Digital Culture, Knowledge, and Commitment to Digital Transformation

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		António Cardoso	--	4388	2024-01-06 20:44:09	\|
2	layout	Jessie Wu	Meta information modification	4388	2024-01-08 03:17:33	\|

This entry is adapted from the peer-reviewed paper 10.3390/admsci14010008

Digital culture (DC) is considered an important and integral part of any organization’s strategy and dynamics, together with knowledge, learning, and continuous improvement. They are key concepts that enable companies to keep up with mature and competitive markets and to be fully adapted to constantly changing scenarios.

digital culture commitment knowledge of digital transformation adoption of digital technologies

1. Introduction

Given the onset of Industry 4.0 in 2011 and the rapid development of digital technologies thereafter, digital transformation has become a hot topic in the global manufacturing industry. To be successful, digital transformation requires a commitment to digital leadership 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 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).

2. Digital Culture

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 mistakes. 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 innovation 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 organizational 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), organizations 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.

We are currently witnessing the emergence of different technologies that allow organizations 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 (Jaklič 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 digitalization 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 interconnection 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.

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 (stakeholders). 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. Sá et 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 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 leadership 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 organizations 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. Črešnar 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. Stachová et 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 technicians. 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.

Digital culture creates the environment and mindset necessary for digital transformation (Kotler et al. 2021), while professional knowledge of digital transformation entails the essential skills and practical knowledge required to successfully implement this transformation 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).

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 technologies 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).

Digital cultures create a conducive environment for knowledge management, facilitating 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-sharing platforms. These tools facilitate collaborative knowledge creation and organization (Moreira et al. 2018).

Commitment and understanding of digital transformation are complementary aspects 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 leading, 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).

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.

Employee commitment, including their level of engagement, enthusiasm, and dedication 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).

Digital transformation involves the integration of advanced digital technologies and the redefinition of business processes to enhance efficiency, effectiveness, and competitiveness (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.

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.

The adoption of digital technologies can lead to significant productivity gains in organizations, 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 increase 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).

The effective integration of digital technologies in business operations and strategies 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 models, 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).

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).

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.

