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Citation: Cardoso, António, Manuel
Sousa Pereira, JoséCarlos Sá, Daryl
John Powell, Silvia Faria, and Miguel
Magalhães. 2024. Digital Culture,
Knowledge, and Commitment to
Digital Transformation and Its Impact
on the Competitiveness of Portuguese
Organizations. Administrative Sciences
14: 8. https://doi.org/10.3390/
admsci14010008
Received: 25 October 2023
Revised: 4 December 2023
Accepted: 15 December 2023
Published: 28 December 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
administrative
sciences
Article
Digital Culture, Knowledge, and Commitment to Digital
Transformation and Its Impact on the Competitiveness of
Portuguese Organizations
António Cardoso 1, * , Manuel Sousa Pereira 2, JoséCarlos Sá3, Daryl John Powell 4, Silvia Faria 5
and Miguel Magalhães 5
1Department of Business and Communication Sciences (DBCS), University Fernando Pessoa (UFP),
4294-004 Porto, Portugal
2Escola Superior de Ciências Empresariais, Instituto Politécnico de Viana do Castelo,
4930-600 Valença, Portugal; pereiramanuel@esce.ipvc.pt
3Instituto Superior de Engenharia do Porto-ISEP, Polytechnic of Porto, 4200-072 Porto, Portugal;
cvs@isep.ipp.pt
4Department of Product and Production Development, SINTEF Manufacturing AS, 2830 Raufoss, Norway;
daryl.powell@sintef.no
5Research on Economics, Management and Information Technologies (REMIT), Portucalense University,
4200-072 Porto, Portugal; slvfaria@gmail.com (S.F.); miguel.magalhaes@iees.pt (M.M.)
*Correspondence: ajcaro@ufp.edu.pt
Abstract: This study aimed to understand the impact of digital culture on companies’ knowledge
and constant commitment to digital transformation, as well as its impact on organizations as a
whole. Secondly, it aimed to explore the impact of digital technology adoption on organizational
performance and competitiveness. Finally, the study investigated the role of knowledge management
during digital transformation. A quantitative study was developed using a descriptive design.
A questionnaire was developed on pre-test was carried out withon 15 participants and since no
doubts or difficulties were detected, it was made available on the internet between January and April
2022. A total of 291 questionnaires were collected and validated. Data were imported from Google
Forms for analysis in SPSS, version 25.0, andSmartPLS
®
4.0 software. The questionnaire revealed
good internal consistency (
α
= 0.922). Ten of the twelve hypotheses were confirmed, that is, the
existence of positive and significant relationships between digital culture (DC) and knowledge of
digital transformation (KDT); DC and adoption of digital technologies (ADT); DC and knowledge
management (KM); commitment (C) and KDT; C and productivity (P); KDT and ADT; ADT and KM;
ADT and P; ADT and C; and P and C. The results of regression analyses showed that the variables
that contributed to the model (“competitiveness of organizations”) were productivity, the adoption of
digital technologies, commitment to digital technologies, and knowledge management. The variables
CD and KDT (Knowledge of digital transformation) presented lower and non-significant values.
Keywords: digital culture; commitment; knowledge of digital transformation; adoption of digital
technologies; knowledge management; productivity; competitiveness
1. Introduction
Given the onset of Industry 4.0 in 2011 and the rapid development of digital technolo-
gies thereafter, digital transformation has become a hot topic in the global manufacturing
industry. To be successful, digital transformation requires a commitment to digital lead-
ership based on rigor, transparency, agility, and responsibility among all stakeholders
(Leal-Rodríguez et al. 2023). The process of building a digital transformation strategy
presupposes a predisposition and incentive for change, seeking to change the attitudes and
behaviors of those responsible for the organization. In other words, a digital transformation
Adm. Sci. 2024,14, 8. https://doi.org/10.3390/admsci14010008 https://www.mdpi.com/journal/admsci
Adm. Sci. 2024,14, 8 2 of 25
strategy can simplify the process and reduce obstacles by seeking solutions to problems
(Pereira et al. 2022).
According to Rymarczyk (2022), the so-called fourth (Industry 4.0) will bring about a
radical change in the production paradigm. In the near future, traditional methods in more
or less automated factories using digital at various levels will be replaced by production in
smart factories—fully digitalized, integrated, flexible, and efficient. As a consequence of
digitization, automation, and autonomous cyber-physical devices, production will become
more efficient and effective. However, the author warns that there are also potential
challenges and threats associated with the implementation of intelligent production, such
as layoffs, violation of consumer privacy, security threats, organizational barriers, lack
of international norms and standards, issues with international protection of intellectual
property, and the risk of unforeseen malfunctions in complex cyber-physical systems.
The COVID-19 pandemic led organizations across the planet to become increasingly
“digital” in response to increasingly hostile full of rules that were imposed on the isolation
of individuals. To avoid bankruptcy or insolvency, organizations needed to adjust their
business models to face the impact of the COVID-19 pandemic on the consumption of a
variety of goods and services. Remembering Darwin (1859), the survivors are not those
who are stronger or more intelligent, but rather, those who best adapt to the environment.
The ability to adjust in order to achieve better performance can be compared to the ability
of organizations to adapt to a new reality—in the shortest amount of time in a global and
competitive market—that is constantly changing (Brynjolfsson and Hitt 2000).
In a world where digital technology permeates every aspect of life, indeterminacy
and uncertainty influence digital organizational culture as both a process and a product
(Zhen et al. 2021). According to Davison and Ou (2017), since new technologies are present
in every aspect of organizations, it is essential that every member of those organizations
possess digital literacy in order to navigate a highly complicated environment with ease.
The necessary and imposed confinements have radically changed the way markets
behave, causing mass digital disruption towards increasing resilience. This timely change
demands from any organization the capacity to rapidly adapt in order to recover or maintain
the (previous) levels of performance (Kim et al. 2021;Pascucci et al. 2023). The development
of organizational capabilities and new dynamics as a way to bypass challenges and changes
imposed by the environment has been studied in a considerable number of scientific works
(e.g., Rogers 2016;Magistretti et al. 2021;Pinochet et al. 2021; and Moura and Saroli 2021).
The work of Kraus et al. (2021), based on a systematic review of the literature, showed
that the increasing digitalization of economies is directly related to the digital transforma-
tion of organizations, which allows them to be competitive in the market. The authors
recognize that disruptive changes do not only occur at the company level, there are also
institutional, social, and environmental implications. The systematic literature review
considers that technology is the main driver of change and digital transformation.
The studies identified in the literature have broadly explored digital transformation,
such as the importance of digital culture, the impacts of emerging technologies, and the
relationship between knowledge and organizational performance, among others. How-
ever, there may beis a lack of focus on more specific areas or issues not comprehensively
addressed. Thus, it is recognized that specific aspects of digital culture have not been
adequately explored, for example, the influence of organizational culture on the adop-
tion of disruptive technologies. There are also gaps in understanding the interactions
between digital transformation and organizational outcomes, such as a more in-depth
analysis of how digital competence directly affects organizational performance. Addition-
ally, the impacts of specific areas of emerging technologies on organizations have not been
sufficiently investigated.
Finally, there is a dearth of studies exploring how digital transformation affects differ-
ent industrial sectors or functional areas within organizations.
Adm. Sci. 2024,14, 8 3 of 25
By identifying these gaps, it is possible to justify the definition of more targeted
research goals and questions to fill these spaces and contribute to a deeper and more
specific understanding of digital transformation in organizations.
The aim of this study is threefold: first, it aims to understand the impact of digital
culture and commitment to digital transformation on digital technology adoption. It
aims to explore the impact of digital technology adoption on organizational performance
and competitiveness. Finally, the study investigates the role of knowledge management
during digital transformations. The fundamental research questions are: (1) does digital
culture, knowledge, and commitment to digital transformation influence the adoption of
digital technologies? (2) What is its impact on the organization’s knowledge management,
productivity, and competitiveness?”
As far as structure, this study begins with an introduction. A literature review is
then carried out on the relevant topics, followed by a description of the methodology, data
analysis, and conclusions. As a methodology, a questionnaire was developed using Google
Docs and made available online on social media. A convenience sample was chosen from
the author’s networking. The data allowed us to conclude that, especially in a dynamic
environment, production-oriented companies looking for economic performance need to
use digital platforms. In this study, 81.3% of the competitiveness variability was explained
by the independent variables.
2. Literature Review
Digital culture (DC) is considered an important and integral part of any organization’s
strategy and dynamics, together with knowledge, learning, and continuous improvement
(Kotler et al. 2021;Vial 2019). They are key concepts that enable companies to keep up with
mature and competitive markets and to be fully adapted to constantly changing scenarios.
