a measurement model of building information modelling maturity

A measurement model of buildinginformation modelling maturity

Yunfeng ChenBuilding Construction Management, Purdue University,

West Lafayette, Indiana, USA

Hazar DibBuilding Construction Management & Computer Graphics Technology,

Purdue University, West Lafayette, Indiana, USA, and

Robert F. CoxBuilding Construction Management, Purdue University,

West Lafayette, Indiana, USA

Abstract

Purpose – There is a growing requirement for a rating system of building information modellingmaturity (BIMM) to compare the effectiveness of modelling processes in construction projects.The literature related to BIMM contains theoretical proposals and description of their maturity models.However, the research efforts are limited and lacking substantial theoretical and empiricaljustifications. This paper is a unique attempt to integrate previous models by performing empiricalinvestigations of key factors for measuring BIMM in construction projects. The paper aims to discussthese issues.

Design/methodology/approach – A national survey was designed to extract the perception of124 BIM-related practitioners and academicians about the conceptual model. Then, exploratory andconfirmatory factor analyses were employed to identify and test the key factors underlying the27 areas.

Findings – A principal component factor analysis of the collected data had suggested a five-factormodel, which explained 69.839 per cent of the variance. The construct validity of the model was furthertested by confirmatory factor analysis. The results indicated that all factors were important inmeasuring BIMM; however, compared with the factors of technology and people, more emphasis wasput on the factors of process and information.

Originality/value – The key value of the paper is to increase the understanding of multi-dimensionnature of BIMM through empirical evidence and to provide practitioners and researchers with theinsight regarding particular emphasis on the factors related to modelling process and information.

Keywords Quality management, Factor analysis, Process improvement, Maturity model, BIM,Construction management

Paper type Research paper

IntroductionBuilding information modelling (BIM) has been increasingly adopted and implemented inthe architectural, engineering and construction (AEC) industry since its inception in the1970s, with many professionals expecting it to transform how this businessoperates (Eastman et al., 2011; Hardin, 2009; Succar, 2010; Wong et al., 2011).

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1471-4175.htm

The authors would like to thank the ConstrucTech Magazine and the buildingSMART alliancefor their support in distributing the questionnaire of this research.

Construction InnovationVol. 14 No. 2, 2014pp. 186-209q Emerald Group Publishing Limited1471-4175DOI 10.1108/CI-11-2012-0060

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The 2009 SmartMarket Report showed that there were 48 per cent of the industry claimedemploying BIM or BIM-related tools, which represented a 75 per cent growth in BIM usagecompared to 2007 (McGraw-Hill, 2009). Among those users, some may just employ a fewcopies of BIM-related software occasionally in projects that were dictated by ownersor contacts; some may facilitate BIM implementation in a small scale of projects byequipping staff and infrastructure; others might launch BIM on majority of their projectsand currently focus on developing and optimising internal and external collaborative BIMprocesses. It is obvious that the extent to which BIM is “explicitly defined, managed,integrated and optimised”, which refers to BIM maturity (BIMM), is different among theusers. So while everyone branded themselves as BIM-able, how can one tell the difference?A reliable and valid benchmark of BIMM is needed for meaningful evaluation andcomparison (Chen et al., 2012; NIBS, 2007; Smith and Tardif, 2009; Succar, 2010).

The maturity model specifies the key areas and characteristics a process must possessto qualify as a process of some maturity (Jalote, 2000; Paulk et al., 1995). It originated in thefield of quality management (Cooke-Davies, 2002). Its underlying premise is that thequality of a final product depends largely on the process used to create it (Paulk et al.,1995). It was expected that as the visibility into the process increases, the process“predictability”, process “control”, and business performance improve (Paulk et al.,1995, p. 27). Previous research also showed that the more mature any business process is,the better forecast, control and performance the business can have (Lockamy andMcCormack, 2004; McCormack and Johnson, 2001). Rooted in Walter Shewhart’s principleof statistical quality control in process improvement, maturity models proliferated in themanufacturing and software industries. One of the most well-known and adoptedmaturity models is the capability maturity model (CMM) proposed by the SoftwareEngineering Institute (SEI) (Vaidyanathan and Howell, 2007), which is a framework forsoftware organisations to evaluate and improve their software process (Paulk et al., 1995).Other maturity models extended from CMM include capability maturity modelintegration (CMMI), people-CMM (P-CMM), personal software process (PSP), teamsoftware process (TSP) and trillium (Alshawi, 2007). To adjust to the European market,a process model titled Bootstrap was developed by the European commission forthe software development process improvement soon after CMM (Alshawi, 2007). Theconcept of maturity model was also adopted in other industries, like theProject Management Process Maturity Model (PM)2 in project management industry,manufacturing supply chain maturity model (MSCMM) in the manufacturingindustry and lean enterprise transformation maturity model in the aerospace industry(Vaidyanathan and Howell, 2007). Inspired by the success of maturity model in otherindustries, researchers in the construction industry started to investigate the applicabilityof maturity models. One significant initiative was the Standardised Process Improvementfor Construction Enterprise (SPICE), which concluded that CMM in the software industrycannot be reused directly to the construction industry due to its incapability address thesupply chain issue (Sarshar et al., 2000). The construction supply chain maturity model(CSCMM) was then proposed for supply chain members to improve performance throughoperation excellence (Vaidyanathan and Howell, 2007). This model cannot be used formeasurement of BIMM, because it focuses on the aspect of multi-enterprise supply chainother than BIM. There were four maturity model proposed in the AEC industry thatclaimed the ability to measure BIM when this study was conducted, which was discussedin the section of BIMM.

