Mapping of the selected literature on robotic technology applications in libraries based on Scopus database: a subjective computational review

Abstract

Purpose

Adopting and implementing robotic technology applications in the library is a significant technological up-gradation today. The purpose of this study was to evaluate selected literature focused mainly on robotics technology applications in the field of libraries and to assess the online social attention to research publications.

Design/methodology/approach

The study employed Scientometric and altmetric tools to evaluate the research publications. The bibliographic data of research publications were downloaded from Scopus database and scrutinized one by one and 71 articles were selected which mainly focused on robotic technology in libraries. Altmetric data were collected from the Dimensions.ai database. The analysis was performed using MS Excel, Tableau, Biblioshiny, VOSviewer and SPSS software.

Findings

Research on robotic technology in the field of libraries has been experiencing a gradual increase, marked by an annual growth rate of 12.93%. The United States has prominently led the way as the most active participant and collaborator in this advancement. Among the various journals, Library Hi Tech has notably stood out as a significant contributor to this field. However, the research articles have garnered limited social attention and impact. Furthermore, the patterns of authorship collaboration have demonstrated relatively modest levels within the field, and a weak correlation has been observed between the social attention received and the Scopus citation metrics of the publications.

Practical implications

The research needs to be disseminated more through various social media platforms to increase its visibility. Sharing research information through social media can bridge the gap between academia and society. The findings of this study can serve as a valuable reference for researchers and policymakers.

Originality/value

This study presents a Scientometric analysis of the selected published literature on robotics technology applications in the field of libraries, highlighting the progress and development of worldwide research in this area.

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Introduction

An interdisciplinary subject, robotics incorporates mechanical engineering, electrical engineering and computer science. In the fourth industrial revolution, robotics play a crucial role in the design, construction, manufacturing and other use of robots in different ways represented by the intelligent, virtual and digital performance in large-scale industries. It is one of the emerging and most innovative technological developments in the contemporary era that support humans in many ways and during difficult times and situations (Javaid et al., 2021; Oosthuizen, 2022). It is a device that works on behalf of humans and automatically and continuously performs some steps or procedures (Harada, 2019). It is often described as the subfield of artificial Intelligence (AI) concerned with perceptual and motor tasks. Robotics is another potential application of AI that can support university teaching and research (Huang, 2022). As a part of their study, Maceli (2019) ranked robotics among the top ten technologies in maker space curriculums for postgraduate library and information science programs.

With the accelerating development of enabling technologies, robotics is rapidly expanding, as recognized worldwide (Batcha, 2017). Within the last decade, robotics has attracted increasing attention from academia and industry (Zhou and Li, 2022). The robotic research was primarily conducted in the United States, the United Kingdom, Germany, China, Italy, etc. (Batcha, 2017; Ravichandran et al., 2022). It has also gained traction in India over the last decade, with significant research growth (Batcha, 2017; Gaud, 2019). To guide researchers interested in this area, researchers like Milojevic and Sabanovic (2013) have proposed designing a non-linear digital archive to display advances in robotics research over the past fifty years.

Robotic technology is used in many fields today, including healthcare, agriculture, food processing, construction, manufacturing, military, education, libraries, etc. The library is a technological front organization that is not away from adopting new technology tools for better management and services. One of the significant changes we have seen in libraries is using new innovative technologies. Robots are one of the latest trends of technological transformation that libraries can use to provide ultimate educational services to users. Over the years, Japanese public Libraries have introduced Robotic technology for public services. The “Pepper,” a semi-humanoid robot developed by Softbank Robotics, has worked in the Yamanaka Public Library since 2015, Fukuoka City Public Library from 2016 to 2018, and the Kitami Institute of Technology Library since 2017 (Softbank Robotics, 2022; Harada, 2019). Apart from that, the American Library Association (ALA) quoted Robot and Drone technology as the future trends in the library. Collaborative robots will increasingly perform repetitive tasks and work alongside humans. The drone (flying robot) will become a regular part of life, used in research, transportation, delivery, artistic production, news coverage and reporting, law enforcement and surveillance, and entertainment (ALA, 2022).

