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The Influence of Cognitive Abilities and Cognitive Load on Business Process Models and Their Creation

  • Manuel Neurauter
  • Jakob Pinggera
  • Markus Martini
  • Andrea Burattin
  • Marco Furtner
  • Pierre Sachse
  • Barbara Weber
Conference paper
Part of the Lecture Notes in Information Systems and Organisation book series (LNISO, volume 10)

Abstract

While factors impacting process model comprehension are relatively well understood by now, little is know about process model creation and factors impacting process model quality. This paper proposes a research model to investigate the influence of cognitive abilities and a continuous psycho-physiological measure of task imposed cognitive load of process model designers on process model quality. The proposed research will not only contribute a better understanding of process model creation, but bears significant potential for improving existing modeling notations as well as for developing process modeling environments.

Keywords

Cognitive load Working memory Executive functions Reasoning ability Business process modeling 

Notes

Acknowledgments

This research is funded by Austrian Science Fund (FWF): P26609–N15.

References

  1. 1.
    Burton-Jones, A., Meso, P.: The effects of decomposition quality and multiple forms of information on novices’ understanding of a domain from a conceptual model. J. Assoc. Inf. Syst. 9(12), Article 1 (2008)Google Scholar
  2. 2.
    Recker, J., Rosemann, M., Indulska, M., Green, P.: Business process modeling—a comparative analysis. J. Assoc. Inf. Syst. 10(4), Article 1 (2009)Google Scholar
  3. 3.
    Fettke, P.: How conceptual modeling is used. Commun. Assoc. Inf. Syst. 25(1), Article 43 (2009)Google Scholar
  4. 4.
    Mendling, J.: Metrics for Process Models (vol. 6). Springer, Berlin (2008)CrossRefGoogle Scholar
  5. 5.
    Weber, B., Reichert, M., Mendling, J., Reijers, H.A.: Refactoring large process model repositories. Comput. Ind. 62(5), 467–486 (2011)CrossRefGoogle Scholar
  6. 6.
    Reijers, H.A., Mendling, J.: A study into the factors that influence the understandability of business process models. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 41(3), 449–462 (2011)CrossRefGoogle Scholar
  7. 7.
    Mendling, J., Strembeck, M., Recker, J.: Factors of process model comprehension—findings from a series of experiments. Decis. Support Syst. 53(1), 195–206 (2012)CrossRefGoogle Scholar
  8. 8.
    Mendling, J., Reijers, H.A., Recker, J.: Activity labeling in process modeling: empirical insights and recommendations. Inf. Syst. 35(4), 467–482 (2010)CrossRefGoogle Scholar
  9. 9.
    Figl, K., Recker, J., Mendling, J.: A study on the effects of routing symbol design on process model comprehension. Decis. Support Syst. 54(2), 1104–1118 (2013)CrossRefGoogle Scholar
  10. 10.
    Figl, K., Mendling, J., Strembeck, M.: The influence of notational deficiencies on process model comprehension. J. Assoc. Inf. Syst. 14(6), Article 1 (2013)Google Scholar
  11. 11.
    Recker, J., Reijers, H.A., Van de Wouw, S.G.: Process model comprehension: the effects of cognitive abilities, learning style, and strategy. Commun. Assoc. Inf. Syst. 34(9), 199–222 (2014)Google Scholar
  12. 12.
    Pinggera, J., Zugal, S., Weber, B., Fahland, D., Weidlich, M., Mendling, J., Reijers, H.A.: How the structuring of domain knowledge helps casual process modelers. In: Parsons, J., Saeki, M., Shoval, P., Woo, C., Wand, Y. (eds.) Conceptual Modeling—ER 2010, pp. 445–451. Springer, Berlin (2010)CrossRefGoogle Scholar
  13. 13.
    Sachse, P., Ulich, E.: Psychologie menschlichen handelns: Wissen & Denken–Wollen & Tun. Pabst Science Publishers, Lengerich (2014)Google Scholar
  14. 14.
    Recker, J., Safrudin, N., Rosemann, M.: How novices design business processes. Inf. Syst. 37(6), 557–573 (2012)CrossRefGoogle Scholar
  15. 15.
    Figl, K., Recker, J.: Exploring cognitive style and task-specific preferences for process representations. Requirements Eng. 19(3), 1–23 (2014) Google Scholar
  16. 16.
