Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-04T19:45:58.623Z Has data issue: false hasContentIssue false
  • English
  • Français

Predicting real-time adaptive performance in a dynamic decision-making context

Published online by Cambridge University Press:  16 December 2014

Darren Good
Darren Good*
Affiliation:
Graziadio School of Business & Management, Pepperdine University, Los Angeles, CA, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

Individuals in organizations must frequently enact a series of ongoing decisions in real-time dynamic contexts. Despite the increasing need for individuals to manage dynamic decision-making demands, we still understand little about individual differences impacting performance in these environments. This paper proposes a new construct applicable to adaptation in such real-time dynamic environments. Cognitive agility is a formative construct measuring the individual capacity to exhibit cognitive flexibility, cognitive openness and focused attention. This study predicts that cognitive agility will impact adaptive performance in a real-time dynamic decision-making microworld computer game called the Networked Fire Chief; a simulation developed to study and train Australian fire fighters. Cognitive agility, operationalized through three distinct methods (performance measures, self-reports and external-rater reports), explained unique variance beyond measures of general intelligence on the total score of adaptive performance in the microworld.

Keywords

adaptabilitycognitive flexibilitydynamic decision making
Type
Research Article
Information
Journal of Management & Organization , Volume 20 , Issue 6 , November 2014 , pp. 715 - 732
Copyright
Copyright © Cambridge University Press and Australian and New Zealand Academy of Management 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ackerman, P. L. (1988). Determinants of individual differences during skill acquisition: cognitive abilities and information processing. Journal of Experimental Psychology: General, 117, 288–318.Google Scholar
Ackerman, P. L. (1992). Predicting individual differences in complex skill acquisition: Dynamics of ability determinant. Journal of Applied Psychology, 77, 598–614.Google Scholar
Anderson, P. (1983). Decision making by objection and the Cuban missile crisis. Administrative Sciences Quarterly, 28, 201–222.Google Scholar
Ashford, S. J., & Taylor, M. S. (1990). Adaptation to work transitions: An integrative approach. In G. R. Ferris & K. M. Rowland (Eds.), Research in personnel and human resource management. vol. 8 (pp. 1–41). Greenwich, CT: JAI Press.Google Scholar
Baddeley, A. D., & Hitch, G. (1974). Working memory. In G. A. Bower (Ed.), The psychology of learning and motivation, vol. 8 (pp. 47–89). New York, NY: Academic Press.Google Scholar
Baird, L., & Griffin, D. (2006). Adaptability and responsiveness: The case for dynamic learning. Organization Dynamics, 35, 372–383.Google Scholar
Baltes, P. B., & Staudinger, U. M. (1996). Interactive minds: Life-span perspectives on the social foundation of cognition. New York, NY: Cambridge University Press.Google Scholar
Barron, F. (1988). Putting creativity to work. In R. J. Sternberg (Ed.), The nature of creativity (pp. 76–98). New York, NY: Cambridge University Press.Google Scholar
Baylor, A. L. (2001). A U-shaped model for the development of intuition by level of expertise. New Ideas in Psychology, 19, 237–244.CrossRefGoogle Scholar
Berg, C. A., & Sternberg, R. J. (1985). A triarchic theory of intellectual development during adulthood. Developmental Review, 6, 334–370.Google Scholar
Bodner, T. (2000). On the assessment of individual differences in mindful information processing: A thesis (Doctoral dissertation, Harvard University).Google Scholar
Boyatzis, R. E., Goleman, D., & Rhee, K. (1999). Clustering competence in emotional intelligence: Insights from the Emotional Competence Inventory (ECI). In R. Bar-On & J. D. Parker (Eds.), Handbook of emotional intelligence (pp. 343–362). San Francisco, CA: Jossey-Bass.Google Scholar
Brehmer, B. (1992). Dynamic decision making: Human control of complex systems. Acta Psychologica, 81, 211–241.Google Scholar
