MSc by Research in Leading, Learning and Change
Case Study Research
Dr Heather Skipworth
Research Fellow, Supply Chain Research Centre
heather.skipworth@cranfield.ac.uk
Who am I
1989
BSc Mechanical Engineering, Leicester University
1989 – 1991 Project Engineer, Metal Box
1991 – 1995 Technical Manager, Field Packaging
1995 – 1996 MSc Manufacturing Systems, Cranfield University
1996 – 1998 Senior Manufacturing Systems Engineer,
BICC Cables Limited
1998 – 2003 PhD Programme, Cranfield University
Application of Form Postponement in Manufacturing Industry
2004 to date Research Fellow, Cranfield University
Survey of Cranfield Doctoral Thesis Submissions
 Out of 156 thesis submissions between 1987 & 2007,
– 65 were case-based,
– 32 used statistical methods,
– 10 used repertory grid
 We major on ‘in-depth’ research that’s relevant to
practice
What Case Study Research is not...
 an aid to teaching
 an interesting story
 promotion of a new fad
 a basket of unconnected observations
 your views with illustrations
 someone else’s views with illustrations
What is a Case Study?
 investigates a contemporary phenomenon within its
real life context...
 ...when the boundaries between phenomenon and
context are not clearly evident
Yin, 2003
Prejudices...
 lack of rigour

biased views, data collection, link conclusions to
evidence
 lack of generalisability
 n = 1, narrow relevance, context specific
 too complex
 data asphyxiation
Abstraction
Case Studies in Operations M. Research
Modelling,
Experiments
Large Population
Surveys
Case Studies,
Action Research
Accuracy / Repeatability
Issue
Variable Oriented
Case Oriented
Basis of
Research
Quantitative

Multi-variate statistical
Multiple methods to establish
techniques
Scope
Many data sets
Few data sets
Wide categoies
Broad empirical generalisations
Narrow classes of phenomena
Several combinations of
based on hetrerogeneous
samples
Comparability ignored/skirted
Causality
Conclusions
different views.
Qualitative & quantitative
conditions may yield a certain
outcome
Disaggregated into variables &
Probabalistic relationships not
distributions
Based on analysis of entire
population or sample
Must account for all deviating
Vague & abstract
‘Unreal quality’ of conclusions
More concrete questions do not
accepted
cases
Few general conclusions
Separate contexts
receive the attention they deserve
Theory/data link
Radically analytic
Strictly a priori
Link between research & actual
Rich & elaborate dialogue
Strong link between research &
actual processes
empirical processes strained
Aggregation
Breaks into parts - variables
which are difficult to reassemble
into wholes. Not combinatorial.
Complexity
Average influence across a
variety
Relevance
Broad: general statements linked
to abstract theoretical ideas
about generic properties
Holistic: parts related to context
of whole
Sensitive to complexity &
historical specificity. But difficult
to sustain attention to complexity
across a large number of cases.
Narrow: findings specific to few
cases examined.
Charles Ragin
Variable-oriented Research
 a true statement about a population...
– may not apply to any individual case
 generalising impedes true understanding
– properties shared by all organisations are obvious
 averages show how organisations are the same
– what matters is how they are different
 large samples & ‘statistical significance’...
– generate ‘significant’ findings that have no meaning
 large sample statistics...
– deflect from individuality, complexity & variety
Bill Starbuck
How Case Studies can be Used...
 explore social processes as they unfold
 understand social processes in context
* internal, external
 explore new processes or behaviours
 explore extremes
 capture emergent properties
 explore informal or secret behaviour
 cross-national comparative research
Hartley, 1994
Applications of Case-Based Research
Exploratory
Descriptive
Explanatory
Testing
Theory
Generation
Testing
Research Strategy - induction v deduction?
INDUCTIVE METHODS
DEDUCTIVE METHODS
THEORISING
Theories
Forming concepts
developing &
arranging
propositions
DOING EMPIRICAL
RESEARCH
Empirical
generalisations
Deducing
consequences
making predictions
Tests
Inducing
generalisations
estimating population
parameters
Wallace, 1971 in Blaikie, 1993
Hypotheses
Drawing samples &
devising measuring
instruments
Observations
Research Design Considerations
 research questions
– not just a journey into the unknown
 hypotheses
– balance between induction & deduction
 data collection
– triangulation (data source, method, investigator) for construct validity
– researcher involvement, identity and biase
 data analysis
– within case and cross-case analytic strategies for internal validity
(Yin’s research designs and Pettigrew’s framework)
 interpreting the observations
– explaining variation
Can we learn anything from a
sample of one?
The case of Phineas Gage…
Yin, 2003
Single-case designs
CONTEXT
Multiple-case designs
CONTEXT
CONTEXT
Case
Case
CONTEXT
CONTEXT
Case
Case
CONTEXT
Case
CONTEXT
Case
Case
Holistic
(single unit
of analysis)
CONTEXT
Embedded
(multiple units
of analysis)
Case
Embedded Unit
of Analysis 1
Embedded Unit
of Analysis 2
Embedded Unit
of Analysis 1
Embedded Unit
of Analysis 1
Embedded Unit
of Analysis 2
Embedded Unit
of Analysis 2
CONTEXT
Case
CONTEXT
Case
Embedded Unit
of Analysis 1
Embedded Unit
of Analysis 2
Embedded Unit
of Analysis 1
Embedded Unit
of Analysis 2
Pettigrew’s ‘meta- level’ analytical framework
CONTEXT
Business environment, product/manufacturing process types
CHANGE CONTENT
Reasons for applying FPp & its application in a MTO and MTS environment
OUTCOME VARIABLES
MTS Unit of Analysis
MTO Unit of Analysis
Internal Variables
FPp Unit of Analysis
Internal Variables
Skipworth 2003
External Variables
Internal Variables
Example of Case Study Scope
Production Equipment
Delivery
schedule
Customer
Order
Processing
Skipworth, 2003
Manufacturing
Planning
Process
routings
Duration,
frequency,
capacity plan
Replenishment
factory
orders
Stock
Control
Production
Scheduling
Production
line
schedules
Production
line
records
Production
Facilities
Ex-works
records
Outbound
Logistics
Mode of transport
Product
Data
Bills
of
Material
Process
Specs.
Project Boundary
Project Boundary
Product
Specs.
Selection in Case Study Research
 Case selection for external validity & analytic generalisation
- clarify domain
- sampling using replication logic – theoretical or literal
- extremes and polar types
 Selecting the Unit of Analysis
- differences in outcome
- coming to terms with time - snapshot / longitudinal / retrospective
 Selecting the data sources/methods
- informants - opponents / supporters / doubters
- methods - databases / documents / observations / interviews
Example of different outcomes...
Measures
ETO
(Contract
Motors)
FPp
(Modified in
Production)
FPp
(Modified from
Stock)
MTS
(Direct sale UK
Stock)
Av. actual order
lead-time
13.9 wks
2.6 wks
3.0 wks
0.1 wks
6.6 wks
0.1 wks
0.5 wks
Av. leadtime
prior to….
Av. order leadtime measured
from…..
.works order
release
7.3 wks
.works order
release
N/a
..booking out of engineering
2.5 wks
..booking out of engineering
N/a
Delivery Reliability
Delivery
reliability to the
customer
63%
79%
66%
96%
Analysing Case Studies
 data collection and analysis iterative process
- theory
data
 within case analysis
- between units of analysis or establishing links between
observations
- qualitative and quantitative data
 cross-case analysis
- search for patterns
- similarities & differences
Eisenhardt’s Roadmap – assumes inductive
 getting started
 selection of cases
 selection of research methods
 entering the field
 analysing data
 shaping hypotheses
 enfolding literature
 reaching closure
Eisenhardt, 1989
Step
Activity
Reason
Getting Started
Definition of research question
Possibly a priori constructs

