Innovation stimulants, innovation capacity, and the performance of capital projects.
Chen, Hong Long
Introduction
Successful innovation management largely depends on identifying the
critical determinants of innovation performance. Accordingly, extensive
research examines and identifies a wide variety of measures of
innovation and the inputs that affect innovation outcomes (e.g.,
Jassawalla, Sashittal 2002; Miller et al. 2007; Vaccaro et al. 2011).
One recent finding, for example, is that the firms with more slack
resources and higher levels of managerial ownership innovate less when
firm performance declines (Latham, Braun 2009). Another finding is that
the network density of a firm's partners strengthens the influence
of technological diversity, which in turn increases the firm's
innovation performance (Phelps 2010).
However, relatively few studies explore innovation from a project
perspective. Although several published studies investigate the
relationships between innovation and project performance, they primarily
examine the relationships between innovation capacity and stimulants
(e.g., DeTienne, Koberg 2002; Ebadi, Utterback 1984), between innovation
capacity and project performance (e.g., Danneels 2002; Davies, Hobday
2005), or between innovation stimulants and project performance (e.g.,
Sundstrom, Zika-Viktorsson 2009; Oke, Idiagbon-Oke 2010).
Furthermore, these published studies principally focus on new
product development (NPD) and research and development (R&D) -
despite the fact that capital projects contribute significantly to the
growth of economy. The capital projects industry includes both the
delivery and the maintenance of facilities (e.g., commercial,
institutional, industrial, and residential buildings; as well as
transportation, energy, water, sewage, and communication systems).
Our focus is on the delivery process of capital projects. As a
result, there appears to be a lack of research that models and
quantifies the triangular relationships between innovation factors
(stimulants and capacity) and the performance of capital projects to
provide management a complete picture of how innovation affects project
performance.
The first objective of this study, therefore, is to explore and
assess the relationships between innovation factors and the performance
of capital projects. The second objective is to quantify systematically
the effects of innovation performance on project performance. Both
objectives help stakeholders better measure the impact of improved
innovation performance on capital projects.
The rest of the paper is organized as follows. "Research
background" reviews related studies, "Hypotheses"
delineates the test hypotheses, "Research methods" presents
the research methodology and describes the sample collection, and
"Results" depicts the statistical tests, model-building, and
validation. "Discussions" discusses the implications of the
research results. "Conclusions" presents the research summary
and conclusions.
1. Research background
Innovation is often thought of as a change in thought process or a
useful application of new inventions or discoveries (McKeown 2008), and
it often manifests itself in either a new product, service, procedure,
or method (Brady, Soderlund 2008). Innovation has been an essential
source of competitive advantage since the beginning of the Industrial
Revolution (Prajogo, Ahmed 2006), and existing research (e.g., Prajogo,
Ahmed 2006; Sampson 2007) demonstrates a wide range of benefits for
corporations that are successful in innovation (e.g., increases in
operation efficiency, sales, profitability, and market share). Not
surprisingly, numerous researchers and practitioners (e.g., Abbey,
Dickson 1983; Sampson 2007) conduct extensive studies to develop
innovation models through examining and identifying the key determinants
of success in innovation.
Whilst numerous models (e.g., Miller et al. 2007; Motohashi et al.
2012; Ooi et al. 2012; Wu et al. 2008) developed for organizational
innovation embody technological and human aspects, one group of scholars
(e.g., Adams et al. 2006; Jassawalla, Sashittal 2002; Prajogo, Ahmed
2006; Prajogo, Sohal 2006) highlights the need to integrate
technological aspects with human aspects when modeling innovation
performance. The rationale is straightforward: innovation practices
should be executed within a suitable environment (i.e., leadership,
management, and culture).
For example, Amabile and Conti (1999) show that work environment
plays a particularly important role in team creativity based on the
study of a large high-technology firm before, during, and after a major
downsizing. Shalley et al. (2000) use a survey of 2,200 adults to
illustrate how organizations can foster creativity by ensuring that work
environments complement the creative requirements of jobs. Jassawalla
and Sashittal (2002) note that cultures that highly support innovation,
foster teamwork, and promote risk-taking and creative actions positively
affect innovation performance. They propose that organizations could
develop such cultures by listening to the participants in the NPD
processes at high-technology organizations.
Based on a study of 235 professional R&D workers in large and
small technology-based firms, Bommer and Jalajas (2004) note that
policies supportive of informal communications affect the extent to
which engineers can obtain more valuable information from suppliers,
customers, and employees in other departments, which in turn affect
innovation performance. Additionally, Elenkov and Manev (2005) show that
sociocultural context directly influences leadership and moderates its
relationship with organizational innovation based on a sample of 1,774
individuals from 12 European countries. Using a sample of 463 R&D
alliances in the telecommunications equipment industry, Sampson (2007)
finds that an alliance environment contributes far more to firm
innovation when technological diversity is moderate, rather than when it
is low or high.