Brehmer, B. (1995). Feedback delays in complex dynamic decision tasks. In P. French & J. Funke (Eds.), Complex problem-solving: The European perspective (pp. 103–130). Mahwah, NJ: Lawrence Earlbaum Associates.Google Scholar
Brehmer, B., & Dörner, D. B. (1993). Experiments with computer-simulated microworlds: Escaping both the narrow straits of the laboratory and the deep blue sea of the field study. Computers in Human Behavior, 9, 171–184.Google Scholar
Brehmer, B., Leplat, J., & Rasmussen, J. (1991). Use of simulation in the study of complex decision making. In J. Rasmussen, B. Brehmer, & J. Leplat (Eds.), Distributed decision making: Cognitive models for co-operative work (pp. 373–386). New York, NY: Wiley.Google Scholar
Briscoe, J. P., & Hall, D. T. (1999). Grooming and picking leaders using competency frameworks: Do they work? An alternative approach and new guidelines for practice. Organizational Dynamics, 28, 37–52.Google Scholar
Burns, R., & Gallini, J. (1983). The relation of cognitive and affective measures to achievement during an instructional sequence. Instructional Science, 12, 103–120.Google Scholar
Cañas, J. J., Quesada, J. F., Antolí, A., & Fajardo, I. (2003). Cognitive flexibility and adaptability to environmental changes in dynamic complex problem solving tasks. Ergonomics, 46, 482–501.Google Scholar
Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54, 1–22.CrossRefGoogle Scholar
Chamorro-Premuzic, T. (2006). Creativity versus conscientiousness: Which is a better predictor of student performance? Applied Cognitive Psychology, 20, 521–531.Google Scholar
Chan, D., & Schmitt, N. (2000). Interindividual differences in intraindividual changes in proactivity during organizational entry: A latent growth modeling approach to understanding newcomer adaptation. Journal of Applied Psychology, 85, 190–210.Google Scholar
Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1991). Categorization and representation of physics problems by experts and novices. Cognitive Sciences, 5, 121–152.Google Scholar
Clark, H. H. (1996). Using language. Cambridge: Cambridge University Press.Google Scholar
Costa, P. T., & McCrae, R. R. (1985). The NEO Personality Inventory manual. Odessa, FL: Psychological Assessment Resources.Google Scholar
Davis, J. P., Eisenhardt, K. M., & Bingham, C. B. (2009). Optimal structure, market dynamism, and the strategy of simple rules. Administrative Science Quarterly, 5, 413–452.Google Scholar
Dawes, R. M. (1988). Rational choice in an uncertain world. San Diego, CA: Harcourt Brace Jovanovich.Google Scholar
Derryberry, D., & Rothbart, M. K. (1988). Affect, arousal, and attention as components of temperment. Journal of Personality and Social Psychology, 55, 958–966.Google Scholar
DiFonzo, N., Hantula, D. A., & Bordia, P. (1998). Microworlds for experimental research: Having your (control & collection) cake, and realism too. Behavior Research Methods, Instruments, & Computers, 30, 278–286.Google Scholar
Digman, J. M. (1990). Personality structure: Emergence of the five-factor model. Annual Review of Psychology, 41, 417–440.Google Scholar
Dovidio, J. F., Kawakami, K., & Beach, K. R. (2001). Implicit and explicit attitudes: Examination of the relationship between measures of intergroup bias. In R. Brown & S. L. Gaertner (Eds.), Blackwell handbook of social psychology: Intergroup processes (pp. 175–197). Malden, MA: Blackwell.Google Scholar
Dupuy, H. P. (1974). The rationale, development and standardization of a basic vocabulary test. Washington, DC: US Government Printing Office.Google Scholar
Earley, P. C., & Ang, S. (2003). Cultural intelligence: Individual interactions across cultures. Stanford, CA: Stanford University Press.Google Scholar
Edmondson, A. C., Bohmer, R. M., & Pisano, G. P. (2001). Disrupted routines: Team learning and new technology implementation in hospitals. Administrative Science Quarterly, 46, 685–716.Google Scholar
Ekstrom, R. B., French, J. W., & Harman, H. H. (1976). Manual for kit of factor referenced cognitive tests. Princeton, NJ: Educational Testing Service.Google Scholar
Elliot, T., Welsh, M., Nettelbeck, T., & Mills, V. (2007). Investigating naturalistic decision making in a simulated micro-world: What questions should we ask? Behavior Research Methods, 39, 901–910.Google Scholar
