First Monday

Unveiling 100 years of digitalization as a scholarly object by Katarina L. Gidlund and Leif Sundberg

Digitalization is frequently mentioned in government policies and academic discourse where it is often being associated with expectations of societal rebirth and large-scale changes. However, little attention has been given to evolutionary aspects of the phenomenon of digitalization. Thus, in this paper, we aim to contribute by focusing on the concept of digitalization in a structured manner and answering the following question: How has the concept of digitalization travelled throughout academic discourse? To focus on digitalization as a scholarly object, we utilized bibliometric analyses of research articles ranging across 10 decades. We produced bibliographic maps of keywords and performed visual cluster analysis of these maps using techniques from VOSviewer and ScientoPy bibliometric software. This operation was conducted to facilitate discussion on the evolution of digitalization from a semi-genealogical perspective and to open up a discussion on continuity, density, convergence, and digitalization as a signifier. By combining bibliometric data with genealogical analysis, we identified how digitalization has traveled from specific contexts (medical use and information conversion) to a more general use after the turn of the millennium. Moreover, a pattern of convergence during the last decade surfaced, where digitalization has become associated with ideas of a digitally transformed society. Through these findings, this paper contributes to current literature on digitalization with a novel analysis of the term’s evolution.


1. Introduction
2. Theoretical positioning
3. Materials and methods
4. Results
5. Analysis and discussion
6. Concluding remarks



1. Introduction

Much attention has been devoted to how society is changing in parallel with technological gains in terms of expressed hopes and expectations about “digitalization.” As noted by Schou and Hjelholt (2018), the discursive landscape of digitalization is characterized by stories of societal rebirth and large-scale changes. These grand narratives have come from a variety of actors, such as the World Economic Forum, OECD, and the European Union, and such stories tend to permeate government policies. In parallel, the scientific community has tried to define the concept, for example in the work of Brennen and Kreiss (2016), who differentiated between digitalization and digitization. Whereas digitization refers to the material process of converting analog information to digital data, digitalization is defined as “the way many domains of social life are restructured around digital communication and media infrastructures” [1]. Another approach was that of Reis, et al. (2019), who presented a literature review on digitalization where digitalization was a search criterion and filters were used to improve the review process, yielding 121 articles. Their results demonstrated that few articles distinguished between digitalization and digitization, so that these terms were sometimes used interchangeably in the literature. That study concluded that there seems “to be quite some confusion regarding the usage of terms” [2], arguing that there is an indication that the field is practitioner-driven and followed by academics.

There are also instances where digitalization has been described briefly in a single section, for example, in a highly cited paper from the information systems field, by Tilson, et al. (2010). Tilson, et al. state that digitalization can be understood as “a sociotechnical process of applying digitizing techniques to broader social and institutional contexts that render digital technologies infrastructural.” Digitalization as a part of societal transformation is a frequent topic in the academic literature. For example, Morgan (2004) discusses the notion of physical proximity in relation to a discourse on how the “forces” of digitalization and globalization are associated with reducing the importance of spatial dimensions. In a similar vein, Dunleavy, et al. (2006) describe how digitalization enables a new government paradigm where previously fragmented processes become subject to reintegration. Furthermore, similar accounts have been given by other researchers without explicitly mentioning digitalization. For example, Brynjolfsson and McAfee (2014) devoted a chapter of their book to “the digitization of almost everything,” which they saw as part of a larger societal change they called the second machine age, even though they did not use the term “digitalization.” Beyond such examples, reviews have been written of combinations such as “digitalization and inflation,” “digitalization and innovation” (Charbonneau, et al., 2017; Parida, et al., 2019), or the closely related concept of “digital transformation” (Vial, 2019; Henriette and Boughzala, 2015; Reis, et al., 2018).

Additionally, digitalization is a prominent theme in literature on new conditions in a variety of domains, including business (Legner, et al., 2017; Urbach and Röglinger, 2019; Ritter and Pedersen, 2020; Rachinger, et al., 2019; Parviainen, et al., 2017; Parida, et al., 2019; Lenka, et al., 2017; Hagberg, et al., 2016; Cenamor, et al., 2017) and government (Dunleavy, et al., 2006; Williams, 2020; see also Sundberg [2019] and Gidlund and Sundberg [2021] for critical inquiries of discourses on digitalization within the government domain). Among highly cited papers that mention digitalization, the application of blockchain technology (Mettler, 2016) and big data (Murdock and Detsky, 2013) in health care have been portrayed as a “revolution” and “inevitable,” respectively. The revolutionary aspects of digitalization have also been mentioned in other contexts, such as supply chains, associated with a fourth industrial revolution commonly referred to as Industry 4.0 (see, e.g., Ivanov, et al., 2019). As explained by Schou and Hjelholt (2018), “Despite its recent hype (or because of it), digitalization often remains a vague concept.” They describe how digitalization has often been referred to as a technical concept associated with a mathematical logic. At the same time, they recognize that some scholars have associated the term with ideas similar to Brennen and Kreiss’ aforementioned notion of digitalization. For example, Latham and Sassen (2009) introduced the term “socio-digitization” when describing “the rendering of facets and political life in digitized form” [3]. Clivaz (2020) explains that the word “digitalization” was coined as a critical concept by Wachal (1971) and is sometimes used in postcolonial studies.

However, few studies have systematically investigated how the term “digitalization” has travelled over time through academic discourse — that is, focusing on its origin and historical evolution with a more longitudinal ambition. As mentioned earlier, digitalization is more often singularized as a temporal discontinuity, described as a novelty with strong restructuring capacity that will fundamentally change society. At the same time, a search in the Scopus database reveals that the term has been used in research articles since the 1920s, two decades before Burks, Goldstine, and Von Neumann (1946) formulated the architecture of an electronic, digital computer. Against this backdrop, we argue that there is a need for research aiming at a deeper understanding of digitalization as a scholarly object over time, tracing its history and evolutions and situating it in a wider system. As Nietzsche argued, “only that which is without history can be defined” [4], further explained by Livesey, “since any concept with a history carries a baggage of conflicting meanings” [5]. By tracing the history of a concept and exploring its evolution over time, it is possible to analyze both continuity and discontinuity, homogeneity and heterogeneity, invention and reinvention, coherence and incoherence, showing how digitalization does not exist in isolation and does not emerge independently of wider regimes of thought. In this paper, we examine the concept of digitalization in a structured and longitudinal manner and attempt to answer the following question: How has the concept of digitalization travelled throughout academic discourse during the period 1920–2020?