According to Bumann and Peter (2016), companies need to adopt a ‘culture of failures’,
which means that the organizational culture allows experimenting and learning from mis-
takes. However, establishing such a culture requires strong and ongoing commitment from
the board and C-level executives who must support the digital strategy (Andriole 2017;
Gill and VanBoskirk 2016). Complementarily, the same authors suggest that companies
should have a collaborative, flexible, and iterative approach to technology development
and leverage modern architectures, such as cloud and application programming interfaces
(APIs) to promote flexibility and speed. In this sense, collaboration, technology, and inno-
vation constitute a constant challenge in the search for relevant solutions for stakeholders.
Cavalcanti et al. (2022) highlighted the importance of improving already existing products
and services by betting on digitization and digital innovation resources. DT is a topic that
involves changes in various spheres (Vial 2019;Verhoef et al. 2021): strategy (Matt et al.
2016), people (Navaridas-Nalda et al. 2020), technology (Pillai et al. 2020), culture (Udo et al.
2016), and social and organizational structures (Selander and Jarvenpaa 2016). Therefore,
it affects the way companies interact with their employees (Gill and VanBoskirk 2016),
stakeholders, and customers (Jain et al. 2021).
The results of the study by Puliwarna et al. (2023) indicated that digital competence
has a positive and significant direct effect on organizational performance and organiza-
tional commitment. In turn, digital culture has a direct and significant negative effect on
organizational performance and organizational commitment.
According to The World Economic Forum (World Economic Forum 2021), organiza-
tions with a solid digital culture utilize advanced devices and information-fueled insights to
drive choices and client-centricity while enhancing and teaming up across the organization.
When executed intentionally, advanced culture can drive sustainable activity and create
value for all partners.
Digital transformation is changing the business ecosystem and business models (Reis
and Melão 2023). The authors recognized that organizational and technological dimensions
are fundamental to digital transformation, with two areas, sustainability and smart cities,
deserving further in-depth studies.
Adm. Sci. 2024,14, 8 4 of 25
We are currently witnessing the emergence of different technologies that allow or-
ganizations to embrace the constant need for innovation. Clients are very demanding
and competition is very aggressive, thus brands must act accordingly (Kotler et al. 2021;
Pascucci et al. 2023). In their research, Cavalcanti et al. (2022) evaluated the importance
of adopting different types of disruptive technologies with a transformative focus as a
way of staying competitive in the market. Autonomous vehicles (Manfreda et al. 2021),
the Internet of Things (Arfi et al. 2021), artificial intelligence (Pillai et al. 2020), blockchain
(Queiroz and Wamba 2019), voice-based digital assistants (Vimalkumar et al. 2021), digital
payment (Balakrishnan and Shuib 2021), mobile payment (Patil et al. 2020), mobile health
applications (Alam et al. 2020), digital personal data stores (Mariani et al. 2021), on-demand
service platforms (Delgosha and Hajiheydari 2020), business intelligence and analytics
(Jakliˇc et al. 2018), social assistive technology (Khaksar et al. 2021), and virtual reality (Kunz
and Santomier 2019) allow the development of better products and services and improve
customer experience (Kotler et al. 2021). It is a reality that suits several sectors, such as
governments (Hujran et al. 2020), hospitals (Rahman et al. 2016), schools (Cavalcanti et al.
2022;Seufert et al. 2021), retail stores (Pillai et al. 2020), and banks (Hu et al. 2019). These
are sectors that have demonstrated a constant commitment to digital transformation (DTC),
betting on constant connectivity between people and technology and vice versa in order to
co-create organizational value.
The main conclusions of Almeida’s study (Almeida 2023) indicate that the digital-
ization of ports represents a significant transformation in the maritime industry, offering
numerous benefits but also posing new challenges. The primary challenges identified are
associated with port infrastructure, the organization of business processes, and the intercon-
nection among different architectures, devices, and legacy systems. The study highlights
the importance of sustainability, communication, collaboration, logistics, and technology
in the digitalization process. The author considers partnerships and the involvement of
multiple partners in digital innovation platforms essential to ensuring the implementation
of these initiatives.
According to S.A. McLaughlin (2017), the term “digital” seems to be seeping into all
aspects of senior management conversations (Peppard and Hemingway 2009;Fitzgerald
et al. 2013;Weill and Woerner 2013). McDonald (2012) stated that the topic is not just
limited to IT professionals or IT departments but is being driven and shaped by questions
from all functional units in the organization (marketing, sales, finance, operations, R&D, IT,
HR, etc.). In this sense, we can say that digitalization processes are increasingly present in
both public and private organizations, as stated by Alvarenga et al. (2020); in other words,
the process of digital transformation in public organizations has positively changed the
practices of knowledge management, in turn contributing to organizational performance
and efficiency.
Regarding knowledge of digital transformation (KDT) and its impact on improving the
organization’s performance indicators, Milgrom and Roberts (1995), Milgrom et al. (1991)
and Shakina et al. (2021) stated that resources and technologies are complementary since an
increase in the use of technology leads to an improvement in the overall performance of the
company. This idea was also mentioned by Moreira et al. (2018). The main objective of DT
is to redesign the organizational business through the introduction of digital technologies,
achieving benefits such as improvements in productivity, cost reduction, and innovation
(Matt et al. 2016). Alvarenga et al. (2020) concluded that innovative and competitive
companies that have adopted knowledge management use formalized tacit knowledge
to be efficient and effective at managing processes. According to Lotti (2014), formalized
knowledge based on technology allows us to change complex tasks into easy and agile
tasks, therefore contributing to better results.
According to Busco et al. (2023), organizational culture is seen as a strategic asset that
supports business transformation and the exploration of digital technologies. The results
of this study highlighted the importance of digital strategies and digital leadership factors
in promoting a digital culture in companies in Chile.
Adm. Sci. 2024,14, 8 5 of 25
Adopting digital technologies (ADT) seems to be the right way to improve people’s
well-being, security issues, production processes, and consequently, general company
management (Cavalcanti et al. 2022). That is why organizations need to better understand
the process of adopting transformative technologies, as well as the intention and acceptance
of these technologies by users, to guarantee their survival in such dynamic and competitive
environments (Moreira et al. 2018;Jahanmir et al. 2020).
Knowledge management (KM) is increasingly relevant to the relationship between
people and technology. It is necessary to constantly prepare people for the transformation
of knowledge in the construction of innovative and differentiating solutions (Diogo et al.
2019); this is the only way of satisfying both internal and external organization needs (stake-
holders). Digitization is about changing the existing sociotechnical structures, previously
mediated by non-digital artifacts or relationships, into structures that are mediated by
digitized artifacts and relationships with digital capabilities (Shakina et al. 2021;Yoo et al.
2010). Alvarenga et al. (2020) reported that managing knowledge in a deliberate, systematic,
and holistic way can increase awareness of the benefits for individuals and organizations,
contributing to a distinctive difference in products and services (differentiation, making it
easy for customers to understand benefits).
As far as productivity (IP), the use of digital technologies has played a very important
role, as they allow the optimization of physical resources, time, and people, hence increasing
organizational effectiveness and efficiency (Li et al. 2020), In addition, digital information
processing technologies allow companies to reconfigure production lines and resources for
customized products in a more flexible and efficient way (Dalenogare et al. 2018;Pascucci
et al. 2023). DT also facilitates finding faster and more satisfactory solutions in public
service institutions (Alvarenga et al. 2020), government actions, and public management in
general, therefore contributing to an increasingly well-informed society.
Competitiveness (IP), being the result of systematically gathering and analyzing
information, implies identifying relevant aspects and giving a prompt answer, therefore
contributing to positive results for organizations. Moreira et al. (2018) indicated that digital
transformation should be considered essential to organizations becoming and staying
competitive over time. However, this transformation cannot occur through an ad hoc
process, but rather through a strategically defined and planned process, as its results
impact the entire organization, from processes and activities to business models. In the
same sense, Romero et al. (2019) stated that, in this progression, the role of humans in
manufacturing environments has evolved from human operators loading, operating, and
unloading machines in industry 2.0 to more decision-oriented activities such as systems
supervision in the industry 3.0 and 4.0 eras. In terms of orientation toward production, Li
et al. (2020) stated that production-oriented companies should not rely only on information
processing capabilities through the use of digital technologies, but also need to develop
the best supply chain digital platforms for accessing more appropriate information, thus
achieving better economic and environmental performance, i.e., converting leads and
prospects into actual clients.
This new approach has led companies to the new industrial revolution, which we
are calling Industry 4.0 (Diogo et al. 2019). Increasingly, organizations need to adapt their
equipment to this new reality in order to adapt to the new era of digital transformation. This
need for adaptation is transversal across all companies and has led machine manufacturers
and suppliers to seek continuous improvement of the equipment they offer on the market
(Vieira et al. 2022). Costa et al. (2023) identified problems on the shop floor due to a need to
increase information and control of the production and maintenance processes. With the
integration of Industry 4.0 concepts in the organization, it was possible to make the process
more profitable for the company, since it was no longer necessary for the heads of the
assembly line to regularly stop by to prepare a detailed report of the current status. Sáet al.
