Research Paper INNOVATION IN INFORMATION AND KNOWLEDGE MANAGEMENT

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Available online 9 October 2020 0148-2963/© 2020 Elsevier Inc. All rights reserved.

The role of digital innovation in knowledge management systems: A systematic literature review

Assunta Di Vaio a, *, Rosa Palladino a, Alberto Pezzi b, David E. Kalisz c

a Department of Law, University of Naples “Parthenope”, Via G. Parisi, no. 13, 80132 Naples, Italy b Department of Business Economics, Roma Tre University, Via Silvio D’Amico 77, 00145 Rome, Italy c Campus Cluster Paris Innovation, Paris School of Business, 59 rue Nationale, 75013 Paris, France

A R T I C L E I N F O

Keywords: Digital Transformation (DT) Knowledge Management (KM) Business Model (BM) Sustainable Performance (SP)

A B S T R A C T

This article investigates the literary corpus on digital innovation in knowledge management systems (KMS) to understand its role in business governance.

The study introduces a broad survey of the scientific literature on this topic to understand how digital innovation promotes new business models through the optimization of new knowledge.

We carried out a bibliometric analysis on a database, including 46 articles published in the last three decades (1990–2020). All the articles were written in English.

The results show that research published on the topic reveals interesting implications for business models and business performance. These findings especially highlight the links between innovation and sustainability, revealing that digital transformation tools contribute over the long-term to the value creation process. This research contributes to the existing literature analyzing the KMS topic by considering it from the digital inno- vation processes perspective, pointing out the need to implement new knowledge creation and to share measures which support global and inclusive growth.

1. Introduction

Innovation is a multidimensional concept, which involves organi- zational and procedural aspects of a company, aimed at improving performance in terms of production efficiency, and/or reducing pro- duction costs (Schumpeter, 2000). Openness to innovation measures a company’s propensity for to change, through an approach aimed at obtaining a competitive advantage derived from the exploitation of new ideas and new technologies (Harryson, 2008).

The adoption of technological solutions for the development of new processes and products, habits, and good practices increases the inno- vation capacity of companies, enabling them to meet the needs of a continually changing market (Gil-Gomez, Guerola-Navarro, Oltra- Badenes, & Lozano-Quilis, 2020). In fact, digital transformation (DT) facilitates the dissemination of information and good practices using Big Data (BD).

Using BD (Schwertner, 2017), encourages the acquisition and ex- change of knowledge between the company and the external environ- ment (Scuotto, Santoro, Bresciani, & Del Giudice, 2017). BD, understood as large data sets containing a heterogeneity of information (Rialti et al.,

2019a, 2019b), allows companies to collect, manage and preserve rich digital content for the long term (Candela et al., 2007). In addition, knowing the status of processes and resources through more modern and sophisticated analysis systems, and detecting the degree of in- terrelationships between the information contained in the database generates a competitive advantage for the company (Ferraris, Mazzo- leni, Devalle, & Couturier, 2019). Therefore, innovation is also config- ured as a governance issue, which influences the business model, pushing entrepreneurs to develop intervention strategies capable of satisfying the contingencies of an increasingly globalized and liberalized market (Ghezzi & Cavallo, 2020). In particular, companies have devel- oped specific awareness of the externalities related to the production and consumption processes. Therefore, they try to transform their management models to limit the negative impacts of their business ac- tivity, without reducing the profits (Kamble, Gunasekaran, & Gawankar, 2020).

In this context, space must be found for practices that improve the centrality of knowledge and knowledge management systems (KMS), favoring the creation of shared and integrated systems capable of improving business performance (Abubakar, Elrehail, Alatailat, & Elçi,

* Corresponding author. E-mail address: [email protected] (A. Di Vaio).

Contents lists available at ScienceDirect

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https://doi.org/10.1016/j.jbusres.2020.09.042 Received 4 May 2020; Received in revised form 18 September 2020; Accepted 19 September 2020

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2019; Del Giudice & Della Peruta, 2016; Santoro, Ferraris, & Bresciani, 2019).

The most advanced KMS are based on the integration of BD into corporate strategies, improving the quality of managers’ choices through the “predictive ability” of the analysis processes, based on the association of data (Intezari & Gressel, 2017). In this way, companies are able to direct their behavior towards innovative and sustainable busi- ness models (Intezari & Gressel, 2017; Olivo, Guzmán, Colomo-Palacios, & Stantchev, 2016; Soto-Acosta, Del Giudice, & Scuotto, 2018), increasing the degree of social responsibility and obtaining a reputa- tional advantage with the interested parties (Carayannis, Grigoroudis, Del Giudice, Della Peruta, & Sindakis, 2017; Nagy, Oláh, Erdei, Máté, & Popp, 2018; Raut et al., 2019).

Therefore, considering that knowledge is a critical resource for the company (Friedrich, Becker, Kramer, Wirth, & Schneider, 2020; Uden & He, 2017), it becomes interesting to understand how KMS, pushed by digital innovation, can accelerate the process of creating value in the long term, guiding the corporate strategy towards new, innovative business models.

Using a systematic review of these contributions in the literature, this study helps to identify new directions in the literature on KMS, identi- fying ideas for future research, through a rigorous and replicable process (Massaro, Dumay, & Guthrie, 2016). In more detail, through a biblio- metric analysis, this study aims to investigate how the dissemination of knowledge can influence the DT process (Thomas & Chopra, 2020), revealing that access to more information can influence investment planning and cost evaluation, with positive effects on returns (Gunjal, 2019).

