Research: Why? What? How?

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Why? What? How?
Research

RESEARCH
WHY DO WE PERFORM

Why do we perform research?
To explore To confirmTo explain
Early stage Mid stage Late stage
Quantitative
statistics
Application of
reference theory
Direct observation
Expert interview
..research stage
..method
Hypotheses Explanations Proof..result
Typical..

To satisfy human curiosity To advance human race
Origin
Goal
Outcome
Activity
Cognitive advantage
Science category
Questions
Answers
Theories
Research
Learning
Natural science
Problems
Solutions
Artifacts
Engineering
Problem solving
Design science

SUMMARY
SUMMARY
To explore To confirmTo explain
Hypotheses Explanations Proof
To satisfy human curiosity To advance human race
Knowledge building
Research
Artifact building
Engineering

RESEARCH
WHAT EXACTLY IS

Research(er)
activities
What exactly is research?
SYSTEMATIC
KNOWLEDGE
BUILDING
RESEARCH
+ =
CREATIVE
KNOWLEDGE
BUILDING
EXPLORATION
+ =
SYSTEMATIC
ARTIFACT
BUILDING
DEVELOPMENT
+ =
CREATIVE
ARTIFACT
BUILDING
DESIGN
+ =

Difference between
Explorative research Exploration
Systematic
Hypotheses
(Full) research paper
Journal or conference
Creative
Ideas
Idea paper
Conference

SUMMARY
SUMMARY
Knowledge building Artifact building
Research
Exploration
Development
Design
Systematic
Creative

RESEARCH
HOW TO PERFORM

How to perform research?
Deduction Abduction
Context + Result

Explanation
Induction
Context + Result

Rule
Rule + Context

Result
Effect Mechanism Cause
PossibilityProbabilityCertainty
Logical
reasoning
Typically used
to reveal
Result
Inference from
general theories
Generalization of
observations
Search for best
possible explanation
Definition
All elephants are grey
Lucy is an elephant
 Lucy is grey
40 swans were observed
All of them are white
 All swans are white
The tiger cage is empty
The cage door is open
 The tiger escaped
Example

Artifact buildingKnowledge building Artifact buildingKnowledge building
Engineering
cycle
Research
cycle
Nested cycles of research activities
Problem
investigation
Problem
investigation
Results
evaluation
Solution
evaluation
Design
evaluation
Design
evaluation
Research
execution
Research
design
Solution
implementation
Solution
design
Problem
investigation
Problem
investigation
Results
evaluation
Solution
evaluation
Design
evaluation
Design
evaluation

Work soon on bigger circles
=
Make many assumptions
Work first on smaller circles
=
Slow progress
never-ending
set of nested
circles
=
RESEARCH

SUMMARY
SUMMARY
Problem investigation
Research design
Problem investigation
Solution design
Design evaluation
Research execution
Design evaluation
Solution implementation
Results evaluation Solution evaluation
R
R
R
R
R
R

NATURAL
SCIENCE
HOW TO PERFORM

How to perform natural science?
Descriptive
theory
Explanatory
theory
Predictive
theory
Whole
theory
Prescriptive
theory
Which types of theories exist?

How to perform natural science?
How to evaluate theories?
Usability
Falsifiability
Novelty
Parsimony
Consistency
Plausibility
Actual use is only measurable on a longer term
Measurable and testable (= internal validity)
Novel relations or existing relations in a novel way
Using small number of constructs and associations
Applies on multiple datasets (train set versus test set)
Accurate and profound description
Credibility
Transferability
Not conflicting with other theories
Applies in different contexts (= external validity)

DESIGN
SCIENCE
HOW TO PERFORM

How to perform design science?
Which types of artifacts exist?
Constructs
Models
Methods
Instantiations
Symbols, languages, terminology, definitions, measures
Keyword: Concepts
Abstractions, representations, frameworks
Keyword: Relations
Approaches, strategies, algorithms
Keyword: Order
Prototypes, software implementations
Keyword: Practical

Construct validity Extent to which is measured what is claimed to be measured
How to evaluate constructs? – Measure validation
Validity
Content validity
Criteria validity
Reliability
Computability
Measure (only) measures what it is supposed to measure
Extent to which all facets of the construct are represented
Extent to which a measure is related to an outcome
Consistency of measurements over time
Calculable in a finite time and preferably quickly
Ease of implementation
Intuitiveness
Computation implementation has reasonable difficulty
Easy to understand and interpret the definition
Independence Independence to other related properties

How to evaluate models? – The physics of notations
Visual expressiveness
Perceptual discriminability
Graphic economy
Dual coding
Semiotic clarity
Semantic transparency
Use: shape, size, color, brightness, orientation, texture,
horizontal position, vertical position
Visual distance matches concept difference
Maximum 6-7 values for each of the graphical variables
Combine visual and textual information
Exactly one symbol for one concept and vice versa
Intuitiveness via analogies and standards
Complexity management
Cognitive integration
Modularization and hierarchical structuring
Clear links between different visualizations
Cognitive fit Maximize fit with task and executor

