PhD Seminar Series Qualitative Research Methodology Defining and conducting case-based research Analysing case study data Klas Eric Soderquist, DBA, Brunel

PhD Seminar SeriesQualitative Research Methodology

Defining and conducting case-based research

Analysing case study data

Klas Eric Soderquist, DBA, Brunel University

ΟΙΚΟΝΟΜΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΘΗΝΩΝΤΜΗΜΑ ΔΙΟΙΚΗΤΙΚΗΣ ΕΠΙΣΤΗΜΗΣ & ΤΕΧΝΟΛΟΓΙΑΣ

• A Case of Case Study Research

• Recalling the Process of Defining the Research Problem

• Defining the Unit of Analysis

• Research Strategy, Relation to Theory, Methodological Choice

• Data Collection– Interviews, Direct observation, Participant observation

– From research question to interview guide

• Data analysis (case or other qualitative approaches)– Open coding

– Axial coding

– The paradigm model

– Validity and Reliability

Seminar Outline

PhD Seminar Series. Qualitative Research MethodologyK.E. Soderquist

My Case of Case Study Research

Inside the Tier Model: Product Development Organisationand Strategies in Automotive Expert Supplier Firms

Carmaker

First tier

Second tier

Third and fourthtier


Definition of the Research Problem

Match

REAL WORLD

CONTEXT

RESEARCHQUESTIONS

RESEARCHOBJECTIVE

OVERALLPHICALPOSITION

PHILOSO-

Match /Mismatch

Match /Mismatch

Match /Mismatch

Match

REAL WORLD

CONTEXT

RESEARCHQUESTIONS

RESEARCHOBJECTIVE

OVERALLPHICALPOSITION

PHILOSO-

Match /MismatchMatch /Mismatch




My Real World Context

• Background

– The Lean Production Framework

– The Tier Model

– The Partnership Concept

– Transaction Cost Theory, Strategic Collaboration Theory, Operational Coordination Theory

• Research Problem

– Supplier Perspective

– Operational Perspective

OverallResearchQuestions


Overall Research Questions

• They tell you what you want to focus on and what you want to know

• They set the rough boundaries of the research: you will study some issues in some context with some actors

• They are oriented towards action and process

• The way they are (implicitly) formulated will determine research strategy later on

• They set the vision for the research project and helps focusing activities


My Overall Research Questions

• How does the emergence of new industrial principles take place in expert supplier firms?

• What is the place and role of expert suppliers in the automotive supply chain?

• What lean production techniques are used and how are they adopted for satisfying the needs of the organisation?

• How in practice takes integrated component development place?

• How are organisations and processes designed to support integration?


Research Objective

• To explain the causality between different observations or the reasons behind a certain situation concerning the phenomenon

• To explore a vague problem or a new area of research

• To describe, i.e., observe and visualise the situation of certain phenomena

One can distinguish between mainly three objectives or purposes with a research project:

The research objective does not automatically define a quantitative or qualitative logic


Research Objective

• WHAT questions of descriptive nature in the sense “how much” or “how many” call for a quantitative approach

• WHAT questions of explanatory or exploratory nature call for a qualitative approach

• HOW questions and WHY questions call for a qualitative approach

The research questions implicitly determine the research objective, and together they indicate quantitative vs. qualitative research:

Qualitative research is needed when we want to come to terms with the meaning, not the “right” or “wrong” with the phenomena under investigation


Match / Mismatch

Match

ANALYSIS

Match / Mismatch

UNIT OF

Mismatch Mismatch

Methodological choice

Quantitative

Deductive Logic

Quantitative

Inductive Logic

Qualitative Qualitative

Deductive Logic Inductive Logic Data collection and data

Deduction Induction

Predefinition and test of

a theoretical model.

THEORY EXTENSION

Determination of theory

from observations.

