Introduction to Advanced Qualitative Research Methods PDF

Introduction to advanced qualitative research methods Differences between quantitative and qualitative research Methods All research based on underlying assumptions Different academic communities share different assumptions = different philosophical positions Philosophy of science: 1. Why – to what ends- do we engage in scientific research (level of ends) 2. Which methodological tools do we use to acquire scientific knowledge? (Level of means) Level of ends for qualitative research: o Social reality is socially constructed = mediated through systems of signs and representation (language, meaning, symbols, culture) o Researcher has to address what is meaningful to people in situation studied o Researcher’s perspective and interpretative nature of social reality matters Why qualitative research? o Uncover unexpected and explore new avenues o To expand develop new theory or expand previously developed theory o To capture inner events, backstage, insider perspective o To bridge the gulf between research and practice o To „rehumanize“ management and organization research and theory Deduction vs. induction in theory-use Level of means of qualitative research “The label qualitative methods […] is at best an umbrella term covering an array of interpretive techniques which seek to describe, decode, translate, and otherwise come to terms with the meaning, not the frequency, of certain more or less naturally occurring phenomena in the social world.” (Van Maanen, 1979, p. 520; own emphazis) Current standings of qualitative research in management studies o 20% of AMJ articles o Many best paper prizes awarded to qualitative studies o Challenges: Gaining access: It takes very long to gain access to your field, gather the data, make sense of your data, and write a qualitative piece Overcoming preconceptions: It is harder to publish a qualitative study as qualitative researchers are more scrutinized than their colleagues doing quant. Research Philosophical foundations of qualitative research Research = original investigation undertaken to contribute to knowledge and understanding in particular field, based on underlying assumptions about what constitutes valid research and how it should be done Underlying assumptions o Ontology: what entities do exist in the world? o Epistemology: What is knowledge and how can it be acquired? o Cognitive interests: Why – to what ends - do we engage in research? Paradigm = worldview consisting of basic philosophical assumptions, specific approaches to research and applications to research problems, carried on through socialization (all researchers become socialized into specific paradigm), imperfectly demarcated Three core paradigms in qualitative research: (Post-)Positivism o Primary paradigm in quantitative research, but also qualitative research based on this worldview o Ontology: Objectivism o Epistemology: Empiricism o Correspondence theory of truth o Methodological focus: Measurement, objectivity, law- generalizations, verification/falsification; most closely associated with hypothetico-deductive quantitative methodologies o Logic: o & Karl Popper’s critical rationalism “No matter how many instances of white swans we may have observed, this does not justify the conclusion that all swans are white.” (Karl Popper, 2002) Reality only known probabilistically Falsification instead of verification Goal: compare facts and theory to challenge prior knowledge No big sample per se necessary for falsification Interpretivism (and social constructivism) o Multiple possible interpretations are equally valid o Ontology: social reality is socially constructed o Epistemology: 1. Access to reality always mediated through system of signification (language, meaning, symbols, culture, etc.) that are contingent 2. Knowledge does not simply mirror or depict but actively brings about reality o Methodological consequences: goal of research “not to capture some preexisting or ready-made world presumed to be available out there but to understand [the] process of symbolic ‘worldmaking’ […] through which the social world is ongoingly accomplished” Critical-postmodern paradigm o Like interpretive research with additions: ▪ More radical break with modernist/positivist ideals of truth, objectivity, facticity and science ▪ Complex relationships between interests, knowledge and power ▪ Focus on critique and deconstruction ▪ Ethically based stance, suggests individual emancipation/improvements in society ▪ Closely associated methodologies: discourse analysis, narrative analysis, postmodern ethnography, postmodern historiography Designing qualitative research projects Research design: o Circular process, guided by research question Developing relevant research questions o Emerges from previous research o Aims to fill relevant gap in literature o Specific enough to guide research process o Open enough to allow surprising findings to emerge o Quantitative vs. qualitative research questions: o Qualitative research questions aim to… … reconstruct subjective interpretative schemes … identify people’s perceptions and evaluations of a certain phenomenon … examine culture, beliefs, norms, motivations, morality, imagination … analyze non-explicable routines, practices, „ways of doing“ … investigate complex processes that unfold over time … develop theoretical constructs based on data … explore phenomena about which little is known Conceptual framework o Helps to sort empirical insights o Connects insights to ongoing research debates o Enables to develop scientific explanations, concepts and models (go beyond descriptive level) o Which theory and concepts help to answer the research question? o What can we see with a certain concept that we would not see without it? Case selection o Guided by research question ▪ What is a good case to analyze the research question? ▪ How many cases are needed to analyze the research question? o Theoretical sampling: selecting cases based on theoretical criteria ▪ Typical cases or extreme cases ▪ Comparing similar cases to search for differences ▪ Comparing different cases to search for similarities o If cases don’t fit research question and theoretical framework, question cannot be answered → start from the beginning Data collection Data analysis Developing explanations, concepts and models Case study research Logic: o “Case studies are rich, empirical descriptions of particular instances of a phenomenon that are typically based on a variety of data sources.” o Case = bounded system, can be person, group, organization, relationship, event, process, problem or other specific entity o Examining context and other complex conditions is integral to understanding o Addresses how and why questions Key assumptions: o Can be conducted within realist or interpretive/constructivist tradition o May serve variety of purposes o Key objectives: theory building, extension of existing theory, (testing theory) Single case studies o In-depth examination of single case providing detailed understanding of specific phenomenon o Objective: developing novel theory o Types: (1) revelatory case, (2) critical case, (3) extreme case, (4) representative case or (5) longitudinal case o Decision of suitable case depends on research question o May have multiple embedded units of analysis: study of multiple aspects within single case → comprehensive understanding of case’s complexity Multiple case studies o Examination and comparison of several cases to gain deeper understanding of specific phenomenon o Objective: produce theory that is parsimonious, generalizable and testable through propositions o Often referred to as “Eisenhardt method” o Decision of cases driven by research question o May include multiple embedded units of analysis: various aspects within each case → enhancing the generalizability of findings Procedure o Using theory in design work o theoretical lens (i.e., institutional theory or practice theory) helps to refine your research question, selecting your case(s), adapt your case study design, and defining the relevant data o use of theory helps to organize data analysis o perspective could limit ability to make discoveries, prepare to discard it after initial data collection o Gaining field access and collecting data o Gaining access via: − Personal ties (e.g., through previous or current employment or personal relationships) − Industry conferences or specific networking events − “Cold acquisition” (e.g., sending well-drafted and specific emails) − Snowballing techniques (e.g., asking informants about other informants) o Collecting data via: − Observations − Interviews − Archival records, documents, pictures, videos, etc. → Ideally, researchers triangulate multiple data sources o Analyzing data o Presenting findings Opportunities and limitations Opportunities Limitations In-depth exploration of complex Time and resource intensive: invest phenomena within real-life context substantial time in conducting interviews, observations and document analysis Contextual understanding: capture Limited (formal) generalization: focus unique circumstances, relationships on specific contexts, so findings not and dynamics, providing more holistic easily applicable to broader & nuances understanding populations Theory development and refinement: Analytic generalization possible potential to contribute to theory development Ethnographic research Origins: o Roots in anthropology o Developed from attempts to study other cultures o Management and organization studies interested in “culture with small c” o Studying lifeworld of organizational member, understanding organizing process and everyday practices Date gathering o Multiple data sources important to triangulate interpretations ▪ Observations (capture in field diaries, using thick description) ▪ Interviews ▪ Documents Data analysis o First and second order concepts Writing o Let readers participate in lived experiences o Convey credible interpretations o Embed vignettes (excerpts of field diary) o Consider use of visual evidence (videos or photos from field) Limitations Additional considerations Design science research Nature: o Two major paradigms in management and organizational studies: ▪ Behavioral science: studies human or organizational behavior to understand, explain and predict it by creating and testing theory ▪ Design science: creates innovative artefacts that serve human purposes by solving technical or organizational problems o Employs techniques for data collection from qualitative and quantitative research o Pragmatic element: relevance lies in artifacts’ efficacy Artifacts o Outcome of design process o Varies depending on real-world problem o Can be managerial, socio-technical or technical by nature o Examples: Research question o Types: Design science process o Different models: five process steps by Kuechler and Vaishnavi (2008) o Outlines steps sequentially but actual process is iterative Step 1: Problem awareness o Identifying and understanding problem space o Questions: − What is the need or what is wanted? − What is the goal, purpose or intended outcome? − What requirements, both from stakeholders and knowledge base, exist? − Who are the involved stakeholders and how do their needs and requirements differ? o Often return to this step as subsequent design steps produce knowledge that flows back Step 2: design suggestions o Iterative exploration of: ▪ Knowledge base ▪ Design suggestions ▪ Reflections within problem space o Goal: suggesting possible solutions and assess feasibility to improve problem awareness and identify best possible solution Step 3: artefact development o Core of design science research process o Based on knowledge base, data collection and design solution, solution is developed o Usually unfolds iteratively o Can involve building: − Models, frameworks, or other representations of the artefact − Prototype of an application or tool, e.g., mock-up, or − Software code that implements a technical solution o Write notes on deign decisions to better re-construct design process Step 4: artefact evaluation o Assess efficacy whether it solves real-world problem o Produces knowledge o Knowledge flows back into design and builds foundation for design’s contribution to existing knowledge base o Differentiate evaluation by purpose (formative vs. summative) or type (artificial vs. naturalistic) Step 5: conclusion and presentation o Writing-up and presenting findings o Presentations and detailed description of: − The real-world problem, motivation, research question and adequacy of taking a design approach − The existing knowledge base that informed the design − The rigorous use of research methods − The designed artefact and its efficacy (i.e., evaluation) o Critical to understand the why of artefact’s design Evaluating design science research o Involve designing artefact for real-world problem o Relevance and rigor o Research should make contribution beyond designed artefact: − Contributing design knowledge to design similar artefacts − Contributing to theory within the problem domain − Contributing to methods underpinning design science research Opportunities and limitations Critical Reflection on Using Interviews and Observations as Data Sources Types of Interviews: Structured: o Definition: Highly organized interviews with a fixed set of questions asked in a specific order. o Characteristics: ▪ Uses standardized questions with predetermined answers, allowing easy comparison across interviews. ▪ Provides reliable and consistent data, making it useful for quantitative analysis. ▪ Reduces interviewer bias and ensures all respondents have a similar experience. o Drawback: Limited flexibility, as follow-up questions or deeper exploration of responses are constrained. Semi-structured: o Definition: Flexible interviews that have a guiding set of questions or topics but allow for spontaneous follow-ups. o Characteristics: ▪ Uses open-ended questions, giving the interviewer freedom to probe or explore unexpected topics. ▪ Balances consistency (with a general interview guide) and flexibility (with room for variation in responses). ▪ Useful for qualitative insights, as it allows for a deeper understanding of complex topics. o Drawback: Data can be more challenging to analyze and compare due to the variation in responses. Open (unstructured) o Definition: Conversational and informal interviews without a predefined structure. o Characteristics: ▪ Minimal guidance is provided, allowing the interview to flow freely based on the participant's responses. ▪ Facilitates in-depth exploration, as the interviewer can delve deeply into the participant's perspectives and experiences. ▪ Commonly used in exploratory research or when the goal is to understand subjective experiences. o Drawback: Lacks consistency, making data hard to quantify, compare, or analyze systematically. Interviewing 1. Opportunities to construct meaning with interviewee (interviewer involved in interview 2. Social practice and interviews are situated & embodied: not just listen but observe 3. Interviewees may want to impress or manage reputation o Consider: Where and when to conduct the interview (enough time; familiar and comfortable situation for interviewee; in person) Using artefacts to trigger responses, e.g. images (Abildgaard, 2018) Structured, semi-structured, or open? When interested in how people do things, ask for stories (Liuberte & Feuls 2022) Recording and taking notes (Czarniawska, 2014) How to interpret what the interviewee says? (Alvesson, 2009) How to: Make an interview guide Formulate your questions with everyday language During interview: Ask for examples or anecdotes to enhance your understanding Take notes Record Put your interview guide aside whenever you can in order to dive deeper into the conversation Come back to the interview guide in order to check whether all themes have been covered Let the other person speak! Questions to ask yourself after interview: What are the challenges of interviewing? What were the surprises in the interview? Which questions worked well (both previously designed and spontaneous)? What do you take away from this experience for your future interviews? Observations What to focus on? o rely on your research interest/question but keep an open mind o You are the main instrument: ‘drop your tools’ (Weick, 1996) (intuitive & perception) o ask yourself: what is happening here? What is at stake? What is being accomplished? Who benefits? What to observe? o people, practices, things, technologies, non-human actors o ask what differences things or actions make o use yourself as an instrument o shadowing as ‘observation on the move’ (Czarniawska, 2014) →following something, logistics and planning Observations as data What to do when you cannot take field notes in the moment? 1. Scribble down shorthand (‘jottings’) and write out notes later (on the day itself or shortly after) 2. Recording devices 3. Memory (pay attention) How to use observations during the research? 1. Share insights in conversations (as questions) 2. Refine your interview questions or what to pay attention to in your observation 3. Consult the literature Full fieldnotes are focus on thick description (Geertz, 1973), rich & detailed, allowing you to re-enter the scene (full sentences) Ideally you try to write them the same day, or the day after Length: Some say 4 hours in the field = 4 hours of writing; 10 pages for every hour (Goffman,1989; Lindlof & Taylor, 2019): 3-5 pages → lush can be meaningful but also cumbersome, if you are overwhelmed or bored by your own fieldnotes, this might be a clue that something has gone awry Style: Loose and informal, write quickly rather than forcing a certain style; structure around themes or chronologies helps, clear headings! Optional: keep separate ‘characters files’ in which you document information on specific informants How is knowledge gained through interviews and observations? Qualitative observation: a multi-sensory approach Research methods relying on observation stressed seeing as main method of perception multi-sensory ethnography (Pink, 2015), affective ethnography (Gherardi, 2019), diffractive methodology (Kuismin, 2022; Schneider 2002) and research on ‘sensing’ (Cnossen, 2022; Willems,2018): more senses are involved in qualitative observation Sensitizing questions you can ask: o What does this space feel like (Hare, 2020)? o What is this situation reminding me of? o What seems to matter in this space/situation/organization? (matter = materializing) (Cooren, 2020) o What is my intuition telling me? o What is “hanging in the air” (Stewart, 2007)? o What micro-moments become consequential (Brummans & Vézy, 2022)? o After that: verify and fact check with subsequent observations and speak to informants Critical reflections on artifacts as data sources Artifacts & relevance in qualitative research In working and organizing, artifacts are all around us! Think about: − Documents (e.g., strategy plans, PPT slides, models, prototypes, sketches) − Visuals (e.g., pictures, visual timelines, Gantt charts, models, drawings, logos, videos) − Spatial aspects and artifacts (e.g., interior design, rooms, furniture) − Embodied aspects and artifacts (e.g., particular gestures, postures, facial expressions) − Other artifacts (e.g., measuring instruments, technologies, books) Artifacts enable or constrain action, convey meaning beyond words, are not neutral but political, are not part of the background but active contributors to action Decentering analytical position: A “turn to things” Linguistic turn → material/visual/relational turn A ‘turn to things’ (Gieryn, 2002) Interest in ‘how matter matters’ (Carlile et al., 2013) Sociomateriality (Leonardi, 2012; Orlikowski, 2007) Actor-network-theory and new materialism (Latour, 2005) Regarding the material, verbal, visual and other modes not as separable, but as co-emergent (Zilber, 2018): multimodality Findings of exemplary studies: Comi & Whyte, 2018 Demonstrating visual artifacts’ performativity: bringing an imagined future into the present and making it amenable to further work, performing different roles over time Unfolding representations, enabling professionals to produce images of the future but also perform spontaneous actions = ways of building & dwelling Their usage is sensorial Exemplary studies: Knight et al., 2018 Insights: Presentation slides help broker divergent interpretations of strategy and give rise to new strategic understandings Demonstrating how presentation slides do more than they show: They not only provide strategists/consultants with a concrete way of “seeing” strategy meanings, but they also generate novel extensions to these meanings by provoking conversations, debate, and dialogue Exemplary studies: Cnossen & Bencherki, 2018 How space plays a role in constituting new organizations and making them last Space constraining or enabling practices, and providing them with meaning Observations, field notes, interviews Exemplary studies: Leybold & Nadegger, 2023 Critically interrogating and making heard the hidden voices and struggles of stigmatized groups, through studying visual, digital artifacts Helping make the invisible visible (e.g., silenced bodies, taken-for- grantedness, taboos), unmask social reality, and make heard more voices Inherently political agenda of artifacts: power relations influence which ideas and voices are expressed through artifacts – manipulative nature of things? But artifacts can also be leveraged for resistance, offering alternative ways to express suppressed views and voices And: artifacts themselves are not neutral – things like interior design and tools in the workplace can express certain norms about which work practices are valued by themselves, too (Wasserman & Frenkel, 2015) Gathering data: Online data Virtual worlds are part of human life: examples Types of Online data Text-based or visual data Asynchronous or synchronous (real-time) online data “Public” or “Private” accessible data Broadcast or networked/co-created data Data from online blogs