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With the proliferation of BIM in the AEC industry, there is a growing awarenessamong the professionals about the need of a BIMM matrix. However, the establishmentof a BIMM matrix can be difficult even not impossible, because of the multi-dimensionalnature of BIM (Smith and Tardif, 2009). BIM is a concept functioning differently todifferent professions. Architects use BIM as “a process and technology” to model“the physical and functional characteristics” of a building (AIA, 2008, A295,Section 1.3.5). Contractors use BIM as a computer software model to improve decisionmaking and facility delivery process (AGC, 2006). Compared with other stakeholders,owners perceive “BIM as more of a collaborative process” (McGraw-Hill, 2009). After all,BIM “is more than a technology” (Ashcraft, 2008); it is a set of interacting process,human, information, and technology issues. According to the technical report by theCenter for Integrated Facility Engineering (CIFE, 2012), the impediments of BIMadoption were shifting from “technical issues such as contractual language andhardware and software to people issues such as training and availability of qualifiedstaff” (Gilligan and Kunz, 2007, p. 1). The adoption of BIM is more than the equipment ofstaff and technology infrastructure; it is a systematic approach to the lifecycleinformation related to a building (Smith and Tardif, 2009). According to SmartMarketReport, the focus of BIM investment changes over time; beginners rated BIM softwareand training as their highest priority investment, while experienced users rankedcollaborative BIM procedures and marketing as their top priority investment(McGraw-Hill, 2009). The employment of any IT solution cannot reach its fullpotential when focusing only on technology (Alshawi, 2007). Unlike other standalonesolution of information technology, the value of BIM can be maximised when it is used asa collaborative platform and process to facilitate communication and interdependenceamong stakeholders (Ashcraft, 2008). Although there have been some attempts topropose metrics for measuring BIM implementation, most studies account only for onedimension of BIM, and focus mainly on the final BIM model other than the process usedto create it. Moreover, because many research efforts lacked substantial theoretical andempirical justifications, the reliability and validity of the models remain questionable.This study was conducted to fill these gaps by integrating previous efforts throughperforming an empirical investigation of key factors for measuring BIMM. Suchresearch is necessary because the results may help non-users to orient their BIMinitiation, as well as current users to locate and improve their BIM implementation.It also offered empirical evidence to the multi-dimensional nature of BIM, and may alsoserve as a starting point for future researches to quantify BIM and its values, as well asthe development of metrics with lower-level items.

Research methodologyEvery project is different, which can result in different requirements for BIM and thereforeBIMM. However, it was believed by the authors through a post-positivism lens that thereshould be a common framework underlying BIMM. To identify the key areas formeasuring BIMM, a two-step quantitative exploratory and confirmatory factor analyticapproach was used, which is a typical methodology for the development of valid andreliable scale (Cudeck and Browne, 1983; Brown, 2006; Hair et al., 2009; Tabachnick andFidell, 2012). A non-experimental design with questionnaire survey was adopted here,because of the factorial analysis’s requirement for large amount of data, the limit of timeand money, as well as the difficulty to find experiment subjects and provide 27 treatments.

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As shown in Figure 1, this study commenced with the development of 27 BIMM areasfor measuring BIMM by reviewing literature. The 27 BIMM areas, their correspondingoriginal areas, as well as their descriptions/definitions were presented in Table AI ofAppendix. The BIM-related academicians’ and practitioners’ perceived importance ofBIMM areas was then collected through a questionnaire survey based on the 27 areas.Qualified responses from 124 participants were received and screened for potentialproblems before formal analysis. Then the exploratory factor analysis (EFA) was usedto identify the key areas and their underlying factors, while confirmatory factor analysis(CFA) was employed to test the construct validity of the factorial structure.

BIM maturityAmong all the maturity models proposed in the AEC industry, only four modelsclaimed the ability to measure BIMM when this study was conducted. They includedthe CMM by the National Institute of Building Science (NIBS, 2007), the BIM

Figure 1.Research methodology

Literature review

Identification of BIMM areas

Assessment of content validity

Phase I:DevelopmentofMeasurementAreas

Design of questionnaire based on theidentified BIMM areas

Review of questionnaire

Development of web-based questionnaire

Phase II:Developmentof surveyinstrument

Exploratory factor analysis (EFA)

Confirmatory Factor Analysis (CFA)

Research conclusions

Phase IV:Dataanalysis andconclusions

Identification of target respondents

Data collection

Data screening

Phase III:Datacollectionandscreening


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deliverable matrix by the Alliance for Construction Excellence (ACE, 2008), the BIMproficiency matrix by Indiana University (IU, 2009), and the BIM competency sets bySuccar (2010). NIBS’s (2007) CMM was developed for the users to evaluate theirinternal business process and practices. There are two versions of NIBS’s CMM. Thefirst is the static tabular CMM consisting of eleven areas of interest againstten maturity level (NIBS, 2007). Based on the tabular CMM, the second is theinteractive CMM that offered output through interaction with the users (NIBS, 2007).The NIBS’s CMM is “a good first step toward establishing BIM implementationbenchmarks” (Smith and Tardif, 2009, p. 44); a minimum BIM is defined in terms of the11 areas and only the users meet the minimum requirements can claim themselves asBIM-able. CMM was validated by a test bed of BIM Award winners in 2007(McCuen et al., 2012) and was considered as the default standard for the measurementof information management (McGraw-Hill, 2008). Nevertheless, its inability inassessing any other metrics above information management has limited itsapplicability (Succar, 2010). Given that the success of a project team depends on theclear regulation of the project scope and deliverables for each stakeholder, ACE’s (2008)BIM deliverable matrix lists BIM services and deliverables at each phase of a typicalBIM project against three implementation levels, as well as the types of software usedby different stakeholders. This matrix concentrates on the description of the digitalmodels of each phase other than the process to create the models. IU’s (2009) BIMproficiency matrix is used to evaluate the BIM capability and proficiency of a firm, theresult of which serves as a selection criterion for a given project. The matrix contained32 credit areas under eight categories. There are few documents about the developmentprocess of BIM proficiency matrix and the rationale of the credit areas. Based on thelimited examples corresponding to each credit area in the enhanced BIM proficiencymatrix, it was perceived that “the matrix focuses on the accuracy and richness ofdigital model data and has less focus on the process of creating that digital model”(Succar, 2010, p. 102). Succar’s (2010) BIM competency set is intended to evaluate thecapability and maturity of a BIM player. It is a hierarchical set of competency areasunder four granularities. Competency areas with varied breadth and specificity can beused for the different goals of measurement, including discovery, evaluation,certification and auditing. The BIM competency set is comprehensive in covering thekey areas related to technology, process and policy. However, there is limiteddescription about the areas related to information management. What is more, there isredundancy of the areas. For example, the area of “networking solution” overlaps“network deliverables” (Succar, 2010, p. 72). Additionally, although the competency setwas claimed to be generated based on previous frameworks related to performance,excellence, and quality management, there is no explicit explanation or comparison ofthe competency areas with items exposed in previous frameworks. More importantly,the competency set was not validated empirically.

