Content uploaded by Andrew Pidgeon
Author content
All content in this area was uploaded by Andrew Pidgeon on Dec 06, 2021
Content may be subject to copyright.
The development of an adaptable outcome-focused measurement and
management methodology, incorporating risk mitigation assignment in
support of Collaborative BIM
Authors
Andrew Pidgeon, Nashwan Dawood
(Teesside University, United Kingdom)
ABSTRACT: This research explores the current availability of BIM measurement systems, processes, and toolsets
as well as their limitations and opportunities for development through the utilization of a two-phase data collection
and analysis approach. The design of the study initiates phase one with a thematic review of underpinning literature
alongside a focus group survey questionnaire as part of the data collection exercise which included 15 BIM experts
from both academia and industrial environments respectively. The second phase of the study explores and analyses
the existing underpinning literature supporting for the implementation of adopted BIM measurement and
management methodologies and practices.
Following the exploration and data collection phases, analysis of the findings show that existing measurement
systems developed by both academia (63.89%) and industry (36.11%) to support the measurement of BIM are
represented by 36 dominant methodologies, with a heightened yet limiting focus on providing the ability to
measure BIM performance (42%), followed by BIM maturity (30%) and BIM competency (28%) which are mostly
aligned with measuring the standardized compliant approach to BIM implementation. Furthermore, due to the
subjective nature of collaborative BIM on a project-by-project basis, issues were raised such as the complexity of
implementing and measuring BIM in a manner that best positions people and teams to harness their abilities and
thusly increasing the likelihood of achieving the required outcome(s) progressively throughout and across the
entire BIM project.
In response to these gaps and opportunistic findings, a novel measurement and mitigation methodology was
developed to support the measurement, management, and implementation of collaborative BIM for a range of
diverse practitioners, focused on ascertaining the complexity, confidence and impacts alongside risk mitigation
that the task/objective may inherit. Further, this approach enables practitioners to move away from static and
restrictive high-level BIM measurement and towards an active BIM management methodology for implementation
across a heterogenous range of BIM project types, at varying stages of their lifecycle.
KEYWORDS: Building Information Modelling, Measurement Techniques, Collaboration, Project Delivery,
Decision Making.
1. INTRODUCTION
Building Information Modelling (BIM) is a collaborative process driven framework that supports the digital
delivery of projects, supported by technology and applications (Eadie et al, 2013; Kirby-Turner and Whittington,
2018), through the integration of teams of people focused on delivering specified outputs utilising a single source
of truth (Vanlande et al, 2008; Vernikos, 2016). Regarding the latter, Common Data Environments (CDE’s) holds
the data that a project produces, exchanges and archives in order to create information clarity and certainty
supported by stage gates and assurance workflows following the check, review, approve methodology (Forcael et
al, 2020). Adding to these points, Singh et al (2010) research defines that almost all of the complexities of
collaborative multi-disciplinary activities are visible across a range of AEC projects, which is where in theory the
prospect of BIM holds potential value as it can aid in removing ambiguity and information silos (Azhar, 2011) and
positions clients and their supply chain with a robust way of understanding their requirements and aspirations in
delivering outputs in an efficient, digitally focused and collaborative manner (Harty, 2005). However, and adding
to the simplified outline above and of the intent of BIM (Oh et al, 2015), the qualitative and quantitative
measurement of BIM is typically an elusive and ambiguous area (Howard and Björk, 2007; Doloi, 2013; Hicks,
1992;), with items such as Return On Investment (ROI) being consistently difficult to ascertain and represent
(Eadie et al, 2014), alongside seemingly insufficient tangible, unified and connected methods to capture the rate
of delivered progress (Da Silva et al, 2019) against the information requirements or client expectations
respectively, in a clear and succinct manner (CIOB, 2020) and at an agreed implementable level. Further, Iyer and
Jha (2005) reaffirm that for projects to be truly measurably successful, a clear approach to delivery must be agreed
and adopted by all parties, together alongside project managers and client stakeholders, respectively. Therefore, in
light of the above, this research study as outlined in the developed sections which follow explores further the
existing applied measurement methodologies, processes and frameworks with a focus on evidencing how they
effectively manage and deliver the expectations and aspirations at a delivery level, from an achieving outcomes
biased perspective. Latter sections state the opportunities to learn and develop these further through the creation
of a new adaptable measurement process across all stages of the project lifecycle (Glick and Guggemos, 2009) and
by a diverse range of stakeholders.
