research topics in digital transformation

The Value of Digital Transformation

by Eric Lamarre , Shital Chheda , Marti Riba , Vincent Genest and Ahmed Nizam

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Research trends in digital transformation in the service sector: a review based on network text analysis

• Empirical article
• Published: 07 February 2022
• Volume 16 , pages 77–98, ( 2022 )

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• Jin Sung Rha 1 &
• Hong-Hee Lee   ORCID: orcid.org/0000-0001-8144-2588 2  

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Digital transformation has influenced value chain operations of both manufacturing and service firms. This study examined digital transformation in the service sector through network text analysis of 330 related articles published during the past 10 years. The selected papers’ keyword link relations were analyzed to create network maps of research topics, ranging from traditional to emerging ideas of researchers. Dominant research topics and their clusters were identified using centrality and community analyses, and research trends were identified. The results of this study will help researchers and practitioners in the relevant fields capture the overall picture of the field.

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1 Introduction

Digitalization has accelerated innovation to a point never imagined before (Lee and Lim 2018 ). Businesses have been implementing digitalization to support flexible changes in operational processes, information systems, and society at large (Parviainen et al. 2017 ). Digitalization enables service innovations (Tronvoll et al. 2020 ) and has caused recent changes in the business environment of various industries (Kapadia and Madhav 2020 ). Therefore, digitalization, if well utilized, can help the enterprise develop dynamic capabilities for agility, flexibility, and resilience in delivering the products and services that customers want (Teece 2014 ; Lee and Trimi 2021 ). Thus, firms can utilize digital technologies to continuously improve their value proposition (Coreynen et al. 2020 ). Digitalization is also seen as a source of organizational sustainability, allowing firms to continuously pursue internal efficiency and external opportunity to create value and increase the market share (Parviainen et al. 2017 ; Kamalaldin et al. 2020 ).

Unlike digitization, which means utilizing digital technologies, digitalization includes value creation for the customers after utilizing the technologies (Seyedghorban et al. 2020 ). Digitalization is defined as “the use of digital technologies and digitized data to impact how work gets done, transform how customers and companies engage and interact, and create new digital revenue streams” (Strønen 2020 ). Recently, various digital technologies have triggered service business growth through digitalization or digital transformation (Gebauer et al. 2021 ). Major technologies for digitalization are AI, Internet of Things (IoT), cloud computing, and big data (Kretschmer and Khashabi 2020 ). IoT, cloud computing, and big data analytics, often considered as base technologies for digitalization, have enabled firms to explore new opportunities to execute customer-oriented business models (Lee and Lim 2018 ; Frank et al. 2019 ; Paiola and Gebauer 2020 ). IoT has made various entities, from physical devices to software, connected with each other through networks. The data exchange in real-time between the entities provides deep insights into material and information flows in the supply chain. IoT, also known as Internet of Everything (IoE), helps construct sophisticated knowledge networks for value creation through real-time communication (Lee and Lim 2018 ). Cloud services are the backbone of digital transformation because they help store and analyze large sets of data at reasonable cost (Abolhassan 2016 ). According to Statista.com ( 2021 ), the number of IoT-connected devices is expected to reach more than 30 billion by 2025, from 11.7 billion in 2020. Big data analytics deals with “the use of advanced analytic techniques against very large, diverse big data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes” (IBM.com 2021 ). Since traditional database management systems are not powerful enough to handle big data, advanced technologies such as AI and IoT are deployed for the analysis. Many organizations rely on big data analytics to extract valuable information and knowledge from the data collected through various channels. Important topics on big data analytics such as storage capacity, visualization, and wireless sensor networks have attracted the attention of big data researchers (Choi et al. 2017 ). Using the document co-citation analysis, Ardito et al. ( 2019 ) found four clusters representing big data analytics and management in the literature: (1) conceptual evolution of big data analytics, (2) management transformation by big data analytics, (3) effects of big data analytics on resource management and performance, and (4) transformation of supply chain management by big data analytics (Ardito et al. 2019 ). Big data analytics facilitates the internal process, helps detect errors, and enhances customer engagement (Kretschmer and Khashabi 2020 ).

Digital transformation in the service sector has recently emerged as an important research topic, and an increasing number of studies have been published in conference proceedings and journals. The present study analyzes the trends of the research on digital transformation by conducting network text analysis on the papers that have recently been presented in conferences and published in academic journals to shed light on future directions of digital transformation in the service sector. Network text analysis examines semantic relations among keyword nodes by constructing matrices of co-occurrences and presenting a visual form of a network (Lee and Su 2010 ). Unlike conventional studies that investigated research trends based on simple frequency analysis of research topics and methodologies used, this study analyzes the relationships among central research topics and keyword clusters of recently published papers on digital transformation in the service sector through network text analysis using NetMiner4.0. This study is organized as follows. Section  2 reviews previous studies on digital transformation. Section  3 provides an overview of network text analysis and examines the published papers digital transformation in services that have been frequently cited. Section  4 summarizes the analysis results and proposes future research directions. Finally, Sect.  5 describes the implications of the results, limitations of the study and future research needs.

2 Literature review

2.1 digital transformation strategy.

Digital transformation should be an important part of corporate strategy since its effective implementation would have such significant impacts on organizational agility, flexibility, and resilience which in turn result in positive performance outcomes (Kretschmer and Khashabi 2020 ). For example, Amazon has used very effective and sophisticated big data analytics, which has been a major enabler of the firm’s success in sales performance (Jannapureddy et al. 2019 ).

Digitalization helps establish high-level collaboration with customers, which can generate new revenue sources (Scherer et al. 2017 ). The most significant benefit of E-commerce is learning about customers over time, without needing to deploy additional channels to push products or services to them (Strønen 2020 ). Competitive E-commerce service providers, such as Amazon.com and Alibaba.com, gather a vast amount of customer information about the relationship between age and access times or customer characteristics of a specific product. Kretschmer and Khashabi ( 2020 ) found digitalization helps enhance interaction with consumers. Companies need to unlearn inflexible product-oriented strategies and balance product and service assets to assure the transformation journey is smooth toward a comprehensive service mindset (Tronvoll et al. 2020 ).

Digitalization has helped companies develop and implement innovative business models (Hokkanen et al. 2021 ). Digitalization has attracted many knowledge workers to handle cognitive tasks, for both business and society (Loebbecke and Picot 2015 ). According to Kretschmer and Khashabi ( 2020 ), digital transformation has guided changes in organizational structures through the evolution of internal processes, which they labeled a strategic renewal. The digital transformation process in the service-dominant age should be supported by the strategic shifts of organizations. Tronvoll et al. ( 2020 ) suggested three facilitators of the shift: identification, dematerialization, and collaboration. By identifying real-time information, a company can make effective move from planning to making both short-term and long-term decisions that are most appropriate in the environment. The firm can transition from physical insufficiency to abundance and doing more with less, by dematerialization enabled by digital servitization. Collaboration among partners in a value chain allows the participants to develop horizontal relationships rather than constrained by vertical ones in the organization.

The digital transformation process presents many potential challenges as well as opportunities. However, many organizations encounter difficulties in changing their business models tailored to the transformation environment (Loebbecke and Picot 2015 ; Kretschmer and Khashabi 2020 ). Digitalization allows advanced data analytics, connectivity among products and services, and blurred boundaries among suppliers, customers, competitors, and even markets (Porter et al. 2014 ). Many large organizations are tradition bound and afraid to abandon their existing arrangements, the transformation process becomes disjointed, thus making it difficult to develop desired business models. For this reason, flexible and innovative startups are generally more effective than their large counterparts in radically changing their business models even under budget constraints (Loebbecke and Picot 2015 ). In sum, digital transformation is rapidly becoming an imperative driver of competitive advantage in the rapidly changing market environment (Kretschmer and Khashabi 2020 ).

2.2 Digital servitization

Fierce competition and technological advances drive firms toward implementing a digital servitization strategy (Coreynen et al. 2020 ). Like digitalization, digital servitization requires organizations to make drastic changes in their business models and operations (Tronvoll et al. 2020 ). Many companies are increasing their service offerings through digital transformation. Mobidoo, a Korean venture firm, offers a secure and easy-to-use mobile payment service to credit card users, using encrypted inaudible sound waves. As the world adopts digital payment systems, the users are increasingly concerned about security issues and compatibility with international standards (Mridha et al. 2017 ). For another example, wearable devices in healthcare such as smart health trackers and blood pressure monitors have helped deliver various healthcare services through smartphone apps (Lee and Lee 2020a ). Firms are moving from the primary focus on goods they offer to integrated ecosystems with services, and this trend appears prevalent in both manufacturing and service industries (Coreynen et al. 2020 ).

The term “servitization” was coined by Vandermerwe and Rada ( 1988 ) and digital servitization is defined as “the transition toward smart product-service-software systems that enable value creation and capture through monitoring, control, optimization, and autonomous function” (Coreynen et al. 2020 ). The servitization trend has been embraced by various disciplines with different labels such as the product-service system, transition from product to solutions, hybrid offerings, and others (Paiola and Gebauer 2020 ). Digitalization is the main driver of servitization. Martín-Peña et al. ( 2018 ) argued that any firm interested in adopting servitization must first consider implementing digital transformation.

Digital servitization is defined as “the transformation in processes, capabilities, and offerings within industrial firms and their associated ecosystems to progressively create, deliver, and capture increased service value arising from a broad range of enabling digital technologies” (Sjödin et al. 2020 ). Digital services activate relational interactions with customers to enhance service quality, brand loyalty, and customer satisfaction (Kamalaldin et al. 2020 ). As digital technology adoption has enabled product-oriented companies to develop service-oriented business models through digital servitization. To benefit from digitalization, value chain stakeholders are shifting their attention from the product-oriented transactional model to the service-oriented relational arrangement (Kamalaldin et al. 2020 ).

Digital servitization has two organizational perspectives, a front-end perspective and a back-end one (Kryvinska et al. 2020 ). The front-end servitization supports deeper interactions with customers while back-end servitization helps the organization achieve operational efficiency and improved resource allocation (Coreynen et al. 2017 ). To be competitive in the dynamic marketplace, a critical resource is firm’s capacity to collect and analyze data that are essential for developing competitive advantage (Paiola and Gebauer 2020 ). Coreynen et al. ( 2020 ) also suggested separating organizational efforts that are directed toward exploitation allied with digital servitization and those focused on exploration. According to that study, exploration seems more effective when the two efforts are made together even though both exploitation and exploration support digital servitization.

3 Methodology

3.1 network text analysis.

This study performed a network text analysis to identify the research trends regarding digital transformation in the service industry. Network text analysis encompasses text mining and network analysis. Text mining is used to analyze and obtain meaningful information from unstructured textual data. Network analysis consists of a network of nodes and links based on data matrices and is used to determine the network structure and effect of each node. By combining these analytical techniques, network text analysis helps obtain key information from texts and build various networks based on co-occurrence matrices (Ferstl et al. 2008 ).

Previous research trend analyses classified the articles based on the researchers’ subjectivity or sorted them manually based on the applied methodologies and theories (Rha 2020 ). However, network text analysis can help analyze the given texts exploratively. Therefore, this method is advantageous in that it can quantify and explain the research trends objectively, through using the text data of research articles.

Recently, many studies in various business fields explored research trends using network analysis. Fahimnia et al. ( 2015a , b ) searched published articles on supply chain management on Scopus and built document, citation, and co-citation networks. They also used cluster and Page Rank analysis to identify key researchers in subfields over time and determined influential articles in the network using the citation index. Feng et al. ( 2017 ) analyzed the clusters of key research articles by building a co-citation network of articles on corporate social responsibility, extracted the main keywords by building a co-word network, and built the clusters of subtopics. Lee and Rha ( 2018 ) developed a network using keywords as nodes extracted from articles published in Service Business: An International Journal for 10 years and analyzed the trends in subtopics in service business areas by calculating the degree of centrality and betweenness centrality.

3.2 Procedures of network text analysis

Network text analysis is generally conducted based on the following procedures (Pyun and Rha 2021 ). First, a database is selected to search the research articles for analysis. This study used Scopus, a widely known academic database, to search the articles. Scopus encompasses most major articles in business and social science fields. Thus, it is suitable for capturing the research trends in specific subfields (Rha 2020 ; Yu and Rha 2021 ). Second, keywords were extracted from the articles searched on the database, after which they were cleansed. On Scopus, indexed keywords and those presented by researchers can be extracted from the selected articles. This study mostly used the keywords presented by authors. After keyword extraction, we performed the following additional steps: supplementing keyword omissions, unifying the terms, and deleting useless words. The whole process is explained comprehensively in the Results section. Third, using the extracted keywords, their frequency in the articles was transformed into a matrix to construct a two-mode network comprised articles and keyword nodes. A two-mode network refers to a network with two dimensions of nodes. In a two-mode network, articles and keywords are connected with links, rather than with another article or another keyword. Therefore, relations among keywords cannot be analyzed directly with just two-mode networks. Fourth, the two-mode network comprised articles and keywords was transformed into a one-mode network comprised only keyword nodes. Network transformation is based on the co-occurrence of keywords. Suppose article A presents keywords a and b; article B presents keywords a, b, and c; and article C presents keywords a, b, and d. Then, keywords a and b are likely to be in co-occurrence, hence highly correlated. In this case, keyword nodes a and b are connected with a link when building a keyword network. This study used the cosine similarity algorithm to transform a two-mode network of articles and keywords into a one-mode network with just keywords. This study conducted centrality and cluster analyses to identify the characteristics of keyword nodes. Degree centrality and betweenness centrality were also analyzed. Keywords with a high degree of centrality indicate that the relevant field is the most actively researched, while those with a high betweenness centrality indicate that the keywords are highly expandable. Using cluster analysis, research topics can be grouped based on the network structure. The procedures of network text analysis performed in this study is shown in Table 1 .

