Simulation as Research Method: Modeling Social Interactions in Management Science

Happach, Roland Maximilian; Tilebein, Meike

doi:10.1007/978-3-319-15515-9_13

Cited by 5 publications

(6 citation statements)

References 33 publications

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“…Rural inter-professional learning is framed using simulation methodology, focusing primarily on human interactions during events that mimic possible real-life health scenarios in which health care professionals may be exposed to working in an interdisciplinary team environment. 9 While the simulated events provide students with opportunities to practice clinical skills, the primary objective is to identify and improve aspects of the patient journey where collaborative care teams may improve patient outcomes and the health care experience. Simulations are specifically written to incorporate rural-specific challenges, such as limited workforce and resource availability, distance to speciality care and/or farming or family responsibilities.…”

Section: What This Study Addsmentioning

confidence: 99%

Interprofessional education in the rural environment to enhance multidisciplinary care in future practice: Breaking down silos in tertiary health education

Reed

Bailey

et al. 2021

Australian J Rural Health

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Objective: Western Sydney University has implemented a rural interprofessional learning programme to promote collaborative care approaches to enhance crossdiscipline communications, improve knowledge and clarity of roles and improve patient care and outcomes. Design: Rural interprofessinal learning is an interprofessional educational approach, consisting of simulations of complex health events. Simulation methodology frames the study with a focus on human interaction. A mixed-methods evaluation has been conducted, incorporating pre-and post-event participant surveys along with semistructured focus groups. Setting: Simulations are conducted in the rural setting, including community settings, working farms and rural hospitals. Main Outcome Measures: Reflexive thematic analysis was used to identify themes measuring students' perceptions of interdisciplinary care, knowlede of other health discipline roles and skills and how they believe the exercise will influence their future practice. Facilitator feedback regarding the efficacy of the simulations was also recorded and analysed using reflexive thematic analysis. Participants: Care of simulated patient(s)/bystander(s) is primarily provided by paramedicine, nursing and medical students; however, increasing interest has expanded the programme to include students from a range of allied health professions.Simulations are facilitated by a multidisciplinary team of experienced practitioners and specialists. Intervention: Four rural interprofessional learning events have been held. Results: 120 students have participated in the evaluation. Findings include increased understanding of the contributions of other disciplines in enhancing patient care, team approaches, cross-discipline communication and a need to engage in collaborative care in future practice. Conclusion: Creating a collaborative learning environment creates a culture of multidisciplinary care, enhancing patient care and improving outcomes. The rural 128 | REED Et al. How to cite this article: Reed K, Reed B, Bailey J, et al. Interprofessional education in the rural environment to enhance multidisciplinary care in future practice: Breaking down silos in tertiary health education.

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Section: What This Study Addsmentioning

confidence: 99%

Interprofessional education in the rural environment to enhance multidisciplinary care in future practice: Breaking down silos in tertiary health education

Reed

Bailey

et al. 2021

Australian J Rural Health

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“…Simulation is popular as a methodological approach in management science (Dooley, 2017;Harrison et al, 2007) and is defined as applying computer software to create and code a quantitative model of real-world events (Happach and Tilebein, 2015). Simulation models are computational representations of a theoretical logic that links constructs (Davis et al, 2007).…”

Section: Methodsmentioning

confidence: 99%

Unraveling the mystery: exploring managers' attraction to excessive markets and investigating behavioral biases in market entry decisions

Soleymanzadeh,

Hajipour

2024

JSMA

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PurposeThe purpose of this study is to address why managers enter the excessive market. A comparison of the facts and perceptions of entrants relative to success in the market shows that many entrants are confident about the viability of their businesses and enter the market. Accordingly, the authors simulate market entry decisions to detect behavioral biases.Design/methodology/approachThe authors adapted the entry decisions simulation method, which is supported by the theoretical foundations of signal detection theory (SDT) and signaling theory. The simulation model is implemented on the Anaconda platform and written in Python 3.FindingsThe results of this study suggest that overestimation relates to excess market entry. Also, the proportion of excess entry under difficult conditions is always higher than under easy conditions.Practical implicationsThis research helps managers and firms think about their and their competitors' abilities and evaluate them before entering the market. Policymakers and practitioners can also design programs such as experiential learning to help entrants assess their skills.Originality/valueSo far, no research has investigated the role of overconfidence under different market conditions. Accordingly, this study contributes to the current market entry literature by disentangling the debate between absolute and relative confidence and by considering the role of task difficulty.

