Seeing the Trees or the Forest? The Effect of IT Project Managers’ Mental Construal on IT Project Risk Management Activities

Published Online:https://doi.org/10.1287/isre.2019.0853

References

  • Abdel-Hamid TK (1988) Understanding the “90% syndrome” in software project management: A simulation-based case study. J. Systems Software 8(4):319–330.CrossrefGoogle Scholar
  • Abdel-Hamid TK, Madnick SE (1989) Lessons learned from modeling the dynamics of software development. Comm. ACM 32(12):1426–1438.CrossrefGoogle Scholar
  • Alexander CS, Becker HJ (1978) The use of vignettes in survey research. Public Opinion Quart. 42(2):93–104.CrossrefGoogle Scholar
  • Alter AL, Oppenheimer DM, Zemla JC (2010) Missing the trees for the forest: A construal level account of the illusion of explanatory depth. J. Personality Soc. Psych. 99(3):436–451.CrossrefGoogle Scholar
  • Armor DA, Sackett AM (2006) Accuracy, error, and bias in predictions for real vs. hypothetical events. J. Personality Soc. Psych. 91(4):583–600.CrossrefGoogle Scholar
  • Atzmüller C, Steiner PM (2010) Experimental vignette studies in survey research. Methodology: Eur. J. Res. Methods Behav. Soc. Sci. 6(3):128–138.Google Scholar
  • Bachmann L, Mühleisen A, Bock A, Riet G, Held U, Kessels A (2008) Vignette studies of medical choice and judgment to study caregivers’ medical decision behavior: Systematic review. BMC Res. Methodology 8(50):1–8.Google Scholar
  • Bajaj N, Tyagi A, Agarwal R (2006) Software estimation: A fuzzy approach. Software Engrg. Notes 31(3):1–5.CrossrefGoogle Scholar
  • Barki H, Rivard S, Talbot J (1993) Toward an assessment of software development risk. J. Management Inform. Systems 10(2):203–225.CrossrefGoogle Scholar
  • Barki H, Rivard S, Talbot J (2001) An integrative contingency model of software project risk management. J. Management Inform. Systems 17(4):37–69.CrossrefGoogle Scholar
  • Benbasat I (1990) Laboratory experiments in information systems with a focus on individuals: A critical appraisal. Benbasat I, ed. The Information Systems Research Challenge: Experimental Research Methods (Harvard Business School, Boston), 33–47.Google Scholar
  • Bloch M, Blumberg S, Laartz J (2012) Delivering large-scale IT projects on time, on budget, and on value. Report, McKinsey & Company, New York, https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/delivering-large-scale-it-projects-on-time-on-budget-and-on-value.Google Scholar
  • Boehm BW (1991) Software risk management: principles and practices. IEEE Software 8(1):32–41.CrossrefGoogle Scholar
  • Brooks FP (1995) The Mythical Man-Month: Essays on Software Engineering (Addison-Wesley, Boston).Google Scholar
  • Colquitt JA, Zapata-Phelan CP (2007) Trends in theory building and theory testing: A five-decade study of the Academy of Management Journal. Acad. Management J. 50(6):1281–1303.CrossrefGoogle Scholar
  • Connolly T, Dean D (1997) Decomposed vs. holistic estimates of effort required for software writing tasks. Management Sci. 43(7):1029–1045.LinkGoogle Scholar
  • Cook TD, Campbell DT (1979) Quasi-experimentation: Design and Analysis for Field Settings (Houghton-Mifflin, Boston).Google Scholar
  • DeMarco T, Lister T (2003) Waltzing with Bears: Managing Risk on Software Projects (Dorset House Publishing, New York).Google Scholar
  • Dhar R, Kim EY (2007) Seeing the forest or the trees: Implications of construal level theory for consumer choice. J. Consumer Psych. 17(2):96–100.CrossrefGoogle Scholar
  • Dobbins GH, Lane IM, Steiner DD (1988) A note on the role of laboratory methodologies in applied behavioural research: Don’t throw out the baby with the bath water. J. Organ. Behav. 9(3):281–286.CrossrefGoogle Scholar
