Anchoring in Project Duration Estimation

Lorko, Matej and Servátka, Maroš and Zhang, Le (2018): Anchoring in Project Duration Estimation.

Abstract

The success of a business project often relies on the accuracy of its project duration estimates. Inaccurate and overoptimistic project schedules can result in significant project failures. In this paper, we explore whether the presence of anchors, such as relatively uninformed suggestions or expectations of the duration of project tasks, play a role in the project estimating and planning process. We conduct a controlled laboratory experiment to test the effect of anchors on task duration estimates. We find strong anchoring effects and systematic estimation biases that do not vanish even after the task is repeatedly estimated and performed. We also find that such persisting biases can be caused by not only externally provided anchors, but also by the planner’s own initial task duration estimate.

• Item Type: MPRA Paper
• Language: English
• Keywords: Project management, project planning, time management, anchors, anchoring effect, task duration, duration estimation, time estimation, anchoring bias
• Subjects: C - Mathematical and Quantitative Methods > C9 - Design of Experiments > C91 - Laboratory, Individual Behavior; D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D83 - Search ; Learning ; Information and Knowledge ; Communication ; Belief ; Unawareness; D - Microeconomics > D9 - Intertemporal Choice > D92 - Intertemporal Firm Choice, Investment, Capacity, and Financing; O - Economic Development, Innovation, Technological Change, and Growth > O2 - Development Planning and Policy > O21 - Planning Models ; Planning Policy; O - Economic Development, Innovation, Technological Change, and Growth > O2 - Development Planning and Policy > O22 - Project Analysis

References

Alevy, J. E., Landry, C. E., & List, J. A. (2015). Field experiments on the anchoring of economic valuations. Economic Inquiry, 53(3), 1522–1538. https://doi.org/10.1111/ecin.12201

Aranda, J., & Easterbrook, S. (2005). Anchoring and adjustment in software estimation. ACM SIGSOFT Software Engineering Notes, 30(5), 346. https://doi.org/10.1145/1095430.1081761

Ariely, D., Loewenstein, G., & Prelec, D. (2003). “Coherent Arbitrariness”: Stable Demand Curves Without Stable Preferences. The Quarterly Journal of Economics, 118(1), 73–106. https://doi.org/10.1162/00335530360535153

Blankenship, K. L., Wegener, D. T., Petty, R. E., Detweiler-Bedell, B., & Macy, C. L. (2008). Elaboration and consequences of anchored estimates: An attitudinal perspective on numerical anchoring. Journal of Experimental Social Psychology, 44(6), 1465–1476. https://doi.org/10.1016/j.jesp.2008.07.005

Boltz, M. G., Kupperman, C., & Dunne, J. (1998). The role of learning in remembered duration. Memory & Cognition, 26(5), 903–921. https://doi.org/10.3758/BF03201172

Cervone, D., & Peake, P. K. (1986). Anchoring, efficacy, and action: The influence of judgmental heuristics on self-efficacy judgments and behavior. Journal of Personality and Social Psychology, 50(3), 492–501. https://doi.org/10.1037/0022-3514.50.3.492

Chapman, G. B., & Johnson, E. J. (1999). Anchoring, Activation, and the Construction of Values. Organizational Behavior and Human Decision Processes, 79(2), 115–153. https://doi.org/10.1006/obhd.1999.2841

Cox, J. C., Sadiraj, V., & Schmidt, U. (2015). Paradoxes and mechanisms for choice under risk. Experimental Economics, 18(2), 215–250. https://doi.org/10.1007/s10683-014-9398-8

Critcher, C. R., & Gilovich, T. (2008). Incidental environmental anchors. Journal of Behavioral Decision Making, 21(3), 241–251. https://doi.org/10.1002/bdm.586

Davis, H. L., Hoch, S. J., & Ragsdale, E. K. E. (1986). An Anchoring and Adjustment Model of Spousal Predictions. Journal of Consumer Research, 13(1), 25–37. https://doi.org/10.1086/209045

Englich, B., Mussweiler, T., & Strack, F. (2006). Playing Dice With Criminal Sentences: The Influence of Irrelevant Anchors on Experts’ Judicial Decision Making. Personality and Social Psychology Bulletin, 32(2), 188–200. https://doi.org/10.1177/0146167205282152

