Understanding Understandability of Conceptual Models – What Are We Actually Talking about?

  • Constantin Houy
  • Peter Fettke
  • Peter Loos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7532)

Abstract

Investigating and improving the quality of conceptual models has gained tremendous importance in the past years. In general, model understandability is regarded one of the most important model quality goals and criteria. A considerable amount of empirical studies, especially experiments, have been conducted in order to investigate factors influencing the understandability of conceptual models. However, a thorough review and reconstruction of 42 experiments on conceptual model understandability conducted in this research shows that there is a variety of different understandings and conceptualizations of the term model understandability. As a consequence, this term remains ambiguous, research results on model understandability are hardly comparable and partly imprecise, which shows the necessity of clarification what the conceptual modeling community is actually talking about when the term model understandability is used. In order to overcome this shortcoming, our research classifies the different observed dimensions of model understandability in a reference framework. Moreover, implications of the findings are presented and discussed and some guidelines for future model understandability research are given.

Keywords

conceptual modeling model understandability model comprehensibility model quality experimental research 

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References

  1. 1.
    Germonprez, M., Hovorka, D., Gal, U.: Secondary Design: A Case of Behavioral Design Science Research. Journal of the AIS 12, 662–683 (2011)Google Scholar
  2. 2.
    Lindland, O.I., Sindre, G., Sølvberg, A.: Understanding Quality in Conceptual Modeling. IEEE Software 11, 42–49 (1994)CrossRefGoogle Scholar
  3. 3.
    Krogstie, J.: Modelling of the People, by the People, for the People. In: Krogstie, J., Opdahl, A.L., Brinkkemper, S. (eds.) Conceptual Modelling in Information Systems Engineering, pp. 305–318. Springer, Berlin (2007)CrossRefGoogle Scholar
  4. 4.
    Cooper, H., Hedges, L.V.: Research Synthesis As a Scientific Enterprise. In: Cooper, H., Hedges, L.V. (eds.) The Handbook of Research Synthesis, pp. 3–14. Russell Sage Foundation, New York (1994)Google Scholar
  5. 5.
    Fettke, P., Houy, C., Vella, A.-L., Loos, P.: Towards the Reconstruction and Evaluation of Conceptual Model Quality Discourses – Methodical Framework and Application in the Context of Model Understandability. In: Bider, I., Halpin, T., Krogstie, J., Nurcan, S., Proper, E., Schmidt, R., Soffer, P., Wrycza, S. (eds.) EMMSAD 2012 and BPMDS 2012. LNBIP, vol. 113, pp. 406–421. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Wand, Y., Storey, V.C., Weber, R.: Analyzing the Meaning of a Relationship. ACM Trans. Database Systems 24, 494–528 (1999)CrossRefGoogle Scholar
  7. 7.
    Wand, Y., Weber, R.: Research Commentary: Information Systems and Conceptual Modeling - A Research Agenda. Information Systems Research 13, 363–377 (2002)CrossRefGoogle Scholar
  8. 8.
    Moody, D.L.: Cognitive Load Effects on End User Understanding of Conceptual Models: An Experimental Analysis. In: Benczúr, A.A., Demetrovics, J., Gottlob, G. (eds.) ADBIS 2004. LNCS, vol. 3255, pp. 129–143. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Moody, D.L., Sindre, G., Brasethvik, T., Sølvberg, A.: Evaluating the Quality of Process Models: Empirical Testing of a Quality Framework. In: Spaccapietra, S., March, S.T., Kambayashi, Y. (eds.) ER 2002. LNCS, vol. 2503, pp. 380–396. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  10. 10.
    Patig, S.: A practical guide to testing the understandability of notations. In: Proceedings of the Fifth Asia-Pacific Conference on Conceptual Modelling (APCCM 2008), Wollongong, Australia (2008)Google Scholar
