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Process Research Workshop: A Spectrum of Methods, AOM PDW,

Saturday, August 6 102 SPDW: (RM, BPS, OB, OMT, TIM) Process Research

Workshop I & II A Spectrum of Methods8:30am - 11:30am; 1 – 4 pm Hawaii Convention Center: Room 311

Pre-registration required at https://spears.okstate.edu/rmdpdwregister. There is a $15 fee for non members of the Research Methods Division.

Presenters: Ann Langley, HEC Montreal; Kevin Dooley, Arizona State U.; Marshall S. Poole, Texas A&M U.; Andrew H. Van de Ven, U. of Minnesota

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Process Research Workshop: A Spectrum of MethodsAgenda

8:30 Welcome & Introductions8:45 Process Research Epistemology – Scott Poole9:15 Small Group Exercise in studying a problem as a process9:45 Discussion and Break10:15 Designing Process Research Studies – Andy Van de Ven10:30 Small Group Exercise in designing a process study11:00 Discussion

11:30 Conclusion – Lunch on your own

1:00 Qualitative Methods for analyzing process data – Ann Langley1:30 Exercise in writing a process study2:00 Discussion and Break2:30 Quantitative Methods for analyzing process data – Kevin Dooley3:00 Small group discussions3:30 Concluding Discussion4:00 End

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Participants’ Questions for Morning Session

1. Discuss representation in process research. (Clive Smallman, Lincoln U. New Zealand). Discuss the relevance of personal experience in field research in light of Bourdieu’s (2003) “Participant Objectivation” (Francois Collet, Oxford)

2. Who offers courses dedicated to process research methods? What are platforms for process scholars to exchange ideas and methods? (Matthias Brauer, U. of St. Gallen).

3. How do you combine different theories? I plan to use the Alternate Templates Strategy to analyze data on inter-organizational relationships and write a narrative using three theoretical lenses: the transaction costs economy -theory, resource based view and evolutionary theory. (Paivi Karjalainen, Teliasonera.com)

4. Process Research has been criticised for its use second hand retrospective reports given by senior executives, the absence of consideration for managerial agency issues, its lack of practical relevance, the absence of consideration for content (diversification, internalization context) and the difficulty to generalise from some in-depth empirical studies. (Johnson, Melin et al. 2003). Are these critics justified ? (Francois Collet, Oxford U.)

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Participants’ Questions for Afternoon Session

1. What are the most appropriate statistical packages to handle process analyses? (Matthias Brauer, U of St. Gallen,Switzerland)

2. When do you use parametric vs. non-parametric tests in event-based process analysis? or put differently: when do you view your events as a sample or an entire population? (Matthias Brauer)

3. How do you apply multiple sensemaking strategies in one paper given the page limitations of ordinary management journals? (Matthias Brauer)

4. How should authors report/frame a "QUAL-quant" process study so that reviewers and editors steeped in variance-theoretic methods won't place inordinate weight on the supporting quantitative methods, killing papers that are mostly qualitative in nature? (Todd Chiles, U.of Missouri-Columbia)

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Sourcebook for Process Research Methods

Marshall S. Poole, Andrew Van de Ven, Kevin Dooley, and Michael Holmes,

New York: Oxford University Press, 2000.1. Perspectives on Change and Development

2. Process Theories and Narrative Explanations

3. Process Theories of Organizational Change

4. Overview: Methods for Process Research

5. The Design of Process Research Studies

6. Stochastic Modeling

7. Phasic Analysis

8. Event Time Series Regression Analysis

9. Nonlinear Dynamical Analysis

10.Conclusions

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Epistemology of Process Research

• The Meaning of Process• Ontological views of Process• Process and Variance Methods• Cognitive Transitions• Exercise: Study a problem as a

process

Marshall Scott PooleTexas A&M University

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Variance and Process Epistemologies

Lawrence Mohr

VARIANCE APPROACH PROCESS APPROACH

Fixed entities with varying attributes Entities participate in events and may change over time

Explanations based on necessary and sufficient causality

Explanations based on necessary causality

Explanations based on efficient causality

Explanations based on final, formal, and efficient causality

Generality depends on uniformity across contexts

Generality depends on versatility across cases

Time ordering among independent variables is immaterial

Time ordering of independent events is critical

Emphasis on immediate causation Explanations are layered and incorporate both immediate and distal causation

Attributes have a single meaning over time

Entities, attributes, events may change in meaning over time

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Variance Theory Process Theory

Attributes of:• Environment (x1)• Technology (x2)• Decision Process (x3)• Resources (x4)

Organization Outcomes

(Y)

Y = f(x1, x2, x3, x4)

• events• activities•choices

StateA

StateB

T0 T1

Langley’s Picture of Variance and Process Theories

Ann Langley

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Bruner’s Two Modes of Thought

Logico-Scientific Mode Narrative ModePurpose Develop and test a theory that

explains the causes or consequences of a general phenomenon in its context.