  • Eisenhardt KM, Graebner ME (2007) Theory building from cases: Opportunities and challenges. Acad. Management J. 50(1):25–32.CrossrefGoogle Scholar
  • Evans SC, Roberts MC, Keeley JW, Blossom JB, Amaro CM, Garcia AM, Stough CO, Canter KS, Robles R, Ree GM (2015) Vignette methodologies for studying clinicians’ decision-making: Validity, utility, and application in ICD-11 field studies. Internat. J. Clinical Health Psych. 15(2):160–170.CrossrefGoogle Scholar
  • Eyal T, Liberman N, Trope Y, Walter E (2004) The pros and cons of temporally near and distant action. J. Personality Soc. Psych. 86(6):781–795.CrossrefGoogle Scholar
  • Fitzsimmons JR, Douglas EJ (2011) Interaction between feasibility and desirability in the formation of entrepreneurial intentions. J. Bus. Venturing 26(4):431–440.CrossrefGoogle Scholar
  • Flyvbjerg B (2006) From Nobel prize to project management: Getting risks right. Project Management J. 37(3):5–15.CrossrefGoogle Scholar
  • Flyvbjerg B, Budzier A (2011) Why your IT project may be riskier than you think. Harvard Bus. Rev. 89(9):2–4.Google Scholar
  • Forsyth DK, Burt CD (2008) Allocating time to future tasks: The effect of task segmentation on planning fallacy bias. Memory Cognition 36(4):791–798.CrossrefGoogle Scholar
  • Freitas AL, Gollwitzer P, Trope Y (2004) The influence of abstract and concrete mindsets on anticipating and guiding others’ self-regulatory efforts. J. Experiment. Soc. Psych. 40(6):739–752.CrossrefGoogle Scholar
  • Fujita K, Trope Y, Liberman N, Levin-Sagi M (2006) Construal levels and self-control. J. Personality Soc. Psych. 90(3):351–367.CrossrefGoogle Scholar
  • Gilbert DT, Wilson TD (2007) Prospection: Experiencing the future. Science 317(5843):1351–1354.CrossrefGoogle Scholar
  • Gino F, Schweitzer ME, Mead NL, Ariely D (2011) Unable to resist temptation: How self-control depletion promotes unethical behavior. Organ. Behav. Human Decision Processes 115(2):191–203.CrossrefGoogle Scholar
  • Halkjelsvik T, Jørgensen M (2012) From origami to software development: A review of studies on judgment-based predictions of performance time. Psych. Bull. 138(2):238–271.CrossrefGoogle Scholar
  • Hill J, Thomas LC, Allen DE (2000) Experts’ estimates of task durations in software development projects. Internat. J. Project Management 18(1):13–21.CrossrefGoogle Scholar
  • Hotz RL (1999) Mars probe lost due to simple math error. Los Angeles Times (October 1), http://articles.latimes.com/1999/oct/01/news/mn-17288.Google Scholar
  • Jones MM, McLean ER (1970) Management problems in large-scale software development projects. Indust. Management Rev. 11(3):1–15.Google Scholar
  • Jørgensen M (2004) Top-down and bottom-up expert estimation of software development effort. Inform. Software Tech. 46(1):3–16.CrossrefGoogle Scholar
  • Jørgensen M, Grimstad S (2011) The impact of irrelevant and misleading information on software development effort estimates: A randomized controlled field experiment. IEEE Trans. Software Engrg. 37(5):695–707.CrossrefGoogle Scholar
  • Jørgensen M, Molokken-Ostvold K (2004) Reasons for software effort estimation error: Impact of respondent role, information collection approach, and data analysis method. IEEE Trans. Software Engrg. 30(12):993–1007.CrossrefGoogle Scholar
  • Kahneman D, Tversky A (1979) Prospect theory: An analysis of decision under risk. Econometrica 47(2):263–291.CrossrefGoogle Scholar
  • Kahneman D, Tversky A (1982) Intuitive prediction: Biases and corrective procedures. Kahneman D, Slovic P, Tversky A, eds. Judgement under Uncertainty: Heuristics and Biases (Cambridge University Press, London), 414–421.CrossrefGoogle Scholar
  • Kahneman D, Krueger A, Schkade D, Schwarz N, Stone AA (2006) Would you be happier if you were richer? A focusing illusion. Science 312(5782):1908–1910.CrossrefGoogle Scholar
