Business Research

, Volume 6, Issue 2, pp 196–213 | Cite as

Improving Environmental Scanning Systems Using Bayesian Networks

Open Access
Article

Abstract

As companies’ environment is becoming increasingly volatile, scanning systems gain in importance. We propose a hybrid process model for such systems’ information gathering and interpretation tasks that combines quantitative information derived from regression analyses and qualitative knowledge from expert interviews. For the latter, we apply Bayesian networks. We derive the need for such a hybrid process model from a literature review. We lay out our model to find a suitable set of business environment indicators to forecast a company’s key financials. Deriving lessons learned from a prototype in the industrial sector, we evaluate the utility of our model following the design science research paradigm. We find our model to especially convince in completeness, transparency and transportability when compared with “pure” mathematical models.

JEL classification

C02 C11 C53 G17 M49 

Keywords

corporate management balancing opportunities and threats regression analyses Bayesian networks information systems (IS) design design science research in IS case study 

References

  1. Aaker, David A. (1983): Organizing a Strategic Information Scanning System, California Management Review, 25 (2): 76–83.CrossRefGoogle Scholar
  2. Abraham, Jocelyne, Jean-Yves Saulquin, and Richard Soparnot (2011): Evaluation of the Value of Experts to a Business: Proposal of a Theoretical Model, Problems and Perspectives in Management, 9 (3): 122–130.Google Scholar
  3. Aguilar, Francis J. (1967): Scanning the Business Environment, Macmillan: New York, NY.Google Scholar
  4. Ahn, Jae-Hyeon and Kazuo J. Ezawa (1997): Decision Support for Real-Time Telemarketing Operations Through Bayesian Network Learning, Decision Support Systems, 21 (1): 17–27.CrossRefGoogle Scholar
  5. AIS (2010): MIS Journal Rankings, http://ais.affiniscape.com/displaycommon.cfm?an=1&subarticlenbr=432 (Access date: 2010-11-20).Google Scholar
  6. Ansoff, Harry I. (1975): Managing Strategic Surprise by Response to Weak Signals, California Management Review, 18 (2): 21–33.CrossRefGoogle Scholar
  7. Ansoff, Harry I. (1980): Strategic Issue Management, Strategic Management Journal, 1 (2): 131–148.CrossRefGoogle Scholar
  8. Barnett, F. William (1988): Four Steps to Forecast Total Market Demand, Harvard Business Review, 66 (4): 28–38.Google Scholar
  9. Baskerville, Richard (2012): Reviving the IT in the IS, European Journal of Information Systems, 21 (6): 587–591.CrossRefGoogle Scholar
  10. Ben-Gal, Irad (2007): Bayesian Networks, in: Fabrizio Ruggeri, Ron S. Kenett, and Frederick W. Faltin (eds.):Encyclopedia of Statistics in Quality and Reliability, Wiley: Chichester, UK, 179–184.Google Scholar
  11. Benbasat, Izak, David K. Goldstein, and Melissa Mead (1987): The Case Research Strategy in Studies of Information Systems, MIS Quartely, 11 (3): 369–386.CrossRefGoogle Scholar
  12. Bielza, Concha, Manuel Gomez, and Prakash P. Shenoy (2010): Modeling Challenges with Influence Diagrams: Constructing Probability and Utility Models, Decision Support Systems, 49 (4): 354–364.CrossRefGoogle Scholar
  13. Boehm, Ernst A. and Peter M. Summers (1999): Analyzing and Forecasting Business Cycles with the Aid of Economic Indicators, International Journal of Management Reviews, 1 (3): 245–277.CrossRefGoogle Scholar
  14. Brinkkemper, Sjaak (1996): Method Engineering: Engineering of Information Systems Development Methods and Tools, Journal of Information and Software Technology, 38 (4): 275–280.CrossRefGoogle Scholar
