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Impact of knowledge-based organizational
support on organizational performance
through project management
Claudia-Inés Sep
ulveda-Rivillas, Joaquin Alegre and Victor Oltra
Abstract
Purpose –The purpose of this study is to empirically investigate how knowledge-based organizational
support (KOS) influences organizational performance through project management.
Design/methodology/approach –Data were obtained from a survey and from archival sources with a
time lag for the dependent variable; structural equation modeling was used to analyze the data. The
sample was made up of 106 organizations in Colombia, considering two key respondents from each
organization: general manager and project manager.
Findings –Results show that KOS is an antecedent of project management and project performance.
Furthermore, project management and project performance play a mediating role between KOS and
organizational performance.
Research limitations/implications –Research limitations are the following: use of cross-sectional data
with a time lag, one single unit of analysis, organizational performance analyzed only from a financial
perspective. Despite these limitations, the paper puts forward relevant implications that bridge knowledge
management and project management literature by clarifying the conditions under which knowledge
organizational support generates a significant impact on organizational performance. Intellectual capital
and knowledge management dynamic capabilities play a relevant role in this connection.
Practical implications –The findings have important practical implications: decision-makers are to
allocate effectively hard and soft resources to configure a knowledge-based infrastructure, through the
development of intellectual capital and knowledge management dynamic capabilities.
Social implications –The findings are generalizable to projects management in the context of non-
government organizations or other social-oriented initiatives.
Originality/value –This study assumes and operationalizes organizational support from a knowledge-
based perspective, represented by intellectual capital and knowledge management dynamic
capabilities, providing empirical evidence of the way KOS influences organizational performance
through project management and project performance.
Keywords Organizational performance, Project performance, Knowledge management,
Intellectual capital, Dynamic capabilities, Knowledge-based organizational support
Paper type Research paper
1. Introduction
Projects allow organizations to create or adapt to environmental changes and are, therefore,
a core activity for most of them. For this reason, project management (PM) is increasingly
regarded as a relevant determining factor in achieving organizational objectives and
competitive advantage. Within the context of projects, organizational support refers to the
backing provided by an organization for the execution of its projects to boost performance
(Fossum et al.,2020;Jugdev et al.,2020;Irfan et al.,2019;Aarseth et al.,2011).
Organizational support is considered one of the most important factors for project success.
Organizational support involves tangible aspects such as physical and technological
Claudia-Ine
´s Sep
ulveda-
Rivillas is based at the
Department of
Administrative Sciences,
Faculty of Economics
Sciences, University of
Antioquia, Medellin,
Colombia.
Joaquin Alegre and Victor
Oltra are both based at the
Department of Business
Management, University of
Valencia, Valencia, Spain.
Received 11 December 2020
Revised 1 April 2021
21 May 2021
Accepted 29 May 2021
The authors acknowledge the
finance received from the
Spanish Ministry of Science,
Innovation and Universities
(PGC2018-097981-B-I00) and
the Spanish Ministry of
Economics and
Competitiveness (ECO2015-
69704-R) to do this research.
Dr Sep
ulveda-Rivillas also
acknowledges support from
Universidad de Antioquia and
Fundaci
on Carolina.
DOI 10.1108/JKM-12-2020-0887 VOL. 26 NO. 4 2022, pp. 993-1013, ©Emerald Publishing Limited, ISSN 1367-3270 jJOURNAL OF KNOWLEDGE MANAGEMENT jPAGE 993
infrastructure and intangible aspects including organizational culture, knowledge management
or incentive schemes. However, the intangible characteristics of organizational support have
not been studied in depth. Knowledge management stands out consistently as a key factor for
project success (Fossum et al., 2020;Dosko
cil and Lacko, 2018;Gunasekera and Chong,
2018;Liu, 2015;Young and Jordan, 2008).
Notwithstanding, organizational support has not been addressed from a knowledge-based
perspective. More research is needed to conceptualize and operationalize organizational
support from a knowledge-based perspective, as well as to analyze its influence on project
performance (PP) and organizational performance (OP). In previous studies, organizational
support has generally been limited to support for employees, but many additional
knowledge-based issues should also be taken into account (Fuentes-Ardeo et al., 2017;
Gasik, 2011;Gelbard and Carmeli, 2009).
Knowledge-based organizational support (KOS) is, therefore, a relevant topic in both the
context of PM and in the topic of knowledge management. KOS refers to the infrastructure
that, supported by knowledge management and arranged by the organization, optimizes
PM with the aim of improving performance (Fossum et al., 2020;Han et al.,2019).
KOS infrastructure is represented by two key facets: intellectual capital (IC) and knowledge
management dynamic capabilities (KMDC). IC is justified as a source for the development,
management and use of knowledge-based resources (Garcia-Perez et al.,2020). KMDC
becomes relevant once these capabilities emerge from knowledge-creation and sharing
practices within organizations and projects (Faccin et al.,2019).
It is worth noting that the organization will display reasonable KOS traits insofar as it
guarantees an infrastructure supported by IC and KMDC, which will be likely to improve
PM, PP and OP. In this vein, there is evidence in previous literature that IC and KMDC have
a positive impact on some OP variables such as new knowledge acquisition, innovative
performance and financial performance (Garcia-Perez et al.,2020;Ansari et al.,2016;Hsu
and Wang, 2012;Bollinger and Smith, 2001).