(2021) developed a decision support system based on system dynamics to assist producers
and managers operating in the wine sector define strategies for action that can respond to
variations in various factors that influence the price, production, and quality of wine. The
Adm. Sci. 2024,14, 8 6 of 25
system presented can be integrated with other 4.0 tools, such as sensors, and consequent
analysis of real-time data on the quality of the soil and the climate is then included in the
model developed. McDermott et al. (2022) considered Industry 4.0 as the revolution of
process digitalization in companies that completely changed the way products, processes,
and services were delivered to customers. According to McDermott et al. (2022), who
developed their research in the “MedTech Industry”, Industry 4.0 is the transformation of
digital technologies, such as cloud computing, big data, big data analytics, cyber-physical
systems, systems integration, cybersecurity, 3D printing, and the IoT, to change the way this
industry does business. Digital technologies help organizations deliver processes, products,
and services efficiently and effectively to their customers and, for now, have a positive
impact on regulatory compliance.
A study conducted in South Korea by Shin et al. (2023) concluded that digital leader-
ship has a direct positive effect on organizational performance and indirect effects through
its impact on digital culture and employees’ digital capabilities. The study found that
both digital culture and employees’ digital capabilities partially mediate the relationship
between digital leadership and organizational performance. The results suggest that orga-
nizations operating in the era of digital transformation require digitally skilled leaders to
influence employees to enhance their capabilities and maintain a consistent digital culture
for improved performance. Additionally, the study highlighted the importance of leaders’
support in enhancing employees’ digital capabilities to increase organizational performance.
Overall, the study emphasized the crucial role of sustainability management in the current
digital era and the necessity for organizations to pay more attention to employees with
digital skills to enhance performance.
The attitudes of future employees, particularly Generation Z, toward the challenges
of Industry 4.0 are complex and multifaceted. ˇ
Crešnar and Nedelko (2020) found that
while these individuals possess values that align with the changing workplace, such as self-
enhancement and openness to change, they may not be inclined toward the benevolence
and universalism required in Industry 4.0. Stachováet al. (2019) emphasized the need for
external partnerships in employee education and development to address these challenges,
particularly in innovative countries. Schaar et al. (2019) highlighted the importance of job
attributes such as tasks, flexibility, family-friendliness, and salary in attracting future staff
to the digitalized workplace. Goh and Lee (2018) provided insights into Generation Z’s
positive attitudes toward the hospitality industry, suggesting that they may be open to the
challenges of Industry 4.0.
According to Anastasiei et al. (2023), network centrality and density have a significant
impact on the likelihood of participating in electronic word-of-mouth (eWOM) in online
social networks. The authors found that individuals with higher network centrality and
density were more likely to engage in both positive and negative eWOM. Additionally,
the use of social networks could moderate the effect of density on the intention to post
negative eWOM, but not the effect of centrality. The authors suggested that companies
should consider these findings when developing their online marketing strategies and
focus on identifying and changing negative online advertising.
This insight, in addition to the impacts on the various industries, will impact the
skills that managers need to develop. Regarding specifically the competencies that quality
managers and technicians will need to have in the so-called Quality 4.0, Santos et al. (2021)
conducted a survey of Portuguese companies to identify which quality management and
continuous improvement competencies were expected from future managers and techni-
cians. The results of the survey showed that these new Quality 4.0 managers should have
skills such as creative thinking, leadership, communication, and teamwork; furthermore,
the results also showed that they should have knowledge of new technologies, such as
cyber-physical production systems, and combine them with best quality management
practices where their decision-making will be based on Big Data.
Based on the previous literature review, the following hypotheses were defined:
Adm. Sci. 2024,14, 8 7 of 25
H1. There is a significant relationship between digital culture and job knowledge of digital
transformation.
Digital culture creates the environment and mindset necessary for digital transforma-
tion (Kotler et al. 2021), while professional knowledge of digital transformation entails the
essential skills and practical knowledge required to successfully implement this transfor-
mation within organizations. Both are crucial for the success of digital transformation in an
increasingly digitized business landscape (Diogo et al. 2019).
Digital culture encompasses the awareness and appreciation of the importance of
technology and digital innovation in the workplace. This is reflected in the mindset
and attitudes of employees toward technology, as well as their willingness to adopt and
experiment with new digital tools and approaches (Gill and VanBoskirk 2016;Udo et al.
2016). Professional knowledge of digital transformation necessitates a solid understanding
of these principles to effectively lead and implement digital transformation (Gill and
VanBoskirk 2016;Cavalcanti et al. 2022).
Several studies (Peláez et al. 2020;Zhen et al. 2021;Teng et al. 2022;Puliwarna et al.
2023) have suggested that digital culture, digital skills, and digital transformation strategies
are interrelated, and have a significant impact on fostering innovation and performance in
SMEs and addressing competency gaps between different groups.
Both digital culture and professional knowledge of digital transformation depend on
a commitment to continuous learning (Puliwarna et al. 2023).
H2. There is a significant relationship between digital culture and the adoption of digital technologies.
Studies in the literature (Magsamen-Conrad and Dillon 2020;Pirhonen et al. 2020;Zhen
et al. 2021;Pereira et al. 2022) indicate a significant correlation between digital culture and
the adoption of digital technologies. Factors such as organizational culture, interpersonal
communication, and socioeconomic disparities influence the adoption process and the
overall digital strategy and performance.
Digital culture creates a conducive environment for the adoption of digital technolo-
gies as it shapes attitudes, behaviors, and mindsets toward technology (Da Silva et al.
2020). Organizations and individuals with a positive digital culture are better prepared to
embrace, integrate, and effectively use digital technologies in their operations and daily
lives (Alvarenga et al. 2020;World Economic Forum 2021;Pereira et al. 2022). Digital
culture is often associated with a greater willingness to take risks, especially when it comes
to experimenting with new technologies (Da Silva et al. 2020); people and organizations
with a digital culture are willing to embrace the risk of trying something new in the digital
world.
Digital culture also promotes adaptability, which is crucial to the adoption of digital
technologies given that the technological landscape is constantly evolving (Diogo et al.
2019).
H3. There is a significant relationship between digital cultures and knowledge management.
Digital cultures create a conducive environment for knowledge management, facilitat-
ing the capture, sharing, and effective use of knowledge through digital technologies (Zhen
et al. 2021). The adoption of a digital culture can enhance the efficiency and effectiveness
of knowledge management in organizations and communities, helping them to remain
relevant and innovative in a constantly evolving digital world (Yoo et al. 2010;Shakina et al.
2021;Alvarenga et al. 2020). Studies by both Tang (2017) and Zhen et al. (2021) have shown
a significant correlation between digital organizational culture and digital capabilities with
regard to digital innovation in SMEs operating within the digital economy. Social networks
and online communities provide opportunities for people to share their experiences and
knowledge with a wide audience. On the other hand, digital cultures encourage the use of
collaboration tools such as wikis, intranets, project management systems, and document-
Adm. Sci. 2024,14, 8 8 of 25
sharing platforms. These tools facilitate collaborative knowledge creation and organization
(Moreira et al. 2018).
H4. There is a significant relationship between commitment and knowledge of digital transformation.
Commitment and understanding of digital transformation are complementary as-
pects that mutually reinforce each other and are necessary to achieve the goals of digital
transformation. Engagement with digital transformation often begins with comprehension
and awareness, and knowledge of digital transformation is essential to successfully lead-
ing, implementing, and adopting digital transformation in organizations (Da Silva et al.
2020). Some studies (Kamalaldin et al. 2020;Ko et al. 2021;Teng et al. 2022) suggest that
commitment plays a pivotal role in the success of digital transformation, underscoring
the significance of factors like business and management commitment, complementary
digitalization capabilities, and knowledge-sharing routines.
Digital transformation often requires a cultural shift within organizations (Pereira et al.
2022). Commitment helps drive this change, while knowledge of digital transformation
aids in creating strategies to promote a digital culture by incorporating technology and
innovation into the organization’s values and practices (Shakina et al. 2021;Li et al. 2020;
Cavalcanti et al. 2022).
H5. There is a significant relationship between commitment and adoption of digital technologies.
Commitment is a significant determinant in the adoption of digital technologies as
it influences acceptance, motivation, resilience, and effective usage of these technologies.
Research conducted by Santos et al. (2021), Shapiro and Mandelman (2021), and Cavalcanti
et al. (2022) indicate that commitment to digital technologies is influenced by factors such
as interpersonal communication, cost, trust, and various elements of commitment. These
factors ultimately impact technology adoption, utilization, and performance.