Although there have been some efforts toward BIMM, most research studied BIM asa one-dimensional concept or an end-product. More significantly, most studies werelimited to the theoretical proposal, and there is limited empirical research to test thereliability and validity of the models and frameworks. Key themes were identified andextracted based on a review of previous literature of maturity, quality, and performancemodels. It turned out that the areas included in the CMM of NIBS and Succar’s BIMcompetency set covered most of the key themes. Considering all above, 27 areas were

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extracted and combined from the NIBS’s CMM and Succar’s BIM competency set toestablish the initial pool of areas for measuring BIMM. The content validity of the27 areas was confirmed by aligning with areas/items exposed in previous models ofmaturity, excellence, and quality management. The 27 BIMM areas of this research,their corresponding original areas in NIBS’s CMM and Succar’s BIM competency set andsimilar areas in previous research were listed in Table I of Chen et al. (2012). The 27 areaswere then evaluated by two BIM-related experts with academic and industry experienceas having validity to tap the BIMM. The content validity of the areas was furtherconfirmed by participants, since most of them commented that the questionnaire wascomprehensive and thorough in measuring BIMM.

Data collection and screeningData collectionAn on-line questionnaire survey was conducted for data collection. The questionnairewas designed to solicit the perception of BIM-related experts about the degree ofimportance of the 27 areas in measuring BIMM through a seven-point Likert scale.A sample questionnaire can be found in Dib et al. (2012). To ensure the relevance of theresponses, the questionnaires were only sent to the academicians conducting researchin BIM and practitioners with BIM-related experience. In order to safeguard thereliability of the received responses, both types of professionals were asked about theirBIM-related experience. If the academic respondent replied that s/he had notBIM-related experience of project or research, her/his response was not used for theanalysis. Similarly, if the practical respondent claimed to be involved in less thantwo BIM-implemented projects or had less than one-year direct working experience withBIM, her/his was discarded as well. The survey was conducted to a total of579 BIM-related experts in the USA. 131 of them responded to the survey,which represented a response rate of 22.63 per cent. Based on the criteria listed above,124 qualified responses were filtered out for further data analysis. The detailedinformation of the responses was shown in Table I. The industry sample represented awide spectrum of professional disciplines working as owner, architect or contractor.These profiles were listed in Tables II and III.

Data screeningBefore the data analysis, the raw data should be screened for potential problems,including missing data, departure from normality and collinearity. First, the data profileincluded 0.96 per cent missing values, which was then substituted with the mean ofthe corresponding areas. Second, considering that most responses lied between 4 and 7,the skewness index (SI) and kurtosis index (KI) were used to examine the normality

Questionnaires sentResponses received

(%)Valid responses

(%)Qualified responses

(%)

Academicians 206 52 (25.24%) 50 (24.27%) 49 (23.79%)Practitioners 373 92 (24.66%) 81 (21.72%) 75 (20.11%)Total 579 144 (24.87%) 131(22.63%) 124 (21.42%)

Source: Adapted from Chen et al. (2012)

Table I.Information about

response fromacademicians and

practitioners in the USA


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of the distribution of each area. Because the absolute value of SI and KI for each area wasless than the cut-off point of 3 and 10 (Kline, 2010), the normality of the distribution ofeach area was not rejected. Finally, in order to use factor analysis, the data matrix shouldhave sufficient correlations greater than 0.30 while less than 0.7 (Hair et al., 2009, p. 104).There are multiple ways with different “rules of thumb” to detect multi-collinearity,including the bivariate correlation (r), the squared multiple correlation (R2

sme ¼ 0:778),and the variance inflation factor (VIF). Commonly, an r above 0.7 for small sample or 0.85for larger sample (Berry and Feldman, 1985), a R2

sme above 0.9 (Kline, 2010), and a VIFmore than 4 or 10 (O’Brien, 2007) may indicate extreme collinearity. Based on therecommended approaches, it was found that there was collinearity between hardwareequipment (HE) (r ¼ 0.846, R2

sme ¼ 0:835, VIF ¼ 6.065) and hardware upgrade (HU)(r ¼ 0.846, R2

sme ¼ 0:778, VIF ¼ 4.505). Considering the nature of the two areas, HE andHU were combined to form a new area of hardware, the score of which was the average ofthe original two areas (Kline, 2010). The further examination of the data set revealed thatthere may be collinearity between the areas of standard operation procedure (SOP)(r ¼ 0.731, R2

sme ¼ 0:648, VIF ¼ 2.841) and documentation and modelling standard(DMS) (r ¼ 0.731, R2

sme ¼ 0:659, VIF ¼ 2.933). Although the related detection indices forthese two areas were not as extreme as the ones for HD and HU, it still posed considerablethreat. Besides, from the theoretical perspective, SOP and DMS aligned on thestandardisation of modelling and operation. To be more conservative, it was decided torun exploratory factor analyses, of the 26 areas with HE and HU combined intohardware and of the 25 areas with SOP and DMS combined into Standardisation,respectively.

Business types Amount (%)

General contractor/construction manager 21 (28.00%)Architect/engineer 19 (25.33%)Consultant 12 (16.00%)Owner/developer 6 (8.00%)Software vendor 6 (8.00%)Subcontractor 5 (6.67%)Others 6 (8.00%)Total 75 (100%)

Table II.Profile of industryrespondents(by business types)

Professions Amount (%)

Company manager (e.g. president/VP/CEP/owner) 18 (24.00%)Project manager (e.g. project/construction/product/program manager) 13 (17.33%)Model director (e.g. VDC/BIM/CAD director) 14 (18.66%)Model designer (e.g. designer/engineer/specialist/coordinator/consultant) 23 (30.67%)Model involver (e.g. estimator/account manager/sales manager) 3 (4.00%)Not specified 4 (5.33%)Total 75 (100%)

Table III.Profile of industryrespondents(by professions)

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Data analysis and resultTo ensure the reliability and validity of a scale development, a two-step exploratory andconfirmatory factor analytic approach was typically applied (Cudeck and Browne, 1983;Brown, 2006; Hair et al., 2009; Tabachnick and Fidell, 2012). The same developmentprocess was used to develop measurement scale for knowledge management maturity(Chen and Fong, 2012), construction project success (Tabish and Jha, 2012) and climatesafety (Hon et al., 2013; Zhou et al., 2011). EFA was a data-driven approach used toidentify the key areas and their underlying factors, which was usually applied in theearly stage of scale development (Brown, 2006). CFA was typically used to test theconstruct validity of the identified factorial solution by evaluating how well itreproduces the sample correlation matrix (Brown, 2006). In this research, step I involvedthe development of BIMM framework based on EFA. Step II used CFA to test theconstruct validity of the first-level and second-level measurement models originatedempirically from EFA result at step I. Throughout this process, a five-factor second-levelmeasurement model was developed to satisfactorily fit the structure of BIMM.

