THE EFFECTS OF THE ENTREPRENEURIAL …iamot2016.org/proceedings/papers/IAMOT_2016_paper_216.pdf · effects of an organization’s entrepreneurial orientation on project success. The

THE EFFECTS OF ENTREPRENEURIAL ORIENTATION ON PROJECT SUCCESS

FRANKLIN JEAN MACHADO* ([email protected])

CRISTINA DAI PRÁ MARTENS* ([email protected])

MAURO LUIZ MARTENS** ([email protected])

HENRIQUE MELLO RODRIGUES DE FREITAS* ([email protected])

*University Nove de Julho (Administration), São Paulo - São Paulo, Brazil

**Methodist University of Piracicaba (Production Engineering), Piracicaba - São Paulo, Brazil

Abstract: Project development and its management are a strategy commonly used by

companies while working on their goals. The large use of this strategy evolves in parallel with

an awareness of the importance of achieving success with the strategy application, in other

words, project success. This awareness is clearly visible in the large number of studies

concerning the understanding of variables affecting project success. Similarly, entrepreneurial

orientation is a research topic with a consolidated significance in the entrepreneurship research

field, and with significant developments over the last decades. This study aimed to analyze the

effects of an organization’s entrepreneurial orientation on project success. The methodology

that grounded this research is quantitative analysis, through the structural equation modeling

method, and using the survey as the data-gathering tool to find 100 valid answers from project

managers. The main result observed was a positive correlation between the organization’s

entrepreneurial orientation and the success of its projects, which will allow the development of

this research subject in the academic area, as well as the definition of corporative practices that

can contribute to project success. In practical terms, one benefit has been observed that will be

added to the list of those generated by entrepreneurial orientation, among others that have

already been verified in other research works.

Keywords: Project management; Project Success; Entrepreneurial orientation; structural

equation modeling; SmartPLS.

Introduction

Recent market dynamics levels are largely responsible for stimulating growth and

innovation, which puts pressure on organizations to respond to more complex demands using

increasingly competitive cost approaches. To meet the concerns arising from this scenario, a

natural decrease occurs in the organization's set of operations, making room for an escalation

of activities through projects (Shenhar & Dvir, 2007). A constant rise in the complexity of the

challenges facing executives is a reality that will occur over the next decade (Kerzner, 2011).

The development of project-based activities in the organizational setting is naturally

accompanied by approaches aimed at evolving the understanding of project success (PS), as

well as its influence on the organization's performance. Great is the difficulty encountered by

researchers to analyze the success of thematic projects, notoriously recognized as complex,

given the diversity of perspectives that can be taken to evaluate this subject (Carvalho &

Rabechini, 2011). As pointed out by Patah and Carvalho (2012), there has been a number of

studies in recent years addressing the issue of project success and how it can be measured

(Belout & Gauvreau, 2004; Besner & Hobbs, 2006; Bizan, 2003; Dvir, Raz & Shenhar, 2003;

International Association for Management of Technology IAMOT 2016 Conference Proceedings

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Gray, 2001; Kendra & Taplin, 2004; Lipovetsky et al., 2005; Raz; Shenhar & Dvir, 2002;

Repiso, Setchi & Salmeron, 2007).

Another issue to consider in this study is organizational entrepreneurship. The seminal

study of Miller (1983) characterized entrepreneurial organizations as those committed to

innovating their products, willing to take risks, and being proactively innovative. The results of

his qualitative study highlighting the existence of these factors in organizations directed what

would eventually become established as the first three dimensions of an organization’s

entrepreneurial orientation (EO). Covin and Slevin (1991) reinforced the characteristics

presented by Miller (1983) while developing a conceptual model correlating entrepreneurship

and organizational behavior. This model pointed to a positive relationship between an

organization’s entrepreneurial attitude orientation and enhanced performance.

A few years after the contributions of Covin and Slevin (1991), Lumpkin and Dess

(1996) proposed two new dimensions of EO —autonomy and competitive aggressiveness—

which were added to the three existing ones, thus constituting a multidimensional approach to

the EO construct. Innovativeness, risk taking, proactiveness, autonomy and competitive

aggressiveness then started to be taking into account to characterize and distinguish key

entrepreneurial procedures (Lumpkin & Dess, 1996).

In the entrepreneurial literature, the relationship between an organization’s EO and

increased performance has already been explored in various studies (Miller, 1983; Covin &

Slevin, 1989; Covin & Slevin, 1991; Wiklund, 1999; Rauch et al., 2009) and considered to have

positive effects. However, there is still a gap in the research on the relationship between EO

and project success. Similarly, when examining studies on project management, it is noted that

the positive impact of the use of project management methods on project performance has also

been demonstrated (Ibbs & Kwak, 2000; Shenhar et al., 2002; Dvir et al., 2003; Zwikael &

Globerson, 2004; Crawford, 2005; Gowan & Mathieu, 2005; Ling et al., 2009). However, there

is still a literary gap when it comes to analyzing EO attitude and its impact on project success.

