Process Research Workshop: A Spectrum of Methods, AOM PDW, Saturday, August 6

1. 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

2. Process Research Workshop: A Spectrum of MethodsAgenda 8:30 Welcome & Introductions 8:45 Process Research Epistemology – Scott Poole 9:15 Small Group Exercise in studying a problem as a process 9:45 Discussion and Break 10:15 Designing Process Research Studies – Andy Van de Ven 10:30 Small Group Exercise in designing a process study 11:00 Discussion 11:30 Conclusion – Lunch on your own 1:00 Qualitative Methods for analyzing process data – Ann Langley 1:30 Exercise in writing a process study 2:00 Discussion and Break 2:30 Quantitative Methods for analyzing process data – Kevin Dooley 3:00 Small group discussions 3:30 Concluding Discussion 4:00 End

3. Participants’ Questions for Morning Session • 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) • 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). • 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) • 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.)

4. Participants’ Questions for Afternoon Session • What are the most appropriate statistical packages to handle process analyses? (Matthias Brauer, U of St. Gallen,Switzerland) • 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) • How do you apply multiple sensemaking strategies in one paper given the page limitations of ordinary management journals? (Matthias Brauer) • 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)

5. Sourcebook for Process Research MethodsMarshall S. Poole, Andrew Van de Ven, Kevin Dooley, and Michael Holmes,New York: Oxford University Press, 2000. • Perspectives on Change and Development • Process Theories and Narrative Explanations • Process Theories of Organizational Change • Overview: Methods for Process Research • The Design of Process Research Studies • Stochastic Modeling • Phasic Analysis • Event Time Series Regression Analysis • Nonlinear Dynamical Analysis • Conclusions

6. 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 Poole Texas A&M University

7. Variance and Process Epistemologies Lawrence Mohr

8. Langley’s Picture of Variance and Process Theories Variance Theory Process Theory • Attributes of: • Environment (x1) • Technology (x2) • Decision • Process (x3) • Resources (x4) State B Organization Outcomes (Y) State A • events • activities • choices Ann Langley T0 T1 Y = f(x1, x2, x3, x4)

9. Bruner’s Two Modes of Thought Jerome Bruner (1915 - )

10. EVOLUTION (Competitive Change) DIALECTIC (Conflictual Change) Variation Selection Retention Multiple Thesis Entities Conflict Synthesis Antithesis Population Scarcity Pluralism (Diversity) Environmental Selection Confrontation Competition Conflict Unit of Change LIFE CYCLE (Regulated Change) TELEOLOGY (Planned Change) Dissatisfaction 4 (Terminate) ImplementGoals Search/Interact Stage 3(Harvest) Stage 1 (Startup) Single Entity Stage 2(Grow) Set/Envision Goals Immanent Program Purposeful enactment Regulation Social construction Compliant adaptation Consensus Mode of Change Constructive Prescribed Process Models of Organization Change Note: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.

11. Approach I Approach IV 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 Variance study of change in organizations Causal analysis of independent variables explaining change (dependent variable) Newtonian view of time Variance Methods Epistemology Approach III Approach II Process study narrating emergent organizing activities Qualitative narrative interpretation of complexity metaphor Social construction view of time 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 Methods a verb, a process A noun, a thing Organizational Ontology Alternative Approaches for Studying Organization Change

12. Designing Process Research Studies • Basics of Process Research • Designing Field Studies • Analyzing Process Data • Exercise: Design a process study Andy Van de Ven U. Of Minnesota

13. 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

15. Steps & Decisions in Designing Process Study

16. 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.

17. Steps & Decisions for Analyzing Process Data

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

20. Qualitative Methods for Analyzing Process Data • Narrative Strategy • Template Matching • Grounded Theorizing • Visual Mapping • Temporal Bracketing • Synthetic Strategy • Quantitative Strategy Ann Langley HEC, Montreal

21. 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 data Kevin Dooley Arizona State University

22. 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

23. Temporal analysis

24. 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

25. Which entrepreneurial process is more likely to be successful? Activity Month SAME RATE Lower concentration, average timing Higher concentration, late (high) timing Month Lichtenstein, Dooley, Carter, & Gartner, 2005

26. 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

27. ebXML event sequences What story can you see? Code R—Requirements S—Search M—Model D—Design I—Internal review E—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

28. 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

29. Where is/are the change point(s) in each series? What story is told? Lichtenstein, Dooley, & Lumpkin JBV 2005

30. DCP 18 also • corresponded to • most “central” • Interview • time of • incorporation

31. 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

32. Dooley & Corman, 2004 NAME INFLUENCE (STACKED GRAPHS) DAYS POST 9-11

33. ARMA(2,1) with (stochastic) four day cycle MA(1), correlated with binLaden at one day lag NAME INFLUENCE (STACKED GRAPHS) Shift; white noise Episodic Sustained episode DAYS POST 9-11

34. Dynamical Systems:Patterns over Time

35. Induction Dooley & Van de Ven, 2000

36. 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

37. 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?

38. Why does temporal analysis work? • 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)}

39. 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

40. 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

41. Additional Slides For display on questions or issues discussed

43. Date:__________ Event #: ______ Event:_____________________________________ __________________________________________ __________________________________________ Observation: _______________________________ __________________________________________ Source: ____________________________________ Keywords: __________________________________ 2. Data Entry Forms A Sample Event Data Entry Form Data Entry Forms

44. A Sample Event Report • CIP 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 is • a promising business opportunity. However, exclusive rights • and 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 published • on-going research in this type area. (As with heart pacers, the first • company in the market can dominate). There is a good fit with existing • 3M 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 proposal • is rather vague about exclusivity after investments by 3M. • Leader: S C SD

46. Example of Visual Mapping Strategy in CIP Case Source: Van de Ven, Polley, Garud & Venkataraman, The Innovation Journey, NY: Oxford, 1999.

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

49. Example of 3D Graphing of Event Sequences

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