Furthermore, it also aims to find out how the previous studies were developed from the KMS approach to strategic innovation and the implementation of new business models (Hock-Doepgen, Clauss, Kraus, & Cheng, 2020) revealing that KMS guiding role in implementation and corporate governance (Maroli, 2019; Pauleen & Wang, 2017). There- fore, it should be structured to include BD, in order to support more effective strategic decisions (Intezari & Gressel, 2017; Kitsios & Kamariotou, 2017; Olivo et al., 2016; Soto-Acosta et al., 2018).

Thus, our research questions are:

– (Q1) How have the digital transformation issues been analyzed by KMS scholars?

– (Q2) What main orientations do scholars adopt in this field, especially in the business governance framework?

Therefore, this article proposes a theoretical framework of knowl- edge management (KM), analyzing the outputs achieved by reviewing the 46 relevant articles identified. As mentioned above, the most striking challenge for academic scholars and strategists is to increase knowledge of, and links between, digital innovation and KM. Hence, analyzing the linkages and connections in those scientific fields could be an interesting contribution to management sciences. However, there are two signifi- cant theoretical problems in this regard:

1) The outcome of knowledge management does not necessarily take into account the impact of processes linked with digital innovation;

2) The above-mentioned orientations linked with governance frame- works seem to ignore the impact of DT on KM.

The remainder of this article is organized as follows. Section 2 in- troduces the theoretical background, while Section 3 describes the methodology using to develop the research. Section 4 provides the re- sults of the review, and Section 5 contains the discussion. Finally, Sec- tion 6 provides conclusions and reveals future implications.

2. Theoretical background to KMS in digital innovation

The availability of information and knowledge management directs

corporate innovation processes towards a more significant competitive advantage (Adams & Lamont, 2003; Cardinal, Allessandri, & Turner, 2001; Darroch & McNaughton, 2002; Dias & Bresciani, 2006; Mao, Liu, Zhang, & Deng, 2016; Pyka, 2002). In fact, keeping up with the rapid progress of innovation is becoming increasingly difficult for companies, which are forced to make use of a collaborative network (Najafi-Tavani, Najafi-Tavani, Naudé, Oghazi, & Zeynaloo, 2018) inside and outside the organization, which is useful for promoting the sharing of knowledge for innovation (Cavusgil, Calantone, & Zhao, 2003).

According to open innovation theory (Alexy, Bascavusoglu-Moreau, & Salter, 2016), a holistic cognitive approach should allow the company to exploit efficiently internal knowledge, and absorb external knowledge concerning the dynamic environment (Del Giudice & Maggioni, 2014; Ferraris, Santoro, & Dezi, 2017; Santoro, Vrontis, Thrassou, & Dezi, 2018). On the other hand, innovation has been defined as a tool that “recombines existing knowledge in new ways” (Du Plessis, 2007, p. 24), highlighting the limits and potential of the organization’s cognitive substrate to encourage development and sustainable innovations.

KMS allows the use of tangible resources to be maximized (Grant, 1996), because it is aimed at the acquisition and exploitation of data to increase performance and improve process management (Bresciani, 2010). Therefore, the construction of a robust cognitive architecture capable of guaranteeing the exploitation and conservation of informa- tion can support corporate innovation processes through intelligent in- frastructures and collaborative techniques based on interaction (Santoro et al., 2018). Hence, KMS influences the company’s performance as it leads to innovation, which consequently increases the competitive advantage (Martín-de Castro, López-Sáez, Delgado-Verde, Andreeva, & Kianto, 2011; Costa & Monteiro, 2016; Zack, McKeen, & Singh, 2009).

The stratification of the knowledge collected by the company (Lee, Choi, & Lee, 2020), favoring the exploitation of existing information as a driver for innovation, in order to combine it with new knowledge ac- quired through performance of this innovation (Ferraris et al., 2017). This highlights the role of KMS not only in terms of the efficiency of the processes of allocating internal and external knowledge to the organi- zation, but also in the exploitation of the innovative potential of the company at several levels (Shujahat et al., 2019). This affects the corporate business model, favoring dialogue between corporate actors and alignment of strategies and capabilities (including resources).

3. Methodology

This study was conducted using a qualitative methodology based on examination of the content of articles focused on KMS, DT, and the impact on transformation processes. Following the series of steps for an indexed search (Fink, 2010), we collected all the articles that make up our database by performing a content analysis to systematize the collected results in a replicable way (Krippendorff, 1980). Notably, we used the ISI Web of Science (WoS), which is a website that allows access to multiple databases, ensuring the availability of data from a wide range of scientific disciplines. Moreover, the database was enriched thanks to a manual collection process by Google Scholar (GS), so as not to neglect any vital contribution to our analysis (Massaro et al., 2016). To be more precise, other articles which contained citations consistent with the topic investigated were selected from journals placed high in the international rankings (Rashman, Withers, & Hartley, 2009). The journals that were selected because of marked interest shown in the topics related to KMS, innovation, DT, and business performance are the Journal of Knowledge Management, the Journal of Intellectual Capital, and Technological Forecasting & Social Change (Okoli & Schabram, 2010).

