evolution of technology research paper

Theories of the Evolution of Technology Based on Processes of Competitive Substitution and Multi-Mode Interaction Between Technologies

Journal of Economics Bibliography, vol. 6, n. 2, pp. 99-109, 2019

14 Pages Posted: 23 Jul 2019

Mario Coccia

National Research Council of Italy (CNR)

Date Written: July 22, 2019

Evolution of technology is a stepwise advancement of a complex system of artifact, driven by interaction with sub-systems and other systems, considering technical choices, technical requirements and science advances, which generate new and/or improved products or processes for use or consumption to satisfy increasing needs and/or to solve complex problems of people in society. This study explains evolution of technology with two different approaches: theories based on processes of competitive substitution of a new technology for the old one and theories considering a multi-mode interaction between technologies, such as the theory of technological parasitism. These theories described here can encourage further theoretical and empirical exploration in the terra incognita of the evolution of technology to explain economic and social change in human society.

Keywords: Evolution of Technology, Technological Evolution, Technological Change, Technological Progress, Technological Advances, Technological Parasitism.

JEL Classification: O31, O32, O33, O39

Suggested Citation: Suggested Citation

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• Published: 12 February 2024

Education reform and change driven by digital technology: a bibliometric study from a global perspective

• Chengliang Wang 1 ,
• Xiaojiao Chen 1 ,
• Teng Yu   ORCID: orcid.org/0000-0001-5198-7261 2 , 3 ,
• Yidan Liu 1 , 4 &
• Yuhui Jing 1  

Humanities and Social Sciences Communications volume  11 , Article number:  256 ( 2024 ) Cite this article

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• Development studies
• Science, technology and society

Amidst the global digital transformation of educational institutions, digital technology has emerged as a significant area of interest among scholars. Such technologies have played an instrumental role in enhancing learner performance and improving the effectiveness of teaching and learning. These digital technologies also ensure the sustainability and stability of education during the epidemic. Despite this, a dearth of systematic reviews exists regarding the current state of digital technology application in education. To address this gap, this study utilized the Web of Science Core Collection as a data source (specifically selecting the high-quality SSCI and SCIE) and implemented a topic search by setting keywords, yielding 1849 initial publications. Furthermore, following the PRISMA guidelines, we refined the selection to 588 high-quality articles. Using software tools such as CiteSpace, VOSviewer, and Charticulator, we reviewed these 588 publications to identify core authors (such as Selwyn, Henderson, Edwards), highly productive countries/regions (England, Australia, USA), key institutions (Monash University, Australian Catholic University), and crucial journals in the field ( Education and Information Technologies , Computers & Education , British Journal of Educational Technology ). Evolutionary analysis reveals four developmental periods in the research field of digital technology education application: the embryonic period, the preliminary development period, the key exploration, and the acceleration period of change. The study highlights the dual influence of technological factors and historical context on the research topic. Technology is a key factor in enabling education to transform and upgrade, and the context of the times is an important driving force in promoting the adoption of new technologies in the education system and the transformation and upgrading of education. Additionally, the study identifies three frontier hotspots in the field: physical education, digital transformation, and professional development under the promotion of digital technology. This study presents a clear framework for digital technology application in education, which can serve as a valuable reference for researchers and educational practitioners concerned with digital technology education application in theory and practice.

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Introduction.

Digital technology has become an essential component of modern education, facilitating the extension of temporal and spatial boundaries and enriching the pedagogical contexts (Selwyn and Facer, 2014 ). The advent of mobile communication technology has enabled learning through social media platforms (Szeto et al. 2015 ; Pires et al. 2022 ), while the advancement of augmented reality technology has disrupted traditional conceptions of learning environments and spaces (Perez-Sanagustin et al., 2014 ; Kyza and Georgiou, 2018 ). A wide range of digital technologies has enabled learning to become a norm in various settings, including the workplace (Sjöberg and Holmgren, 2021 ), home (Nazare et al. 2022 ), and online communities (Tang and Lam, 2014 ). Education is no longer limited to fixed locations and schedules, but has permeated all aspects of life, allowing learning to continue at any time and any place (Camilleri and Camilleri, 2016 ; Selwyn and Facer, 2014 ).

The advent of digital technology has led to the creation of several informal learning environments (Greenhow and Lewin, 2015 ) that exhibit divergent form, function, features, and patterns in comparison to conventional learning environments (Nygren et al. 2019 ). Consequently, the associated teaching and learning processes, as well as the strategies for the creation, dissemination, and acquisition of learning resources, have undergone a complete overhaul. The ensuing transformations have posed a myriad of novel issues, such as the optimal structuring of teaching methods by instructors and the adoption of appropriate learning strategies by students in the new digital technology environment. Consequently, an examination of the principles that underpin effective teaching and learning in this environment is a topic of significant interest to numerous scholars engaged in digital technology education research.

Over the course of the last two decades, digital technology has made significant strides in the field of education, notably in extending education time and space and creating novel educational contexts with sustainability. Despite research attempts to consolidate the application of digital technology in education, previous studies have only focused on specific aspects of digital technology, such as Pinto and Leite’s ( 2020 ) investigation into digital technology in higher education and Mustapha et al.’s ( 2021 ) examination of the role and value of digital technology in education during the pandemic. While these studies have provided valuable insights into the practical applications of digital technology in particular educational domains, they have not comprehensively explored the macro-mechanisms and internal logic of digital technology implementation in education. Additionally, these studies were conducted over a relatively brief period, making it challenging to gain a comprehensive understanding of the macro-dynamics and evolutionary process of digital technology in education. Some studies have provided an overview of digital education from an educational perspective but lack a precise understanding of technological advancement and change (Yang et al. 2022 ). Therefore, this study seeks to employ a systematic scientific approach to collate relevant research from 2000 to 2022, comprehend the internal logic and development trends of digital technology in education, and grasp the outstanding contribution of digital technology in promoting the sustainability of education in time and space. In summary, this study aims to address the following questions:

RQ1: Since the turn of the century, what is the productivity distribution of the field of digital technology education application research in terms of authorship, country/region, institutional and journal level?

RQ2: What is the development trend of research on the application of digital technology in education in the past two decades?

RQ3: What are the current frontiers of research on the application of digital technology in education?

Literature review

Although the term “digital technology” has become ubiquitous, a unified definition has yet to be agreed upon by scholars. Because the meaning of the word digital technology is closely related to the specific context. Within the educational research domain, Selwyn’s ( 2016 ) definition is widely favored by scholars (Pinto and Leite, 2020 ). Selwyn ( 2016 ) provides a comprehensive view of various concrete digital technologies and their applications in education through ten specific cases, such as immediate feedback in classes, orchestrating teaching, and community learning. Through these specific application scenarios, Selwyn ( 2016 ) argues that digital technology encompasses technologies associated with digital devices, including but not limited to tablets, smartphones, computers, and social media platforms (such as Facebook and YouTube). Furthermore, Further, the behavior of accessing the internet at any location through portable devices can be taken as an extension of the behavior of applying digital technology.

The evolving nature of digital technology has significant implications in the field of education. In the 1890s, the focus of digital technology in education was on comprehending the nuances of digital space, digital culture, and educational methodologies, with its connotations aligned more towards the idea of e-learning. The advent and subsequent widespread usage of mobile devices since the dawn of the new millennium have been instrumental in the rapid expansion of the concept of digital technology. Notably, mobile learning devices such as smartphones and tablets, along with social media platforms, have become integral components of digital technology (Conole and Alevizou, 2010 ; Batista et al. 2016 ). In recent times, the burgeoning application of AI technology in the education sector has played a vital role in enriching the digital technology lexicon (Banerjee et al. 2021 ). ChatGPT, for instance, is identified as a novel educational technology that has immense potential to revolutionize future education (Rospigliosi, 2023 ; Arif, Munaf and Ul-Haque, 2023 ).

Pinto and Leite ( 2020 ) conducted a comprehensive macroscopic survey of the use of digital technologies in the education sector and identified three distinct categories, namely technologies for assessment and feedback, mobile technologies, and Information Communication Technologies (ICT). This classification criterion is both macroscopic and highly condensed. In light of the established concept definitions of digital technology in the educational research literature, this study has adopted the characterizations of digital technology proposed by Selwyn ( 2016 ) and Pinto and Leite ( 2020 ) as crucial criteria for analysis and research inclusion. Specifically, this criterion encompasses several distinct types of digital technologies, including Information and Communication Technologies (ICT), Mobile tools, eXtended Reality (XR) Technologies, Assessment and Feedback systems, Learning Management Systems (LMS), Publish and Share tools, Collaborative systems, Social media, Interpersonal Communication tools, and Content Aggregation tools.

Methodology and materials

Research method: bibliometric.

The research on econometric properties has been present in various aspects of human production and life, yet systematic scientific theoretical guidance has been lacking, resulting in disorganization. In 1969, British scholar Pritchard ( 1969 ) proposed “bibliometrics,” which subsequently emerged as an independent discipline in scientific quantification research. Initially, Pritchard defined bibliometrics as “the application of mathematical and statistical methods to books and other media of communication,” however, the definition was not entirely rigorous. To remedy this, Hawkins ( 2001 ) expanded Pritchard’s definition to “the quantitative analysis of the bibliographic features of a body of literature.” De Bellis further clarified the objectives of bibliometrics, stating that it aims to analyze and identify patterns in literature, such as the most productive authors, institutions, countries, and journals in scientific disciplines, trends in literary production over time, and collaboration networks (De Bellis, 2009 ). According to Garfield ( 2006 ), bibliometric research enables the examination of the history and structure of a field, the flow of information within the field, the impact of journals, and the citation status of publications over a longer time scale. All of these definitions illustrate the unique role of bibliometrics as a research method for evaluating specific research fields.

This study uses CiteSpace, VOSviewer, and Charticulator to analyze data and create visualizations. Each of these three tools has its own strengths and can complement each other. CiteSpace and VOSviewer use set theory and probability theory to provide various visualization views in fields such as keywords, co-occurrence, and co-authors. They are easy to use and produce visually appealing graphics (Chen, 2006 ; van Eck and Waltman, 2009 ) and are currently the two most widely used bibliometric tools in the field of visualization (Pan et al. 2018 ). In this study, VOSviewer provided the data necessary for the Performance Analysis; Charticulator was then used to redraw using the tabular data exported from VOSviewer (for creating the chord diagram of country collaboration); this was to complement the mapping process, while CiteSpace was primarily utilized to generate keyword maps and conduct burst word analysis.

Data retrieval

This study selected documents from the Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI) in the Web of Science Core Collection as the data source, for the following reasons:

(1) The Web of Science Core Collection, as a high-quality digital literature resource database, has been widely accepted by many researchers and is currently considered the most suitable database for bibliometric analysis (Jing et al. 2023a ). Compared to other databases, Web of Science provides more comprehensive data information (Chen et al. 2022a ), and also provides data formats suitable for analysis using VOSviewer and CiteSpace (Gaviria-Marin et al. 2019 ).

(2) The application of digital technology in the field of education is an interdisciplinary research topic, involving technical knowledge literature belonging to the natural sciences and education-related literature belonging to the social sciences. Therefore, it is necessary to select Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI) as the sources of research data, ensuring the comprehensiveness of data while ensuring the reliability and persuasiveness of bibliometric research (Hwang and Tsai, 2011 ; Wang et al. 2022 ).

After establishing the source of research data, it is necessary to determine a retrieval strategy (Jing et al. 2023b ). The choice of a retrieval strategy should consider a balance between the breadth and precision of the search formula. That is to say, it should encompass all the literature pertaining to the research topic while excluding irrelevant documents as much as possible. In light of this, this study has set a retrieval strategy informed by multiple related papers (Mustapha et al. 2021 ; Luo et al. 2021 ). The research by Mustapha et al. ( 2021 ) guided us in selecting keywords (“digital” AND “technolog*”) to target digital technology, while Luo et al. ( 2021 ) informed the selection of terms (such as “instruct*,” “teach*,” and “education”) to establish links with the field of education. Then, based on the current application of digital technology in the educational domain and the scope of selection criteria, we constructed the final retrieval strategy. Following the general patterns of past research (Jing et al. 2023a , 2023b ), we conducted a specific screening using the topic search (Topics, TS) function in Web of Science. For the specific criteria used in the screening for this study, please refer to Table 1 .

Literature screening

Literature acquired through keyword searches may contain ostensibly related yet actually unrelated works. Therefore, to ensure the close relevance of literature included in the analysis to the research topic, it is often necessary to perform a manual screening process to identify the final literature to be analyzed, subsequent to completing the initial literature search.

The manual screening process consists of two steps. Initially, irrelevant literature is weeded out based on the title and abstract, with two members of the research team involved in this phase. This stage lasted about one week, resulting in 1106 articles being retained. Subsequently, a comprehensive review of the full text is conducted to accurately identify the literature required for the study. To carry out the second phase of manual screening effectively and scientifically, and to minimize the potential for researcher bias, the research team established the inclusion criteria presented in Table 2 . Three members were engaged in this phase, which took approximately 2 weeks, culminating in the retention of 588 articles after meticulous screening. The entire screening process is depicted in Fig. 1 , adhering to the PRISMA guidelines (Page et al. 2021 ).

The process of obtaining and filtering the necessary literature data for research.

Data standardization

Nguyen and Hallinger ( 2020 ) pointed out that raw data extracted from scientific databases often contains multiple expressions of the same term, and not addressing these synonymous expressions could affect research results in bibliometric analysis. For instance, in the original data, the author list may include “Tsai, C. C.” and “Tsai, C.-C.”, while the keyword list may include “professional-development” and “professional development,” which often require merging. Therefore, before analyzing the selected literature, a data disambiguation process is necessary to standardize the data (Strotmann and Zhao, 2012 ; Van Eck and Waltman, 2019 ). This study adopted the data standardization process proposed by Taskin and Al ( 2019 ), mainly including the following standardization operations:

Firstly, the author and source fields in the data are corrected and standardized to differentiate authors with similar names.

Secondly, the study checks whether the journals to which the literature belongs have been renamed in the past over 20 years, so as to avoid the influence of periodical name change on the analysis results.

Finally, the keyword field is standardized by unifying parts of speech and singular/plural forms of keywords, which can help eliminate redundant entries in the knowledge graph.

Performance analysis (RQ1)

This section offers a thorough and detailed analysis of the state of research in the field of digital technology education. By utilizing descriptive statistics and visual maps, it provides a comprehensive overview of the development trends, authors, countries, institutions, and journal distribution within the field. The insights presented in this section are of great significance in advancing our understanding of the current state of research in this field and identifying areas for further investigation. The use of visual aids to display inter-country cooperation and the evolution of the field adds to the clarity and coherence of the analysis.

Time trend of the publications

To understand a research field, it is first necessary to understand the most basic quantitative information, among which the change in the number of publications per year best reflects the development trend of a research field. Figure 2 shows the distribution of publication dates.

Time trend of the publications on application of digital technology in education.

From the Fig. 2 , it can be seen that the development of this field over the past over 20 years can be roughly divided into three stages. The first stage was from 2000 to 2007, during which the number of publications was relatively low. Due to various factors such as technological maturity, the academic community did not pay widespread attention to the role of digital technology in expanding the scope of teaching and learning. The second stage was from 2008 to 2019, during which the overall number of publications showed an upward trend, and the development of the field entered an accelerated period, attracting more and more scholars’ attention. The third stage was from 2020 to 2022, during which the number of publications stabilized at around 100. During this period, the impact of the pandemic led to a large number of scholars focusing on the role of digital technology in education during the pandemic, and research on the application of digital technology in education became a core topic in social science research.

Analysis of authors

An analysis of the author’s publication volume provides information about the representative scholars and core research strengths of a research area. Table 3 presents information on the core authors in adaptive learning research, including name, publication number, and average number of citations per article (based on the analysis and statistics from VOSviewer).

Variations in research foci among scholars abound. Within the field of digital technology education application research over the past two decades, Neil Selwyn stands as the most productive author, having published 15 papers garnering a total of 1027 citations, resulting in an average of 68.47 citations per paper. As a Professor at the Faculty of Education at Monash University, Selwyn concentrates on exploring the application of digital technology in higher education contexts (Selwyn et al. 2021 ), as well as related products in higher education such as Coursera, edX, and Udacity MOOC platforms (Bulfin et al. 2014 ). Selwyn’s contributions to the educational sociology perspective include extensive research on the impact of digital technology on education, highlighting the spatiotemporal extension of educational processes and practices through technological means as the greatest value of educational technology (Selwyn, 2012 ; Selwyn and Facer, 2014 ). In addition, he provides a blueprint for the development of future schools in 2030 based on the present impact of digital technology on education (Selwyn et al. 2019 ). The second most productive author in this field, Henderson, also offers significant contributions to the understanding of the important value of digital technology in education, specifically in the higher education setting, with a focus on the impact of the pandemic (Henderson et al. 2015 ; Cohen et al. 2022 ). In contrast, Edwards’ research interests focus on early childhood education, particularly the application of digital technology in this context (Edwards, 2013 ; Bird and Edwards, 2015 ). Additionally, on the technical level, Edwards also mainly prefers digital game technology, because it is a digital technology that children are relatively easy to accept (Edwards, 2015 ).

Analysis of countries/regions and organization

The present study aimed to ascertain the leading countries in digital technology education application research by analyzing 75 countries related to 558 works of literature. Table 4 depicts the top ten countries that have contributed significantly to this field in terms of publication count (based on the analysis and statistics from VOSviewer). Our analysis of Table 4 data shows that England emerged as the most influential country/region, with 92 published papers and 2401 citations. Australia and the United States secured the second and third ranks, respectively, with 90 papers (2187 citations) and 70 papers (1331 citations) published. Geographically, most of the countries featured in the top ten publication volumes are situated in Australia, North America, and Europe, with China being the only exception. Notably, all these countries, except China, belong to the group of developed nations, suggesting that economic strength is a prerequisite for fostering research in the digital technology education application field.

This study presents a visual representation of the publication output and cooperation relationships among different countries in the field of digital technology education application research. Specifically, a chord diagram is employed to display the top 30 countries in terms of publication output, as depicted in Fig. 3 . The chord diagram is composed of nodes and chords, where the nodes are positioned as scattered points along the circumference, and the length of each node corresponds to the publication output, with longer lengths indicating higher publication output. The chords, on the other hand, represent the cooperation relationships between any two countries, and are weighted based on the degree of closeness of the cooperation, with wider chords indicating closer cooperation. Through the analysis of the cooperation relationships, the findings suggest that the main publishing countries in this field are engaged in cooperative relationships with each other, indicating a relatively high level of international academic exchange and research internationalization.

In the diagram, nodes are scattered along the circumference of a circle, with the length of each node representing the volume of publications. The weighted arcs connecting any two points on the circle are known as chords, representing the collaborative relationship between the two, with the width of the arc indicating the closeness of the collaboration.

Further analyzing Fig. 3 , we can extract more valuable information, enabling a deeper understanding of the connections between countries in the research field of digital technology in educational applications. It is evident that certain countries, such as the United States, China, and England, display thicker connections, indicating robust collaborative relationships in terms of productivity. These thicker lines signify substantial mutual contributions and shared objectives in certain sectors or fields, highlighting the interconnectedness and global integration in these areas. By delving deeper, we can also explore potential future collaboration opportunities through the chord diagram, identifying possible partners to propel research and development in this field. In essence, the chord diagram successfully encapsulates and conveys the multi-dimensionality of global productivity and cooperation, allowing for a comprehensive understanding of the intricate inter-country relationships and networks in a global context, providing valuable guidance and insights for future research and collaborations.

