Knowledge areas, themes and future research on open data: A co-word analysis

Open data-set identifier for open innovation and knowledge management

Purpose

This paper aims to highlight the importance of open data and the role that knowledge management and open innovation can play in its identification and use. Open data has great potential to create social and economic value, but its main problem is that it is often not easily reusable. The aim of this paper is to propose a unique identifier for open data-sets that would facilitate search and access to them and help to reduce heterogeneity in the publication of data in open data portals.

Design/methodology/approach

Considering a model of the impact process of open data reuse and based on the digital object identifier system, this paper develops a proposal of a unique identifier for open data-sets called Open Data-set Identifier (OpenDatId).

Findings

This paper presents some examples of the application and advantages of OpenDatId. For example, users can easily consult the available content catalogues, search the data in an automated way and examine the content for reuse. It is also possible to find out where this data comes from, solving the problems caused by the increasingly frequent federation of data in open data portals and enabling the creation of additional services based on open data.

Originality/value

From an integrated perspective of knowledge management and open innovation, this paper presents a new unique identifier for open data-sets (OpenDatId) and a new concept for data-set, the FAIR Open Data-sets.

The evolution of e-government research over two decades: applying bibliometrics and science mapping analysis

Purpose

This study uses a bibliometric approach to analyze the overall status of e-government research by revealing patterns and trends that would help gain a broad understanding of global developments in the field and future directions.

Design/methodology/approach

All documents related to e-government published from 2000 to 2019 were extracted from the Scopus and the Digital Government Reference Library databases. Bibexcel, Biblioshiny, and VOSviewer were used to perform the analyses and visualize the science mapping.

Findings

The results showed that 21,320 documents related to e-government research were published and cited 263,179 times. The annual growth rate of e-government research has reached 21.50%. The regression analysis showed that the growth rate is expected to increase gradually over the coming years. Despite the significant role that conference papers play in the e-government literature, the impact of articles far exceeds conference papers. The University of Albany (SUNY) has played an important role in e-government research in terms of production and impact. Furthermore, the study revealed some countries that are expected to play a prominent role in e-government research, as well as several topics that may attract more attention soon.

Originality/value

The results presented in this study and the comprehensive picture obtained about the e-government field make it an effective supplement to the expert evaluation. Thus, researchers, research managers, policymakers, institutions, funding agencies, and governments can rely on it.

Public Social Media Discussions on Agricultural Product Safety Incidents: Chinese African Swine Fever Debate on Weibo

Public concern over major agricultural product safety incidents, such as swine flu and avian flu, can intensify financial losses in the livestock and poultry industries. Crawler technology were applied to reviewed the Weibo social media discussions on the African Swine Fever (ASF) incident in China that was reported on 3 August 2018, and used content analysis and network analysis to specifically examine the online public opinion network dissemination characteristics of verified individual users, institutional users and ordinary users. It was found that: (1) attention paid to topics related to "epidemic," "treatment," "effect" and "prevent" decrease in turn, with the interest in "prevent" increasing significantly when human infections were possible; (2) verified individual users were most concerned about epidemic prevention and control and play a supervisory role, the greatest concern of institutional users and ordinary users were issues related to agricultural industry and agricultural products price fluctuations respectively; (3) among institutional users, media was the main opinion leader, and among non-institutional users, elites from all walks of life, especially the food safety personnel acted as opinion leaders. Based on these findings, some policy suggestions are given: determine the nature of the risk to human health of the safety incident, stabilizing prices of relevant agricultural products, and giving play to the role of information dissemination of relevant institutions.

Network Extraction and Analysis of Character Relationships in Chinese Literary Works

Character relationships in literary works can be interpreted and analyzed from the perspective of social networks. Analysis of intricate character relationships helps to better understand the internal logic of plot development and explore the significance of a literary work. This paper attempts to extract social networks from Chinese literary works based on co-word analysis. In order to analyze character relationships, both social network analysis and cluster analysis are carried out. Network analysis is performed by calculating degree distribution, clustering coefficient, shortest path length, centrality, etc. Cluster analysis is used for partitioning characters into groups. In addition, an improved visualization method of hierarchical clustering is proposed, which can clearly exhibit character relationships within clusters and the hierarchical structure of clusters. Finally, experimental results demonstrate that the proposed method succeeds in establishing a comprehensive framework for extracting networks and analyzing character relationships in Chinese literary works.

A Review of Digital Era Governance Research in the First Two Decades: A Bibliometric Study

The emergence of digital technologies has profoundly affected and transformed almost every aspect of societal relations. These impacts have also reached public administration, including its governance. Digital technologies’ rise has paved the way for the surfacing of a new public governance model called the Digital Era Governance (DEG) model (often referred to as e-government, digital government, e-governance, or digital governance) in which digital technologies play a central role. Therefore, the main aim of this paper is to provide a comprehensive and in-depth examination of DEG research over the past two decades. Bibliometric analysis is based on the Scopus database that contains 9175 documents published between 2001 and 2020. In this context, several established and innovative bibliometric approaches are applied. The results reveal the growth of DEG research over the last two decades, especially in recent years, as accelerated by several of the most relevant documents published in reputable journals such as Government Information Quarterly. Most DEG research has been conducted in Anglo-Saxon countries, as confirmed while examining the most relevant authors’ affiliations and collaborations. The results also indicate that DEG has advanced from conventional public services to citizen-oriented e-services by including citizens’ participation and, most recently, even to smart services by facilitating emerging and disruptive technologies. The findings add to the stock of scientific knowledge and support the evidence-based policymaking needed to successfully pursue a sustainable future.

