations that are operating in multinational environments and are planning their operations and structures at either a managerial and/or team level. Even though the research has been conducted in one business simulation, the methodological and operational aspects may be applied to the study of other simulations, and to some extent other types of learning domains where semi-structured data is generated, such as digital services.},
keywords = {business simulation, englanninkieliset väitöskirjat, LASSO selection, logistic regression, multinomial logistic regression, real-time dynamic decision-making, regression analysis, regression tree, sequence analysis},
pubstate = {published},
tppubtype = {phdthesis}
}
This dissertation brings new insights to the analysis of decision-making. It studies the behavior of teams in a real-time business simulation context. The study uses multiple perspectives to reveal new information about patterns in decision-making.
The research project was conducted in "RealGame", which is a real-time business simulation. While much research has been done in the analysis of decision-making in static situations, little is known about how decision-making happens in dynamic contexts where time plays a crucial role. Therefore, it is important to produce new information about the behavior of decision-makers in such environments.
The research uses concepts of real-time and dynamic decision-making theory and is based on the framework of prescriptive decision-making. The research questions in this study are related to the formation of decision-making teams, the structure of successful decisions and the different decision-making strategies in a real-time context. It contributes to previously published studies of decision-making in simulated business environments. It is one of the few studies which considers small teams' decision-making behavior in a real-time simulated business environment.
The use of multiple evaluations brings new and complementary insights into the behavior of decision-making teams in business simulation environments. The results give preliminary information about real-time behavior. Thus, they contribute to the existing literature of decision-making theory. The results also give practical advice for organizations that are operating in multinational environments and are planning their operations and structures at either a managerial and/or team level. Even though the research has been conducted in one business simulation, the methodological and operational aspects may be applied to the study of other simulations, and to some extent other types of learning domains where semi-structured data is generated, such as digital services.
The research project was conducted in "RealGame", which is a real-time business simulation. While much research has been done in the analysis of decision-making in static situations, little is known about how decision-making happens in dynamic contexts where time plays a crucial role. Therefore, it is important to produce new information about the behavior of decision-makers in such environments.
The research uses concepts of real-time and dynamic decision-making theory and is based on the framework of prescriptive decision-making. The research questions in this study are related to the formation of decision-making teams, the structure of successful decisions and the different decision-making strategies in a real-time context. It contributes to previously published studies of decision-making in simulated business environments. It is one of the few studies which considers small teams' decision-making behavior in a real-time simulated business environment.
The use of multiple evaluations brings new and complementary insights into the behavior of decision-making teams in business simulation environments. The results give preliminary information about real-time behavior. Thus, they contribute to the existing literature of decision-making theory. The results also give practical advice for organizations that are operating in multinational environments and are planning their operations and structures at either a managerial and/or team level. Even though the research has been conducted in one business simulation, the methodological and operational aspects may be applied to the study of other simulations, and to some extent other types of learning domains where semi-structured data is generated, such as digital services.