An in-depth examination of the publishing institutions is provided in Table 5 , showcasing the foremost 10 institutions ranked by their publication volume. Notably, Monash University and Australian Catholic University, situated in Australia, have recorded the most prolific publications within the digital technology education application realm, with 22 and 10 publications respectively. Moreover, the University of Oslo from Norway is featured among the top 10 publishing institutions, with an impressive average citation count of 64 per publication. It is worth highlighting that six institutions based in the United Kingdom were also ranked within the top 10 publishing institutions, signifying their leading position in this area of research.

Analysis of journals

Journals are the main carriers for publishing high-quality papers. Some scholars point out that the two key factors to measure the influence of journals in the specified field are the number of articles published and the number of citations. The more papers published in a magazine and the more citations, the greater its influence (Dzikowski, 2018 ). Therefore, this study utilized VOSviewer to statistically analyze the top 10 journals with the most publications in the field of digital technology in education and calculated the average citations per article (see Table 6 ).

Based on Table 6 , it is apparent that the highest number of articles in the domain of digital technology in education research were published in Education and Information Technologies (47 articles), Computers & Education (34 articles), and British Journal of Educational Technology (32 articles), indicating a higher article output compared to other journals. This underscores the fact that these three journals concentrate more on the application of digital technology in education. Furthermore, several other journals, such as Technology Pedagogy and Education and Sustainability, have published more than 15 articles in this domain. Sustainability represents the open access movement, which has notably facilitated research progress in this field, indicating that the development of open access journals in recent years has had a significant impact. Although there is still considerable disagreement among scholars on the optimal approach to achieve open access, the notion that research outcomes should be accessible to all is widely recognized (Huang et al. 2020 ). On further analysis of the research fields to which these journals belong, except for Sustainability, it is evident that they all pertain to educational technology, thus providing a qualitative definition of the research area of digital technology education from the perspective of journals.

Temporal keyword analysis: thematic evolution (RQ2)

The evolution of research themes is a dynamic process, and previous studies have attempted to present the developmental trajectory of fields by drawing keyword networks in phases (Kumar et al. 2021 ; Chen et al. 2022b ). To understand the shifts in research topics across different periods, this study follows past research and, based on the significant changes in the research field and corresponding technological advancements during the outlined periods, divides the timeline into four stages (the first stage from January 2000 to December 2005, the second stage from January 2006 to December 2011, the third stage from January 2012 to December 2017; and the fourth stage from January 2018 to December 2022). The division into these four stages was determined through a combination of bibliometric analysis and literature review, which presented a clear trajectory of the field’s development. The research analyzes the keyword networks for each time period (as there are only three articles in the first stage, it was not possible to generate an appropriate keyword co-occurrence map, hence only the keyword co-occurrence maps from the second to the fourth stages are provided), to understand the evolutionary track of the digital technology education application research field over time.

2000.1–2005.12: germination period

From January 2000 to December 2005, digital technology education application research was in its infancy. Only three studies focused on digital technology, all of which were related to computers. Due to the popularity of computers, the home became a new learning environment, highlighting the important role of digital technology in expanding the scope of learning spaces (Sutherland et al. 2000 ). In specific disciplines and contexts, digital technology was first favored in medical clinical practice, becoming an important tool for supporting the learning of clinical knowledge and practice (Tegtmeyer et al. 2001 ; Durfee et al. 2003 ).

2006.1–2011.12: initial development period

Between January 2006 and December 2011, it was the initial development period of digital technology education research. Significant growth was observed in research related to digital technology, and discussions and theoretical analyses about “digital natives” emerged. During this phase, scholars focused on the debate about “how to use digital technology reasonably” and “whether current educational models and school curriculum design need to be adjusted on a large scale” (Bennett and Maton, 2010 ; Selwyn, 2009 ; Margaryan et al. 2011 ). These theoretical and speculative arguments provided a unique perspective on the impact of cognitive digital technology on education and teaching. As can be seen from the vocabulary such as “rethinking”, “disruptive pedagogy”, and “attitude” in Fig. 4 , many scholars joined the calm reflection and analysis under the trend of digital technology (Laurillard, 2008 ; Vratulis et al. 2011 ). During this phase, technology was still undergoing dramatic changes. The development of mobile technology had already caught the attention of many scholars (Wong et al. 2011 ), but digital technology represented by computers was still very active (Selwyn et al. 2011 ). The change in technological form would inevitably lead to educational transformation. Collins and Halverson ( 2010 ) summarized the prospects and challenges of using digital technology for learning and educational practices, believing that digital technology would bring a disruptive revolution to the education field and bring about a new educational system. In addition, the term “teacher education” in Fig. 4 reflects the impact of digital technology development on teachers. The rapid development of technology has widened the generation gap between teachers and students. To ensure smooth communication between teachers and students, teachers must keep up with the trend of technological development and establish a lifelong learning concept (Donnison, 2009 ).

In the diagram, each node represents a keyword, with the size of the node indicating the frequency of occurrence of the keyword. The connections represent the co-occurrence relationships between keywords, with a higher frequency of co-occurrence resulting in tighter connections.

2012.1–2017.12: critical exploration period

During the period spanning January 2012 to December 2017, the application of digital technology in education research underwent a significant exploration phase. As can be seen from Fig. 5 , different from the previous stage, the specific elements of specific digital technology have started to increase significantly, including the enrichment of technological contexts, the greater variety of research methods, and the diversification of learning modes. Moreover, the temporal and spatial dimensions of the learning environment were further de-emphasized, as noted in previous literature (Za et al. 2014 ). Given the rapidly accelerating pace of technological development, the education system in the digital era is in urgent need of collaborative evolution and reconstruction, as argued by Davis, Eickelmann, and Zaka ( 2013 ).

In the domain of digital technology, social media has garnered substantial scholarly attention as a promising avenue for learning, as noted by Pasquini and Evangelopoulos ( 2016 ). The implementation of social media in education presents several benefits, including the liberation of education from the restrictions of physical distance and time, as well as the erasure of conventional educational boundaries. The user-generated content (UGC) model in social media has emerged as a crucial source for knowledge creation and distribution, with the widespread adoption of mobile devices. Moreover, social networks have become an integral component of ubiquitous learning environments (Hwang et al. 2013 ). The utilization of social media allows individuals to function as both knowledge producers and recipients, which leads to a blurring of the conventional roles of learners and teachers. On mobile platforms, the roles of learners and teachers are not fixed, but instead interchangeable.

In terms of research methodology, the prevalence of empirical studies with survey designs in the field of educational technology during this period is evident from the vocabulary used, such as “achievement,” “acceptance,” “attitude,” and “ict.” in Fig. 5 . These studies aim to understand learners’ willingness to adopt and attitudes towards new technologies, and some seek to investigate the impact of digital technologies on learning outcomes through quasi-experimental designs (Domínguez et al. 2013 ). Among these empirical studies, mobile learning emerged as a hot topic, and this is not surprising. First, the advantages of mobile learning environments over traditional ones have been empirically demonstrated (Hwang et al. 2013 ). Second, learners born around the turn of the century have been heavily influenced by digital technologies and have developed their own learning styles that are more open to mobile devices as a means of learning. Consequently, analyzing mobile learning as a relatively novel mode of learning has become an important issue for scholars in the field of educational technology.

The intervention of technology has led to the emergence of several novel learning modes, with the blended learning model being the most representative one in the current phase. Blended learning, a novel concept introduced in the information age, emphasizes the integration of the benefits of traditional learning methods and online learning. This learning mode not only highlights the prominent role of teachers in guiding, inspiring, and monitoring the learning process but also underlines the importance of learners’ initiative, enthusiasm, and creativity in the learning process. Despite being an early conceptualization, blended learning’s meaning has been expanded by the widespread use of mobile technology and social media in education. The implementation of new technologies, particularly mobile devices, has resulted in the transformation of curriculum design and increased flexibility and autonomy in students’ learning processes (Trujillo Maza et al. 2016 ), rekindling scholarly attention to this learning mode. However, some scholars have raised concerns about the potential drawbacks of the blended learning model, such as its significant impact on the traditional teaching system, the lack of systematic coping strategies and relevant policies in several schools and regions (Moskal et al. 2013 ).

2018.1–2022.12: accelerated transformation period

The period spanning from January 2018 to December 2022 witnessed a rapid transformation in the application of digital technology in education research. The field of digital technology education research reached a peak period of publication, largely influenced by factors such as the COVID-19 pandemic (Yu et al. 2023 ). Research during this period was built upon the achievements, attitudes, and social media of the previous phase, and included more elements that reflect the characteristics of this research field, such as digital literacy, digital competence, and professional development, as depicted in Fig. 6 . Alongside this, scholars’ expectations for the value of digital technology have expanded, and the pursuit of improving learning efficiency and performance is no longer the sole focus. Some research now aims to cultivate learners’ motivation and enhance their self-efficacy by applying digital technology in a reasonable manner, as demonstrated by recent studies (Beardsley et al. 2021 ; Creely et al. 2021 ).

The COVID-19 pandemic has emerged as a crucial backdrop for the digital technology’s role in sustaining global education, as highlighted by recent scholarly research (Zhou et al. 2022 ; Pan and Zhang, 2020 ; Mo et al. 2022 ). The online learning environment, which is supported by digital technology, has become the primary battleground for global education (Yu, 2022 ). This social context has led to various studies being conducted, with some scholars positing that the pandemic has impacted the traditional teaching order while also expanding learning possibilities in terms of patterns and forms (Alabdulaziz, 2021 ). Furthermore, the pandemic has acted as a catalyst for teacher teaching and technological innovation, and this viewpoint has been empirically substantiated (Moorhouse and Wong, 2021 ). Additionally, some scholars believe that the pandemic’s push is a crucial driving force for the digital transformation of the education system, serving as an essential mechanism for overcoming the system’s inertia (Romero et al. 2021 ).

The rapid outbreak of the pandemic posed a challenge to the large-scale implementation of digital technologies, which was influenced by a complex interplay of subjective and objective factors. Objective constraints included the lack of infrastructure in some regions to support digital technologies, while subjective obstacles included psychological resistance among certain students and teachers (Moorhouse, 2021 ). These factors greatly impacted the progress of online learning during the pandemic. Additionally, Timotheou et al. ( 2023 ) conducted a comprehensive systematic review of existing research on digital technology use during the pandemic, highlighting the critical role played by various factors such as learners’ and teachers’ digital skills, teachers’ personal attributes and professional development, school leadership and management, and administration in facilitating the digitalization and transformation of schools.

The current stage of research is characterized by the pivotal term “digital literacy,” denoting a growing interest in learners’ attitudes and adoption of emerging technologies. Initially, the term “literacy” was restricted to fundamental abilities and knowledge associated with books and print materials (McMillan, 1996 ). However, with the swift advancement of computers and digital technology, there have been various attempts to broaden the scope of literacy beyond its traditional meaning, including game literacy (Buckingham and Burn, 2007 ), information literacy (Eisenberg, 2008 ), and media literacy (Turin and Friesem, 2020 ). Similarly, digital literacy has emerged as a crucial concept, and Gilster and Glister ( 1997 ) were the first to introduce this concept, referring to the proficiency in utilizing technology and processing digital information in academic, professional, and daily life settings. In practical educational settings, learners who possess higher digital literacy often exhibit an aptitude for quickly mastering digital devices and applying them intelligently to education and teaching (Yu, 2022 ).

The utilization of digital technology in education has undergone significant changes over the past two decades, and has been a crucial driver of educational reform with each new technological revolution. The impact of these changes on the underlying logic of digital technology education applications has been noticeable. From computer technology to more recent developments such as virtual reality (VR), augmented reality (AR), and artificial intelligence (AI), the acceleration in digital technology development has been ongoing. Educational reforms spurred by digital technology development continue to be dynamic, as each new digital innovation presents new possibilities and models for teaching practice. This is especially relevant in the post-pandemic era, where the importance of technological progress in supporting teaching cannot be overstated (Mughal et al. 2022 ). Existing digital technologies have already greatly expanded the dimensions of education in both time and space, while future digital technologies aim to expand learners’ perceptions. Researchers have highlighted the potential of integrated technology and immersive technology in the development of the educational metaverse, which is highly anticipated to create a new dimension for the teaching and learning environment, foster a new value system for the discipline of educational technology, and more effectively and efficiently achieve the grand educational blueprint of the United Nations’ Sustainable Development Goals (Zhang et al. 2022 ; Li and Yu, 2023 ).

Hotspot evolution analysis (RQ3)

The examination of keyword evolution reveals a consistent trend in the advancement of digital technology education application research. The emergence and transformation of keywords serve as indicators of the varying research interests in this field. Thus, the utilization of the burst detection function available in CiteSpace allowed for the identification of the top 10 burst words that exhibited a high level of burst strength. This outcome is illustrated in Table 7 .

According to the results presented in Table 7 , the explosive terminology within the realm of digital technology education research has exhibited a concentration mainly between the years 2018 and 2022. Prior to this time frame, the emerging keywords were limited to “information technology” and “computer”. Notably, among them, computer, as an emergent keyword, has always had a high explosive intensity from 2008 to 2018, which reflects the important position of computer in digital technology and is the main carrier of many digital technologies such as Learning Management Systems (LMS) and Assessment and Feedback systems (Barlovits et al. 2022 ).

Since 2018, an increasing number of research studies have focused on evaluating the capabilities of learners to accept, apply, and comprehend digital technologies. As indicated by the use of terms such as “digital literacy” and “digital skill,” the assessment of learners’ digital literacy has become a critical task. Scholarly efforts have been directed towards the development of literacy assessment tools and the implementation of empirical assessments. Furthermore, enhancing the digital literacy of both learners and educators has garnered significant attention. (Nagle, 2018 ; Yu, 2022 ). Simultaneously, given the widespread use of various digital technologies in different formal and informal learning settings, promoting learners’ digital skills has become a crucial objective for contemporary schools (Nygren et al. 2019 ; Forde and OBrien, 2022 ).

Since 2020, the field of applied research on digital technology education has witnessed the emergence of three new hotspots, all of which have been affected to some extent by the pandemic. Firstly, digital technology has been widely applied in physical education, which is one of the subjects that has been severely affected by the pandemic (Parris et al. 2022 ; Jiang and Ning, 2022 ). Secondly, digital transformation has become an important measure for most schools, especially higher education institutions, to cope with the impact of the pandemic globally (García-Morales et al. 2021 ). Although the concept of digital transformation was proposed earlier, the COVID-19 pandemic has greatly accelerated this transformation process. Educational institutions must carefully redesign their educational products to face this new situation, providing timely digital learning methods, environments, tools, and support systems that have far-reaching impacts on modern society (Krishnamurthy, 2020 ; Salas-Pilco et al. 2022 ). Moreover, the professional development of teachers has become a key mission of educational institutions in the post-pandemic era. Teachers need to have a certain level of digital literacy and be familiar with the tools and online teaching resources used in online teaching, which has become a research hotspot today. Organizing digital skills training for teachers to cope with the application of emerging technologies in education is an important issue for teacher professional development and lifelong learning (Garzón-Artacho et al. 2021 ). As the main organizers and practitioners of emergency remote teaching (ERT) during the pandemic, teachers must put cognitive effort into their professional development to ensure effective implementation of ERT (Romero-Hall and Jaramillo Cherrez, 2022 ).

The burst word “digital transformation” reveals that we are in the midst of an ongoing digital technology revolution. With the emergence of innovative digital technologies such as ChatGPT and Microsoft 365 Copilot, technology trends will continue to evolve, albeit unpredictably. While the impact of these advancements on school education remains uncertain, it is anticipated that the widespread integration of technology will significantly affect the current education system. Rejecting emerging technologies without careful consideration is unwise. Like any revolution, the technological revolution in the education field has both positive and negative aspects. Detractors argue that digital technology disrupts learning and memory (Baron, 2021 ) or causes learners to become addicted and distracted from learning (Selwyn and Aagaard, 2020 ). On the other hand, the prudent use of digital technology in education offers a glimpse of a golden age of open learning. Educational leaders and practitioners have the opportunity to leverage cutting-edge digital technologies to address current educational challenges and develop a rational path for the sustainable and healthy growth of education.

Discussion on performance analysis (RQ1)

The field of digital technology education application research has experienced substantial growth since the turn of the century, a phenomenon that is quantifiably apparent through an analysis of authorship, country/region contributions, and institutional engagement. This expansion reflects the increased integration of digital technologies in educational settings and the heightened scholarly interest in understanding and optimizing their use.

Discussion on authorship productivity in digital technology education research

The authorship distribution within digital technology education research is indicative of the field’s intellectual structure and depth. A primary figure in this domain is Neil Selwyn, whose substantial citation rate underscores the profound impact of his work. His focus on the implications of digital technology in higher education and educational sociology has proven to be seminal. Selwyn’s research trajectory, especially the exploration of spatiotemporal extensions of education through technology, provides valuable insights into the multifaceted role of digital tools in learning processes (Selwyn et al. 2019 ).

Other notable contributors, like Henderson and Edwards, present diversified research interests, such as the impact of digital technologies during the pandemic and their application in early childhood education, respectively. Their varied focuses highlight the breadth of digital technology education research, encompassing pedagogical innovation, technological adaptation, and policy development.

Discussion on country/region-level productivity and collaboration

At the country/region level, the United Kingdom, specifically England, emerges as a leading contributor with 92 published papers and a significant citation count. This is closely followed by Australia and the United States, indicating a strong English-speaking research axis. Such geographical concentration of scholarly output often correlates with investment in research and development, technological infrastructure, and the prevalence of higher education institutions engaging in cutting-edge research.

China’s notable inclusion as the only non-Western country among the top contributors to the field suggests a growing research capacity and interest in digital technology in education. However, the lower average citation per paper for China could reflect emerging engagement or different research focuses that may not yet have achieved the same international recognition as Western counterparts.

The chord diagram analysis furthers this understanding, revealing dense interconnections between countries like the United States, China, and England, which indicates robust collaborations. Such collaborations are fundamental in addressing global educational challenges and shaping international research agendas.

Discussion on institutional-level contributions to digital technology education

Institutional productivity in digital technology education research reveals a constellation of universities driving the field forward. Monash University and the Australian Catholic University have the highest publication output, signaling Australia’s significant role in advancing digital education research. The University of Oslo’s remarkable average citation count per publication indicates influential research contributions, potentially reflecting high-quality studies that resonate with the broader academic community.

The strong showing of UK institutions, including the University of London, The Open University, and the University of Cambridge, reinforces the UK’s prominence in this research field. Such institutions are often at the forefront of pedagogical innovation, benefiting from established research cultures and funding mechanisms that support sustained inquiry into digital education.

Discussion on journal publication analysis

An examination of journal outputs offers a lens into the communicative channels of the field’s knowledge base. Journals such as Education and Information Technologies , Computers & Education , and the British Journal of Educational Technology not only serve as the primary disseminators of research findings but also as indicators of research quality and relevance. The impact factor (IF) serves as a proxy for the quality and influence of these journals within the academic community.

The high citation counts for articles published in Computers & Education suggest that research disseminated through this medium has a wide-reaching impact and is of particular interest to the field. This is further evidenced by its significant IF of 11.182, indicating that the journal is a pivotal platform for seminal work in the application of digital technology in education.

The authorship, regional, and institutional productivity in the field of digital technology education application research collectively narrate the evolution of this domain since the turn of the century. The prominence of certain authors and countries underscores the importance of socioeconomic factors and existing academic infrastructure in fostering research productivity. Meanwhile, the centrality of specific journals as outlets for high-impact research emphasizes the role of academic publishing in shaping the research landscape.

As the field continues to grow, future research may benefit from leveraging the collaborative networks that have been elucidated through this analysis, perhaps focusing on underrepresented regions to broaden the scope and diversity of research. Furthermore, the stabilization of publication numbers in recent years invites a deeper exploration into potential plateaus in research trends or saturation in certain sub-fields, signaling an opportunity for novel inquiries and methodological innovations.

