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12. Project Organising
Competitive Session
The Impact of Value Co-creation on Project Performance Moderated by
Requirements Uncertainty—Evidence from Chilean Construction Industry
Boris Heredia-Rojas
School of Civil Engineering, University of Sydney, Australia
Department of Construction Management, Universidad Católica del Norte, Chile
Email: [email protected]
Dr Li Liu
School of Civil Engineering, University of Sydney, Australia
Email: [email protected]
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12. Project Organising
Competitive Session
The Impact of Value Co-creation on Project Performance Moderated by
Requirements Uncertainty—Evidence from Chilean Construction Industry
ABSTRACT
Drawing from the relational contracting approach, value co-creation is essential to project success in
certain types of construction project delivery models, such as design-construction, ECI and strategic alliance. However, there is little empirical evidence on the impact of value co-creation on project
performance. Based on prior co-creation research and a cross-sectional survey of 120 Chilean project
managers, this study provides much-needed evidence on the effects of value co-creation on project performance. Value co-creation is modelled as a higher-order construct underpinned through
relational engagement, collaboration and innovativeness. The positive influence of value co-creation
on project performance is moderated by project’s requirement uncertainty. The study contributes to
the literature by conceptualising the moderated impact on project performance and validates the
relationships empirically.
Keywords: Value co-creation, project performance, requirements uncertainty, moderating effect, PLS. Prior management literature has recognised value creation as a dynamic process where a firm
produces perceived use value about client needs and generates exchange values when the product or
service is sold (Bowman & Ambrosini, 2000). In other words, the firm creates value for the client
through goods and services. More recently, researchers have emphasised the importance of the client
and the contractors work collaboratively to maximise the created value—the so-called value co-
creation (Prahalad & Ramaswamy, 2004). Value co-creation perspective stresses that the client and
the contractor hold similar roles to generate value, integrating resources and applying competencies to
collaborate based on trust, continuous interactions, engagement, effective information exchange and
gains/pains sharing to enhance and maximize benefits for all project stakeholders (Chang, Chih,
Chew, & Pisarski, 2013; Grönroos & Voima, 2013; Prahalad & Ramaswamy, 2004; Ramaswamy &
Ozcan, 2013; Ranjan & Read, 2014; Rod, Lindsay, & Ellis, 2014; Roser, DeFillippi, & Samson, 2013;
Vargo, Maglio, & Akaka, 2008).
In the traditional construction procurement model, architects were engaged first by the client
to come up with a design before putting the construction of the facility to tender. The construction
contractor is selected after the design has been completed, therefore precludes the contributions of
construction contractors on constructability during the design stage (Song, Mohamed, & AbouRizk,
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2009). After the construction starts, architects and contractors work at arms-length and, as the project
progresses, it becomes more and more challenging and costly to change the design in order the create
value (Nord, 2012). Further, the client often does not exactly know what they want until late stages of
the project (i.e. higher requirements uncertainty), therefore introduce changes in requirements or
scope during the project which can be costly and time-consuming. With limited knowledge and
experience about the construction process and management, the ability of the designer to come up
with a design that maximises project value while minimises unnecessary changes occur during
construction stage is very limited. Instead, integrating design and delivery through the close
collaboration between the architect and the construction contractor is most likely to “co-create”
project value above and beyond the traditional design-tender-construct process.
Value Co-creation is a key approach essential to project performance in certain types of
construction project delivery models, such as design & construction, early contractor involvement
(ECI), strategic alliance and other relationship-based models (Ahola, Laitinen, Kujala, & Wikström,
2008; Eriksson & Westerberg, 2011; Hartmann, Roehrich, Frederiksen, & Davies, 2014; Jacobsson &
Roth, 2014; Ning & Ling, 2015; Nord, 2012). According to transaction costs economics (TCE), the
dominant logic underlying such delivery models assumes that when project uncertainty is high, it is
hard and costly to write a complete contract and safeguard the contract (Poppo & Zenger, 2002; von
Branconi & Loch, 2004; Williamson, 1985). Therefore, the alternative is to nurture and maintain
collaborative relationships with project counterparties by looking after their interests as well and in
some challenging environments sharing gains and pains (Colledge, 2005; Harper, Molenaar, &
Cannon, 2016; Lahdenperä, 2012; Ning & Ling, 2015; M Rahman & Kumaraswamy, 2002) —the so-
called relational contracting (RC) approach.