  • Keil M, Rai A, Liu S (2013) How user risk and requirements risk moderate the effects of formal and informal control on the process performance of IT projects. Eur. J. Inform. Systems 22(6):650–672.CrossrefGoogle Scholar
  • Keil M, Cule PE, Lyytinen K, Schmidt RC (1998) A framework for identifying software project risks. Comm. ACM 41(11):76–83.CrossrefGoogle Scholar
  • Keil M, Tan BCY, Wei K-K, Saarinen T, Tuunainen V, Wassenaar A (2000) A cross-cultural study on escalation of commitment behavior in software projects. MIS Quart. 24(2):299–325.CrossrefGoogle Scholar
  • Kutsch E, Hall M (2009) The rational choice of not applying project risk management in information technology projects. Project Management J. 40(3):72–81.CrossrefGoogle Scholar
  • Lampel J, Shamsie J, Shapira Z (2009) Experiencing the improbable: Rare events and organizational learning. Organ. Sci. 20(5):835–845.LinkGoogle Scholar
  • Lee JS, Keil M, Kasi V (2012) The effect of an initial budget and schedule goal on software project escalation. J. Management Inform. Systems 29(1):53–78.CrossrefGoogle Scholar
  • Liberman N, Trope Y (1998) The role of feasibility and desirability considerations in near and distant future decisions: A test of temporal construal theory. J. Personality Soc. Psych. 75(1):5–18.CrossrefGoogle Scholar
  • Liberman N, Sagristano M, Trope Y (2002) The effect of temporal distance on level of mental construal. J. Experiment. Soc. Psych. 38(6):523–535.CrossrefGoogle Scholar
  • Liu S (2015) Effects of control on the performance of information systems projects: The moderating role of complexity risk. J. Oper. Management 36(1):46–62.CrossrefGoogle Scholar
  • Lloyd R (1999) Metric mishap caused loss of NASA orbiter. CNN (September 30), http://www.cnn.com/TECH/space/9909/30/mars.metric.02/.Google Scholar
  • Lovallo D, Kahneman D (2003) Delusions of success: how optimism undermines executives’ decisions. Harvard Bus. Rev. 81(7):56–63.Google Scholar
  • Lyytinen K, Mathiassen L, Ropponen J (1996) A framework for software risk management. J. Inform. Tech. 11(4):275–285.CrossrefGoogle Scholar
  • Lyytinen K, Mathiassen L, Ropponen J (1998) Attention shaping and software risk—a categorical analysis of four classical risk management approaches. Inform. Systems Res. 9(3):233–255.LinkGoogle Scholar
  • MacKenzie SB, Podsakoff PM, Podsakoff NP (2011) Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS Quart. 35(2):293–334.CrossrefGoogle Scholar
  • March JG, Shapira Z (1987) Managerial perspectives on risk and risk taking. Management Sci. 33(11):1404–1418.LinkGoogle Scholar
  • Matta NF, Ashkenas RN (2003) Why good projects fail anyway. Harvard Bus. Rev. 81(9):109–114.Google Scholar
  • Mignerat M, Rivard S (2012) The institutionalization of information system project management practices. Inform. Organ. 22(2):125–153.CrossrefGoogle Scholar
  • Moeini M, Rivard S (2019a) Responding - or not - to information technology project risks: An integrative model. MIS Quart. 43(2):475–500.CrossrefGoogle Scholar
  • Moeini M, Rivard S (2019b) Sublating tensions in the IT project risk management literature: A model of the relative performance of intuition and deliberate analysis for risk assessment. J. Assoc. Inform. Systems 20(3):243–284.Google Scholar
  • Muraven M, Tice DM, Baumeister RF (1998) Self-control as limited resource: Regulatory depletion patterns. J. Personality Soc. Psych. 74(3):774–789.CrossrefGoogle Scholar
  • Oberg J (1999) Why the Mars Probe went off course. IEEE Spectrum (December 1), https://spectrum.ieee.org/aerospace/robotic-exploration/why-the-mars-probe-went-off-course.Google Scholar
  • Project Management Institute (PMI) (2009) Practice Standard for Project Risk Management (Project Management Institute (PMI), Newtown Square, PA).Google Scholar