Develop a plausible story that interprets meaning to a particular experience or sequence of events

Method Logical “if-then” propositions

that derive testable hypotheses among variables in specified context

Plot linking intentional actions of characters in in events and settings.

Discourse:

Triggers assumptions,

Is reflexive, and

open to multiple views

Evaluation Criteria

Valid argument

Empirical truth

Boundary conditions

Verisimilitude

A good story

Reflexive

Open to multiple views

Jerome Bruner(1915 - )

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EVOLUTION (Competitive Change) DIALECTIC (Conflictual Change)

MultipleEntities

Unit ofChange

SingleEntity

LIFE CYCLE (Regulated Change)

Pluralism (Diversity)ConfrontationConflict

TELEOLOGY (Planned Change)

Variation Selection RetentionThesis

AntithesisConflict Synthesis

4 (Terminate)

Stage 2(Grow)

Dissatisfaction

ImplementGoals

Search/Interact

Set/Envision Goals

Mode of ChangePrescribed Constructive

Population ScarcityEnvironmental SelectionCompetition

Immanent ProgramRegulationCompliant adaptation

Purposeful enactmentSocial constructionConsensus

Process Models of Organization ChangeNote: Arrows on lines represent likely sequences among events, not causation between events.Source: Van de Ven & Poole, Explaining Development and Change in Organizations, AMR, 1995.

Stage 1(Startup)

Stage 3(Harvest)

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Approach I Approach IV

VarianceMethods

Epistemology

ProcessMethods

Approach II Approach III

OrganizationalOntology

A noun, a thing a verb, a process

Variance study of change in organizations

Causal analysis of independent variablesexplaining change (dependent variable)

Newtonian view of time

Variance study of process patterns

Quantitative analysis of event time series:Markov, time series, event history, & nonlinear complex adaptive systems

Time is a variable of change process

Process study narrating sequence of change events in organization

Progressions of change (stages, cycles, etc)In the development of org. entity

Transaction or event-based view of time

Process study narrating emergent organizing activities

Qualitative narrative interpretation ofcomplexity metaphor

Social construction view of time

Alternative Approaches for Studying Organization Change

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Designing Process Research Studies

• Basics of Process Research• Designing Field Studies• Analyzing Process Data• Exercise: Design a process

study

Andy Van de VenU. Of Minnesota

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Basics of Process Research

• Define the meaning of process:• A logic that explains a causal relationship• A category of concepts or variables• A narrative of how things change over time

• Clarify theory of process (vs. variance theory)• process vs. variance theories• life cycle, teleology, dialectic, & evolution process theories

• Adopt a process vocabulary • simple, multiple, cumulative, conjunctive & iterative

progressions• Design research to observe and analyze process

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Steps & Decisions in Designing Process Study

Key Step Key Decision(s) Suggestions 1. The topic What is the research question or

problem? How/why an organization changes? How a change process unfolds?

2. The research question Variance or process research? Variance for causal questions Process is geared to how questions

3. Frame of reference Who’s viewpoint is featured? What is the researcher’s role?

Observe change process from a specific participant’s viewpoint

4. Mode of inquiry Sound general argument or good particular story?

General explanations – causal theories Particular understanding - narratives

5. Conceptual model One or more models/stories? Which ones?

Compare plausible alternative models

6. Observational method Real-time or historical observations?

Observe before outcomes are known

7. Field research design How design the field research? Develop parallel, synchronic, and Diachronic research design

8. Sample diversity Homogeneous or heterogeneous? Compare the broadest range possible. Compare different viewpoints.

9. Sample size Number of events and cases? Focus on number of temporal intervals and granularity of events

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A Critical Realist Call for Intellectual Pluralism

• There is a real world out there, but our understanding of it is limited

• All facts, observations & data are theory laden

• Social science has no absolute, universal, error-free truths or laws

• No form of inquiry can be value free & impartial; each is value full

• Knowing a complex reality demands use of multiple perspectives

• Robust knowledge is invariant (in common) across multiple models

• Models that better fit the problems they are intended to solve are selected, producing an evolutionary growth of knowledge.

