Requirements Engineering

, Volume 21, Issue 1, pp 63–85 | Cite as

Exploring cognitive style and task-specific preferences for process representations

Original Article

Abstract

Process models describe someone’s understanding of processes. Processes can be described using unstructured, semi-formal or diagrammatic representation forms. These representations are used in a variety of task settings, ranging from understanding processes to executing or improving processes, with the implicit assumption that the chosen representation form will be appropriate for all task settings. We explore the validity of this assumption by examining empirically the preference for different process representation forms depending on the task setting and cognitive style of the user. Based on data collected from 120 business school students, we show that preferences for process representation formats vary dependent on application purpose and cognitive styles of the participants. However, users consistently prefer diagrams over other representation formats. Our research informs a broader research agenda on task-specific applications of process modeling. We offer several recommendations for further research in this area.

Keywords

Conceptual modeling Business process modeling Representation forms Model evaluation User preferences Cognitive style 

Notes

Acknowledgments

Dr Recker’s contributions to this research have been supported by a grant from the Australian Research Council (DE120100776).

References

  1. 1.
    Davies I, Green P, Rosemann M, Indulska M, Gallo S (2006) How do practitioners use conceptual modeling in practice? Data Knowl Eng 58(3):358–380CrossRefGoogle Scholar
  2. 2.
    Recker J, Rosemann M, Indulska M, Green P (2009) Business process modeling: a comparative analysis. J Assoc Inf Syst 10(4):333–363Google Scholar
  3. 3.
    Fettke P (2009) How conceptual modeling is used. Commun Assoc Inf Syst 25(43):571–592Google Scholar
  4. 4.
    Kock N, Verville J, Danesh-Pajou A, DeLuca D (2009) Communication flow orientation in business process modeling and its effect on redesign success: results from a field study. Decis Support Syst 46(2):562–575CrossRefGoogle Scholar
  5. 5.
    Indulska M, Green P, Recker J, Rosemann M (2009) Business process modeling: perceived benefits. In: Castano S, Dayal U, Laender AHF (eds) Conceptual modeling–ER 2009. Lecture notes in computer science. Springer, Gramado, pp 458–471CrossRefGoogle Scholar
  6. 6.
    Wolter C, Meinel C (2010) An approach to capture authorisation requirements in business processes. Requirements Eng 15(4):359–373CrossRefGoogle Scholar
  7. 7.
    Phalp KT, Vincent J, Cox K (2007) Improving the quality of use case descriptions: empirical assessment of writing guidelines. Softw Qual J 15(4):383–399CrossRefGoogle Scholar
  8. 8.
    Lee J, Wyner GM, Pentland BT (2008) Process grammar as a tool for business process design. MIS Q 32(4):757–778Google Scholar
  9. 9.
    Moody DL (2009) The “Physics” of notations: toward a scientific basis for constructing visual notations in software engineering. IEEE Trans Softw Eng 35(6):756–779CrossRefGoogle Scholar
  10. 10.
    Recker J, Safrudin N, Rosemann M (2012) How novices design business processes. Inf Syst 37(6):557–573CrossRefGoogle Scholar
  11. 11.
    Boekelder A, Steehouder M (1998) Selecting and switching: some advantages of diagrams over tables and lists for presenting instructions. IEEE Trans Prof Commun 41(4):229–241CrossRefGoogle Scholar
  12. 12.
