Qualitative Methods For Research

1
Qualitative Methods For Research

• Dr Susan GassonCollege of Information Science
  TechnologyDrexel UniversityEmail
  sgasson_at_cis.drexel.edu

2
Agenda

• What is qualitative research?
• Issues of rigor and differences from quantitative
  research
• Methods for qualitative analysis
• Data collection methods
• Analysis methods
• A Study of Knowledge Management in a
  Boundary-Spanning, Global IS Devt. Group
• Rigor and validity issues
• Exercise coding qualitative data
• Useful resources and references

3
What is qualitative analysis?

• Non-quantifiable (or non-quantified) data are
  analyzed using a variety of methods, to
  understand patterns in the data.
• Whereas quantitative data are analyzed
  statistically, qualitative data are organized,
  categorized (coded) and then analyzed through
  inferential reasoning processes.
• Organization of qualitative data involves
  identification of relevant data samples, e.g.
• sections from tape-recorded interviews
• time-stamped episodes from a video-recorded
  activity
• field notes from observed behavior in the
  situation being studied).

4
Example Coding Observations

• Categorize a description of the voting process in
  a specific country.
• Focus is on
• (i) how the vote-counting process works,
• (ii) the reliability of the process
• (iii) the role of technology.
• Code each new idea in the printout (may be a
  sentence or may be a paragraph) with
• Category code (may have gt1)
• Attribute(s) of the category

5
Examples Coding Voting Description
Observation Category Code Attribute Code
The officials check that no one person's vote is used more than once, and tally up the total number of ballot papers issued in order to help verify that all the ballots make it safely to the count ??? ???
Note that the count can be observed in the count room by the candidates and their agents no press or news organization is allowed access, though they can typically watch from a balcony ??? ???

• Focus on
• (i) How the vote-counting process works,
• (ii) The reliability of the process
• (iii) The role of technology (can you make any
  observations from this data?).

6
Example Coding Voting Description
Observation Category Code Attribute Code
The officials check that no one person's vote is used more than once, and tally up the total number of ballot papers issued in order to help verify that all the ballots make it safely to the count Vote-counting process Reliability Manual Secure
Note that the count can be observed in the count room by the candidates and their agents no press or news organization is allowed access, though they can typically watch from a balcony Vote-counting process Reliability Visible Trustworthy
7
Coding Scheme

• Process (of vote-counting)
• Manual vs. electronic
• Hidden vs. visible
• Auditable vs. no-paper-trail
• Reliability (of the process)
• Secure vs. insecure
• Trustworthy vs. untrustworthy
• Objective vs. partisan
• Technology (role of)
• Registering vote
• Counting votes
• Tallying totals

8
Philosophical Questions

• What are you measuring, in a scientific
  experiment?
• Does it exist independently of your perception?
• Is it universal?
• Is it true?
• What are you measuring, in an interview or
  observation study of people performing daily
  work?
• Does it exist independently of your perception?
• Is it universal?
• Is it true?
• If you have 5 different researchers performing
  the same study, will they reach the same
  conclusions?

9
Research Paradigms in IS Info. Science

• 1. Positivist Research
• Positivists generally assume that reality is
  objectively given and can be described by
  measurable properties which are independent of
  the observer (researcher) and his or her
  instruments.
• Positivist studies generally attempt to test
  theory, to increase the predictive understanding
  of phenomena (hypothesis testing).
• 2. Interpretive/Constructivist Research
• Interpretive researchers start out with the
  assumption that reality is socially
  constructed. Phenomena can be understood only
  through the meanings that people assign to them,
  accessed via social constructions such as
  language, consciousness, shared meanings.
• Interpretive research does not predefine
  dependent and independent variables, but focuses
  on the full complexity of human sense making in
  context as the situation emerges.
• 3. Critical Research
• Critical researchers assume that social reality
  is historically constituted and that peoples
  ability to change their social and economic
  circumstances is constrained by various forms of
  social, cultural and political domination.
• Critical research focuses on the oppositions,
  conflicts and contradictions in organizations and
  society. It is emancipatory in intent it seeks
  to eliminate causes of alienation and domination.