Consequently, the present study explores the link between KOS and OP based on the
following research question: How does KOS influence OP, taking into consideration the role
of PM? Following previous research on the topic, our research question is universalistic in
nature (Davison and Martinsons, 2016). The relationships we propose are designed and
assessed using the universalist concepts and measures proposed in previous literature.
2. Conceptual framework
2.1 Knowledge-based organizational support
Organizational support –the way in which the organization promotes its projects to enable
better performance –is considered an important factor for improving PP and achieving
successful results. Previous research identifies intangible organizational support as the most
relevant factor for PP. This includes PM-oriented organizational culture, top management
support for project development, incentive schemes, trust, commitment and open
communication (Fossum et al., 2020;Dosko
cil and Lacko, 2018;Gunasekera and Chong, 2018;
Lin et al., 2018;Aarseth et al., 2011;Gelbard and Carmeli, 2009;Fortune and White, 2006).
In the context of projects, knowledge management is a key success factor. Organizational
support facilitates knowledge management processes in the macro-environment of the
project (the organization) to crucially support its management and performance. However,
organizational support has not been approached from a knowledge-based perspective. For
this reason, the concept of KOS we propose includes two dimensions: IC and KMDC.
On the one hand, IC includes knowledge, skills, stakeholder connections, processes,
routines and individual and collective learning (Oh, 2019;Fuentes-Ardeo et al.,2017;Gasik,
2011). On the other hand, KMDC allows the organization to adapt and move in dynamic
PAGE 994 jJOURNAL OF KNOWLEDGE MANAGEMENT jVOL. 26 NO. 4 2022
environments by reconfiguring knowledge management practices. KMDC and IC are a
manifestation of knowledge-based capabilities facilitating the achievement of objectives
(Garcia-Perez et al.,2020;Paoloni et al., 2020;Martı
´nez-Martı
´nez et al.,2020;Oh, 2019).
Therefore, KOS is defined as knowledge management-supported infrastructure that the
organization uses to assist its PM to attain better performance at both project and
organizational levels. IC refers to the organization’s set of intangible resources, namely,
knowledge, experience, technologies, designs and processes, information and
relationships, etc. The Project Management Institute (PMI) considers IC as one of the
organizational factors influencing PM through support for project planning, development
and execution (PMI, 2017; Bontis, 1998).
When examining in detail how IC is structured, three dimensions stand out: human capital,
structural capital and relational capital. Human capital refers to the individual capabilities,
knowledge, abilities and experience of the project’s members and stakeholders. In turn,
structural capital refers to the mechanisms that the organization makes available to the
project team such as systems, procedures, organizational routines or culture. Such
mechanisms aim to facilitate the accomplishment of the project’s goals in terms of time,
cost, scope and value creation. Finally, relational capital is concerned with the quality of the
firm’s interactions with its internal and external stakeholders, including customers,
suppliers, government, unions, investors, etc. (Alexandru et al., 2020;Garcia-Perez et al.,
2020;Cegarra-Navarro et al.,2019;Bontis, 1998).
KMDC refers to the organizational capabilities that are used to reconfigure knowledge
management practices to adapt to environmental changes. These capabilities arise from
creating and sharing knowledge practices implemented in projects and organizations.
KMDC is important for initiating, planning, executing, monitoring, controlling and closing
projects, as they are conceived as knowledge-based formalized initiatives for the renewal of
the organization (Asiaei et al.,2021;Faccin et al.,2019;Easterby-Smith and Prieto, 2008;
Cepeda and Vera, 2007).
KMDC includes two dimensions: external learning and internal learning. External learning
refers to the firm’s abilities to create and integrate new knowledge by interacting with the
environment and other organizations. With regard to projects, the interaction of the project
members with the project’s macro-environment (the organization) and with the external
environment facilitates new knowledge absorption and integration, benefiting PM.
Conversely, internal learning refers to the new knowledge created by the firm’s own
cumulative experience using its own resources to meet project goals (Wang et al.,2021;
PMI, 2017; Alegre et al., 2013).
2.2 Project management, project performance and organizational performance
International standards such as those published by the PMI and the International Project
Management Association (IPMA) concur that PM is the application of knowledge, methods,
tools, techniques, skills and competencies to project activities to efficiently and effectively
achieve goals through processes, including the integration of the various phases of the
project life cycle (PMI, 2017; IPMA, 2015).
The core functions of PM refer to characteristics, processes, activities or conditions
established throughout the project life cycle that significantly influence its outcome. When
these functions are identified and managed promptly, they facilitate effective decision-
making and improve project results. Currently, PM is understood from an organizational
perspective; that is, projects are considered as temporary organizations in close interaction
with a permanent organization (PMI, 2017; Andersen, 2016;Yun et al., 2016;Winter et al.,
2006).
The outcome of PM is PP. Following previous literature, we define PP as the completion of a
project within the scope, timeline and budget established, assuring end users’ and
VOL. 26 NO. 4 2022 jJOURNAL OF KNOWLEDGE MANAGEMENT jPAGE 995
stakeholders’ satisfaction (Ling et al.,2009;Todorovi
cet al., 2015; PMI, 2017; Irfan et al.,
2019).
