Comprehension of Procedural Visual Business Process Models: A Literature Review

Business and Information Systems Engineering

Volumn 59, Issue 1, 2017, Pages 41-67

  Figl, Kathrin ^a  

^a VIENNA UNIVERSITY OF ECONOMICS AND BUSINESS   (Austria)

Author keywords

Comprehension; Human information behavior; Literature review; Process modeling

Indexed keywords

EID: 85011845620     PISSN: None     EISSN: 18670202     Source Type: Journal    
DOI: 10.1007/s12599-016-0460-2     Document Type: Article

References (142)

1. Aguilar ER, García F, Ruiz F, Piattini M, Visaggio CA, Canfora G (2008) Evaluation of BPMN models quality-a family of experiments. In: 3rd International conference on evaluation of novel approaches to software engineering, Funchal, pp 56–63
2. Aysolmaz B, Reijers HA (2016) Towards an integrated framework for invigorating process models: a research agenda. In: Reichert M, Reijers HA (eds) Business process management workshops 2015, LNBIP 256, Innsbruck, Austria. Springer International Publishing, pp 552–558. doi:10.1007/978-3-319-42887-1_44
3. Azim A, Ghani A, Koh Tieng WG, Muketha M, Wen WP (2008) Complexity metrics for measuring the understandability and maintainability of business process models using goal-question-metric. Int J Comput Sci Netw Secur 8(5):219–225
4. Baddeley AD (1992) Working memory. Science 255(5044):556–559
5. 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):347–360. doi:10.1057/palgrave.ejis.3000546
6. Becker J, Rosemann M, Schütte R (1995) Grundsätze ordnungsmäßiger Modellierung. WIRTSCHAFTSINFORMATIK 37(5):435–445
7. Becker J, Delfmann P, Dreiling A, Knackstedt R, Kuropka D (2004) Configurative process modeling–outlining an approach to increased business process model usability. In: Khosrow-Pour M (ed) 14th Înformation resources management association international conference. IRM, Hershey, pp 615–619
8. Bera P (2012) Does cognitive overload matter in understanding BPMN models? J Comput Inf Syst 52(4):59–69
9. Bernstein V, Soffer P (2015) Identifying and quantifying visual layout features of business process models. In: Enterprise, business-process and information systems modeling. Springer, Heidelberg, pp 200–213
10. Bichler M, Frank U, Avison D, Malaurent J, Fettke P, Hovorka D, Krämer J, Schnurr D, Müller B, Suhl L (2016) Theories in business and information systems engineering. Bus Inf Syst Eng 58(4):291–319
11. Browne GJ, Pitts MG (2004) Stopping rule use during information search in design problems. Organ Behav Hum Decis Process 95(2):208–224
12. Browne GJ, Pitts MG, Wetherbe JC (2007) Cognitive stopping rules for terminating information search in online tasks. MIS Q 31(1):89–104
13. Burton-Jones A, Wand Y, Weber R (2009) Guidelines for empirical evaluations of conceptual modeling grammars. J Assoc Inf Syst 10(6):495–532
14. Cardoso J (2006) Process control-flow complexity metric: an empirical validation. In: IEEE International conference on services computing, SCC’06, pp 167–173
15. Chandler P, Sweller J (1996) Cognitive load while learning to use a computer program. Appl Cognit Psychol 10(2):151–170
16. Charette RN (2005) Why software fails. IEEE Spectr 42(9):42–49. doi:10.1109/MSPEC.2005.1502528
17. Chen C, Yu Y (2000) Empirical studies of information visualization: a meta-analysis. Int J Hum Comput Stud 53(5):851–866
18. Claes J, Vanderfeesten I, Reijers HA, Pinggera J, Weidlich M, Zugal S, Fahland D, Weber B, Mendling J, Poels G (2012) Tying process model quality to the modeling process: the impact of structuring, movement, and speed. In: Barros A, Gal A, Kindler E (eds) Business process management, LNCS, vol 7481. Springer, Heidelberg, pp 33–48
19. Dimoka A, Banker RD, Benbasat I, Davis FD, Dennis AR, Gefen D, Gupta A, Ischebeck A, Kenning P, Pavlou PA (2010) On the use of neurophysiological tools in IS research: developing a research agenda for NeuroIS. MIS Q 36(3):679–702