10
The Research Life-Cycle In Theory Generation
Tests/extends theory
Generates/explores theory
11
Positivist vs. Interpretivist Beliefs
Positivist / Functionalist Interpretive / Constructivist
Ontological (beliefs about the nature of reality) Real-world phenomena relationships exist independently of the individuals perceptions Phenomena relationships are viewed as social constructs by which an individual makes sense of the external world/reality
Epistemological (beliefs about knowledge how we know reality) Natural laws govern all aspects of existence. These laws may be observed from outside the situation and abstracted to provide generally-applicable models and theories. Rules governing behavior in various situations are dependent on context. Inferred relationships between contextual factors and observed behaviors may be transferred to similar situations.
Human Nature(how we account for human behavior) The behavior of individuals en masse (with exceptions that can be explained by a lack of rationality or variance from the mean) can be viewed as determined by the situation. Human beings have complete autonomy their actions are dictated by free will (which may be constrained by external forces). So they do not act according to any laws of rational behavior.
Methodological(beliefs about how we apply inquiry methods) Researchers derive generalizable models or theories of behavior through the analysis of small-scope findings from large samples and systematic methods to construct scientific theories regarding the real world. Researchers infer transferable, in-depth subjective accounts of situations, that analyze observations from small samples in great detail. The presence of the observer is accounted for.
12
Constructivism The Hermeneutic Circle
The whole (the big picture)

• Hermeneutics is (literally) the interpretation of
  a text
• its intent
• its content, and
• its context.

The parts (analysis of minutiae or components)
Methodologically, the assemblage of an
understanding of the whole through an analysis
of its parts, e.g. WHOLE PARTGeneral/typical
case Instance of complicated caseLearning
process Instances of learningDecision process
Instances of decision making
Gadamer, H-G (1989), "Text and Interpretation,"
in Dialogue and Deconstruction The
Gadamer-Derrida Encounter, edited/translated by
D. P. Michelfelder and R. E. Palmer, SUNY Press,
Albany, NY, pp 21-51.
13
Use Of Multiple Methods

• Most often (but not always), the term
  qualitative research refers to qualitative
  content analysis, performed interpretively.
• Tenet of interpretivism is that researcher
  interprets data.
• So can use multiple qualitative methods for both
  data collection and data analysis, e.g.
• Data collection observation, formal interviews,
  interactive (facilitated analysis) interviews and
  workshops, document analysis, investigative
  surveys, etc.
• Data analysis qualitative coding (using
  different sets of constructs, to examine
  different aspects of the data), inferential
  analysis (usually simple frequency
  co-concurrence), statistical analysis, discourse
  analysis, etc.

14
Use Of Mixed Methods

• The use of mixed methods indicates the comparison
  of findings across multiple data collection
  techniques and analysis methods.
• This approach
• Provides multiple perspectives of the research
  problem
• Guards against limiting the scope of the inquiry
• Yields a stronger substantiation of the derived
  constructs
• (Cavaye, 1995 Eisenhardt, 1989 Orlikowski,
  1994 Wolfe, 1994).
• Mixed methods may (but does not have to) combine
  qualitative and quantitative analysis.

15
Qualitative Data Collection Vs. Qualitative
Analysis

• DATA

Qualitative Quantitative
Qualitative Interpretive content analysis studies. Hermeneutics, Phenomenology,Grounded Theory. Search for and presentation of meaning in quantitative results. Explanations of findings Interpretation of statistical results Graphical displays of data Naming factors/clusters in factor analysis cluster analysis
Quantitative Post-positivist Content AnalysisTurning words into numbers Word Counts, Free Lists, Pile Sorts, etc. Statistical analysis of text frequencies code co-occurrence Positivist Research Statistical mathematical analysis of numeric data (e.g. regression). Multivariate analysis.
ANALYSIS
Source Bernard, H.R. (1996) Qualitative Data,
Quantitative Analysis, CAM, The Cultural
Anthropology Methods Journal, Vol. 8 no. 1,
available at http//www.analytictech.com/borgatti/
qualqua.htm
16
Contributions of Qualitative Research

• The contribution of qualitative research studies
  in IS can be
• The development of concepts
• e.g. automate vs. informate" (Zuboff, 1988)
• The generation of theory
• e.g. Orlikowski Robey (1991) organizational
  consequences of IT.
• The drawing of specific implications
• e.g. Walsham Waema (1994) the relationship
  between design and development and business
  strategy.
• The contribution of rich insight
• e.g. Suchman (1987) contrast of situated action
  with planned activity and its consequences for
  the design of organizational IT.

