Identification of Critical Success Factors (CSFs) of BIM Software Selection: A Combined Approach of FCM and Fuzzy DEMATEL

Abstract

The architecture, engineering, and construction (AEC) industry has seen a significant rise in the adoption of Building Information Modeling (BIM) in the last few years. BIM software have launched with numerous robust capabilities and features to satisfy the ever-demanding needs of the AEC industry. Various factors are associated with the selection of BIM software depending on a company’s requirements and constraints. BIM software selection is a daunting process as most AEC industries are unaware of the factors to consider when making this important decision. This study focuses on identifying the critical success factors (CSFs) and their interrelationship for efficient BIM software selection. For this research, a questionnaire was developed and disseminated in two stages in India, the United States of America (U.S.A.), Germany, and the United Kingdom (U.K.). In the first stage, a total of twenty-six identified CSFs were analyzed with the factor comparison method (FCM) to identify the top fifteen CSFs. Subsequently, the identified top fifteen CSFs were further assessed by implementing Fuzzy DEMATEL to categorize them into cause-and-effect groups based on respective influence strength, depicted with a causal diagram. Out of fifteen CSFs, five and ten factors were grouped into the cause group and effect group for BIM software selection, respectively. The most important factors were identified as software functionality, BIM adoption strategies and processes, interoperability, staff competencies, BIM standards and regional regulations. The outcome of this research can help BIM user companies improve their BIM software selection framework and decision-making process during purchasing software.

Formwork System Selection Criteria for Building Construction Projects: A Structural Equation Modelling Approach

“…This sampling technique is shown to be successful since the sample effectively represents the population while avoiding any voluntary response bias [49]. The questionnaire was developed using https://docs.google.com/forms (accessed on 10 October 2021), which is a common online survey system [50,51]. The survey link was sent to more than 2500 respondents through the Union of Chambers of Turkish Engineers and Architects (UCTEA) and the Association of Formwork and Scaffold Manufacturers (IKSD) in Turkey.…”

Importance Analysis of System Related Fault Based on Improved Decision-Making Trial and Evaluation Laboratory

“…Chen, Jih-Kuang (2021) [6] ISM Determined the threshold by the maximum mean de-entropy method and an additional transitivity check procedure Lopez, DS et al (2021) [7] Rough set theory A systematic evaluation and analysis approach under ambiguity is proposed for evaluating, analyzing, and measuring aspects of internal healthcare performance Li, JS, Xu, KL. (2021) [8] CM Established evaluation index model by identifying relevant hazards of converter steelmaking system You, XL, Hou, FJ (2021) [9] HFLTS Proposed expert weights model based on the similarity and entropy of HFLTSs and introduced the consensus reaching process Ortiz-Barrios, M et al (2021) [10] FAHP Exploited the advantages of FAHP, DEMATEL methods for evaluating performances of GDs Kou, G, et al (2021) [11] fuzzy TOPSIS [14] FCM Help BIM user companies improve their BIM software selection framework and decision-making process during software purchases El-Garaihy, WH (2021) [15] DEA BSC Determined the relationship between the four dimensions of the BSC by identifying supply chain metrics and applying the DEMATEL approach Cui, HY, et al (2021) [16] QFD MOORA Defined green supply chain criteria for each stage of QFD and build IVIF-DEMATEL and IVIF-MOORA hybrid models Hosseini, A, et al (2021) [17] VIKOR Based on Analytical network process of DEMATEL. Using a Hybrid Modified Fuzzy VIKOR Approach to assess and mitigate tourism risks to close the gap Tsuei, HJ, et al (2020) [18] VIKOR Built a decision model of search engine ranking for administrators to improve the performances of websites that satisfy users' needs Li, TC, et al (2020) [19] GRNN Based on the data of my country's marine engineering equipment industry, the grounded theory, and identifying the key influencing factors Pourjavad, E, Shahin, A (2020) [20] AHP TOPSIS Developed by integrating fuzzy DEMATEL, fuzzy hierarchical analysis and by preference order techniques similar to the ideal solution TOPSIS method Chen, TL, et al (2020) [21] VIKOR Help decision makers choose the most appropriate design solution; designers can also improve the gap between product design and expectations Rostamnezhad, M, et al (2020) [22] SD Quantified the importance of the various influencing factors, Considered their complex interactions Titiyal, R, et al (2020) [23] DANP Investigated the relationship between performance levels and performance aspects and calculated their weights Gul, S. (2020) [24] Spherical fuzzy set…”

“…According to iterative formula (13), the power method is used to solve the 𝐷𝐷 𝐼𝐼 value, as shown in formula (14). (1) .…”

“…(1) . (14) In theory and algorithm, Brin and Page ensured that the value of initial value 𝑫𝑫 (1) did not affect the convergence of node estimation value, and did not change the final ranking relation of important value [38], [39]. Therefore, the 𝑫𝑫 𝐾𝐾 (1) value and 𝑫𝑫 𝐼𝐼 (1) value in formula (12) and formula ( 14) are taken to facilitate the operation of 𝑫𝑫 𝐾𝐾 (1) = [1,1, ⋯ ⋯ ,1] value and 𝑫𝑫 𝐼𝐼 (1) = [1,1, ⋯ ⋯ ,1] value, respectively.…”

“…According to the calculation formula of influencing degree and influenced degree (11), ( 12), ( 13), (14), the influencing degree 𝐷𝐷 𝐼𝐼 and influenced degree 𝐷𝐷 𝐾𝐾 of each system component are obtained by using the MATLAB software. Taking 𝐷𝐷 𝐾𝐾 as an example, the algorithm flow is shown in Fig.…”