4.1 Articles selected for analysis

This study collected and analyzed published articles on digitalization in the service industry. We used Scopus to search for the relevant articles. As shown in Table 2 , various search words were entered to analyze the related articles. Articles were selected using the search words that are related to the keywords presented by the authors or indexed by Scopus. The research areas were limited to “Business, Management, and Accounting” and “Decision Science”. Further, only peer reviewed articles published in English, including those in journals or presented in conferences or symposiums, were selected for analysis. Three hundred and thirty articles were selected as the study sample, excluding those searched redundantly using various search words.

Figure  1 summarizes 330 articles by year of publication. Since 2016, the number of studies on digital transformation began to accelerate. This study searched only articles published up to June 2021. Table 3 shows major journals that published the articles in our sample.

The number of articles by year

4.2 Preprocessing to build a keyword network

A total of 1126 keywords were collected from 330 articles in our research sample. Keywords presented in the articles were used preferentially. Further, in case the keywords were omitted, some keywords were extracted from the article titles as done by previous studies (Rha 2020 ). Cleansing was further conducted. Of the 1126 keywords, some were similar in meaning but expressed in different ways. For example, “Internet of Things” and “IoT” carry the same meaning and were therefore unified into “IoT”, and “Artificial Intelligence”, and “AI” as “AI”. Similarly, “Healthcare Industry”, “Health Services”, “E-Healthcare”, and “M-Healthcare” were all dealt with “Healthcare” and were therefore unified as “Healthcare”. When keyword cleansing was necessary according to the content of the article, two researchers in the service field were consulted.

This study used “Service”, “Digitalization”, and “Digital Transformation” as search keywords. As these keywords were presented in most articles, they could not be considered as influential keywords despite their high frequency and centrality in network analysis. Further, it is more important to analyze the keywords that are linked to digital transformation than digital transformation itself as this study analyzed articles on digital transformation in the service industry. Therefore, this study eliminated “Service”, “Digitalization”, and “Digital Transformation” from the gathered keywords. Ultimately 1049 keywords were used in network analysis through keyword cleansing. Of the 1049 keywords, those with a high frequency were “Innovation”, “Industry 4.0”, “Servitization”, “Business Model”, “IoT”, “E-government”, “Financial Services”, “Healthcare”, and “Big Data”. Fig.  2 shows a word cloud created based on keyword frequency, with higher frequency shown in bigger texts.

4.3 Building two-mode and one-mode networks

By collecting keywords, a matrix is naturally formed with two dimensions: articles and keywords. The keywords presented in each article were used. The frequency of each keyword of an article could be either “1”, or “0” if there was no keyword. If a certain keyword occurred in a certain article, the value between the two would be “1”. Thus, these two are connected with a link in the network. This is how a two-mode network is comprised article nodes and keyword nodes. In this case, article nodes are not linked with each other, neither are keyword nodes. Figure  3 shows a two-mode network comprised articles and keywords, with most articles and keywords forming a huge network. This indicates that most articles researched related topics. A small number of other articles had keywords that were not presented by other articles. These articles, therefore, were separated from the two-mode network.

Two-mode network between keywords and research articles

To transform the two-mode network comprised articles and keywords into a one-mode network comprised keyword nodes only, this study calculated cosine similarity as shown in Eq.  1 . This study included only the keywords that had the occurrence frequency of 2 or above when developing a one-mode network. The cut off value of cosine similarity was 0.2.

where C ik  = number of occurrences of keyword i , C jk  = number of occurrences of keyword j .

The keyword network is shown in Fig.  4 . Keyword nodes in the one-mode keyword network are connected with links when they occur in multiple articles, as the higher the co-occurrence the greater the cosine similarity. The locations of keyword nodes in the network are insignificant; they are located close when they are relevant. Moreover, the links have no weights or directions.

One-mode network among keywords

4.4 Centrality analysis

Centrality analysis was conducted using the keyword network. This study analyzed degree centrality and betweenness centrality. Degree centrality shows how many links a particular node has with other nodes in the network. Nodes with high degree centrality are actively linked to many other nodes in the network, indicating their keywords are closely related to subtopics that are most actively examined. Betweenness centrality increases when a particular node is frequently located on the paths between different nodes. Nodes with high betweenness centrality mediate other nodes and are therefore analyzed as keywords connecting subtopics or expanding concepts in the keyword network. Degree centrality and betweenness centrality can be calculated as shown in Eqs.  2 and 3 .

where g jk  = the number of shortest paths that connect nodes j and k , g jk ( n i ) = the number of paths that passthrough node i among the shortest paths that connect nodes j and k , [( g  − 1)( g  − 2)/2] = the number of all node pairs not including n i ).

Table 4 shows the results of the degree centrality analysis. The keywords with a high degree centrality were “Business Model”, “Servitization”, “Innovation”, “Customer Experience”, “Industry 4.0”, “Big Data”, and “Business Ecosystem”. Many studies on digital transformation in the service industry explored the development and application of new business models in line with digital transformation. Moreover, many studies were on servitization based on sensing, data storage and analysis technology as well as pursuing innovation through digitalization. They examined business ecosystems for developing new business models and innovation.

Table 5 shows the results of the betweenness centrality analysis. They are generally similar to the results of the degree centrality analysis. However, keywords such as “Banking”, “Financial Services”, and “Healthcare” showed a relatively high betweenness centrality. This implies that many studies on digital transformation in the service industry were focused on topics related to finance and healthcare.

A cluster analysis was conducted using the structural characteristics of the keyword network. The process was based on the algorithm of Blondel et al. ( 2008 ) and provided by NetMiner. Keywords with high cohesion can be placed in one group through cluster analysis. Therefore, clusters comprised keyword nodes can be analyzed as subtopics on digital transformation in the service industry. Consequently, 6 clusters were formed as shown in Fig.  5 . Table 6 shows the main keywords of each cluster.

Keyword network clustering analysis

The first cluster is about analyzing new ecosystems by business firms regarding the digitalization of services and new collaborative mechanisms. Further, the main keywords were “Ecosystem”, “Platforms”, “Value Creation”, “Fintech”, “Information Sharing”, and “Collaboration”. Articles related to the first cluster among our sample of 330 articles are as follows. Liu ( 2020 ) argued that real-time information sharing and co-value creation between firms have become important in digitalized services, and that it is necessary to consider a new business ecosystem based on new collaboration methods as firms depend more on one another. Endres et al. ( 2021 ) proved that digital innovation in the process of new software product development promotes entrepreneurial ecosystems and improves performance.

The second cluster is about major digital technologies that drive the digital transformation of services. The main keywords were “Blockchain”, “Industry 4.0”, “IoT”, “Product Services Systems”, and “Cybersecurity”. Li et al. ( 2020 ) analyzed the rapid increase in the application of blockchain technology in financial service and conducted a scientometric analysis, which showed that much attention was focused on legal matters and the security advantage of blockchain. The data on a blockchain is secured through cryptography, the decentralized peer-to-peer network infrastructure, and distributed ledger technology. Sestino et al. ( 2020 ) indicated that IoT is one of the most important technologies in the digitalization of services as customer behavior and interests can be databased and identified on a real-time basis.

The third cluster is about the acceleration of digital transformation owing to COVID-19 and the digitalization of the healthcare industry. The main keywords were “COVID-19”, “Healthcare”, “SME”, and “Smart City”. Rapaccini et al. ( 2020 ) conducted a survey and found that the COVID-19 pandemic accelerated digital servitization and caused service providers to concentrate their competencies on developing new products based on digital technology. Denicolai and Previtali ( 2020 ) found that digital transformation in the healthcare industry drives the development of precision medicine, increases the intensity of collaboration among organizations in the healthcare value chains, and drives the optimization of treatment performance and cost reduction by promoting information sharing in the ecosystem.

The fourth cluster is about the development and application of new business models as an outcome of digital transformation in the service industry and servitization using digital technologies. The main keywords were “Dynamic Capabilities”, “Servitization”, “Value Co-Creation”, and “Business Model”. Volberda et al. ( 2021 ) argued that unlike traditional business models, digital transformation enables new business models for value co-creation with partners or customers. Here, dynamic capabilities are important for adapting to the fast changing environment and breaking free from the barriers of conventional methods and rules. Payne et al. ( 2021 ) explained that the diversification of services that can be aligned with products in the era of digital transformation enables all kinds of innovation by providing new customized value to customers based on service-oriented business models.

The fifth cluster is about E-government, E-commerce, and E-services that drove the digitalization of services. The main keywords were “E-Government”, “E-Commerce”, “E-Services”, and “Marketing”. Loukadounou et al. ( 2020 ) showed that the Greek government increased the citizens’ satisfaction with administrative services and also reduced related costs through digital transformation of administrative processes. Case ( 2019 ) conducted a case analysis on B2B firms and discovered that digital transformation resulted in considerable changes in E-commerce among the firms studied. Case further found that the level of customer experience anticipated by B2B buyers through E-commerce had increased substantially.

The sixth cluster is about the digitalization of financial business. The main keywords were “Financial Services”, “Banking”, and “Insurance”. Niemand et al. ( 2021 ) showed that banks with entrepreneurship and a strategic vision for digital transformation can improve their performance based on digitalization. In the same context, Breidbach et al. ( 2019 ) argued that digitalization does not guarantee better performance in financial business; rather, it is necessary to examine various managerial options, such as orchestration with existing services, effectively executing safety measures for security, assessing the performance of new financial services and value co-creation efforts with customers.

5 Discussion and conclusion

This study identified the research trends of digital transformation in the service industry through network text analysis. Today, organizations strive to become agile organizations that are adaptable to the dynamic business environment, as customers are increasingly demanding various forms of new services in convergence with the digital media (Lee and Lim 2018 ). Thus, there has been an increasing number of studies on relevant topics. This study searched 330 articles on Scopus and used them to conduct a network analysis. The results of the centrality analysis using the keyword network can be summarized as follows. The most actively studied subtopics in relation to digital transformation in the service industry were “Business Model”, “Ecosystem”, “Servitization”, and “Customer Experience”. Digital transformation enables firms to collaborate with all of their stakeholders for value co-creation. This naturally leads them to develop new business models and concentrate on creating and operating new business ecosystems with partners and customers. Moreover, as the development of digital technologies has enabled product-oriented firms to develop service-oriented business models, they began to focus on digital servitization. Value creation through new customer experience also became an important organizational strategy. Many studies in our research sample dealt with healthcare and financial services, as identified by the betweenness centrality analysis.

This study identified major topics of research on digital transformation in the service industry through cluster analysis of the keyword network. The identified topics were classified into six groups. The first cluster was about building new business ecosystems for digitalization. Whereas digitization is the process of converting analog to digital, digitalization is the transformation process of making business processes over to use digital technologies (Gobble 2018 ). Many studies showed that digitalization in the service industry requires new management procedures and practices, demanding significant changes in organizational structure and culture (Sklyar et al. 2019 ; Pelletier and Cloutier 2019 ; Liu and Guo 2021 ; Endres et al. 2021 ). The second cluster included studies on technologies that are prevalently used in digital transformation, mostly Industry 4.0 technologies including IoT and blockchain. Many researchers have pointed out that IoT and blockchain technologies enable service providers to enhance monitoring, traceability, and full transparency over business processes with secure network platforms (Chehri and Jeon 2019 ; Rosete et al. 2020 ; Li et al. 2020 ). Because of these advantages, the market size of digital healthcare, fintech, and other untact services are expected to grow sharply (Lee and Lee 2020b ). The third cluster showed that the COVID-19 pandemic has accelerated digitalization in all social sectors, including the service industry and the healthcare area particularly. Even though face-to-face care plays a critical role in the current healthcare system, digital healthcare is suggested as an alternative face-to-face with a virtual visit to prevent infection and accelerate telemedicine services in the COVID-19 era (Yamamoto 2021 ; Tortorella et al. 2021 ). Many studies maintained that service organizations are speeding up the adoption of digital transformation to respond quickly and decisively to the COVID-19 pandemic era with resilience (Bartsch et al. 2020 ; Agostino et al. 2021 ; Abdel-Basset et al. 2021 ). The fourth cluster highlighted that digital transformation led to the development of new business models in the service industry. Further, various studies were conducted on the topic of digital servitization. Digital servitization creates new innovative services such as add-on services to smart product-service systems, enabling organizations to sense, seize, and reconfigure new business opportunities (Linde et al. 2021a ). Previous literature pointed out that digital servitization allows service organizations to build a platform for better interactions with customers, improving data collection, storage, analysis, and utilization (Sklyar et al. 2019 ; Linde et al. 2021b ). The fifth cluster was about E-government and E-commerce that drove digitalization in the service industry even before digital transformation. E-government systems aimed at providing more responsive and efficient services to the public with digital technologies, enhancing citizens’ trust and confidence in government (Uyar et al. 2021 ). Likewise, E-commerce has provided customers with a personalized shopping experience and broken down the barriers of time, place, and space (Ameen et al. 2021 ). The articles in this cluster indicate that various managerial insights in conventional E-service are important and they need to be finetuned to be suitable for the digital era. The final cluster was about digital transformation in the financial industry. Even though COVID-19 crisis has accelerated the digital transformation, there are some barriers in adoption of digital transformation in the financial industries such as functional and psychological barriers (Santos and Ponchio 2021 ). Functional barriers include product value and risk related to product usage, and psychological barriers are associated with the tradition and norms of the person (Mani and Chouk 2018 ). Many articles not only focused on the importance of fintech and smart banking services but also argued that operational excellence is imperative for maximizing customer experience and security improvement for the successful digital transformation in the financial industry.