Stage I: measurement model – EFAThe underlying constructs of the initial pool of areas for measuring BIMM wereexplored by the principal component factor analysis (PCFA) with a varimax rotation.Based on the discussion in the section of data screening, the 26 areas with HE and HUcombined was analysed separately from the 25 areas with the additional combinationof SOP and DMS. The two measurement models were then compared from theperspectives of quantitative indices and theory.

EFA of 26 areasThe Kaiser-Meyer-Olkin (KMO) measures of sampling adequacy of 0.857 and thesignificant Bartlett test of sphericity (1,537.952, p , 0.000) indicated that there weremeritoriously significant intercorrelation among the areas and the factor analysis wasappropriate (Hair et al., 2009). In order to obtain a simpler solution with factors that hadclearer interpretation, the areas with factor loadings less than 0.5 (Hair et al., 2009) orthe difference of cross-loadings less than 0.2 (Statwiki, 2012) were removed. Aftertwo-iteration of PCFA, nine inconsistent areas were removed. The acceptance of eacharea explained by the factor solution was confirmed by its correspondingcommunality, all of which were bigger than 0.510. The identified five-factor modelexplained 68.127 per cent of the variance. The structure of the model and its relatedstatistics were listed in Table IV. For the interpretation of the five factors, please alsorefer to the data analysis and interpretation of Chen et al. (2012).

EFA of the 25 areasThe results from the KMO measure of sampling adequacy (0.850) and the Bartlett testof sphericity (1,453.401, p , 0.000) indicated the appropriateness of factor analysis.Following the same criteria about convergent validity and discriminant validity, nineinconsistent areas were discarded and 16 significant areas were retained afterthree-iteration of PCFA. Table V presented the detail about the model structure andstatistical measures.

The five factors accounted for 69.839 per cent of the variance. Factor 1 (processdefinition and management (PDM)) explained 22.670 per cent of the variance and loaded


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on areas of standardisation, role, strategic planning, senior leadership, quality controland information accuracy (IAc). Factor 2 (13.627 per cent) concerned informationmanagement, including areas of lifecycle process, work flow, and geospatial capability.Factor 3 (11.488 per cent) loaded on items concerning training, involving trainingprogram and training delivery method. Factor 4 (11.083 per cent) included areas ofapplications, hardware and process and technology innovation (PTI), all of whichaligned on technology. Factor 5 (10.971 per cent) focused on information delivery,consisting areas of information delivery method and information assurance (IA).

Comparison of the two identified five-factor measurement modelsThe two models were quite close since the key structure of the measurement models stayedquite the same, as well as the statistical measures. This was understandable because theonly difference between the two initial pools lied in the combination of SOP and DMS.However, compared with the first model accounting for 68.127 per cent of the total variancewith 17 areas, the second model accounted for more variance (69.839 per cent) with fewer

BIMM areas Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Communalities

Doc. and modellingstandards 0.811 0.213 20.066 20.029 0.032 0.709Standard operatingprocess 0.809 0.159 0.050 20.084 0.095 0.699Role 0.738 0.229 0.067 0.090 0.008 0.610Quality control 0.733 0.113 0.304 0.193 20.054 0.682Strategic planning 0.684 0.098 0.145 0.358 0.063 0.630Senior leadership 0.681 20.084 0.211 0.297 0.213 0.649Work flow 0.102 0.807 0.128 0.128 0.060 0.698Lifecycle process 0.069 0.789 0.046 0.166 0.137 0.675Geospatial capability 0.265 0.683 0.215 20.004 0.045 0.586Real-time data 0.199 0.588 20.003 0.355 0.012 0.512Training program 0.160 0.177 0.848 0.037 0.088 0.786Training deliverymethod 0.151 0.112 0.841 20.067 0.178 0.779Information deliverymethod 0.119 0.214 0.056 0.836 0.072 0.767Information assurance 0.157 0.182 20.087 0.835 0.053 0.776Applications 20.047 20.010 0.137 0.093 0.821 0.704Hardware 0.118 0.035 0.425 0.072 0.702 0.694Process and tech.innovation 0.206 0.371 20.131 20.022 0.662 0.637Eigenvalue 5.369 1.913 1.865 1.321 1.115% variance 21.154 14.711 11.106 10.921 10.235Cronbach’s a 0.848KMO measure ofsampling adequacy 0.797Bartlett’s test ofsphericity: approx.x 2 (sig.) 822.620 (0.000)

Notes: Significant factor loadings are highlighted; the factors represent: 1 – PDM; 2 – informationmanagement; 3 – training; 4 – information delivery; 5 – technologySource: Adapted from Chen et al. (2012)

Table IV.Factor structure andvariance explained basedon responses from theexperts in the USA

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areas (16 areas). In addition, from the theoretical perspective, SOP and DMS wereperceived to measure the different aspects of standardisation: SOP focused more on thestandardisation of process while DMS emphasised on final product. What is more,the author tested both models with CFA, the result of which turned out that the overallfitting of higher-level measurement model (x2

99 2 146:250, CFI ¼ 0.931, RMSEA ¼ 0.062)based on the 16-area structure was better than the one with further modification based onthe 17-area structure (x2

113 2 177:523, CFI ¼ 0.915, RMSEA ¼ 0.068). Considering allabove, the second model extracted from 25 areas was used for further CFA.

Stage II: measurement models: CFAThe result from EFA provided empirically derived hypotheses as to the number andnature of factors of BIMM. The hypothesised measurement model was then tested forits goodness-of-fit and validity using the first-order CFA. Then the common constructof BIMM underlying the five factors were further examined by second-order CFA.

First-order measurement modelBased on the result from EFA, the model to be tested hypothesised a priori that BIMM wasa five-factor structure composed of PDM, information management (Info Management),training, technology and information delivery (Info Delivery), as shown in Figure 2.