Finally, outstanding PP should improve OP. OP refers to measuring organizational results;
that is, evaluating the level of organizational effectiveness. The focus of corporate strategic
management is to improve OP overtime. OP measurement comprises financial and non-
financial metrics. Financial performance includes profitability indicators, sales growth rate
and economic value-added, while non-financial performance covers aspects such as
innovation performance, market share, productivity and quality (Jugdev et al.,2020;Irfan
et al.,2019
;Tseng and Lee, 2014).
3. Research model
According to contingency theory, the chances of project success will increase insofar
as the permanent organization –the macro-environment of the project –has an
infrastructure suitable for PM, consisting mainly of intangible aspects such as KOS. In
turn, organizations will be willing to adopt a PM strategy provided that it leads to a
significant improvement in OP (Aubry and Hobbs, 2011;Lawrence and Lorch, 1967).
Tables 1 and 2display previous empirical evidence supporting direct and indirect
connections (through PM) between KOS and OP.
Given that knowledge is considered the most important intangible resource for PM,
organizational support must focus on building knowledge-based organizational
infrastructures. KOS supports decision-making, facilitates problem-solving and fosters
knowledge creation and knowledge exchange (Han et al., 2019;Le and Lei, 2019;
Fuentes-Ardeo et al., 2017;Liu et al.,2015;Gasik, 2011;Young and Jordan, 2008;
Gosain et al.,2005). PM benefits from all these advantages deriving from KOS
(Table 1). Hence, we put forward the following hypothesis:
H1. KOS has a positive effect on PM.
PM represents an organizational strategy for achieving competitive advantages. As
organizations develop mature PM processes, a significant impact will be generated on
Table 1 Indirect effects of KOS on OP
Effect on PM References Effect on PP (through PM) References
Effect on OP
(through PM and PP) References
Supports decision-
making and facilitates
problem-solving
Young and
Jordan, 2008;
Gosain et al.,
2005;Liu
et al., 2015
Improvement of “hard” aspects of the
project such as time, cost and quality
Albert et al.,
2017;Fossum
et al., 2020;
Sabden et al.,
2020
Enhances
knowledge creation
and competitive
advantage
Jugdev and
Mathur, 2006;
Jugdev et al.,
2019
Fosters knowledge
creation and
knowledge exchange
Han et al.,
2019;Le and
Lei, 2019
Improvement of “soft” aspects of the
project such as motivation,
communication and stakeholders’
management
Gustavsson and
Hallin, 2014;
Larsson et al.,
2018
Table 2 Direct effects of KOS on OP
Direct effects on OP References
Improves employees’ performance Chen et al., 2020;Astuty and Udin, 2020;Ridwan et al., 2020
Improves project team performance Kim, 2017;Haar and Brougham, 2020;Abuzid and Abbas, 2017
Facilitates knowledge exchange and learning processes Yang et al., 2020;Shateri et al., 2020;Correia-Lima et al., 2019
PAGE 996 jJOURNAL OF KNOWLEDGE MANAGEMENT jVOL. 26 NO. 4 2022
project success and on OP. In consequence, project success or failure is closely linked to
the proper application of PM methods and tools, including hard and soft aspects (Fossum
et al., 2020;Sabden et al.,2020;Ronald and Tamara, 2018;Larsson et al.,2018;Albert
et al.,2017
;Gustavsson and Hallin, 2014;Yazici, 2009). PP might be enhanced as a result
of these PM methods and tools (Table 1). Therefore, we hypothesize:
H2. PMhasapositiveeffectonPP.
Organizations are willing to adopt a PM strategy only if it is proven to represent a
source of value creation. However, previous literature has not yet found strong
empirical evidence for this impact. In fact, although the advantages of PM have been
extensively studied, project failure rates remain high, suggesting that further research
is needed to gain a better understanding of this phenomenon (Aubry and Hobbs,
2011;PM, 2017).
Previous studies have recognized that even though the tangible resources of PM are valuable,
they are not sufficient to develop a competitive advantage. It is intangible resources such as
knowledge that lead to competitive advantages (Jugdev et al., 2019;Jugdev and Mathur,
2006). Consequently, because PP generates new knowledge (Table 1), we propose the
following hypothesis:
H3. PP has a positive effect on OP.
Recent research has underscored the positive effects of KOS on OP. More precisely,
recent findings (Table 2) highlight the contribution of KOS to employees’ performance,
project team performance and knowledge exchange and learning processes (Li and
Liu, 2021;Imran and Aldaas, 2020;Park et al.,2020;Yang et al., 2020;Shateri et al.,
2020;Chen et al., 2020;Astuty and Udin, 2020;Ridwan et al., 2020;Haar and
Brougham, 2020;Correia-Lima et al., 2019;Kim, 2017;Abuzid and Abbas, 2017).
Following this line of research, we hypothesize:
H4. KOS has a positive effect on OP.