20. Döhring M, Reijers HA, Smirnov S (2014) Configuration vs. adaptation for business process variant maintenance: an empirical study. Inf Syst 39:108–133. doi:10.1016/j.is.2013.06.002
21. Dumas M, La Rosa M, Mendling J, Mäesalu R, Reijers H, Semenenko N (2012) Understanding business process models: the costs and benefits of structuredness. In: Ralyté J, Franch X, Brinkkemper S, Wrycza S (eds) Advanced information systems engineering, LNCS, vol 7328. Springer, Heidelberg, pp 31–46. doi:10.1007/978-3-642-31095-9_3
22. Effinger P, Jogsch N, Seiz S (2011) On a study of layout aesthetics for business process models using BPMN. In: Mendling J, Weidlich M, Weske M (eds) Business process modeling notation, LNBIP, vol 67. Springer, Heidelberg, pp 31–45. doi:10.1007/978-3-642-16298-5_5
23. Fahland D, Lübke D, Mendling J, Reijers HA, Weber B, Weidlich M, Zugal S (2009) Declarative versus imperative process modeling languages: the issue of understandability. In: Halpin T, Krogstie J, Nurcan S et al (eds) Enterprise, business-process and information systems modeling. Lecture Notes in Business Information Processing, vol 29. Springer, Berlin, Heidelberg, pp 353–366. doi:10.1007/978-3-642-01862-6_29
24. Fernández-Ropero M, Pérez-Castillo R, Cruz-Lemus JA, Piattini M (2013) Assessing the best-order for business process model refactoring. In: 28th Annual ACM symposium on applied computing, Coimbra
25. Fettke P, Vella AL, Loos P (2012) From measuring the quality of labels in process models to a discourse on process model quality: a case study. In: 45th Hawaii international conference on system sciences (HICSS), pp 197–206. doi:10.1109/HICSS.2012.290
26. Figl K, Laue R (2011) Cognitive complexity in business process modeling. In: Mouratidis H, Rolland C (eds) Advanced information systems engineering, LNCS, vol 6741. Springer, Heidelberg, pp 452–466. doi:10.1007/978-3-642-21640-4_34
27. Figl K, Laue R (2015) Influence factors for local comprehensibility of process models. Int J Hum Comput Stud 82:96–110. doi:10.1016/j.ijhcs.2015.05.007
28. Figl K, Recker J (2016) Exploring cognitive style and task-specific preferences for process representations. Requir Eng 21(1):63–85. doi:10.1007/s00766-014-0210-2
29. Figl K, Strembeck M (2014) On the importance of flow direction in business process models. In: 9th International conference on software engineering and applications (ICSOFT-EA). SCITEPRESS, Vienna
30. Figl K, Strembeck M (2015) Findings from an experiment on flow direction of business process models. In: International workshop on enterprise modelling and information systems architectures (EMISA), Innsbruck
31. Figl K, Mendling J, Strembeck M (2009) Towards a usability assessment of process modeling languages. In: 8th GI-Workshop Geschäftsprozessmanagement mit Ereignisgesteuerten Prozessketten (EPK), CEUR-WS, vol 554. Berlin, pp 138–156
32. Figl K, Mendling J, Strembeck M, Recker J (2010) On the cognitive effectiveness of routing symbols in process modeling languages. In: 13th international conference on business information systems (BIS), Berlin
33. Figl K, Mendling J, Strembeck M (2013a) The influence of notational deficiencies on process model comprehension. J Assoc Inf Syst 14(6):312–338
34. Figl K, Recker J, Mendling J (2013b) A study on the effects of routing symbol design on process model comprehension. Decis Support Syst 54(2):1104–1118. doi:10.1016/j.dss.2012.10.037
35. Gemino A, Wand Y (2004) A framework for empirical evaluation of conceptual modeling techniques. Requir Eng 9(4):248–260
36. Genon N, Heymans P, Amyot D (2010) Analysing the cognitive effectiveness of the BPMN 2.0 visual syntax. In: Software language engineering, LNCS. Springer, Heidelberg, pp 377–396