In this context, this research aims to answer the following question: “What are the effects

of an organization’s entrepreneurial orientation (EO) on project success (PS)?” Thus, the main

objective of this research is to analyze the effects of this orientation on the success of the

projects an organization develops. To this end, a survey was developed together with

professionals engaged in projects, and the data was analyzed using the structural equation

modeling (SEM). The results show a positive relationship between an organization’s

entrepreneurial orientation and the success of the projects it develops, contributing to the

development of this research topic, as well as for identifying organizational practices that can

contribute to project success.

Following this introduction, a brief conceptual review is carried out on project success

and entrepreneurial orientation; next, the research method is described, results are presented

and discussed, and, finally, closing remarks are made.

Literature review

Project success

The theme of project success has been addressed in different ways in the literature. By

and large, the approaches used are either similar to the Iron Triangle model, also known as one

dimensional (Turner, 2009; Müller & Turner, 2007; Wateridge, 1995), or they are more

complex, multidimensional concept approaches comprising a wider range of attributes (Jugdev


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& Muller, 2005; Atkinson, 1999; Shenhar et al., 2001). Patah and Carvalho (2012) pointed out

that research undertaken over the previous year concluded that different forms of measurement

were used at different times (Barber, 2004; Bryde, 2003; Ika, 2009). From the perceptual

proposal of Katz and Allen (1985), which measured project performance through interviews

with managers, to the multidimensional one presented by Shenhar and Dvir (2007), there have

been different attempts seeking to assess project performance individually. In fact, there has

been a continuing search for identifying the factors that positively affect project success (Mir

& Pinnington, 2014). Table 1 consolidates some of these approaches.

Table 1. Criteria adopted over time to measure project success Source: Adapted from Patah & Carvalho (2012); Borges & Carvalho (2015).

AUTHOR CRITERIA

Shenhar & Dvir (2007),

(1985)

Schedule, budget, cost performance, project innovation, adaptability, ability to

cooperate with other organizational areas

Larson & Gobeli (1989) Cost control, adherence to deadlines, technical performance

Markowitz (1990) Uncertainty

Dvir et al. (1998) Fulfillment of pre-set goals, benefits to the customers

Archer & Ghasemzadeh

(1999) Economic gains, cost-benefit analysis, risks, impact on the market

Gray (2001) Budget, schedule, technical specifications, stakeholders’ opinion

White& Fortune (2002)

Meets customer’s requirements; completed within budget; completed within

schedule; meets organizational objectives; yields business benefits; minimize

business disruption; meets quality and safety standards

Ibbs & Reginato (2002) Measured value of process

Ling (2004) Cost; time; quality; customer satisfaction

Thamhain (2004) Project team environment; Project team performance

Rad & Levin (2006) Company; People

Patah & Carvalho (2007) Comparative measure between project’s costs at its initiation and at its

completion

Shenhar & Dvir (2007) Project efficiency, Impact on the customer, Impact on the team, Business

success, Preparing for the future

Elattar (2009)

Clarity in communication, high satisfaction of staff, economic measures (sales,

profit, ROI, ROE), compliance with requirements and specifications (scope),

respect for the environment, safety

PMI (2013) Product quality, project quality, punctuality, budget compliance, customer

satisfaction degree

This diversity makes it almost impossible to define a unique concept to be adopted, and

thus supports an approach that considers different perspectives and dimensions to understand

the construct. Different projects have specific characteristics that must be considered to

determine criteria combinations and success factors (Thomas & Fernandez, 2008). An effective

evaluation of project success should include the organization’s goals achievement, the

efficiency in the project development, its impact on the organizational strategy and the

profitability of the stakeholders (Cserháti & Szabó, 2014). Moreover, some studies suggest that

the success criteria should also encompass benefits generated for the organization where they

are developed, as well as their preparation for the future in terms of innovation and skills

development (Papke-Shields et al. 2010).

Shenhar and Dvir’s proposal of 2007 was build on their earlier study (Shenhar et al.,

2001) and demonstrated the evolution of the model they proposed, as well as the empirical

validations they developed. The multidimensional concept of successful project is presented in

order to find the answer to the question that seeks to understand how projects contribute to the

success and effectiveness of the organization (Shenhar & Dvir, 2007:23). Their model consists

of five independent dimensions, i.e., efficiency, impact on the customer, impact on the team,


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business and direct success, and preparation for the future, which enables the understanding of

the impact of projects on each of these dimensions independently.

According to the authors’ approach (Shenhar & Dvir, 2007), the efficiency dimension

is a short-term measure that assesses whether the project was completed according to plan

(schedule, budget and scope). The impact on customer dimension must point out how the

project’s result impacted the customer’s life or business in its effort to meet customer needs.