References

Leal-Rodríguez, Antonio, Carlos Sanchís-Pedregosa, Antonio Moreno-Moreno, and Antonio Leal-Millán. 2023. Digitalization beyond technology: Proposing an explanatory and predictive model for digital culture in organizations. Journal of Innovation & Knowledge 8: 100409.
Pereira, Manuel Sousa, António Cardoso, José Carlos Sá, Manuel Magalhães, and Silvia Faria. 2022. Digital Transformation in Organizations and Its Impact on Knowledge Management: A Quantitative Study. In Implementing Automation Initiatives in Companies to Create Better-Connected Experiences. Edited by Jorge Remondes and Sondrina Teixeira. Hershey: IGI Global, pp. 1–13.
Rymarczyk, Jan. 2022. The Change in the Traditional Paradigm of Production under the Influence of Industrial Revolution 4.0. Businesses 2: 188–200.
Darwin, Charles. 1859. On the Origin of Species by Means of Natural Selection, or Preservation of Favoured Races in the Struggle for Life. London: John Murray.
Brynjolfsson, Erik, and Lorin M. Hitt. 2000. Beyond computation: Information technology, organizational transformation and business performance. Journal of Economic Perspectives 14: 23–48.
Zhen, Zhang, Zahid Yousaf, Magdalena Radulescu, and Muhammad Yasir. 2021. Nexus of digital organizational culture, capabilities, organizational readiness, and innovation: Investigation of SMEs operating in the digital economy. Sustainability 13: 720.
Davison, Robert, and Carol Xj Ou. 2017. Digital work in a digitally challenged organization. Information & Management 54: 129–37.
Kim, Seunghyun, Byungchul Choi, and Yong Kyu Lew. 2021. Where is the age of digitalization heading? The meaning, characteristics, and implications of contemporary digital transformation. Sustainability 13: 8909.
Pascucci, Federica, Elisabetta Savelli, and Giacomo Gistri. 2023. How digital technologies reshape marketing: Evidence from a qualitative investigation. Italian Journal of Marketing 2023: 27–58.
Rogers, David. 2016. The Transformation Digital Playbook—Rethink Your Business for The Digital Age. New York: Columbia Business School Publishing, pp. 20–169.
Magistretti, Stefano, Cristina Tu Anh Pham, and Claudio Dell’Era. 2021. Enlightening the dynamic capabilities of design thinking in fostering digital transformation. Industrial Marketing Management 97: 59–70.
Pinochet, Pinochet, Luis Hernan, Guilherme de Camargo Belli Amorim, Durval Lucas Júnior, and Cesar Alexandre de Souza. 2021. Consequential factors of Big Data’s Analytics Capability: How firms use data in the competitive scenario. Journal of Enterprise Information Management 34: 1406–28.
Moura, Graziela Breitenbauch, and Letícia Godoy Saroli. 2021. Sustainable value chain management based on dynamic capabilities in small and medium-sized enterprises (SMEs). The International Journal of Logistics Management 32: 168–89.
Bumann, Jimmy, and Marc Peter. 2016. Action Fields of Digital Transformation—A Review and Comparative Analysis of Digital Tran formation Maturity Models and Frameworks. In Digitalisierung und andere Innovationsformen im Management. Edited by Mark Aeschbacher, Knut Hinkelmann and Arie Verkuil. Innovation und Unternehmertum, Band 2. Basel: Edition Gesowip, pp. 13–40.
Andriole, Stephen. 2017. Five Myths About Digital Transformation. MIT Sloan Management Review 58: 20–22. Available online: https://sloanreview.mit.edu/article/five-myths-about-digital-transformation/ (accessed on 4 September 2021).
Gill, Martin, and Shar VanBoskirk. 2016. The Digital Maturity Model 4.0. Benchmarks: Digital Transformation Playbook. Available online: https://www.forrester.com/report/the-digital-maturity-model-40/RES131801 (accessed on 15 September 2022).
Cavalcanti, Diego Rodrigues, Tiago Oliveira, and Fernando de Oliveira Santini. 2022. Drivers of digital transformation adoption: A weight and meta-analysis. Heliyon 8: e08911.
Vial, Gregory. 2019. Understanding digital transformation: A review and a research agenda. Journal of Strategic Information Systems 28: 118–44.
Verhoef, Peter, Thijs Broekhuizen, Yakov Bart, Abhi Bhattacharya, John Qi Dong, Nicolai Fabian, and Michael Haenlein. 2021. Digital transformation: A multidisciplinar reflection and research agenda. Journal of Business Research 122: 889–901.
Matt, Christian, Thomas Hess, Alexander Benlian, and Florian Wiesbock. 2016. Options for Formulating a Digital Transformation Strategy. MIS Quarterly Executive 15: 123–39. Available online: https://aisel.aisnet.org/misqe/vol15/iss2/6 (accessed on 13 September 2022).
Navaridas-Nalda, Fermín, Mónica Clavel-San, Rúben Fernández-Ortiz, and Mario Arias-Oliva. 2020. The strategic influence of school principal leadership in the digital transformation of schools. Computers in Human Behavior 112: 106481.