    Simon, H.A.: The Sciences of the Artificial, 3rd edn. The MIT Press, Cambridge (1996)Google Scholar
  17. 17.
    Soffer, P., Kaner, M., Wand, Y.: Towards understanding the process of process modeling: theoretical and empirical considerations. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) Business Process Management Workshops, pp. 357–369. Springer, Berlin (2012)CrossRefGoogle Scholar
  18. 18.
    Cinaz, B.: Monitoring of cognitive load and cognitive performance using wearable sensing. Dissertation No. 21091, ETH Zurich, Zurich, Switzerland (2013)Google Scholar
  19. 19.
    Wickens, C.D., Hollands, J.G., Parasuraman, R., Banbury, S.: Engineering Psychology and Human Performance, 4th edn. Pearson, Boston (2012)Google Scholar
  20. 20.
    Wilmont, I., Hengeveld, S., Barendsen, E., Hoppenbrouwers, S.: Cognitive mechanisms of conceptual modelling. In: Ng, W., Storey, V.C., Trujillo, J.C. (eds.) Conceptual Modeling, pp. 74–87. Springer, Berlin (2013)CrossRefGoogle Scholar
  21. 21.
    Boduroglu, A., Minear, M., Shah, P.: Working memory. In: Durso, F.T., Nickerson, R.S., Dumais, S.T., Lewandowsky, S., Perfect, T.J. (eds.) Handbook of Applied Cognition, 2nd edn, pp. 55–82. Wiley, Chichester (2007)CrossRefGoogle Scholar
  22. 22.
    Lin, T., Imamiya, A., Mao, X.: Using multiple data sources to get closer insights into user cost and task performance. Interact. Comput. 20(3), 364–374 (2008)CrossRefGoogle Scholar
  23. 23.
    Novak, D., Beyeler, B., Omlin, X., Riener, R.: Workload estimation in physical human-robot interaction using physiological measurements. Interact. Comput. (2014)Google Scholar
  24. 24.
    Sternberg, R.J., Kaufman, S.B. (eds.): The Cambridge Handbook of Intelligence. Cambridge University Press, Cambridge (2011)Google Scholar
  25. 25.
    Zugal, S., Pinggera, J., Reijers, H., Reichert, M., Weber, B.: Making the case for measuring mental effort. In: Proceedings of the Second Edition of the International Workshop on Experiences and Empirical Studies in Software Modelling, pp. 6:1–6:6. ACM, New York, NY, USA (2012)Google Scholar
  26. 26.
    Hart, S.G., Staveland, L.E.: Development of NASA-TLX (task load index): results of empirical and theoretical research. In: Hancock, P.A., Meshkati, N. (eds.) Human Mental Workload, pp. 139–183. North-Holland Press, Amsterdam (1988)CrossRefGoogle Scholar
  27. 27.
    Gobet, F., Waters, A.J.: The role of constraints in expert memory. J. Exp. Psychol. Learn. Mem. Cognition 29(6), 1082–1094 (2003)CrossRefGoogle Scholar
  28. 28.
    Horn, J., Masunaga, H.: A merging theory of expertise and intelligence. In: The Cambridge Handbook of Expertise and Expert Performance, pp. 587–611. Cambridge University Press, New York, NY (2006)Google Scholar
  29. 29.
    Proctor, R.W., Vu, K.-P.L.: Laboratory studies of training, skill acquisition, and retention of performance. In: Ericsson, K.A., Charness, N., Feltovich, P.J., Hoffman, R.R. (eds.) The Cambridge Handbook Of Expertise And Expert Performance, pp. 265–286. Cambridge University Press, Cambridge (2006)CrossRefGoogle Scholar
  30. 30.
    Raven, J.J.: Raven progressive matrices. In: McCallum, R.S. (ed.) Handbook of Nonverbal Assessment, pp. 223–237. Springer, US (2003)Google Scholar
  31. 31.
    Burgess, G.C., Gray, J.R., Conway, A.R.A., Braver, T.S.: Neural mechanisms of interference control underlie the relationship between fluid intelligence and working memory span. J. Exp. Psychol. Gen. 140(4), 674–692 (2011)CrossRefGoogle Scholar
  32. 32.
    Cowan, N., Elliott, E.M., Scott Saults, J., Morey, C.C., Mattox, S., Hismjatullina, A., Conway, A.R.A.: On the capacity of attention: its estimation and its role in working memory and cognitive aptitudes. Cognitive Psychol. 51(1), 42–100 (2005)CrossRefGoogle Scholar
  33. 33.
    Just, M.A., Carpenter, P.A.: A capacity theory of comprehension: individual differences in working memory. Psychol. Rev. 99(1), 122–149 (1992)CrossRefGoogle Scholar
  34. 34.
    Kyllonen, P.C., Stephens, D.L.: Cognitive abilities as determinants of success in acquiring logic skill. Learn. Individ. Differ. 2(2), 129–160 (1990)CrossRefGoogle Scholar
  35. 35.