Recently, subsequent work based on a sample of 145 firms suggests
that service suppliers that retain management control over their
intellectual output are more innovative (Leiponen 2008). Based on a
longitudinal investigation of 77 telecommunications equipment
manufacturers, Phelps (2010) concludes that the network density of a
firm's allies and partners strengthens the influence of
technological diversity, which in turn increases the firm's
innovation performance. More recently, Vaccaro et al. (2012) conclude
that smaller, less complex organization environments benefit more from
transactional leadership in realizing management innovation. The study
is based on a sample of 151 companies.
In addition, Tang et al. (2012) use Tobit-censored normal
regression analysis to examine the relationships among executive hubris,
organization environment, and firm innovation. Based on a sample of
2,820 manufacturing firms in China and 3,285 U.S. firms in high-tech
industries, they conclude that executive hubris positively affects firm
innovation performance, but the relationship between executive hubris
and firm innovation becomes weaker when the environment is more
munificent and complex.
Despite the panoply of studies that use a wide variety of measures
to describe innovation outcomes and the input characteristics that
affect those outcomes as well as firm performance (e.g., Kessler,
Chakrabarti 1996; Tang et al. 2012; Vaccaro et al. 2012), most studies
focus on firms engaged in innovation (e.g., Nohria, Gulati 1996; Sampson
2007); relatively few studies explore projects engaged in innovation.
Further, although some existing studies describe the relationships
between innovation and project performance (e.g., Oke, Idiagbon-Oke
2010; Sundstrom, Zika-Viktorsson 2009), these studies principally focus
on examining the relationships between the technological and human
aspects of innovation (e.g., DeTienne, Koberg 2002; Ebadi, Utterback
1984), between innovation's technological aspects and project
performance (e.g., Davies, Hobday 2005; Kazanjian et al. 2000), or
between innovation's human aspects and project performance (e.g.,
Calamel et al. 2012; Sundstrom, Zika-Viktorsson 2009; Oke, Idiagbon-Oke
2010).
Furthermore, most published studies primarily focus on NPD and
R&D projects (e.g., Danneels 2002; Sundstrom, Zika-Viktorsson
2009)--despite the fact that capital projects contribute significant
growth to the economy (Chen 2011; Mallick, Mahalik 2010). As a result,
there appears to be a lack of research that models and quantifies the
triangular relationships between innovation factors (technological
aspects and human aspects) and the performance of capital projects,
providing management a total picture of how innovation affects project
performance.
2. Hypotheses
The preceding section critiques existing studies of innovation and
project performance. Now the question is: How does project innovation
affect the performance of capital projects?
To answer this question, we first examine the relationships between
innovation factors and the performance of capital projects. Then, we
model and quantify the effects of innovation factors on the performance
of capital projects.
To investigate the relationships between innovation factors and the
performance of capital projects, we need to develop a series of test
hypotheses. A review of literature on innovation suggests that both
technological and human aspects affect organizational innovation (e.g.,
Adams et al. 2006; Jassawalla, Sashittal 2002; Prajogo, Ahmed 2006; Tang
et al. 2012). We posit that technological and human issues should not be
examined in isolation when modeling the effects of innovation
performance on the performance of capital projects. We define the
technological factors of innovation performance as innovation capacity
concerning the accumulation of knowledge and the creativity and
experience of existing and emerging technologies. The human factors of
innovation performance that we define as innovation stimulants concern
leadership, team-building, communication management, and productive
culture.
To articulate the triangular relationships between innovation
factors (stimulants and capacity) and the performance of capital
projects, we propose three hypotheses:
H1: Stimulant factors of project innovation positively affect the
innovation capacity of capital projects.
H2: Stimulant factors of project innovation positively affect the
performance of capital projects.
H3: Project innovation capacity positively affects the performance
of capital projects.
3. Research methods
3.1. Participants and procedures
Of the 500 members of Taiwan's Chinese National Association of
General Contractors (CNAGC) that we randomly selected and invited to
participate in this research, 121 companies participated - a 24.2%
response rate (CNAGC has over 1,000 members). Of the 121 firms, 24 have
less than US$5 million in revenue; 30 have US$5 million US$15 million in
revenue; 37 have US$15 million-US$25 million; and 30 have more than
US$25 million in revenue.
Each of the 121 companies in the sample had a project manager who
had just completed a capital project. The 121 capital projects fall into
three categories: buildings (69 projects), transportation facilities (22
projects), and industrial facilities (30 projects). Project managers
average between one and 26 years of experience; 30 participants had
fewer than five years of experience; 51 had between five and 10 years;
33 had between 10 and 20 years; and seven participants had over 20 years
of experience.