Blogs primarily feature text, some visual or embedded video Asynchronous communication, reverse chronologically sorted “publicly” available data Co-created comments by users Online data in quantitative research Traces which are stored and recorded Accumulation of digital trace data inro big datasets Online data in qualitative research As archive: o Accumulation online data and treat them as collection of documents ▪ Characteristics of this data: Multi-modality (e.g., text, emoticons, images, memes) Reverse-chronologically ordered communication instances Co-created content (e.g., comments on posts) Evaluative infrastructures (ratings, views) Identities (user statistics, user profile) o Useful for comparative case studies relying on “document analysis”, study a phenomenon ex-post o Passive role of researcher As process: o Process: Collecting data as the phenomenon unfolds in a virtual setting. For instance, by participating in online meetings via Zoom/Teams/Webex/Jitsi, conducting work on crowdwork platforms or immersing oneself in virtual worlds (e.g., games). o Characteristics: ▪ Multi-modal data (e.g., video and text) ▪ Synchronous communication, which is less structured and not always recordable ▪ Co-created content (e.g., commenting live videos) o Fits to research designs emphasizing ethnographic or participant observation approaches; Pioneering work of Kozinets (=Netnography) (2002) o Researcher has active part of data collection/creation process Combining both approaches o Process approach to collecting data usually includes archive approach whenever possible (vice-versa possible too) (Akemu & Abdelnour, 2018; Kozinets, 2002) Advantages and challenges when using online data Reflecting upon the challenges Contextualizing online data and layered platforms o Ensure fit between the purpose of study and the type of data (Pousti et al., 2021). For instance, online data as the only data source is great to study phenomena where things happen in the open (or now moved into the open). Use theoretical lenses/concept that are compatible with relying on online data such as stakeholder management, CSR, legitimacy, brand, openness/closeness, CCO, impression management, etc. o Pay attention to communicative culture in online settings. For example, sensitive firm audience interaction may not occur via the blog but in closed private settings such as e-mails or DMs. Online data prevents gaining insights what happens behind the ‘digital veil’. o Some platforms provide more functionalities and behavioral options for more experienced users than for ‘beginners’ – this aspect needs to be considered when doing observations on platforms. o For certain research questions you will need additional data sources (e.g., interviews, for example, Gegenhuber et al., 2021) Ethical issues o You need to think about on how to „ethical data collection, data analysis and reporting“ when you design a qualitative study using online data (Pousti et al., 2021, p. 364) o Primary goal: Avoiding harm! (e.g., Levina & Vaast, 2016) o Considerations (Whiting & Pritchard, 2017): ▪ Degree of human participation (archive vs data as part of the process) ▪ Public vs. private data (platform infrastructure vs. perception of users) ▪ Seeking informed consent and from whom (users, platform provider, feasibility) ▪ Anonymization vs. attribution (challenges of ‘cloaking’) Gathering and processing data o Gathering the data: ▪ Manuel downloading of data as a pdf-files. Make trial download on transfer it to MAXQDA and make sure text selection works to ensure proper coding process. ▪ Automated scraping of data. o Processing the data: ▪ − In my research I coded hundreds of blog posts and thousands of comments manually (seevGegenhuber & Dobusch, 2017; Gegenhuber & Naderer, 2019). Up to a certain point manual analysis isvpossible and gives you an in-depth understanding of the phenomenon. ▪ At some point, a digital data set may simple be too large. In this case, automating some parts of the analysis may be useful (e.g., sentiment analysis and topic modelling – see advanced topics slides at the end). In any case, it makes sense to qualitatively dig into the data set to get an initial insight what is going on. Analyzing data based on grounded theory Definition: Grounded theory is an inductive approach to help build theory based on actors’ interpretations of relevant phenomena. Inductive approach: empirically attempts to avoid imposing theoretical explanations from the outset Unit of analysis: actors’ views and understandings of phenomena Typical data sources: semi-structured interviews, observation, also documents, newspaper articles, blog posts, etc. → theories are grounded in data Data analysis Guiding principles: o Partial simultaneity of data collection and analysis (start analyzing right after collecting data) o Constant comparison (similarities & differences of codes) Overall procedure: o First-order analysis ▪ Open coding: developing & assigning categories to pieces of data & reflect informants’ words and experiences as closely as possible ▪ Either analytical code (description of what’s going on) pr in- vivo code (wording in field) ▪ Developing first-order categories: merging categories that are similar in character & relabel them that continues to reflect actors’ views and understandings (reflections of broader patterns of what actors in the field have said and done → not theoretical ▪ Getting lost is part of the process (many codes → 25-30 categories) o Second-order analysis ▪ Axial coding: “what’s going on here, theoretically”, drawing on literature → make sense of similarities and differences between first-order categories ▪ Developing second-order themes: merging first-order categories similar in theoretical terms, assigning labels that convey a theoretical understanding ▪ Selective coding: grouping second-order themes to aggregate dimensions (context, enablers, mechanisms, outcomes) o Model development ▪ Ordering second-order themes around aggregate dimensions ▪ Elaborating interrelationships between second-order themes ▪ Dialogue between data and literature ▪ Go back to data and prior literature Some merits and pitfalls Merits Pitfalls Developing rigorous and credible Risk of creating stylized images of theoretical conclusions from messy research process (non-linearity, qualitative data creative leaps) Popularity of grounded theory led to Factor-analytic style: slicing complex stronger position of qualitative studies phenomena into decontextualized, in management research quantifiable pieces De-facto standard grounded theory suppresses plurality of approaches to qualitative research Analyzing process data Process studies: Focus on how and why things emerge, develop, grow or terminate over time Considering phenomena dynamically (movement, activity, events, change and temporal evolution) Processes Strategies for analyzing processes Narrative strategy o Developing thick descriptions o Composing story of what happened o Synthesizing corpus of raw data in empirical narrative o Empirical narrative is detailed and context-sensitive o High in accuracy, low on simplicity & generality (close to data) Quantification strategy Alternate templates strategy Visual mapping strategy o What happened when? o Use of graphical displays: matrices, timelines, etc. o Reduction of potentially large quantities to visual forms o Revealing patterns of precedence, parallelism/co-evolution & passage of time o Moderate on accuracy, simplicity & generality Temporal bracketing strategy o Structuring description of events (e.g. narratives, timelines) around phases o Identifying turning points or key events o Phases more or less separable units that can be compared o (how do activities in one phase lead to changes in another? How can passage from one phase to another be explained?) o Moderate on simplicity & generality, accuracy depends on adequacy of decomposing processes into phases Synthetic strategy Grounded theory strategy o Deconstructing process data into comparable incidents o Developing categories, themes and dimensions by comparing incidents o High on accuracy, low to moderate on simplicity, generality depends on level of theoretical abstraction No analysis strategy will produce theory without an uncodifiable creative leap Process model Theorizing from qualitative data Theorizing and the facts, evidence, theory triad What is a theory and what are forms or styles of theorizing? Theory: set of well-developed categories that are systematically interrelated through statements of relationship to form theoretical framework that explains or predicts relevant phenomenon Style of theorizing: distinctive approach or way of thinking in which scholars develop and present their theories Three forms of explanatory theorizing Propositional theorizing Identifying categories or variables in causal & probabilistic relationships Develop graphical model, capturing key variables + relationships If-then argumentations Example simple propositional relation: case of organizational failure o Black-box model of presumed causal relation Expanding model with moderator & mediator Configurational theory Form of simplification Holistic explanation: seeks explanation of topic by grouping attributes of a kind into ideal types Interdependencies of attributes: focuses on mutually interdependent attributes that group together differently in each configuration Limited number of possible configurations: assumes that only limited number of configurations are viable Configuration 1: the laggard (organizational failure) Configuration 2: the imperialist (organizational failure) There may be further configurations Process theorizing Temporality: recognizes that phenomena are not static but unfold over time Dynamics and change: focuses on dynamics and change Mechanisms: seeks to identify mechanisms & processes of becoming Types: Example organizational failure: mechanisms of change the villain Summary of respective readings 01 Van Manen (Introduction to Advanced QRM) "Reclaiming Qualitative Methods for Organizational Research: A Preface" by John Van Maanen: The Limitations of Quantitative Methods The "Territory is Not the Map" Metaphor: Van Maanen uses this metaphor to illustrate the limitations of quantitative data. Quantitative methods, like surveys, provide a simplified "map" of organizational reality, but they fail to capture the richness and complexity of the "territory" itself – the lived experiences and nuanced meanings within the organization. The map is a representation, not the reality. Advanced understanding involves recognizing the inherent limitations of any single research method and the potential for bias in data collection and interpretation. Gresham's Law in Organizational Studies: Van Maanen suggests that "programmed research" (quantitative methods) is driving out "unprogrammed research" (qualitative methods). This implies that the emphasis on easily quantifiable data overshadows the need for in-depth understanding of complex organizational phenomena. This is a critical point for