6 Project management importance

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The influence of the eye of
competence on project success:
exploring the indirect effect of
people on both perspective
and practice
La influencia del ojo de la
competencia en el �exito del
proyecto: explorando el efecto
indirecto de las personas en la
perspectiva y la pr�actica
Cristiane Esteves Cruz, Gabriela Scur and
Ana Paula Vilas Boas Viveiros Lopes
Department of Production Engineering, Centro Universit�ario FEI,
S~ao Bernardo do Campo, Brazil, and
Marly M. Carvalho
Engenharia de Produç~ao, Universidade de S~ao Paulo, Sao Paulo, Brazil
Abstract
Purpose –There is a lack of investigation on three areas of competence in the Individual Competence Baseline
4 (ICB4) (IPMA). Furthermore, some studies pointed out the importance of soft skills over hard skills, but this
relationship was not explored from the project manager’s competence perspective. This paper aims to analyze
the influence of project manager competencies on project success.
Design/methodology/approach – The survey involved 100 Brazilian project management professionals.
Structural equationmodeling (SEM) using a partial least squares (PLS) approachwas employed for data analysis.
Findings – The competence people was the protagonist of all project success. It affects practice with indirect
effects on the impact on the customer. The paper highlights the project manager’s soft skills in reaching
customer perception. Besides, the competence people also impacts perspective and, indirectly, preparation for
the future.
Research limitations/implications – Personal and interpersonal skills enable the project manager to
interrelate with the project environment (organization strategy, governance, structures, processes, standards,
power and interest, culture and values) and, therefore, to open a panorama for opportunities as a new market,
product or technology. Thus, the new competence area perspective introduced in ICB4 brings an important
insight for this research and future studies.
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JEL Classification — J2, M0, M1, M5, M100
Management area: management
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1012-8255.htm
Received 25 November 2021
Revised 24 May 2022
29 July 2022
Accepted 17 September 2022
Academia Revista
Latinoamericana de
Administraci�on
Vol. 35 No. 4, 2022
pp. 516-536
© Emerald Publishing Limited
1012-8255
DOI 10.1108/ARLA-11-2021-0218
https://doi.org/10.1108/ARLA-11-2021-0218
Originality/value – Besides investigating the effect of behavioral competencies on project performance, this
research addressed the importance of looking at the indirect effects when exploring models and testing
hypotheses for a complete understanding of the relationship between variables.
Keywords Project manager skills, Competence, Project success, ICB4, IPMA
Paper type Research paper
Resumen
Prop�osito – Hay una falta de investigaci�on en tres �areas de competencia del ICB4 (IPMA). Adem�as, algunos
estudios han se~nalado la importancia de las habilidades blandas en las habilidades duras, pero esta relaci�on no
ha sido explorada desde la perspectiva de la competencia del gerente de proyecto. El objetivo de esta
investigaci�on es analizar la influencia de las competencias del director de proyectos (ICB4) en el �exito del
proyecto.
Metodolog�ıa – La encuesta involucr�o a 100 profesionales brasile~nos de gesti�on de proyectos. Para el an�alisis
de datos se emple�o un modelo de ecuaciones estructurales utilizando un enfoque de m�ınimos cuadrados
parciales (PLS).
Resultados – La competencia “Personas” fue la protagonista de todo el�exito del proyecto. Tiene efectos sobre
la “Pr�actica” con efectos indirectos sobre el “Impacto en el Cliente”. El art�ıculo destaca la influencia de las
habilidades blandas del gerente de proyecto en la percepci�on del cliente. Adem�as, “Personas” tambi�en incide en
“Perspectiva” y, con efectos indirectos, en “Preparaci�on para el Futuro”.
Originalidad – adem�as de investigar el efecto de las competencias de comportamiento en el desempe~no del
proyecto, esta investigaci�on abord�o la importancia de observar los efectos indirectos al explorar modelos y
probar hip�otesis para una comprensi�on completa de la relaci�on entre las variables.
Implicaciones de la investigaci�on – Las habilidades personales e interpersonales permiten al director del
proyecto relacionarse con el entorno del proyecto (estrategia organizacional, gobierno, estructuras, procesos,
est�andares, poder e inter�es, cultura y valores) y, por lo tanto, abren un panorama de oportunidades como
nuevas mercados, productos o tecnolog�ıa. As�ı, la nueva “Perspectiva” del �area de competencia introducida en
ICB4 trae una visi�on importante para esta investigaci�on y para estudios futuros.
Palabras clave Habilidades del director de proyectos, Competencia. �Exito del proyecto, ICB4, IPMA
Tipo de papel Trabajo de investigaci�on
1. Introduction
Projects are required in a competitive world marked by several changes; besides driving
innovation, it makes organizations more agile and efficient (Andersen and Grude, 2018).
Therefore, recognizing the strategic importance of project management in the corporate
environment is the focus of organizational leaders (Aubry and Hobbs, 2011; Yang et al., 2014).
However, according to Kerzner (2019), few firms recognize project management as a core
competence. The author points out some reasons for this, such as firms seeing project
management as a scheduling tool for the employees and executives failing to endorse the
advantages it could bring. Moreover, if project management is seen as a core competence, it
could bring the decision-making to project managers and, thus, undermine the executive’s
power and authority.
Project managers are recruited according to their competencies, which refer to a
collection of knowledge, personal attitudes, skills and relevant experience. These are core
assets that must be taken seriously (Chen et al., 2019), and some recent studies have devoted
attention to the competencies demanded in the selection process (Vale et al., 2018),
competence-building trajectories (Takey and Carvalho, 2015) and on competence retention
(Ekrot et al., 2016).