  15. Chang, Pei-Chann, Chien-Yuan Lai, and K. Robert Lai (2006): A Hybrid System by Evolving Case-Based Reasoning with Genetic Algorithm in Wholesaler’s Returning Book Forecasting, Decision Support Systems, 42 (3): 1715–1729.CrossRefGoogle Scholar
  16. Chen, Hsinchun, Michael Chau, and Shu-Hsing Li (2011): Enterprise Risk and Security Management: Data, Text and Web Mining, Decision Support Systems, 50 (4): 649–650.CrossRefGoogle Scholar
  17. Choudhury, Vivek and Jeffrey L. Sampler (1997): Information Specificity and Environmental Scanning: An Economic Perspective, MIS Quarterly, 21 (1): 25–53.CrossRefGoogle Scholar
  18. Cinar, Didem and Gulgun Kayakutlu (2010): Scenario Analysis Using Bayesian Networks: A Case Study in Energy Sector, Knowledge-Based Systems, 23 (3): 267–276.CrossRefGoogle Scholar
  19. Cowell, Robert G., Richard J. Verrall, and Y. K. Yoon (2007): Modeling Operational Risk with Bayesian Networks, Journal of Risk and Insurance, 74 (4): 795–827.CrossRefGoogle Scholar
  20. Davies, Jonathan, Mike Finlay, Tara McLenaghen, and Duncan Wilson (2006): Key Risk Indicators: Their Role in Operational Risk Management and Measurement, in: Ellen Davis (ed.): The Advanced Measurement Approach to Operational Risk, Risk Books: London, UK, 215–246.Google Scholar
  21. Day, George S. and Paul J. H. Schoemaker (2005): Scanning the Periphery, Harvard Business Review, 83 (11): 135–148.Google Scholar
  22. DiCicco-Bloom, Barbara and Benjamin F. Crabtree (2006): The Qualitative Research Interview, Medical Education, 40 (4): 314–321.CrossRefGoogle Scholar
  23. Diliello, Trudy C., Jeffery D. Houghton, and David Dawley (2011): Narrowing the Creativity Gap: The Moderating Effects of Perceived Support for Creativity, Journal of Psychology, 145 (3): 151–172.CrossRefGoogle Scholar
  24. Dreyfus, Hubert L. and Stuart E. Dreyfus (2005): Peripheral Vision: Expertise in Real World Contexts, Organization Studies, 26 (5): 779–792.CrossRefGoogle Scholar
  25. Duncan, Robert B. (1972): Characteristics of Organizational Environments and Perceived Environmental Uncertainty, Administrative Science Quarterly, 17 (3): 313–327.CrossRefGoogle Scholar
  26. Eden, Colin, Sue Jones, David Sims, and Tim Smithin (1981): The Intersubjectivity of Issues and Issues of Intersubjectivity, Journal of Management Studies, 18 (1): 37–47.CrossRefGoogle Scholar
  27. Ediger, Volkan S. and Sertac Akar (2007): ARIMA Forecasting of Primary Energy Demand by Fuel in Turkey, Energy Policy, 35 (3): 1701–1708.CrossRefGoogle Scholar
  28. Ericsson, K. Anders, Michael J. Prietula, and Edward T. Cokely (2007): The Making of an Expert, Harvard Business Review, 85 (7/8): 114–121.Google Scholar
  29. Fontela, Emilio (1976): Industrial Applications of Cross-Impact Analysis, Long Range Planning, 9 (4): 29–33.CrossRefGoogle Scholar
  30. Frolick, Mark N., Monica J. Parzinger, R. Kelly Rainer Jr., and Narender K. Ramarapu (1997): Using EISs for Environmental Scanning, Information Systems Management, 14 (1): 35–40.CrossRefGoogle Scholar
  31. Garg, Vinay K., Bruce A. Walters, and Richard L. Priem (2003): Chief Executive Scanning Emphases, Environmental Dynamism, and Manufacturing Firm Performance, Strategic Management Journal, 24 (8): 725–744.CrossRefGoogle Scholar
  32. Gregor, Shirley (2006): The Nature of Theory in Information Systems, MIS Quarterly, 30 (3): 611–642.Google Scholar
  33. Gutzwiller, Thomas (1994): Das CC RIM-Referenzmodell für den Entwurf von betrieblichen, transaktionsorientierten Informationssystemen, Physica: Heidelberg.CrossRefGoogle Scholar
  34. Hevner, Alan R., Salvatore T. March, Jinsoo Park, and Sudha Ram (2004): Design Science in Information Systems Research, MIS Quarterly, 28 (1): 75–105.Google Scholar
