April 11-12, 2011DIMETIC, Strasbourg

Technological and Organizational Dynamics(a problem solving perspective)

Stefano Brusoni stefano.brusoni@unibocconi.it

Patton M. Q. (2002) Qualitative Research and Evaluation Methods, Sage.

Flick, U. (2002) An Introduction to Qualitative Research, Sage.

Creswell, J. W. (2003) Research Design: Qualitative, quantitative and mixed methods approaches, CA: Sage

Reading list

Table of Contents

Definition of QRM (somewhat idiosyncratic?)

SamplingQuestions (and theory)Entering the fieldSampling strategies Leaving the field

Examples (as I go along)

Discussion, discussion, discussion

What is the goal of qualitative research?

The goal of qualitative research is the development and validation of concepts which help us to understand, and evaluate, social phenomena in natural settings, giving due emphasis to the views and experiences of all the participants, in order to devise ways to improve human life.

Why is it done?(more and more!!)

Big emphasis on social relations and, generally, social change. Induction is better than deduction when things change rapidlyNeed to create and explore new categories and relationships(E.g. dear old Ronald Coase, 1937)

(General?) Disenchantment with the old ideals of positivism and objectivityEveryone is a bit post-something todayLittle faith left in ‘general theories’, more emphasis on ‘appropriate’ theories(unless you work in Dept of Economics, of course)(but don’t get me started on Strategy!)

Concerns about lack of applicability of social sciences.Too far removed from everyday questions and problems(‘Why didn’t you predict the financial crisis?’ asked the Queen …)(But see Perez C, 2002)

?

Prediction vs. explanation

Example 1

Cirillo, Valentini and Brusoni (two weeks ago) Cool again! Corporate spin outs and the rejuvenation of old timers

Build on March 1991 argument of exploiration and exploitationSocialization crowds out diversity which leads to inertia.

Key argument: spin outs enable de-socialization and hence old timers become explorative again

control group of similar inventors without spin out experiencesplit samples according to levels of tenuren different model specifications

t-test on groups mean of inventors extent of exploration pre- and post-the spinout event

Groups mean trend of inventors’ extent of exploration. Reference year: spin-out year

What is qualitative research?(or at least what does it look like?)

Part I – the ‘basic ingredients’

Data

Data

InterviewsOpen ended questions and probes yield in-depth responses about people’s experiences, perceptions, opinions, feelings and knowledge. Data consist of verbatim quotations with sufficient context to be interpretable.

ObservationsFieldwork descriptions of activities, behaviors, actions, conversations, interpersonal interactions, organizational or community processes, or any other aspect of observable human experience. Data consist of field notes: rich, detailed descriptions, including the context within which the observations were made.

DocumentsWritten materials and other documents form organizational, clinical, or program records; memoranda and correspondence; official publication and reports, personal diaries, letters, artistic works, photographs, and memorabilia, written reports to open-ended surveys.Data consists of excerpts from documents captured in a way that records and preserves context.

(the ladder of analytical abstraction)(Miles and Huberman, p. 92)

Reconstruction of interview tapes as written notes. Synopses of individual interviews.

Creating a text to work on

Coding of data. Writing of analytical notes on linkages to various frameworks of interpretation

Trying out coding categories to find a set that fits

1. SUMMARIZING AND PACKAGING THE DATA

Searching for relationships in the data. Writing analytical memos. Finding out where the emphases and gaps in the data are

Identifying themes and trends in the data

2. REPACKAGING AND AGGREGATING

Cross checking tentative findings. Matrix analysis of major themes

Testing hypotheses and reducing the bulk of the data for analysis of trends in it

Synthesis: integrating the data into one explanatory framework

Delineating the deep structure

3. DEVELOPING AND TESTING PROPOSITIONS TO CONSTRUCT AND EXPLANATORY FRAMEWORK

On validities

Conclusion validityIs there a relationship between these two variables?

Internal validityIf there is, is there a causal one?

Construct validityIf it is causal, do your variables really reflect your constructs?

External validityCan you generalize your constructs and cause-effect relationship to other settings?

The validity staircase – an example

Conclusion validityIs there a relationship between these two variables?E.g. between cheating and lecturers’ goal orientation?E.g. between cheating and educational level?E.g. between cheating and punishment?

Internal validityIf there is, is there a causal one?E.g. Does a certain goal orientation reduce cheating attitude?E.g. Does ‘better’ punishment curb cheating?E.g. Does certain ‘institutional forces’ sustain or prevent cheating?