Exploration of Topic Classification in the Tourism Field with Text Mining Technology—A Case Study of the Academic Journal Papers

This study collects abstracts of SSCI tourism journal papers between 2010 and 2019 from the WoS (Web of Science) database and uses a novel method of topic classification to explore the vocabulary characteristics of the classified articles. The corpora of abstracts are given quantitative Term Frequency–Inverse Document Frequency (TF–IDF) weights. A hierarchical K-means cluster analysis is then performed to automatically classify the articles; co-word analysis techniques are used to show the characteristics of feature words for distinct clusters, titles, and the consistency of the classified articles. Based on the results for 5783 abstracts, cluster analysis classifies the number of K-means clusters into six categories: travel, culture, sustainability, model, behavior, and hotel. A cross-check method is applied to assess the consistency of the topic classifications, list titles and keywords of the documents with the three smallest distances in each category and apply a strategic diagram to present the features of the distinct categories.

Mapping the intellectual structure of the coronavirus field (2000-2020): a co-word analysis

Over the two last decades, coronaviruses have affected human life in different ways, especially in terms of health and economy. Due to the profound effects of novel coronaviruses, growing tides of research are emerging in various research fields. This paper employs a co-word analysis approach to map the intellectual structure of the coronavirus literature for a better understanding of how coronavirus research and the disease itself have developed during the target timeframe. A strategic diagram has been drawn to depict the coronavirus domain's structure and development. A detailed picture of coronavirus literature has been extracted from a huge number of papers to provide a quick overview of the coronavirus literature. The main themes of past coronavirus-related publications are (a) "Antibody-Virus Interactions," (b) "Emerging Infectious Diseases," (c) "Protein Structure-based Drug Design and Antiviral Drug Discovery," (d) "Coronavirus Detection Methods," (e) "Viral Pathogenesis and Immunity," and (f) "Animal Coronaviruses." The emerging infectious diseases are mostly related to fatal diseases (such as Middle East respiratory syndrome, severe acute respiratory syndrome, and COVID-19) and animal coronaviruses (including porcine, turkey, feline, canine, equine, and bovine coronaviruses and infectious bronchitis virus), which are capable of placing animal-dependent industries such as the swine and poultry industries under strong economic pressure. Although considerable research into coronavirus has been done, this unique field has not yet matured sufficiently. Therefore, "Antibody-virus Interactions," "Emerging Infectious Diseases," and "Coronavirus Detection Methods" hold interesting, promising research gaps to be both explored and filled in the future.

Mapping the intellectual structure of GIS-T field (2008–2019): a dynamic co-word analysis

Using geographic information systems (GIS) widely for dealing with transportation problems (is well-known as GIS-T), has made it nessasary for researchers to discover the current state-of-the-art and predict the trends of future research. This paper aims to contribute to a better understanding of GIS-T research area from a longitudinal perspective, over the period 2008–2019. A co-word analysis was used to illustrate all the underlying subfields of GIS-T based on published papers in the Web of Science (WoS) database service. The main knowledge areas representing the intellectual structure of GIS-T including (a) sustainability, (b) health, (c) planning and management, and (d) methods and tools, were detected. Finally, in order to illustrate the structure and development of the identified clusters, two-dimensional maps and strategic diagrams for each period were drawn. This study is the first attempt to employ a text mining method so as to detect the conceptual structure of GIS-T research area from a complex and interdisciplinary literature.

Social Life-Cycle Assessment: A Review by Bibliometric Analysis

This study examined the literature on social life-cycle assessment (S-LCA) published in the last 15 years (2003–2018) using bibliometric methods. Applying scientific mapping and analyzing publication performance, the study describes the structure of and trends in S-LCA publications in terms of related subject categories, authors, journals, countries, and highly cited articles. Challenges and research gaps in the S-LCA literature were also explored. The content of related papers published in the ISI Web of Science databases was examined to identify the main themes investigated, evolution of publication activity, and most representative elements. Analyses were conducted with SciMAT software. This tool enables researchers to map research specialties by extracting qualitative information in the specialized literature and representing it using quantitative measures. The results show rapid and exponential growth of the S-LCA research line in the past ten years, with a clear upward trend in related publications (mostly case studies), especially after publication of the UNEP/SETAC Guidelines for Social Life Cycle Assessment of Products in 2009: 66% of all articles published on S-LCA were published during the period 2015–2018, primarily by European authors. The findings also delineate S-LCA as a highly fragmented research field that has been applied to diverse sectors (agriculture, bioenergy, transport, water management, chemical products, electronics, etc.), mainly in non-European countries. Critical questions concerning methods, framework, paradigms, and indicators remain to be resolved. This study provides insight into the publication performance of S-LCA, characterizing its intellectual structure and salient authors and works. In identifying hotspots in the S-LCA research, the study provides a useful state-of-the-art reference guide for academics and reveals critical research gaps and potential research avenues for future studies to advance in consolidating the discipline.