37. Gregor S (2006) The nature of theory in information systems. MIS Q 30(3):611–642
38. Grover V, Lyytinen K (2015) New state of play in information systems research: the push to the edges. MIS Q 39(2):271–296
39. Gruhn V, Laue R (2006) Adopting the cognitive complexity measure for business process models. In: 5th IEEE International conference on cognitive informatics, pp 236–241. doi:10.1109/COGINF.2006.365702
40. Gruhn V, Laue R (2007) Good and bad excuses for unstructured business process models. In: 12th European conference on pattern languages of programs (EuroPLoP)
41. Haisjackl C, Zugal S (2014) Investigating differences between graphical and textual declarative process models. In: Advanced information systems engineering workshops. Springer, Heidelberg, pp 194–206
42. Haisjackl C, Barba I, Zugal S, Soffer P, Hadar I, Reichert M, Pinggera J, Weber B (2016) Understanding declare models: strategies, pitfalls, empirical results. Softw Syst Model 15(2):325–352
43. Heggset M, Krogstie J, Wesenberg H (2015) The influence of syntactic quality of enterprise process models on model comprehension. CAiSE forum. CEUR, Stockholm
44. Heinrich LJ, Riedl R, Stelzer D (2014) Informationsmanagement: Grundlagen, Aufgaben, Methoden. De Gruyter, Berlin
45. Hemmer E, Heinzl A (2011) Where is the “I” in “IS research”? The quest for a coherent research stream in the context of human information behavior. In: Theory-guided modeling and empiricism in information systems research. Springer, Heidelberg, pp 223–246
46. Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. MIS Q 28(1):75–105
47. Hipp M, Strauss A, Michelberger B, Mutschler B, Reichert M (2014) Enabling a user-friendly visualization of business process models. Business process management workshops, LNBIP. Springer, Heidelberg, pp 395–407
48. Houy C, Fettke P, Loos P (2012) Understanding understandability of conceptual models—what are we actually talking about? In: Atzeni P, Cheung D, Ram S (eds) Conceptual modeling, LNCS, vol 7532. Springer, Heidelberg, pp 64–77. doi:10.1007/978-3-642-34002-4_5
49. Houy C, Fettke P, Loos P (2014) On the theoretical foundations of research into the understandability of business process models. In: 22nd European conference on information systems, Tel Aviv
50. 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, LNCS. Springer, Heidelberg, pp 458–471
51. Jeyaraj A, Sauter VL (2014) Validation of business process models using swimlane diagrams. J Inf Technol Manag 25(4):27
52. Johannsen F, Leist S, Braunnagel D (2014) Testing the impact of wand and weber’s decomposition model on process model understandability. In: International conference on information systems, Auckland
53. Jošt G, Huber J, Heričko M, Polančič G (2016) An empirical investigation of intuitive understandability of process diagrams. Comput Stand Interfac 48:90–111. doi:10.1016/j.csi.2016.04.006
54. Kathleen N, Ross B, Kriglstein S (2014) Storyboard augmentation of process model grammars for stakeholder communication. In: IEEE International conference on information visualization theory and applications (IVAPPI), pp 114–121
55. King WR, He J (2005) Understanding the role and methods of meta-analysis in IS research. Commun Assoc Inf Syst 16(1):32
56. Kirschner PA (2002) Cognitive load theory: implications of cognitive load theory on the design of learning. Learn Instr 12(1):1–10
57. Kocbek M, Jošt G, Heričko M, Polančič G (2015) Business process model and notation: the current state of affairs. Comput Sci Inf Syst 12(2):509–539. doi:10.2298/CSIS140610006K
58. Kock N, Danesh A, Komiak P (2008) A discussion and test of a communication flow optimization approach for business process redesign. Knowl Process Manag 15(1):72
59. 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):562–575. doi:10.1016/j.dss.2008.10.002