This study provides several significant contributions. First, it quantified and analyzed the research trends of digital transformation in the service industry using only those articles with unstructured text data. Using network text analysis, this study identified major research topics dealing with digital transformation in the service industry. There has been no systematic literature review done on digital transformation in the service industry. The results of this study will help researchers in the relevant fields capture the overall picture of the field. Second, the research trend analysis conducted in this study also reveals the topics that need thorough future research. Despite the extensive scope of the service industry, many studies in our sample focused on healthcare and financial services, indicating the accelerating importance of these services. The results of our study also found that further research on the digitalization of other service areas are needed, such as hospitality and tourism, aviation, and logistics. Moreover, many studies briefly explained the impact of COVID-19 on the digitalization of services. As the COVID-19 pandemic has become the new normal, it is necessary to conduct systematic research on how the post COVID-19 era will affect digitalization in the service industry. In addition, more research is needed on new service strategies in the digital era (Lee and Trimi 2021 ). Moreover, service organizations should pursue strategic innovations for new business models and “untact” technologies such as AI, robots, IoT, and big data (Lee and Lee 2020b ). Thus, it is necessary to carry out research on customer-centric service strategies, convergence of disruptive digital technologies and services, and service innovations that can create new value and competitive advantage in the digital era.

This study has the following limitations. First, the research sample we used in this study may have omitted some relevant articles for analysis as we used only Scopus rather than multiple databases for article selection. Moreover, only the articles that are directly related to digital transformation in the service industry were searched. Second, this study analyzed articles based on the keyword network analysis, while further analysis can be done based on citations, co-citations, and researcher networks. These limitations provide opportunities and directions for future research.

Abdel-Basset M, Chang V, Nabeeh NA (2021) An intelligent framework using disruptive technologies for COVID-19 analysis. Technol Forecast Soc Change. https://doi.org/10.1016/j.techfore.2020.120431

Article   Google Scholar  

Abolhassan F (ed) (2016) The drivers of digital transformation: why there’s no way around the cloud, 1st edn. Springer, New York

Google Scholar  

Agostino D, Arnaboldi M, Lema MD (2021) New development: COVID-19 as an accelerator of digital transformation in public service delivery. Public Money Manage 41:69–72. https://doi.org/10.1080/09540962.2020.1764206

Ameen N, Tarhini A, Shah M, Madichie NO (2021) Going with the flow: smart shopping malls and omnichannel retailing. J Serv Mark 35:325–348. https://doi.org/10.1108/JSM-02-2020-0066

Ardito L, Scuotto V, Del Giudice M, Petruzzelli AM (2019) A bibliometric analysis of research on big data analytics for business and management. Manage Decis 57:1993–2009. https://doi.org/10.1108/MD-07-2018-0754

Bartsch S, Weber E, Büttgen M, Huber A (2020) Leadership matters in crisis-induced digital transformation: how to lead service employees effectively during the COVID-19 pandemic. J Serv Manage 32:71–85. https://doi.org/10.1108/JOSM-05-2020-0160

Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008:P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008

Breidbach CF, Keating BW, Lim C (2019) Fintech: research directions to explore the digital transformation of financial service systems. J Serv Theory Pract 30:79–102. https://doi.org/10.1108/JSTP-08-2018-0185

Case C (2019) A fortune 14 company’s journey to delivering a modern e-commerce customer experience. J Brand Strategy 8:10–19

Chehri A, Jeon G (2019) The industrial internet of things: examining how the IIoT will improve the predictive maintenance. In: Chen Y-W, Zimmermann A, Howlett RJ, Jain LC (eds) Innovation in medicine and healthcare systems, and multimedia. Springer, Singapore, pp 517–527

Chapter   Google Scholar  

Choi T-M, Chan HK, Yue X (2017) Recent development in big data analytics for business operations and risk management. IEEE Trans Cybern 47:81–92. https://doi.org/10.1109/TCYB.2015.2507599

Coreynen W, Matthyssens P, Van Bockhaven W (2017) Boosting servitization through digitization: pathways and dynamic resource configurations for manufacturers. Ind Mark Manage 60:42–53. https://doi.org/10.1016/j.indmarman.2016.04.012

Coreynen W, Matthyssens P, Vanderstraeten J, van Witteloostuijn A (2020) Unravelling the internal and external drivers of digital servitization: a dynamic capabilities and contingency perspective on firm strategy. Ind Mark Manage 89:265–277. https://doi.org/10.1016/j.indmarman.2020.02.014

Denicolai S, Previtali P (2020) Precision medicine: implications for value chains and business models in life sciences. Technol Forecast Soc Change 151:119767. https://doi.org/10.1016/j.techfore.2019.119767

dos Santos AA, Ponchio MC (2021) Functional, psychological and emotional barriers and the resistance to the use of digital banking services. Innov Manage Rev 18:331–348. https://doi.org/10.1108/INMR-07-2020-0093

Endres H, Huesig S, Pesch R (2021) Digital innovation management for entrepreneurial ecosystems: services and functionalities as drivers of innovation management software adoption. Rev Manage Sci. https://doi.org/10.1007/s11846-021-00441-4

Fahimnia B, Sarkis J, Davarzani H (2015a) Green supply chain management: a review and bibliometric analysis. Int J Prod Econ 162:101–114. https://doi.org/10.1016/j.ijpe.2015.01.003

Fahimnia B, Tang CS, Davarzani H, Sarkis J (2015b) Quantitative models for managing supply chain risks: a review. Eur J Oper Res 247:1–15. https://doi.org/10.1016/j.ejor.2015.04.034

Feng Y, Zhu Q, Lai K-H (2017) Corporate social responsibility for supply chain management: a literature review and bibliometric analysis. J Clean Prod 158:296–307. https://doi.org/10.1016/j.jclepro.2017.05.018

Ferstl EC, Neumann J, Bogler C, von Cramon DY (2008) The extended language network: a meta-analysis of neuroimaging studies on text comprehension. Hum Brain Mapp 29:581–593. https://doi.org/10.1002/hbm.20422

Frank AG, Dalenogare LS, Ayala NF (2019) Industry 4.0 technologies: implementation patterns in manufacturing companies. Int J Prod Econ 210:15–26. https://doi.org/10.1016/j.ijpe.2019.01.004

Gebauer H, Paiola M, Saccani N, Rapaccini M (2021) Digital servitization: crossing the perspectives of digitization and servitization. Ind Mark Manage 93:382–388. https://doi.org/10.1016/j.indmarman.2020.05.011

Gobble MM (2018) Digitalization, digitization, and innovation. Res-Technol Manage 61:56–59. https://doi.org/10.1080/08956308.2018.1471280

Hokkanen H, Walker C, Donnelly A (2021) Business model opportunities in brick and mortar retailing through digitalization. J Bus Models 8:33–61. https://doi.org/10.5278/JBM.V8I3.5803

IBM.com (2021) Big data analytics. https://www.ibm.com/analytics/hadoop/big-data-analytics . Accessed 20 July 2021

Jannapureddy R, Vien Q-T, Shah P, Trestian R (2019) An auto-scaling framework for analyzing big data in the cloud environment. Appl Sci 9:1417. https://doi.org/10.3390/app9071417

Kamalaldin A, Linde L, Sjödin D, Parida V (2020) Transforming provider-customer relationships in digital servitization: a relational view on digitalization. Ind Mark Manage 89:306–325. https://doi.org/10.1016/j.indmarman.2020.02.004

Kapadia SB, Madhav VV (2020) The impact of digitization on banking and financial services industry in India. IUP J Bank Manage 19:24–31

Kretschmer T, Khashabi P (2020) Digital transformation and organization design: an integrated approach. Calif Manage Rev 62:86–104. https://doi.org/10.1177/0008125620940296

Kryvinska N, Kaczor S, Strauss C (2020) Enterprises’ servitization in the first decade—retrospective analysis of back-end and front-end challenges. Appl Sci 10:2957. https://doi.org/10.3390/app10082957

Lee SM, Lee D (2020a) Healthcare wearable devices: an analysis of key factors for continuous use intention. Serv Bus 14:503–531. https://doi.org/10.1007/s11628-020-00428-3

Lee SM, Lee D (2020b) “Untact”: a new customer service strategy in the digital age. Serv Bus 14:1–22. https://doi.org/10.1007/s11628-019-00408-2

Lee SM, Lim S (2018) Living innovation: from value creation to the greater good, 1st edn. Emerald Publishing Limited, Bingley

Book   Google Scholar  

Lee SM, Rha JS (2018) A network text analysis of published papers in service business, 2007–2017: research trends in the service sector. Serv Bus 12:809–831. https://doi.org/10.1007/s11628-018-0377-6

Lee P-C, Su H-N (2010) Investigating the structure of regional innovation system research through keyword co-occurrence and social network analysis. Innovation 12:26–40. https://doi.org/10.5172/impp.12.1.26

Lee SM, Trimi S (2021) Convergence innovation in the digital age and in the COVID-19 pandemic crisis. J Bus Res 123:14–22. https://doi.org/10.1016/j.jbusres.2020.09.041

Li J, Huang X, Wu C, et al. (2020) How can blockchain shape digital transformation: a scientometric analysis and review for financial services. In: 2020 management science informatization and economic innovation development conference (MSIEID). IEEE, Guangzhou, China, pp 264–267

Linde L, Frishammar J, Parida V (2021a) Revenue models for digital servitization: a value capture framework for designing, developing, and scaling digital services. IEEE Trans Eng Manage. https://doi.org/10.1109/TEM.2021.3053386

Linde L, Sjödin D, Parida V, Wincent J (2021b) Dynamic capabilities for ecosystem orchestration A capability-based framework for smart city innovation initiatives. Technol Forecast Soc Change 166:120614. https://doi.org/10.1016/j.techfore.2021.120614

Liu K (2020) Digital innovation and transformation to business ecosystems. In: The 222h international conference on enterprise information systems (ICEIS 2020). Online Streaming, organised by INSTICC Portugal, pp 1–31

Liu K, Guo H (2021) Digital innovation and transformation to business ecosystems. In: Filipe J, Śmiałek M, Brodsky A, Hammoudi S (eds) Enterprise information systems. Springer, Cham, pp 793–803

Loebbecke C, Picot A (2015) Reflections on societal and business model transformation arising from digitization and big data analytics: a research agenda. J Strateg Inf Syst 24:149–157. https://doi.org/10.1016/j.jsis.2015.08.002

Loukadounou S, Koutsona V, Loukis E (2020) Analyzing a frugal digital transformation of a widely used simple public service in greece. In: Themistocleous M, Papadaki M, Kamal MM (eds) European, Mediterranean, and Middle Eastern Conference on Information System. Springer, Cham, pp 223–237

Mani Z, Chouk I (2018) Consumer resistance to innovation in services: challenges and barriers in the internet of things era. J Prod Innov Manage 35:780–807. https://doi.org/10.1111/jpim.12463

Martín-Peña ML, Díaz-Garrido E, Sánchez-López JM (2018) The digitalization and servitization of manufacturing: A review on digital business models. Strateg Change 27:91–99. https://doi.org/10.1002/jsc.2184

Mridha MF, Nur K, Kumar A, Akhtaruzzaman Md (2017) A new approach to enhance internet banking security. Int J Comput Appl 160:35–39. https://doi.org/10.5120/ijca2017913093

Niemand T, Rigtering JPC, Kallmünzer A et al (2021) Digitalization in the financial industry: a contingency approach of entrepreneurial orientation and strategic vision on digitalization. Eur Manage J 39:317–326. https://doi.org/10.1016/j.emj.2020.04.008

Paiola M, Gebauer H (2020) Internet of things technologies, digital servitization and business model innovation in BtoB manufacturing firms. Ind Mark Manage 89:245–264. https://doi.org/10.1016/j.indmarman.2020.03.009

Parviainen P, Tihinen M, Kääriäinen J, Teppola S (2017) Tackling the digitalization challenge: how to benefit from digitalization in practice. Int J Inf Syst Proj Manage. https://doi.org/10.12821/ijispm050104