Commitment is often an indicator of people’s willingness to embrace change. The
introduction of new digital technologies typically involves changes in routines and work
processes. Committed individuals are more likely to embrace these changes and adapt to
new technologies effectively (Cavalcanti et al. 2022). In organizations, the commitment of
the leadership and the team plays a crucial role in fostering a culture of digital technology
adoption (Santos et al. 2021;Puliwarna et al. 2023). When the leadership is committed, it
sets a positive example and promotes technological adoption throughout the organization.
H6. There is a significant relationship between commitment and productivity.
Employee commitment, including their level of engagement, enthusiasm, and dedica-
tion toward their work and organization, significantly influences productivity at various
levels, including the individual, team, and organizational levels (Gill and VanBoskirk 2016;
McLaughlin 2017;Alvarenga et al. 2020;Puliwarna et al. 2023). The evidence identified
in the literature suggests that commitment to digital technologies is positively associated
with higher productivity outcomes, improved quality of life, and innovation (Ko et al.
2021;Teng et al. 2022;Puliwarna et al. 2023). Employee commitment positively impacts
productivity as it relates to focus, dedication, work quality, collaboration, innovation, job
satisfaction, and goal achievement (McLaughlin 2017). Therefore, organizations seek to
foster an environment that encourages commitment as it results in a more productive and
effective workforce (Cavalcanti et al. 2022).
H7. There is a significant relationship between knowledge of digital transformation and adoption of
digital technologies.
Digital transformation involves the integration of advanced digital technologies and
the redefinition of business processes to enhance efficiency, effectiveness, and competi-
Adm. Sci. 2024,14, 8 9 of 25
tiveness (Moreira et al. 2017;Shakina et al. 2021). Understanding digital transformation
is a prerequisite for effective adoption of digital technologies. It informs the selection,
implementation, and use of these technologies, as well as ongoing adaptation to changes in
the digital landscape (Alvarenga et al. 2020). Having a solid grasp of digital transformation
is essential for competitiveness and relevance in an increasingly digitalized world.
H8. There is a significant relationship between the adoption of digital technologies and knowledge
management.
The adoption of digital technologies is closely linked to knowledge management,
as digital technologies play a fundamental role in the creation, capture, storage, sharing,
and application of knowledge within organizations (Diogo et al. 2019). Several studies
(Alvarenga et al. 2020;Magsamen-Conrad and Dillon 2020;Pereira et al. 2022;Cavalcanti
et al. 2022) suggest that the adoption of digital technologies is associated with the quality
of knowledge management, influencing the behavioral intention to use technologies and
playing an important role in the improvements and sustainability of organizations. Digital
technologies enable efficient knowledge capture, whether through electronic documents,
databases, content management systems, or social and collaborative media platforms
(Pereira et al. 2022;Cavalcanti et al. 2022). Digital information systems facilitate knowledge
storage and organization as well as agile knowledge sharing.
H9. There is a significant relationship between adopting digital technologies and productivity.
The adoption of digital technologies can lead to significant productivity gains in orga-
nizations, ranging from process automation to improved communication and information
access (Shapiro and Mandelman 2021). Digital technologies have the ability to automate
routine and repetitive tasks, saving time and human resources. This allows employees to
focus on more strategic and creative activities, thus increasing productivity (Alvarenga
et al. 2020;Shakina et al. 2021).
Furthermore, the adoption of digital technologies often stimulates innovation and
the creation of new products and services that can drive organizational productivity and
growth. Similarly, using digital technologies to enhance the customer experience can in-
crease customer loyalty and satisfaction, resulting in higher productivity through increased
sales and customer success (Järvinen and Karjaluoto 2015;Moreira et al. 2017;Li et al. 2020;
Pascucci et al. 2023).
H10. There is a significant relationship between adopting digital technologies and competitiveness.
The effective integration of digital technologies in business operations and strate-
gies can have a significant impact on an organization’s ability to compete in the market
(Magsamen-Conrad and Dillon 2020). Organizations that embrace digital transformation
are better positioned to adapt to market changes, meet customer demands, innovate, and
operate more efficiently, thus becoming more competitive within their industries (Matt et al.
2016;Moreira et al. 2017;Li et al. 2020;Puliwarna et al. 2023).
Digital technologies, such as automation systems and management software, can
enhance the efficiency of operational processes, reducing costs and production time. This
enables organizations to be more competitive in terms of pricing and delivery schedules
(Alvarenga et al. 2020;Da Silva et al. 2020). Digital technologies also stimulate innovation,
allowing companies to develop new products and services, create innovative business mod-
els, explore new markets, enhance the customer experience, reach global markets, respond
more agilely to market changes, and reduce operational costs, thus making products and
services more competitive in terms of price. Furthermore, these innovations can attract and
retain talent who value a digitalized work environment (Matt et al. 2016).
H11. There is a significant relationship between knowledge management and competitiveness.
Adm. Sci. 2024,14, 8 10 of 25
Knowledge management involves the collection, sharing, organization, and efficient
utilization of knowledge within an organization; this practice can bring several benefits
that enhance competitiveness (Alvarenga et al. 2020;Shakina et al. 2021). Several studies
(Moreira et al. 2017;Kim et al. 2021;Pereira et al. 2022;Aziz et al. 2022) have indicated that
knowledge management has a positive impact on competitiveness through factors such as
technical and administrative innovations, product innovation, and enhanced organizational
performance. A robust knowledge management strategy can contribute significantly to
an organization’s success and competitiveness. Therefore, knowledge management helps
organizations innovate, make more informed decisions, continuously learn, avoid errors,
collaborate effectively, and adapt to market changes (Moreira et al. 2017;Pereira et al. 2022).
H12. There is a significant relationship between productivity and competitiveness.
Productivity plays a crucial role in the success and ability of an organization to compete
effectively (Kim et al. 2021). Companies and organizations that can produce more with
fewer resources while maintaining high quality and agility are well-positioned to compete
effectively in their markets (Moura and Saroli 2021;Li et al. 2020). Therefore, improving
productivity is often a strategic priority for companies looking to maintain and enhance
their competitiveness.
The theoretical model that supports this study contains seven constructs (latent vari-
ables: digital culture, commitment, knowledge of digital transformation, adoption of digital
technologies, knowledge management, productivity, and competitiveness). The measure-
ment model presented in Figure 1was prepared using the SmartPLS
®
4.0 software. The
observable or measured variables (VO) and their respective connections in the constructs
can be measured.
Adm. Sci. 2023, 13, x FOR PEER REVIEW 10 of 27
enables organizations to be more competitive in terms of pricing and delivery schedules
(Alvarenga et al. 2020; Da Silva et al. 2020). Digital technologies also stimulate innovation,
allowing companies to develop new products and services, create innovative business
models, explore new markets, enhance the customer experience, reach global markets,
respond more agilely to market changes, and reduce operational costs, thus making prod-
ucts and services more competitive in terms of price. Furthermore, these innovations can
attract and retain talent who value a digitalized work environment (Matt et al. 2016).
H11. There is a significant relationship between knowledge management and competitiveness.
Knowledge management involves the collection, sharing, organization, and efficient
utilization of knowledge within an organization; this practice can bring several benefits
that enhance competitiveness (Alvarenga et al. 2020; Shakina et al. 2021). Several studies
(Moreira et al. 2017; Kim et al. 2021; Pereira et al. 2022; Aziz et al. 2022) have indicated that
knowledge management has a positive impact on competitiveness through factors such
as technical and administrative innovations, product innovation, and enhanced organiza-
tional performance. A robust knowledge management strategy can contribute signifi-
cantly to an organization’s success and competitiveness. Therefore, knowledge manage-
ment helps organizations innovate, make more informed decisions, continuously learn,
avoid errors, collaborate effectively, and adapt to market changes (Moreira et al. 2017;
Pereira et al. 2022).
H12. There is a significant relationship between productivity and competitiveness.
Productivity plays a crucial role in the success and ability of an organization to com-
pete effectively (Kim et al. 2021). Companies and organizations that can produce more
with fewer resources while maintaining high quality and agility are well-positioned to
compete effectively in their markets (Moura and Saroli 2021; Li et al. 2020). Therefore,
improving productivity is often a strategic priority for companies looking to maintain and
enhance their competitiveness.
The theoretical model that supports this study contains seven constructs (latent var-
iables: digital culture, commitment, knowledge of digital transformation, adoption of dig-
ital technologies, knowledge management, productivity, and competitiveness). The meas-
urement model presented in Figure 1 was prepared using the SmartPLS® 4.0 software. The
observable or measured variables (VO) and their respective connections in the constructs
can be measured.
Figure 1. Path Model.
To validate the hypotheses using the model created, a questionnaire was prepared for
data collection and subsequent statistical analysis.