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Communalities

Standardisation 0.764 0.200 20.002 0.042 20.057 0.629Quality control 0.762 0.123 0.278 20.069 0.138 0.697Role 0.755 0.252 0.029 20.023 20.008 0.636Strategic planning 0.755 0.101 0.094 0.054 0.268 0.664Senior leadership 0.750 20.078 0.157 0.201 0.180 0.667Info accuracy 0.666 0.078 0.134 0.244 0.300 0.617Lifecycle process 0.080 0.832 0.028 0.112 0.222 0.762Work flow 0.098 0.824 0.129 0.039 0.184 0.741Geospatial capability 0.287 0.642 0.218 0.050 20.022 0.545Training program 0.166 0.170 0.857 0.094 0.057 0.803Training deliverymethod 0.180 0.116 0.825 0.189 20.083 0.769Applications 20.050 20.021 0.153 0.820 0.110 0.710Hardware 0.149 0.048 0.407 0.703 0.050 0.687Process and tech.innovation 0.245 0.396 20.174 0.655 20.047 0.678Information assurance 0.203 0.138 20.081 0.047 0.848 0.788Info delivery method 0.174 0.186 0.054 0.064 0.842 0.782Eigenvalue 5.232 1.849 1.684 1.332 1.078% variance 22.67 13.627 11.488 11.083 10.971Cronbach’s a 0.845KMO measure ofsampling adequacy 0.812Bartlett’s test ofsphericity: approx. x 2

(sig.) 748.178 (0.000)

Notes: Significant factor loadings are highlighted; the factors represent: 1 – PDM;2 – information management; 3 – training; 4 – technology; 5 – information delivery

Table V.Factor structure and

variance explained basedon responses from the

experts in the USA


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The model and its components were specified as following:. Responses to the measuring instrument of BIMM can be explained by

five factors, PDM, Info Management, training, technology and Info Delivery.. The five factors are inter-correlated.. Each observed variable loads on only one factor.. Error terms associated with each observed variable were uncorrelated.

Different goodness-of-fitness (GOF) measured different facets of a model’s ability torepresent the data. Based on the recommendation from previous literature, the absolute fitof the normed x 2, the goodness-of-fit index (GFI) and root mean square error ofapproximation (RMSEA) were reported (Tabachnick and Fidell, 2012), as well as theincremental fit of comparative fit index (CFI) and the parsimony fit of adjusted goodness offit index (AGFI) (Hair et al., 2009). The satisfactory threshold for model fit can be achievedby attaining x 2/df , 2.00 (Tabachnick and Fidell, 2006), GFI . 0.85 (Maruish, 2004),RMSEA , 0.07 (Steiger, 2007), CFI . 0.95 (Hair et al., 2009) and AGFI . 0.8 (Maruish,2004). The fit statistics suggested good support for the model. Specifically, x 2/94 ¼ 1.361,

Figure 2.Hypothesised modelof factorial structurefor the BIMM

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GFI ¼ 0.891, RMSEA ¼ 0.054, CFI ¼ 0.951 and AGFI ¼ 0.842. The standardised outputof the first-order measurement model was presented in Figure 3. The parameter estimatesfor all the factor loadings were significant and in the expected positive direction. Except theparameter estimate for the area of innovation, all loadings were higher than 0.5, whichsuggested that the areas were strongly related to their associated factors and indicated theconstruct validity (Hair et al., 2009). The modification indices (MIs) were examined todetect any possible misspecification (Byrne, 2009). Table VI presented the information ofthe biggest ten MIs. However, given the meaningless and trivialness of these MIs, as wellas the adequate fit of the existing model (Byrne, 2009), no additional parameters wereincluded in the model.

Second-order measurement modelTo facilitate theoretical understanding of multi-dimensional nature of BIMM, thefirst-order measurement model was specified into a second-order measurement model.The difference between the two specifications is that a structure is “imposed on thecorrelational pattern among the first-order factors” (Byrne, 2009, p. 143). In our casehere, BIMM was specified as the construct underlying the five first-order factors,as schematically shown in Figure 4.

Figure 3.Standardised output

of the first-order modelof factorial structure

for the BIMM


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The CFA model to be tested postulated priorities that:. Responses to the instrument of BIMM can be explained by five first-order factors

(PDM, Info Mgmt, training, technology and Info Delivery) and one second-orderfactor (BIMM).

Path M.I. Par change

Innovation ˆ lifecycle process 10.964 0.215Innovation ˆ Info Mgmt 9.948 0.284e14 $ Info Mgmt 9.526 0.236Geospatial ˆ quality control 7.596 0.239Innovation ˆ work flow 7.336 0.229e1 $ e3 7.107 0.159e9 $ PDM 5.921 0.123e4 $ e10 5.574 20.112e5 $ Info Mgmt 5.483 20.171Innovation ˆ info accuracy 5.474 0.171

Table VI.Ten largestmodification indexes

Figure 4.Hypothesisedsecond-order modelof factorial structurefor the BIMM

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. Each observed variable loads on only one first-order factor.

. Error terms associated with each observed variable were uncorrelated.

. Co-variation among the five first-order factors is fully explained by theirregression on the second-order factor (BIMM).

The adequacy of the parameter estimates and the overall model suggested good supportto the validity of the BIMM measurement models (Byrne, 2009). Specifically, all thefactor loadings were significant and in the expected direction, as indicated by thestandardised output in Figure 5. The adequacy of the model as a whole was evidenced byx 2/99 ¼ 1.477, GFI ¼ 0.877, RMSEA ¼ 0.062, CFI ¼ 0.931 and AGFI ¼ 0.830. Bothabove support that the hypothesised model fitted the data quite well. The biggest tenMIs were listed in Table VII. In light of the nonsense and triviality of the MIs, as well asthe good fit of the model, it was concluded that the second-order model shown in Figure 4was the optimal representation for measuring BIMM for the US BIM-related experts.

The multi-dimensional hypothesis of the BIMM was not rejected by the test result ofparameter estimates. All the five factors were statistically significant, which furthersuggested that all factors were important in measuring BIMM. Among the five factors,

Figure 5.Standardised output

of the second-ordermodel of factorial

structure for the BIMM


199

PDM had the highest standardised second-order factor loading of 0.71, followed by IMof 0.63, technology of 0.576, training of 0.574 and ID of 0.545. It indicated thatcompared with factors of people and technology, the factors of process and informationwere perceived to be more important in measuring BIMM.

DiscussionThe key findings of this research were further compared with other independent BIMand maturity related research.

Theoretically, the proposed maturity framework was considered ascomprehensive because the key factors and content of other BIM and maturityrelated research can be categorised under the four dimensions. Based on the nature ofthe five factors, the five factors of BIMM can be reduced to four dimensions oftechnology (Factor 4: technology), process (Factor 1: PDM), people (Factor 3: training),and information (Factor 2: information management; Factor 5: information delivery).The four dimensions of this research were validated due to their consistencywith the key content of global BIM-related research. As listed in Table AII ofAppendix, the four dimensions of this research covered the areas/content/indices ofBIM standards/guides/protocols/specifications in Australia, UK, China, Finland andSingapore (BCA, 2012). The factorial structure of BIMM was further confirmed bycomplying with the key factors of other maturity models, as listed in Table VIII.