Payne EHM, Dahl AJ, Peltier J (2021) Digital servitization value co-creation framework for AI services: a research agenda for digital transformation in financial service ecosystems. J Res Interact Mark 15:200–222. https://doi.org/10.1108/JRIM-12-2020-0252

Pelletier C, Cloutier LM (2019) Conceptualising digital transformation in SMEs: an ecosystemic perspective. J Small Bus Enterp Dev 26:855–876. https://doi.org/10.1108/JSBED-05-2019-0144

Porter K, Simons R, Harris J (2014) Comparison of three techniques for scour depth measurement: photogrammetry, echosounder profiling and a calibrated pile. Coast Eng Proc 1:64. https://doi.org/10.9753/icce.v34.sediment.64

Pyun J, Rha JS (2021) Review of research on digital supply chain management using network text analysis. Sustainability 13:9929. https://doi.org/10.3390/su13179929

Rapaccini M, Saccani N, Kowalkowski C et al (2020) Navigating disruptive crises through service-led growth: the impact of COVID-19 on Italian manufacturing firms. Ind Mark Manage 88:225–237. https://doi.org/10.1016/j.indmarman.2020.05.017

Rha JS (2020) Trends of research on supply chain resilience: a systematic review using network analysis. Sustainability 12:4343. https://doi.org/10.3390/su12114343

Rosete A, Soares B, Salvadorinho J et al (2020) Service robots in the hospitality industry: an exploratory literature review. In: Nóvoa H, Drăgoicea M, Kühl N (eds) Exploring service science. Springer, Cham, pp 174–186

Scherer R, Rohatgi A, Hatlevik OE (2017) Students’ profiles of ICT use: identification, determinants, and relations to achievement in a computer and information literacy test. Comput Hum Behav 70:486–499. https://doi.org/10.1016/j.chb.2017.01.034

Sestino A, Prete MI, Piper L, Guido G (2020) Internet of things and big data as enablers for business digitalization strategies. Technovation 98:102173. https://doi.org/10.1016/j.technovation.2020.102173

Seyedghorban Z, Samson D, Tahernejad H (2020) Digitalization opportunities for the procurement function: pathways to maturity. Int J Oper Prod Manage 40:1685–1693. https://doi.org/10.1108/IJOPM-04-2020-0214

Sjödin D, Parida V, Kohtamäki M, Wincent J (2020) An agile co-creation process for digital servitization: a micro-service innovation approach. J Bus Res 112:478–491. https://doi.org/10.1016/j.jbusres.2020.01.009

Sklyar A, Kowalkowski C, Sörhammar D, Tronvoll B (2019) Resource integration through digitalisation: a service ecosystem perspective. J Mark Manage 35:974–991. https://doi.org/10.1080/0267257X.2019.1600572

Statista.com (2021) Global IoT and non-IoT connections 2010–2025. In: Statista. https://www.statista.com/statistics/1101442/iot-number-of-connected-devices-worldwide/ . Accessed 24 June 2021

Strønen F (2020) Drivers for digitalization in retail and service industries. In: Proceedings of the 16th European conference on management leadership and governance. ACPI

Teece DJ (2014) A dynamic capabilities-based entrepreneurial theory of the multinational enterprise. J Int Bus Stud 45:8–37. https://doi.org/10.1057/jibs.2013.54

Tortorella GL, Fogliatto FS, Saurin TA et al (2021) Contributions of healthcare 4.0 digital applications to the resilience of healthcare organizations during the COVID-19 outbreak. Technovation. https://doi.org/10.1016/j.technovation.2021.102379

Tronvoll B, Sklyar A, Sörhammar D, Kowalkowski C (2020) Transformational shifts through digital servitization. Ind Mark Manage 89:293–305. https://doi.org/10.1016/j.indmarman.2020.02.005

Uyar A, Nimer K, Kuzey C et al (2021) Can E-government initiatives alleviate tax evasion? The moderation effect of ICT. Technol Forecast Soc Change 166:120597. https://doi.org/10.1016/j.techfore.2021.120597

Vandermerwe S, Rada J (1988) Servitization of business: adding value by adding services. Eur Manage J 6:314–324. https://doi.org/10.1016/0263-2373(88)90033-3

Volberda HW, Khanagha S, Baden-Fuller C et al (2021) Strategizing in a digital world: overcoming cognitive barriers, reconfiguring routines and introducing new organizational forms. Long Range Plann. https://doi.org/10.1016/j.lrp.2021.102110

Yamamoto S (2021) E-healthcare service design using model based jobs theory. In: Zimmermann A, Schmidt R, Jain LC (eds) Architecting the digital transformation. Springer, Cham, pp 383–395

Yu S-J, Rha J-S (2021) Research trends in accounting fraud using network analysis. Sustainability 13:5579. https://doi.org/10.3390/su13105579

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Rha, J.S., Lee, HH. Research trends in digital transformation in the service sector: a review based on network text analysis. Serv Bus 16 , 77–98 (2022). https://doi.org/10.1007/s11628-022-00481-0

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84 Digital Transformation Essay Topic Ideas & Examples

🏆 best digital transformation topic ideas & essay examples, 📃 good research topics about digital transformation, 🎓 interesting topics to write about digital transformation, ❓ digital transformation research questions.

• General Electric Company’s Digital Transformation Strategy The introduction of digital products is something that should be supported by the current business model. Such an initiative required a superior business model to make the company competitive and successful.
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• The Digital Transformation and Innovation Nexus The practical orientation of the study ensures its applicability in the current economic environment characterized by the increasing complexity of the organizational landscape.
• Information Governance and Digital Transformation By involving information technology, innovations in technology, and data, organizations must oversee the right implementation of digital transformation to address security and privacy concerns.
• Digital Transformation: Job Satisfaction among Academic Family Physicians Some stakeholders may resist the application of Industry 4 in the manufacturing sector based on the concerns raised above. This study aims to investigate the relevance of digital transformation, specifically focusing on Industry 4, in […]
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• Supply Chain Digital Transformation To improve the present system, it is essential to utilize analytics, simulation, and optimization approach as a digitalization extension means.
• Sadara Company’s Digital Transformation The digital transformation and the transition to the online environment used to be seen as the prerogative of the organizations that provided solely the services that could be easily translated into the online context due […]
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• How Digital Transformation Is Affecting the Oil and Gas Industry The research will assess the contributions of digital transformation in the oil and gas industry. What is the impact of digital transformation in the oil and gas industry?
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• Bossard Company’s Digital Transformation As the size of the clients grew to industrial companies and factories, the demands for parts increased. The SmartBin technology was innovative and has become the centerpiece of Bossard’s business model and approach to customers.
• Digital Transformation: Hyper-Connectedness and Collaboration The guiding principles for E2E economy formulated by the authors include the ability of organizations to provide optimal customer experiences through the right partnerships, capacity to use contextual and predictive analytics to generate customer value, […]
• American Entertainment Industry: Digital Transformation The purpose of this paper is to examine the aspects of the current competition between streaming companies and television networks with the focus on observed digital transformations in sharing information and to discuss what further […]
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• The Productivity and Unemployment Effects of the Digital Transformation
• Analysis of the Key Elements of Digital Transformation
• How the Digital World May Influence Teaching
• Data, Measurement and Initiatives for Inclusive Digitalization, and Future of Work
• Digitalization and Smartening Public Governance of the European High North Regions
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• How Decarbonization, Digitalization, and Decentralization Are Changing Key Power Infrastructures
• Digitalization, Multinationals, and Employment: An Empirical Analysis of Their Causal Relationships
• How Digital Transformation Has Reshaped the Mass Media
• Managing Digitalization: Challenges and Opportunities for Business
• Organizing for Digitalization Through Mutual Constitution: The Design Firm Case
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• How Has Technology Impacted Digital Transformation?
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Digital transformation: A meta-review and guidelines for future research

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Corresponding author. [email protected]

Received 2022 May 24; Revised 2022 Dec 28; Accepted 2023 Jan 3; Collection date 2023 Jan.

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

The emergence of digital transformation has changed the business landscape for the foreseeable future. As scholars advance their understanding and digital transformation begins to gain maturity, it becomes necessary to develop a synthesis to create solid foundations. To do so, significant steps need to be taken to critically, rigorously, and transparently examine the existing literature. Therefore, this article uses a meta-review with the support of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Protocol. As a result, we identified six dimensions and seventeen categories related to digital transformation. The organizational, technological, and social dimensions are still pivotal in digital transformation, while two new dimensions (sustainability and smart cities) still need to be explored in the existing literature. The need to deepen knowledge in digital transformation and refine the dimensions found is of paramount importance, as it involves some complexity due to organizational dynamics and the development of new technologies. It was also possible to identify opportunities, challenges, and future directions.

Keywords: Digital transformation, Meta-review, PRISMA, Organizations, Social, Technologies, Sustainability, Smart cities

1. Introduction

In recent years, academics have provided in-depth knowledge regarding Digital Transformation (DT). These contributions were carried out in the production industry [ 1 ], service industry [ 2 ], healthcare [ 3 ], and education [ 4 ], just to name a few areas. However, these studies are dispersed across several academic fields. As the academic community realized this limitation, researchers became interested in gaining a broader view of DT through systematic literature reviews (SLR) within each field [ 5 – 7 ] and some of them about the DT phenomenon itself [ 8 ]. Although the aforementioned works have contributed to significant advances in knowledge, there are no records of articles providing a detailed holistic view of DT. To fill this gap in the literature, we followed the suggestions of notable scholars [ 9 , 10 ] and set out to undertake a meta-review. Along with this, we also identified reports of other phenomena about DT, such as the paradox of digital technologies [ 11 ]. If, on the one hand, there is a belief in the benefits of adopting DT, on the other hand, there has been some frustration with DT and its impacts on organizations. Conceptually, DT benefits organizations with better operational efficiency [ 6 , 12 ], greater innovation [ 13 ], and cost reduction [ 14 ] in the medium-long term. However, the implementation of DT is complex as it entails initial costs, requires changes, and creates resistance from workers [ 15 ]. Therefore, DT adoption may be risky without models and tools that assist its implementation across organizations. Viewed in isolation, this meta-review may be considered ambitious; however, it can become a relevant work if viewed from a holistic perspective, along with other systematic reviews. We opted for a meta-review because it can ensure reproducibility and transparency of the entire review process. To this end, we explained the methodological process in detail and included the content analysis process (see Appendix A) to make the entire process visible to readers. With DT changing rapidly, the need to identify opportunities, challenges, and future directions is critical. In this regard, we developed the following research question: What are the drivers of DT promoting scientific growth? The answer to the previous question can be achieved by addressing the following objectives: (1) identifying the most relevant thematic areas; (2) categorize the literature on DT; and (3) propose future research based on recent studies. We consider this study original and innovative because it fills an important gap in the literature. In November 22nd, 2022, after performing a search on Elsevier Scopus with the search terms “digital transforming” and “meta-review” in the title of the document, no result was found; in title-abstract-keyword only four documents were found, but they were not directly related to the theme. These results obtained in one of the most important international databases are surprising, especially considering the exponential growth of research on DT in recent years.

The next section provides a conceptualization of DT and associated terms. We then explain the PRISMA process and how the data was collected and analyzed. The results section presents a holistic theoretical-conceptual model of DT and a research agenda. Finally, the conclusions section focuses on managerial, theoretical, and original contributions.

2. Conceptual overview

In the existing literature, concepts referring to DT are still inconsistent or treated simplistically [ 16 , 17 ]. Although there is still some difficulty in accepting a consensual definition of DT, this section describes the relationship between digitation, digitalization, and DT. If it was common to find conceptual miscellanea in the past between digitization, digitalization, and DT, this issue now seems to be overcome. In that regard, Kohli & Johnson [ 18 ] stress that digitization is commonly associated with transforming traditional processes into digital ones. Loske & Klumpp [ 19 ] also consider that digitization is a “process of converting analog data into digital data sets.” Furthermore, recent research argues that digitization encodes or shifts analog tasks and information into a digital format so that computers can store, process, or transmit information without altering value-creating activities [ 20 ]. An excellent example of digitization is e-books or downloadable music, i.e., converting tangible products into products delivered digitally [ 18 ].

Digitalization, in turn, is described as digital technologies that can be used to alter existing business processes. In that regard, companies are investing in products and process innovation through new digital solutions, allowing them to deal with more data and information [ 21 ]. One example is the creation of online or mobile communication channels allowing customers to connect with companies more conveniently than through traditional interactions [ 22 ]. Thus, within the scope of digitalization, companies must apply digital technologies that allow the optimization of existing business processes, i.e., better coordination between processes and creating value for the customer. In short, the difference between digitation and digitalization lies in creating value and improving the customer experience.