By testing H4 we will assess the importance of the direct effect of KOS on OP
while including the mediating and indirect effects through PM and PP (H1,H2
and H3). Figure 1 depicts the research model and the hypotheses proposed in
this study.
Figure 1 Research model
VOL. 26 NO. 4 2022 jJOURNAL OF KNOWLEDGE MANAGEMENT jPAGE 997
4. Method
4.1 Sample and data collection
The target population was constructed from public databases in Colombia. The unit of
analysis was the organization. Before launching the survey, a pre-test was conducted with
three PM experts to validate the content of the indicators, as well as to verify the translation
from original sources in the context of the organizations under study (Cegarra-Navarro
et al., 2020;Ferreras-Me
´ndez et al.,2016). These experts were a university professor, a
university researcher and an industry consultant.
Following Bono and McNamara’s (2011) research design recommendations, the information
was obtained from two individuals surveyed in each organization (general manager for
organizational issues and project manager for project issues) to avoid common method
variance bias. An online questionnaire addressed to general managers and project
managers from Colombian organizations was administered from October 2017 to March
2018. We accessed information on a project for each company, corresponding to the latest
project managed by the respondent. We obtained 106 valid questionnaires, which is an
adequate sample to test the model with a statistical power of 80% (Hair et al.,2017;Kock
and Hadaya, 2018;Arias-Pe
´rez et al., 2020). Table 3 summarizes the distribution of the
sample.
4.2 Measures
KOS: This concept is conceived as a multidimensional construct made up of IC and KMDC.
IC is measured with the scale developed by Wang et al. (2016) based on Bontis (1998).
KMDC is measured with the scale proposed by Alegre et al. (2013). These scales are
provided in the Appendix. They were chosen for the following reasons:
䊏they facilitate the connection with the knowledge management literature,
䊏they have been recently published in relevant academic journals; and
䊏their validity and reliability have been satisfactorily tested in previous studies (Alegre
et al., 2013;Villar et al., 2014;Wang et al., 2016;Asiaei et al., 2018).
PM: Following Yun et al. (2016), we conceive PM from the recent core functions perspective
as a multidimensional construct with two latent factors: management of project stakeholders
and project risk management. Core functions are characteristics, processes, activities or
conditions established throughout the project life cycle. They significantly influence its
outcome (PMI, 2017). The PM scale is provided in the Appendix.
Table 3 Sociodemographic characteristics of the participants
Organizations Levels (%)
Sector
Service/trade 64
Industrial 36
Size
Micro 6.5
Small 23.5
Medium 39
Large 31
Age (years)
<20 26.4
[20, 40) 34
40 39.6
PAGE 998 jJOURNAL OF KNOWLEDGE MANAGEMENT jVOL. 26 NO. 4 2022
PP: PP is measured with the scale proposed by Ling et al. (2009), which has a
comprehensive approach, including project delivery, competency at the organizational
level and profitability. This scale is provided in the Appendix and has been previously used
with satisfactory results (Yang, 2013).
OP: OP was measured through return on assets (ROA). ROA has been used extensively in
the management literature to assess OP. Furthermore, it is an objective measurement that
increases the reliability of our analyzes when performed together with perceptual measures
(Bono and McNamara, 2011;Ul-Haq, 2021). The organizations’ financial information was
obtained from Colombian public databases and corresponds to the end of December 2018,
representing a one-year time lag with respect to the online questionnaire applied to collect
primary information. This time lag between the independent variables and the dependent
variable is recommended by Bono and McNamara (2011): KOS and PM need to be
implemented for a period of time to have observable effects on OP.
4.3 Procedure
The variance-based structural equations models technique was applied through estimation
by partial least squares (PLS), using SmartPLS (v. 3.3.3) software. The PLS technique was
used for the following reasons:
䊏the complexity of the structural model, which includes direct and indirect relationships
with third-order constructs;
䊏the use of aggregated scores to model the multidimensional construct following the
three-stage approach;
䊏the use of secondary data, specifically financial indicators, to operationalize OP;
䊏the fact that data do not follow a normal distribution; and
䊏latent variables are composites, which is very common in the knowledge management
research field (Vatamanescu et al., 2020;Cepeda-Carrion et al., 2019;Hair et al., 2019;
Rigdon, 2012).
A three-stage approach was followed to analyze the multidimensional constructs. In the first
stage, the aggregated scores of the first-order dimensions were estimated. In the second
stage, these scores were used to model the second-order constructs. In the third stage, the
aggregated scores of the second-order constructs were estimated and they were used to
model the third-order constructs (Sarstedt et al., 2019).
Following Hayes (2015), the conditional indirect effect was analyzed through the moderated
mediation index. Finally, data analysis was carried out in two stages: the measurement
model was assessed and the structural model was tested (Hair et al., 2017).
The quality of the scales was verified considering the goodness of fit, convergent and
discriminant validity and reliability measures (Hair et al.,2017). Following Henseler et al.
(2016), the goodness of fit was assessed through the following bootstrap-based fit tests
with 5,000 subsamples for the saturated model: standardized root mean square residual
(SRMR) <0.08, SRMR <95% bootstrap quantile (HI95 of SRMR), unweighted least squares
discrepancy (dULS) <95% bootstrap quantile (HI95 of dULS) and geodesic discrepancy
(dG) <95% bootstrap quantile (HI95 of dG).