THEORY DEVELOPMENT

RELATION TO THEORY

REAL WORLD

CONTEXT

RESEARCHQUESTIONS

RESEARCH

OBJECTIVE

DEFINITION OF THE RESEARCH PROBLEM

OVERALL

Match

STRATEGY

Match / Mismatch

RESEARCH

Match

Mismatch Mismatch

PHICALPOSITION

PHILOSO-

Match /Mismatch

Match /Mismatch

Match /Mismatch

Mismatch

Match

analysis methods, appropriate

for the chosen methodology

“Full“ProcessModel ofMethodologicalChoice

Unit of Analysis – What is the Case?

It is the Research Object – or unit in the real world context that you will observe

It can be:• An individual,• A role• A group• A process • An organisational entity• An organisation

Or any other definable and observable unit


Unit of Analysis – Example

Units of analysis in change management research:• Change efforts (e.g., installing new technology, downsizing, a particular

restructuring, effort to change corporate culture…)

• Change events (e.g. a crises);

• The leaders of change (i.e., the individuals that drive through "corporate revolutions" with research focus on their personal traits, leadership styles and leadership levers);

• Sectors undergoing change (i.e., comparative studies of different business sectors in search of similarities and specificities basically in the content of change)

• Employees facing change and acting in change processes.


My Unit of Analysis

• Could be a component development project

• Could be the supplier interface

• Could be the product development project

I did chose the Product Development Process in each studied firm because studying the PDP would enable answering all the questions (at least in theory)


Match / Mismatch

Match

ANALYSIS

Match / Mismatch

UNIT OF

Mismatch Mismatch


Quantitative

Deductive Logic

Quantitative

Inductive Logic



Deduction Induction



THEORY EXTENSION


from observations.

THEORY DEVELOPMENT

RELATION TO THEORY

REAL WORLD

CONTEXT

RESEARCHQUESTIONS

RESEARCH

OBJECTIVE


OVERALL

Match

STRATEGY

Match / Mismatch

RESEARCH

Match

Mismatch Mismatch

PHICALPOSITION

PHILOSO-

Match /Mismatch

Match /Mismatch

Match /Mismatch

Mismatch

Match




Research Strategy

Five Basic Strategies:

• Experiment

• Survey

• Archival Analysis

• History

• Case Study


Case Study Research Strategy

Selecting the Case Study Organization: Criterion Sampling

• Purposive rather than random samples

• Samples can evolve as research moves on

• Identify theory driven criteria in order to frame the research

• Inclusive or contradictory samples


Case Study Research Strategy

My Criterion Sampling

Case Study Organisations should:

• Have a good record for internally generated innovation.

• Innovation has a high profile in the organization.

• Have established links with research laboratories.

• Have an explicit strategy for R&D and R&D driven organizational development


Match / Mismatch

Match

ANALYSIS

Match / Mismatch

UNIT OF

Mismatch Mismatch


Quantitative

Deductive Logic

Quantitative

Inductive Logic



Deduction Induction



THEORY EXTENSION


from observations.

THEORY DEVELOPMENT

RELATION TO THEORY

REAL WORLD

CONTEXT

RESEARCHQUESTIONS

RESEARCH

OBJECTIVE


OVERALL

Match

STRATEGY

Match / Mismatch

RESEARCH

Match

Mismatch Mismatch

PHICALPOSITION

PHILOSO-

Match /Mismatch

Match /Mismatch

Match /Mismatch

Mismatch

Match




Observations

EmpiricalGeneralizations

HypothesesTest

INDUCTION DEDUCTION

DEDUCTIONINDUCTION

Construction

of theory

Application

of theory

Theoretical work

Empirical work

Theoretical work

Empirical work

Theory

Relation to Theory

Theory ExtensionTheory Development


Methodological Choice


Quantitative

Deductive Logic

Quantitative

Inductive Logic






• Participant Observation – longitudinal presence The researcher is part of the organisation on a contractual basis and has a

contextual professional role and reporting responsibility

• Direct Observation – (longitudinal) presence The researcher is present in the organisation as an external observer