60. Koschmider A, Figl K, Schoknecht A (2015a) A comprehensive overview of visual design of process model element labels. In: Business process management workshops, Innsbruck
61. Koschmider A, Ullrich M, Heine A, Oberweis A (2015b) Revising the vocabulary of business process element labels. In: Zdravkovic J, Kirikova M, Johannesson P (eds) Advanced information systems engineering, LNCS, vol 9097. Springer, Heidelberg, pp 69–83. doi:10.1007/978-3-319-19069-3_5
62. Kummer T-F, Recker J, Mendling J (2016) Enhancing understandability of process models through cultural-dependent color adjustments. Decis Support Syst 87:1–12. doi:10.1016/j.dss.2016.04.004
63. La Rosa M, ter Hofstede AHM, Wohed P, Reijers HA, Mendling J, van der Aalst WMP (2011) Managing process model complexity via concrete syntax modifications. IEEE Trans Ind Inform 7(2):255–265
64. Laue R, Awad A (2011) Visual suggestions for improvements in business process diagrams. J Vis Lang Comput 22(5):385–399. doi:10.1016/j.jvlc.2011.04.003
65. Laue R, Gadatsch A (2011) Measuring the understandability of business process models—are we asking the right questions? In: Business process management workshops, LNBIP, vol 66. Springer, Heidelberg, pp 37–48
66. Leopold H, Smirnov S, Mendling J (2010) Refactoring of process model activity labels. Natural language processing and information systems. Springer, Heidelberg, pp 268–276
67. Leopold H, Eid-Sabbagh R-H, Mendling J, Azevedo LG, Baião FA (2013) Detection of naming convention violations in process models for different languages. Decis Support Syst 56:310–325. doi:10.1016/j.dss.2013.06.014
68. Leopold H, Mendling J, Günther O (2016) Learning from quality issues of BPMN models from industry. IEEE Softw 33(4):26–33. doi:10.1109/MS.2015.81
69. Lindland OI, Sindre G, Solvberg A (1994) Understanding quality in conceptual modeling. IEEE Softw 11(2):42–49
70. Mayring P (2003) Qualitative Inhaltsanayse (Qualitative Content Analysis), 8th edn. Beltz, Weinheim
71. Melcher J, Seese D (2008) Towards validating prediction systems for process understandability: measuring process understandability. In: 10th International symposium on symbolic and numeric algorithms for scientific computing (SYNASC’08), pp 564–571
72. Melcher J, Mendling J, Reijers HA, Seese D (2010) On measuring the understandability of process models. In: Business process management workshops, LNBIP, vol 43. Springer, Heidelberg, pp 465–476
73. Mendling J (2007) Detection and prediction of errors in EPC business process models. Vienna University of Economics and Business Administration
74. Mendling J (2013) Managing structural and textual quality of business process models. Data-driven process discovery and analysis. Springer, Heidelberg, pp 100–111
75. Mendling J, Strembeck M (2008) Influence factors of understanding business process models. In: Abramowicz W, Fensel D (eds) Business information systems (BIS), vol 7. Springer, Heidelberg, pp 142–153
76. Mendling J, Reijers H, van der Aalst WMP (2010a) Seven process modeling guidelines (7PMG). Inf Softw Technol 52(2):127–136
77. Mendling J, Reijers HA, Recker J (2010b) Activity labeling in process modeling: empirical insights and recommendations. Inf Syst 35(4):467–482
78. Mendling J, Sánchez-González L, García F, La Rosa M (2012a) Thresholds for error probability measures of business process models. J Syst Softw 85(5):1188–1197. doi:10.1016/j.jss.2012.01.017
79. Mendling J, Strembeck M, Recker J (2012b) Factors of process model comprehension—findings from a series of experiments. Decis Support Syst 53(1):195–206
80. Mendling J, Leopold H, Pittke F (2014) 25 challenges of semantic process modeling. IJISEBC 1(1):78–94
81. Milani F, Dumas M, Matulevičius R, Ahmed N, Kasela S (2016) Criteria and heuristics for business process model decomposition. Bus Inf Syst Eng 58(1):7–17
