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Response of contractor behavior to
hierarchical governance: effects on
the performance of mega-projects
Response of
contractor
behavior
Hang Yin
College of Management and Economics, Tianjin University, Tianjin, China
Dan Wang
School of Public Policy and Administration, Chongqing University, Chongqing, China
Yilin Yin
Received 30 January 2020
Revised 14 July 2020
17 December 2020
5 February 2021
Accepted 27 March 2021
School of Management, Tianjin University of Technology, Tianjin, China
Henry Liu
School of Design and the Built Environment, University of Canberra, Canberra,
Australia, and
Binchao Deng
School of Management, Tianjin University of Technology, Tianjin, China
Abstract
Purpose – This study aims to examine the impacts of formal and informal hierarchical governances (HGs) on
the performance of mega-projects and the mediating role of contractor behavior (i.e. perfunctory and
consummate behaviors) in these relationships.
Design/methodology/approach – A total of 375 valid data entries from managers representing 375 megaprojects were analyzed through path analysis.
Findings – Both formal and informal HGs exert positive effects on the performance of mega-projects. While
formal HG positively affects contractor perfunctory behavior and contractor consummate behavior, informal
HG affects contractor perfunctory behavior only. Contractor behavior mediates the relationship between
formal HG and project performance.
Research limitations/implications – The impacts of potential moderators (e.g. institutional arrangement
and complexity) on the relationship between HG and contractor behavior have not been considered in
this study.
Practical implications – This study is useful for owners to enhance formal HG to improve contractor
perfunctory and consummate behaviors, which in turn can enhance the performance of mega-projects.
Originality/value – This study expands the knowledge of mega-project performance management from the
perspective of HG. It also contributes to the literature of contractor behavior within the context of megaprojects.
Keywords Hierarchical governance, Contractor behavior, General contractor, Mega-project, Performance
Paper type Research paper
Introduction
Mega-projects account for approximately 8% of global GDP (Laing and Connor, 2020). Due to
their complexity, uncertainty and multi-interface management, mega-projects [1] impose
extensive difficulties and challenges on their owners (Flyvbjerg et al., 2003). Many cases
reported that the owners’ poor management of project delivery was the main cause of the
budget and schedule overruns of mega-projects (El-Sabek and McCabe, 2018). Thus, a general
This research is funded by the National Natural Science Foundation of China (Project Numbers:
71472135, 71602144 and 71772136).
Disclosure Statement: No potential conflict of interest is reported by the authors.
Engineering, Construction and
Architectural Management
© Emerald Publishing Limited
0969-9988
DOI 10.1108/ECAM-01-2020-0073
ECAM
contract mode with hierarchical governance (HG) has become attractive within the context of
mega-projects (Wang et al., 2019a).
The owners of mega-projects, for example, in China, normally appoint groups and their
subsidiaries as general contractors and subcontractors, respectively. In such organizations, a
stable hierarchy exists between general contractors and subcontractors (Yuan et al., 2019).
Notably, there are two forms of HG: (1) formal HG, which depends on vertical integration of
official positions and (2) informal HG relying on social relationships (Weber, 1980). Compared
with the traditional contracting model, the hierarchy between general contractor and
subcontractors (Figure 1) can enable instructions to be transmitted more efficiently (Li
et al., 2018).
The relationship between HG and project performance is controversial, although research
indicates that HG can hinder the efficiency of information exchange in an organization
(Reitzig and Maciejovsky, 2015; Chen, 2017). One perspective holds that a flat and horizontal
management model is more efficient for task coordination and organizational control than
vertical management in projects (Pilkien_e et al., 2018). However, HG has led to many
successful mega-projects, such as the Beijing–Shanghai High-Speed Rail and Hong Kong–
Zhuhai–Macao Bridge (Qiu et al., 2019). Essentially, they have been considered critical to
organizational performance (e.g. He and Huang, 2011; Brahm and Tarzijan, 2012; Bunderson
et al., 2016). Therefore, it is important to explore the way by which HG influences the
performance of mega-projects. As addressed above, the underlying dynamics leading to the
impacts of formal and informal HGs on organizational performance vary (e.g. official control
and social relationships). Thus, it is essential to identify how these two different HGs
determine the performance of mega-projects.
Contractor behavior refers to the behavior of the contractor during contract enforcement
(between the owner and the contractor) (Hart, 2008), and it has been recognized as a
determinant of project success. Transaction cost theory states that governance changes the
behavioral choices of both parties in the transaction (Williamson, 1971). Williamson (1991)
further identified that HG reduces the transaction costs of the organization by decreasing
the uncertainty of behavior. Notably, formal HG constrains organizational behavior mainly
through a control system (e.g. rewards/penalties and personnel arrangements), thereby
improving individual efforts to drive project success (Sihag and Rijsdijk, 2019). By contrast,
Hierarchical governance
Owner
General contractor
Group
Subcontractor 1
Subsidiary A
Hierarchy
Subcontractor 2
Subsidiary B
Subcontractor 3
Subsidiary C
Figure 1.
General contract mode
with HG
Contract
Hierarchy
Hierarchical
governance
informal HG can contribute to coordinating individual and/or group interactions, which
lead to key stakeholders’ engagement with the goals of the organizations (He and Huang,
2011). Therefore, contractor behavior can build a bridge between the HG and project
performance.
Against the contextual backdrop above, this study aims to develop an integrative model to
examine how HG affects the performance of mega-projects. It also seeks to differentiate the
influence of formal and informal HGs on a mega-project’s performance, and the mediating
role contractor behavior plays in these relationships.
Literature review and hypothesis development
Hierarchical governance in construction projects
HG is the process by which an authorized decision-maker commands others to engage in
various activities within a certain range (Weber, 1980). While formal HG is an official system
supported by a top-down command and control of the organization, informal HG is
underpinned by social norms and interaction between dominating and dominated parties, for
example, values, cultural background and unofficial communication (Diefenbach and Sillince,
2011). In construction projects, HG is described as the governance that the groups (general
contractors) implement on their subsidiaries (subcontractors).
Formal and informal management practices relate to the cost, schedule and safety of
construction projects (Castillo et al., 2018). Formal HG comprises such mechanisms as
strategic, financial and personnel controls (Chang and Hong, 2000; Liao, 2005; White et al.,
2008). Groups can strengthen their risk response capabilities in projects by improving
financial performance through formal HG (Wang et al., 2020b). The partner organization of a
project can improve the efficiency and agility of relevant management through a consistent
strategic layout, that is, formal HG (Hoffmann et al., 2020). In contrast, informal HG comprises
social interdependence (Noorderhaven and Harzing, 2009). High interdependence between
groups and their subsidiaries will achieve synergies by sharing their values, intelligence and
technology (White et al., 2008). As a result, group interdependence from synergy effects
enables significant cost savings and risk sharing for subsidiaries in terms of construction
activities.