Surveys collected the data. Prior to the data collection, several
experienced researchers and a panel of experts from CNAGC critiqued the
questionnaire for structure, readability, clarity, and completeness.
These researchers and experts also appraised the extent to which the
indicators sufficiently addressed the subject area (Dillman 1978). Based
on the feedback from these researchers and experts, the survey
instrument was then modified to strengthen its validity.
The final version of the survey questionnaire comprises two
sections. The first section, composed of open-ended questions, gathers
detailed background information such as annual revenue; project type;
project cost, including contract price, budget, contract price for
project changes, and actual cost; as well as the project schedule
including the contract schedule, scheduled time, contract schedule for
project change, and actual schedule.
The second section gathers data for the project innovation
variables and measures that data using scales based on a synthesis of
literature from the project management, innovation management, group
effectiveness, and organizational theory fields. Section two consists of
multiple-choice questions in which respondents indicate on a 10-point
scale the extent to which certain project variables likely affect the
innovation and project performance. Because of space limitations,
complete survey questionnaires are available from the authors on
request.
3.2. Measures and analysis
Cost, time, and performance are the typical measures of project
success (Kloppenborg, Opfer 2002). In other words, a project is often
considered successful if it finishes within its budget estimate,
finishes within its scheduled time frame, and performs as designed
(Scott-Young, Samson 2008). Whilst the research literature in project
management engages in a fruitful debate over the nature of project
performance (Dvir et al. 1998), project performance criteria have become
multifaceted.
For example, Shenhar et al. (2001) use project efficiency, customer
benefit, organizational success, and potential benefit to the
organization to assess project performance. Yu et al. (2005) develop a
value-centered model based on net project execution cost and net project
operation value to evaluate project performance. The Project Management
Institute (2008) assesses project success with cost, time, quality, and
stakeholder satisfaction.
Thus, this study chooses project time, cost, profitability, and
customer satisfaction as the criteria for capital project performance.
The rationales are straightforward: delays in completion time may turn a
promising investment opportunity into an expensive failure (Scott-Young,
Samson 2008), cost overrun directly encroaches on profit (Teerajetgul et
al. 2009), and project profitability and customer satisfaction ensure
business growth and development (Chen 2011).
Further, based on an extensive review of the interdisciplinary
literature and in an effort to generate a more parsimonious measurement,
we choose widely accepted constructs and their respective key measures
of organizational innovation to gauge project innovation stimulants and
capacity. Constructs measuring project innovation stimulants that
concern project leadership, project team-building, project
communication, and culture are Leadership (Lead) and People Management
(PM). Those measuring project innovation capacity that relate to the
accumulation of project knowledge and project creativity as well as the
experience of existing and emerging project technologies are Knowledge
Management (KM), Creativity Management (CM), Research and Development
(R&D), and Technology Management (TIM). Table 1 lists the taxonomy
of measures of these constructs, the means and standard deviations of
their respective measures, and the constructs' corresponding
Cronbach's a values for the reliability analysis for the 121 sample
projects. If not otherwise indicated, all measures use a scale in which
1 is "strongly disagree" and 10 is "strongly agree".
High scores suggest good performance; low scores indicate poor
performance.
Lead ([alpha] = .92) is measured according to a four-item scale
(see the respective Variable, Measure, Mean, Standard Deviation, and
Cronbach's a columns in Table 1) based on Bart (2002), Linton and
Walsh (2004), O'Neil et al. (1998), Prajogo and Ahmed (2006), and
Prajogo and Sohal (2006). PM ([alpha] = .97) is measured according to a
five-item scale (see Table 1) based on Abbey and Dickson (1983), Amabile
and Conti (1999), Prajogo and Ahmed (2006), Prajogo and Sohal (2006),
and Shalley et al. (2000). KM ([alpha] = .96) is measured according to a
four-item scale (Table 1) based on Herrera et al. (2010), Miller et al.
(2007), Prajogo and Ahmed (2006), Subramaniam and Youndt (2005), Wu et
al. (2008), and Youndt et al. (2004).
CM ([alpha] = .96) is measured according to a seven-item scale
based on Amabile and Conti (1999), Dulaimi et al. (2005), Kratzer et al.
(2006), Prajogo and Ahmed (2006), Shalley et al. (2000). TM ([alpha] =
.95) is measured according to a four-item scale (Table 1) based on Hayes
and Wheelwright (1984), Prajogo and Ahmed (2006), Urban and von Hippel
(1988). R&D ([alpha] = .96) is measured according to a five-item
scale (Table 1) based on Adams et al. (2006), Bessant and Francis
(1997), Prajogo and Ahmed (2006), Prajogo and Sohal (2006).
Finally, Project Performance (PP) is measured on a four-item scale
that includes Profitability, Cost, Time, and Customer Satisfaction (CS).