82. Moher TG, Mak DC, Blumenthal B, Leventhal LM (1993) Comparing the comprehensibility of textual and graphical programs: the case of petri nets. In: Cook CR, Scholtz JC, Spohrer JC (eds) Empirical studies of programmers: fifth workshop, pp 137–161
83. Moody DL (2004) Cognitive load effects on end user understanding of conceptual models: an experimental analysis. In: 8th East European conference on advances in databases and information systems. Springer, Heidelberg, pp 129–143
84. Moody DL (2005) Theoretical and practical issues in evaluating the quality of conceptual models: current state and future directions. Data Knowl Eng 15(3):243–276
85. Moody DL (2009) The “physics” of notations: towards a scientific basis for constructing visual notations in software engineering. IEEE Transact Softw Eng 35(5):756–779
86. Moody DL, Shanks G (2003) Improving the quality of data models: empirical validation of a quality management framework. Inf Syst 28(6):619–650
87. Müller-Putz GR, Riedl R, Wriessnegger SC (2015) Electroencephalography (EEG) as a research tool in the information systems discipline: foundations, measurement, and applications. Commun Assoc Inf Syst 37(1):46
88. Natschläger C (2011) Deontic BPMN. In: Hameurlain A, Liddle S, Schewe K-D, Zhou X (eds) Database and expert systems applications, LNCS, vol 6861. Springer, Heidelberg, pp 264–278. doi:10.1007/978-3-642-23091-2_23
89. Olsson M (2005) Beyond ‘needy’ individuals: conceptualizing information behavior. Proc Am Soc Inf Sci Technol 42(1). doi:10.1002/meet.1450420161
90. 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):596–606
91. Overhage S, Birkmeier DQ, Schlauderer S (2012) Quality marks, metrics, and measurement procedures for business process models. Bus Inf Syst Eng 54(5):217–235. doi:10.1007/s11576-012-0335-1
92. Palmer SE (1992) Common region: a new principle of perceptual grouping. Cognit Psychol 24(3):436–447
93. Parsons J, Cole L (2005) What do the pictures mean? Guidelines for experimental evaluation of representation fidelity in diagrammatical conceptual modeling techniques. Data Knowl Eng 55(3):327–342
94. 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, LNCS, vol 6336. Springer, Heidelberg, pp 13–28. doi:10.1007/978-3-642-15618-2_4
95. Peters N, Weidlich M (2009) Using glossaries to enhance the label quality in business process models. In: 8th GI-Workshop Geschäftsprozessmanagement mit Ereignisgesteuerten Prozessketten (EPK), vol 554. CEUR, Berlin, pp 75–90
96. Petre M (1995) Why looking isn’t always seeing: readership skills and graphical programming. Commun ACM 38(6):33–44. doi:10.1145/203241.203251
97. Petre M (2006) Cognitive dimensions ‘beyond the notation’. J Vis Lang Comput 17(4):292–301
98. Petrusel R, Mendling J (2013) Eye-tracking the factors of process model comprehension tasks. In: Salinesi C, Norrie MC, Pastor Ó (eds) Advanced information systems engineering: 25th international conference, CAiSE 2013, Valencia. Springer, Heidelberg, pp 224–239. doi:10.1007/978-3-642-38709-8_15
99. Petrusel R, Mendling J, Reijers HA (2016) Task-specific visual cues for improving process model understanding. Inf Softw Technol 79:63–78
100. Pichler P, Weber B, Zugal S, Pinggera J, Mendling J, Reijers HA (2012) Imperative versus declarative process modeling languages: an empirical investigation. In: Business process management workshops, LNBIP, vol 99, pp 383–394
101. Pinggera J, Furtner M, Martini M, Sachse P, Reiter K, Zugal S, Weber B (2013) Investigating the process of process modeling with eye movement analysis. Business Process Management Workshops. Springer, Heidelberg, pp 438–450
102. Purchase HC, Carrington D, Allder J-A (2000) Experimenting with aesthetics-based graph layout. In: Anderson M, Cheng P, Haarslev V (eds) Theory and application of diagrams: 1st international conference, Diagrams, Edinburgh. Springer, Berlin, pp 498–501. doi:10.1007/3-540-44590-0_46
