Models of Strategic Management Scanning Based on Trend Heuristics as the Least Information Intensive Quantifiers
DOI:
https://doi.org/10.46661/revmetodoscuanteconempresa.3260Keywords:
strategic, scanning, trend, transition, information shortage, complexAbstract
SS (Strategic Scanning) is unique, partially subjective, inconsistent, interdisciplinary, vague and multidimensional process. Its description and optimisation suffers from IS (Information Shortage) and heterogeneity. IS eliminates straightforward application of traditional statistical methods. Heterogeneity problems are caused by heterogeneous nature of scanned information structures. Artificial Intelligence has developed some tools to solve such problems. Qualitative reasoning is one of them. It is based on the least information intensive quantifiers i.e. trends. There are four different trends i.e. qualitative values and their derivatives: plus/increasing; zero/constant; negative/decreasing; any value / any trend. The paper studies SS models based on ELEs (Equationless Heuristics). An example of ELE is – If novelity is increasing then confidence is decreasing. A solution of a qualitative model is represented by a set S of scenarios and a set T of time transitions among these scenarios. The key information input into an ELE model is subjective knowledge of experts. A consensus among SS experts is often not reached because of inconsistencies of experts’ knowledge. The SS case study is 12 dimensional (e.g. Freshness, Relevance) and based on 12 ELEs. There are 29 scenarios.
Downloads
References
Cai, S., Lin, X., Xu, D., & Fu, X. (2016). Judging online peer-to-peer lending behavior: A comparison of first-time and repeated borrowing requests. Information and Management, 53(7), 857-867.
De Kleer, J., & Brown, J.S. (1984). A qualitative physics based on confluences. Artificial Intelligence, 24(1-3), 7-83.
Dohnal, M. (1988). Naive models as active expert system in bioengineering and chemical engineering. Collection of Czechoslovak Chemical Communications, 53(7), 1476-1499.
Dohnal, M. (1991). A Methodology for Common-sense Model Development. Computers in Industry, 16(2), 141-158.
Dohnal, M., Kocmanova, A., & Rašková, H. (2008). Hi tech microeconomics and information non-intensive calculi. Trendy ekonomiky a management, II, 20-26.
Garnett, K., Lickorish, F.A., Rocks, S.A., Prpich, G., Rathe, A.A., Pollard, S.J.T. (2016). Integrating horizon scanning and strategic risk prioritisation using a weight of evidence framework to inform policy decisions. Science of The Total Environment, 560-561, 82-91.
Hayes, P.J. (1985a). Naive Physics I: Ontology For Liquids. In J.R. Hobbs & B. Moore (eds), Formal Theories of the Commonsense World, Ablex (pp. 71-89). California: SRI International.
Hayes, P.J. (1985b). The Second Naive Physics Manifesto. In J.R. Hobbs & B. Moore (eds), Formal Theories of the Commonsense World, Ablex (pp. 1-36). California: SRI International.
Hendry, D.F. (1995). Dynamic Econometrics. Oxford: Oxford University Press.
Kuipers, B. (1994). Qualitative reasoning: modeling and simulation with incomplete knowledge. Cambridge: MIT Press.
Lesca, N., Caron-Fasan, M.-L., & Falcy, S. (2012). How managers interpret scanning information. Information and Management, 49(2), 126-134.
Lipmann, O., & Bogen, H. (1923). Naive Physik: Arbeiten aus dem Institut für Angewandte Psychologie in Berlin; theoretische und experimentelle Untersuchungen über die Fähigkeit zu intelligentem Handeln. Leipzig: Johann Ambrosius Barth.
Ljung, L. (1999). System Identification: Theory For the User. New Jersey: Prentice Hall.
Loève, M. (1977). Probability Theory I. New York: Springer-Verlag.
Lorenz, H.-W. (1989). Nonlinear dynamical economics and chaotic motion. Berlin: Springer.
Meluzin, T., & Zinecker, M. (2014). Research into Determinants Influencing IPO Decisions on the Polish Capital Market (Determinanten der Entscheidung für eine Börseneinführung unter den Bedingungen des polnischen Kapitalmarktes - Ergebnisse einer empirischen Untersuchung). Betriebswirtschaftliche Forschung und Praxis, 66(6), 652-671.
Michalewicz, Z., & Fogel, D.B. (2004). How to solve it: Modern Heuristics. Berlin: Springer.
Mu, E., Kirsch, L.J., & Butler, B.S. (2015). The assimilation of enterprise information system: An interpretation systems perspective. Information and Management, 52(3), 359-370.
Mueller, E.T. (2014). Commonsense Reasoning: An Event Calculus Based Approach. San Francisco: Morgan Kaufmann.
Myers, M.D., & Newman, M. (2007). The qualitative interview in IS research: Examining the craft. Information and Organization, 17(1), 2-26.
Parsons, S., Kubat, M., & Dohnal, M. (1995). A rough set approach to reasoning under uncertainty. Journal of Experimental, 7(2), 175-193.
Ramakrishnan, T., Jones, M.C., & Sidorova, A. (2012). Factors influencing business intelligence (BI) data collection strategies: An empirical investigation. Decision Support Systems, 52(2), 486-496.
Russell, B. (1948). Human knowledge: Its scope and limits, Routledge classics. London: George Allen and Unwin.
Seidl, D., & Werle, F. (2017). Inter-organizational sensemaking in the face of strategic meta-problems: Requisite variety and dynamics of participation. Strategic Management Journal, 39(3), 830-858. DOI: 10.1002/smj.2723.
Sen, P.K., & Singer, J.M. (1993). Large sample methods in statistics: an introduction with applications. New York: Chapman.
Singh, S., & Dey, L. (2005). A rough-fuzzy document grading system for customized text information retrieval. Information Processing and Management, 41(2), 195-216.
Van Wyk, R.J. (1997). Strategic technology scanning. Technological Forecasting and Social Change, 55(1), 21-38.
Vicha, T., & Dohnal, M. (2008). Qualitative identification of chaotic systems behaviours. Chaos, Solitons, 38(1), 70-78.
Walters, B.A., Jiang, J.J., & Klein, G. (2003). Strategic information and strategic decision making: the EIS/CEO interface in smaller manufacturing companies. Information and Management, 40(6), 487-495.
Young, L.F. (1987). A systems architecture for supporting senior managers' messy tasks. Information, 13(2), 85-94.
Zhang, Y.-L., Li, C.-Q., Lin, M.-L., & Lin, Y.-J. (2015). Relationships between generalized rough sets based on covering and reflexive neighborhood system. Information Sciences, 319, 56-67.
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Journal of Quantitative Methods for Economics and Business Administration
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Submission of manuscripts implies that the work described has not been published before (except in the form of an abstract or as part of thesis), that it is not under consideration for publication elsewhere and that, in case of acceptance, the authors agree to automatic transfer of the copyright to the Journal for its publication and dissemination. Authors retain the authors' right to use and share the article according to a personal or instutional use or scholarly sharing purposes; in addition, they retain patent, trademark and other intellectual property rights (including research data).
All the articles are published in the Journal under the Creative Commons license CC-BY-SA (Attribution-ShareAlike). It is allowed a commercial use of the work (always including the author attribution) and other derivative works, which must be released under the same license as the original work.
Up to Volume 21, this Journal has been licensing the articles under the Creative Commons license CC-BY-SA 3.0 ES. Starting from Volume 22, the Creative Commons license CC-BY-SA 4.0 is used.