Hierarchical governance and mega-project performance
Formal HG is recognized as a stable organizational management model. Yun et al. (2015)
argued that an authoritative hierarchical relationship within an organization and clear
instructions from its managers are fundamental for project control. To deal with external
risks and achieve expected project performance, maintaining the stability of the management
structure within the organization is critical (Levander et al., 2011; Yun et al., 2020). In this
stance, the following hypothesis is proposed:
H1a. Formal HG correlates positively with mega-project performance.
One determinant of project performance is the subcontractors’ obedience to the general
contractor based on unified social norms, values and cultural background (i.e. informal HG)
(Sedita and Apa, 2015; Liu et al., 2017). Differences in values and cultural norms between
participants may create conflicts between them, thereby potentially resulting in the failure of
the project (Liu et al., 2017). Based on a case study, Nguyen and Watanabe (2017) also
identified the importance of informal HG in determining project performance. Therefore,
another hypothesis is derived as follows:
H1b. Informal HG correlates positively with mega-project performance.
Response of
contractor
behavior
ECAM
Contractor behavior in construction projects
Drawing upon transaction cost theory, Yan et al. (2018) defined that there are two types of
contractor behavior in construction projects, involving (1) contractor perfunctory behavior
(CPB) and (2) contractor consummate behavior (CCB). CPB refers to the contractor’s
verifiable and remunerative actions specified in the contract. It includes four main
components: (1) correctly performing the contractual obligations, (2) fully completing the
work within the contract, (3) achieving the construction targets agreed in the contract and
(4) performing construction according to the construction drawings and specifications
(Anvuur and Kumaraswamy, 2011; Xu et al., 2018). The CCB describes the contractor’s
proactive actions to execute the contract in the spirit of mutual trust and cooperation,
including (1) controlling risks, (2) making up for loopholes in the contract and (3) acting as
an altruistic party (Liu et al., 2019; Yan et al., 2018). The classification of contractor behavior
above is similar to the distinction between in-role and extra-role behaviors. Put simply, CCB
significantly relates to extra-role or organizational citizenship behaviors (Wang et al., 2018),
which are referred to as the informal and voluntary behavior where a party is willing to
carry out extra activities and assist the other party to deal with the task-related problems
(Zhang et al., 2018). However, rewards for such voluntary behavior are not contractually
guaranteed (Braun et al., 2013).
A plethora of studies have been undertaken to investigate the influences of contract
governance and institutional support on contractor behavior and performance of traditional
projects (Ke et al., 2015; Zheng et al., 2019). Owing to unpredictable contractor behavior in
mega-projects, the existing arguments regarding the relationship between governance and
behavior for traditional projects are invalid in mega-projects (Park et al., 2017). Although
extant research has emphasized the importance of effective organizational systems for
guiding and encouraging contractor behavior in the project governance of mega-projects, the
relationship between HG and contractor behavior is ambiguous (Lim et al., 2011; Jiang
et al., 2017).
Hierarchical governance and contractor behavior
Given the different roles of formal and informal mechanisms in managing mega-projects, the
influence of the two types of HG (i.e. formal HG and informal HG) on the different forms of
contractor behavior may be different (Benıtez-Avila
et al., 2018).
Formal HG incentivizes contractors to exhibit contractor behavior; it is based on
inequality in member power or status. Zhang et al. (2018) pointed out that power is the
determinant of interorganizational relations and has a great influence on contractor behavior.
Because general contractors are dominant in the HG system, they can control subcontractors’
CPB through punishments, such as unfair financial distribution as well as restrictions on
executive promotion (Xu et al., 2018). Furthermore, given the power difference between
general contractors and subcontractors in the long-term arrangement of mega-projects,
subcontractors must strictly commit to general contractors (i.e. groups) (Wang et al., 2017).
The commitments include not only CPB but also those not specified in the contract, that is,
CCB (Shiu et al., 2014). This perspective has led to the following hypothesis:
H2. Formal HG correlates positively with CPB (2a) and CCB (2b).
Informal HG breeds group norms and common values, which can enhance contractor
behavior. An organization with an efficient informal HG will benefit from a unified goal and
value system (He and Huang, 2011). Specifically, once common values and guidelines are
established within the organization, the general contractor is more likely to exhibit CPB.
Zhang et al. (2016) further claimed that the shared values and continuous social interaction in
an organization tend to reinforce the subcontractors’ perception of affective commitment to
the organization. Put simply, subcontractors who perceive that they are parts of the
organization are more likely to make additional efforts. Hence, the following relevant
hypothesis can be made:
H3. Informal HG correlates positively with CPB (3a) and CCB (3b).
Contractor behavior and project performance
The contract terms serve as the standard for the two parties to perform. The contract not only
describes the work, powers and obligations of both parties but also sets out the goals of
project performance and remuneration methods (Schepker et al., 2014). As CPB guarantees
the effective execution of the contract, the contract’s requirements regarding project
performance (e.g. project duration, investment and quality) can be realized (Zhang et al., 2016).
Additionally, CCB is characterized as altruism, which manifests as proactive risk control and
compensation for flaws in the contracts, increasing the possibility of achieving the expected
performance (Yan et al., 2018). Xia et al. (2018) maintained that the use of risk-management
strategies and technology to internalize project risks is a foundation to achieve predetermined
outputs and prevent the risk of being confronted by the owner. Thus, a hypothesis can be
made as follows:
H4. CPB (4a) and CCB (4b) correlate positively with mega-project performance.
Mediating role of contractor behavior
Foss and Weber (2015) argued that HG improves organizational performance by enhancing
members’ behavior and work efficiency. Based on hypotheses H2–H4, this study proposes
that contractor behavior mediates the relationship between HG and the performance of
mega-projects. CPB reflects the owner’s interests and code of conduct in the contract (Zhang
et al., 2019). This ensures that formal HG within the contractor organization allows project
performance to more closely meet the expectations of the owner (Liu et al., 2019). When the
general contractor determines what actions are acceptable to the owner, the code of conduct
constructed by the general contractor and subcontractors through informal control will be
closer to the owner’s desires. Considering this view, the following hypothesis can be
derived:
H5. CPB mediates the relationship between HG (formal HG, H5a; informal HG, H5b) and
mega-project performance.
