Figure 1 shows the evolution of publications from 1999 to 2020.

Figure 1
Evolution of publications over the years
Source: Elaborated by authors.

The first publication related to the topic was in 1999. Although this paper does not deal with the current DT, the study sheds light on the preponderant role of leadership in the networks era. In other words, after a case study, Kodama (1999) recommends new strategies to deal with the era of connectivity, such as new business styles, superior leadership capable of generating new skills and innovation. After this period, publications remained stable. As of the year 2014, the field has a rise in publications. In addition, more than half of the articles were published between 2019 and 2020, which demonstrates the subject’s relevance and topicality, the need for future studies on DT and its relationship with knowledge.

The analysis of journals indicates a concentration of publication sources, as the articles in the sample were published in only 6 journals. The journal “Information and Management” has the largest number of publications (Table 1). The journal “International Journal of Information Management” is also highlighted as a pioneer in the subject, since of its 9 publications in the sample, 6 took place before the rise of the subject, in 2014.

Table 1
Distribution of the papers by journals

Source: Elaborated by authors

Regarding the regions that produced most of the publications, there was a concentration of studies from Europe and Asia (Table 2). The countries that stood out in the regions were: United Kingdom (7) and China (4).

Table 2
Geographic regions

Source: Elaborated by authors.

The quantitative approach was the most used in the studies analysed, that was applied in 20 studies (Table 3). The most used data collection instrument was the questionnaire and structural equation modelling was the main means for data analysis, present in 15 articles.

The qualitative approach was also representative in the sample. Of the 14 articles that used this approach, almost all collected data through interviews and document research. The main form of analysis was content analysis, present in 13 articles. Few studies used mixed methods (qualitative and quantitative approaches) which may indicate research possibilities. It should be noted that review studies were not found in this sample.

Table 3
Research approach

Source: Elaborated by authors.

The keywords are very useful for authors, researchers and editors as they signal central themes in articles. The keywords were examined through the analysis of social networks and the results, supported by the use of VOSviewer®, showing seven clusters (Figure 2).

Figure 2 demonstrates the occurrences of different keywords in the papers and the relationship between them. The analysis reveals that digital innovation and business analysis capability are the most recurrent keywords (4 times), followed by survey (3), knowledge-based view (2), social media (2), organizational performance (2), capabilities (2), and business value (2). The results revealed that DT is associated with organizational capabilities and that it effects on business value and organizational performance. The knowledge-based view theory and the survey tool were shown as useful to investigate the theme and, in addition, the results suggest that the research field is still under construction due to the wide variety of keywords used by actors to characterize the themes of research.

Figure 2.
Groups of papers keywords
Source: VOSviewer.

This research area represents a junction of clusters 2 and 3 (Table 4). It features a greater number of articles spanning the years 2007 to 2020. The area emphasizes tangible, intangible and human resources that will allow companies to develop capabilities to deal with the new digital economy and possibly achieve competitive advantage.

The literature has a wide classification of the types of resources. In this research, the investigated papers are mainly based on the definitions of Grant (2010); Bharadwaj (2000), and Santhanam and Hartono (2003), in which: tangible resources are understood as physical and financial resources that companies use in their operations; human resources include the experience, knowledge and wisdom of the individuals associated with a company and; Intangible resources include the attributes of a group of individuals associated with a company, involving organizational story, organizational learning, among others. Table 5 shows how these features are addressed in the articles.

Table 5
Definition of Resources

Source: Elaborated by authors using VOSviewer.

The Gupta and George’s (2016) study is central to this area due to the scope of the article's connections with other ones (Figure 3) and the approach to tangible, intangible and human resources as well as the Shamim et al. (2018) and David-West et al.’s (2018) studies. Shamim et al. (2018) investigated the influence of big data management challenges, showing that companies demand several tangible and intangible resources. Similarly, David-West et al. (2018) also found the need for tangible, intangible and human resources for Nigeria's digital financial services providers.