2. METHODOLOGY
The methodology and principles set out as part of design of this research are summarized below, in terms of the
progressive workflow order.
1) Explore and assess through a systematic literature review the current state of BIM in respect to its
implementation nuances, across academic and industrial literature.
2) Investigate the existing and thus developed measurement methodologies, tools and processes that are
readily available to support BIM execution of projects, across academic and industry literature.
3) Undertake a semi-structured electronic questionnaire with a predetermined focus group, including 15
BIM experts from academia and industry, to expose their knowledge and experience of how BIM is
currently measured and how it could be improved.
4) Review and compile a consolidated list of existing measurement methodologies, processes and tools
that are available to support the implementation and management of BIM projects.
5) Summarize and review the gaps, opportunities and reference to existing measurement systems from
data collected from the 15 BIM expert focus group participants.
6) Analyze the findings post literature review and data collection of how BIM is measured, managed, and
delivered through academic and industry data collection.
7) Develop a novel alternative measurement methodology that is biased towards achieving qualitative and
quantitative proactive outcome assignment and measurement of collaborative BIM due to the
opportunities presented, including risk mitigation; and
8) Conclude, apply limitations, and outline future works.
2.1 AIM AND OBJECTIVES
The key aim, objectives and methods used as part of the design of this research are stipulated below in Table 1,
following the developing research ‘aims and objectives model’ proposed by Thomas and Hodges (2010).
Table 1: Aim and objectives of the research study (Thomas and Hodges, 2010)
Aim
Objectives
Methods
To develop a novel approach to
measuring both qualitatively and
quantifiably the outcomes of BIM
objective implementation, following a
systematic and thematic exploration of
academic and industry literature
alongside a semi-structure questionnaire
of BIM experts (academic and
Undertake a review of the underpinning
theoretical literature, as well as industry
practices and advances.
Systematic and thematic review of
academic and industry literature.
Develop and execute a semi-structured
survey questionnaire to understand
further the currently available and
adopted measurement systems and
toolsets.
Utilise an electronic questionnaire
platform (Microsoft Forms) to facilitate
the mixed methods of data.
industrial).
Outline the core gaps between current
measurement methodologies and future
opportunities for BIM.
Through analysis of the research
findings.
Propose an alternative measurement
system focused on simplicity and
adaptability of use for both qualitative
and quantitative data for collaborative
BIM delivery.
Develop a novel measurement
methodology based upon existing gaps
and opportunities presented by the
conclusive evidence.
2.2 PROCESS FLOW METHODOLOGY
To further support the thematic analysis approach to this research (Attride-Stirling, 2001), a process methodology
flow developed by Braun and Clarke (2006) for the socially led exploration of research has been utilized as shown
in Figure 1 below, as part of the schema design. This four-stage process enables in order of flow 1) discovery of
information 2) review of collated evidence 3) a proposal based on the key outcomes and 4) conclusive
formalization on the thematic themes, feedback, and findings.
Figure 1: High-level process flow methodology (Redrawn from Braun and Clarke, 2006)
Presenting the developed methodology of this research more clearly and visually, a flow diagram has been created
in Figure 2 below which represents the process leading towards the research’s ultimate aims in developing an
alternative outcome focused measurement and management methodology.
Figure 2: Visual research process methodology
2.3 STAKEHOLDER INCLUSION
In order to facilitate, capture and incorporate data from a range of diverse, experienced and educated participants,
fifteen BIM experts were invited from both academic and industry background to support the thematic data
collection process (Attride-Stirling, 2001). Furthermore, this predetermined focus group was a prolongation of
participants designed and developed from previous research by the author (Pidgeon and Dawood, 2021a), with
their continued utilization to create consistency from a focus group perspective; gathering expert opinions,
experiences, and perspectives from their collective average experience of almost 23 years (as described below in
Table 2).