Despite progress made to understand the phenomenon of value co-creation (Ramaswamy
(2009)), there is limited understanding of the composition of value co-creation in the construction
industry. This study argues that relational engagement, collaboration, and innovativeness constitute
values co-creation in the construction industry and therefore jointly impact on project performance.
THEORY AND HYPOTHESES DEVELOPMENT
In this section, the theoretical background literature on value co-creation and its impacts on
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project performance are reviewed, and hypotheses developed. The conceptual model on the influence
of value co-creation on project performance moderated by project’s requirement uncertainty is
presented in Figure 1.
Insert Figure 1 about here
Value co-creation in projects
As Prahalad and Ramaswamy (2004) proclaim, value co-creation is the process where clients
and suppliers jointly create value through mainly high-quality interactions above and beyond the
traditional focus where value generated inside the firm (i.e. supplier) through its products, activities
and competencies. Although value co-creation theory has been developed on a service-dominant (S-
D) logic in a business-to-business (B2B) context (Edvardsson, Tronvoll, & Gruber, 2011; Grönroos,
2011b; Payne, Storbacka, & Frow, 2008; Prahalad & Ramaswamy, 2004; Vargo et al., 2008), projects
and programs can be understood as processes of value co-creation (Chang et al., 2013; Winter &
Szczepanek, 2008) when they are analysed as an interconnected network of relationships and
interdependencies between parties (Mele, 2011). Client-contractor interactions at the front-end or
design phase are critical to value maximisation in projects because such interactions could reduce the
requirement uncertainty of projects and the costly late changes in requirements and scope
(Matinheikki, Artto, Peltokorpi, & Rajala, 2015). Relational engagement (i.e. quality of interactions
and relational norms), collaboration (i.e. strategic information exchange and joint problem-solving)
and innovativeness are fundamental drivers underpin value co-creation across a project’s lifecycle
(Aarikka-Stenroos & Jaakkola, 2012; Chang et al., 2013; Jacobsson & Roth, 2014; Liu, Fellows, &
Chan, 2014; Matinheikki et al., 2015; Mele, 2011; Nord, 2012; Rod et al., 2014).
Relational engagement enables co-creation because it requires parties to a relationship to
engage in active dialogue and interaction under a set of relational norms (Grönroos, 2011a; Prahalad
& Ramaswamy, 2004; Ranjan & Read, 2014) which lays the foundation for close collaborations
between the parties (Jacobsson & Roth, 2014). The contractors apply their professional skills,
methods and expertise to solve problems for the client, while the client, drawing from their business
domain knowledge, clarifies its needs, defines the problem and scrutinises the design and solutions
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through close interactions with the contractors (Austin & Seitanidi, 2012; Nord, 2012). Value co-
creation demands collaboration among parties (i.e. client, designer and contractor) to share their
resource complementarity, distinctive competencies and linked interests (Austin & Seitanidi, 2012).
Hence, collaborative work means the joint activities whereby two or more parties actively and
reciprocally solving complex problems, exchanging necessary and critical information and achieving
shared goals, reducing risks, sharing gains and/or pains (Aarikka-Stenroos & Jaakkola, 2012; Bedwell
et al., 2012; Cheung, Myers, & Mentzer, 2010; Gulati, Wohlgezogen, & Zhelyazkov, 2012; Hadaya &
Cassivi, 2012; Prahalad & Ramaswamy, 2004; Vargo et al., 2008; Wang & Wei, 2007). Further,
innovativeness is often necessary for complex and uncertain projects to co-create value through
solving technical difficulties or management challenges (Matinheikki et al., 2015; Prahalad &
Ramaswamy, 2004). Consequently, co-creation based on relational engagement, collaboration and
innovativeness are essential for adding value to the stakeholders of and for the effective delivery of
projects with high complexity and uncertainty (Caldwell, Roehrich, & Davies, 2009; Hartmann et al.,
2014; Liu et al., 2014; Nord, 2012).
Value co-creation and project performance
Value co-creation establishes an engaged platform (Jacobsson & Roth, 2014) for effective
client-supplier interactions based on a transparent flow and access to critical information, trust and,
active dialogue (Caldwell et al., 2009; Prahalad & Ramaswamy, 2004). These continuous interactions
conducive to solving complex problems throughout the project and encouraging innovate solutions to
optimise project outcomes (Ramaswamy, 2009). As an example, the ‘T5 project’ in London is an
exceptional case where the close relationship between parties improves the project results regarding
client satisfaction, supplier business success and some short-term benefits as schedule and budget, as
well (Caldwell et al., 2009). Therefore,
Hypothesis 1. Value co-creation impacts positively on project performance.