understanding the historical context of the article. Further research could explore the specific ways in which this "law" manifests in different organizational settings. Distance Between Generalized Principles and Contextual Understandings: A major critique is the disconnect between general theoretical principles about organizational behavior and the specific, context-dependent explanations provided by organizational members themselves. Quantitative studies often struggle to bridge this gap. Advanced study would involve exploring different theoretical frameworks that attempt to reconcile these two perspectives. Growing Gap Between Theory and Testable Data: The article points out a widening gap between the theoretical constructs used in research and the availability of data to test those theories. Quantitative methods, with their focus on pre-defined variables, may not be suitable for exploring complex, emergent phenomena. Advanced considerations involve exploring alternative research designs that better integrate theory and data collection. Complex Data Manipulation and Loose Interpretive Frameworks: A paradox exists: data manipulation techniques have become increasingly sophisticated, yet the interpretive frameworks used to make sense of the data remain loose and contingent. This suggests a need for more robust theoretical grounding for qualitative analysis. Advanced study would involve exploring different approaches to qualitative data analysis and the development of more rigorous interpretive frameworks. Distrust of Conventional Data Collection Techniques: The article expresses skepticism about the ability of conventional methods (formal interviews, surveys, etc.) to accurately capture organizational phenomena without distortion. These methods may impose artificial structures on the data, leading to misinterpretations. Advanced study would involve a critical evaluation of different data collection methods and their potential biases. The Power of Qualitative Methods This section details the strengths of qualitative methods as presented in the article. Emphasis on Meaning, Not Frequency: Qualitative methods prioritize understanding the meaning of social phenomena, rather than simply their frequency. This involves interpreting symbols, decoding meanings, and translating observations into a coherent narrative. Advanced study would involve exploring different theoretical perspectives on meaning-making in social contexts. Reducing the Distance Between Theory and Data: Qualitative research aims to minimize the distance between theory and data by gathering data "in vivo," close to the point of origin. This allows for a more nuanced and context-rich understanding. Advanced study would involve exploring different approaches to data collection and analysis that enhance this connection. Idiographic Maps and Nomothetic Statements: Qualitative studies create idiographic (specific, detailed) descriptions of a particular organizational context. These descriptions can then be used to generate nomothetic (generalizable) statements about organizational behavior. Advanced study would involve exploring the relationship between idiographic and nomothetic research approaches. The Importance of Context: Qualitative researchers emphasize the importance of understanding the context in which behavior occurs. Meaning is derived from the interaction between behavior and its context. Advanced study would involve exploring different theoretical approaches to contextual analysis. Empathetic Objectives and Direct Knowledge: Qualitative research often involves direct, firsthand, and intimate knowledge of the research setting. This allows researchers to develop empathetic understandings of the perspectives of organizational members. Advanced study would involve exploring ethical considerations related to researcher involvement and participant observation. Everyday Life as a Model: Qualitative methods are similar to the interpretive processes we use in everyday life. We constantly make sense of symbolic, contextual, and cryptic information. Advanced study would involve exploring the philosophical underpinnings of interpretive research. Qualitative Research Methods Participant Observation: Researchers immerse themselves in the organizational setting to observe and understand behavior firsthand. Advanced study would involve exploring different techniques for participant observation, including the role of the researcher and ethical considerations. Ethnographic Interviews: In-depth interviews designed to elicit rich descriptions of organizational experiences and perspectives. Advanced study would involve exploring different interview techniques and strategies for analyzing qualitative interview data. Life Histories: Collecting narratives of individuals' experiences within the organization to understand their perspectives and how they shape organizational life. Advanced study would involve exploring different methods for collecting and analyzing life histories. Content Analysis: Analyzing textual data (documents, transcripts, etc.) to identify patterns and themes. Advanced study would involve exploring different techniques for content analysis, including quantitative and qualitative approaches. Semiotics: The study of signs and symbols to understand how meaning is created and communicated within the organization. Advanced study would involve exploring different semiotic theories and their application to organizational research. Conversational Analysis: Analyzing conversations to understand how interactions shape organizational processes and relationships. Advanced study would involve exploring different techniques for conversational analysis, including transcription and coding methods. Case Studies: In-depth investigations of a single organization or event to provide a rich understanding of a specific context. Advanced study would involve exploring different approaches to case study research, including comparative case studies and embedded case studies. Qualitative vs. Quantitative Methods: A Comparison Feature Qualitative Methods Quantitative Methods Focus Meaning, interpretation, context Measurement, frequency, generalizability Data Type Textual, visual, observational Numerical Sample Size Small, purposive Large, representative Data Analysis Interpretive, thematic Statistical Generalizability Limited to the specific context studied Aims for broader generalizability Researcher Role Immersed, participatory More detached, objective Strengths Rich detail, in-depth understanding, context Generalizability, statistical power, objectivity Weaknesses Limited generalizability, subjective interpretation Lack of depth, potential for bias, oversimplification Criteria for Evaluating Qualitative Research Description over Prescription: Preference for studies that prioritize detailed description of organizational phenomena over prescriptive advice or methodological pronouncements. Practical Importance: Focus on studies with practical relevance for organizational researchers. Disciplinary Mix: Inclusion of studies from diverse disciplines to demonstrate the broad applicability of qualitative methods. Novel Themes: Emphasis on studies that uncover new insights into organizational behavior, rather than simply replicating existing findings. Unresolved Problems in Organizational Inquiry Gap between generalized principles and contextual understandings: The disconnect between broad theoretical statements and the specific, context- dependent experiences of organizational members. Growing gap between theoretical constructions and available data: The difficulty of finding data to test existing theories, particularly those dealing with complex organizational phenomena. Increasingly complex data manipulation techniques and looser interpretive frameworks: The paradox of sophisticated data analysis methods paired with less rigorous interpretive frameworks. Distrust of conventional data collection techniques: Concerns about the validity and reliability of traditional methods like surveys and interviews. Facts to Memorize: 1. "The territory is not the map": Quantitative data only provides a simplified representation of complex organizational reality. 2. Gresham's Law: The dominance of quantitative methods may be crowding out valuable qualitative research. 3. Idiographic vs. Nomothetic: Qualitative research aims for detailed, specific descriptions (idiographic) that can inform broader generalizations (nomothetic). 4. In vivo data collection: Qualitative research emphasizes gathering data directly within the organizational setting. 5. Contextual understanding: Meaning is derived from the interaction between behavior and its context. 6. Empathetic objectives: Qualitative researchers strive to understand organizational phenomena from the perspectives of those involved. 7. Participant observation: A key qualitative method involving immersion in the research setting. 8. Ethnographic interviews: In-depth interviews designed to elicit rich descriptions of experiences. 9. Life histories: Narratives of individuals' experiences within the organization. 10. Content analysis: Analyzing textual data to identify patterns and themes. 11. Semiotics: Studying signs and symbols to understand meaning-making. 12. Conversational analysis: Analyzing conversations to understand interactions. 13. Case studies: In-depth investigations of a single organization or event. 14. Description over prescription: Qualitative research should prioritize detailed description over methodological pronouncements. 15. Practical importance: Qualitative research should have relevance for organizational researchers. 16. Disciplinary mix: Qualitative methods should be applicable across various disciplines. 17. Novel themes: Qualitative research should uncover new insights into organizational behavior. 18. Gap between theory and data: A major challenge in organizational research is the difficulty of finding data to test existing theories. 19. Distrust of conventional methods: Concerns about the validity and reliability of traditional data collection techniques. 20. Qualitative research prioritizes meaning over frequency: Understanding the why behind organizational phenomena is central. 