103. Recker J (2013) Empirical investigation of the usefulness of gateway constructs in process models. Eur J Inf Syst 22(6):673–689
104. Recker J, Dreiling A (2007) 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) Australasian conference on information systems. The University of Southern Queensland, pp 356–366
105. 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(1):65–84
106. Recker J, Mendling J (2016) The state of the art of business process management research as published in the BPM conference. Bus Inf Syst Eng 58(1):55–72
107. Recker J, Rosemann M, van der Aalst WMP (2005) On the user perception of configurable reference process models–initial insights. In: 16th Australasian Conference on Information Systems, Paper 66
108. 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):199–222
109. Reijers HA, Mendling J (2011) A study into the factors that influence the understandability of business process models. IEEE Trans Sys Man Cybern Part A 41(3):449–462
110. Reijers HA, Recker JC, van de Wouw SG (2010) An integrative framework of the factors affecting process model understanding: a learning perspective. In: 16th Americas conference on information systems: sustainable IT collaboration around the globe. Association for Information Systems, Paper 184
111. Reijers HA, Freytag T, Mendling J, Eckleder A (2011a) Syntax highlighting in business process models. Decis Support Syst 51:339–349
112. Reijers HA, Mendling J, Dijkman RM (2011b) Human and automatic modularizations of process models to enhance their comprehension. Inf Syst 36(5):881–897
113. Riedl R (2009) Zum Erkenntnispotenzial der kognitiven Neurowissenschaften für die Wirtschaftsinformatik: Überlegungen anhand exemplarischer Anwendungen. NeuroPsychoEconomics 4(1):32–44
114. Riedl R, Banker RD, Benbasat I, Davis FD, Dennis AR, Dimoka A, Gefen D, Gupta A, Ischebeck A, Kenning P (2010) On the foundations of NeuroIS: reflections on the Gmunden Retreat 2009. Commun Assoc Inf Syst 27(1):15
115. Rodrigues RDA, Barros MDO, Revoredo K, Azevedo LG, Leopold H (2015) An experiment on process model understandability using textual work instructions and BPMN models. In: 29th Brazilian symposium on software engineering (SBES), pp 41–50. doi:10.1109/SBES.2015.12
116. Rosa ML, Wohed P, Mendling J, ter Hofstede AHM, Reijers HA, van der Aalst WMP (2011) Managing process model complexity via abstract syntax modifications. IEEE Trans Ind Inform 7(4):614–629. doi:10.1109/TII.2011.2166795
117. Sánchez-González L, García F, Mendling J, Ruiz F (2010) Quality assessment of business process models based on thresholds. In: Meersman R, Dillon T, Herrero P (eds) On the move to meaningful internet systems: OTM 2010, LNCS, vol 6426. Springer, Heidelberg, pp 78–95. doi:10.1007/978-3-642-16934-2_9
118. Sánchez-González L, García F, Ruiz F, Mendling J (2012) Quality indicators for business process models from a gateway complexity perspective. Inf Softw Technol 54(11):1159–1174. doi:10.1016/j.infsof.2012.05.001
119. Sandkuhl K, Wiebring J (2015) Experiences from selecting a BPM notation for an enterprise. Business information systems workshops. Springer, Heidelberg, pp 126–138
120. Sarshar K, Loos P (2005) Comparing the control-flow of epc and petri net from the end-user perspective. International conference on business process management. Springer, Heidelberg, pp 434–439
121. Schrepfer M, Wolf J, Mendling J, Reijers HA (2009) The impact of secondary notation on process model understanding. In: The practice of enterprise modeling, 2nd IFIP WG8.1 working conference (PoEM 2009), Stockholm, Sweden
122. Siegmund J, Kästner C, Apel S, Parnin C, Bethmann A, Leich T, Saake G, Brechmann A (2014) Understanding understanding source code with functional magnetic resonance imaging. In: 36th International conference on software engineering, ACM, pp 378–389