Discussion on the evolutionary trends (RQ2)

The evolution of the research field concerning the application of digital technology in education over the past two decades is a story of convergence, diversification, and transformation, shaped by rapid technological advancements and shifting educational paradigms.

At the turn of the century, the inception of digital technology in education was largely exploratory, with a focus on how emerging computer technologies could be harnessed to enhance traditional learning environments. Research from this early period was primarily descriptive, reflecting on the potential and challenges of incorporating digital tools into the educational setting. This phase was critical in establishing the fundamental discourse that would guide subsequent research, as it set the stage for understanding the scope and impact of digital technology in learning spaces (Wang et al. 2023 ).

As the first decade progressed, the narrative expanded to encompass the pedagogical implications of digital technologies. This was a period of conceptual debates, where terms like “digital natives” and “disruptive pedagogy” entered the academic lexicon, underscoring the growing acknowledgment of digital technology as a transformative force within education (Bennett and Maton, 2010 ). During this time, the research began to reflect a more nuanced understanding of the integration of technology, considering not only its potential to change where and how learning occurred but also its implications for educational equity and access.

In the second decade, with the maturation of internet connectivity and mobile technology, the focus of research shifted from theoretical speculations to empirical investigations. The proliferation of digital devices and the ubiquity of social media influenced how learners interacted with information and each other, prompting a surge in studies that sought to measure the impact of these tools on learning outcomes. The digital divide and issues related to digital literacy became central concerns, as scholars explored the varying capacities of students and educators to engage with technology effectively.

Throughout this period, there was an increasing emphasis on the individualization of learning experiences, facilitated by adaptive technologies that could cater to the unique needs and pacing of learners (Jing et al. 2023a ). This individualization was coupled with a growing recognition of the importance of collaborative learning, both online and offline, and the role of digital tools in supporting these processes. Blended learning models, which combined face-to-face instruction with online resources, emerged as a significant trend, advocating for a balance between traditional pedagogies and innovative digital strategies.

The later years, particularly marked by the COVID-19 pandemic, accelerated the necessity for digital technology in education, transforming it from a supplementary tool to an essential platform for delivering education globally (Mo et al. 2022 ; Mustapha et al. 2021 ). This era brought about an unprecedented focus on online learning environments, distance education, and virtual classrooms. Research became more granular, examining not just the pedagogical effectiveness of digital tools, but also their role in maintaining continuity of education during crises, their impact on teacher and student well-being, and their implications for the future of educational policy and infrastructure.

Across these two decades, the research field has seen a shift from examining digital technology as an external addition to the educational process, to viewing it as an integral component of curriculum design, instructional strategies, and even assessment methods. The emergent themes have broadened from a narrow focus on specific tools or platforms to include wider considerations such as data privacy, ethical use of technology, and the environmental impact of digital tools.

Moreover, the field has moved from considering the application of digital technology in education as a primarily cognitive endeavor to recognizing its role in facilitating socio-emotional learning, digital citizenship, and global competencies. Researchers have increasingly turned their attention to the ways in which technology can support collaborative skills, cultural understanding, and ethical reasoning within diverse student populations.

In summary, the past over twenty years in the research field of digital technology applications in education have been characterized by a progression from foundational inquiries to complex analyses of digital integration. This evolution has mirrored the trajectory of technology itself, from a facilitative tool to a pervasive ecosystem defining contemporary educational experiences. As we look to the future, the field is poised to delve into the implications of emerging technologies like AI, AR, and VR, and their potential to redefine the educational landscape even further. This ongoing metamorphosis suggests that the application of digital technology in education will continue to be a rich area of inquiry, demanding continual adaptation and forward-thinking from educators and researchers alike.

Discussion on the study of research hotspots (RQ3)

The analysis of keyword evolution in digital technology education application research elucidates the current frontiers in the field, reflecting a trajectory that is in tandem with the rapidly advancing digital age. This landscape is sculpted by emergent technological innovations and shaped by the demands of an increasingly digital society.

Interdisciplinary integration and pedagogical transformation

One of the frontiers identified from recent keyword bursts includes the integration of digital technology into diverse educational contexts, particularly noted with the keyword “physical education.” The digitalization of disciplines traditionally characterized by physical presence illustrates the pervasive reach of technology and signifies a push towards interdisciplinary integration where technology is not only a facilitator but also a transformative agent. This integration challenges educators to reconceptualize curriculum delivery to accommodate digital tools that can enhance or simulate the physical aspects of learning.

Digital literacy and skills acquisition

Another pivotal frontier is the focus on “digital literacy” and “digital skill”, which has intensified in recent years. This suggests a shift from mere access to technology towards a comprehensive understanding and utilization of digital tools. In this realm, the emphasis is not only on the ability to use technology but also on critical thinking, problem-solving, and the ethical use of digital resources (Yu, 2022 ). The acquisition of digital literacy is no longer an additive skill but a fundamental aspect of modern education, essential for navigating and contributing to the digital world.

Educational digital transformation

The keyword “digital transformation” marks a significant research frontier, emphasizing the systemic changes that education institutions must undergo to align with the digital era (Romero et al. 2021 ). This transformation includes the redesigning of learning environments, pedagogical strategies, and assessment methods to harness digital technology’s full potential. Research in this area explores the complexity of institutional change, addressing the infrastructural, cultural, and policy adjustments needed for a seamless digital transition.

Engagement and participation

Further exploration into “engagement” and “participation” underscores the importance of student-centered learning environments that are mediated by technology. The current frontiers examine how digital platforms can foster collaboration, inclusivity, and active learning, potentially leading to more meaningful and personalized educational experiences. Here, the use of technology seeks to support the emotional and cognitive aspects of learning, moving beyond the transactional view of education to one that is relational and interactive.

Professional development and teacher readiness

As the field evolves, “professional development” emerges as a crucial area, particularly in light of the pandemic which necessitated emergency remote teaching. The need for teacher readiness in a digital age is a pressing frontier, with research focusing on the competencies required for educators to effectively integrate technology into their teaching practices. This includes familiarity with digital tools, pedagogical innovation, and an ongoing commitment to personal and professional growth in the digital domain.

Pandemic as a catalyst

The recent pandemic has acted as a catalyst for accelerated research and application in this field, particularly in the domains of “digital transformation,” “professional development,” and “physical education.” This period has been a litmus test for the resilience and adaptability of educational systems to continue their operations in an emergency. Research has thus been directed at understanding how digital technologies can support not only continuity but also enhance the quality and reach of education in such contexts.

Ethical and societal considerations

The frontier of digital technology in education is also expanding to consider broader ethical and societal implications. This includes issues of digital equity, data privacy, and the sociocultural impact of technology on learning communities. The research explores how educational technology can be leveraged to address inequities and create more equitable learning opportunities for all students, regardless of their socioeconomic background.

Innovation and emerging technologies

Looking forward, the frontiers are set to be influenced by ongoing and future technological innovations, such as artificial intelligence (AI) (Wu and Yu, 2023 ; Chen et al. 2022a ). The exploration into how these technologies can be integrated into educational practices to create immersive and adaptive learning experiences represents a bold new chapter for the field.

In conclusion, the current frontiers of research on the application of digital technology in education are multifaceted and dynamic. They reflect an overarching movement towards deeper integration of technology in educational systems and pedagogical practices, where the goals are not only to facilitate learning but to redefine it. As these frontiers continue to expand and evolve, they will shape the educational landscape, requiring a concerted effort from researchers, educators, policymakers, and technologists to navigate the challenges and harness the opportunities presented by the digital revolution in education.

Conclusions and future research

Conclusions.

The utilization of digital technology in education is a research area that cuts across multiple technical and educational domains and continues to experience dynamic growth due to the continuous progress of technology. In this study, a systematic review of this field was conducted through bibliometric techniques to examine its development trajectory. The primary focus of the review was to investigate the leading contributors, productive national institutions, significant publications, and evolving development patterns. The study’s quantitative analysis resulted in several key conclusions that shed light on this research field’s current state and future prospects.

(1) The research field of digital technology education applications has entered a stage of rapid development, particularly in recent years due to the impact of the pandemic, resulting in a peak of publications. Within this field, several key authors (Selwyn, Henderson, Edwards, etc.) and countries/regions (England, Australia, USA, etc.) have emerged, who have made significant contributions. International exchanges in this field have become frequent, with a high degree of internationalization in academic research. Higher education institutions in the UK and Australia are the core productive forces in this field at the institutional level.

(2) Education and Information Technologies , Computers & Education , and the British Journal of Educational Technology are notable journals that publish research related to digital technology education applications. These journals are affiliated with the research field of educational technology and provide effective communication platforms for sharing digital technology education applications.

(3) Over the past two decades, research on digital technology education applications has progressed from its early stages of budding, initial development, and critical exploration to accelerated transformation, and it is currently approaching maturity. Technological progress and changes in the times have been key driving forces for educational transformation and innovation, and both have played important roles in promoting the continuous development of education.

(4) Influenced by the pandemic, three emerging frontiers have emerged in current research on digital technology education applications, which are physical education, digital transformation, and professional development under the promotion of digital technology. These frontier research hotspots reflect the core issues that the education system faces when encountering new technologies. The evolution of research hotspots shows that technology breakthroughs in education’s original boundaries of time and space create new challenges. The continuous self-renewal of education is achieved by solving one hotspot problem after another.

The present study offers significant practical implications for scholars and practitioners in the field of digital technology education applications. Firstly, it presents a well-defined framework of the existing research in this area, serving as a comprehensive guide for new entrants to the field and shedding light on the developmental trajectory of this research domain. Secondly, the study identifies several contemporary research hotspots, thus offering a valuable decision-making resource for scholars aiming to explore potential research directions. Thirdly, the study undertakes an exhaustive analysis of published literature to identify core journals in the field of digital technology education applications, with Sustainability being identified as a promising open access journal that publishes extensively on this topic. This finding can potentially facilitate scholars in selecting appropriate journals for their research outputs.

Limitation and future research

Influenced by some objective factors, this study also has some limitations. First of all, the bibliometrics analysis software has high standards for data. In order to ensure the quality and integrity of the collected data, the research only selects the periodical papers in SCIE and SSCI indexes, which are the core collection of Web of Science database, and excludes other databases, conference papers, editorials and other publications, which may ignore some scientific research and original opinions in the field of digital technology education and application research. In addition, although this study used professional software to carry out bibliometric analysis and obtained more objective quantitative data, the analysis and interpretation of data will inevitably have a certain subjective color, and the influence of subjectivity on data analysis cannot be completely avoided. As such, future research endeavors will broaden the scope of literature screening and proactively engage scholars in the field to gain objective and state-of-the-art insights, while minimizing the adverse impact of personal subjectivity on research analysis.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/F9QMHY

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Acknowledgements

This research was supported by the Zhejiang Provincial Social Science Planning Project, “Mechanisms and Pathways for Empowering Classroom Teaching through Learning Spaces under the Strategy of High-Quality Education Development”, the 2022 National Social Science Foundation Education Youth Project “Research on the Strategy of Creating Learning Space Value and Empowering Classroom Teaching under the background of ‘Double Reduction’” (Grant No. CCA220319) and the National College Student Innovation and Entrepreneurship Training Program of China (Grant No. 202310337023).

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Wang, C., Chen, X., Yu, T. et al. Education reform and change driven by digital technology: a bibliometric study from a global perspective. Humanit Soc Sci Commun 11 , 256 (2024). https://doi.org/10.1057/s41599-024-02717-y

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The societal impacts of technological change can be seen in many domains, from messenger RNA vaccines and automation to drones and climate change. The pace of that technological change can affect its impact, and how quickly a technology improves in performance can be an indicator of its future importance. For decision-makers like investors, entrepreneurs, and policymakers, predicting which technologies are fast improving (and which are overhyped) can mean the difference between success and failure.

New research from MIT aims to assist in the prediction of technology performance improvement using U.S. patents as a dataset. The study describes 97 percent of the U.S. patent system as a set of 1,757 discrete technology domains, and quantitatively assesses each domain for its improvement potential.

“The rate of improvement can only be empirically estimated when substantial performance measurements are made over long time periods,” says Anuraag Singh SM ’20, lead author of the paper. “In some large technological fields, including software and clinical medicine, such measures have rarely, if ever, been made.”

A previous MIT study provided empirical measures for 30 technological domains, but the patent sets identified for those technologies cover less than 15 percent of the patents in the U.S. patent system. The major purpose of this new study is to provide predictions of the performance improvement rates for the thousands of domains not accessed by empirical measurement. To accomplish this, the researchers developed a method using a new probability-based algorithm, machine learning, natural language processing, and patent network analytics.

Overlap and centrality

A technology domain, as the researchers define it, consists of sets of artifacts fulfilling a specific function using a specific branch of scientific knowledge. To find the patents that best represent a domain, the team built on previous research conducted by co-author Chris Magee, a professor of the practice of engineering systems within the Institute for Data, Systems, and Society (IDSS). Magee and his colleagues found that by looking for patent overlap between the U.S. and international patent-classification systems, they could quickly identify patents that best represent a technology. The researchers ultimately created a correspondence of all patents within the U.S. patent system to a set of 1,757 technology domains.

To estimate performance improvement, Singh employed a method refined by co-authors Magee and Giorgio Triulzi, a researcher with the Sociotechnical Systems Research Center (SSRC) within IDSS and an assistant professor at Universidad de los Andes in Colombia. Their method is based on the average “centrality” of patents in the patent citation network. Centrality refers to multiple criteria for determining the ranking or importance of nodes within a network.

“Our method provides predictions of performance improvement rates for nearly all definable technologies for the first time,” says Singh.

Those rates vary — from a low of 2 percent per year for the “Mechanical skin treatment — Hair removal and wrinkles” domain to a high of 216 percent per year for the “Dynamic information exchange and support systems integrating multiple channels” domain. The researchers found that most technologies improve slowly; more than 80 percent of technologies improve at less than 25 percent per year. Notably, the number of patents in a technological area was not a strong indicator of a higher improvement rate.

“Fast-improving domains are concentrated in a few technological areas,” says Magee. “The domains that show improvement rates greater than the predicted rate for integrated chips — 42 percent, from Moore’s law — are predominantly based upon software and algorithms.”

TechNext Inc.

The researchers built an online interactive system where domains corresponding to technology-related keywords can be found along with their improvement rates. Users can input a keyword describing a technology and the system returns a prediction of improvement for the technological domain, an automated measure of the quality of the match between the keyword and the domain, and patent sets so that the reader can judge the semantic quality of the match.

Moving forward, the researchers have founded a new MIT spinoff called TechNext Inc. to further refine this technology and use it to help leaders make better decisions, from budgets to investment priorities to technology policy. Like any inventors, Magee and his colleagues want to protect their intellectual property rights. To that end, they have applied for a patent for their novel system and its unique methodology.

“Technologies that improve faster win the market,” says Singh. “Our search system enables technology managers, investors, policymakers, and entrepreneurs to quickly look up predictions of improvement rates for specific technologies.”

Adds Magee: “Our goal is to bring greater accuracy, precision, and repeatability to the as-yet fuzzy art of technology forecasting.”

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SYSTEMATIC REVIEW article

The effects of technological developments on work and their implications for continuous vocational education and training: a systematic review.

• Faculty of Human Sciences, University of Regensburg, Regensburg, Germany

Technology is changing the way organizations and their employees need to accomplish their work. Empirical evidence on this topic is scarce. The aim of this study is to provide an overview of the effects of technological developments on work characteristics and to derive the implications for work demands and continuous vocational education and training (CVET). The following research questions are answered: What are the effects of new technologies on work characteristics? What are the implications thereof for continuous vocational education and training? Technologies, defined as digital, electrical or mechanical tools that affect the accomplishment of work tasks, are considered in various disciplines, such as sociology or psychology. A theoretical framework based on theories from these disciplines (e.g., upskilling, task-based approach) was developed and statements on the relationships between technology and work characteristics, such as complexity, autonomy, or meaningfulness, were derived. A systematic literature review was conducted by searching databases from the fields of psychology, sociology, economics and educational science. Twenty-one studies met the inclusion criteria. Empirical evidence was extracted and its implications for work demands and CVET were derived by using a model that illustrates the components of learning environments. Evidence indicates an increase in complexity and mental work, especially while working with automated systems and robots. Manual work is reported to decrease on many occasions. Workload and workflow interruptions increase simultaneously with autonomy, especially with regard to digital communication devices. Role expectations and opportunities for development depend on how the profession and the technology relate to each other, especially when working with automated systems. The implications for the work demands necessary to deal with changes in work characteristics include knowledge about technology, openness toward change and technology, skills for self- and time management and for further professional and career development. Implications for the design of formal learning environments (i.e., the content, method, assessment, and guidance) include that the work demands mentioned must be part of the content of the trainings, the teachers/trainers must be equipped to promote those work demands, and that instruction models used for the learning environments must be flexible in their application.

Introduction

In the face of technology-driven disruptive changes in societal and organizational practices, continuous vocational education and training (CVET) lacks information on how the impact of technologies on work must be considered from an educational perspective ( Cascio and Montealegre, 2016 ). Research on workplace technologies, i.e., tools or systems that have the potential to replace or supplement work tasks, typically are concerned with one out of two areas of interest: First, economic and sociological research repeatedly raises the question on technological mass-unemployment and societal inequality as a result of technological advances ( Brynjolfsson and McAfee, 2014 ; Ford, 2015 ; Frey and Osborne, 2017 ). And second, management literature questions the suitability of prevailing organizational structures in the face of the so-called “fourth industrial revolution” ( Schwab, 2017 ), taking visionary leaps into a fully automated future of digital value creation ( Roblek et al., 2016 ).

Many of the contributions of scholars discuss the enormous potential of new technologies for work and society at a hypothetical level, which led to a large number of position papers. Moreover, the question on what consequences recent developments, such as working with robots, automated systems or artificial intelligence will have for different professions remain largely unclear. By examining what workplace technologies actually “do” in the work environment, it was suggested that work tasks change because of technological developments ( Autor et al., 2003 ; Autor, 2015 ). This is due to technologies substituting different operations or entire tasks and thus leave room for other activities. Jobs are defined by the work tasks and the conditions under which the tasks have to be performed. This in turn defines the necessary competences, that is the potential capacity to carry out a job (e.g., Ellström, 1997 ). Therefore, CVET needs to be informed on the changes that technology causes in work tasks and the consequential characteristics of work. Only then CVET is able to derive the required competences of employees and organize learning environments that foster the acquirement of these competences. These insights can be used to determine the implications thereof for the components of formal learning environments: content, didactics, trainer behavior, assessment, and resources (e.g., Mulder et al., 2015 ).

The aim of this systematic literature review is to get insight into the effects of new technological developments on work characteristics in order to derive the necessary work demands and their implications for the design of formal learning environments in CVET.

Therefore, the following research questions will be answered:

RQ 1 : What are the effects of new technologies on work characteristics?

RQ 2 : What are the implications thereof for continuous vocational education and training?

Theoretical considerations on the relationships between technology and work characteristics are presented before the methods for searching, selecting and analyzing suitable studies are described. Regarding the results section, the structure is based on the three main steps of analyzing the included studies: First, the variables identified within the selected studies are clustered and defined in terms of work characteristics. Second, a comprehensive overview of evidence on the relationships between technologies and work characteristics is displayed. Third, the evidence is evaluated regarding the work demands that result from technologies changing work characteristics. Finally, the implications for CVET and future research as well as the limitations of this study will be discussed.

Theoretical Framework

In this section, a conceptualization of technology and theoretical assumptions on relationships between technology and work characteristics will be outlined. Research within various disciplines, such as sociology, management, economics, educational science, and psychology was considered to inform us on the role of technology within work. Completing this section, an overview of the various components of learning environments is provided to be used as a basis for the analyses of the empirical evidence.