Table 2: Stakeholder participants, expertise, and experience
Function
Participant
Reference
Experience
Average Experience
Expertism bias
% Split
Professor
P1
20
c. 23 years
Academia
33%
Professor
P2
28
Academia
Professor
P3
33
Academia
Professor of
Construction
Management
P4
40
Academia
Professor of Digital
Construction
P5
12
Academia
BIM Manager
C1
22
Construction
33%
Senior Project
Manager
C2
22
Construction
BIM Consultant
C3
35
Construction
Project Manager
C4
30
Construction
Deputy Regional Chief
Engineer
C5
17
Construction
BIM Consultant
D1
12
Design
33%
BIM Manager
D2
10
Design
Project Information
Manager
D3
10
Design
Operations Director
D4
21
Design
Business Unit Director
D5
30+
Design
3. RESULTS AND ANALYSIS
This section and sub-sections which follow capture the data collected via the focus group BIM experts (15no.)
spanning academia and industry, using the semi-structured questionnaire survey technique, to better understand
which measurement systems are currently adopted, their successes and any opportunities that may be presented
from their opinions and knowledge due to their poor usefulness and gaps they bring. Additionally, this was
complimented by a thematic literature review of the currently available underpinning literature produced by
academic and industry respectively, with a focus again on the developed measurement and management systems
specifically focused towards supporting BIM.
Further, the specific area of interest and thus exploration across both survey and literature review explore the
existence of these measurement systems (both theoretically and practically) in order to provide a further detailed
insight towards the current state, availability, benefits and disadvantages of available BIM measurement systems
and thusly, opportunities for developing an alternative approach to support collaborative BIM implementation and
execution.
3.1 SURVEY DATA COLLECTION TECHNIQUE
To gain further knowledge into the existing measurement methodologies which are currently available to support
the implementation of collaborative BIM which have equally successful and unsuccessful elements, three
questions were designed and distributed to the focus group via a semi-structured questionnaire to understand
further how BIM outcomes were measured, their success and failures as well as proposals for alternative practices
that could benefit the BIM operational environment further.
Moreover, three questions were positioned to the participants as part of the survey questionnaire to gain a wider
detailed perspective towards understanding how the existing applied methodologies were structured, what trends
they share, their advantages and as well as inefficiencies and outlining any opportunities for improvement. These
questions are part of the design in response to the former items were as follows.
1. How are BIM criteria and alternatives currently measured?
2. What are their successes and disadvantages?
3. How do you propose this could be alternatively measured and/or undertaken in the future?
In response to the assigned items above, Table 3 below outlines in summary the conclusive observations and
commonalities stated, which has been summarized by the author in response to the participants feedback received
through completion of the interview questionnaire.
Table 3: Stakeholder participants, expertise, and experience
Participant reference
Observations by the author to the responses
A1
- Current methods are aligned more to PAS1192 and equivalent standards.
- No directly referenced/actively used measurement systems at the BIM execution stage.
A2
- Requests for Information are largely the benchmark for successful collaboration.
- No directly referenced/actively used measurement systems at the BIM execution stage.
A3
- Return On Investment metrics are rarely implemented.
- No directly referenced/actively used measurement systems at the BIM execution stage.
A4
- No directly referenced/actively used measurement systems at the BIM execution stage.
A5
- Reduced clarity on how measurement is practically undertaken.
- Evidenced a vast range of theoretical tools developed as part of a research study but reduced data on
implementation referencing.
- No directly referenced/actively used measurement systems at the BIM execution stage.
C1
- Compliance is documented but not measured (quantifiably)
- No directly referenced/actively used measurement systems at the BIM execution stage.
C2
- No directly referenced/actively used measurement systems at the BIM execution stage.
C3
- Development, at a project and organisational level, of measurement methodologies towards better clarity on
implementation themes would be beneficial to a range of team members.
- No directly referenced/actively used measurement systems at the BIM execution stage.