Researchers have put forth specific robotics applications in the library environment, such as Huang (2022), who proposed that robotic on-demand batch scanning systems would facilitate users to browse printed material through a web interface in the future. Hong Kong academic librarians' attitudes toward robotic process automation (RPA) have been examined by Lin et al. (2022). They conclude that software-based robots, or bots, can automate manual, repetitive and rule-based processes. They further argue that RPA bots can be deployed in library environments in the activities such as electronic form processing, data collection, conversion and import, searching, harvesting and reconciliation, and API integration. In an experimental study conducted by Midwestern University, robot pet therapy reduces stress and improves happiness and relaxation among library users (Edwards et al., 2022). A smart talking robot named Xiaotu (female) has been developed by Yao et al. (2015) that can be used for online reference services allowing participants to contribute to the collection of resources and co-create content. As noted by Weng et al. (2022), robotics can be used to develop computational thinking in university students, and curricula should be designed with advanced robotic kits to allow students to gain a deeper understanding of robotics.

Similarly, Miglino et al. (2008) argue that by using Breedbot, a form of robotics as a teaching tool, evolutionary biology can be taught in schools via tools like Breedbot. Park and Kwon (2016) have studied the adoption of teaching assistant robots using the technology acceptance model and found that the user's intention to use the robots was influenced by perceived usefulness, enjoyment, and service quality. Some studies, such as those by Tung and Campos (2022), have focused on the superior user experience of social robots. In their studies, they argue that these robots should exhibit human-like social behavior and be capable of expressing human-like feelings and emotions.

Several research evaluations have also been carried out to determine the research growth and trends concerning robotics in various sectors, such as robotics in Logistics 4.0 (Atzeni et al., 2021), robotics in construction (Aghimien et al., 2020; Cai et al., 2019; Onososen and Musonda, 2022), robotic in surgery (Connelly et al., 2020; Li et al., 2021; Musbahi et al., 2022), robotics in disaster response (Dadvar and Habibian, 2022), soft robotic (Zhou and Li, 2022), autonomous vehicles (Gandia et al., 2019), robotic in agriculture (Pivoto et al., 2018; Gonzalez-de-Santos et al., 2020; Lytridis et al., 2021), etc. In addition, a number of studies were conducted regarding the applicability and potential of using robotic technology in education, teaching and learning (Benitti, 2012; Mubin et al., 2013; Kim et al., 2015; Yolcu and Demirer, 2017; Rosanda and Starcic, 2020; Mahdi et al., 2021; Papakostas et al., 2021; Mwangi et al., 2022). Some researchers argued that the robot teacher's performance in the classroom is not like that of a human teacher due to the senses that human beings own (Mahdi et al., 2021). However, the majority of the studies highlighted the positive outcomes for teaching concepts related to the Science, technology, engineering, and mathematics (STEM) areas (Benitti, 2012; Kim et al., 2015; Anwar et al., 2019; Mwangi et al., 2022) and robot can take on the role of a tutor, tool or peer in the learning activities (Mubin et al., 2013). A study was conducted based on the Web of Science and Emerald database to review the literature on the application of intelligent systems in the libraries with a special reference to expert systems (ES), AI and robots (Asemi et al., 2021). The study revealed that ES are useable intelligent systems with the ability to mimic librarian expert behaviors to support decision making and management.

However, there needs to be evidence of research progress on robotic technologies in libraries to inform researchers, practitioners and other key stakeholders related to the field. None of the previous research analyzed the performance of scientific players, social, conceptual and intellectual structure of the research publications, network analysis and altmetric-based analysis to determine the social attention to the research on robotic technologies in libraries. Hence, this study intended to reveal the present status by analyzing research growth, trends, cognitive structure and social attention received by the selected article publications mainly focused on robotic technologies in libraries and indexed in the Scopus database. As per our knowledge, it is the first study that shed light on the characteristics and development of research publications on robotic technology in libraries. The findings would be of interest to both researchers and key stakeholders in the field.