Neither theory nor hypotheses
Focuses efforts
Provides better grounding of
construct measures
Retains theoretical flexibility
Selecting Cases
Specified population

Theoretical, not random sampling
Constrains extraneous variation &
sharpens external validity
Focuses efforts on theoretically
useful cases - ie those that replicate
or extend theory
Crafting
Instruments and
Protocols
Multiple data collection methods

Qualitative and quantitative data
combined
Multiple investigators
Strengthens grounding of theory by
triangulation of evidence
Synergistic view of evidence
Entering the Field
Overlap data collection and analysis,
including field notes
Flexible and opportunistic data
collection methods
Speeds analyses and reveals helpful
adjustments to data collection
Allows investigators to take
advantage of emergent themes and
unique case features
Analysing Data
Within case displays
Gains familiarity with data and
preliminary theory generation
Forces investigators to look beyond
initial impressions and see evidence
though multiple lenses
Cross-case pattern search using
divergent techniques’
Fosters divergent perspectives and
strengthens grounding
Shaping
Hypotheses
Iterative tabulation of evidence for
each construct
Replication, not sampling, logic
across cases
Search evidence for ‘why’ behind
relationships
Sharpens construct definition,
validity, and measurability
Confirms, extends and sharpens
theory
Build internal validity
Enfolding
Literature
Comparison with conflicting
literature

Comparison with similar literature
Builds internal validity, raises
theoretical level, and sharpens
construct definitions
Sharpens generalisability, improves
construct definition, and raises
theoretical level
Reaching Closure
Theoretical saturation when possible
Ends process when marginal
improvement becomes small
Eisenhardt’s Roadmap
Analysing Case Study Evidence
 Analysing case studies is always challenging
because of the detail. It is helped by:
–
–
–
–
–
–
–
–
being clear about research objectives
being clear about the unit of analysis & study questions
coming to terms with time
making your research method explicit
making your meta level framework explicit
making your hypotheses explicit
identifying themes that cut across the data
using techniques of data reduction & display
Cross-Case Comparisons
elements in common, which provide evidence
about what might indeed be ‘universal best practice’
uniqueness, which provides evidence about opportunities
for application of specific practices in given situations.
Uniqueness can also propose potential pitfalls,
which act to hobble the process of change.