Although there is a wide range of studies portraying project managers’ skills, Baccarini,
(1999), Cheng et al. (2005), Ahsan et al. (2013), Chipulu et al. (2012) and Nahod and Radujkovi�c
(2013) found that the competencies of the project manager have not been sufficiently
investigated in real-life executed projects. Moradi et al. (2020) argue that there still is a gap in
considering the context of the project and the probable discrepancies between the standards
of practices and the empirical results.
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We found many references on the project management competencies in the practitioner
literature as the Individual Competence Baseline (ICB) from IPMA. Despite this, it is possible
to notice several unsuccessful projects (Varaj~ao et al., 2018). A bibliometric analysis shows
few studies on the lens of the eye of competence areas proposed by ICB4 (IPMA, 2015).
Despite some studies investigating the effect of behavioral competencies on project
performance (Gruden and Stare, 2018, M€uller and Turner, 2010a, b), there is a lack of
investigation on the three areas of competencies suggested in the eye of competence, i.e.
personal, perspective and practice (IPMA, 2015). Moreover, there is a lack of understanding of
the relationship between them. Some studies pointed out the importance of soft skills over
hard skills (Carvalho and Rabechini, 2015), but this relationship was not explored from the
project manager’s competence perspective. Sampaio et al. (2021) also argue that there is a
shortage of quantitative analysis on this topic.
Therefore, the article aims to analyze the influence of the project manager’s competencies
(ICB4) on project success. Further, it explores the relationship among the areas of the eye of
competence, investigatingthe effect of personal competencies on practice and perspective.
The research design was survey-based research applying structural equation
modeling (SEM).
This study contributes to the literature showing that the competence “people” is the
protagonist for all project success dimensions. Besides, personal competence influences
practice competence with significant indirect effects on the impact on “customer”. Thus, it
highlights the project manager’s influence of soft skills (personal competencies) on hard skills
(practice competencies). Besides, “people” competence also impacts “perspective” with
indirect effects on “preparation for the future”. It means that personal competencies enable
the project manager to interrelate with the project environment organization strategy,
governance, structures, processes, standards, power and interest, culture and values) and,
therefore, to open a panorama for new opportunities.
2. Literature review
2.1 Project management competences
Over the years, many researchers have sought to define the concept of competencies. “A
competency is a set of related but different sets of behavior organized around an underlying
construct” (Boyatzis, 1982). The OPM (US Office of Personnel Management) defines
competency as “a measurable pattern of knowledge, skill, abilities, behaviors, and other
characteristics that an individual needs to perform work roles or occupational functions
successfully”. “Competence is a combination of knowledge (qualification), skills (ability to do
a task), and core personality characteristics that lead to superior results” (Crawford, 2007).
Vukomanovi�c et al. (2016) argue that individuals’ competence comes from possessing
attributes (such as knowledge, skills, values and attitudes) and proof of good performance.
Alvarenga et al. (2019) define competence as a combination of knowledge, skill and attitude.
Similarly, IPMA (2015) described individual competence as applying knowledge, skills and
abilities to achieve the desired results. Pinto et al. (2017) argue that effective project managers
have technical capabilities and critical managerial attributes, including problem-solving and
behavioral management skills.
Stevenson and Starkweather (2010) identified leadership, communication skills in the
various spheres, verbal and written skills, attitude and ability to deal with ambiguity as
critical skills of the project manager. Gruden and Stare (2018) found the most effective
behavioral competencies to improve the successful delivery of projects. These competencies
are leadership, results-orientation, assertiveness, reliability and efficiency. Similarly,
Sampaio et al. (2021) identified leadership, communication, result orientation, emotional
intelligence, ethics, creativity and motivation as critical to project success.
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The project manager must have other orientations besides simply technical aspects since
each phase of the project demands a different approach: methods and techniques of project
management are crucial in the initial stage and planning of an event, while during execution,
relationships and communication can determine the success of the project (Cserh�ati and
Szab�o, 2014). By focusing only on objective criteria and using traditional management
techniques, the project manager can cause the perception of project failure in front of
stakeholders (Wateridge, 1999). Therefore, the project manager must have both skills known
as hard, associated with management issues, and soft, associated with leadership issues
(S€oderlund and Maylor, 2012; Gustavsson and Hallin, 2014).
M€uller and Turner (2007b) associated the impact of the project manager’s leadership style
and the type of project on project success. Alvarenga et al. (2019) and Sang et al. (2018)
investigated project managers and found that leadership is one of the most critical skills for
promoting project success. In another research, M€uller and Turner (2007a) studied leadership
styles based on the three main competencies: emotional, managerial and intellectual.
Emotional competence is associated with motivation, meticulousness, sensitivity, influence,
self-awareness, emotional resilience and intuition. Managerial competence relates to resource
management, communication, development, training and reach. Finally, strategic
perspective, vision and critical thinking are part of intellectual competence (M€uller and
Turner, 2007a).
Standards represent a common norm or procedure consensus-built, repeatable to a desired
result. Usually, standards can be descriptive or prescriptive; the vast majority of literature on
project management is based on process-oriented standards (Vukomanovi�c et al., 2016).
However, according to Nahod and Radujkovi�c (2013), processes alone are not enough in terms
of management. The competencies of those leading the project are also necessary.