The moderating effect of requirements uncertainty
In the contingent project management literature, project characteristics such as project
complexity (Tyssen, Wald, & Heidenreich, 2014), time pressure (Thomas, Esper, & Stank, 2010) and,
project uncertainty (Nidumolu, 1995) have been argued to impact on project success. Particularly,
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requirement uncertainty reflects the extent the client is unsure about the project purpose or how to
achieve the purpose. It will be much more challenging to co-create value through a project’s lifecycle
where requirement uncertainty is high than when it is low. When there is a high level of uncertainty,
collaborative client-contractor relationships supports effective project delivery and thus conducive to
project performance maximisation (Eriksson & Westerberg, 2011; Pesämaa, Eriksson, & Hair Jr,
2009; M. Rahman & Kumaraswamy, 2005). In contrast, for small, straightforward and routine
projects (i.e. low levels of requirement uncertainty) (Eriksson & Westerberg, 2011), the need for close
collaboration between project stakeholders is less imperative. As Ning and Ling (2015) pointed out,
when the project context becomes complex or uncertain, there is a stronger demand for collaborative
project partnership adaptation. Therefore,
Hypothesis 2. The effect of value co-creation on project performance is moderated by
requirements uncertainty.
RESEARCH DESIGN
Data collection
Data was collected through a cross-sectional questionnaire survey. The unit of analysis is ‘the
project’. The data was gathered from general contractors in Chile. The Project Management Institute
(PMI®) Santiago Chile Chapter Antofagasta Branch invited its members to complete the online survey
of project managers by email between October and November 2015. A total of 362 project managers
were asked, 120 full and valid responses from construction project managers were received. The
response rate was 33.15%, which represents a reasonable sample for a Web-based questionnaire in
business and management research that commonly is between 30 and 50% (Saunders, Lewis, &
Thornhill, 2016). Respondents were asked questions about one of their recently completed projects.
Tables 1 and 2 show the profiles of the respondents and projects, respectively. The majority of those
surveyed (70.00%) were top project managers, and 60.84% had more than ten years working
experience in projects. Additionally, 62.50% surveyed projects had a planned budget more than $A10
million whereas 59.17% had a total planned duration over one year.
Insert Table 1 about here
Insert Table 2 about here
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Survey instrument
The survey instrument was adapted from existing and validated multi-item scales used in
prior research. Table 3 lists key constructs and the indicators used in this study. Value co-creation was
operationalized as a third-order reflective construct composed of three constructs namely relational
engagement, collaboration, and innovativeness. First, relational engagement is a second-order latent
variable shaped by the quality of interactions and relational norms. The quality of interactions was
measured by two indicators (i.e. interactions produce novel insights and display a sound strategic
understanding) adapted from Grayson and Ambler (1999) and one indicator (i.e. partners’ proactive
role) from Ranjan and Read (2014). Relational norms refer to the high degree of reciprocal values
between clients and suppliers when they work collaboratively, and it may be measured through four
items (i.e. honesty, trustworthy, best effort and no blame culture) proposed by Suprapto, Bakker,
Mooi, and Hertogh (2015). Second, collaboration is also a second-order construct formed by strategic
information exchange and joint problem-solving. Thus, strategic information exchange was
operationalized by four items from Cheung et al. (2010) (i.e. shared information on successful and
unsuccessful experiences, user’s needs and behaviours, organizations’ strategies and policies, and
financial performance and organizational know-how); and, joint problem-solving was quantified
through four items, three questions from Wang and Wei (2007) (i.e. finding out proper solutions,
client’s suggestions, and soon information sharing to solve arisen problems) and, one new proposed
question about working closely to reduce risks and sharing gains and/or pains. Lastly, from Koys and
DeCotiis (1991) we measured innovativeness by three items that reflect the co-creating process to try
out new ideas for the project, new ways of doing things for the project and creativity in operating
methods.
