Presentation on theme: "Case Study Research Dr Heather Skipworth Research Fellow, Supply Chain Research Centre MSc by Research in Leading, Learning."— Presentation transcript:
Case Study Research Dr Heather Skipworth Research Fellow, Supply Chain Research Centre email@example.com MSc by Research in Leading, Learning and Change
Who am I 1989BSc Mechanical Engineering, Leicester University 1989 – 1991Project Engineer, Metal Box 1991 – 1995Technical Manager, Field Packaging 1995 – 1996MSc Manufacturing Systems, Cranfield University 1996 – 1998Senior Manufacturing Systems Engineer, BICC Cables Limited 1998 – 2003PhD 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
Case Studies in Operations M. Research Modelling, Experiments Large Population Surveys Case Studies, Action Research Abstraction Accuracy / Repeatability
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 Generation Testing Theory
Theories Observations Hypotheses Empirical generalisations Deducing consequences making predictions Forming concepts developing & arranging propositions Inducing generalisations estimating population parameters Drawing samples & devising measuring instruments Tests DEDUCTIVE METHODSINDUCTIVE METHODS THEORISING DOING EMPIRICAL RESEARCH Wallace, 1971 in Blaikie, 1993 Research Strategy - induction v deduction?
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…
CONTEXT Case CONTEXT Case CONTEXT Case CONTEXT Case CONTEXT Case Embedded Unit of Analysis 1 Embedded Unit of Analysis 2 CONTEXT Case Embedded Unit of Analysis 1 Embedded Unit of Analysis 2 CONTEXT Case Embedded Unit of Analysis 1 Embedded Unit of Analysis 2 CONTEXT Case Embedded Unit of Analysis 1 Embedded Unit of Analysis 2 CONTEXT Case CONTEXT Case Embedded Unit of Analysis 1 Embedded Unit of Analysis 2 Multiple-case designs Single-case designs Holistic (single unit of analysis) Embedded (multiple units of analysis) Yin, 2003
Pettigrew’s ‘meta- level’ analytical framework OUTCOME VARIABLES CHANGE CONTENT Reasons for applying FPp & its application in a MTO and MTS environment External Variables MTS Unit of Analysis MTO Unit of Analysis FPp Unit of Analysis Internal Variables CONTEXT Business environment, product/manufacturing process types Skipworth 2003
Example of Case Study Scope Product Specs. Process Specs. Manufacturing Planning Production Scheduling Customer Order Processing Stock Control Production Outbound Logistics Product Data Bills of Material Process routings Duration, frequency, capacity plan Production line schedules Delivery schedule Replenishment factory orders Production line records Ex-works records Production Equipment FacilitiesMode of transport Project Boundary Skipworth, 2003
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...
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
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