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how novices model
business processes
Jan Recker | Niz Safrudin | Michael Rosemann

Business Process Management Group
Information Systems Discipline
Faculty of Science and Technology
    Queensland University of Technology
    Brisbane Australia
A picture replaces
                     1,000 words,
               or do I need 1,000
               words to explain a
                         picture?


the standard
BPMN 2.0
                               Voelzer (2009)
OUR
INTERPRETATION
        OF THE
      PROBLEM
 We tend to force users to think like
                 process modelers,

when process modelers should think
                        like users.
THE RESEARCH QUESTIONS
THE RESEARCH MODEL
METHOD & FINDINGS
DISCUSSION OF RESULTS




                AGENDA
                 TODAY
Research Questions
RQ1   How do novice analysts carry out business process modeling
      when uninformed of formal modeling method(s)?

RQ2   How ‘good’ are the different types of process designs in
      representing important business elements of a particular
      process scenario?
Our Research Model
 Prior Experience
                                                         Process Design Work
    F: Method Knowledge
    O: Process Modeling
       Experience                                          F: Process Design
       Data Modeling                                          Representation Type
       Experience
                                                           O: Diagram Classification
       Object-Oriented
       Modeling Experience



    F: Domain Knowledge
    O: Experience with Airport
       Domain
                                                           F: Process Design
                                                              Representation
    F: Artistic Competency                                    Quality
    O: Drawing skill                                       O: Semantic Correctness
       Assessment                                             Assessment




                                 KEY
                                 F: Theoretical Factor       O: Operationalisation of Factor
Data Collection

QUASI‐EXPERIMENT

Part 1: Demographics Survey
Data Collection

QUASI‐EXPERIMENT

Part 2: Drawing Skills
Data Collection

QUASI‐EXPERIMENT

Part 3: Solving a modeling problem
Mark is going on a trip to Sydney. He decides to call a taxi from home to the
airport. The taxi arrives after 10 minutes, and takes half an hour for the 20
kilometers to the airport. At the airport, Mark uses the online check-in counter
and receives his boarding pass. Of course, he could have also used the ticket
counter. He does not have to check-in any luggage, and so he proceeds
straight to the security check, which is 100 meters down the hall on the right.
The queue here is short and after 5 minutes he walks up to the level with the
departure gates. Mark decides not to go to the Frequent Flyer lounge and
instead walks up and down the shops for 15 minutes and buys a newspaper
before he returns to the gate. After ten minutes waiting, he boards the plane.
Mark is going on a trip to Sydney. He decides to call a taxi
  from home to the airport. The taxi arrives after 10 minutes,
  and takes half an hour for the 20 kilometers to the airport. At
  the airport, Mark uses the online check-in counter and
  receives his boarding pass. Of course, he could have also
  used the ticket counter. He does not have to check-in any
  luggage, and so he proceeds straight to the security check,
  which is 100 mtrs down the hall on the right. The queue here
  is short and after 5 minutes he walks up to the level with the
  departure gates. Mark decides not to go to the Frequent Flyer
  lounge and instead walks up and down the shops for
  15 minutes and buys a newspaper before he returns to the
  gate. After ten minutes waiting, he boards the plane.


    HOW NOVICES MODEL BUSINESS PROCESSES

... for instance
Coding and Analysis
                                  Process Design Work


Process Design Type [DT]            F: Process Design
                                       Representation
                                       Type
•Iterative multi-coder approach     O: Diagram
                                       Classification
•Classifying diagrams per:
   •Graphical constructs
   •Textual information
   •Control flow                    F: Process Design
                                       Representation
                                       Quality
                                    O: Semantic
                                       Correctness
                                       Assessment
Research Findings

  TYPE I                               TYPE II     TYPE III        TYPE IV           TYPE V




No graphics               Negligible graphics    Some graphics   Lots of graphics   All graphics
All text                  Lots of text           Lots of text    Some text          Negligible text
No. of Diagram           54 / 75
 Percentage of Students   72%


DT2 Flowchart Design
No. of Diagram           6 / 75
 Percentage of Students   8%


DT3 Hybrid Design
No. of Diagram           11 / 75
 Percentage of Students   14%


DT4 Storyboard Design
Research Findings

Predicting the chosen
 Process Design Type [DT]

•DT2 (Flowchart Design):
   •PDK a significant predictor (Beta = 1.47,
    p = 0.04)
•DT4 (Storyboard Design):
   •OMK a significant negative predictor (Beta =
    -3.62, p = 0.01)
Coding and Analysis
                                                     Process Design Work

Process Design Quality [DQ]                            F: Process Design
                                                          Representation
•Multi-coder approach                                     Type
                                                       O: Diagram
•Semantic Correctness                                     Classification
   • based on (Yang et al., 2005; Mendling et al.,
     2009; Nickerson et al., 2008)
•Representation of:
   • Activities      • Temporal Information            F: Process Design
   • States          • Geospatial Information             Representation
                                                          Quality
   • Events
                                                       O: Semantic
   • Business Rule                                        Correctness
                                                          Assessment
Research Findings
Predicting the
 Process Design Quality [DQ]


