Improving Environmental Scanning Systems Using Bayesian Networks

• Published in: Business Research Vol. 6; no. 2; pp. 196 - 213
• Main Authors: Welter, Simon, Mayer, Jörg H., Quick, Reiner
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.11.2013; Springer Nature B.V
• Subjects: Accounting/Auditing; Bayesian analysis; Business and Management; Business Strategy/Leadership; Case studies; Corporate Finance; Corporate management; Economic indicators; Environmental scanning; Executives; Information systems; Marketing; Mathematical models; Regression analysis; Risk management; Scanners; Trends; C53; corporate management; C11; M49; regression analyses; C02; information systems (IS) design; Bayesian networks; G17; balancing opportunities and threats; case study; design science research in IS
• ISSN: 2198-3402; 1866-8658; 2198-2627
• DOI: 10.1007/BF03342749

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.