  35. Jensen, Finn V. and Thomas D. Nielsen (2007): Bayesian Networks and Decision Graphs, Springer: Berlin et al.CrossRefGoogle Scholar
  36. Kajüter, Peter (2004): Die Regulierung des Risikomanagements im Internationalen Vergleich, Controlling und Management, 48 (3 Supplement): 12–25.CrossRefGoogle Scholar
  37. Kaplan, Robert S. and David P. Norton (1992): The Balanced Scorecard: Measures That Drive Performance, Harvard Business Review, 70 (1): 71–79.Google Scholar
  38. Kornmeier, Martin (2007): Wissenschaftstheorie und Wissenschaftliches Arbeiten: Eine Einführung für Wirtschaftswissenschaftler, Physica: Heidelberg.Google Scholar
  39. Kreilkamp, Edgar (1987): Strategisches Management und Marketing: Markt- und Wettbewerbsanalyse, Strategische Frühaufklärung, Portfolio-Management, De Gruyter: Berlin.CrossRefGoogle Scholar
  40. Krishnan, Jagan, Dasaratha V. Rama, and Yinghong Zhang (2008): Costs to Comply with SOX Section 404, Auditing: A Journal of Practice & Theory, 27 (1): 169–186.CrossRefGoogle Scholar
  41. Kuo, Ren J. and K. C. Xue (1998): A Decision Support System for Sales Forecasting Through Fuzzy Neural Networks with Asymmetric Fuzzy Weights, Decision Support Systems, 24 (2): 105–126.CrossRefGoogle Scholar
  42. Lam, Monica (2004): Neural Network Techniques for Financial Performance Prediction: Integrating Fundamental and Technical Analysis, Decision Support Systems, 37 (4): 567–581.CrossRefGoogle Scholar
  43. Lane, David C. (2000): Should System Dynamics Be Described as a ‘Hard’ or ‘Deterministic’ Systems Approach?, Systems Research and Behavioral Science, 17 (1): 3–22.CrossRefGoogle Scholar
  44. Lauria, Eitel J. M. and Peter J. Duchessi (2006): A Bayesian Belief Network for IT Implementation Decision Support, Decision Support Systems, 42 (3): 1573–1588.CrossRefGoogle Scholar
  45. Leigh, William, Russell Purvis, and James M. Ragusa (2002): Forecasting the NYSE Composite Index with Technical Analysis, Pattern Recognizer, Neural Network, and Genetic Algorithm: A Case Study in Romantic Decision Support, Decision Support Systems, 32 (4): 361–377.CrossRefGoogle Scholar
  46. Lenz, R. T. and Jack L. Engledow (1986): The Applicaibility of Current Theory, Strategic Management Journal, 7 (4): 329–346.CrossRefGoogle Scholar
  47. Lesca, Nicolas and Marie-Laurence Caron-Fasan (2008): Strategic Scanning Project Failure and Abandonment Factors: Lessons Learned, European Journal of Information Systems, 17 (4): 371–386.CrossRefGoogle Scholar
  48. Levanon, Gad (2010): Evaluating and Comparing Leading and Coincident Economic Indicators, Business Economics, 45 (1): 16–27.CrossRefGoogle Scholar
  49. Li, Lianfa, Jinfeng Wang, Hareton Leung, and Chengsheng Jiang (2010): Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data, Risk Analysis, 30 (7): 1157–1175.CrossRefGoogle Scholar
  50. Liu, Shuhua (2000): Agent Based Environmental Scanning System: Impacts on Managers and Their Strategic Scanning Activities, AMCIS 2000 Proceedings, Paper 173, http://aisel.aisnet.org/amcis2000/173 (Access date: 2013-06-27).Google Scholar
  51. Lyneis, James M. (2000): System Dynamics for Market Forecasting and Structural Analysis, System Dynamics Review, 16 (1): 3–25.CrossRefGoogle Scholar
  52. Makridakis, Spyros, Robin M. Hogarth, and Anil Gaba (2010): Why Forecasts Fail: What to Do Instead?, MIT Sloan Management Review, 51 (2): 83–90.Google Scholar
  53. March, Salvatore T. and Gerald F. Smith (1995): Design and Natural Science Research on Information Technology, Decision Support Systems, 15 (4): 251–266.CrossRefGoogle Scholar
  54. Mayer, Jörg H. (2012): Powering Up Companies’ Crystal Balls: Analysis Of A Multicase Study Towards More Applicable Environmental Scanning Systems, ECIS 2012 Proceedings, Paper 97, http://aisel.aisnet.org/ecis2012/97 (Access date: 2013-06-26).Google Scholar