    Coll RA, Coll JH, Thakur G (1994) Graphs and tables: a four-factor experiment. Commun ACM 37(4):76–86CrossRefGoogle Scholar
  13. 13.
    Riding R, Cheema I (1991) Cognitive styles—an overview and integration. Educ Psychol: Int J Exp Educ Psychol 11(3):193–215CrossRefGoogle Scholar
  14. 14.
    Thomas PR, McKay JB (2010) Cognitive styles and instructional design in university learning. Learn Individ Differ 20(3):197–202CrossRefGoogle Scholar
  15. 15.
    Recker J (2010) Opportunities and constraints: the current struggle with BPMN. Bus Process Manag J 16(1):181–201CrossRefMathSciNetGoogle Scholar
  16. 16.
    Dehnert J, van der Aalst WMP (2004) Bridging the gap between business models and workflow specifications. Int J Coop Inf Syst 13(3):289–332CrossRefGoogle Scholar
  17. 17.
    Bandara W, Gable GG, Rosemann M (2005) Factors and measures of business process modelling: model building through a multiple case study. Eur J Inf Syst 14(4):347–360CrossRefGoogle Scholar
  18. 18.
    Reijers HA, Mendling J (2011) A study into the factors that influence the understandability of business process models. IEEE Trans Syst Man Cybern A 41(3):449–462CrossRefGoogle Scholar
  19. 19.
    Simon HA (1996) The sciences of the artificial, 3rd edn. MIT Press, CambridgeGoogle Scholar
  20. 20.
    Kalpic B, Bernus P (2006) Business process modeling through the knowledge management perspective. J Knowl Manag 10(3):40–56CrossRefGoogle Scholar
  21. 21.
    Kim H-W, Kim Y-G (1997) Dynamic process modeling for BPR: a computerized simulation approach. Inf Manag 32(1):1–13CrossRefGoogle Scholar
  22. 22.
    Ouyang C, van der Aalst WMP, Dumas M, ter Hofstede AHM, Mendling J (2009) From business process models to process-oriented software systems. ACM Trans Softw Eng Methodol 19(1):2–37CrossRefGoogle Scholar
  23. 23.
    Dumas M, La Rosa M, Mendling J, Reijers HA (2013) Fundamentals of business process management. Springer, BerlinCrossRefGoogle Scholar
  24. 24.
    Burton-Jones A, Meso P (2008) 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):784–802Google Scholar
  25. 25.
    Mendling J, Strembeck M, Recker J (2012) Factors of process model comprehension: findings from a series of experiments. Decis Support Syst 53(1):195–206CrossRefGoogle Scholar
  26. 26.
    Campbell DJ (1988) Task complexity: a review and analysis. Acad Manag Rev 13(1):40–52Google Scholar
  27. 27.
    Akin Ö, Akin C (1998) On the process of creativity in puzzles, inventions, and designs. Autom Constr 7(2–3):123–138CrossRefGoogle Scholar
  28. 28.
    Kettinger WJ, Teng JTC, Guha S (1997) Business process change: a study of methodologies, techniques, and tools. MIS Q 21(1):55–80CrossRefGoogle Scholar
  29. 29.
    Sarkar P, Chakrabarti A (2008) The effect of representation of triggers on design outcomes. Artif Intell Eng Des Anal Manuf 22(2):101–116CrossRefGoogle Scholar
  30. 30.
    Gorla N, Pu H-C, Rom WO (1995) Evaluation of process tools in systems analysis. Inf Softw Technol 37(2):119–126CrossRefGoogle Scholar
  31. 31.
    Friedrich F, Mendling J, Puhlmann F (2011) Process model generation from natural language text. In: Mouratidis H, Rolland C (eds) Advanced information systems engineering–CAiSe 2011. Lecture notes in computer science, vol 6741. Springer, London, pp 482–496Google Scholar
  32. 32.
    Blumberg R, Atre S (2003) The problem with unstructured data. DM Rev 13:42–49Google Scholar
  33. 33.
    Cockburn A (2000) Writing effective use cases. Addison-Wesley Longman Publishing Co., IncGoogle Scholar
  34. 34.