Vukomanovi�c et al. (2016) argue that a competency-based standard allows firms to possess
employees who can perform a project, program and portfolio tasks. It will act as a
complementary tool to processes and procedures. Nijhuis et al. (2015) found that previous
studies on project management competencies have used no standard set of project
management competencies.
The IPMA ICB for the individual project, program and portfolio management (3 PM)
competencies version 3 (ICB3) was launched in 2006, proposing the model known as “the eye
of competence”, which presents contextual, technical and behavioral competences. Version 4
(ICB4) was launched on IPMA’s 50th anniversary in 2015. ICB4 competencies are represented
by the three competence areas: people, practice and perspective focusing on the individual
development of the people involved in project management.
The literature demonstrated a varying understanding of behavioral competencies
(Gruden and Stare, 2018). The competence area people encompasses personal and
interpersonal skills necessary to succeed in projects, programs and portfolios (IPMA,
2015). According to ICB4, all individual competence starts with the ability to self-reflect and
self-management and finishes with the task successfully fulfilled in terms of outcomes and
the satisfaction of the stakeholders (result orientation). Among these competencies are eight
more: personal integrity and reliability; personal communication; relationships and
engagement; leadership; teamwork; conflict and crisis; resourcefulness and negotiation and
however a lack of a uniform list of competencies (Nijhuis et al., 2015). At the same time, the
competence area practice comprehends technical skills in projects, programs and portfolio
management to realize their success. It defines 14 competence elements: project design,
requirements and objectives, scope, time organization and information, quality, finance,
resources, procurement, plan and control, risk and opportunities, and stakeholder.
Although knowledge, skills, and abilities are crucial, we must always put them into a
particular context. Thus, ICB4 considers another competence area, described as perspective.
Every project and program is influenced by organizational societal and political contexts.
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Indeed, there are formal and explicit goals andmore informal and implicit interests regarding
the different stakeholders. Thus, perspective comprises five competence elements
encompassing tools, methods and techniques through which people interact with the
environment, such as strategy, governance, structures and processes, compliance, standards
and regulations, power and interest, and culture and values.
2.2 Project success
The vision of project success has been transformed over the years (Atkinson, 1999; Jugdev
and M€uller, 2005; Kerzner, 2019; Toor and Ogunlana, 2010; Serrador and Turner, 2015). For
many years, the predominant measurement of project success has been the performance of
the iron triangle, a term coined by Atkinson (1999) to indicate cost, time and quality criteria.
Currently, the definition of projectsuccess includes more comprehensive measures, such as
completion of the project within the allotted time, the budgeted cost, the appropriate
performance or specification level, with acceptance by the customer, with minimal or
mutually agreed scope changes, andwithout changing themainworkflow and organizational
culture (Serrador and Turner, 2015).
Pereira et al. (2021) argue that determining the success of a project can be ambiguous and
difficult to measure, mainly because it is challenging to reach all the criteria raised by the
literature. Baccarini (1999) already pointed out that success is not necessarily achieved in all
aspects and can be partial.
Project success is a multifaceted construct (Pinto and Slevin, 1988) that varies throughout
the project’s life cycle (Jugdev and M€uller, 2005), in addition to being influenced by
stakeholders (De Wit, 1988). Shenhar et al. (2001) combined qualitative and quantitative
methods to develop a multidimensional framework for assessing project success in four
dimensions: project efficiency (schedule and budget); impact on customer (functional
performance, technical specifications, customer needs, customer problem-solving, product
use and customer satisfaction); organizational success (commercial success and increase in
market share); preparation for the future (newmarket, new product line and new technology).
2.3 Conceptual model and research hypotheses
This paper analyzes the influence of project managers’ competencies (ICB4) on project
success. Voukomanovic et al. (2016) argue that a competent person possesses the attributes
(the input and personal competencies) necessary for job performance (the output
competencies). Similarly, Nahod and Radujkovi�c (2013) state that competencies are related
to job performance (immediate business success, preparation for the future, efficiency and
impact on the customer). Therefore, following the IPMA eye of competence, we built the
conceptual model (Figure 1).
Several authors study project success as a multidimensional construct (Shenhar and Dvir,
2007; Carvalho and Rabechini, 2015; Martens and Carvalho, 2016, 2017) and suggest a
hierarchical measuring model (Wetzels et al., 2009). There are four combinations of
hierarchical measurement models (Jarvis et al., 2003), and we decided on Type I, which is
reflective first-order and reflective second-order variables. Thus, we designed success as a
multidimensional strategic concept adapted from Shenhar and Dvir (2007) with two second-
order latent variables, business success (BS) and project success (PS), as shown in Figure 2.
We deployed BS into two first-order latent variables: direct success (BSDS) and preparation
for future (BSPF). PS has three first-order latent variables: efficiency (PSE), impact on the
customer (PSIC) and impact on the team (PSIT).
The emotional intelligence of project managers is a fundamental competence to sustain
high-quality decision-making and development, directly influencing project success (Mazur
et al., 2014). The study byMir and Pinnington (2014) demonstrated that the project manager’s
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performance explains at least 44.9% of the variance in the project’s success in project-based
organizations in the United Arab Emirates. Therefore, the following hypotheses are
considered:
H1a. There is a statistically significant and positive relationship between the project
manager’s competence “people” and business direct success.
H1b. There is a statistically significant and positive relationship between the competence
of the project manager “perspective” and business direct success.
H1c. There is a statistically significant and positive relationship between the competence
of the project manager “practice” and business direct success.
H2a. There is a statistically significant and positive relationship between the project
manager’s competence “people” and efficiency.
H2b. There is a statistically significant and positive relationship between the competence
of the project manager’s “perspective” and efficiency.