Outlining Technology and Recent Technological Developments

A clear definition of technology often lacks in studies, what may be due to the fact that the word itself is an “equivoque” ( Weick, 1990 , p. 1) and a “repository of overlapping inconsistent meanings” ( McOmber, 1999 , p. 149). A suitable definition can be provided by analyzing what technologies actually “do” ( Autor et al., 2003 , p. 1,280). The primary goal of technology at work is to save or enhance labor in the form of work tasks, defined as “a unit of work activity that produces output” ( Autor, 2013 , p. 186). Technology can therefore be defined as mechanical or digital devices, tools or systems. These are used to replace work tasks or complement the execution of work tasks (e.g., McOmber, 1999 ; Autor et al., 2003 ). According to this view, technology is conceptualized according to “its status as a tool” (“instrumentality”; McOmber, 1999 , p. 141). Alternatively, technology is understood as “the product of a specific historical time and place,” reflecting a stage of development within a predefined historical process (“industrialization”; McOmber, 1999 , p. 143) or as the “newest or latest instrumental products of human imagination” (“novelty”; McOmber, 1999 , p. 143), reflecting its nature that is rapidly replacing and “outdating” its predecessors. The definition according to “instrumentality” is particularly suitable for this research, as the interest focuses on individual-level effects of technologies and its use for accomplishing work. Therefore, the technology needs to be mentioned explicitly (e.g., “robot” instead of “digital transformation”) and described specifically in the form with which the employee is confronted at the workplace. Different definitions may reflect different perspectives on the role of technology for society and work. These perspectives in the form of paradigmatic views ( Liker et al., 1999 ) include philosophical and cultural beliefs as well as ideas on organizational design and labor relations. They differ with regard to the complexity in which the social context is believed to determine the impact of technology on society. Listed in accordance to increasing social complexity, the impact may be determined by technology itself (i.e., “technological determinism”), established power relations (i.e., “political interest”), managerial decisions (i.e., “management of technology”), or the interaction between technology and its social context (i.e., “interpretivist”) ( Liker et al., 1999 ). Later research added an even more complex perspective, according to which the effects of technology on society and organizations are determined by the relations between the actors themselves (i.e., “sociomateriality”; Orlikowski and Scott, 2008 ). Paradigmatic views may guide research in terms of content, purpose and goals, which in turn is likely to affect the methods and approach to research and may be specific to disciplines. For instance, Marxist sociological research following the view of “political interest” or research in information systems following the view of “management of technology.”

New technological developments are widely discussed in various disciplines. For instance, Ghobakhloo (2018) summarizes the expected areas of application of various technological concepts within the “smart factory” in the manufacturing industry: The internet of things as an umbrella term for independent communication of physical objects, big data as procedure to analyse enormous amounts of data to predict the consequences of operative, administrative, and strategic actions, blockchain as the basis for independent, transparent, secure, and trustworthy transaction executed by humans or machines, and cloud computing as an internet-based flexible infrastructure to manage all these processes simultaneously ( Cascio and Montealegre, 2016 ; Ghobakhloo, 2018 ). The central question to guide the next section is to what extent these new technologies, and also well-established technologies such as information and communication technologies (ICT), which are constantly being expanded with new functions, could influence work characteristics on a theoretical basis.

Theories on the Relationships Between Technology and Work Characteristics

A central discussion on technology can be found in the sociological literature on deskilling vs. upgrading ( Heisig, 2009 ). The definition of “skill” in empirical studies on this subject varies regarding its content by describing either the level of complexity that an employee is faced with at work, or the level of autonomy that employees are able to make use of Spenner (1990) . Theories advocating the deskilling of work (e.g., labor process theory; Braverman, 1998 ) propose that technology is used to undermine workers' skill, sense of control, and freedom. Employees need to support a mechanized workflow under constant surveillance in order to maximize production efficiency ( Braverman, 1998 ). Other authors, advocating “upskilling” ( Blauner, 1967 ; Bell, 1976 ; Zuboff, 1988 ), propose the opposite by claiming that technology frees employee's from strenuous tasks, leaving them with more challenging and fulfilling tasks ( Francis, 1986 ). In addition, issues of identity at work were raised by Blauner (1967) who acknowledged that employees may feel “alienated” as soon as technologies change or substitute work that is meaningful to them, leaving them with a feeling of powerlessness, meaninglessness, or self-estrangement ( Shepard, 1977 ). In sum, sociological theories suggest that technology has an impact on the level of freedom, power and privacy of employees, determining their identity at work and the level of alienation they experience.

According to contingency theories ( Burns and Stalker, 1994 ; Liker et al., 1999 ) technology is a means to reduce uncertainty and increase competitiveness for organizations ( Parker et al., 2017 ). Therefore, the effects of technology on the employee depend on strategic decisions that fit the organizational environment best. When operational uncertainty is high, organizations get more competitive by using technology to enhance the flexibility of employees in order to enable a self-organized adaption to the changing environment ( Cherns, 1976 ). This increases employee's flexibility by allowing them to identify and decide on new ways to add value to the organization (“organic organization”; Burns and Stalker, 1994 ). When operational uncertainty is low, organizations formalize and standardize procedures in order to optimize the workflow and make outputs more calculable (“mechanistic organization”; Burns and Stalker, 1994 ). This leads to less opportunities for individual decision-making and less flexibility for the employees. In sum, contingency theories suggest, that the effects of technology depend on the uncertainty and competitiveness in the external environment and may increase or decrease employee's flexibility and opportunities for decision-making and self-organization.

Economic research following the task-based approach from Autor et al. (2003) suggests, that technology substitutes routine tasks and complements complex (or “non-routine”) ones. Routine manual and cognitive tasks usually follow a defined set of explicit rules, which makes them susceptible to automation. By analyzing qualification requirements in relation to employment rates and wage development, it was argued that workplace automation substitutes routine and low-skill tasks and thus favors individuals who can carry out high-skilled complex work due to their education and cognitive abilities ( Card and DiNardo, 2002 ; Autor et al., 2003 ). This means, that the accomplishment of tasks “demanding flexibility, creativity, generalized problem-solving, and complex communications” ( Autor et al., 2003 , p. 1,284) becomes more important. Complex tasks, so far, posed a challenge for automation, because they required procedural and often implicit knowledge ( Polanyi, 1966 ; Autor, 2015 ). However, recent technological developments such as machine learning, are capable of delivering heuristic responses to complex cognitive tasks by applying inductive thinking or big data analysis ( Autor, 2015 ). Regarding complex manual tasks, mobile robots are increasingly equipped with advanced sensors which enable them to navigate through dynamic environments and interactively collaborate with human employees ( Cascio and Montealegre, 2016 ). In sum, economic research following the task-based approach argues that technology affects the routineness and complexity of work by substituting routine tasks. However, new technologies may be able to increasingly substitute and complement not only routine tasks, but complex tasks as well. According to the theories, this will again increase the complexity of work by creating new demands for problem-solving and reviewing the technology's activity.

Useful insights can be gained from psychological theories that explicitly take the role of work characteristics into account. Work characteristics are often mentioned by for instance sociological theories (e.g., autonomy and meaningfulness) without clearly defining the concepts. Particularly the job characteristics model of Hackman and Oldham (1975) and the job-demand-control model of Karasek (1979) and Karasek et al. (1998) are consulted to further clarify the meaning of autonomy and meaningfulness at work. With regard to autonomy, Hackman and Oldham's model 1975 conceptualizes autonomy as a work characteristic, defined as “the degree to which the job provides substantial freedom, independence, and discretion to the employee in scheduling the work and in determining the procedures to be used in carrying it out” ( Hackman and Oldham, 1975 , p. 162). According to the authors, autonomy facilitates various work outcomes, such as motivation and performance. In a similar vein, Karasek et al. (1998) stress the role of autonomy in the form of “decision authority” that interacts with more demanding work characteristics, such as workload or frequent interruptions and therefore enables a prediction of job strain and stress ( Karasek et al., 1998 ). With regard to meaningfulness, Hackman and Oldham (1975) clarify that different core job dimensions, such as the significance of one's own work results for the work and lives of other people, the direct contribution to a common goal with visible outcomes, and the employment of various skills, talents and activities all enhance the perception of meaningfulness at work. In sum, psychological theories on employee motivation and stress clarify the concepts of autonomy and meaningfulness by illustrating the factors that contribute to their experience in relation to challenging and rewarding aspects of work.

Components of CVET

In order to formulate the implications for CVET of the studied effects of technology on work characteristics, a framework with the different components of CVET is needed. The objective of the VET system and continuous education is to qualify people by supporting the acquirement of required competences, for instance by providing training. Competences refer to the potential capacity of an individual in order to successfully carry out work tasks ( Ellström, 1997 ). They contain various components such as work-related knowledge and social skills (e.g., Sonntag, 1992 ). Competences are considered here as “the combination of knowledge, skills and attitude, in relation to one another and in relation to (future) jobs” ( Mulder and Baumann, 2005 , p. 106; e.g., Baartman and de Bruijn, 2011 ).

Participants in CVET enter the system with competences, such as prior knowledge, motivation, and expectations. It is argued that these have to be considered when designing learning environments for CVET. Next to making the distinction between the different components of learning environments content, guidance, method, and assessment, it is considered important that these components are coherent and consistent ( Mulder et al., 2015 ). For instance, the content of the training needs to fit to the objectives and the background of the participants. The same goes for the method or didactics used (e.g., co-operative learning, frontal instruction) and the guidance of teachers, mentors or trainers. In addition, assessment needs to be consistent with all these components. For instance, problem based learning or competence based training requires other forms of assessment than more classical teacher centered forms of didactics, which makes a classic multiple choice test not fitting ( Gulikers et al., 2004 ). Figure 1 contains an overview of the components of learning environments for CVET.

Figure 1 . Components of CVET learning environments (adapted from Mulder et al., 2015 , p. 501).

Three steps are necessary to answer the research questions. Firstly, a systematic search and review of empirical studies reporting evidence on the direct relationships between new technologies and work characteristics. Secondly, an analysis of the evidence with regard to its implications for work demands. Thirdly, deriving the work demands and their implications for CVET.

Systematic Search Strategy

Due to the interdisciplinary nature of our research, specific databases were selected for each of the disciplines involved: Business Source Premier (business and management research) and PsycArticles (psychology) were searched via EBSCOhost, and ERIC (educational science), and Sociological Abstracts (sociology) were searched via ProQuest.

Identifying suitable keywords for technological concepts is challenging due to the rapidly changing and inconsistent terminology and the nested nature of technological concepts ( Huang et al., 2015 ). Therefore, technological terms were systematically mapped by using the different thesauri provided by each of the chosen databases. After exploding a basic term within a thesaurus, the resulting narrower terms and related terms were documented and examined within the following procedure: (a) Checking the compatibility with our definition of technology reflecting its instrumentality, (b) Adjustment of keywords that are too broad or too narrow, (c) Disassembling nested concepts. The procedure was repeated stepwise for each of the databases. Finally, 45 terms that reflect new technologies were documented and used for the database search.

Keywords reflecting work characteristics are derived from the theoretical conceptualizations previously outlined. Synonyms for different concepts within the relevant theories were identified and included. In order to narrow our search results, additionally operators for empirical studies conducted in a workplace setting were added.

In order to avoid unnecessary redundancy, the use of asterisks was carefully considered, provided that the search results did not lose significantly in precision or the number of hits did not grow to an unmanageable number of studies. The final search string is shown in Table 1 .

Table 1 . Final search string.

Eligibility Criteria and Study Selection

Technical criteria included methodological adequacy. This was ensured by only including studies published in peer-reviewed journals. In addition, the studies had to provide quantitative or qualitative data on relationships between technology and work characteristics. Only English-language studies were considered, because most of the studies are published in English and therefore the most complete overview of the existing knowledge on this topic can be obtained. This also enables as many readers as possible to have access to the original studies and analyse the findings of the empirical studies themselves.

Concerning technology, variables had to express the direct consequence or interaction with a certain technology (e.g., the amount of computer-use or experience with robots in the workplace) and indirect psychological states that conceptually resulted from the presence of the technology (e.g., a feeling of increased expectations concerning availability). Regarding work characteristics, variables had to describe work-related aspects associated with our conceptualization of work characteristics (e.g., a change in flexibility or the perception of complexity).

Regarding the direction of effects, only studies that focused on the implementation or use of technologies for work-related purposes were included. Studies were excluded, if they (a) tested particular designs or features of technologies and evaluated them without considering effects on work characteristics, (b) regarded technology not as a specific tool but an abstract process (e.g., “digital transformation”), (c) were published before 1990 due to the fact that the extent of usability and usefulness of technologies before that time should be substantially limited compared to today (e.g., Gattiker et al., 1988 ), and (d) investigated the impact of technologies on society in general without a specific relation to professional contexts (e.g., McClure, 2018 ).

Studies that were found but that did not report empirical findings on the relationships between technology and work characteristics, but rather on the relationships between technology and work demands (e.g., specific knowledge or skills) or work outcomes (e.g., performance, job satisfaction) were documented. Since the aim for this study was to derive the work demands from the work characteristics in any case, the studies that reported a direct empirical relationship between technology and work demands were analyzed separately ( N = 7).

Data Extraction

The variables expressing technology and work characteristics were listed in a table, including the quantitative or qualitative data on the relationships. Pearson's r correlations were preferred over regression results to ensure comparability. For qualitative data, the relevant passages documenting data were included. Finally, methodological information as well as sample characteristics and size are listed.

Analysis of the Results

Firstly, the variables containing work-related aspects are clustered thematically into a comprehensive final set of work characteristics. This is necessary to reduce complexity due to variations in naming, operationalization and measurement and to make any patterns in the data more visible. Deviations from the theoretically expected clusters are noted and discussed before synthesizing the evidence narratively in accordance to the research questions ( Rodgers et al., 2009 ). As proposed, the evidence on changing work characteristics is analyzed with respect to the resulting work demands in the sense of knowledge, skills, attitude and behavior, which in turn are used to determine the implications for the different components of CVET.

Figure 2 depicts a flowchart documenting the literature search. In sum, 21 studies providing evidence on relationships between technology and work characteristics were included. In addition, seven supplementary studies containing empirical evidence on relationships between technology and specific work demands were identified. These studies are taken into account when deriving the work requirements. Next, the descriptive characteristics of the included studies will be reported. After that, the evidence on relationships between technologies and work characteristics of the 21 included studies will be summarized, before finally deriving the work demands based on the evidence found.

Figure 2 . Flowchart of literature search process.

Characteristics of Studies

Table 2 contains an overview of the characteristics of selected studies. Most of the studies were published between 2015 and 2019 (52%). Nearly half of the studies were conducted in Europe (48%), followed by North America (33%). Most of the studies reported qualitative data collected with methods such as interviews (62%).

Table 2 . Characteristics of the studies.

The studies investigated a variety of technologies, such as computers (1, 7), various forms of Information and Communication technologies (ICTs; 2, 3, 17, 18, 21) in a broad sense, including specific examples of work-extending technologies and other tools for digital communication, information technology (IT) systems supporting information dissemination and retrieval within organizations (4, 9), automated systems supporting predominantly physical work procedures (5, 6, 11, 12, 13, 14, 20), robots (15, 19), social media enabling professional networking and participation in organizational and societal practices (8, 16), and more domain-specific technologies such as clinical technology supporting professional decisions (9) and field technology for labor management (10).

Relationships Between Technology and Work Characteristics

In sum, nine work characteristics were identified and defined distinctively. Table 3 contains the operational definitions of the final work characteristics and the work-related aspects they consist of. The final work characteristics are: Workflow interruptions, workload, manual work, mental work, privacy, autonomy, complexity, role expectations, and opportunities for development.

Table 3 . Overview for final work characteristics and the exemplary work-related aspects assigned to them.

The complete overview of the selected studies and results for the relationships between technology and work characteristics is provided in Table 4 (for quantitative data) and Table 5 (for qualitative data). To further increase comprehensibility, the variables within the tables were labeled according to their function in the respective study (e.g., independent variable, mediating variable, dependent variable; see notes).

Table 4 . Studies providing quantitative evidence for the relationship between technology and work-related aspects.

Table 5 . Studies providing qualitative evidence for the relationship between technology and work-related aspects.

There is quantitative evidence on positive relationships between IT system use and complexity reported by two studies (4, 9). On a similar note, qualitative evidence suggests lower situational awareness within automated systems indicating an increase in complexity (12), and clinical technology being associated with an increase in complexity for nurses (9).

There is mixed quantitative evidence on the relationships between computer work and autonomy (1). The amount of computer work is positively related to autonomy, while technological pacing is negatively related to autonomy. Working within automated systems is negatively (5, 6) or not related (6) to different measures of autonomy. ICT use shows mixed relationships with job decision latitude (3) depending on ICT features that describe negative or positive effects of use. Evidence indicates a positive relationship between social media use and autonomy. Qualitative evidence suggests that ICT use increases autonomy (21) and flexibility (17, 18, 21).

Quantitative studies indicate strong positive relationships between computer work (1) and ICT use (2) and workload. The relationships are not consistent due to the fact that certain ICT features differ in their effects on workload. ICT characteristics such as presenteeism and pace of change are positively related to feelings of increasing workload, while a feeling of anonymity is negatively associated with workload. Evidence indicates positive relationships between time or workload pressure in the context of computer work (7), working in an automated system (5), as well as social media use (8) and provide evidence for positive relationships between various technologies and workload. Qualitative studies report similar outcomes. ICT use (18), automated systems (12, 13) as well as clinical technology (9) are reported to increase the workload.

Workflow Interruptions

Quantitative evidence indicates positive relationships between computer work and increasing levels of interruptions as well as an increasing demand for multitasking (7). Qualitative evidence suggests that ICT use is positively associated with an increased level of interruptions on the one hand and workflow support on the other hand (21). Further qualitative evidence suggests that robots at the workplace have positive effects on workflow support (19), and automated systems seem to increase the level of multitasking required in general (12).

Manual Work

Qualitative evidence suggests a decrease in the amount of physically demanding tasks when working with automated systems (11) and robots (15). In one study, qualitative evidence suggests an increase in manual work for technical jobs where automated systems are used (14).

Mental Work

Quantitative evidence indicates no relationships between monitoring tasks or problem-solving demands for technical jobs within automated systems (6). Qualitative evidence however suggests positive relationships between work within automated systems and various cognitive tasks and demands, such as problem-solving and monitoring (11, 13), while working with robots increases the amount of new and challenging mental tasks (15).

Quantitative evidence indicates that different ICT characteristics show different relationships with invasion of privacy (2). Some features are negatively related to invasion of privacy (anonymity) and others are positively related to it (presenteeism, pace of change). Qualitative evidence suggests that IT systems are not related to the perception of managerial surveillance (9), while social media is positively related to peer-monitoring (16), and field technology is negatively related to employee data control (10).

Role Expectations

Quantitative evidence indicates that ICT use is inconsistently related to role ambiguity depending on specific characteristics of the technology (2). Regarding automated systems, quantitative evidence indicates no relationship between working in an automated system and opportunities for role expansion in the form of an increased perceived responsibility (6). Qualitative evidence suggests that ICT use increases the expectations for availability and connectivity (21), and social media positively affects networking pressure (16). Qualitative evidence suggests that IT systems (9) decrease meaningful job content and role expansion. Qualitative evidence suggests that automated systems vary with regard to enhancing meaningfulness at work, dependent on whether the work tasks are complemented by the system or revolve around maintaining the system (20).

Opportunities for Development

Qualitative evidence suggests that ICT use (12) as well as working with an automated system (17) increase the demands for continuing qualification. Qualitative evidence suggests that opportunities for learning and development are prevalent with clinical technology (9) and absent when working with robots (19). Mixed qualitative evidence regarding automated systems and learning opportunities suggests that the effects depend on the differences in work roles in relation to being supported by the system or supporting the system (20).