3. Methodology
The following research questions were defined to meet the general purpose of the
study: (1) does digital culture, knowledge, and commitment to digital transformation
influence the adoption of digital technologies? (2) What is its impact on the organization’s
knowledge management, productivity, and competitiveness? A quantitative study was
developed (Pestana and Gageiro 2014;Malhotra 2019) with the objective of analyzing how
Adm. Sci. 2024,14, 8 11 of 25
digital culture and commitment to digital transformation influence the adoption of new
technologies and their impact on knowledge management, productivity, and competitive-
ness of organizations.
To measure these constructs, we chose to use 38 indicators (Table 1and Appendix A).
Table 1. Conceptual model’s variables.
Latent Variables No of
Items Authors Scale
Digital Culture (DC) 7 Gill and VanBoskirk (2016);
Diogo et al. (2019)
From 1 (strongly disagree) to 5
(strongly agree)
Commitment to digital
transformation (CDT) 7
Gill and VanBoskirk (2016);
McLaughlin (2017);
Alvarenga et al. (2020);
Cavalcanti et al. (2022)
From 1 (strongly disagree) to 5
(strongly agree)
Knowledge of digital
transformation (KDT) 5
Moreira et al. (2017);
Alvarenga et al. (2020);
Shakina et al. (2021)
From 1 (strongly disagree) to 5
(strongly agree)
Adoption of digital technologies
(ADT) 3
Gill and VanBoskirk (2016);
Moreira et al. (2017);
Cavalcanti et al. (2022)
From 1 (strongly disagree) to 5
(strongly agree)
Knowledge Management (KM) 5
Alvarenga et al. (2020);
Shakina et al. (2021);
Cavalcanti et al. (2022)
From 1 (strongly disagree) to 5
(strongly agree)
Productivity (IP) 5
Moreira et al. (2017);
Alvarenga et al. (2020);
Li et al. (2020)
From 1 (strongly disagree) to 5
(strongly agree)
Competitiveness (IC) 6 Moreira et al. (2017);
Li et al. (2020)
From 1 (strongly disagree) to 5
(strongly agree)
To evaluate the latent variables, previous scales (Table 1) were used: digital culture
(Gill and VanBoskirk 2016;Diogo et al. 2019); commitment to digital transformation (Gill
and VanBoskirk 2016;McLaughlin 2017;Alvarenga et al. 2020;Cavalcanti et al. 2022);
knowledge of digital transformation (Moreira et al. 2017;Alvarenga et al. 2020;Shakina
et al. 2021); adoption of digital technologies (Gill and VanBoskirk 2016;Moreira et al. 2017;
Cavalcanti et al. 2022); knowledge management (Alvarenga et al. 2020;Shakina et al. 2021;
Cavalcanti et al. 2022); productivity (Moreira et al. 2017;Alvarenga et al. 2020;Li et al.
2020); and competitiveness (Moreira et al. 2017;Li et al. 2020).
The questionnaire was developed on Google Forms. A pre-test was carried out on 15
participants (selected on the basis of their relevance to the study and their willingness to
participate. They included researchers with experience in questionnaire design and senior
managers from some organizations) and since no doubts or difficulties were detected, it
was made available on the Internet between January and April 2022. Participants answered
the questions based on a 5-point Likert scale (Pestana and Gageiro 2014;Malhotra 2019)
varying from 1 (totally disagree) to 5 (totally agree). In order to reach respondents with
knowledge of digital transformation in organizations (senior managers, executives, IT
managers, and senior staff from different functional areas), we used company email lists as
well as a professional social media platform (LinkedIn). The link to the questionnaire was
distributed to respondents via email or social media groups.
Overall, the selection of respondents likely aimed to gather feedback or insights from
individuals who had relevant experience or knowledge of the subject in the questionnaire,
ensuring that the collected data would be meaningful for the research or study objectives.
Respondents responded freely and were not rewarded for their answers.
Adm. Sci. 2024,14, 8 12 of 25
Data were analyzed using the structural equation model (SEM), a multivariate tech-
nique that combines aspects of multiple regression with factor analysis to simultaneously
estimate a series of interrelated dependence relationships (Henseler et al. 2009;Hair et al.
2014).
Although this is a non-probabilistic convenience sample (Pestana and Gageiro 2014),
the use of G*Power software (Faul et al. 2009), as suggested by Hair et al. (2014), allows a
minimum sample of 189 respondents (f2 of 0.15).
Table 2summarizes the sample’s main characteristics. Respondents were mostly
male (62.2%), with the majority of participants aged between 41 and 50 years (40.5%).
Most respondents had bachelor’s (45%) or master ’s (29.6%) degrees. Another interesting
characteristic is the fact that a considerable number of participants reported being in the
same job for 10 or more years (44.7%).
Table 2. Sample characterization.
Variables Categories N %
Gender Female 110 37.8
Male 181 62.2
Age groups
21–30 years 44 15.1
31–40 years 71 24.4
41–50 years 118 40.5
51–60 years 46 15.8
61–70 years 12 4.1
Education
Doctorate 42 14.4
Master’s degree 86 29.6
Bachelor’s degree 131 45.0
High school 18 6.2
Basic education 14 4.8
Service time
+20 years 58 19.9
15–19 years 31 10.7
10–14 years 41 14.1
4–9 years 67 23.0
Up to 3 years 94 32.3
Activity sector
Education/training 89 30.6
Services (Banking, security, etc.) 53 18.2
Industry/Manufacturing 94 32.2
Technologies 14 4.5
Others 62 21.7
4. Results
A total of 291 questionnaires were collected and validated. Data were imported
from Google Forms for analysis in SPSS, version 25.0 (Armonk NY: US), and SmartPLS
®
4.0 software. Descriptive statistics (demographic information, frequencies, mean, and
standard deviation) were generated in SPSS, and other statistical analyses were conducted
in SmartPLS 4.0. Exploratory and confirmatory factor analysis, reliability and convergent
validity, discriminant validity, path coefficients, hypothesis testing, and PLS-SEM were
used to investigate the relationships between the variables.
4.1. Reliability and Convergent Validity of the Scale
We used Cronbach’s Alpha to assess internal consistency and performed factor analy-
sis using principal component analysis (PCA) (Pestana and Gageiro 2014;Malhotra 2019)
to assess dimensionality and estimate the validity of each group of questions in the ques-
tionnaire.
The questionnaire revealed good internal consistency (Alpha = 0.922), considering the
38 items that make up the scale. Cronbach’s alpha values for the seven dimensions varied
between 0.657 (adoption of digital technologies (ADT)) and 0.882 (commitment to digital
Adm. Sci. 2024,14, 8 13 of 25
transformation (CDT)) which reveals, in general, a reasonable (ADT, KDT, PKM, IC) or
good (DC, CDT, IP) internal consistency (Table 3). The Cronbach’s alpha value of 0.657 for
the ‘Adoption of Digital Technologies’ (ADT) dimension falls within an acceptable range
for exploratory studies or early stages of research (Pestana and Gageiro 2014;Malhotra
2019). While it approaches the lower limit, it remains sufficient for the study’s objectives,
particularly in this social science domain, given the complexity of the construct under
investigation. Removing item three from the scale resulted in a Cronbach’s alpha value of
0.693. However, since this adjustment does not significantly enhance reliability, we opted
to retain all three items in the scale.
Table 3. Descriptive statistics and results of validity analysis.
Variable Items Component
Cronbach’s αPrincipal Components
Analysis (PCA)
123456 7
Digital culture (DC)
Mean: 3.87
Sdt: 0.977
DC1
0.818
0.805
Variance explained by
factor 1 = 47.241
KMO = 0.838
Bartlett’s test
χ2= 634.259
df = 21
Sig. = 0.000
DC2
0.813
DC3
0.810
DC4
0.691
DC5
0.620
DC6
0.583
DC7
0.545
Knowledge of
digital
transformation
(KDT)
Mean: 4.03
Sdt: 0.916
KDT1
0.852
0.754
Variance explained by
factor 1 = 51.76
KMO = 0.698
Bartlett’s test
χ2= 432.782
df =10
Sig. = 0.000
KDT2
0.823
KDT3
0.818
KDT4
0.761
KDT5
0.604
KDT6
0.536
Commitment to
digital
transformation
(CDT)
Mean: 3.91
Sdt: 1.006
CDT1
0.901
0.882
Variance explained by
factor 1 = 59.11
KMO = 0.844
Bartlett’s test
χ2= 1206.666
df = 21
Sig. = 0.000
CDT2
0.845
CDT3
0.845
CDT4
0.836
CDT5
0.747
CDT6
0.584
CDT7
0.548
Adoption of digital
technologies
(ADT)
ADT1
0.847
0.657
Variance explained by
factor 1 = 59.56
KMO = 0.602
Bartlett’s test
χ2= 141.385
df = 3
Sig. = 0.000
ADT2
0.801
ADT3
0.653
Performance of
knowledge
management (PKM)
Mean: 3.89
Sdt: 0.896
PKM1
0.855
0.765
Variance explained by
factor 1 = 51.63
KMO = 0.744
Bartlett’s test
χ2= 441.569
df = 10
Sig. = 0.000
PKM2
0.848
PKM3
0.819
PKM4
0.654
PKM5
0.567
Impact on
productivity
(IP)
Mean: 3.98
Sdt: 0.876
IP1
0.790
0.803
Variance explained by
factor 1 = 56.39
KMO = 0.800
Bartlett’s test
χ2= 432.999
df = 10
Sig. = 0.000
IP2
0.782
IP3
0.743
IP4
0.727
IP5
0.709
Adm. Sci. 2024,14, 8 14 of 25
Table 3. Cont.