  • Project Management Institute (PMI) (2017) A Guide to the Project Management Body of Knowledge, 5th ed. (Project Management Institute (PMI), Newtown Square, PA).Google Scholar
  • Ropponen J, Lyytinen K (1997) Can software risk management improve system development: an exploratory study. Eur. J. Inform. Systems 6(1):41–50.CrossrefGoogle Scholar
  • Ropponen J, Lyytinen K (2000) Components of software development risk: How to address them? A project manager survey. IEEE Trans. Software Engrg. 26(2):98–112.CrossrefGoogle Scholar
  • Schmidt R, Lyytinen K, Keil M, Cule P (2001) Identifying software project risks: An international Delphi study. J. Management Inform. Systems 17(4):5–36.CrossrefGoogle Scholar
  • Shadish WR, Cook TD, Campbell DT (2002) Experimental and Quasi-experimental Designs for Generalized Causal Inference (Houghton-Mifflin, Boston).Google Scholar
  • Shmueli O, Pliskin N, Fink L (2016) Can the outside-view approach improve planning decisions in software development projects? Inform. Systems J. 26(4):395–418.CrossrefGoogle Scholar
  • Sitkin SB, Pablo AL (1992) Reconceptualizing the determinants of risk behavior. Acad. Management Rev. 17(1):9–38.CrossrefGoogle Scholar
  • Slovic P (1987) Perception of risk. Science 236(4799):280–285.CrossrefGoogle Scholar
  • Smith HJ, Keil M (2003) The reluctance to report bad news on troubled software projects: A theoretical model. Inform. Systems J. 13(1):69–95.CrossrefGoogle Scholar
  • Stephan E, Liberman N, Trope Y (2011) The effects of time perspective and level of construal on social distance. J. Experiment. Soc. Psych. 47(2):397–402.CrossrefGoogle Scholar
  • Taylor H, Artman E, Woelfer JP (2012) Information technology project risk management: bridging the gap between research and practice. J. Inform. Tech. 27(1):17–34.CrossrefGoogle Scholar
  • Trope Y, Liberman N (2010) Construal-level theory of psychological distance. Psych. Rev. 117(2):440–463.CrossrefGoogle Scholar
  • Trope Y, Liberman N, Wakslak C (2007) Construal levels and psychological distance: Effects on representation, prediction, evaluation, and behavior. J. Consumer Psych. 17(2):83–95.CrossrefGoogle Scholar
  • Tversky A, Kahneman D (1974) Judgment under uncertainty: Heuristics and biases. Science 185(4157):1124–1131.CrossrefGoogle Scholar
  • Wakslak CJ, Trope Y (2009) The effect of construal level on subjective probability estimates. Psych. Sci. 20(1):52–58.CrossrefGoogle Scholar
  • Wakslak CJ, Trope Y, Liberman N, Alony R (2006) Seeing the forest when entry is unlikely: Probability and the mental representation of events. J. Experiment. Psych. 135(4):641–653.CrossrefGoogle Scholar
  • Wallace L, Keil M (2004) Software project risks and their impact on outcomes. Comm. ACM 47(4):68–73.CrossrefGoogle Scholar
  • Wallace L, Keil M, Rai A (2004) How software project risk affects project outcomes: An investigation of the dimensions of risk and an exploratory model. Decision Sci. 35(2):289–321.CrossrefGoogle Scholar
  • Wallander L (2009) 25 years of factorial surveys in sociology: A review. Soc. Sci. Res. 38(3):505–520.CrossrefGoogle Scholar
  • Ward S, Chapman C (2003) Transforming project risk management into project uncertainty management. Internat. J. Project Management 21(2):97–105.CrossrefGoogle Scholar
  • Watson D, Clark LA, Tellegen A (1988) Development and validation of brief measures of positive and negative affect: The PANAS Scales. J. Personality Soc. Psych. 54(6):1063–1070.CrossrefGoogle Scholar
  • Windeler JB, Maruping L, Venkatesh V (2017) Technical systems development risk factors: The role of empowering leadership in lowering developers’ stress. Inform. Systems Res. 28(4):775–796.LinkGoogle Scholar
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