Objectives of the study

To analyze year-wise and subject-wise distribution of research article publications, citations and AAS on robotics technologies in the libraries;
To identify the most prolific authors, countries, and journals participated in the research on robotic technologies in the libraries;
To analyze the social, intellectual, and conceptual structure of the research on robotic technologies in the libraries;
To analyze the regional differences of the research on robotic technologies in the libraries;
To assess the social media attentions to research publications and trace the geographical distribution of twitter and Mendeley readers;
To examine the correlation between social attentions and Scopus citations of the publications;

Data and methodology

The study employed Scientometric and altmetric tools to evaluate the research publications on robotic technology applications in the libraries. Scientometric study is concerned with the quantitative aspects of science, technology, and innovation (Kim and Zhu, 2018). It evaluates the research publications through two main approaches, i.e., performance analysis and science mapping (Donthu et al., 2021; Basumatary et al., 2022a, 2023a). Performance analysis aims at evaluating the activity of scientific actors (researchers, countries, organizations, and departments) and the impact of their activity. In contrast, science mapping analysis monitors a scientific field to determine its (cognitive) structure, its evolution, and the main actors within it (Noyons et al., 1999). It includes citation analysis, co-citation analysis, bibliographic coupling, co-word analysis and co-authorship network analysis (Chen, 2017). Scientometric tools evolve rapidly and become more computational, primarily used to assess every aspect of published literature. Scientometric indicators can be used for the three levels of research evaluation, i.e., macro (countries, scientific disciplines), meso (research centers, university departments, scientific subdisciplines) and micro (single papers, individual researchers) (Vinkler, 1988; Glänzel and Moed, 2002; Fiala, 2014).

While the altmetric tools provide alternative metrics rather than citations for research publications from different social media platforms in a very short period (Basumatary et al., 2022b, 2023b). It complements the traditional method for measuring the impact of scholarly articles on the social web. Altmetrics visualizes how much attention has been paid to particular scholarly papers on social networking sites. It helps the researcher, publishers, and other stakeholders identify the topic's popularity, trends and societal impacts on research publications.

Data collection

Two phases of data collection have been carried out for this study. In the first stage, we acquired the bibliographical data of core robotic research publications concerning the libraries published between 2003 and 2022 from the Scopus database as indexed by 23-11-2022 (Figure 1). Scopus is the largest abstract and citation database of peer-reviewed literature, i.e., scientific journals, books and conference proceedings (Elsevier, 2022). It is one of the international scientific committee's best bibliographic databases (Fernandez-Llimos, 2018; Sau, 2020; Sau and Nayak, 2020). Scopus has covered 70 million items and 1.4 billion cited references since 1970, making it the most extensive research publication database. Scopus bibliographic information is extensively used in Scientometric analysis. Data were searched using the search term “robot*” OR “robotic*” AND “library” OR “libraries” OR “librarian” in the “Article titles, Abstract, Keywords” section. The result was refined by the publication year (2003–22), document type (article), and source type (journal, trade journal). While refining the results, 1,536 document results were found. They were again scrutinized one by one by visiting abstracts and full-text articles, and 71 core research articles publications on robotics in the field of the libraries were found relevant and extracted from the database.

The second phase of data collection aimed to measure the social attention to research publications on robotics in the library. We chose the Dimensions.ai database to collect online social attention and Mendeley reading statistics to perform the assessment. Dimensions.ai is the world's largest linked research information dataset comprising 136 million publications maintained by Digital Science and Research Solutions that provides altmetric attention data based on the Altmetric.com database (Dimensions, 2023). To obtain the altmetric attention and Mendeley readership metrics, we used the Digital Object Identifier (DOI) of respective articles. First, we picked up the DOI of each article and pasted it into the search box in the Dimensions.ai database in the DOI section (Figure 2). Based on the search result, each article's altmetric attention and Mendeley reading statistics were copied manually into the MS Excel sheet for further analysis.

Tools and techniques

Collected data were tabulated using the statistical tool MS Excel. The performance measurement and scientific mapping were analyzed using Tableau, VOSviewer, and Biblioshiny (Bibliometrix R package). Further, the altmetric analysis was performed with Tableau and SPSS. The correlation between AAS, Scopus citations, and Mendeley readership was computed through Pearson Correlation techniques using IBM SPSS software based on the range of Pearson correlation coefficient (r) strengths presented in Table 1. Previous researchers, Meghanathan et al. (2016), Liang et al. (2019) and Basumatary et al. (2023b), have also adopted this value.