On this basis, this article developed in two phases. The first was aimed at identifying, extracting, and studying the individual articles consistent with the aims of the research, while the second developed the bibliometric study of these articles.

In the first phase, in order to ensure a robust methodology, it was necessary to proceed by stages: (1) extraction of the articles; (2)

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verification of congruence with the RQs; (3) manual integration of the articles of the collection, and (4) database processing the final findings. The overall approach for our data collection is highlighted in Fig. 1.

In the first phase of our research, we studied the scientific articles collected by WoS and GS to identify and systematize the main

orientations of scientific research. In order to collect all relevant publications developed on the topic

investigated, no time restrictions were imposed. Thus, we collected all the scientific articles on these topics from 1990 to 2019 (results of the default WoS settings: Table 1).

Fig. 1. Summarizes research.

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To identify the articles relevant to our research, we combined trun- cated words. Specifically, we used the following sets:

– Set 1: knowledge management AND innovation; – Set 2: knowledge management AND digital transformation OR Big

Data OR IoT; – Set 3: knowledge management AND process; – Set 4: knowledge management AND business model; – Set 5: knowledge management AND sustainable performance; – Set 6: knowledge management AND business performance.

The extraction process of articles was driven by the mix of three words allowing the relationships among the articles from several research clusters to be established and the most significant number of contributions on the topic under investigation to be identified.

Thanks to this phase, the search was extended to research on KM from the innovation perspective and DT, including BD and the Internet of Things (IoT). It also included the impact on business performance (BP) and sustainable performance (SP). In fact, KM is the substrate of our scientific research, from which the ramifications on the sphere of innovation and the effects on performance emerged.

As regards the second phase, in order to identify the most relevant articles, each article was studied by reading keywords and the abstract to establish whether it was in line with the aims of our research. All co- authors were actively involved in this phase. They worked systemati- cally and independently, analyzing each article and highlighting the key points of the research aims. Their conclusions were subsequently compared. Individual study of the documentation and comparison of the results is an essential step in this type of methodology, because it gua- rantees greater solidity to the results of the analysis. All keywords were verified to ensure that they were in line with the intentions of our investigation. Then, the abstract of each article was read in depth to ensure its relevance to the field of KM, innovation, or BD, highlighting its affinity with the issues examined in terms of processes and performance.

Regarding the third phase, considering the limited ability of WoS to identify all the scientific articles significant for our research, we carried

out a manual Google search. We used identical conditions. In the last stage, each co-author involved in this research acted individually and independently. Specifically, the authors painstakingly analyzed each article to highlight the crucial issues favorable to our investigation. Any articles not relevant to the research and any duplicates were removed from the database. Finally, the authors compared their results, devel- oping the sections of the literature review. The final list used for our analysis was composed of 46 contributions. Section 4.1. includes the bibliometric analysis of the selected articles.

4. Findings

Bibliometric boxes, concepts, and categorizations by topic, are the main dimensions of this qualitative analysis, discussed in the next section.

4.1. Bibliometric box

Contributions identified were analyzed on Bibliometrix, to process interactive and descriptive information to summarizing the investiga- tion, highlighting the dimension of the findings obtained in time and space. Bibliometric testing enables “transparent” as well as “reproduc- ible” reviews (Aria & Cuccurullo, 2017, p. 959), giving safer results in the collection of scientific documents and news, without the risk of ignoring the most relevant contributions, regardless of the date of publication.

Bibliometric analysis allows the reconstruction of the network of correlations between the documents, measuring the impact of each contribution within the research field examined, starting from the analysis of the keywords (Ellegaard & Wallin, 2015).

Initially, the articles were examined with the “abstract’s top 20 words”, as shown in Fig. 2. This allows the words which occur most often in the abstracts of the selected articles to be highlighted. It is interesting to observe that the word “innovation” has the highest occurrence index; it is repeated 50 times in the database. “Innovation” is followed by: “management”, “data”, “digital” and “business”, which have an equal number of appearances in the abstracts. This sequence of words is

Fig. 2. Top 20 abstract’s words.

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Fig. 3. Conceptual map and keyword clusters.

Fig. 4. Trend of scientific productions.

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particularly significant concerning the subject of our investigation, as it confirms the close relationship between the topics investigated, in particular the impact of innovation in knowledge management pro- cesses and the effects on business models (Gil-Gomez et al., 2020; Hock- Doepgen et al., 2020; Del Giudice, Garcia-Perez, Scuotto, & Orlando, 2019a; Del Giudice, Scuotto, Garcia-Perez, & Petruzzelli, 2019b; Gupta & Bose, 2019; Huesig & Endres, 2019; Kamble et al., 2020; Raut et al., 2019; Santoro et al., 2019; Lokshina & Lanting, 2019; Scuotto, Del Giudice, Tarba, Petruzzelli, & Chang, 2019a; Bogers, Chesbrough, & Moedas, 2018; Nielsen, 2018; Bresciani, Ferraris, & Del Giudice, 2018; Lin, Lin, & Lu, 2018; Pappas, Mikalef, Giannakos, Krogstie, & Lekakos, 2018; Carayannis et al., 2017; Seele, 2017; Xia, Yu, Gao, & Cheng, 2017; Del Giudice & Della Peruta, 2016; Parmentier & Mangematin, 2014). Subsequently, the words “knowledge”, “model”, “performance” and “transformation” occurred in most of the articles, as presented in Fig. 2 below.

