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Front. Eng    2021, Vol. 8 Issue (1) : 32-47    https://doi.org/10.1007/s42524-019-0082-8
RESEARCH ARTICLE
Understanding innovation diffusion and adoption strategies in megaproject networks through a fuzzy system dynamic model
Yan ZHANG1, His-Hsien WEI2, Dong ZHAO3, Yilong HAN4, Jiayu CHEN1()
1. Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China
2. Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China
3. School of Planning, Design and Construction, Michigan State University, East Lansing, MI 48824, USA
4. Department of Construction Management and Real Estate, School of Economics and Management, Tongji University, Shanghai 200092, China
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Abstract

Innovation and knowledge diffusion in megaprojects is one of the most complicated issues in project management. Compared with conventional projects, megaprojects typically entail large-scale investments, long construction periods, and conflicting stakeholder interests, which result in a distinctive pattern of innovation diffusion. However, traditional investigation of innovation diffusion relies on subjective feedback from experts and frequently neglects inter-organizational knowledge creation, which frequently emerges in megaprojects. Therefore, this study adopted project network theory and modeled innovation diffusion in megaprojects as intra- and inter-organizational learning processes. In addition, system dynamics and fuzzy systems were combined to interpret experts’ subject options as quantitative coefficients of the project network model. This integrated model will assist in developing an insightful understanding of the mechanisms of innovation diffusion in megaprojects. Three typical network structures, namely, a traditional megaproject procurement organization (TMO), the environ megaproject organization (EMO), and an integrated megaproject organization (IMO), were examined under six management scenarios to verify the proposed analytic paradigm. Assessment of project network productivity suggested that the projectivity of the TMO was insensitive to technical and administrative innovations, the EMO could achieve substantial improvement from technical innovations, and the IMO trended incompatibly with administrative innovations. Thus, industry practitioners and project managers can design and reform agile project coordination by using the proposed quantitative model to encourage innovation adoption and reduce productivity loss at the start of newly established collaborations.
Keywords megaproject      innovation adoption      project network      system dynamic      fuzzy logic     
Corresponding Author(s): Jiayu CHEN   
Just Accepted Date: 27 December 2019   Online First Date: 27 February 2020    Issue Date: 15 January 2021
 Cite this article:   
Yan ZHANG,His-Hsien WEI,Dong ZHAO, et al. Understanding innovation diffusion and adoption strategies in megaproject networks through a fuzzy system dynamic model[J]. Front. Eng, 2021, 8(1): 32-47.
 URL:  
https://academic.hep.com.cn/fem/EN/10.1007/s42524-019-0082-8
https://academic.hep.com.cn/fem/EN/Y2021/V8/I1/32
Fig.1  Schematic plot of the proposed assessment model.
Working experience
£ 1 year 12.50%
1–2 years 37.50%
2–5 years 37.50%
5–10 years 6.25%
10–20 years 6.25%
Working experience on PPP projects
1–2 years 42.86%
2–5 years 57.14%
Number of experts in PPP project categories
Water supply, heating, sewerage 5
Refuse treatment 3
Utility tunnel 9
Road 15
Railway 6
Airport 10
Urban metro 7
Healthcare building 1
Tourism 1
Education 1
Elderly center 2
Heritage 2
Sports center 2
Government-subsidized housing 3
Water utilities 3
Agriculture 3
Forestry 1
Resources and environment protection 1
Urban redevelopment 3
New development of the area 3
Tab.1  Basic information of experts in the Delphi survey
Code Influencing Factors for Membership Functions (IFMF) Description Mean Weighting Reference
IFMF1 Consistency of innovation adoption The innovativeness should consistent with the nature of innovations after adoption 3.73 0.090 Gambatese and Hallowell (2011)
IFMF2 Time of innovation adoption The times of adoption practices have been taken for one innovation 2.36 0.057 Kramer et al. (2009)
IFMF3 Number of innovation adoption The number of innovations that adopted after the consistency discussion 3.09 0.075 Gambatese and Hallowell (2011); Nikas et al. (2007)
IFMF4 Formalization Existence of formal job descriptions, policies, and procedures for personnel 3.69 0.089 Damanpour and Schneider (2006)
IFMF5 Centralization* Level of centralized authority for decision-making 4.09 0.099 Damanpour and Schneider (2006)
IFMF6 Specialization Existence of personnel with specialized skills in various functional areas in an organization 2.36 0.057 Gambatese and Hallowell (2011)
