The Role of Big Data Analytics and Decision-Making in Achieving Project Success

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The Role of Big Data Analytics and Decision-Making in Achieving Project Success
Riaz Ahmed a, Sumayya Shaheenb, Simon P. Philbinc
a Bahria University, Islamabad, Pakistan
bAfiniti – Pair Better, Islamabad, Pakistan
cLondon South Bank University, UK
Abstract
Big data analytics and decision-making are critical to project success. Therefore, this study aims to
investigate the impact of big data analytics on project success and the moderating effect of decision-making
from the resource-based view perspective. The study adopted a survey instrument and collected data from
135 respondents engaged in big data analytics in the IT and telecommunications sector of Pakistan. The
findings identify implications based on the significant positive impact of big data analytics on project
success as well as enhancement of relationships through the interaction of decision-making between big data
analytics and the three dimensions of project success.
Keywords: Big data analytics, decision-making, project success, resource-based view, IT and
telecommunication.
1. Introduction
Organizations are continuing to witness an explosion in the level of data (Tien, 2017) that is created every
year and includes the volume of data (i.e. in terms of bytes of data), the velocity (i.e. the speed of data
creation) as well as the variety (i.e. the diversity of different forms of data) (McAfee and Brynjolfsson,
2012). Indeed, this so-called ‘big data’ (Favaretto et al., 2020) has been described as a game-changer in the
way businesses operate across many industries and this can be associated with the increased use of
structured data as well as unstructured data, such as from images and video format (Lee, 2017). Moreover,
people are using smartphones, laptops, desktops, tablets and other smart machines that inherently produce a
variety of data in large volumes – this variety of large data sets is also known as big data (Russom, 2011).
Big data includes massive levels of data from data mining, data analysis, and data sharing (Wang et al.,
(2020), manufacturing data from cyber-physical systems (Cui et al., 2020) as well as other applications
related to Industry 4.0, such as digital twin systems (Cheng et al., (2020). The related field of big data
analytics has been articulated in terms of six main components, which are as follows: data generation, data
acquisition, data storage, advanced data analytics, data visualization, and decision-making for value-creation
Cited As: Ahmed, R., Shaheen, S., & Philbin, S. P. (2022). The role of big data analytics and decision-making in
achieving project success. Journal of Engineering and Technology Management, 65, 101697.

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(Saggi & Jain, 2018). Big data analytics (BDA) has been explored across a range of different applications,
including operational excellence for optimizing sustainable supply chain performance (Bag et al., 2020),
manufacturing internet-of-things (Dai et al., 2020), and digital transformation and value creation in the
context of industrial marketing (Wang & Wang, 2020).
In the era of digital transformation, organizations realize the value and importance of making the right
decisions at the right time, which can only be made through relying on the relevant and timely availability of
data and information that are processed as part of the decision-making process (Joseph & Gaba, 2020). At
any level of an organization, the decision-making process is supported through information that can be
processed in a meaningful manner. The sequence of collecting, processing, and visualizing big data can help
an organization’s management function to make informed decisions about the operations and strategy of the
organization (Koscielnaik & Puto, 2015). In the constantly changing business environment, organizations
across the globe are striving to gain a competitive advantage through utilizing the latest technologies to
process data and enhance strategic decision-making. In this context, Mazzei and Noble (2017) have argued
that large, diverse, complex, and/or longitudinal data sets are having a direct impact on the formulation of
organizational strategy and along with the increased level of data is leading to analytic capabilities and
processes for re-definition of innovation, competition, and productivity across industrial sectors.
Indeed, analytic processes and corresponding abilities are required to translate big data into valuable
insights, which can improve decision-making both at the project and organizational levels; which in turn can
support and enhance the prospects of project success (Thirathon et al., 2017). The protagonists of the
concept of big data analytics suggest that the benefits of using data analytics exist, however, they still need
to be confirmed through rigorous empirical research (Thirathon et al., 2017). Although managers recognize
the likely benefits that arise through investing more in big data analytics practices and infrastructure
(Schroeck et al. 2012). However, whether or not decision-making renders any impact on this relationship is
an area that still requires exploration. With an increasing technical know-how and data awareness, it is now
possible to analyze large data sets to support the decision-making process and this includes the use of data
visualization techniques (Killen et al., 2020). This data-driven decision-making can potentially be utilized to
improve the way resources are managed and thereby increase the success rate of projects.
The resource-based view (RBV) of strategy explains how resources, capabilities, and competitive advantage
of organizations are linked (Barney, 2001). Furthermore, the resource-based view describes how resources
play an important role in reducing the toxicity of project environments. This contributes to energizing and
motivating team members to increase performance and productivity, which ultimately improves

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organizational and project performance (Wang, Zaman, Rasool, uz Zaman, & Amin, 2020). On the other
hand, to improve an organization’s competitive advantage through the delivery of projects, organizations are
rapidly implementing business process re-engineering and taking effective decisions to adopt new
technologies (Akter et al., 2019). It can be observed that RBV perspectives are ideal for the adoption of big
data and digital transformation since these technological transitions demand significant investment to
capitalize project success in the organizations (Akter et al., 2019; Sundarakani, Ajaykumar, & Gunasekaran,
2021). According to the RBV, the utilization of valuable resources at the disposal of companies is the basis
for competitive advantage (Chae, Olson, & Sheu, 2014; Wernerfelt, 1984). Indeed, organizations are facing
problems in transforming data into meaningful information and improving their business opportunities
(Pramanik, Mondal, & Haldar, 2020). Consequently, organizations adopt big data analytics for the efficient
utilization of resources and to support effective decision-making (Khan, 2022).
Frisk and Bannister (2017) have postulated that the adoption of a design approach enabled by big data can
support organizations to alter the decision-making culture and thereby achieve more effective decisions.
Whereas, Schrage (2016) advocated the use of the RACI (responsible, accountable, consulted, and informed)
framework to improve decision-making through creating auditable and accountability networks for project
and process management. In this approach, big data can be harnessed according to the different stages of the
framework (i.e. responsible, accountable, consulted, and informed) to enhance the decision-making process
and help support positive project outcomes. Furthermore, Power (2013) proposes that big data eventually
leads to improved health services and improved education systems as well as improved decision-making.
Conversely, Oukil and Govindaluri (2020) have developed a hybrid multi‐attribute decision‐making
procedure that can be utilized in ranking project proposals to use historical project data and reduce the
problems associated with predicting project performance, such as cost and schedule overruns. Previous
research studies also suggested that efficient and effective use of big data sets affects the quality of decisions
taken in projects (Kościelniak & Puto, 2015). However, despite these studies on the influence of decision-
making and big data on projects in the extant literature, there are still ambiguities in the literature on whether
big data and big data analytics can eventually lead to enhancing the success rate of projects (Narayan & Tan,
2019). Thus, there is a need to further explore the linkage between big data analytics and project success by
investigating whether big data impacts the decision-making ability of project managers (Papadaki, Bakas,
Karamitsos, & Kirkham, 2019; Thirathon, Wieder, Matolcsy, & Ossimitz, 2017; Wang, Zhang, & Song,
2020). This leads to the identification of a gap in the knowledge base on whether there is a relationship
between big data analytics and project success. There is also a need to understand the role of decision-
making in this context and how decision-making impacts the processing of big data in the project

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environment (Miller, 2018). To address this gap in the extant literature, the following research questions
have been formulated:
a) Does a relationship exist between big data analytics and project success? and
b) Does decision-making moderate the relationship between big data analytics and project success?
The motivation and uniqueness of this empirical study can be related to the literature that reveals the scarcity
of research examining the moderating effect of decision-making on big data analytics and project success.
However, earlier studies were limited to other areas, such as conducting a systematic review on BDA (Cui et
al., 2020; Dai et al., 2020), a review on analytical-based decision making (Akter et al., 2019), identifying
barriers to BDA adoption (Khan, 2022), the relationships between BDA and supply chain management (Bag
et al., 2020; Sundarakani et al., 2021), BDA and e-procurement (Al Nuaimi et al., 2021), BDA and
institutional resources (Bag et al., 2021), BDA and sustainable competitive advantage (Shah, 2022), and
decision support systems and supply chain performance (Khan et al., 2021). Consequently, there is a scarcity
of research investigating the relationship between big data analytics and project success in the presence of
effective decision-making (Miller, 2019; Narayan & Tan, 2019; Papadaki et al., 2019; Thirathon et al., 2017;
Y. Wang et al., 2020). To fill the aforementioned research gaps, this study aims to examine the moderating
effect of decision-making on the relationship between big data analytics and project success in the
information technology and telecommunication sector in Pakistan.
The study contributes to the knowledge base and practice by utilizing the RBV to advance the theoretical
understanding of BDA and decision-making for IT and telecommunication projects. This is enabled through
the lens of interrelated resources that need to be combined to maximize the rate of project success.
Furthermore, by making a distinction between human and technological resources and understanding the
process of decision-making based on project performance, this approach provides a more robust explanation
for the role of BDA in maintaining the outcome of projects. Additionally, this study extends prior research
based on the RBV by exploring key predictors of BDA in decision-making to improve the likelihood of
project success. More specifically, the study findings validate the fundamental roles of human capabilities
(i.e. decision-making) and technological capabilities (i.e. big data analytics) in enhancing competitive
advantage through the implementation of projects in a big data environment. This is consistent with Khan’s
(2022) proposition that big data should be considered during the planning and implementation of projects
from a holistic perspective. Finally, the study validates the moderating influence of decision-making on the
relationship between big data analytics and the various factors of project success.