C4
- Disconnected teams despite active BIM
- Procurement and contracts supporting information exchange are required.
- Silos of what is to be delivered, when and by whom is sometimes mysterious.
- User friendly and understandable interfaces to the benefits as well as making clear the negatives would be
advantageous.
- No directly referenced/actively used measurement systems at the BIM execution stage.
C5
- Regular metrics reported as part of organization operating model but disconnected from direct project activities
and inconsistently produced.
- No directly referenced/actively used measurement systems at the BIM execution stage.
D1
- Limited to zero tools available to practically measure active delivery/task outputs.
- Success typically driven and perceived as cost alignment (on time, on budget)
- Information clarity and quality should be a driver for useful measurement.
- No directly referenced/actively used measurement systems at the BIM execution stage
D2
- Lack of client understanding of BIM requirements and therefore importance factors.
- Applying understandable focus for and from the client towards output requirements and goals would be
advantageous.
- No directly referenced/actively used measurement systems at the BIM execution stage
D3
- No measurable input or out from, by or with the delivery partners.
- Completion as well as way points checks required to support outcomes (and get them back on track).
- No directly referenced/actively used measurement systems at the BIM execution stage.
D4
- As goals aren’t typically measured but stated there are reactive measures taken to gain realignment which is
disruptive and costly (time, cost, reputation etc.).
- Applying a system that focuses on right first-time approach would reduce rework and thus improve efficiencies
of collaborative BIM.
No directly referenced/actively used measurement systems at the BIM execution stage
D5
- Measurement systems require friendliness to them so all can use and gain benefits, steer and guidance on how
to achieve tasks/reduce risks.
- Risk reduction and performance output driven approach would be valuable.
- No directly referenced/actively used measurement systems at the BIM execution stage.
3.2 EXISTING MEASUREMENT AND MANAGEMENT METHODOLOGIES
Complimenting the survey questionnaire feedback responses as evidenced above in Table 3, a thematic literature
review of the existing BIM measurement systems from both academic and industry literature was undertaken
towards further understanding the available systems developed to achieving the expected outcomes for
collaborative BIM. These findings as part of the thematic literature review are shown below in Table 4, along with
a categorization of their bias and whether they have been developed by industry or academia.
Table 4: Stakeholder participants, expertise, and experience
Methodology
Categorization (bias)
Academic or Industry developed
Citation
BIM Assessment
Maturity
Academia
Pennsylvania State
University, 2013
BIM Maturity Measurement
Maturity
Industry
Arup, 2015
BIM Benefits Management
Performance
Industry
Transport for London, 2017
BIM Return on Investment Tool
Performance
Industry
CDBB, 2019
CPIx BIM Assessment
Competency
Industry
CPIx, 2011
Project Management Process
Maturity Model (PM2)
Performance
Academic
Kwak and Ibbs, 2002
BIM Measurement
Methodology (BMM)
Performance
Industry
PwC, 2018
BIM Value Management
Performance
Industry
NATSPEC, 2019
Interactive Capability Maturity
Model (I-CMM)
Maturity
Academic
Suermann et al., 2008
BIM Excellence Online
Platform (BIMe OP)
Competency
Academic
Succar et al, 2013
BIM Maturity Measurement
Tool
Maturity
Industry
ICE, 2014
BIM Maturity Assessment
Maturity
Industry
National Federation of
Builders, 2019
BIM Benefits
Performance
Academic
University of Cambridge,
2018
BIM Performance
Benchmarking
Performance
Academic