Entrepreneurship Through Open Data: An Opportunity for Sustainable Development

Entrepreneurship and open data are key elements in the sustainable development field, improving economic, social, and environmental dimensions. However, entrepreneurship and open data are barely studied together in the literature from a theoretical perspective. Therefore, this study identifies the main themes in the previous studies and proposes a conceptual model for analyzing entrepreneurship through open data. For this purpose, a descriptive analysis and a co-word analysis were performed. Results show that the subject is multidisciplinary, and the main theme of study is how different agents reuse information released by public administrations to generate new entrepreneurial initiatives, especially novel business models associated with new mobile applications. Open data sources, innovation, and business models are studied as critical factors for analyzing entrepreneurship through open data. Likewise, a conceptual model is presented and emerging themes for future research are proposed. Among them, the importance of encouraging collaboration between different agents in the open data ecosystem for service development and improvement is emphasized. Our study identifies an emerging theme that is still in an early phase: The study of sustainable entrepreneurship through open data as a value creation initiative to address global sustainable development.

Uncertain risk: assessing open data signals

Purpose

Open data resources contain few signals for assessing their suitability for data analytics. The purpose of this paper is to characterize the uncertainty experienced by open data consumers with a framework based on economic theory.

Design/methodology/approach

Drawing on information asymmetry theory about market exchanges, this paper investigates the practical challenges faced by data consumers seeking to reuse open data. An inductive qualitative analysis of over 2,900 questions asked between 2013 and 2018 on an internet forum identified how a community of 15,000 open data consumers expressed uncertainty about data sources.

Findings

Open data consumers asked direct questions that expressed uncertainty about the availability, interoperability and interpretation of data resources. Questions focused on future value and some requests were devoted to seeking data that matched known sources. The study proposes a data signal framework that explains uncertainty about open data within the context of control and visibility.

Originality/value

The proposed framework bridges digital government practice to information signaling theory. The empirical evidence substantiates market aspects of open data portals. This paper provided a needed case study of how data consumers experience uncertainty. The study integrates established theories about risk to improve the reuse of open data.

Knowledge System Analysis on Emergency Management of Public Health Emergencies

The Coronavirus Disease 2019 (COVID-19) infectious pneumonia pandemic highlights the importance of emergency management of public health emergencies (EMPHE). This paper addresses the challenge of building a knowledge system for EMPHE research that may contribute to understand the spatial and temporal characteristics of knowledge distribution, research status, cutting-edge research and development trends, and helps to identify promising research topics and guide research and practice of EMPHE. Based on the Web of Science, this paper retrieves 1467 articles about EMPHE published from 2010 to date. Then, based on high-frequency keywords, we use CiteSpace to analyze their knowledge co-occurrence network, clustering network and knowledge evolution. Furthermore, we summarize the features and gaps in EMPHE research, providing references for future research directions. Based on the above analysis, this work constructs a knowledge system about EMPHE research, providing a comprehensive visual summary of the existing research in the field of EMPHE, with the aim to guide future research and practice.

Visual Analysis of Research Paper Collections Using Normalized Relative Compression

The analysis of research paper collections is an interesting topic that can give insights on whether a research area is stalled in the same problems, or there is a great amount of novelty every year. Previous research has addressed similar tasks by the analysis of keywords or reference lists, with different degrees of human intervention. In this paper, we demonstrate how, with the use of Normalized Relative Compression, together with a set of automated data-processing tasks, we can successfully visually compare research articles and document collections. We also achieve very similar results with Normalized Conditional Compression that can be applied with a regular compressor. With our approach, we can group papers of different disciplines, analyze how a conference evolves throughout the different editions, or how the profile of a researcher changes through the time. We provide a set of tests that validate our technique, and show that it behaves better for these tasks than other techniques previously proposed.

Open Data for Open Innovation: An Analysis of Literature Characteristics

In this paper, we review some characteristics of the literature that studies the uses and applications of open data for open innovation. Three research questions are proposed about both topics: (1) What journals, conferences and authors have published papers about the use of open data for open innovation? (2) What knowledge areas have been analysed in research on open data for open innovation? and (3) What are the methodological characteristics of the papers on open data for open innovation? To answer the first question, we use a descriptive analysis to identify the relevant journals and authors. To address the second question, we identify the knowledge areas of the studies about open data for open innovation. Finally, we analyse the methodological characteristics of the literature (type of study, analytical techniques, sources of information and geographical area). Our results show that the applications of open data for open innovation are interesting but their multidisciplinary nature makes the context complex and diverse, opening up many future avenues for research. To develop a future research agenda, we propose a theoretical model and some research questions to analyse the open data impact process for open innovation.