Exploring cognitive style and task-specific preferences for process representations

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.

[1]  Andrew Gemino,et al.  A framework for empirical evaluation of conceptual modeling techniques , 2004, Requirements Engineering.

[2]  Edgar Erdfelder,et al.  G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences , 2007, Behavior research methods.

[3]  David V. Budescu,et al.  A Comparative Study of Measures of Partial Knowledge in Multiple-Choice Tests , 1997 .

[4]  Jan Mendling,et al.  On the Usage of Labels and Icons in Business Process Modeling , 2010, Int. J. Inf. Syst. Model. Des..

[5]  Richard E. Mayer,et al.  Multimedia Learning , 2001, Visible Learning Guide to Student Achievement.

[6]  Maria Kozhevnikov,et al.  The new object‐spatial‐verbal cognitive style model: Theory and measurement , 2009 .

[7]  Bill Curtis,et al.  Experimental evaluation of software documentation formats , 1989, J. Syst. Softw..

[8]  Donald G. Saari,et al.  Mathematical structure of voting paradoxes , 2000 .

[9]  Per Runeson,et al.  Using Students as Experiment Subjects – An Analysis on Graduate and Freshmen Student Data , 2003 .

[10]  Mark Strembeck,et al.  Factors of process model comprehension - Findings from a series of experiments , 2012, Decis. Support Syst..

[11]  Venkataraman Ramesh,et al.  Understanding Conceptual Schemas: Exploring the Role of Application and IS Domain Knowledge , 2006, Inf. Syst. Res..

[12]  R. Stebbins Exploratory research in the social sciences , 2001 .

[13]  Remco M. Dijkman,et al.  Human and automatic modularizations of process models to enhance their comprehension , 2011, Inf. Syst..

[14]  Michael A. Motes,et al.  Object-Spatial Imagery: A New Self-Report Imagery Questionnaire , 2006 .

[15]  Susanne Patig,et al.  IT Requirements of Business Process Management in Practice - An Empirical Study , 2010, BPM.

[16]  Jan Mendling,et al.  From business process models to process-oriented software systems , 2009, TSEM.

[17]  Ned Kock,et al.  Communication flow orientation in business process modeling and its effect on redesign success: Results from a field study , 2009, Decis. Support Syst..

[18]  Patrick R. Thomas,et al.  Cognitive styles and instructional design in university learning , 2010 .

[19]  Jan Recker,et al.  Process Model Comprehension: The Effects of Cognitive Abilities, Learning Style, and Strategy , 2014, Commun. Assoc. Inf. Syst..

[20]  Christophe Ley,et al.  Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median , 2013 .

[21]  M. Kozhevnikov,et al.  Creativity, visualization abilities, and visual cognitive style. , 2013, The British journal of educational psychology.

[22]  Joan H. Coll,et al.  Graphs and tables: a four-factor experiment , 1994, CACM.

[23]  Jan Mendling,et al.  A Study Into the Factors That Influence the Understandability of Business Process Models , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[24]  R. Riding,et al.  Cognitive Styles—an overview and integration , 1991 .

[25]  Hee-Woong Kim,et al.  Dynamic process modeling for BPR: A computerized simulation approach , 1997, Inf. Manag..

[26]  Michael Rosemann,et al.  Factors and measures of business process modelling: model building through a multiple case study , 2005, Eur. J. Inf. Syst..

[27]  Jan Recker,et al.  Empirical investigation of the usefulness of Gateway constructs in process models , 2013, Eur. J. Inf. Syst..

[28]  B. Tabachnick,et al.  Using Multivariate Statistics , 1983 .

[29]  Narasimhaiah Gorla,et al.  Evaluation of process tools in systems analysis , 1995, Inf. Softw. Technol..

[30]  Richard A. Harshman,et al.  Factor analysis of a questionnaire on imagery and verbal habits and skills. , 1983 .