02 Gephart (Philosophical Foundations of QRM) "Qualitative Research and the Academy of Management Journal: Challenges and Opportunities" by Sara Rynes and Robert P. Gephart Jr. (2004): What is Qualitative Research? Qualitative research is a multimethod approach that uses interpretive and naturalistic methods to understand social phenomena. It prioritizes the qualities of experiences, processes, and meanings as they naturally occur. Unlike quantitative research, which focuses on measuring and analyzing causal relationships between variables, qualitative research is inductive and interpretive. Key Concepts: Interpretive: Focuses on understanding the meanings individuals and groups ascribe to their experiences. Naturalistic: Studies phenomena in their natural settings, avoiding artificial manipulation. Inductive: Develops theories and explanations from observed data, rather than testing pre-existing hypotheses. Multimethod: Employs various data collection techniques (interviews, observations, document analysis, etc.) to gain a comprehensive understanding. Subjectivity: Acknowledges the researcher's influence on the research process and the interpretation of findings. This is a key difference from quantitative research's aim for objectivity. Thick Description: Detailed accounts of social phenomena that capture the context, meanings, and processes involved. Concepts-in-use: The concepts and meanings used by social actors in their everyday lives, which form the basis of analysis in interpretive research. Member Checking: A process of verifying the accuracy of interpretations with the individuals or groups being studied. Reflexivity: A critical self-reflection on the researcher's role and potential biases in shaping the research process and interpretations. Triangulation: Using multiple data sources and methods to confirm findings and enhance the validity of the research. Theoretical Perspectives in Qualitative Research Postpositivism: Realism: Assumes an objective reality that can be studied, although it may be imperfectly known. Probabilistic Knowledge: Recognizes that knowledge is always provisional and subject to revision. Falsification: Emphasizes testing hypotheses by attempting to disprove them, rather than solely verifying them. Qualitative Methods in Postpositivism: Qualitative methods are used to gather data that can be used to test hypotheses or compare findings to existing knowledge. This often involves looking for patterns and evidence that either support or contradict pre-existing theories. Example: A study using qualitative data to test a hypothesis about the relationship between organizational culture and employee performance. Interpretive Research: Relativism: Assumes multiple realities shaped by individual and group perspectives. Meaning-Making: Focuses on understanding how individuals and groups create and share meaning. Inductive Reasoning: Develops theories and explanations from the data collected. Qualitative Methods in Interpretive Research: Qualitative methods are used to explore the meanings and interpretations of social actors. This often involves in-depth interviews, observations, and analysis of texts and documents. Example: A study exploring how employees make sense of organizational change. Critical Postmodernism: Historical Realism: Acknowledges that reality is socially constructed and shaped by power relations. Critique of Power Structures: Aims to uncover hidden power dynamics and inequalities. Emancipation: Seeks to empower marginalized groups and challenge dominant ideologies. Qualitative Methods in Critical Postmodernism: Qualitative methods are used to expose power imbalances and challenge dominant narratives. This often involves critical discourse analysis, deconstruction, and other methods that focus on language and representation. Example: A study examining how gender inequality is reproduced in organizational practices. Qualitative Methodologies Case Studies: Definition: In-depth investigation of a single case (individual, organization, event). Data Sources: Interviews, observations, documents, archival data. Strengths: Rich, detailed understanding of a specific context. Limitations: Limited generalizability. Example: A case study of a successful organizational turnaround. Interviews: Definition: Structured, semi-structured, or unstructured conversations to gather information from individuals. Types: Ethnographic interviews, long interviews, focus groups. Strengths: Access to individual perspectives and experiences. Limitations: Potential for bias and subjectivity. Example: Interviews with managers to understand their decision-making processes. Observations: Definition: Systematic observation of behavior and interactions in natural settings. Types: Participant observation, ethnography, ethnomethodology. Strengths: Direct observation of behavior and context. Limitations: Observer bias and reactivity. Example: Participant observation in a team meeting to understand team dynamics. Grounded Theory: Definition: An inductive approach to theory building, where theories emerge from the data. Process: Data collection, coding, constant comparison, theoretical sampling. Strengths: Development of new theories grounded in empirical data. Limitations: Can be time-consuming and resource-intensive. Example: Developing a theory of organizational learning from interviews with employees. Textual Analysis: Definition: Analysis of written or spoken texts to understand meanings and interpretations. Types: Semiotics, narrative analysis, discourse analysis. Strengths: Access to a wide range of data sources. Limitations: Interpretation can be subjective. Example: Analyzing organizational documents to understand the organization's culture. Data Analysis in Qualitative Research Qualitative data analysis is an iterative and interpretive process. It involves systematically reviewing data to identify patterns, themes, and meanings. Several approaches exist, and researchers often combine them. Key Concepts: Coding: Assigning labels or codes to segments of data to identify recurring themes. Theme Development: Identifying and refining major themes that emerge from the data. Narrative Analysis: Analyzing stories and narratives to understand how individuals make sense of their experiences. Discourse Analysis: Analyzing language and communication to understand how meaning is constructed and negotiated. Content Analysis: A systematic approach to analyzing the content of texts or other data sources. Computer-Assisted Qualitative Data Analysis Software (CAQDAS): Software tools that assist in managing and analyzing large qualitative datasets. Challenges and Opportunities in Qualitative Research for AMJ Challenges: "One-off" studies: Lack of integration into larger research programs. Inadequate literature reviews: Insufficient review of relevant literature. Poorly defined research questions: Unclear or ambiguous research questions. Underspecified methodologies: Lack of detail in describing research methods. Insufficient data analysis: Lack of systematic and rigorous data analysis. Weak theoretical grounding: Insufficient connection to existing theory. Opportunities: Unique contributions: Qualitative research can offer unique insights into organizational phenomena. Memorable examples: Qualitative research can provide compelling examples to illustrate key concepts. Socially important insights: Qualitative research can address important social and organizational issues. Theoretical advancement: Qualitative research can contribute to the development of new theories. Improving Qualitative Research Submissions to AMJ Recommendations: Embed research in ongoing programs: Conduct research as part of a larger research agenda. Conduct thorough literature reviews: Provide comprehensive reviews of relevant literature. Clearly define research questions: Formulate clear and focused research questions. Specify methodologies in detail: Provide detailed descriptions of research methods and procedures. Conduct rigorous data analysis: Employ systematic and rigorous data analysis techniques. Ground research in theory: Connect research findings to existing theory. Provide thick descriptions: Include detailed descriptions of data and context. Compare and contrast examples: Analyze data systematically to reveal patterns and themes. Show how findings emerged from data: Clearly link findings to the data. Address research questions in discussion: Connect findings to research questions and goals. Discuss broader implications: Explain the significance of findings for management theory and practice. Facts to Memorize Qualitative research is multimethod and uses interpretive, naturalistic approaches. It emphasizes qualities, processes, and meanings. Three main theoretical perspectives are postpositivism, interpretive research, and critical postmodernism. Postpositivism seeks probabilistic knowledge and falsification of hypotheses. Interpretive research focuses on meaning-making and inductive reasoning. Critical postmodernism critiques power structures and seeks emancipation. Key qualitative methodologies include case studies, interviews, observations, grounded theory, and textual analysis. Data analysis involves coding, theme development, and various analytical approaches. Challenges in AMJ submissions include "one-off" studies, inadequate literature reviews, and underspecified methodologies. Opportunities include unique contributions, memorable examples, and socially important insights. Improving submissions requires embedding research in programs, conducting thorough literature reviews, clearly defining research questions, specifying methodologies, conducting rigorous data analysis, and grounding research in theory. Member checking and reflexivity are crucial for enhancing the rigor and validity of qualitative research. Triangulation strengthens the credibility of findings by using multiple data sources and methods. Thick description is essential for capturing the context and nuances of social phenomena. CAQDAS can assist in managing and analyzing large qualitative datasets. 03 Gehman (Designing Qualitative Research projects) Finding Theory-Method Fit: A Comparison of Three Qualitative Approaches to Theory Building (Gehman et al., 2017) The Rise of Qualitative Research in Management Qualitative research methods have significantly increased in management studies. The number of qualitative papers published between 2000 and 2010 doubled the output of the previous two decades. This growth reflects a broader acceptance of qualitative approaches within the field. Impact: Qualitative research has not only increased in volume but has also substantially influenced the field by generating novel theories that have reshaped scholars' understanding of core theoretical constructs. Heterogeneity: It's crucial to understand that qualitative research isn't monolithic. It encompasses a diverse range of approaches, each with its own philosophical underpinnings and methodological tools. This diversity is both a strength and a challenge. Theory-Method Fit: The increasing use of diverse qualitative methods necessitates a heightened awareness of the unique assumptions associated with each approach. A critical aspect is achieving theory-method fit, where the chosen methodology aligns with the research question and theoretical goals. Ignoring this can lead to flawed or unconvincing research. Methodological Mashups: A common issue is the indiscriminate citation of various qualitative methods without considering their potentially incompatible assumptions. This "mashup" approach undermines the rigor and coherence of the research. Three Key Approaches to Qualitative Theory Building This section details three influential approaches to qualitative theory building in management research: Gioia's methodology, Eisenhardt's theory building from cases, and