Concerning tangible resources, some studies have emphasized their importance for digital innovation and companies’ performance. Aboelmaged (2014) assessed that the readiness for technology and innovation were proportional to the technological infrastructure and capabilities of companies. Wong et al. (2019) analyzed that cost, competitive pressure and complexity can interfere with the intention to adopt blockchain in operations and supply chain management among Small and Medium Enterprises. Torres and Sidorova (2019) reinforce the importance of system quality, data quality and staff experience in big data analysis in the digital age. Corte-Real et al. (2020) also highlighted the importance of data quality in BDA and IoT capabilities, stating that data quality allows companies to expand and reshape their resource base. Zhang and Ravishankar (2019) indicate that the infrastructure for platform development, implementation, commercialization and improvement were crucial for the adoption of cloud computing.

Regarding to intangible resources, other group of studies emphasized their importance for digital innovation and company performance. Alberti-Alhtaybat et al. (2019) analyzed how a startup-minded business manages to make decisions quickly and has a flexible business model for value creation in DT. Another intangible resource pointed out as crucial for the success of companies in the digital economy was its culture. Upadhyaya and Kumar (2020) analyzed how this resource measures internal analytic knowledge, big data capacity and company performance. Al-Dmour et al. (2020) also addressed the impact of knowledge on digital innovation by validating and empirically examining the impact of KM functions on digital financial innovation through the moderating role of the demographic characteristics of managers. Furthermore, Abrell (2015) found that both types of customer knowledge (explicit and tacit) assist in the long-term digital innovation of companies, so investing in this intangible asset is promising for companies.

Human resources were also highlighted with emphasis on the companies' innovation and competitiveness. Xu et al. (2016) postulated that companies that adopt a high level of traditional marketing analytics (TMA) and big data analytics (BDA) have higher levels of knowledge fusion and, consequently, product development success (NPS). Shujahat et al. (2019) analyzed the mediating role of knowledge worker productivity (employees who use ICT systems, Big Data) in the relationships between knowledge management processes and innovation. Low and Johnston (2012) highlighted the leadership and importance of staying at the forefront of emerging technologies for companies to create more vibrant

and adaptive incrementalism behaviors, which, for them, is the role of leadership. Merendino et al. analyzed whether big

data (BD) changed the decision-making process at the board level, showing that DB can vary according to the cognitive and dynamic capabilities of a company. Krishnamoorthi and Mathew (2018) concluded that changing a business involves human and technology assets combined.

The second search area represents a joining of clusters 1, 4 and 5 (Table 4). Studies in this area focus mainly on the role of organizational capabilities for innovation and knowledge processes (acquisition, exploitation, transfer) in the digital age. They highlighted the new digital technologies (social media, big data, IoT, cloud technology, digital platforms) encouraging companies to innovate in products, services and processes (Chi et al., 2018) by exchanging their knowledge with external agents. Consequently, this demanded organizational capabilities from companies to manage internal and external knowledge and thus respond to new market demands (Braojos et al., 2020). Table 6 shows how these capabilities are addressed in the articles as well as their respective effects.

Table 6
Capability definitions

Source: Elaborated by authors using VOSviewer

Pilav-Veli´ce Marjanovic (2016) found that companies that promote open innovation usually are more willing to process innovation, however, an intensive source of knowledge is not enough for innovation and, in addition, a high level of absorptive capability was necessary. Cui et al. (2017) aiming to understand the stages of the innovation process evidenced the influence of the absorptive capability enabled by IT affecting the performance of open innovation projects, which demonstrates the absorption of knowledge promoting open innovation.

On the other hand, Braojos et al. (2020) postulated that due to an opportunistic behavior, companies may be interested not only in the absorption of knowledge, but also in its disabsorption. Knowledge disabsorption refers to a

process of transferring superficial/secondary knowledge from inside to outside the company. To check the validity of this theory, Braojos et al. (2020) analyzed how a company's IT integration capability affects the business's KM capabilities to create value. The results showed the ability of IT integration allowing the company to absorb and unabsorb knowledge with other organizations, which, in turn, improved the company's performance. Hock-Doepgena et al. (2021) also addressed KM capabilities in their studies by analysing the conditional effects of knowledge management capabilities on business model innovation in the context of SMEs. The results showed that external KM capabilities allow SMEs to innovate their business model.

Castagna et al. (2020) found digital technologies enabling Italian SMEs to operate in creative industries in their customer knowledge management strategies. They noticed that these SMEs adopted and made intensive use of traditional technologies for customer KM, despite the more profitable use of digital technologies demonstrating how this scenario is still disturbing for companies.

