ABSTRACT

Science mapping and Bibliometrics techniques will greatly facilitate the life of the researcher, providing the necessary elements for a systematic writing, revealing to the researcher the theoretical bases of a given study with a focus on results and publication. Therefore, the Smart method was developed with the objective of facilitating the bibliometric analysis and selection of theoretical references, materializing itself by a Business Intelligence system that promises to bring intelligence, dynamism, and agility to the scientific writing process. The system called Smart Bibliometrics, the result of this research, is easy to navigate, has interactive and compelling visuals, allows teams to integrate and perform advanced analysis in a systematic way. One of the differentials of the tool is the complete development in a state-of-the-art technological platform when it comes to information and sharing. In addition, it manages to speed up processes that would be time-consuming. To use the resources, it will not even be necessary to download any software on the user’s computer. Another advantage is the simultaneous processing of data from Scopus and Web of Science databases, increasing the solution’s versatility. The performed analysis will be available, and its updating will be facilitated by an automated process. In this research, in addition to developing a system on a state-of-the-art technological platform when it comes to information and sharing, the solution was validated through the Alpha Test application to verify the user’s experience with the developed features, receiving feedback from a group of users who used the platform. The solution proved to be viable, it can be scalable and may be replicated in several institutions that are interested in the presented methodology.

Introduction

Science mapping, as the name suggests, seeks to achieve an overview of the “state-of-the-art” scientific knowledge in each research area. Additionally, bibliometrics is the application of mathematical and statistical indicators to measure and compare the evolution of science and technique in any research area. The bibliometric analysis embraces the performance analysis of contributions on specific subjects, complemented by an interaction analysis between the researchers and their subject of study (DONTHU et al., 2021), in other words, it covers the analysis of numerical indicators and interactions detected in the body of the scientific data.

The techniques of science mapping have evolved considerably in recent years thanks to advances in information technologies tools. The first discussions on mapping scientific literature indicated the importance of applying software that could perform bibliometric analyses (AHLGREN; JARNEVING, 2008; CHAVALARIAS; COINTET, 2008; NOYONS; MOED; VAN RAAN, 1999; SMALL, 1997; SOÓS, 2011; SOÓS; KAMPIS, 2012) combining metrics and data visualization.

The use of science mapping and bibliometric techniques have been consolidated as regular practices at the very beginning of the research when several bibliometric information are raised to assist scientific writing (ARIA; CUCCURULLO, 2017; MURGADO-ARMENTEROS et al., 2015; PAGANI; KOVALESKI; RESENDE, 2015; PALLOTTINO et al., 2018; RODRÍGUEZ-BOLÍVAR; ALCAIDE-MUÑOZ; COBO, 2018). It is a method adopted to make a scientific drawing of the subjects that have been addressed with complex discussions, which requires management since the techniques of science mapping and bibliometrics are composed of systematic steps based on different software. It is worth mentioning that some tools don’t provide free access (ARIA; CUCCURULLO, 2017).

Bibliometric analysis is a method applied to explore and analyze volumes of data, searching for evolutionary nuances of a specific field of knowledge, while shedding light on emerging areas (DONTHU et al., 2021). The application of these bibliometric techniques seeks to raise insights about the evolution of science, directing the efforts of researchers to a systematic review of the literature focusing on discussions of the academic community about a specific point based on the organization and summary of theoretical constructions as per the main scientific documents produced.

A methodology initially developed for the selection of bibliography, known as ProKnow-C (Knowledge Development Process – Constructivist) (ENSSLIN et al., 2015), aims to build a bibliometric analysis in 4 different stages: selection of a portfolio of relevant publications, descriptive bibliometric analysis such as the analysis of numerical metrics, systematic review of publications according to the selected portfolio, definition of new researching questions according to the previous steps applied. The development of the ProKnow-C method started in the early 2000s based on the activities of the Laboratory of Multi-criteria Methodologies for Decision Support (LabMCDA) at the Federal University of Santa Catarina, Brazil, to develop a structured process for the selection of bibliographic references scientifically recognized and its analysis on a specific subject (ENSSLIN et al., 2015). In managing the data, spreadsheets support receiving consecutive filters to select a set of scientific outputs related to distinct research subjects.

Procknow-C, Pagani, Kovaleski, and Resende (2015) proposed a method of articles classification, known as “Methodi Ordinatio”, which is expressed by a metric used to rank and classify scientific outputs. It means that in addition to the filters applied systematically to scientific outputs, this methodology develops a metric that allows the classification of results based on the variables number of citations, year of publication, and influence of journals. In the Ordinatio method, it is necessary to manage data extracted from scientific portals to generate the InOrdinatio classification index. In this case, the user needs to know spreadsheets to develop the calculations suggested by the methodology and apply a mathematical equation of classification that allows selecting the most relevant scientific outputs of the selected sample.

However, with the addition of a representative metric to generate a classification of importance among sample elements extracted from scientific portals, it is essential to develop visuals to expand the capacity of bibliometric analysis. Neuroscience proved the importance of data visualization and the comprehension of certain phenomena since images increase comprehension abilities (DWYER et al., 2020), justifying the importance of visual impressions in cognitive processes. Thus, Rodríguez-Bolívar, Alcaide-Muñoz and Cobo (2018) proposed a method of scientific mapping to analyze the evolution of specific research subjects, combining different bibliometric tools able to identify subject fields and show their progress by employing different visualization tools in the research planning.