Although the concept of DT has gained significant notoriety only recently, it dates back to the 90's [ 23 ]. DT goes beyond digitalization as it involves changing organizational processes and tasks, which typically lead to developing new business models [ 17 ]. Thus, DT consists of integrating information technologies in companies' operations, whether internal or external [ 24 ]. It can also be considered as a change that occurs with the implementation of technologies in a system within a company [ 19 ]. This transformation is supported by the adoption of new technologies from which new performance, new processes, and new business models emerge [ 25 , 26 ]. In addition, DT is not only linked to technology, but also to an improvement in the business model, collaboration, and culture [ 27 ]. This transformation arises with the use of digital tools in the daily activities and processes of the company, being subsequently achieved through its promotion inside and outside it [ 28 ]. For instance, DT can be employed in several domains, such as the healthcare sector; in this regard, the wide and deep use of information technologies changes how health services are delivered and processed [ 29 ]. A company that opts for DT seeks to offer a product and/or service through new digital formats, thus achieving a link between physical processes and virtual processes [ 23 ]. Some authors identify several possible contributions of DT in a company, such as: (1) optimization of physical and digital resources; (2) obtaining greater competitive advantage; (3) greater creation of value for the customer; and (4) cost reduction [ 30 , 31 ].

However, not all industries have been able to keep up with this technological pace and adopt digital technologies, either due to investment difficulties or lack of adaptation of their business model [32, p. 141]. In a digital company, success involves accepting market uncertainty and volatility, identifying opportunities and having the ambition to realize them, as well as making quick decisions taking into account innovation, customers and competitors [ 33 ]. DT has played a disruptive role in various sectors of activity. However, the retail sector was considered one of the sectors most prone to DT [ 30 , 32 ]. This is due to the emergence of new consumers called “digital natives”, who have driven the use of digital platforms and, consequently, the need for innovation in current business models [ 7 ]. The next section discusses the data collection process, the content analysis, and the research limitations.

3. Materials and methods

This article uses a meta-review, as it aims to synthesize the existing body of completed and recorded work produced by researchers [ 34 ]. Meta-reviews are methods known to be able to gather the literature and which can have a significant influence on research, practice, and policy [ 35 ]. A Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) also supports the meta-review to discover new ideas, concepts, and debates in a critical, rigorous, and transparent way. PRISMA included a checklist of 27 items and four-phase flowchart ( Fig. 1 ), enabling data extraction from two of the largest abstract and citation databases of peer-reviewed literature.

PRISMA flowchart.

The search was conducted in Elsevier's Scopus and Web of Science Core Collection (WoS) on December 8, 2021 ( Fig. 1 ). This search combined the terms “digital transformation” and “systematic literature review” in the Title-Abstract-Keywords (TITLE-ABS-KEY) to identify the manuscripts within the area of research (identification phase). Then, we applied pre-selected filters (i.e., language, source, and document type) to identify the most relevant manuscripts (screening phase). The next phase included accessibility criteria (eligibility phase), which encompassed removing duplicated articles and those that were not strictly related to the topic. Finally, articles not identified in the Scopus and WoS databases were included (inclusion phase). Incorporating additional articles allowed to justify and/or reinforce the arguments used in the results section. That is, highly cited conference papers in DT [ 17 ] can also be relevant and should not be left out. We were careful with the issue of transparency, and, for that reason, we included the flowchart ( Fig. 1 ) and their respective explanation. As mentioned earlier, data collection in the Scopus and WoS databases was carried out until the end of 2021. Both databases were selected because they are considered the largest international and multidisciplinary research databases of peer-reviewed manuscripts. This argument is also used by researchers who have published articles on DT in top-tier Journals, such as Benavides et al. [ 5 ] and Lombardi and Secundo [ 36 ], or in some cases, just one of the selected databases, such as the WoS, by Zhu [ 8 ], and Scopus by de Bem Machado et al. [ 37 ]. A more objective argument that justifies using Scopus and WoS is related to the coverage of journals in the area of Natural Sciences and Engineering [ 38 ], areas typically associated with DT. Moreover, we could have much broader data coverage [ 39 ] and free access if we selected Google Scholar. However, despite being a powerful search engine, it does not guarantee that the documents included have been peer-reviewed.

After performing the search using the terms “digital transformation” and “systematic literature review” in the TITLE-ABS-KEY, we identified 262 manuscripts. Following this, we applied the filter by full-text journal articles to obtain high-quality research articles. For readability and interpretation reasons, we selected only articles in English; otherwise, difficulties in interpretation could lead to biased results. This phase resulted in the selection of 79 scientific journal articles. The eligibility phase allowed the elimination of 17 duplicate articles and 33 articles that did not correspond to the research objectives, resulting in 29 articles. The last phase included 5 more articles, so in the end, we were left with 34 articles to analyze. The PRISMA protocol we followed uses the same process of identification, screening, eligibility, and inclusion of other relevant scientific articles published in Q1 journals, whose databases were Scopus and WoS [ 40 ].

Data were encoded twice. First, the articles were manually encoded. That is, the articles were read in full, and repeated words and text excerpts were identified ( Appendix A). Data analysis was performed using low-tech material (e.g., Excel). However, as a significant number of articles were being examined, text analysis using a computer-assisted data analysis package is recommended. Therefore, the second step included using NVivo12 [ 41 ], a qualitative data analysis software for researchers. Data were analyzed using the content analysis technique [ 42 ]. This technique allowed coding the most important phrases and words [ 43 ], making it possible to identify patterns in emerging codes and ideas. Specifically, the process was carried out in four stages: first, we read the entire texts to identify the most relevant phrases and ideas, followed by a coding process; second, we associated excerpts/codes from the selected articles with the categories and added new ones as necessary; third, we identified emerging patterns and ideas (dimensions); lastly, we revised the previous categories, making adjustments, until redundancies and contradictions were clarified and the results were easily interpreted. In short, this technique enabled to code and analyze a large volume of data. After the content analysis, we also followed a verification process: first, we compared the two analyzes, the aforementioned manual cross-analysis with NVivo12; secondly, a verification that included the analysis of the articles’ keywords. The latter step included cross-checking the categories and sub-categories (i.e., our manual categorization) with the 34 articles’ keyword statistics (i.e., authors' choice) and which can be retrieved directly from Scopus. This process allowed to identify discrepancies in the data analysis. As we found similarities, we consolidated the coding process.

Despite the advantages of meta-review, this methodology also has limitations. Applying filters may have excluded relevant documents from other databases (PubMed, etc.), search engines (e.g., Google scholar), or other forms of publication (e.g., books, chapters). However, the PRISMA technique has an advantage over traditional systematic reviews because, unlike the latter, PRISMA (last phase) allows the inclusion of relevant articles overcoming the aforementioned limitation. Lastly, this article presents a “snapshot” of the reality, as both databases are permanently being updated.

4. Results and discussion

4.1. digital transformation overview – influential topics and subject areas.

This section aims to respond to the first research objective. To transparently identify the most relevant thematic areas, we use the graphs provided directly by the Scopus database, which is the leading database for this article (similarly used by Lombardi and Secundo [ 36 ]). Compared to WoS, Scopus was selected for covering a wider range of journals, both in keyword search and citation analysis [ 16 ]. Additionally, most papers indexed in WoS are included in Scopus [ 44 ]. Indeed, when we exclude repeated articles (i.e., screening phase, Fig. 1 ), most of the selected articles come from Scopus. Therefore, for this section, the first initial terms “digital transformation” and “systematic literature review” were used in the Scopus TITLE-ABS-KEY (resulting in 157 articles), which allowed us to identify the most relevant thematic areas. This graphical analysis aims to provide the most holistic view possible in order to provide readers with an overview of the results. For example, from this analysis, the reader can easily infer that the topic is growing exponentially ( Fig. 2 ) and that only 30% of Scopus documents have been analyzed ( Fig. 3 ). For quality reasons, the content analysis had to focus only on journal articles, being therefore more restricted.

Documents by year (retrieved from Elsevier Scopus).

Documents by type (retrieved from Elsevier Scopus).

Fig. 2 shows the upward scientific interest in DT, especially from 2018 onwards. This phenomenon is probably explained by the maturity of the topic, making it possible to analyze the existing literature with some relevance. In particular, we can see that published studies have mainly focused on business model strategies [ 45 – 49 ], digital business [ 48 , 50 , 51 ], the use of disruptive technologies [ 47 , [52] , [53] , [54] ], sustainability [ 55 , 56 ], human resources [ 57 – 59 ], and smart cities [ 45 ]. In turn, Fig. 3 shows the types of documents focused on DT. The publication in conference proceedings is an indicator that DT is arousing the interest of researchers in the scope of the discussion of ideas and the search for solid knowledge on the subject. In terms of article publishing, we have seen that the appetite of top-tier indexed Journals is high, as 45% of the articles are from Q1 Journals and 31% from Q2 Journals.

Regarding the distribution of papers by country, we can see that Germany, the United Kingdom, and Brazil are the ones that stood out the most ( Fig. 4 ). Germany stands out from the other economies, as German industry is one of the main drivers of Industry 4.0 (I4.0). To do so, Germany has made a significant investment in research, which is essential for initiatives aimed at digitizing the manufacturing industry [ 56 ]. For instance, Siemens has formed a research alliance in industrial automation and digitization with the state-funded Technical University of Munich, the Ludwig-Maximilians University, the German Research Center for Artificial Intelligence, and the Fraunhofer Institute for Applied and Integrated Security Applications [ 60 ].

Documents by country or territory (retrieved from Elsevier Scopus).

Considering that one of the drivers of the German economy has been I4.0, it is not surprising that the areas with the greatest scientific research are computer science (26.6%) and engineering (15.1%) in the context of the development of cyber-physical systems, cybersecurity, cloud computing, advanced robotics, just to name a few. Fig. 5 , with no surprise, also includes the subject area of business, management, and accounting (17.7%), given the impact of its coverage in different countries, industries, companies, and people. In that regard, Kraus et al. [ 61 ] argue that DT has led to considerable changes in many organizations, no longer seen as just a technological opportunity but as a way to introduce new processes that can improve the main structures of how companies do business.

Documents by subject area (retrieved from Elsevier Scopus).

4.2. Digital transformation overview – dimensions and categories

This section presents a general view of the existing literature regarding DT, thus responding to the second research objective. We focused exclusively on the analysis of the 34 articles that were selected from Scopus and WoS ( Fig. 1 ). Table 1 shows the dimensions and categories identified during data analysis. Appendix A presents a series of tables with more detailed information (including codes/phrases). Although it is not common to see tables with the complete content analysis available in scientific articles, we decided to make all the information available to the reader for transparency and reproducibility reasons.

Dimensions and categories.

4.2.1. Business models

The first dimension addresses (but not limited to) topics, such as ( Table A1 ): (1) business process innovation, which is improving the competitive position of organizations [ 45 , 54 ] and bringing disruptive DT to the global industrial value chain [ 53 , 60 , 62 ]; (2) digital business strategy that enhances productivity [ 46 , 63 , 64 ] and creates new value for customers [ 65 ].

With regard to innovation, the trend is for organizations in DT environments to implement value-added innovation by integrating social and economic dimensions from different types of innovation, such as product-service and process innovation, as well as innovation in business and organizational models [ 54 , 60 ]. Developing a digital business strategy is critical for organizations as DT involves business and technology issues, transcending organizational boundaries [ 46 ]. Furthermore, selecting technologies (i.e., tech-oriented) is vital to the business strategy and can significantly add value to the business [ 63 ]. Initially, Information Technology (IT) strategy was seen as a functional- and secondary-level strategy component; however, nowadays, DT is the central pillar of the strategy, driving the emergence of the “digital strategy” concept [ 48 ]. Thus, in the context of the digital age, the organizational environment is also more volatile, uncertain, complex, and ambiguous (VUCA), so the rapid changes in competition, demand, technology, and regulations are more challenging than ever. In that regard, the pressure on companies to align their business strategy with the changing technological environment has increased significantly with the emergence and growing importance of new disruptive digital technologies [ 60 , 64 ]. Therefore, a digital business strategy demands strong leadership, an agile and scalable core, and a clear focus on customer engagement or a digitized solutions strategy [ 65 ]. The “tech-oriented” view fails to capture the more fundamentally important role of the “procedural” character of DT, demanding a deeper and more complete “transformational” effort on vision, strategy, culture, human skills, resources and infrastructures, business model, and company's competitiveness [ 48 , 61 ].

In short, with regard to business models, we found that process innovation is changing the business landscape, increasing competitiveness through the development of new digital services and products. In that regard, the business strategy focuses on disruptive technologies. The VUCA environment pushes for a more comprehensive and transformational strategy where people and resources adapt to organizational needs.

4.2.2. Digital business

The second dimension addresses (but not limited to) topics, such as ( Table A2 ): (1) digital culture, literacy, and digital skills that are enhancing DT efforts [ 52 , 58 , 64 ]; (2) digital economy and the challenge of measuring the potential generated by digital technologies [ 65 , 66 ]; (3) innovation and socio-technological shared values, being seen as an opportunity to balance the responsibilities assigned to humans and machines [ 54 , 65 ].

When it comes to digital business, organizations wanting to benefit from their technology investments need to strengthen the digital skills of their workforce [ 58 ]. Therefore, the workforce is one of the key actors in transforming the organization, as digitally capable human resources will be managing and using technology [ 48 , 66 ]. Furthermore, employees working in digitally mature organizations describe their culture as more collaborative and innovative than traditional ones [ 64 ].