An empirical study by Wan et al. (2020) supports the idea that paternalistic leadership with a
clear hierarchy is an effective style in managing large projects as it can enhance team
cohesion and facilitate voluntary behaviors. The voluntary rationalization advice that is
provided by contractors concerning the improvement of design defects can reduce
construction costs (Liu et al., 2019). As discussed above, HG can encourage subcontractors
to be more proactive in communicating, cooperating and reciprocating with the general
contractor, in turn allowing contractors to better deal with external risks promptly, which is
necessary for the development of the project. Therefore, this study proposes the following
hypothesis:
H6. CCB mediates the relationship between HG (formal HG, H6a; informal HG, H6b) and
mega-project performance.
Based on the hypotheses proposed above, a conceptual model can be developed (see
Figure 2). This illustrates the influence of formal and informal HGs on the performance
of mega-projects as well as the mediating roles of CPB and CCB within these
relationships.
Response of
contractor
behavior
H1a
ECAM
Mediation: H5a/H5b
Formal
hierarchical
governance
H2a
H2b
Contractor
perfunctory
behavior
H4a
Mega-project
performance
H3a
Informal
hierarchical
governance
Figure 2.
Proposed model
H3b
H1b
Contractor
consummate
behavior
H4b
Mediation: H6a/H6b
Methodology
Participants and procedures
The survey for this study was conducted following a four-stage process: (1) identify and
select cases from the top 4 construction groups in China (ENR, 2017) and the Mega Project
Case Study and Data Center (China) (MPCSC, 2018); (2) generate mega-project list based on
the identified cases; (3) contact project managers (owners and general contractors) for their
consent to participate and (4) invite managers to complete the questionnaires (Figure 3). The
respondents engaged in the survey were asked to complete a questionnaire, which clearly
indicated the purpose of this study in the first page to ensure the reliability of the responses.
The respondents of the survey provided responses voluntarily and were assured of the
confidentiality of their identities.
In this survey, conducted between January 2018 and April 2020, a questionnaire was
distributed to managers from a variety of projects (e.g. subway, highway, bridge and tunnel
construction) across 26 cities (e.g. Beijing, Tianjin, Guangzhou and Shenzhen) in China. A
total of 375 valid questionnaires representing 375 mega-projects were returned (response
rate 5 25%). Respondents included owners, general contractors, all with more than five years
of experience in their respective fields. Table 1 shows the demographic information of the
respondents.
Figure 3.
Survey procedure
Variable
Category
Project type
Subway
Highway
Railway
Bridge
Other
1–10bn
10–20bn
20–30bn
more than 30bn
less than 2 years
2–5 years
more than 5 years
Project investment (CNY)
Project duration
Frequency
%
154
50
74
32
65
81
183
76
35
36
249
90
41.1
13.3
19.7
8.5
17.3
21.6
48.8
20.3
9.3
9.6
66.4
24.0
Note(s): 1 USD ≈ 6.46 CNY
Measures
The scales used in this study are based on the well-established scales used by the extant
literature in relevant fields. The items were translated into Chinese, and the descriptions were
modified to fit the construction domain. Three experienced scholars were asked to check the
accuracy of the constructs. To improve readability, 21 practitioners in the construction
domain were invited to review the wording of the items. The measures are evaluated on a fivepoint Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree).
Hierarchical governance (HG). HG was measured using two subscales (i.e. formal and
informal HG). Formal HG was measured using a ten-item scale (Liao, 2005; White et al., 2008).
An example item is “General contractor understands subcontractors’ strategy.” Informal HG
was measured using a six-item scale (Noorderhaven and Harzing, 2009). A sample item is
“General contractor and subcontractors often communicate informally.”
Contractor behavior. Contractor behavior was measured using a nine-item scale, including
four items for CPB and five items for CCB (Yan et al., 2018). Example items are “The
contractor can carry out the construction according to the construction drawings and
standard specifications provided by the owner” (CPB) and “The contractor will volunteer to
make an extra effort for the project (such as active overtime when necessary)” (CCB).
Mega-project performance. A six-item scale was used to assess the performance of megaprojects (Pinto et al., 2009). A sample item is “The project results, or deliverables, are in line
with the client objectives.”
Control variables – Control variables were used for this research, including previous
cooperation, future business prospects, project type, project cost and project duration. The first
two variables indicate whether the participants have experience in cooperating with other
organizations and future business prospects (0 5 no, 1 5 yes). The remaining three are
regarding the factors that have been acknowledged as being critical for the performance of
mega projects (Schepker et al., 2014; Buvik and Rolfsen, 2015; You et al., 2018; Zheng et al.,
2018), that is, project type (e.g. “1” means a subway), project cost (e.g. “1” stands for the project
with the investment of 1bn to 10bn CNY) and project duration (e.g. “1” denotes the duration
less than 2 years).
Data analysis
A two-stage analytic procedure (Anderson and Gerbing, 1988) was conducted to test the
hypotheses using the software Mplus 7.1. First, confirmatory factor analysis (CFA) was
performed to examine the robustness of the measurement model. Second, path analysis was
Response of
contractor
behavior
Table 1.
Demographic
information of
respondents
ECAM
conducted to test the hypothesized relationships between the latent variables. The
significance of the indirect effects was examined by the bootstrapping method with 1,000
bootstrap samples and a confidence interval (CI) of 95% (Shrout and Bolger, 2002).
Empirical results and findings
Preliminary analysis
Table 2 presents the descriptive statistics, including means, standard deviations and
intercorrelations of the studied variables. The Pearson correlation coefficients showed that
formal HG correlates significantly with CPB (r 5 0.358, p < 0.001) and CCB (r 5 0.418,
p < 0.001), while informal HG correlates significantly with CPB (r 5 0.419, p < 0.001) and CCB
(r 5 0.201, p < 0.01). Both CPB (r 5 0.438, p < 0.001) and CCB (r 5 0.511, p < 0.001) correlate
significantly with mega-project performance.
Measurement model
CFA was used to test measurement validity. Table 3 presents a comparison of the models. The
five-factor model provided a generally good fit to the data (χ 2/df 5 1.426, CFI 5 0.974,
TLI 5 0.971, RMSEA 5 0.034). It performed considerably better than the alternative four-factor
model (χ 2/df 5 3.976, CFI 5 0.817, TLI 5 0.799, RMSEA 5 0.089), the three-factor model (χ 2/
df 5 7.788, CFI 5 0.575, TLI 5 0.542, RMSEA 5 0.135), and the two-factor model (χ 2/df 5 8.388,
CFI 5 0.535, TLI 5 0.501, RMSEA 5 0.140), supporting the discriminant validity of the variables.