    Vessey I, Weber R (1986) Structured tools and conditional logic: an empirical investigation. Commun ACM 29(1):48–57CrossRefGoogle Scholar
  35. 35.
    Moody DL (2009) The “Physics” of notations: towards a scientific basis for constructing visual notations in software engineering. IEEE Trans Software Eng 35(5):756–779CrossRefMathSciNetGoogle Scholar
  36. 36.
    Curtis B, Sheppard SB, Kruesi-Bailey E, Bailey J, Boehm-Davis DA (1989) Experimental evaluation of software documentation formats. J Syst Softw 9(2):167–207CrossRefGoogle Scholar
  37. 37.
    Larkin JH, Simon HA (1987) Why a diagram is (sometimes) worth ten thousand words. Cogn Sci 11(1):65–100CrossRefGoogle Scholar
  38. 38.
    Ottensooser A, Fekete A, Reijers HA, Mendling J, Menictas C (2012) Making sense of business process descriptions: an experimental comparison of graphical and textual notations. J Syst Softw 85(3):596–606CrossRefGoogle Scholar
  39. 39.
    Masri K, Parker DC, Gemino A (2008) Using iconic graphics in entity-relationship diagrams: the impact on understanding. J Database Manag 19(3):22–41CrossRefGoogle Scholar
  40. 40.
    Mendling J, Recker J, Reijers HA (2010) On the usage of labels and icons in business process modeling. Int J Inf Syst Mdel Des 1(2):40–58CrossRefGoogle Scholar
  41. 41.
    Malaga RA (2000) The effect of stimulus modes and associative distance in individual creativity support systems. Decis Support Syst 29(2):125–141CrossRefGoogle Scholar
  42. 42.
    Zajonc RB, Hazel M (1982) Affective and cognitive factors in preferences. J Consum Res 9(2):123–131CrossRefGoogle Scholar
  43. 43.
    Blazhenkova O, Kozhevnikov M (2009) The new object-spatial-verbal cognitive style model: theory and measurement. Appl Cogn Psychol 23(5):638–663CrossRefGoogle Scholar
  44. 44.
    Vessey I, Galletta DF (1991) Cognitive fit: an empirical study of information acquisition. Inf Syst Res 2(1):63–84CrossRefGoogle Scholar
  45. 45.
    Vessey I (1991) Cognitive fit: a theory-based analysis of the graphs versus tables literature. Decis Sci 22(2):219–240CrossRefGoogle Scholar
  46. 46.
    Blazhenkova O, Kozhevnikov M (2008) The new object-spatial-verbal cognitive style model: theory and measurement. Appl Cogn Psychol 23(5):638–663CrossRefGoogle Scholar
  47. 47.
    Stebbins RA (2001) Exploratory research in the social sciences. Qualitative research methods, vol 48. Sage, Thousand OaksGoogle Scholar
  48. 48.
    Kumar S, Karoli V (2011) Handbook of business research methods. Thakur PublishersGoogle Scholar
  49. 49.
    Recker J, Reijers HA, van de Wouw SG (2014) Process model comprehension: the effects of cognitive abilities, learning style and strategy. Commun Assoc Inf Syst 34(9):199–222Google Scholar
  50. 50.
    Fromkin HL, Streufert S (1976) Laboratory experimentation. Rand McNally College Publishing Company, ChicagoGoogle Scholar
  51. 51.
    Recker J, Mendling J, Hahn C (2013) How collaborative technology supports cognitive processes in collaborative process modeling: a capabilities-gains-outcome model. Inf Syst 38(8):1031–1045CrossRefGoogle Scholar
  52. 52.
    Lichtenstein S, Slovic P (eds) (2006) The construction of preference. Cambridge University Press, New YorkGoogle Scholar
  53. 53.
    Recker J (2010) Continued use of process modeling grammars: the impact of individual difference factors. Eur J Inf Syst 19(1):76–92CrossRefMathSciNetGoogle Scholar
  54. 54.
    Brehm JW (1956) Post-decision changes in the desirability of alternatives. J Abnorm Soc Psychol 52(3):384–389CrossRefGoogle Scholar
  55. 55.