Sebastian and Berlo, 2010
NBIMS CMM
Maturity
Industry
NBIMS, 2007
Business Process Orientation
Maturity Model (BPO)
Competency
Academic
Lockamy and McCormack,
2004
COBIT Model
Competency
Academic
Lainhart, 2000
Vico BIM Scorecard
Competency
Industry
Vico, 2019
IU BIM Proficiency Index
Competency
Academic
Indiana University, 2012
BIM ROI Framework
Performance
Academic
Giel and Issa, 2013
BIM Competency Assessment
Tool (BIMCAT)
Competency
Academic
Giel and Issa, 2014
i-BIM Maturity Model (Level
0-3)
Maturity
Academic
Bew and Richards, 2008
Capability Maturity Model
Integration for Development
(CMMI-DEV)
Maturity
Industry
SEI (2006)
Lean Enterprise Self-
Assessment Tool
Maturity
Academic
Nightingale and Mize, 2002
BIM Quick Scan
Performance
Industry
Sebastian and Berlo, 2010
Portfolio, Programme and
Project Management Maturity
Model (P3M3)
Performance
Academic
OGC, 2008
BIM Assessment Profile
Competency
Industry
CIC, 2012
BIM Cloud Score (BIMCS)
Performance
Academic
Du et al, 2014
Construction Supply Chain
Maturity Model (CSCMM)
Performance
Academic
Vaidyanathan and Howell,
2007
VDC Scorecard
Performance
Academic
Kam et al, 2014
Owner’s BIMCAT
Competency
Academic
Azzouze et al, 2015
BIM Characterization
Framework
Maturity
Academic
Gao, 2011
BIM Maturity Matrix
Maturity
Academic
Succar, 2010
Five Performance Metrics for
BIM
Performance
Academic
Succar et al, 2012
Knowledge Retention Maturity
Levels
Competency
Academic
Arif et al., 2009
Interorganizational BIM
Performance
Academic
Fox and Hietanen, 2007
Total BIM Measurement and Management Methodologies
36
Summarizing on Table 4 above, Figures 3 and 4 which follow highlight the percentage share of existing
measurement and management methodologies, as well as a graphical representation of their dominance of focus
and developed areas.
Figure 3: Existing BIM measurement and management focus
Figure 4: Analysis of dominant development areas
4. DEVELOPMENT POST ANALYSIS
Following analysis of the focus group feedback in response to the semi-structured questionnaire alongside a
thematic literature review of existing academic and industry measurement and management methodologies to
support the delivery of collaborative BIM, a novel methodology has been produced in Table 5 below, provided by
the gaps, opportunities, limitations, and inefficiencies that the existing methodologies present and of course inhibit.
This newly developed system advancing on from core findings from the existing underpinning literature focus
along extrapolation of data provided through the mixed method survey collection technique focuses on both
qualitative and quantitative application and reasoning from user input towards achieving specific BIM goals and
objectives and risk mitigation.
Table 5: Developed measurement and management methodology proforma
Confidence, Complexity, and Impact (CCI) Measurement and Management Framework
Total Sum
Focal Point (objective)
Confidence (in being able to
deliver the task)
Complexity (of the task/goal)
Impact (on wider
objectives if not achieved)
A
1=high
2=medium
3=low
1=non-complex
2=complex
3=highly complex
1=low
2=medium
3=high
C + C+ I
B
1=high
2=medium
3=low
1=non-complex
2=complex
3=highly complex
1=low
2=medium
3=high
C + C+ I
C
1=high
2=medium
3=low
1=non-complex
2=complex
3=highly complex
1=low
2=medium
3=high
C + C+ I
Risk Factor (from total sum above)
Low
1-3
Medium*
4-6
High*
7-9
What (needs to be undertaken or utilized to improve the likelihood of success?)
A
B
C
Who (needs to support the task for effective delivery?)
A
B
C
When (will this start, be re-evaluated i.e., monthly, and finish?)
A
B
C
*Mitigations (if risks are ‘medium’ and/or ‘high’)
A
B
C
Mathematically, the formula for calculating each sum for the Confidence, Complexity, and Impact (CCI) factor as
defined by each practitioner or team for their particular focal point(s) is shown below.