According to Aria and Cuccurullo (2017), this analysis allows the creation of a graphical representation of the network of relationships between the concepts, starting from the keywords. Fig. 3 highlights two visual structures in which we can observe the concentration of concepts. More specifically, we distinguish two groups by using two different colors. A RED core symbolizes the framework of BD analytics challenges, and a BLUE core stands for dimensions of knowledge, branching out into aspects of management, organization, strategy and performance. Graphical representations are hierarchical structures that express in- terrelationships between concepts organically by facilitating significant comprehension of cognitive structures. The cognitive force of this con- ceptual mapping is useful for grasping the conceptual substrate of the topics and understanding how they are connected and related (Liu, 2004). Analyzing the conceptual plan, we observe that words linked to “knowledge”, “innovation”, “performance”, “strategy”, “big data”, “in- formation technology”, “value creation”, “environmental performance”, “organizational knowledge”, “efficiency”, “business” and “model” are concentrated primarily in the BLUE core. Otherwise, BD analytics issues, which are related to the following words: “integration”, “implementa- tion”, “supply chain management”, “challenges”, “framework”, “future” and “research agenda”, are included in the RED core.

Considering the time period of this study (1990–2020), we observe increasing scholarly interest in the themes since 2016, as illustrated in Fig. 4. The dynamic analysis of the most recurrent words in the set of

data indicates that performance studies of management and firms grew in parallel with knowledge and innovation, reaching a peak of interest between 2019 and 2020. This is very significant for our analysis, as it attests to the growing interrelation between the topics investigated, and confirms that performance management implies the integration of knowledge and innovation.

The greatest interest was generally registered by scholars from France and the USA (Fig. 5), followed by Austria, Denmark, Germany, Italy, Switzerland and the United Arab Emirates. Fig. 5 describes the intra-country (SCP – green) and inter-country (MCP – orange) collabo- ration indices. From this figure, it can be seen that in France, there is not only a higher production of research articles on our topic but also a greater willingness of French scholars to collaborate with other countries.

4.2. Content of the selected articles

Considering the different steps developed in this research on 46 ar- ticles, the bibliometric analysis highlighted the following findings.

Table 1 (see appendix) includes an exhaustive characterization of the database using the following categories:

i) year; ii) author;

iii) paper; iv) article type; v) subtopic;

vi) methodology.

Most studies suggest that the innovative footprint of business man- agement requires an attitude of openness on the part of companies, both towards the systems of transformation of products and services, and towards the mechanisms for implementing and sharing internal and external knowledge (Bagherzadeh, Markovic, Cheng, & Vanhaverbeke, 2019; Bogers et al., 2018; Parmentier & Mangematin, 2014).

Adapting to digital transformation processes also requires a “dy- namic ability” on the part of companies, to reinvent and reshape basic resources (Luppicini, 2020) in order to absorb technological manage- ment in the context of decision-making strategies aimed at obtaining competitive advantages, as in the case of ambidextrous organizations

Fig. 5. Collaboration index.

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(Ammirato, Sofo, Felicetti, & Raso, 2019; Bresciani et al., 2018; Kon- lechner, Müller, & Güttel, 2018; Scuotto, Arrigo, Candelo, & Nicotra, 2019b; Warner & Wäger, 2019). In practical terms this means that the exchange with the user communities facilitates the diffusion and the mutual exchange of knowledge by breaking traditional patterns and favoring the implementation of interactive digital platforms, without losing control of the processes and their returns (Gil-Gomez et al., 2020; Randhawa, Josserand, Schweitzer, & Logue, 2017).

Trantopoulos, von Krogh, Wallin, and Woerter (2017) observed the behavior of several Swiss manufacturing companies over a period of nine years, demonstrating that the performance of process innovation is positively influenced by the use of new information technologies (i.e. IoT), which encourage access to large databases that exploit vast amounts of information (Dai, Wang, Xu, Wan, & Imran, 2019), leading to significant improvements in profits. This suggests that companies should implement investment strategies aimed at implementing IoT to meet the new needs of the digitized market, promoting the exchange of information with the outside world in real-time (Bresciani et al., 2018; Kamble et al., 2020). This data is valuable if inserted into an intuitive BD analysis system, which allows it to be processed and to generate a competitive advantage (Carayannis et al., 2017; Nagy et al., 2018; Raut et al., 2019). This data derives from a mixture of sources. It therefore requires new and more modern methods of analysis through information technologies.

In addition, the use of BD allows the maintenance of open manage- ment of business processes which, through the involvement of stake- holders (Gupta & Bose, 2019), and also encourages the achievement of sustainability objectives by increasing corporate social responsibility (CSR) (Bogers et al., 2018; Huesig & Endres, 2019; Pappas et al., 2018; Raut et al., 2019; Seele, 2017; Xia et al., 2017). In choosing the tech- nological options to be adopted, companies can cross-evaluate the in- dicators and sustainable development features of each product, facilitating decisions to obtain a more sustainable performance (Xia et al., 2017). They can also use systems to measure the efficiency of the outputs generated by the use of sustainable resources, correcting any unwanted results, in order to align the management and control systems with smarter and more sustainable business models (Lin et al., 2018). This has a significant impact on performance (Huesig & Endres, 2019; Pappas et al., 2018; Raut et al., 2019), because IoT raises the levels of knowledge in a prognostic and holistic sense (Rodríguez-Rodríguez, Rodríguez, Elizondo-Moreno, Heras-González, & Gentili, 2020), allow- ing the company to evaluate all the economic, environmental, social, digital and innovative aspects of the business models that best meet the needs of the market (Brenner, 2018; Ghezzi & Cavallo, 2020). A crucial element is the “predictive” skills of the algorithms that regulate IoT systems, which carry out checks in terms of the sustainability of the choices, in order to prevent future complications and possible damage (Ammirato et al., 2019; Ferraris et al., 2019; Seele, 2017).