The results indicate that fit indices were not adequate for the one-factor model (χ 2/df 5 10.108,
CFI 5 0.426, TLI 5 0.385, RMSEA 5 0.156). To examine and exclude the common method
variance (CMV), a common factor that was uncorrelated with the other factors was constructed,
which is associated with same loadings (Podsakoff et al., 2003; Malhotra et al., 2006; Chang et al.,
2010). The relatively small values of the mean difference in the correlation suggest that the impact
of CMV on the relationships being observed in this study is insignificant.
Table 4 reports Cronbach’s alpha, standardized factor loading (SFL), construct reliability
(CR) and average variance extracted (AVE). Cronbach’s alpha values for the variables were
all higher than the accepted threshold of 0.8 (Nunnally, 1978), suggesting good reliability.
SFLs were greater than 0.7, AVEs were greater than 0.5 and CRs were greater than 0.7 (Hair,
1995). Thus, the convergence of the constructs is assured. The square root of the AVEs of all
constructs was greater than the off-diagonal correlation coefficients (see Table 2), so the
discriminate validity is acceptable (Fornell and Larcker, 1981).
Hypothesis analysis
The proposed hypotheses were examined using a two-step path analysis on a relatively
simple initial model as well as on a final model. First, a path analysis was conducted on a
Variable
Mean
SD
1
2
3
4
5
1. Formal HG
3.649
0.615
(0.790)
(0.869)
2. Informal HG
3.544
0.880
0.301***
0.419***
(0.797)
3. CPB
3.189
0.912
0.358***
***
0.201**
0.203**
(0.802)
4. CCB
3.754
0.828
0.418
***
***
0.422
0.438***
0.511***
(0.789)
5. Mega-project performance
3.594
0.784
0.528
Note(s): SD 5 standard deviation. The square roots of AVE are values in brackets. HG 5 hierarchical
Table 2.
***
Descriptive statistics of governance, CPB 5 contractor perfunctory behavior, CCB 5 contractor consummate behavior. p < 0.001,
**
p < 0.01, two-tailed p-value, n 5 375
studied variables
Model
χ2
df
χ 2/df
CFI
TLI
RMSEA
SRMR
Five-factor model
597.383 419
1.426 0.974 0.971
0.034
0.039
Four-factor model (CPB and CCB as one
1,681.952 423
3.976 0.817 0.799
0.089
0.108
factor)
Three-factor model (formal and informal
3356.654 431
7.788 0.575 0.542
0.135
0.146
HGs as one factor)
Two-factor model (CPB, CCB and mega3632.053 433
8.388 0.535 0.501
0.140
0.132
project performance as one factor)
One-factor model
4386.924 434 10.108 0.426 0.385
0.156
0.139
Note(s): χ 2 5 chi-square, df 5 degree of freedom, HG 5 hierarchical governance, CPB 5 contractor
perfunctory behavior, CCB 5 contractor consummate behavior
simpler model (HG, project performance and control variables). Figure 4 illustrates that both
formal HG (β 5 0.445, p < 0.001) and informal HG (β 5 0.327, p < 0.001) have significantly
positive effects on mega-project performance, supporting H1a and H1b.
The final model, including all variables, fits well: χ 2 /df 5 1.436 < 3, CFI 5 0.964 > 0.9,
TLI 5 0.961 > 0.9, RMSEA 5 0.034 < 0.08, SRMR 5 0.050 < 0.06. Figure 5 presents the direct
path estimates for the partial mediation model. Formal HG has significantly positive direct
effects on CPB (β 5 0.495, p < 0.01) and CCB (β 5 0.643, p < 0.001), supporting H2a and H2b.
Informal HG has a significantly positive direct effect on CPB (β 5 0.442, p < 0.001) but not
CCB (β 5 0.093, p > 0.05), which supports H3a but not H3b. Moreover, both CPB (β 5 0.158,
p < 0.01) and CCB (β 5 0.275, p < 0.001) have significantly positive direct effects on megaproject performance, which supports H4a and H4b.
The bootstrapping results (see Table 5) indicate that the indirect effects of formal HG on
project performance via CPB (95% CI: lower bound 5 0.030; upper bound 5 0.186) and via
CCB (95% CI: lower bound 5 0.079; upper bound 5 0.336) are both significant, thus providing
evidence for their mediation of their respective relationships and supporting hypotheses H5a
and H5b. The indirect effect of informal HG on project performance via CPB (95% CI: lower
bound 5 0.026; upper bound 5 0.146) is significant, but that via CPB (95% CI: lower
bound 5 0.014; upper bound 5 0.080) is not significant, which supports H6a but not H6b.
Discussion
This study develops and examines an integrated model of HG and the performance of megaprojects. HG has a positive effect on the performance of mega-projects. Moreover, two types of
contractor behavior (i.e. CPB and CCB) mediate the relationship between HG and project
performance.
Theoretical implications
This research empirically demonstrates the significance of HG in project organization and
enables an understanding of how HG can lead to better performance of mega-projects. Compared
with previous studies (e.g. Huang and Lien, 2012; Castillo et al., 2018), this study draws a
distinction between formal and informal HG and demonstrates their positive influence on project
performance. Formal HG has a greater impact on performance than informal HG. A reasonable
explanation is that formal mechanisms usually have clear directions and goals, and informal
mechanisms are often intangible and difficult to implement and maintain (Bygballe et al., 2018).
Furthermore, this study enriches the research on the principal–agent relationship between
general contractor and subcontractors by clarifying the relationship between HG-based general
contractor and subcontractors and the effects of informal HG in project organization.
Response of
contractor
behavior
Table 3.