  55. Mayer, Jörg H., Neon Steinecke, and Reiner Quick (2011): Improving the Applicability of Environmental Scanning Systems: State of the Art and Future Research, in: Markus Nuettgens, Andreas Gadatsch, Karlheinz Kautz, Ingrid Schirmer, and Nadine Blinn (eds): Governance and Sustainability in Information Systems: Managing the Transfer and Diffusion of IT, Springer: Heidelberg et al., 207–223.CrossRefGoogle Scholar
  56. Mayer, Jörg H., Neon Steinecke, Reiner Quick, and Timm Weitzel (2012): More Applicable Environmental Scanning Systems Leveraging “Modern” Information Systems, http://link.springer.com/article/10.1007/s10257-012-0207-7 (Access date: 2013-07-02).Google Scholar
  57. McDoniel, Phillip B. and Patrick J. Monteleone (2001): Simulation and Optimisation in Direct Marketing. Part 1: Using Simulation Models to Develop Forecasts, Journal of Database Marketing, 9 (1): 35–44.CrossRefGoogle Scholar
  58. Merritt, Thomas P. (1974): Forecasting the Future Business Environment: The State of the Art, Long Range Planning, 7 (3): 54–62.CrossRefGoogle Scholar
  59. Myers, Michael D. and Michael Newman (2007): The Qualitative Interview in IS Research: Examining the Craft, Information and Organization, 17 (1): 2–26.CrossRefGoogle Scholar
  60. Nadkarni, Sucheta and Fiona Fui-Hoon Nah (2003): Aggregated Causal Maps: An Approach to Elicit and Aggregate the Knowledge of Multiple Experts, Communications of the Association for Information Systems, 12: 406–436.Google Scholar
  61. Nadkarni, Sucheta and Prakash P. Shenoy (2001): A Bayesian Network Approach to Making Inferences in Causal Maps, European Journal of Operational Research, 128 (3): 479–498.CrossRefGoogle Scholar
  62. Nadkarni, Sucheta and Prakash P. Shenoy (2004): A Causal Mapping Approach to Constructing Bayesian Networks, Decision Support Systems, 38 (2): 259–281.CrossRefGoogle Scholar
  63. Narchal, R. M., K. Kittappa, and P. Bhattacharya (1987): An Environmental Scanning System for Business Planning, Long Range Planning, 20 (6): 96–105.CrossRefGoogle Scholar
  64. Olson, David L., Dursun Delen, and Yanyan Meng (2012): Comparative Analysis of Data Mining Methods for Bankruptcy Prediction, Decision Support Systems, 52 (2): 464–473.CrossRefGoogle Scholar
  65. Österle, Hubert, Jörg Becker, Ulrich Frank, Thomas Hess, Dimitris Karagiannis, Helmut Krcmar, Peter Loos, Peter Mertens, Andreas Oberweis, and Elmar J. Sinz (2010): Memorandum zur gestaltungsorientierten Wirtschaftsinformatik, Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung, 62 (6): 664–672.Google Scholar
  66. Peffers, Ken, Tuure Tuunanen, Charles E. Gengler, Matti Rossi, Wendy Hui, Ville Virtanen, and Johanna Bragge (2006): The Design Science Research Process: A Model for Producing and Presenting Information Systems Research, http://6a.1b.7aae.static.theplanet.com/sites/default/files/documents/000designscresearchproc_desrist_2006.pdf (Access date: 2013-06-26).Google Scholar
  67. Porter, Michael E. and Victor E. Millar (1985): How Information Gives You Competitive Advantage, Harvard Business Review, 63 (4): 149–160.Google Scholar
  68. Pourret, Olivier, Patrick Naim, and Bruce Marcot (2008): Bayesian Networks: A Practical Guide to Applications, Wiley: Chichester, UK.CrossRefGoogle Scholar
  69. Rohrbeck, Rene (2012): Exploring Value Creation from Corporate-Foresight Activities, Futures, 44 (5): 440–452.CrossRefGoogle Scholar
  70. Sanford, Andrew D. and Imad A. Moosa (2012): A Bayesian Network Structure for Operational Risk Modelling in Structured Finance Operations, Journal of the Operational Research Society, 63 (4): 431–444.CrossRefGoogle Scholar