Hensen and Dong (2019) postulated that the company's internal use of IT to collect and share scientific or market knowledge influences the benefits of innovation at the process level and the performance of innovation at the organizational level. They verified the use of IT to search for knowledge positively influencing internal innovation. Chi et al. (2018) when analyzing how digital technologies help companies to reach collaborative innovation resources, they evidenced the capability of collaborative innovation improving another organizational capability: the capability of digital collaboration, that is, the capability of coordinating information between distributors on the internet. Fisher et al. (2020) analyzed digital transformation in different environmental settings and then recommend that companies carefully analyze their environment and determine their business needs in order to effectively address the technological, organizational, and operational requirements of DT. The authors provided a list of basic requirements for companies, such as process-oriented knowledge management.

Another research group investigated social media and innovation. Braojos et al. (2018) analyzed how IT resources affect company performance and evidenced IT resources, social media and e-commerce influencing individually and positively on company performance through online customer engagement. However, this result is conditioned on social media and e-commerce capabilities. Zhang et al. (2020) analyzed how social media-based customer co-creation influences business value in new product development and found social media-based customer-company co-creation indirectly affecting the performance of companies through their knowledge and development of dynamic capabilities. Muninger et al., (2019) also examined the use of social media in innovation processes and highlighted the need for strategic and operational resources, as well as the involvement of people from various departments and levels of the organization in acquiring and disseminating social media knowledge.

From the analysis, the importance of tangible, intangible and human resources in the development of organizational capabilities in the DT process in companies was identified (Gupta & George; 2016; Shamim et al., 2018; David-West et al., 2018). Understanding which resources and how they can help companies in the social DT process seems relevant. Concerning the resources, knowledge stands out, recognized as one of the most important resources in the digital age. Knowledge-based theory establishes that the proper use of knowledge provides companies with a sustainable competitive advantage (Al-Dmour et al., 2020). However, according to the theory, there must be structured processes for the acquisition, exploitation and transfer of knowledge. Therefore, competitiveness occurs through the application of knowledge and not through the possession of knowledge itself. Furthermore, it is the efficient management of knowledge that results in innovations. Therefore, understanding the functions of knowledge (Boeker et al., 2019) can help companies implement more effective processes to deal with this new scenario. Institutional and economic issues as well as the company's life cycle are also highlighted (Fischer et al., 2020). These factors influence not only the resources available to companies, but also their funding strategies and growth objectives. Thus, a future research agenda in this area includes some research questions exemplified below:

Q1.1 How are knowledge functions (acquisition, exploitation and transfer) shaped by the advance of digital technologies? What roles should companies give back in this transformation contex?

Q1.2 How does the digital maturity of a company influence its knowledge processes? What are the implications on types of innovation?

Q1.3 How can cultural and economic differences and institutional pressures affect knowledge and its management in the context of DT?

Q1.4. To what extent does the quality of resources influence DT in companies?

Most of the analyzed studies showed that some organizational capability is needed to manage the changes caused by DT, and a large part of these capabilities are dynamic (i.e., capabilities that allow them gather new knowledge and apply it) (Hock-Doepgena et al., 2021), thus modifying the companies' resource base. These higher-order capabilities also reflect the complexity of the DT phenomenon and the use of innovative digital technologies by companies. As highlighted by Orlandi et al. (2020), digital technologies provided companies with a large amount of data, often disorganized and unstructured. For the authors, they must make sense of these data. In the same sense, Braojos et al. (2020) stated that when opening their innovation processes, companies must understand how to manage external knowledge, arising from collaboration, and incorporate it into the company. It is postulated that it is not enough for companies to develop operational capabilities, but dynamic capabilities are needed to reconfigure resources, especially knowledge. As highlighted by Winter (2003) and Teece (2014), dynamic capabilities are higher-order capabilities that lead companies to operate, extend, modify and create new capabilities. Therefore, to be successful with DT, companies should consider recommendations about their own resources beyond the capabilities needed to take advantage of DT opportunities and manage threats and changes in the environment (Tecee, 2014). Thus, a future agenda in this area includes some research questions, such as:

Q 2.1. How is knowledge quality affected by DT? What is its impact on company performance? What is the role of KM capabilities?

Q2.2 What is the role of knowledge absorption and disabsorption capabilities in the success of DT?

Q2.3 What is the role of collaboration (as an external source of knowledge) in building dynamic capabilities for the digital age?