Furthermore, the use of BD can facilitate the distribution of new skills in the business context, combining economic profit and social well- being (Pappas et al., 2018; Savastano, Amendola, Bellini, & D’Ascenzo, 2019). El-Kassar and Singh (2019) also spoke of “green innovation” as a catalyst for beneficial practices, using all tangible and intangible re- sources for the firm and the external environment (Kamble et al., 2020; Rothberg & Erickson, 2017).

In this scenario, managers use BD analysis tools to support decision- making strategies (Rialti et al., 2019a, 2019b) that combine the spirit of innovation with the realization of long-term value (Singh & El-Kassar, 2019). Digital innovation means interaction between IoT, tools, and people, favoring the diffusion of information and the exchange of knowledge, assuming that knowledge is the first engine of profit, espe- cially in the era of digital innovation (Pauleen & Wang, 2017). Digital innovation is encouraged by the company’s commitment towards the use of technologies capable of improving the company’s knowledge levels and performance sustainability through adequate training courses for human workforce (Singh & El-Kassar, 2019). For this reason, it

becomes fundamental to develop better systems to protect the exchange and strategic sharing of information in order to reduce the risk of knowledge dispersion or abuse (Ilvonen, Thalmann, Manhart, & Sil- laber, 2018).

These strategies converge in a digital business model in which “the underlying business logic deliberately recognizes the characteristics of digitalization and takes advantage of it, both in interaction with cus- tomers and commercial partners, and in its internal functioning” (Bärenfänger & Otto, 2015, p. 18). Digital initiatives increase the degree of learning within the company, which improves its usefulness compared to other competitors (Gupta & Bose, 2019).

Digital innovation stimulates the processes of implementation and renewal of corporate knowledge (Arfi & Hikkerova, 2019), thanks to the push of internal social capital, understood as a network of relationships between internal units of the company and external social capital, this latter intended as a network of exchange between external units (Del Giudice, Maggioni, Jiménez-Jiménez, Martínez-Costa, & Sanz-Valle, 2014).

Therefore, a good business management system should institution- alize a continuous learning and sharing protocol (Carayannis et al., 2017; Del Giudice & Della Peruta, 2016), where digitalization, IoT and BD systems are the engines of a corporate strategy that is based on knowledge (Del Giudice et al., 2019a). Through an adequate “strategic learning” system, economic operators build a core of knowledge and skills in support of the goals planned in the strategic and operational sharing (Gupta & Bose, 2019; Huesig & Endres, 2019).

In this sense, innovation becomes a driver of corporate governance (Yin & Sheng, 2019), acting as a catalyst for the planning, management, and strategic command of corporate processes and investments, in the direction of new innovative business models (Gupta & Bose, 2019).

5. Discussion

Bibliometric analysis shows that digital innovation involves business processes from within, influencing the strategic design of companies that use new information technologies to guide their business model, especially in a sustainable sense (Bogers et al., 2018; Carayannis et al., 2017; Ghezzi & Cavallo, 2020; Gupta & Bose, 2019; Huesig & Endres, 2019; Lin et al., 2018; Nagy et al., 2018; Pappas et al., 2018; Raut et al., 2019; Seele, 2017; Xia et al., 2017).

Primarily, innovation allows the best use of the company’s knowl- edge: encouraging the implementation of KMS that guarantee access to more information (Gunjal, 2019); influencing investment planning; evaluating costs, and generating positive effects on returns (Bresciani et al., 2018; Del Giudice et al., 2014; Intezari & Gressel, 2017). KMS has a leading role in the implementation and governance of BD (Pauleen & Wang, 2017). Therefore, it should be structured to include BD, to facilitate corporate governance and support more effective strategic decisions (Intezari & Gressel, 2017; Olivo et al., 2016; Soto-Acosta et al., 2018). The degree of transfer, sharing, and exploitation of knowledge requires the cooperation of all company departments, through the implementation of collaborative and inter-organizational learning pro- cesses that exploit large flows of information. IoT tools, in particular, contain vast amounts of data and simplify the ways of identifying exploitable knowledge along the entire organizational chain (Bresciani et al., 2018; Del Giudice & Della Peruta, 2016; Ferraris et al., 2019; Tian, 2017). The “predictive ability” of BD analysis systems elevates the de- gree of interrelation between information, allowing the company to make conscious decisions achieving higher performance (Ferraris et al., 2019). Thus, innovation takes on a radical character because it affects business choices and has a spillover effect towards other related com- panies (Del Giudice et al., 2019a, 2019b; Scuotto et al., 2020).