We used the average variance extracted (AVE) to test convergent validity, accepting values
equal to or above 50% (Fornell and Larcker, 1981). We used the Fornell and Larcker
criterion to assess discriminant validity, verifying that the AVE was greater than the squared
correlation between factors (Fornell and Larcker, 1981). Discriminant validity was also
assessed satisfactorily through the Heterotrait-Monotrait (HTMT) criterion: all values were
lower than or equal to 0.9 (Henseler et al.,2016). Finally, to assess the scales’ reliability we
VOL. 26 NO. 4 2022 jJOURNAL OF KNOWLEDGE MANAGEMENT jPAGE 999
used Cronbach’s alpha, Dijkstra-Henseler’s (
r
A) index and Dillon-Goldstein’s (
r
c) index,
accepting values greater than 0.7 in all of them.
The possible influence of common factor bias was considered using the ex-ante and ex-
post perspectives (Podsakoff et al.,2003). From the ex-ante perspective, two respondents
from each organization were surveyed. The anonymity of participants was respected and all
responses were considered valid; that is, there were no right or wrong answers. The
response scale was different for the dependent and independent variables. Both primary
and secondary sources were used and the organizations’ financial information included a
one-year delay. A pre-test with experts was also carried out before administering the
questionnaire. All these procedures are in line with Bono and McNamara’s (2011)
recommendations.
From the ex-post perspective, Harman’s single factor test was applied. All the indicators
making up the constructs analyzed were included in factor analysis. This model showed an
adequate fit (
x
2: 3,264.677, p: 0.00, df: 860,
x
2/df: 3.80; SRMR: 0.185, RMSEA: 0.163, CFI:
0.370, GFI: 0.280, AGFI: 0.208, NFI: 0.308), suggesting that common method variance does
not represent a significant problem (Podsakoff et al.,2003).
In the structural equations model, the hypotheses were tested through a re-sampling
procedure with 5,000 samples. A one-tail test was performed, reporting R
2
and adjusted R
2
of the endogenous variables, the path coefficient (magnitude, sign), significance (p-value,
confidence interval), endogenous constructs’ variance inflation factor (VIF) and size effect
(f
2
)(Henseler et al.,2016). Because the multidimensional constructs are estimated with the
three-stage approach, the structural model is assessed in the third stage (Sarstedt et al.,
2019).
Additionally, the global fit of the model was analyzed using the following bootstrap-based fit
tests: SRMR <0.08, SRMR <95% bootstrap quantile (HI95 of SRMR), unweighted least
squares discrepancy (dULS) <95% bootstrap quantile (HI95 of dULS) and geodesic
discrepancy (dG) <95% bootstrap quantile (HI95 of dG) (Henseler et al.,2016).
5. Results
5.1 Evaluation of the measurement model
Following Henseler et al. (2016), the goodness of fit of the saturated model was assessed in
the three stages, obtaining SRMR, dULS and dG values lower than the values
corresponding to the 95% quantile, as shown in Table 4. Fit criteria were met in the three
stages.
The evaluation of the measurement model yields favorable results, as all constructs and
dimensions present a Cronbach’s alpha >0.7, AVE >0.5, composite reliability >0.7
(Table 5) and discriminant validity is confirmed (Table 6), confirming that the scales meet
the psychometric properties. Three indicators (IC3, KMDC8 and PP1) were eliminated in the
process for presenting loadings <0.7 and considering that their elimination does not affect
the constructs’ content validity. Table 5 shows the outcome of the model’s first stage,
providing evidence that all validity and reliability criteria are met. These criteria were also
assessed satisfactorily in the second and third stages.
Table 4 Goodness of fit of the saturated model
First stage (first-order constructs) Original sample HI95
SRMR 0.058 0.060
dULS 0.220 0.238
dG 0.082 0.113
PAGE 1000 jJOURNAL OF KNOWLEDGE MANAGEMENT jVOL. 26 NO. 4 2022
5.2 Structural model contrasting
Figure 2 presents the structural model (Model 1) with parameter estimation. Table 7 shows
the results of hypothesis testing. The goodness of fit of the estimated model was assessed,
obtaining SRMR, dULS and dG values lower than the values corresponding to the 95%
quantile (Table 8).
R
2
and adjusted R
2
were used as criteria to analyze the explained variance of the
endogenous variables, yielding these acceptable results: PP (R
2
= 0.198, R
2
adjusted =
0.185), OP (R
2
= 0.030, adjusted R
2
= 0.011), PM (R
2
= 0.073, adjusted R
2
= 0.064).