•ANOVA Analysis
   •DT a significant predictor
    (F = 12.46, p = 0.00)
   •PDK a significant predictor
    (F = 9.57, p = 0.01)
Research Findings
 Predicting the
  Process Design Quality [DQ]
DT with      State        Task      Event    Business       Time    Distance
highest                                       Rules
mean 
results
DT1        5.00        5.00       1.00      4.00         4.00      5.00
DT2        2.98*       3.81*      2.81*     4.06         3.15 *    3.07
DT3        2.50        3.00       1.33      3.17         3.00      3.67
DT4        2.73        2.82       1.27      3.09         2.91      3.73*
DT5        1.00        1.00       1.00      1.00         1.00      1.00

                     Multivariate ANOVA Selected Results
Research Findings
QUALITY DIMENSION   PRIOR EXPERIENCE OF INDIVIDUALS




  MANOVA Significant Results of Prior Experience
Qualitative Analysis
“Dual Coding Theory”
Paivio (1990)   Effective conveyance of information
                • Interdependency – text and graphics
Qualitative Analysis
“Physics of Notation”
Moody (2009)   Theory of effective visual notations
               •Monosemy


                                              independent
                                                 symbol


                                            has established
                                               meaning
Qualitative Analysis
“Spatial Contiguity”
Mayer & Moreno (2003)   Inclusion of text and graphics
                        • Rather than segregation
Qualitative Analysis
“Temporal Information”
Boroditsky (2000)   DT2 Flowcharts
                    • Textual captions within abstract shapes
Qualitative Analysis
“Geospatial Information”
DT4 Storyboards
• Notable: Effective and intuitive representation
Discussion



                                     RESEARCH LIMITATIONS
Students as novice analysts
Inter-Subjectivity in coding
Drawing, not designing, skill assessment
Explanatory power of statistics
Coding by professional modeler
Conclusions
Implications
  ACADEMIC CURRICULUM
    Introduce Business Process Modeling informally
    General teaching practice

  INDUSTRY PRACTICE
     Communication amongst uninformed stakeholders
     Leverage intuitive articulations in process (re-) design initiatives

  RESEARCH
    How can creative problem-solving (for process innovation) be
    supported through process models?
Contact Us
Jan Recker, Niz Safrudin,Michael Rosemann

Business Process Management Group
Queensland University of Technology
126 Margaret Street
Brisbane QLD 4000 Australia

e {j.recker; norizan.safrudin; m.rosemann}@qut.edu.au
t janrecker, nizzsafrudin, ismiro
w http://www.bpm.fit.qut.edu.au

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How Novices Model Business Processes @BPM2010