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Steps & Decisions for Analyzing Process

Data Key Step Key Decision(s) Suggestions 1. Developing process concepts

What concepts or issues will you look at?

Begin with sensitizing concepts and revise with field observations

2. Defining incidents & events

What activities or incidents are indicators of what events?

Incidents are observations, events are unobserved constructs

3. Specifying an incident What is the qualitative datum? Develop decision rules to bracket or code observations

4. Measuring an incident What is a valid incident? Ask informants to verify incidents 5. Identifying events What strategies are available to

tabulate and organize field data? Apply a mix of qualitative and quantitative data analysis methods

6. Developing process theory

How move from surface observations to a process theory?

Identify characteristics of narrative theory

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Barley’s Field Research Design

Barley, S (1990) “Images of Imagining: Notes on Doing Longitudinal Field Work,” Organization Science, 1, 226.

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Qualitative Methods for Analyzing Process Data

• Narrative Strategy• Template Matching• Grounded Theorizing• Visual Mapping• Temporal Bracketing• Synthetic Strategy• Quantitative Strategy

Ann LangleyHEC, Montreal

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Quantitative Methods for Analyzing Process Data

• Analyzing Event Sequence Data

• Structures of Event Time Series

• Models for examining different structures of time series• Orderly data• Chaotic data• Random dataKevin Dooley

Arizona State University

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Quantitative & qualitative

Some sources will naturally be quantitative But many will be qualitative

Symbolic time series• Event type sequences

Numerical time series• Number of events per fixed time period• Quantification of qualitative content (manifest/computerized

content analysis)

When change qualitative to quantitative?• Large volumes of qualitative data• Modeling skills present on research team

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Temporal analysis

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Descriptive analysis example

RQ: Are there temporal patterns of new venture activities (start-up events) which are predictive of venture emergence? Does the type of event matter?

Method PSED sample Case = nascent entrepreneur A priori event list Respondent indicates month of event completion Time series formed by number of events each month

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Month

Month

Lower concentration, average timing

Higher concentration, late (high) timing

Activity

Which entrepreneurial process is more likely to be successful?

SAME RATE

Lichtenstein, Dooley, Carter, & Gartner, 2005

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Sequence analysis example• RQ: Are there temporal patterns of activities in

large-scale, group-based development activities?– Does process depend on task?

• Method– Development of ebXML standards– Case = Task force– Almost all of process on-line (20k emails)– Text analysis to identify dominant narrative theme

(activity) in each month

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ebXML event sequencesCodeR—RequirementsS—SearchM—ModelD—DesignI—Internal reviewE—External review

Business process standard: R—S—S—R—M—M—M—R—R—R—M--I –I—I—I—E—R—I—I—E—

R—E—E—E

Technical standard:R—R—R—R—R—D—I—I—I—I—D—D—I—I—D—D—E—E—D—D

—E—E—E—E

Choi, Raghu, Vinze, & Dooley 2005

What story can you see?

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Change point analysis example

RQ: What happens during organizational emergence? Method

Biweekly interviews and empirical data from entrepreneur

Change point analysis of multiple time series Temporal analyst blind to case details

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Where is/are the change point(s) in each series?What story is told?

Lichtenstein, Dooley, & Lumpkin JBV 2005

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Variable Data Estimated CP Degree of Change

EXPENSES Mean DCP 18 * Decreased by 58%

Variance DCP 18 ** Decreased by 55%

B/W Mean DCP 18 ** Decreased by 55%

Variance DCP 18 *** Decreased by 82%

TOTAL Mean DCP 18 * Decreased by 34%

Variance none –

JOB Mean none –

Variance DCP 18 * Decreased by 27%

DCP 18 also corresponded to• most “central”Interview• time ofincorporation

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Dynamical analysis example

RQ: What are the generative mechanisms behind media attention to 9-11 players?

Methods All Reuters articles related to 9-11 over 66 days

(approx. 100 pages/text per day) Text analysis to identify influence of name in media

texts Time series

• ARMA models• Spectral analysis• Chaos detection

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NA

ME

INF

LU

EN

CE

(S

TA

CK

ED

GR

AP

HS

)

DAYS POST 9-11

Dooley & Corman, 2004

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ARMA(2,1) with (stochastic) four day cycle

NA

ME

INF

LU

EN

CE

(S

TA

CK

ED

GR

AP

HS

)

DAYS POST 9-11

MA(1), correlated with binLaden at one day lag

Shift; white noise

Episodic

Sustained episode

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Dynamical Systems:Patterns over Time

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Induction

Dooley & Van de Ven, 2000

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Correlative analysis example

RQ: What are the semiotic processes occurring in business news genre?