Where each element is:
a1= Confidence factor
a2= Complexity factor
a3= Impact factor
5. CONCLUSION
This research study was designed to expose the existing measurement and management methodologies that support
collaborative BIM via a systematic and thematic literature review of academic and industrial literature, as well and
alongside the inclusion of a BIM focus group inclusive of 15 academic and industry experts. Findings show that
although there are 36 dominant and available measurement methodologies, they are primarily attentive to three
core areas of focus: performance (42%), maturity (30%) and competency (28%). Additionally, existing methods
are heavily focused on high-level compliance with standards (such as ISO19650) and aim to confirm the ability of
a project or person to be able to deliver performance to said standards and/or protocols. The development of an
alternative measurement approach as defined as part of this research is intended to be implemented across a broad
range of diverse stakeholders throughout various stages of the BIM lifecycle of a project, and thus unrestrictive
and adaptable. Moreover, a three pillar approach of confidence, complexity and impact (CCI) has been developed
in light of these findings, allowing objectively focused outcomes to be assigned quantitative and qualitative values
in a simplified manner and measured in a progressive form, complimented by risk management and mitigation
fields which can be defined, populated, remeasured and even used as escalation mediums to increase the likelihood
of BIM goal attainment, seek continual improvement and benefit from risk reduction in achieving successful
collaborative BIM outcomes.
6. REFERENCES
Arif, M., Egbu, C., Alom, O. and Khalfan, M.M.A. (2009), “Measuring knowledge retention: a case study of a
construction consultancy in the UAE”, Engineering, Construction and Architectural Management, Vol. 16 No. 1,
pp. 92-108, available at: https://doi.org/10.1108/09699980910927912.
Arup (2015), New BIM Maturity Measure Model Launches, https://www.arup.com/news-and-events/new-bim-
maturity-measure-model-launches (accessed 24 August 2021).
Attride-Stirling, J. (2001), Thematic networks: An analytic tool for qualitative research, Qualitative Research,
1, 385–405. doi:10.1177/146879410100100307.
Azhar, S. (2011), Building Information Modeling (BIM): Trends, Benefits, Risks, and Challenges for the AEC
Industry. Leadersh. Manag. Eng. 2011, 11, 241–252.
Azzouz, A., Copping, A. & Shepherd, P. (2015). An investigation into Building Information Modelling Assessment
Methods, Proceedings of 51st ASC Annual International Conference, University of Bath, 17-19.
Bew, M. and Richards, M. (2008), “Bew-Richards BIM maturity model”, BuildingSMART Construct IT Autumn
Members Meeting, Brighton.
Braun, Virginia & Clarke, Victoria. (2006), Using thematic analysis in psychology. Qualitative Research in
Psychology. 3. 77-101. 10.1191/1478088706qp063oa.
CDBB (2019), Scottish Futures Trust, High-Level Introduction and Summary of the SFT BIM ROI Tool,
https://www.cdbb.cam.ac.uk/files/sft_bim_roi_tool_2019.pdf (accessed 24 August 2021).
CIC. (2012). BIM Planning Guide for Facility Owners Pennsylvania State University: University Park, PA.
Retrieved from: http://bim.psu.edu.
CIOB (2020), The Golden Thread: Understanding the capability and capacity of the UK built environment to
deliver and retain digital information, https://www.goldenthread.co.uk/Golden-Thread-Review.pdf (accessed 07
September 2021).
CPIx (2011), BIM Assessment Form, https://bimuk.co.uk/cpix-protocol/cpix-bim-assessment-form/ (accessed 24
August 2021).
Da Silva, Paula & Crippa, Julianna & Scheer, Sergio. (2019). BIM 4D in Construction Scheduling: Details,
Benefits and Difficulties. 10. 19010. 10.20396/parc.v10i0.8650258.
Doloi, Hemanta. (2013), Cost Overruns and Failure in Project Management: Understanding the Roles of Key
Stakeholders in Construction Projects. Journal of Construction Engineering and Management. 139. 267-279.
10.1061/(ASCE)CO.1943-7862.0000621.
Du, J., Liu, R. and Issa, R.R. (2014) ‘BIM cloud score: benchmarking BIM performance’, Journal of Construction
Engineering and Management, 140(11), p.04014054.
Eadie, Robert & Odeyinka, Henry & Browne, Mike & Mckeown, Clare & Yohanis, Michael. (2014), Building
Information Modelling Adoption: An Analysis of the Barriers to Implementation. Journal of Engineering and
Architecture. 2. 77-101.