Pillai, Rajasshrie, Brijesh Sivathanu, and Yogesh K. Dwivedi. 2020. Shopping intention at AI-powered automated retail stores (AIPARS). Journal of Retailing and Consumer Services 57: 102207.
Udo, Godwin, Kallol Bagchi, and Moutusy Maity. 2016. Exploring factors affecting digital piracy using the norm activation and UTAUT models: The role of national culture. Journal of Business Ethics 135: 517–41. Available online: https://www.jstor.org/stable/24736069 (accessed on 4 September 2021).
Selander, Lisen, and Sirkka L. Jarvenpaa. 2016. Digital action repertoires and transforming a social movement organization. MIS Quarterly 40: 331–52. Available online: https://www.jstor.org/stable/26628909 (accessed on 5 June 2021).
Jain, Geetika, Justin Paul, and Archana Shrivastava. 2021. Hyper-personalization, co-creation, digital clienteling and transformation. Journal of Business Research 124: 12–23.
Puliwarna, Tunggul, Pantja Djati, and Elisabeth Tanti. 2023. The Effect of Digital Leadership, organizational culture, digital competence and organization’s commitment on Organizational Performance: Information Technology System in Indonesian Navy. International Journal of Scientific Research and Management (IJSRM) 11: 4833–46.
World Economic Forum. 2021. Digital Culture: The Driving Force of Digital Transformation. Available online: https://www3.weforum.org/docs/WEF_Digital_Culture_Guidebook_2021.pdf (accessed on 4 September 2021).
Reis, João, and Nuno Melão. 2023. Digital transformation: A meta-review and guidelines for future research. Heliyon 9: e12834.
Kotler, Philip, Hermawan Kartajaya, and Iwan Setiawan. 2021. Marketing 5.0 Tecnologia para a Humanidade. Conjuntura. Lisboa: Actual Editora. ISBN 978-989-694-623-4.
Manfreda, Anton, Klara Ljubi, and Aleš Groznik. 2021. Autonomous vehicles in the smart city era: An empirical study of adoption factors important for millennials. International Journal of Information Management 58: 102050.
Arfi, Wissal Ben, Imed Ben Nasr, Galina Kondrateva, and Lubica Hikkerova. 2021. The role of trust in intention to use the IoT in eHealth: Application of the modified UTAUT in a consumer context. Technological Forecasting and Social Change 167: 120688.
Queiroz, Maciel, and Samuel Fosso Wamba. 2019. Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management 46: 70–82.
Vimalkumar, M., Sujeet Kumar Sharma, Jang Bahdur Singh, and Yogesh K. Dwivedi. 2021. ‘Okay google, what about my privacy?’: User’s privacy perceptions and acceptance of voice based digital assistants. Computers in Human Behavior 120: 106763.
Balakrishnan, Vimala, and Nor Liyana Mohd Shuib. 2021. Drivers and inhibitors for digital payment adoption using the Cashless Society Readiness-Adoption model in Malaysia. Technology in Society 65: 101554.
Patil, Pushp, Kuttimani Tamilmani, Nripendra P. Rana, and Vishnupriya Raghavan. 2020. Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. International Journal of Information Management 54: 102144.
Alam, Mohammad Zaheduk, Wang Hu, Md. Abdul Kaium, Md Rakibul Hoque, and Mirza Mohammad Didarul Alam. 2020. Understanding the determinants of mHealth apps adoption in Bangladesh: A SEM-Neural network approach. Technology in Society 61: 101255.
Mariani, Marcello, Maria Ek Styven, and Fréderic Teulon. 2021. Explaining the intention to use digital personal data stores: An empirical study. Technological Forecasting and Social Change 166: 120657.
Delgosha, Mohammad Soltan, and Nastaran Hajiheydari. 2020. On-demand service platforms pro/anti adoption cognition: Examining the context-specific reasons. Journal of Business Research 121: 180–94.
Jaklič, Jurij, Tanja Grublješič, and Aleš Popovič. 2018. The role of compatibility in predicting business intelligence and analytics use intentions. International Journal of Information Management 43: 305–18.
Khaksar, Seyed Mohammad Sadegh, Rajiv Khosla, Stephen Singaraju, and Bret Slade. 2021. Carer’s perception on social assistive technology acceptance and adoption: Moderating effects of perceived risks. Behaviour & Information Technology 40: 337–60.
Kunz, Reinhard E., and James P. Santomier. 2019. Sport content and virtual reality technology acceptance. Sport, Business and Management: An International Journal 10: 83–103.
Hujran, Omar, Emad Abu-Shanab, and Ali Aljaafreh. 2020. Predictors for the adoption of e-democracy: An empirical evaluation based on a citizen-centric approach. Transforming Government: People, Process and Policy 14: 523–44.
Rahman, Mohammed Sajedur, Myung Ko, John Warren, and Darrell Carpenter. 2016. Healthcare Technology SelfEfficacy (HTSE) and its influence on individual attitude: An empirical study. Computers in Human Behavior 58: 12–24.
Seufert, Sabine, Josef Guggemos, and Michael Sailer. 2021. Technology-related knowledge, skills, and attitudes of pre- and in-service teachers: The current situation and emerging trends. Computers in Human Behavior 115: 106552.