This “domino effect” of knowledge transfer also overcomes the dif- ficulties related to the high costs of technological and digital updating, increasing the employees’ know-how of new technological skills that the company uses to be more competitive (Del Giudice et al., 2019b; Uden &

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Table 1 Data collection and classification.

Year References Journal Article Type

Subtopic Methodology

2000 Schumpeter, J. A. Entrepreneurship: The social science view

ARTICLE Innovation, Entrepreneurship, Business Model, Big Data

Qualitative Study

2008 Harryson, S. J. R&d Management ARTICLE Innovation, Management, Business Model, Performance

Qualitative Study: Case Study

2014 Del Giudice, M., Maggioni, V., Jiménez-Jiménez, D., Martínez-Costa, M., & Sanz-Valle, R.

Journal of Knowledge Management

ARTICLE KMS; Business Model, Innovation, Performance

Qualitative Study

Parmentier, G., & Mangematin, V. Technological Forecasting and Social Change

ARTICLE Innovation, Strategy, Performance Qualitative Study: Case Study

2015 Bärenfänger, R., & Otto, B. 2015 IEEE 17th Conference on Business Informatics

ARTICLE Innovation, Business Model Qualitative Study

2016 Del Giudice, M., & Della Peruta, M. R. Journal of Knowledge Management

ARTICLE Innovation, KMS, CSR, Performance Quantitative Study

Olivo, J. F. L., Guzmán, J. G., Colomo-Palacios, R., & Stantchev, V.

Journal of Knowledge Management

ARTICLE IT, Business Model, Big Data, Strategy

Quantitative Study

2017 Carayannis, E. G., Grigoroudis, E., Del Giudice, M., Della Peruta, M. R., & Sindakis, S.

Journal of Knowledge Management

ARTICLE Innovation, Strategy, Big Data, Performance

Qualitative Study

Intezari, A., & Gressel, S. Journal of Knowledge Management

ARTICLE KMS, Business Model, Big Data, Performance

Quantitative and Qualitative Study

Pauleen, D. J., & Wang, W. Y. Journal of Knowledge Management

ARTICLE KM, KMS, Innovation Qualitative Study

Randhawa, K., Josserand, E., Schweitzer, J., & Logue, D.

Journal of Knowledge Management

ARTICLE KMS, Business Model, Big Data Qualitative Study: Case Study

Rothberg, H. N., & Erickson, G. S. Journal of Knowledge Management

ARTICLE KMS, Business Model, Big Data, Innovation

Quantitative and Qualitative Study

Scuotto, V., Santoro, G., Bresciani, S., & Del Giudice, M.

Creativity and Innovation Management

ARTICLE innovation, ICT, Business Model, Performance

Quantitative Study

Seele, P. Journal of Cleaner Production ARTICLE Innovation, Quantitative Study Tian, X. Journal of Knowledge

Management ARTICLE Sustainability, Business Model, Big

Data, innovation Qualitative Study

Trantopoulos, K., von Krogh, G., Wallin, M. W., & Woerter, M.

MIS Quarterly ARTICLE Innovation, Business model Quantitative Study

Uden, L., & He, W. Journal of Knowledge Management

ARTICLE KMS, Business Model, IoT, Performance

Qualitative Study: Case Study

Xia, D., Yu, Q., Gao, Q., & Cheng, G. Journal of Cleaner Production ARTICLE Sustainability, Innovation, Business Model, Performance

Quantitative Study

2018 Bogers, M., Chesbrough, H., & Moedas, C. California Management Review ARTICLE Open Innovation, Business Model, Big Data

Qualitative Study

Brenner, B. Sustainability ARTICLE Sustainability, Innovation, Business Model, Performance

Qualitative Study

Bresciani, S., Ferraris, A., & Del Giudice, M. Technological Forecasting and Social Change

ARTICLE Ambidexterity, Business Model, IoT Quantitative Study

Ferraris, A., Mazzoleni, A., Devalle, A., & Couturier, J.

Management Decision ARTICLE KMS, Performance, Innovation Quantitative Study

Ilvonen, I., Thalmann, S., Manhart, M., & Sillaber, C. Knowledge Management Research & Practice

ARTICLE Innovation, KMS, Performance Qualitative Study

Konlechner, S., Müller, B., & Güttel, W. H. International Journal of Technology Management

ARTICLE Ambidexterity, Business Model, IoT Quantitative Study

Lin, F., Lin, S. W., & Lu, W. M. Sustainability ARTICLE Innovation, Sustainability, Business Model

Quantitative Study

Nagy, J., Oláh, J., Erdei, E., Máté, D., & Popp, J. Sustainability ARTICLE Digitalization, Business Model, Big Data

Qualitative Study

Pappas, I. O., Mikalef, P., Giannakos, M. N., Krogstie, J., & Lekakos, G.

Information Systems and Business Management

ARTICLE Big Data, Innovation, Performance Qualitative Study

Soto-Acosta, P., Del Giudice, M., & Scuotto, V. Baltic Journal of Management ARTICLE KMS, Innovation, Big Data Qualitative Study Usai, A., Scuotto, V., Murray, A., Fiano, F., & Dezi, L. Journal of Knowledge

Management ARTICLE Innovation, Entrepreneurial, KMS Quantitative Study

2019 Ammirato, S., Sofo, F., Felicetti, A. M., & Raso, C. European Journal of Innovation Management

ARTICLE IoT, Business Model, Big Data Quantitative and Qualitative Study

Del Giudice, M., Garcia-Perez, A., Scuotto, V., & Orlando, B.