Model comparison
ECAM
Variables
SFL
Formal HG
Strategic control (Cronbach’s α 5 0.840; CR 5 0.841; AVE 5 0.639)
SC1 The general contractor understands subcontractors’ strategy
SC2 The general contractor understands subcontractors’ principal competitors
SC3 The general contractor and subcontractors jointly develop strategic initiatives
0.797
0.768
0.832
Financial control (Cronbach’s α 5 0.858; CR 5 0.858; AVE 5 0.669)
FC1 The general contractor will evaluate the sales of subcontractors
FC2 The general contractor will assess the profit growth of subcontractors
FC3 The general contractor will assess the rate of return on assets of subcontractors
0.795
0.817
0.841
Personnel control (Cronbach’s α 5 0.879; CR 5 0.879; AVE 5 0.645)
PC1 The general contractor decides the appointment of the senior executives of the subcontractors
PC2 The general contractor decides the promotion of the executives of the subcontractors
PC3 Subcontractor executives will accept the performance appraisal of the general contractor
PC4 Subcontractor executives will be bound by the general contractor’s reward and punishment
mechanism
Informal HG
0.805
0.813
0.812
0.783
Group interdependence (Cronbach’s α 5 0.853; CR 5 0.853; AVE 5 0.660)
GI1 Subcontractors and the general contractor can share R&D resources
GI2 Subcontractors and the general contractor can jointly conduct marketing scales and share
advertising resources
GI3 Subcontractors and the general contractor can jointly use market distribution channels
0.796
0.812
0.829
Social interaction (Cronbach’s α 5 0.868; CR 5 0.869; AVE 5 0.689)
SI1 Subcontractor executives often participate in social activities organized by the general contractor
SI2 Subcontractor executives often share the values of the general contractor
SI3 The general contractor and subcontractors often communicate informally
0.769
0.884
0.833
Contractor perfunctory behavior (Cronbach’s α 5 0.874; CR 5 0.873; AVE 5 0.635)
CPB1 The contractor can carry out the construction according to the construction drawings and
standard specifications provided by the employer
CPB2 The contractor can carry out the construction according to the contract requirements or
agreement
CPB3 The contractor can complete all the ancillary tasks required by the project
CPB4 The contractor can complete the construction tasks stipulated in the contract or agreement
Contractor consummate behavior (Cronbach’s α 5 0.900; CR 5 0.900; AVE 5 0.644)
CCB1 The contractor will volunteer to make an extra effort for the project (such as active overtime
when necessary)
CCB2 The contractor will take the initiative to put forward reasonable proposals for the employer (such
as the cost saving plan)
CCB3 The contractor will help the relevant participants to adapt to the construction site
CCB4 The contractor will voluntarily inform the employer of the drawings or the errors or omissions in
the contract
CCB5 The contractor will actively control and internalize project risks and reflow the risk back to the
employer
Table 4.
Cronbach’s alpha,
standardized factor
loading, construct
reliability and average
variance
extracted (AVE)
Mega-project performance (Cronbach’s α 5 0.908; CR 5 0.908; AVE 5 0.623)
PMP1 The project results, or deliverables, are in line with the client objectives
PMP2 The project is within the budget
PMP3 This project is on schedule
PMP4 The construction and deliverables quality accord with the standard
PMP5 The project passed the quality inspection
PMP6 The participants of this project maintain good cooperation
0.832
0.880
0.723
0.741
0.780
0.835
0.793
0.813
0.791
0.776
0.789
0.804
0.802
0.804
0.758
Response of
contractor
behavior
0.445***
Formal
hierarchical
governance
Mega-project
performance
Control variables
PT (0.122**)
Informal
hierarchical
governance
PC (0.036, ns)
PD (–0.048, ns)
PCO (0.210***)
0.327***
FBP (0.127**)
Note(s): *p < 0.05, **p < 0.01, ***p < 0.001, ns means p > 0.05, two-tailed P-value, n = 375,
PT = project type, PC = project cost, PD = project duration, PCO = previous cooperation,
FBP = future business prospects.
Figure 4.
Direct path estimates
for the simpler model
0.408**
Formal
hierarchical
governance
0.495**
Contractor
perfunctory
behavior
0.643***
0.275***
0.442***
Informal
hierarchical
governance
0.093, ns
0.158**
Mega-project
performance
Control variables
PT (0.070**)
Contractor
consummate
behavior
PC (0.005, ns)
PD (–0.020, ns)
PCO (0.138***)
FBP (0.052**)
0.238**
Note(s): *p < 0.05, **p < 0.01, ***p < 0.001, ns means p > 0.05, two-tailed P-value, n = 375
Path
Estimate
Lower CI
Upper CI
Figure 5.
Direct path estimates
for the partial
mediation model
Hypothesis
0.030
0.186
H5a
Formal HG → CPB → mega-project performance
0.078*
0.079
0.336
H5b
Formal HG → CCB → mega-project performance
0.177**
0.026
0.146
H6a
Informal HG → CPB → mega-project performance
0.070*
Informal HG → CCB → mega-project performance
0.026, ns
0.014
0.080
H6b
Note(s): **p < 0.01, *p < 0.05, two-tailed p-value, n 5 375. Bootstrapping 5 1,000 times, 95% confidence
intervals [CI]). HG 5 hierarchical governance, CPB 5 contractor perfunctory behavior, CCB 5 contractor
consummate behavior
A contribution is also made to the performance management of mega-projects by providing
evidence demonstrating how the pattern of HG affects mega-project performance via
contractor behavior. The extant literature (e.g. Castillo et al., 2018; Sirisomboonsuk et al., 2018;
Table 5.
Results of indirect
effects between
variables
ECAM
Wang et al., 2019b) is replete with research into the impacts of governance and organizational
characteristics on project performance. However, in this study, contractor behavior has been
organized as an intermediary within the relationship between HG and mega-project
performance. This has provided insight into managing the key stakeholders of mega-projects
from a different perspective.
Apart from the contributions above, this study confirms the different relationships
between the two forms of HG and contractor behavior. The perspective that HG promotes the
contractor’s perfunctory behavior but is insignificant for consummate behavior has been
proven by the empirical evidence above. Our results echo the argument by Oedzes et al. (2019)
that informal HG has a weaker relationship with contractor behavior. Informal HG between
general contractors and subcontractors is vulnerable to external complexity (Brahm and
Tarzijan, 2015; Kabiri and Hughes, 2018), which rarely stimulates interactions between
contractors and owners.
Practical implications
Based on the empirical evidence, the owner of a mega-project is advised to choose a general
contractor with a sound hierarchy to conduct construction management to ensure project
performance. In the pre-qualification stage, the owner should check whether the general
contractor has a clear and complete control system (i.e. strategic, financial and personnel
control) to ensure a high level of formal HG. Furthermore, the owner should pay attention to
the informal HG between the general contractor and their subcontractors. Put simply, it is
essential for general contractors to improve the level of the HG with their subcontractors to
enhance management efficiency in a mega-project.
Notably, promoting formal and informal HGs in general contractors can enhance the
contractor’s perfunctory and consummate behaviors. The owner should take measures to
focus more on motivating CCB, as it has a stronger impact on the performance of megaprojects than CPB. Compared with CCB, CPB is observable and, therefore, assessment and
control of CPB are more feasible; for example, these could consist of a clear definition of the
contractor’s job duties/responsibility and an appropriate risk-sharing mechanism.
Conclusions
Building upon transaction cost theory, this study develops an integrated model of HG, contractor
behavior and performance of mega-projects. Formal HG affects project performance both directly
and indirectly through CPB and CCB, while informal HG affects project performance primarily
via CPB. These findings enrich project governance research and expand the application of HG in
mega-projects. This study also benefits project governance in which the owners of mega-projects
choose general contractors and stimulate contractor behaviors.