Empirically, the research findings were validated by comparing them with those ofSmartMarket Report by McGraw-Hill, which was the “default gold standard” of BIMimplementation status in North America (Suermann, 2009). First, all the BIMM factorswere found to be important in measuring BIMM. This finding was confirmed giventhat the four dimensions of BIMM were corresponding to the four most importantfactors for increasing BIM benefits, as indicated in the SmartMarket Report byMcGraw-Hill. Specifically, the four factors included “Improved Interoperabilitybetween Software Applications”, “Improved BIM Software Functionality”, “MoreClearly-Defined BIM Deliverables between Parties” and “More Owners Asking forBIM” (2012, p. 22), which corresponded to the four dimensions of information,technology, process and people. Second, it was found that factors related to process andinformation were more important than factors of technology and people. This findingwas consistent with findings of SmartMarket Report, which concluded that the topobstacles to BIM improvement were related to information and process (2009).

Path M.I. Par change

Hardware ˆ training delivery 9.530 0.230Innovation ˆ lifecycle process 9.329 0.195e13 $ F3 8.602 0.226e14 $ F2 8.499 0.220Innovation ˆ Info Mgmt 7.855 0.251Innovation ˆ Info Mgmt 7.855 0.251e11 $ e13 7.504 0.233e11 $ F5 7.177 20.136Applications ˆ strategic planning 6.880 20.210Geospatial ˆ quality control 6.876 0.226e1 $ e3 6.531 0.151

Table VII.Ten largestmodification indexes

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Last, the factorial structure of BIMM was generalisable due to the consistency ofBIMM measurement models based on the perception of USA and non-USA BIM-relatedprofessionals. This research is part of a study of BIMM measurement models based onperception and experience of global BIM-related professionals. Given that the samplesfrom different countries might be heterogeneous due to the inference of issues asculture, industry practice and regulation, infrastructure and BIM adoption status,separate measurement models were developed for the USA and non-USAprofessionals, respectively, (Dib et al., 2012). Similar factors related to information,process, people and technology were recognised. In addition, the factors of informationand process ranked higher than the factors related to technology and people for bothmodels. The key difference of the two measurement models lied in the emphasis ofsimilar factors. For example, for the factors related to people, the USA experts focusedon the training while the non-USA experts emphasised the regulation of teamstructure. This may suggest that the USA experts perceived BIM more as a technology,while the non-USA experts treated BIM more as a process. For more detaileddiscussion, please refer to Chen (2013).

Study\factor Technology Process People Information

Leanenterprise self-assessmenttool (MIT andUW, 2001)

“EnablingInfrastructure”:“must support andimplementation ofLean Principles,practices andbehaviour” (p. 11)

“Life-cycleProcesses”: refers tothe “Implement leanpractices across life-cycle processes” (p. 8)

“LeanTransformation/Leadership”: refers tothe development of“lean implementationplans” (p. 6)

Constructionsupply chainmaturitymodel(Vaidyanathanand Howell,2007)

“TechnologyAssessment”: refersto “the various tools”used to businessprocess efficiency”(p. 175)

“ProcessAssessment”: is “toidentify the currentas-is business processmethodology” (p. 175)

“Strategyassessment”: “is todetermine the currentbusiness strategy”with customersand suppliers (p. 176)“Value assessment”:

is to “assess thecurrent pain points”of “as-is businessprocess” (p. 176)

NIBS (2007) “InformationManagement”(p. 80)

BIM maturitymatrix(Succar, 2010,p. 69)

Technology: this field“clusters a group ofplayers whospecialise indeveloping software,hardware, equipmentand networkingsystems [. . .]”

Process: this field“clusters a group ofplayers who procure,design, construct,manufacture, use,manage and maintainstructures”

Policy: this field“clusters a groupof players focusedon preparingpractitioners,delivering research,distributing benefits,allocating risks andminimising conflicts[. . .]”

Table VIII.Comparison of BIMM

dimensions of thisresearch with other

maturity models


201

ConclusionWith the proliferation of BIM adoption in the AEC industry, there is a need for a scientificrating system to benchmark the different levels of BIM implementation (NIBS, 2007).Based on a substantial theoretical and empirical justification, a valid and reliablemeasurement model of BIMM with five factors under four dimensions was proposed atthe end of this study. There were several theoretical and empirical implications from thisresearch. The key value of this research is to increase the understanding ofmulti-dimensional nature of BIMM through empirical evidence, and to providepractitioners and researchers with the insight regarding particular emphasis on thefactors related to process and information besides the factors of technology and people.In addition, the BIMM measurement model can be used by the industry to benchmarkBIMM across AEC projects, by the current 71 per cent BIM users of the USA AECprojects as a scientific checklist to evaluate their BIM implementation, as well as aguideline for the non-users of the remaining 29 per cent AEC projects to initiate their BIMefforts. What is more, different projects have different requirements for BIM. BIM can beengineered according to project stakeholders’ perceived priority of the BIMM areas tooptimise their investment. Case study can be conducted to examine the efficiency ofthe application of the proposed BIMM model to real-world projects. Finally, thequantification of BIM’s impact had been considered impossible due to themultidimensional nature of BIM and methodological difficulties. The BIMMmeasurement model here can serve as an initial point for quantifying the impact ofBIM on its intangible benefits like trust and collaboration or tangible benefits such asproductivity and construction performance. Specifically, hypothesised relationshipbetween BIMM and its benefits can be established based their respective measurementmatrixes, which can be further tested by data collected from surveys or projects.

The BIMM measurement was considered as generalisable from three perspectives.First, the key dimensions and areas of BIMM measurement models were consistentwith other maturity models and BIM-related standards. Second, the four dimensionsand their ranking in this research aligned with the four most important factorsimpacting BIMM benefits as indicated in the SmartMarket Report by McGraw-Hill.Third, similar factors were perceived as important by both the USA and the non-USABIM-related professionals.

