CompetitiveIntelligenceNetworks
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This document analyzes the patterns of scientific collaboration in competitive intelligence studies from 1995 to 2012. It finds that the collaboration network exhibits isolated authors and groups with weak links. Topics studied have diversified from early focuses on technology and management to newer areas like open sources, economic intelligence, and visualization. However, the field lacks common descriptors and consolidated channels of communication like academic journals. Overall, the scientific community in this area demonstrates weak interconnectivity and dispersion.
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CompetitiveIntelligenceNetworks
- 1. Networks of scientific collaboration in competitive intelligence studies. Eva Ortoll Espinet ( eortoll@uoc.edu) and Montserrat Garcia Alsina (mgarciaals@uoc.edu) UOC: Universitat Oberta de Catalunya (SPAIN)
- 2. Objectives a) What are the patterns of collaboration of scientific community in CI field? b) In which topics does scientific community work and how do they evolve? c) What instruments does scientific community use to collaborate?
- 3. Methodology Social Network Analysis and Bibliometric Analysis
- 4. Data gathering Papers from ISI Knowledge Web of Science (1995 to 2012) Keywords: “Competitive intelligence”, “Marketing intelligence”, “Economic Intelligence”, “Intelligence analysis”, “Territorial intelligence”, and “Environmental scanning”. 679 papers were gathered.
- 5. Data analysis Periods: 1995-2000; 2001-2006 and 2007- 2012 Groups of data: a) Co-authorship networks, b) Co-words networks and c) Journals statistics
- 6. Processes for research method are literature retrieval and filtering keyword revision and statistical analysis author revision and statistical analysis journal revision and statistical analysis visualization of keyword network visualization of co-authorship network
- 7. Collaboration networks Micro-level analysis: coauthorship 1995-2000 2001-2006 2007-2012 Number of authors 175 398 675 % of authors with jointly papers 73,71% 75,62% 73,48% % redundant collaboration 0,35% 2,85% 6,95% Average degree of collaboration 3,17 2,81 2,77
- 9. Collaboration networks Micro-level analysis: coauthorship 1995-200 2001-2006 2007-2012 Institutions 45 81 186 % interinstitutional collaboration 51,11% 48,14% 55,78% Institutions with redudant collaboration 1 12 2 Average degree of collaboration 1,68 1,87 1,96
- 10. Topics of research Difficult analyse topics Descriptors' evolution Great variety of descriptors Increasing synonyms to identify the same area of research Progressive connexion among clustered topics
- 11. Topics of research Great variety of descriptors Information / intelligence sources Visualization DESCRIPTORS competitive Intelligence environmental scanning Information retrieval strategic planning Intelligence analysis terrorism information systems evaluation criteria strategy Knowledge management marketing intelligence security criminal behavior security incidents security vulnerability scanning intelligence agents strategic management information sources monitoring criminal intelligence analysis evaluation economic intelligence data mining visual analytics business intelligence information visualization crime scenarios data analysis national security text mining strategy formulation strategy implementation visual knowledge discovery data visualization regional development visualization open sources social networks analysis counterterrorism knowledge visualization information sourcing Information search and retrieval HUMINT OSINT human infomation sources Criminal network analysis intelligence sources information retrieval effectiveness information retrieval models human competitive intelligence human intelligence network open information source 1995-2000 2001-2006 2007-2012 17,47 20,82 38,28 7,23 13,08 9,78 7,23 3,63 0,74 7,23 0 0 6,63 18,89 16,09 6,02 3,15 0 6,02 1,69 0,74 5,42 0 0 4,82 0 2,21 4,22 8,23 5,36 3,61 5,57 7,89 3,61 0,73 0 3,61 0 0 3,61 0 0 3,61 0 0 1,81 2,91 0 1,81 1,94 0,00 1,81 1,81 1,2 1,2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6,05 4,84 4,36 3,39 3,15 3,15 3,15 2,91 2,66 2,42 2,42 1,45 1,45 1,45 0,97 0,97 0,73 0,48 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3,05 2,1 0 0 1,16 1,68 3,89 4,31 6,1 4 0 0,42 1,05 0,84 0 0 0 0 0 0 2,1 0,42 2,42 1,16 1,05 1,05 0,95 0,74 0,74 0,63 0,53 0,53 0,42 0,42 0,32 0,32 0,32
- 12. Topics of research Intelligence analysis on the top but decreasing Information retrieval high decrease Now, marketing on the top Data visualization is increasing Intelligence analysis Information retrieval Knowledge management Marketing intelligence Information sources
- 16. Publications Journals Congress Increasing predominance of: Computer science, Business & Economics Information % library science (except in congress)
- 17. Discussion and conclusions Patterns of collaboration Authors and groups with low productivity and weak links among them Authors with very few relations among them Isolated teams with low interaction among them Slight interdisciplinary collaboration Weak bridges among institutions Slight increase of interinstitutional collaboration
- 18. Discussion and Conclusions Knowledge topics and evolution First period: technological issues and management Second period: technological issues maintain their presence, and there is and increase in subtopics about Open Sources, Economic Intelligence, visualization, data and text mining Third period: Open Sources and Visualization issues continues, Information Analysis is increasing
- 19. Discussion and Conclusions Channels to communicate Few academic journals Few congresses specific to CI (only 2) Lack of descriptors homogenization The interdisciplinary nature of the field makes the consolidation of channels for knowledge interaction difficult