Langley's process research. Gioia's Methodology: Grounded Theory and Interpretivism Core Principle: Gioia's approach emphasizes grounded theory, building theory directly from data. It's deeply rooted in interpretivism, prioritizing the meanings and interpretations of organizational actors. Knowledgeable Agents: A central assumption is that informants are "knowledgeable agents"—they understand their actions and can articulate their thoughts, emotions, and intentions. This contrasts with views that treat individuals as passive "cultural dopes." First-Order vs. Second-Order Analysis: The methodology distinguishes between first-order analysis (codes derived directly from informants' terms) and second-order analysis (researcher-centric concepts and themes derived from the first-order codes). Data Structure: Gioia stresses the importance of a data structure to organize and present the progression from raw data to first-order codes to second-order themes. He famously states, "You got no data structure, you got nothing." Dynamic Model: The data structure is a static representation; the ultimate goal is to create a dynamic model (a "movie" rather than a "photograph") that illustrates the relationships between concepts and explains the phenomenon under study. Grand Shazzam!: This refers to the moment of insightful discovery, the "aha!" moment where the researcher grasps the underlying processes and mechanisms. Rigor: Gioia's methodology aims to demonstrate rigor in qualitative research by systematically presenting evidence and clearly illustrating the data-to-theory connections. Exemplar Studies: Gioia and Chittipeddi (1991), Gioia et al. (2010), Corley and Gioia (2004) Eisenhardt's Theory Building from Cases: Inductive and Deductive Integration Core Principle: Eisenhardt's method views theory building and theory testing as two sides of the same coin. It's an inductive approach that closely integrates with deductive theory testing. Goal: The primary goal is to develop strong, generalizable theory that is parsimonious, testable, logically coherent, and empirically accurate. Case Studies: The method relies on case studies, rich empirical instances of a phenomenon, often using multiple data sources. Multiple cases are preferred for greater generalizability. Replication Logic: Each case is analyzed independently, and the emergent theory is "tested" in each case. This replication logic contrasts with pooling data into summary statistics. Research Designs: Various research designs are employed, including the "racing design" (following multiple cases over time), "polar types" (comparing contrasting cases), and controlling for antecedents. Data Collection: Deep immersion in the setting is crucial, utilizing diverse data sources (interviews, observations, archives, etc.). Grounded Theory Building: The process involves collecting data, identifying measures and constructs (similar to Gioia's first- and second-order codes), and abstracting to a higher theoretical level. "The Whys": The method emphasizes explaining the underlying logic ("the whys") that connect constructs and propositions. Exemplar Studies: Eisenhardt (1989a), Eisenhardt & Graebner (2007), Ozcan & Eisenhardt (2009) Langley's Process Research: Temporal Dynamics and Flow Core Principle: Langley's approach emphasizes the importance of studying processes over time. It focuses on understanding the temporal dynamics, activities, and flows that shape organizational phenomena. Variance vs. Process Thinking: Langley contrasts variance thinking (correlational relationships between variables) with process thinking (understanding how things evolve over time). Longitudinal Data: Process research requires rich longitudinal data to capture the evolution of processes. Data sources can include interviews, observations, and archival materials. Analytic Strategies: Multiple analytic strategies can be used, including narrative, quantification, alternate templates, grounded theory, visual mapping, temporal bracketing, and comparative cases. These can be mixed and matched. Temporal Bracketing: This technique simplifies temporal flows by breaking processes into phases, which can then be compared across cases. Longitudinal Replication: Replication in process research often involves identifying recurring patterns across temporal phases within a single case or across multiple cases. Theoretical Contributions: Successful process research goes beyond simply describing events; it must offer theoretical explanations for observed patterns and mechanisms. Exemplar Studies: Langley (1999), Langley et al. (2013), Denis et al. (2001) Similarities and Differences Among Approaches While distinct, these three approaches share common ground: Deep Immersion: All three emphasize deep immersion in the research setting and a commitment to understanding the phenomenon from the perspective of the actors involved. Grounded Theory Building: All three, in their own ways, involve building theory from data, although the specific processes and outcomes differ. Theoretical Sampling: All three utilize theoretical sampling, a non-random sampling strategy that guides data collection based on emerging theoretical insights. However, key differences exist: Focus: Gioia's approach prioritizes understanding informants' interpretations, Eisenhardt's focuses on building generalizable theory, and Langley's emphasizes temporal dynamics. Data Structure: Gioia strongly advocates for a formal data structure, while Eisenhardt prefers a more flexible approach. Generalizability vs. Transferability: Eisenhardt's method aims for generalizability, while Gioia's emphasizes transferability of insights. Variance Control: Eisenhardt's multi-case approach often involves controlling for variance, while Gioia's interpretivist approach embraces variability as a source of insight. Pet Peeves and Critical Reflections The authors also shared their "pet peeves" regarding common pitfalls in qualitative research: Researcher Pet Peeve Explanation Gioia Overuse and misapplication of his methodology Criticizes the use of his methodology as a mere presentational tactic rather than a systematic approach; worries about an "arms race" in demonstrating rigor. Eisenhardt Rigor mortis (excessive rigidity in methods) Argues against overly prescriptive methods and data presentation formats; emphasizes the importance of focusing on the data and sampling rather than excessive transparency. Langley Weak theoretical contributions in process research Identifies several problematic approaches: narrative without theorization, anti-theorizing, illustrative theorizing, and pattern theorizing without explanation; stresses the need for coherent and integrated theoretical contributions. The Creative Process in Qualitative Research The creative process in qualitative research involves a dialectic between immersion in the data and detachment from it. Researchers must become deeply familiar with their data, allowing insights to emerge organically. However, they also need to step back and critically evaluate their findings, seeking fresh perspectives and integrating insights from other sources. Theoretical Sampling: This iterative process of data collection and analysis is crucial for generating new insights. Disciplined Imagination: The creative process requires both systematic discipline and free imagination, a balance between rigorous analysis and open- ended exploration. Abduction: Langley suggests that qualitative research is better described as abduction than induction, as it involves integrating empirical observations with existing theory to generate new theoretical insights. Achieving Theory-Method Fit: Key Takeaways The symposium concluded with three key takeaways for conducting rigorous qualitative research: 1. Clear Theoretical Goals: Begin with a clear theoretical goal and objective. This guides research design and methodological choices. 2. Customization: Customize the chosen methodology to the specific research context. While templates and exemplars are helpful, they should not be applied rigidly. 3. Theory-Method Fit: Ensure a strong theory-method fit, where the methods align with the research question and theoretical aims. This involves considering the ontological and epistemological assumptions of different approaches. Facts to Memorize 1. Qualitative research in management has significantly increased in recent decades, demonstrating its growing acceptance and impact. 2. Qualitative research is heterogeneous, encompassing diverse approaches with unique assumptions. 3. Theory-method fit is crucial for rigorous qualitative research. 4. Gioia's methodology emphasizes grounded theory and interpretivism, prioritizing informants' interpretations. 5. Gioia's methodology distinguishes between first-order and second-order analysis and stresses the importance of a data structure. 6. Eisenhardt's theory building from cases integrates inductive and deductive approaches, aiming for generalizable theory. 7. Eisenhardt's method utilizes replication logic in analyzing multiple cases. 8. Langley's process research focuses on temporal dynamics and flows, requiring rich longitudinal data. 9. Langley advocates for multiple analytic strategies in process research, including visual mapping and temporal bracketing. 10. All three approaches emphasize deep immersion and theoretical sampling. 11. Key differences exist in focus (interpretations, generalizability, temporal dynamics), data structure, and generalizability vs. transferability. 12. Gioia criticizes the overuse and misapplication of his methodology. 13. Eisenhardt cautions against "rigor mortis" (excessive rigidity in methods). 14. Langley highlights the need for strong theoretical contributions in process research. 15. The creative process in qualitative research involves a dialectic between immersion and detachment. 16. Abduction, not induction, better describes the process of integrating empirical observations with existing theory. 17. Achieving theory-method fit requires clear theoretical goals, customization, and alignment between methods and research questions. 