123. Simon HA (1955) A behavioral model of rational choice. Q J Econ 69(1):99–118
124. Škrinjar R, Bosilj-Vukšic V, Indihar-Štemberger M (2008) The impact of business process orientation on financial and non-financial performance. Bus Process Manag J 14(5):738–754
125. Soffer P, Wand Y (2007) Goal-driven multi-process analysis. J Assoc Inf Syst 8(3):175–203
126. Soffer P, Wand Y, Kaner M (2015) Conceptualizing routing decisions in business processes: theoretical analysis and empirical testing. J Assoc Inf Syst 16(5):2
127. Stitzlein C, Sanderson P, Indulska M (2013) Understanding healthcare processes. An evaluation of two process model notations. In: Human factors and ergonomics society annual meeting, vol 1. Sage, New York, pp 240–244
128. Storch A, Laue R, Gruhn V (2013) Measuring and visualising the quality of models. In: IEEE 1st International workshop on communicating business process and software models quality, understandability, and maintainability (CPSM), pp 1–8. doi:10.1109/CPSM.2013.6703084
129. Sweller J (1988) Cognitive load during problem solving: effects on learning. Cognit Sci Multidiscip J 12(2):257–285
130. Sweller J (2010) Element interactivity and intrinsic, extraneous, and germane cognitive load. Educ Psychol Rev 22(2):123–138
131. Trkman M, Mendling J, Krisper M (2016) Using business process models to better understand the dependencies among user stories. Inf Softw Technol 71:58–76. doi:10.1016/j.infsof.2015.10.006
132. Turetken O, Rompen T, Vanderfeesten I, Dikici A, van Moll J (2016) The effect of modularity representation and presentation medium on the understandability of business process models in BPMN. In: International conference on business process management. Springer, Heidelberg, pp 289–307
133. Vom Brocke J, Riedl R, Léger P-M (2013) Application strategies for neuroscience in information systems design science research. J Comput Inf Syst 53(3):1–13
134. Vom Brocke J, Simons A, Riemer K, Niehaves B, Plattfaut R, Cleven A (2015) Standing on the shoulders of giants: challenges and recommendations of literature search in information systems research. Commun Assoc Inf Syst 37(9):205–224
135. Wagemans J, Elder JH, Kubovy M, Palmer SE, Peterson MA, Singh M, von der Heydt R (2012) A century of Gestalt psychology in visual perception: I. Perceptual grouping and figure–ground organization. Psychol Bull 138(6):1172
136. Wand Y, Weber R (1995) On the deep structure of information systems. Inf Syst J 5(3):203–223
137. Weber B, Reichert M, Mendling J, Reijers HA (2011) Refactoring large process model repositories. Comput Ind 62(5):467
138. Webster J, Watson RT (2002) Analyzing the past to prepare for the future: writing a literature review. MIS Q 26(2):13–23
139. Weitlaner D, Guettinger A, Kohlbacher M (2013) Intuitive comprehensibility of process models. In: Fischer H, Schneeberger J (eds) S-BPM ONE—running processes, vol 360. Communications in computer and information science. Springer, Heidelberg, pp 52–71. doi:10.1007/978-3-642-36754-0_4
140. Wilson TD (2000) Human information behavior. Inf Sci 3(2):49–56
141. Zugal S, Pinggera J, Weber B, Mendling J, Reijers HA (2012) Assessing the impact of hierarchy on model understandability—a cognitive perspective. In: Kienzle J (ed) Models in software engineering: workshops and symposia at MODELS 2011, Wellington, New Zealand, Reports and revised selected papers. Springer, Heidelberg, pp 123–133. doi:10.1007/978-3-642-29645-1_14
142. Zugal S, Soffer P, Haisjackl C, Pinggera J, Reichert M, Weber B (2015) Investigating expressiveness and understandability of hierarchy in declarative business process models. Softw Syst Model 14(3):1081–1103. doi:10.1007/s10270-013-0356-2