A combined technique using SEM and TOPSIS for the commercialization capability of R&amp;D project evaluation

Karaveg, Charttirot; Thawesaengskulthai, Natcha; Chandrachai, Achara

doi:10.5267/j.dsl.2015.3.004

Cited by 16 publications

(13 citation statements)

References 49 publications

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“…To examine project success opportunity and risk, devise management practices and strategies, and formulate a business plan, market analysis is imperative [35]. Addressing market conditions, market components like competition and economic trends should be examined properly [36].…”

Section: Economic Market Climatementioning

confidence: 99%

Bayesian Structural Equation Modeling for Examining the Relationship between Multidimensional Factors and Construction Project Cost

Zhao¹,

Liu²,

Mbachu³

2019

Preprint

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Construction projects are usually operating in a complex and dynamic environment in which the accumulation of many interrelated factors causes high uncertainty. Construction projects are complex and frequently involve substantial uncertainties including process complicatedness, intricate organization structure, dynamic environment, and financial strain. The study aims to categorize the influencing factors into three groups, namely construction project system, economic-market climate, and external environment. It attempts to adopt a novel analysis tool to examine the relationship between the project cost and multiple influencing factors by using Bayesian SEM. While the Bayesian SEM method has been receiving increasing attention in exploring the relationship between latent variables, construction studies still heavily rely on the covariance-based SEM approach. This study introduces several advantages of Bayesian SEM that make it more flexible and powerful than covariance-based SEM and provides the foundation of Bayesian SEM estimation and inference by illustrating this method in a project cost application.

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Section: Economic Market Climatementioning

confidence: 99%

Bayesian Structural Equation Modeling for Examining the Relationship between Multidimensional Factors and Construction Project Cost

Zhao¹,

Liu²,

Mbachu³

2019

Preprint

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“…Other researchers have used the Grey theory to evaluate different organizations' strategies while others have used it in selecting suppliers, assessing the relationship between organization's aspects and performance (Shukla et al, 2014;Kung & Wen, 2007). Karaveg et al (2015) proposed a combined technique using SEM and TOPSIS for the commercialization capability of R&D project evaluation. Shukla et al (2014) presented a decisionmaking model for selecting the best manufacturing system using grey system theory.…”

Section: Introductionmentioning

confidence: 99%

Application of Grey-TOPSIS approach to evaluate value chain performance of tea processing chains

Nyaoga¹,

Magutu²,

Wang³

2016

10.5267/j.dsl

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This study develops an effective method to measure value chain performance and rank them based on qualitative criteria and to determine the ranking order of the various forms of performance under study. This approach integrates the advantage of grey systems theory and TOPSIS to evaluate and rank value chain performance. Grey-TOPSIS approach has been applied to measure and rank the value chain performance of various firms. The results indicate that the proposed model is useful to facilitate multi-criteria decision-making (MCDM) problem under the environment of uncertainty and vagueness. The model also provides an appropriate ranking order based on the available alternatives. The Grey-TOPSIS approach that will be useful to the managers to use for solving the similar type of decision-making problems in their firms in the future has been discussed. Even though, the problem of choosing a suitable performance option is often addressed in practice and research, very few studies are available in the literature of Grey-TOPSIS decision models. Also, Grey-TOPSIS model application in the tea processing firms is non-existence hence this study is the very first to apply this model in evaluating value chain performance in the tea processing firms.

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“…Öztaysi [29] used AHP and interval type-2 fuzzy sets in a group to solve the problem of ERP project selection. Karaveg et al [30] proposed a mixed-method combining structural equation modeling and TOPSIS for evaluation of R&D commercialization capacity. Yousefi and Hadi-Vencheh [31] combined the three techniques of AHP, TOPSIS and DEA for the selection of the optimal six-sigma projects.…”

Section: Vikormentioning

confidence: 99%

A Consistent Fuzzy Preference Relations Based ANP Model for R&D Project Selection

2017

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In today's rapidly changing economy, technology companies have to make decisions on research and development (R&D) projects investment on a routine bases with such decisions having a direct impact on that company's profitability, sustainability and future growth. Companies seeking profitable opportunities for investment and project selection must consider many factors such as resource limitations and differences in assessment, with consideration of both qualitative and quantitative criteria. Often, differences in perception by the various stakeholders hinder the attainment of a consensus of opinion and coordination efforts. Thus, in this study, a hybrid model is developed for the consideration of the complex criteria taking into account the different opinions of the various stakeholders who often come from different departments within the company and have different opinions about which direction to take. The decision-making trial and evaluation laboratory (DEMATEL) approach is used to convert the cause and effect relations representing the criteria into a visual network structure. A consistent fuzzy preference relations based analytic network process (CFPR-ANP) method is developed to calculate the preference-weights of the criteria based on the derived network structure. The CFPR-ANP is an improvement over the original analytic network process (ANP) method in that it reduces the problem of inconsistency as well as the number of pairwise comparisons. The combined complex proportional assessment (COPRAS-G) method is applied with fuzzy grey relations to resolve conflicts arising from differences in information and opinions provided by the different stakeholders about the selection of the most suitable R&D projects. This novel combination approach is then used to assist an international brand-name company to prioritize projects and make project decisions that will maximize returns and ensure sustainability for the company.

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A combined technique using SEM and TOPSIS for the commercialization capability of R&D project evaluation

Cited by 16 publications

References 49 publications

Bayesian Structural Equation Modeling for Examining the Relationship between Multidimensional Factors and Construction Project Cost

Bayesian Structural Equation Modeling for Examining the Relationship between Multidimensional Factors and Construction Project Cost

Application of Grey-TOPSIS approach to evaluate value chain performance of tea processing chains

A Consistent Fuzzy Preference Relations Based ANP Model for R&D Project Selection

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