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“…Most empirical data concerning functional diversity are based on team-level observations rather than on observations of individual team members' behavior (Kreidler and Tilebein, 2013;Kreidler and Tilebein, 2016). The System Dynamics simulation method is able to capture the underlying phenomena and structures of a problem (Happach and Tilebein, 2015;Kreidler and Tilebein, 2016;Schieritz and Milling, 2003;Stolarski and Tilebein, 2009). System Dynamics can thus be applied to represent mechanisms of functional diversity on a team basis and integrate the changing dominant effects of functional diversity in teams and capture the dynamic effects.…”

Section: Simulation Modelmentioning

confidence: 99%

“…Because most empirical studies in the area of diversity research are cross-sectional, they cannot entirely capture the effects of functional team diversity on innovation, especially with regard to dynamics. Therefore, a more systemic approach is used in this paper to take a closer look at the complex and interconnected phenomena of team diversity (based on Davis et al, 2007;Happach and Tilebein, 2015). Davis et al (2007 propose using simulation as an alternative and complementary research method in management science, when dealing with complexity and dynamic behavior (Davis et al, 2007, Happach andTilebein, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, a more systemic approach is used in this paper to take a closer look at the complex and interconnected phenomena of team diversity (based on Davis et al, 2007;Happach and Tilebein, 2015). Davis et al (2007 propose using simulation as an alternative and complementary research method in management science, when dealing with complexity and dynamic behavior (Davis et al, 2007, Happach andTilebein, 2015). By applying a systemic perspective to the contradictory forces and dynamic behavior of diversity in NPD teams, with our simulation model, we aim to contribute to the explanation of those effects found in literature.…”

Section: Introductionmentioning

confidence: 99%

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Driver or inhibitor for innovation?

Kreidler

Tilebein

2018

Self Cite

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Purpose Literature is unanimous about the effects of functional diversity in new product development teams. This paper uses simulation modeling to investigate the contradictory and dynamic effects of functional team diversity on innovation revealed by empirical literature. This paper aims to start a discussion on this dynamic perspective of team diversity. Design/methodology/approach This paper presents a systemic approach toward investigating the contradictory and dynamic effects of functional team diversity on innovation by creating a simplified System Dynamics model of functional diversity in new product development teams. Findings Although the simulation model is highly simplified, it can integrate the contradictory results of empirical data and the dynamic component of teamwork. Therefore, it offers a new approach to investigating the effects of functional diversity on team innovation. Research limitations/implications The model is highly simplified and exemplary. No actual data are included, thus limiting the results as fully theoretical. Originality/value Empirical studies often analyze the effects of functional diversity on innovation in new product development teams. However, empirical data are unclear regarding the nature of the effects of functional diversity on innovation. Therefore, functional diversity is chosen for the simulation model as being the most controversially discussed diversity attribute. By applying a simulation model to the problem and adding a dynamic component to teamwork, we are contributing to the explanation for the contradictory findings in literature.

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Simulation as Research Method: Modeling Social Interactions in Management Science

Cited by 5 publications

References 33 publications

Interprofessional education in the rural environment to enhance multidisciplinary care in future practice: Breaking down silos in tertiary health education

Interprofessional education in the rural environment to enhance multidisciplinary care in future practice: Breaking down silos in tertiary health education

Unraveling the mystery: exploring managers' attraction to excessive markets and investigating behavioral biases in market entry decisions

Driver or inhibitor for innovation?

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