The third dimension –impact on the team– assesses the cumulative impact of the project, i.e.,

project team satisfaction, morale, overall loyalty to the organization, as well as the retention of

team members in the organization after project completion. The business and direct success

dimension is an expression of the project's business success. It is focused on answering the

question, “Has it contributed to the construction of the final result of the organization?” Finally,

the preparation for the future dimension has a long-term nature and evaluates how well the

project helps the organization to prepare its infrastructure for the future, or even how it creates

new opportunities for the organization.

This model (adopted by the present study) enables project success assessment using

metrics related to dimensions focused on different time horizons —from the short to the long

term—, given the fact that some information that makes up the metrics can only be collected

after specific periods of project life cycle. About this peculiarity, Zahra and Covin (1995)

emphasized that the pay-off of certain EO variables are based on long-term time horizons, and

exemplified it citing the study developed by Von Hippel in 1977 about 18 businesses, which

reported a success rate of 60% (obtaining a 10% gross profit) within a horizon of 3-5 years after

the launch of the project.

This approach makes it possible to grasp the authors’ intention in characterizing this

model of project success evaluation as a dynamic model. While the project efficiency can be a

short-term measure, business success can be measured only when the product / service / process

—the final delivery of the project— is available to customers, under a long-term approach.

Although the relationship between the existence of EO and project success is not clear,

many authors have developed studies that show that organizations with a higher EO also tend

to perform better (Rauch et al 2009; Castanhar, Dias & Esperança, 2006; Moreno & Casillas,

2008). Thus, for this study’s goal, understanding project success is as important as

understanding EO, the aspects of which are presented in the following section.

Entrepreneurial orientation

EO is deemed as one of the broadest and most promising research streams in the field

of entrepreneurship, with several studies already conducted and many still to come (George &

Marino, 2011). Conceptually, EO is related to basic policies and practices for the development

of entrepreneurial actions and decisions, and the processes that decision makers use to enhance

the purpose of their organizations, support their vision and create competitive advantages

(Rauch et al., 2009; Freitas et al., 2012).

Even though entrepreneurship and EO are similar concepts, a basic distinction between

both can be made (Lumpkin & Dess, 1996): whereas entrepreneurship is directly related to the

"new entrant" and questions such as "In which venture must we invest "and" How to make it

successful?”, EO is more focused on methods, practices and decision-making styles that

managers use. According to these authors, EO is characterized by five dimensions in the

organizational context: innovativeness, risk taking, proactiveness, autonomy and competitive

aggressiveness.


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Innovativeness entails the organization’s need to renew, innovate and seek new

opportunities (Miller, 1983). It involves a tendency to engage in creativity and experimentation,

through R&D (Rauch et al., 2009), supporting initiatives that can result in new products,

services or processes (Lumpkin & Dess, 1996). Morris et al. (1994) also address innovativeness,

as a creative search for new solutions —technologies, processes, products or services— for

problems and needs. The dimension of risk-taking is very close to that of innovativeness,

involving bold actions that venture into the unknown or the commitment of significant

resources to uncertain ventures (Rauch et al. 2009), with the expectation of obtaining high

returns (financial or opportunity-wise) (Lumpkin & Dess, 1996). According to Miller (1983), a

risk-adverse enterprise cannot be entrepreneurial, even if it seeks to "innovate" in order to

imitate its competitors.

Proactiveness, in turn, is characterized as the organization’s tendency to be ahead of the

competition when launching new products, technologies or services, rather than just following

market initiatives (Miller, 1983). It is related to the capacity to anticipate and seek new

opportunities (Lumpkin & Dess, 1996) and act in anticipation of future demands (Rauch et al.

2009), with the expectation of a share in emerging markets. Another dimension, somehow close

to that of proactivity, is competitive aggressiveness, with some authors tending to equate these

two concepts (Covin & Covin, 1990; Covin & Slevin, 1991). Other authors, however, see

proactivity as a response to opportunities, and competitive aggression as a response to market

threats (Lumpkin & Dess, 2001). Competitive aggressiveness is related to the tendency to

challenge competition (Lumpkin & Dess, 1996) aimed at overcoming it (Rauch et al. 2009) by

adopting a combative stance in order to improve the organization's position (Martens et al,

2011). Finally, autonomy can be characterized as independent action by individuals or a team

to bring an idea or vision to its completeness (Lumpkin & Dess, 1996), aiming at bringing forth

a project (Rauch et al., 2009).

The presence of these dimensions (all or some) in an organization characterizes its EO,

which may consist of different combinations (Lumpkin & Dess, 1996; Covin et al., 2006). The

interface between EO and organizational performance mentioned in the literature (Wiklund,

1999, Rauch et al., 2009) refers to the research question of this study, to the attempt to relate

EO and project success. In this sense, the next section seeks to approximate these two issues.