Forcael, E.; Martínez-Rocamora, A.; Sepúlveda-Morales, J.; García-Alvarado, R.; Nope-Bernal, A.; Leighton, F
(2020), Behavior and Performance of BIM Users in a Collaborative Work Environment. Appl. Sci. 2020, 10, 2199.
https://doi.org/10.3390/app10062199.
Fox, S., & Hietanen, J. (2007). Interorganizational use of building information models: Potential for automational,
informational and transformational effects. Construction Management and Economics, 25(3), 289–296.
Gao, J. (2011). A Characterization Framework to Document and Compare BIM Implementations on Construction
Projects, Doctoral dissertation, Stanford University.
Giel, B. and Issa, R.R.A. (2013), “Synthesis of existing BIM maturity toolsets to evaluate building owners”,
Computing in Civil Engineering, ASCE International Workshop on Computing in Civil Engineering, Los Angeles,
CA, June 23-25, pp. 451-458.
Giel, B. and Issa, R. (2014) ‘Framework for Evaluating the BIM Competencies of Building Owners’, 2014
International Conference on Computing in Civil and Building Engineering, June 23-25, Orlando, Florida, United
States, pp. 552-559. DOI: https://doi.org/10.1061/9780784413616.069.
Harty, C. (2005). “Innovation in construction: a sociology of technology approach.” Building Research and
Information, 33(6): 512-522.
Hicks, J. C. (1992). “Heavy construction estimates, with and without computers.” J. Constr. Eng. Manage., 118(3),
545–560.
Howard, R., & Björk, B. (2007). Building information models – experts’ views on BIM/IFC developments.
ICE (2014), BIM Maturity Measurement Tool, https://www.ice.org.uk/knowledge-and-resources/best-
practice/bim-maturity-measurement-tool (accessed 24 August 2021).
Indiana University. (2012). IU BIM Proficiency Matrix. [www Document]. URL
http://www.indiana.edu/~uao/docs/standards/IU%20BIM%20Proficiency%20Matrix.xls (accessed 24 August
2021).
Iyer, K.C. and Jha, K.N. (2005) Factors Affecting Cost Performance: Evidence from the Indian Construction
Projects. International Journal of Project Management, 23, 283-295.
https://doi.org/10.1016/j.ijproman.2004.10.003.
Kam, C., Senaratna, D., McKinney, B., Xiao, Y. and Song, M. (2014) ‘The VDC Scorecard: Formulation and
Validation’, CIFE, Stanford, UK.
Kirby-Turner and Whittington (2018), BIM, collaboration and NEC4- Construction Manager Magazine (accessed
15-08-21).
Kwak, Y.H. and Ibbs, C.W. (2002), “Project management process maturity (PM) 2 model”, Journal of Management
in Engineering, Vol. 18 No. 3, pp. 150-155, available at: https://doi.org/10.1061/ (ASCE)0742-597X(2002).
Lainhart, J.W. (2000), “COBITTM: a methodology for managing and controlling information and information
technology risks and vulnerabilities”, Journal of Information Systems, Vol. 14 No. S-1, pp. 21-25, available at:
https://doi.org/10.2308/jis.2000.14.s-1.21.
Lockamy, A. and McCormack, K. (2004), “The development of a supply chain management process maturity
model using the concepts of business process orientation”, Supply Chain Management: An International Journal,
Vol. 9 No. 4, pp. 272-278, available at: https://doi.org/10. 1108/13598540410550019.
National Federation of Builders (2019), BIM Online Maturity Assessment, https://bimuk.co.uk/cpix-
protocol/cpix-bim-assessment-form/ (accessed 24 August 2021).
NATSPEC (2019), BIM Value Tool, https://bimvaluetool.natspec.org/ (accessed 24 August 2021).
NBIMS. (2007). National Building Information Modelling Standard, Version 1.0, part 1: overview, principles and
methodologies.
Nightingale, D.J. and Mize, J.H. (2002), “Development of a lean enterprise transformation maturity model”,
Information, Knowledge, Systems Management, Vol. 3 No. 1, pp. 15-30.