To accomplish this goal, we used a combination of bibliometric analysis (see Section 3) and a genealogically inspired analysis (see Section 2). The aim as such was not to make a close reading with an ambition to contribute to the definition of digitalization in line with earlier research, but rather to situate the concept historically and to generate an overview of the term’s evolution. By studying the use of the concept in academia and identifying related attributes, relations, scope, and temporality, we argue that it is possible to explore and expose “digitalization” in relation to a wider evolutionary system and to unearth “resemblances, repetitions, and natural criss-crossings” [6].



2. Theoretical positioning

Before proceeding to methodological considerations when conducting bibliometric analysis, in this section we present the theoretical underpinnings that guided data collection and analysis. As mentioned earlier, this paper focuses on the evolution of digitalization and does so with a genealogy-inspired analysis (Foucault, 1970).

In line with Munir and Jones (2004), this paper argues that our positioning is likely to contribute important insights to the study of technological evolution by analyzing traces of continuity within a discourse that emphasizes radical change. Furthermore, this study facilitates the understanding of technology as part of a complex interplay shaped and co-shaped by multiple institutions and structures (Latour, 1987). As highlighted by Schafer (2022), historicizing a relatively vast digital corpus (in this case, 100 years of published digitalization research) that covers different frames and periods is a daunting task requiring a constant balancing act to incorporate ever-changing environments and meanings (for a concrete example of such challenges, see Birkner, et al.’s [2018] analysis of the German history of journalism from 1914 to 2014). Or, as noted by Balbi and Magaudda (2018), an evolutionary excursion into digital history unveils symbolic narratives that legitimize existing social and cultural practices. Such myths may not correspond to the “real world,” but they may still influence collective thought, “with its own heroes, example legacy and narratives.” Ultimately, dominant narratives and myths form discourses that support the construction of cultural hegemonies. In line with these authors, our approach aims at creating a more profound analysis of the evolution of digitalization, which may serve as a counter-hegemonic narrative. This has been done, for example, in earlier studies on shared collective experiences of “old” media, where these experiences are used to aim a critique against the “new” (digital) media (see Menke and Schwarznegger, 2019) or in studies that discuss the notion of simply “disconnecting” from the (normalized) digital (Schwarznegger and Lothmeier, 2021). In the same manner, we focus on patterns but also add relations and topics in between. By doing so, we avoid temporal disattachment commonly associated with grand narratives of the superiority of new technology and open the way for several interpretations regarding the origin of digitalization, including non-digital ones.

To inform our analysis and to identify themes, temporal patterns of continuity and discontinuity, and convergences (Jenkins and Deuze, 2008; Balbi, 2017), we ground our analysis in Foucauldian genealogical analysis (Foucault, 2005, 1970; Hook, 2005; Kearins and Hooper, 2002; Borger, et al., 2013). The reason behind this positioning is the possibility that it brings to address narratives of digitalization that frame it as a unique expression of technological development with transformative mechanisms to restructure society. This positioning provides a way of tracing and exploring the discursive origin and evolution of the idea of digitalization in a more continuous manner and situate the concept in relation to a wider system.

As such, this investigation involves analyzing how scholarly ways of writing and thinking about digitalization have developed over time, including various uses, translations, borders, and relations. The choice of a Foucauldian-inspired genealogical aspect to the analysis also endows it with the richness of different uses of the concept of digitalization, combined with a sensitivity to the vagueness with which digitalization as a concept is often surrounded. Here we take a standpoint in that everything cannot be “digitalization.” This idea paraphrases what Precora (1991) explains as an incongruously inclusive position that masks a wide variety of socio-political goals and “produces the most disturbing example of intellectual adaptation” and inscription of self-reflection [7]. In turn, genealogy “unmasks the apparently rational or coherent within time” [8] or, as Donald Broady puts it, unmasks “academia’s effort to construct” [9]. Scholars rationalize their epistemological efforts, and rationality grows through scholars’ work to objectify [10], which in this case means objectifying the concept of digitalization.

We therefore argue that the vague and sometimes all-embracing use of “digitalization” demands a more reflective criticism of scholarly practices, focusing on the locations of “digitalization,” the channels it takes to these locations, and the relations in between locations, to reveal plausible alternatives to more pervasive discourses [11]. In his book Power/knowledge, Foucault provisionally defines genealogy as “the union of erudite knowledge and local memories which allows us to establish a historical knowledge of struggles and to make use of this knowledge tactically today” [12]. Thus, a Foucauldian genealogy “emphasises a reconceptualization of the current order, rejecting what is tacitly accepted but known to be flawed, and problematizing it in terms of its historical production” [13]. That is, through a retrospective analysis, such a genealogy unveils the formations within which we are trapped.

Thus, this analysis is inspired by genealogical dimensions but might not fully qualify as genealogical. We are aware that our choices of empirical material (academic publications only) and analytical tools (a strict bibliometric analysis without close readings) entail limitations. However, our try-outs, search strings, and analyses were constantly reflexive and sensitive toward historicity, knowledge claims, and traces of materiality.



3. Materials and methods

In order to analyze the evolution and emergence of the scholarly discourse on digitalization (i.e., scholarly ways of thinking and writing about this subject), we chose to do a bibliometric analysis of instances where digitalization is mentioned during the time period 1920–2020 in the Scopus database (further explained below). As we expected the literature on digitalization to be vast, we needed tools to allow the making of “cuts in the environment” and to establish categories and basic objects (see, e.g., Alaimo and Kalinikos, 2021; Bowker and Star, 1999). The tools chosen for the purpose of this paper were VOSviewer (a tool that uses visualization of similarities) and ScientoPy (a Python-based scientometric analysis tool).