The success of the digital economy is expected to be ensured by strengthening the position of companies through the quality of corporate governance and financial structure, aligned with the latest technologies. The digital economy is seen as an economy that accelerates the DT of existing economic sectors, promotes new ecosystems enabled by digital technologies, and develops a digital industry [ 66 ]. Thus, the digital economy includes a combination of digital infrastructure, socio-technical processes, and information and communication technologies [ 56 ]. The risk of the digital economy is associated with the large-scale acceleration of the development of new technologies, which seems almost unstoppable due to the intensive innovation trend. Moreover, recent studies have also stressed that the greatest challenge many organizations face when investing in DT is finding a way for equating, reimagining and redefining the employees experience and bringing their digital literacy up to date. At this level, artificial intelligence (AI) is demanding greater skill in terms of problem solving, as it begins to outperform human performance in executing analytically complex cognitive tasks. Thus, the challenges appear to be twofold, both from the point of view of technological acceleration and the digital literacy of the workforce.

4.2.3. Technologies

The third dimension addresses (but not limited to) topics, such as ( Table A3 ): (1) technology and innovation management, which has been one of the main drivers of DT [ 48 , 52 , 61 , 64 , 65 , 67 ]; (2) AI and big data, which have been propelling significant developments in carrying out analytical-cognitive activities both in organizations and in the industry [ 55 , 56 , 58 , 62 , 64 , 68 ]; and the (3) Internet of Things (IoT) and I4.0, which involves the interconnection of computing power and intelligent data flow, enabling process control in the service and production industry [ 48 , 62 ].

Technology is one of the main drivers of DT, giving a significant boost to organizations that integrate this key factor into their strategy [ 62 ]. As mentioned earlier, technology is an enabler of DT that is causing a change in value creation, as it supports the development of new business models and a focus on acquiring new skills and competencies [ 67 ]. One of the largest consultancies, McKinsey & Company, proposed a model based on six building blocks that allows implementing a successful end-to-end transformation for industrial companies. These six blocks naturally go beyond the simple technology upgrade and are: (1) Create a business-led technology roadmap; (2) Talent development and qualification; (3) Adopt an agile delivery methodology; (4) Moving to a modern technology environment; (5) Focus on enriching data management; (6) Conduct the adaptation and scaling of digital initiatives [ 52 ]. With regard to technology, DT has aroused interest in specific digital technologies, such as AI and big data [ 65 ]. Due to VUCA pressure, companies are aligning their business strategy with digital technological change (e.g., AI, Big Data) [ 64 ]. In that regard, AI is defined as the transformation of service-product processes into automated processes, dependent on intelligent computer systems or robots that do not require human intervention to perform tasks associated with intelligence [ 6 , 47 ]. Despite the well-known advantages of AI and robotics, current discussion often covers the risks of automation. Debates have focused more on the adaptability of jobs in DT than on replacing human labor [ 69 ]. Most studies suggest that complex socioemotional tasks continue to be performed by human beings, while cognitive-analytic tasks will be increasingly migrated to machines [ 70 ]. DT has therefore led to the formation of the digital organization, whose most volatile asset is AI and computational capital, evidenced in the continuous growth of automated information and the creation of digital products [ 56 ]. Digital technologies such as AI, big data analytics, and social platforms generate positive improvements for society (smart cities) and industry (I4.0) [ 55 ]. Thus, DT has been described as the change in an organization's structure, processes, functions and business models due to the adoption of digital technologies such as IoT, AI, machine learning, augmented reality, just to mention a few [ 17 , 58 ]. Therefore, DT does not focus only on organizations, but on almost all domains of knowledge, as it radically changes the concepts traditionally defined in organizational and management science [ 68 ].

4.2.4. Sustainability

The fourth dimension addresses (but not limited to) topics, such as ( Table A4 ): (1) sustainable businesses that focus on the integration of new and disruptive technologies [ 53 , 55 , 56 ]; (2) sustainable competitive advantage by integrating these technologies into the companies’ business processes [ 47 ]; (3) sustainable development with an emphasis on the United Nations Sustainable Development Goals (SDGs) [ 56 ]; and (4) sustainable innovation with an emphasis on open innovation theory [ 53 ].

Transformation to I4.0 has involved occupational adaptations to ensure quality and sustainable business models [ 56 ], leading to carbon emissions reductions [ 55 ] and an augmented degree of social responsibility [ 53 ]. Within the scope of DT, industry-specific IT resources are valued because they reduce costs, supporting sustainable competitive advantages as a result [ 62 ]. Therefore, the objective of companies is to establish sustainable performance and competitive advantage by integrating technology in the decision-making process with corporate strategy [ 47 ]. Additionally, the open innovation paradigm suggests that a holistic and cognitive approach to corporate governance, based on a regime of cooperation between internal and external resources for value creation, opens the possibility of redefining business models in which knowledge develops horizontally. This is achieved by involving all actors in the corporate ecosystem to gain a long-term sustainable competitive advantage [ 53 ]. The interest is in understanding and presenting the impact of digitization initiatives on economic growth and the achievement of the United Nations SDG [ 56 ].

4.2.5. Human resources (HR)

The fifth dimension addresses (but not limited to) topics, such as ( Table A5 ) employee experience, career dynamics, and type of human-machine relationships [ 57 , 58 ].

Within DT, HR concerns have been about the ability of employees to establish Human-Robot Interaction and Collaboration (HRI-C) relationships. At this level, the discussion is broad and involves a change in culture, mindset, and skills required from employees [ 58 ]. However, dealing with DT and the establishment of HRI-C dynamics can be challenging, particularly if employees are not ready for them. Therefore, the pressure to create HRI-Cs can create information overload and employee anxiety [ 58 ]. On top of that, while the benefits of a diverse workforce are well known, the career dynamics of individuals with technical differences over the rest are not well understood [ 57 ]. These different levels of expertise conflict with the balance between the professional and personal lives of the workforce. Therefore, companies must find strategies to balance professional and personal life for individuals who move to more specialized fields.

Furthermore, the literature also highlights that “a change management strategy to gradually change the mindset of the workforce and senior management, and instill the idea that there is no end to change” [52, p. 15]. It is recommended that organizations should develop change management models in DT environments, similar to traditional models (e.g., Lewin's or Kotter's change management models). In that regard, Attaran and Attaran [ 63 ] go further, stating that organizations fail to change because leaders do not pay enough attention to change management, which negatively affects the companies’ HR, making the next change more challenging to implement.

4.2.6. Smart cities

The sixth dimension addresses (but not limited to) ( Table A6 ) smart manufacturing [ 45 , 55 , 60 ], in particular the use of disruptive technologies to produce high-value products and services. Smart cities are not exactly smart manufacturing; however, smart manufacturing contributes to a larger scenario, acting as an enabler of smart cities. This aspect emerges from our analysis and is in line with the arguments of Suvarna et al. [ 71 ]. According to these authors, smart manufacturing contributes to smart cities not only from a technological point of view but also because it satisfies sustainability issues, which are important indices that make up a smart city. Other authors, such as Lom et al. [ 72 ], followed the same argument when they stated that process-based I4.0 with smart city transportation systems could create very effective, demand-driven, and highly productive manufacturing companies, while contributing to the sustainable development of society.

DT has attracted increasing interest from academics and practitioners regarding sustainability and intelligence/automation, such as smart cities, smart homes, smart governments, and smart production [ 45 ]. In particular, the alliance between sustainability and intelligence is at the center of academic discussion, highlighting themes such as sustainable smart manufacturing being enabled by digital technologies, such as IoT, cloud computing, big data, cyber-physical systems, AI, etc. [ 55 ]. These disruptive technologies have been offering unprecedented opportunities to create and develop value-added products and services [ 73 ]. In that regard, we identified that smart cities work as an extensive smart ecosystem, including different value activities and specific business functions and technologies [ 60 ]. To stimulate research on smart cities, there have been numerous special issues published by top-tier journals [ 73 , 74 ]. Thus, according to our analysis, smart cities are in increasing development, being a promising research area.

4.3. Proposed research agenda

The meta-review sets the stage for a research agenda. This review documents what is already known and, using critical knowledge gap analysis, helps to refine research questions, concepts, and theories to point the way for future research [ 75 ]. The articulation between the research question and the DT dimensions allowed the definition of the research agenda. Thus, the proposed research agenda defines the research areas and priorities that guide scholars.

Early in this article, we presented four figures that allowed us to identify the publication of documents by year ( Fig. 2 ), type ( Fig. 3 ), country ( Fig. 4 ), and subject area ( Fig. 5 ). The areas of research identified with the most remarkable growth are open innovation ( Table A4 . Sustainability) and I4.0 ( Table A1 . Business Model and Table A3 . Technologies), within the scope of (1) Computer Science; (2) Business, Management, and Accounting; (3) Engineering ( vide Fig. 5 ). An example that illustrates the scientific development of the areas above (i.e., open innovation and I4.0); is given by Savastano et al. [ 60 ], referring to the case of the alliance between Siemens with the state-funded Technical University of Munich, the German Research Center for Artificial Intelligence, and the Fraunhofer Institute for Applied and Integrated Security Applications.

Some topics described above were also identified in the content analysis stage (i.e., six dimensions and respective categories), allowing us to pinpoint the research priorities for DT. Below, the reader can find the main contributions of the article that frame the research agenda:

According to the literature, VUCA environments are pushing for comprehensive and transformational digital strategies, changing the business landscape by increasing competitiveness in developing new services and products. To streamline research on the development of smart services and products, several special issues have been published by leading journals [ 76 ]. Therefore, disruptive technologies (AI, Big data, etc.) and innovation have been one of the main drivers of DT in building new digital services and products, and this trend is likely to continue.

Compared with an early DT literature review, published in 2018 by Reis et al., new dimensions have been highlighted in this article. The three dimensions identified by Reis et al. [ 17 ] are still widely explored, namely organizational ( Table A1 and A2 ), technological ( Table A3 ), and social ( Table A5 ). However, the new dimensions, namely sustainability ( Table A4 ) and smart cities ( Table A6 ) are still underdeveloped. What is new in this article is that while sustainability and smart cities are widely explored in other research domains (e.g., social sciences, engineering, etc.), within the scope of DT (i.e., business and management), it still falls far short of expectations. This argument may be also supported by a quick search in Elsevier Scopus (dated May 15th, 2022) with the keyword “sustainability” in TITLE-ABS-KEY, which indicates that the top 3 subject areas are Environmental Sciences (18.2%), Social Sciences (15.2%), and Engineering (11.3%); Business, Management, and Accounting represents only 7.5% of worldwide research. With regard to “smart cities”, a similar search shows that the top 3 subject areas are Computer Sciences (31.7%), Engineering (19.6%), and Social Sciences (11.2%); Business, Management, and Accounting represents only 2.6% of the worldwide research. This is a significant gap, considering that, in the scope of DT, the subject area Business, Management, and Accounting is in the top two with 17.7% ( Fig. 5 ).

From our analysis, future research may focus on the latter two dimensions (i.e., sustainability and smart cities). In that regard, researchers point out that empirical studies linking DT and sustainability are still scarce [ 77 ]. At the same time, recent growth in digital technologies is enabling cities to streamline smart services and offering new products [ 78 ]. This argument is also pointed out by some recent studies that have investigated the literature on DT in the context of meta-reviews Reis et al. [ 73 ] or meta-synthesis [ 79 ] in smart cities. Therefore, we argue that additional efforts are needed to reduce the knowledge gap between these two concepts (sustainability and smart cities) and DT.

During data analysis, we tried to use the MECE rule (mutually exclusive and collectively exhaustive). MECE is a framework that allows solving complex problems by dividing them into sub-problems that are mutually exclusive (they do not overlap) and comprehensively exhaustive (cover all possibilities). The application of MECE rule was impossible in this context because of the difficulty of developing mutually exclusive sub-dimensions; nevertheless, the attempt presented interesting results. We delved deeper into this issue and realized that MECE is particularly important for creating taxonomies, as vague definitions cause overlaps between dimension characteristics [ 80 ]. An example is represented by the difficulty in the past in distinguishing between digitization, digitization, and DT. Since then, DT has been extensively investigated, with a clear conceptual distinction. But DT is so comprehensive that the concept crosses several research domains and dimensions (such as those identified in this article). For instance, the HR dimension is transversal to all other dimensions, such as technology (i.e., redefinition of HR skills) or digital business (sociotechnical values). In real terms, the dimensions identified are closely related to each other, covering all possibilities (i.e., comprehensively exhaustive). The MECE rule may still be used in the future, for mixed studies that incorporate literature review and empirical research for each of the dimensions identified in this article.

Lastly, the research agenda includes the suggestion to analyze the impact of incorporating various technologies and how they can influence companies at different levels – individual, departmental, and organizational. In this regard, Kozanoglu and Abedin [ 58 ] argue that future studies could investigate one or several technologies to determine how their number and/or qualities can influence employees at an individual and company level. More specifically, they give the example of the article by Du et al. [ 81 ] that analyzes the use of blockchain in the business processes of a financial company.