Profitability is measured on a one-item scale (see Table 1) based on
Hartley and Watt (1981). Cost and Time are both measured according to
one-item scale (see Table 1) based on Anbari (2004). CS ([alpha] = .97)
is measured according to a 10-item scale based on Bettencourt et al.
(2001), Chen (2011), Ling et al. (2009), Luu et al. (2008), Qureshi et
al. (2009), and Tohumcu and Karasakal (2010).
Principal component analysis reveals that all the factor loadings
of the measurement items of Lead, PM, KM, CM, TM, and R&D are all
0.63 or greater and thus exceed the threshold value of 0.50 (Hair et al.
1998). We therefore include the variables in the innovation
performance-measurement model. Principal component analysis also shows
that the factor loadings of PP's Time, Cost, Proitability, and CS
are 0.57, 0.74, 0.28, and 0.71, respectively. (For comparison purpose,
percentile ranks categorize time performance on a 10-point scale based
on the computed values of Time from the 121 sample projects using the
revised estimated duration/actual duration equation in Table 1. The same
technique also applies the Cost and Proitability equations in Table 1.)
We therefore delete the Proitability measurement item. PP (a = .73) is
measured by Time, Cost, and CS.
The methodology to analyze the relationships between innovation
factors and performance of capital projects and to quantify the impact
of innovation on the performance of capital projects is threefold.
First, to test the hypotheses, this study uses the absolute values of
the kurtosis indexes to verify normality, followed by maximum likelihood
(ML) and asymptotically distribution-free (ADF) estimation methods of
structural equation modeling (SEM), respectively, when the data is
normally and abnormally distributed. Second, based on the test results
of the hypotheses, this study conducts a hierarchical robust regression
analysis using a maximum R-square improvement procedure to obtain the
optimum subset of regressor variables. Use of robust regression analysis
not only dampens the influence of outlying observations, but also
ensures that the forecasts and the model estimation are unbiased when
the normality of the residuals is violated (Neter et al. 1996).
Though this study already applies a maximum R-square improvement
procedure, which is a very popular method for combating the
multicollinearity (Freund, Wilson 1998) that may impair the usefulness
of a model's estimated parameters, there is a need to examine if
multicollinearity still exists. This study uses incomplete
principal-component analysis (Littell, Freund 2000) to detect and
rectify the problem of multicollinearity.
Third, this study validates its optimal models using an
out-of-sample test. Specifically, this study develops a hypothesis to
test whether a significant discrepancy exists in the mean value of the
differences between estimated and actual project performance for both
the estimation data and the out-of-sample data. To examine the
hypothesis, this study uses the Kolmogorov-Smirnov test to verify
normality, followed by T-tests and Mann-Whitney tests, respectively,
when the data is normally and abnormally distributed. We first use all
121 sample capital projects to test our hypotheses. We then split the
sample into two subsamples: the estimation data and the out-of-sample
validation data. The estimation data, composed of 61 projects randomly
selected from 121 capital projects, are used for model-building. We use
the out-of-sample validation data--the remaining 60 projects--to
validate the model.
4. Results
4.1. Results of hypothesis tests
Figure 1 provides the analysis results of SEM's ML estimation
for innovation's effects on the performance of capital projects.
This study uses the ML estimation method because the absolute values of
the kurtosis indexes are all smaller than 1.24, indicating that the data
are normally distributed. The structural model provides an adequate fit
to the data, where the model chi-square ([X.sup.2]) = 27.36, the degree
of freedom (df) = 22, [X.sup.2]/df = 1.24, the root mean square error of
approximation (RMSEA) = 0.05, the comparative fit index (CFI) = 0.94,
and the Tucker-Lewis index (TLI) = 0.90.
As seen in Figure 1, the path coefficients support all but the
second hypothesis. Specifically, project stimulant factors positively
affect the innovation capacity of capital projects (Hypothesis 1), and
project innovation capacity positively affects the performance of
capital projects (Hypothesis 3). On the other hand, the rejection of
Hypothesis 2 suggests that stimulant factors of project innovation
insignificantly affect the performance of capital projects. The test
results suggest that project innovation capacity serves as a mediator
between innovation stimulants and the performance of capital projects.
To confirm our findings, we compare the fit of our hypothesized
model to the alternate model, where the direct path between innovation
stimulant and project performance is deleted. The rationale behind this
test is straightforward: the alternate model would result in a poorer
fit to the data if stimulant factors have a direct impact on project
performance.
[FIGURE 1 OMITTED]
The alternate model exhibits an almost identical fit to the data,
with [X.sup.2] = 27.4, df =23, [X.sup.2]/df = 1.19, RMSEA = 0.04, CFI =
0.95, and TLI = 0.92. This result suggests that deleting the path
between stimulant and performance does not make our hypothesized model
inferior and therefore verifies that innovation capacity mediates
innovation stimulants and the performance of capital projects.
