An evaluation of competitive and technological intelligence tools: A cluster analysis of users’ perceptions

Fatma Fourati-Jamoussi, Claude-Narcisse Niamba, Julien Duquennoy


The purpose of this article is to discuss and evaluate the use of competitive and technological intelligence (CTI) tools by students to help designers of these tools get the best efficiency out of a monitoring process. This article introduces an application of the cluster analysis method and the competitive and technological intelligence literature. In order to evaluate the use of CTI tools, we deal with two evaluation models: Task-Technology Fit (TTF) and the Technology Acceptance Model (TAM). A survey was sent to users of CTI tools addressed to engineering students and the most pertinent replies were examined. The responses were analyzed by using the statistical software SPAD. Results showed a typology from the various profiles of users of this technology by using the method of classification. We note different perceptions between student users. Although this study remains focused on the individual perspective, it requires more examination about the organizational impact of the use of CTI tools. The identification of the different user profiles was done by using a cluster analysis. For the designers of CTI tools these results highlight the importance of user perception, suggesting designers take into account the perception of all user types. As these tools develop, more and more companies will be looking for skills of future engineers for monitoring and management of strategic information. That’s why practical courses in CTI are taught to the students in order to take into account the companies’ needs. 


Competitive and technological intelligence, cluster analysis, TTF model, TAM model, user perception

Full Text:



Abzaltynova, Z. and Williams, J. (2013). Developments in Business Intelligence Software. Journal of Intelligence Studies in Business, (II): 40-54.

Amara, Y., Solberg Soilen, K. and Vriens, D. (2012). Using the SSAV model to evaluate Business Intelligence Software. Journal of Intelligence Studies in Business, (III): 29-40.

Anderberg, M. R. (1973). Cluster Analysis for Applications. New York: Academic Press, 372pp.

Anderberg, M. R. (2014).Cluster Analysis for Applications: Probability and Mathematical Statistics. A series of monographs and textbooks. New York: Academic Press,

Bel Hadj, T. and and Aouadi, S. (2014). Effects of corporate economic intelligence on international competitiveness of Tunisian firms. Knowledge Horizons-Economics. 6 (1): 113-121.

Bel Hadj, T., Ghodbane, A. and Aouadi, S. (2016). The relationship between ‘competitive intelligence’ and the internationalization of North African SMEs. Competition & Change. 20 (V) : 326-336.

Benbasat, I. and Nault, B. R. (1990). An evaluation of empirical research in managerial support systems. Decision Support Systems, 6 (III): 203-226.

Berkhin, P. (2006). A survey of clustering data mining techniques, grouping multidimensional data, Springer, 25-71.

Calof, J., and Smith, J. (2010). The integrative domain of foresight and competitive intelligence and its impact on R&D management. R&D Management. 40 (1): 31-39. doi:10.1111/j.1467-9310.2009.00579.x

Choi, S., Park, H., Kang, D., Lee, J. and Kim, K. (2012). A SAO-based text mining approach to building a technology tree for technology planning. Expert Systems with Applications. 39: 11443–11455.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technologies. MIS Quarterly, 13 (III): 319-340.

Delone, W. H. and McLean, E. R. (2003). The Delone and McLean model of information success: A ten-year update. Journal of Management Information Systems, 19 (IV): 9-30.

Doll, W. J. and Torkzadeh, G. (1998). Developing a multidimensional measure of system-use in an organizational context. Information and Management, 33 (IV): 171-185.

Du Toit, A. S. A. (2015). Competitive intelligence research: an investigation of trends in the literature. Journal of Intelligence Studies in Business, 5 (II): 14-21.

Everitt B. S. (1998). Dictionary of statistics. Cambridge University Press, London.

Fourati-Jamoussi, F. (2014). Veille stratégique: l’évaluation de l’utilisation des agents intelligents. Presses Académiques Francophones, ISBN 978-3-8381-4777-2, 448p.

Fourati-Jamoussi, F. (2015). E-reputation: a case study of organic cosmetics in social media. In: 2015 6th international conference on Information Systems and Economic Intelligence, IEEE, pp 125–132.

Fourati-Jamoussi, F. and Niamba, C. N. (2016). An evaluation of business intelligence tools: a cluster analysis of users’ perceptions. Journal of Intelligence Studies in Business. 6 (1): 37-47.