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This article is structured in the following manner. After the introduction, the most relevant literature is
presented, followed by a synthesis of the hypotheses and thereafter the methods used in this research study.
Then, data analysis and results are explained. Finally, discussion, implications, conclusions, and future
recommendations are presented.
2. Literature review and research hypotheses
It has been reported that over 60% of projects experience some form of failure, which results in cost
overruns, schedule delays, and/or deficiencies and inadequacies in the desired project outcomes (Fauser et
al., 2015). There are presently no conventional tools that can be used to adequately forecast which projects
will fail and what can be done to counteract the risks of failure and thereby increase the possibilities of
project success. Big data analytics provides a potential pathway to address the shortcoming by enabling
improved access to data and information on projects. Moreover, big data analytics allows organizations to
gain an improved understanding of project complexity according to existing governance models and control
mechanisms to increase project success. Fauser et. al (2015) also suggested that since predictions are not
entirely conclusive, it is important to utilize the understanding developed after data analysis in conjunction
with other decision support approaches thereby enhancing the likelihood of project success. In this context,
the literature review has been organized according to the following areas: big data analytics, project success
and efficiency, and decision-making in projects. This is followed by the definition of the research hypotheses
and the research model.
2.1. Big data analytics
The resource-based view has been used to theorize big data analytics (BDA) as a tacit resource in the digital
era and successful project implementation is dependent on organizational capabilities (Bag, Pretorius, Gupta,
& Dwivedi, 2021). According to Laney, Taylor, and Gartner (2013), big data is the "high-volume, high-
velocity, and high-variety information assets that demand cost-effective, innovative forms of information
processing for enhanced insight and decision making". Moreover, Power (2013) elaborated that data volume
refers to the units of data stored on various media, data variety includes different types of different digital
data formats that can be used, data velocity refers to the speed with which data becomes available for use,
and lastly, data variability means that there is inconsistency and fluctuation in the data flow. Big data is
massive and because of its huge volume, variety, ever-increasing velocity, and complexity, presents major
challenges for existing databases and system architectures in managing and storing such data (Carter, 2011).

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Big data’s four dimensions were also put forth by Zikopoulos et al. (2013) who described big data in terms
of; a) volume, b) velocity, c) variety, and d) veracity.
Big data analytics can be viewed from a holistic perspective through the collecting, handling, processing,
and evaluating of data according to the ‘5V’ dimensions (i.e. variety, volume, veracity, velocity, and value)
to generate meaningful and practical concepts for generating continual value, determining performance and
creating viable returns (Wamba et al., 2015). Indeed, some experts have suggested that big data analytics is
the “the next frontier for innovation, competition, and productivity” (Manyika et al., 2011, p.1), a “new
paradigm of knowledge assets” (Hagstrom, 2012, p. 2), and even a “fourth paradigm of science” (Strawn,
2012, p.34). The application of big data analytics in the current business environment yields a competitive
edge and also elevates the effectiveness of data exploration (Koscielnaik and Puto, 2015). Elgendy and
Elragal (2014) established through a research study that big data analytics could enhance decision-making
and enable the extraction of unforeseen insights and knowledge. The literature on big data analytics has
presented a significant association between the usage of data analytics and the performance of an
organization (Germann et al., 2014). For instance, big data analytics permits firms to investigate, evaluate
and accomplish strategic business goals through the lens of data (Brands, 2014). Moreover, it can be
observed that big data analytics is becoming a critical element of decision-making procedures that are
practiced across businesses (Hagel, 2015).
2.2. Project success and efficiency
Researchers in the past defined project success in terms of several different variables. Karlsen and
Gottschalk (2004) used five criteria to define project success, which include: project performance, system
implementation, project outcome, benefits to stakeholders, and benefits to users. Philbin and Kennedy
(2014) found through case study research that the success of engineering and technology projects can be
considered through adopting a systems model that is informed by six underlying sub-systems, which are as
follows: process, technology, resources, impact, knowledge, and culture. Whereas Shenhar and Dvir (2007)
formulated a project success measurement model that comprises five dimensions, which are: project
efficiency, impact on the customer, impact on the team, direct business and organizational success, and
preparing for the future. Nixon et. al (2012) also argued that dimensions such as time, cost, and scope are not
sufficient to measure project success. With time, it can be observed that the fact that the project remained
within the resource constraints becomes less significant, and most of the time it even becomes irrelevant
several years after project completion; in contrast, the dimension of ‘impact on the customer’ becomes more
relevant and visible (Turner and Zolin, 2012). Consequently, it is useful to consider further the project

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success model parameters defined by Shenhar and Dvir (2007) and operationalized by Ahmed and Mohamad
Azmi bin (2016).
In projects, the term ‘project efficiency’ is concerned with achieving the project’s cost, time, and scope goals
(Shenhar & Dvir, 2007). Project efficiency is considered an important factor of project success (Turner and
Zolin, 2012), which measures the overall performance of budget, schedule, and utilization of other resources,
to yield long-term benefits for the organization (Ahmed & Mohamad Azmi bin, 2016). Indeed, a project is
considered successful when it ensures that customers’ requirements have been fulfilled and the required
level of customer satisfaction is realized. To fulfill the project customer’s requirements, an adequate focus is
required on team morale, team loyalty, team skills development, and retention after the project completion
(Shenhar & Dvir, 2007). Furthermore, successful implementation of projects contributes directly towards the
organization and business success, in addition to preparing for the future in terms of development of
organizational competence level, creating a new product line, developing new organizational processes,
creating a new market/niche, and technological advancement (Ahmed et al., 2021).
2.3. Decision-making in projects
Effective decisions can be viewed as the central transactions in both projects and organizations. Successful
organizations outperform their competitors by either making high-quality decisions, making faster decisions
or implementing more effective decisions (Grušovnik et al. 2017). The efficient and effective use of
available data sets (including both small scale and large scale) impacts the quality of decisions taken across
current operations of organizations. It can often be a complex task since the present-day information and
communications technology (ICT) systems are elaborate and include software, hardware, and organizational
solutions managed with large sets of data. In this regard, it has been highlighted that informed decision-
making processes that integrate large data sets as inputs, can be harnessed to address the modern-day
challenges in complex organizations (Koscielnaik and Puto, 2015). Furthermore, Koscielnaik & Puto, .
(2015) argue that only the organizations that develop and implement modern solutions in the field of
decision-making processes satisfy their clients’ expectations and gain a competitive edge over competitors.
For this reason, project managers need to focus on decision-making processes based on the processing of
data sets if they aim to stay ahead of rivals.
Decision-making is a multi-dimensional and intricate process that is often spontaneous and occurs naturally;
while at other times, it may have to be systematically planned with much consideration and foresight
(Malakooti, 2010). Moreover, Kościelniak & Puto (2015) studied the usability of big data in decision-
making processes in multiple organizations and formulated four stages of the decision-making process based