4.2. Model-building
As suggested, project innovation capacity (composed of TM, KM, CM,
and R&M) significantly affects the performance of capital projects.
Based on the results, this study conducts a series of hierarchical
robust regression analyses using a maximum R-squared improvement
procedure to develop optimal innovation-effect models from the
estimation data of the 61 projects. Table 2 reports the model-building
results.
As seen in the table, the optimal innovation-effect model at step 1
(Model 1) includes the TM variable and explains 31.78% of the variation
in the PP data. At step 2, the optimal innovation effect model (Model 2)
is composed of TM and KM, capable of explaining 33.91% of the variation
in the PP data, which is 2.13% more than that of Model 1. The optimal
models at steps 3 and 4 (Models 3 and 4) are composed of TM, KM, and CM,
and TM, KM, CM, and R&D, respectively. The corresponding R-squares
of Models 3 and 4 are 33.91% and 34.42%, suggesting that Model 4 is the
optimum model among Models 1 to 4.
The bottom of Table 2 shows the chi-square values of the White test
(White 1980) for Models 1 to 4. The White test establishes whether the
residual variance of a variable in a regression model is constant
(homoscedasticity) or not (heteroskedasticity). Diagnostics for
heteroskedasticity in regression models are essential because
heteroskedasticity leads to inefficient parameter and covariance-matrix
estimates. As seen in the table, the chisquare value for the White test
of Model 4 is 11.23, and the associated p-value is larger than 0.05,
suggesting the acceptance of the null hypothesis of no
heteroskedasticity in the residuals at the 0.05 level.
Further, multicollinearity, in which two or more independent
variables in a multivariate regression model are highly correlated, may
impair the usefulness of the model's estimated parameters by
inflating their variances (Freund, Wilson 1998). Hence, this study uses
the eigenvalues (Freund, Wilson 1998), the variance of
principal-component regression analysis, to determine if the effects of
multicollinearity are present in the model.
The multicollinearity diagnostics of Model 4 are in the left-hand
side Table 3, where the "Condition Number" column, the square
root of the ratio of the largest to smallest eigenvalue, indicates the
degree of near-linear dependencies. Eigenvalues have condition numbers
larger than 30, and variables with variation proportions greater than
0.5 for each of these eigenvalues are involved in the near-linear
dependency (Belsley et al. 1980). As seen in the table, although the KM
and R&D variables of the fourth eigenvalue have respective variation
proportions of 0.70 and 0.84, the fourth eigenvalue has a condition
number of 7.33--smaller than 30. Consequently, multicollinearity does
not exist in the model.
4.3. Model validation
This study validates its optimal innovation-effect model (Model 4)
using an out-ofsample test. We use Model 4 to estimate the performance
of the 60 out-of-sample capital projects, which we then compare to the
60 projects' actual performance. To determine conclusively whether
Model 4 (developed from the estimation data for the 61 capital projects
using the proposed estimation method) provides equal estimation power
for the out-of-sample data, we form the hypothesis:
[H.sub.0]: [[mu].sub.E[absolute value of PP - [??]P]] =
[[mu].sub.OS[absolute value of PP - [??]P]],
[H.sub.a]: [[mu].sub.E[absolute value of PP - [??]P]] [not equal
to] [[mu].sub.OS[absolute value of PP - [??]P]],
where [[mu].sub.E[absolute value of PP - [??]P]] is the absolute
average value of the differences between estimated and actual project
performance for the 61 estimation projects, and [[mu].sub.OS[absolute
value of PP - [??]P]] is the absolute average value of the differences
between estimated and actual project performance for the 60
out-of-sample projects.
The hypothesis examines whether a significant discrepancy exists in
the mean value of the differences between the estimated and actual
project performance of the 61 estimation projects and the 60
out-of-sample projects. If no significant discrepancy exists, we can
confidently claim that our optimal innovation-effect model (Model 4)
explains 34.42% of the variation in project performance.
The right-hand side of Table 3 shows the results of the
Kolmogorov-Smirnov and MannWhitney tests. We use the Mann-Whitney test
because the significant 0.13 value from the Kolmogorov-Smirnov sets
suggests that the data sets of [[mu].sub.E[absolute value of PP -
[??]P]] are abnormally distributed. The sample data is also unpaired
(there are 61 estimation projects versus 60 out-of-sample projects).
As the right-hand side of Table 3 shows, the Mann-Whitney U is
1473.00 and the associated p-value is larger than 0.05, confirming an
insignificant discrepancy. We therefore accept the null hypothesis,
implying that no significant discrepancy exists in the mean value of the
differences between the estimated and actual project performance of the
61 estimation projects and the 60 out-of-sample projects in the optimal
innovation-effect model (Model 4). Figure 2 plots the actual project
performance against the estimated project performance for the 60
out-of-sample projects in Model 4.