H2c. There is a statistically significant and positive relationship between the competence
of the project manager “practice” and efficiency.
H3a. There is a statistically significant and positive relationship between the competence
of the project manager “people” and the impact on the customer.
H3b. There is a statistically significant and positive relationship between the competence
of the project manager’s “perspective” and the impact on the customer.
H3c. There is a statistically significant and positive relationship between the competence
of the project manager’s “practice” and the impact on the customer.
H4a. There is a statistically significant and positive relationship between the project
manager’s competence “people” and preparation for the future.
Figure 1.
Conceptual model
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H4b. There is a statistically significant and positive relationship between the project
manager’s “perspective” competence and preparation for the future.
H4c. There is a statistically significant and positive relationship between the competence
of the project manager’s “practice” and preparation for the future.
H5. There is a statistically significant and positive relationship between the competence
of the project manager “people” and the competence of the project manager
“practice”.
H6. There is a statistically significant and positive relationship between the competence
of the project manager “people” and the competence of the project manager
“perspective”.
3. Research methods
This paper aims to analyze the influence of project managers’ competencies on project
success. Consequently, we adopted a quantitative and confirmatory research design through
a survey for the data collection (Forza, 2002) and SEM for data analysis (Hair et al., 2014).
Figure 2.
Measurement model
validation: path
algorithm output
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3.1 Sample and data collection
We considered the population of interest and project managers with at least three years of
experience in this area. For dimensioning sample size and analyzing internal validity (Forza,
2002), we used a computer aid approach applying G*Power 3.1.9.2 software (Faul et al., 2009).
We analyzed the sample size through the power test (Cohen, 1988). We took into account
the effect size of 0.15 (average value), the power level of 0.80, the maximum allowed error of
5% (Cohen, 1988) and the number of predictors equal to five, accordingly, the research model
(see Figure 1). The test result returns a suggested sample size of 92 respondents. Thus, a post-
hoc compute achieved power in G*Power shows a power equal to 0.8430 for our sample of 100
respondents.
A questionnaire (Appendix) composed of three sections (C-1 characterization of the
respondent, the company and the project, C-II competencies of the project manager and C-III
project performance) was used to collect the data based on the literature review. The first
version of the questionnaire was validated and adjusted with a pilot sample of five project
managers (Netemeyer et al., 2003). The validated questionnaire was sent to professionals with
the role of project manager via LinkedIn. We sent 245 questionnaires; of which, 100 (41%)
were answered and validated. The five-point Likert scale was used in the research questions.
To avoid the project managers’ perceptions of performance being biased in predictable
ways, we use other performance indicators that are not directly linked to the perception of
success (Meier and O’Toole, 2013). Similar to Zuo et al. (2018), we used neutral linguistic terms
in the Likert scale to avoid biases in the respondents’ subjective judgments. Another strategy
to prevent bias was that the competencies were randomized and not grouped according to
any hierarchical model (Alvarenga et al., 2019).
3.2 Variable operationalization
Aligned with the literature review and the research instrument presented previously, we
designed the measurement model that relates the constructto their manifest variables
(Tenehaus et al., 2005).
We operationalized the three independent latent variables (people, practice, and
perspective) as reflective, following the theoretical structure of IPMA eye of competence
(IPMA, 2015), which is appropriate for the target theoretical constructs (Hair et al., 2014).
The four dependent latent variables (business direct success, preparation for the future,
efficiency, and impact on the customer) were operationalized accordingly to the project
success definition proposed by Shenhar and Dvir (2007). Similarly, these measurement
models were designed as reflective as previous confirmatory work by Carvalho and
Rabechini (2015).
3.3 Data analysis
SEM is a multivariate data analysis technique that allows the simultaneous estimation of
multiple dependency relationships between latent variables (Hair et al., 1995). Dependent
variables are called endogenous variables and are predicted by other endogenous constructs.
The independent variables are exogenous and are not predicted by other variables in
the model.
A distinction is made between two families of SEM: one based on covariance and the other
based on partial least squares (PLS-SEM) applied in this study. The PLS model is composed
of the measurement model (it relates the measurable manifest variables with its latent
variables) and the structural model (it relates the latent variables to each other) (Tenehaus
et al., 2005). PLS-SEM was selected based on Hair et al. (2014) recommendation, mainly
because the theoretical model is complexwithmany constructs (seven latent variables), many
manifest variables (see Appendix), and the variables do not need to be normal.
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We applied the SmartPLS 3.0 software (Bido et al., 2014) to validate the measurement and
structural models. The structural model was validated by applying a bootstrapping
algorithm with 5,000 resamplings.
4. Results
4.1 Sample demographics
The sample was primarily composed of administrators, engineers and information
technology professionals, and significant male representation (91%). Regarding their
educational level, 70% have only bachelor’s degree, 23% are postgraduates, 6% have
master’s degree and 1% has doctoral degree. The experience as project manager was also
analyzed, with 85% having more than five years working in this function.
The projects surveyed can be considered relevant once 78%have a high investment (more
than US$ 250 thousand), and 69% have more than ten team members. Besides, they are
represented by different sectors, with a predominance of companies in energy and gas (22%),
information technology (21%) and machines and equipment (16%). The primary source of
capital was foreign, with 65% from multinational companies.
