Skip to main content

Bibliometric analysis of fourth industrial revolution applied to heritage studies based on web of science and scopus databases from 2016 to 2021

Abstract

Using past material and spiritual remains, cultural heritage examines communities’ identity formation across time. Cultural heritage requires public and private institutions to care about its restoration, maintenance, conservation, and promotion. Through a bibliometric perspective, this study has analyzed, quantified, and mapped the scientific production of the fourth industrial revolution applied to heritage studies from 2016 to 2021 in the Scopus and Web of Science databases. Biblioshiny software from RStudio was employed to categorize and evaluate the contribution of authors, countries, institutions, and journals. In addition, VOSviewer was used to visualize their collaboration networks. As a main result, we found that augmented reality and remote sensing represent the research hotspot concerning heritage studies. Those techniques have become common in archaeology, as well as museums, leading to an increase in their activity. Perhaps, more recent tools, such as machine learning and deep learning, will provide future pathways in cultural heritage from data collected in social networks. This bibliometric analysis, therefore, provides an updated perspective of the implementations of technologies from industry 4.0 in heritage science as a possible guideline for future worldwide research.

Introduction

Cultural heritage studies have become relevant worldwide due to their impact on the revaluation of human history, leading to initiatives to increase social and economic impacts in several countries. In recent years, heritage studies have started to be boosted by technologies from the fourth industrial revolution -industry 4.0- such as the internet of things (IoT), artificial intelligence, augmented reality, remote sensing, etc., extending the known horizons of the field.

These technologies have varied non-invasive applications for heritage sites, as the following examples demonstrate. IoT has enabled the conservation of cultural heritage by managing data collected by sensors and by enhancing the interaction and experience of visitors with cultural objects as much as by preserving the related records [1,2,3]. Additionally, three-dimensional documentation of cultural heritage has been used to fully visualize tall and heavy sculptures through aerial laser scanners and photographs, without which it would not have been possible to be observed [4]. Likewise, artistic heritage can be digitalized with slow-motion technology techniques [5].

Another emerging technology in cultural tourism is augmented reality (AR), which improves consumer satisfaction with the introduction of easy-to-use technological devices, rich in information easier to catch for the public [6]. Moreover, most tourists have claimed that meaningful designs, valuable contents, and intuitive interfaces are key factors for successful AR mobile apps [7]. The use of machine learning techniques has made it feasible for the preservation of intangible heritage such as cultural dances; in such cases, dancers’ movements have been captured with graphics to visually preserve, analyze and understand choreographic patterns [8]. Meanwhile, satellite remote sensing has allowed the discovery of historical places as well as the creation of strategies for their conservation and management by identifying factors that may endanger them in the future [9, 10].

On the other hand, bibliometric analysis has been increasing in heritage studies associated with computer sciences, focusing on industrial heritage [11], intangible heritage [12], and creative and cultural heritages [13,14,15]. Bibliometrics has primarily been used to identify industry 4.0 as a disruptive phenomenon with potential implications in various disciplines and production processes [16]. Limited attention has been paid to bibliometric analysis based on the recent timeframe 2016–2021. Using the software biblioshiny we were able to collect a large number of articles, from Scopus and Web of Science (WoS) databases, across various emerging topics for analysis. Therefore, this research aims to compile and process-wide data associated with technologies from industry 4.0 applicated to heritage studies in the last six years. The following research questions were addressed:

Q1: How many research articles were annually published between 2016 and 2021 in industry 4.0 that were applied to heritage science?

Q2: Who are the most cited authors in studies associated with industry 4.0?

Q3: Which papers are the most cited in heritage studies in conjunction with industry 4.0?

Q4: Which journals host the highest quantities of papers in this research area?

Q5: What leaders’ institutions are found in the focused research field?

Q6: What sponsor institutions are the most active in the selected period?

Q7: What are the top ten countries publishing on this subject?

Bibliometric research can lead to the development and discovery of trends in a field, helping the scientific community to identify new hotbeds of innovation based on a recent window of observation [17]. This bibliometric analysis provides an updated perspective of the implementations of technologies from industry 4.0 in heritage research as a scientific reference for subsequent research. We provide the latest progress related to research, hotspots, and future possible development trends.

Methodology

Study design

This study employed a bibliometric analysis as a tool commonly used to map research in indistinct academic fields. This area is also named scientometrics, which uses math and statistic to numerically describe the scientific activity and relevance across a certain period [17].

Data source

Scopus and Web of Science databases were selected due to their wide renown for hosting high-quality journals and research documents. Institutional access was required to download and corroborate the content of the study files.