  71. Sarkar, Sumit and Ram S. Sriram (2001): Bayesian Models for Early Warning of Bank Failures, Management Science, 47 (11): 1457–1475.CrossRefGoogle Scholar
  72. Scavarda, Annibal J., Tatiana Bouzdine-Chameeva, Susan M. Goldstein, Julie M. Hays, and Arthur V. Hill (2006): A Methodology for Constructing Collective Causal Maps, Decision Sciences, 37 (2): 263–283.CrossRefGoogle Scholar
  73. Schadt, Eric E., Michael D. Linderman, Jon Sorenson, Lawrence Lee, and Garry P. Nolan (2010): Computational Solutions to Large-Scale Data Management and Analysis, Nature Reviews Genetics, 11 (9): 647–657.CrossRefGoogle Scholar
  74. Sherman, W. Scott and Valrie Chambers (2009): SOX as Safeguard and Signal: The Impact of the Sarbanes-Oxley Act of 2002 on US Corporations’ Choice to List Abroad, Multinational Business Review, 17 (3): 163–180.CrossRefGoogle Scholar
  75. Shirazi, Mohsen A. and Javad Soroor (2007): An Intelligent Agent-Based Architecture for Strategic Information System Applications, Knowledge-Based Systems, 20 (8): 726–735.CrossRefGoogle Scholar
  76. Simon, Herbert A. (1996): The Sciences of the Artificial, 3rd ed., MIT Press: Cambridge et al., MA.Google Scholar
  77. Sommerville, Ian (2007): Software Engineering, Addison-Wesley: Harlow et al., UK.Google Scholar
  78. Spender, J.-C. and Bernard Marr (2005): A Knowledge-Based Perspective on Intellectual Capital, in: Bernard Marr (ed.): Perspectives on Intellectual Capital, Elsevier: Amsterdam, 183–195.CrossRefGoogle Scholar
  79. Stratford, Jean S. (1988): United States Economic Indicators: Definitions, Sources and Resources, Government Publications Review, 15 (3): 231–236.CrossRefGoogle Scholar
  80. Sun, Lili and Prakash P. Shenoy (2007): Using Bayesian Networks for Bankruptcy Prediction: Some Methodological Issues, European Journal of Operational Research, 180 (2): 738–753.CrossRefGoogle Scholar
  81. Sundstrom, Eric, Paul L. Busby, and Warren S. Bobrow (1997): Group Process and Performance: Interpersonal Behaviors and Decision Quality in Group Problem Solving by Consensus, Group Dynamics: Theory, Research, and Practice, 1 (3): 241–253.CrossRefGoogle Scholar
  82. Vom Brocke, Jan (2003): Referenzmodellierung, Gestaltung und Verteilung von Konstruktionsprozessen, Logos: Berlin.Google Scholar
  83. Vom Brocke, Jan, Alexander Simons, Björn Niehaves, Kai Riemer, Ralf Plattfaut, and Anne Cleven (2009): Reconstructing the Giant: On the Importance of Rigour in Documenting the Literature Search Process, ECIS 2009 Proceedings, Paper 372, http://aisel.aisnet.org/ecis2009/372 (Access date: 2013-06-27).Google Scholar
  84. Webster, Jane and Richard T. Watson (2002): Analyzing the Past to Prepare for the Future: Writing a Literature Review, MIS Quarterly, 26 (2): xiii–xxiii.Google Scholar
  85. Wong, Man L. and Yuan Y. Guo (2008): Learning Bayesian Networks from Incomplete Databases Using a Novel Evolutionary Algorithm, Decision Support Systems, 45 (2): 368–383.CrossRefGoogle Scholar
  86. Yasai-Ardekani, Masoud and Paul C. Nystrom (1996): Designs for Environmental Scanning Systems: Tests of a Contingency Theory, Management Science, 42 (2): 187–204.CrossRefGoogle Scholar
  87. Yin, Robert K. (2009): Case Study Research: Design and Methods, 4th ed., Sage: Los Angeles et al., CA.Google Scholar

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© The Author(s) 2013

This article is published under license to BioMed Central Ltd. Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Authors and Affiliations

  1. 1.A.T. Kearney GmbHGermany
  2. 2.Institute of Information ManagementUniversity St. GallenSwitzerland
  3. 3.Department of Accounting and AuditingDarmstadt University of TechnologyGermany

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