4.2 Measuring model validation
We applied a path algorithm in SmartPLS 3 software to validate all variable measurement
models. The indicators’ reliability of all latent variables showsmanifest variableswith significant
loading factors higher than 0.6 and average variance extracted (AVE) higher than 0.5 (seeTable 1
and Figure 2). The discriminant validity was analyzed through the AVE’s square root, which
should be higher than the correlation among the variables (see diagonal in Table 1). The AVE of
all variables demonstrates the convergent validity with values superior to 0.5. Finally, composite
reliability (CR) shows internal consistency with values superior to 0.7 (see Table 1).
The last validation of themeasurement model is performed by the discriminant validation
of the constructs, by the Fornell–Larcker calculation, whose result can be seen in Table 2.
4.3 Structural model and hypothesis testing
4.3.1 Model and direct effects. We tested the structural model and hypotheses by applying
bootstrapping simulation with 5,000 resamplings in the SmartPLS 3.0 software (see Table 3
and Figure 3).
PEOPLE PERSPECTIVE PP_BS PP_E PP_IC PP_PF PRACTICE
PEOPLE 0.727
PERSPECTIVE 0.627 0.729
PP_BS 0.260 0.272 0.855
PP_E 0.288 0.150 0.281 0.893
PP_IC 0.275 0.113 0.472 0.310 0.763
PP_PF 0.213 0.263 0.440 0.232 0.108 0.938
PRACTICE 0.642 0.686 0.297 0.288 0.289 0.123 0.725
Composite reliability
(CR)
0.871 0.849 0.845 0.887 0.874 0.936 0.869 > 0.7
Average variance
extracted (AVE)
0.529 0.531 0.731 0.797 0.583 0.879 0.526 > 0.5
Note(s):The diagonal contains theAVE square root, which is higher than the correlation among variables. All
correlations are significant at the 1% level. The discriminant validity was analyzed through the AVE’s square
root, which should be higher than the correlation among the variables. The AVE of all variables demonstrates
the convergent validity with values superior to 0.5. Composite reliability (CR) shows internal consistency with
values superior to 0.7
Table 1.
Measurement model
validation
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Cross-loading PEOPLE PERSPECTIVE PP_BS PP_E PP_IC PP_PF PRACTICE
EP01 0.241 0.150 0.210 0.905 0.348 0.180 0.301
EP02 0.274 0.116 0.297 0.881 0.197 0.237 0.209
IC01 0.196 0.103 0.432 0.180 0.722 0.091 0.138
IC02 0.316 0.099 0.306 0.417 0.859 0.043 0.346
IC03 0.147 0.084 0.445 0.106 0.778 0.183 0.184
IC04 0.035 �0.001 0.357 �0.002 0.712 0.071 0.071
IC06 0.156 0.102 0.414 0.144 0.736 0.084 0.144
PEO04-2 0.699 0.615 0.361 0.125 0.192 0.241 0.491
PEO05-1 0.699 0.365 0.136 0.310 0.287 0.252 0.477
PEO06-3 0.763 0.376 0.109 0.207 0.094 0.184 0.485
PEO06-4 0.770 0.428 0.058 0.119 0.088 0.083 0.402
PEO08-1 0.711 0.447 0.128 0.297 0.151 0.067 0.486
PEO10-1 0.718 0.445 0.267 0.189 0.348 0.067 0.430
PER01-1 0.505 0.806 0.265 0.229 0.143 0.211 0.589
PER03-1 0.261 0.629 0.193 0.039 �0.014 0.284 0.445
PER04-1 0.455 0.781 0.144 0.082 0.074 0.133 0.476
PER04-2 0.403 0.708 0.134 0.123 �0.018 0.196 0.467
PER05-2 0.578 0.705 0.230 0.045 0.162 0.165 0.499
PF01 0.227 0.283 0.462 0.243 0.149 0.954 0.147
PF02 0.166 0.202 0.350 0.186 0.041 0.921 0.076
PRA01-1 0.322 0.527 0.244 0.106 0.122 0.114 0.718
PRA03-1 0.450 0.412 0.206 0.260 0.302 0.066 0.760
PRA08-1 0.415 0.524 0.179 0.221 0.200 0.026 0.728
PRA10-3 0.477 0.469 0.209 0.246 0.130 0.176 0.756
PRA11-1 0.423 0.471 0.200 0.233 0.288 0.020 0.695
PRA13-1 0.625 0.577 0.249 0.166 0.190 0.127 0.691
SO01 0.223 0.209 0.864 0.273 0.474 0.298 0.281
SO02 0.222 0.258 0.846 0.206 0.330 0.459 0.225
Note(s): Fornell–Larcker calculation performed the discriminant validation of the constructs
Original
Standard
deviation T-statistics
Hypothesis Sample (O) (Stdev) (jO/Stdevj) p values R square
PEOPLE → PP_BS H1a 0.087 0.126 0.692 0.489 0.101
PERSPECTIVE → PP_BS H1b 0.099 0.174 0.570 0.569
PRACTICE→ PP_BS H1c 0.173 0.143 1.207 0.228
PEOPLE → PP_E H2a 0.229 0.152 1.503 0.133 0.115
PERSPECTIVE → PP_E H2b �0.170 0.156 1.090 0.276
PRACTICE→ PP_E H2c 0.258 0.195 1.323 0.186
PEOPLE → PP_IC H3a 0.229 0.126 1.824 0.068 0.125
PERSPECTIVE → PP_IC H3b �0.241 0.171 1.412 0.158
PRACTICE → PP_IC H3c 0.307 0.140 2.201 0.028
PEOPLE → PP_PF H4a 0.136 0.135 1.008 0.314 0.085
PERSPECTIVE → PP_PF H4b 0.290 0.153 1.897 0.058
PRACTICE→ PP_PF H4c �0.163 0.151 1.076 0.282
PEOPLE → PRACTICE H5 0.642 0.054 11.957 0.000 0.412
PEOPLE → PERSPECTIVE H6 0.627 0.058 10.790 0.000 0.393
Note(s): Italic values represent significant
Table 2.