As such, the study design created possibilities to identify and display clusters based on keywords (van Eck and Waltman 2010; Waltman, et al., 2010; Perianes-Rodriguez, et al., 2016); and overlay visualizations (showing evolution over time). As explained by Pesta, et al. (2016), a focus on keywords has several advantages. First, keywords represent what authors think is important in their articles. Second, the analysis of keywords enables detection of trends in both past and present research topics. Third, a bibliometric keyword analysis can answer several pertinent questions about research topics, citations, and the development over time. In sum, this approach provides material to analyze the evolution of the concept of digitalization as a scholarly object. In the following section, we explain this study’s materials and chosen methodological steps.

3.1. Materials

The search for relevant literature was conducted in the Scopus database with no restrictions. Two terms were used in the search in titles, abstracts, and keywords: digitalization and digitalisation (TITLE–ABS–KEY (digitalization OR digitalisation). The search was performed on 1 August 2021, and this search string generated 18,690 documents, which were exported to CSV files via Scopus’ export function. Not using any filters while searching returned a comprehensive body of work that inevitably will contain a degree of “noise” in the form of irrelevant papers, but we justify this approach on the grounds that we aimed to employ an inclusive approach while searching. Nevertheless, the analysis using bibliometric software enabled us to perform limitations to make the material manageable (for example, by setting requirements on the number of times a certain keyword or source had to occur to be included in the analysis).

Scopus was selected as a source of articles since it is the largest scientific database. As noted by Boyle and Sherman (2006), the Web of Science (WoS) is the oldest database and may include more information, dating back to 1900. However, a search for “digitalization” or “digitalisation” resulted in more hits before 1950 in Scopus compared to WoS. Moreover, as the size of the Scopus dataset was fairly comprehensive, a coherent single-database sample was preferred to combining two different datasets.

3.2. Keyword co-occurrence analysis

First, a map based on text data was generated (term co-occurrence) for all keywords (terms present in titles, abstracts, and author/index keywords of the papers). We used all the keywords in this step, since they are useful for investigating the knowledge structure of a scientific field (Zhang, et al., 2016). To make the material manageable, the minimum number of occurrences of a term was set to 20 (out of 67,883 keywords, 1,328 met this threshold). Next, we removed irrelevant keywords, such as “article” and “priority journal,” from the analysis. Overall, this analysis generated four clusters. To analyze the items and clusters, the following properties were studied: size (of items), distance (between items and between clusters), and temporal positioning (via overlay visualization). The size of an item reflects the number of publications in which it was found, and the distance between two items approximates the relatedness of these terms. Colors represent groups (clusters) of terms that are relatively strongly related to each other based on the analysis of co-occurrences of terms in the abstracts, titles, and keywords of the articles. The post-analysis of these clusters was performed in three ways: a) a visual inspection of the clusters, sizes, and distances between items and other clusters; b) an interpretation of what each cluster represents; and, c) an overlay visualization, which allowed us to identify temporal patterns in the material (older clusters are darker, and newer clusters are lighter in VOSviewer) and thus to support an evolutionary perspective.

Figure 1 illustrates three different clusters, with two items in each. By interpreting the content (items) of the clusters and the distance between the clusters, a researcher can use previous experiences to describe the clusters and their relationships with each other. For example, Cluster 1 could be considered the “fruit” cluster, residing in close proximity to Cluster 2, the “berries” cluster. Both clusters are relatively far from Cluster 3, the “telephone” cluster, due to a limited number of shared characteristics (e.g., keywords). However, these clusters may share some common references to “apple.” Additionally, this logic applies to the items within the clusters, which may be closer to or more distant from each other.


Exemplary visualization
Figure 1: Exemplary visualization.


We expected at least two outcomes from this analysis. First, that the results would highlight specific aspects of digitalization in the forms of clusters thematically different from each other in order to touch upon convergence and/or singularization and temporal discontinuity (of digitalization as more isolated or as part of a wider discursive ecosystem). Second, through the overlay visualization, we aimed to reflect on patterns of both continuity and discontinuity regarding the temporal positioning of the clusters, thereby informing an evolutionary analysis of how digitalization has traveled over time in academic texts. To complement the bibliometric analyses, we refer to exemplary papers in the results section, illustrating the content of the clusters.

3.3. Temporal scientometric analysis

Additionally, another tool for bibliometric analysis was used: ScientoPy (Ruiz-Rosero, et al., 2019). ScientoPy is an open source, scientometric tool that consists of a Python script with a graphical user interface. A feature useful for the purpose of this paper is that ScientoPy specializes in applying temporal scientometric analysis to visualize trending topics over time, also known as “topic evolution”. We used ScientoPy on the previously described dataset exported from Scopus and divided the literature into temporal periods. These spans were 1920–1980 (as we noticed that a major use of the terms digitalization and digitalisation during this period consists of medical research not associated with the use of digital technology); 1981–1990; 1991–2000; 2001–2010; and, 2011–2020. These periods were analyzed using the feature authorKeywords (keywords presented by authors in the papers) as “topics” to generate graphs over the ten most common topics from each period using the “evolution” visualization in ScientoPy.

The following methodological choices were made in ScientoPy during this step (terms from the software are in parentheses): focus on the evolution of the top 10 (Topic length: 10) author Keywords (authorKeywords) during five periods (1920–1980, 1981–1990, 1991–2000, 2001–2010, 2011–2020). Depending on how they affected the visualizations, the search terms (digitalization or digitalisation) were sometimes removed. When the search terms were kept in the visualizations, the topic length in ScientoPy was adjusted to ensure that the top ten topics were included with the search term. Therefore, this method focused on the evolution of digitalization through the authors’ positioning of the research over time, allowing the detection of patterns of specificity, continuity, discontinuity, or a combination of these three factors.