Table 5 Psychometric properties
First stage (first-order constructs)
Construct Dimension
Cronbach’s
alpha
Composite
reliability (
r
c)
Dijkstra-Henseler’s
(
r
A) AVE
Knowledge-based organizational
support (KOS)
Intellectual capital (IC) knowledge
management dynamic capabilities
(KMDC)
0.673 0.849 0.85 0.739
Project management (PM) Management of project stakeholders
(MPS) project risk management
(PRM)
0.689 0.856 0.847 0.75
Project performance (PP) 0.881 0.914 0.90 0.645
Organizational performance (OP) 1 1 1 1
Note: All loadings are significant and above 0.7
Table 6 Discriminant validity
Fornell-Larcker criterion Heterotrait-Monotrait (HTMT) criterion
Constructs OP PM PP KOS OP PM PP KOS
OP 1.000 OP
PM 0.085 0.866 PM 0.120
PP 0.035 0.439 0.803 PP 0.134 0.534
KOS 0.166 0.267 0.052 0.860 KOS 0.197 0.334 0.127
Figure 2 Model 1
VOL. 26 NO. 4 2022 jJOURNAL OF KNOWLEDGE MANAGEMENT jPAGE 1001
Because we want to achieve a better understanding of the relationships between PP and
OP (not supported) and between KOS and OP (supported with negative coefficient), an
additional test is performed. This test explores the moderating effects using the following
firm variables: age (0: young firms <25years of existence in the market, 1: consolidated
firms >25 years of existence); size (0: small firms <60 employees, 1: large firms >60
employees); and sector (0: trade and services, 1: industrial).
Our main interests are to explain in which scenarios PP can generate a significant impact on
OP and to understand under what conditions the effect of KOS on OP is positive and
significant. The resulting model (Model 2) is depicted in Figure 3; it considers the
statistically significant moderating effects using the orthogonalization method. Table 9
shows the results of the moderated mediation model.
The moderating effects present positive and statistically significant coefficients, thereby
supporting the hypotheses. A theoretical argumentation on the pertinence of these
moderating effects is presented in the discussion section to provide a better understanding
of the conditions under which KOS has a positive effect on OP. Moreover, as the moderated
Table 7 Model 1 hypotheses testing
Structural relation Hypotheses Standardized coefficient p-value Bootstrapping interval 1 at 95% Result
KOS-PM H1 0.267 0.014 (0.080, 0.478) Supported
PM-PP H2 0.439 0.000 (0.325, 0.566) Supported
PP-OP H3 0.044 0.302 (0.095, 0.178) Not supported
KOS-OP H4 0.168 0.009 (0.285, 0.054) Supported with negative coefficient
Notes: p<0.1, p<0.05, p<0.01,
1
Based on 5,000 sub-samples
Table 8 Goodness of fit of the estimated model
Goodnes of fit indices Original sample HI95
SRMR 0.063 0.073
dULS 0.262 0.351
dG 0.085 0.116
Figure 3 Model 2
PAGE 1002 jJOURNAL OF KNOWLEDGE MANAGEMENT jVOL. 26 NO. 4 2022
mediation index is significant, the indirect effect of KOS on OP, mediated by PM and PP, is
conditioned by the sector.
The explained variance of the endogenous variables presents acceptable results: PP (R
2
=
0.193, adjusted R
2
= 0.185), OP (R
2
= 0.154, adjusted R
2
= 0.103), PM (R
2
= 0.071, adjusted
R
2
= 0.062). Moreover, the VIF values of the exogenous constructs are below 5 (KOS:
1.024, PM: 1.000, PP: 1.005), suggesting that our results are not affected by collinearity.
6. Discussion
Organizational support is recognized as one of the most important critical factors for
effective PM (Liu et al.,2015;Young and Jordan, 2008). In this vein, Gelbard and Carmeli
(2009) argue that the interactions between team dynamics and organizational support are
significantly related to budget, functionality and time performance in projects.
Organizational support is approached in the present study from a knowledge-based
perspective (KOS), represented by two dimensions, IC and KMDC, recognizing that
knowledge leads to configuring organizational infrastructures suitable for PM (Fuentes-
Ardeo et al., 2017;Gasik, 2011). The scale proposed to represent this construct meets the
psychometric properties of reliability and validity, which indicates that IC and KMDC, acting
together, are a reasonable representation of KOS. Therefore, this new perspective in
analyzing organizational support represents an important contribution to knowledge
management and PM literature.
Our findings from testing H1 and H2 show that KOS positively and significantly influences
PM and PP. This is consistent with the tenets of contingency theory and advances
understanding on the reasons leading to project success or failure (Mathur et al.,2014;
Young and Jordan, 2008).
H3 and H4 were not fully supported in our first analysis. Empirical management research
usually focuses on contrasting preconceived hypotheses. Nevertheless, data analysis may
hide an analytical value surpassing any a priori conception (Wenzel and Van Quaquebeke,
2018). In fact, according to some recent findings (Al Yami et al.,2021;Seymour and
Hussein, 2014), age and industry could be relevant contextual factors playing a role in our
research model. Therefore, with the aim of achieving a greater understanding of the
relationships between:
䊏PP and OP; and
䊏KOS and OP, additional testing was carried out, which uncovered patterns not
contemplated in the research design.
Hence, our findings provide support for the existence of two moderating factors:
䊏firm age plays a moderating role between KOS and OP; and
䊏business sector moderates the relationship between PP and OP.