    Hambrick, D.Z., Engle, R.W.: Effects of domain knowledge, working memory capacity, and age on cognitive performance: an investigation of the knowledge-is-power hypothesis. Cognitive Psychol. 44(4), 339–387 (2002)CrossRefGoogle Scholar
  36. 36.
    Oberauer, K., Süss, H.-M., Schulze, R., Wilhelm, O., Wittmann, W.W.: Working memory capacity—facets of a cognitive ability construct. Pers. Individ. Differ. 29(6), 1017–1045 (2000)CrossRefGoogle Scholar
  37. 37.
    Oberauer, K., Süβ, H.-M., Wilhelm, O., Wittmann, W.W.: Which working memory functions predict intelligence? Intelligence 36(6), 641–652 (2008)CrossRefGoogle Scholar
  38. 38.
    Daneman, M., Carpenter, P.A.: Individual differences in working memory and reading. J. Verbal Learn. Verbal Behav. 19(4), 450–466 (1980)CrossRefGoogle Scholar
  39. 39.
    Pinggera, J., Zugal, S., Weber, B., Fahland, D., Weidlich, M., Mendling, J., Reijers, H.A.: How the structuring of domain knowledge helps casual process modelers. In: Parsons, J., Saeki, M., Shoval, P., Woo, C., Wand, Y. (eds.) Conceptual Modeling—ER 2010, pp. 445–451. Springer, Berlin (2010)CrossRefGoogle Scholar
  40. 40.
    Zugal, S., Pinggera, J., Weber, B.: Assessing process models with cognitive psychology. EMISA 2011, 177–182 (2011)Google Scholar
  41. 41.
    Claes, J., Gailly, F., Poels, G.: Cognitive aspects of structured process modeling. In: Franch, X., Soffer, P. (eds.) Advanced Information Systems Engineering Workshops, pp. 168–173. Springer, Berlin (2013)CrossRefGoogle Scholar
  42. 42.
    Friedman, N.P., Miyake, A., Young, S.E., Defries, J.C., Corley, R.P., Hewitt, J.K.: Individual differences in executive functions are almost entirely genetic in origin. J. Exp. Psychol. Gen. 137(2), 201–225 (2008)CrossRefGoogle Scholar
  43. 43.
    Miyake, A., Friedman, N.P., Emerson, M.J., Witzki, A.H., Howerter, A., Wager, T.D.: The unity and diversity of executive functions and their contributions to complex “Frontal Lobe” tasks: a latent variable analysis. Cognitive Psychol. 41(1), 49–100 (2000)CrossRefGoogle Scholar
  44. 44.
    Kane, M.J., Engle, R.W.: The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: an individual-differences perspective. Psychon. Bull. Rev. 9(4), 637–671 (2002)CrossRefGoogle Scholar
  45. 45.
    Ackerman, P.L.: A correlational analysis of skill specificity: learning, abilities, and individual differences. J. Exp. Psychol. Learn. Mem. Cogn. 16(5), 883–901 (1990)CrossRefGoogle Scholar
  46. 46.
    Reder, L.M., Schunn, C.D.: Bringing together the psychometric and strategy worlds: predicting adaptivity in a dynamic task. In: Gopher, D., Koriat´, A. (eds.) Attention and Performance XVII: Cognitive Regulation of Performance: Interaction of Theory and Application, pp. 315–342. The MIT Press, Cambridge, MA, US (1999)Google Scholar
  47. 47.
    Engle, R.W., Kane, M.J., Tuholski, S.W.: Individual Differences in Working Memory Capacity and What they Tell us about Controlled Attention, General Fluid Intelligence, and Functions of the Prefrontal Cortex. In: Miyake, A., Shah, P. (eds.) Models of working memory: mechanisms of active maintenance and executive control, pp. 102–134. Cambridge University Press (1999)Google Scholar
  48. 48.
    Stahl, C., Voss, A., Schmitz, F., Nuszbaum, M., Tüscher, O., Lieb, K., Klauer, K.C.: Behavioral components of impulsivity. J. Exp. Psychol. Gen. 143(2), 850–886 (2014)CrossRefGoogle Scholar
  49. 49.
    Krogstie, J., Sindre, G., Jørgensen, H.: Process models representing knowledge for action: a revised quality framework. Eur. J. Inf. Syst. 15(1), 91–102 (2006)CrossRefGoogle Scholar
  50. 50.
    Van der Aalst, W.M.P.: Verification of workflow nets. In: Azéma, P., Balbo, G. (eds.) Application and Theory of Petri Nets, pp. 407–426. Springer, Berlin (1997)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Manuel Neurauter
    • 1
  • Jakob Pinggera
    • 1
  • Markus Martini
    • 1
  • Andrea Burattin
    • 1
  • Marco Furtner
    • 1
  • Pierre Sachse
    • 1
  • Barbara Weber
    • 1
  1. 1.University of InnsbruckInnsbruckAustria

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