Cross-loading
Table 3.
Structural model
validation: direct
effects
The influence
of the eye of
competence
525
For the one side, Table 2 shows first that hypotheses H1–H4 relating the eyes competencies
with project success variables were validated for two latent dependent variables (preparation
for future – PP_PF and impact on the customer_– PP_IC) but by different independent
variables. Two types of competencies (people and practice) affect the PP_IC, validating H3a
and H3c, while PP_PF is affected by the perspective competence (H4b). No significant direct
effects were identified for the other independent variables (business direct success – PP_BS
and efficiency PP_E).
Looking at the dependent variable perspective, we can see that the competence eye variables
affect the project success variables differently. However,the overall magnitude of the effect is
small, with 11.5% on PP_E, 12.5% on PP_IC, 10.1% on PP_BS and 8.5% on PP_PF.
On the other side, the hypotheses relating the people’s competencies with the two others
(Practice –H5 and Perspective –H6) were validated significantly at the 1% level. Besides, the
magnitude of the effect is substantial in both 41.2% on practice and 39.3% on perspective. It
is essential to look at the indirect effects, as discussed in the following section.
4.3.2 Exploring the indirect and total effects. The strong effect of people on practice and
perspective competencies shows the crucial role of the indirect impacts in the research model.
Note that the people latent variable has potential indirect effects in all four dependent latent
variables, besides the direct effect, according to the research model (see Figure 3).
Figure 3.
The structural model
validated
ARLA
35,4
526
Table 4 shows people’s direct, indirect and total effects on all dependent variables. The total
effect of people is significant for all dependent variables. Two indirect effects have to acknowledge
both significant at 5% level: People→ Practice→ PP_IC and People→ Perspective→ PP_PF.
Summarizing the structural model’s results, the importance–performance maps (see
Figure 4) show how the competence eye variables affect the project success dimensions.
Figure 4 shows that people plays a crucial role in all project success dimension. In PP_PF,
as expected, the perspective competency is the most relevant, while practices competency
appears suitable in PP_E and PP_IC near people competency.
5. Discussion
Our findings confirmed that people’s competence was the protagonist of the project’s success.
This is consistent with previousworks (e.g. Sang et al., 2018; Alvarenga et al., 2019; Bashir et al.,
2021; Sampaio et al., 2021) which found the importance of behavioral competencies to project
success. However, few studies verified it using empirical quantitative analysis (Gruden and
Stare, 2018). Therefore, our study provides empirical evidence to extend quantitative research
and confirms the effects of project managers’ behavioral competencies on project success. The
competence people’s effects on practice and, consequently, on the impact on the customer were
significant. It highlights the influence of the project manager’s soft skills (personal and
interpersonal abilities) on his/her hard skills (technical abilities), reaching customerperception of
performance, technical specifications, product use, needs, problem-solving and satisfaction. It
aligns with previous studies (S€oderlund and Maylor, 2012; Gustavsson and Hallin, 2014),
associating both skills as essential to better manage projects. Pereira et al. (2021) conducted an
exploratory survey about the evaluation of information systems project success. They found
that beyond themore classic criteria of success of project management (scope, quality, time and
cost), criteria related to stakeholders’ satisfaction were mentioned, as well as “Contribution for
the development of the organization”, “Preparation for the future,” and “Social impact”. People’s
variable impact on perspective variable, which together affects preparation for the future.
According to Pereira et al. (2021), preparation for the future is a long-term dimension that
involves preparing the organization for future opportunities, such as exploring new markets,
prospecting new ideas, innovations or products, and promoting new essential skills and
competencies. Ribeiro et al. (2021) argue that soft skills in the Industry 4.0 context will transform
project stakeholders’ communication. In other words, personal and interpersonal skills enable
the project manager to interrelate with the project environment (organization strategy,
governance, structures, processes, standards, power and interest, culture and values) and,
therefore, to open a panorama for opportunities as a new market, product or technology. In a
study conducted by Moradi et al. (2021) about the performance improvement of project
managers in collaborative construction projects in Norway and Finland, trust-based
collaboration and cooperation shared risk-reward system require specific core competencies.
In contrast, the unique elements, e.g. culture and contracting parties, require specific context-
oriented competencies. Thus, the newcompetence area perspective introduced in ICB4 brings an
important insight for this research and future studies.
5.1 Limitations
Despite the achievement of the objectives, this study, unavoidably, has some limitations.
A limitation underlying this model is not being generalized due to using a nonprobabilistic
sample. In addition, there was a large concentration of professionals in three main areas
(energy and gas, information technology and machines and equipment). Therefore, the result
can be more favorable to the reality of projects in these segments. It leads to a disadvantage,
that is, the cohort differences. The sample can be influenced by cohort differences that arise
from the particular experiences of a unique group of people. Another limitation is the report
The influence
of the eye of
competence
527
O
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6
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N
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ic
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at
a
5%
le
v
el
Table 4.
Direct, indirect and
total effects
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35,4
528
biases. Finally, a limitation is the inability to assess the incidence, study success projects and
infer a causal relationship (cause and effect). Cross-sectional studies often need to select a
sample of subjects from a large and heterogeneous study population (Wang and Cheng, 2020).