Variable Items Component
Cronbach’s αPrincipal Components
Analysis (PCA)
123456 7
Impact on
competitiveness
(IC)
Mean: 3.84
Sdt: 0.942
IC1 0.804
0.773
Variance explained by
factor 1 = 47.24
KMO = 0.788
Bartlett’s test
χ2= 445.388
df = 15
Sig. = 0.000
IC2 0.783
IC3 0.746
IC4 0.651
IC5 0.620
IC6 0.558
Factor analysis showed the existence of one factor per dimension, with the Kaiser–
Meyer–Olkin (KMO) value being greater than 0.602 (KMO varies between 0.602 and 0.844),
which does not cause problems in the interpretation of the data since there is a correlation
between the variables (Bartlett with sig = 0.000), as recommended in the literature (Pestana
and Gageiro 2014;Malhotra 2019).
The model improvement strategy was used for the construction of the PLS-PM model.
The criteria used to implement the adjustments included removing the variables that
showed a correlation of less than 0.6 with their constructs and the variables that showed
commonalities below 0.4 (DC1, DC6, DC7, KDT3, KDT4, KDT5, KDT6, CDT1, CDT7,
ADT3, PKM4, PKM5, IC2, and IC3); we obtained a final model (Figure 2) by implementing
these modifications.
Adm. Sci. 2023, 13, x FOR PEER REVIEW 16 of 27
Figure 2. A structural model with standardized path coefficients.
To verify whether the variables are associated with the respective proposed factors
and to evaluate the measurement model, a confirmatory factor analysis (CFA) was con-
ducted.
The Fornell–Larcker criterion (Henseler et al. 2009) was used, that is, the average var-
iance extracted (AVE) values must be greater than 0.50 (AVE > 0.50), as mentioned by
Ringle et al. (2018). The tests of the convergent validity of the constructs, above 0.5, of the
1st Order LV, attest to the convergent validity of the scale. On the other hand, the factor
loadings of the VO in the original constructs (VL) were always larger than those in others,
meaning that the model has discriminant validity (Chin 1998).
The structural model was found to satisfy all relevant reliability and validity require-
ments, as mentioned in the literature (Tenenhaus et al. 2005). Table 4 shows that
Cronbach’s alpha > 0.7, rho_a > 0.7, composite reliability (rho c) > 0.7, and average variance
extracted (AVE) > 0.5.
Table 4. Correlations and discriminant validity based on the Fornell–Larcker criterion.
Variables Cronbach’s Alpha 1 2 3 4 5 6
1. ADT 0.702 0.876
2. CDT 0.904 0.724 0.851
3. IC 0.781 0.770 0.794 0.833
4. DC 0.837 0.725 0.707 0.740 0.821
5. PKM 0.842 0.641 0.657 0.713 0.743 0.872
6. IP 0.806 0.747 0.771 0.861 0.726 0.657 0.749
Composite reliability (rho-a) 0.709 0.906 0.790 0.851 0.846 0.833
Composite reliability (rho-c) 0.868 0.929 0.852 0.892 0.905 0.761
Average variance extracted (AVE) 0.767 0.724 0.537 0.675 0.761 0.561
Note: n = 291.
Cross loads allow us to verify that each item has a greater relationship/weight with
the construct to which it is related than with the others (Henseler et al. 2015), as shown in
Table 5.
Figure 2. A structural model with standardized path coefficients.
To verify whether the variables are associated with the respective proposed factors and
to evaluate the measurement model, a confirmatory factor analysis (CFA) was conducted.
The Fornell–Larcker criterion (Henseler et al. 2009) was used, that is, the average
variance extracted (AVE) values must be greater than 0.50 (AVE > 0.50), as mentioned by
Ringle et al. (2018). The tests of the convergent validity of the constructs, above 0.5, of the
1st Order LV, attest to the convergent validity of the scale. On the other hand, the factor
loadings of the VO in the original constructs (VL) were always larger than those in others,
meaning that the model has discriminant validity (Chin 1998).
The structural model was found to satisfy all relevant reliability and validity require-
ments, as mentioned in the literature (Tenenhaus et al. 2005). Table 4shows that Cronbach’s
alpha > 0.7, rho_a > 0.7, composite reliability (rho c) > 0.7, and average variance extracted
(AVE) > 0.5.
Adm. Sci. 2024,14, 8 15 of 25
Table 4. Correlations and discriminant validity based on the Fornell–Larcker criterion.
Variables Cronbach’s Alpha 1 2 3 4 5 6
1. ADT 0.702 0.876
2. CDT 0.904 0.724 0.851
3. IC 0.781 0.770 0.794 0.833
4. DC 0.837 0.725 0.707 0.740 0.821
5. PKM 0.842 0.641 0.657 0.713 0.743 0.872
6. IP 0.806 0.747 0.771 0.861 0.726 0.657 0.749
Compositereliability(rho-a) 0.709 0.906 0.790 0.851 0.846 0.833
Composite reliability (rho-c) 0.868 0.929 0.852 0.892 0.905 0.761
Average variance extracted (AVE) 0.767 0.724 0.537 0.675 0.761 0.561
Note: n = 291.
Cross loads allow us to verify that each item has a greater relationship/weight with
the construct to which it is related than with the others (Henseler et al. 2015), as shown in
Table 5.
Table 5. Outer loadings matrix: cross loads convergent validity criterion.
ADT CDT IP DC PKM KDT IP
ADT1 0.839
ADT2 0.911
CDT2 0.778
CDT3 0.879
CDT4 0.873
CDT5 0.892
CDT6 0.829
DC2 0.844
DC3 0.883
DC4 0.713
DC5 0.837
IC1 0.668
IC2 0.631
IC4 0.806
IC5 0.746
IC6 0.797
IP1 0.688
IP2 0.711
IP3 0.809
IP4 0.823
IP5 0.703
KTD1 0.956
KTD2 0.738
PKM1 0.892
PKM2 0.897
PKM3 0.825
Convergent validity: all factor loadings are significant at 1%.
Based on these results, it can be concluded that the model meets the criteria of con-
vergent and discriminant validity, guaranteeing the consistency of its construction and
statistical inference.
4.2. Structural Model Assessment
After validating the model measurements, the next step was to calculate the structural
model criteria. Considering that the study used correlations and linear regressions, the
level of significance of these relationships was evaluated (p
≤
0.05). For correlations, the
null hypothesis (Ho) was established as r = 0 while for regression, it was established as Ho:
Adm. Sci. 2024,14, 8 16 of 25
Γ
= 0 (path coefficient = 0). If p> 0.05, the Ho was accepted and the inclusion of VL or VO
in SEM was reconsidered.
To verify the statistically significant hypotheses, significance tests were performed
using the Smart PLS 4.0 software. Results were obtained by bootstrapping with 500 sub-
samples. According to Henseler et al. (2015), three aspects should be analyzed during the
evaluation of the structural model: (1) path coefficients, (2) determination coefficients (R
and R2), and (3) relevance of the f2 coefficients.
After analyzing the path coefficients at the level of significance and relevance of the
coefficients, we found that not all the hypotheses initially proposed were confirmed. As
can be seen in Table 6, hypotheses H1, H2, H3, H4, H6, H7, H8, H9, H10, and H12 were
statistically significant (p< 0.05) and were therefore confirmed; however, hypotheses H5
and H11 were not confirmed (p> 0.05).
Table 6. Significance results and hypothesis testing.