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on suitably organized and processed data. It is presumed that decision-making is an activity that every
management performs and the information, which is the material requirement of decision making, is used as
a tool by project managers (Kościelniak & Puto, 2015). The decision-making process, as a quality, is
correlated to the extent to which market opportunities are realized as well as the elimination of any market
threats (Botterhuis et al., 2010).
2.4. Definition of research hypotheses and research model
Organizations in the data-driven age need to enhance their existing business models by involving process
automation or by adopting innovative and creative models to maximize their business value (O'Driscoll,
2014). The literature suggests that interpreting big data will enable organizational leaders to make more
effective use of the insights from the data and thereafter make well-informed decisions. This will also help to
generate, deliver and efficiently secure greater business value in both the short-term and long-term horizons
of projects. Therefore, the research model has been developed (see Figure 1) and the following hypothesis
has been synthesized to test the relationship between big data analytics and project efficiency.
H1: There is a significant relationship between big data analytics and project efficiency.
Big data perceptions and insights enable organizations to produce new products and services as well as
business models to satisfy customer needs. Apart from the fact that meaningful insights help an organization
in achieving a competitive advantage, Das et al. (2013) posited that there are other benefits to be gained
from analyzing data. For instance, big data helps to make time-sensitive decisions faster and supports firms
to quickly and efficiently monitor the contemporary trends of the industry they are operating within. This
allows organizations to leverage organizational capabilities arising from projects through developing new
business opportunities and creating improved products or services for customers (Halaweh and Massry,
2015). Therefore, the following hypothesis has been synthesized:
H2: There is a significant relationship between big data analytics and the project’s impact on the customer.
Big data analytics has the potential to improve the decision-making ability of project teams because of the
availability of valuable insights arising from the data and information that is provided. In this regard,
Manyika et al. (2011) argued that the sophisticated analysis of data impacts the team’s performance and
skills level. On the other hand, project team members learn to read and make sense of the big data and
resulting patterns, and this whole exercise equips the project team to decide upon matters promptly because

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of the availability and accessibility of relevant projects data. Therefore, the following hypothesis has been
synthesized:
H3: There is a significant relationship between big data analytics and the project’s impact on the team.
Organizations that invest in setting up systems and management processes to analyze big data can secure
greater value for their businesses as Bayer & Laney (2012) discussed Wal-Mart’s case, in which they used
big data to increase profits by using clickstream data from its 45 million monthly online shoppers. This
increased the number of online shoppers by 10-15%, which in turn increased sales by several billion dollars.
Therefore, the following hypothesis has been synthesized:
H4: There is a significant relationship between big data analytics and the project’s direct organizational and
business success.
In projects, big data has become an emerging field and its challenges are real where researchers need to play
a more important role in this arena (Ahmed & Yüzbaşı, 2016). Indeed, big data analytics facilitate in
improving the prospects of the project and organizational performance (Singpurwalla, 2016). Big data
analytics can be used to capitalize on large data sets to extract valuable information for preparing future
project initiatives, and this includes the organizational data related to project governance and privacy
concerns (Pramanik et al., 2020). Using data analytics to improve processes helps organizations prepare in
advance for future initiatives by enabling them to adjust processes to provide for a smoother ride ahead.
Therefore, the following hypothesis has been synthesized:
H5: There is a significant relationship between big data analytics and how the project helps to prepare for
the future.
It is critical to define project resources precisely and practice decision-making in the early stages of the
project during the planning stage to enhance the chances of project success. Caniëls et al. (2011) found
empirical evidence that project data and information are directly and indirectly related to timely decision-
making and consequently project efficiency. The quality of decision-making depends on the information
produced by information systems used to manage the project resources. This finding concurs with the study
by Saeed and Abdinnour-Helm (2008), which indicated that higher quality of information leads to improved
decisions by project managers, thereby improving the performance of projects as well as project efficiency.
Therefore, the following hypothesis has been synthesized:

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H6: Decision-making significantly moderates the relationship between big data analytics and project
efficiency.
In strategically managed projects, management teams deliberately spend more time and focus more attention
on such activities and decisions that are aimed at improving the long-run business results. In contrast, the
project teams focus more on competitive gain, future market success, and customer needs (Shenhar & Dvir,
2007). Furthermore, other areas such as meeting the performance criteria, functional specifications, and
technical requirements also impact the customer’s satisfaction that can be seen as part of the project’s
success. Therefore, the following hypothesis has been synthesized:
H7: Decision-making significantly moderates the relationship between big data analytics and the project’s
impact on the customer.
Decision-making involves taking the right decisions about individual activities regarding controlling the
project activities and the work of the project team members to ensure that progress is satisfactorily
maintained. In the case where team members are geographically scattered and working virtually, a
supportive project environment must be in place that enables effective and efficient teamwork across the
project (Shim et al, 2002). The availability of data on the performance of projects in conjunction with
effective decision-making allows the project team members to control the project, although there is always a
possibility that a potentially good project fails because of poor management of the project (Ahmed et al.,
2021). On the other hand, successful delivery of projects enables the team members to gain improved skills
and competencies, thereby improving their employment prospects and overall skills base. Hence,
participating in successful projects can significantly impact the project team members (Ahmed & Mohamad
Azmi bin, 2016; Shenhar & Dvir, 2007). Therefore, the following hypothesis has been synthesized:
H8: Decision-making significantly moderates the relationship between big data analytics and the project’s
impact on the team.
The high-quality project information has critical importance which enables the organizations to make
improved and timely decisions that eventually contribute to project success (Caniëls et al. 2011). Dietrich
and Lehtonen (2005) articulated that a statistically significant correlation exists between the availability,
relevance, and legitimacy of the information and the success of a project; this was also found to be impacted
by suitable and relevant decision-making. It is imperative to have effective and efficient decision-making
processes that utilize the relevant data to create meaningful and data-driven insights. Such trends are gaining

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attention from organizations and practitioners that want to be more agile, efficient, and run their businesses
smartly (Demirkan and Delen, 2013). Therefore, the following hypothesis has been synthesized:
H9: Decision-making significantly moderates the relationship between big data analytics and the project’s
direct organizational and business success.
The concept of being a service-oriented organization is becoming one of the fastest-growing models of
modern economic structures. Many organizations rely on decision support systems and data analytics
capabilities, which are in the process of building their data-driven decision support systems (Demirkan and
Delen, 2013). Furthermore, organizations are keeping themselves up-to-date with the latest technologies and
trends, and investing in using data analytics to make effective decisions for long-term development.
Consequently, organizations are focusing on the right path to tackle the challenges of the future by taking
effective decisions and predicting their performance by employing data analytics. Therefore, the following
hypothesis has been synthesized:
H10: Decision-making significantly moderates the relationship between big data analytics and how the
project helps to prepare for the future.
Figure 1: Research Model

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3. Methods
The research design chosen for this study was explanatory, which used positivism as the underlying
philosophy for the reason that observation and experiment can be used to gain the required knowledge. This
approach is also known as the scientific method or quantitative research (Rahi, 2017). A deductive model
was adopted by using a questionnaire to collect data for testing the aforementioned research hypotheses
synthesized from the literature review.
3.1. Measurement of variables
Shenhar and Dvir’s (2007) developed the ‘Project Success Assessment Questionnaire (PSAQ)’, and
operationalized by Ahmed & Mohamad (2016) which was adopted to measure the multi-facet project
success. Furthermore, the ‘Project Management Effectiveness Construct (PMEC)’, designed by Morrison
and Brown (2004) was used to assess the multi-dimensional decision-making process. For this research
study, project management effectiveness is operationally defined as rational decision-making as measured by
the PMEC. The BDA constructs-based questionnaire was adopted from Kim, Shin, and Kwon (2012) to
measure big data analytics. The BDA constructs-based questionnaire about the decision-making dimension
was merged with the PMEC questionnaire and a total of 12 questions were finalized for measuring variables.
All questions used in the survey were anchored on a 5-point Likert scale to capture the responses of
participants in the study.
3.2. Population and sample
The population of the study was organizations working in the information technology (IT) and
telecommunications sector of Pakistan, employing big data analytics, and making effective decisions to
ensure the success of projects. The sample of 180 respondents for the study was selected according to the
guidelines of Peduzzi, Concato, Feinstein, and Holford (1995), which indicate that an established rule of
thumb for the required sample size is present to ensure at least events or observations per variable.
Moreover, this sample size is also consistent with the sample size of earlier related studies (Mikalef, Boura,
Lekakos, & Krogstie, 2019a, 2019b; Queiroz & Telles, 2018; Zhang & Xiao, 2020). The selection of the
sample from the various organizations in IT and telecommunication sector as well as the project managers
and team members as the respondents was carried out to maximize the generalizability of this research and
minimize the chances of bias in the sample data. To further ensure the sample adequacy, Kaiser-Meyer-
Olkin (KMO) analysis was performed, which is explained in the factor analysis section.