Walsham, G. (1995) Interpretive Case Studies In
IS Research Nature and Method, European Journal
of Information Systems, No. 4, pp 74-81
17
Distributed Knowledge Coordination Across Virtual
Organization Boundaries

• Dr Susan GassonEdwin M. ElrodDrexel University

18
Knowledge Management For Virtual Collaboration

• Organizational KM view
• Knowledge-as-process
• Knowledge processes are embedded within
• Best practices (tacit knowledge),
• Contexts (localized knowledge) and
• Genres of communication (legitimate knowledge).
• Effective knowledge management depends on sharing
  understanding that is only meaningful in the
  context and community of practice within which it
  is applied.

• KM Systems View
• Knowledge-as-thing
• Knowledge can be defined independently of human
  action.
• Knowledge can be divorced from practice
• Knowledge can be abstracted into rules or
  algorithms, independent of context
• Knowledge can be defined objectively.
• Effective KM depends on knowledge capture,
  codification transfer across many different
  places and many different CoPs.

How do we resolve this tension?
19
Research Question

• How are different forms of knowledge managed and
  coordinated across the boundaries of a virtual,
  global organization?

20
eCommerce Group Functional Boundaries
Executive Management
Vendor Projects
Europe
Technical Operations
Client FacingApplications
Financial Client Performance Evaluation
BackendApplications
21
Corporate and Geographic Boundaries
eServCorpEU Operations
eServCorp eCommerce
VendorCorp
eServCorp EU Customer Service
eServCorp Asia Pacific
eServCorp N. American Operations
ParentCorp
eServCorp Corporate
22
Field Observations

• Researchers observe transcribe telephone
  conferences and other (face-to-face) meetings
• Supplemented with monthly ad hoc interviews with
  management team.
• Sample statistics through June 2006
• 338 conference calls/group meetings
• Average length 0 30
• Shortest 004
• Longest 135
• 8 group interviews.
• Over 1000 pages of transcription
• Longitudinal, ethnographic, exploratory

23
Thematic Analysis Of Meetings (Initial)

• Thematic analysis What are the most common
  themes?
• Categories of behavior or phenomena, meaningful
  in context of the study.
• Are there notable exceptions?
• E.g. individuals who do not discuss specific
  themes or who say very different things about
  particular topics?
• What concept-categories or event-categories can
  be identified ?
• What is the range of views expressed with regard
  to a topic?
• Can you identify any sub-categories?
• Variations on your themes, further
  distinctions/qualifications?
• What language is used?
• Are there common synonyms or metaphors that
  indicate a specific meaning or category of
  behavior?
• What respondent characteristics are associated
  with particular views?
• Do people with different expertise express
  different views?
• What patterns emerge, across various samples, or
  over time?

