2017
DOI: 10.3390/su9050800
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Country Selection Model for Sustainable Construction Businesses Using Hybrid of Objective and Subjective Information

Abstract: An important issue for international businesses and academia is selecting countries in which to expand in order to achieve entrepreneurial sustainability. This study develops a country selection model for sustainable construction businesses using both objective and subjective information. The objective information consists of 14 variables related to country risk and project performance in 32 countries over 25 years. This hybrid model applies subjective weighting from industrial experts to objective information… Show more

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Cited by 9 publications
(7 citation statements)
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“…Abundant studies provided decision-making processes and methods for contractors' IMS, including mapping managerial decision-making model (Andersen and Strandskov, 1997), neural network model (Dikmen and Birgonul, 2004), fuzzy logic framework (Levy and Yoon, 1995), CBR-INT (a case-based reasoning decision support tool-with human intervention) (Ozorhon et al, 2006), scale-based profit prediction model (Han et al, 2007), fuzzy preference relations-based analytic hierarchy process (Fuzzy LinPreRa-based AHP) (Lee et al, 2017), OLI þ S entry decision model (Isa et al, 2017), risk-based entry decision model (Odediran and Windapo, 2018), TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) based on information entropy theory and cluster analysis (Lin and Su, 2019), adaptive neuro fuzzy information system (ANFIS) (Utama et al, 2019) and evidential reasoning (ER) approach (Li et al, 2021). Some studies related to IMS are specific to project selection (Dikmen and Birgonul, 2004;Han et al, 2007;Li et al, 2020a, b;Ozorhon et al, 2006;Utama et al, 2019;.…”
Section: Ims and Ems Modelsmentioning
confidence: 99%
“…Abundant studies provided decision-making processes and methods for contractors' IMS, including mapping managerial decision-making model (Andersen and Strandskov, 1997), neural network model (Dikmen and Birgonul, 2004), fuzzy logic framework (Levy and Yoon, 1995), CBR-INT (a case-based reasoning decision support tool-with human intervention) (Ozorhon et al, 2006), scale-based profit prediction model (Han et al, 2007), fuzzy preference relations-based analytic hierarchy process (Fuzzy LinPreRa-based AHP) (Lee et al, 2017), OLI þ S entry decision model (Isa et al, 2017), risk-based entry decision model (Odediran and Windapo, 2018), TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) based on information entropy theory and cluster analysis (Lin and Su, 2019), adaptive neuro fuzzy information system (ANFIS) (Utama et al, 2019) and evidential reasoning (ER) approach (Li et al, 2021). Some studies related to IMS are specific to project selection (Dikmen and Birgonul, 2004;Han et al, 2007;Li et al, 2020a, b;Ozorhon et al, 2006;Utama et al, 2019;.…”
Section: Ims and Ems Modelsmentioning
confidence: 99%
“…In order to eliminate environmental constraints, construction companies adopt business model innovation to maintain a sustainable competitive advantage in a dynamic environment. The categories range from business models themed on industrial parks, cultural tourism real estate, recreation centers, and TOD models to circular economy business models ( Lee et al, 2016 , 2017 ; Heesbeen and Prieto, 2020 ; Das et al, 2021 ; Gosselin et al, 2021 ). This paper argues that in a dynamic environment, companies adopt different strategic orientations to form a sustainable competitive advantage for the company ( Spanjol et al, 2012 ; Cheng and Sheu, 2017 ; Han and Zhang, 2021 ).…”
Section: Introductionmentioning
confidence: 99%
“…Competitiveness evaluation methods can be divided into subjective evaluation and objective information evaluation. Subjective evaluation methods are often conducted by means of questionnaires and expert interviews [5] and evaluated by various research methods, such as analytic hierarchy process [6], fuzzy comprehensive evaluation [7], and factor analysis [8]. Subjective evaluation methods can reflect the specific strategic information of different enterprises, but they come with some problems like subjective bias and expert bias.…”
Section: Introductionmentioning
confidence: 99%
“…Subjective evaluation methods can reflect the specific strategic information of different enterprises, but they come with some problems like subjective bias and expert bias. Contrarily, objective information evaluation methods are often based on publicly available secondary data [5], including grey relational analysis [9], backpropagation neural network (BPNN) [10,11], and data envelopment analysis (DEA) [12]. Objective information evaluation methods can overcome the shortcomings of subjective evaluation methods, but it is necessary to consider premise assumptions and use conditions of different models in the process of evaluation.…”
Section: Introductionmentioning
confidence: 99%