Atman: Intelligent information gap detection for learning organizations: First steps toward computational collective intelligence for decision making
DOI:
https://doi.org/10.37380/jisib.v10i2.582Keywords:
Contingency theory, environmental scanning, knowledge-based view, learning organization, machine learningAbstract
Companies’ environments change constantly and very quickly, so each company must be aligned with its environment and understand what is happening to maintain and improve its performance. To constantly adapt to its environment, the company must integrate a learning process in relation to what is happening and become a "learning company." This posture will ensure organizational effectiveness in relation to changes in the environment and allow companies to achieve goals under the best conditions. Our project aims at delivering a competitive and collective intelligence service allowing to support decision making processes through the diagnostic of alignment between internal knowledge of the organization and available external information.
References
de Almeida, F. C., Lesca, H. (2019) Collective intelligence process to interpret weak signals and early warnings. Journal of Intelligence Studies in Business
Bilal, M., Israr, H., Shahid, M., & Khan, A. (2016) Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques. Journal of King Saud University-Computer and Information Sciences, 28(3), 330-344.
Boiy, E., & Moens, M. F. (2009) A machine learning approach to sentiment analysis in multilingual Web texts. Information retrieval, 12(5), 526-558.
Camponovo, G, Pigneur, Y. (2004) Extending technology roadmapping for environmental analysis. Proceedings of the Colloque sur la Veille stratégique, scientifique et technologique
Camponovo, G, Pigneur, Y. (2004) Information Systems alignment in uncertain environments. Proceedings of Decision Decision Support Systems (DSS)
Camponovo, G. (2009) Concepts for designing environment scanning information systems. International Journal of Business and Systems Research
Cangelosi, V. E., and W. R. Dill. 1965. "Organizational Learning: Observations Toward a Theory," Administrative Science Quarterly (10:2), Sep., pp. 175-203.
Choo, C. (2001) Environmental scanning as information seeking and organizational learning. Information Research, Vol. 7; Number1, October 2001, p. 1
Dogson, M. (1993) Organizational learning: a review of some literatures. Organization studies.
Fiedler, F. E. (1964). A Contingency Model of Leadership Effectiveness. Advances in Experimental Social Psychology (Vol.1). 149-190. New York: Academic Press.
Grèzes, V., Liu, Z., Crettol, O., Perruchoud, A. (2012) From business model design to environmental scanning: the way to a new semantic tool to support SMEs' strategy. Proceedings of eChallenges e-2012
Nasukawa, T., & Yi, J. (2003) Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of the 2nd international conference on Knowledge capture (pp. 70-77). ACM.
Neethu, M. S., & Rajasree, R. (2013) Sentiment analysis in twitter using machine learning techniques. In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.
Patil, G., Galande, V., Kekan, V., & Dange, K. (2014) Sentiment analysis using support vector machine. International Journal of Innovative Research in Computer and Communication Engineering, 2(1), 2607-2612.
Vinodhini, G., & Chandrasekaran, R. M. (2012) Sentiment analysis and opinion mining: a survey. International Journal, 2(6), 282-29
Vroom, V.H. and Yetton, P.W. (1973). Leadership and decision-making. Pittsburgh: University of Pittsburgh Press
Weill, Peter; Olson, Marorethe H. (1989). An Assessment of the Contingency Theory of Management Information Systems. Journal of Management Information Systems, 6(1), 63.
Wan, Y., & Gao, Q. (2015) An ensemble sentiment classification system of twitter data for airline services analysis. In 2015 IEEE international conference on data mining workshop (ICDMW) (pp. 1318-1325). IEEE.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 Journal of Intelligence Studies in Business
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).