Despite these contributions, there are some limitations to this study. First, the survey was
limited to the Chinese mega-project industry. Future research should be undertaken within a
wider context. Second, this study neglects the moderating factors that may exist in the
relationship between HG and project performance, such as institutional arrangement and
organizational diversity. Therefore, exploring the potential influence of contractual
arrangements, legal frameworks and cultural differences is promising. Finally,
organization ownership and project complexity, which may relate to the performance of
mega-projects, were not controlled. Hence, further discussion is needed.
Note
1. A mega-project that is defined in this study is referred to as a large-scale infrastructure capital
investment with a contract value that is more than one billion Chinese Yuan (≈US$155m), generating
significant influences on social productivity, economic growth, individual livelihoods and the natural
environment (Wang et al., 2020a; He et al., 2021).
References
Anderson, J.C. and Gerbing, D.W. (1988), “Structural equation modeling in practice: a review and
recommended two-step approach”, Psychological Bulletin, Vol. 103 No. 3, pp. 411-423.
Anvuur, A.M. and Kumaraswamy, M.M. (2011), “Measurement and antecedents of cooperation in
construction”, Journal of Construction Engineering and Management, Vol. 138 No. 7,
pp. 797-810.
Benıtez-Avila,
C., Hartmann, A., Dewulf, G. and Henseler, J. (2018), “Interplay of relational and
contractual governance in public-private partnerships: the mediating role of relational norms,
trust and partners’ contribution”, International Journal of Project Management, Vol. 36 No. 3,
pp. 429-443.
Brahm, F. and Tarzijan, J. (2012), “The impact of complexity and managerial diseconomies on
hierarchical governance”, Journal of Economic Behavior and Organization, Vol. 84 No. 2,
pp. 586-599.
Brahm, F. and Tarzijan, J. (2015), “Does complexity and prior interactions affect project procurement?
Evidence from mining mega-projects”, International Journal of Project Management, Vol. 33
No. 8, pp. 1851-1862.
Braun, T., Ferreira, A.I. and Sydow, J. (2013), “Citizenship behavior and effectiveness in temporary
organizations”, International Journal of Project Management, Vol. 31 No. 6, pp. 862-876.
Bunderson, J., Vegt, G.S., Cantimur, Y. and Rink, F. (2016), “Different views of hierarchy and why they
matter: hierarchy as inequality or as cascading influence”, Academy of Management Journal,
Vol. 59 No. 4, pp. 1265-1289.
Buvik, M.P. and Rolfsen, M. (2015), “Prior ties and trust development in project teams - a case study
from the construction industry”, International Journal of Project Management, Vol. 33 No. 7,
pp. 1484-1494.
Bygballe, L.E., Endresen, M. and F
alun, S. (2018), “The role of formal and informal mechanisms in
implementing lean principles in construction projects”, Engineering Construction and
Architectural Management, Vol. 25 No. 10, pp. 1322-1338.
Castillo, T., Alarcon Luis, F. and Pellicer, E. (2018), “Influence of organizational characteristics on
construction project performance using corporate social networks”, Journal of Management in
Engineering, Vol. 34 No. 4, pp. 1-9.
Chang, S.J. and Hong, J. (2000), “Economic performance of group-affiliated companies in Korea:
intragroup resource sharing and internal business transactions”, Academy of Management
Journal, Vol. 43 No. 3, pp. 429-448.
Chang, S.J., Van Witteloostuijn, A. and Eden, L. (2010), “From the Editors: common method variance
in international business research”, Journal of International Business Studies, Vol. 41 No. 2,
pp. 178-184.
Chen, C. (2017), “Management quality and firm hierarchy in industry equilibrium”, American
Economic Journal: Microeconomics, Vol. 9 No. 4, pp. 203-244.
Diefenbach, T. and Sillince, J.A.A. (2011), “Formal and informal hierarchy in different types of
organization”, Organization Studies, Vol. 32 No. 11, pp. 1515-1537.
El-Sabek, L.M. and McCabe, B.Y. (2018), “Coordination challenges of production planning in the
construction of international mega-projects in the Middle East”, International Journal of
Construction Education and Research, Vol. 14 No. 2, pp. 118-140.
ENR (2017), “ENR’s 2017 top 250 international contractors 1-100”, available at: https://www.enr.com/
toplists/2017-Top-250-International-Contractors-1 (accessed January 2018).
Response of
contractor
behavior
ECAM
Flyvbjerg, B., Bruzelius, N. and Rothengatter, W. (2003), Megaprojects and Risk: An Anatomy of
Ambition, Cambridge University Press, Cambridge.
Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable
variables and measurement error”, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50.
Foss, N.J. and Weber, L. (2015), “Moving opportunism to the back seat: bounded rationality, costly
conflict, and hierarchical forms”, Academy of Management Review, Vol. 41 No. 1, pp. 61-79.
Hair, J.F. (1995), Multivariate Data Analysis with Reading, Macmillan, New York.
Hart, O. (2008), “Economica coase lecture reference points and the theory of the firm”, Economica,
Vol. 75 No. 299, pp. 404-411.
He, J. and Huang, Z. (2011), “Board informal hierarchy and firm financial performance: exploring a
tacit structure guiding boardroom interactions”, Academy of Management Journal, Vol. 54
No. 6, pp. 1119-1139.
He, Q., Wang, T., Chan, A.P.C. and Xu, J. (2021), “Developing a list of key performance indictors for
benchmarking the success of construction megaprojects”, Journal of Construction Engineering
and Management, Vol. 147 No. 2, pp. 1-12.
Hoffmann, D., Ahlemann, F. and Reining, S. (2020), “Reconciling alignment, efficiency, and agility in
IT project portfolio management: recommendations based on a revelatory case study”,
International Journal of Project Management, Vol. 38 No. 2, pp. 124-136.
Huang, C.F. and Lien, H.C. (2012), “An empirical analysis of the influences of corporate social
responsibility on organizational performance of Taiwan’s construction industry: using
corporate image as a mediator”, Construction Management and Economics, Vol. 30 No. 4,
pp. 263-275.
Jiang, W., Zhao, X. and Ni, J. (2017), “The impact of transformational leadership on employee
sustainable performance: the mediating role of organizational citizenship behavior”,
Sustainability, Vol. 9 No. 9, p. 1567.
Kabiri, S. and Hughes, W. (2018), “The interplay between formal and informal elements in analysing
situations of role conflict among construction participants”, Construction Management and
Economics, Vol. 36 No. 12, pp. 651-665.