Insert Table 3 about here
Requirements uncertainty is defined in this study as the difference between amount
information required and the amount of information available for specifying project requirements
(Kossmann, 2013; Winch, 2010). It was quantified through two items that represent requirements
instability (i.e. project requirements identified at the beginning were quite different from those at the
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end) and requirements diversity (i.e. project’s users/stakeholders often differed among themselves in
the requirements to be met). Both questions were adapted from Jiang, Klein, Wu, and Liang (2009).
On the other hand, project performance was defined as a second-order reflective construct
shaped by the impact on the client and the impact on the supplier. The indicators were taken from
Shenhar and Dvir (2007). Thus, the impact on the client was measured by two indicators of project
efficiency (i.e. budget and schedule) and five indicators of client satisfaction (i.e. client’s performance
improvement, client’s satisfaction, met client’s requirements, come back for future work and future
projects contribution). Whereas impact on the supplier (i.e. contractor) was evaluated with four items
namely, economic business success, profitability, return on investment and supplier’s direct
performance.
All questions about value co-creation (i.e. relational engagement, collaboration and
innovativeness) and requirements uncertainty were rated on a seven-point Likert scale (1=fully
disagree, 2=disagree, 3=partially disagree, 4=unsure or don’t know, 5=partially agree, 6=agree and
7=fully agree). As Saunders et al. (2016, p. 458) point out “this inclusion of a neutral point allows the
respondent to ‘sit of the fence’ by ticking in the middle ‘not sure’ category when considering an
implicitly negative statement”. By contrast following the suggestions of Podsakoff, MacKenzie, Lee,
and Podsakoff (2003), project performance was rated through a different Likert scale (1=not at all to
7=to a great extent). This different scale was established because we tried to eliminate or minimise the
method variance error using procedural remedies as separating methodologically the measures of the
predictor (i.e. co-creation) and criterion variables (i.e. project performance).
Data Analysis
We applied partial least square structural equation modelling (PLS-SEM) to analyse the
conceptual model. PLS-SEM was chosen due to three main reasons. First, PLS-SEM is suggested to
perform exploratory studies or to extend existing structural theory (Hair Jr, Hult, Ringle, & Sarstedt,
2016; Urbach & Ahlemann, 2010). Second, PLS-SEM needs lower demands of sample size and not
requires normal-distributed data (Hair Jr et al., 2016). Third, PLS-SEM handles complex models (i.e.
large number of constructs and indicators and/or hierarchical component models) (Hair Jr et al., 2016;
Urbach & Ahlemann, 2010). Because our research was more exploratory than confirmatory, the
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sample size was 120 observations, the gathered data was non-normal and, the conceptual model was
shaped by higher-order latent variables, PLS-SEM was the most appropriate approach to obtain more
robust results in this study.
RESULTS
Smart PLS 3 (Ringle, Wende, & Becker, 2015) was used to assess the measurement models
and the structural model. Additionally, we followed the PLS-SEM’s guidelines mainly suggested by
Chin (1998), Hair Jr et al. (2016) and Urbach and Ahlemann (2010).
Measurement models assessment
Our measurement models consist of eight reflective constructs. According to Hair Jr et al.
(2016), the criteria for evaluating the reliability and validity of reflective measurement models are
four. (1) Internal consistency reliability (Cronbach’s alpha and composite reliability >0.70). (2)
Indicator reliability (outer loadings >0.70, but indicators with outer loadings between 0.40 and 0.70
should be considered for removal only if its deletion leads an increase in composite reliability and
average variance extracted –AVE). (3) Convergent validity (AVE >0.50). (4) Discriminant validity
(Fornell-Larcker criterion where the square root of the AVE of each construct should be higher than
the maximum correlation with any other construct and heterotrait-monotrait ratio (HTMT) approach
where the confidence interval of the HTMT statistic should not include the value 1 for all
combinations of constructs).
The evaluation of all eight reflective constructs was completely satisfactory. Table 4 shows
the results summary of measurement models assessment. First, internal consistency reliability was
satisfied because all constructs had Cronbach’s alpha and composite reliability scores equal or above
0.70. Second, all indicators’ outer loadings reached sufficient levels of indicator reliability higher than
0.70, except IC2 which was 0.69. It was not removed because its deletion did not lead to an increase
in CR and AVE. Third, as all AVE scores were higher than 0.5 convergent validity was achieved to all
constructs. Finally, discriminant validity was accomplished because confidence interval of HTMT
statistics did not include 1 for all combinations of constructs. Also, Fornell-Larcker criterion analysis
demonstrated that square roots of AVE of each construct were greater than their inter-correlations
(Table 5).