Epley, N., & Gilovich, T. (2001). Putting adjustment back in the anchoring and adjustment heuristic: differential processing of self-generated and experimenter-provided anchors. Psychological Science, 12(5), 391–6. https://doi.org/10.1111/1467-9280.00372

Epley, N., & Gilovich, T. (2005). When effortful thinking influences judgmental anchoring: differential effects of forewarning and incentives on self-generated and externally provided anchors. Journal of Behavioral Decision Making. https://doi.org/10.1002/bdm.495

Fischbacher, U. (2007). Z-Tree: Zurich toolbox for ready-made economic experiments. Experimental Economics, 10(2), 171–178. https://doi.org/10.1007/s10683-006-9159-4

Furnham, A., & Boo, H. C. (2011). A literature review of the anchoring effect. Journal of Socio-Economics, 40(1), 35–42. https://doi.org/10.1016/j.socec.2010.10.008

Galinsky, A. D., & Mussweiler, T. (2001). First offers as anchors: The role of perspective-taking and negotiator focus. Journal of Personality and Social Psychology, 81(4), 657–669. https://doi.org/10.1037/0022-3514.81.4.657

Galton, F. (1907). Vox populi (The wisdom of crowds). Nature, 75(7), 450–451.

Geman, S., Bienenstock, E., & Doursat, R. (1992). Neural Networks and the Bias/Variance Dilemma. Neural Computation, 4(1), 1–58. https://doi.org/10.1162/neco.1992.4.1.1

Greiner, B. (2015). Subject pool recruitment procedures: organizing experiments with ORSEE. Journal of the Economic Science Association, 1(1), 114–125. https://doi.org/10.1007/s40881-015-0004-4

Grimstad, S., & Jørgensen, M. (2007). Inconsistency of expert judgment-based estimates of software development effort. Journal of Systems and Software, 80(11), 1770–1777. https://doi.org/10.1016/j.jss.2007.03.001

Halkjelsvik, T., & Jørgensen, M. (2012). From origami to software development: A review of studies on judgment-based predictions of performance time. Psychological Bulletin, 138(2), 238–271. https://doi.org/10.1037/a0025996

Hertwig, R., & Ortmann, A. (2001). Experimental practices in economics: a methodological challenge for psychologists? Behavioral and Brain Sciences, 24(3), 383-403; discussion 403-451. https://doi.org/10.1037/e683322011-032

Hinds, P. (1999). The curse of expertise: The effects of expertise and debiasing methods on prediction of novice performance. Journal of Experimental Psychology: Applied, 5(2), 205–221.

Holt, C. A. (1986). Preference reversals and the independence axiom. The American Economic Review, 76(3), 508–515.

Holt, C. A., & Laury, S. K. (2002). Risk aversion and incentive effects. American Economic Review, 92(5), 1644–1655. https://doi.org/10.1257/000282802762024700

Jacowitz, K. E. ., & Kahneman, D. (1995). Measures of anchoring in estimation tasks. Personality and Social Psychology Bulletin, 21(11), 1161–1166. https://doi.org/10.1177/01461672952111004

Joørgensen, M., & Sjøberg, D. I. K. (2004). The impact of customer expectation on software development effort estimates. International Journal of Project Management, 22(4), 317–325. https://doi.org/10.1016/S0263-7863(03)00085-1

Jørgensen, M., & Grimstad, S. (2008). Avoiding Irrelevant and Misleading Information When Estimating Development Effort. IEEE Software, 25(3), 78–83.

Jørgensen, M., & Grimstad, S. (2011). The impact of irrelevant and misleading information on software development effort estimates: A randomized controlled field experiment. IEEE Transactions on Software Engineering, 37(5), 695–707. https://doi.org/10.1109/TSE.2010.78

Josephs, R. A., & Hahn, E. D. (1995). Bias and Accuracy in Estimates of Task Duration. Organizational Behavior and Human Decision Processes. https://doi.org/10.1006/obhd.1995.1016

Kahneman, D., & Tversky, A. (1979). INTUITIVE PREDICTION: BIASES AND CORRECTIVE PROCEDURES. TIMS Studies in the Management Sciences, 12, 313–327. https://doi.org/citeulike-article-id:3614496