Search strategy

We introduced an extended list of keywords in both databases, covering heritage and industry 4.0 topics (see Fig. 1). On behalf of industry 4.0, the words used were the following: data science, industry 4.0, augmented reality, computer science, remote sensing, artificial intelligence, 3D scanning, data mining, data analytics, data handling, data processing, big data, data visualization, internet of things, and machine learning. Representing heritage areas, the selected words were the following: heritage, museums, monuments, paintings, conservatory, archeology, cultural tourism, art restoration, arts and humanities, preventive conservation, collections care, and art conservation. These keywords were obtained in a cyclical process in which, starting from the articles thrown by the databases, more words were incorporated, covering initially unforeseen topics. The established timeline covered data between 2016 and 2021 while the search was reduced to titles and keywords to increase the effectiveness of the equation to collect papers from the objective fields. Only original articles were considered as the document type. Both web pages were consulted for the last time on June 15th, 2022.

Fig. 1
figure 1

Flowchart of used bibliometric methodology

Bibliometric analysis

Plots and tables combined the separately processed and analyzed data downloaded in BibTeX files from Scopus and WoS databases. The Biblioshiny app from the RStudio cloud served as a tool to obtain and organize both databases prior to manual manipulation. Biblioshiny offers data about the most productive countries, institutions, authors, research fields, and journals, as well as about keywords, h-index, impact factor, total citations, etc. [18]. Moreover, VOSviewer was included for data mining, mapping, and visualization of collaborative networks [19].

Limitations

Scopus and WoS databases are not perfectly adapted to bibliometric analyses, since they tend to throw up a certain amount of erroneous data that limits the conclusions to be drawn from them. Qualitative statements can be subjective since this type of study is quantitative [20]. This type of academic exercise offers a short-term forecast of the area under investigation [21].

Results and discussion

The arguments about the established objectives are addressed in the upcoming subsections, based on the data taken from WoS and Scopus, which indicates that Scopus is the preferred database to spread research articles related to industry 4.0 in the heritage studies, largely doubling the number of documents hosted in WoS (see Fig. 1).

Trends in the annual production of original papers

Although the number of documents indexed in Scopus was found higher than in WoS in the studied timeframe, the overall average of total citations is slightly higher in WoS compared to Scopus, reaching 3.98 citations per year against the 3.60 of Scopus (see Fig. 2). In both cases, the research production represents a linear sustained growth -as expected with the apparition of new technologies in heritage science [22]- with a larger jump in productivity from 2019 to 2020 in Scopus, and from 2020 to 2021 in WoS. One of the likely reasons for these increments is related to the incorporation of new technologies into cultural tourism [23], and the opening of access to journals during the COVID-19 pandemic [24]. Meanwhile, the decay of total citations (TC) over years may respond to a phenomenon associated with the time required by researchers to identify the newly published works, their novelty, and their accessibility, in combination with today’s science productivity, etc. [25,26,27].

Fig. 2
figure 2

Annual trend of publication a and total citation b from WoS and Scopus from 2016 to 2021

Most cited authors and their collaborations

Researchers’ performances in a given field are usually quantitatively measured through indicators such as the number of published papers, total citations, h-index, and other derivatives [28]. In this case, Scopus and WoS databases coincide in the conclusion that Piccialli F. is one of the more productive researchers in heritage studies combined with technologies from industry 4.0 (see Table 1). As a general observation of the data, Agapiou A. and Lasaponara R. have delivered the largest quantity of articles whereas Dieck MC. is the author with the most elevated number of citations. As shown in Fig. 3, Lasaponara R. is the researcher with the most collaborations (67) in Scopus, followed by Chen F. (63), and Massini N. (52). Piccialli F., however, stands out as the author with greatest link strength from WoS, with 16 connections (see Table 7 from appendices). These findings, including the h-index, may indicate that Lasaponara R. is the most influential author in studies developed around industry 4.0 and its application to heritage concerns. It is worth noting that images from Fig. 3 are not tailored to the data of Table 1 since these networks’ charts are focused on searching for collaborations -total link strength- and they depend on the minimum article per author selected and on the decision of presenting the interconnection of nodes. In this case, the node size is proportional to the number of authors’ partnerships. The same logic applies to the following VOSviewer figures and analysis along with the document.