6. Conclusion
This paper analyzed the influence of project managers’ competencies (ICB4) on project
success. The model proved consistent once indicators’ reliability of all latent variables
showed significant loading factors.
Regarding competencies’ effects on project success, the research hypotheses H3a, H3c and
H4b were confirmed. It means people and practice impact directly on the customer, and
perspective impacts on preparation for the Future. Besides that, there was a statistically
significant and positive relationship between the projectmanager’s competence People and
both Perspective and Practice, confirming hypotheses H5 and H6. Then, our study points out
that project success begins and ends with people.
Our findings contribute to the body of knowledge on project managers’ competencies by
providing academic insights since it reveals the influence of the eye of competence on project
success. Another interesting finding was the importance of looking at the indirect effects
when exploring models and testing hypotheses. This enabled the most exciting and complete
analysis in this research.
Asapractical implication,wesuggest thatprojectmanagersbeawareofbehavioral competencies
conducive to their success and develop them. It could include self-reflection and self-management,
personal integrity, reliability, leadership, teamwork, conflict and crisis, negotiation, results
orientation, resourcefulness, relationships and engagement). We stimulate companies to promote
project management training and empower managers to develop behavioral competencies.
Future studies could also discover important relations between variables in proposed
models. Moreover, we suggest gathering data from different countries, using larger sample
size, expanding the scope by adding industries or areas and combining different perspectives,
including various project stakeholders.
Figure 4.
Importance–
performance maps
The influence
of the eye of
competence
529
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Appendix
Questionnaire
Item Section I: general questions
The questions below refer to the characterization of the respondent
1 Gender Male
Female
2 Educational level Bachelor
Postgraduate
Master
Doctor
3 Educational background
4 Work experience as project manager Last than 5 years
5–10 years
11–20 years
More than 20 years
The questions below refer to the characterization of the company
5 Economic activity Automotive
Cellulose and paper
Chemistry
Commerce
Construction
Education
Electronics
Energy and gas
Entertainment
Financial and secure
Food
Health
Information technology
Machines and equipment
Oil, derivatives and biofuel
Pharmacy
Public administration and defense
Sanitation
Telecommunication
Transport and storage
Other
6 Company size (annual revenue, considering exchange rate
1 USD 5 4 BRL
Micro (5 or less than US$ 90
thousand)
Small (90 to US$ 1.2 million)
Medium (1.2 - US$ 75 million)
Large (more than US$ 75 million)
7 Company capital Multinational
National
(continued )
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533
Item Section I: general questions
Mixed
The questions below refer to the characterization of the project
8 Project amount Less than US$ 25 thousand
25–250 thousand
More than US$ 250 thousand
9 Team size Less than 5 members
5 - 10 members
11–20 members
More than 20 members
Item Section II: project management competences Scale
Perspective
PER01-1 Determine, assess and review key performance indicators 1 to 5
PER02-1 Align the project with finance and control processes and functions 1 to 5
PER03-1 Assess, use and develop professional standards and tools for the project 1 to 5
PER04-1 Assess the personal ambitions and interests of others and the potential impact of these on
the project
1 to 5
PER04-2 Assess the informal influence of individuals and groups and its potential impact on the
project
1 to 5
PER04-3 Assess the personalities and working styles of others and employ them to the benefit of
the project
1 to 5
PER05-1 Assess the culture and values of the society and their implications for the project 1 to 5
PER05-2 Align the project with the formal culture and corporate values of the organization 1 to 5
People
PEO01-1 Identify and reflect on the ways in which own values and experiences affect the work 1 to 5
PEO02-1 Complete tasks thoroughly in order to build confidence with others 1 to 5
PEO02-2 Acknowledge and apply ethical values to all decisions and action 1 to 5
PEO03-1 Provide clear and structured information to others and verify their understanding 1 to 5
PEO03-2 Employ humor and sense of perspective when appropriate 1 to 5
PEO04-1 Share own vision and goals in order to gain the engagement and commitment of others 1 to 5
PEO04-2 Demonstrate empathy through listening, understandingand support 1 to 5
PEO05-1 Take ownership and show commitment 1 to 5
PEO05-2 Provide direction, coaching and mentoring to guide and improve the work of individuals
and teams
1 to 5
PEO06-1 Promote cooperation and networking between team members 1 to 5
PEO06-2 Empower teams by delegating tasks and responsibilities 1 to 5
PEO06-3 Support, facilitate and review the development of the team and its members 1 to 5
PEO06-4 Select and build the team 1 to 5
PEO07-1 Mediate and resolve conflicts and and/or their impact 1 to 5
PEO07-2 Mediate and resolve crises and/or their impact 1 to 5
PEO08-1 Stimulate and support an open environment 1 to 5
PEO08-2 Stimulate and support and creative environment 1 to 5
PEO08-3 Promote and apply creative techniques to find alternatives and solutions 1 to 5
PEO09-1 Identify and analyze the interests of all parties involved in the negotiation 1 to 5
PEO09-2 Reach negotiated agreements with other parties that are in line with own objectives 1 to 5
PEO10-1 Promote and ‘sell’ the project, its processes and outcomes 1 to 5
PEO10-2 Evaluate all decisions and actions against their impact on project success and the
objectives of the organization
1 to 5
Practice
PRA01-1 Design the project execution architecture 1 to 5
PRA02-1 Identify and analyze the project stakeholder needs and requirements 1 to 5
(continued )
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About the authors
Cristiane Esteves Cruz holds a master degree in Mechanical Production Engineering by University
Center of FEI (2017), she is postgraduated in Business and Project Management by Institute of
Management Foundation - FIA (2012), Mechanical Production Engineer by University Center of FEI
(2009) and Technician in Data Processing by Foundation Technological Institute of Osasco - FITO
(2003). Cristiane has worked over two years as a project manager being responsible for national and
international projects of hydromechanical equipment at Voith Siemens Hydro Power Generation Ltda
and seven years of professional experience in the financial area working with controllership,
organizational cash management, financial project management, training and business intelligence.