• Interviews – occasional visits (can be longitudinal)

• Documentary Analysis - background info and event analysis

Data Collection MethodsCase-Based Research


• The overall research questions starts broadly and are gradually narrowed by asking sub questions and sub-sub questions

• Research questions should be asked «crude» to managers and experts in an exploratory phase: Pilot interviews or pilot case

Data CollectionFrom Research Question to Interview Guide


Data Collection through InterviewsFrom Research Question to Interview Guide (I)

The process of generating more detailed questions

Sub, sub-sub… ResearchQuestions

Mental Processing

Researcher‘s

Exploratory Data(Real World)

Overall ResearchQuestions

Personal Experience(Real World)


Data Collection through InterviewsFrom Research Question to Interview Guide (II)

My Interview Guide (minor extract). Semi-structured, open ended• How are organisations and processes designed to support integration?

•What coordination activities exist in the product development process?• When and how is manufacturing staff involved in new development projects?• What are the benefits of their involvement?• What are the drawbacks?• How is coordination with customers organised?• How is coordination with suppliers organised?

•What communication structures exist in the product development process?• Who talks to who in the process?• What channels are used?• Frequency of communication between different players?

• How does communication support coordination?


Data Collection through InterviewsBasic Interview Techniques


• Not too active, not too passive - The guide serves as a tool for navigating the interviews

• Use of the critical incident technique to start out the interviews or enter into new subjects.

It is efficient when managers have difficulties in articulating answers to specific questions and consists of asking the interviewee to describe specific events that have had a critical impact on the way a specific issue has been managed

• Use re-launch questions:

- Could you tell me more about this?

- Could you specify further? - What are you thinking about in particular?

It is important in such cases not to give any suggestions to interviewees

Between Collection and AnalysisInterview (or observation) Transcripts


• For serious research, tape record interviews!

• Always keep supportive notes

• Proceed to immediate transcript

• Transcript should reflect the interview at 100 percent

• Ask for feed-back on transcripts

• Fill in uncertainties with a second (phone) interview

Data Analysis / CodingBasics


• If we talk about case research, we will have at least multiple interviews, possibly a mix of interviews and field notes

• Our analytical problem is that of analysing a mass of text

• Analysis / Coding represents the operations by which data are:

– Broken down

– Conceptualized,

– Put back together in new ways

• The objective is to build theory from data – a “Grounded Theory” approach

Data Analysis / CodingOpen Coding (I)


• Labelling events: Consists of placing conceptual labels on discrete happenings, events and instances in data

Data are broken down and conceptualized by taking apart an observation, a sentence, a paragraph and giving each separate incident, idea or event a name that represents a phenomenon. Incident after incident in the field notes are compared in order to give similar events the same name -conceptual label- and nuance and enrich each concept. The process involves asking questions to the data so as to identify properties and dimensions of the incidents

Data Analysis / CodingOpen Coding (II)


• Categorising: Consists of grouping conceptual labels into categoriesConcepts that seem to pertain to the same phenomena are then regrouped -categorized- and given a name that should be more abstract than that given during labeling. Categories have conceptual power because they are able to pull together around them groups of concepts. Practically, categorising consists of going through all concepts and asking questions such as 'What is this concept about?' or 'Is this concept similar or different from the one before or after?' In this way, categories are discovered when concepts are compared against one another, and concepts become characteristic components of a category - so called subcategories. If a concept seems not to pertain to an already identified category, it is left aside and might become the entry to a new category as data analysis goes on.

• Categorising can be bottom-up or top-down

Data Analysis / CodingAxial Coding


• Condensation of data, i.e., regrouping and linking categories to each other

Open coding fractures the data and allows the researcher to identify some categories, their properties and dimensions. Axial coding puts these data back together in new ways by making connections between categories.