Table 1 Top 10 most cited authors in heritage-industry 4.0 research (2016–2021)
Fig. 3
figure 3

Most collaborative authors in heritage studies combined with industry 4.0 from 2016 to 2021 in a Scopus and b WoS, considering a minimum of two documents per institution in VOSviewer

Most cited research articles

As shown in Table 2, the three most cited papers were written by Sun Y. [31], Dieck MC., and Alletos S. The number of citations observed in both databases is similar, highlighting the work of Sun Y. [31] with 476 average total citations, considering both databases. Augmented reality and the internet of things are subjects of great interest in these publications, with 6 and 2 published works from the top ten, respectively. The use of augmented reality in cultural heritage has grown notably in recent years due to its great public appeal [29]. People enjoy the interactive experience provided by digital tools, which translate the content of high scientific value into easily understandable elements, making it more attractive to society [30]. As previously mentioned, IoT contributes to the conservation of cultural heritage -sustainability- by assisting the management of data collected from sensors, as better explained in the work of Sun Y. [1, 31].

Table 2 Top 10 most cited articles in heritage-industry 4.0 research (2016–2021)

Journals that host the highest number of articles

According to both, Scopus and WoS databases, Table 3 shows that the journals with the most participation in heritage studies including tools from industry 4.0 are Remote sensing from the United States, Sustainability from Switzerland, and Journal on computing and cultural heritage from the United States. Approximately, 15% of the published articles in the studied fields are hosted in said three journals from both databases. That is, there exists a large spectrum of journals (85%) in which to publish articles regarding heritage issues mixed with computer science technologies. Moreover, most of the topics covered by the journals’ scope are related to the deterioration of attractions, monuments, artifacts, and places of great heritage value, aiming to contribute to their reconstruction and preservation [32, 33], as well as to encourage presential and virtual tourism [34, 35].

Table 3 Top 10 Journals that hosted the most articles in heritage-industry 4.0 research (2016–2021)

Through introducing Bradford's law, it was feasible to classify sources into core areas, related areas, and not relevant areas regarding the field addressed by the articles hosted in the journals, as expressed in Eq. (1). This law emerges as a plausible indicator to explain why many authors prefer to publish their research works in the journals presented in Table 3.

$${r}_{0}=2\mathrm{ln}({e}^{\gamma }Y)$$
(1)

where \({r}_{0}\) represents the number of journals conforming the core area, \(\gamma\) is the Euler’s constant \((\sim 0.577)\), and \(Y\) is the number of papers in the journal with more hosted documents [36]. In this case, since we are studying two databases, \({Y}_{1}=85\) in Scopus and \({Y}_{2}=25\) in WoS. Thus, \({r}_{0-1}\left(Scopus\right)\cong 10\) and \({r}_{0-2}\left(WoS\right)\cong 8\). As a result, only the source Multimedia tools and applications from WoS is out of the core collection, considering that Sensors is into the Scopus list. Also, it is notable that Remote sensing is the preferred journal to publish articles around industry 4.0 linked with heritage studies.

Most productive institutions and their collaborations

Scopus and WoS provided different results regarding the most productive institutions, considering at least two publications per institute in VOSviewer while omitting unconnected nodes. The top 3 most productive universities in Scopus publish less than in the WoS case, positioning the University of Naples Federico II as the most contributive institution; followed by Cyprus and Manchester Universities (see Table 4). The success of the University of Naples Federico II in this field may be explained by initiatives such as the creation of the”High-Tech FabLab DIETI Unina” (HT FabLab) in recent years, which aimed to spread high-technology digital design/implementation to students through the development of practical projects for their training and research initiatives [37].

Table 4 Top 10 most productive institutions of heritage-industry 4.0 research (2016–2021)

Meanwhile, from the collaborative point of view, the University of Chinese Academy of Science (UCAS), with six visible nodes (see Fig. 4a) plus 4 hidden (see Table 8 in Appendix B), stands out as the most associative institution according to Scopus. Other bibliometric studies related have obtained a similar outcome [38]. However, the WoS data did not provide visual information in Fig. 4b about the leader institution but showed the limited collaboration of most universities. Also, Appendix B indicates that for WoS, UCAS University occupied the second position, with three links less than the Chinese Academy of Sciences -manager of UCAS-, which reaches 13 connections. Moreover, Fig. 4 points out that new organizations are emerging in the past few years.