Endres, M. L., Chowdhury, S., & Milner, M. (2009). Ambiguity tolerance and accurate assessment of self-efficacy in a complex decision task. Journal of Management & Organization, 15, 31–46.Google Scholar
Endsley, M. R. (1995). Measurement of situation awareness in dynamic systems. Human Factors, 37, 65–84.Google Scholar
Fernandez-Duque, D., Baird, J. A., & Posner, M. I. (2000). Executive attention and metacognitive regulation. Consciousness and Cognition, 9, 288–307.Google Scholar
Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive development inquiry. American Psychologist, 34, 906–911.Google Scholar
Funke, J. (1991). Solving complex problems: Human identification and control of complex systems. In R. J. Sternberg & P. A. Frensch (Eds.), Complex problem solving: Principles and mechanisms (pp. 185–222). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Glynn, M. A. (1996). Innovative genius: A framework for relating individual and organizational intelligences to innovation. Academy of Management Review, 21, 1081–1111.CrossRefGoogle Scholar
Gonzalez, C., Vanyukov, P., & Martin, M. K. (2005). The use of microworlds to study dynamic decision making. Computers in Human Behavior, 21, 273–286.Google Scholar
Good, D. J., & Michel, E. J. (2013). Individual ambidexterity: Exploring and exploiting in dynamic contexts. Journal of Psychology: Interdisciplinary and Applied, 147, 435–453.CrossRefGoogle ScholarPubMed
Gottfredson, L. S. (1997). Intelligence and social policy. Intelligence, 24, 1–320.Google Scholar
Gough, H. G. (1979). A creative personality scale for the adjective check list. Journal of Personality and Social Psychology, 37, 1398–1405.Google Scholar
Guilford, J. P., Christensen, P. R., Merrifield, P. R., & Wilson, R. C. (1978). Alternate uses: Manual of instructions and interpretation. Orange, CA: Sheridan Psychological Services.Google Scholar
Gupta, A. K., Smith, K. G., & Shalley, C. E. (2006). The interplay between exploration and exploitation. Academy of Management Journal, 4, 693–706.Google Scholar
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (4th ed.). New Jersey, USA: Prentice Hall.Google Scholar
Haynie, M. J., Shepherd, D., Mosakowski, E., & Earley, C. P. (2010). A situated metacognitive model of the entrepreneurial mindset. Journal of Business Venturing, 25, 217–229.Google Scholar
Hesketh, B. (1997). Dilemmas in training for transfer and retention. Applied Psychology: An International Review, 46, 317–319.Google Scholar
Hooijberg, R., & Quinn, R. E. (1992). Behavioral complexity and the development of effective managers. In R. L. Phillips & J. G. Hunt (Eds.), Strategic leadership: A multiorganizational-level perspective (pp. 161–176). Westport, CT: Quorum Books.Google Scholar
Horn, J. L. (1985). Remodeling old models of intelligence. In B. B. Wolman (Ed.), Handbook of intelligence: Theories, measurements, and applications (pp. 267–300). New York, NY: Wiley.Google Scholar
Hunter, J. E., & Hunter, R. F. (1984). Validity and utility of alternative predictors of job performance. Psychological Bulletin, 76, 72–93.Google Scholar
Isen, A. M., Daubman, K. A., & Nowicki, G. P. (1987). Positive affect facilitates creative problem solving. Journal of Personality and Social Psychology, 52, 1122–1131.Google Scholar
Jacobs, J. E., & Paris, S. G. (1987). Children’s metacognition about reading: Issues in definition, measurement, and instruction. Educational Psychologist, 22, 255–278.Google Scholar
Jarvis, C., MacKenzie, S., & Podsakoff, P. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30, 199–218.Google Scholar
Keilp, J. G., Sackeim, H. A., & Mann, J. J. (2005). Correlates of trait impulsiveness in performance measures and neuropsychological tests. Psychiatry Research, 135, 191–201.Google Scholar
Koch, C. (2003). Self-monitoring, need for cognition, and the Stroop effect: A preliminary study. Perceptual and Motor Skills, 96, 212–214.Google Scholar
Kozlowski, S. W. J., Gully, S. M., Brown, K. G., Salas, E., Smith, E. M., & Nason, E. R. (2001). Effects of training goals and goal orientation traits on multidimensional training outcomes and performance adaptability. Organizational Behavior & Human Decision Processes, 85, 1–31.Google Scholar