How to evaluate methods? – Method Evaluation Model
Actual
effectiveness
Actual
efficiency
Perceived
usefulness
Perceived
ease of use
Intention
to use
Actual
usage
How to evaluate instantiations? – Technology Acceptance Model

SUMMARY
SUMMARY
How to perform science?
Descriptive theory
Explanatory theory
Constructs (concepts)
Models (relations)
Predictive theory
Whole theory
Methods (order)
Instantiations (practical)
Prescriptive theory

RESEARCH
HOW TO DESIGN

How to design research?
Research
gap
Literature
gap
Research
objectives
Research
goal
What is missing?
Difference between what could and what does exist
What is the problem?
Why is this (still) a problem?
What do you want to do?
What general state do you aim to achieve?
How do you want to do this?
What measurable state(s) do you aim to achieve?
Research
questions
What do you want to learn from this?
What is the knowledge contribution?

How to design research?
Maximize focus
Computer, software,
notes, data,
lighting, food & drinks
Few days in a row,
loading & offloading
Maximize inspiration
Conversations & activities,
stimulating places,
pen & paper, ambiance
Clean desk, music or silence
No email, visitors, phone calls & texts, social media
Creative tasksHard work
Goal
All tools ready
No distraction
Long enough

More at janclaes.info/resources
Thank you!

References
Slide 4
 March ST, Smith GF (1995) Design and natural science research on information technology. Decis. Support Syst. 15(4):251–266.
Slide 12
 Wieringa RJ, Heerkens JMG (2006) The methodological soundness of requirements engineering papers: a conceptual framework and two case studies. Requir. Eng.
11(4):295–307.
Slide 13
 Wieringa RJ (2009) Design science as nested problem solving. Proc. 4th Int. Conf. Des. Sci. Res. Inf. Syst. Technol. - DESRIST ’09. (ACM Press, New York, New York, USA).
Slide 16
 Gregor S (2006) The nature of theory in information systems. MIS Q. 30(3):611–642.
 Example descriptive theory: 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. Barros A, Gal A, Kindler E, eds. Proc. 10th Int. Conf. Bus. Process Manag. (BPM ’12), Tallinn, Est.
Sept. 3, 2012. (LNCS 7481, Springer), 33–48.
 Example explanatory theory: Claes J, Vanderfeesten I, Gailly F, Grefen P, Poels G (2015) The Structured Process Modeling Theory (SPMT) - A cognitive view on why and
how modelers benefit from structuring the process of process modeling. Inf. Syst. Front. 17(6):1401–1425.
 Example prescriptive theory: Claes J, Vanderfeesten I, Gailly F, Grefen P, Poels G (2017) Towards a structured process modeling method: Building the prescriptive modeling
theory. Proc. BPM 2016 Conf. Work. (LNBIP 281, Springer), 168–179.
 (Example method): Claes J, Vanderfeesten I, Gailly F, Grefen P, Poels G (2017) The Structured Process Modeling Method (SPMM) what is the best way for me to construct a
process model? Decis. Support Syst. (in press)
Slide 17
 Gregor S (2006) The nature of theory in information systems. MIS Q. 30(3):611–642.
 Grover V, Lyytinen K, Srinivasan A, Tan BCY (2008) Contributing to rigorous and forward thinking explanatory theory. J. Assoc. Inf. Syst. 9(2):40–47.
 Weber R (2012) Evaluating and developing theories in the information systems discipline. J. Assoc. Inf. Syst. 13(1):1–31.
 Weick KE (1989) Theory construction as disciplined imagination. Acad. Manag. Rev. 14(4):516–531.
 Example: Claes J, Vanderfeesten I, Gailly F, Grefen P, Poels G (2015) The Structured Process Modeling Theory (SPMT) - A cognitive view on why and how modelers benefit
from structuring the process of process modeling. Inf. Syst. Front. 17(6):1401–1425.
Slide 19
 Hevner ARR, March ST, Park J, Ram S (2004) Design science in information systems research. MIS Q. 28(1):75–105.
 Slide 20
 Smith SM, Albaum GS (2005) Fundamentals of Marketing Research (SAGE).
 Polančič G, Cegnar B (2016) Complexity metrics for process models - A systematic literature review. Comput. Stand. Interfaces 51(July 2016):104–117.
Slide 21
 Moody DL (2009) The “physics” of notations: Toward a scientific basis for constructing visual notations in software engineering. IEEE Trans. Softw. Eng. 35(6):756–779.
 Example: Claes J, Vanderfeesten I, Pinggera J, Reijers HA, Weber B, Poels G (2015) A visual analysis of the process of process modeling. Inf. Syst. E-bus. Manag. 13(1):147–
190.
Slide 22
 Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3):319–340.
 Moody DL (2003) The method evaluation model: A theoretical model for validating information systems design methods. In: Ciborra CU, Mercurio R, Marco M de, Martinez
M, Carignani A, eds. Proc. 11th Eur. Conf. Inf. Syst. (ECIS ’03). (AIS Electronic Library, Naples, Italy), 1327–1336.
 Example: Claes J, Poels G (2014) Merging Event Logs for Process Mining: A Rule Based Merging Method and Rule Suggestion Algorithm. Expert Syst. Appl. 41(16):7291–
7306.

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