colloquium@tue

Ghent University & Eindhoven University of [email protected] - www.janclaes.info

TU/e Colloquium 201310 April 2023

FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION

The process of process modeling and process model quality

Jan ClaesTeaching assistant : PhD 2009 – 2015 : Joint PhD

Supervisors : Geert Poels (UGent) and Paul Grefen (TU/e)Co-supervisors : Frederik Gailly (UGent) and Irene Vanderfeesten (TU/e)

TU/e Colloquium 20132/41


Outline

Process of process modeling (PPM) PPMChart visualization Structured process modeling (SPM)

Future work: preliminary ideas Process model quality Experiments to link SPM with quality



Process of Process Modeling (PPM)

Properties of textual description

Properties of modeler

Properties of modeling process

Properties of resulting model

PRIMARY RESEARCH FOCUSProperties of real process

Properties of observation process

Properties of softwareand modeling language

Properties of model reader

Properties of reading process

Properties of process engine




Observational modeling sessions People construct models Every action on modeling canvas is logged Different datasets

• 120 students in Eindhoven 2010• 14 experts in Berlin 2010• 14 experts in Eindhoven 2011• 118 students in Eindhoven 2012• 146 students in Gent 2013




Properties of the modeling processActivity Timestamp Attributes

Create start event 10:00 Id = 1; x = 10; y = 10

Create activity 10:04 Id = 2; x = 40; y = 10; name = “Receive order”

Create edge 10:05 Id = 3; from = 1, to = 2

Move activity 10:07 Id = 2; x = 15; y = 10

Create gateway 10:08 Id = 4; x = 65; y = 10; type = “XOR”

Create edge 10:09 Id = 5; from = 2, to = 4

Create activity 10:24 Id = 6; x = 80; y = 0; name = “Reject order”

Create activity 10:25 Id = 7; x = 80; y = 20; name = “Prepare order”

Create gateway 10:27 Id = 8; x = 105; y = 10; type = “XOR”



CREATE_ACTIVITY CREATE_START_EVENT CREATE_END_EVENT CREATE_AND CREATE_XOR CREATE_EDGE MOVE_ACTIVITY MOVE_START_EVENT MOVE_END_EVENT MOVE_AND MOVE_XOR DELETE_ACTIVITY DELETE_START_EVENT DELETE-END_EVENT DELETE_AND DELETE_XOR DELETE_EDGE NAME_ACTIVITY RENAME_ACTIVITY NAME_EDGE RENAME_EDGE


Visualization in PPMChart

time

mod

el e

lem

ents




9 design principles of visual notations (Moody 2009)

Visual expressiveness Perceptual discriminability Graphic economy Semantic transparency Semiotic clarity Dual coding Cognitive fit Complexity management Cognitive integration




Visual expressiveness Optimal use of graphical variables 8 graphical variables: shape, size, color, brightness,

orientation, texture, horizontal position and vertical position (Bertin, 2010)

Color is most effective (Lohse, 1993; Treisman, 1982; Winn, 1993)

But can also cause problems (e.g., color blindness, black-and-white printers)




8 graphical variables Shape: model element type ( ) Size: not used Color: operation type ( ) Brightness: model element type ( ) Orientation: not used Texture: not used Horizontal position: time Vertical position: model element




Perceptual discriminability Symbols are clearly distinguishable The more two concepts differ, the more the

corresponding symbols should differ (Winn, 1990)

Visual distance is determined by • Number of different values for the graphical variables• Size of these differences

CREATE_ACTIVITY CREATE_START_EVENT CREATE_END_EVENT CREATE_AND CREATE_XOR CREATE_EDGE

MOVE_ACTIVITY MOVE_START_EVENT MOVE_END_EVENT MOVE_AND MOVE_XOR

DELETE_ACTIVITY DELETE_START_EVENT DELETE-END_EVENT DELETE_AND DELETE_XOR DELETE_EDGE

NAME_ACTIVITY RENAME_ACTIVITY NAME_EDGE RENAME_EDGE




Graphic economy Limited amount of values for each variable Assures cognitive effectiveness (Nordbotten & Crosby, 1999)

Span of absolute judgment• Is the amount of distinct observable perceptual values• Estimated at seven (Miller, 1956)

Span of attention• Is the amount of different objects that can be

distinguished at a glance• Estimated at six objects (Miller, 1956)




Semantic transparency If a novice would be able to guess the meaning of

each symbol Achieved through natural mappings (Norman, 2002)

Shapes similar to bpmn ( ) Logical colors (creation, deletion, movement) Horizontal timing




Semiotic clarity Every concept is represented by exactly one symbol

and every symbol represents exactly one concept (Goodman, 1968)

Same default symbol for XOR and AND gateway () Same default symbol for start and end event ()




Dual coding For information processing Graphical representation is better than textual Combination has highest cognitive effectiveness

(Paivio, 1990)

Textual line identifiers and time intervals No textual code on the dots Textual information in pop-up on selected items




Cognitive fit Optimal representation depends on the task Cognitive load is lower for experts (Vessey & Galletta, 1991)

(Optimal representation depends on the modeler)

View is customizable through various options View can be filtered (e.g. hide deleted elements)




Complexity management Reduce complexity (R. Weber, 1997)

• by modularization (divide the diagram in smaller subsystems)

• hierarchical structuring (make separate diagrams of the same information at different levels of abstraction)

