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  • 200127_ABS_Scenario_based_questionnaire_JOS_R1_SE_SO_DSU

    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Simulation on 26/04/2020 available online: https://www.tandfonline.com/doi/full/10.1080/17477778.2020.1753251

    Accepted author manuscript, 1.3 MB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Eliciting Agents’ Behaviour using Scenario-Based Questionnaire in Agent-Based Dairy Supply Chain Simulation

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

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Eliciting Agents’ Behaviour using Scenario-Based Questionnaire in Agent-Based Dairy Supply Chain Simulation. / Utomo, Dhanan; Onggo, Stephan; Eldridge, Stephen et al.
In: Journal of Simulation, Vol. 16, No. 1, 31.01.2022, p. 58-72.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Utomo, D, Onggo, S, Eldridge, S, Daud, AR & Tejaningsih, S 2022, 'Eliciting Agents’ Behaviour using Scenario-Based Questionnaire in Agent-Based Dairy Supply Chain Simulation', Journal of Simulation, vol. 16, no. 1, pp. 58-72. https://doi.org/10.1080/17477778.2020.1753251

APA

Utomo, D., Onggo, S., Eldridge, S., Daud, A. R., & Tejaningsih, S. (2022). Eliciting Agents’ Behaviour using Scenario-Based Questionnaire in Agent-Based Dairy Supply Chain Simulation. Journal of Simulation, 16(1), 58-72. https://doi.org/10.1080/17477778.2020.1753251

Vancouver

Utomo D, Onggo S, Eldridge S, Daud AR, Tejaningsih S. Eliciting Agents’ Behaviour using Scenario-Based Questionnaire in Agent-Based Dairy Supply Chain Simulation. Journal of Simulation. 2022 Jan 31;16(1):58-72. Epub 2020 Apr 26. doi: 10.1080/17477778.2020.1753251

Author

Utomo, Dhanan ; Onggo, Stephan ; Eldridge, Stephen et al. / Eliciting Agents’ Behaviour using Scenario-Based Questionnaire in Agent-Based Dairy Supply Chain Simulation. In: Journal of Simulation. 2022 ; Vol. 16, No. 1. pp. 58-72.

Bibtex

@article{8786890a9db84dc88789277817c180e2,
title = "Eliciting Agents{\textquoteright} Behaviour using Scenario-Based Questionnaire in Agent-Based Dairy Supply Chain Simulation",
abstract = "A scenario-based questionnaire is a survey method that aims to identify the respondents{\textquoteright} decision rules using their responses to a series of scenarios. It is rarely used in agent-based modelling and simulation (ABMS) with most researchers preferring a survey with closed questions as the data collection method. This is particularly true for ABMS studies in agri-food supply chains. In our paper, we design a scenario-based questionnaire to elicit the behaviour of agents in ABMS and apply it in a dairy supply chain case. Our findings suggest that respondents respond well to a scenario-based questionnaire as it relates more closely to their actual decision-making process. Furthermore, our experiment shows that the decision rules extracted using a scenario-based questionnaire improve ABMS validity.",
keywords = "Agent-based modelling and simulation, data-collection, decision rule elicitation, supply chain, dairy",
author = "Dhanan Utomo and Stephan Onggo and Stephen Eldridge and A.R. Daud and S. Tejaningsih",
note = "This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Simulation on 26/04/2020 available online: https://www.tandfonline.com/doi/full/10.1080/17477778.2020.1753251",
year = "2022",
month = jan,
day = "31",
doi = "10.1080/17477778.2020.1753251",
language = "English",
volume = "16",
pages = "58--72",
journal = "Journal of Simulation",
issn = "1747-7778",
publisher = "Palgrave Macmillan Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - Eliciting Agents’ Behaviour using Scenario-Based Questionnaire in Agent-Based Dairy Supply Chain Simulation

AU - Utomo, Dhanan

AU - Onggo, Stephan

AU - Eldridge, Stephen

AU - Daud, A.R.

AU - Tejaningsih, S.

N1 - This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Simulation on 26/04/2020 available online: https://www.tandfonline.com/doi/full/10.1080/17477778.2020.1753251

PY - 2022/1/31

Y1 - 2022/1/31

N2 - A scenario-based questionnaire is a survey method that aims to identify the respondents’ decision rules using their responses to a series of scenarios. It is rarely used in agent-based modelling and simulation (ABMS) with most researchers preferring a survey with closed questions as the data collection method. This is particularly true for ABMS studies in agri-food supply chains. In our paper, we design a scenario-based questionnaire to elicit the behaviour of agents in ABMS and apply it in a dairy supply chain case. Our findings suggest that respondents respond well to a scenario-based questionnaire as it relates more closely to their actual decision-making process. Furthermore, our experiment shows that the decision rules extracted using a scenario-based questionnaire improve ABMS validity.

AB - A scenario-based questionnaire is a survey method that aims to identify the respondents’ decision rules using their responses to a series of scenarios. It is rarely used in agent-based modelling and simulation (ABMS) with most researchers preferring a survey with closed questions as the data collection method. This is particularly true for ABMS studies in agri-food supply chains. In our paper, we design a scenario-based questionnaire to elicit the behaviour of agents in ABMS and apply it in a dairy supply chain case. Our findings suggest that respondents respond well to a scenario-based questionnaire as it relates more closely to their actual decision-making process. Furthermore, our experiment shows that the decision rules extracted using a scenario-based questionnaire improve ABMS validity.

KW - Agent-based modelling and simulation

KW - data-collection

KW - decision rule elicitation

KW - supply chain

KW - dairy

U2 - 10.1080/17477778.2020.1753251

DO - 10.1080/17477778.2020.1753251

M3 - Journal article

VL - 16

SP - 58

EP - 72

JO - Journal of Simulation

JF - Journal of Simulation

SN - 1747-7778

IS - 1

ER -