[FIGURE 2 OMITTED]
6. Discussions
This study examines and models the triangular relationships between
innovation factors (stimulants and capacity) and the performance of
capital projects. Drawing on the literature in innovation management
(e.g., Adams et al. 2006; Jassawalla, Sashittal 2002; Prajogo, Ahmed
2006; Prajogo, Sohal 2006), we posit that innovation stimulants and
capacity should not be examined in isolation when modeling the effects
of innovation performance on the performance of capital projects.
The results of hypothesis tests show that project stimulant factors
positively affect the innovation capacity of capital projects, and
project innovation capacity positively affects the performance of
capital projects. However, stimulant factors of project innovation
insignificantly affect the performance of capital projects. In fact,
drawing on the structural relationships shown in Figure 1, project
innovation capacity serves as a mediator between innovation stimulants
and the performance of capital projects. In other words, the stimulant
factors do not have a direct impact on capital project performance but
rather have an indirect impact realized through project innovation
capacity.
Our test results reported here provide a comprehensive framework
for analyzing the relationship between project practices and innovation
performance. As mentioned, most prior research on project innovation
focuses on examining the relationships between innovation capacity and
stimulants, between innovation capacity and project performance, or
between innovation stimulants and project performance. From a managerial
perspective, constraining findings of one or another of these studies
might be potentially misleading.
For example, based on the findings of the relationship between
innovation capacity and project performance, this study quantifies the
effects of innovation performance on project performance using a series
of hierarchical robust regression analyses. The optimal
innovation-effect model, composed of TM, KM, CM, and R&D, explains
34.42% of the variation in project performance. In other words, this
model indicates that whilst a capital project may improve innovation by
TM, KM, CM, and R&D, the innovation also improves the performance of
the capital project by 34.42%. This indication may mistakenly suggest
that having excellent technology, R&D, knowledge, and creativity
management is sufficient for accomplishing high project performance.
In sum, our findings suggest that project innovation capacity
positively affects the performance of capital projects, and project
stimulant factors are fundamental enablers that affect innovation
performance and, by extension, the performance of capital projects.
Therefore, in order to create innovative projects, project leaders need
to build project environments that foster leadership, team-building,
communication, and a productive culture for innovation. Such
environments provide momentum that motivates project team members to
innovate. More important, such environments allow project-based
organizations to leverage their innovative capacity to deliver
innovative outcomes and project performance.
Conclusions
Extensive research in the innovation-management field examines and
identifies a wide variety of measures that describe innovation outcomes
and the inputs that affect those outcomes; however, relatively little
research explores innovation from a project perspective. Although
several published studies delineate the relationships between innovation
and project performance, most studies concentrate on examining the
relationships between innovation capacity and stimulants, between
innovation capacity and project performance, or between innovation
stimulants and project performance. Further, these studies primarily
focus on NPD and R&D projects--despite the fact that the capital
projects industry also contributes significantly to the growth of
economy.
This study develops an innovation performance-measurement model for
capital projects by incorporating technological factors (capacity) and
human factors (stimulants). The results show that innovation capacity
mediates the relationship between innovation stimulants and innovation
performance (and thus, by extension, project performance) that is
consistent with prior research at the firm level. This study
reclassifies creativity and knowledge management into the technological
aspect (capacity) and extends the findings to the project level.
This study performs hierarchical robust regression analyses using a
maximum R-squared improvement procedure to develop optimal
innovation-effect models that are based on capacity variables.
Out-of-sample validation demonstrates that our optimal model explains
34.42% of the variation in project performance. This result in turn
implies that as the innovation performance of a capital project
improves, the project's performance improves by 34.42%.
In conclusion, this study clarifies and explains the relationship
between the technological and human aspects of innovation performance at
the project level, and it offers managers a practical way to measure the
impact of project innovation performance on project performance. As an
extension of this research, a study of the longitudinal relationships
between project innovation and performance throughout the
project-delivery process would benefit decision-making, management, and
project control.
doi: 10.3846/16111699.2012.711361
Acknowledgment
We would like to thank the Taiwan National Science Council for
financially supporting this research.
Caption: Fig. 1. Innovation's effects on the performance of
capital projects
Caption: Fig. 2. Plots of the actual versus fitted values of Model
4 for the 60 out-of-sample projects
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Hong Long Chen
Department of Business and Management, National University of
Tainan, No. 33, Sec. 2, Shu-Lin St., Tainan 700, Taiwan
E-mail: along314@mail.nutn.edu.tw
Received 07 November 2011; accepted 09 July 2012
Hong Long CHEN (PhD, University of Florida) is a Professor in the
department of Business and Management at the National University of
Tainan, Taiwan. His research interests are project finance, corporate
finance, performance management, and supply chain management. He is a
reviewer of several prestigious journals, such as the IEEE Transactions
on Engineering Management, International Journal of Project Management,
Supply Chain Management: An International Journal, International Journal
of Production Economics, Journal of Management in Engineering, Journal
of Construction Engineering and Management, and Construction Management
and Economics. He is also a member of the editorial board of
International Journal of Information Technology Project Management.