24
Knowledge Sharing
(Johnson, et al, 2002)(Polanyi, 1958)(Zack,
2001)
Boundary Object Mechanism Knowledge Sharing Form Knowledge Sharing Form Knowledge Sharing Form Knowledge Sharing Form
Boundary Object Mechanism Know-What Know-Why Know-How Who-knows-what
Repositories
Standardized Forms, Methods, Procedures
Models
Maps
Observed knowledge translation and
transformational activities.
(Star, 1989) (Carlile, 2002)
25
Know-How
Boundary Object Mech. Knowledge Sharing Form Knowledge Sharing Form Knowledge Sharing Form Knowledge Sharing Form
Boundary Object Mech. Know-What Know-Why Know-How Who-knows-what
Repositories
Std Forms, ...
Models
Maps
Make work practices explicit through discussion
and debate.
Standardized Procedures
Ms CorpSys Some system reports have problems.
Mr VendorTech This was fixed in acceptance, but
it didn't move with the release. Mr EVP How
many times does this happen? About 50. Why are
we paying ltthe vendorgt for the same mess up 50
of the time? Ms CorpSys We go through a
rollout plan after every test. Moving code over
always catches us. Mr ClientSys There should be
some established best practice. Mr EVP I'm
sure there's a best practice 'cause it's been
going on since the 1960s.
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Know-Why
Boundary Object Mech. Knowledge Sharing Form Knowledge Sharing Form Knowledge Sharing Form Knowledge Sharing Form
Boundary Object Mech. Know-What Know-Why Know-How Who-knows-what
Repositories
Std Forms, ...
Models
Maps
Establish boundaries of eCommerce group.
Maps
Mr ClientSys It turns out that a vendor that the
EU office has is one that everyone else
uses. Mr EVP Yes and develops stuff for
everyone else and shares the information. It
depends whether we consider that a system for
constitutes a competitive advantage, Ms Europe I
think that outcome analysis and project sourcing
has to become a strategic area. ? ? ?
27
Who-Knows-What
Boundary Object Mech. Knowledge Sharing Form Knowledge Sharing Form Knowledge Sharing Form Knowledge Sharing Form
Boundary Object Mech. Know-What Know-Why Know-How Who-knows-what
Repositories
Std Forms, ...
Models
Maps
Identify relevant stakeholders in other groups.
Maps
Ms Europe Mr Support and June visited the French
vendor, so I have asked them to do a write-up for
us, so that we understand what the issues are
etc. and if there is an opportunity to take some
of the stuff like the product site, like the
project bank for Europe, since its already
built. But we need to look at the how we host
it, where we do it so I have asked them to
write it up for us. Mr EVP OK, let them write
it up. Then lets talk about it you, me and Mr
ClientSys. The reason I want to discuss this
other stuff - you, me and Mr ClientSys - is that
I want to make sure that whatever they put
together, you have vetted. With a broader
understanding of the global perspective than they
might have. ...
Ms Europe Mr Support and June visited the French
vendor, so I have asked them to do a write-up for
us, so that we understand what the issues are
etc. and if there is an opportunity to take some
of the stuff like the product site, like the
project bank for Europe, since its already
built. But we need to look at the how we host
it, where we do it so I have asked them to
write it up for us. Mr EVP OK, let them write
it up. Then lets talk about it you, me and Mr
ClientSys. The reason I want to discuss this
other stuff - you, me and Mr ClientSys - is that
I want to make sure that whatever they put
together, you have vetted. With a broader
understanding of the global perspective than they
might have. ...
Formal knowledge sharing
28
Concept Map Early Themes From Analysis of
Meetings
Project Collaboration Knowledge
Project Knowledge
Organization
Distribution
Problem
Diverse set of global groups collaborate
according to focus
Who-knows-what more important than who-can-do-what
Informal, distributed social context of project
Too complex for one person to understand
Formal knowledge often local and undocumented
Problem emerges thro negotiation
Project roles responsibilities change frequently
Knowledge located in peoples heads
Project goals are subjective various groups
individuals define project in different ways
Project definition is ad hoc (memory-dependent)
Group memory of project changes
Definition of project changes frequently little
coordination or persistence of knowledge (group
memory)
29
Analytical Framework Categorize Collaborations
By Modes of Organizational Problem-Solving

• Well-Structured Problems
• Clear problem-structure defines change
  requirements
• Unambiguous goals for change
• Knowledge accessed via pattern recognition
  (problem-solvers in similar domains develop
  repertoire of solutions).
• Ill-Structured Problems
• Uncertain problem-structure indicates multiple
  alternative solutions
• Need to bound and structure problem to analyze
  requirements (complexity reduction)
• Explore unfamiliar knowledge-domains through
  consultation with experts to resolve ambiguity re
  change-goals and scope.
• Wicked Problems
• Problem emerges has no objective definition,
  boundary, or structure
• Stakeholders see partial subsets ? multiple goals
  for change
• Problem, solutions, scope of inquiry, and
  relevant expertise are negotiated (equivocality
  reduction) .
• Explore emergent knowledge-domains thro
  iterative cycles of inquiry.