  11. 11.
    Lazarsfeld, P.F., Pasanella, A.K., Rosenberg, M. (eds.): Continuities in the Language of Social Research. The Free Press, New York (1972)Google Scholar
  12. 12.
    Babbie, E.R., Mouton, J.: The Practice of Social Research. Oxford University Press, Cape Town (2001)Google Scholar
  13. 13.
    Edwards, J.R., Bagozzi, R.P.: On the Nature and Direction of Relationships Between Constructs and Measures. Psychological Methods 5, 155–174 (2000)CrossRefGoogle Scholar
  14. 14.
    Petter, S., Straub, D., Rai, A.: Specifying Formative Constructs in Information Systems Research. MIS Quarterly 31, 623–656 (2007)Google Scholar
  15. 15.
    Costner, H.L.: Theory, deduction, and the rules of correspondence. American Journal of Sociology 75, 245–263 (1969)CrossRefGoogle Scholar
  16. 16.
    Khatri, V., Vessey, I., Ramesh, V., Clay, P., Park, S.-J.: Understanding conceptual schemas: Exploring the role of application and IS domain knowledge. Information Systems Research 17, 81–99 (2006)CrossRefGoogle Scholar
  17. 17.
    Burton-Jones, A., Meso, P.N.: Conceptualizing Systems for Understanding: An Empirical Test of Decomposition Principles in Object-Oriented Analysis. Information System Research 17, 38–60 (2006)CrossRefGoogle Scholar
  18. 18.
    Reijers, H.A., Mendling, J.: A Study Into the Factors That Influence the Understandability of Business Process Models. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans 41, 449–462 (2011)CrossRefGoogle Scholar
  19. 19.
    Shanks, G.: Conceptual Data Modelling. An Empirical Study of Expert and Novice Data Modellers. Australian Journal of Information Systems 4, 63–73 (1997)Google Scholar
  20. 20.
    Hardgrave, B.C., Dalal, N.P.: Comparing Object-Oriented and Extended-Entity Relationship Data Models. Journal of Database Management 6, 15–21 (1995)Google Scholar
  21. 21.
    Agarwal, R., De, P., Sinha, A.: Comprehending object and process models: An empirical study. IEEE Trans. Softw. Eng. 25, 541–556 (1999)CrossRefGoogle Scholar
  22. 22.
    Bavota, G., Gravino, C., Oliveto, R., De Lucia, A., Tortora, G., Genero, M., Cruz-Lemus, J.A.: Identifying the Weaknesses of UML Class Diagrams during Data Model Comprehension. In: Whittle, J., Clark, T., Kühne, T. (eds.) MODELS 2011. LNCS, vol. 6981, pp. 168–182. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  23. 23.
    Bodart, F., Patel, A., Sim, M., Weber, R.: Should Optional Properties Be Used in Conceptual Modelling? A Theory and Three Empirical Tests. Information Systems Research 12, 384–405 (2001)CrossRefGoogle Scholar
  24. 24.
    Burton-Jones, A., Weber, R.: Understanding relationships with attributes in entity-relationship diagrams. In: 20th Annual International Conference on Information Systems (ICIS 1999), Charlotte, NC, USA, pp. 214–228 (1999)Google Scholar
  25. 25.
    Juhn, S., Naumann, J.: he Effectiveness of Data Representation Characteristics on User Validation. In: 6th International Conference on Information Systems, pp. 212–226 (1985)Google Scholar
  26. 26.
    Palvia, T., Lio, C., To, P.: The Impact of Conceptual Data Models on End User Performance. Journal of Database Management 3, 4–14 (1992)Google Scholar
  27. 27.
    Shoval, P., Fruerman, I.: OO and EER Schemas: A Comparison of User Comprehension. Journal of Database Management 5, 28–38 (1994)Google Scholar
  28. 28.
    Hardgrave, B.C., Dalal, N.P.: Comparing Object Oriented and Extended Entity Relationship Models. Journal of Database Management 6, 15–22 (1995)Google Scholar
  29. 29.
    Kim, Y.-G., March, S.T.: Comparing Data Modelling Formalisms. Communications of the ACM 38, 103–115 (1995)CrossRefGoogle Scholar
  30. 30.
    Shanks, G.: Conceptual Data Modelling: An Empirical Study of Expert and Novice Data Modellers. Australasian Journal of Information Systems 4, 63–73 (1997)Google Scholar
