• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.179-200
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• 2019

Word-of-mouth (WoM) is defined by consumer activities that share information concerning consumption. WoM activities have long been recognized as important in corporate marketing processes and have received much attention, especially in the marketing field. Recently, according to the development of the Internet, the way in which people exchange information in online news and online communities has been expanded, and WoM is diversified in terms of word of mouth, score, rating, and liking. Social media makes online users easy access to information and online WoM is considered a key source of information. Although various studies on WoM have been preceded by this phenomenon, there is no meta-analysis study that comprehensively analyzes them. This study proposed a method to extract major researches by applying text mining techniques and to grasp the main issues of researches in order to find the trend of WoM research using scholarly big data. To this end, a total of 4389 documents were collected by the keyword 'Word-of-mouth' from 1941 to 2018 in Scopus (www.scopus.com), a citation database, and the data were refined through preprocessing such as English morphological analysis, stopwords removal, and noun extraction. To carry out this study, we adopted main path analysis (MPA) and word co-occurrence network analysis. MPA detects key researches and is used to track the development trajectory of academic field, and presents the research trend from a macro perspective. For this, we constructed a citation network based on the collected data. The node means a document and the link means a citation relation in citation network. We then detected the key-route main path by applying SPC (Search Path Count) weights. As a result, the main path composed of 30 documents extracted from a citation network. The main path was able to confirm the change of the academic area which was developing along with the change of the times reflecting the industrial change such as various industrial groups. The results of MPA revealed that WoM research was distinguished by five periods: (1) establishment of aspects and critical elements of WoM, (2) relationship analysis between WoM variables, (3) beginning of researches of online WoM, (4) relationship analysis between WoM and purchase, and (5) broadening of topics. It was found that changes within the industry was reflected in the results such as online development and social media. Very recent studies showed that the topics and approaches related WoM were being diversified to circumstantial changes. However, the results showed that even though WoM was used in diverse fields, the main stream of the researches of WoM from the start to the end, was related to marketing and figuring out the influential factors that proliferate WoM. By applying word co-occurrence network analysis, the research trend is presented from a microscopic point of view. Word co-occurrence network was constructed to analyze the relationship between keywords and social network analysis (SNA) was utilized. We divided the data into three periods to investigate the periodic changes and trends in discussion of WoM. SNA showed that Period 1 (1941~2008) consisted of clusters regarding relationship, source, and consumers. Period 2 (2009~2013) contained clusters of satisfaction, community, social networks, review, and internet. Clusters of period 3 (2014~2018) involved satisfaction, medium, review, and interview. The periodic changes of clusters showed transition from offline to online WoM. Media of WoM have become an important factor in spreading the words. This study conducted a quantitative meta-analysis based on scholarly big data regarding WoM. The main contribution of this study is that it provides a micro perspective on the research trend of WoM as well as the macro perspective. The limitation of this study is that the citation network constructed in this study is a network based on the direct citation relation of the collected documents for MPA.

• The Journal of Information Systems
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• v.28 no.3
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• pp.39-58
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• 2019

Purpose A digital twin is a digital replication of a physical system. Gartner identified the digital twin as one of the Gartner Top 10 Strategic Technology Trend for three years from 2017. The rapid development of the digital twin market is expected to bring about innovation and change throughout society, and much research has been done recently in academia. In this research, we tried to explore the main research trends for digital twin research. Design/methodology/approach We collected the digital twin research from Web of Science, and analyzed 804 articles that was published during time span of 2010-2018. A total of 41 key authors were selected based on the frequency of citation. We created a co-citation matrix for the core authors, and performed multivariate analysis such as cluster analysis and multidimensional scaling. We also conducted social network analysis to find the influential researchers in digital twin research. Findings We identified four major sub- areas of digital twin research: "Infrastructure", "Prospects and Challenges", "Security", and "Smart Manufacturing". We also identified the most influential researchers in digital twin research: Lee EA, Rajkumar R, Wan J, Karnouskos S, Kim K, and Cardenas AA. Limitation and further research suggestion were also discussed as a concluding remarks.

• The Journal of Information Systems
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• v.30 no.1
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• pp.21-44
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• 2021

Purpose The opportunities and approaches to big data have grown in various ways in the digital era. Business analytics is nowadays an inevitable strategy for organizations to earn a competitive advantage in order to survive in the challenged environments. The purpose of this study is to analyze the intellectual structure of business analytics literature to have a better insight for the organizations to the field. Design/methodology/approach This research analyzed with the data extracted from the database Web of Science. Total of 427 documents and 23,760 references are inserted into the analysis program CiteSpace. Author co-citation analysis is used to analyze the intellectual structure of the business analytics. We performed clustering analysis, burst detection and timeline analysis with the data. Findings We identified seven sub- areas of business analytics field. The top four sub-areas are "Big Data Analytics Infrastructure", "Performance Management System", "Interactive Exploration", and "Supply Chain Management". We also identified the top 5 references with the strongest citation bursts including Trkman et al.(2010) and Davenport(2006). Through timeline analysis we interpret the clusters that are expected to be the trend subjects in the future. Lastly, limitation and further research suggestion are discussed as concluding remarks.

• Journal of Korea Technology Innovation Society
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• v.14 no.spc
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• pp.1171-1186
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• 2011

Identifying Emerging Technology Trends is crucial for decision makers of nations and organizations in order to use limited resources, such as time, money, etc., efficiently. Many researchers have proposed emerging trend detection systems based on a popularity analysis of the document, but this still needs to be improved. In this paper, an emerging trend detection classifier is proposed which uses both academic and industrial data, SCOPUS and PATSTAT. Unlike most pre-vious research, our emerging technology trend classifi-er utilizes supervised, semi-automatic, machine learning techniques to improve the precision of the results. In addition, the citation information from among the SCOPUS data is analyzed to identify the early signals of emerging technology trends.