Fig. 4
figure 4

The most collaborative institutions in heritage studies combined with industry 4.0 from 2016 to 2021 in a Scopus and b WoS in VOSviewer

Most participative funding agencies

Scopus and WoS showed differences in their hosted most sponsoring agencies. The top 3 institutions in Scopus doubled the WoS ones. The Horizon 2020 Framework Programme is the principal funding agency, which represents the world’s most extensive research and innovation program created by the European Union to support and encourage research in the European Research Area (ERA) [39]. The European Commission has played an important role in heritage studies. For instance, it inaugurated the European Year of Cultural Heritage (EYCH) in 2018 to boost engagement with Europe’s past [40]. The National Natural Science Foundation of China rounded out the top three (see Table 5), due to its commitment to research in multiple disciplines, which has led to several researchers achieving the Chinese National Natural Science Award [41].

Table 5 Top 10 most participative funding agencies in heritage-industry 4.0 research (2016–2021)

Most contributing countries and their collaborations

Both databases were consistent about the leading top countries in heritage studies developed under the outlook of industry 4.0, including at least ten publications per institute in VOSviewer, omitting unconnected nodes. Italy stands as the principal country that has successfully incorporated ideas, technology, and innovation from computers to heritage sciences. This statement is sustained by the number of citations received, which highly overpasses the numbers obtained by China and the United Kingdom (see Table 6). Italy is not only the leader in production but also in the impact of their 4.0 heritage studies. The success of Italy and China must be framed in the implementation of national policies by both governments. Italy launched the ‘Piano Nazionale Industria 4.0’ national plan in 2018, which aimed to support companies investing in new technologies, modernizing machinery, creating new patents, and pushing innovation [42]. China’s actual empowerment was thoroughly planned in the “Made in China 2025” strategy from 2015, which was directed to catch up with industry 4.0 technologies [43].

Table 6 Top 10 countries in heritage-industry 4.0 research (2016–2021)

One of Italy’s recent initiatives is reflected in the resilience of its museums during the COVID-19 pandemic in 2020, where online cultural material and other initiatives migrated to social media -Twitter, Instagram, and Facebook-, showing a sharp rise in their regular online activity [44]. This practice proved the utility of digital technologies to boost the number of visitors, reduce costs, improve the visitor experience, and adapt to competitors. Hence, the COVID-19 pandemic accelerated the adoption of digital technologies. However, more funding is essential for the inclusion of more digital tools [45].

Figure 5 shows that the most collaborative countries are Italy and UK whereas USA and China intercalate the third place depending on the database explored (see Table 9 of Appendix C). Otherwise, the same figure shows that China, Spain, Australia, Norway, and Netherlands are countries that have been increasing their collaborations in the past two years.

Fig. 5
figure 5

Most collaborative countries in heritage studies combined with industry 4.0 from 2016 to 2021 in a Scopus and b WoS, considering a minimum of ten documents per country in VOSviewer

Relationship among institutions, countries, and keywords

Keywords are loyal representations of scientific research works in articles, and their frequent implementations may reflect the hotspots of a particular study field. The word-cloud visualization of Scopus and WoS database in Biblioshiny (see Fig. 6), allowed us to define the most relevant keyword introduced by authors in heritage studies linked with industry 4.0 technologies. These results highlight that the key terms for heritage science are archaeology, museum, and tourism while those for computer science are remote sensing, augmented reality, and machine learning (see Table 10 in Appendix D).

Fig. 6
figure 6

Word clouds of authors’ keywords in heritage studies combined with industry 4.0 from 2016 to 2021 in a Scopus and b WoS

Remote sensing provides data for mapping the surface of the earth, identification of landslides, and environmental monitoring for cultural heritage decision-making [46]. Augmented reality may combine with virtual reality, since both include electronic devices -tablets, smartphones, laptops, etc. to attract tourists and encourage them to visit museums [47]. Machine learning has been adopted by archaeologists to study geospatial, material cultural, textual, natural, and artistic data. These algorithms are trained to identify and classify archaeological features and objects [48]. Institutions may fluctuate but in general terms, remote sensing and augmented reality are the most prioritized technologies in heritage studies (see Fig. 8 in Appendix D). Furthermore, these technologies can be introduced together for education purposes in several heritage areas [49].

Figure 7 shows that machine learning and deep learning technologies are gaining importance in heritage studies. For instance, social networks are useful for mapping values and attributes transmitted by the public to cultural heritage through the collection of images, texts, geographic locations, and so forth, by means of graph-based machine learning algorithms [50]. Artistic paintings have been analyzed through automatic classification by deep learning, extracting detailed features of oil painting images to provide accurate results [51]. We believe that these applications belong to new pathways of heritage studies addressed to industry 4.0, and serve as indicators for future routes to be explored by scientists.