König, C. J. (2005). Anchors distort estimates of expected duration. Psychological Reports, 96(2), 253–256. https://doi.org/10.2466/PR0.96.2.253-256

König, C. J., Wirz, A., Thomas, K. E., & Weidmann, R.-Z. (2014). The Effects of Previous Misestimation of Task Duration on Estimating Future Task Duration. Current Psychology, 34(1), 1–13. https://doi.org/10.1007/s12144-014-9236-3

Løhre, E., & Jørgensen, M. (2016). Numerical anchors and their strong effects on software development effort estimates. Journal of Systems and Software, 116, 49–56. https://doi.org/10.1016/j.jss.2015.03.015

Morgenshtern, O., Raz, T., & Dvir, D. (2007). Factors affecting duration and effort estimation errors in software development projects. Information and Software Technology, 49(8), 827–837. https://doi.org/10.1016/j.infsof.2006.09.006

Mussweiler, T. (2001). The durability of anchoring effects. European Journal of Social Psychology, 31, 431–442.

Mussweiler, T., & Strack, F. (1999). Hypothesis-consistent testing and semantic priming in the anchoring paradigm: A selective accessibility model. Journal of Experimental Social Psychology, 35(2), 136–164. https://doi.org/10.1006/jesp.1998.1364

Plous, S. (1989). Thinking the Unthinkable: The Effects of Anchoring on Likelihood Estimates of Nuclear War. Journal of Applied Social Psychology, 19(1), 67–91. https://doi.org/10.1111/j.1559-1816.1989.tb01221.x

Project Management Institute. (2013). A guide to the project management body of knowledge (PMBOK ® guide). Project Management Institute. https://doi.org/10.1002/pmj.20125

Project Management Institute. (2017). PMI’s Pulse of the Profession 2017.

Ritov, I. (1996). Anchoring in Simulated Competitive Market Negotiation. Organizational Behavior and Human Decision Processes, 67(1), 16–25. https://doi.org/10.1006/obhd.1996.0062

Roy, M. M., & Christenfeld, N. J. S. (2007). Bias in memory predicts bias in estimation of future task duration. Memory & Cognition, 35(3), 557–564. https://doi.org/10.3758/BF03193294

Roy, M. M., Christenfeld, N. J. S., & McKenzie, C. R. M. (2005). Underestimating the Duration of Future Events: Memory Incorrectly Used or Memory Bias? Psychological Bulletin, 131(5), 738–756. https://doi.org/10.1037/0033-2909.131.5.738

Roy, M. M., Mitten, S. T., & Christenfeld, N. J. S. (2008). Correcting memory improves accuracy of predicted task duration. Journal of Experimental Psychology. Applied, 14(3), 266–275. https://doi.org/10.1037/1076-898X.14.3.266

Shepperd, M., Mair, C., & Jørgensen, M. (2018). An Experimental Evaluation of a De-biasing Intervention for Professional Software Developers.

Smith, V. L. (1991). Rational Choice: The Contrast between Economics and Psychology. Journal of Political Economy, 99(4), 877–897. https://doi.org/10.2307/2069710

Thomas, K. E., & Handley, S. J. (2008). Anchoring in time estimation. Acta Psychologica, 127(1), 24–29. https://doi.org/10.1016/j.actpsy.2006.12.004

Tobin, S., & Grondin, S. (2015). Prior task experience affects temporal prediction and estimation. Frontiers in Psychology, 6(July), 1–7. https://doi.org/10.3389/fpsyg.2015.00916

Tversky, A., & Kahneman, D. (1974). Judgments under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124

Wilson, T. D., Houston, C. E., Etling, K. M., & Brekke, N. (1996). A new look at anchoring effects: basic anchoring and its antecedents. Journal of Experimental Psychology: General, 125(4), 387–402. https://doi.org/10.1037/0096-3445.125.4.387

Woods, D., & Servátka, M. (2016). Testing psychological forward induction and the updating of beliefs in the lost wallet game. Journal of Economic Psychology, 56, 116–125. https://doi.org/10.1016/j.joep.2016.06.006

Yang, C., Sun, B., & Shanks, D. R. (2017). The anchoring effect in metamemory monitoring. Memory & Cognition. https://doi.org/https://doi.org/10.3758/s13421-017-0772-6