Table 1. Taxonomy, means, standard deviations, and reliabilities
Variable Measure Research Reference
Leadership (Lead) Project leaders O'Neil et al. 1998; Prajogo,
share similar Ahmed 2006; Miller et al. 2007
beliefs
Project leaders Linton, Walsh 2004; Prajogo,
encourage Ahmed 2006
learning and
improvement
Project leaders Prajogo, Ahmed 2006
encourage
change and
sharing
Unity of Bart 2002; Prajogo, Ahmed 2006;
purpose Prajogo, Sohal 2006
People management Team member Prajogo, Ahmed 2006; Prajogo,
(PM) training and Sohal 2006
development
exists
Project Prajogo, Ahmed 2006
maintains team
member
communication
Team member Prajogo, Ahmed 2006; Prajogo,
satisfaction Sohal 2006
regularly
measured
Team member Prajogo, Ahmed 2006; and
training Prajogo, Sohal 2006
multi-skilling
used
Project's work Abbey, Dickson 1983; Amabile,
environment Conti 1999; Prajogo, Ahmed
is positive 2006; Prajogo, Sohal 2006;
Shalley et al. 2000
Knowledge Project Prajogo, Ahmed 2006;
management (KM) intellectual Subramaniam, Youndt 2005;
capital Youndt et al. 2004
build-up is
important
Regular Herrera et al. 2010; Prajogo,
upgrades in Ahmed 2006
project-related
knowledge and
skills
Company shares Miller et al. 2007; Prajogo,
and Ahmed 2006
disseminates
project-related
information and
knowledge
Project-related Prajogo, Ahmed 2006; Wu et al.
intellectual 2008
assets managed
well
Creativity Top management Amabile, Conti 1999; Dulaimi
management (CM) support for et al. 2005; Shalley et al.
innovative 2000
ideas/solutions
is high
Project manager Amabile, Conti 1999; Dulaimi
decision et al. 2005; Shalley et al.
authority is 2000
high
Time and Amabile, Conti 1999; Prajogo,
resources Ahmed 2006
provided for
generating
innovative
ideas/solutions
Groups have Prajogo, Ahmed 2006
diverse skills
and communicate
openly
Cognitive Kratzer et al. 2006
conflicts
moderately high
Manager has Dulaimi et al. 2005
bottom-up
problem-solving
style
Creativity is Amabile, Conti 1999; Prajogo,
rewarded and Ahmed 2006
recognized
Technology At the leading Urban, von Hippel 1988
management (TM) edge of project
practices/
technologies
Evaluating Hayes, Wheelwright 1984;
potential of Prajogo, Ahmed 2006
using new
project
technologies/
practices
Acquire project Hayes, Wheelwright 1984;
technological Prajogo, Ahmed 2006
capabilities
in advance of
needs
Continuously Prajogo, Ahmed 2006
thinking of
next-generation
technology
Research and Team education Kessler, Chakrabarti 1996
development (R&D) and confidence
are high
Physical Adams et al. 2006
resources
adequate
Financial Adams et al. 2006
resources
adequate
Tools and Bessant, Francis 1997
systems
adequate
R&D Prajogo, Ahmed 2006;
strategically Prajogo, Sohal 2006
important
Profitability Revised profit Hartley, Watt 1981
performance
= (revised
contract
price - actual
cost
Cost Revised cost Anbari 2004
performance
= revised
estimated
cost/actual
cost
Time Revised time Anbari 2004
performance
= revised
estimated
duration/
actual duration
Customer Meets customer Tohumcu, Karasakal 2010
satisfaction (CS) budget estimate
Meets customer Tohumcu, Karasakal 2010
scheduled time
frame
Low defect rate Tohumcu, Karasakal 2010
Resolves Ling et al. 2009
defects quickly
and effectively
Customer Luu et al. 2008
complaints low
Responsiveness Qureshi et al. 2009; Tohumcu,
to customer Karasakal 2010
requests/
complaints
Conforms to Ling et al. 2009
contract
requirements
Courtesy of Ling et al. 2009
staff
Understanding Bettencourt et al. 2001
of customer's
company and
industry
Communication Chen 2011; Ling et al. 2009
with the
customer is
effective
Variable Measure Mean Standard Cronbach's
N = 121 Deviation [alpha]
Leadership (Lead) Project 7.28 1.65 0.92
leaders
share similar
beliefs
Project 7.33 1.44
leaders
encourage
learning and
improvement
Project 5.71 1.82
leaders
encourage
change and