04 Yin (Case Study Research) Applied Social Research Methods Series: A Collection of Research Guides (Yin, 2003) Case Study Research: A Deep Dive What is Case Study Research? Case Study Research: A research strategy employed to investigate a contemporary phenomenon within its real-life context, especially when the boundaries between the phenomenon and context are not clearly evident. It's particularly useful for answering "how" and "why" questions. Note: Unlike experiments that isolate variables, case studies embrace complexity. Advanced concepts involve understanding the philosophical debates surrounding qualitative vs. quantitative approaches and their reconciliation within case study methodology. Types of Case Studies There are three main types: Exploratory Case Studies: Used to develop hypotheses and propositions for further research. They involve a clear purpose, rationale, and success criteria, even without pre-defined propositions. Descriptive Case Studies: Aim to provide a detailed account of a phenomenon. They focus on a comprehensive description of the subject, often using metaphors or frameworks to organize the information. Explanatory/Causal Case Studies: Seek to understand the "how" and "why" behind a phenomenon, establishing causal relationships between events. They often involve pattern matching and the consideration of rival explanations. Note: Advanced understanding involves recognizing the overlap between these types and the possibility of using multiple types within a single study. Designing Case Studies: The Blueprint for Success A robust research design is crucial. It links data collection, analysis, and conclusions to the initial research questions. Five key components are: 1. Study Questions: Clearly defined research questions, often focusing on "how" and "why." 2. Propositions (Hypotheses): Statements about the expected relationships between variables. These are crucial for explanatory studies but may be absent in exploratory ones. 3. Unit(s) of Analysis: Precise definition of what constitutes the "case" (e.g., individual, organization, event). This includes specifying boundaries in time and space. 4. Logic Linking Data to Propositions: The method used to connect the collected data to the propositions (e.g., pattern matching, explanation building). 5. Criteria for Interpreting Findings: Pre-defined standards for judging the success of the study and interpreting the results. Note: Advanced design work involves developing a theoretical framework that guides the entire process, from question formulation to generalization of findings. This framework should incorporate rival theories and potential threats to validity. Four Major Case Study Designs Case study designs are categorized using a 2x2 matrix: Design Type Single Case Multiple Case Holistic Type 1: Single case, holistic analysis Type 3: Multiple cases, holistic analysis Embedded Type 2: Single case, multiple units of analysis Type 4: Multiple cases, multiple units of analysis Note: Multiple-case designs utilize a replication logic, not a sampling logic. Cases are selected to either confirm similar results (literal replication) or contrasting results for predictable reasons (theoretical replication). The "two-case" design is particularly powerful for enhancing external validity. Judging Research Design Quality: Four Key Criteria Four criteria assess research design quality: 1. Construct Validity: Ensuring accurate operationalization of concepts. Multiple sources of evidence and informant review are key tactics. 2. Internal Validity (Explanatory Studies Only): Establishing true causal relationships, ruling out spurious correlations. Pattern matching and addressing rival explanations are crucial. 3. External Validity: Determining the generalizability of findings. Analytic generalization, based on theory, is preferred over statistical generalization. Replication logic in multiple-case studies is essential. 4. Reliability: Ensuring that the study's procedures can be replicated with consistent results. A detailed case study protocol and a well-organized database are vital. Note: These criteria are addressed throughout the research process, not just at the design stage. Data Collection: Gathering the Evidence Six Sources of Evidence Case studies draw on diverse sources: 1. Documentation: Reports, memos, letters, etc. Requires careful interpretation due to potential bias. 2. Archival Records: Administrative records, databases, surveys, etc. Requires attention to the context of their creation. 3. Interviews: Open-ended, focused, or structured interviews. Requires skilled questioning and interpretation, considering potential biases. 4. Direct Observation: Formal or informal observation of events. Multiple observers enhance reliability. 5. Participant-Observation: Active participation in the phenomenon being studied. Offers unique insights but carries risks of bias. 6. Physical Artifacts: Technological devices, tools, etc. Provides concrete evidence of processes and activities. Note: Advanced data collection involves using multiple sources to triangulate findings and create a converging line of inquiry. Three Principles of Data Collection 1. Multiple Sources of Evidence: Triangulation of data from multiple sources enhances validity and reliability. 2. Case Study Database: A formal, organized collection of all evidence, separate from the final report, to ensure transparency and replicability. 3. Chain of Evidence: Clear links between research questions, data collection methods, analysis, and conclusions, allowing for traceability and scrutiny. Note: The database should include notes, documents, tabular materials, and narratives, all organized for easy retrieval. Data Analysis: Making Sense of the Evidence General Analytic Strategies Three main strategies guide analysis: 1. Relying on Theoretical Propositions: Using pre-defined propositions to focus the analysis and prioritize relevant data. 2. Rival Explanations: Identifying and testing alternative explanations to strengthen internal validity. 3. Case Descriptions: Developing a descriptive framework to organize the data, particularly useful for descriptive studies. Note: Advanced strategies involve iteratively refining explanations based on the evidence and considering threats to validity. Specific Analytic Techniques Five techniques are commonly used: 1. Pattern Matching: Comparing empirically observed patterns with predicted patterns to assess internal validity. 2. Explanation Building: Constructing a causal explanation through an iterative process of examining evidence, revising propositions, and considering rival explanations. 3. Time-Series Analysis: Analyzing changes in variables over time to identify causal relationships and trends. 4. Logic Models: Mapping out a chain of cause-and-effect relationships to analyze complex interventions or processes. 5. Cross-Case Synthesis (Multiple Cases Only): Comparing and contrasting findings across multiple cases to enhance external validity and identify general patterns. Note: These techniques often complement each other and can be used in combination. Reporting Case Studies: Communicating Your Findings Targeting Case Study Reports Case study reports cater to diverse audiences: academic colleagues, policymakers, practitioners, and funders. The report's format, style, and emphasis should be tailored to the specific audience. Note: Multiple versions of the report may be necessary to effectively communicate with different audiences. Illustrative Structures for Case Study Compositions Six compositional structures are presented: 1. Linear-Analytic: A standard structure presenting the problem, methods, findings, and conclusions. 2. Comparative: Repeatedly presenting the case using different models or perspectives. 3. Chronological: Presenting events in temporal order. Requires careful attention to avoid disproportionate emphasis on early events. 4. Theory-Building: Organizing the report to build a theoretical argument. 5. Suspense: Presenting the outcome first and then building the explanation. 6. Unsequenced: Presenting sections or chapters without a strict order, suitable for descriptive studies. Note: The choice of structure should be made early in the research process and may be adjusted based on emerging findings. Procedures in Doing a Case Study Report 1. Compose Early: Begin drafting sections (e.g., bibliography, methodology) early in the research process. 2. Case Identities: Decide whether to disclose or disguise the identities of the case and participants, considering ethical and practical implications. 3. Informant Review: Have the draft report reviewed by participants and informants to enhance construct validity and identify potential biases. Note: Rewriting and editing are crucial for producing a high-quality report. Characteristics of an Exemplary Case Study 1. Significance: The study addresses important theoretical or practical issues. 2. Completeness: The study thoroughly investigates the case, considering boundaries, evidence, and potential biases. 3. Alternative Perspectives: The study considers and addresses rival interpretations. 4. Sufficient Evidence: The report presents the most relevant evidence in a clear and unbiased manner. 5. Engaging Composition: The report is well-written, clear, and captivating. Facts to Memorize Case Study Research Definition: An empirical inquiry investigating a contemporary phenomenon within its real-life context, especially when boundaries are unclear. Useful for "how" and "why" questions. Three Types of Case Studies: Exploratory, descriptive, and explanatory/causal. Five Components of a Research Design: Study questions, propositions, unit(s) of analysis, logic linking data to propositions, criteria for interpreting findings. Four Criteria for Judging Research Design Quality: Construct validity, internal validity, external validity, reliability. Six Sources of Evidence: Documentation, archival records, interviews, direct observation, participant-observation, physical artifacts. Three Principles of Data Collection: Multiple sources of evidence, case study database, chain of evidence. Three General Analytic Strategies: Relying on theoretical propositions, rival explanations, case descriptions. Five Specific Analytic Techniques: Pattern matching, explanation building, time-series analysis, logic models, cross-case synthesis. Six Compositional Structures: Linear-analytic, comparative, chronological, theory-building, suspense, unsequenced. Three Procedures for Composing a Case Study Report: Compose early, decide on case identities (real or anonymous), have the draft reviewed by informants. Five Characteristics of an Exemplary Case Study: Significance, completeness, alternative perspectives, sufficient evidence, engaging composition. Replication Logic: In multiple-case studies, cases are selected to either confirm similar results (literal replication) or contrasting results for predictable reasons (theoretical replication). Analytic Generalization: Generalizing findings to a broader theory, rather than to a statistical population. Data Triangulation: Using multiple sources of evidence to corroborate the same fact or phenomenon. Case Study Protocol: A detailed plan guiding data collection, including questions, procedures, and reporting guidelines. Case Study Database: A formal, organized collection of all evidence gathered during the study. Persuasion with Case Studies: A Reflection on the Challenges of Convincing Readers (Siggelkow, 2007) Challenges in Persuasive Case Studies This section addresses the primary hurdles researchers face when using case studies to build convincing arguments. Siggelkow highlights three key challenges: 1. Small Sample Size: A common criticism of case studies is their limited sample size. Reviewers often question the generalizability of findings based on a single case or a small number of cases. However, Siggelkow argues that a well-chosen, unique case can be incredibly powerful, offering insights unavailable through larger, more representative samples. The analogy of a talking pig illustrates this point: one compelling example can be more persuasive than many weak ones. Advanced Concept: The strength of a single case hinges on its uniqueness and the richness of the data it provides. A case study should offer a deep understanding of a phenomenon, revealing intricate details and causal mechanisms that might be missed in broader studies. The selection of the case itself is crucial; it must be strategically chosen to illuminate the research question. 