[31]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

[32]  J. Brehm Postdecision changes in the desirability of alternatives. , 1956, Journal of abnormal psychology.

[33]  Jan Mendling,et al.  Making sense of business process descriptions: An experimental comparison of graphical and textual notations , 2012, J. Syst. Softw..

[34]  Daniel L. Moody,et al.  The “Physics” of Notations: Toward a Scientific Basis for Constructing Visual Notations in Software Engineering , 2009, IEEE Transactions on Software Engineering.

[35]  R. Zajonc,et al.  Affective and Cognitive Factors in Preferences , 1982 .

[36]  R. Fisher Social Desirability Bias and the Validity of Indirect Questioning , 1993 .

[37]  Wil M. P. van der Aalst,et al.  Bridging The Gap Between Business Models And Workflow Specifications , 2004, Int. J. Cooperative Inf. Syst..

[38]  Jan Recker,et al.  The Effects of Content Presentation Format and User Characteristics on Novice Developers' Understanding of Process Models , 2011, Commun. Assoc. Inf. Syst..

[39]  Ömer Akin,et al.  On the process of creativity in puzzles, inventions, and designs , 1998 .

[40]  Jan Recker,et al.  Continued use of process modeling grammars: the impact of individual difference factors , 2010, Eur. J. Inf. Syst..

[41]  W. Shadish,et al.  Experimental and Quasi-Experimental Designs for Generalized Causal Inference , 2001 .

[42]  M. F. Steehouder,et al.  Selecting and switching: some advantages of diagrams for presenting instructions , 1998 .

[43]  H. Simon,et al.  The sciences of the artificial (3rd ed.) , 1996 .

[44]  Paul Slovic,et al.  The Construction of Preference: References , 2006 .

[45]  M. F. Steehouder,et al.  Selecting and switching: some advantages of diagrams over tables and lists for presenting instructions , 1998 .

[46]  William J. Kettinger,et al.  Business Process Change: A Study of Methodologies, Techniques, and Tools , 1997, MIS Q..

[47]  Herbert A. Simon,et al.  Why a Diagram is (Sometimes) Worth Ten Thousand Words , 1987, Cogn. Sci..

[48]  Jan Recker,et al.  How Much Language Is Enough? Theoretical and Practical Use of the Business Process Modeling Notation , 2008, CAiSE.

[49]  Alfredo Campos,et al.  Gender differences in imagery , 2014 .

[50]  Jan Mendling,et al.  Process Model Generation from Natural Language Text , 2011, CAiSE.

[51]  Mark Strembeck,et al.  The Influence of Notational Deficiencies on Process Model Comprehension , 2013, J. Assoc. Inf. Syst..

[52]  Jan Recker,et al.  How novices design business processes , 2012, Inf. Syst..

[53]  Jan Mendling,et al.  Syntax highlighting in business process models , 2011, Decis. Support Syst..

[54]  Peter Bernus,et al.  Business process modeling through the knowledge management perspective , 2006, J. Knowl. Manag..

[55]  A. Glenberg,et al.  Comprehension of illustrated text: Pictures help to build mental models☆ , 1992 .

[56]  Peter C.-H. Cheng,et al.  Why Diagrams Are (Sometimes) Six Times Easier than Words: Benefits beyond Locational Indexing , 2004, Diagrams.

[57]  Jan Mendling,et al.  How collaborative technology supports cognitive processes in collaborative process modeling: A capabilities-gains-outcome model , 2013, Inf. Syst..

[58]  Peter Fettke,et al.  How Conceptual Modeling Is Used , 2009, Commun. Assoc. Inf. Syst..

[59]  Yvonne Rogers,et al.  External cognition: how do graphical representations work? , 1996, Int. J. Hum. Comput. Stud..

[60]  Ron Weber,et al.  Structured tools and conditional logic: an empirical investigation , 1986, CACM.