Volunteer as English and Industrial processes teacher at Formare from Iochpe Foundation for 5 years.
Gabriela Scur is business bachelor, master in Business and PhD in Production Engineering by the
Polytechnic School of the University of S~ao Paulo. Professor Gabriela Scur works in the Production
Item Section II: project management competences Scale
PRA03-1 Define the project deliverables 1 to 5
PRA04-1 Sequence project activities and create a schedule 1 to 5
PRA04-2 Monitor progress against the schedule and make any necessary adjustments 1 to 5
PRA05-1 Implement, monitor and maintain the organization of the project 1 to 5
PRA06-1 Develop and monitor the implementation of and revise a quality management plan for the
project
1 to 5
PRA07-1 Monitor project financials in order to identify and correct deviations from the project plan 1 to 5
PRA08-1 Define the quality and quantity of resources required 1 to 5
PRA09-1 Supervise the execution of contracts, address issues and seek redress where necessary 1 to 5
PRA10-1 Control project performance against the project plan and take any necessary remedial
actions
1 to 5
PRA10-2 Initiate and manage the transition to a new project phase 1 to 5
PRA10-3 Assess, get agreement on and implement project changes 1 to 5
PRA11-1 Develop and implement a risk management framework 1 to 5
PRA11-2 Assess the probability and impact of risks and opportunities 1 to 5
PRA12-1 Develop and maintain a stakeholder strategy and communication plan 1 to 5
PRA12-2 Organize and maintain networks and alliances 1 to 5
PRA13-1 Assess and review the impacts of changes affecting the portfolio 1 to 5
PRA14-1 Prioritize programs and projects based on the organization’s priorities 1 to 5
Item Section III: Project success Scale
Project efficiency
PE01 Meeting schedule goal 1 to 5
PE02 Meeting budget goal 1 to 5
Impact on client
IC01 Meeting functional performance 1 to 5
IC02 Meeting technical specifications 1 to 5
IC03 Fulfilling customer needs 1 to 5
IC04 Solving a customer’s problem 1 to 5
IC05 The customer is using the product 1 to 5
IC06 Customer satisfaction 1 to 5
Business success
BS01 Commercial success 1 to 5
BS02 Creating a large market share 1 to 5
Preparing for the future
PF01 Creating a new market 1 to 5
PF02 Creating a new product line 1 to 5
PF03 Developing a new technology 1 to 5
Note(s): Likert scale: 1 (Strongly disagree), 2 (Disagree), 3 (Neutral), 4 (Agree) and (Strongly agree)
The influence
of the eye of
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535
Engineering Department at University of FEI, located in S~ao Bernardo do Campo, state of S~ao Paulo,
Brazil, since 2006. Dr. Gabriela Scur is currently Adjunct Professor in Operations Strategy and
Management. Gabriela has experience in research projects in Operations Strategy, acting mainly on the
following themes: sustainability, innovative capabilities, industrial organization and clusters of
companies. The findings of the researches have already been published in peer-reviewed journals such
as Journal of Cleaner Production, Competitiveness Review, Review of Business Management and
Innovation andManagement Review. Gabriela Scur is the corresponding author and can be contacted at:
gabriela@fei.edu.br
Ana Paula Vilas Boas Viveiros Lopes holds BSc in Textile Engineering from the University of FEI.
She was part of the trainee program of the company Alpargatas-Santista Têxtil and worked at the
company Vicunha Têxtil. She is a member of the Project Management Laboratory - LGP (www.pro.poli.
sup.b/lgp). Worked as a teacher in the Production Engineering undergraduate course at the School of
Engineering and Management (ESEG). she teaches in the Project Management specialization course at
Fundaç~ao Vanzolini. She worked as a teacher in undergraduate and graduate courses at University
of FEI.
Marly M. Carvalho is a Full Professor at the University of S~ao Paulo (USP) in the Production
Engineering Department of the Polytechnic School in Brazil. She is the coordinator of the Project
Management Lab (http://www.pro.poli.usp.br/lgp) and Quality and Product Engineering (QEP) CNPq
research group. She holds a BSc in Production Engineering from the University of S~ao Paulo, and MSc
and PhD degrees in the same area from the Federal University of Santa Catarina, and the Post-Doctoral
Program at the Polytechnic of Milan. Marly has published 12 books and a number of articles in relevant
journals.
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mailto:gabriela@fei.edu.br
http://www.pro.poli.sup.b/lgp
http://www.pro.poli.sup.b/lgp
http://www.pro.poli.usp.br/lgp
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	The influence of the eye of competence on project success: exploring the indirect effect of people on both perspective and ...
	titlink2
	Introduction
	Literature review
	Project management competences
	Project success
	Conceptual model and research hypotheses
	Research methods
	Sample and data collection
	Variable operationalization
	Data analysis
	Results
	Sample demographics
	Measuring model validation
	Structural model and hypothesis testing
	Model and direct effects
	Exploring the indirect and total effects
	Discussion
	Limitations
	Conclusion
	References
	Questionnaire
	About the authors