Hypothesis Original
Sample
Sample Mean
(M) STDEV T Statistics pValues Confirmation of
the Hypothesis
H1
ADT →IC 0.258 0.256 0.047 5.471 0.000 Confirmed
H2
ADT →PKM 0.215 0.211 0.069 3.111 0.002 Confirmed
H3
ADT →IP 0.398 0.398 0.049 8.114 0.000 Confirmed
H4
CDT →ADT 0.424 0.426 0.054 7.810 0.000 Confirmed
H5
CCT →KDT −0.033 −0.032 0.038 0.869 0.385 Not Confirmed
H6
DCT →IP 0.486 0.486 0.048 10.029 0.000 Confirmed
H7
DC →ADT 0.398 0.394 0.114 3.498 0.000 Confirmed
H8
DC →PKM 0.587 0.592 0.069 8.547 0.000 Confirmed
H9
DC →KDT 0.957 0.956 0.026 37.218 0.000 Confirmed
H10
PKM →IC 0.251 0.250 0.042 5.994 0.000 Confirmed
H11
KDT →ADT 0.030 0.034 0.103 0.288 0.773 Not confirmed
H12
IP →IC 0.460 0.462 0.046 10.084 0.000 Confirmed
According to the criteria developed by Cohen (1988) and Chin (1998), the results of the
evaluation of Pearson’s coefficients of determination (R2), as shown in Table 7, point to a
high degree of adjustment and adherence of the explanation of variables “ADT” (R2 = 0.615),
“IC” (R2 = 0.799), “PKM” (R2 = 0.574), “KDT” (R2 = 0.872), and “IP” (R2 = 0.669).
Table 7. Determination coefficient (R-squared).
R-Squared R-Squared Adjusted
Adoption of digital technologies (ADT) 0.615 0.611
Competitiveness (IC) 0.799 0.797
Knowledge management (PKM) 0.574 0.571
Knowledge of digital transformation (KDT)
0.872 0.871
Productivity (IP) 0.669 0.667
The results of the evaluation of Pearson’s coefficients of determination (R2), as shown
in Table 7, point to a high degree of adjustment and adherence regarding the explanation of
the variables “anxiety” (R2 = 0.729), “satisfaction” (R2 = 0.757), “turn-over” (R2 = 0.589),
“happiness” (R2 = 0.78), and “performance” (R2 = 0.196), with the latter being considered
as having a weak effect based on Cohen’s criteria (Cohen 1988) and Chin’s criteria (Chin
1998), and thus not explained by the model.
As can be seen in Figure 2and Table 7, changes in “DC” and CDT affect KDT, with
R2 = 0.872, that is, KDT is affected by DC and CDT, with a contribution of 87.2%. Likewise,
KDT, CDT, and DC play a crucial role in ADT (R2 = 0.616). ADT and CDT affect IP
(R2 = 0.675), DC and ADT influence KM (R2 = 0.574), and IC is affected by KM, ADT, and
IP (R2 = 0.753).
Adm. Sci. 2024,14, 8 17 of 25
Finally, a multiple regression analysis (MRA) was used to assess whether the im-
provement in the competitiveness of organizations depends on the set of variables studied
(DC, KDT, CDT, ADT, IP, and PKM). Table 8shows the model summary and the multiple
correlation coefficients. Since R
2
a (Adjust R-squared) = 0.813 (F = 206.276; p-value: 0.000),
we can say that 81.3% of the variability in competitiveness is explained by the independent
variables present in the adjusted linear regression model. The model is highly significant
and expressed as follows:
IC = 0.183 CTD + 0.184 ADT + 0.363 IP + 0.176 PKM.
Table 8. Results of regression: model summary and coefficients.
R R2 R2adjust F Sig.
0.902 a0.813 0.809 206.276 0.000 b
Unstandardized coefficients
Standardized
coefficients
Beta
t Sig.
Model B Std. Error
(Constant)
7.173
×
10
−18 0.026 0.000 1.000
DC 0.021 0.059 0.021 0.362 0.718
KDT 0.087 0.050 0.087 1.743 0.082
CTD 0.183 0.046 0.183 4.002 0.000
ADT 0.184 0.048 0.184 3.825 0.000
IP 0.363 0.055 0.363 6.639 0.000
PKM 0.176 0.044 0.176 3.985 0.000
aDependent variable: Competitiveness (IC). bPredictors: (Constant), DC, KDT, CTD, ADT, IP, PKM.
Table 8shows that the variables that contribute most to the model are productivity
(IP; R = 0.363; p= 0.000), adoption of digital technologies (ADT; R = 0.184; p= 0.000), com-
mitment to digital technologies (CPD; R = 0.183; p= 0.000), and knowledge management
(PKM; R = 0.176; p= 0.000). The variables DC and KDT had lower and non-significant
values (p> 005).
5. Discussion
The results obtained in this study allowed us to confirm 10 of the 12 hypotheses pre-
sented: H1—relationship between digital culture and knowledge of digital transformation
work; H2—relationship between digital culture and adoption of digital technologies; H3—
relationship between digital cultures and knowledge management; and H4—Relationship
between commitment and knowledge of digital transformation—were all confirmed. Hy-
pothesis H5—relationship between commitment and adoption of digital technologies—
was not confirmed. Hypotheses H6—relationship between commitment and productivity;
H7—relationship between knowledge of digital transformation and adoption of digital
technologies; H8—relationship between adoption of digital technologies and knowledge
management; H9—relationship between the adoption of digital technologies and productiv-
ity; and H10—relationship between adoption of digital technologies and competitiveness—
were also confirmed. Hypothesis H11—relationship between knowledge management and
competitiveness—was not confirmed, while H12—the relationship between productivity
and competitiveness—was confirmed.
This means that, contrary to the studies identified in the literature, we did not find
evidence of a positive and significant relationship between commitment and the adoption
of digital technologies (H5) (Shapiro and Mandelman 2021;Santos et al. 2021;Cavalcanti
et al. 2022), as well as between knowledge management and organizational competitiveness
(H11) (Moreira et al. 2017;Alvarenga et al. 2020;Shakina et al. 2021;Kim et al. 2021;Pereira
et al. 2022;Aziz et al. 2022).
Adm. Sci. 2024,14, 8 18 of 25
The literature review confirmed that digital transformation, and more specifically
Industry 4.0, is currently of great importance due particularly to the competitive advantage
that it can bring to organizations (Moreira et al. 2018;Jahanmir et al. 2020;Cavalcanti et al.
2022). On the othr hand, there is empirical evidence that recognizes other benefits, such
as increased productivity and efficiency, improved organizational performance, increased
revenue, and reduced costs (Li et al. 2020). To help create this competitive advantage, there
is a set of Industry 4.0 technologies and tools that help organizations improve products
and processes.
Several authors (Moreira et al. 2018;Jahanmir et al. 2020;Cavalcanti et al. 2022;
Jahanmir et al. 2020) considered that the adoption of digital technologies would ensure
growth and sustainability in dynamic and competitive environments such as those that
currently affect the socio-economic context characterized by unpredictability and turbulence
caused by crises (for example COVID-19, the war in Ukraine, etc.).
The data obtained allowed us to verify that, in general, the majority of companies
are at a low (initial) level when it comes to adopting Industry 4.0 practices and tools.
Although respondents recognized the importance and urgency of carrying out digital
transformation—similar to the studies by Järvinen and Karjaluoto (2015) and Shakina et al.
(2021) —they recognized several barriers and difficulties in this process (Alvarenga et al.
2020).
As mentioned by Matt et al. (2016), Alvarenga et al. (2020), and Moreira et al. (2018),
the objective of DT is to redesign the organizational business through the introduction
of digital technologies, achieving benefits such as improvements in productivity, cost
reduction, and innovation. As mentioned by Li et al. (2020), the adoption of digital
technologies allows the optimization of physical, time, and people resources, resulting
in increased organizational effectiveness and efficiency. The integration of the physical
and digital worlds within the company and between companies in its supply chain is
important in implementing Industry 4.0 practices and tools. In this sense, the requirement
for new knowledge, skills, and qualifications of human resources is recognized (Gill and
VanBoskirk 2016), capable of dealing safely and confidently with new technologies and,
consequently, facilitating the adoption of Industry 4.0 practices, as evidenced by Da Silva
et al. (2020).
However, digital transformation presents new challenges and barriers to the adoption
of these technologies, particularly in terms of security and protection, initial investment,
trust, research and development, technological barriers, organizational management, hu-
man capital, and the lack of financial resources (Costa et al. 2023). In this study, the majority
of respondents identified the efficiency of production and management systems as the
main positive impacts resulting from the adoption of Industry 4.0 tools.
The majority of companies under study still have very low levels of implementation of
new digital tools (which means low levels of maturity), meaning that they have a long way
to go before digitalization. Digital transformation is transversal to the entire organization,
constituting a change that will affect the different areas of a business (Vieira et al. 2022).
This means that digital strategies must be formulated in accordance with the company’s
objectives and based on the advantages that digital technologies offer.