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3.3. Data collection and respondents
An online survey questionnaire was developed to collect data for the measurement of the variables of this
empirical research study. The questionnaire was divided into four parts. The first part consisted of questions
on demographics. The second part contained 25 questions about project success. The third part contained 12
questions that were targeted to acquire information related to decision-making. The fourth and the last part
consisted of 44 questions that covered big data analytics (BDA).
To qualify as a participant, the respondents needed to be key informants of the concepts and theories being
investigated and have meaningfully been involved in the activities being studied (Pesämaa, Zwikael, HairJr,
& Huemann, 2021). Therefore, project managers and team members were chosen as targeted respondents
who are ultimately responsible for the successful implementation of projects. The rationale for the selection
of the respondents was that project managers play a key role in decision making and are responsible for
making informed decisions that eventually lead to project success. Similarly, project team members know
the dynamics of big data analytics due to their role in employing, modeling, analyzing, and interpreting data
trends.
Accordingly, an online survey questionnaire was disseminated among 180 project managers and team
members of different IT and telecommunications projects, out of which 91 responses were collected through
an online survey instrument. The second round of data collection was conducted by sending out reminders to
the participants and 45 more responses were received. A total of 136 responses were collected and it was
found that one survey used unreliable names for their organization and their industry, so it was discarded and
the remaining 135 responses were used for further data analyses. The overall response rate was 75% and the
summary of demographic data is presented in Table 1.
Table 1: Summary of demographic data
Demographics
Characteristics
N
Percentage
Gender
Female
48
36%
Male
87
64%
Education
MS/MPhil
37
27%
Masters
51
38%
Bachelors
47
35%
Industry
Information technology (IT)
99
73%
Telecommunications
36
27%
Years of experience
More than 15 Yrs
19
14%
11 - 15 Yrs
25
18%
6 - 10 Yrs
52
39%
3 - 5 Yrs
23
17%

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Less than 3 Yrs
16
12%
4. Findings
Factor analysis was used to validate the study constructs and the ‘Process’ tool developed by Hayes (2012)
was used to observe the moderation effects. The conditional effect of moderation was analyzed using the
Jhonson-Neyman model. There were no missing values in the data. Data was also observed for skewness and
kurtosis and was found to be within the acceptable limits of -1.96 and +1.96 to prove a normal distribution of
the data (George & Mallery, 2010; Gravetter & Wallnau, 2014). A summary of descriptive data is presented
in Table 2.
Table 2: Summary of descriptive statistics
Min
Max
Mean
SD
Skewness
Kurtosis
Statistic
Statistic
Statistic
Statistic
Statistic
SE
Statistic
SE
Project efficiency
1.00
5.00
3.52
0.688
-0.239
0.209
0.040
0.414
Impact on customer
1.00
5.00
4.05
0.692
-0.501
0.209
-.316
0.414
Impact on team
1.00
5.00
3.51
0.706
-0.252
0.209
-0.123
0.414
Org. business success
1.00
5.00
3.76
0.690
-0.272
0.209
0.590
0.414
Preparing for future
1.00
5.00
3.96
0.699
-0.136
0.209
-0.562
0.414
Decision-making
1.00
5.00
3.53
0.678
0.077
0.209
-0.407
0.414
Big data analytics
1.00
5.00
3.42
0.760
-0.249
0.209
0.173
0.414
4.1. Factor analysis
Regarding the validity of the constructs used in this study, factor analysis was employed as the statistical
technique for validation of the hypothesized structure of the constructs (Ahmed & Mohamad, 2016). It was
performed with varimax rotation and the factor loadings for project success items ranged from 0.638 to
0.959, which are above the cut-off value of 0.40. None of the items was dropped, and to further assess the
appropriateness of factor analysis, two additional tests of Kaiser-Meyer-Olkin (KMO) and Bartlett’s test of
sphericity were also performed. The KMO value for measuring the sampling adequacy for project success
factors were 0.757, 0.745, 0.781, 0.764, and 0.796, which are acceptable as they are all above the 0.60 cut-
off value. KMO values for big data analytics and decision-making were 0.911 and 0.856 respectively, which
were well above the cut-off value. Bartlett’s Test of Sphericity also revealed significant results for all
variables with p < 0.05, thereby indicating that the correlation matrix is not an identity matrix, and therefore
construct validity exists.
As the first step in sifting the data for further analysis, Principal Component Analysis (PCA), one of the two
approaches for factor analysis, was chosen. PCA was performed to find out the number of variables that

15
account for and explain the most variance in the correlation matrix pattern to transform the original variables
into a smaller set of linear combinations. After PCA, it was found that all components measuring the
variables were significant, except for one that measured big data analytics and had the component value of -
0.079, hence it was dropped and the rest of the components were considered for further analyses.
4.2. Correlation analysis
Regarding the testing of hypotheses, Pearson’s Correlation was used as the bivariate correlation, which
explains the strength of the relationship between two variables. The correlation value was computed to find
the strength of the relationship between big data analytics and each of the five dimensions of project success.
It is evident from the results presented in Table 3 that a significant and positive relationship exists between
all five dimensions of project success, big data analytics, and decision making.
Table 3: Summary of correlation analysis among variables
Sr.
Variable
Mean
SD
1
2
3
4
5
6
7
1
Project efficiency
3.521
0.689
1
2
Impact on customer
4.055
0.692
0.565**
1
3
Impact on team
3.521
0.689
0.978**
0.548**
1
4
Org. business success
3.766
0.691
0.457**
0.585**
0.458**
1
5
Preparing for future
3.925
0.648
0.449**
0.509**
0.436**
0.700**
1
6
Big data analytics
3.492
0.707
0.446**
0.390**
0.446**
0.566**
0.358**
1
7
Decision making
3.418
0.739
0.441**
0.380**
0.441**
0.566**
0.393**
0.809**
1
**Correlation is significant at the 0.01 level (2-tailed).
4.3. Regression analysis
The next step in the data analysis procedure was to understand how much variance existed in the response
variables (i.e. the five dimensions of the outcome variable; project success), which is explained by the
predictor variable of big data analytics. Additionally, analyses were carried out to study the strength of the
relationship, if any, between these variables. For this purpose, linear regression was executed. The steps
followed to run the regression analysis were to first enter the outcome variable and then the predictor
variable. The same steps were repeated five times to run the regression for each of the five hypotheses, H1 to
H5. The results are presented in Table 4.
The p-value for the H1 test was 0.000, which is <0.005 and hence this indicates that every unit of change in
the use of big data analytics, will bring about changes in the response variable, project efficiency, and so the
relationship is significant. The value of R-squared is a statistical measure of the closeness of data to the
regression line and is known as the coefficient of determination, which explains the percentage of variation