In short, when answering the research question, we found six dimensions of DT, along with seventeen categories and sixty-six codes. Four dimensions, out of six, have already been explored in early reviews of DT literature [ 17 ]. Thus, this article is original insofar as we evidenced that “sustainability” dimension has been driven by open innovation in the context of improving new business models; and the “smart city” dimension has been driven by disruptive technologies in the context of the development of smart systems.

5. Conclusion

5.1. theoretical contributions.

To the best of our knowledge, this is the first time a meta-review on DT has been carried out. For that reason alone, this article is already original, bringing a timely contribution. From what we could extract from the analysis, there was a significant growth in literature reviews on the subject. Therefore, the academic interest in meta-reviews per se justifies publication. The article contributes to the theory as it provides clear guidance on research paths. The main contribution is, therefore, the definition of a research agenda focused on six dimensions, namely: 1) business models; 2) digital business; 3) technologies; 4) sustainability; 5) human resources; 6) smart cities. In that regard, we also provided the categories that emerged from the analysis, giving a clearer perspective of each dimension.

In general terms, it was possible to identify two new dimensions compared to previous studies – sustainability and smart cities. The existing literature points out that empirical studies link DT and sustainable business. While the most skeptical readers of this article might claim that sustainability is a widely explored dimension, it seems to fall short of expectations in the context of DT. In this context, sustainability has been driven by open innovation in terms of improving new business models. With regard to smart cities, the development of disruptive technologies has been the key driver of progress. It seems pertinent, thus, to reduce the knowledge gap on sustainability and smart cities in the context of DT.

5.2. Managerial contributions

With regard to managerial contributions, the results of this article are somewhat limited. First, because this article follows a literature review strategy; second, because the article's objective was to define a scientific agenda. Nevertheless, we were able to identify some contributions. In particular, it was possible to verify that due to the link between DT and technology, the significant areas of development are connected to computer sciences and engineering. Thus, for companies that intend to invest in DT, from the point of view of recruiting and training of HR, it may be helpful to consider investments in the areas of industrial engineering, computer engineering, and management. At the organizational level and in the context of the digital age, managers who intend to pursue a DT strategy should pay special attention to the open innovation ecosystem (e.g., n-Helix), rather than investing in company-centric innovation. From a business point of view, there are opportunities within the scope of smart cities that should be explored, namely in developing new technologies and sustainable development.

5.3. Original contributions

According to the results of the meta-review, we found that the most relevant concern is the need to reduce the gap regarding sustainability and smart cities in the context of DT. Crossing that gap in the literature and what is new and original in this article, we would like to highlight some frustration with the DT implementation, specifically with sustainable HR, a neglected dimension both empirically and theoretically. In that regard, the literature stresses that a change management strategy is essential to develop sustainable HR by instilling the idea that there is no end to change. Thus, organizations must develop management models for change in DT environments, similar to those traditional models that already exist, such as the ADKAR model or Kotter's change management model. The suggestion of developing new DT HR models is particularly relevant in digital business. Technological acceleration is forcing organizations to strengthen the digital skills of their workforce. The debates around adapting the workforce to DT contexts are not new. However, we advocate the development of HR sustainability models to adapt the workforce to Digital VUCA environments, where technological acceleration persists. Moreover, the existing literature refers the need to develop comprehensive transformational organizational efforts, particularly from a socio-technical perspective [ 48 ]. From our analysis, the smart cities dimension is very focused on smart production/manufacturing. Thus, in our view, the socio-technical approach is underdeveloped in this context. The same is not valid regarding the business model and digital model dimensions. We may have found our mutually exclusive sub-dimension in the sociotechnical issue. In other words, the socio-technical issue is a subset that still is not transversal to the different DT dimensions. However, as far as we know, there are already several articles outside the context of this research that analyze the socio-technical issue in smart cities [ 82 , 83 ] (although not focused on DT), which leads us to believe that a greater degree of scientific deepening is needed.

Appendix A.