Data analysis

Year-wise and topic-wise distribution of publications, citations and AAS

Figure 3 shows the year-wise distribution of articles, citations, and altmetric attention score (AAS) of robotic technology research in the field of libraries published between 2003 and 2022. It shows that the numbers of publications are in increasing trend. No single publication was found in 2003, 2010 and 2011. The publication was growing with an Annual Growth Rate of 12.93%. Although the publication was fluctuated and increased gradually till 2016 and accelerated from 2017. It was also found that 69.01% of articles were published in the open-access category in different reputed peer-reviewed journals.

The most number of articles were published in 2021 counted, 17 (23.94%), followed by 2020 counted, 9 (12.68%). The red trend line in the figure indicates the total citation received by articles over the years. The articles published in 2020 received the most number of citations (TC = 47), followed by 2019 (TC = 40) and 2017 (TC = 39). Furthermore, the blue trend line in the figure represents the AAS received by the articles in particular years. The articles published in 2021 received the most number of AAS (AAS = 46), followed by 2022 (AAS = 14) and 2020 (AAS = 13).

Topic-wise distribution of publications

The research was conducted on diverse robotic technologies concerning library management and services. The publications' abstracts were reviewed one by one and categorized broadly based on the themes of the research. 53.52% of research was conducted focusing on the applications of robotic technology in different library management and services (Figure 4). Moreover, 42.25% of research was based on the strategy and implementation of robotic technology in libraries, including research on readiness, adoption, future planning, etc. However, 4.23% of research was conducted to understand the different opinions of the professionals, stakeholders, and their perception of adopting robotic technology in the library.

Most prolific authors

Table 2 presents the list of authors who contributed more than one article. They are ranked by the number of publications, total citation, h-index, g-index, article fractionalized and AAS. A total of 178 authors contributed articles on robotic technology research in the library in the last two decades. Seven authors contributed two papers each, and the remaining 171 contributed one article each. Most relevant authors per fractionalized number of authored documents were calculated to quantify an author's contribution to a published set of papers.

Most prolific countries

Table 3 shows the top ten most prolific countries participating in robotic research in the field of libraries. The countries were ranked based on their total number of publications, citations, and Altmetric Attention Score (AAS) received against the publications. The United States conducted tremendous research in the domain and produced the most publications (n = 13, TC = 79, AAS = 33) in the last two decades. It was followed by China (n = 7, TC = 32, AAS = 4). The United States was the leading country in robotic research, as previous researchers Gupta and Dhawan (2018) reported. Taiwan also conducted significant research on the field and published (n = 5, TC = 3, AAS = 13) articles. Other countries also contributed pieces of their scientific publications to the domain, even though the number of publications could have been more impressive.

Most prolific journals

A total of 55 peer-reviewed journals contributed to the robotic technology research in the library. Table 4 shows the list of the top ten most prolific journals that produced the most articles. Journals were ranked by the number of citations received and the altmetric attention score (AAS). Library Hi Tech and Library Hi Tech News, published by Emerald Publishing, was the top contributor journal of robotic research concerning the library. They publish articles in Library and Information Science, Computer science, and the Information Systems domain as a multidisciplinary journal. Many articles were found that were related to robotics (Araujo et al., 2022), artificial intelligence (Ali et al., 2021; Borgohain et al., 2022), Internet of Things (Xie et al., 2019; Yang et al., 2020; De Sarkar, 2022) and other emerging technologies. However, the core publications on robotic technology concerning libraries were found in four articles. Researchers preferred to publish mainly in Emerald Publishing's journals, followed by the journal published by MDPI, Taylor and Francis, University of Nebraska-Lincoln, Elsevier, and Pleiades Publishing were also contributed significantly to the robotic technology research in the field of libraries.

Social structure of research publications

The social structure of the research publication can be determined by analyzing the collaboration between authors, organizations, and countries (Basumatary et al., 2023c, d). Collaboration in a particular research project makes a study more efficient and impactful as multiple researchers contribute their ideas and learn new things. Collaborative research has the capabilities for exchanging ideas across disciplines, learning new skills, access to funding, higher quality results, radical benefits, and personal factors such as fun and pleasure (Bansal, 2019). The co-authorship of papers stems from the researchers' desire to increase their scientific productivity, both in quality and quantity (Beattie and Goodacre, 2004; Clark et al., 2006). Further, it helps a researcher identify the subject experts and collaborators for future research. This study analyzes the co-authorship of authors, organizations and countries to determine how the authors from different geographical regions had been socially associated with each other and conducted research on robotic technology in the field of libraries.

