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This study aims to consider assurance of learning among undergraduate business students enrolled in capstone business strategy courses using the GLO-BUS competitive simulation. Gender, academic major and business core course performance were examined.

Participants were 595 undergraduate capstone business students from 21 course sections taught over a four-year period. Variables included learning assurance measures, simulation performance, gender, major, business core course grades, capstone course grade and cumulative grade point average. Correlations, linear regression, multiple regression and multivariate analysis of variance (MANOVA) were used to analyze the data.

Learning assurance report scores were strongly related to simulation performance. Simulation performance was related to capstone course grade, which, in turn, was significantly related to the grade point average (GPA). Core business courses were related to learning assurance and performance indicators. Significant differences for gender and degree major were found for academic performance measures. Women and men did not differ in simulation performance.

Limitations include the use of one simulation (GLO-BUS) and studying students at one university taught by one professor. Assurance of learning measures needs further study as factors in business program evaluation. Future research should analyze post-graduate performance and career achievements in relation to assurance of learning outcomes.

This study conducts empirical analyses of simulation learning that focuses entirely on direct measures, including student characteristics (gender, major), learning assurance measures, business core course grades, capstone course grades and student GPAs.

The study emanated from initial attempts to determine whether two computer simulations used in teaching a college business course delivered a meaningful learning experience. This paper aims to investigate whether students' level of performance in the simulation game was due to the application of skill or largely a matter of “luck”.

Applying a method similar to that of Wellington et al., the study evaluated the consistency of performance across two different rounds of each simulation game. It also compared performance levels across both simulations, and examined the relationship between game performance and academic achievement.

The significant consistency between performance levels suggests that with respect to the simulations used in this study, the game score reflected the player's application of skill rather than reliance on “luck”. However, there is no significant relationship between game performance and academic achievement.

While this study is based on two specific games, other simulation users can use it as a yardstick to ascertain the educational value of the simulations that they use.

The purpose of this paper is to provide a method for analysing and improving the operational performance of business processes (BPs).

The method employs two standards, Business Process Modelling Notation (BPMN 2.0) and Business Processes Simulation (BPSim 1.0), to measure key performance indicators (KPIs) of BPs and test for potential improvements. The BP is first modelled in BPMN 2.0. Operational performance can then be measured using BPSim 1.0. The process simulation also enables execution of reliable “what-if” analysis, allowing improvements of the actual processes under study. To confirm the validity of the method the authors provide an application to the healthcare domain, in which the authors conduct several simulation experiments. The case study examines a standardised patient arrival and treatment process in an orthopaedic-emergency room of a public hospital.

The method permits detection of process criticalities, as well as identifying the best corrective actions by means of the “what-if” analysis. The paper discusses both management and research implications of the method.

The study responds to current calls for holistic and sustainable approaches to business process management (BPM). It provides step-by-step process modelling and simulation that serve as a “virtual laboratory” to test potential improvements and verify their impact on operational performance, without the risk of error that would be involved in ex-novo simulation programming.

Evaluating warfighter lethality is a critical aspect of military performance. Raw metrics such as marksmanship speed and accuracy can provide some insight, yet interpreting subtle differences can be challenging. For example, is a speed difference of 300 milliseconds more important than a 10% accuracy difference on the same drill? Marksmanship evaluations must have objective methods to differentiate between critical factors while maintaining a holistic view of human performance.

Monte Carlo simulations are one method to circumvent speed/accuracy trade-offs within marksmanship evaluations. They can accommodate both speed and accuracy implications simultaneously without needing to hold one constant for the sake of the other. Moreover, Monte Carlo simulations can incorporate variability as a key element of performance. This approach thus allows analysts to determine consistency of performance expectations when projecting future outcomes.

The review divides outcomes into both theoretical overview and practical implication sections. Each aspect of the Monte Carlo simulation can be addressed separately, reviewed and then incorporated as a potential component of small arms combat modeling. This application allows for new human performance practitioners to more quickly adopt the method for different applications.

Performance implications are often presented as inferential statistics. By using the Monte Carlo simulations, practitioners can present outcomes in terms of lethality. This method should help convey the impact of any marksmanship evaluation to senior leadership better than current inferential statistics, such as effect size measures.

This paper aims to cover the development of a methodology for hybrid fuzzy Monte Carlo agent-based simulation (FMCABS) and its implementation on a parametric study of construction crew performance.

The developed methodology uses fuzzy logic, Monte Carlo simulation and agent-based modeling to simulate the behavior of construction crews and predict their performance. Both random and subjective uncertainties are considered in model variables.

The developed methodology was implemented on a real case involving the parametric study of construction crew performance to assess its applicability and suitability for this context.

This parametric study demonstrates a practical application for the hybrid FMCABS methodology. Though findings from this study are limited to the context of construction crew motivation and performance, the applicability of the developed methodology extends beyond the construction domain.

This paper will help construction practitioners to predict and improve crew performance by taking into account both random and subjective uncertainties.

This paper will advance construction modeling by allowing for the assessment of social interactions among crews and their effects on crew performance.