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Insert Table 4 about here
Insert Table 5 about here
Structural model assessment
The structural model assessment determines the model’s capability to predict the relationships
between the constructs. The rules of thumb for structural model evaluation are defined by Hair Jr et al.
(2016). (1) Collinearity (predictor construct’s tolerance through variance inflation factor (VIF) value
should be higher than 0.20 and lower than 5). (2) Path coefficients (analysing magnitude and
significance through bootstrapping). (3) Coefficient of determination –R2 (in overall values, around
0.67 are substantial, around 0.33 moderate and, around 0.19 are weak (Chin, 1998)). (4) The effect
size f 2 (values of 0.02, 0.15, and 0.35 indicate an exogenous construct has a small, medium, or large
effects respectively on an endogenous construct). (5) Predictive relevance –Q2 (through blindfolding,
values greater than 0 indicate that the exogenous latent variables have predictive relevance for the
endogenous latent variables). (6) The effect size q2 (values of 0.02, 0.15, and 0.35 indicate an
exogenous construct has a small, medium, or large effects, respectively on an endogenous construct).
Specifically, in our study, the structural model evaluation’s results were satisfactory. First,
because there were not sets of predictor variables in our structural model, collinearity was not critical.
Indeed, all VIF values were 1, in other words, VIF values were higher than 0.2 and lower than 5.
Second, the significance of path coefficients was evaluated through bootstrapping routine with 5000
subsamples, bias-corrected and accelerated (BCA) bootstrap and no sign changes option. The analysis
showed that the path coefficient between value co-creation (CoC) and project performance (PP) was
0.53, where its confidence intervals did not include zero, t-value was 6.33, and ***p-value was
<0.001. These scores indicated that co-creation and project performance relationship was significant
at 5% for a two-tailed test. Third, R2 value was 0.277, in other words, 27.7% of the variance in project
performance. This score is considered moderate (Chin, 1998; Urbach & Ahlemann, 2010). Although
this measurement indicates a medium level of predictive accuracy, “selecting a model solely based on
the R2 value is not a good approach” (Hair Jr et al., 2016, p. 199). In consequence, we complemented
R2 assessment through analysing all combinations of endogenous variables and related predictors
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variables. In our model, the effect size (f2) amongst co-creation and project performance was f 2=0.384
that means a large effect size (>0.35). Fifth, blindfolding results showed a Q2 value equal to 0.14 for
project performance (i.e. the endogenous latent variable). The value was positive (>0) then we may
confirm that there was a model’s predictive relevance in respect to value co-creation construct.
Finally, q2 is a relative measure of predictive relevance between exogenous construct (i.e. value co-
creation) and endogenous construct (i.e. project performance). Following the procedure indicates by
Hair Jr et al. (2016), value co-creation has a medium predictive effect on project performance because
q2 was 0.17 (>0.15). In sum, we predict that value co-creation has a positive impact on project
performance (hypothesis 1). The results demonstrate that this relationship is highly significant
(***p<0.001). Therefore, hypothesis 1 was supported.
Moderation analysis
According to guidelines proposed by Hair Jr et al. (2016), first, we should inspect the type of
measurement model for exogenous and moderator constructs. In this study, the exogenous construct
(i.e. value co-creation) and the moderator (i.e. requirements uncertainty) were reflective. A two-stage
approach was undertaken to analyse the moderating effect of requirements uncertainty. As reported in
in Tables 4 and 5, requirements uncertainty construct met all reliability and validity criteria.
The effect size of the interaction (f 2) was used to assess the moderation. It indicates how
much the moderator (i.e. requirements uncertainty) contributes to explain the endogenous latent
variable (i.e. project performance). The f 2 value greater than 0.025 indicates large effect size (Hair Jr
et al., 2016). Hence, f 2 of 0.06 showed a considerable effect size.
Then, bootstrapping was used to determine the significance of the interaction effect, and the
results indicate significance at 5% level (t-value=2.10 and *p-value=0.04). Figure 2 shows a slope
plots of the relationship between value co-creation and project performance for low (one standard
deviation unit below its mean) and high (one standard deviation unit above its mean) levels of the
moderator construct (i.e. requirements uncertainty - RU) values. Thus, when RU is high, the
relationship between value co-creation and project performance is stronger than when RU is low.