Table 9 Model 2
Structural relation Standardized coefficient p-value f
2
Bootstrapping interval
a
at 95% Result
KOS Age –OP 0.228 0.024 0.053 (0.075, 0.418) Supported
PP Sector –OP 0.266 0.001 0.067 (0.149, 0.432) Supported
Mediat moder:
Structural relation Bootstrapping interval1at 95% Result
KOS –PM –PP Sector –OP (0,0078, 0,0703) Supported
Notes: p<0.1, p<0.05, p<0.01,
a
Based on 5,000 sub-samples
VOL. 26 NO. 4 2022 jJOURNAL OF KNOWLEDGE MANAGEMENT jPAGE 1003
Regarding firm age, previous studies have shown that because of the difficulties in
managing knowledge (S
anchez-Polo et al.,2019), the impact of KOS on OP is not direct but
depends on contextual and structural factors.
In fact, for young companies, it is very costly to develop and maintain a knowledge-based
infrastructure because of their lack of financial resources (Al Yami et al.,2021). Therefore,
as young organizations develop a greater KOS, PM and PP are enhanced, but OP remains
unaltered. In the case of consolidated firms, the impact of KOS on OP presents an upward
trend; that is, the organization’s investment in IC and KMDC development is reflected in a
positive impact on OP.
Regarding the second of our moderating effects, results show that in organizations from the
manufacturing sector, PP has a positive effect on OP, while in the trade and service sector,
this effect is not significant. One explanation for this finding is that the manufacturing sector
has been a pioneer in the development of project work (Seymour and Hussein, 2014). We
suggest that the long experience of PM in the manufacturing sector, as compared to
the trade and service sector, has enabled it to configure capabilities for the effective
management of projects, minimizing risks and generating higher success rates and,
consequently, a positive impact on OP. In this vein, some previous studies such as Raz
et al. (2002), have found significant sector effects on PP.
Additionally, we found a significant indirect effect of KOS on OP mediated by PM and PP.
This represents a moderating mediation (Hayes, 2015) and suggests that both PM and
PP in the manufacturing sector are mechanisms that enhance the indirect effect of KOS on
OP. In the trade and service sector, we find the opposite situation.
7. Conclusions
The present study empirically analyzed how KOS influences OP, both directly and mediated
by PM and PP. Structural equation modeling was applied, meeting global fit, validity,
reliability, parsimony and replicability criteria.
Our results show that projects are mechanisms through which KOS generates a positive
and significant impact on OP. KOS is also found to be an important antecedent of PM and
PP. Additionally, we found that firm age plays a moderating role between KOS and OP,
while the business sector moderates the relationship between PP and OP. Moreover, the
indirect effect of KOS on OP, through PM and PP, is found to be conditioned by the sector.
This study makes three contributions. First, it underscores the direct impact of KOS on OP.
Second, it proposes PM and PP as mediating mechanisms enhancing the impact of KOS on
OP. Third, KOS is suggested as an antecedent to PM and PP.
One further contribution of this study is the evidence it provides from Colombia. Like most
emerging economies, Colombia has been understudied in the literature on knowledge-
based support systems and PM. Delving into context-related concerns, previous research
in KOS and PM has been mainly universalistic. As a result, our research framework also
takes a universalistic approach. The relationships we propose and test come directly from
previous universalistic findings. Although our theoretical model was based on Colombian
data, we assume it should also be supported by data from any country, as the relationships
we test are fairly generic.
However, “context is king,” as Davison and Martinsons (2016) rightly point out. As research
in KOS and PM is further developed, theoretical extensions dealing with context (e.g.
emerging economies vs developed economies) are required. The concepts we focus on
and the generic relationships we suggest in this study could be further developed to better
adapt to differentiated contexts in which projects are undertaken. Some recent studies
highlight bureaucratic and hierarchical organizations, difficulties in accessing financing
resources, economic instability and corruption as some features that project managers in
PAGE 1004 jJOURNAL OF KNOWLEDGE MANAGEMENT jVOL. 26 NO. 4 2022
emerging economies may have to deal with (Rincon-Gonzalez et al., 2019;Guerrero and
Urbano, 2020). Further developments including these specific features would provide a
more detailed picture of the interactions between KOS, PM and performance in
differentiated contexts.
These findings have important implications for decision-making. They provide a greater
understanding of the conditions under which KOS generates a significant impact on OP, in a
direct manner and through projects. Therefore, allocating resources to configure a knowledge-
based infrastructure, through the development of IC and KMDC, is a vital management task.
As for IC, the organization should promote the development of the capabilities, knowledge,
skills and experience of the project manager and team, as well as those of the organization’s
personnel charged with offering advice, support and assistance to PM. Managers should
also improve the mechanisms, as well as the hard and soft resources required to guarantee
the achievement of project goals, while at the same time strengthening the quality of
the interaction between the firm and its internal and external stakeholders. Regarding KMDC,
we recommend facilitating the interaction between the project manager and team with the
macro-environment of the project (organization), as well as with the external environment,
enabling absorption, integration and creation of new knowledge.
This study also has some limitations. A cross-sectional design was used, so longitudinal
analyzes could be conducted in future research to gain a better understanding of the
phenomenon. Different levels of analysis could also be considered (organization, project
and stakeholders) to perform multilevel analyzes. Furthermore, a non-probabilistic sample
was used for this study; the results should, therefore, be generalized with caution. Finally,
OP was analyzed from a financial perspective, so future research could include non-
financial aspects for a more comprehensive view.