Fig. 7
figure 7

Keywords most used by authors in heritage studies combined with industry 4.0 from 2016 to 2021 in a Scopus and b WoS, considering a minimum of eight and five occurrences in VOSviewer, respectively

Conclusions

This bibliographic review demonstrates the recent growing interest shown by institutions, journals, researchers, countries, and funding agencies in the study of cultural heritage associated with the use of technological tools provided by industry 4.0. The main conclusions delivered by responding to each one of the settled research questions are the following:

  • The production of original papers in the explored fields is currently under linear growth.

  • A minimum of 4 published papers, with more than one hundred citations, is required to become one of the most cited authors on the tracked type of research.

  • The most cited articles on these issues deal with augmented reality and IoT applications in cultural heritage.

  • The main magazines that disseminate industry 4.0 initiatives linked to cultural heritage have a JCR greater than 2, with remote sensing being the preferred repository.

  • The most productive institutions delivered at least 10 documents to be part of the top ten.

  • Funding agencies pursuing the top ten of given awards needed to finance a minimum of 4 papers.

  • Italy and China are the most concerned countries regarding the fourth industrial revolution applied to material science, whose success stems from the incorporation of specific public policies.

  • Augmented reality and remote sensing represent the most attractive technologies to perform new studies in heritage science.

In general terms, this bibliometric analysis offers an updated standpoint on heritage sciences for developing subsequent research and generating more consciousness about the impact of introducing new technologies in the promotion, discovery, management, and conservation of world cultural and historical heritage. Although augmented reality and remote sensing are the research hotspots technologies in heritage studies, and they have become usual in archeology and museums to trigger education and tourism, exist future possible pathways such as machine learning and deep learning that are as well used in heritage areas like artistic paintings. Finally, we suggest that future research will validate the quantitative trends found in each of the addressed questions presented in the introduction section.

Availability of data and materials

The databases used can be freely downloaded from the Web of Science and Scopus websites following the procedure explained in the methodology section.

References

  1. Dinesh M, Arvind C, Sreeja-Mole S, Subash-Kumar C, Chandra-Sekar P, Somasundaram K, Srihari K, Chandragandhi S, Prabhu-Sundramurthy V. An energy efficient architecture for furnace monitor and control in foundry based on industry 40 using IoT. Sci Program. 2022. https://doi.org/10.1155/2022/1128717.

    Article  Google Scholar 

  2. M. T. Marshall, “Interacting with heritage: on the use and potential of iot within the cultural heritage sector,” 2018 5th Int. conf. internet things syst. manag. secur. IoTSMS 2018. Nov 2018; pp. 15–22. Doi: https://doi.org/10.1109/IOTSMS.2018.8554899.

  3. M. Maksimović and M. Cosović, “Preservation of Cultural Heritage Sites using IoT,” 2019 18th int. symp. INFOTEH-JAHORINA, INFOTEH 2019—Proc. May 2019; Doi: https://doi.org/10.1109/INFOTEH.2019.8717658.

  4. F. Chiabrando, F. D’Andria, G. Sammartano, and A. T. Spanò, “Metodología y aplicacion práctica para la digitalización de patrimonio cofrade mediante la fusion de datos de láser escaner y fotogramétricos,” 8th Int. Congr. Archaeol. Comput. Graph. Cult. Herit. Innov. Oct 2016; pp. 110–117. Doi: https://doi.org/10.4995/Arqueologica8.2015.3517.

  5. Truong SN, Dinh LN, Minh HN, Khac DL, Van SQ. Proposals on technologies and contents to digitize the art of the Trống quân response singing in Hưng Yên province, Vietnam. AIP Conf Proc. 2021;2406(1):030023. https://doi.org/10.1063/5.0066533.

    Article  Google Scholar 

  6. N. Chung, J. Jia, T. Xiaorui, and C. Koo, “The influence of technology readiness on satisfaction and destination loyalty toward augmented reality technologies”. 2019. https://aisel.aisnet.org/pacis2019/36. Accessed 15 June 2022.

  7. Han D-I, Jung T, Han D-I, Jung T. Identifying tourist requirements for mobile AR tourism applications in urban heritage tourism. In: Timothy MJ, Claudia DT, editors. Augmented reality and virtual reality. Cham: Springer International Publishing; 2018.

    Google Scholar 

  8. Rallis I, Voulodimos A, Bakalos N, Protopapadakis E, Doulamis N, Doulamis A. Machine learning for intangible cultural heritage: a review of techniques on dance analysis. Springer Ser Cult Comput. 2020. https://doi.org/10.1007/978-3-030-37191-3_6/TABLES/1.