4. Results

The results are presented in the following order: In Section 4.1, the keyword analysis conducted in VOSviewer is presented. Subsequently, the results from the analysis of author keywords over time are displayed in Section 4.2.

4.1. Keyword co-occurrence analysis

The keyword analysis revealed four clusters, which are further described below. As explained earlier in Section 3, the size of an item reflects the number of publications in which it was found, and the distance between two items approximates the relatedness of the terms. The colors represent groups (clusters) of terms that are relatively strongly related to each other based on the analysis of co-occurrences of terms in abstracts, titles, and keywords of the articles. As revealed by Figure 3, the clusters display variations in temporal position, where darker items are older, and lighter items represent more recent research.


Keywords, network visualization
Figure 2: Keywords, network visualization.



Keywords, overlay visualization
Figure 3: Keywords, overlay visualization.


The following section examines the four clusters by examining their items, relations, and distances to other items and clusters.

Cluster 1 (155 items, blue in Figure 2): “Digitalis”

This cluster contains research on the use of an extract “digitalis” from the foxglove plant as a therapeutic treatment. “Digitalization” refers to the process of using digitalis for a variety of medical conditions. Reid (1927) reacted against a contemporary editorial, referring to digitalization as “digitalis poisoning.” This author refers to Gould’s (1926) Medical dictionary, where digitalization is defined as “[s]ubjection to the effects of digitalin or digitalis.” The largest items in this cluster refer to medical experiments, with exemplary terms such as “female” (621 occurrences), “male” (572 occurrences), “adult” (555 occurrences), “digoxin” (374 occurrences), “aged” (333 occurrences), “major clinical study” (327 occurrences), and “digitalis” (287 occurrences). Hence, the items in the cluster have very little to do with the current discourse on digitalization, being unrelated to the use of digital technology. Nevertheless, an interesting overlap between this cluster and current discourse is found in a paper by Steinhubl and Topol (2015) titled “Moving from digitalization to digitization in cardiovascular care: why is it important, and what could it mean for patients and providers?” These authors describe how the use of digitalis in the eighteenth century is credited as the launch of the modern therapeutic era in medicine. They further describe how cardiovascular care has evolved through the incorporation of state-of-the-art technologies, and express optimism regarding the use of new digital technologies in the field.

Cluster 2 (134 items, yellow in Figure 2): “Computer-aided medicine”

In this cluster, terms are associated with both medicine and digital technology. The items are relatively few, which is due to a limited number of occurrences compared with the other clusters. Terms such as “clinical article” and “controlled study” border Clusters 1 and 3, but the left part of the cluster heavily emphasizes the increased use of computers in society, using terms such as “algorithms,” “computer-aided design,” “signal processing,” and “computer simulation.” Thus, this cluster is an intersection between medicine and increasing use of (digital) computers, sometimes in conjunction with each other, suggested by keywords such as “diagnostic imaging” and “computer assisted diagnosis.” Hence, this cluster provides insights into the emerging use of digital techniques for medical aids, with an emphasis on image analysis (e.g., Giger, et al., 1988).

Cluster 3 (206 items, green in Figure 2): “E-health”

Cluster 3 also relates to medical research but contains more items related to the topic of health care in general, as suggested by terms such as “human” or “humans,” “telemedicine” (e.g., Zdravković, 2008), “health care delivery,” and “e-health”, as well as terms such as “education”. An exemplary article on the topic of health care education titled “The digitalization aliens” is presented by Schmitz, et al. (2020). Notably, the impact of the COVID-19 pandemic has generated terms such as “COVID-19,” “SARS-CoV-2,” “pandemic,” “pandemics,” and “coronavirus disease 2019” as researchers investigates challenges and opportunities of digitalization during the global pandemic (see, e.g., Almeida, et al., 2020; Hacker, et al., 2020)

Cluster 4 (503 items, red in Figure 2): “Digital transformation”

This cluster is the largest cluster, with the highest density and the largest items. It is also the least distinct cluster, its items ranging across a variety of topics, sectors, and applications of digital technology. The largest items in this cluster are “digital transformation” (906 occurrences), “industry 4.0” (860 occurrences), “digital technologies” (745 occurrences), “artificial intelligence” (604 occurrences), and “digital economy” (557 occurrences). These terms represent the current discourse on digitalization mentioned in the introduction, but they also reflect a more general use of the term, lacking the common ground in medicine or information conversion found in the other clusters.

As shown in Figure 3, the clusters roughly correspond to different time periods, the earliest academic work using “digitalization” or “digitalisation” being located at the right of the figure. Additionally, a darker color reflects older research, and the latest research is found to the left of Figure 3. Hence, by visually inspecting the clusters in Figures 2 and 3, the following timeline may be constructed from old (dark) to new (light) based on the clusters’ contrast: Digitalis → Computer-aided medicine → [E-health & Digital transformation].

4.2. Topic evolution analysis

The analysis of author keywords divided into periods generated a modest number of documents published before 2010. Overall, the results reflect key terms scholars have associated with “digitalization” or “digitalisation” over time. As seen in Figure 4, before 1980 the most common uses for these terms occurred in medical research, as mentioned earlier. During 1981–1990 and 1991–2000, medical terms were still common, but during these periods digitization efforts, such as digitization of telecommunications networks and advancements in computing, also impact the results. This impact is suggested by terms such as “telecommunication network,” “analog-to-digital conversion,” “image analysis,” and “image processing.” These findings correspond with the results of the keyword analysis in Section 4.1, where a gradual shift occurs from a pure medical use of the concept of digitalization to an increased use of digital computers in health care and the digitization of society as a whole.


Author keyword evolution, 19201980
Figure 4: Author keyword evolution, 1920–1980.



Author keyword evolution, 19811990
Figure 5: Author keyword evolution, 1981–1990.