Ke, Y., Ling, F.Y.Y. and Zou, P.X.W. (2015), “Effects of contract strategy on interpersonal relations and
project outcomes of public-sector construction contracts in Australia”, Journal of Management
in Engineering, Vol. 31 No. 4, pp. 1-10.
Laing, L. and Connor, V. (2020), “Joint ventures – delivering global mega infrastructure projects”,
available at: https://www.pinsentmasons.com/thinking/special-reports/joint-ventures-globalinfrastructure-projects?pageNumber51 (accessed April 2020).
Levander, E., Engstr€om, S., Sarden, Y. and Stehn, L. (2011), “Construction clients’ ability to manage
uncertainty and equivocality”, Construction Management and Economics, Vol. 29 No. 7, pp. 753-764.
Li, Y., Lu, Y., Ma, L. and Kwak, Y.H. (2018), “Evolutionary governance for mega-event projects (meps):
a case study of the world expo 2010 in China”, Project Management Journal, Vol. 49 No. 1,
pp. 57-78.
Liao, Y. (2005), “Business strategy and performance: the role of human resource management control”,
Personnel Review, Vol. 34 No. 3, pp. 294-309.
Lim, B.T.H., Ling, F.Y.Y., Ibbs, C.W., Raphael, B. and Ofori, G. (2011), “Empirical analysis of the
determinants of organizational flexibility in the construction business”, Journal of Construction
Engineering and Management, Vol. 137 No. 3, pp. 225-237.
Liu, J., Yang, P., Xia, B. and Skitmore, M. (2017), “Effect of perceived justice on subcontractor
willingness to cooperate: the mediating role of relationship value”, Journal of Construction
Engineering and Management, Vol. 143 No. 9, pp. 1-8.
Liu, J., Wang, Z., Skitmore, M. and Yan, L. (2019), “How contractor behavior affects engineering project
value-added performance”, Journal of Management in Engineering, Vol. 35 No. 4, pp. 1-12.
Malhotra, N.K., Kim, S.S. and Patil, A. (2006), “Common method variance in IS research: a comparison
of alternative approaches and a reanalysis of past research”, Management Science, Vol. 52
No. 12, pp. 1865-1883.
MPCSC (2018), “Case list”, available at: http://www.mpcsc.org/case_list.htm (accessed March 2018).
Nguyen, L.H. and Watanabe, T. (2017), “The impact of project organizational culture on the
performance of construction projects”, Sustainability, Vol. 9 No. 5, p. 781.
Noorderhaven, N. and Harzing, A.W. (2009), “Knowledge-sharing and social interaction within MNEs”,
Journal of International Business Studies, Vol. 40 No. 5, pp. 719-741.
Nunnally, J. (1978), Psychometric Methods, McGraw-Hill Book, New York.
Oedzes, J.J., Van der Vegt, G.S., Rink, F.A. and Walter, F. (2019), “On the origins of informal hierarchy:
the interactive role of formal leadership and task complexity”, Journal of Organizational
Behavior, Vol. 40 No. 3, pp. 311-324.
Park, H., Kim, K., Kim, Y.W. and Kim, H. (2017), “Stakeholder management in long-term complex
megaconstruction projects: the saemangeum project”, Journal of Management in Engineering,
Vol. 33 No. 4, pp. 1-11.
Pilkien_e, M., Alonderien_e, R., Chmieliauskas, A., Simkonis,
S. and M€
uller, R. (2018), “The governance
of horizontal leadership in projects”, International Journal of Project Management, Vol. 36 No. 7,
pp. 913-924.
Pinto, J.K., Slevin, D.P. and English, B. (2009), “Trust in projects: an empirical assessment of owner/
contractor relationships”, International Journal of Project Management, Vol. 27 No. 6,
pp. 638-648.
Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y. and Podsakoff, N.P. (2003), “Common method biases in
behavioral research: a critical review of the literature and recommended remedies”, Journal of
Applied Psychology, Vol. 88 No. 5, pp. 879-903.
Qiu, Y., Chen, H., Sheng, Z. and Cheng, S. (2019), “Governance of institutional complexity in megaproject
organizations”, International Journal of Project Management, Vol. 37 No. 3, pp. 425-443.
Reitzig, M. and Maciejovsky, B. (2015), “Corporate hierarchy and vertical information flow inside the
firm - a behavioral view”, Strategic Management Journal, Vol. 36 No. 13, pp. 1979-1999.
Schepker, D.J., Oh, W.Y., Martynov, A. and Poppo, L. (2014), “The many futures of contracts: moving
beyond structure and safeguarding to coordination and adaptation”, Journal of Management,
Vol. 40 No. 1, pp. 193-225.
Sedita, S.R. and Apa, R. (2015), “The impact of inter-organizational relationships on contractors’
success in winning public procurement projects: the case of the construction industry in the
Veneto region”, International Journal of Project Management, Vol. 33 No. 7, pp. 1548-1562.
Shiu, E., Jiang, Z. and Zaefarian, G. (2014), “Antecedents of behavioural commitment in interorganizational relationships: a field study of the UK construction industry”, Construction
Management and Economics, Vol. 32 No. 9, pp. 888-903.
Shrout, P.E. and Bolger, N. (2002), “Mediation in experimental and nonexperimental studies: new
procedures and recommendations”, Psychological Methods, Vol. 7 No. 4, pp. 422-445.
Sihag, V. and Rijsdijk, S.A. (2019), “Organizational controls and performance outcomes: a metaanalytic assessment and extension”, Journal of Management Studies, Vol. 56 No. 1, pp. 91-133.
Sirisomboonsuk, P., Gu, V.C., Cao, R.Q. and Burns, J.R. (2018), “Relationships between project
governance and information technology governance and their impact on project performance”,
International Journal of Project Management, Vol. 36 No. 2, pp. 287-300.
Wan, J., Le, Y., Wang, G., Xia, N. and Liu, X. (2020), “Carrot or stick? The impact of paternalistic
leadership on the behavioral integration of top management teams in megaprojects”,
International Journal of Managing Projects in Business, Vol. 13 No. 5, pp. 937-960.
Wang, G., He, Q., Meng, X., Locatelli, G., Yu, T. and Yan, X. (2017), “Exploring the impact of
megaproject environmental responsibility on organizational citizenship behaviors for the
Response of
contractor
behavior
ECAM
environment: a social identity perspective”, International Journal of Project Management,
Vol. 35 No. 7, pp. 1402-1414.