• Journal of Information Science Theory and Practice
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• v.3 no.2
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• pp.42-57
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• 2015

Purpose. : The present study is an attempt to find out the impact of self-citations on Impact Factor (IF) across disciplines. The study examines the number of research articles published across 27 major subject fields covered by SCImago, encompassing as many as 310 sub-disciplines. The study evaluates aspects like percentage of self-citations across each discipline, leading self-citing countries and continents, and the impact of self-citation on their IF. Scope. : The study is global in nature, as it evaluates the trend of self-citation and its impact on IF of all the major subject disciplines of the world, along with countries and continents. IF has been calculated for the year 2012 by analyzing the articles published during the years 2010 and 2011. Methodology/Approach. : The study is empirical in nature; as such, statistical and mathematical tools and techniques have been employed to work out the distribution across disciplines. The evaluation has been purely under-taken on the secondary data, retrieved from SCImago Journal and Country Ranking. Findings. : Self-citations play a very significant part in inflating IF. All the subject fields under study are influenced by the practice of self-citation, ranging from 33.14% to 52.38%. Compared to the social sciences and the humanities, subject fields falling under the purview of pure and applied sciences have a higher number of self-citations, but a far lesser percentage than the social sciences and humanities. Upon excluding self-citations, a substantial amount of change was observed in the IF of subject fields under study, as 18 (66.66%) out of 27 subjects fields faced shuffle in their rankings. Variation in rankings based on IF with and without self-citation was observed at subject level, country level, and continental level.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2011.11a
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• pp.219-229
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• 2011

• The Journal of Industrial Distribution & Business
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• v.13 no.6
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• pp.1-7
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• 2022

Purpose: This study aims to investigate an overview of the reputation of e-commerce from 2001-2021. Research design, data and methodology: This study uses a bibliometrics technique involving published results from the Scopus database. Keyword tracking uses the terms e-commerce + reputation. The data collected meets the criteria for the type of journal publication. Data was collected using the Publish or Perish (PoP) program and exported into VOSviewer. Bibliometrics examines certain fields of science based on several components such as author and co-author, citation and co-citation, keywords related to theme mapping, origin, and source of publication. Data was collected using the Publish or Perish (PoP) program and exported into VOSviewer. Results: The results show the total citations from 118 papers are 1429, with citations per paper of 12.11 and citations per year of 68.05. The trend of publications from 2001-2021 shows the dynamics of increasing or decreasing, but this trend is still developing. Conclusions: This paper also presents articles that have contributed greatly to the study of e-commerce reputation, the most productive authors, and clustered themes regarding e-commerce reputation. Reputation is an important area of e-commerce research. Reputation is also essential factors for e-commerce in facing business competition and needs to be a consideration for digital business practitioners.

• Journal of Korean Library and Information Science Society
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• v.49 no.2
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• pp.245-266
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• 2018

This study proposes a novel method to rank highly-cited papers that incorporate the likelihood of receiving future citations. Instead of using the total citation count, the proposed method ranks most-papers based on the yearly citation growth rate (YCGR). The rank of YCGR can be obtained by calculating the average ranks of five individual citation related variables: 1) Total Citation Count, 2) Leftside-Slope, 3) Publication Year, 4) Peak Year, and 5) Rightside-Slope. To empirically test the proposed method, yearly citation counts with other relevant bibliographic records of the 50 most-cited papers in Library and Information Science (LIS) journals used in the study conducted by Walters and Wilder were collected from the Scopus database for the years 1996 to 2016. The result indicated that the YCGR appears to reflect the degree to which the paper is likely to receive future citations, and the ranked list based YCGR offered an alternative viewing feature of the highly-cited papers in LIS. Although more empirical analyses are needed, the rank based on YCGR in conjunction with variables related to YCGR can be used as an alternative method in recognizing influential papers in LIS.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.2
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• pp.1-24
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• 2013

The research about customer value has developed over the past years in the marketing field. On the other hand, the stream of the idea has not fully been structured yet. It is the purpose of this research to present the process of development together with the intellectual structure in the field of customer value researches using "Author Co-citation Analysis" (ACA). For the purpose of the research, authors chosen were ranked in order of frequency according to their citations which were used for network analysis. Further, it was of advantage in finding the development process for this research from 1996 to 2011. The trend were set into three time-line groups/trends (1996~2000, 2001~2005, and 2006~2011) that were respectively analyzed. In conclusion, the research represents the intellectual structure of customer value in each period. The research having been tried, influenced a variable field in other marketing researches. While still, many researches limit their focus on a "one-way customer value, used by companies in the past and some in the present, many researches now have a wider perspective about the value and relationship of their customer and their company, together with the society at large.

• Agribusiness and Information Management
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• v.3 no.1
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• pp.23-37
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• 2011

The development of informatization impacts all sectors, including agriculture. Agricultural informatization builds the knowledge linkage structures of agricultural innovation systems globally. This study investigated the global knowledge linkage structures in agricultural innovation pertinent to information technology (IT) for agricultural research and development (R&D) investments and activities. We explored the longitudinal trend of systemness within the networked research relationships in the triple helix (TH) of the university, industry and government (UIG). We collected data from publications in the Science Citation Index (SCI), the Social Sciences Citation Index (SSCI), and the Arts and Humanities Citation Index (A&HCI) to analyze the TH network dynamics. We also performed a scientometrics analysis to quantitatively identify the knowledge and insights of global agricultural innovation structures. These results could be informative for individual countries. Our findings reveal that the global knowledge linkage structures in the agricultural sector that are pertinent to IT fluctuate widely and fail to increase the capacity of agricultural innovation research due to a neglect of the network effects of the TH dynamics of UIG.