    Article  Google Scholar 

  9. Moise C, Negula I, Mihalache C, Lazar AM, Luminita-Dudulescu A, Tiberiu-Rustoiu G, Constantin-Inel I, Badea A. Remote sensing for cultural heritage assessment and monitoring: the case study of Alba Iulia. Sustain. 2021;13:1406. https://doi.org/10.3390/SU13031406.

    Article  Google Scholar 

  10. Chen F, You J, Tang P, Zhou W, Masini N, Lasaponara R. Unique performance of spaceborne SAR remote sensing in cultural heritage applications: overviews and perspectives. Archaeol Prospect. 2018;25(1):71–9. https://doi.org/10.1002/ARP.1591.

    Article  Google Scholar 

  11. Zhang J, Cenci J, Becue V, Koutra S, Ioakimidis CS. Recent evolution of research on industrial heritage in Western Europe and China based on bibliometric analysis. Sustain. 2020;12(13):5348. https://doi.org/10.3390/su12135348.

    Article  Google Scholar 

  12. Su X, Li X, Kang Y. A bibliometric analysis of research on intangible cultural heritage using citeSpace. SAGE Open. 2019. https://doi.org/10.1177/2158244019840119.

    Article  Google Scholar 

  13. Kovaitė K, Šūmakaris P, Korsakienė R. Sustainability in creative and cultural industries: a bibliometric analysis. Creat Stud. 2022;15(1):278–98. https://doi.org/10.3846/cs.2022.16565.

    Article  Google Scholar 

  14. Zhang J, Xiong K, Liu Z, He L. Research progress and knowledge system of world heritage tourism: a bibliometric analysis. Herit Sci. 2022;10(1):1–18. https://doi.org/10.1186/s40494-022-00654-0.

    Article  Google Scholar 

  15. X. Zhou, C. Zhang, Z. Pan, and P. Wei, “Tourism industry is leading the digital transformation of cultural heritage management: Bibliometric analysis based on web of science database,” Proc—2020 Manag. Sci. Informatiz. Econ. Innov. Dev. Conf. MSIEID 2020. 2020; pp. 273–276. Doi: https://doi.org/10.1109/MSIEID52046.2020.00056.

  16. D. Trotta and P. Garengo, “Industry 4.0 key research topics: a bibliometric review,” 2018 7th int. conf. ind. technol. manag. ICITM 2018. 2018; pp. 113–117. Doi: https://doi.org/10.1109/ICITM.2018.8333930.

  17. Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. How to conduct a bibliometric analysis: an overview and guidelines. J Bus Res. 2021;133:285–96. https://doi.org/10.1016/J.JBUSRES.2021.04.070.

    Article  Google Scholar 

  18. Aria M, Cuccurullo C. bibliometrix: An R-tool for comprehensive science mapping analysis. J Informetr. 2017;11(4):959–75. https://doi.org/10.1016/j.joi.2017.08.007.

    Article  Google Scholar 

  19. Karahan S, Gül LF. Mapping current trends on gamification of cultural heritage. In: Cordan Ö, Dinçay DA, Toker ÇY, Öksüz EB, Semizoğlu S, editors. Game + design education. Cham: Springer International Publishing; 2021. p. 281–93.

    Chapter  Google Scholar 

  20. Gaur A, Kumar M. A systematic approach to conducting review studies: an assessment of content analysis in 25 years of IB research. J World Bus. 2018;53(2):280–9. https://doi.org/10.1016/J.JWB.2017.11.003.

    Article  Google Scholar 

  21. Wallin JA. Bibliometric methods: pitfalls and possibilities. Basic Clin Pharmacol Toxicol. 2005;97(5):261–75. https://doi.org/10.1111/J.1742-7843.2005.PTO_139.X.

    Article  CAS  Google Scholar 

  22. Da Xu L, Xu EL, Li L. Industry 4.0: state of the art and future trends. Int J Prod Res. 2018;56(8):2941–62. https://doi.org/10.1080/00207543.2018.1444806.

    Article  Google Scholar 

  23. Espeso-Molinero P. Tendencias del turismo cultural. PASOS Rev Tur y Patrim Cult. 2019;17(6):1101–12. https://doi.org/10.25145/j.pasos.2019.17.076.

    Article  Google Scholar 

  24. Vecco M, Clarke M, Vroonhof P, De-Weerd E, Ivkovic E, Minichova S, Nazarejova M. The impact of the COVID-19 pandemic on creative industries, cultural institutions, education and research. Geneva: World Intellectual Property Organization; 2022.