The relationship between PS and EO

The topics of project management and entrepreneurship have been increasingly

developed in the literature, but few studies address the connection between them and their

subareas (Semolic & Kovac, 2008; Kuuraa, Blackburn & Lundin, 2014; Lundin et al., 2015).

Martens et al. (2015) studied the relationship between entrepreneurial orientation and project

management and identified a positive impact of entrepreneurship on project management,

characterized in terms of integration management, scope management, time management, cost

management, quality management, human resources management, communications

management, risk management and procurement management. This study was the first

approximation between EO and project management issues and pointed out contributions of

organizational entrepreneurship to project management. However, there is gap in the literature

of studies addressing the relationship between entrepreneurial orientation and project success,

which motivates this study.

The relationship between EO and good organizational performance has traditionally

been tackled in the literature on entrepreneurship (Andersen, 2010; Filser & Eggers, 2014), with

performance being one of the most explored themes in EO studies over about 30 years of


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research on it (Rauch et al., 2009). A central argument in this connection is that "EO may be

viewed the entrepreneurial strategy-making processes that key decision makers use to enact

their firm's organizational purpose, sustain its vision, and create competitive advantage (s)"

(Rauch et al., 2009:762).

Project-oriented companies have their results closely related to the success of the

projects they develop. In this sense, the evidence from the literature that EO contributes to good

organizational performance refers to the possibility of a relationship between EO and project

success. In a study aimed to understand project success factors, Vezzoni et al. (2013) pointed

out that preparation to face risks and empowerment were the two most important project success

factors, which may be related to two dimensions of EO, respectively, risk-taking and autonomy.

Some authors suggest that a gain in economic performance can be caused by the attitude

of innovative organizations that develop and introduce new products and technologies on the

market (Schumpeter, 1934; Brown & Eisenhardt, 1998). Organizations that behave proactively

are able to create an advantage for being the first to position themselves in the market, thereby

benefiting from all aspects that this advantage provides, such as charging higher prices and

exploring specific markets ahead of other competitors (Zahra & Covin, 1995). Firms that invest

in risky strategies are likely to become more profitable in the long term (March, 1991; McGrath,

2001).

Thus, taking into account the context just presented, the following hypothesis is

proposed:

H1. An organization’s entrepreneurial orientation positively impacts the success of the projects

it develops.

Method

The research used in this study is characterized as descriptive (Hair et al., 2005) and

made use of quantitative data. A survey research model was adopted for data collection

(Gerhard & Silveira, 2009) together with project managers of Brazilian firms selected through

convenience non-probability sampling.

The research instrument was a questionnaire based on four studies that present

indicators for the constructs EO and PS, and their dimensions. The EO axle included indicators

drawn from the studies conducted by Covin and Slevin (1989) (the innovativeness, risk-taking

and proactiveness dimensions, with 3 indicators each), Lumpkin and Dess (2001) (competitive

aggressiveness dimension, with 2 indicators) and Lumpkin et al. (2009) (autonomy dimension,

4 indicators). For the PS theme, we adopted the study by Shenhar and Dvir (2010), with

indicators to measure PS through its five dimensions (efficiency, with 4 items; impact on the

team, with 6; impact on the customer, with 4; business and direct success with 6; preparing for

the future, with 6). Thus, the data collection instrument included 15 indicators of EO and 27 of

PS (Table 2).

Table 2. Conceptual framework of the study: constructs, dimensions, indicators and codes

Source: Based on Covin & Slevin (1989), Lumpkin & Dess (2001), Lumpkin et al. (2009) and Shenhar & Dvir (2010).

CO

NS

-

TR

UC

TS

DIMENSIONS

/ LATENT

VARIABLES

CODES INDICATORS / OBSERVABLE VARIABLES

EO

–

En

trep

ren

e

uri

al

Ori

enta

tio

n

IN

Innovativeness

EOIN_1

EOIN_2

EOIN_3

Innovation through R & D

Innovation through new products or services

Innovation through changes in the production of products or services

RT

Risk taking

EORT_1

EORT_2

Tendency for high-risk projects development


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EORT_3 Tendency to assume a bold attitude in a hostile environment