Some limitations of this research need to be acknowledged. One issue is about howwell the sample represented the population of the USA BIM users. There was a widerange of respondents in terms of their experience, education, position and demography.The BIMM measurement model might differ with different composition of participantswith distinct experience and background. Therefore, the measurement model extractedhere might not reflect the perception of the population. A natural extension of this line ofresearch would be to consider the differences in the perception of BIMM across theprofessionals with different backgrounds, such as the difference between the group ofarchitects and the group of constructors or the difference between practitioners withdifferent levels of industry experience. Another limitation is that the findings from thisstudy may not be generalised directly to other specific countries; however, as discussedbefore, similar factors were identified by both the USA and the non-USA experts.Therefore, the research findings here can be adapted for use in other countries andregions. Future studies of the perception of the experts in other countries and globalexperts could also be conducted and compared with the findings in this research.

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Further reading

FMI and Construction Management Association of America (CMAA) (2007), The Eighth AnnualSurvey of Owners, available at: http://images.autodesk.com/flashassets/company/BIM/downloads/FMI_CMAA_report.pdf (accessed 28 June 2011).

IBM (2011), Workflow Process, available at: http://publib.boulder.ibm.com/infocenter/cmgmt/v8r5m0/index.jsp?topic¼%2Fcom.ibm.administeringcm.doc%2Fmwf00008.htm(accessed 8 December 2011).

About the authorsYunfeng Chen is a graduate student in the Department of Building Construction Management withthe research focus on BIM, maturity and process models, project management, collaboration andtrust. Yunfeng Chen is the corresponding author and can be contacted at: [email protected]

Dr Hazar Dib is an Assistant Professor in a joint appointment with the Department ofComputer Graphics Technology and the Department of Building Construction Management withthe research focus on BIM, object-oriented-modelling (OOM), construction delivery, visualization,and sustainability.

Dr Robert F. Cox is the Associate Dean for Globalization and Engagement in the College ofTechnology and the Interim Department Head and Professor of the Department of BuildingConstruction Management with the research focus on construction management, partnering,trust, and technique application.

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ual

ity

of“a

llfa

ctor

s”d

uri

ng

pro

du

ctio

n(C

has

e,20

07)

24.

Sp

ecifi

cati

on“S

erv

ice

spec

ifica

tion

”/”p

rod

uct

spec

ifica

tion

”(S

ucc

ar)

Sp

ecifi

cati

ons

are

“req

uir

emen

tsto

be

sati

sfied

by

ap

rod

uct

orse

rvic

e”(D

OT

,20

12)

25.

Com

pet

ency

pro

file

“Ex

per

ien

ce”,

“kn

owle

dg

e”,

“sk

ills

”,“d

yn

amic

s”(S

ucc

ar)

Com

pet

ency

pro

file

refe

rsto

ase

tof

kn

owle

dg

e,sk

ills

and

abil

itie

s(K

SA

s)th

ata

pos

itio

nre

qu

ires

(Ste

ele

and

Kir

kp

atri

ck,

2011

)26

.T

rain

ing

pro

gra

m“T

rain

ing

pro

gra

mm

es[S

IC]”

(Su

ccar

)T

rain

ing

pro

gra

mre

fers

toa

set

ofre

late

del

emen

tsth

atfo

cus

onad

dre

ssin

ga

pro

ject

’str

ain

ing

nee

ds

(SE

I,20

08)

(con

tinued

)

Table AI.BIMM areas and theircorresponding sources

and descriptions


207

BIM

Mar

eas

Sou

rce

for

BIM

Mar

eas

Des

crip

tion

s/d

efin

itio

ns

27.

Tra

inin

gd

eliv

ery

met

hod

“Res

earc

hef

fort

s,ed

uca

tion

alp

rog

ram

mes

[SIC

]”/

”del

iver

able

s”(S

ucc

ar)

Tra

inin

gd

eliv

ery

met

hod

refe

rsto

the

met

hod

chos

ento

trai

np

arti

cip

ants

inu

seof

BIM

(Bla

nch

ard

and

Th

ack

er,

2006

)2.

Inte

rop

erab

ilit

y“I

nte

rop

erab

ilit

y/I

FC

sup

por

t”(N

IBS

,200

7)“I

nte

rop

erab

ilit

y”

isth

eab

ilit

yof

two

orm

ore

syst

ems

toex

chan

ge

info

rmat

ion

that

can

be

use

d(I

EE

E,

1991

)5.

Info

rmat

ion

del

iver

ym

eth

od“D

eliv

ery

met

hod

”(N

IBS

)In

form

atio

nd

eliv

ery

met

hod

refe

rsto

asy

stem

orap

pro

ach

use

db

ya

pro

ject

tob

rin

gin

form

atio

nto

use

rs(N

IBS

,20

07)

10.

Dat

ari

chn

ess

“Dat

ari

chn

ess”

(NIB

S)

“Dat

ari

chn

ess”

refe

rsto

the

com

ple

ten

ess

and

val

ue

ofd

ata

orin

form

atio

nw

ith

ina

syst

em(N

IBS

,20

07)

11.

Rea

l-ti

me

dat

a“T

imel

ines

s/re

spon

se”

(NIB

S)

“Rea

l-ti

me

dat

ad

enot

esin

form

atio

nth

atis

del

iver

ed”

oru

pd

ated

“im

med

iate

lyaf

ter

coll

ecti

on”

orch

ang

e(E

ncy

clop

edia

,20

11)

12.

Info

rmat

ion

accu

racy

“In

form

atio

nac

cura

cy”

(NIB

S)

IAc

defi

nes

the

clos

enes

sof

the

info

rmat

ion

rece

ived

toth

etr

uth

(Iv

anov

,19

72)

13.

Gra

ph

ics

“Gra

ph

ical

info

rmat

ion

”(N

IBS

)“G

rap

hic

s”ar

e“v

isu

al”

pre

sen

tati

ons

onco

mp

ute

rsc

reen

orp

aper

toin

form

oril

lust

rate

.(T

FD

,20

11)

14.

Geo

spat

ial

cap

abil

ity

“Sp

atia

lca

pab

ilit

y”

(NIB

S)

Geo

spat

ial

cap

abil

ity

refe

rsto

the

cap

abil

ity

ofa

syst

emto

cap

ture

,st

ore,

anal

yse

,m

anag

e,an

dp

rese

nt

dat

aw

ith

refe

ren

ceto

geo

gra

ph

iclo

cati

ond

ata

(NIB

S,

2007

)15

.L

ifec

ycl

ep

roce

sses

“Lif

e-cy

cle

vie

ws”

(NIB

S)

Lif

ecy

cle

pro

cess

esar

eth

ep

roce

sses

resp

onsi

ble

for

afa

cili

tyfr

omco

nce

pti

onth

rou

gh

oper

atio

nan

dm

ain

ten

ance

(Nig

hti

ng

ale

and

Miz

e,20

02)

17.