    Google Scholar 

  25. Saberi MK, Abedi H. Accessibility and decay of web citations in five open access ISI journals. Internet Res. 2012;22(2):234–47. https://doi.org/10.1108/10662241211214584/FULL/XML.

    Article  Google Scholar 

  26. Parolo PDB, Pan RK, Ghosh R, Huberman BA, Kaski K, Fortunato S. Attention decay in science. J Informetr. 2015;9(4):734–45. https://doi.org/10.1016/J.JOI.2015.07.006.

    Article  Google Scholar 

  27. Repiso R, Moreno-Delgado A, Aguaded I. Factors affecting the frequency of citation of an article. Iberoam J Sci Meas Commun. 2021;1(1):007–007. https://doi.org/10.47909/IJSMC.08.

    Article  Google Scholar 

  28. Valérie D, Pierre AG. Bibliometric indicators: quality measurements of scientific publication 1. Radiology. 2010;255(2):342–51. https://doi.org/10.1148/RADIOL.09090626.

    Article  Google Scholar 

  29. Cárdenas MIZ. Aplicación en realidad aumentada para divulgación del patrimonio cultural. Kepes. 2016;13(14):33–59. https://doi.org/10.17151/KEPES.2016.13.14.3.

    Article  Google Scholar 

  30. Spadoni E, Porro S, Bordegoni M, Arosio I, Barbalini L, Carulli M. Augmented reality to engage visitors of science museums through interactive experiences. Heritage. 2022;5:1370–94. https://doi.org/10.3390/heritage5030071.

    Article  Google Scholar 

  31. Sun Y, Song H, Jara AJ, Bie R. Internet of things and big data analytics for smart and connected communities. IEEE Access. 2016;4:766–73. https://doi.org/10.1109/ACCESS.2016.2529723.

    Article  Google Scholar 

  32. Corso J, Roca J, Buill F. Geometric analysis on stone façades with terrestrial laser scanner technology. Geosci. 2017;7:103. https://doi.org/10.3390/GEOSCIENCES7040103.

    Article  Google Scholar 

  33. Bec A, Moyle B, Schaffer V, Timms K. Virtual reality and mixed reality for second chance tourism. Tour Manag. 2021;83:104256. https://doi.org/10.1016/J.TOURMAN.2020.104256.

    Article  Google Scholar 

  34. Ibañez-Etxeberria A, Gómez-Carrasco CJ, Fontal O, García-Ceballos S. Virtual environments and augmented reality applied to heritage education an evaluative study. Appl Sci. 2020;10:2352. https://doi.org/10.3390/APP10072352.

    Article  Google Scholar 

  35. Sandoval C, Pirogova E, Lech M. Two-stage deep learning approach to the classification of fine-art paintings. IEEE Access. 2019;7:41770–81. https://doi.org/10.1109/ACCESS.2019.2907986.

    Article  Google Scholar 

  36. Zhu Z, et al. Visualization and mapping of literature on the scientific analysis of wall paintings: a bibliometric analysis from 2011 to 2021. Herit Sci. 2022;10(1):1–13. https://doi.org/10.1186/S40494-022-00735-0/TABLES/6.

    Article  Google Scholar 

  37. Angrisani L, Arpaia P, Bonavolonta F, Lo Moriello RS. Academic FabLabs for industry 4.0: experience at University of Naples Federico II. IEEE Instrum Meas Mag. 2018;21(1):6–13. https://doi.org/10.1109/MIM.2018.8278802.

    Article  Google Scholar 

  38. Song H, Chen P, Zhang Y, Chen Y. Study progress of important agricultural heritage systems (IAHS): a literature analysis. Sustainability. 2021;13(10859):1–22.

    CAS  Google Scholar 

  39. Kim J, Yoo J. Science and technology policy research in the EU: from framework programme to HORIZON 2020. Soc Sci. 2019;8(5):153. https://doi.org/10.3390/socsci8050153.

    Article  Google Scholar 

  40. Florian Niebling HM, Münster Sander. Research and education in urban history in the age of digital libraries. Cham: Springer International Publishing; 2021. https://doi.org/10.1007/978-3-030-93186-5.

    Book  Google Scholar 

  41. Yang W, District H. National natural science foundation of China. Sci Found China. 2009;16(2):47–53. https://doi.org/10.1088/1005-0841/16/2/005.