A comprehensive summary of the outcomes can be found in Table 6 . The information in this table gives a summary of the evidence found for the different technologies and their relationships to work characteristics, more specifically to work related aspects. Important distinctive characteristics such as sample characteristics are listed in Tables 4 , 5 .

Table 6 . Overview over identified relationships between technology and work characteristics.

Subsequently, the results shown are now used as a basis for the identification of work demands that lead to the need for adapting to changes in work characteristics.

Relationships Between Technologies and Work Demands

Three sources are considered for the identification of work demands: Work demands mentioned in the studies on technology and work characteristics, work demands mentioned by the supplementary studies found during the database search ( N = 7), and work demands analytically derived from the results.

Some studies that examined the effects of technology on work characteristics also reported concrete work demands. Regarding the increasing complexity and the associated mental work, qualitative evidence suggests an increasing demand for cognitive as well as digital skills (11) in automated systems. With regard to IT systems, quantitative evidence indicates positive relationships with computer literacy (9), and analytical skills (4). With regard to the increase in workflow interruptions and the role expectations for constant availability and connectivity, time and attention management strategies are proposed in order to cope with the intrusive features of technology (2). Other strategies mentioned in the studies include self-discipline for disengaging from the ubiquitous availability resulting from mobile communication devices (18, 8) as well as the need for reflecting on individual responsiveness when working overtime due to self-imposed pressure to be available at all times (18, 21). Concerning opportunities for development, the willingness and ability to learn and adapt to technological changes and the associated changes in work (15, 4, 12) is emphasized. Moreover, employability is facilitated by using technological tools for professional networking (16).

The supplementary studies provide evidence on the direct relationships between technologies and work demands without the mediating consideration of work characteristics. This evidence is listed in Table 7 .

Table 7 . Supplementary studies on the relationship between technology and work-related demands.

There is quantitative evidence for positive relationships between the perception of controllability and exploratory use of computers (22), first-hand experience with robots and readiness for robotization (23, 24), and perceived usefulness and positive attitudes toward telemedicine technology (25), blockchain technology (26), and IT systems in general (27). Further quantitative evidence indicates mixed effects of perceived ease of use. Evidence indicates a positive relationship between perceived ease of use and perceived technological control with regard to telemedicine (25), no relationship between ease of use and attitude regarding blockchain technology (26), and a positive relationship between ease of use and attitude toward using IT systems (27). Quantitative evidence indicates that information processing enabled by technology is positively related to an increasing demand of cognitive skills (e.g., synthesizing and interpreting data) and interpersonal skills (e.g., coordinating and monitoring other people), but not related to an increasing demand in psychomotor skills (e.g., manual producing and precise assembling) (28). The level of standardization of work is positively related to interpersonal skills, but not related to cognitive and psychomotor skills (28). A high variety of tasks is positively related to the demand for cognitive skills and interpersonal skills and not related to psychomotor skills (28).

By analyzing the evidence on relationships between technology and work characteristics, further work demands can be derived. Knowledge about the specific technology at hand may be useful to decrease the perception of complexity as new technologies are introduced. This seems evident when comparing the effects of a simple computer with the effects of work within an automated system. For instance, while evidence indicates no relationship between computer work and complexity (6), work within an automated system is suggested to be associated with increasing complexity (12). Moreover, problem-solving skills (13) and cognitive skills such as diagnosing and monitoring (11, 15) increase when employees work within automated systems. Increasing autonomy suggests the need for personal skills regarding self-organizing and self-management due to greater flexibility and the associated possibilities for structuring work in many ways, particularly when working with ICTs (18, 21). Workflow interruptions and an increasing workload also increases the importance of communication skills for explicating the boundaries of one's own engagement to colleagues and leaders (17, 18, 21). Furthermore, reflecting the professional role at work may be critical due to changes in role expectations. The example of self-imposed need for availability underlines this argument (21). All this has implications for self-regulatory activities, such as reflection, and could benefit from experimenting and monitoring one's own strategies for time and attention management.

Implications for CVET: Objectives and Characteristics

The aforementioned studies describe several required behavioral aspects that are considered important due to technology at work. Emphasized is the need for components related to the organization of one's own work, namely self-discipline and time and attention management.

The identified need for reflection on one's own professional actions, for experimentation, and also for professional networking (for instance by using tools) can be seen as parts of further professional development by oneself or in interaction with others. In addition, the need for demonstrating employability is mentioned. From all these professional and career development aspects can be derived that problem-solving skills, self-regulation skills, and communication skills are required as well as proactive work behavior and coping and reflection strategies.

Various relevant skills, such as psychomotor skills, analytical skills, management skills, and interpersonal skills are mentioned. In addition, the need for diagnostic and monitoring skills as well as digital skills is emphasized. All these components can be used in relation to two explicitly mentioned needs: ability to learn and computer literacy. The demand for generic and transferable skills is emphasized. As a basis for the skills, knowledge is required, for instance on the technology itself, although not explicitly discussed in the studies. In contrast, several components of attitude are explicitly mentioned and considered to be a requirement for the ability to deal with challenges caused by new technologies at work. Firstly, the more generic willingness to learn, adaptability, and perceived behavioral control. Secondly, attitudes that are directly linked to technology, namely a positive attitude and trust, especially toward technology (e.g., robots), and technological readiness and acceptance.

Next to the opportunity of acquiring the mentioned components of competences at work, CVET can organize training interventions in the form of adequate learning environments to foster these. The ability of employees to carry out, develop and use the mentioned behavioral aspects, skills, knowledge, and attitudes, can be considered as required objectives of CVET and have concrete consequences for the characteristics of the learning environments.

As for the content of the learning environments, derived from the aforementioned requirements, it can be argued that attention should be paid to different categories of learning objectives: acquiring knowledge about and learning how to use technology, how to manage work and oneself, and how to continue one's own professional development. In addition, the relevance of attitude tells us that these components need to be fostered in the training and therefore need to be part of the content of the learning environments as well.

In relation to the methods or the didactics, only one study explicitly mentioned a suggestion, namely experience based learning for fostering adaptability (12). In relation to the guidance of trainers or teachers no suggestions are provided. The same goes for assessment, diagnoses or monitoring, and the coherence of components of the learning environments.

This systematic literature review aimed at identifying effects of new technological developments on work characteristics, identifying associated work demands, and determining their implications for the design of formal CVET learning environments.

Effects of New Technologies on Work Characteristics and Word Demands

Based on a systematic review focusing on empirical evidence, several effects of technology on work characteristics were found, thus answering RQ 1. Evidence suggests that complexity and mental work increases with ongoing automation and robotization of work, for instance due to the automatization of procedures which “hides” certain processes from employees. The automatization of tasks introduces new mental tasks, such as monitoring the machine's activities and solving problems. A decrease in manual work depends on the relation between the job and the technology in use (supporting vs. being supported).

Workload and workflow interruptions increase as a general consequence of the ubiquity of technology, mainly due to a higher level of job speed and the associated time and workload pressure. A higher level of autonomy seems to be associated with a higher workload and more workflow interruptions. This applies in particular to work with ICTs and domain-specific technologies, such as field technology.

Role expectations and opportunities for development depend on the relation between the job and the technology in use (supporting vs. being supported). With regard to role expectations, the need for being available or connected via digital devices and a new division of responsibilities between employees and technology are repeatedly mentioned in the studies. This applies particularly to work with automated systems, robots, and domain-specific technologies such as clinical technology.

With regard to work demands, employees need strategies to deal with higher levels of workload, autonomy, and complexity. Required skill demands contain mental, analytical, cognitive, and self-regulatory demands. In addition, opportunities for role expansion and learning, which do not seem to automatically result from the implementation and use of new technologies, need to be created (pro)actively by the employees. Employees need to take more responsibility with regard to their own development and professional work identity (for instance considering the pressure for constant availability). They need to be able to effectively deal with a high workload and number of interruptions, increasing flexibility, complexity, and autonomy, a demand for constant availability, changes in meaningfulness of tasks, changes in work roles, and the need to create and use learning opportunities. In the light of ongoing changes and challenges, skills to further develop and adapt one's own skills gain in importance. Regarding attitudes, the willingness to learn, adapt and experiment may be a central work demand.

Implications for the Practice of CVET

Various required objectives of CVET can be concluded from the reported results. For instance, developing the ability of employees to carry out the mentioned behaviors, as well as the skills, knowledge and attitudes that are necessary for those behaviors. These objectives have consequences for the content of CVET learning environments. From the empirical studies on the relationships between technology and work, we derived the need for employees to organize their own work, for instance through time management. Furthermore, many issues relating to own professional development and career development are important, to acquire individually and independently as well as by interacting with others. Ultimately, this refers to the skills of self-initiated learning and development. With regard to fostering helpful attitudes, raising awareness of the relevance of trust or training the social skills to promote trust in the workplace can be included in the content of CVET learning environments. In research on creating trust within organizations, regularly giving and receiving relevant information was shown to be important for creating trust toward co-workers, supervisors and top-management, which in turn fostered the perception of organizational openness and employee involvement as a result ( Thomas et al., 2009 ). In the research on creating trust in virtual teams, the importance of frequent interaction was important to develop trust on a cognitive as well as an affective level (e.g., Germain, 2011 ). These research results however need to be adapted to the context of technology at work.

Although there is no information provided on the guidance of employees, informal guidance through leadership ( Bass and Avolio, 1994 ) as well as formal guidance by trainers and teachers during interventions contain possibilities for fostering the required competences. Attention should be paid not only to acquiring relevant knowledge (digital literacy), but also to skills in applying the knowledge and therefore dealing with technology. Even more challenging might be the task of supporting attitude development (e.g., technological acceptance and openness to changes), fostering transfer of skills, and preparation for future development. Especially future professional development, which includes the ability to learn in relation to current and future changes, needs to be focused on. Teachers, trainers and mentors need to be equipped to be able to foster these competences.

In relation to the use of didactical methods, methods that do not merely focus on knowledge acquisition but also provide opportunities for skill acquisition and changes in attitude need to be applied. For example, one study explicitly suggested experience based learning for fostering the adaptability of employees when faced with ongoing technological developments. Other solutions for instruction models as a profound basis for learning environments may be found in more flexible approaches, for instance according to the cognitive flexibility theory ( Spiro et al., 2003 ), where learners are meant to find their own learning paths in ill-structured domains. By applying such models, that are often based on constructivist learning theories, in a coherent way, the development of strategies for self-organizing and self-regulation may be facilitated.

Furthermore, the use of technology within learning environments may have the potential to increase participants interactions, which are focused in for instance collaborative and co-operative learning ( Dillenbourg et al., 2009 ). Next to increasing interactions in learning and being able to co-operate, technology in learning environments can used to foster the other required competences, if adequately designed ( Vosniadou et al., 1996 ; Littlejohn and Margaryan, 2014 ).

When keeping in mind, that the coherence of components is an important requirement for the design of learning environments ( Mulder et al., 2015 ), the component that describes assessment needs further attention. There is evidence supporting the idea, that the type of assessment has an impact on how learning takes place ( Gulikers et al., 2004 ; Dolmans et al., 2005 ). Therefore, it can be used to deliberatively support and direct learning processes.

Only when all these aspects are considered can CVET interventions effectively and sustainably foster the mentioned objectives, such as promoting a willingness to change in relation to technologies, the effective use of technology, and personal development in the context of technological developments.

Limitations and Implications for Future Research

Regarding the search methods, the use of databases is challenging when investigating technologies ( Huang et al., 2015 ). Technological and technical terms are widespread outside the research in which they are regarded as the object of investigation. Therefore, it produces a large amount of studies that concern technology with diverse research objectives that can be difficult to sort. An interesting focus for future research would be the systematic mapping of journals dealing specifically with technology in order to identify research that could complement the results of the present study as well as consider specificities regarding the domains in which the data is collected and disciplines by which the research is conducted. For instance, domain-specific databases from healthcare or manufacturing might provide additional insights into the effects of technology on work. Another limitation is the absence of innovative new technologies, such as artificial intelligence, blockchain, or the internet of things as object of investigation. Broad technological categories, such as ICTs and social media have received some attention in research, especially in relation to questions beyond the scope of this review. Newer technological developments as discussed by Ghobakhloo (2018) are virtually not present in current research. This gap in empirical research needs to be filled. In addition, future research should ensure that it does not miss opportunities for research where effects of these innovative technologies can be examined in detail, for instance by conducting an accompanying case study of the implementation process. Research investigating changes over time regarding the use of technology and its effects is needed. In doing so, research could capture the actual dynamics of change and development of processes as they happen in order to inform truly effective interventions in practice. Moreover, a classification of technological characteristics according to their effects may be valuable by enabling a more in-depth analysis of new technologies and their effects on specific groups of employees and different types of organizations. These analyses will also allow a breakdown of effects in relation to differences in jobs, hierarchy levels and levels of qualification, which could be very important for organizations and employers in order to adapt the CVET strategy to the specific demands of specific groups of employees. The present review takes a first step in this direction by identifying work characteristics that are affected by different technologies. In addition, future research could also take into account non-English language research, which might increase insight in for instance cultural differences in the use and the effects of technology at work.

Regarding theory, some of the relevant theories considering technology stem from sociology (e.g., Braverman, 1998 ) or economics ( Autor et al., 2003 ). For instance, the task-based approach ( Autor et al., 2003 ) showed some explanatory value by suggesting that complexity may increase as a consequence of technology. Furthermore, it suggested that this effect may depend on job specifics. Those propositions are reflected in the aforementioned empirical evidence. Psychological theories on work characteristics do not conceptualize technology explicitly (e.g., Hackman and Oldham, 1975 ; Karasek, 1979 ). As of the present study, the large variation regarding the concepts and variables derived from theory might limit the comparability of results. To foster systematic research, further theory development needs to more explicitly consider the role of technology at multiple levels (i.e., individual level, team level, organizational level) and with regard to the characteristics and demands of work. In the context of theory, the paradigmatic views also deserve attention (e.g., Liker et al., 1999 ; Orlikowski and Scott, 2008 ). These views could be reflected in the subject of research, as exemplified for instance in the study of field technologies and its effects on privacy from a managerial control and power perspective, potentially reflecting the view of political interest ( Tranvik and Bråten, 2017 ). Most of the studies, however, do not take a clear stand on what exactly they mean when they investigate technology. This complicates interdisciplinary inquiry and integration, as it is not always clear which understanding of technology is prevalent. We therefore encourage future research to explicitly define technology, for instance as in the present paper using the proposed framework of McOmber (1999) . In doing so, characteristics of technology may be defined more clearly and distinctive which in turn would enable the formation of the strongly needed categorization of technologies, as was proposed earlier.

And, although there are theories and models on the use of technology in education (e.g., E-Learning, Technology enhanced learning), they are not focussing on fostering the competences required to deal with new technologies in a sustainable manner. In general, the same gap needs to be filled for instruction models and instructional design models, for instance to promote changes in attitude and professional development. In addition, there is hardly any attention for the consequences of new technologies at work for CVET yet ( Harteis, 2017 ). All this requires more systematic evaluation studies. The research gaps identified need to be filled in order to provide evidence-based support to employees in dealing with new technologies at work in a sustainable manner, taking charge of their own performance and health, as well as seeking and using opportunities for their own professional and career development.

Data Availability Statement

All datasets generated for this study are included in the article/supplementary material.

Author Contributions

PB and RM have jointly developed the article, and to a greater or lesser extent both have participated in all parts of the study (design, development of the theoretical framework, search, analyses, and writing). The authors approved this version and take full responsibility for the originality of the research.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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* Studies included in the systematic review.

** Supplementary studies.

Keywords: technology, work characteristics, continuous vocational education and training, automation, work demands, systematic review

Citation: Beer P and Mulder RH (2020) The Effects of Technological Developments on Work and Their Implications for Continuous Vocational Education and Training: A Systematic Review. Front. Psychol. 11:918. doi: 10.3389/fpsyg.2020.00918

Received: 14 February 2020; Accepted: 14 April 2020; Published: 08 May 2020.

Reviewed by:

Copyright © 2020 Beer and Mulder. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Patrick Beer, patrick.beer@ur.de

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Preparing for the Revolution: Information Technology and the Future of the Research University (2002)

Chapter: 1. introduction, 1 introduction.

O ur society is now being reshaped by rapid advances in information technologies—computers, telecommunications networks, and other digital systems—that have vastly increased our capacity to know, achieve, and collaborate (Attali, 1992; Brown, 2000; Deming and Metcalfe, 1997; Kurzweil, 1999). These technologies allow us to transmit information quickly and widely, linking distant places and diverse areas of endeavor in productive new ways, and to create communities that just a decade ago were unimaginable.

Of course, our society has been through other periods of dramatic change before, driven by such innovations as the steam engine, railroad, telephone, and automobile. But never before have we experienced technologies that are evolving so rapidly (increasing in power by a hundredfold every decade), altering the constraints of space and time, and reshaping the way we communicate, learn, and think.

The rapid evolution of digital technologies is creating not only new opportunities for our society but challenges to it as well, 1 and institutions of every stripe are grappling to respond by adapting their strategies and activities. Corporations and governments are reorganizing to enhance productivity, improve quality, and control costs. Entire industries have been restructured to better align themselves with the realities of the digital age. It is no great exaggeration to say that information technology is fundamentally changing the relationship between people and knowledge.

Yet ironically, at the most knowledge-based entities of all— our colleges and universities—the pace of transformation has been relatively modest in key areas. Although research has in many ways been transformed by information technology, and it

is increasingly used for student and faculty communications, other higher-education functions have remained more or less unchanged. Teaching, for example, largely continues to follow a classroom-centered, seat-based paradigm.

Nevertheless, some major technology-aided teaching experiments are beginning to emerge, and several factors suggest that digital technologies may eventually drive significant change throughout academia (Newman and Scurry, 2000; Hanna, 2000; Noble, 2001). Because these technologies are expanding by orders of magnitude our ability to create, transfer, and apply information, they will have a profound impact on how universities define and fulfill their missions. In particular, the ability of information technology to facilitate new forms of human interaction may allow the transformation of universities toward a greater focus on learning. 2

American academia has undergone significant change before, beginning with the establishment of secular education during the 18 th century (Rudolph, 1991). Another transformation resulted from the Land-Grant College Act of 1862 (Morrill Act), which created institutions that served agriculture and industries; academia was no longer just for the wealthy but charged with providing educational opportunities to the working class as well. Around 1900, the introduction of graduate education began to expand the role of the university in training students for careers both scholarly and professional. The middle of the twentieth century saw two important changes: the G. I. Bill, which provided educational opportunities for millions of returning veterans; and the research partnership between the federal government and universities, which stimulated the evolution of the research university. Looking back, each of these changes seems natural. But at the time, each involved some reassessment both of the structure and mission of the university (Wulf, 1995).

Already, higher education has experienced significant technology-based change, particularly in research, 3 even though it presently lags other sectors in some respects. And we expect that the new technology will eventually also have a profound impact on one of the university’s primary activities—teaching— by freeing the classroom from its physical and temporal bounds and by providing students with access to original source materials (Gilbert, 1995). The situations that students will encounter as citizens and professionals can increasingly be

simulated and modeled for teaching and learning, and new learning communities driven by information technology will allow universities to better teach students how to be critical analyzers and consumers of information.

The information society has greatly expanded the need for university-level education; lifelong learning is not only a private good for those who pursue it but also a social good in terms of our nation’s ability to maintain a vibrant democracy and support a competitive workforce.