  • 1. how novices model business processes Jan Recker | Niz Safrudin | Michael Rosemann Business Process Management Group Information Systems Discipline Faculty of Science and Technology Queensland University of Technology Brisbane Australia
  • 2. A picture replaces 1,000 words, or do I need 1,000 words to explain a picture? the standard BPMN 2.0 Voelzer (2009)
  • 3. OUR INTERPRETATION OF THE PROBLEM We tend to force users to think like process modelers, when process modelers should think like users.
  • 4. THE RESEARCH QUESTIONS THE RESEARCH MODEL METHOD & FINDINGS DISCUSSION OF RESULTS AGENDA TODAY
  • 5. Research Questions RQ1 How do novice analysts carry out business process modeling when uninformed of formal modeling method(s)? RQ2 How ‘good’ are the different types of process designs in representing important business elements of a particular process scenario?
  • 6. Our Research Model Prior Experience Process Design Work F: Method Knowledge O: Process Modeling Experience F: Process Design Data Modeling Representation Type Experience O: Diagram Classification Object-Oriented Modeling Experience F: Domain Knowledge O: Experience with Airport Domain F: Process Design Representation F: Artistic Competency Quality O: Drawing skill O: Semantic Correctness Assessment Assessment KEY F: Theoretical Factor O: Operationalisation of Factor
  • 9.
  • 10. Data Collection QUASI‐EXPERIMENT Part 3: Solving a modeling problem Mark is going on a trip to Sydney. He decides to call a taxi from home to the airport. The taxi arrives after 10 minutes, and takes half an hour for the 20 kilometers to the airport. At the airport, Mark uses the online check-in counter and receives his boarding pass. Of course, he could have also used the ticket counter. He does not have to check-in any luggage, and so he proceeds straight to the security check, which is 100 meters down the hall on the right. The queue here is short and after 5 minutes he walks up to the level with the departure gates. Mark decides not to go to the Frequent Flyer lounge and instead walks up and down the shops for 15 minutes and buys a newspaper before he returns to the gate. After ten minutes waiting, he boards the plane.
  • 11. Mark is going on a trip to Sydney. He decides to call a taxi from home to the airport. The taxi arrives after 10 minutes, and takes half an hour for the 20 kilometers to the airport. At the airport, Mark uses the online check-in counter and receives his boarding pass. Of course, he could have also used the ticket counter. He does not have to check-in any luggage, and so he proceeds straight to the security check, which is 100 mtrs down the hall on the right. The queue here is short and after 5 minutes he walks up to the level with the departure gates. Mark decides not to go to the Frequent Flyer lounge and instead walks up and down the shops for 15 minutes and buys a newspaper before he returns to the gate. After ten minutes waiting, he boards the plane. HOW NOVICES MODEL BUSINESS PROCESSES ... for instance
  • 12. Coding and Analysis Process Design Work Process Design Type [DT] F: Process Design Representation Type •Iterative multi-coder approach O: Diagram Classification •Classifying diagrams per: •Graphical constructs •Textual information •Control flow F: Process Design Representation Quality O: Semantic Correctness Assessment
  • 13. Research Findings TYPE I                               TYPE II TYPE III TYPE IV TYPE V No graphics Negligible graphics Some graphics Lots of graphics All graphics All text Lots of text Lots of text Some text Negligible text
  • 14. No. of Diagram 54 / 75 Percentage of Students 72% DT2 Flowchart Design
  • 15. No. of Diagram 6 / 75 Percentage of Students 8% DT3 Hybrid Design
  • 16. No. of Diagram 11 / 75 Percentage of Students 14% DT4 Storyboard Design
  • 17. Research Findings Predicting the chosen Process Design Type [DT] •DT2 (Flowchart Design): •PDK a significant predictor (Beta = 1.47, p = 0.04) •DT4 (Storyboard Design): •OMK a significant negative predictor (Beta = -3.62, p = 0.01)
  • 18. Coding and Analysis Process Design Work Process Design Quality [DQ] F: Process Design Representation •Multi-coder approach Type O: Diagram •Semantic Correctness Classification • based on (Yang et al., 2005; Mendling et al., 2009; Nickerson et al., 2008) •Representation of: • Activities • Temporal Information F: Process Design • States • Geospatial Information Representation Quality • Events O: Semantic • Business Rule Correctness Assessment
  • 19. Research Findings Predicting the Process Design Quality [DQ] •ANOVA Analysis •DT a significant predictor (F = 12.46, p = 0.00) •PDK a significant predictor (F = 9.57, p = 0.01)
  • 20. Research Findings Predicting the Process Design Quality [DQ] DT with  State Task Event Business  Time Distance highest  Rules mean  results DT1 5.00 5.00 1.00 4.00 4.00 5.00 DT2 2.98* 3.81* 2.81* 4.06 3.15 * 3.07 DT3 2.50 3.00 1.33 3.17 3.00 3.67 DT4 2.73 2.82 1.27 3.09 2.91 3.73* DT5 1.00 1.00 1.00 1.00 1.00 1.00 Multivariate ANOVA Selected Results
  • 21. Research Findings QUALITY DIMENSION PRIOR EXPERIENCE OF INDIVIDUALS MANOVA Significant Results of Prior Experience
  • 22. Qualitative Analysis “Dual Coding Theory” Paivio (1990) Effective conveyance of information • Interdependency – text and graphics
  • 23. Qualitative Analysis “Physics of Notation” Moody (2009) Theory of effective visual notations •Monosemy independent symbol has established meaning
  • 24. Qualitative Analysis “Spatial Contiguity” Mayer & Moreno (2003) Inclusion of text and graphics • Rather than segregation
  • 25. Qualitative Analysis “Temporal Information” Boroditsky (2000) DT2 Flowcharts • Textual captions within abstract shapes
  • 26. Qualitative Analysis “Geospatial Information” DT4 Storyboards • Notable: Effective and intuitive representation
  • 27. Discussion RESEARCH LIMITATIONS Students as novice analysts Inter-Subjectivity in coding Drawing, not designing, skill assessment Explanatory power of statistics Coding by professional modeler
  • 28. Conclusions Implications ACADEMIC CURRICULUM Introduce Business Process Modeling informally General teaching practice INDUSTRY PRACTICE Communication amongst uninformed stakeholders Leverage intuitive articulations in process (re-) design initiatives RESEARCH How can creative problem-solving (for process innovation) be supported through process models?
  • 29.
  • 30. Contact Us Jan Recker, Niz Safrudin,Michael Rosemann Business Process Management Group Queensland University of Technology 126 Margaret Street Brisbane QLD 4000 Australia e {j.recker; norizan.safrudin; m.rosemann}@qut.edu.au t janrecker, nizzsafrudin, ismiro w http://www.bpm.fit.qut.edu.au