Developing a generic risk maturity model (GRMM) for evaluating risk management in construction projects

Hoseini, Erfan; Hertogh, Marcel; Bosch‐Rekveldt, Marian

doi:10.1080/13669877.2019.1646309

Cited by 33 publications

(37 citation statements)

References 40 publications

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“…The project objectives can be considered as several elements like “project scope”, “total project time”, “total project cost” and “product quality”, thus these four attributes are selected as decision attributes of constructed decision system (Monghasemi et al ., 2015). Table 2 shows explanations and the domain of each decision attribute (Bilal et al ., 2020; Hoseini et al ., 2019; Arnuphaptrairong, 2011; Dey et al ., 2007; Ropponen and Lyytinen, 2000). The objects of constructed decision system (U) are 692 finished banking software projects within the last three years in the biggest banking information technology ecosystem of Iran.…”

Section: Methodsmentioning

confidence: 99%

Developing a risk assessment model for banking software development projects based on rough-grey set theory

Karimi

Yahyazade

2021

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PurposeRisk management is one of the most influential parts of project management that has a major impact on the success or failure of projects. Due to the increasing use of information technology in all fields and the high failure rate of software development projects, it is essential to predict the risk level of each project effectively before starting. Therefore, the main purpose of this paper is proposing an expert system to infer about the risk of new banking software development project.Design/methodology/approachIn this research, the risk of software developing projects is considered from four dimensions including risk of cost deviation, time deviation, quality deviation and scope deviation, which is examined by rough set theory (RST). The most important variables affecting the cost, time, quality and scope of projects are identified as condition attributes and four initial decision systems are constructed. Grey system theory is used to cluster the condition attributes and after data discretizing, eight rule models for each dimension of risk as a decision attribute are extracted using RST. The most validated model for each decision attribute is selected as an inference engine of the expert system, and finally a simple user interface is designed in order to predict the risk level of any new project by inserting the data of project attributesFindingsIn this paper, a high accuracy expert system is designed based on the combination of the grey clustering method and rough set modeling to predict the risks of each project before starting. Cross-validation of different rule models shows that the best model for determining cost deviation is Manual/Jonson/ORR model, and the most validated models for predicting the risk of time, quality and scope of projects are Entropy/Genetic/ORR, Manual/Genetic/FOR and Entropy/Genetic/ORR models; all of which are more than 90% accurateResearch limitations/implicationsIt is essential to gather data of previous cases to design a validated expert system. Since data documentation in the field of software development projects is not complete enough, grey set theory (GST) and RST are combined to improve the validity of the rule model. The proposed expert system can be used for risk assessment of new banking software projectsOriginality/valueThe risk assessment of software developing projects based on RST is a new approach in the field of risk management. Furthermore, using the grey clustering for combining the condition attributes is a novel solution for improving the accuracy of the rule models.

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Section: Methodsmentioning

confidence: 99%

Developing a risk assessment model for banking software development projects based on rough-grey set theory

Karimi

Yahyazade

2021

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“…Risk maturity models help understand the degree of sophistication of risk management practices and their effectiveness at each stage (Wieczorek-Kosmala, 2014). Meanwhile (Hoseini et al, 2019)…”

Section: Risk Maturitymentioning

confidence: 99%

Risk Management of Mice Tourism in XYZ Firm During the Covid-19 Pandemic

Tan¹,

Ardilo²

2021

bmss

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This paper explores the implementation of risk management maturity of MICE tourism-sector organisations in XYZ firm which located in Indonesia, precisely in Bandung City, which has a population of 2.510.103 people. The research used a qualitative-descriptive design in the form of a case study at XYZ firm. This study uses primary data and secondary data as supplementary. Primary data was collected by conducting semi-structured interviews with subjects whose roles and functions are relevant to facilitating XYZ firm's strategic initiatives and programs. The findings indicate that XYZ firm was at the initial level in implementing risk management which positively corresponds with its resilience and sustainability—XYZ had a difficult time bouncing back, surviving, and thriving this global pandemic. The results of this research may assist decision-makers on the importance and the dynamics of risk management implementation, particularly for the MICE tourism industry in Indonesia.

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“…Hoje em dia, eles têm aplicações em muitos campos (WENDLER, 2012), incluindo gestão de risco. Um dos primeiros modelos foi proposto por (HILLSON, 1997) e atualmente vários modelos de maturidade de gestão de risco são encontrados, embora concentrados em algumas áreas: setor de serviços públicos de água (MacGILLIVRAY et al, 2007a(MacGILLIVRAY et al, , 2007bPOLLARD, 2008), gerenciamento de projeto (ZOU; CHEN; CHAN, 2010;MU et al, 2014;YEO;REN;REN, 2016;HWANG;LOW, 2016;CHAPMAN, 2019;HARTONO;WIJAYA;ARINI, 2019;HOSEINI;HERTOGH;REKVELDT, 2019), cibersegurança (BOYSON, 2014) e enterprise risk management (ERM) (OLIVA, 2016).…”

Section: Modelos De Maturidade Para Gestão De Risco E Lacuna Na Liter...unclassified

Anais do(a) 16º CONGRESSO BRASILEIRO DE SISTEMAS

2021

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ResumoCada vez mais pessoas têm contado com a renda variável para diversificar seus investimentos e aumentar seus ganhos. Em geral, esse tipo de investimento começa como uma fonte secundária de recursos. O processo de compreensão do funcionamento desse tipo mercado conta com diversas iniciativas educacionais independentes. Com o objetivo de vivenciar a dinâmica e principalmente os riscos associados a tais investimentos, propomos aqui a aplicação de um modelo de predição utilizando árvore de regressão como algoritmo de aprendizado para prever se existe um período ideal em um dia de negociação para realizar uma transação conveniente, conforme os interesses do investidor pela venda ou compra de ativos. Selecionamos uma ação brasileira que faz parte do Índice IBovespa (IBOV) para acompanhar durante um período de dois meses e meio. Nossas conclusões mostram que a negociação de curto prazo é muito arriscada devido à volatilidade dos preços do mercado de ações ao longo de um mesmo dia. Ainda, sugerimos que investidores inexperientes devem contar com técnicas combinadas e informações adicionais para uma negociação bem-sucedida, ou mesmo considerar produtos financeiros mais seguros. Além disso, uma visão holística e realista, que vai além da busca por ganhos imediatos, é necessária entre o crescente público que investe o qual está excessivamente exposto às propagandas que argumentam que este risco compensa.

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Developing a generic risk maturity model (GRMM) for evaluating risk management in construction projects

Cited by 33 publications

References 40 publications

Developing a risk assessment model for banking software development projects based on rough-grey set theory

Developing a risk assessment model for banking software development projects based on rough-grey set theory

Risk Management of Mice Tourism in XYZ Firm During the Covid-19 Pandemic

Anais do(a) 16º CONGRESSO BRASILEIRO DE SISTEMAS

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