16
in the response variable. The regression test results illustrated that big data analytics explains 19.9% of the
variance in the project efficiency (ΔF=33.07, p<0.001). The standardized beta value was also positive and
significant (β=0.446, p<0.001). Next, the same steps were repeated for testing H2, with impact on the
customer as the response variable, and the results indicated a significant relationship between the predictor
and the response variables, with p<0.001. Big data analytics explained 15.2% of the variance in impact on
the customer (ΔF=23.819, p<0.001). The standardized beta value was also positive and significant (β =0.39,
p<0.001).
Table 4: Summary of research hypotheses results
Hyp
Variables
Coefficients
Model Summary
ANOVA
B
β
t
Sig.
R
R²
Adj R²
ΔF
Sig.
H1
Project efficiency
0.404
0.466
5.751
0.000
0.446
0.199
0.193
33.070
0.000
H2
Impact on customer
0.355
0.390
4.880
0.000
0.39
0.152
0.146
23.819
0.000
H3
Impact on team
0.415
0.466
5.749
0.000
0.446
0.199
0.193
33.051
0.000
H4
Org. business success
0.514
0.566
7.915
0.000
0.566
0.320
0.315
62.649
0.000
H5
Preparing for future
0.329
0.358
4.427
0.000
0.358
0.128
0.122
19.594
0.000
Regression analysis for H3 yielded positive results indicating the significance, where big data analytics
explains 19.9% of the variance in outcome variable of impact on customer (ΔF=33.051, p<0.001). The
standardized beta value was also positive and significant (β=0.415 p<0.001). A substantial outcome was
yielded through regression for testing H4, where the regressor variable big data analytics explained 32% of
the variance in the outcome variable dimension of organizational business success (ΔF=19.594, p<0.001).
The standardized beta value also indicated a positive and significant relationship (β=0.566 p<0.001). Similar
steps were repeated for testing H5, with preparing for the future as the last dimension of the outcome variable
of project success. The results showed that big data analytics explained 12.8% of the variance in the outcome
variable of preparing for the future (ΔF=62.649, p<0.001), and the standardized beta value also indicated a
positive and significant relationship (β=0.358 p<0.001).
In this model, hypotheses H6 to H10 tested the moderator variable and its impact on the response variable of
project success, through five of its dimensions. The PROCESS tool developed by Hayes (2012) was used to
carry out moderator analysis in the SPSS software. The steps included entering the response variable in the
model followed by the predictor variable and the moderator variable in the model and moderator analysis
was run to test the hypotheses one by one. The results of moderation analysis for H6 indicated that decision-
making (DM) does not have any significant impact on the relationship between the predictor and the
response variable because the interaction between the variables indicated that the p-value is 0.9480, which is
greater than the cut-off value of p=0.05 (see Table 5). The values of the upper and lower limits of the
confidence interval also suggested the same. Hence, it was concluded that H6 is not supported.

17
Table 5: Summary of hierarchical multiple regression model (moderation analysis)
Hyp
DMxBDA
Coeff
se
t
p
LLCI
ULCI
H6
PEF
0.0053
0.0806
0.0654
0.9480
-0.1542
0.1647
H7
IOC
0.1765
0.0894
1.9747
0.0504
-0.0003
0.3533
H8
IOT
0.0107
0.0824
0.1299
0.8968
-0.1523
0.1737
H9
OBS
0.1944
0.0789
2.4623
0.0151
0.0382
0.3506
H10
PFF
0.1772
0.0859
2.0618
0.0412
0.0072
0.3472
Moderated regression results for H7 indicated the existence of an impact of the moderator variable on the
relationship between the predictor and outcome variables of this study. The p-value of 0.05 was just about at
the borderline of the acceptance criteria of significance, so the conditional effect of moderation was analyzed
further to see if the degree of moderation is at the low, medium, or high levels; which represents standard
deviation below the mean, at mean, and above the mean respectively. For a high level of decision-making
(b= 0.3, t (131) = 2.4, p =0.01), which indicated that for every decision made, using big data analytics
increased the chances of project success by 0.3 points (see Table 5). It was also observed from the data
above that moderation when set at low and medium levels did not have any positive significant impact on
the relationship between the variables. Decision-making only started to moderate the relationship between
big data analytics and project success when the high levels of moderation were applied (Bauer & Curran,
2005). Based on these results, H7 was supported.
The results of the moderation analysis for H8 indicated that decision-making does not significantly moderate
the relationship between the big data analytics and the project’s impact on the team, as it is evident from the
interaction between these variables that the p-value is 0.896, which is greater than the cut-off value of
p=0.05 (see Table 5). The values of the upper and lower limits of the confidence interval also suggested the
non-existence of any moderation effect between the variables in the study. Hence, H8 was not supported.
Next, the interaction of the moderator was studied by including direct organizational and business success in
the model, since the outcome variable and a significant moderation effect were observed for H9. With a p-
value below 0.05 and unidirectional scale values of the confidence interval, it was concluded that moderation
existed and hence H9 was supported. The conditional effect of moderation is summarized in Table 5, where
an enhancing effect was observed since increasing the use of decision-making with big data analytics
resulted in a gradual increase in the probability of direct organizational and business success.
For high-level decision-making, it was observed that big data analytics increased the chances of project
success by 0.47 points (b=0.47, t (131) = 4.2, p=0.00). The interaction of decision-making with big data

18
analytics, and preparing for the future was studied by performing moderator analysis, which yielded positive
and significant results for H10, with the value of p=0.041, which was less than the cut-off value of p=0.05.
The unidirectionality of scale in the upper and lower limits of the confidence interval also confirmed the
moderation effect of decision-making and hence H10 was supported. Moreover, an overall summary of
research hypotheses supported or not supported is presented in Table 6.
Table 6: Summary of research hypotheses (supported/ not supported)
Hypothesis
Significance (<0.01)
Supported/ Not supported
H1
0.000
Supported
H2
0.000
Supported
H3
0.000
Supported
H4
0.000
Supported
H5
0.000
Supported
H6
0.948
Not supported
H7
0.001
Supported
H8
0.896
Not supported
H9
0.000
Supported
H10
0.041
Supported
5. Discussion
The findings from the research confirmed that there is a positive effect of big data analytics on organization
performance through increased project success. Previously, the success associated with big data analytics
was largely through case-based studies (LaValle et al. 2011, CGMA 2013) and therefore the research
findings reported herein provide an empirical extension to this field of academic research in the literature.
Upon testing whether using big data analytics results in increased project efficiency, it was revealed that big
data analytics provide the depth of information that is required at an organizational level. This enhanced
level of information can be translated into timely decisions, eventually leading to more and more projects
completed within specified schedules and budgets – the measures of project efficiency. In studying the
moderating effect of decision-making, the results confirmed that the indirect effect is stronger and it is one
of the main drivers of success (Naor et al. 2008). The results further strengthen the claims about utilizing big
data analytics in decision-making, which translates into significantly improved organizational performance
(Thirathon et al., 2017). This is because decision-making impacts and strengthens the relationship between
big data analytics and overall organizational business success, which is a response variable of this study. The
regression analysis yielded positive and significant results with ΔF=19.594, p<0.001, and β=0.566 p<0.001
for organizational business success.
As previously discussed in the literature, overall project success is a broad concept that has moved beyond
purely being a measure of project efficiency (Turner & Zolin, 2012; Collyer & Warren, 2009; Thomas et al.,

19
2008; Shenhar & Dvir, 2007). The results of this research study indicate that project efficiency can be
enabled through the insights yielded from large data sets and along with other success contributors that result
in the desired outcomes for projects. The use of data for understanding project dynamics and steering
projects towards successful completion also helps organizations to prepare for the future, which is evident
from this study. This is consistent with the study of Turner and Zolin (2012) that decision-makers need
improved insights into projects to go one step ahead and use the information proactively for future planning
and control to maximize project success (Serrador and Turner, 2015).
The research study identified that a positive and significant relationship exists between big data analytics and
the project success dimensions. Therefore, organizations investing in big data analytical solutions can be
viewed in terms of maximizing the level of success arising from projects across the broad range of
dimensions that have been evaluated in this study (i.e. project efficiency, impact on the customer, impact on
the team, organizational business success and preparing for future). Specifically, the use of big data analytics
to support the management of projects was found to be a strong predictor of success as it explains one-third
of the total variance in overall organizational business success. The moderation effects of decision-making
were observed but were not found to impact all the dimensions of project success equally; two of the
dimensions did not reveal any influence; which were project efficiency and impact on the customer (p >
0.05). The findings indicate that the dimensions of project success can be independently explained through
the use of big data analytics but not all are significantly affected by the moderator. Thus, big data analytics
involves making decisions about business models and processes, and effective and faster decisions have
been proven to steer organizations towards exceedingly high performance (Meissner & Wulf, 2014).
The absence of moderating effects between decision-making and project efficiency also brings up an
interesting fact that big data analytics can lead to the more efficient delivery of projects, which do not
necessarily need any decision-based influence to yield the desired project results. The moderation analysis or
hierarchical multiple regression analysis revealed the level of moderation for each of the five dimensions of
project success. Furthermore, the conditional effect of the focal predictor was analyzed at the three different
levels of the moderator values which can be termed as low, medium, and high, or one standard deviation
below the mean, at the mean, and one standard deviation above the mean respectively. This analysis enabled
visualization of the moderation effect associated with increasing or decreasing when different values of the
moderator variable are used. However, H6 and H8 were not supported because of insignificant moderation,
while hypotheses H7, H9, and H10 were substantiated. It was observed that decision-making moderated the
relationship between big data analytics and project success when one level standard deviation above the
mean was used in moderation (Bauer & Curran, 2005).
Examining the results individually for moderator analysis, it was evident that the greatest percentage of
variance (37%) was due to the three variables, big data analytics, decision-making, and their interaction,