But while information technology has the capacity to enhance and enrich teaching and scholarship, it also appears to pose certain threats to our colleges and universities (Duderstadt, 2000a; Katz, 1999) in their current manifestations. We can now use powerful computers and networks to deliver educational services to anyone—any place, any time. Technology can create an open learning environment in which the student, no longer compelled to travel to a particular location in order to participate in a pedagogical process involving tightly integrated studies based mostly on lectures or seminars by local experts, is evolving into an active and demanding consumer of educational services. 4

Similarly, faculty’s scholarly communities are shifting from physical campuses to virtual ones, globally distributed in cyberspace. And technological innovations are stimulating the growth of powerful markets for educational services and the emergence of new for-profit competitors, which could also help reshape the higher-education enterprise (Goldstein, 2000; Shea, 2001).

Technological change also has the potential for transforming how the research university accomplishes its social mission. In an increasingly global culture linked together by technology, with no single cultural context to provide a “filter,” the role of traditional disciplinary canons is changing.

It is clear that the digital age poses many questions for academia. For example, what will it mean to be “educated” in the twenty-first century? How will academic research be organized and financed? As the constraints of time and space are relaxed by information technology, how will the role of the university’s physical campus change?

In the near term it seems likely that the campus, a geographically concentrated community of scholars and a center of culture, will continue to play a central role, though the current

manifestations of higher education may shift. For example, students may choose to distribute their college experience among residential campuses, commuter colleges, and online (virtual) universities. They may also assume more responsibility for, and control over, their education. 5 The scholarly activities of faculty will more frequently involve technology to access distant resources and enhance interaction with colleagues around the world. The boundaries between the university and broader society may blur, just as its many roles will become ever more complex and intertwined with those of other components of the knowledge and learning enterprise (Brown and Duguid, 1996).

Thus we must take care not simply to extrapolate the past but instead to examine the full range of options for the future, even though their precise impacts on society and its institutions will be difficult to predict. In any case, we must be ready for disruption. Just as these technologies have driven rapid, significant, and frequently discontinuous and unforeseen change in other sectors of our society, so too will they present university decision makers not only with exciting prospects but a decidedly bumpy ride.

CONTEXT FOR THE STUDY

Given their mandate from Congress to advise the federal government on scientific and technological matters, the presidents of the National Academies (National Academy of Sciences, National Academy of Engineering, and Institute of Medicine) acted on the above concerns. They launched a project in early 2000, through the National Research Council (NRC), to better understand the implications of information technology for the research university. This institution is a key element of the national research enterprise, a prime mover of the economy, and a critical source of scientists and engineers. Its wide range of academic functions also makes it an important model for analysis, with broad applicability elsewhere in the university community.

Primary support for the National Academies project was provided by the National Research Council, with additional support from the W.K. Kellogg Foundation, the National Science Foundation, and the Woodrow Wilson Fellowship Foundation.

The project was organized under the Policy and Global Affairs Division of the NRC, with staff and program support from the Government-University-Industry Research Roundtable.

The premise of this study was simple. Although the rapid evolution of digital technology will present numerous challenges and opportunities to the research university, there is a sense that many of the most significant issues are not well understood by academic administrators, their faculty, and those who support or depend on the institution’s activities.

The study had two objectives:

To identify those information technologies likely to evolve in the near term (a decade or less) that could ultimately have major impact on the research university.

To examine the possible implications of these technologies for the research university—its activities (teaching, research, service, outreach) and its organization, management, and financing—and the impacts on the broader higher-education enterprise.

In addressing the second point, the panel examined those functions, values, and characteristics of the research university most likely to change as well as those most important to preserve.

In pursuit of these ends, a panel was formed consisting of leaders from industry, higher education, and foundations with expertise in the areas of information technology, the research university, and public policy. Since first convening in February 2000, the Steering Committee has held a number of meetings— including site visits to major technology-development centers such as Lucent (Bell) Laboratories and IBM Research Laboratories—to identify and discuss trends, issues, and options. The major themes addressed by these activities were:

The pace of evolution of information technology.

The ubiquitous character of the Internet.

The relaxation of the conventional constraints of space, time, and institution.

The pervasive character of information technology (the potential for near-universal access to information, education, and research).

The changing ways in which we handle digital data, information, and knowledge.

The growing importance of intellectual capital relative to physical or financial capital.

In January 2001 a two-day workshop was held at the National Academies—with the invited participation of about 80 leaders from higher education, industry, and government—to explore possible strategies for the research university and its various stakeholders and to provide input on possible follow-up initiatives. The presentations and discussions of the workshop were videotaped and broadcast on the Research Channel, and they are currently being videostreamed from its web site (programs.researchchannel.com) to help stimulate public discussion. Members of the panel also participated in a discussion of the project at the June 2001 meeting of the Government-University-Industry Research Roundtable.

This report, finalized through a series of conference calls and email exchanges during the second half of 2001, discusses what the panel learned during the study process. Chapter 2 describes the likely near-future of information technology;

Chapter 3 discusses the implications of this technology for the research university; and Chapter 4 summarizes the panel’s findings and calls for a continued dialogue between the research university and its stakeholders on these issues.

The panel has tried to maintain a clear and focused presentation of the issues. In a number of places, it makes assertions based on its collective judgment, while taking care to alert readers and appropriately qualify those assertions. Where possible, the report references the growing literature on information technology and education in order to complement the panel’s opinions. Yet change is occurring so rapidly there is high risk that any specific assertion made by individual experts or a panel such as this one may be proved wrong within a few years. Indeed, a central theme of the report is that the research university must be prepared to cope with constant shifts and continued uncertainty regarding information technology and its implications.

In addition, while this report focuses on the 261 U.S. doctoral/research universities, one of the inevitable consequences of the march of information technology is that these universities will become much more interconnected with the rest of higher education. Therefore much of the discussion deals with the broader academic context, of which the research university is but one component.

However, in seeking to gain a broad view of the issues facing the research university and information technology, the panel was unable (given the available time and resources) to examine several issues in the depth it would have liked. Therefore some important topics, such as the service mission of the university, are discussed but briefly.

Finally, although its original charge was to provide specific conclusions and recommendations on a range of policy issues— including some, such as the altered funding environment for the research university and the changes to intellectual-property protection wrought by the digital revolution that are spurring legislative actions, roiling campuses, and finding their way to court—the panel ultimately decided that specificity at this point would be inappropriate and premature. Digital technology is evolving so rapidly that an overly prescriptive set of conclusions and recommendations would be in danger of becoming irrelevant soon after the report’s publication. However, the

priorities for action that the panel identified are in areas that institutions and the overall higher-education enterprise can themselves consider and begin to address. And academia might get some assistance in that regard. The digital revolution will undoubtedly create barriers and opportunities that permit new federal and state approaches to provide significant leverage in helping the research university anticipate and manage change.

The rapid evolution of information technology (IT) is transforming our society and its institutions. For the most knowledge-intensive entities of all, research universities, profound IT-related challenges and opportunities will emerge in the next decade or so. Yet, there is a sense that some of the most significant issues are not well understood by academic administrators, faculty, and those who support or depend on the institution's activities. This study identifies those information technologies likely to evolve in the near term (a decade or less) that could ultimately have a major impact on the research university. It also examines the possible implications of these technologies for the research university—its activities (learning, research, outreach) and its organization, management, and financing—and for the broader higher education enterprise. The authoring committee urges research universities and their constituents to develop new strategies to ensure that they survive and thrive in the digital age.

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• Int J Environ Res Public Health

Digital Transformation in Healthcare: Technology Acceptance and Its Applications

Angelos i. stoumpos.

1 Healthcare Management Postgraduate Program, Open University Cyprus, P.O. Box 12794, Nicosia 2252, Cyprus

Fotis Kitsios

2 Department of Applied Informatics, University of Macedonia, 156 Egnatia Street, GR54636 Thessaloniki, Greece

Michael A. Talias

Associated data.

Not applicable.

Technological innovation has become an integral aspect of our daily life, such as wearable and information technology, virtual reality and the Internet of Things which have contributed to transforming healthcare business and operations. Patients will now have a broader range and more mindful healthcare choices and experience a new era of healthcare with a patient-centric culture. Digital transformation determines personal and institutional health care. This paper aims to analyse the changes taking place in the field of healthcare due to digital transformation. For this purpose, a systematic bibliographic review is performed, utilising Scopus, Science Direct and PubMed databases from 2008 to 2021. Our methodology is based on the approach by Wester and Watson, which classify the related articles based on a concept-centric method and an ad hoc classification system which identify the categories used to describe areas of literature. The search was made during August 2022 and identified 5847 papers, of which 321 fulfilled the inclusion criteria for further process. Finally, by removing and adding additional studies, we ended with 287 articles grouped into five themes: information technology in health, the educational impact of e-health, the acceptance of e-health, telemedicine and security issues.

1. Introduction

Digital transformation refers to the digital technology changes used to benefit society and the healthcare industry. Healthcare systems need to use digital technology for innovative solutions to improve healthcare delivery and to achieve improvement in medical problems. The digital transformation of healthcare includes changes related to the internet, digital technologies, and their relation to new therapies and best practices for better health management procedures. The quality control of massive data collected can help improve patients’ well-being and reduce the cost of services. Digital technologies will also impact medical education, and experts will deceive new ways to train people. Now in this way, practitioners will face new opportunities.

Digital transformation is an ongoing process that can create opportunities in the health sector, provided the necessary infrastructure and training are available. Under Regulation (EU) 2021/694 of the European Parliament and of the Council of 29 April 2021, establishing the Digital Europe Program and repealing Decision (EU) 2015/2240, digital transformation is defined as the use of digital technologies for the transformation of businesses and services. Some technologies that contribute to digital transformation are the digital platform of the Internet of Things, cloud computing and artificial intelligence. At the same time, the sectors of society that are almost affected are telecommunications, financial services and healthcare.

Digital health can play a role in innovation in health, as it facilitates the participation of patients in the process of providing health care [ 1 ]. The patient can overcome his poor state of health when they are no longer in a state of well-being. In this case, the patient is given the to participate in the decision-making regarding their health care. Searching for information through the patient’s internet or using digital health applications (e.g., via mobile phone) is essential for the patient to make the right decision about their health.

In the coming years, health change is expected to focus primarily on the patient, who will take on the “health service consumer” role as the patient seeks control over their health management. The healthcare industry will be shaped based on the needs and expectations of this new “consumer of health services”, which will require upgraded experiences with the main characteristics of personalisation, comfort, speed and immediacy in the provision of services. Gjellebaek C. et al. argue that new digital technologies will shift healthcare towards digitalisation, bringing significant benefits to patients and healthcare infrastructure [ 2 ]. Some of the benefits listed by Gjellebaek C. are the increase in employee productivity, the improvement of the efficiency and effectiveness of the operation of the health units, and the reduction of their operating costs.

On the other hand, in terms of health infrastructure, a typical example is the United States, where 75% of hospitals use electronic health record systems, according to Rebekah E. et al. [ 3 ]. However, clinicians often report side effects using digital technologies, which can be attributed to their misuse [ 3 ]. In addition, some health professionals oppose using these systems and develop solutions that jeopardise patient care. In some countries, such as the United States, the government provides incentives for the “effective use” of e-health technologies, but their results remain uncertain [ 3 ].

Rebekah E. et al. focus more specifically on U.S. hospitals, observing that the remaining countries are relatively in the early stages of transformation [ 4 ]. The United Kingdom, for example, has recently pursued troubled e-health initiatives, and Australian hospitals have only recently participated in investments in the digitalisation of their hospital services [ 4 ]. At the European Union level, digital health is a critical key strategic priority, in line with the European Strategic Plan 2019–2024 (European Commission).

Today, digital transformation in health is spreading and consolidating rapidly [ 5 ]. The purpose of this paper is to provide an assessment of the current literature on digital health transformation, as well as to identify potential vulnerabilities that make its implementation impossible. The ultimate goal is to see how digital technologies facilitate patients’ participation in health and their health.

Due to the rapid development of e-health and digitalisation, data from previous studies are becoming potentially irrelevant. Most studies evaluating digitalisation have relied heavily on quantitative research-based methods. Although quantitative evaluations are required, some of their effects could be omitted.

According to Gopal G. et al., healthcare has the lowest level of digital innovation compared to other industries, such as media, finance, insurance and retail, contributing to limited labour productivity growth [ 6 ]. With this article, we seek to reverse this picture and contribute to the emergence of digitalisation as a factor of health innovation while optimising patient outcomes and the cost of services provided. However, to achieve this innovation, systemic changes are needed in healthcare finances, the education of healthcare staff and healthcare infrastructure.

The following section analyses the methodology and its steps, which then contributed to the emergence of our results.

2. Material and Methods

2.1. search strategy and bibliography reviews.

Our research approach is based on the methodology of Webster and Watson, who developed a concept-centric method and an ad hoc classification system in which categories are used to describe areas of literature [ 7 ]. Initially, the existing bibliographic reviews were searched to select the databases based on keywords. A retrospective search was then performed to examine the reports of the selected works. Finally, the references of selected works were investigated to increase the search sample through the future search. After selecting the articles, they were grouped according to their content.

Systematic reviews were conducted to place this paper on existing knowledge of digital health, as well as to review prior knowledge in this area and to discuss recognised research questions based on the results of previous studies. A comprehensive review of the published literature was reported by Marques, I. C., & Ferreira, J. J. [ 8 ]. The authors explored the potential of existing digital solutions to improve healthcare quality and analysed the emerging trend in digital medicine to evaluate the research question of how stakeholders apply and manage digital technologies for business purposes [ 9 ]. The main question is: How and what could be done sustainably and inclusively through innovation to achieve sustainable development goals by taking advantage of Information and Communication Technologies? Recently, researchers have expressed concern about secure communication and user authentication within providing information to patients. In contrast with data storage, information exchange, and system integration, new approaches and uses of patient care processes are envisaged with the prospect of monitoring not only diagnostic statistics but also in-depth analysis of signs and symptoms before and after treatment, essential sources for new research. Table 1 presents the previous bibliographic reviews on which our study was based.

Previous Bibliographic Reviews.

2.2. Network Analysis

Network analysis is considered a branch of graph theory. Our network analysis is based on the similarity of keywords found in identifying the eligible papers. We used visualisation of similarities (VOS) software, version 1.6.18, to construct graphical networks to understand the clustering of the keywords and their degree of dissimilarity. Our network analysis is based on the similarity of keywords found in identifying the eligible papers.

Initial Search

The search was performed on the following databases: Scopus, Science Direct, and PubMed, using the keywords “digital transformation”, “digitalisation”, “Ehealth or e-health”, “mhealth or m-health”, “healthcare” and “health economics”. We selected publications from the search of international journals and conference proceedings. We collected papers from 2008 until 2021. The documents sought belonged to strategy, management, computer science, medicine, and health professions. Finally, the published works were in English only. The total number of articles collected using the keywords as shown in Table 2 was 5847.

Search Strategy.

We systematically checked the total number of papers 5847 by reading their titles, abstracts, and, whenever necessary, the article’s first page to conclude if each document was relevant as a first step as shown in the Figure 1 .

The diagram for the first phase of the selection process.

Then, we looked at the titles of the 378 articles, and after reading their summary, we accepted 321 articles. Further studies were rejected because their full text was not accessible. As a result, there were 255 articles in our last search. Of the selected 255 articles, 32 more were added based on backward and forward research. The investigation was completed by collecting common standards from all databases using different keyword combinations. According to the systematic literature review, we follow the standards of Webster and Watson (2002) to reject an article. Since then, we have collected the critical mass of the relevant publications, as shown in Figure 2 .

The diagram of the article selection process.

3.1. Chronological Development of the Publications

The categorisation of the articles was based on their content and the concepts discussed within them. As a result, we classify articles into the following categories: information technology in health, the educational impact on e-health, the acceptance of e-health, telemedicine, and e-health security.

Although researchers in Information and Communication Technology and digitalisation conducted studies almost two decades ago, most publications have been published in the last eight years. This exciting finding highlights the importance of this field and its continuous development. Figure 3 shows a clear upward trend in recent years. More specifically, the research field of Information and Communication Technology, in combination with digital transformation, appeared in 2008. However, the most significant number of articles was found in 2019, 2020 and 2021. The number of articles decreased to the lowest in 2009–2011 and 2013–2014. Due to the expansion of the field to new technologies, the researchers studied whether the existing technological solutions are sufficient for implementing digital transformation and what problems they may face.

Number of articles and citations per publication by year.

Figure 3 shows a combination of the articles per year and the number of citations per publication per year.

3.2. Document Type

Of the document types, 59.51 per cent of the articles were categorised as “survey”, while a smaller percentage were in: “case study” (32.53%), “literature review” (5.88%) and “report” (2.08%). However, these documents focused on specific concepts: “information technology in health” (45%), “education impact of e-health” (11%), “acceptance of e-health” (19%), “telemedicine” (7%), “security of e-health” (18%).

As we can see from the following Figure 4 , we used network analysis, where the keywords related to digitalisation and digital transformation were identified in the research study. Network analysis, using keywords, came with VOSviewer software to find more breadth and information on healthcare digitalisation and transformation exploration. It was created by analysing the coexistence of keywords author and index. This analysis’s importance lies in the structure of the specific research field is highlighted. In addition, it helped map the intellectual structure of scientific literature. Keywords were obtained from the title and summary of a document. However, there was a limit to the number of individual words. The figure represents a grid focused on reproducing keywords in the literature on the general dimensions of digitalisation. The digitalisation network analysis showed that e-health, telemedicine, telehealth, mobile health, electronic health/medical record, and information systems were the main relevant backgrounds in the literature we perceived. In the healthcare literature, keywords such as “empowerment” and “multicenter study” usually do not lead to a bibliographic search on digitalisation. Figure 4 shows how e-health and telemedicine have gone beyond the essential and most crucial research framework on how they can affect hospitals and the health sector. The potentially small gaps in network analysis can be filled by utilising data in our research study, contributing to future research.

Bibliometric map of the digital transformation and healthcare.

Figure 5 shows the network analysis with the keywords concerning time publication. The yellow colour indicates keywords for most recent years.

Network visualisation of keywords per year.

Figure 6 presents the density visualisation of keywords.

Heat map of keywords.

Figure 7 shows the number of articles per each method (survey, literature review etc.) for each year.

The map of number of articles per method for each year.

It is evident from Figure 7 that the most used method paper is the survey type and that in the year 2021, we have a high number of surveys compared to previous years.

3.3. Summary of the Included Articles

In Figure 2 , we have explained how we collected the critical mass of the 255 relevant publications. We added another 32 articles based on further research with the backward and research methods, which resulted in a total number of 287 articles.

Then, the articles were categorised according to their content. The concepts discussed in the papers are related to information technology in health, the educational impact of e-health, the acceptance of e-health, telemedicine, and e-health security. For this purpose, the following table was created, called the concept matrix table.

4. Concept Matrix

In this section, we provide the Concept matrix table. Academic resources are classified according to if each article belongs or not to any of the five concepts shown in Table 3 .

Concept Matrix Table.

5. Analysis of Concepts

From the articles included in the present study between 2008 and 2021, they were grouped into five categories identified: (i) information technology in health, (ii) acceptance of e-health, (iii) telemedicine, (iv) security of e-health, and (v) education impact of e-health.

5.1. Information Technology in Health

Researchers have studied several factors to maximise the effectiveness and success of adopting new technology to benefit patients. Hospitals can benefit from information technology when designing or modifying new service procedures. Health units can use information and communication technology applications to analyse and identify patients’ needs and preferences, enhancing their service innovation processes. Previous findings conclude that technological capability positively influences patient service and innovation in the service process [ 301 ]. These results have significant management implications as managers seek to increase technology resources’ efficiency to achieve patient-centred care as the cornerstone of medical practice [ 207 ].