Hu, Zhongqing, Shuai Ding, Shizheng Li, Luting Chen, and Shanlin Yang. 2019. Adoption intention of fintech services for bank users: An empirical examination with an extended technology acceptance model. Symmetry 11: 340.
Almeida, Fernando. 2023. Challenges in the Digital Transformation of Ports. Businesses 3: 548–68.
McLaughlin, Stephen A. 2017. Dynamic capabilities: Taking an emerging technology perspective. International Journal of Manufacturing Technology and Management 31: 62–81.
Peppard, Joe, and Christopher Hemingway. 2009. “Design-for-Use”: A Paradigm for Successful Information Technology Projects. Business Horizons. Available online: https://www.som.cranfield.ac.uk/som/dinamic-content/media/ISRC/Design%20for%20Use%20-%20a%20paradigm%20for%20successful%20IT%20projects.pdf (accessed on 5 June 2021).
Fitzgerald, Michael Nina Kruschwitz, Didier Bonnet, and Michael Welch. 2013. Embracing digital technology: A new strategic imperative. MIT Sloan Management Review Research Report 55: 1–12.
Weill, Peter, and Stephanie L. Woerner. 2013. Managing total digitization: The next frontier. CISR Research Briefing 8: 1–4.
McDonald, Mark. 2012. Digital strategy do not equal IT strategy. Harvard Business Review 90: 85–92. Available online: https://hbr.org/2012/11/digital-strategy-does-not-equa (accessed on 3 June 2021).
Alvarenga, Ana, Florinda Matos, Radu Godina, and João Matias. 2020. Digital Transformation and Knowledge Management in the Public Sector. Sustainability 12: 5824.
Milgrom, Paul, and John Roberts. 1995. Complementarities and fit strategy, structure, and organizational change in manufacturing. Journal of Accounting and Economics 19: 179–208.
Milgrom, Paul, Yingyi Qian, and John Roberts. 1991. Complementarities, momentum, and the evolution of modern manufacturing. American Economic Review 81: 84–88. Available online: http://www.jstor.org/stable/2006831 (accessed on 3 June 2021).
Shakina, Elena, Petr Parshakov, and Artem Alsufiev. 2021. Rethinking the corporate digital divide: The complementarity of technologies and the demand for digital skills. Technological Forecasting and Social Change 162: 120405.
Moreira, Fernando, Maria João Ferreira, and Isabel Seruca. 2018. Enterprise 4.0—The emerging digital transformed enterprise? Procedia Computer Science 138: 525–32.
Lotti, Oliva F. 2014. Knowledge management barriers, practices and maturity model. Journal of Knowledge Management 18: 1053–74.
Busco, Carolina, Felipe González, and Michelle Aránguiz. 2023. Factors that favor or hinder the acquisition of a digital culture in large organizations in Chile. Frontiers Psychology Section Organizational Psychology 14: 1153031.
Jahanmir, Sara, Graça Miranda Silva, Paulo Gomes, and Helena Martins Gonçalves. 2020. Determinants of users’ continuance intention toward digital innovations: Are late adopters different? Journal of Business Research 115: 225–33.
Diogo, Ricardo Alexandre, Armando Kolbe Junior, and Neri Santos. 2019. A transformação digital e a gestão do conhecimento: Contribuições para a melhoria dos processos produtivos e organizacionais. P2P & INOVAÇÃO Rio de Janeiro 5: 154–75.
Yoo, Youngjin, Kalle Lyytinen, Veeresh Thummadi, and Aaron Weiss. 2010. Unbounded innovation with digitalization: A Case of Digital Camera. Annual Meeting of the Academy of Management 1–41. Available online: http://www.youngjinyoo.com/aom2010-digital-camera.pdf (accessed on 4 September 2021).
Li, Ying, Jing Dai, and Li Cui. 2020. The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model. International Journal of Production Economics 229: 107777.
Dalenogare, Lucas Santo, Guilherme Benitiez, Nestor Ayla, and Alejandro Frank. 2018. The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics 204: 383–94.
Romero, David, Paolo Gaiardelli, Daryl Powell, Thorsten Wuest, and Matthias Thürer. 2019. Rethinking Jidoka Systems under Automation & Learning Perspectives in the Digital Lean Manufacturing World. IFAC-PapersOnLine 52: 899–903.
Vieira, Valter Afonso Marcos, Marcos Almeida, Colin Gabler, Ricardo Limongi, Milena Costa, and Miriam da Costa. 2022. Optimising digital marketing and social media strategy: From push to pull to performance. Journal of Marketing Management 38: 709–39.
Costa, Ruben Marta Barbosa, Francisco Silva, José Sá, Luis Ferreira, and Bebiana Pinto. 2023. Improving Procedures for Production and Maintenance Control Towards Industry 4.0 Implementation. In International Conference Innovation in Engineering. Cham: Springer, pp. 58–67.
Sá, Jéssica, Luís Pinto Ferreira, Teresa Dieguez, José Carlos Sá, and Francisco José Gomes da Silva. 2021. Industry 4.0 in the Wine Sector–Development of a Decision Support System Based on Simulation Models. In International Conference Innovation in Engineering. Cham: Springer, pp. 371–84.