Author keyword evolution, 19912000
Figure 6: Author keyword evolution, 1991–2000.


After the turn of the millennium, the breakthrough of the Internet is evident in the results, and the most-used terms by authors now refer to the use of digital technology (with the exception of “education,” a domain where technology is taught and applied), as suggested by Figure 7. Finally, Figure 8 reveals the exponential-like growth of publications after 2010, as seen at the right of the figure, where a majority of documents containing the key terms were produced within the last two years. In addition to reflecting breakthroughs and new technologies, such as the Internet of things, artificial intelligence, and blockchains, the keywords reveal that many scholars are now using terms to describe macro changes in society, as suggested by terms such as “Industry 4.0” (where 4.0 refers to a fourth industrial revolution), “big data” (reflecting the generation of vast amounts of digital data),“digital transformation,” and “digital economy.” These findings are coherent with the analysis in Section 4.1, where many of the “grand narratives” of digitalization manifest in cluster 4 (the red cluster).


Author keyword evolution, 20012010
Figure 7: Author keyword evolution, 2001–2010.



Author keyword evolution, 20112020
Figure 8: Author keyword evolution, 2011–2020.




5. Analysis and discussion

As stated earlier, in this paper, we focus on the concept of digitalization in an evolutionary manner and, in a systematic way, we attempt to illustrate how the concept of digitalization has travelled throughout academic discourse in the time period studied. The goal of this investigation is to explore and expose “digitalization” as a concept in relation to a wider evolutionary system, in order to facilitate a historical understanding that counterbalances the hegemonic narratives of uniqueness, newness, and “historylessness.” The underlying argument is that concepts like digitalization — or the earlier concepts of information technology (IT) and information and communication technology (ICT) — do not exist in isolation; “they don’t emerge independently to wider regimes of thought” as Hook puts it [14]. A genealogical analysis helps us understand digitalization’s conceptual situatedness vis-à-vis other concepts in longer and wider perspective.

In this study, we made two initial choices: to perform a bibliometric study with documents spanning an extensive period of time (i.e., a hundred years, from 1920 to 2020) and to make as few exclusions as possible when gathering data (e.g., no disciplinary selections were coupled with digitalization). The chosen approach allowed us to perform a retrospective, inclusive study of scholarly knowledge production to gain a deeper understanding of digitalization in terms of historical events, patterns, clusters, and topics. Based on the results in the previous section, this analysis encompasses two identified patterns in the material.

The first and most obvious pattern is that of the scholarly production of knowledge from 1920 to 1980, which was revealed – somewhat surprisingly, given the discourse on digitalization in the latest decade – to be in medical disciplines. “Digitalization” in the academic literature from this period is devoted to different forms of heart treatments originating from the flower of Digitalis purpurea flower (also known as foxglove). Hence, the origin of the concept of digitalization and its founders and users in academia are likely not those one would first consider today. The detachment of this use from other uses of the concept is illustrated in the keyword analysis in Figures 2 and 3, where the oldest cluster is loosely coupled with the other clusters through a few items. The concept of digitalization has, so to speak, been appropriated by a different set of academic disciplines.

The results also reveal a gradual focal shift toward digitization (that is, information conversion), followed by a clear cut and focus shift after the turn of the millennium. The referent has thus changed over time; in fact, the term “digitalization” has been used to mean various significant objects, and even if these are related, continuous, or cumulative to some degree, they are not as coherent as one might think. These results indicate that it is important to go deeper and address focal points to make the concept more tangible and primed for discussion. If we use the concept in an uninterrogated way without touching upon, or declaring, its relations, reference points, and creators (see Gidlund and Sundberg, 2021) we might animate an understanding of digitalization as something given, unreflected, and inevitable. While the contemporary concept may be subject to a range of signifiers, it is simultaneously static in the sense that the various signifiers often adhere to narratives of societal change associated with digital technologies.

The second pattern is one of convergence around digitalization as “digital transformation,” as suggested by the analysis from the previous section. Through several cuts, breaks, and shifts, less through struggles and more through various unifying processes, digitalization has become strongly related to digital transformation. By tracing digitalization back in time, from medicine via digitization to the current usage, it is evident that the concept is used much more vaguely in recent research than it once was. In line with Balbi (2017), we argue that this convergence is a continuous process with overlapping layers, and thus it is reversible and resistible; as we contextualize digitalization in history and unveil patterns of convergence, we acknowledge that “partly it will remain stable and partly it will change again in the future” [15], and we encourage future studies to analyze the “relationship between change and continuity” [16]. To understand digitalization on a deeper level, we stress the need for closer readings that situate the concept and analyze narratives. As Nietzsche puts it, “however suddenly or arbitrary [concepts] appear to emerge in history of thought, they nonetheless belong just as much to a system as do the members of the fauna of a continent” [17]. When digitalization converges into the more elusive digital transformation, and before then, the systemic relation to its meaning as a medical significance of the flower of Digitalis purpurea is diluted, there is a void open for translation. Thus, the reason behind this transformation and the way the digital holds a pivotal role appear almost insignificant. The field is, so to speak, open to a vast variety of significant objects and raison d’êtres.



6. Concluding remarks

In this paper, we have focused on digitalization as an evolving phenomenon in scholarly work to answer the following question: How has the concept of digitalization travelled in academic discourse during the time period 1920–2020? By combining bibliometric data with genealogical analysis, we have gained insights into how digitalization has evolved and changed in 10 decades of scholarly work. First, we have identified how the current use of digitalization has travelled, from specific contexts (medical use and information conversion) to a more general use after the turn of the millennium. Second, we identified a pattern of convergence during the last decade, where digitalization is associated with narratives of digital transformation.