Method Media articles across multiple cases Text analysis to identify theme influence, tone, and

intensity Change point analysis to identify epochs Correlate themes with tone, intensity within epochs

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Table shows whether theme was positively or negatively influenced with tone or intensity during a particular epoch• Tone: Ratio of positive to negative words• Intensity: Ratio of emotive words to non-emotive words

HOW ARE TONE AND INTENSITY USED BY THE MEDIA?

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Why does temporal analysis work?

1. Only time tells stories

2. The dynamics of the parts embed the dynamics of the whole

Dynamics of (e.g.) g (w, x, y, z) =

Dyn. of g {w(t), w(t-K), w(t-2K), w(t-3K)}

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Pragmatics

Plot temporal data! Software

Sequence analysis—Social network software based on transition matrix (e.g. UCINet)

Time series analysis—Most advanced stats programs (e.g. Statistica, SPSS)

Nonlinear dynamics—Chaos Data Analyzer Change point analysis—quality control charts; Change Point

Analyzer Challenges

Skills in exploratory statistical modeling Communicating to readers Can only examine dynamics, change points, and correlation in a

hierarchical manner

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Process as generative

Dominant closed path is “normative” (R-M-I-E).

Requirements-centric Lots of transitivity

• No closed path• Design-centric (hub)

AGILE WATERFALL

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Additional Slides

For display on questions or issues discussed

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2. Data Entry Forms

Data Entry FormsDate:__________ Event #: ______

Event:_________________________________________________________________________________________________________________________Observation: _________________________________________________________________________

Source: ____________________________________

Keywords: __________________________________

A Sample Event Data Entry Form

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A Sample Event ReportCIP Event Printout as of 02/25/94 Number: 38 Date: 02/01/77 Event: University of Melbourne approaches 3M on a joint venture to develop and manufacture CI. News of the development of a "bionic ear" triggers interest of executives at 3M. Observ: The relationship was not established, and 3M decides to pursue the "bionic ear" idea separately. Leader: I S SD Number: 41 Date: 12/15/77 Event: 3M evaluates U. of Melbourne, Australia proposal for the

"bionic ear." A report to 3M executives states the project isa promising business opportunity. However, exclusive rightsand patent protection is reported as unclear.

Observ: On the surface the project is very promising -- the US market potential using $ 1000 device (conservative) is $ 1000 mm. The device

is an emerging technology, I am not aware of any publishedon-going research in this type area. (As with heart pacers, the firstcompany in the market can dominate). There is a good fit with existing3M technology. On the minus side, I have some doubts about the patent protection. The Australian proposal does not indicate a strong position.There is also the problem with the distance involved and the proposalis rather vague about exclusivity after investments by 3M.

Leader: S C SD

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Source: Van de Ven, Polley, Garud & Venkataraman, The Innovation Journey, NY: Oxford, 1999.

Example of Visual Mapping Strategy in CIP Case

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Source: R. Garud & A. Van de Ven, “An Empirical Evaluation of the Internal Corporate Venturing Process,”Strategic Management Journal, 13 (1992): 93-109.

Example of Temporal Bracketing Strategy in CIP Case

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Example of 3DGraphing ofEvent Sequences

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Langley, A. (1999) “Strategies for Theorizing From Process Data,” AMR, 24, 1, p. 696.

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Narrative Features of Process Theory

• In narrative theory, the the story includes more than just event sequence. A process theory should include:• Sequence in Time• Focal Actor(s)• Narrative Voice• Evaluative Frame of Reference• Indicators of Content and Context

Brian Pentland, “Building Process Theory with Narrative: From description to explanation,” Academy of Management Review, 24, 4

(1999): 711-724.

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k = 1.8

k = 3.2

k = 3.7

Xt = kXt-1(1-Xt-1)

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Bifurcation Structure of the Limit Set of Logistic Map Xt = kXt-1 (1 – Xt-1) for varying values of k

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Dooley, K. and Van de Ven, A. (1999) “Explaining Complex Organzational Dynamics,”Organization Science, 10, 3: p. 367.

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I II III IV100 % Deterministic 100 % Stochastic

Mathematics

10

100

1000

D

imen

sion

- P

hysi

cs

I. Solvable dynamic system, e.g. gear trains, physical pendulumII. Amenable to perturbation theory, e.g. satellite orbitsIII. Chaotic dynamic systems, e.g. climatology, Lorenz equationsIV. Turbulent/stochastic systems, e.g. quantum mechanics, turb. flow

(Source: Morrison, 1991)

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