20
which is explained [F (3,131) = 25.78, p<0.001, R2=0.37] for the organizational business success. The
second most significant relationship was observed in the moderation interaction with project efficiency, with
34% variance [F (3,131) = 22.57, p<0.001, R2=0.34]. For each of the other three dimensions, namely,
impact on the customer, impact on the team, and preparing for the future, the percentage of variance was
20%, 34%, and 27% respectively. The Jhonson Neyman analysis of moderation was used to compute the
exact level of moderator required at p = 0.05, which indicates that there is a significant effect on the
variables. It was concluded that for the variable impact on the customer, [b = 0.23, t (131) = 1.98, p=0.05], at
least 3.7 times decision-making is practiced, and consequently, big data analytics is significantly related to
project success. As the practice of decision-making is increased, the relationship between big data analytics
and its impact on customers becomes more significant. Similarly, decision-making regarding overall
organizational business success and preparing for the future plays a significant role as evident from the
results [b=0.22, t (131) = 1.98, p=0.05. and b=0.32, t (131) = 1.94, p=0.05].
6. Implications, directions, and conclusions
6.1. Theoretical implications
This study utilizes the resource-based view theory to advance the theoretical understanding of big data
analytics relating to decision-making and maximizing project success, in addition to acknowledging that
these resources are interrelated, specifically, in IT and telecommunications projects. Based on the resource-
based view, diverse subsets and bundles of resources (i.e. data analytics and skills) aid in the development of
capabilities of organizations where few resources are easy to imitate by competitors for competitive
advantage (Bag et al., 2021). Thus, the impact of big data analytics on project success has been measured
and analyzed using a dataset from the IT and telecommunications sector, which can be explored and applied
to other industries and sectors for further validating the relationship. Furthermore, the findings have
substantiated the benefits associated with the use of big data analytics as opposed to previously discussed
cases that were not necessarily fully validated success stories (LaValle et al. 2011).
This research study supports the resource-based view theory by providing empirical evidence that efficient
resource usage and management, together with the successful implementation of big data analytics, improve
organizational productivity and performance through effective decision-making. The outcomes of this study
align with previous research that suggests big data analytics can considerably boost organizational
effectiveness (Bag et al., 2020). Furthermore, BDA implementation in companies from developing countries
(such as Pakistan and others) is still in its infancy; in such circumstances, management is often either
hesitant to invest in technological efforts or is unaware of the long-term benefits that organizations and

21
society might reap from such investments. Based on the study findings, this can form the basis of future
research on a sub-dimensional level of big data analytics, which can pinpoint the necessary ingredients and
resources that project managers and team members need to focus on while dealing with huge volumes of
data to steer project activities towards achieving success. The results also support the existing literature,
which suggests that extensively using big data analytics translates into meaningfully improved
organizational performance (Thirathon et al., 2017).
6.2. Practical implications
This research has implications for project managers, IT managers, project coordinators, and policymakers
who are implementing big data analytics to improve project performance and decision-making processes.
Effective methods and procedures, as well as modern project management standards and protocols, should
be adopted to build employees' analytical and decision-making skills. As a result, project managers should
make integrating data analytic approaches with strategic business goals a top priority for improved
organizational and project outcomes. Indeed, the moderating role of big data analytics contributes to a
growing body of knowledge relevant to practitioners about the necessity of effective and timely decision-
making, as well as resource optimization through the implementation of best practices based on RBV.
Moreover, this study acknowledges current literature by emphasizing the importance of resource availability
and suitable utilization, as well as big data analytics, which is necessary for firms to enhance efficiency and
gain a competitive advantage (Bag et al., 2020). However, greater attention is required towards the
development of strategic level policies for the optimum use of big data analytics at the organizational level
that can assist successful decision-making during project planning and execution. Furthermore, establishing
effective implementation of BDA and decision-making guidelines and policies at the project level can also
help organizations to enhance their productivity and performance.
Further practical implications are that improving decision-making leads to improved project outcomes since
big data analytics in conjunction with data-driven decision-making influences organizational and business
success resulting from successful management of projects. Organizations investing in big data analytics can
also consider investing in training to support project managers and decision-makers to properly utilize and
extract valuable information through the systematic use of big data analytics. Therefore, leading to analyzing
such data to steer projects towards success across the holistic dimensions of project success included in this
study. The absence of moderating effects of decision-making on project efficiency and impact on the team
implies that big data analytics independently influence these aspects and the relationship does not need any
additional input from decision-makers in the organizations as the team dynamics as well as project efficiency
can enable the desired results for projects. However, project managers must ensure that project teams should
have adequate access to information and have the required infrastructure for collecting, storing, analyzing,

22
and interpreting large and complex volumes of project data. Investing in this infrastructure can provide a
competitive edge for IT and telecommunications as well as other organizations enabling them to develop a
contemporary talent pool to meet the business demands both internally and externally. Indeed, project
managers can ensure that all such aspects are included in the planning and control process to improve the
overall project results and thereby support the achievement of organizational goals.
6.3. Limitations and future directions
Firstly, this cross-sectional research was geographically limited to the IT and telecommunications sector in
Pakistan, and in the future, there is scope to adopt a more global perspective across different industrial
sectors. Longitudinal research can be conducted to study the impact of decision-making on several stages of
a project, from the conceptual phase to the final delivery and handover, in addition to validating this model
and understanding how big data analytics and decision-making impact across different types of projects as
well as industrial sectors. Secondly, the research issues and specific research gaps highlighted in the recent
literature suggested the need for examination of the impact of big data analytics on multi-dimensional
project success in the presence of decision-making. However, big data analytics, project success, and
decision-making can be used as multi-dimensional constructs in different environments. Therefore, it would
be fruitful for further researchers to explore the relationships by adding multi-dimensions of all constructs of
big data analytics, project success, and decision making in various cultures and project environments, to
provide a more comprehensive framework that may apply to all types of projects, organizations, and
industries, sectors, and countries. Thirdly, the quality of decision-making can be explored in a controlled
environment, with and without the use of big data analytics and further impact of analytics-based decision-
making processes can be studied with a wider array of participants. Additionally, a case study-based
approach can be used to explore how project success is perceived in different settings of big data analytics
usage and infrastructure, as well as applying more promising theories other than RBV. Finally, it is proposed
that the moderating effect of multi-faceted decision-making on the relationship between big data analytics
and project success can be further explored to identify if there are other factors and theories that moderate or
mediate this relationship, alone or in combination with decision-making in different types of projects and
organizations.
6.4. Conclusion
This study contributes to the body of knowledge by identifying that decision-making is a critical part of
managing projects, which has a significant influence on the relationship between big data analytics and the
dimensions of project success. Thus, decisions can be made based on the experience and expert judgment of
those involved in project delivery, which can be further enhanced by gaining insight into the trends and

23
obscured information in the big data by systematically sorting and analyzing data sets across the project
lifecycle. This study also emphasizes the effectiveness of big data analytics for projects in the ICT sector,
although the findings can potentially be applied to other industrial sectors. Statistical analyses of the data
from the empirical research study have revealed that big data analytics has a positive and significant
relationship with the dimensions of project success. Furthermore, the moderating effect of decision-making
was identified in the interaction between the predictor and outcome variables, where the moderating effect of
decision-making was observed in the interaction between big data analytics and three out of five dimensions
of project success. Through conditional moderation analysis, it was concluded that project efficiency and
impact on the team dimensions were the two dimensions of project success for which decision-making did
not play a moderating role.
The decisions made in organizational environments involve big data analytics, which has an impact on how
information is processed and utilized to ensure improved organizational performance and this includes the
delivery of projects. Through this study, it is evident that the highest level of moderation was observed when
big data analytics interacted with the organizational business success dimension of the outcome variable (i.e.
project success). This study is based upon the established constructs from previously published literature and
the evidence found for the role of decision-making in project success further validates the findings. The
study is consistent with the current discussion around big data and its impact on project management and
thereby contributes to the knowledge base in this important area of academic research. The study provides
evidence that employing big data analytics in projects results in improved performance of projects thereby
leading to enhanced organizational capabilities.
Acknowledgments
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-
profit sectors.
Declaration of Competing Interests
We have read and understood the JET-M policy on the declaration of interests and declare that the authors
have no competing interests.