Collaboration networks of authors

The most tangible forms of research collaboration among researchers are the co-authorship of the particular research article. Authorship collaboration has many benefits, including the ability to share ideas, knowledge and resources; the development of new and innovative ideas; and the opportunity to build relationships with other authors. Additionally, authorship collaboration can help to improve the quality of writing by providing constructive feedback and editing. Figure 5 shows the co-authorship networks among authors. Based on the dataset, we identified the 172 authors who shared at least one collaborative article. Among them, the eight authors have 2 collaborative papers each. In the figure, the size of circular nodes represents the strength of the collaboration relationship, and circle nodes represent the number of publications of the particular author. In this study, Lin W (National Taiwan University, Department of Library and Information Science, Taipei, Taiwan) and Yueh H-P (National Taiwan University, Department of Bio-Industry Communication and Development, Taipei, Taiwan) were the most collaborative researchers in the domain with 3 publications and 31 citations each and collaborated with 8 different authors. Del Pobil AP follows them (Universidad Jaume I, Department of Computer Science and Engineering, Castellon de la Plana, Spain) and Fu LC (National Taiwan University, Taipei, Taiwan) with 2 publications, 28 and 29 citations each, respectively. Followed by other authors, as seen in Figure 5.

Collaboration networks of countries

A total of 36 countries were associated with the 71 article publications. A network was visualized using VOSviewer, open-source software, to identify the collaboration pattern between countries (Figure 6). The circle nodes represent the publications, and the size of the circle node represents the number of publications. A bigger size of the node means more publications, and small nodes represent less number of publications. The thickness of the connecting line represents the strengths of the collaboration link between countries. The visualization resulted that the United States has the most extensive collaboration publications. Although, all the collaborated countries in particular research work will get similar collaboration credits. However, the collaboration total link strength may vary based on the number of collaborated works. As a result of the United States (TLS = 3), South Korea (TLS = 3), Taiwan (TLS = 1), and Spain (TLS = 1). They are followed by South Africa (TLS = 2), Nigeria (TLS = 2), the United Kingdom (TLS = 1), Canada (TLS = 1), Germany (TLS = 1), and Australia (TLS = 1).

Intellectual structure of research publications

The intellectual structure of research publications can be identified, traced, and visualized by counting the frequency with which an author's work is co-cited with another author in the references of citing documents (Bayer et al., 1990; van Eck and Waltman, 2017; Basumatary et al., 2023a). The co-citation analysis aids in the exploration of the intellectual linkages between the influential articles in a discipline and the mapping of the intellectual structure of the discipline (Culnan, 1987; White and Griffith, 1981; White and Mc Cain, 1998; Calabretta et al., 2011). It can help map the intellectual structure of a research field and identify the most active research areas, discover front-line research, and bring out high-impact transformative discoveries.

Co-citation analysis of cited authors

The total number of 71 articles had 3,236 cited authors. The co-citation analysis of the cited authors is one of the best ways to determine the intellectual structure of scientific communication on a particular subject. The author's co-citation network visualizes the influential documents in a data set (Olawumi et al., 2017). Co-citation represents the close relationship between two articles, either because they belong to the same topic or because the topic is closely connected. Co-citation analysis is based on the frequency of two documents cited together. Co-citations constitute a complex network of ideas that researchers have connected, associated with, and organized (Garfield, 1993). Thus, it represents a method to map the intellectual structure of a field (White and Griffith, 1981). Out of 3,236 cited authors, 64 with at least 4 citations were selected for network visualization to identify the most influential cited authors. However, some of the most cited authors were not visible in the network graph due to the limitation of the visualization software interface and their weak co-citations link strengths. As seen in Figure 7, the authors in the largest circle nodes are the most cited in the robotic technology research in the library. Chi DKW was the most influential cited author among the 3,236 authors with 18 total citations (TC) and 237 total link strengths (TLS), followed by Howard M (TC = 15, TLS = 240), Prats M (TC = 15, TLS = 153), Etc., as seen in the figure.