Given the challenges and barriers inherent to this change, the management of digital
transformation must be controlled and carried out gradually, always managing all the
impacts it will have on the organization and its employees (Shakina et al. 2021;Alvarenga
et al. 2020;Santos et al. 2021). In fact, “Human Resources” has a fundamental role in
the adoption of new technologies and digitalization practices (Diogo et al. 2019;Santos
et al. 2021), as it may require the acquisition of new qualifications and skills. Portuguese
organizations can obtain a competitive advantage and many of the known benefits if
they are able to adopt Industry 4.0 practices. Although this study showed that there
is a significant number of companies that have not yet adopted or have adopted few
Industry 4.0 practices and tools, respondents believed in the potential for change and in
the willingness of their organizations to improve and adopt Industry 4.0 practices and
Adm. Sci. 2024,14, 8 19 of 25
tools. This perspective is corroborated by Alvarenga et al. (2020) and Shakina et al. (2021)
who considered that knowledge management and the consequent training and training of
human resources can increase awareness about the benefits of digital transformation and
facilitate the implementation of Industry 4.0 practices and tools.
Several authors (Milgrom et al. 1991;Milgrom and Roberts 1995;Moreira et al. 2018;
and Shakina et al. 2021) considered that resources and technologies are complementary
to one another because an increase in technology improves the performance of the entire
business. According to Matt et al. (2016), the primary goal of digital transformation (DT)
is to restructure organizational business through the use of digital technology, resulting
in advantages like increased productivity, cost savings, and innovation. According to
Alvarenga et al. (2020), formalized tacit knowledge is a useful tool for controlling processes
in creative and competitive businesses that have implemented knowledge management.
Finally, multiple regression analysis (MRA) allowed us to determine that competitive-
ness (IC) depends on productivity, the adoption of digital technologies, commitment to
digital influence, and knowledge management. Based on the model tested, KDT (knowl-
edge of digital transformation) and DC (digital culture) did not contribute significantly to
the model, which means that they do not influence the competitiveness of organizations.
We also saw an improvement in competitiveness, as mentioned by Li et al. (2020).
The authors indicated that, especially in a dynamic environment, production-oriented
companies looking for economic performance need to use digital platforms. In this study,
81.3% of the competitiveness variability was explained by the independent variables.
6. Conclusions
This study revealed that organizational competitiveness depends on productivity, the
adoption of digital technology, commitment to digital transformation, and knowledge man-
agement. Uncovered empirical evidence supports improved productivity, efficiency, organi-
zational performance, increased revenue, and cost reduction due to digital transformation.
Not all initially proposed hypotheses included in the conceptual model were con-
firmed. Hypotheses H1, H2, H3, H4, H6, H7, H8, H9, H10, and H12 (p< 0.05) were
confirmed, whereas hypotheses H5 and H11 were not confirmed (p> 0.05). Thus, we can
state that there is a significant relationship between digital culture and job knowledge of
digital transformation (H1), digital culture and the adoption of digital technologies (H2),
digital cultures and knowledge management (H3), commitment and knowledge of digital
transformation (H4), commitment and productivity (H6), knowledge of digital transfor-
mation and adoption of digital technologies (H7); the adoption of digital technologies and
knowledge management (H8), the adoption of digital technologies and productivity (H9),
the adoption of digital technologies and competitiveness (H10), knowledge management
and competitiveness (H11), and productivity and competitiveness (H12).
On the contrary, there was no significant relationship between commitment and
adoption of digital technologies (H5) or between knowledge management and competitive-
ness (H11).
Responses to the question on digital culture and knowledge and commitment to digital
transformation showed that it positively influences the adoption of digital technologies and,
in a complementary way, productivity and competitiveness. As far as commitment and
knowledge management are concerned, the respective correlations were not confirmed.
This research contributes to a comprehensive understanding of the impact of digi-
tal transformation and Industry 4.0 practices and tools on Portuguese organizations. It
underscores the challenges and opportunities associated with digitalization and high-
lights the crucial role of human resources and knowledge management in this journey.
Overall, the findings have important implications for businesses seeking to enhance their
competitiveness and productivity in an increasingly digital world.
The data obtained are important, as they allow us to have a real perspective of what is
happening in the “transition and digital transformation” in organizations and, thus, allow
Adm. Sci. 2024,14, 8 20 of 25
organizational managers and political decision-makers the possibility of creating tools for
support appropriate to the reality of the Portuguese industry.
One limitation of this study is associated with the use of a non-probabilistic sample
that makes it difficult to extrapolate the results with 100% reliability. Another limitation is
the fact that this is a quantitative study that may have neglected some important qualitative
factors for a more comprehensive understanding of the theme under analysis.
Because the participants who responded to the questionnaire were anonymous, it was
not possible to objectively verify the impact that industry practices have on organizations,
particularly at a financial level. Another limitation is the fact that the study did not use
any model (for example, the Pathfinder i4.0 model: https://pathfinder.i40.de/en/demo/)
(accessed on 4 June 2021) that would allow evaluating and comparing maturity levels
between organizations (for example, by sector and/or business size). Additionally, the
study did not assess the impact of COVID-19 on the adoption of Industry 4.0 practices
and tools.
It will therefore be important in the future to assess the level of maturity of Portuguese
industries, for example, using the Pathfinder i4.0 model. It will also be important to
monitor the evolution of digital transformation in Portuguese organizations, as well as its
impact on the competitiveness of companies and the country. Likewise, it is recommended
that comparative studies be conducted between regions, nations, sectors, and sizes of
organizations. The impact of COVID-19 on the adoption of Industry 4.0 practices and tools
should also be conducted.
For future research, we suggest employing qualitative methods, specifically conduct-
ing in-depth interviews with a substantial cross-section of business leaders. This approach
has the potential to enhance our comprehension of the subject, particularly within a busi-
ness context.
Author Contributions: Conceptualization, A.C. and M.S.P.; Methodology, A.C. and M.S.P.; Software,
A.C.; Validation, A.C., J.C.S., S.F. and M.M.; Formal analysis, A.C., M.S.P. and S.F.; Investigation, A.C.
and M.S.P.; Data curation, J.C.S. and S.F.; Writing–original draft, A.C., M.S.P. and M.M.; Writing–
review & editing, M.S.P., J.C.S., D.J.P., S.F. and M.M.; Visualization, J.C.S. and D.J.P.; Supervision, A.C.
and M.S.P. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: Data are contained within the article.
Conflicts of Interest: The authors declare no conflicts of interest.
Appendix A. Dimensions and Indicators of the Latent Variables
Latent Variables Items
Digital culture (DC) We believe that our competitive strategy depends on digital technologies
Top management promotes digital transformation
We have the right leaders to execute our digital strategy
The organization invests in targeted digital education and training for all employees
We clearly communicate our digital vision both internally and externally
We allocate appropriate resources and means for implementing the digital strategy
Customer perceptions are considered in the digital design and development of the organization
Commitment
to digital transformation
(CDT)
In my organization, there are policies that prioritize the use of information technologies
My organization is prepared for the evolution of digital transformation
Our organizational change example can promote other digital transformation projects
Organizational leadership is prepared for digital change
My immediate supervisors are committed to digital change
Our supervisors alert us to what is important to know
I feel comfortable expressing my opinion and presenting my point of view to my colleagues and
superiors. I feel I will be heard
Adm. Sci. 2024,14, 8 21 of 25
Latent Variables Items
Knowledge
of digital transformation
(KDT)
I am aware of the objectives of digital transformation in my organization
I seek to understand the vision, mission, and strategies defined in my organization and apply them
in my daily activities.
In the digital transformation process, I don’t feel resistance to change.
Digital transformation has modified internal processes
Digital transformation is the future of organizational management
Adoption
of digital technologies
(ADT)
In my day-to-day work, I use digital technologies and products. In processes, management, and
internal communication, meetings, etc.
Processes in my service are fully digitized
Through technological innovation, manual operations have been changed and become digital
Knowledge management
(KM)
The implementation of the platform contributed to increased knowledge sharing among colleagues
Knowledge gained during and after digital transformation can improve service delivery to citizens
I consider that digital transformation contributed to improving knowledge management practices
I have knowledge of the importance of knowledge management and its impacts on
digital transformation
Digital transformation is fundamental to better organizational performance
Productivity
(IP)
The digital transformation contributed to the improvement of internal processes
Digital transformation increased productive efficiency and effectiveness
Technological change and innovation have the advantage of optimizing work methodologies
I feel that with digital transformation, I can be faster and more efficient in performing my tasks
The digital transformation contributed to an increase in the organization’s productivity
Competitiveness (IC) Digital transformation made services more transparent and secure
Digital transformation significantly contributed to reducing the organization’s costs
I believe that digital transformation improved the organization’s competitiveness
Digital transformation contributed to the organization’s innovation
Digital transformation allowed for a competitive advantage in the market
Digital transformation allowed for exploring new markets and opportunities
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