Kuhl, J., & Kazen-Saad, M. (1988). A motivational approach to volition: Activation and de-activation of memory representations related to unfulfilled intentions. In V. Hamilton, G. H. Bower, & N. H. Firjda (Eds.), Cognitive perspectives on emotion and motivation (pp. 63–85). Dordrecht, The Netherlands: Martinus Nijhoff.Google Scholar
Kuhn, D. (1989). Children and adults as intuitive scientists. Psychological Review, 96, 674–689.Google Scholar
Langer, E. (1989). Minding matters: The consequences of mindlessness-mindfulness In L. Berkowitz (Ed.), Advances in experimental social psychology (pp. 137–173). San Diego, CA: Academic Press.Google Scholar
LePine, J. A., Colquitt, J. A., & Erez, A. (2000). Adaptability to changing task contexts: Effects of general cognitive ability, conscientiousness, and openness to experience. Personnel Psychology, 53, 563–593.Google Scholar
Lerch, F. J., & Harter, D. E. (2001). Cognitive support for real-time dynamic decision making. Information Systems Research, 12, 63–82.Google Scholar
Littman, J. A. (2005). Curiosity and the pleasures of learning: Wanting and liking new information. Cognition and Emotion, 19, 793–814.Google Scholar
Louis, M. R., & Sutton, R. I. (1991). Switching cognitive gears: From habits of mind to active thinking. Human Relations, 44, 55–76.Google Scholar
Luchins, A. S., & Luchins, E. H. (1959). Rigidity in behavior. Eugene, OR: University of Oregon Press.Google Scholar
Lustig, C., May, C. P., & Hasher, L. (2001). Working memory span and the role of proactive interference. Journal of Experimental Psychology General, 130, 199–207.Google Scholar
MacLeod, C. M. (1991). Half a century of research on the Stroop effect: An integrative review. Psychological Bulletin, 109, 163–203.Google Scholar
Mainemelis, C., Boyatzis, R., & Kolb, D. A. (2002). Learning styles and adaptive flexibility: Testing experiential learning theory. Management Learning, 33, 5–33.Google Scholar
Martin, M. M., & Anderson, C. M. (1998). The cognitive flexibility scale: Three validity studies. Communication Reports, 11, 1–9.Google Scholar
Martin, M. M., & Rubin, R. B. (1995). A new measure of cognitive flexibility. Psychological Reports, 76, 623–626.Google Scholar
Martindale, C., Anderson, K., Moore, K., & West, A. N. (1996). Creativity, oversensitivity, and rate of habituation. Personality and Individual Differences, 20, 423–427.Google Scholar
Matthews, G., & Deary, I. J. (1998). Personality traits. Cambridge, UK: Cambridge.Google Scholar
Mayer, R. (1998). Cognitive, metacognitive, and motivational aspects of problem solving. Instructional Science, 26, 49–63.Google Scholar
Mitroff, S. R., Simons, D. J., & Franconeri, S. L. (2002). The siren song of implicit change detection. Journal of Experimental Psychology: Human Perception and Performance, 28, 798–815.Google Scholar
Mom, T. J., van den Bosch, F. J., & Volberda, H. W. (2007). Investigating managers’ exploration and exploitation activities: The influence of top-down, bottom-up, and horizontal knowledge inflows. Journal of Management Studies, 44, 910–931.Google Scholar
Morrison, R. (1977). Career adaptivity: The effective adaptation of managers to changing role demands. Journal of Applied Psychology, 62, 549–558.Google Scholar
Moses, L. J., & Baird, J. A. (1999). Metacognition. The MIT Encyclopedia of the Cognitive Sciences, 533–535.Google Scholar
Mumford, M. D., Zaccaro, S. J., Harding, F. D., Jacobs, T. O., & Fleishman, E. A. (2000). Leadership skills for a changing world: Solving complex social problems. Leadership Quarterly, 11, 11–35.Google Scholar
Neisser, U. (1967). Cognitive psychology. New York, NY: Appleton-Century-Crofts.Google Scholar
Omodei, M. M., & Wearing, A. J. (1995). The fire chief microworld generating program: An illustration of computer-simulated microworlds as an experimental paradigm for studying complex decision-making behavior. Behavior Research Methods, Instruments & Computers, 27, 303–316.Google Scholar