McDermott, Olivia, Ida Foley, Jiju Antony, Michael Sony, and Mary Butler. 2022. The Impact of Industry 4.0 on the Medical Device Regulatory Product Life Cycle Compliance. Sustainability 14: 14650.
Shin, Jinkyo, Md Alamgir Mollah, and Jaehyeok Choi. 2023. Sustainability and Organizational Performance in South Korea: The Effect of Digital Leadership on Digital Culture and Employees’ Digital Capabilities. Sustainability 15: 2027.
Črešnar, Rok, and Zlatko Nedelko. 2020. Understanding Future Leaders: How Are Personal Values of Generations Y and Z Tailored to Leadership in Industry 4.0? Sustainability 12: 4417.
Stachová, Katarína, Ján Papula, Zdenko Stacho, and Lucia Kohnová. 2019. External Partnerships in Employee Education and Development as the Key to Facing Industry 4.0 Challenges. Sustainability 11: 345.
Schaar, Anne Kathrin, André Calero Valdez, Tatjana Hamann, and Martina Ziefle. 2019. Industry 4.0 and its future staff. Matching millennials perceptions of a perfect job with the requirements of digitalization. Paper presented at the International Conference on Competitive Manufacturing (COMA2019): Proceedings, Stellenbosch, South Africa, January 30–February 1; Edited by Dimiter Dimitrov, Devon Hagedorn-Hansen and Konrad von Leipzig. Stellenbosch: Department of Industrial Engineering, Stellenbosch University, pp. 246–52. Available online: https://hdl.handle.net/10019.1/105429 (accessed on 5 June 2021).
Goh, Edmund, and Cindy Lee. 2018. A workforce to be reckoned with: The emerging pivotal Generation Z hospitality workforce. International Journal of Hospitality Management 73: 20–28.
Anastasiei, Bogdan, Nicoleta Dospinescu, and Octavian Dospinescu. 2023. Word-of-Mouth Engagement in Online Social Networks: Influence of Network Centrality and Density. Electronics 12: 2857.
Santos, Gilberto, Jose Carlos Sá, Maria João Félix, Luís Barreto, Filipe Carvalho, Manuel Doiro, Kristína Zgodavová, and Miladin Stefanović. 2021. New Needed Quality Management Skills for Quality Managers 4.0. Sustainability 13: 6149.
Peláez, Antonio López, Amaya Erro-Garcés, and Emilio José Gómes Ciriano. 2020. Young people, social workers and social work education: The role of digital skills. Social Work Education 39: 825–42.
Teng, Xiaoyan, Zhong Wu, and Feng Yang. 2022. Research on the Relationship between Digital Transformation and Performance of SMEs. Sustainability 14: 6012.
Magsamen-Conrad, Kate, and Jeanette Muhleman Dillon. 2020. Mobile technology adoption across the lifespan: A mixed methods investigation to clarify adoption stages, and the influence of diffusion attributes. Computers in Human Behavior 112: 106456.
Pirhonen, Jari, Luciana Lolich, Katariina Tuominen, Outi Jolanki, and Virpi Timonen. 2020. These devices have not been made for older people’s needs—Older adults’ perceptions of digital technologies in Finland and Ireland. Technology in Society 62: 101287.
Da Silva, Vander luiz, João Luis Kovaleski, Regina Negri Pagani, Jaqueline de Matos Silva, and Alana Corsi. 2020. Implementation of industry 4.0 concept in companies: Empirical evidences. International Journal of Computer Integrated Manufacturing (Brazil) 33: 325–42.
Tang, Hongmei. 2017. A Study of the Effect of Knowledge Management on Organizational Culture and Organizational Effectiveness in Medicine and Health Sciences. Eurasia Journal of Mathematics, Science and Technology Education 13: 1831–45.
Kamalaldin, Anmar, Lina Linde, David Sjödin, and Vinit Parida. 2020. Transforming provider-customer relationships in digital servitization: A relational view on digitalization. Industrial Marketing Management 89: 306–25.
Ko, Andrea, Péter Fehér, Tibor Kovács, Ariel Mitev, and Zoltán Szabó. 2021. Influencing factors of digital transformation: Management or IT is the driving force? International Journal of Innovation Science 14: 1–20.
Shapiro, Alan Finkelstein, and Federico S. Mandelman. 2021. Digital adoption, automation, and labor markets in developing countries. Journal of Development Economics 151: 102656.
Moreira, Fernando, Manuel Au-Yong-Oliveira, Ramiro Goncalves, and Carlos Costa. 2017. Digital transformation—Opportunities and Threats for More Consistent Competitiveness. Faro: Ed. Syllables & Challenges.
Järvinen, Joel, and Heikki Karjaluoto. 2015. The use of Web analytics for digital marketing performance measurement. Industrial Marketing Management 50: 117–27.
Aziz, Iffat, Muhammad Shafiq, and Iram Fatima. 2022. Investigation of knowledge management and firm competitiveness: Core competence as a mediator. F1000Research 11: 1114.

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Business

Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Miguel Magalhães

View Times: 571

Update Date: 09 Jan 2024

Table of Contents

Video Upload Options

Confirm

1. Introduction

2. Digital Culture

References