  31. 31.
    Nordbotten, J.C., Crosby, M.E.: The Effect of Graphic Style on Data Model Interpretation. Information Systems Journal 9, 139–155 (1999)CrossRefGoogle Scholar
  32. 32.
    Moody, D.L.: Complexity Effects on End User Understanding of Data Models: An Experimental Comparison of Large Data Model Representation Methods. In: Wrycza, S. (ed.) 10th European Conference on Information Systems (ECIS 2002), Gdansk, Poland, pp. 482–496 (2002)Google Scholar
  33. 33.
    Purchase, H.C., Colpoys, L., McGill, M., Carrington, D.: UML collaboration diagram syntax: an empirical study of comprehension. In: First International Workshop on Visualizing Software for Understanding and Analysis (2002)Google Scholar
  34. 34.
    Parsons, J.: Effects of local versus global schema diagrams on verification and communication in conceptual data modeling. Journal of MIS 19, 155–184 (2003)Google Scholar
  35. 35.
    Moody, D.L.: Cognitive Load Effects on End User Understanding of Conceptual Models: An Experimental Analysis. In: Benczúr, A.A., Demetrovics, J., Gottlob, G. (eds.) ADBIS 2004. LNCS, vol. 3255, pp. 129–143. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  36. 36.
    Serrano, M., Calero, C., Trujillo, J., Lujan, S., Piattini, M.: Empirical Validation of Metrics for Conceptual Models of Data Warehouses. In: Persson, A., Stirna, J. (eds.) CAiSE 2004. LNCS, vol. 3084, pp. 506–520. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  37. 37.
    Gemino, A., Wand, Y.: Complexity and Clarity in Conceptual Modeling: Comparison of Mandatory and Optional Properties. Data and Knowledge Engineering 55, 301–326 (2005)CrossRefGoogle Scholar
  38. 38.
    Poels, G., Gailly, F., Maes, A., Paemeleire, R.: Object Class or Association Class? Testing the User Effect on Cardinality Interpretation. In: Akoka, J., Liddle, S.W., Song, I.-Y., Bertolotto, M., Comyn-Wattiau, I., van den Heuvel, W.-J., Kolp, M., Trujillo, J., Kop, C., Mayr, H.C. (eds.) ER Workshops 2005. LNCS, vol. 3770, pp. 33–42. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  39. 39.
    Sarshar, K., Loos, P.: Comparing the Control-Flow of EPC and Petri Net from the End-User Perspective. In: van der Aalst, W.M.P., Benatallah, B., Casati, F., Curbera, F. (eds.) BPM 2005. LNCS, vol. 3649, pp. 434–439. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  40. 40.
    Cruz-Lemus, J.A., Genero, M., Morasca, S., Piattini, M.: Using Practitioners for Assessing the Understandability of UML Statechart Diagrams with Composite States. In: Hainaut, J.-L., Rundensteiner, E.A., Kirchberg, M., Bertolotto, M., Brochhausen, M., Chen, Y.-P.P., Cherfi, S.S.-S., Doerr, M., Han, H., Hartmann, S., Parsons, J., Poels, G., Rolland, C., Trujillo, J., Yu, E., Zimányie, E. (eds.) ER Workshops 2007. LNCS, vol. 4802, pp. 213–222. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  41. 41.
    Mendling, J., Reijers, H.A., Cardoso, J.: What Makes Process Models Understandable? In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 48–63. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  42. 42.
    Recker, J., Dreiling, A.: Does it matter which process modelling language we teach or use? An experimental study on understanding process modelling languages without formal education. In: Toleman, M., Cater-Steel, A., Roberts, D. (eds.) Proceedings of the 18th ACIS 2007, Toowoomba, Australia, pp. 356–366 (2007)Google Scholar
  43. 43.
    Serrano, M., Trujillo, J., Calero, C., Piattini, M.: Metrics for data warehouse conceptual models understandability. Inf. Softw. Technol. 49, 851–870 (2007)CrossRefGoogle Scholar
  44. 44.
    Burton-Jones, A., Meso, P.N.: The Effects of Decomposition Quality and Multiple Forms of Information on Novices’ Understanding of a Domain from a Conceptual Model. Journal of the AIS 9, 748–802 (2008)Google Scholar
  45. 45.