Construct validityIf it is causal, do your variables really reflect your constructs?E.g. image? –> codes and triangulationE.g. punishment and teachers’ learning model –> codes and triangulationE.g. the ‘meaning’ of cheating (e.g. cooperative vs. selfish)

External validityCan you generalize your constructs and cause-effect relationship to other settings? BIG WORRY!!E.g. look at different universities/programsE.g. look at different foundations crimonologyE.g. develop testable typologies of ‘characters’

Conclusion validity

Hard to define criteria Missing p-levels: Type I vs Type II error

Think of alternatives. Can you exclude them on the basis of yourdata?

Problem is: thinking of alternative explanations ain’t easy. Research designTHEORETICAL SamplingInterviews, observations, documentsInterplay of data analysis and data gatheringMajor weakness in all papers I have refereed (and written) over the years.

Example.Burt R. 1998. The gender of social capital. Rationality in Society.

What is qualitative research?(or at least what does it look like?)

Part II – Sampling

(from Patton)

(What sampling is about)(M&H, chapter 6)

Why do we have graffiti on subway cars?

Because kids want to express their identity

Why do they want to express their identity?

Because they are alienated

Why are they alienated?Because they have no jobs

Why do they have no jobs?Because they are unskilled.

Why do we have graffiti on subway cars?

Because cars are not protected in their yards at night

Why are they not protected?

Because the transit budget does not permit it.

Why … ?

Sampling is about:

Entry point Select and identify key contacts and informants, which implies choices about theories and variables

Causality which ‘chain’ you follow depends on which chain you generate through sampling

Sampling is NOT about statistical generalizability(or external validity)

Select information rich cases strategically and purposefullySpecific type and number of cases

selected depends on study purpose and resources

Purposeful (or theoretical) sampling

Key problem here is identifying criteriato guide the enlargement of the sample

Gradual sampling

RepresentativenessProbability samplingSimple random sampling

Stratified random sampling

PurposeType of sampling

Key trade offs in research design, both related to unit of analysisBreadth vs. depth

Sample size

Theoretical vs. statistical sampling(Flick, chapter 7)

Sampling is finished when whole population been studied

Sampling is finished when theoretical saturation is reached

Samples size is known in advanceSamples size is not defined in advance

One shot drawing following predefined planRepeated drawings with criteria to be defined again at each step

Features of the basic population known in advance

Features of the basic population not known in advance

Extension of basic population is known in advance

Extension of basic population not known in advance

Statistical samplingTheoretical sampling

Theoretical Sampling (example 2)

Sampling and causality (not generalization)

An example: Canato and Brusoni (2010 WP)RQ is about understanding unevenness in routines change

Motivation

How do established organizations reinvent themselves?Loads about technological innovationRelatively less on managerial innovation

Practical relevance: companies engage constantly in renewal initiatives, with mixed results.

Surviving crisis? Adopting new business models?

Theoretical relevance: micro-dynamics of organisational and institutional change are still under-explored.

Conscious vs. Intentional? Conscious and intentional?(Search and decision processes)

Imperial College Business School ©

Empirical setting

Imperial College Business School ©

Adoption of new procedures

Adaptation phase

Organizational change (e.g. new CEO)

Departure of the change leader

Established procedures

Two key analytical building blocks:

• organisational routines

• organisational identity

Organisational routines

Routines are recurrent and repeated patterns of behaviour, learned over time, which capture the actual ways in which activities are carried out in organisations (Cohen & Bacdayan, 1994), embodying the solutions to problems solved in the past (Nelson and Winter,1982; Feldman, 2000)

Routines set roles, rules and responsibilities (Nelson and Winter, 1982; Cohen & Bacdayan, 1994; Becker, 2004)

Data sources

Objective for the analysisData sources

Gain knowledge about the history of the company and the overall organisational context

Internal and external documentsCorporate biographies (5)Books and reports about the companyArchive of internal magazinesBusiness press coverage

Build trust and deepen understanding of company’s features

Non participant observation26 weeks of non participant observation

Elicit understandings ofcurrent company’s featuresintroduction of new procedureschange outcome

Interviews59 in UE17 in US headquartersConducted in 2005, 2006, 2008

Sampling in Canato and Brusoni

Nested strategyWhy this company? big, old, successful and ‘insular’Why this division? typical example of large divisionsWhy Six Sigma? the new process-based architectureWhy these routines within it? typical examples (no production though)Which individuals? pre- and post-Six Sigma experience

Do note that upfront we had no clue as to where we would end up.