Consequently, hypothesis 2 was supported.
Insert Figure 2 about here
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DISCUSSION AND CONCLUSIONS
The findings from the results reported above show that value co-creation underpinned by
relational engagement, collaboration and innovativeness impacts significantly on project performance.
Also, this relationship is moderated by requirements uncertainty. Specifically, higher levels of
requirements uncertainty entail a stronger relationship between co-creation and project performance,
while lower levels of requirements uncertainty lead to a weaker association between both.
Theoretical and managerial implications
Anecdotal evidence suggests that that clients and suppliers can co-create value under an
atmosphere based on mutual engagement, collaboration and innovation. This study adds to the current
value creation and project management literature empirical evidence about the relationship between
value co-creation and project performance. Additionally, project requirements uncertainty has been
identified as a moderator between co-creation and project performance. Our findings give strong
evidence about the moderating effect of requirements uncertainty on co-creation and project
performance relationship. This finding suggests that when a project is more uncertain regarding client
and other stakeholders’ requirements, the project governance should be underpinned by collaborative
arrangements. Thus, client-supplier engagement based on trust and continuous interactions enables
more effective information and knowledge exchange to solve jointly complex problems.
For practitioners, our findings empirically show that collaborative project delivery models are
best suited to a client and other stakeholders' requirements, which is imperative to the successful
delivery of construction projects. Recognising the most suitable delivery model under diverse contexts
can reduce the project's risk of failure and help maximise project value. When requirements
uncertainty is low, the co-creating value is less critical to project performance; whereas, when
requirements uncertainty is high, choosing a collaborative project delivery model such as design and
construction, ECI or strategic alliancing, is critical to superior project performance.
This cross-sectional survey research has some limitations. We collected data to study the
client-supplier relationships completely from contractor’s point of view and one industry in particular
(i.e. construction). Further research could extend this work to include a wider spectrum of project
stakeholders (e.g., client) in a broader range of project-organised industries.
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FIGURES
Figure 1: Conceptual Model
Figure 2: Moderating Effect of Requirements Uncertainty
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TABLES
Table 1: Summary of Respondent’s Profile (n = 120)
Profile items Number Percentage
Designation Top project managers (e.g. executive project
manager, senior project manager, project director,
contract manager)
84 70.00
Middle project managers (e.g. project engineer,
project planning and controlling manager, project
technical manager)
36 30.00
Project experience
(years)
Under 10 47 39.16
Between 10 to 19 50 41.67
Between 20 to 29 17 14.17
Over 30 6 5.00
Age (years) Under 30 6 5.00
Between 30 and 39 50 41.67
Between 40 and 49 41 34.16
Between 50 and 59 20 16.67
Over 60 3 2.50
Level of education Bachelor/Professional 21 17.50
Master degree 99 82.50
Table 2: Summary of Project’s Profile
Profile items Number Percentage
Total planned budget
(millions of AU$)
Less than 10 45 37.50
Between 10 and 99.99 39 32.50
Between 100 and 999.99 14 11.67
More than 1000 22 18.33
Total planned duration
(in months)
Less than 12 49 40.83
Between 13 and 24 44 36.67
Between 25 and 36 11 9.17
Between 37 and 48 6 5.00
More than 48 10 8.33
People involved Less than 100 44 36.67
Between 100 and 249 31 25.83
Between 250 and 499 18 15.00
Between 500 and 999 6 5.00
More than 1000 21 17.50
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Table 3: Constructs and indicators
Value co-creation (CoC) (third-order reflective construct)
Relational engagement (RE) (second-order reflective construct)
Quality of interactions (INT)
INT1. The interactions between both parties produced novel insights.
INT2. Both parties displayed a sound strategic understanding of each other in their interactions.
INT3. Both parties played a proactive role during the interaction.
Relational norms (RN)
RN1. Both parties were intentionally open and honest in their interactions.
RN2. Both parties felt confident that another party was reliable and trustworthy.
RN3. Both parties believed the other party made its best efforts.
RN4. Both parties adopted ‘no blame culture’ whenever problems arise.
Collaboration (CL) (second-order reflective construct)
Strategic information exchange (SIE)
SIE1. [Both parties exchanged information] on successful and unsuccessful experiences with
deliverables exchanged in the relationship.
SIE2. …related to changes in the users’ needs, preferences, and behaviour.