Co-citation analysis of cited references

A total number of 2,050 cited references were found in the 71 articles. Out of the total cited references, 17 cited references were found in the network that has at least 2 citations of a cited reference. As seen in Figure 8, the paper authored by Calvert (2017) Robots, the quiet workers, are you ready to take over?, Public library quarterly, 36 (2), pp. 167-172 was most cited reference with 4 total citations (TC) and 10 total link strengths (TLS) followed by Arlitsch, K., Newell, B., (2017) Thriving in the age of accelerations: a brief look at the societal effects of artificial intelligence and the opportunities for libraries, Journal of library administration, 57 (7), pp. 789–798 with (TC = 3, TLS = 16), etc. as seen in the figure.

Conceptual structure analysis

The conceptual structure of the publication can be measured by analyzing the keywords assigned by the author in the particular research publication. The prominent keyword used by the researcher in their paper indicates the focused theme of the research on a particular discipline. It gives an idea of the potential areas of research that have been under-explored. Figure 9 illustrates the most prominent keywords used by the authors in their works on robotic technology research concerning the library in the last two decades. A total number of 254 keywords were found that were used in the 71 articles. In order to identify the most frequently appeared keywords, 20 keywords with a robust set of connections were selected for network analysis and visualization using VOSviewer. The occurrence of keywords is indicated with colored circle nodes, and the smooth lines represent the relationship between keywords. The bigger size nodes represent the most frequently appeared keywords in the research. As a result, the keyword “artificial intelligence” was the most frequently appeared keyword with 13 occurrences (OC) and 31 total link strengths (TLS), followed by “robotics” (OC = 8, TLS = 14), “robots” (OC = 6, TLS = 16), etc. as seen in Figure 9.

Regional differences of the research focused

The three-field plot in Biblioshiny was employed to visually assess the regional differences of the research focused on robotic technology in the libraries. Figure 11 shows the association between the author (left), country (middle), and keywords (right). It signifies how different authors from different countries are researching particular topics (Basumatary et al., 2023c, d). The connecting lines in the graph represent the association between the author, country, and keywords. The height of the rectangles depends on the number of studies conducted by countries, authors, and research topics. As the number of studies conducted by each of these entities increases, the rectangle will become taller. As countries and authors conduct more research on the subject, connecting lines will be thicker. For example-the country China (33 flow counts) and the USA (32 flow counts) are conducting a series of research on artificial intelligence, robot, deep learning, machine learning, etc., Iran (16 flow counts) focused on data mining, artificial intelligence, public library, exploratory factor analysis (EPA), etc., Pakistan (15 flow counts) focused on artificial intelligence, robotics, chatbots, machine learning, etc. as seen in the figure. It was found that the USA and China were the countries that researched diverse topics on robotics concerning the library.

Social attentions to research publications

Due to the significant advancement of Information Communication Technology (ICT), almost all publishers are switching the publication method from traditional to digital. Social media platforms are becoming the primary channels for disseminating and instantly accessing information after publication. Hence, analyzing the users' online attention to published research articles through various social media platforms is one of the most appropriate ways of evaluating the impact of research on society and its popularity trends. The library research publications on robotic technology were disseminated through six social webs, including Mendeley's reference management tool (Table 5). Twitter was the most prominent social media platform that people used to disseminate information regarding publications. The publications were mentioned in different Blog posts, Facebook pages, Wikipedia pages, and Redditors. The publications received very little attention from the audiences. The findings suggest that publications in this field should be promoted and disseminated through various social media platforms to make more visibility of the research activities on robotic technology concerning libraries.

Moreover, the Twitter and Mendeley readers of the articles were traced to identify the most influential geographical regions of twitter and readers. Because of the more people engagement on these two platforms was selected for in-depth evaluation of the characteristics of users. It was found that only 12 (16.90%) articles were mentioned in the different Twitter posts, and the majority of the articles, 59 (83.10%), were not. However, 43.37% of tweets were tweeted from unidentifiable regions (Figure 10a). Most Twitters were affiliated with Australia, followed by the United States and the United Kingdom.

In contrast, out of 71 articles, only 16 were read by 526 times on Mendeley. However, only 3 publications had 9 (1.71%) identified readers from 6 different countries and readers of 13 articles, 98.29% were from unidentifiable regions (Figure 10b). Most readers were affiliated with the United States, Brazil, and Portugal.