Taking a bold stance against confrontations

PR

Proactiveness

EOPR_1

EOPR_2

EOPR_3

Proactiveness toward Competitors

Proactiveness launching new products or services

Adoption of a competitive stance

AU

Autonomy

EOAU_1

EOAU_2

EOAU_3

EOAU_4

Support to autonomous efforts

Autonomy to choose ventures to develop

Autonomy for decision-making

Team’s appreciation of autonomous action

CA

Competitive

aggressiveness

EOCA_1

EOCA_2

Organization’s competitive aggressiveness

Adoption of a competitive stance

PS

– P

roje

ct S

ucc

ess

EF

Efficiency

PSEF_1

PSEF_2

PSEF_3

PSEF_4

Compliance with schedule

Compliance with budget

Compliance with scope

Compliance with other efficiency measures

IT

Impact on Team

PSIT_1

PSIT_2

PSIT_3

PSIT_4

PSIT_5

PSIT_6

Project team’s motivation

Project Team’s loyalty

Project team’s moral

Project team’ satisfaction

Project team’s personal development

Possibility of project team retention

IC

Impact on

Customer

PSIC_1

PSIC_2

PSIC_3

PSIC_4

PSIC_5

Improved customer performance

Customer satisfaction

Customer requirements met

Product use by customer

Possibility of customer repurchase

DS

Business and

Direct Success

PSBD_1

PSBD_2

PSBD_3

PSBD_4

PSBD_5

PSBD_6

Project’s business success

Profitability through the Project

Project rentability

Market share gain through the project

Value Creation for Shareholders through the project

Organizational performance gain through the project

PF

Preparation for

the Future

PSPF_1

PSPF_2

PSPF_3

PSPF_4

PSPF_5

PSPF_6

Generation of contributions for future projects through the project

Development of additional products through the Project

Exploration of new markets through the Project

Development of new technologies through the Project

Development of new processes through the Project

Development of administrative capacities through the project

Data collection was conducted via the Web, by sending the survey link to potential

respondents working on project management, making use of data bases of project managers and

social networks (especially LinkedIn). The sample needed for this study was calculated with

the help of G * Power v.3.1.9.2 software (http://www.gpower.hhu.de/en.html) (Faul et al.,

2009). Two parameters were taken into consideration: the power that was set to 1 and the f²

effect size, whose value was set to 0.15 (Cohen, 1988; Hair et al., 2014.). The result indicated

that the minimum sample should be composed of 89 observations (Ringle, Silva & Bido, 2014).

Initially, a pre-test was developed with three project managers. Its results were used only for

the purpose of evaluating the questionnaire developed, as suggested by Kinnear and Taylor

(1996) and Babbie (2003). Finally, 100 valid questionnaires were obtained, which were used in

the study.

Figure 1 shows the theoretical model that illustrates the EO and PS constructs, with their

dimensions, as well as this study’s hypothesis. Structural equation modeling in this research

adopts the reflective measurement model (Jarvis et al., 2003) for the latent variables, where the

direction of the causality is from the construct to the measure, and the lack of an indicator does


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not change the meaning of the construct. The adoption of the reflective model for the EO

construct is in line with the recommendations made by George Marino (2011) and Covin and

Wales (2012), and for PS construct as well.

Figure 1. Proposed model for the study Source: Authors

Structural equation modeling was adopted for data analysis. It is a multivariate statistical

analysis technique that is capable of measuring latent variables, linked to an unobservable

theoretical concept, which is verifiable in an approximate manner by means of manifest

variables (Hair et al. 2005). The latent variables typically represent constructs that, unable to

be measured directly, have their measure linked to indicators of the respective model (Hair et

al. 2005).

The multivariate statistical analysis proposed in this study included the following tests:

normality verification, measurement model analysis by checking the convergent validity of the

model, discriminant validity verification, analysis of loadings of variables and constructs

(internal consistency), and analysis of the structural model through Pearson’s correlation

analysis (R²), Student’s t-test (bootstrapping), predictive relevance test (Q²), and Cohen’s effect

size test (f²) (Hair et al. 2005). This analysis was developed using IBM SPSS v.21 software,

and Smart PLS 3.2 (Ringle et al. 2015). Taking these method parameters into consideration, the

next section presents the results of the data analysis.

Results

We received 101 questionnaires, but because one had more than 10% of missing values,

our sample in this study consisted of 100 reliable and valid questionnaires. Respondents were

from Brazilian organizations: 73% in the service segment, 16% from industries and 11% from

the trade sector. Most (70%) work in project management positions (as managers, directors or

coordinators), and 30% in project-related positions. About 45% have worked with projects for

over five years, and 55% for less than 2 years or 2 to 5 years.

Initially, we used the Kolmogorov-Smirnov test to assess normality with the aid of IBM

SPSS software version.3.2.0. The parameters considered for this analysis were suggested by

Field (2009): p > 0.5 for a normal distribution and p < 0.5 for a non-normal distribution. All

observations yielded results of p < 0.5, which determines the distribution of the model as non-

standard, i.e., the sample data significantly differ from a normal distribution (Field, 2009).


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Given this non-normality, the PLS method was adopted to develop the structural equation

modeling (SEM) of this study.

After the assessment of normality, tests were applied for the SEM model’s

measurement. First, convergent validity analysis was performed to evaluate the degree to which

two measurements of the same concept were related (Hair et al. 2005). According to these

authors, results greater than 0.5% of average variance extracted (AVE) of the constructs with

their variables are expected.