Thus, we contribute to current literature on digitalization with an evolutionary perspective, positioning the concept in a historical context and following its trajectory over 10 decades of scholarly work. By doing so, our contribution clarifies digitalization as a concept by providing an overview of its various meanings and its shifting narrative residency. Our study also contributes to the literature by a novel attempt to aggregate and theorize results from bibliometric data. By tracing digitalization back in time and assembling it through different trajectories on its path to today’s notion, we counteract a naturalized view purely based on the sovereignty of new technology. On the contrary, we show how digitalization has evolved over the course of 10 decades, in which today’s notion constitutes one part, and it likely will shift again. Thus, we encourage current and future scholars to follow these shifts using various sources of data and diverse and novel methodological approaches. End of article


About the authors

Katarina L. Gidlund is a professor in the Department of Information Systems and Technology at Mid Sweden University.
E-mail: katarina [dot] l [dot] gidlund [at] miun [dot] se

Leif Sundberg is a researcher in the Department of Information Systems and Technology at Mid Sweden University.
E-mail: leif [dot] sundberg [at] miun [dot] se



1. Brennen and Kreiss, 2016, pp. 1–11.

2. Reis, et al., 2019, p. 446.

3. Latham and Sassen, 2009, p. 16.

4. Nietzsche, 1996, p. 60.

5. Livesey, 2021, p. 474.

6. Foucault 1994, p. 120.

7. Pecora, 1991, p. 107.

8. Pecora, 1991, p. 108.

9. Broady, 1991, p. 379.

10. Ibid.

11. Kearins and Hooper, 2002, p. 733.

12. Foucault, 1980, p. 83.

13. Kearins and Hooper, 2002, p. 735.

14. Hook, 2007, p. 101.

15. Balbi, 2017, p. 46.

16. Ibid.

17. Nietzsche, 1996, p. 120.



Cristina Alaimo and Jannis Kallinikos, 2021. “Managing by data: Algorithmic categories and organizing,” Organization Studies, volume 42, number 9, pp. 1,385–1,407.
doi:, accessed 23 July 2022.

Fernando Almeida, José Duarte Santos, and José Augusto Monteiro, 2020. “The challenges and opportunities in the digitalization of companies in a post-COVID-19 world,” IEEE Engineering Management Review, volume 48, number 3, pp. 97–103.
doi:, accessed 23 July 2022.

Gabriele Balbi, 2017. “Deconstructing ‘media convergence’: A cultural history of the buzzword, 1980s2010s,” In: Sergio Sparviero, Corinna Peil, and Gabriele Balbi (editors). Media convergence and deconvergence. Cham, Switzerland: Palgrave Macmillan, pp. 31–51.
doi:, accessed 23 July 2022.

Gabriele Balbi and Paolo Magaudda, 2018. A history of digital media: An intermedia and global perspective. New York: Routledge.
doi:, accessed 23 July 2022.

Thomas Birkner, Erik Koenen, and Christian Schwarzenegger, 2018. “A century of journalism history as challenge: Digital archives, sources, and methods,” Digital Journalism, volume 6, number 9, pp. 1,121–1,135.
doi:, accessed 23 July 2022.

Merel Borger, Anita Van Hoof, Irene Costera Meijer, and José Sanders, 2013. “Constructing participatory journalism as a scholarly object: A genealogical analysis,” Digital Journalism, volume 1, number 1, pp. 117–134.
doi:, accessed 23 July 2022.

Geoffrey C. Bowker and Susan Leigh Star, 1999. Sorting things out: Classification and its consequences. Cambridge, Mass.: MIT Press.

Frances Boyle and Damien Sherman, 2006. “Scopus™: The product and its development,” Serials Librarian, volume 49, number 3, pp. 147–153.
doi:, accessed 23 July 2022.

Scott Brennen and Daniel Kreiss, 2016. “Digitalization,” In: Klaus Bruhn Jensen and Robert T. Craig (editors). International encyclopedia of communication theory and philosophy. Chichester: Wiley, pp. 1–11.
doi:, accessed 23 July 2022.

D. Broady, 1991. Sociologi och epistemologi: Om Pierre Bourdieus författarskap och den historiska epistemologin. 2 korr. uppl. Stockholm: HLS Förlag,

Erik Brynjolfsson and Andrew McAfee, 2014. The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York: Norton.

Arthur. W. Burks, Herman H. Goldstine, and John von Neumann, 1946. “Preliminary discussion of the logical design of an electronic computer instrument,” Institute for Advanced Study, Princeton University; version at, accessed 23 July 2022.

J. Cenamor, D. Rönnberg Sjödin, and V. Parida, 2017. “Adopting a platform approach in servitization: Leveraging the value of digitalization,” International Journal of Production Economics, volume 192, pp. 54–65.
doi:, accessed 23 July 2022.

Karyne Charbonneau, Alexa Evans, Subrata Sarker, and Lena Suchanek, 2017. “Digitalization and inflation: A review of the literature,” Bank of Canada, Staff Analytical Note/Note analytique du personnel, 2017–20, at, accessed 23 July 2022.

Claire Clivaz, 2020. “Digitized and digitalized humanities: Words and identity,” Atti del IX Convegno Annuale dell’Associazione per l’Informatica Umanistica e la Cultura Digitale (AIUCD), pp. 67–73.
doi:, accessed 23 July 2022.

Patrick Dunleavy, Helen Margetts, Simon Bastow, and Jane Tinkler, 2006. “New public management is dead *mdash; Long live digital-era governance,” Journal of Public Administration Research and Theory, volume 16, number 3, pp. 467–494.
doi:, accessed 23 July 2022.

Michel Foucault, 2005. The order of things: An archaeology of the human sciences. London: Routledge.

Michel Foucault, 1994. The order of things: An archaeology of the human sciences. New York: Vintage Books.

Michel Foucault, 1980. Power/knowledge: Selected interviews and other writings, 1972-1977. Edited by Colin Gordon. Translated by Colin Gordon, Leo Marshall, John Mepham and Kate Sopers. New York: Vintage.

Michel Foucault, 1970. The order of things: An archaeology of the human sciences. London: Tavistock Publications.