Before starting the research, it is required to choose a data analysis method to manage workflows that employ software to organize the data extracted from scientific portals that provide visual information, such as the method known as Bibliometrix (ARIA; CUCCURULLO, 2017). In this methodology, the data extracted from scientific portals are processed in the R software to feed the bibliometric system and generate information and visualizations. The process requires downloading and installing the R software and users’ ability to interact with a not-so-intuitive programming language, which considerably hinders researchers’ work. The execution algorithm elaborated in the R language, besides being complex, can present some problems during the process, being necessary configuring the environment and thorough execution of commands. Any script error may result in processing problems, and the steps are complex execution.

By considering the dynamic research environment and the need to raise more responsive bibliometric information, the Business Intelligence (BI) tools are widely applicable and powerful to assist researchers in guiding better bibliography choices. The evolution of the information systems grows exponentially, and the BI tools follow this pattern by offering technological solutions in line with the concept of “big data” that lead to positive decisions based on data, information, knowledge, and intelligence (SHOLLO; GALLIERS, 2016).

BI embraces the concept of Business Intelligence oriented to the processes of collecting, organizing, analyzing, and monitoring information, decision-making elements in any business centered on data and knowledge analyses(BOŽIČ; DIMOVSKI, 2019; CHEN; H.L.CHIANG; C. STOREY, 2018). According to López-Robles et al. (2019), the definition of BI could be comprehended as the collection, analysis, interpretation, and dissemination of high-value information about strategic areas, transmitted to decision-makers at the proper time. Making choices based on information and knowledge is a prerequisite for the success of any enterprise, whether in the business or in the scientific environment where it is possible to use, from the beginning of the research, appropriate references aligned with a relevant gap.

Based on the methods of the study, a BI system was built on the method called Smart Bibliometrics to group the methodologies applied (ARIA; CUCCURULLO, 2017; ENSSLIN et al., 2015; PAGANI; KOVALESKI; RESENDE, 2015) in a system that associates an assertive classification metric of scientific documents with strategically developed visualizations. This solution combines science mapping and bibliometric techniques, covering automated data manipulation and elaboration of advanced visual analyses to drive the selection of superior references through the Smart Bibliometrics. This new methodology aims to facilitate the work by automating various routines, not requiring the user to understand spreadsheets management, R programming, or even downloading any software, besides being freely accessible.

Essentially, the proposed method is convenient for offering a free access solution and cloud processing capabilities, joining representative metrics of strategic theoretical and visual reference selection, intuitive and user-friendly interface, allowing the experience of data searching and interaction, which offers an overview of scientific research on a specific theme. The Smart Bibliometrics application aims to assist the automation of diverse routine data collection by building a simple and user-friendly system capable of indicating any possible research gaps in the scientific area, as well as to reverse intelligence in scientific writing focused on the evolution of knowledge in the most varied fields of expertise.

FINAL CONSIDERATIONS

The information age has significantly changed organizations’ decision-making processes. If recently emotions and feelings were strong triggers to justify options, currently choices are centered on data, information, and knowledge. And in academia, these processes grow in importance. The data available on scientific portals are essential sources to reveal strategic information that may indicate new research trends. We have moved from the “Information Age” to the “Age of Knowledge”, a new configuration where data volumes analysis and interpretation are the base for decisions, especially when considering the scientific research environment.
The Knowledge Age carries the debate on open science. Years ago, science practices were restricted to laboratories, today, they have broad and democratic participation with the contribution of researchers worldwide, promoting diversity of ideas. Arising queries about open science are related to the access to data as a critical characteristic for an efficient and progressive system and the importance of data sharing for the evolution of science (Hardwicke et al., 2018). That said, new systems need to promote collaborative science, materializing bibliometric theories for democratic access and sharing of scientific discovery.
The world of technological solutions and the hyper connection is currently on solid ground, connecting people and finding solutions to studies developed in new democratic practices of bibliometrics and science in general. A related issue is a political dimension that falls within the broader field of open science (Lyon, 2016). The technological transformation and the emergence of systems developed for open access, with unrestricted participation, can influence public policies in the natural path of science democratization. New technologies impact the way we communicate and interact, then science is characterized by construction in multiple perspectives and experiences.
Distributing the system open version with free of charge advanced cloud computing capabilities, strengthens this movement of democratization of science, expanding the possibilities of overcoming research challenges and problems that would be dealt with collectively.
Regular updating has become a challenge for any professional in the age of knowledge, especially in the academy. Producing relevant scientific material requires considerable dedication and time. Therefore, the developmental method has a high potential to optimize scientific production and facilitate researchers’ studies on bibliometric analysis considering the volume of data currently available.
Specialized journals have become increasingly demanding. In this way, a work based on consistent bibliometric analysis is fundamental in raising the chances of publication success. Having a broad overview of the “state-of-the-art” subject at the beginning of the research is a major attribute that determines a work approval for publication in a renowned scientific journal.
Finally, science mapping and bibliometric processes, through the Smart Bibliometrics, can potentially automate manual and routine processes, connect people and ideas, providing more agile analysis in choosing relevant scientific productions in an innovative, simplified, and accessible manner. With this system, scientists will have a powerful tool that provides strategic information, increasing the chances of success in publications by identifying significant research gaps and contribute to an open science. The expected result is a solution that simplifies researchers’ work, expands assertiveness in scientific production, and contributes to knowledge development.
 
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