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Appendix
Questionnaire
A) Knowledge-based organizational support
-Intellectual capital Likert scale ranging from (1) strongly disagree to (7) strongly agree
-Knowledge management dynamic capabilities Likert scale ranging from (1) strongly
disagree to (7) strongly agree.
Please state the performance of your company as compared with your competitors in the
following terms:
Human capital
IC1 Employees hold suitable work experience for accomplishing their job successfully in our
company
IC2 Employees of our company have excellent professional skills in their particular jobs and
functions
IC3 The company provides well-designed training programs
IC4 The employees of our company often develop new ideas and knowledge
IC5 Employees are creative in our company
Structural capital
IC6 The overall operations procedure of our company is very efficient
IC7 Our company responds to changes very quickly
IC8 Our company has an easily accessible information system
IC9 Systems and procedures of our company support innovation
IC10 Our company’s culture and atmosphere are flexible and comfortable
IC11 Our company emphasizes new market development investment
IC12 There is a supportive culture/atmosphere between the departments of our company
Relational capital
IC13 Our company maintains appropriate interactions with its stakeholders
IC14 Our company maintains long-term relationships with customers
IC15 Our company has many excellent suppliers
IC16 Our company has good, stable relationships with its strategic partners
Source: Wang et al. (2016),Bontis (1998)
External learning competence
KMDC1 Ability to obtain information about state-of-the-art scientific and technological
developments through technological surveillance systems
KMDC2 Effective and updated competitive intelligence
KMDC3 Ability to create knowledge through cooperation with industry associations
KMDC4 Ability to create knowledge through cooperation with R&D institutions such as
universities and technological institutes
KMDC5 Technology acquisition (patents, equipment, etc.)
Internal learning competence
KMDC6 Degree of academic qualification of employees in the R&D function
KMDC7 Ability to be positioned on the technological front line/frontier
KMDC8 Ability to manage the innovation effort
KMDC9 Ability to assess innovation projects
KMDC10 Suitability of human resources devoted to the R&D function
KMDC11 Ability to coordinate and integrate the different innovation project phases and the
consequent inter-functional interphases between engineering, production and marketing
Source: Alegre et al. (2013)
VOL. 26 NO. 4 2022 jJOURNAL OF KNOWLEDGE MANAGEMENT jPAGE 1011
B) Project management Likert scale ranging from (1) strongly disagree to (5) strongly
agree.
C) Project performance Likert scale ranging from (1) expectations are not strongly met to (7)
expectations are strongly exceeded.
About the authors
Claudia-Ine
´s Sep
ulveda-Rivillas is an Associate Professor in the Department of
Administrative Sciences at the Faculty of Economics Sciences at the University of Antioquia
in Colombia. Her teaching and research interests include corporate finance, project
management and strategic management using quantitative methodologies, mainly
structural equation models.
Joaquin Alegre is a Professor in Innovation Management at the University of Valencia. His
teaching and research interests focus on different issues dealing with the innovation
process within organizations. Innovation, organizational learning and knowledge
management are frequent topics in his research. His investigations have focused on the
organizational level and on the employees’ level. He has been the principal investigator in
four competitive research projects funded by the Spanish Government. He has published
his findings at top journals such as Research Policy, Journal of Product Innovation
Management, Technovation or British Journal of Management. Joaquin Alegre is the
corresponding author and can be contacted at: joaquin.alegre@uv.es
Effective management of project team (EMP)
PM10 Team members were familiar with the project execution plan and used it to manage their
work
PM12 The project team was well aligned in terms of objectives and expectations
PM18 The project management team was made up of appropriate personnel
PM19 The people worked effectively as a team on the project
PM22 Key members of the project team understood the goals and objectives of the project
owner
Management of interaction with stakeholders (MIS)
PM21 The interrelationships among the project stakeholders were well managed
PM29 The plan and progress, including changes, were clearly and frequently communicated to
project stakeholders
PM30 There was a high degree of trust, respect and transparency among the companies that
worked on the project
PM33 When problems arose, effective mechanisms existed to ensure that they were solved
Project risk management (PRM)
PM15 The project had an effective process of risk identification and management
PM23 All key members of the project team were involved in the risk assessment process
Source: Based on Yun et al. (2016), PMI (2017).
PP1 Budget performance (actual cost versus budget)
PP2 Schedule performance (actual versus plan)
PP3 Quality performance
PP4 Owner satisfaction
PP5 Profitability
PP6 Public satisfaction (with the project)
PP7 Project scope
Source: Ling et al. (2009).
PAGE 1012 jJOURNAL OF KNOWLEDGE MANAGEMENT jVOL. 26 NO. 4 2022
Victor Oltra is an Associate Professor of Management at the University of Valencia. His
teaching and research interests revolve around topics such as innovation, knowledge
management, human resource management, business ethics and corporate social
responsibility. He has published his research outcomes in a diversity of leading
international journals such as Journal of Knowledge Management, Human Resource
Management, Journal of Business Ethics, International Journal of Human Resource
Management, Corporate Social Responsibility and Environmental Management or Business
Ethics: A European Review.
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