Wang, G., He, Q., Xia, B., Meng, X. and Wu, P. (2018), “Impact of institutional pressures on
organizational citizenship behaviors for the environment: evidence from megaprojects”, Journal
of Management in Engineering, Vol. 34 No. 5, pp. 1-11.
Wang, D., Fang, S. and Fu, H. (2019a), “The effectiveness of evolutionary governance in mega
construction projects: a moderated mediation model of relational contract and transaction cost”,
Journal of Civil Engineering and Management, Vol. 25 No. 4, pp. 340-352.
Wang, D., Fang, S. and Li, K. (2019b), “Dynamic changes of governance mechanisms in mega
construction projects in China: the mediating role of opportunism”, Engineering, Construction
and Architectural Management, Vol. 26 No. 4, pp. 723-735.
Wang, G., Locatelli, G., Wan, J., Li, Y. and Le, Y. (2020a), “Governing behavioral integration of top
management team in megaprojects: a social capital perspective”, International Journal of Project
Management, Available online, pp. 1-12.
Wang, G., Zhang, H., Xia, B., Wu, G. and Han, Y. (2020b), “Relationship between internationalization
and financial performance: evidence from ENR-listed Chinese firms”, Journal of Management in
Engineering, Vol. 36 No. 2, pp. 1-11.
Weber, M. (1980), Wirtschaft und Gesellschaft, J. C. B. Mohr (Paul Siebeck), T€
ubingen.
White, R.E., Hoskisson, R.E., Yiu, D.W. and Bruton, G.D. (2008), “Employment and market innovation
in Chinese business group affiliated firms: the role of group control systems”, Management and
Organization Review, Vol. 4 No. 2, pp. 225-256.
Williamson, O.E. (1971), “The vertical integration of production: market failure considerations”,
American Economic Review, Vol. 61 No. 2, pp. 112-123.
Williamson, O.E. (1991), “Comparative economic organization: the analysis of discrete structural
alternatives”, Administrative Science Quarterly, Vol. 36 No. 2, pp. 269-296.
Xia, N., Zou, P.X.W., Griffin, M.A., Wang, X. and Zhong, R. (2018), “Towards integrating construction
risk management and stakeholder management: a systematic literature review and future
research agendas”, International Journal of Project Management, Vol. 36 No. 5, pp. 701-715.
Xu, Z., Yin, Y., Li, D. and Browne, G.J. (2018), “Owner’s risk allocation and contractor’s role behavior in
a project: a parallel-mediation model”, EMJ - Engineering Management Journal, Vol. 30 No. 1,
pp. 14-23.
Yan, L., Wang, Z. and Deng, J. (2018), “Research on structural dimensions and measurement of
contractors’ performance behavior in construction project based on theory of contract reference
point”, Tumu Gongcheng Xuebao/China Civil Engineering Journal, Vol. 51 No. 8, pp. 105-117, [In
Chinese].
You, J., Chen, Y., Wang, W. and Shi, C. (2018), “Uncertainty, opportunistic behavior, and governance in
construction projects: the efficacy of contracts”, International Journal of Project Management,
Vol. 36 No. 5, pp. 795-807.
Yuan, L., Wu, M. and Yin, H. (2019), Theory and Practice of Investment Control in Megaproject: Taking
Guangzhou Metro Line 11 as Example, China Architecture & Building Press, Beijing.
Yun, S., Jung, W., Han, S.H. and Park, H. (2015), “Critical organizational success factors for public
private partnership projects - a comparison of solicited and unsolicited proposals”, Journal of
Civil Engineering and Management, Vol. 21 No. 2, pp. 131-143.
Yun, L., Wan, J., Wang, G., Bai, J. and Zhang, B. (2020), “Exploring the missing link between top
management team characteristics and megaproject performance”, Engineering Construction
and Architectural Management, Vol. 27 No. 5, pp. 1039-1064.
Zhang, S.B., Fu, Y.F., Gao, Y. and Zheng, X.D. (2016), “Influence of trust and contract on dispute
negotiation behavioral strategy in construction subcontracting”, Journal of Management in
Engineering, Vol. 32 No. 4, pp. 1-11.
Zhang, S., Fu, Y. and Kang, F. (2018), “How to foster contractors’ cooperative behavior in the Chinese
construction industry: direct and interaction effects of power and contract”, International
Journal of Project Management, Vol. 36 No. 7, pp. 940-953.
Zhang, L., Fenn, P. and Fu, Y. (2019), “To insist or to concede? Contractors’ behavioural strategies
when handling disputed claims”, Engineering Construction and Architectural Management,
Vol. 26 No. 3, pp. 424-443.
Zheng, X., Lu, Y. and Chang, R. (2019), “Governing behavioral relationships in megaprojects:
examining effect of three governance mechanisms under project uncertainties”, Journal of
Management in Engineering, Vol. 35 No. 5, pp. 1-16.
Zheng, X., Lu, Y., Le, Y., Li, Y. and Fang, J. (2018), “Formation of interorganizational relational
behavior in megaprojects: perspective of the extended theory of planned behavior”, Journal of
Management in Engineering, Vol. 34 No. 1, pp. 1-16.
About the authors
Hang Yin is a PhD candidate at the College of Management and Economics, Tianjin University, PR
China. His research interests include mega-project, project governance and trust. His current research
focuses on the intellectual structure and research front of trust in construction projects.
Dan Wang is an assistant professor at the School of Public Policy and Administration, Chongqing
University, PR China. Her research interests include project performance, public–private partnerships
(PPPs), transport resilience and occupational health and safety. She has published papers in Q1 journals,
such as Accident Analysis and Prevention, and Safety Science. Dan Wang is the corresponding author
and can be contacted at: wangxiaodan@cqu.edu.cn
Yilin Yin is a professor at the School of Management and head of the Institute of Public Projects and
Cost Engineering at the Tianjin University of Technology, PR China. His research interests include
project governance, public–private partnerships and trust.
Henry Liu is an assistant professor at Building and Construction Management, University of
Canberra, Australia. He holds a PhD in Civil Engineering, Master of Construction Management and
Bachelor of Law. His research interests include public–private partnerships (PPPs), infrastructure asset
management, performance measurement, construction production forecasting and transport resilience.
He has published papers in Q1 journals, such as Production Planning and Control, Environment and
Planning C: Politics and Space, ASCE Journal of Construction Engineering and Management, ASCE
Journal of Management in Engineering, ASCE Journal of Infrastructure Systems, Transportation
Research Part D: Transport and Environment, Cities and International Journal of Project Management.
Binchao Deng is a lecturer at the School of Management at the Tianjin University of Technology, PR
China. His research interests include public–private partnerships and project governance.
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Response of
contractor
behavior