Correlation analysis

A Pearson correlation coefficient (Pearson's R) was computed to assess the linear relationship between Scopus Citation (SC) and Altmetric Attention Score (AAS) (Table 6). The Pearson correlation method is the most common method for numerical variables used to measure the strength and direction of the relationship between variables. The correlation coefficient is the decimal number between −1.00 and 1.00, where 0 means no correlation, −1.00 is a total negative correlation, and 1.00 is a total positive correlation between variables. The AAS and Scopus citations had weak positive correlations (r = 0.026, p = 0.831).

Discussion

This study explored the research trends on robotic technology applications in the libraries using the Scientometric and altmetric tools. The research publications are growing gradually, with a 12.93% annual growth rate in the field. However, compared to the research on other emerging technologies in the libraries, for example-artificial intelligence (Borgohain et al., 2022), RFID (Kumar et al., 2017; Singh et al., 2016), digital library (Mahadevagouda and Pavithrabai, 2022), Internet of Things (Deshpande, 2020), Etc. the research growth on robotic was very less in the last two decades. In addition, the research published in the field received little attention from the audiences, and it needs more promotions through various social media platforms. Promoting the research through social media profiles like Facebook, Twitter, etc., is important (Verma and Yuvaraj, 2022). By sharing research publications on social media, researchers and journals can bridge the gap between academia, society, and the scholarly community and boost citation impact because people can only cite articles they know. Writing a high-quality article in journals will only give it a fifty percent chance of getting cited, while the promotion and broad dissemination of the publications will be needed to complete the other fifty percent (Ebrahim, 2012; Bong and Ebrahim, 2017; Fagbule, 2018).

The performance measurement revealed that Lin W and Yueh HP (National Taiwan University, Taiwan) were the most prolific authors as well as most active collaborators with (n = 3, h-index = 2, g-index = 2, AAS = 3), the United States was the most prolific country as well as a most active collaborator with (n = 13, TC = 79, ASS = 33). Library Hi Tech was the most prolific journal (n = 4, TC = 29, AAS = 12) that contributed significant contributions to the field in the last two decades. The USA is leading in the research on various robotic technology applications (Nature, 2022), for example, soft robotics (Zhou and Li, 2022), robotic gynecologic surgery (Vidal et al., 2022), construction robotics (Onososen and Musonda, 2022), Etc. The reason may be that they invested heavily in developing robotics and artificial intelligence technologies. The US government has also encouraged the development of advanced robotic technologies through funding and grants from organizations such as the National Science Foundation (nsf.gov, 2022) and the Defense Advanced Research Projects Agency (DARPA.MIL, 2022).

Additionally, several US universities have established robotics engineering and research centers, which have been instrumental in developing robotic technologies. The USA has a strong presence in the robotics industry, with several major companies such as Google, Amazon, and Microsoft developing robotics technology for decades. All these factors have contributed to the US's leadership in robotic technology applications in various fields, including the library.

Co-citation analysis allows researchers to identify important authors in a research field and clusters of authors associated with particular research topics. By understanding the co-citation of cited authors, researchers can gain insight into how authors influence each other and how their research affects the field. The intellectual structure of research can be identified by analyzing the co-citations (Calabretta et al., 2011; Culnan, 1987; White and Griffith, 1981; White and McCain, 1998). As a result, Chi DKW was the most influential cited author among the 3,236 authors with 18 total citations (TC) and 237 total link strengths (TLS) and Calvert (2017) Robots, the quiet workers, are you ready to take over?, Public library quarterly, 36 (2), pp. 167–172, was most cited reference with 4 total citations (TC) and 10 total link strengths (TLS).