Technological Forecasting and Social Change

ARTICLE Innovation, Technological, Entrepreneurial, KMS

Quantitative Study

Del Giudice, M., Scuotto, V., Garcia-Perez, A., & Petruzzelli, A. M.

Technological Forecasting and Social Change

ARTICLE Spillover, Innovation, Knowledge Qualitative Study

El-Kassar, A. N., & Singh, S. K. Technological Forecasting and Social Change

ARTICLE Innovation, Stakeholder, Sustainability, Performance

Qualitative Study

Gupta, G., & Bose, I. Technological Forecasting and Social Change

ARTICLE Digital, Business Model, Innovation Quantitative Study

Huesig, S., & Endres, H. European Journal of Innovation Management

ARTICLE Digital, Business Model, Innovation Quantitative Study

Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. International Journal of Production Economics

ARTICLE Innovation, Sustainability, Business Model, Big Data

Qualitative Study

Raut, R. D., Mangla, S. K., Narwane, V. S., Gardas, B. B., Priyadarshinee, P., & Narkhede, B. E.

Journal of Cleaner Production ARTICLE Innovation, Sustainability, Business Model, Big Data

Qualitative Study

Santoro, G., Ferraris, A., & Bresciani, S. ARTICLE Qualitative Study

(continued on next page)

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He, 2017). Numerous studies confirm the positive effect of the employment of IT

or BD on performance, demonstrating that the use of open, innovative systems develops an integrated strategic capability in business sectors, based on sharing and exchanging multidisciplinary knowledge (Huesig & Endres, 2019; Scuotto et al., 2017; Singh & El-Kassar, 2019; Xia et al., 2017).

In this way, our study reveals that the literature on KMS recognizes the impact of digital innovation on business performance, it improves efficiency and the quality of knowledge in organizational and strategic processes, confirming that the combined use of human and technolog- ical resources generates a competitive advantage (Ferraris et al., 2017; Lee et al., 2020; Shujahat et al., 2019). Above all, this systematic liter- ature review demonstrates that, in the current globalized market, IoT strategies, combined with KMS, constitute an engine for the develop- ment of new BMs (Kiel, Arnold, & Voigt, 2017) driven by innovative practices, towards sustainable economic development, which increases the degree of social responsibility and enhances the company’s reputa- tion (Carayannis et al., 2017; Nagy et al., 2018; Raut et al., 2019). The open innovation paradigm suggests that a holistic, cognitive approach to corporate governance, based on a regime of cooperation between in- ternal and external resources for the creation of value, opens the pos- sibility of redefining business models in which knowledge develops horizontally (Furukawa, 2015). This is achieved through the involve- ment of all the actors involved in the corporate ecosystem to achieve a long-term, sustainable competitive advantage.

6. Conclusion, limitations and future perspective of the research

This study analyzed the existing literature on KMS, with the aim of investigating the role of KMS in the era of digital transformation, especially in terms of corporate governance. The results revealed that tools such as IoT and BD enables the current world economy signifi- cantly by increasing the competitiveness of companies, guaranteeing access to large flows of data and information, processed through powerful software, capable of highlighting the degree of correlation between useful knowledge in different company departments (Ghezzi & Cavallo, 2020; Gupta & Bose, 2019; Huesig & Endres, 2019; Nagy et al., 2018; Pappas et al., 2018; Raut et al., 2019; Tian, 2017). Furthermore, knowledge expresses its maximum potential when it is adequately exploited by the company (Usai, Scuotto, Murray, Fiano, & Dezi, 2018), through internal and external sharing processes, which enrich the company’s know-how (Bogers et al., 2018; Huesig & Endres, 2019; Pappas et al., 2018; Raut et al., 2019; Seele, 2017; Xia et al., 2017).

However, there is still plenty of room for debate on the role of KMS in the framework of corporate governance and business models towards digital innovation, which remains limited. Our findings also highlight that BD has become a “need for management” because it allows the

analysis of user preferences and cost trends, as well as forecasting the behavior of markets (Franklin, Serra.Diaz, Syphard, & Regan, 2017). More specifically, digital transformation and its tools provide an inte- grated strategic solution that operationally guides business governance.

In this scenario, KMS has a crucial role in ensuring the optimization of technologies and resources, developing knowledge-sharing strategies available to all company operators, and supporting managers in their making-decision processes. At the same time, the innovation tools adopted in KMS allow the processes to be optimized, directing the company towards innovative and sustainable business models to achieve improved performance. These business models are characterized by “open” platforms, oriented towards the free exchange of news and allocation of knowledge, through the exploitation of company potential. By adopting innovative strategies, companies can also support more sustainable behaviors, which increase CSR and improve the company’s image with stakeholders. Stakeholders are increasingly sensitive to the need to reconcile economic profit and social well-being, using innova- tive tools capable of measuring the environmental impact of company activities, promoting the creation of long-term value. Therefore, com- panies’ development of an open culture of innovation could enhance the use of KMS to support governance strategies oriented towards new forms of sustainable business over time. If innovation does not lead to the construction of lasting business models, capable of adapting to the changing conditions of the market and the needs of the stakeholders, it becomes an end in itself. Indeed, this access to advanced digital inno- vation systems requires significant investment by companies, which expose themselves to high costs and enormous risks associated with the non-recovery of the capital used. Therefore, it would be desirable to implement incentives and support measures, aimed at companies and the world of production, to support the development and sharing of new knowledge initiatives for new services or for perfecting existing ones, with the goal of an inclusive and sustainable economy.