Omodei, M. M., Wearing, A. J., McLennan, J., Hansen, J., Clancy, J. M., Elliott, G. C., Ley, T., Taranto, P., & Thorsteinsson, E. B. (2001). Human decision making in complex systems interim summary report: Research agreement #2, unpublished manuscript Melbourne: La Trobe University.Google Scholar
Pallier, G., Roberts, R. D., & Stankov, L. (2000). Biological versus psychometric intelligence: Halstead’s (1947) distinction re-visited. Archives of Clinical Neuropsychology, 13, 205–226.Google Scholar
Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). Report 91-B-004. Ann Arbor, MI: National Center for Research to Improve Postsecondary Teaching and Learning. 87pp.Google Scholar
Pulakos, E. D., Arad, S., Donovan, M. A., & Plamondon, K. E. (2000). Adaptability in the workplace: Development of a taxonomy of adaptive performance. Journal of Applied Psychology, 85, 612–624.Google Scholar
Quinn, R. E., Spreitzer, G. M., & Hart, S. (1992). Challenging the assumptions of bipolarity: Interpenetration and effectiveness. In S. Srivastva & R. Fry (Eds.), Executive continuity (pp. 222–252). San Francisco, CA: Jossey-Bass.Google Scholar
Rabbit, P. M. A. (1966). Errors and error correction in choice reaction tasks. Journal of Experimental Psychology, 71, 264–272.Google Scholar
Reder, L. M., & Schunn, C. D. (1996). Metacognition does not imply awareness: Strategy choice is governed by implicit learning and memory. In L. M. Reder (Ed.), Implicit memory and metacognition (pp. 45–78). Hillsdale, NJ: Erlbaum.Google Scholar
Rende, B. (2000). Cognitive flexibility: Theory, assessment, and treatment. Seminars in Speech and Language, 21, 121–133.Google Scholar
Rigas, G., Carling, E., & Brehmer, B. (2002). Reliability and validity of performance measures in microworlds. Intelligence, 30, 463–480.Google Scholar
Robertson, I. H., Manly, T., Andrade, J., Baddeley, B. T., & Yiend, J. (1997). Oops!: Performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologia, 35, 747–758.Google Scholar
Rossiter, J. R. (2002). The C-OAR-SE procedure for scale development in marketing. International Journal of Research in Marketing, 19, 305–335.Google Scholar
Salgado, J. F. (1999). Personnel selection methods. International Review of Industrial an Organizational Psychology, 14, 1–54.Google Scholar
Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19, 460–475.CrossRefGoogle Scholar
Shapiro, K. L., & Raymond, J. E. (1994). Temporal allocation of visual attention: Inhibition or interference. In D. Dagenbach & T. Carr (Eds.), Inhibitory process in attention, memory and language (pp. 151–188). New York, NY: Academic Press.Google Scholar
Shanteau, J. (1988). Psychological characteristics and strategies of expert decision makers. Acta Psychologica, 68, 203–215.CrossRefGoogle Scholar
Sitkin, S. B., & Pablo, A. L. (1992). Reconceptualizing the determinants of risk behavior. Academy of Management Review, 17, 9–38.Google Scholar
Spiro, R. J., & Jehng, J. C. (1990). Cognitive flexibility and hypertext: Theory and technology for the nonlinear and multi-dimensional traversal of complex subject matter. In D. Nix & R. J. Spiro (Eds.), Cognition, education and multi-media: Exploring ideas in high technology, Chapter 7 (pp. 163–205). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Sternberg, R. J. (1997). Thinking styles. New York, NY: Cambridge University Press.Google Scholar
Sternberg, R. J. (1999). The theory of successful intelligence. Review of General Psychology, 3, 292–316.Google Scholar
Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 12, 643–662.Google Scholar
Theeuwes, J. (1994). Endogenous and exogenous control of visual selection. Perception, 23, 429–440.Google Scholar
Tversky, A., Sattath, S., & Slovic, P. (1988). Contingent weighting in judgment and choice. Psychological Review, 95, 371–384.Google Scholar
Wallach, M. A., & Kogan, N. (1965). Modes of thinking in young children. New York: Holt, Rinehart & Winston.Google Scholar
Yantis, S. (1993). Stimulus-driven attentional capture. Current Directions in Psychological Science, 2, 156–161.Google Scholar
Zaccaro, S. J. (2001). The nature of executive leadership: A conceptual and empirical analysis of success. Washington, DC: APA Books.Google Scholar
Zimmermann, P., & Fimm, B. (2000). Test for attentional performance (TAP). Herzogenrath: PSYTEST.Google Scholar