Informatics facilitates the exchange of knowledge necessary for creating ideas and the development process. The internet supports health organisations in developing and distributing their services more efficiently [ 206 ]. Also, Information Technology improves the quality of services, reduces costs, and helps increase patient satisfaction. As new technologies have created opportunities for companies developing high-tech services, healthcare units can increase customer value, personalise services and adapt to their patient’s needs [ 209 ]. To this end, the “smart hospitals” should represent the latest investment frontiers impacting healthcare. Their technological characteristics are so advanced that the public authorities need know-how for their conception, construction, and operation [ 228 ].

A new example is reshaping global healthcare services in their infancy, emphasising the transition from sporadic acute healthcare to continuous and comprehensive healthcare. This approach is further refined by “anytime and everywhere access to safe eHealth services.” Recent developments in eHealth, digital transformation and remote data interchange, mobile communication, and medical technology are driving this new paradigm. Follow-up and timely intervention, comprehensive care, self-care, and social support are four added features in providing health care anywhere and anytime [ 289 ]. However, the healthcare sector’s already precarious security and privacy conditions are expected to be exacerbated in this new example due to the much greater monitoring, collection, storage, exchange, and retrieval of patient information and the cooperation required between different users, institutions, and systems.

The use of mobile telephony technologies to support health goals contributes to the transformation of healthcare benefits worldwide. The same goes for small and medium-sized healthcare companies, such as pharmacies. A potent combination of factors between companies and customers is the reason for creating new relationships. In particular, mobile technology applications represent new opportunities for integrating mobile health into existing services, facilitating the continued growth of quality service management. Service-based, service-focused strategies have changed distribution patterns and the relationship between resellers and consumers in the healthcare industry, resulting in mobile health and significant pharmacy opportunities. It has been an important research topic in the last decade because it has influenced and changed traditional communication between professionals and patients [ 211 ]. An example of a mobile healthcare platform is “Thymun”, designed and developed by Salamah et al. aiming to create intelligent health communities to improve the health and well-being of autoimmune people in Indonesia [ 225 ].

5.2. Acceptance of E-Health

In a long-term project and a population study (1999–2002), Hsu et al. evaluated e-health usage patterns [ 302 ]. The authors conclude that access to and use of e-health services are rapidly increasing. These services are more significant in people with more medical needs. Fang (2015) shows that scientific techniques can be an essential tool for revealing patterns in medical research that could not be apparent with traditional methods of reviewing the medical literature [ 303 ]. Teleradiology and telediagnosis, electronic health records, and Computer-Aided Diagnosis (CAD) are examples of digital medical technology. France is an example of a country that invests and leads in electronic health records, based on what is written by Manard S. et al. [ 243 ]. However, the impact of technological innovation is reflected in the availability of equipment and new technical services in different or specialised healthcare sectors.

On the other hand, Mariusz Duplaga (2013) argues that the expansion of e-health solutions is related to the growing demand for flexible, integrated and cost-effective models of chronic care [ 304 ]. The scope of applications that can support patients with chronic diseases is broad. In addition to accessing educational resources, patients with chronic diseases can use various electronic diaries and systems for long-term disease monitoring. Depending on the disease and the symptoms, the devices used to assess the patient’s condition vary. However, the need to report symptoms and measurements remains the same. According to Duplaga, the success of treatments depends on the patient’s involvement in monitoring and managing the disease. The emphasis on the role of the patient is parallel to the general tendency of people and patients to participate in decisions made about their health. Involving patients in monitoring their symptoms leads to improved awareness and ability to manage diseases. Duplaga argues that the widespread use of e-health systems depends on several factors, including the acceptance and ability to use information technology tools, combined with an understanding of disease and treatment.

Sumedha Chauhan & Mahadeo Jaiswal (2017) are on the same wavelength. They claim that e-health applications provide tools, processes and communication systems to support e-health practices [ 305 ]. These applications enable the transmission and management of information related to health care and thus contribute to improving patient’s health and physicians’ performance. The human element plays a critical role in the use of e-health, according to the authors. In addition, researchers have studied the acceptance of e-health applications among patients and the general public, as they use services such as home care and search for information online. The meta-analysis they use combines and analyzes quantitative findings of multiple empirical studies providing essential knowledge. However, the reason for their research was the study of Holden and Karsh (2010) [ 306 ].

To provide a comprehensive view of the literature acceptance of e-health applications, Holden and Karsh reviewed 16 studies based on healthcare technology acceptance models [ 306 ]. Findings show them that the use and acceptance of technological medical solutions bring improvements but can be adopted by those involved in the medical field.

5.3. Telemedicine

On the other hand, telemedicine is considered one of the most important innovations in health services, not only from a technological but also from a cultural and social point of view. It benefits the accessibility of healthcare services and organisational efficiency [ 215 ]. Its role is to meet the challenges posed by the socio-economic change in the 21st century (higher demands for health care, ageing population, increased mobility of citizens, need to manage large volumes of information, global competitiveness, and improved health care provision) in an environment with limited budgets and costs. Nevertheless, there are significant obstacles to its standardisation and complete consolidation and expansion [ 300 ].

At present, there are Telemedicine centres that mediate between the patient and the hospital or doctor. However, many factors make this communication impossible [ 300 ]. Such factors include equipment costs, connectivity problems, the patient’s trust or belief in the system or centre that applies telemedicine, and resistance to new and modern diagnostics, especially in rural and island areas. Therefore, telemedicine would make it easier to provide healthcare systems in remote areas than having a specialist in all the country’s remote regions [ 300 ]. Analysing the concept further, one can easily argue that the pros outweigh the disadvantages. Therefore, telemedicine must be adopted in a concerted effort to resolve all the obstacles we are currently facing. Telemedicine centres and services such as teleradiology, teledermatology, teleneurology, and telemonitoring will soon be included. This means that a few years from now, the patient will not have to go to a central hospital and can benefit remotely from the increased quality of health services. This will save valuable time, make good use of available resources, save patient costs, and adequately develop existing and new infrastructure.

In 2007, the World Health Organisation adopted the following broad description of telemedicine: “The delivery of health care services, where distance is a critical factor, by all health care professionals using information and communication technologies for the exchange of valid information for the diagnosis, treatment and prevention of disease and injuries, research and evaluation, and for the continuing education of health care providers, all in the interests of advancing the health of individuals and their communities ” [ 307 ]

According to the Wayback Machine, Canadian Telehealth Forum, other terms similar to telemedicine are telehealth and e-health, which are used as broader concepts of remote medical therapy. It is appropriate to clarify that telemedicine refers to providing clinical services. In contrast, telehealth refers to clinical and non-clinical services, including education, management and research in medical science. On the other hand, the term eHealth, most commonly used in the Americas and Europe, consists of telehealth and other elements of medicine that use information technology, according to the American Telemedicine Association [ 308 ].

The American Telemedicine Association divides telemedicine into three categories: storage-promotion, remote monitoring, and interactive services. The first category includes medical data, such as medical photographs, cardiograms, etc., which are transferred through new technologies to the specialist doctor to assess the patient’s condition and suggest the appropriate medication. Remote monitoring allows remote observation of the patient. This method is used mainly for chronic diseases like heart disease, asthma, diabetes, etc. Its interactive services enable direct communication between the patient and the treating doctor [ 309 ].

Telemedicine is a valuable and efficient tool for people living or working in remote areas. Its usefulness lies in the health access it provides to patients. In addition, it can be used as an educational tool for learning students and medical staff [ 310 ].

Telemedicine is an open and constantly evolving science, as it incorporates new technological developments and responds to and adapts to the necessary health changes within societies.

According to J.J. Moffatt, the most common obstacles to the spread of telemedicine are found in the high cost of equipment, the required technical training of staff and the estimated time of a meeting with the doctor, which can often be longer than the use of a standard doctor [ 311 ]. On the other hand, the World Health Organisation states that telemedicine offers excellent potential for reducing the variability of diagnoses and improving clinical management and the provision of health care services worldwide. The World Health Organisation claims, according to Craig et al. and Heinzelmann PJ, that telemedicine improves access, quality, efficiency and cost-effectiveness [ 312 , 313 ]. In particular, telemedicine can help traditionally under-served communities by overcoming barriers to the distance between healthcare providers and patients [ 314 ]. In addition, Jennett PA et al. highlight significant socio-economic benefits for patients, families, health professionals and the health system, including improved patient-provider communication and educational opportunities [ 315 ].

On the other hand, Wootton R. argues that telemedicine applications have achieved different levels of success. In both industrial and developing countries, telemedicine has yet to be used consistently in the healthcare system, and few pilot projects have been able to be maintained after the end of their initial funding [ 316 ].

However, many challenges are regularly mentioned and responsible for the need for more longevity in many efforts to adopt telemedicine. One such challenge is the complexity of human and cultural factors. Some patients and healthcare workers resist adopting healthcare models that differ from traditional approaches or home practices. In contrast, others need to have the appropriate educational background in Information and Communication Technologies to make effective use of telemedicine approaches [ 314 ]. The need for studies documenting telemedicine applications’ economic benefits and cost-effectiveness is also a challenge. Strong business acumen to persuade policymakers to embrace and invest in telemedicine has contributed to a need for more infrastructure and program funding [ 312 ]. Legal issues are also significant obstacles to the adoption of telemedicine. These include the need for an international legal framework that allows health professionals to provide services in different jurisdictions and countries. Furthermore, the lack of policies governing data confidentiality, authentication and the risk of medical liability for health professionals providing telemedicine services [ 314 ]. In any case, the technological challenges are related to legal issues. In addition, the systems used are complex, and there is a possibility of malfunction, which could cause software or hardware failure. The result is an increase in patient morbidity or mortality as well as the liability of healthcare providers [ 317 ].

According to Stanberry B., to overcome these challenges, telemedicine must be regulated by definitive and comprehensive guidelines, which are ideally and widely applied worldwide [ 318 ]. At the same time, legislation must be enacted governing health confidentiality, data access, and providers’ responsibility [ 314 ].

5.4. Security of eHealth

The possibility of the patients looking at the electronic patient folder in a cloud environment, through mobile devices anytime and anywhere, is significant. On the one hand, the advantages of cloud computing are essential, and on the other hand, a security mechanism is critical to ensure the confidentiality of this environment. Five methods are used to protect data in such environments: (1) users must encrypt the information before storing it; (2) users must transmit information through secure channels; (3) the user ID must be verified before accessing data; (4) the information is divided into small portions for handling and storage, retrieved when necessary; (5) digital signatures are added to verify that a suitable person has created the file to which a user has access. On the other hand, users of these environments will implement self-encryption to protect data and reduce over-reliance on providers [ 210 ].

At the same time, Maliha S. et al. [ 227 ] proposed the blockchain to preserve sensitive medical information. This technology ensures data integrity by maintaining a trace of control over each transaction. At the same time, zero trusts provide that medical data is encrypted and that only certified users and devices interact with the network. In this way, this model solves many vulnerabilities related to data security [ 227 ]. Another alternative approach is the KONFIDO project, which aims at the safe cross-border exchange of health data. A European H2020 project aims to address security issues through a holistic example at the system level. The project combines various cutting-edge technologies in its toolbox (such as blockchain, photonic Physical Unclonable Functions, homomorphic encryption, and trusted execution) [ 234 ]. Finally, Coppolino L. et al. [ 271 ] proposed using a SIEM framework for an e-healthcare portal developed under the Italian National eHealth Net Program. This framework allows real-time monitoring of access to the portal to identify potential threats and anomalies that could cause significant security issues [ 271 ].

5.5. Education Impact of E-Health

But all this would only be feasible with the necessary education of both users and patients [ 11 ]. As the volume and quality of evidence in medical education continue to expand, the need for evidence synthesis will increase [ 295 ]. On the other hand, Brockers C. et al. argued that digitalisation changes jobs and significantly impacts medical work. The quality of medical data provided for support depends on telemedicine’s medical specialisation and knowledge. Adjustments to primary and further education are inevitable because physicians are well trained to support their patients satisfactorily and confidently in the increasingly complex digitalisation of healthcare. The ultimate goal of the educational community is the closest approach of students to the issues of telemedicine and e-health, the creation of a spirit of trust, and the acceptance and transmission of essential knowledge [ 268 ].

Noor also moved in this direction, seeking to discover the gaps in Saudi education for digital transformation in health [ 248 ]. The growing complexity of healthcare systems worldwide and the growing reliance of the medical profession on information technology for precise practices and treatments require specific standardised training in Information Technology (IT) health planning. Accreditation of core Information Technology (IT) is advancing internationally. Noor A. examined the state of Information Technology health programmes in the Kingdom of Saudi Arabia (KSA) to determine (1) how well international standards are met and (2) what further development is required in the light of recent initiatives of the Kingdom of Saudi Arabia on e-health [ 248 ]. Of the 109 institutions that participated in his research, only a few offered programmes specifically in Health Information Technology. As part of Saudi Vision 2030, Saudi digital transformation was deemed an urgent need. This initiative calls for applying internationally accepted Information Technology skills in education programmes and healthcare practices, which can only happen through greater collaboration between medical and technology educators and strategic partnerships with companies, medical centres and government agencies.

Another study by Diviani N. et al. adds to the knowledge of e-health education, demonstrating how online health information affects a person’s overall behaviour and enhances patients’ ability to understand, live and prepare for various health challenges. The increasing digitalisation of communication and healthcare requires further research into the digital divide and patients’ relationships with health professionals. Healthcare professionals must recognise the online information they seek and engage with patients to evaluate online health information and support joint healthcare-making [ 235 ].

6. Discussion

The selected studies comprise a conceptual model based on bibliographic research. Using an open-ended technique, we analyse the selected 287 articles, which are grouped into categories based on their context. This methodology provides readers with a good indication of issues concerning the timeliness of health digitalisation. A limitation of the methodology is that selected criteria of the method might be subjective in terms of the search terms and how the papers are selected. The articles indicate that this field is initial, and further research is needed. Although several articles have created a theoretical basis for corporate sustainability and strategic digital management, only limited studies provided guidelines on the strategic digital transformation process and its health implementation stages. However, studies have also developed sustainable models, software or applications in this area. This is also the reason for creating opportunities for future researchers, who will be closed to investigate this gap and improve the viability of digital health strategies. In addition, any work carried out in case studies provides fruitful results by facilitating researchers through deep penetration into sustainable digitalisation. No generalised frameworks are available to guide the wording and implementation of digital action plans. Thus, the need for quantitative or qualitative research is created, providing conclusions on the impact of internal or external factors in the sustainability process, implementation, adoption, planning, and challenges of digital health solutions in general, as well as the impact of digital transformation. Most existing studies explore the issue of digitalisation in a particular part of a nursing institution or a disease rather than the management strategy perspective. In this way, researchers ignore a debate on obstacles and problems that often face in practice during integration. Such an analysis could lead to more profound knowledge.

7. Conclusions

In conclusion, our research observed a timeless analysis of systematised studies focusing on digital health developments. These studies broaden the researchers’ vision and provide vital information for further investigation. This article focuses on understanding digitalisation in healthcare, including, for the most part, the digitalisation of information and adopting appropriate parameters for further development. To build a more holistic view of digital health transformation, there is a great need for research on the management implications of digitalisation by different stakeholders. Finally, the development of telemedicine, the further enhancement of digital security and the strengthening of technological information systems will contribute to the universal acceptance of the digital health transformation by all involved.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualisation, A.I.S., F.K. and M.A.T.; methodology, F.K. and M.A.T.; software, A.I.S.; validation, A.I.S.; data curation, A.I.S.; writing—original draft preparation, A.I.S. and M.A.T.; writing—review and editing, A.I.S. and M.A.T.; visualisation, A.I.S.; supervision, M.A.T.; project administration, M.A.T. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

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Distributed acoustic sensing technology in marine geosciences

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• Published: 14 September 2024
• Volume 2 , article number  26 , ( 2024 )

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• Jiayi Wei 1   na1 ,
• Wende Gong 3   na1 ,
• Junhui Xing   ORCID: orcid.org/0000-0002-2608-0366 1 , 2 &
• Haowei Xu 1  

Distributed acoustic sensing (DAS) is an emerging vibration signal acquisition technology that transforms existing fiber-optic communication infrastructure into an array of thousands of seismic sensors. Due to its advantages of low cost, easy deployment, continuous measurement, and long-distance measurement, DAS has rapidly developed applications in the field of marine geophysics. This paper systematically summarizes the status of DAS technology applications in marine seismic monitoring, tsunami and ocean-current monitoring, ocean thermometry, marine target monitoring, and ocean-bottom imaging; analyzes the problems faced during its development; and discusses prospects for further applications in marine geoscience and future research directions.

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1 Introduction

As human exploration of the Earth’s deep-sea environment continues, the demand for efficient and reliable marine geoscience data acquisition and analysis technologies is increasing. As an innovative monitoring and detection method, distributed acoustic sensing (DAS) technology has gradually become one of the key technologies to address this challenge.

DAS originated in the 1970s as a novel vibration signal acquisition technology, using optical fiber as a sensing and transmission medium. It is based on phase-sensitive optical time-domain reflectometry, achieving continuous distributed detection of acoustic signals through the interference effect of backscattered Rayleigh light in the optical fiber (Hartog 2017 ). DAS enables continuous spatial sampling of seismic wavefields with meter-level precision. This technology, initially used in military defense and engineering structure monitoring, inherits the advantages of conventional optical fiber sensing technologies, such as resistance to electromagnetic interference, good concealment, corrosion resistance, insulation, and low deployment costs (Wang et al. 2019 ). Thanks to the sensitivity of phase to propagation distance in optical transmission, DAS technology can sense a wide frequency range of vibration signals. As a new measurement method in geophysics, it can transform existing fiber-optic communication infrastructure into an array of thousands of seismic sensors. Over the past decade, the gradual development of DAS technology has been increasingly useful in fields such as oil and gas exploration, downhole vertical seismic profiling (VSP), and traffic monitoring (Parker et al. 2012 ; Daley et al. 2013 ; Madsen et al. 2013 ; Hartog et al. 2014 ; Mateeva et al. 2014 ; Jiang et al. 2016 ).

In marine geophysical research, the application of underwater seismic sensors such as ocean-bottom seismometers (OBS) has matured. The data collected from deployed OBS can be used for volcano structure detection, earthquake detection, and seabed structure exploration. However, the deployment cost of OBS is extremely high for observing larger areas. In contrast, DAS systems are simple in structure, have modest maintenance costs, feature real-time data transmission capabilities, are easy to deploy, allow for continuous measurement, and can measure over long distances. Because DAS is sensitive to optical fiber deformations, it can be directly applied to existing communication cables. Currently, millions of kilometers of optical fiber cables have been laid worldwide, with a sizable proportion in the ocean as communication fibers or dark fibers. DAS technology can leverage these fibers to establish a large-scale seabed observation network for marine geophysics applications (Marra et al. 2018 ; Lindsey et al. 2019 ). To provide readers with a clearer understanding of the marine geoscience applications of this technology, this work explains the status of DAS equipment and parameters. It also details the application of DAS in marine seismic monitoring, seabed imaging, tsunami and typhoon monitoring, sea water temperature monitoring, and marine target monitoring. Based on this, it outlines prospects for the future development and application of DAS technology.

2 Equipment parameters

The distributed fiber-optic sensing system consists of the DAS demodulation and the sensing fiber. There are many options for distributed acoustic sensing demodulations, such as the iDAS from Silixa in the UK ( https://silixa.com ), the FEBUS A1-R from FEBUS Optics in France ( https://www.febus-optics.com ), the DAS N51/N52 series from AP Sensing in Germany ( https://www.apsensing.com ), the QuantX DAS from OptaSense in the US ( https://www.optasense.com ), the USTC ZD-DAS developed by the University of Science and Technology of China ( https://36kr.com/p/2410165669176072 ), or the DITEST DAS series from Omnisens in Switzerland ( http://www.omnisens.com.cn/ ) (Fig.  1 ). The parameters of these devices are listed in Table  1 .