    De Lucia, A., Gravino, C., Oliveto, R., Tortora, G.: Data model comprehension an empirical comparison of ER and UML class diagrams. In: IEEE International Conference on Program Comprehension, Amsterdam, pp. 93–102 (2008)Google Scholar
  46. 46.
    Genero, M., Poels, G., Piattini, M.: Defining and validating metrics for assessing the under-standability of entity-relationship diagrams. Data and Knowledge Engineering 64, 534–557 (2008)CrossRefGoogle Scholar
  47. 47.
    Mendling, J., Strembeck, M.: Influence factors of understanding business process models. In: Abramowicz, W., Fensel, D. (eds.) BIS 2008. LNBIP, vol. 7, pp. 142–153. Springer, Berlin (2008)Google Scholar
  48. 48.
    Reijers, H.A., Mendling, J.: Modularity in Process Models: Review and Effects. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 20–35. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  49. 49.
    Vanderfeesten, I., Reijers, H.A., Mendling, J., Van Der Aalst, W.M.P., Cardoso, J.: On a Quest for Good Process Models: The Cross-Connectivity Metric. In: Bellahsène, Z., Léonard, M. (eds.) CAiSE 2008. LNCS, vol. 5074, pp. 480–494. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  50. 50.
    Fuller, R.M., Murthy, U., Schafer, B.A.: The effects of data model representation method on task performance. Information & Management 47, 208–218 (2010)CrossRefGoogle Scholar
  51. 51.
    Sánchez-González, L., García, F., Mendling, J., Ruiz, F., Piattini, M.: Prediction of Business Process Model Quality Based on Structural Metrics. In: Parsons, J., Saeki, M., Shoval, P., Woo, C., Wand, Y. (eds.) ER 2010. LNCS, vol. 6412, pp. 458–463. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  52. 52.
    Figl, K., Laue, R.: Cognitive Complexity in Business Process Modeling. In: Mouratidis, H., Rolland, C. (eds.) CAiSE 2011. LNCS, vol. 6741, pp. 452–466. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  53. 53.
    Ottensooser, A., Fekete, A., Reijers, H.A., Mendling, J., Menictas, C.: Making sense of business process descriptions: An experimental comparison of graphical and textual notations. Journal of Systems and Software 85, 596–606 (2012)CrossRefGoogle Scholar
  54. 54.
    Parsons, J.: An Experimental Study of the Effects of Representing Property Precedence on the Comprehension of Conceptual Schemas. Journal of the AIS 12, 441–462 (2011)Google Scholar
  55. 55.
    Recker, J., Dreiling, A.: The Effects of Content Presentation Format and User Characteristics on Novice Developers Understanding of Process Models. Communications of the AIS 28, 65–84 (2011)Google Scholar
  56. 56.
    Reijers, H.A., Mendling, J.: A Study Into the Factors That Influence the Understandability of Business Process Models. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans 41, 449–462 (2011)CrossRefGoogle Scholar
  57. 57.
    Reijers, H.A., Mendling, J., Dijkman, R.M.: Human and automatic modularizations of process models to enhance their comprehension. Information Systems 36, 881–897 (2011)CrossRefGoogle Scholar
  58. 58.
    Sánchez-González, L., Ruiz, F., García, F., Cardoso, J.: Towards thresholds of control flow complexity measures for BPMN models. In: Proceedings of the ACM Symposium on Applied Computing, SAC 2011, TaiChung, pp. 1445–1450 (2011)Google Scholar
  59. 59.
    Schalles, C., Creagh, J., Rebstock, M.: Usability of Modelling Languages for Model Interpretation: An Empirical Research Report. In: Bernstein, A., Schwabe, G. (eds.) 10th International Conference on Wirtschaftsinformatik, Zurich, Switzerland, pp. 787–796 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Constantin Houy
    • 1
  • Peter Fettke
    • 1
  • Peter Loos
    • 1
  1. 1.Institute for Information Systems (IWi), at the German Research Center for Artificial Intelligence (DFKI)Saarland UniversitySaarbrückenGermany

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