OGC (2008), “Portfolio, Programme, and Project Management Maturity Model (P3M3)”, Office of Government
Commerce, available at: https://goo.gl/k6OcnV (accessed 24 August 2021).
Oh, M.; Lee, J.; Hong, S.W.; Jeong, Y. (2015), Integrated system for BIM-based collaborative design. Autom.
Constr. 2015, 58, 196–206.
Pennsylvania State University (2013), Organisational BIM Assessment,
https://lp.constantcontact.com/su/77NsQ0E/BIMforOwners (accessed 06 September 2021).
Pidgeon A, Dawood N (2021). BIM Adoption Issues in Infrastructure Construction Projects: Analysis and
Solutions, ITcon Vol. 26, pg. 263-285, https://doi.org/10.36680/j.itcon.2021.015.
Andrew Pidgeon, Nashwan Dawood (2021a). Bridging the gap between theory and practice for adopting
meaningful collaborative BIM processes in infrastructure projects, utilising multi-criteria decision making
(MCDM). Journal of Information Technology in Construction (ITcon), Special issue: ‘Construction 4.0:
Established and Emerging Digital Technologies within the Construction Industry (ConVR 2020)’, Vol. 26, pg. 783-
811, DOI: 10.36680/j.itcon.2021.043
PriceWaterhouseCooper (2018), BIM Level 2 Benefits Measurement Methodology, Available at:
https://www.cdbb.cam.ac.uk/files/3._pwc_benefits_measurement_methodology.pdf (accessed 8 June 2021).
Sebastian, R., & Van Berlo, L. (2010). Tool for benchmarking BIM performance of design, engineering and
construction firms in the Netherlands. Architectural Engineering and Design Management (Special Issue:
Integrated Design and Delivery Solutions), 6, 254–263.
SEI. (2006). Capability Maturity Model Integration for Development (CMMI-DEV), improving processes for
better products. Pittsburgh, PA: Software Engineering Institute/Carnegie Melon.
Vishal Singh, Ning Gu, Xiangyu Wang (2010), A theoretical framework of a BIM-based multi-disciplinary
collaboration platform, Automation in Construction, Volume 20, Issue 2, 2011, Pages 134-144, ISSN 0926-5805,
https://doi.org/10.1016/j.autcon.2010.09.011.
Succar, Bilal. (2010). Building Information Modelling Maturity Matrix. 10.4018/978-1-60566-928-1.ch004.
Succar, B., Sher, W., & Williams, A. (2012). Measuring BIM performance: Five metrics, Architectural Engineering
and Design Management, 8(2), 120-142.
Suermann, P.C., Issa, R.R.A. and McCuen, T.L. (2008), “Validation of the US national building information
modeling standard interactive capability maturity model”, 12th International Conference on Computing in Civil
and Building Engineering, Beijing.
David R Thomas & Ian Hodges (2010), Designing and Planning Your Research Project: Core Skills for Social and
Health Researchers. Sage Publications. 2010.
Transport for London (2017), BIM Strategy, http://foi.tfl.gov.uk/FOI-4380-1718/4380-1718-
TfL%20BIM%20Strategy.pdf (accessed 24 August 2021).
University of Cambridge (2018), BIM Maturity Assessment Tool (BMAT),
https://www.ifm.eng.cam.ac.uk/research/asset-management/research-projects/bim-maturity-assessment/
(accessed 24 August 2021).
R. Vanlande, C. Nicolle, C. Cruz (2008), IFC and building lifecycle management, Autom. Constr. 18 (1) (2008)
70–78.
Vaidyanathan, K. and Howell, G. (2007), “Construction Supply Chain Maturity Model – conceptual framework”,
International Group for Lean Construction (IGLC-15), MI, July, pp. 170-180.
Vernikos, V.K. (2016), Realising offsite construction in the civil engineering and infrastructure sector.
Vico (2019), Vico BIM Scorecard, Survey Monkey, https://www.surveymonkey.com/r/9YCHVXC (accessed 24
August 2021)