• The objective is to identify the core category (basically derived from the unit of analysis) and to logically relate other main categories to the core

• The “Paradigm Model” structures the axial coding process

5. Identify the outcomes related to the phenomenon - predictable/intended or not (competences, ressources, knowledge, know-how...).

CONSEQUENCES

The ParadigmModel

1. Identify the phenomenon to be studied (activity, business process...).

PHENOMENON

2. Identify the conditions of directive nature, influencing the phenomenon (driving forces, objectives...).

CAUSAL CONDITIONS

3. Specify the context in which the phenomenon takes place (organization, information....).

CONTEXT

4. Identify the conditions of shaping nature that intervene in carrying out and managing the i phenomenon (reactions from stakeholders, cognitive processes, continuous improvement...).

INTERVENING CONDITIONS


Data Analysis / CodingAn IterativeProcess


My ResearchProcess

Initial literature review,development of researchproposal and topic guide

Pilot interviews, testingof topic relevanceSuppliers 1, 2, 3 and 4

Final version ofthe topic guide

Formal interviews withproduct development managersSuppliers 3 and 4

Formal interviewswith general managersSuppliers 3 and 4

Data analysis:open coding

First case studySupplier 3

Data analysis: open coding,data reduction and develop-ment of case study topic guide

Second case studySupplier 4

1993

1994

1995

1996

Data analysis:Axial coding,comparison of findingswith relevant literature

Second series of formalinterviews (product developmentmanagers) Suppliers 5, 6, 7 and 8

Data analysis: open coding,data reduction and comparishonof concepts with case study one

Category: Problem SolvingSub categories: Objectives, Efficiency, Support StructuresProperties of “Efficiency”: Nature of Topic, Nature of ParticipationDimensions of “Nature of Topic”: Problems, Problems & Causes, Problems, Causes & possible le actionsDimensions of “Nature of Participation”: Participant Driven Management Driven

Data Analysis / CodingMy Open Coding


Sentence extracted from interview:

«We need performance measurements that enhance the value of inter-functional problem solving»

Data Analysis / CodingMy Axial Coding (I)


Core Category: Operational DesignThe analysis of the category “Problem Solving” led to the identification of four distinctive levels of operational design

Individual workIndividual work

Group work

Intrafunctional between designtechnicians and engineers

Group work

Intrafunctional between designtechnicians and engineers

Project work

Interfunctional within thesupplier company

Project work

Interfunctional within thesupplier company

Systemic work

Involving customers, interface suppliersand lower-tier suppliers

Systemic work

Involving customers, interface suppliersand lower-tier suppliers

Data Analysis / CodingMy Axial Coding (II)


Causal Conditions: Means of Guidance– Guiding Visions, Performance Measurements

Context: Design Support Structures– Specifications, Information transmission support technology,

technology scanning, knowledge repositories

Intervening Conditions– Learning dynamics,

learning model

Intra-company,

intra-functional learning

Intra-company,

intra-functional learning

Purchasing

ProcessEngineering

Sales

Intra-company, inter-functional learning

Purchasing

ProcessEngineering

Sales


Purchasing

ProcessEngineering

Sales


Inter-company, intra-functional learning

Designfunction ininterfacingsup. firms

Design function incustomer

firms

Inter-company, intra-functional learning



firms



firms

Data Analysis / CodingMy Axial Coding (II)


Causal Conditions: Means of Guidance– Guiding Visions, Performance Measurements

Context: Design Support Structures– Specifications, Information transmission support technology,

technology scanning, knowledge repositories

Intervening Conditions– Learning dynamics,

learning model

Consequences– Model of the process of

building core capabilities

SHARED

VALUES

DevelopmentTechnological

CollectiveLearning

DevelopmentTechnological

CollectiveLearningCollectiveLearning

CAPABILITIESCORE

CAPABILITIESCORE

CAPABILITIESCORE

Adequation with customer needs

Differentiation from competitors

Adequation with customer needs

Differentiation from competitors

Manage-ment

Sys-tems

Manage-ment

Sys-tems

StrategicObjec-

tives

Meansof

Guidance

StrategicObjec-

tives

Meansof

Guidance

Basic Resources

Skills

Basic Resources

Skills

Basic Resources

Skills

Data Analysis / CodingSelective Coding and «Story»


