Abstract
Declarative process modeling languages, such as Declare, represent processes by means of temporal rules, namely constraints. Those languages typically come endowed with a graphical notation to draw such models diagrammatically. In this paper, we explore the effects of diagrammatic representation on humans’ deductive reasoning involved in the analysis and compliance checking of declarative process models. In an experiment, we compared textual descriptions of business rules against textual descriptions that were supplemented with declarative models. Results based on a sample of 75 subjects indicate that the declarative process models did not improve but rather lowered reasoning performance. Thus, for novice users, using the graphical notation of Declare may not help readers properly understand business rules: they may confuse them in comparison to textual descriptions. A likely explanation of the negative effect of graphical declarative models on human reasoning is that readers interpret edges wrongly. This has implications for the practical use of business rules on the one hand and the design of declarative process modeling languages on the other.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Houy, C., Fettke, P., Loos, P.: On the theoretical foundations of research into the understandability of business process models. In: European Conference on Information Systems, Tel Aviv (2014)
van der Aalst, W.M.P., Pesic, M.: DecSerFlow: towards a truly declarative service flow language. In: Bravetti, M., Núñez, M., Zavattaro, G. (eds.) WS-FM 2006. LNCS, vol. 4184, pp. 1–23. Springer, Heidelberg (2006). https://doi.org/10.1007/11841197_1
Bose, R.P.J.C., Maggi, F.M., van der Aalst, W.M.P.: enhancing declare maps based on event correlations. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 97–112. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40176-3_9
Fahland, D., et al.: Declarative versus imperative process modeling languages: the issue of understandability. In: International Workshop BPMDS and International Conference EMMSAD, pp. 353–366 (2009)
Figl, K.: Comprehension of procedural visual business process models. Bus. Inf. Syst. Eng. 59, 41–67 (2017)
Johnson-Laird, P.N.: Deductive reasoning. Wiley Interdisciplinary Rev. Cogn. Sci. 1, 8–17 (2010)
Boritz, J.E., Borthick, A.F., Presslee, A.: The effect of business process representation type on assessment of business and control risks: diagrams versus narratives. Issues Account. Educ. 27, 895–915 (2012)
Haisjackl, C., Zugal, S.: Investigating differences between graphical and textual declarative process models. In: Iliadis, L., Papazoglou, M., Pohl, K. (eds.) CAiSE 2014. LNBIP, vol. 178, pp. 194–206. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07869-4_17
López, H.A., Debois, S., Hildebrandt, T.T., Marquard, M.: The process highlighter: from texts to declarative processes and back. BPM (Dissertation/Demos/Industry) 2196, 66–70 (2018)
De Smedt, J., De Weerdt, J., Serral, E., Vanthienen, J.: Discovering hidden dependencies in constraint-based declarative process models for improving understandability. Inf. Syst. 74, 40–52 (2018)
Recker, J.: Empirical investigation of the usefulness of gateway constructs in process models. European J. Inf. Syst. 22, 673–689 (2013)
Figl, K., Laue, R.: Influence factors for local comprehensibility of process models. Int. J. Hum Comput Stud. 82, 96–110 (2015)
Steinke, G., Nickolette, C.: Business rules as the basis of an organization’s information systems. Ind. Manage. Data Syst. 103, 52–63 (2003)
Ross, R.G.: What’s wrong with if-then syntax for expressing business rules ~ one size doesn’t fit all. Bus. Rules J. 8 (2007)
Bauer, M.I., Johnson-Laird, P.N.: How diagrams can improve reasoning. Psychol. Sci. 4, 372–378 (1993)
Byrne, R.M.J., Johnson-Laird, P.N.: If and the problems of conditional reasoning. Trends Cogn. Sci. 13, 282–287 (2009)
Cummins, D., Lubart, T., Alksnis, O., Rist, R.: Conditional reasoning and causation. Memory Cogn. 19, 274–282 (1991)