The developed hybrid FMCABS methodology represents an original contribution, as it allows agent-based models to simultaneously process all types of variables (i.e. deterministic, random and subjective) in the same simulation experiment while accounting for interactions among different agents. In addition, the developed methodology is implemented in a novel and extensive parametric study of construction crew performance.

Total enterprise simulations are often used and included as graded elements by instructors in business degree capstone “policy courses” under the assumption that their use enriches the learning experience. This suggests a belief that people who perform best in simulations have learned how to play the game better. The studies reported on here attempt to determine and evaluate the relationship between learning and simulation performance. These studies have consistently found that, while simulation‐related learning does occur, there appears to be no direct, positive relationship between the two variables. Suggestions for further research are proposed.

The purpose of this paper is to develop a simulation-based value function (VF) that combines multiple key performance indicators (KPIs) into a unified Sigma rating (SR) for system-level performance assessment and improvement.

Simulation is used as a platform for assessing the multiple KPIs at the system level. A simple additive VF is formed to combine the KPIs into a unified SR using the analytical hierarchy process and the entropy method. Value mapping is utilized to resolve the conflict among KPIs and generate a unified value. These methods are integrated into the standard Six Sigma define-measure-analyze-improve-control (DMAIC) process.

Simulation results provided the Six Sigma DMAIC process with system-level performance measurement and analysis based on multiple KPIs. The developed VF successfully generated unified SRs that were used to assess various performance improvement plans.

The accuracy and credibility of the results obtained from using the proposed VF are highly dependent on the availability of pertinent data and the accuracy of the developed simulation model.

The proposed approach provides Six Sigma practitioners and performance mangers with a mechanism to assess and improve the performance of production and service system based on multiple KPIs when conducting Six Sigma studies.

This paper contributes to the previous research by handling multiple KPIs in Six Sigma studies conducted at the system level using simulation and VF. The research also provides guidelines for using the different methods of weights assessment to form the VF within the DMAIC process.

This paper aims to investigate the impact of simulation laboratory on continuing education engineering students’ academic performance.

The investigation consists of establishing the student learning levels then mapping the student learning levels (knowledge, comprehension, application, analysis, synthesis and evaluation) through program outcomes with appropriate evaluation components. 270 continuing education students enrolled during six years were selected to be observed as part of this study. These students were divided into two subgroups, one with 135 students who were offered simulation lab (G2) and the other 135 students were not offered simulation lab (G1) in this investigation. Subsequently, a comparative analysis was carried out on these two groups to assess the student performance in multiple evaluation components with respect to student learning level and program outcome achievement.

It was identified that student performance in the application, analysis, synthesis and evaluation learning levels has improved for the group with simulation lab, and no change or minimal change was observed for the group without simulation lab. It was revealed that the simulation lab practice problems needs to be aligned with the theoretical concepts in the course to get a better performance from the students.

The study was conducted in one of the leading institutes with 270 students’ performance observed over a period of six years. It is the comprehensive work done on a complete program with data collated over a period of six years in multiple courses and multiple assessments.

The purpose of this paper is to investigate how dimensions related to teamwork and team climate can influence decision making and learning of teams (performance). In order to understand which factors are more effective, several relevant group and team characteristics drawn from classical literature on groups and more recent empirical team simulation research have been considered.

The paper presents the results of a longitudinal study carried out during four months. A total of 183 Italian participants, divided into 50 teams of three (n=24), four (n=19) and five (n=7) members, have been involved in a business game developed by several European savings banks and simulating a real stock market environment. The aim of each team is not only to earn virtual money, but also learning long-term strategies to develop profitable investments without losing sight of economic factors.

Based on literature review, the authors tested three group levels (intragroup relations level, self-member level and group-design level) by making three hypotheses concerning the teams involved in the simulation and investigated the communication and innovation (CI) dimension from the Italian version of the team climate inventory (TCI) by Ragazzoni et al. A correlation between team performance and CI was found (r=0.301 p=0.048), which is in line with the hypothesis that such factors as communication and support for innovation can affect the decision-making performance.

The results presented in the paper let practitioners understand which dynamics characterize teamwork activities and how such aspects as communication and support for innovation can lead to group learning and decision-making performance. The simulation used in this research is an empirical way to study team performance and group learning without other noise variables.

This paper investigates how individuals' decision-making approach and perceptions of a game's cognitive realism affect the performance of virtual businesses in a web-based simulation game.

Survey data are collected from 274 business simulation game users and is analysed using the fsQCA technique.

The study identifies three alternative pathways to high and low performance in a business simulation game. Results indicate that a flexible decision-making approach exists in all high performance pathway solutions. Where a game is perceived to be realistic, a more focused decision-making approach is associated with high performance. However, where perceived cognitive realism is absent, a less focused experimental decision-making approach is employed, which increases the chances to achieve low performance. Finally, perceived cognitive realism and an experimental decision-making approach are found to be mutually exclusive for achieving high performance.

Whilst the learning benefits of web-based simulation games are widely acknowledged, the complex interplay amongst factors affecting performance in games is under-researched. Limited research exists on how perceptions of a game's cognitive realism interact with user decision-making approaches to affect performance.