    Ben-Simon A, Budescu DV, Nevo B (1997) A comparative study of measures of partial knowledge in multiple-choice tests. Appl Psychol Meas 21(1):65–88CrossRefGoogle Scholar
  56. 56.
    Blajenkova O, Kozhevnikov M, Motes MA (2006) Object-spatial imagery: a new self-report imagery questionnaire. Appl Cogn Psychol 20(2):239–263CrossRefGoogle Scholar
  57. 57.
    Kozhevnikov M, Blazhenkova O, Becker M (2010) Trade-off in object versus spatial visualization abilities: restriction in the development of visual-processing resources. Psychon Bull Rev 17(1):29–35CrossRefGoogle Scholar
  58. 58.
    Kozhevnikov M, Kozhevnikov M, Yu CJ, Blazhenkova O (2013) Creativity, visualization abilities, and visual cognitive style. Br J Educ Psychol 83(2):196–209CrossRefGoogle Scholar
  59. 59.
    Campos A (2014) Gender differences in imagery. Personal Individ Differ 59:107–111CrossRefGoogle Scholar
  60. 60.
    Occelli V, Lin JB, Lacey S, Sathian K (2014) Loss of form vision impairs spatial imagery. Front Hum Neurosci 8, Art No 159. doi:10.3389/fnhum.2014.00159
  61. 61.
    Kraemer DJ, Hamilton RH, Messing SB, DeSantis JH, Thompson-Schill SL (2014) Cognitive style, cortical stimulation, and the conversion hypothesis. Front Hum Neurosci 8, Art No 15. doi:10.3389/fnhum.2014.00015
  62. 62.
    Aggarwal I, Woolley AW (2013) Do you see what I see? The effect of members’ cognitive styles on team processes and errors in task execution. Organ Behav Hum Decis Process 122(1):92–99CrossRefGoogle Scholar
  63. 63.
    OMG (2010) BPMN 2.0 by example. http://www.omg.org/spec/BPMN/2.0/examples/PDF. Accessed 27 Aug 2014
  64. 64.
    Khatri V, Vessey I, Ramesh V, Clay P, Sung-Jin P (2006) Understanding conceptual schemas: exploring the role of application and is domain knowledge. Inf Syst Res 17(1):81–99CrossRefGoogle Scholar
  65. 65.
    Patig S, Casanova-Brito V, Vögeli B (2010) IT requirements of business process management in practice: an empirical study. In: Hull R, Mendling J, Tai S (eds) Business process management: BPM 2010. Lecture notes in computer science, vol 6336. Springer, Hoboken, pp 13–28CrossRefGoogle Scholar
  66. 66.
    Reijers HA, Freytag T, Mendling J, Eckleder A (2011) Syntax highlighting in business process models. Decis Support Syst 51(3):339–349CrossRefGoogle Scholar
  67. 67.
    zur Muehlen M, Recker J (2008) How much language is enough? Theoretical and practical use of the business process modeling notation. In: Léonard M, Bellahsène Z (eds) Advanced information systems engineering: CAiSE 2008. Lecture notes in computer science. Springer, Montpellier, pp 465–479CrossRefGoogle Scholar
  68. 68.
    Saari DG (2000) Mathematical structure of voting paradoxes: II. Positional voting. Econ Theor 15(1):55–102CrossRefMathSciNetMATHGoogle Scholar
  69. 69.
    Cook C, Heath F, Thompson RL, Thompson B (2001) Score reliability in Webor internet-based surveys: unnumbered graphic rating scales versus Likert-type scales. Educ Psychol Measur 61(4):697–706CrossRefGoogle Scholar
  70. 70.
    Gemino A, Wand Y (2004) A framework for empirical evaluation of conceptual modeling techniques. Requirements Eng 9(4):248–260CrossRefGoogle Scholar
  71. 71.
    Compeau DR, Marcolin BL, Kelley H, Higgins CA (2012) Generalizability of information systems research using student subjects: a reflection on our practices and recommendations for future research. Inf Syst Res 23(4):1093–1109CrossRefGoogle Scholar
  72. 72.