30
Three Spans of Collaboration

• (i)  Local coordination of projects
• Core e-Commerce group manage project define
  goals, scope, timescales, deliverables, and
  rationale
• Boundaries functional, role, geographic.
• (ii) Conjoint agency
• Core e-Commerce group control project act as
  hub, incorporating knowledge/expertise from
  external groups
• e-Commerce define goals, scope, and
  responsibilities
• Collaboration with hardware or software vendors,
  other eServCorp business units, client project
  groups
• (iii) Distributed Collaboration
• e-Commerce group part of a web of collaborating
  groups
• Goals, scope, system definitions,
  business-process changes negotiated, implemented,
  and evaluated jointly
• e-Commerce group subject to joint or external
  project-leadership by groups from eServCorp,
  ParentCo., associated companies, or vendors.

31
Problem-Coordination Distance
Knowledge coordination strategy depends on
problem coordination-distance
Problem-Solving Mode Collaboration Span Collaboration Span Collaboration Span
Problem-Solving Mode Local Coordination Conjoint Agency Distributed Collaboration
Well-structured problems
Ill-structured problems
Wicked problems
32
Relative Incidence of Problems
33
Modes of Organizational Problem-Solving
Well-Structured Problems Ill-Structured Problems Wicked Problems
Local Coordination Situation interpretation stories analogies create shared resource to identify similar problems Group identity construction plans, processes checklists formalize procedural memory Framing collective strategy group agrees evolving goals of change, to clarify approach to problem
Conjoint Agency Scope interpretation stories analogies communicate rules, evaluation-criteria, responsibilities at boundary Delegated knowledge- leadership domain expert roles assumed. Rules procedures at coordinate knowledge transfer at boundary Defining a collective response delegated boundary-spanner locates knowledge controls evolving boundary procedures
Distributed Coordination Coordinating division of labor functional domain-expert roles and social network leveraged for knowledge exchange Managing external networks of influence group domain-experts jointly formulate problem, negotiate group responsibilities Collective knowledge networking leader negotiates group role group members become expert in evolving set of knowledge-domains
34
Conclusions and Contributions

• Knowledge is coordinated by means of a web of
• Functional and domain-expert roles
• Distributed knowledge resources
• Imposed or negotiated procedures.
• Knowledge coordination strategy depends on
  problem coordination-distance. This concept
  combinesorganizational span of coordination with
  problem-type.
• Central role of a cohesive group identity
• Informs semi-autonomous decision making by group
  members
• Provides conceptual patterns for action at group
  boundaries
• Adapted collaboratively through distributed,
  improvisational sense making to deal with novel
  situations.

35
Two Dimensions of KM Coordination
36
KMS Implications

• Knowledge Management Systems must expand beyond
  communicating management decisions to embrace
  distributed, emergent, collaborative decision
  formation
• Well-structured problems require rule-based KMS.
• Ill-structured problems require adaptive KMS.
• Wicked problems require evolutionary dynamic
  KMS, supplemented by human contact.
• KMS must be supplemented with face-to-face
  mechanisms that permit social networks to be
  formed and maintained.
• KMS must be supplemented with face-to-face
  mechanisms that permit domain expertise to be
  acquired and translated across domains.

37
Analyzing Qualitative Data

• Principles and Practice(!)

38
Qualitative data coding

• Data are be transcribed into a textual form
  (recommended) and/or analyzed in its raw form
  (e.g. video/audio, with items of interest
  identified by time-stamp).
• Data analysis (coding) can take two forms
• Data are classified according to a conceptual
  schema or a theoretical model, which leads to
  explanations dependent upon, or the further
  development of the conceptual model
• Data are classified according to patterns that
  emerge from interpretation of the data. As themes
  and patterns emerge from the data, these are
  tested against further data samples to derive a
  substantive (grounded) theory.

39
A Question
Q If two researchers are presented with the same
data, will they derive the same results if they
use the same methods, applied rigorously?