According to the first AVE analysis, a need was noted for a deeper understanding of the

dimension (first-order construct) preparing for the future (PF, with an observed result of 0.444),

which had a score below 0.5%. Thus it was decided to exclude the observable variables

(indicators) with smaller loadings of this dimension, as follows: PSPF_6 (0.513) and PSPF_4

(0.578), and then recheck the convergent analysis of the model. In the final model, the minimum

requirement of 0.5% in the AVE was met, as shown in Table 3.

Table 3. Analysis of the results of the SEM’s measurement model

Source. Survey data extracted from the Smart PLS 3.0 software (Ringle et al, 2015).

AVE

Composite

Reliability

Cronbach’s

alpha R²

Q² f²

CA 0.811 0.895 0.77 0.561 0.436 0.386

RT 0.666 0.857 0.753 0.469 0.292 0.333

AU 0.589 0.85 0.765 0.427 0.234 0.309

EF 0.507 0.797 0.663 0.513 0.246 0.205

IC 0.523 0.843 0.765 0.709 0.363 0.289

IT 0.579 0.891 0.852 0.569 0.318 0.404

IN 0.582 0.806 0.64 0.587 0.323 0.176

EO 0.348 0.886 0.862 - - 0.253

PF 0.555 0.833 0.734 0.427 0.214 0.247

PR 0.762 0.906 0.843 0.605 0.454 0.507

DS 0.594 0.896 0.859 0.623 0.35 0.425

PS 0.319 0.919 0.908 0.203 0.055 0.257

The new assessment also resulted in a subtle improvement in the AVE, composite

reliability and Cronbach's alpha indices related to the second-order construct project success.

The values of the adjusted loadings of the measurement model are presented in Figure 2 and

show the internal consistency of the measurement model.


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Figure 2. Estimated standardized loadings of the indicators of the adjusted SEM’s measurement model

Source. Survey data extracted from the Smart PLS 3.0 software (Ringle et al, 2015). PAREI

According to the studies of Cohen (1988), the minimum value for R² must be greater

than 0.26, which is endorsed by Hair et al. (2009). Based on this parameter, the R² for the latent

variables (dimensions or first-order constructs) of the proposed model is considered high.

Once the pre-set criteria for confirming the convergent validity of the model were met,

the next step was to confirm the model’s discriminant validity, which occurs when it is verified

that two conceptually similar concepts are distinct (Hair et al., 2005). In this case, the

explanatory power of the observable variables (or indicators) of a particular construct is more

substantial for it than for the other constructs of the model (Chin, 1998). The discriminant

validity also indicates whether the latent variables are independent from one another (Chin,

2010; Götz et al, 2010; Hair et al, 2014; Tenenhaus et al, 2005; Fornell & Larcker, 1981; Ringle

et al., 2014). Two tests were used: the first showed bigger factor loadings of the indicators in


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their respective dimension than those relating to any other dimension of the model, by means

of the cross-loadings criterion; in the second test, the confirmation could be achieved by

comparing the dimensions and their respective square root values of the AVE analysis, also

known as Fornell-Larcker criterion (Table 4).

After performing the necessary calculations, it was found that the AVE square root

values of each dimension were checked as higher than the correlations found for the other

dimensions, thereby confirming the assumption proposed by Chin (1998) (Table 4). Once the

analysis confirming the model’s discriminant and convergent validities were completed, the

loadings of observable variables and their respective dimensions were then considered as

definitive.

Table 4. Discriminant validity analysis (Fornell-Larcker criterion)

Source. Survey data extracted from the Smart PLS 3.0 software (Ringle et al, 2015).

CA RT AU EF IC IT IN EP PF PR DS PS

CA 0.9

RT 0.651 0,816

AU 0.393 0.286 0.767

EF 0.161 0.201 0.214 0.712

IC 0.174 0.204 0.114 0.565 0.723

IT 0.155 0.302 0.261 0.51 0.536 0.761

IN 0.451 0.322 0.329 0.235 0.138 0.392 0.763

EO 0.749 0.685 0.654 0.33 0.282 0.451 0.766 0.59

PF 0.052 0.134 0.082 0.314 0.436 0.389 0.195 0.193 0.745

PR 0.35 0.319 0.359 0.346 0.35 0.469 0.662 0.778 0.212 0.873

DS 0.239 0.143 0.243 0.436 0.609 0.335 0.31 0.372 0.49 0.38 0.771

PS 0.222 0.267 0.257 0.716 0.842 0.754 0.353 0.451 0.653 0.483 0.789 0.565

After the analysis of the measurement model, evaluations were made of the structural

model. As a resampling technique, this study made use of the Student’s t-test through the

bootstrapping technique, where the original data is successively sampled with replacements to

determine the model sample (Hair et al. 2005). For its application, we used 100 cases with 500

repetitions (subsamples) for the observation of the t-test (Student), where the t value represents

a real difference between the groups, taking the standard error into account. The value that can

be considered significantly is that of t-value> 1.96 (Hair et al. 2005). Table 5 shows the values

obtained. Table 5. Final structural model of adjusted SEM

Source. Survey data extracted from the software Smart PLS 3.0 (Ringle et al, 2015).