    Runeson P (2003) Using students as experiment subjects: an analysis on graduate and freshmen student data. In: Linkman S (ed) 7th International conference on empirical assessment & evaluation in software engineering, Staffordshire, England. Keele University, pp 95–102Google Scholar
  73. 73.
    Faul F, Erdfelder E, Lang A-G, Axel B (2007) G*Power 3: a flexible statistical power analysis for the social, behavioral, and biomedical sciences. Behav Res Methods 39(2):175–191CrossRefGoogle Scholar
  74. 74.
    Leys C, Ley C, Klein O, Bernard P, Licata L (2013) Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J Exp Soc Psychol 49(4):764–766CrossRefGoogle Scholar
  75. 75.
    Recker J (2013) Empirical investigation of the usefulness of gateway constructs in process models. Eur J Inf Syst 22(6):673–689CrossRefGoogle Scholar
  76. 76.
    Figl K, Mendling J, Strembeck M (2013) The influence of notational deficiencies on process model comprehension. J Assoc Inf Syst 14(6):312–338Google Scholar
  77. 77.
    Reijers HA, Mendling J (2011) A study into the factors that influence the understandability of business process models. IEEE Trans Syst Man Cybern A 41:449–462CrossRefGoogle Scholar
  78. 78.
    Stevens JP (2001) Applied multivariate statistics for the social sciences. Applied Multivariate STATS, 4th edn. Lawrence Erlbaum Associates, Hillsdale, New JerseyGoogle Scholar
  79. 79.
    Tabachnick BG, Fidell LS (2007) Using multivariate statistics. Pearson Education Inc, BostonGoogle Scholar
  80. 80.
    Kozhevnikov M, Hegarty M, Mayer RE (2002) Revising the visualizer-verbalizer dimension: evidence for two types of visualizers. Cogn Instr 20(1):47–77CrossRefGoogle Scholar
  81. 81.
    Glenberg AM, Langston WE (1992) Comprehension of illustrated text: pictures help to build mental models. J Mem Lang 31(2):129–151CrossRefGoogle Scholar
  82. 82.
    Cheng PC (2004) Why diagrams are (sometimes) six times easier than words: benefits beyond locational indexing. In: Diagrammatic representation and inference. Springer, pp 242–254Google Scholar
  83. 83.
    Scaife M, Rogers Y (1996) External cognition: how do graphical representations work? Int J Hum-Comput Stud 45(2):185–213CrossRefGoogle Scholar
  84. 84.
    Mayer RE (2009) Multimedia learning, 2nd edn. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  85. 85.
    Shadish WR, Cook TD, Campbell DT (2002) Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin Company, BostonGoogle Scholar
  86. 86.
    Recker J, Dreiling A (2011) The effects of content presentation format and user characteristics on novice developers’ understanding of process models. Commun Assoc Inf Syst 28(6):65–84Google Scholar
  87. 87.
    Reijers HA, Mendling J, Dijkman RM (2011) Human and automatic modularizations of process models to enhance their comprehension. Inf Syst 36(5):881–897CrossRefGoogle Scholar
  88. 88.
    Fisher RJ (1993) Social desirability bias and the validity of indirect questioning. J Consum Res 20(2):303–315CrossRefGoogle Scholar
  89. 89.
    Paivio A, Harshmann R (1983) Factor analysis of a questionnaire on imagery and verbal habits and skills. Can J Psychol 37(4):461–483CrossRefGoogle Scholar
  90. 90.
    Byström K, Järvelin K (1995) Task complexity affects information seeking and use. Inf Process Manage 31(2):191–213CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Institute for Information Systems and New MediaWU - Vienna University of Economics and BusinessViennaAustria
  2. 2.Information Systems SchoolQueensland University of TechnologyBrisbaneAustralia

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