2. Non-Representativeness: Another frequent concern is the perceived bias in case selection. Reviewers may argue that the chosen case is not representative of the broader population, limiting the generalizability of the findings. Siggelkow counters this by emphasizing that the goal is often not representativeness but rather the identification of unique insights. The Phineas Gage case in neurology serves as a prime example: while not representative, it provided invaluable insights into brain function. Advanced Concept: The selection of a non-representative case is often deliberate. Researchers might choose a case precisely because of its unusual characteristics, allowing them to explore extreme scenarios or exceptional circumstances that reveal underlying mechanisms. The key is to carefully articulate the rationale for case selection and the limitations of generalizing the findings. 3. Insufficient Data: Reviewers may also demand more data, suggesting the study of additional cases. Siggelkow points out that in some instances, obtaining more data is simply not feasible. If the case under study is exceptionally rare (like Phineas Gage's injury), collecting more data is impossible. The focus should then shift to the depth of analysis within the existing data. Advanced Concept: The value of a case study lies not just in the quantity of data but also in its quality and the depth of analysis. Researchers should focus on extracting rich insights from the available data, carefully examining causal mechanisms and considering alternative explanations. The limitations of the data should be acknowledged and addressed. Uses of Case Studies in Building Persuasive Arguments This section explores the various ways case studies can contribute to a persuasive research paper. Siggelkow identifies three key uses: 1. Motivation: Case studies can effectively motivate a research question by presenting a compelling real-world example of the phenomenon under investigation. A well-chosen case can highlight the importance and relevance of the research problem, making it more engaging for the reader. Advanced Concept: The motivational use of a case study goes beyond simply describing the phenomenon. It should clearly link the case to the research question, demonstrating why the question is important and worthy of investigation. The case should serve as a springboard for the theoretical development. 2. Inspiration: Case studies can serve as a source of inspiration for new theoretical ideas. When limited theoretical knowledge exists, an inductive approach, where theory emerges from the data, can be valuable. The rich details of a case study can spark new insights and lead to the development of novel theoretical frameworks. Advanced Concept: Inductive theory building from case studies requires careful attention to data analysis and interpretation. Researchers must avoid overfitting the theory to the specific case, ensuring that the resulting framework has broader applicability. Rigorous testing and validation are crucial. 3. Illustration: Case studies can effectively illustrate theoretical concepts and causal mechanisms. They bridge the gap between abstract theoretical constructs and concrete empirical observations, making the argument more accessible and persuasive. By providing a concrete example, researchers can clarify the meaning of theoretical constructs and demonstrate how they operate in practice. Advanced Concept: The illustrative use of a case study requires careful selection of details. Researchers should focus on those aspects of the case that directly support the theoretical argument, avoiding unnecessary details that might distract the reader. The case should serve as a clear and concise illustration of the theoretical framework. Structuring a Persuasive Case Study Paper This section focuses on the effective structuring and writing of a case study paper to maximize its persuasive power. 1. Conceptual Argument: The core of a persuasive case study is a strong conceptual argument. The theory should be internally consistent and logically sound, even without the supporting case study. The case study then serves to strengthen and illustrate the argument, not to carry it alone. Advanced Concept: The conceptual argument should be clearly articulated and well- supported by existing literature. It should address potential counterarguments and demonstrate the originality and significance of the contribution. 2. Data Selectivity: Researchers should be selective in the data they present, focusing only on those details that directly support the conceptual argument. Avoid overwhelming the reader with unnecessary details. The goal is to tell a clear and concise story that effectively illustrates the theoretical points. Advanced Concept: The selection of data should be guided by the theoretical framework. Researchers should identify the key variables and mechanisms that are central to the argument and focus on presenting data that directly relates to these elements. 3. Alternative Explanations: A strong case study addresses potential alternative explanations for the observed phenomena. By systematically considering and refuting alternative interpretations, researchers can strengthen the persuasiveness of their argument. This demonstrates a thorough understanding of the complexities of the issue and enhances the credibility of the findings. Advanced Concept: The consideration of alternative explanations should be rigorous and systematic. Researchers should identify potential confounding factors and biases and explain why these factors are unlikely to account for the observed results. This strengthens the causal inferences drawn from the case study. 4. Ex Post Obviousness: A common challenge is the perception that the findings are "obvious" in hindsight. Researchers should strive to present findings that are surprising and insightful, challenging existing assumptions and offering new perspectives. The contribution should be more than just a confirmation of existing knowledge. Advanced Concept: To avoid ex post obviousness, researchers should focus on developing novel theoretical frameworks and testing them rigorously. The findings should offer new insights and contribute to a deeper understanding of the phenomenon under investigation. The originality and significance of the contribution should be clearly articulated. Case Study Examples: Liz Claiborne and Vanguard This section examines two case studies discussed by Siggelkow to illustrate different approaches to using case studies in research. Case Study Primary Use of Case Study Key Findings Liz Claiborne Illustration Demonstrated the relationship between inertia and internal fit in the face of external shocks. Vanguard Motivation & Inspiration Showed the complex evolution of a firm towards tight internal fit; inspired a framework for analyzing firm evolution. Facts to Memorize Three main challenges in persuasive case studies: small sample size, non- representativeness, insufficient data. Three key uses of case studies: motivation, inspiration, illustration. Key elements of a persuasive case study paper: strong conceptual argument, data selectivity, addressing alternative explanations, avoiding ex post obviousness. Liz Claiborne case study: primarily used for illustration, demonstrating the relationship between inertia and internal fit. Vanguard case study: used for motivation and inspiration, leading to a framework for analyzing firm evolution. Talking pig analogy: illustrates the power of a single, compelling case. Phineas Gage case: exemplifies the value of a unique, non-representative case. Importance of conceptual argument: the theory should stand on its own, even without the case study. Data selectivity: focus on details directly supporting the argument. Addressing alternative explanations: strengthens the persuasiveness of the argument. Avoiding ex post obviousness: findings should be surprising and insightful. Inductive theory building: theory emerges from the data in cases with limited prior knowledge. Illustrative use of case studies: clarifies theoretical constructs and demonstrates their operation. Motivational use of case studies: highlights the importance and relevance of the research problem. Generalizability vs. Unique Insights: Case studies prioritize unique insights over representativeness. 05 Jarzabkowski (Ethnographic Research) Introduction to Ethnographic Research in Strategy This study guide focuses on the article "Producing Persuasive Findings: Demystifying Ethnographic Textwork in Strategy and Organization Research" by Jarzabkowski and Bednarek. What is Ethnography? Ethnography: A qualitative research method involving immersive fieldwork to understand a culture or social group from an insider's perspective. In organizational studies, this means deeply embedding oneself within an organization to observe and understand its practices, interactions, and culture. Immersive Fieldwork: Extended periods of observation and participation within the research setting. This allows researchers to gain a deep understanding of the context and nuances of organizational life. Insider's Perspective: Ethnographers strive to understand the world from the perspective of the people they study, rather than imposing external frameworks. This involves understanding the meanings and interpretations that individuals within the organization ascribe to their actions and experiences. Why Ethnography in Strategy Research? Ethnography offers unique insights into the complexities of strategy-making, moving beyond traditional quantitative approaches. It allows researchers to: Capture the process: Observe how strategies unfold in real-time, revealing the messy, iterative nature of strategic decision-making. Understand tacit knowledge: Explore the unspoken rules, assumptions, and practices that shape strategic action. Analyze power dynamics: Examine how power relations influence strategic choices and outcomes. Provide rich context: Offer detailed descriptions that provide a deep understanding of the organizational setting and its influence on strategy. The Nature of Ethnographic Data: Fieldnotes Ethnographic data primarily comes from fieldnotes. These are not simply verbatim transcripts of conversations, but rather detailed records of the researcher's observations, experiences, and interpretations. Fieldnotes: Content and Characteristics Descriptive ob

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