IFMF7 Public policy Potential government policies that may have effects on the megaproject execution 2.64 0.064 Pauget and Wald (2013)
IFMF8 Frequent supervision Intensity and sequencing of the supervision in the innovation process 2.82 0.068 Koskela and Vrijhoef (2001)
IFMF9 Organization structure A clear hierarchy of people, their function, the workflow, and the reporting system 3.18 0.077 Egbu (2004)
IFMF10 Organization size The size of the organization during the adoption and implementation of innovations 2.55 0.062 Egbu (2004)
IFMF11 Firms funding If the organizations provide sufficient funding to perform new innovations 3.88 0.094 Chan et al. (2014)
IFMF12 Knowledge limitation The expertise in an organization who master and is familiar with the advanced technologies and innovations 3.23 0.078 Javernick-Will (2012);
Ozorhon et al. (2016)
IFMF13 Slack resources Existence of surplus resources that are available for experimenting with innovations 3.79 0.092 Dikmen et al. (2005)
Tab.2  List of identified influencing factors
CI TI NI Fo Ce Sp PP FS OS1 OS2
CI 1.00 0.35 0.36 0.63 0.45 0.23 0.27 0.35 -0.22 -0.26
TI 0.35 1.00 0.40 0.52 0.33 0.26 0.30 0.27 -0.39 -0.20
NI 0.36 0.40 1.00 0.64 0.48 0.23 0.41 0.40 -0.36 -0.22
Fo 0.63 0.52 0.64 1.00 0.63 0.46 0.54 0.58 -0.44 -0.41
Ce 0.45 0.33 0.48 0.63 1.00 0.51 0.62 0.59 -0.31 -0.43
Sp 0.23 0.26 0.23 0.46 0.51 1.00 0.63 0.44 -0.28 -0.40
PP 0.27 0.30 0.41 0.54 0.62 0.63 1.00 0.53 -0.27 -0.37
FS 0.35 0.27 0.40 0.58 0.59 0.44 0.53 1.00 -0.21 -0.37
OS1 -0.22 -0.39 -0.36 -0.44 -0.31 -0.28 -0.27 -0.21 1.00 0.32
OS2 -0.26 -0.20 -0.22 -0.41 -0.43 -0.40 -0.37 -0.37 0.32 1.00
Tab.3  Correlation of the influencing factors
Fig.2  p membership function (VL–very low, L–low, M–moderate, H–high, VH–very high).
Innovation factors Weighting Membership function (degree factors)
VL L M H VH
Management Factors1
Time of innovation adoption 0.077 0.45 0.00 0.36 0.09 0.09
Number of innovation adoption 0.101 0.09 0.36 0.09 0.27 0.18
Formalization 0.121 0.09 0.27 0.18 0.36 0.09
Centralization 0.134 0.09 0.18 0.36 0.27 0.09
Specialization 0.077 0.18 0.36 0.36 0.09 0.00
Public policy 0.087 0.09 0.36 0.36 0.18 0.00
Frequent supervision 0.092 0.09 0.27 0.36 0.27 0.00
Organization structure 0.104 0.09 0.18 0.27 0.36 0.09
Organization size 0.084 0.09 0.27 0.64 0.00 0.00
Technical Factors2
Consistency of innovation adoption 0.114 0.27 0.09 0.45 0.18 0.00
Time of innovation adoption 0.072 0.09 0.09 0.45 0.36 0.00
Number of innovation adoption 0.094 0.09 0.18 0.27 0.36 0.09
Formalization 0.113 0.09 0.27 0.27 0.27 0.09
Centralization 0.125 0.09 0.18 0.55 0.09 0.09
Specialization 0.072 0.09 0.27 0.27 0.18 0.18
Organization size 0.078 0.09 0.18 0.25 0.29 0.19
Firms funding 0.118 0.09 0.18 0.34 0.36 0.03
Knowledge limitation 0.099 0.09 0.18 0.47 0.22 0.04
Slack resources 0.116 0.09 0.18 0.36 0.23 0.14
Tab.4  Membership functions for influencing factors
Fig.3  System dynamic models for technical innovation learning rate.
Fig.4  System dynamic models for administrative innovation learning rate.
Fig.5  Contractual scheme of the traditional megaproject procurement organization (TMO).
Fig.6  Contractual scheme of the environ megaproject organization (EMO).
Fig.7  Contractual scheme of the integrated megaproject organization (IMO).
Code Scenarios Coefficients setting
– Baseline All coefficients of the system dynamic models were set based on the Delphi survey results
Administrative variations
S1 Simplified organization Lower organization size coefficient
S2 Financially sufficient organization Higher firm funding coefficient
S3 Centralized organization Higher centralization coefficient
Technical variations
S4 Technology with supervision interference Higher supervision frequency coefficient
S5 Technology with specialization Higher specialization coefficient
S6 Technology with government incentives Higher public policy coefficient
Tab.5  Summary of the potential scenarios to promote the innovation diffusion in megaproject organizations
Fig.8  Normalized project network productivity under different scenarios.
Fig.9  The normalized project network productivity of TMO for all scenarios.
Fig.10  The normalized project network productivity of EMO for all scenarios.
Fig.11  The normalized project network productivity of IMO for all scenarios.
R-square S1 S2 S3 S4 S5 S6
TMO -0.069
(0.4666)		 -0.104
(0.6739)		 -0.082
(0.4265)		 0.004
(0.0061)		 -0.001
(0.0001)		 -0.024
(0.2111)
EMO 0.013
(0.0559)		 0.037
(0.3178)		 0.029
(0.2538)		 0.193
(0.7705)		 0.146
(0.4911)		 0.109
(0.5580)
IMO 0.021
(0.0893)		 0.025
(0.1543)		 0.038
(0.3575)		 -0.008
(0.0220)		 -0.008
(0.0251)		 -0.005
(0.0080)
Tab.6  The equivalent project network learning rates over iterations
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