24
References
Ahmed, R., Hussain, A., & Philbin, S. P. (2021). Moderating Effect of Senior Management Support on the
Relationship Between Schedule Delay Factors and Project Performance. Engineering Management
Journal, 33(3), 1-20.
Ahmed, R., & Azmi bin Mohamad, N. (2016). Exploring the relationship between multi-dimensional top
management support and project success: An international study. Engineering Management
Journal, 28(1), 54-67.
Ahmed, E., & Yüzbaşı, B. (2016). Big data analytics: integrating penalty strategies. International Journal of
Management Science and Engineering Management, 11(2), 105-115.
Akter, S., Bandara, R., Hani, U., Wamba, S. F., Foropon, C., & Papadopoulos, T. (2019). Analytics-based
decision-making for service systems: A qualitative study and agenda for future research.
International Journal of Information Management, 48, 85-95.
AlNuaimi, B. K., Khan, M., & Ajmal, M. M. (2021). The role of big data analytics capabilities in greening
e-procurement: A higher order PLS-SEM analysis. Technological Forecasting Social Change, 169,
120808.
Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role of institutional pressures and resources
in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing
practices and circular economy capabilities. Technological Forecasting Social Change, 163, 120420.
Bag, S., Wood, L. C., Xu, L., Dhamija, P., & Kayikci, Y. (2020). Big data analytics as an operational
excellence approach to enhance sustainable supply chain performance. Resources, Conservation and
Recycling, 153, 104559.
Barney, J. B. (2001). Resource-based theories of competitive advantage: A ten-year retrospective on the
resource-based view. Journal of Management, 27(6), 643-650.
Bauer, D. J., & Curran, P. J. (2005). Probing interactions in fixed and multilevel regression: Inferential and
graphical techniques. Multivariate Behavioral Research, 40, 373-400.
Beyer, M. A., & Laney, D. (2012). The importance of ‘big data’: a definition. Stamford, CT: Gartner, 2014-
2018.
Brands, K. C. M. A. (2014). Big data and business intelligence for management accountants. Strategic
Finance, 95, 64–65.
Botterhuis L,van der Duin P, de Ruijter P, van Wijck P. Monitoring the future. Building an early warning
system for the Dutch Ministry of Justice, Futures2010; 42: pp. 454-465.
Carter, P. (2011) Big Data Analytics: Future Architectures, Skills and Roadmaps for the CIO, IDC
Whitepaper. Retrieved from: URL http://free-doc-lib.com/book/big-data-analytics-future-architectures-skills-and-
roadmaps.pdf.
Caniëls, M., & Bakens, R. (2012). The effects of Project Management Information Systems on decision
making in a multi project environment. International Journal Of Project Management, 30(2), 162-
175.
CGMA, (2018). Big Data: Readying business for the big data revolution. (2018). Retrieved from: URL
https://www.cgma.org/resources/reports/downloadabledocuments/cgma-briefing-big-data.pdf.
Chae, B., Olson, D., & Sheu, C. (2014). The impact of supply chain analytics on operational performance: a
resource-based view. International Journal of Production Research, 52(16), 4695-4710.
Cheng, J., Zhang, H., Tao, F., & Juang, C. F. (2020). DT-II: Digital twin enhanced Industrial Internet
reference framework towards smart manufacturing. Robotics and Computer-Integrated
Manufacturing, 62, 101881.

25
Cohen, L., Manion, L., & Morrison, K. (2013). Validity and reliability. In Research methods in
education (pp. 203-240). Routledge.
Collyer, S., & Warren, C. M. (2009). Project management approaches for dynamic
environments. International Journal of Project Management, 27(4), 355-364.
Cui, Y., Kara, S., & Chan, K. C. (2020). Manufacturing big data ecosystem: A systematic literature review.
Robotics and Computer-Integrated Manufacturing, 62, 1018-61.
Dai, H. N., Wang, H., Xu, G., Wan, J., & Imran, M. (2020). Big data analytics for manufacturing internet of
things: opportunities, challenges and enabling technologies. Enterprise Information Systems, 14(9-
10), 1279-1303.
Das, T. K., & Kumar, P. M. (2013). Big data analytics: A framework for unstructured data analysis.
International Journal of Engineering Science & Technology, 5(1), 153-000.
Demirkan, H., & Delen, D. (2013). Leveraging the capabilities of service-oriented decision support systems:
Putting analytics and big data in cloud. Decision Support Systems, 55(1), 412-421.
Dietrich, P., & Lehtonen, P. (2005). Successful management of strategic intentions through multiple project
management: reflections from empirical study. International Journal of Project Management, 23(5),
386-391.
Elgendy, N., & Elragal, A. (2014, July). Big data analytics: a literature review paper. In Industrial
Conference on Data Mining (pp. 214-227). Springer, Cham.
Fauser, J., Schmidthuysen, M., & Scheffold, B. (2015). The Prediction of Success in Project
Management. Predictive Project Analytics. Projektmanagement aktuell, 26, 66-74.
Favaretto, M., De Clercq, E., Schneble, C. O., & Elger, B. S. (2020). What is your definition of Big Data?
Researchers’ understanding of the phenomenon of the decade. PloS one, 15(2), 228-247.
Frisk, J.E. & Bannister, F. (2017). Improving the use of analytics and big data by changing the decision-
making culture: A design approach. Management Decision, 55(10), 2074-2088.
George, D., & Mallery, P. (2010). SPSS for Windows step by step. A simple study guide and reference (10.
Baskı).
Germann, F., Lilien, G. L., Fiedler, L., & Kraus, M. (2014). Do retailers benefit from deploying customer
analytics? Journal of Retailing, 90(Issue), 587-593.
Gravetter, F. J., & Wallnau, L. B. (2014). Introduction to the t-statistic. Essentials of Statistics for the
Behavioral Sciences, 8, 252.
Grušovnik, D., Kavkler, A., & Uršič, D. (2017). Dimensions of Decision-Making Process Quality and
Company Performance: A Study of Top Managers in Slovenia. Naše gospodarstvo/Our
economy, 63(4), 66-75.
Hagel, J. (2015). Bringing analytics to life. Journal of Accountancy, 219(2), 24-40.
Hagstrom, M. (2012). High-performance analytics fuels innovation and inclusive growth: Use big data,
hyperconnectivity and speed to intelligence to get true value in the digital economy. Journal of
Advanced Analytics, 3–4.
Halaweh, M., & Massry, A. E. (2015). Conceptual model for successful implementation of big data in
organizations. Journal of International Technology and Information Management, 24(2), 2.
Hayes, A. F. (2012). PROCESS: A versatile computational tool for observed variable mediation,
moderation, and conditional process modeling.
Joseph, J., & Gaba, V. (2020). Organizational structure, information processing, and decision-making: a
retrospective and road map for research. Academy of Management Annals, 14(1), 267-302.
Karlsen, J. T., & Gottschalk, P. (2004). Factors affecting knowledge transfer in IT projects. Engineering
Management Journal, 16(1), 3-11.