• Lets find out!
• Organize in groups of three(-ish) people.
• Discuss themes arising from coded data (10
  minutes)
• Present findings 5 minutes per group

40
How to Code Data

• RQ What are differences in the ways that various
  types of IS professional or manager define the
  core problems skills of IS design
  development?
• Read the transcript or data record through.
• Ask yourself what is it that is going on here?
• Make notes about themes that you see in the
  data
• Dont attempt to be systematic/comprehensive at
  this point
• Categorize (code) your observations
• Relate category-codes to research question
• Define attributes of categories (attribute codes)
• Define categories and sub-categories (coding
  families)
• Ask so what?
• Relate categories and their attributes to
  contextual factors and/or type of subject
• Draw conclusions about what the data tells you,
  in answer to the research question.

41
Issues With Qualitative Research

• How much data is enough?
• How do you know that what you found is not what
  you were looking for?
• Is it difficult to publish qualitative research
  studies?
• Is qualitative research considered less
  acceptable than quantitative research?
• Is this something that a PhD student should
  consider?

42
Intercoder Reliability/Agreement

• Intercoder reliability is a measure of agreement
  among coders in their coding of data
• High reliability scores indicate that
• Categories are well-defined (agreed) and can be
  replicated by others applying the same schema, OR
• Multiple coders are applying a pre-defined set of
  categories consistently, when coding data
  samples.
• Assess by comparing (co-coding) several data
  samples (e.g. 10)
• Or analyze data from a pilot study to see what
  codes emerge across researchers before main study
  starts
• Measures of intercoder agreement)
• Coefficient of reliability (Holsti, 1969, p. 140)
• Scotts pi (Holsti, 1969, p. 140)
• Cohens kappa (Krippendorff, 1980, p. 138)
• Agreement coefficient (Krippendorff, 1980, p.
  138)
• Composite reliability (Holsti, 1969, p. 137)
• Good website http//astro.temple.edu/lombard/rel
  iability/

43
Summary Issues in Qualitative Research

• Qualitative research methods are used differently
  by researchers working within various
  philosophical approaches and various qualitative
  traditions.
• Data collection methods include action research,
  case studies, ethnography.
• Data analysis methods include statistical
  sampling of coded data and the inductive
  generation of relationships between variables.
• In the interpretive approach
• Rigor is achieved through comparison of findings
  across data samples and reflexivity.
• Validity is communicated through trustworthiness
  and subject validation of interpretations, rather
  than statistical significance.
• Can protect yourself against allegations of
  subjective interpretation (lack of rigor), by
  testing for co-coder reliability.

44
The Qualitative Quantitative Debate
Qualitative Quantitative

• Constructivist/Interpretivist
• Find answers to questions
• Social science view
• Explanatory
• Goal understand the subjects perspective, in
  context
• Investigation oriented
• Emergent themes and issues
• Researcher is part of situation being studied

• Realist/Positivist
• Test hypotheses
• Natural science view
• Confirmatory
• Goal find probabilities and correlations
• Verification oriented
• Controlled variables
• Researcher distanced from situation being studied

• BUT
• Differences are not as simple as this it is
  possible to perform qualitative research in a
  positivist way, or quantitative analysis of
  interpreted findings.
• Positivist research is also subjective but the
  subjectivity occurs earlier in the research
  life-cycle, in selection of theory to be tested
  and research instrument(s).