Katarina L. Gidlund and Leif Sundberg, 2021. “Undisclosed creators of digitalization: A critical analysis of representational practices,” Information Polity, volume 26, number 1, pp. 3–20.
doi:, accessed 23 July 2022.

Lissak M. Giger, Kunio Doi, and Heber MacMahon, 1988. “Image feature analysis and computeraided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields,” Medical Physics, volume 15, number 2, pp. 158–166.
doi:, accessed 23 July 2022.

George M. Gould, 1926. Gould’s medical dictionary, containing all the words and phrases generally used in medicine and the allied sciences, with their proper pronunciation, derivation, and definition. Edited by R.J.E. Scott. Philadelphia, Pa.: P. Blakiston.

Janine Hacker, Jan vom Brocke, Joshua Handali, Markus Otto, and Johannes Schneider, 2020. “Virtually in this together — How Web-conferencing systems enabled a new virtual togetherness during the COVID-19 crisis,” European Journal of Information Systems, volume 29, number 5, pp. 563–584.
doi:, accessed 23 July 2022.

Johan Hagberg, Malin Sundström, and Niklas Egels-Zandén, 2016. “The digitalization of retailing: An exploratory framework,” International Journal of Retail & Distribution Management, volume 44, number 7, pp. 694–712.
doi:, accessed 23 July 2022.

Emily Henriette, Mondher Feki, and Imed Boughzala, 2015. “The shape of digital transformation: A systematic literature review,” MCIS 2015 Proceedings, at, accessed 23 July 2022.

Derek Hook, 2007. “Discourse, knowledge, materiality, history: Foucault and discourse analysis,” In: Foucault, psychology and the analytics of power. London: Palgrave Macmillan, pp. 100–137.
doi:, accessed 23 July 2022.

Derek Hook, 2005. “Genealogy, discourse,‘effective history’: Foucault and the work of critique,” Qualitative Research in Psychology, volume 2, number 1, pp. 3–31.
doi:, accessed 23 July 2022.

Dmitry Ivanov, Alexandre Dolgui, and Boris Sokolov, 2019. “The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics,” International Journal of Production Research, volume 57, number 3, pp. 829–846.
doi:, accessed 23 July 2022.

Henry Jenkins and Mark Deuze, 2008. “Editorial: Convergence culture,” Convergence, volume 14, number 1, pp. 5–12.
doi:, accessed 23 July 2022.

Kate Kearins and Keith Hooper, 2002. “Genealogical method and analysis,” Accounting, Auditing & Accountability Journal, volume 15, number 5, pp. 733–757.
doi:, accessed 23 July 2022.

Robert Latham and Saskia Sassen, 2009. “Introduction digital formations: Constructing an object of study,” In: Robert Latham and Saskia Sassen (editors). Digital formations: IT and new architectures in the global realm. Princeton, N.J.: Princeton University Press, pp. 1–34.

Bruno Latour, 1987. Science in action: How to follow scientists and engineers through society. Harvard University Press.

Christine Legner, Torsten Eymann, Thomas Hess, Christian Matt, Tilo Böhmann, Paul Drews, Alexander Mädche, Nils Urbach, and Frederik Ahlemann, 2017. “Digitalization: Opportunity and challenge for the business and information systems engineering community,” Business & Information Systems Engineering, volume 59, number 4, pp. 301–308.
doi:, accessed 23 July 2022.

Sambit Lenka, Vinit Parida, and Joakim Wincent, 2017. “Digitalization capabilities as enablers of value cocreation in servitizing firms,” Psychology & Marketing, volume 34, number 1, pp. 92–100.
doi:, accessed 23 July 2022.

Michael Livesey, 2021. “Historicising ‘terrorism’: How, and why?” Critical Studies on Terrorism, volume 14, number 4, pp. 474–>doi:, accessed 23 July 2022.

Manuel Menke and Christian Schwarzenegger, 2019. “On the relativity of old and new media: A lifeworld perspective,” Convergence, volume 25, number 4, pp. 657–672.
doi:, accessed 23 July 2022.

Matthias Mettler, 2016. “Blockchain technology in healthcare: The revolution starts here,&redquo; 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom).
doi:, accessed 23 July 2022.

Kevin Morgan, 2004. “The exaggerated death of geography: Learning, proximity and territorial innovation systems,” Journal of Economic Geography, volume 4, number 1, pp. 3–21.
doi:, accessed 23 July 2022.

Kamal A. Munir and Matthew Jones, 2004. “Discontinuity and after: The social dynamics of technology evolution and dominance,” Organization Studies, volume 25, number 4, pp. 561–581.
doi:, accessed 23 July 2022.

Travis B. Murdoch and Allan S. Detsky, 2013. “The inevitable application of big data to health care,” Journal of the American Medical Association, volume 309, number 13 (3 April), pp. 1,351–1,352.
doi:, accessed 23 July 2022.

Friedrich Nietzsche, 1996. On the genealogy of morals Translated with an introduction and notes by Douglas Smith. Oxford: Oxford University Press.

Vinit Parida, David Sjödin, and Wiebke Reim, 2019. “Reviewing literature on digitalization, business model innovation, and sustainable industry: Past achievements and future promises,” Sustainability, volume 11, number 2, 391.
doi:, accessed 23 July 2022.

Päivi Parviainen, Maarit Tihinen, Jukka Kääriäinen, and Susanna Teppola, 2017. “Tackling the digitalization challenge: How to benefit from digitalization in practice,” International Journal of Information Systems and Project Management, volume 5, number 1, pp. 63–77.
doi:, accessed 23 July 2022.

Vincent P. Pecora, 1991. “Nietzsche, genealogy, critical theory,” New German Critique, number 53, pp. 104–130.
doi:, accessed 23 July 2022.

Antonio Perianes-Rodriguez, Ludo Waltman, and Nees Jan van Eck, 2016. “Constructing bibliometric networks: A comparison between full and fractional counting,” Journal of Informetrics, volume 10, number 4, pp. 1,178–1,195.
doi:, accessed 23 July 2022.