PATHS ORIGINAL

SAMPLE AVERAGE

STANDARD

DEVIATION

T-

TEST

P- VALUE

EO -> CA 0.749 0.745 0.058 13.004 0.000

EO -> RT 0.685 0.686 0.065 10.553 0.000

EO -> AU 0.654 0.658 0.074 8.803 0.000

EO -> IN 0.766 0.769 0.056 13.788 0.000

EO -> PR 0.778 0.777 0.062 12.566 0.000

EO -> PS 0.451 0.450 0.103 4.376 0.000

PS -> EF 0.716 0.715 0.066 10.818 0.000


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PS -> IC 0.842 0.844 0.029 28.616 0.000

PS -> IT 0.754 0.753 0.065 11.621 0.000

PS -> PF 0.653 0.659 0.062 10.567 0.000

PS -> DS 0.789 0.789 0.056 14.084 0.000

The T-test values can be considered relevant because they are above the given level of

relevance, thus indicating the causal relationship between the two constructs EO and PS. This

analysis showed that the EO construct positively affects PS, according to the Student’s t-test

(4.376), thereby explaining 20.3% of the EO effect on PS, and confirming H1.

The predictive relevance test (see Table 3), also known as Stone-Geisser’s Q² test,

indicates whether the model is able to satisfactorily predict the indicators classified as

endogenous. This is achieved through the reuse of the aggregate sample and the omission of

certain D data points, making use of estimates to predict these same omissions (Hair et al. 2005).

The same authors suggest values of 0.02 as having a low explanatory power, 0.15 as possessing

an average explanatory power and 0.35 with a high explanatory power. Moreover, they suggest

D omission values between 5 and 10, and 7 was the value used for this study. The Stone-Geisser

indicator can be obtained by calculating the average overall redundancy of the adjusted model

with the help of SmartPLS, in the blindfolding report.

It was observed that the latent variables (first-order constructs or dimensions)

competitive aggressiveness (CA), customer impact (CI), proactive (PR) and business and direct

success (DS) have loadings of predictive relevance of high explanatory power, whereas risk-

taking (RT), autonomy (AU), efficiency (EF), impact on the team (IT), innovativeness (IN) and

preparation for the future (PF) have loadings of predictive relevance with a medium explanatory

power.

Chin (1998) and Hair et al. (2005) indicate that Cohen’s f² effect size, also given by the

blindfolding test, is obtained by including and excluding the model’s constructs in order to

evaluate the usefulness of each construct for the proposed model. Cohen (1988) proposes that,

by calculating the double estimation (with and without the dependent variable) in structural

models, it is possible to calculate the coefficient of determination for each of latent variable.

Thus, in the analysis of f², the impact of independent latent variables on dependent ones to

adjust the model is considered low if the values are up to 0.02, moderate if up to 0.15, and

substantial if 0.35 for each latent variable analyzed (Hair et al. 2005) (see Table 3 for the f²

values for this study)

As it can be observed, the latent variables (first-order constructs or dimensions) of

competitive aggressiveness (CA), impact on team (IT), proactive (PR), business and direct

success (DS) had a substantial impact, whereas risk taking (RT), autonomy (AU), efficiency

(EF), impact on the customer (IC), innovativeness (IN), entrepreneurial orientation (EO) and

preparation for the future (PF) had a moderate influence. No latent variable had little influence.

Discussion of Results

The results of this study present empirical evidence of a positive and significant

relationship between the constructs of the theoretical model of EO and PS, with a 99%

probability. This relationship was statistically proven in this study and explained by two tests

that confirm the hypothesis (H1) that entrepreneurial orientation variables positively influence

the achievement of success in projects. In the first test, there was a highly significant causal

relationship between the EO and PS constructs given by the Student’s t-test (4.376), with a p


1626

value <0.01 (see Table 5) greater than 1.96, as suggested by Hair et al. (2014). Pearson’s R²

coefficient of determination test or explained variance showed that 20.3% on the effects on the

project success construct are explained by the entrepreneurial orientation construct (Cohen,

1988). Figure 3 shows the final structural model.

Figure 3. Final Structural Model

Source. Survey data extracted from the Smart PLS 3.0 software (Ringle et al, 2015)

These results indicate that it is possible to obtain better PS indices when the organization

presents an entrepreneurial behavior characterized by innovativeness, risk taking,

proactiveness, autonomy and competitive aggressiveness. The current EO literature addresses

the clear relationship between EO and organizational performance improvement (Rauch et al,

2009; Andersen, 2010; Filser & Eggers, 2014, among others). Considering the success of the

projects, this relationship has been confirmed in the present study, suggesting that the effects

of EO are beneficial to project success, which ultimately reflects in the performance of project-

oriented organizations.