    Article  Google Scholar 

  42. Cimini C, Boffelli A, Lagorio A, Kalchschmidt M, Pinto R. How do industry 4.0 technologies influence organisational change? An empirical analysis of Italian SMEs. J Manuf Technol Manag. 2021;32(3):695–721. https://doi.org/10.1108/JMTM-04-2019-0135/FULL/PDF.

    Article  Google Scholar 

  43. Bongomin O, Nganyi EO, Abswaidi MR, Hitiyise E, Tumusiime G. Sustainable and dynamic competitiveness towards technological leadership of industry 40: implications for East African community. J Eng. 2020. https://doi.org/10.1155/2020/8545281.

    Article  Google Scholar 

  44. Agostino D, Arnaboldi M, Lampis A. Italian state museums during the COVID-19 crisis: from onsite closure to online openness. Museum Manag Curatorsh. 2020. https://doi.org/10.1080/09647775.2020.1790029.

    Article  Google Scholar 

  45. Raimo N, De Turi I, Ricciardelli A, Vitolla F. Digitalization in the cultural industry: evidence from Italian museums. Int J Entrep Behav Resdoi. 2021. https://doi.org/10.1108/IJEBR-01-2021-0082.

    Article  Google Scholar 

  46. Noszczyk T, Gawronek P. Remote sensing and GIS for environmental analysis and cultural heritage. Remote Sens. 2020;12:3960. https://doi.org/10.3390/RS12233960.

    Article  Google Scholar 

  47. Vargas JCG, Fabregat R, Carrillo-Ramos A, Jové T. Survey: using augmented reality to improve learning motivation in cultural heritage studies. Appl Sci. 2020;10(3):897. https://doi.org/10.3390/app10030897.

    Article  Google Scholar 

  48. Bickler SH. Machine learning arrives in archaeology. Adv Archaeol Pract. 2021;9(2):186–91. https://doi.org/10.1017/aap.2021.6.

    Article  Google Scholar 

  49. O’Banion MS, Lewis NS, Boyce MW, Laughlin J, Majkowicz DC. Use of an augmented reality sand table for satellite remote sensing education. J Geogr High Educ. 2022. https://doi.org/10.1080/03098265.2022.2087215.

    Article  Google Scholar 

  50. Brovelli MA, Kainz W, Bai N, Nourian P, Luo R, Roders AP. Heri-graphs: a dataset creation framework for multi-modal machine learning on graphs of heritage values and attributes with social media. ISPRS Int J Geo-Inf. 2022;11:469. https://doi.org/10.3390/IJGI11090469.

    Article  Google Scholar 

  51. Wu M. Subdivision of artistic objectives of oil painting creation under the evaluation of deep learning model. J Sensors. 2022. https://doi.org/10.1155/2022/6825863.

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank researcher Katherine Anne Mills of the Department of History of Art and Architecture at Harvard University for her editing and comments to improve the quality of the paper.

Funding

This study was financed by the Universidad Señor de Sipán.

Author information

Authors and Affiliations

Authors

Contributions

Alviz-Meza A. and Vásquez-Coronado M. carried out the literature review, data analysis, and discussion of results, and drafted the manuscript. Delgado J. and Blanco-Victorio D. helped with extracting data, and manipulation of the used software. Alviz-Meza A. communicates with the editor. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Anibal Alviz-Meza.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix

Appendix A

See Table 7.

Table 7 Top 5 collaborative authors in heritage-industry 4.0 research (2016–2021)

Appendix B

See Table 8.

Table 8 Top 5 collaborative institutions in heritage-industry 4.0 research (2016–2021)

Appendix C

See Table 9.

Table 9 Top 5 collaborative countries in heritage-industry 4.0 research (2016–2021)

Appendix D

See Table 10, Fig. 8.

Table 10 Top 5 most used keywords in heritage-industry 4.0 research (2016–2021)
Fig. 8
figure 8

Relationship between institutions, countries, and most used keywords in heritage studies combined with industry 4.0 from 2016 to 2021 in a Scopus and b WoS

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Alviz-Meza, A., Vásquez-Coronado, M.H., Delgado-Caramutti, J.G. et al. Bibliometric analysis of fourth industrial revolution applied to heritage studies based on web of science and scopus databases from 2016 to 2021. Herit Sci 10, 189 (2022). https://doi.org/10.1186/s40494-022-00821-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40494-022-00821-3

Keywords

  • Bibliometric
  • Heritage
  • Industry 4.0
  • Scopus
  • Web of Science
  • Biblioshiny
  • VOSviewer