• The narrative about the core category must identify and define the basic content of the other categories

• Narrative about each category, crossing the results of axial coding with research questions

• Theoretical sampling: Strengthen weak relationships between categories inductively and integrate literature relevant for the results emerging

• Specify action / interaction strategies for how the holistic model or theory operates

Problem solving 1*

Product planning

Productengineering

Processengineering

RunningProduction /Minor modifications

Project 1

Project 2

Project 3

Project 4

SUPPLIER CARMAKER

Design

Transfer of learning

through formal and

Shared knowledge base

Component design

Problem solving 2*

Problem solving 4*

Problem solving 3*

technician S1

Design

technician S2

Design

technician S3

informal group work,

organizational routines

and support structures

Design

Transfer of learning

through formal and

technician C1

Design

technician C3

Design

technician C4

informal group work,

organizational routines

and support structures

* Transfer through supplier - customerproblem solving cycles

Design

technician C2

Transfer of experience

Example: The answer to one of the research questions emerges from the narratives about the core category, the context category and the intervening conditions category

How are organisations and processes designed to support integration?

Reliability and Validity (I)


• Reliability consists of demonstrating that the operations of a study -such as the data collection procedures- can be repeated, with the same results

• It is a question of documenting the research procedure

• Reliability is ensured by keeping data in different forms:- Directly taken field notes - from interviews and observations,

- Expanded typed notes made as soon as possible after the field work (this includes comments on problems and ideas that arise during each stage of the fieldwork and that will guide further research),

- A running record of analysis and interpretation (open coding and axial coding).

Reliability and Validity (II)


• Construct validity means establishing correct operational measures for the concepts being studied. It is ensured through:- The use of multiple sources of evidence,

- The establishment of a chain of evidence,

- Letting key informants review draft result reports

• External validity means establishing the domain to which a study's findings can be generalized. It is ensured through the use of a replication logic “Analytical Generalisation”

- Relate case findings to existing or emerging bodies of literature, part of which will have been analysed in the literature section of the thesis

Sources

• Easterby-Smith, M., Thorpe, R., Lowe, A., (1991), Management Research, An Introduction, London: Sage Publications.

• Dey, I. (1993), Qualitative Data Analysis. A User-Friendly Guide for Social Scientists, London: Routledge.

• Miles, M. B. & Huberman, A. M., (1994), Qualitative Data Analyses, An Expanded Sourcebook, Thousand Oaks, CA: Sage Publications.

• Pras, B. & Tarondeau, J-C., (1979), "Typologie de la recherche en gestion" (Typology of Management Research, in French), Enseignement et Gestion, Nouveau Serie no. 9, p. 5-11

• Silverman, D., (1993), Interpreting Qualitative Data, Methods for Analysing Talk, Text and Interaction, London: Sage Publications.

• Strauss, A., & Corbin, J., (1990), Basics of Qualitative Research, Grounded Theory Procedures and Techniques, Newbury Park CA: Sage Publications

• Van Maanen, J., Dabbs, J.M., Faulkner, R.R. (1982), Varieties of Qualitative Research. Beverley Hills, CA: Sage Publications

• Van Maanen, J., (1983), Qualitative Methodology, Beverley Hills, CA: Sage Publications. • Yin, R., K., (2003), Case Study Research, Design and Methods, 3rd edition, Newbury Park,

CA: Sage Publications.


Documents

PhD Seminar Series Qualitative Research Methodology Defining and conducting case-based research Analysing case study data Klas Eric Soderquist, DBA, Brunel