SIE3: …related to changes in the two organisations’ strategies and policies. SIE4: …that is sensitive for them, such as financial performance and organisational know-how. Joint problem-solving (JPS)
JPS1. When conflicts arose, both parties found out a proper solution jointly.
JPS2. When the contractor’s performance did not match with client’s expectation, the client helped it
or provided suggestions.
JPS3. Both parties worked closely to reduce risks, sharing gains and/or pains throughout the project.
JPS4. Both parties shared information as soon as any unexpected problems arise.
Innovativeness (INN)
INN1. Both parties collaboratively and frequently tried out new ideas for the project.
INN2. Both parties collaboratively and frequently seek new ways of doing things for the project.
INN3. Both parties were creative in its operating methods during the project.
Project performance (PP) (second-order reflective construct)
Impact on the Client (IC)
IC1. The project was completed within or below budget.
IC2. The project was completed on time or earlier.
IC3. The product improved the client’s performance.
IC4. The client was satisfied.
IC5. The product met the client’s requirements.
IC6. The client came/will come back for future work.
IC7. The project outcome contributed/will contribute to future projects.
Impact on the Supplier (IS)
IS1. The project was an economic business success to the contractor.
IS2. The project increased the contractor’s profitability.
IS3. The project has a positive return on investment.
IS4. The project contributed to the contractor’s direct performance.
Requirements uncertainty (RU)
RU1. Project requirements identified at the beginning were quite different from those at the end.
RU2. Project’s users/stakeholders often differed among themselves in the requirements to be met.
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Table 4: Results Summary of Measurement Models Assessment
Construct Item Indicator
reliability
Internal consistency
reliability
Convergent
validity
Discriminant
validity
Loadingsa
CR CA AVE HTMT
> 0.70 > 0.70 > 0.70 > 0.50 Confidence
interval does not
include 1
Value co-creation (CoC) 0.96 0.95 0.56
Relational engagement (RE) 0.94 0.93 0.69
Quality of interaction (INT)
INT1 0.86 0.92 0.86 0.79 Yes
INT2 0.91
INT3 0.89
Relational norms (RN)
RN1 0.90 0.94 0.91 0.79 Yes RN2 0.91 RN3 0.91 RN4 0.84
Collaboration (CL) 0.91 0.89 0.56
Strategic information exchange (SIE)
SIE1 0.84 0.87 0.81 0.63 Yes SIE2 0.81 SIE3 0.77 SIE4 0.76
Joint problem solving (JPS)
JPS1 0.87 0.89 0.84 0.68 Yes JPS2 0.74 JPS3 0.83 JPS4 0.85
Innovativeness (INN)
INN1 0.90 0.92 0.87 0.79 Yes
INN2 0.89
INN3 0.88
Project performance (PP) 0.93 0.92 0.55
Impact on the client (IC)
IC1 0.75 0.92 0.89 0.61 Yes IC2 0.69b
IC3 0.73 IC4 0.90 IC5 0.88 IC6 0.78 IC7 0.75
Impact on the supplier (IS)
IS1 0.93 0.93 0.90 0.77 Yes IS2 0.92 IS3 0.84 IS4 0.81
Requirements
uncertainty(RU)
RU1 0.73 0.85 0.70 0.74 Yes
RU2 0.97
Note: CA=Cronbach’s alpha; CR=Composite reliability; AVE=average variance extracted; HTMT= Heterotrait-monotrait ratio; aAll outer loadings are significant at 0.05 level; bIt was not removed because its deletion does not lead to an increase in CR and AVE.
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Table 5: Constructs Correlation and Discriminant Validity (Fornell-Larcker Criterion)
Constructs IC INN INT IS JPS RN RU SIE
Impact on the client – IC 0.78 Innovativeness – INN 0.44 0.89 Quality of interactions – INT 0.53 0.70 0.89 Impact on the supplier – IS 0.65 0.30 0.36 0.87 Joint problem solving – JPS 0.51 0.69 0.74 0.31 0.83 Relational norms – RN 0.55 0.64 0.76 0.39 0.78 0.89 Requirements uncertainty – RU -0.26 -0.04 -0.10 -0.09 -0.17 -0.08 0.86 Strategic information exchange –SIE 0.37 0.71 0.69 0.23 0.71 0.61 -0.09 0.80
Note: Scores below diagonal are correlations; bolded scores on diagonal are the square root of AVE values.
