The anchoring effect in business intelligence supported decision-making

ABSTRACT This article reports on a study that is part of a larger project on how Business intelligence (BI) can effectively support a range of decisions made by different decision-makers through the lens of behavioural economics. This study examines one cognitive bias, the anchoring effect. A laboratory experiment was conducted where participants used a BI system to make a forecast. Two anchors with the same value were presented; a spurious anchor and a plausible anchor. We were interested if the use of a BI system would mitigate the negative consequences of the anchoring effect. Our results show BI system use mitigates the effect of a spurious anchor, but not a plausible anchor. That is, despite the significant expenditure on BI, decision-makers can still be subject to major biases and make less rational decisions. This study indicates that cognitive bias is an important topic for BI supported decision makingresearch and practice.

[1]  F. Pukelsheim The Three Sigma Rule , 1994 .

[2]  Van-Hau Trieu,et al.  Getting value from Business Intelligence systems: A review and research agenda , 2017, Decis. Support Syst..

[3]  Sang M. Lee,et al.  An exploratory cognitive DSS for strategic decision making , 2003, Decis. Support Syst..

[4]  Vernon L. Smith,et al.  Theory and Experiment: What are the questions? , 2009 .

[5]  Duane T. Wegener,et al.  Elaboration and numerical anchoring: Implications of attitude theories for consumer judgment and decision making , 2010 .

[6]  D. Kahneman,et al.  Representativeness revisited: Attribute substitution in intuitive judgment. , 2002 .

[7]  R. Kelly Rainer,et al.  Business intelligence: an analysis of the literature , 2008, IEEE Engineering Management Review.

[8]  N. McGlynn Thinking fast and slow. , 2014, Australian veterinary journal.

[9]  K. Stanovich,et al.  Heuristics and Biases: Individual Differences in Reasoning: Implications for the Rationality Debate? , 2002 .

[10]  Thomas Gilovich,et al.  Incidental environmental anchors , 2008 .

[11]  Jonathan Evans In two minds: dual-process accounts of reasoning , 2003, Trends in Cognitive Sciences.

[12]  H. Raiffa,et al.  The hidden traps in decision making. , 1998, Harvard business review.

[13]  Roger Blake,et al.  Discovering Data and Information Quality Research Insights Gained through Latent Semantic Analysis , 2012, Int. J. Bus. Intell. Res..

[14]  Chuan-Hoo Tan,et al.  Consumer-based decision aid that explains which to buy: Decision confirmation or overconfidence bias? , 2012, Decis. Support Syst..

[15]  F. Strack,et al.  Explaining the Enigmatic Anchoring Effect: Mechanisms of Selective Accessibility , 1997 .

[16]  Robert M. Davison,et al.  Context is king! Considering particularism in research design and reporting , 2016, J. Inf. Technol..

[17]  Daniel M. Oppenheimer,et al.  Anchors aweigh: A demonstration of cross-modality anchoring and magnitude priming , 2008, Cognition.

[18]  B. Newell Judgment Under Uncertainty , 2013 .

[19]  David Arnott,et al.  Cognitive biases and decision support systems development: a design science approach , 2006, Inf. Syst. J..

[20]  Markus Grünwald,et al.  Business Intelligence , 2009, Informatik-Spektrum.

[21]  Ahmad Rizal Mohd Yusof,et al.  The Study on the Application of Business Intelligence in Manufacturing: A Review , 2013, Int. J. Bus. Intell. Res..

[22]  Eric J. Johnson,et al.  Anchoring, Activation, and the Construction of Values. , 1999, Organizational behavior and human decision processes.

[23]  Eldar Shafir,et al.  Anchoring on the "here" and "now" in time and distance judgments. , 2009, Journal of experimental psychology. Learning, memory, and cognition.

[24]  Jack Shih-Chieh Hsu,et al.  Understanding the role of computer-mediated counter-argument in countering confirmation bias , 2012, Decis. Support Syst..

[25]  M. Bazerman Judgment in Managerial Decision Making , 1990 .

[26]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[27]  G. Northcraft,et al.  Experts, amateurs, and real estate: An anchoring-and-adjustment perspective on property pricing decisions , 1987 .

[28]  Feng-Yang Kuo,et al.  An exploratory study of cognitive effort involved in decision under Framing - an application of the eye-tracking technology , 2009, Decis. Support Syst..

[29]  Peter Buxmann,et al.  Investigating Business Intelligence And Analytics From A Decision Process Perspective: A Structured Literature Review , 2013, ECIS.

[30]  Graham Pervan,et al.  A critical analysis of decision support systems research revisited: the rise of design science , 2014, J. Inf. Technol..

[31]  Marc A. Koopmanschap,et al.  With a little help from an anchor: Discussion and evidence of anchoring effects in contingent valuation , 2006 .

[32]  Eric J. Johnson,et al.  The limits of anchoring. , 1994 .

[33]  Anna Sidorova,et al.  Business intelligence success: The roles of BI capabilities and decision environments , 2013, Inf. Manag..

[34]  Paul Slovic,et al.  The affect heuristic , 2007, Eur. J. Oper. Res..

[35]  A. Furnham,et al.  A literature review of the anchoring effect , 2011 .

[36]  Leon A. Kappelman,et al.  The 2016 SIM IT Issues and Trends Study , 2019, MIS Q. Executive.