The study also assessed the co-occurrences of the most prominent keywords to identify the trending research topics in the domain. This analysis can help researchers identify patterns in research topics and trends in the development of research fields over time. Co-occurrence analysis can also be used to compare the impact of different research topics and to detect correlations between research topics and other factors, such as funding and policy. The keywords assigned by the authors represent the focused theme of the research. Hence it signifies that the highly appeared keywords mean the trending research topics in the domain. The keywords analysis revealed that in the last two decades, most research was focused on artificial intelligence, robotics, robots, NLP, big data, cloud computing, Chatbot, etc. (Figure 9). It throws a clear light that more research needs to be carried out in the subfields of robotic domains such as cloud robotics, robotic engineering, and types of robots that can be used in modern library management and services. Cloud robotics involves the integration of robots with cloud computing technologies which could bring several advantages in the library to, enhanced information access, efficiency, collaboration and learning. Similarly, robotic engineering is crucial in creating robots that can effectively interact with users and perform library-related tasks. In libraries, humanoid robots can interact with users in a more human-like manner. Assistive robots can potentially enhance the accessibility and inclusivity of multicultural pre-tertiary classrooms (Papadopoulos et al., 2020). Autonomous mobile robots can locate books, retrieve requested items, and assist visitors with directions, providing efficient and time-saving services. The stationary robots can be reduced waiting times and improve overall service efficiency. The librarian robots are primarily designed to detect and replace books on the shelf (Asemi et al., 2021).

Integrating robotics in libraries aligns with the principles of the new industrial revolution, i.e., Industry 4.0, which focuses on automation, data exchange, and digitalization, which can contribute to the digitization of library services and operations. Likewise, Industry 5.0, which emphasizes human-robot collaboration, can augment their capabilities and support libraries in delivering better services, enhancing productivity, efficiency, and quality and aligning with the vision of next-generation society.

Moreover, the study identified the regional differences of the focused research topics based on the criteria of author, country, and keywords (Figure 11). The line connected to each unit represents the relationship between the author, region, and focused research topic. The thickness of the line represents the number of research conducted on a particular topic.

The publications received less attention from the audience even though the research publications were mostly disseminated through Twitter and significant readers on Mendeley. Most Tweeters were affiliated with Australia, followed by the United States, and most readers were affiliated with the United States, Brazil, and Portugal. There was a weak positive correlations between AAS and Scopus citations (r = 0.026, p = 0.831) and was statistically insignificant at conventional significance levels (p < 0.05), suggesting that AAS may not be a valuable predictor of citation impact.

Conclusion

In conclusion, implementing robotic technology applications is an emerging technological transformation in libraries. This study found that 53.52% of research focused on the applications, 42.25% was based on the strategy and implementation, and 4.23% of research was conducted to understand different opinions of the professionals, stakeholders, and their perception of the adoption of robotic technologies in the libraries. However, more research is required in this area for the implementation of this emerging technology and smooth management of various services in libraries. In addition, a time-to-time review of research progress in the domain is very important to report precise information on the state-of-art development of the research. This study is just the beginning and is limited to the selected scholarly articles primarily focused on robotic technologies in libraries and indexed in the Scopus database. Hence, more research can be carried out in the domain by acquiring comprehensive data from different databases, i.e., Web of Science, Dimensions, Google scholar, Etc. which may provide different results.

Funding: The authors declare that no funds, grants or other support were received for this study.

Competing interests: The authors declare that there is no potential conflict of interest in this research.

Figure 1

Workflow process of bibliographic data collection from Scopus database

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Figure 2

Workflow process of altmetric data collection from Dimensions.ai database

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Figure 3

Year-wise distribution of publications, citations and AAS

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Figure 4

Topic-wise distribution of publications

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Figure 5

Collaboration networks of authors

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Figure 6

Collaboration network of countries

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Figure 7

Co-citation network of cited authors

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Figure 8

Network of cited references

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Figure 9

Network visualization of most prominent keywords

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Figure 10

Geographical distribution of Twitter and Mendeley readers

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Figure 11

Regional differences of the research focused

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Table 1

Range of correlation coefficient values

Range of correlation coefficient values	Level of correlation	Range of correlation coefficient values	Level of correlation
0.80 to 1.00	Very Strong Positive	−1.00 to −0.80	Very Strong Negative
0.60 to 0.79	Strong Positive	−0.79 to −0.60	Strong Negative
0.40 to 0.59	Moderate Positive	−0.59 to −0.40	Moderate Negative
0.20 to 0.39	Weak Positive	−0.39 to −0.20	Weak Negative
0.00 to 0.19	Very Weak Positive	−0.19 to −0.01	Very Weak Negative