Only one PPM instance at a time




Cognitive integration Mechanisms to integrate different diagrams

(Hahn & Kim, 1999; Kim, Hahn, & Hahn, 2000)

Fixed default values for easy comparison Line identifiers correspond to model element id’s Lines are sorted according to model (start to end)



CREATE_ACTIVITY CREATE_START_EVENT CREATE_END_EVENT CREATE_AND CREATE_XOR CREATE_EDGE MOVE_ACTIVITY MOVE_START_EVENT MOVE_END_EVENT MOVE_AND MOVE_XOR DELETE_ACTIVITY DELETE_START_EVENT DELETE-END_EVENT DELETE_AND DELETE_XOR DELETE_EDGE NAME_ACTIVITY RENAME_ACTIVITY NAME_EDGE RENAME_EDGE


Visualization in PPMChart

Start event Edge Activity

Gateway

Edge

Activity

Edge

Edge

Activity

Edge Gateway

Edge

7298

9

32

14

30

31

10

3356

34

time

mod

el e

lem

ents



Structured Process Modeling (SPM)

UNSTRUCTURED(rather) chaotic process




FLOW-ORIENTEDFrom start event to end event



ASPECT-ORIENTEDContent – structure – lay-out




CHUNKINGWork on model part by part





Structured process modeling Applying a modeling strategy consistently Flow-oriented modeling versus aspect-oriented

First content, then structure, then lay-out

Finish aspect before continuing Separate vertical zones

From start to end (according to the process flow)

Finish part before continuing Diagonal zone in charts

With or within chunks




Cognitive aspects Cognitive Load Theory (CLT)

limited capacity of working memory

Cognitive Fit Theory (CFT)effect increase if task representation fits




Conclusion 1How to construct models?

Apply a modeling style consistently!

Properties of modeling process

Properties of resulting model

PRIMARY RESEARCH FOCUSStructured process modeling

Aspect-oriented modeling

Flow-orientedmodeling

Chunkedmodeling



Different studies Apply process mining techniques on historical data (Eo-BS) Develop PPM visualization (Co-DS) Different structuring styles (Ea-BS) Link modeling strategy with model quality (Co-BS) Develop method/tool to increase model quality (Co-DS)

Different research methods Behavioral science (BS) vs. Design science (DS) Explorative (Eo) vs. Explanatory (Ea) vs. Confirmative (Co)

Different studies Apply process mining techniques on historical data Develop PPM visualization Different structuring styles Link modeling strategy with model quality Develop method/tool to increase model quality

Methodology



Business Process Modeling (BPM)

Business process model Graphical, abstract representation of a process Important tool for analysis and improvement

Business process model in BPMN notation



Process model quality

Less nodes Less crossing arcs Less nested gateways …

More realistic More precise More complete …

Which model is better?



Which model is better?Process model quality

Less nodes Less crossing arcs Less nested gateways …

Correctness

More realistic More precise More complete …

Completeness

Understandability

Maintainability

InspectionExecution



Execution Inspection

Process model quality

Conclusion 2Process model quality?

Depends on the goal of the model!




How does SPM influence model quality?Structured process modeling lowers

cognitive efforts and cognitive overloadLess unintentional quality issues in model

Focus on correctness and completeness Focus on understandability and maintainability

But no effect on Missing knowledge of domain or model language Wrong quality focus




Structured process modeling causes model quality improvement! Explanation 1:

• Apply structured modeling style• Lowers cognitive load• Results in improved process model quality

Explanation 2:• Have a lot of modeling experience• Results in a consistent modeling style• And in improved process model quality

Or not?



Experiments

How to prove causality? Take two identical groups of people Give only one group a treatment Let both groups make the same exercise Check for significant difference of the results Difference can only be caused by treatment



Experiments

Two identical groups Randomized: assign participants randomly to group Block randomized: control for secondary variables

• E.g. equal amount of (fe)male participants in each group Check with pre-test: check primary variables

• E.g. check difference in experience of both groups



Experiments

Give only one group a treatment Traditionally pharmaceutical In our case learning a technique Placebo effect Treatment effect (TE) should be verified and

separated from learning effect (LE) Treatment group: TE + LE Control group: LE



Experiments

Triple blinded Blind experiment: participants do not know if they

are in the treatment group or the control group Double blind: participants and administrators do

not know to which group participants belong Triple blind: participants, administrators and data

analysts do not know the group assignment



Experiments

Make the same exercise under the same conditions Keep as much variables constant as possible Try to control for the others Literally the only difference between the two

groups should be the treatment



Participants for comparative experiment High school students? OK! Undergraduates? OK! Experienced modelers? OK! Non human movie characters? Not OK! Cognitive processes in the human mind

Experiments

Participants for comparative experiment High school students? Undergraduates? Experienced modelers? Non human movie characters?



Experiments with undergraduates

When use students? For confirmative, comparative experiments No reason to believe that effect is different

(general human cognitive processes) Very homogeneous group, large groups

When not to use students? For explanatory, observational experiments Use real modelers with varying levels of experience Representative sample



Experiments

Conclusion 3Use undergraduate students in experiments?

Sometimes no, sometimes yes!

ComparativeExplanatory



Contact information

Jan [email protected]

http://www.janclaes.infoTwitter: @janclaesbelgium

Thanks for your attention!Do you have feedback on my research plans?

> explanatory theories <

> behavioral science <

> methodology <

> evaluation <