Distributed acoustic sensing (DAS) equipment diagram. a IDAS; b FEBUS A1-R; c AP SENSING DAS; d OptaSense QuantX DAS; e USTC ZD-DAS; f Omnisens DITEST

The DAS devices developed by various companies exhibit many common features, but each has unique advantages. DAS devices have a wide frequency-response range, high spatial resolution, and long measurement distances. For example, Silixa’s iDAS and AP Sensing’s DAS devices both have a frequency-response capability of up to 20 kHz or more, suitable for capturing high-frequency vibration and acoustic signals. The measurement distances of DAS devices typically reach or exceed 50 km. Silixa’s iDAS and the USTC DAS developed by the University of Science and Technology of China provide long-distance measurements over 50 km while offering sub-meter spatial resolution, allowing these devices to precisely locate acoustic events. These characteristics make them widely applicable in fields such as geophysics and traffic detection.

Current DAS devices have a frequency-response range from 0.001 Hz to 50 kHz (e.g., Silixa iDAS), capable of detecting microseismic signals such as Scholte waves and ocean surface gravity waves with frequencies between 0.01 Hz and 1 Hz (Xiao et al. 2022 ). The broader frequency detection range of DAS devices meets the detection frequency requirements in marine geophysics and enables these devices to better monitor marine targets (e.g., marine mammals like whales).

3 Marine seismic monitoring

The poor spatial coverage of marine observation instruments has long led to a lack of high-density marine observations. This has resulted in biases and low resolution in global seismic tomography models and uncertainties in the specific locations of marine seismic activity (Williams et al. 2019 ). In this context, submarine optical fiber has gradually become a useful tool for us to observe marine seismic activities. There have been many studies on the use of submarine fiber for marine geophysical applications and seismic monitoring, such as the frequency metrology interferometric techniques method (Marra et al. 2018 ) and the state of polarization monitoring technology (Zhan et al. 2021 ). The DAS continuous high-density monitoring capability and convenient layout provide a new means for marine seismic activity observation.

DAS is applicable for seismic monitoring in subsea dark fibers and communication cables (Ajo-Franklin et al. 2019 ; Williams et al. 2019 ). Advances in fiber-optic seismology have directly facilitated research on microseisms and related areas. Research teams have demonstrated the capability of underwater DAS to record and analyze seismic events. Microseismic and teleseismic observations were conducted using a subsea fiber-optic cable approximately 42 km long, buried 0.5–3.5 m below the seabed in the North Sea off the coast of Zeebrugge, Belgium (Williams et al. 2019 ). Data recorded by the DAS system during the August 19, 2018, M w 8.2 earthquake in Fiji were used to separate and identify marine and seismic signals, analyze observed ocean surface gravity waves and Scholte waves, and extract P- and S-wave phases in the 0.01–1 Hz band (Williams et al. 2019 ).

A 41.5-km-long telecommunications cable was deployed near the coast of Toulouse, France, spanned from the shallow continental shelf to the deep-sea plain at a depth of approximately 2500 m for observing and recording periodic oscillatory background signals. The first eight kilometers of the cable, deployed on the shallow continental shelf, detected signals in the 0.1–0.25 Hz range, with an average depth of 100 m. When the cable reached the deep-sea plain, at an average depth of more than 2000 m, the recorded data primarily showed high-frequency waves in the 0.2–0.8 Hz range. The cable data exhibited records related to local small earthquakes, ocean surface gravity waves, and microseismic noise, indicating that DAS can be directly applied to subsea communication cables for seabed dynamics monitoring (Sladen et al. 2019 ).

Backscattered laser pulses and phase-based coherent optical time-domain reflectometry ( \(\upvarphi\) -OTDR) technologies have been used with fibers for optical analysis multiplexing, successfully applying DAS technology to subsea dark fibers. This technique was applied to more than 20 km of ‘MARS’ cable near California for environmental noise analysis, demonstrating the potential of DAS for marine seismic observations (Lindsey et al. 2019 ).

Lior et al. ( 2021 ) conducted a comprehensive analysis of underwater DAS measurements. They analyzed noise sources, including ocean surface gravity waves and Scholte waves, using DAS data from three different telecommunications cables in Greece and compared the DAS records with those from nearby broadband stations. In Lior et al. ( 2021 ), the DAS data were not sensitive to P-waves. However, Tonegawa et al. ( 2022 ) successfully extracted P-waves in the 0.1–0.3 Hz band from submarine ambient-noise data recorded by DAS during a rainstorm. The results in Tonegawa et al. ( 2022 ) indicate that the extraction of P-waves by DAS is currently weather-dependent, with higher P-wave intensities during rainy weather improving DAS data quality, whereas calm, clear weather results in poorer P-wave extraction quality. This may adversely affect the application of DAS to marine seismic monitoring.

DAS can detect active-source-generated hydroacoustic signals, such as from air gun shots (Matsumoto et al. 2021 ). Approximately 50 km of the 128-km Moruto submarine cable near the Dense Ocean-floor Network system for Earthquakes and Tsunamis observatory in Japan was used for DAS measurements. The Muroto cable has a buried section between 0.35 and 2.14 km, with a burial depth ranging from 0.5 to 1.0 m below the seafloor. Beyond this section, due to its own weight, the cable is laid on the seafloor at depths below 500 m (Matsumoto et al. 2021 ). These researchers compared the frequency and sensitivity of DAS transmission data with data from hydrophones near the optical cable. The results showed that DAS fully recorded the vibration signals from the active source and, under strong coupling between the cable and the seabed, DAS could identify seismic signals caused by regional earthquakes.

Microseismic noise is almost ubiquitous in DAS submarine detection data, providing a novel method for exploring the mechanisms of microseism generation. DAS can record the amplitude and frequency of seafloor strains caused by ocean surface gravity waves, as well as secondary microseisms caused by the interaction of incident and reflected gravity waves from the coast. This offers a new opportunity to search for ocean microseism sources and understand the complex seismic phenomena present in the ocean. DAS sensitivity to ocean surface gravity waves and Scholte waves (Spica et al. 2022 ) can be applied to locate high-frequency microseism sources in the ocean.

Xiao et al. ( 2022 ) analyzed the signal-to-noise ratio (SNR) changes in seaward and landward signals monitored by DAS using a submarine cable buried 1-m below the seabed near the coast of Valencia, Spain. Using a short-term cross-correlation function method, they accurately located ocean secondary microseism sources within a few kilometers of error. Figure  2 intuitively reflects the determination of the source by comparing the SNR of seaward signals (Fig.  2 a) and landward signals (Fig.  2 b). The results indicate that the locations of high-frequency microseism sources in the ocean are constantly moving (Xiao et al. 2022 ). A similar analysis was performed for secondary microseism noise sources along the French coast (Guerin et al. 2022 ). These analyses are crucial for studying the mechanisms of microseism generation, as DAS provides a new low-cost method for long-term continuous observation of source location changes.

Signal-to-noise (SNR) variations of high-frequency microseisms (0.5–1 Hz) obtained off the coast of Valencia, Spain, by Xiao et al. ( 2022 ). The three colored circular symbols are the locations of the detected high-frequency microseismic sources. a SNR variation of the seaward signal; b SNR variation of the landward signal. (Modified from Xiao et al. 2022 )

4 Ocean-bottom imaging

Ocean-bottom imaging holds significant value for exploring and developing underwater resources, monitoring ocean environmental changes, and providing early warnings for underwater disasters. Using seismic waves from natural earthquakes to study the source and medium is fundamental to seismology. Analyzing these seismic waves to establish body-wave velocity models is crucial for determining the properties of submarine media and imaging the ocean bottom. The advent of DAS provides a new possibility for imaging ocean sediments in areas unreachable by traditional passive seismic surveys. Currently, research on DAS imaging has two directions: active-source imaging and passive-source imaging.

Ambient-noise tomography is commonly used for seismic imaging. DAS has been extensively used for ambient-noise tomography on land (Dou et al. 2017 ; Ajo-Franklin et al. 2019 ; Yuan et al. 2020 ). Ambient-noise analysis in DAS data can also achieve passive-source ocean-bottom imaging. By extracting and inverting Rayleigh wave multimodal dispersion curves, we can obtain a two-dimensional shear-wave velocity profile around the observation cable, combining this with the autocorrelation images of ambient-noise data recorded by DAS, we can analyze the shallow seafloor structure (Fig.  3 ) (Spica et al. 2020 ; Viens et al. 2023 ). This method has already been practically applied in the ocean to reveal small-scale geological structures such as underwater sedimentary basins and faults (Lior et al. 2021 ). Spica et al. ( 2022 ) also proposed a data processing method (gridded slant-stack, GSS) that can detect coherent surface wave energy in the ocean-bottom DAS wavefield for extraction and inversion.

Images obtained through environmental noise tomography (modified from Spica et al. 2020 ). a Power spectral density plot obtained through each channel of the distributed acoustic sensing. b Two-dimensional (2D) shear-wave velocity profile obtained by inverting the dispersion curves of each channel. c Geological interpretation plot obtained by combining a and b

The application of DAS in ocean-bottom active-source imaging has been demonstrated. Active-source seismic data recorded by a submarine cable in Norway’s Trondheimsfjord was used to generate seismic profiles of the ocean bottom and underlying geological structures (Taweesintananon et al. 2021 ). Their research showed that with increased active-source energy, the quality and resolution of DAS imaging would further improve, proving that DAS technology can produce seismic imaging comparable to traditional active-source methods (Taweesintananon et al. 2021 ).

The quality of DAS observation data is significantly correlated with the coupling degree of the optical cable to the seabed. In areas where the seabed depth changes markedly, DAS observation quality is worse than in areas of consistent depth. Poor seabed coupling of optical cables will adversely affect the quality of the measurement data.

5 Tsunami and ocean-current monitoring

Tsunamis are ocean waves triggered by underwater geological activities such as submarine earthquakes, underwater landslides, or volcanic eruptions, and they are highly destructive. Therefore, real-time monitoring for tsunamis is a significant field in geophysics. Because tsunamis can induce infragravity waves (Bromirski and Stephen 2012 ), they can be monitored by observing infragravity wave anomalies. Signal-processing methods exist to extract infragravity waves (high-frequency tsunamis) and tsunami waves from DAS data. The specific methods include down-sampling continuous strain rate data to 1 Hz, applying spectral whitening methods for preprocessing, converting subsea DAS datasets to the frequency-wavenumber (F-K) domain by adjusting step size, and finally performing apparent velocity correction (Xiao et al. 2024 ).

Researchers successfully inverted infragravity waves and tsunami waves off the coast near Florence, Oregon, using an underwater fiber-optic cable and the FEBUS Optics A1-R demodulator (Xiao et al. 2024 ). The infragravity waves were in the 0.005–0.03 Hz band (Fig.  4 a) and wind waves and microseisms were below 0.3 Hz (Fig.  4 b). Additionally, Becerril et al. ( 2024 ) demonstrated real-time monitoring using DAS technology on submarine cables and analyzed seabed strain induced by tsunami waves through fully coupled three-dimensional (3D) physical simulations, proving that DAS can be used for real-time tsunami monitoring and faster tsunami warnings.

Results of frequency-wavenumber (F-K)-transformed stacking of whole-month data collected by distributed acoustic sensing near Florence, Oregon, by Xiao et al. (Modified from Xiao et al. 2024 ). The red dashed lines correspond to the theoretical results for a water depth of 120 m. a 0.05–0.03 Hz band associated with infragravity waves. b Band below 0.3 Hz where wind waves and microseismicity located

Because current researchers have a limited understanding of the mechanisms by which earthquakes generate infragravity waves, future work will require more extensive data collection and more advanced data interpretation methods.

DAS enables in-situ observations of ocean currents during extreme sea conditions caused by typhoons. Ocean storms are a major source of microseismic noise, which can be effectively used for real-time monitoring and tracking of typhoons (Davy et al. 2014 ). Williams et al. ( 2022 ) first demonstrated the application of DAS technology for seawater flow monitoring by ocean surface gravity wave interferometry. They successfully measured flow velocity variations in the Strait of Gibraltar, showing that ambient-noise interferometry with DAS can monitor ocean currents through gravity waves and ocean waves. Lin et al. ( 2024 ) proposed a method for calculating the speed and direction of horizontal ocean currents and successfully conducted in-situ measurements during the passage of Typhoon Muifa. Moreover, ocean currents exhibit a fluid-solid coupling vibration phenomenon known as vortex-induced vibration (VIV) when passing through narrow gaps, characterized by the periodicity of vortex shedding. Flore et al. ( 2023 ) used VIV theory to convert the oscillation frequency of a poorly connected section of cable off the coast of Toulon, southern France, successfully deriving an ocean-current velocity time series at a depth of 2390 m.

6 Ocean thermometry

In-situ monitoring of seafloor temperature is critical for understanding climate change and its impacts on the marine environment. Currently, various monitoring methods are used, including conductivity, temperature, and depth (CTD) sensors, expendable bathythermographs (XBTs), Argo floats, underwater drones, and crewed submersibles. However, these devices often face limitations when conducting long-term and intensive seawater temperature measurements in deep and remote areas. DAS and distributed temperature sensing (DTS) technologies have the potential to fill this gap.

T-waves are the third arrival waves in seismograms following P- and S-waves, propagating horizontally at a speed of approximately 1.5 km/s (Linehan 1940 ; Tolstoy and Ewing 1950 ). Because the speed of sound in seawater increases with temperature, changes in T-wave travel time through the ocean can be used to infer deep-sea temperature variations, a method known as seismic ocean temperature (SOT) measurement (Wu et al. 2020 ). DAS has successfully observed acoustic T-waves generated by marine earthquakes (Ugalde et al. 2021 ). Shen and Wu ( 2024 ) conducted an observational experiment using two cables off the central coast of Oregon, namely ocean observatory initiative (OOI) North and OOI South. The experiment used two OptaSense QuantX demodulators, one Silixa iDAS demodulator, and one Silixa DTS device, collecting approximately 26 TB of data. Shen and Wu ( 2024 ) proposed a denoising scheme to enhance T-wave signals in DAS, successfully detecting small T-wave events hidden in the noise. They calculated the robustness of each dataset for different magnitudes by comparing the denoised OOI North data with data from the HYS11 OBS and OOI observatory data (Fig.  5 ). The results indicate that despite the significant impact of the ocean environment and instrument noise on its application in the SOT method, DAS performs comparably to traditional OBS and remains feasible for sea temperature measurements (Shen and Wu 2024 ).

Schematic diagram of the performance comparison between DAS and OBS proposed by Shen and Wu (modified from Shen and Wu 2024 ). SOT robustness using HYS11 data, raw OOI North data, and denoised OOI North data, as a function of earthquake magnitude

7 Marine target monitoring

Monitoring marine targets aims to manage ocean resources, protect the environment, study climate change, ensure safety, and provide early warnings for natural disasters. Current monitoring methods include satellite remote sensing, uncrewed submersibles, ocean buoys, and underwater acoustic detection. DAS technology has now been applied to monitor ships and whales within the ocean (Landrø et al. 2022 ). DAS can distinguish different frequencies of whale calls, thereby identifying various whale species.

Bouffaut et al. ( 2022 ) first used a dark fiber located 120 km from the Svalbard Islands in Norway for wildlife monitoring, recording baleen whale signals at three distinct positions on the fiber (Fig.  6 ). Their research indicated that DAS has the potential for real-time, low-cost monitoring of whales globally.

Baleen whale vocalizations recorded by Bouffaut et al. ( 2022 ) using the Svalbard DAS array at three distinct locations (44.2 km, 57.5 km, and 76.5 km from the fiber-optic cable). a Spatio-temporal ( t - x ); b spatio-spectral ( f - x ). (Modified from Bouffaut et al. 2022 )

Rørstadbotnen et al. ( 2023 ) collected DAS data from two 250-km long cables off the central coast of Oregon, recording numerous whale sounds and ship noises. They applied DAS data to both brute force grid search (GS) and Bayesian filter (BF) methods and compared the results. Their study showed that both methods had localization errors of approximately 100 m. In terms of whale track prediction, the GS method had a more dispersed position distribution, whereas the BF method had a smaller error compared to the GS method (Rørstadbotnen et al. 2023 ). Both methods demonstrate the feasibility of locating cetaceans within an area of approximately 1800 m 2 , highlighting the potential of DAS for marine target monitoring.

8 Summary and outlook

This work systematically reviewed the applications of DAS technology in marine geophysics and its equipment parameters. Significant achievements of DAS technology have been reached in various fields, such as marine seismic monitoring, tsunami and ocean-current monitoring, ocean thermometry, marine target monitoring, and ocean-bottom imaging. In the field of seismic monitoring, the high sensitivity and wide coverage of DAS technology have significantly improved the precision and efficiency of deep-sea ambient-noise analysis and oceanic microseismic source location while promoting related research. It is now possible to perform in-situ monitoring of ocean currents during extreme weather. Marine target monitoring, especially locating marine organisms, has also seen practical applications. The feasibility of DAS in tsunami warning, sea temperature measurement, and subsea imaging has also been verified, highlighting the potential of DAS to produce groundbreaking results in these fields.

As DAS technology research has continued, the equipment has seen significant improvements in deployment under various extreme environments, with advancements in deployment distances and spatial resolution. However, the application of DAS-related equipment and technology in marine geophysics is not yet fully mature and faces many challenges. In the future, DAS technology for marine regional observation networks will develop in various fields. To further improve the performance of DAS technology, research should be conducted in two areas as follows:

Cable deployment. The weak coupling of cables to the seabed during deep-sea cable deployment because of terrain variations can affect the resolution and SNR of DAS in local areas. This issue can be addressed by increasing the coupling medium between the cable and the seabed or by improving cable deployment techniques.

Data processing. As the length of seabed cable deployment increases, efficient management of DAS data while maximizing the use of dark fibers and active communication fibers is a key issue for the future. Currently, there are data processing programs like DASPy and DASCore, as well as open-source repositories like PubDAS (Spica et al. 2023 ). There is not yet a systematic processing method for the massive data generated during the DAS data transmission process. Artificial intelligence can be combined with DAS for data preprocessing and denoising, such as the N2N method for signal denoising using machine learning (Lapins et al. 2024 ).

After improving the quality and reliability of DAS data, standardization and popularization will lead to large-scale regional deployment. These advances will increase the applications of DAS technology in marine geophysics, providing dedicated support for future marine scientific research and disaster warnings.

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Acknowledgements

This research was supported by the Fundamental Research Funds for the Central Universities (Grant No. 202262012), and the National Natural Science Foundation of China (Grant No. 42076224).

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Edited by: Wenwen Chen.

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Jiayi Wei and Wende Gong contributed equally to this work.

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Key Laboratory of Submarine Geosciences and Prospecting Techniques, MOE and College of Marine Geosciences, Ocean University of China, Qingdao, 266100, China

Jiayi Wei, Junhui Xing & Haowei Xu

Laboratory for Marine Mineral Resources, Qingdao Marine Science and Technology Center, Qingdao, 266071, China

Junhui Xing

China Oilfield Services Limited, Tianjin, 300450, China

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Jiayi Wei and Junhui Xing: responsible for the research design, literature collection and screening, and drafting of the manuscript. Wende Gong and Haowei Xu: mainly in charge of revising and editing the manuscript.

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Wei, J., Gong, W., Xing, J. et al. Distributed acoustic sensing technology in marine geosciences. Intell. Mar. Technol. Syst. 2 , 26 (2024). https://doi.org/10.1007/s44295-024-00039-y

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Received : 22 July 2024

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Published : 14 September 2024

DOI : https://doi.org/10.1007/s44295-024-00039-y

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