Ch

ang

em

anag

emen

t“C

han

ge

man

agem

ent”

(NIB

S)

“Ch

ang

em

anag

emen

t”id

enti

fies

,ev

alu

ates

and

imp

lem

ents

imp

rov

emen

tto

the

pro

ject

s’d

efin

edB

IMp

roce

sses

ona

con

tin

uou

sb

asis

(IT

GI,

2007

)18

.R

ole

“Rol

e”(N

IBS

)“R

ole”

refe

rsto

au

nit

ofd

efin

edre

spon

sib

ilit

ies

that

may

be

assu

med

by

one

orm

ore

par

ties

(SE

I,19

94)

Notes:

aT

he

area

sw

ere

nu

mb

ered

corr

esp

ond

ing

toth

eor

der

ofth

eit

ems

inth

eq

ues

tion

nai

re;f

orth

ed

etai

lof

the

qu

esti

onn

aire

,ple

ase

refe

rto

app

end

ixof

the

arti

cle

wri

tten

by

Dib

etal.

(201

2);t

he

area

su

nd

erth

eco

lum

ns

of“B

IMM

area

s”an

d“S

ourc

efo

rB

IMM

area

s”w

ere

rep

rod

uce

dd

irec

tly

from

Tab

leI

ofth

ear

ticl

ew

ritt

enb

yC

hen

etal.

(201

2)

Table AI.

CI14,2

208

Stu

dy

Tec

hn

olog

yP

roce

ssP

eop

leIn

form

atio

n

NA

TS

PE

CN

ati

onalB

IMG

uid

e(N

AT

SP

EC

,20

11,

pp

.iv

-v)

“Tec

hn

olog

yP

latf

orm

and

Sof

twar

e”;“

BIM

man

agem

ent

Pla

n(B

MP

)”;

“Req

uir

emen

tsfo

rU

sin

gB

IM”

“Col

lab

orat

ion

Pro

ced

ure

s”“B

IMro

les

and

Res

pon

sib

ilit

ies”

“Mod

elS

har

ing

”;“3

DM

odel

s,F

orm

ats,

and

Mod

elS

tru

ctu

res”

;“F

ile

Sto

rag

ean

dS

ecu

rity

”;“R

equ

irem

ents

for

2DD

raw

ing

s”;

“Mod

elli

ng

Req

uir

emen

ts”

AE

C(U

K)

BIM

Pro

toco

l(A

EC

(UK

)C

omm

itte

e,20

12)

“Res

ourc

es”

(p.

43);

“In

tero

per

abil

ity

”(p

.19

)“P

roje

ctB

IME

xec

uti

onP

lan

”:“P

roje

ctE

xec

uti

onP

lan

”(p

.12)

and

“Pro

ject

BIM

mee

tin

gs”

(p.

13)

“Pro

ject

BIM

Ex

ecu

tion

Pla

n”:

“Rol

esan

dR

esp

onsi

bil

itie

s”(p

.10

)

“Col

lab

orat

ion

BIM

Wor

kin

g”

(p.

14);

“Dat

aS

egre

gat

ion

”(p

.21

)

“Mod

elli

ng

Met

hod

olog

y”

(P.

25);

“Fol

der

Str

uct

ure

and

Nam

ing

Con

ven

tion

s”(p

.32)

;“P

rese

nta

tion

Sty

les”

(p.

39)

Hon

gK

ong

BIM

Pro

ject

Spe

cifica

tion

(HK

I,20

11,

p.

2)

“Cla

shA

nal

ysi

sP

roce

ss”;

“Har

dw

are

Sp

ecifi

cati

ons”

;“S

oftw

are

Sp

ecifi

cati

ons”

“BIM

pro

ject

Ob

ject

ive”

;“B

uil

din

gIn

form

atio

nM

odel

Sp

ecifi

cati

on”;

“BIM

Met

hod

olog

ies

and

Pro

cess

es”;

“Pro

ject

Del

iver

able

sfr

omB

IMP

roce

ss”

“BIM

Man

agem

ent

and

Sta

ffR

esou

rces

”“M

odel

Dat

aan

dL

evel

ofD

etai

l”

Com

mon

BIM

requir

emen

ts(B

uil

din

gS

mar

tF

inla

nd

,20

12)

“Sof

twar

e”(p

.7)

;“M

odel

ing

Too

ls”

(p.

9)“B

IMS

pec

ifica

tion

”(p

.10

)“P

ub

lish

ing

ofM

odel

s”(p

.11

)“R

ole

ofth

eB

IMC

oord

inat

or”

(p.

11)

“BIM

Acc

ura

cy”

(p.

8);

“Th

eB

uil

din

gs,

Flo

orL

evel

san

dD

ivis

ion

s”(p

.9)

;“N

amin

gan

dA

rch

ivin

gof

the

BIM

”(p

.10

);“R

elea

seof

the

BIM

”(p

.7)

;“C

oord

inat

ors

and

Un

its”

(p.

8);

“Wor

kin

gM

odel

s”(p

.11)

;“Q

ual

ity

Ass

ura

nce

ofB

IMs”

(p.

12)

Sin

gapo

reB

IMG

uid

e(B

CA

,20

12,

p.

vii

)

“BIM

Mod

elin

gan

dC

olla

bor

atio

nP

roce

du

res”

:“I

nd

ivid

ual

Dis

cip

lin

eM

odel

ing

”,“C

ross

-Dis

cip

lin

ary

Mod

elC

oord

inat

ion

”

“BIM

Sp

ecifi

cati

ons”

:“B

IMO

bje

ctiv

ean

dR

esp

onsi

bil

ity

Mat

rix

”,“C

omp

ensa

tion

Ex

pec

tati

ons”

“BIM

Sp

ecifi

cati

ons”

:“B

IMD

eliv

erab

les”

,“L

evel

ofD

etai

lan

dP

roje

ctS

tag

esin

the

Sin

gap

ore

BIM

Gu

ide”

;“B

IMM

odel

ing

and

Col

lab

orat

ion

Pro

ced

ure

s”:“

Mod

elan

dD

ocu

men

tati

onP

rod

uct

ion

”

Table AII.Comparison of BIMM

dimensions of thisresearchwith content and

areas of globalBIM-related

standards/guides/protocols/specifications


209

a measurement model of building information modelling maturity

Documents