[61]  Helen Kelley,et al.  Research Commentary - Generalizability of Information Systems Research Using Student Subjects - A Reflection on Our Practices and Recommendations for Future Research , 2012, Inf. Syst. Res..

[62]  Marta Indulska,et al.  Business Process Modeling- A Comparative Analysis , 2009, J. Assoc. Inf. Syst..

[63]  Sharon L. Thompson-Schill,et al.  Cognitive style, cortical stimulation, and the conversion hypothesis , 2014, Front. Hum. Neurosci..

[64]  Marta Indulska,et al.  How do practitioners use conceptual modeling in practice? , 2006, Data Knowl. Eng..

[65]  Brian T. Pentland,et al.  Process Grammar as a Tool for Business Process Design , 2008, MIS Q..

[66]  Amaresh Chakrabarti,et al.  The effect of representation of triggers on design outcomes , 2008, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[67]  Peter Meso,et al.  The Effects of Decomposition Quality and Multiple Forms of Information on Novices' Understanding of a Domain from a Conceptual Model , 2008, J. Assoc. Inf. Syst..

[68]  Alistair Cockburn,et al.  Writing Effective Use Cases , 2000 .

[69]  J. Stevens Applied Multivariate Statistics for the Social Sciences , 1986 .

[70]  R. Gonzalez Applied Multivariate Statistics for the Social Sciences , 2003 .

[71]  Iris Vessey,et al.  Cognitive Fit: A Theory‐Based Analysis of the Graphs Versus Tables Literature* , 1991 .

[72]  Dennis F. Galletta,et al.  Cognitive Fit: An Empirical Study of Information Acquisition , 1991, Inf. Syst. Res..

[73]  Valeria Occelli,et al.  Loss of form vision impairs spatial imagery , 2014, Front. Hum. Neurosci..

[74]  Mary Hegarty,et al.  Revising the Visualizer-Verbalizer Dimension: Evidence for Two Types of Visualizers , 2002 .

[75]  Kalervo Järvelin,et al.  Task complexity affects information seeking and use , 1995 .

[76]  Bruce Thompson,et al.  Score Reliability in Webor Internet-Based Surveys: Unnumbered Graphic Rating Scales versus Likert-Type Scales , 2001 .

[77]  Ross A. Malaga The effect of stimulus modes and associative distance in individual creativity support systems , 2000, Decis. Support Syst..

[78]  Andrew Gemino,et al.  Using Iconic Graphics in Entity-Relationship Diagrams: The Impact on Understanding , 2008, J. Database Manag..

[79]  Jan Recker,et al.  Opportunities and constraints: the current struggle with BPMN , 2010, Bus. Process. Manag. J..

[80]  Maria Kozhevnikov,et al.  Trade-off in object versus spatial visualization abilities: Restriction in the development of visual-processing resources , 2010, Psychonomic bulletin & review.

[81]  D. Campbell Task Complexity: A Review and Analysis , 1988 .

[82]  Donald G. Saari,et al.  Mathematical Structure of Voting Paradoxes: II. Positional Voting , 1999 .

[83]  R. Mayer,et al.  Multimedia Learning: Frontmatter , 2001 .

[84]  Anita Williams Woolley,et al.  Do you see what I see? The effect of members cognitive styles on team processes and errors in task execution , 2013 .

[85]  Keith Phalp,et al.  Improving the quality of use case descriptions: empirical assessment of writing guidelines , 2007, Software Quality Journal.

[86]  Marta Indulska,et al.  Business Process Modeling: Perceived Benefits , 2009, ER.

[87]  Christoph Meinel,et al.  An approach to capture authorisation requirements in business processes , 2010, Requirements Engineering.

[88]  Mark von Rosing,et al.  Business Process Model and Notation - BPMN , 2015, The Complete Business Process Handbook, Vol. I.

[89]  Peter C-H Why Diagrams Are (Sometimes) Six Times Easier than Words: Benefits beyond Locational Indexing , 2004 .