- 20. Conclusions Weak interconnected scientific community Dispersion of topics Lack of common language Weak channels of communication
- 21. Limitations and future research ISI Web of Knowledge data base Hub and authorities will be part of our future approach Shared methodological approaches will be part of our future analysis
Editor's Notes
- Thank you very much for your attention. My colleage Montse Garcia and me, come from Catalonia, Spain, and work at Universitat Oberta de Catalunya. To begin with, we want to notice, even most of the public already knows, that different studies has been proved the benefits and merits of research collaboration, that include: sharing and transferring knowledge and research equipment, connecting scholars to a large scientific network, expediting the research process and increasing the visibility of articles/publications. Secondly, We also must admit that since the growth of information and knowledge economy, Competitive Intelligence has become a topic of academic interest. There are some descriptive studies that identified the scientific production in competitive inteligence, but not from the point of view of research collaboration. And finally, even there’s not doubt that there exits and academic field of competitive intelligence, the question is how mature is it?
- In this sense, for the study of the madurity of CI field, we take into account some of the requirements for an academic field to be consolidate suggested by authors as Vanderstraeton or Khun, such as: networks of experts that configure the scientific community; common paradigms to evaluate the validity of the prescriptions done in the literature, or the specticif scientific publications that allow the interaction of knowledge and the development of new research topics over the years Taking into account these precedents, the aim of our presentation is to elaborate a scientific knowledge map about CI by studying the main research topics and the structure of collaboration among their actors. We’ll do that by analysing: the patterns of collaboration of scientific community ; the topics of interest of that community and the instruments that scientics use to collaborate
- How we did it? The research method that we have used integrates social network analysis and bibliometric analysis Bibliometric Analysis has been done using the BIBEXCEL software, and the PAJEK software has been used for visualization the networks and also to obtain some network mesures that helps in the Analysis and Interpretation of Data The process to obtain data has been the following:
- We gather Papers (journal papers, books and conference proceedings) from ISI Knowledge Web of Science, raging from 1995 until 2012 The literature was retrieved searching with the followed words in the field “Topic”: “Competitive intelligence”, “Marketing intelligence”, “Economic Intelligence”, “Intelligence analysis”, “Territorial intelligence”, and “Environmental scanning”. We use this topics because are those more representative of Competitive Intelligence area, and that have been identified in previous bibliographics reviews about the subject (see for example Bergeron paper) Once obtained the papers we Split the data in three periods
- As we can see on the screen For each period we analysed the following three groups of data: first of all, we build the authors network to identified multiauthored papers, their frequency and their links Secondly, we built co-words networks to identified the main research topics, and finally, we identifiy journals and conference proceedings related to our topic of interest. For the different groups of data we did the following actions:
- ….. After filter the gathered data-set (que entenem per “filtering”??) We make both and author and keyword revision, traying to ommit duplicates or to normalize authors and journal names. Once each group of data was review we applied some of the functions of BIBEXCEL software to build the differents networks we wanted To analyse. Once obtanied each network we make the visualization with PAJEK. Pajel also allows us to draw and identify network propietries that have been used for the analysis. Some of the results we obtained are summarized in the followings slides
- In the first slide we have what we have called “Micro-level analysis”, that is, the coauthorship network at the author level, latter on we could see this collaboration at institutional level. The results show a more or less stable values in the percentatge of authors that signed a jointly paper and the average of degree collaboration It is interesting ot notice that the redundant collaboration that is, group of authos with more than one jointly paper, has increase in each period. This means that there are more productive groups over the years, even those groups are no always the same during the overall period.
- Here we can see the graphic of the coauthorship networks, corresponding to the two last periods of our study. As you can see, the weight of the links (that is, the number of redundant collaborations) has increased in the second period, and also the number and size of groups From the point of view of institutions the data is the follow
- These data are similar with pattrons of other interinstitutional collaboration analysis in Social Sciences identified in a recent study published in xxxxx From the point of view of topics of research…., my colleague Montse, will explain you the main results:
- Even the growth of academic papers in the field of CI during the last 20 years, it seems that CI as an academic discipline is not still in a mature stage, mainly due to: Weak interconnected scientific community, that could act as a barrier for knowledge flow and knowledge creation Weak channels of communication, both formal and informal, perhaps due to the interdisciplinary nature of IC field