45
References (Books and Articles on How-To Do
Qualitative Research)

• Denzin, N.K., and Lincoln, Y.S. Eds. (2000) The
  Handbook of Qualitative Research. Sage Books.
• Eisenhardt, K.M. (1989) "Building Theories From
  Case Study Research," Academy of Management
  Review (144), pp 532-550.
• Gasson, S (2003) Rigor in Grounded Theory
  Research, in M. Whitman and A. Woszczynski
  (Eds.) Handbook for Info. Sys. Research, Idea
  Group, Hershey PA
• Gasson, S. (2009) Employing A Grounded Theory
  Approach For MIS Research, in Dwivedi et al.
  (Eds.), Handbook of Research on Contemporary
  Theoretical Models in Information Systems, Idea
  Group, Hershey PA.
• Glaser, B.G. Strauss, A.L. (1967) The Discovery
  of Grounded Theory, Aldine Publishing, New York
• Guest, G., Bunce, A., Johnson, L. (2006). How
  Many Interviews Are Enough? An Experiment With
  Data Saturation And Variability. Field Methods,
  18(1), 59-82.
• Lincoln, Y. S. and Guba, E. G. (1985),
  Naturalistic inquiry, Sage Publications CA
• Miles, M.B. and Huberman, A.M. (1994) Qualitative
  Data Analysis An Expanded Sourcebook, (2nd.
  Edition) Sage Publications, Thousand Oaks, CA
• Patton, M. Q. (2002). Qualitative research and
  evaluation methods (3rd ed.). Thousand Oaks, CA
  Sage..
• Strauss, A. L., and Corbin, J. (1998) Basics of
  Qualitative Research Grounded Theory Procedures
  And Techniques. 2nd. edition, Sage Publications,
  Newbury Park, CA
• Yin, R. K. Case Study Research, Design and
  Methods, 3rd ed. Newbury Park, Sage Publications,
  2002.

46
More references (recommended examples)
References used in slides are given in notes to
slides

• Barley, S. (1990) Images Of Imaging Notes on
  Doing Longitudinal Field Work, Organization
  Science, Vol. 1, No. 3, pp 220-247
• Cavaye, A.L.M. "User Participation In System
  Development Revisited," Information Management
  (285) 1995, pp 311-323.
• Checkland, P. (1981) Systems Thinking, Systems
  Practice, John Wiley Sons, Chichester.
• Newman, M., and Robey, D.(1992) "A Social Process
  Model of User-Analyst Relationships," MIS
  Quarterly (162) 1992, pp 249-266.
• Orlikowski, W.J. Robey, D. (1991) Information
  Technology and the Structuring of Organizations',
  Information Systems Research, Vol. 2, No. 2, pp
  143-169
• Schutz, A.(1962) Collected papers Vol. I. The
  problem of social reality. Martinus Nijhoff, The
  Hague.
• Suchman, L. (1987) Plans And Situated Action,
  Cambridge University Press, MA, USA
• Tannen, D. "What's In A Frame?" in Framing in
  Discourse, D. Tannen (ed.), Oxford University
  Press, Oxford, UK, 1993.
• Van Maanen, J. (1988) Tales of the Field,
  University of Chicago Press, Chicago, IL
• Walsham, G. (1995) Interpretive Case Studies In
  IS Research Nature and Method, European Journal
  of Information Systems, No. 4, pp 74-81
• Wolfe, R.A. "Organizational Innovation Review
  Critique and Suggested Research Direction,"
  Journal of Management Studies (313) 1994, pp
  405-431.
• Yin, R.K.Case Study Research, Design and Methods,
  2nd ed. Newbury Park, Sage Publications, 1994.

47
Resources

• ISWORLD Qualitative Research website
  http//www.qual.auckland.ac.nz/
• CAQDAS Qualitative Research resources lots of
  software! http//caqdas.soc.surrey.ac.uk/resources
  .htm
• University of Georgia Qualitative Research
  Site http//www.qualitativeresearch.uga.edu/QualP
  age/
• Ethnographic Qualitative Methods Course
  Resources
• Discourse Analysis (Deborah Tannen, 2004)
  http//www.lsadc.org/fields/index.php?aaadiscours
  e.htm
• Good discussion of inter-coder reliability in
  content analysis http//www.temple.edu/sct/mmc/rel
  iability/
• Some freeware for qualitative data analysis -
• Audacity is an audio editor which will record
  sounds, play sounds, import, edit and export WAV,
  AIFF, Ogg Vorbis, and MP3 files
• Express Scribe provides professional audio
  playback control software
• Atlas/ti -- cut-down but usable demo of
  qualitative analysis software
• My web-page interesting readings for PhD
  students http//www.ischool.drexel.edu/faculty/s
  gasson/IS-readings.html