Business Models dimension

Digital Business Ecosystems dimension

Technological dimension

Sustainability dimension

Human Resources dimension

Smart Cities dimension

• 1. Llopis-Albert C., Rubio F., Valero F. Impact of digital transformation on the automotive industry. Technol. Forecast. Soc. Change. 2021;162 doi: 10.1016/j.techfore.2020.120343. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 2. Diener F., Špaček M. Digital transformation in banking: a managerial perspective on barriers to change. Sustainability. 2021;13(4):2032. doi: 10.3390/su13042032. [ DOI ] [ Google Scholar ]
• 3. Ricciardi W., Pita Barros P., Bourek A., Brouwer W., Kelsey T., Lehtonen L., Expert Panel on Effective Ways of Investing in Health (EXPH) Anastasy C., Barros P., Barry M., Bourek A., Brouwer W., De Maeseneer J., Kringos D., Lehtonen L., McKee M., Murauskiene L., Nuti S., Ricciardi W.…Wild C. How to govern the digital transformation of health services. Eur. J. Publ. Health. 2019;29(Supplement_3):7–12. doi: 10.1093/eurpub/ckz165. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 4. Jackson N.C. Managing for competency with innovation change in higher education: examining the pitfalls and pivots of digital transformation. Bus. Horiz. 2019;62(6):761–772. doi: 10.1016/j.bushor.2019.08.002. [ DOI ] [ Google Scholar ]
• 5. Benavides L., Tamayo Arias J., Arango Serna M., Branch Bedoya J., Burgos D. Digital transformation in higher education institutions: a systematic literature review. Sensors. 2020;20(11):3291. doi: 10.3390/s20113291. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 6. Kraus S., Schiavone F., Pluzhnikova A., Invernizzi A.C. Digital transformation in healthcare: analyzing the current state-of-research. J. Bus. Res. 2021;123:557–567. doi: 10.1016/j.jbusres.2020.10.030. [ DOI ] [ Google Scholar ]
• 7. Marques I.C.P., Ferreira J.J.M. Digital transformation in the area of health: systematic review of 45 years of evolution. Health Technol. 2020;10(3):575–586. doi: 10.1007/s12553-019-00402-8. [ DOI ] [ Google Scholar ]
• 8. Zhu X., Ge S., Wang N. Digital transformation: a systematic literature review. Comput. Ind. Eng. 2021;162 doi: 10.1016/j.cie.2021.107774. [ DOI ] [ Google Scholar ]
• 9. Paul J., Merchant A., Dwivedi Y.K., Rose G. Writing an impactful review article: what do we know and what do we need to know? J. Bus. Res. 2021;133:337–340. doi: 10.1016/j.jbusres.2021.05.005. [ DOI ] [ Google Scholar ]
• 10. Paul J., Criado A.R. The art of writing literature review: what do we know and what do we need to know? Int. Bus. Rev. 2020;29(4) doi: 10.1016/j.ibusrev.2020.101717. [ DOI ] [ Google Scholar ]
• 11. Fitzgerald M., Kruschwitz N., Bonnet D., Welch M. Vol. 16. 2014. Embracing Digital Technology. [ Google Scholar ]
• 12. Gebayew C., Hardini I.R., Panjaitan G.H.A., Kurniawan N.B., Suhardi . 2018 International Conference on Information Technology Systems and Innovation (ICITSI) 2018. A systematic literature review on digital transformation; pp. 260–265. [ DOI ] [ Google Scholar ]
• 13. Appio F.P., Frattini F., Petruzzelli A.M., Neirotti P. Digital transformation and innovation management: a synthesis of existing research and an agenda for future studies. J. Prod. Innovat. Manag. 2021;38(1):4–20. doi: 10.1111/jpim.12562. [ DOI ] [ Google Scholar ]
• 14. Saini K. 2018 International Conference on Computing, Power and Communication Technologies (GUCON) 2018. A future's dominant technology blockchain: digital transformation; pp. 937–940. [ DOI ] [ Google Scholar ]
• 15. Matt C., Hess T., Benlian A. Digital transformation strategies. Bus. Info. Syst. Eng. 2015;57(5):339–343. doi: 10.1007/s12599-015-0401-5. [ DOI ] [ Google Scholar ]
• 16. Falagas M.E., Pitsouni E.I., Malietzis G.A., Pappas G. Comparison of PubMed, Scopus, Web of science, and Google scholar: strengths and weaknesses. Faseb. J. 2008;22(2):338–342. doi: 10.1096/fj.07-9492LSF. [ DOI ] [ PubMed ] [ Google Scholar ]
• 17. Reis J., Amorim M., Melão N., Matos P. In: Rocha Em Á., Adeli H., Reis L.P., Costanzo S., editors. Vol. 745. Springer International Publishing; 2018. Digital transformation: a literature review and guidelines for future research; pp. 411–421. (Trends and Advances in Information Systems and Technologies). [ DOI ] [ Google Scholar ]
• 18. Kohli R., Johnson S. Vol. 17. 2011. (Digital Transformation in Latecomer Industries: CIO and CEO Leadership Lessons from Encana Oil & Gas (USA) Inc). [ Google Scholar ]
• 19. Loske D., Klumpp M. Verifying the effects of digitalisation in retail logistics: an efficiency-centred approach. Int. J. Logist. Res. Appl. 2022;25(2):203–227. doi: 10.1080/13675567.2020.1815681. [ DOI ] [ Google Scholar ]
• 20. Verhoef P.C., Broekhuizen T., Bart Y., Bhattacharya A., Qi Dong J., Fabian N., Haenlein M. Digital transformation: a multidisciplinary reflection and research agenda. J. Bus. Res. 2021;122:889–901. doi: 10.1016/j.jbusres.2019.09.022. [ DOI ] [ Google Scholar ]
• 21. Castagna F., Centobelli P., Cerchione R., Esposito E., Oropallo E., Passaro R. Customer knowledge management in SMEs facing digital transformation. Sustainability. 2020;12(9):3899. doi: 10.3390/su12093899. [ DOI ] [ Google Scholar ]
• 22. Ramaswamy V., Ozcan K. Brand value co-creation in a digitalized world: an integrative framework and research implications. Int. J. Res. Market. 2016;33(1):93–106. doi: 10.1016/j.ijresmar.2015.07.001. [ DOI ] [ Google Scholar ]
• 23. de Souza C.A., Szafir-Goldstein C., Aagaard A. 2020 Global Internet of Things Summit (GIoTS) 2020. IoT in the context of digital transformation and business model innovation: the case of a traditional Brazilian wholesaler; pp. 1–6. [ DOI ] [ Google Scholar ]
• 24. Chaparro-Peláez J., Acquila-Natale E., Hernández-García Á., Iglesias-Pradas S. The digital transformation of the retail electricity market in Spain. Energies. 2020;13(8):2085. doi: 10.3390/en13082085. [ DOI ] [ Google Scholar ]
• 25. Allen R. Transformational and digital change: a UK perspective. Organ. Soc. Dyn. 2019;19(2):143–167. doi: 10.33212/osd.v19n2.2019.143. [ DOI ] [ Google Scholar ]
• 26. Nadkarni S., Prügl R. Digital transformation: a review, synthesis and opportunities for future research. Manag. Rev. Quart. 2021;71(2):233–341. doi: 10.1007/s11301-020-00185-7. [ DOI ] [ Google Scholar ]
• 27. Krasonikolakis I., Tsarbopoulos M., Eng T.-Y. Are incumbent banks bygones in the face of digital transformation? J. Gen. Manag. 2020;46(1):60–69. doi: 10.1177/0306307020937883. [ DOI ] [ Google Scholar ]
• 28. Bulovic V., Covic Z. 2020 IEEE 18th International Symposium on Intelligent Systems and Informatics (SISY) 2020. The impact of digital transformation on sustainability in fashion retail; pp. 149–154. [ DOI ] [ Google Scholar ]
• 29. Agarwal R., Gao G., Gordon), DesRoches C., Jha A.K. Research commentary —the digital transformation of healthcare: current status and the road ahead. Inf. Syst. Res. 2010;21(4):796–809. doi: 10.1287/isre.1100.0327. [ DOI ] [ Google Scholar ]
• 30. Cahyadi I. Developing digital application to improve business process sustainability in an Indonesian fast moving consumer goods company. J. Phys. Conf. 2020;1569(3) doi: 10.1088/1742-6596/1569/3/032023. [ DOI ] [ Google Scholar ]
• 31. Wolpert S., Roth A. Development of a classification framework for technology based retail services: a retailers' perspective. Int. Rev. Retail Distrib. Consum. Res. 2020;30(5):498–537. doi: 10.1080/09593969.2020.1768575. [ DOI ] [ Google Scholar ]
• 32. Filotto U., Caratelli M., Fornezza F. Shaping the digital transformation of the retail banking industry. Empirical evidence from Italy. Eur. Manag. J. 2021;39(3):366–375. doi: 10.1016/j.emj.2020.08.004. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 33. Corso M., Giovannetti G., Guglielmi L., Vaia G. In: Conceiving and Implementing the Digital Organization. Bongiorno Em G., Rizzo D., Vaia G., editors. Springer International Publishing; 2018. pp. 181–203. (CIOs and the Digital Transformation). [ DOI ] [ Google Scholar ]
• 34. Fink A. fifth ed. SAGE; 2020. Conducting Research Literature Reviews: from the Internet to Paper. [ Google Scholar ]
• 35. Hennessy E.A., Johnson B.T., Keenan C. Best practice guidelines and essential methodological steps to Conduct rigorous and systematic meta‐reviews. Appl. Psychol.: Health Well-Being. 2019;11(3):353–381. doi: 10.1111/aphw.12169. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 36. Lombardi R., Secundo G. The digital transformation of corporate reporting – a systematic literature review and avenues for future research. Meditari Account. Res. 2021;29(5):1179–1208. doi: 10.1108/MEDAR-04-2020-0870. [ DOI ] [ Google Scholar ]
• 37. de Bem Machado A., Secinaro S., Calandra D., Lanzalonga F. Knowledge management and digital transformation for Industry 4.0: a structured literature review. Knowl. Manag. Res. Pract. 2022;20(2):320–338. doi: 10.1080/14778238.2021.2015261. [ DOI ] [ Google Scholar ]
• 38. Mongeon P., Paul-Hus A. The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics. 2016;106(1):213–228. doi: 10.1007/s11192-015-1765-5. [ DOI ] [ Google Scholar ]
• 39. Martín-Martín A., Thelwall M., Orduna-Malea E., Delgado López-Cózar E. Google scholar, microsoft academic, Scopus, dimensions, Web of science, and OpenCitations' COCI: a multidisciplinary comparison of coverage via citations. Scientometrics. 2021;126(1):871–906. doi: 10.1007/s11192-020-03690-4. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 40. de Jesus Pacheco D.A., ten Caten C.S., Jung C.F., Sassanelli C., Terzi S. Overcoming barriers towards sustainable product-service systems in small and medium-sized enterprises: state of the art and a novel decision matrix. J. Clean. Prod. 2019;222:903–921. doi: 10.1016/j.jclepro.2019.01.152. [ DOI ] [ Google Scholar ]
• 41. Edhlund B.M., McDougall A.G. NVivo 12 essentials: your guide to the world's most powerful data analysis software. Lulu. 2019 [ Google Scholar ]
• 42. Neuendorf K.A. second ed. SAGE; 2017. The Content Analysis Guidebook. [ Google Scholar ]
• 43. Reis J., Amorim M., Melao N. 2017 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI) 2017. Omni-channel service operations: building technology-based business networks; pp. 96–101. [ DOI ] [ Google Scholar ]
• 44. Waltman L. A review of the literature on citation impact indicators. J. Info. 2016;10(2):365–391. doi: 10.1016/j.joi.2016.02.007. [ DOI ] [ Google Scholar ]
• 45. Gökalp E., Martinez V. Digital transformation maturity assessment: development of the digital transformation capability maturity model. Int. J. Prod. Res. 2021:1–21. doi: 10.1080/00207543.2021.1991020. [ DOI ] [ Google Scholar ]
• 46. Hanelt A., Bohnsack R., Marz D., Antunes Marante C. A systematic review of the literature on digital transformation: insights and implications for strategy and organizational change. J. Manag. Stud. 2021;58(5):1159–1197. doi: 10.1111/joms.12639. [ DOI ] [ Google Scholar ]
• 47. Kitsios F., Kamariotou M. Artificial intelligence and business strategy towards digital transformation: a research agenda. Sustainability. 2021;13(4):2025. doi: 10.3390/su13042025. [ DOI ] [ Google Scholar ]
• 48. Leão P., Silva M.M. Impacts of digital transformation on firms' competitive advantages: a systematic literature review. Strat. Change. 2021;30(5):421–441. doi: 10.1002/jsc.2459. [ DOI ] [ Google Scholar ]
• 49. Li F. The digital transformation of business models in the creative industries: a holistic framework and emerging trends. Technovation. 2020:92–93. doi: 10.1016/j.technovation.2017.12.004. 102012. [ DOI ] [ Google Scholar ]
• 50. Hausberg J.P., Liere-Netheler K., Packmohr S., Pakura S., Vogelsang K. Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis. J. Bus. Econ. 2019;89(8–9):931–963. doi: 10.1007/s11573-019-00956-z. [ DOI ] [ Google Scholar ]
• 51. Jedynak M., Czakon W., Kuźniarska A., Mania K. Digital transformation of organizations: what do we know and where to go next? J. Organ. Change Manag. 2021;34(3):629–652. doi: 10.1108/JOCM-10-2020-0336. [ DOI ] [ Google Scholar ]
• 52. Bellantuono N., Nuzzi A., Pontrandolfo P., Scozzi B. Digital transformation models for the I4.0 transition: lessons from the change management literature. Sustainability. 2021;13(23) doi: 10.3390/su132312941. [ DOI ] [ Google Scholar ]
• 53. Di Vaio A., Palladino R., Pezzi A., Kalisz D.E. The role of digital innovation in knowledge management systems: a systematic literature review. J. Bus. Res. 2021;123:220–231. doi: 10.1016/j.jbusres.2020.09.042. [ DOI ] [ Google Scholar ]
• 54. Monteiro Cavalieri Barbosa A., Pego Saisse M.C. Hybrid project management for sociotechnical digital transformation context. Brazil. J. Oper. Product. Manag. 2019;16(2):316–332. doi: 10.14488/BJOPM.2019.v16.n2.a12. [ DOI ] [ Google Scholar ]
• 55. Feroz A.K., Zo H., Chiravuri A. Digital transformation and environmental sustainability: a review and research agenda. Sustainability. 2021;13(3):1530. doi: 10.3390/su13031530. [ DOI ] [ Google Scholar ]
• 56. Gomez-Trujillo A.M., Gonzalez-Perez M.A. Digital transformation as a strategy to reach sustainability. Smart Sustain. Built Environ. 2021 doi: 10.1108/SASBE-01-2021-0011. [ DOI ] [ Google Scholar ]
• 57. Cabot C., Gagnon S. Understanding the career dynamics of IT professionals in digital transformation times: a systematic review of career anchors studies. Int. J. Inform. Syst. Project Manag. 2021;9(2):44–60. doi: 10.12821/ijispm090203. [ DOI ] [ Google Scholar ]
• 58. Cetindamar Kozanoglu D., Abedin B. Understanding the role of employees in digital transformation: conceptualization of digital literacy of employees as a multi-dimensional organizational affordance. J. Enterprise Inf. Manag. 2021;34(6):1649–1672. doi: 10.1108/JEIM-01-2020-0010. [ DOI ] [ Google Scholar ]
• 59. Steiber A., Alänge S. Corporate-startup Co-creation for increased innovation and societal change. Triple Helix J. 2020:1–23. doi: 10.1163/21971927-bja10004. [ DOI ] [ Google Scholar ]
• 60. Savastano M., Amendola C., Bellini F., D'Ascenzo F. Contextual impacts on industrial processes brought by the digital transformation of manufacturing: a systematic review. Sustainability. 2019;11(3):891. doi: 10.3390/su11030891. [ DOI ] [ Google Scholar ]
• 61. Kraus S., Durst S., Ferreira J.J., Veiga P., Kailer N., Weinmann A. Digital transformation in business and management research: an overview of the current status quo. Int. J. Inf. Manag. 2022;63 doi: 10.1016/j.ijinfomgt.2021.102466. [ DOI ] [ Google Scholar ]
• 62. Kraus S., Jones P., Kailer N., Weinmann A., Chaparro-Banegas N., Roig-Tierno N. Digital transformation: an overview of the current state of the art of research. Sage Open. 2021;11(3) doi: 10.1177/21582440211047576. [ DOI ] [ Google Scholar ]
• 63. Attaran M., Attaran S. Digital transformation and economic contributions of 5G networks. Int. J. Enterprise Inf. Syst. 2020;16(4):58–79. doi: 10.4018/IJEIS.2020100104. [ DOI ] [ Google Scholar ]
• 64. Teichert R. Digital transformation maturity: a systematic review of literature. Acta Univ. Agric. Silvic. Mendelianae Brunensis. 2019;67(6):1673–1687. doi: 10.11118/actaun201967061673. [ DOI ] [ Google Scholar ]
• 65. Schneider S., Kokshagina O. Digital transformation: what we have learned (thus far) and what is next. Creativ. Innovat. Manag. 2021;30(2):384–411. doi: 10.1111/caim.12414. [ DOI ] [ Google Scholar ]
• 66. Penmetsa M.K., Bruque-Camara S. A framework for building a sustainable digital nation: essential elements and challenges. Digital Pol. Regulat. Govern. 2021;23(3):262–286. doi: 10.1108/DPRG-10-2020-0148. [ DOI ] [ Google Scholar ]
• 67. Junge A.L. Digital transformation technologies as an enabler for sustainable logistics and supply chain processes – an exploratory framework. Brazil. J. Oper. Product. Manag. 2019;16(3):462–472. doi: 10.14488/BJOPM.2019.v16.n3.a9. [ DOI ] [ Google Scholar ]
• 68. Jagiellonian University, Poland, Batko R., Baliga-Nicholson K. Digital innovation as the key factor in changing organizational identity into a digital organizational identity. Prob. Zarzadzan. 2019;4/2019(84):39–51. doi: 10.7172/1644-9584.84.3. [ DOI ] [ Google Scholar ]
• 69. Reis J., Santo P.E., Melão N. Influence of artificial intelligence on public employment and its impact on politics: a systematic literature review. Brazil. J. Oper. Product. Manag. 2021;18(3) doi: 10.14488/BJOPM.2021.010. [ DOI ] [ Google Scholar ]
• 70. Reis J., Melão N., Salvadorinho J., Soares B., Rosete A. Service robots in the hospitality industry: the case of Henn-na hotel, Japan. Technol. Soc. 2020;63 doi: 10.1016/j.techsoc.2020.101423. [ DOI ] [ Google Scholar ]
• 71. Suvarna M., Büth L., Hejny J., Mennenga M., Li J., Ng Y.T., Herrmann C., Wang X. Smart manufacturing for smart cities—overview, insights, and future directions. Adv. Intell. Syst. 2020;2(10) doi: 10.1002/aisy.202000043. [ DOI ] [ Google Scholar ]
• 72. Lom M., Pribyl O., Svitek M. 2016 Smart Cities Symposium Prague (SCSP) 2016. Industry 4.0 as a part of smart cities; pp. 1–6. [ DOI ] [ Google Scholar ]
• 73. Reis J., Marques P.A., Marques P.C. Where are smart cities heading? A meta-review and guidelines for future research. Appl. Sci. 2022;12(16):8328. doi: 10.3390/app12168328. [ DOI ] [ Google Scholar ]
• 74. Roccotelli M., Mangini A.M. Applied Sciences; Multidisciplinary Digital Publishing Institute; 2021. Advances on Smart Cities and Smart Buildings (NO 2) https://www.mdpi.com/journal/applsci/special_issues/Smart_Cities_Buildings [ Google Scholar ]
• 75. Jesson J., Matheson L., Lacey F.M. SAGE Publications; 2011. Doing Your Literature Review: Traditional and Systematic Techniques. [ Google Scholar ]
• 76. Reis J., Jorge D., Costa J. 2022. Applied Sciences. https://www.mdpi.com/journal/applsci/special_issues/Building_New_Services [ Google Scholar ]
• 77. El Hilali W., El Manouar A., Janati Idrissi M.A. Reaching sustainability during a digital transformation: a PLS approach. Int. J. Innovat. Sci. 2020;12(1):52–79. doi: 10.1108/IJIS-08-2019-0083. [ DOI ] [ Google Scholar ]
• 78. Anthony Jnr B. Managing digital transformation of smart cities through enterprise architecture – a review and research agenda. Enterprise Inf. Syst. 2021;15(3):299–331. doi: 10.1080/17517575.2020.1812006. [ DOI ] [ Google Scholar ]
• 79. Esashika D., Masiero G., Mauger Y. An investigation into the elusive concept of smart cities: a systematic review and meta-synthesis. Technol. Anal. Strat. Manag. 2021;33(8):957–969. doi: 10.1080/09537325.2020.1856804. [ DOI ] [ Google Scholar ]
• 80. Liu X., Werder K., Maedche A. Curran Associates, Inc; Red Hook, NY: 2017. A Taxonomy of Digital Service Design Technique. International Conference on Information Systems. [ Google Scholar ]
• 81. Du W., Derek), Pan S.L., Leidner D.E., Ying W. Affordances, experimentation and actualization of FinTech: a blockchain implementation study. J. Strat. Inf. Syst. 2019;28(1):50–65. doi: 10.1016/j.jsis.2018.10.002. [ DOI ] [ Google Scholar ]
• 82. Carvalho L. Smart cities from scratch? A socio-technical perspective. Camb. J. Reg. Econ. Soc. 2015;8(1):43–60. doi: 10.1093/cjres/rsu010. [ DOI ] [ Google Scholar ]
• 83. Curşeu P.L., Semeijn J.H., Nikolova I. Career challenges in smart cities: a sociotechnical systems view on sustainable careers. Hum. Relat. 2021;74(5):656–677. doi: 10.1177/0018726720949925. [ DOI ] [ Google Scholar ]
• 84. Dahanayake K.C., Sumanarathna N. IoT-BIM-based digital transformation in facilities management: a conceptual model. J. Facil. Manag. 2021;20(3):6437–6451. doi: 10.1108/JFM-10-2020-0076. [ DOI ] [ Google Scholar ]
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