We’ve got about 35 unused interview hours

The outcome of change

RejectedStrong clash

Strong clash

Strong clashLeading

Partially rejectedWeak clashStrong

clashStrong clashNorming

ModifiedNo clashWeak clash

Strong clashDeepening

AcceptedNo clashWeak clashNo clashScoping

OutcomeCooperationFlexibilityAutonomy

Organisational identity traitsR

outin

es

Illustrate what is typical (to others)E.g. Might be used after survey to highlight processes underpinning quantitative data.Note: quite useful in quantitative dissertations to show you know what you are talking about.

Typical case sampling

Focus; reduce variation; simplify analysis; enable group interviewingE.g. symbolic interactionism

Homogeneous sampling

Pick wide range of cases to get variation on dimension of interest. Aim is to identify commonalities.E.g. impact of ICTs on development of similar product in small vs. large firm

Maximum variation sampling

Information-rich cases that manifest the phenomenon intensely, but not extremelyNote: need careful exploratory workE.g. heuristic research (pain, loneliness)E.g. industrial economists’ attention to ‘persistent’ innovators

Intensity sampling

Learning from unusual manifestations of phenomenon. E.g. notable successes/failuresE.g. early cases of HIV not developing AIDSE.g. Ethnomethodologists

Outlier sampling

Testing variation, elaborating and deepening initial analysis. Confirming: examples that fit already emerging patterns. Give richness and depthDisconfirming: try alternative explanations; external validity

Confirming and disconfirming cases

Finding manifestations of a theoretical construct of interest so as to elaborate and examine the construct and its variations. You need to sample people, time periods etc on the basis of the potential manifestation of theoretical constructsE.g. very common in grounded theoryNote: often seen in combination with snowballing

Theory-based sampling

Picking all cases that meet some criterionNote: useful to identify cases from standardized questionnaires worthy of follow-up interviews

Criterion sampling

Identify cases of interest from sampling people who know people who know people who …Note: useful to identify candidate ‘outliers’ or ‘intense’ manifestations of given phenomenon.

Snowball sampling

If it is ‘true’ here, than it must be true in all other casesE.g. physics and experimental designE.g. implementation of new regulations (pick well educated citizens: if they do NOT understand new regulations, nobody else will).Note: need to be very sure about dimensions which define ‘criticality’

Critical case sampling

TriangulationCombination or mixed purposeful sampling

Don’t even think of this!!Convenience sampling

Aim: attract or deflect attention to the studyAlso known (to me) as the ‘talking pig’ strategy, e.g. the Cuban crisis in 1962 (or 3?)

Sampling politically important cases

Add credibility (not representativeness) to program evaluation.Note: cases selected before program outcomes known

Purposeful random sampling

Following new leads during fieldworkNote: good strategy during pilot, make supervisors real nervous

Opportunistic or emergent sampling

Illustrate characteristics of specific subgroupsE.g. combine typical case sampling with max heterogeneity sampling (above average, average and below average cases)

Stratified purposeful sampling

Example 3the making of MIRS (B&P, OS 2006)

Very matureMajor technological innovation in 1920s followed by shake out (Klepperand Simons, 1996)Radial revolution in the late 1960s and acquisitions of US firms (Sull et al. 1996)Very concentrated sector (top 10 firms have over 85% of sales in 2000)

Very odd recent history. Rapid increase in market segmentationRevamping of innovative efforts

Microelectronics as the fastest growing patent class (Acha and Brusoni (2005)Rapid diffusion of radical process innovations (Brusoni and Sgalari, 2005) MIRS

Motivation: how does modularity come into being?

Akron workers, about 1910.

MIRS, Milano Bicocca, about 2000

Traditional tires manufacturing

‘Modular’ know-how and designprocess; ‘non-modular’production process

Innovative tires manufacturing

Integrated know-how anddesign process; modularproduction process(and product)

SamplingModularity literature (rel. technology – organizationOutlier sampling (organization)Snowballing (participants)

Roles (may) change over timeProfessional stranger

Reveal taken-for-granted routinesVisitor

One off or limited interaction with participantsQuestions about routinesStill an ‘external’ perspective

InitiateGive up the external perspective (to some extent)(The ‘village idiot’ metaphor)

InsiderParticipation in everyday’s activities

Entering the field

Final thoughts on sampling

Nested and emergent

Theory-driven, purpose-built

Sampling strategies are chiefly about establishing causality and eliminating alternative explanations

Not generalizability, not external validity

Sampling strategies bound our studies and directly determines our entry point into the field

Methink it is more important than our exit point (theoretical saturation sort of stuff)