Object Management Group: How Business Rules Relate to Business processes from a business person’s point of view. Business Rules Symposium, Minneapolis (2010)
Ross, R.G.: The Business Rules Manifesto. Business Rules Group (2003)
Vessey, I.: Cognitive fit: a theory-based analysis of the graphs versus tables literature. Decision Sci. 22, 219–240 (1991)
Zur Muehlen, M., Indulska, M.: Modeling languages for business processes and business rules: a representational analysis. Inf. Syst. 35, 379–390 (2010)
Knuplesch, D., Reichert, M., Ly, L.T., Kumar, A., Rinderle-Ma, S.: Visual modeling of business process compliance rules with the support of multiple perspectives. In: Ng, W., Storey, V.C., Trujillo, J.C. (eds.) ER 2013. LNCS, vol. 8217, pp. 106–120. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41924-9_10
Vessey, I., Galletta, D.: Cognitive fit: An empirical study of information acquisition. Information Systems Research 2, 63–84 (1991)
Scaife, M., Rogers, Y.: External cognition: How do graphical representations work? Int. J. Hum.-Comput. Stud. 45, 185–213 (1996)
Baddeley, A.D.: Working memory. Science 255, 556–559 (1992)
Glenberg, A.M., Langston, W.E.: Comprehension of illustrated text: Pictures help to build mental models. J. Memory Lang. 31, 129–151 (1992)
Johnson-Laird, P.N.: Mental models and human reasoning. Proc. Nat. Acad. Sci. 107, 18243–18250 (2010)
Dwyer, M.B., Avrunin, G.S., Corbett, J.C.: Patterns in property specifications for finite-state verification. In: International Conference on Software Engineering, pp. 411–420. IEEE (1999)
Di Ciccio, C., Maggi, F.M., Montali, M., Mendling, J.: Resolving inconsistencies and redundancies in declarative process models. Inf. Syst. 64, 425–446 (2017)
Britton, C., Jones, S.: The untrained eye: How languages for software specification support understanding in untrained users. Hum.-Comput. Interact. 14, 191–244 (1999)
Moody, D.L.: The “physics” of notations: towards a scientific basis for constructing visual notations in software engineering. IEEE Trans. Software Eng. 35, 756–779 (2009)
Recker, J., Green, P.: How do individuals interpret multiple conceptual models? a theory of combined ontological completeness and overlap. J. Assoc. Inf. Syst. 20, 1 (2019)
Liberman, N., Klar, Y.: Hypothesis testing in wason’s selection task: social exchange cheating detection or task understanding. Cognition 58, 127–156 (1996)
Wason, P.C.: Reasoning. In: Foss, B. (ed.) New Horizons in Psychology. Penguin, London (1966)
Faul, F., Erdfelder, E., Lang, A.-G., Buchner, A.: G* power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39, 175–191 (2007)
Fox, N., Mathers, N.: Empowering research: statistical power in general practice research. Family Pract. 14, 324–329 (1997)
Tversky, B., Zacks, J., Lee, P., Heiser, J.: Lines, blobs, crosses and arrows: diagrammatic communication with schematic figures. In: Anderson, M., Cheng, _.P., Haarslev, V. (eds.) Diagrams 2000. LNCS (LNAI), vol. 1889, pp. 221–230. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-44590-0_21
Boekelder, A., Steehouder, M.: Selecting and switching: some advantages of diagrams over tables and lists for presenting instructions. IEEE Trans. Professional Commun. 41, 229–241 (1998)
Haisjackl, C., et al.: Understanding declare models: Strategies, pitfalls, empirical results. Software Syst. Model. 15, 325–352 (2016)
Straub, D.W., Boudreau, M.-C., Gefen, D.: Validation guidelines for is positivist research. Commun. Assoc. Inf. Syst. 13, 380–427 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Figl, K., Di Ciccio, C., Reijers, H.A. (2020). Do Declarative Process Models Help to Reduce Cognitive Biases Related to Business Rules?. In: Dobbie, G., Frank, U., Kappel, G., Liddle, S.W., Mayr, H.C. (eds) Conceptual Modeling. ER 2020. Lecture Notes in Computer Science(), vol 12400. Springer, Cham. https://doi.org/10.1007/978-3-030-62522-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-62522-1_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62521-4
Online ISBN: 978-3-030-62522-1
eBook Packages: Computer ScienceComputer Science (R0)