sharing
Unity of 5.80 1.58
purpose
People management Team member 6.74 1.68 0.97
(PM) training and
development
exists
Project 6.21 1.67
maintains team
member
communication
Team member 6.55 1.49
satisfaction
regularly
measured
Team member 6.59 1.72
training
multi-skilling
used
Project's work 6.64 1.53
environment
is positive
Knowledge Project 6.82 1.47 0.96
management (KM) intellectual
capital
build-up is
important
Regular 6.86 1.62
upgrades in
project
-related
knowledge and
skills
Company shares 6.62 1.65
and
disseminates
project
-related
information
and knowledge
Project 6.88 1.54
-related
intellectual
assets managed
well
Creativity Top management 6.41 1.82 0.96
management (CM) support for
innovative
ideas/
solutions
is high
Project 6.55 1.72
manager
decision
authority is
high
Time and 6.55 1.72
resources
provided for
generating
innovative
ideas/
solutions
Groups have 6.47 1.57
diverse skills
and
communicate
openly
Cognitive 6.45 1.56
conflicts
moderately
high
Manager has 6.64 1.71
bottom-up
problem
-solving
style
Creativity is 6.52 1.72
rewarded and
recognized
Technology At the leading 6.63 1.76 0.95
management (TM) edge of
project
practices/
technologies
Evaluating 6.66 1.49
potential of
using new
project
technologies/
practices
Acquire 6.89 1.51
project
technological
capabilities
in advance of
needs
Continuously 6.71 1.63
thinking of
next
-generation
technology
Research and Team education 6.58 1.63 0.96
development (R&D) and confidence
are high
Physical 6.69 1.75
resources
adequate
Financial 6.38 1.70
resources
adequate
Tools and 7.29 1.33
systems
adequate
R&D 6.69 1.69
strategically
important
Profitability Revised profit 5.50 2.94
performance
= (revised
contract
price - actual
cost
Cost Revised cost 5.60 2.87
performance
= revised
estimated
cost/actual
cost
Time Revised time 5.60 2.89
performance
= revised
estimated
duration/
actual
duration
Customer Meets customer 6.89 1.67 0.97
satisfaction (CS) budget
estimate
Meets customer 7.17 1.58
scheduled time
frame
Low defect 7.02 1.65
rate
Resolves 7.40 1.28
defects
quickly
and
effectively
Customer 7.12 1.44
complaints low
Responsiveness 7.43 1.33
to customer
requests/
complaints
Conforms to 7.15 1.52
contract
requirements
Courtesy of 7.02 1.54
staff
Understanding 7.08 1.43
of customer's
company and
industry
Communication 7.55 1.32
with the
customer is
effective
Table 2. Optimal innovation effect models created with robust
regression analysis using a maximum R-squared improvement
Dependent variable: PP
Variables Model 1 Model 2
Coefficient Chi-Square Coefficient Chi-Square
Step 1
Intercept 0.46 0.22 0.36 0.14
TM 0.80 28.35 * 0.43 2.00
Step 2
KM 0.38 1.80
Step 3
CM
Step 4
R&D
[R.sup.2] (%) 31.78 33.91
Changes of
[R.sup.2] (%) 2.13
The White test 2.10 5.12
Variables Model 3 Model 4
Coefficient Chi-Square Coefficient Chi-Square
Step 1
0.35 0.12 0.37 0.71
0.38 1.02 0.34 0.39
0.37 1.65 0.26 0.48
0.06 0.06 0.01 0.99
0.20 0.66
33.91 34.42
0.00 0.51
9.56 11.23
Notes: *p < 0.05; ** p < .01; *** p < .001.
Table 3. Multicollinearity diagnostics, Kolmogorov-Smirnov, and
Mann-Whitney tests of Model 4 (a)
Multicollinearity Diagnostics
Proportion of Variation
Principal Eigenvalue Condition TM KM CM R&D
Component Number
1 3.59 1.00 0.01 0.01 0.01 0.01
2 0.23 3.92 0.01 0.23 0.55 0.01
3 0.12 5.64 0.96 0.06 0.12 0.14
4 0.07 7.33 0.027 0.70 0.32 0.84
Kolmogorov-Smirnov and Mann-Whitney Tests
Principal Source Observation Mean Rank
Component Number
1 [[mu].sub.E][absolute value 61 55.15
of PP-[??]P]
2 [[mu].sub.E][absolute value 60 66.95
of PP-[??]P]
3
4
Kolmogorov-Smirnov and Mann-Whitney Tests
Principal Kolmogorov-Smirnov Sum of Ranks Mann-Whitney
Component U
1 0.13 * 3364.00 1473.00
2 0.08 4017.00
3
4
(a) Model 4 = 0.37 + 0.34TM + 0.26KM + 0.01CM + 0.20R&D, where
[R.sup.2] = 34.42%.
Notes: * p < 0.05; ** p < .01; *** p < .001.