This study presents the limitations of a theoretical analysis: the analysis should also be extended to empirical tests on corporate behavior, to understand the potential impact of KMS through digital innovation, to achieve a sustainability-oriented business model and sustainable competitive advantage.

Acknowledgements

The authors would like to thank the editors and anonymous referees for providing helpful comments and suggestions which led to an improvement of the article.

Funding

This research has been funded by University of Naples “Parthenope” - number 002158 - (Italy).

Table 1 (continued )

Year References Journal Article Type

Subtopic Methodology

Sinergie Italian Journal of Management

Open Innovation, Business Model, KM

Savastano, M., Amendola, C., Bellini, F., & D’Ascenzo, F.

Sustainability ARTICLE Innovation, Digital Transformation, Business Model

Qualitative Study

Scuotto, V., Arrigo, E., Candelo, E., & Nicotra, M. Business Process Management Journal

ARTICLE Ambidexterity, Digital Transformation, Business Model

Quantitative Study

Scuotto, V., Del Giudice, M., Tarba, S., Petruzzelli, A., & Chang, V.

Journal of World Business ARTICLE Innovation, Business model, Develop

Quantitative Study

Singh, S. K., & El-Kassar, A. N. Journal of Cleaner Production ARTICLE Sustainability, Big Data Qualitative Study Warner, K. S., & Wäger, M. Long Range Planning ARTICLE Innovation, Business Mode,

Performance Qualitative Study

2020 Gil-Gomez, H., Guerola-Navarro, V., Oltra-Badenes, R., & Lozano-Quilis, J. A.

Economic Research ARTICLE Innovation, Business Model, Digital transformation

Qualitative Study

Ghezzi, A., & Cavallo, A. Journal of Business Research ARTICLE Entrepreneurship, Business Model, Big Data

Qualitative Study

A. Di Vaio et al.

Journal of Business Research 123 (2021) 220–231

229

Appendix A. Additional data

Table 1 below gives the additional data related to this article.

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Assunta Di Vaio*, is an Associate Professor of Business Administration at University of Naples Parthenope, Italy. She is qualified as Full Professor in the same scientific field. She teaches Business Administration; Sustainable Disclosure and Reporting; Corporate Governance of Maritime companies; Governance of Port Systems. Her research fields include managerial accounting and management information for the decision-making processes in the public and private sector; performance measurement; sustainable ac- counting; non-financial disclosure; human resources disclosure; intellectual capital and sustainable business models; sustainable development and UN 2030 Agenda; digital

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transformation, Artificial Intelligence and Blockchain technology. Her research has been published in various prestigious journals (e.g., Journal of Cleaner Production, Journal of Intellectual Capital, Journal of Business Research, International Journal of Information Management, Energy Policy, Utility Policy, Maritime Policy & Management, and so forth). She is editorial board member of international Journals. She is a peer reviewer for inter- national Journals edited by Elsevier, Emerald, Taylor & Francis, MDPI, Springer. She regularly participates as speaker at many International Conferences on port and maritime issues. She is member of the International Steering Committee of these Conferences. She has been an associate member of UCL Quantitative and Applied Spatial Economic Research Laboratory (QASER) at University College London (UK). Currently, she is Deputy-Director of the Department of Law at University of Naples “Parthenope”.

Rosa Palladino, is PhD Student in Law and economic-social institutions: regulatory, organizational and historical-evolutionary profiles at the University of Naples “Parthe- nope” (Italy). Her research fields include non-financial disclosure; human resources disclosure; intellectual capital and sustainable business models; sustainable development and UN 2030 Agenda; digital transformation, Artificial Intelligence and Blockchain technology. She is has high knowledge about SLR. She is a reviewer for international Journals edited by Emerald and Springer. She regularly attends seminars and conferences on these issues. Her research has been published in international books and journals (e.g.,

Journal of Intellectual Capital, Journal of Cleaner Production, Meditari Accountancy Research, Journal of Business Research, Sustainability).

Alberto Pezzi, Ph.D., is an Associate Professor of Management at University of Roma TRE, Italy. He teaches strategy; business planning; management. He was a visiting professor at important Universities. His research fields include Cross-border Mergers and Acquisitions, digital convergence, digitalisation performance, e-business, strategies and governance models, performance and internationalization strategies, technology and knowledge management. He is editorial board member of international Journals. His research has been published in various significant outlets.

David E. Kalisz, Ph.D., is an Associate Professor at Paris School of Business (Paris, France), Head of Management & Strategy Department. Author of numerous publications in the field of new media, digital, competition strategy and value innovation and author of a book “Competitive Strategies”. Scientifically he is inspired by the influence of the latest tech- nologies on the functioning of enterprises. He is the creator of the program of the Blue Ocean Strategy and the Blue Ocean Shift. He gives lectures in English, French and Polish. He is also interested in exploring future trends and concepts linked with Strategic Fore- sight, member of the Center for Futures Studies.

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