Giald, B. and Giald, T. (1988). The business intelligence system, a new tool for competitive advantage, New York: AMACOM.

Goodhue, D. L. and Thompson R. L. (1995). Task-Technology Fit and Individual Performance. MIS Quarterly, 19 (II): 213-236.

Grublješič, T. and Jaklič, J. (2014). Customer oriented Management practices leading to BIS embeddedness. Online Journal of applied Knowledge Management, 2 (I):11-27.

Herring, J. P. (1998). What is intelligence analysis?. Competitive Intelligence Magazine, 1(II).(current September 1, 2003).

Jacob, R. and Pariat, L. (2002). Gestion des connaissances et compétitivité des entreprises. Réseaux. 3 (II): 4.

Jakobiak, F. (1998). L’intelligence économique en pratique. Paris: Editions d’Organisation, 307 p.

Johnson, R. A. and Wichern, D. W. (1998). Applied multivariate statistical analysis. Prentice Hall, New Jersey.

Kahaner, L. (1998). Competitive Intelligence: How to gather, analyze and use information to move your business to the top, Touchstone.

Kaufman L. and Rousseeuw P. J. (2009). Finding groups in Data: an introduction to cluster analysis. A John Wiley and Sons, INC, ISBN 978-0-470-31748-8, 368 p.

Lesca, N. and Caron-Fasan, M. L. (2006). Veille anticipative, une autre approche de l’intelligence économique. Lavoisier, Paris.

McKercher B. (2008). Segment transformation in urban tourism, Tourism Management, 29 (VI): 1215-1225.

Mooi, E. and Sarstedt, M. (2011). Cluster analysis, in a concise guide to market research, Springer Berlin Heidelberg, ISBN 978-3-642-12540-9, 237-284.

Nyblom, M., Behrami, J., Nikkilä, T. and Solberg Soilen, K. (2012). An evaluation of Business Intelligence Software systems in SMEs-a case study, Journal of Intelligence Studies in Business, (II): 51-57.

Pateyron, E. (1998). La veille stratégique. Paris: Economica, 1998, 212 p.

Porter, M. E. (1999). La concurrence selon Porter. Paris: Editions Village Mondial.

Punj, G. and Stewart, D. W. (1983). Cluster Analysis in Marketing Research: Review and Suggestions for Application, Journal of Marketing Research. 20 (II): 134-148.

Ruach, D. and Santi, P. (2001). Competitive Intelligence Adds Value: Five Intelligence Attitudes, European Management Journal, 19 (V): 552-559.

Sakys, V. and Butleris, R. (2011). Business Intelligence tools and technology for the analysis of university studies management. Transformations in Business and Economics, 10 (II): 125-136.

Salles, M. (2006). Stratégies des PME et intelligence économique: une méthode d’analyse du besoin 2 éme edition. Paris: Economica.

Salvador, M. R., Zamudio, P. C., Carrasco, A. S. A., Benítez, E. O., Bautista, B. A. (2014). Strategic Foresight: Determining Patent Trends in Additive Manufacturing. Journal of Intelligence Studies in Business, 4 (III): 42-62.

Seddon, P. B. (1997). A respecification and extension of the Delone and McLean model of IS success. Information Systems Research, 8 (III): 240-253.

Solberg Soilen, K. (2015). A place for intelligence studies as a scientific discipline, Journal of Intelligence Studies in Business, 5 (III): 34-46.

Solberg Soilen, K. and Hasslinger, A. (2012). Factors shaping vendor differentiation in the Business Intelligence Software industry. Journal of Intelligence Studies in Business, (III): 48 - 54.

Venkatesh, V., Morris, M. G., Davis, G. B. and Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27 (III): 425-478.

Wierenga, B. and Van Bruggen, G.H. (2000). Marketing Management Support Systems: Principles, Tools and Implementation. International Series in Quantitative Marketing. Kluwer Academic Publishers.


  • There are currently no refbacks.

Comments on this article

View all comments
Cookies are small text files that are placed on your computer by websites that you visit. They are widely used in order to make websites work, or work more efficiently, as well as to provide information to the owners of the site.

ISIB is indexed by Web of Science (Emerging list), ESCI, SCOPUS, EBSCO, DOAJ, Google Scholar, EconBib and SCImago, and is ranked as a Level 1 publication by the Norwegian Social Science Data Services and the Finnish Publication Forum. 



 Journal Index by SCIMAGO