On the other hand, there is still 79.7% of influence of other variables on the PS construct,

which denotes how complex the study on project success is (Thomas & Fernandez, 2008;

Carvalho & Rabechini, 2011; Patah & Carvalho, 2012; Mir & Pinnington, 2014; Borges &

Carvalho, 2015), indicating that it can be influenced by various other variables, such as issues

related to the use of project management methods (Ibbs & Kwak, 2000; Shenhar et al., 2002;

Dvir et al. 2003; Zwikael & Globerson, 2004; Crawford, 2005; Gowan & Mathieu., 2005; Ling

et al 2009), and reinforcing the use of more complex approaches to assess the project success

through multidimensional concepts that comprise a broader range of attributes (Jugdev &

Muller, 2005; Atkinson, 1999; Shenhar et al., 2001).

These results suggest that the project management environment, project managers’

vision and entrepreneurship in this context need further studies to understand the relationship

between entrepreneurship and project management, yet little explored in the literature (Martens


1627

et al., 2015; Lundin et al, 2015; Kuuraa, Blackburn, & Lundin, 2014). Similarly, the gains

related to projects under the perspective of an alignment with EO need to be understood in depth

(Martens et al., 2015). Thus, the results of this study suggest that the EO dimensions are being

considered, but they deserve a greater integration in the context of projects aimed at better

results in successful project measurements.

Finally, the results suggest the validity of THE theoretical empirical model looking at

the analysis of measurement and structural model. This is positive because it confirms the

hypothesis (H1) of this study showing that EO significantly contributes to obtaining PS. These

results are in line with studies indicating that EO contributes to organizational performance.

Final considerations

Understanding the factors that contribute to project success can directly impact project

performance and, consequently, that of the organizations that develop them. In this article, the

validation of the theoretical model allows not only pointing out to managers the importance of

entrepreneurial behavior development in their respective organizations in the pursuit of project

success, but also identifying nuances that can be inferred from the path analysis of each

construct dimension. Importantly, given the proportion of the impact of entrepreneurial

orientation on project success (20.3%) identified in this study, it is critical that project

management professionals expand their learning horizon to recognize other factors that affect

project success.

In the academic context, the validation of the proposed hypothesis sheds light on the

contribution of entrepreneurial orientation to project success. More broadly, the article

contributes to the development of studies linking entrepreneurship and project management,

and strengthens the relation evidenced in the literature that this orientation contributes to

organizational performance, in this case by contributing to the success of projects. Additionally,

the article adds knowledge to studies seeking to understand the factors that influence the success

in projects, as well as contributes to the list of benefits of EO for organizations. Taken into

consideration the existing gap in the literature that addresses the relationship between project

success and entrepreneurial orientation, this study helps to minimize it and to further stimulate

research on this topic.

Among the limitations of this study, it is worth noting that the results are limited to the

context of the sample of Brazilian firms, according to the perspective of the project managers

who answered the questionnaire. This limitation creates the opportunity for furthering studies

on model validation for specific business sectors and specific sizes of organizations, as well for

increased samples.

This article encourages the development of other research studies in its continuity. The

study of the specific relationships between each dimension of each construct (entrepreneurial

orientation and project success), as well as their inter-relationships and power of influence, can

evidence the contribution of each EO dimension to project success and even to each category

of success of the adopted model. This can contribute to a better targeting of the aspects of

entrepreneurship to be developed in organizations when a contribution to project success is

desired. Different organizational segments may also have different effects of EO on PS. For

example, in segments or contexts where innovation is more present, it can be speculated that

the innovativeness dimension plays a more important role in project success than in those where

firms do not favor innovation. That can also occur with other dimensions: environments where

teams operate more autonomously, where competition is stronger, where risk behavior is more


1628

or less conservative, or where there is more market proactiveness, thereby opening up

possibilities for future research.

As noted in the analysis of the results, EO can explain 20% of the effects on PS in the

sample studied. The remaining 80% have not been explained by this study, enabling new

approaches to the topic of project success that will allow better understanding the complexity

of this relationship. Another research niche could involve the inclusion of moderator or

mediator variables in the EO-PS relationship model in order to understand new relationships

that could be identified.

Acknowledgements

This research was conducted with support from the Coordination for Improvement of Higher

Education Personnel (CAPES), the National Council for Scientific and Technological

Development (CNPq), and the Research Support Fund of the University Nove de Julho (FAP /

UNINOVE).

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THE EFFECTS OF THE ENTREPRENEURIAL …iamot2016.org/proceedings/papers/IAMOT_2016_paper_216.pdf · effects of an organization’s entrepreneurial orientation on project success. The