Bryan Pesta, John Fuerst, and Emil O.W. Kirkegaard, 2018. “Bibliometric keyword analysis across seventeen years (2000–2016) of Intelligence articles,” Journal of Intelligence, volume 6, number 4, 46.
doi:, accessed 23 July 2022.

Michael Rachinger, Romana Rauter, Christiana Müller, Wolfgang Vorraber, and Eva Schirgi, 2019. “Digitalization and its influence on business model innovation,” Journal of Manufacturing Technology Management, volume 30, number 8, pp. 1,143–1,160.
doi:, accessed 23 July 2022.

William D. Reid, 1927. “Digitalization,” Journal of the American Medical Association, volume 89, number 16, p. 1353.
doi:, accessed 23 July 2022.

João Reis, Marlene Amorim, Nuno Melão, Yuval Cohen, and Mário Rodrigues, 2019. “Digitalization: A literature review and research agenda,” In: Zoran Anisic, Bojan Lalic, and Danijela Gracanin (editors). Proceedings on 25th International Joint Conference on Industrial Engineering and Operations Management — IJCIEOM. The next generation of production and service systems. Cham, Switzerland: Springer, pp. 443–456.
doi:, accessed 23 July 2022.

João Reis, Marlene Amorim, Nuno Melão, and Patricia Matos, 2018. “Digital transformation: A literature review and guidelines for future research,” In: Álvaro Rocha, Hojjat Adeli, Luís Paulo Reis, and Sandra Costanzo (editors). Trends and advances in information systems and technologies. Cham, Switzerland: Springer, volume 1, pp. 411–421.
doi:, accessed 23 July 2022.

Thomas Ritter and Carsten Lund Pedersen, 2020. “Digitization capability and the digitalization of business models in business-to-business firms: Past, present, and future,” Industrial Marketing Management, volume 86, pp. 180–190.
doi:, accessed 23 July 2022.

Juan Ruiz-Rosero, Gustavo Ramírez-González, and Jesus Viveros-Delgado, 2019. “Software survey: ScientoPy, a scientometric tool for topics trend analysis in scientific publications,” Scientometrics, volume 121, number 2, pp. 1,165–1,188.
doi:, accessed 23 July 2022.

Valérie Schafer, 2022. “‘Put it back’. Issues and challenges of historicising online virality,” at, accessed 23 July 2022.

Lisa Schmitz, Jana Aulenkamp, Daniel Bechler, and Jonah Grütters, 2020. “The digitalization aliens,” GMS Journal for Medical Education, volume 37, number 6, Doc55.
doi:, accessed 23 July 2022.

Jannick Schou and Morten Hjelholt, 2018. Digitalization and public sector transformations. Cham, Switzerland: Palgrave Macmillan.
doi:, accessed 23 July 2022.

Christian Schwarzenegger and Christine Lohmeier, 2021. “Creating opportunities for temporary disconnection: How tourism professionals provide alternatives to being permanently online,” Convergence, volume 27, number 6, pp. 1,631–1,647.
doi:, accessed 23 July 2022.

Steven R. Steinhubl and Eric J. Topol, 2015. “Moving from digitalization to digitization in cardiovascular care: Why is it important, and what could it mean for patients and providers?” Journal of the American College of Cardiology, volume 66, number 13, pp. 1,489–1,496.
doi:, accessed 23 July 2022.

Leif Sundberg, 2019. “If digitalization is the solution, what is the problem?” Abstracts of papers presented at the 19th European Conference on Digital Government ECDG 2019, pp. 136–143.

David Tilson, Kalle Lyytinen, and Carsten Sørensen, 2010. “Research commentary — Digital infrastructures: The missing IS research agenda,” Information Systems Research, volume 21, number 4, pp. 748–759.
doi:, accessed 23 July 2022.

Nils Urbach and Maximilian Röglinger, 2019. “Introduction to digitalization cases: How organizations rethink their business for the digital age,” In: Digitalization cases: Management for professionals. Cham, Switzerland: Springer, pp. 1–12.
doi:, accessed 23 July 2022.

Nees Jan van Eck and Ludo Waltman, 2010. “Software survey: VOSviewer, a computer program for bibliometric mapping,” Scientometrics, volume 84, number 2, pp. 523–538.
doi:, accessed 23 July 2022.

Gregory Vial, 2019. “Understanding digital transformation: A review and a research agenda,” Journal of Strategic Information Systems, volume 28, number 2, pp. 118–144.
doi:, accessed 23 July 2022.

Robert Wachal, 1971. “Humanities and computers: A personal view,” North American Review, volume 256, number 1, pp. 30–33.

Ludo Waltman, Nees Jan van Eck, and Ed C.M. Noyons, 2010. “A unified approach to mapping and clustering of bibliometric networks,” Journal of Informetrics, volume 4, number 4, pp. 629–635.
doi:, accessed 23 July 2022.

Idongesit Williams, 2020. “E-government, yesterday, today and the future,” In: Anil Sieben (editor). E-government: Perspectives, challenges and opportunities. Hauppauge, N.Y.: Nova Science Publishers.

Svetozar Zdravković, 2008. “Telemedicine: Perspectives and expectations,” Archive of Oncology, volume 16, numbers 3–4, pp. 69–73.
doi:, accessed 23 July 2022.

Juan Zhang, Qi Yu, Fashan Zheng, Chao Long, Zuxun Lu, and Zhiguang Duan, 2016. “Comparing keywords plus of WOS and author keywords: A case study of patient adherence research,” Journal of the Association for Information Science and Technology, volume 67, number 4, pp. 967–972.
doi:, accessed 23 July 2022.


Editorial history

Received 5 October 2021; revised 22 June 2022; revised 18 July 2022; accepted 18 July 2022.

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Unveiling 100 years of digitalization as a scholarly object
by Katarina L. Gidlund and Leif Sundberg.
First Monday, Volume 27, Number 8 - 1 August 2022