26
Kerzner, H. (2004). Advanced project management: Best practices on implementation. Hoboken, NJ: John
Wiley & Sons.
Khan, S. (2022). Barriers of big data analytics for smart cities development: a context of emerging
economies. International Journal of Management Science and Engineering Management, 17(1), 1-9.
doi:https://doi.org/10.1080/17509653.2021.1997662
Khan, S. A., Hassan, S. M., Kusi-Sarpong, S., Mubarik, M. S., & Fatima, S. (2021). Designing an integrated
decision support system to link supply chain processes performance with time to market.
International Journal of Management Science and Engineering Management, 1-13.
doi:10.1080/17509653.2021.2000900
Killen, C. P., Geraldi, J., & Kock, A. (2020). The role of decision makers’ use of visualizations in project
portfolio decision making. International Journal of Project Management, 38(5), 267-277.
Kim, G., Shin, B., & Kwon, O. (2012). Investigating the value of sociomaterialism in conceptualizing IT
capability of a firm. Journal of Management Information Systems, 29(3), 327-362.
Kościelniak, H., & Puto, A. (2015). BIG DATA in Decision Making Processes of Enterprises. Procedia
Computer Science, 65, 1052-1058. doi: 10.1016/j.procs.2015.09.053
Laney, D., Taylor, P., & Gartner INC. (2013). Big Data Means Big Business, Gartner INC,
LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the
path from insights to value. MIT Sloan Management Review, 52(2), 21.
Lee, I. (2017). Big data: Dimensions, evolution, impacts, and challenges. Business Horizons, 60(3), 293-303.
Malakooti, B. (2010). Decision making process: typology, intelligence and optimization. Journal of
Intelligent Manufacturing, 23(3), 733- 746.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011).
Institute. MG, Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Byers, AH Big Data:
The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute.
Mazzei, M. J., & Noble, D. (2017). Big data dreams: A framework for corporate strategy. Business
Horizons, 60(3), 405-414.
McAfee, A. & Brynjolfsson, E. (2012). Big data: the management revolution. Harvard Business Review,
90(10), 60-68.
Meissner, P., & Wulf, T. (2014). Antecendents and effects of decision comprehensiveness: The role of
decision quality and perceived uncertainty. European Management Journal, 32(4), 625-635.
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019a). Big data analytics and firm performance:
Findings from a mixed-method approach. Journal of Business Research, 98, 261-276.
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019b). Big data analytics capabilities and innovation:
the mediating role of dynamic capabilities and moderating effect of the environment. British Journal
of Management, 30(2), 272-298.
Miller, G. J. (2018). Quantitative Comparison of Big Data Analytics and Business Intelligence Project
Success Factors. In Information Technology for Management: Emerging Research and Applications
(pp. 53-72): Springer.
Miller, G. J. (2019). The influence of big data competencies, team structures, and data scientists on project
success. Paper presented at the 2019 IEEE Technology & Engineering Management Conference
(TEMSCON).
Morrison, J., & Brown, C. (2004). Project management effectiveness as a construct: A conceptual study.
South African Journal of Business Management, 35(4), 73-94.
Naor, M., Goldstein, S. M., Linderman, K. W., & Schroeder, R. G. (2008). The role of culture as driver of
quality management and performance: infrastructure versus core quality practices. Decision
Sciences, 39(4), 671-702.

27
Narayan, S., & Tan, H. C. (2019). Adopting big data to forecast success of construction projects: A review.
Malaysian Construction Research Journal, 6(1), 132-143.
Nixon, P., Harrington, M., & Parker, D. (2012). Leadership performance is significant to project success or
failure: a critical analysis. International Journal of Productivity and Performance Management, 61(2),
204-216.
Oukil, A., & Govindaluri, S. M. (2020). A hybrid multi‐attribute decision‐making procedure for ranking
project proposals: A historical data perspective. Managerial and Decision Economics, 41(3), 461-
472.
O'Driscoll, T. (2014). Can Big Data Deliver Added Value? Training, 51(2), 51.
Papadaki, D., Bakas, D. N., Karamitsos, D., & Kirkham, D. (2019). Big data from social media and
scientific literature databases reveals relationships among risk management, project management and
project success. Project Management Project Success.
Peduzzi, P., Concato, J., Feinstein, A. R., & Holford, T. R. (1995). Importance of events per independent
variable in proportional hazards regression analysis II. Accuracy and precision of regression
estimates. Journal of clinical epidemiology, 48(12), 1503-1510.
Pesämaa, O., Zwikael, O., HairJr, J., & Huemann, M. (2021). Publishing quantitative papers with rigor and
transparency. International Journal of Project Management, 39(3), 217-222.
Philbin, S. P., & Kennedy, D. A. (2014). Diagnostic framework and health check tool for engineering and
technology projects. Journal of Industrial Engineering and Management, 7(5), 1145-1166.
Pramanik, D., Mondal, S. C., & Haldar, A. (2020). A framework for managing uncertainty in information
system project selection: An intelligent fuzzy approach. International Journal of Management
Science and Engineering Management, 15(1), 70-78.
Project Management Institute. (2013). A Guide to the Project Management Body of Knowledge (PMBOK).
Power, D. J. (2014). Using ‘Big Data’ for analytics and decision support. Journal of Decision
Systems, 23(2), 222-228.
Queiroz, M. M., & Telles, R. (2018). Big data analytics in supply chain and logistics: an empirical approach.
The International Journal of Logistics Management.
Rahi, S. (2017). Research design and methods: A systematic review of research paradigms, sampling issues
and instruments development. International Journal of Economics & Management Sciences, 6(2), 1-
5.
Russom, P. (2011). Big data analytics. TDWI Best Practices Report, fourth quarter, 19(4), 1-34.
Saeed, K. A., & Abdinnour-Helm, S. (2008). Examining the effects of information system characteristics
and perceived usefulness on post adoption usage of information systems. Information &
Management, 45(6), 376-386.
Saggi, M. K., & Jain, S. (2018). A survey towards an integration of big data analytics to big insights for
value-creation. Information Processing & Management, 54(5), 758-790.
Schrage, M. (2016). How the big data explosion has changed decision making. Harvard Business Review,
25, 1-6.
Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D., & Tufano, P. (2012). Analytics: The real-world
use of big data. IBM Global Business Services, 12, 1-20.
Serrador, P., & Turner, R. (2015). The relationship between project success and project efficiency. Project
Management Journal, 46(1), 30-39.
Shah, T. R. (2022). Can big data analytics help organisations achieve sustainable competitive advantage? A
developmental enquiry. Technology in Society, 68, 101801.
Shenhar, A. J., & Dvir, D. (2007). Reinventing Project Management. Boston, MA: Harvard Business School.

28
Shim, J., Warkentin, M., Courtney, J., Power, D., Sharda, R., & Carlsson, C. (2002). Past, present, and future
of decision support technology. Decision Support Systems, 33(2), 111-126.
Singpurwalla, N. D. (2016). Seeking relationships in big data: a Bayesian perspective. International Journal
of Management Science Engineering Management, 11(2), 116-121.
Strawn, G. O. (2012). Scientific research: How many paradigms? Educause Review, 47, 26.
Sundarakani, B., Ajaykumar, A., & Gunasekaran, A. (2021). Big data driven supply chain design and
applications for blockchain: An action research using case study approach. Omega, 102, 102452.
Thirathon, U., Wieder, B., Matolcsy, Z., & Ossimitz, M. (2017). Big Data, Analytic Culture and Analytic-
Based Decision Making: Evidence from Australia. Procedia Computer Science, 121, 775-783.
Tien, N. D. (2017). Tune up fuzzy C-means for big data: some novel hybrid clustering algorithms based on
initial selection and incremental clustering. International Journal of Fuzzy Systems, 19(5), 1585-
1602.
Thomas, M., Jacques, P. H., Adams, J. R., & Kihneman-Woote, J. (2008). Developing an effective project:
Planning and team building combined. Project Management Journal, 39(4), 105–113.
Turner, R., & Zolin, R. (2012). Forecasting success on large projects: Developing reliable scales to predict
multiple perspectives by multiple stakeholders over multiple time frames. Project Management
Journal, 43(5), 87–99.
Wang, W. Y. C., & Wang, Y. (2020). Analytics in the era of big data: the digital transformations and value
creation in industrial marketing. Industrial Marketing Management, 86, 12-15.
Wang, J., Yang, Y., Wang, T., Sherratt, R. S., & Zhang, J. (2020). Big data service architecture: a survey.
Journal of Internet Technology, 21(2), 393-405.
Wang, Y., Zhang, H., & Song, M. (2020). Does Big Data–Embedded New Product Development Influence
Project Success? Research-technology management, 63(4), 35-42.
Wang, Z., Zaman, S., Rasool, S. F., uz Zaman, Q., & Amin, A. (2020). Exploring the Relationships Between
a Toxic Workplace Environment, Workplace Stress, and Project Success with the Moderating Effect
of Organizational Support: Empirical Evidence from Pakistan. Risk Management Healthcare Policy,
13, 1055-1067.
Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’can make big
impact: Findings from a systematic review and a longitudinal case study. International Journal of
Production Economics, 165, 234-246.
Wernerfelt, B. (1984). A resource‐based view of the firm. Strategic Management Journal, 5(2), 171-180.
Zhang, H., & Xiao, Y. (2020). Customer involvement in big data analytics and its impact on B2B
innovation. Industrial Marketing Management, 86, 99-108.
Zikopoulos, P., Deroos, D., Parasuraman, K., Deutsch, T., Giles, J., & Corrigan, D. (2013). Harness the
power of big data: The IBM Big Data Platform. New York, NY: McGraw-Hill.