Qualitative research designs provide future-oriented plans for undertaking research. Designs should describe how to effectively address and answer a specific research question using qualitative data and qualitative analysis techniques. Designs connect research objectives to observations, data, methods, interpretations, and research outcomes. Qualitative research designs focus initially on collecting data to provide a naturalistic view of social phenomena and understand the meaning the social world holds from the point of view of social actors in real settings. The outcomes of qualitative research designs are situated narratives of peoples’ activities in real settings, reasoned explanations of behavior, discoveries of new phenomena, and creating and testing of theories.
A three-level framework can be used to describe the layers of qualitative research design and conceptualize its multifaceted nature. Note, however, that qualitative research is a flexible and not fixed process, unlike conventional positivist research designs that are unchanged after data collection commences. Flexibility provides qualitative research with the capacity to alter foci during the research process and make new and emerging discoveries.
The first or methods layer of the research design process uses social science methods to rigorously describe organizational phenomena and provide evidence that is useful for explaining phenomena and developing theory. Description is done using empirical research methods for data collection including case studies, interviews, participant observation, ethnography, and collection of texts, records, and documents.
The second or methodological layer of research design offers three formal logical strategies to analyze data and address research questions: (a) induction to answer descriptive “what” questions; (b) deduction and hypothesis testing to address theory oriented “why” questions; and (c) abduction to understand questions about what, how, and why phenomena occur.
The third or social science paradigm layer of research design is formed by broad social science traditions and approaches that reflect distinct theoretical epistemologies—theories of knowledge—and diverse empirical research practices. These perspectives include positivism, interpretive induction, and interpretive abduction (interpretive science). There are also scholarly research perspectives that reflect on and challenge or seek to change management thinking and practice, rather than producing rigorous empirical research or evidence based findings. These perspectives include critical research, postmodern research, and organization development.
Three additional issues are important to future qualitative research designs. First, there is renewed interest in the value of covert research undertaken without the informed consent of participants. Second, there is an ongoing discussion of the best style to use for reporting qualitative research. Third, there are new ways to integrate qualitative and quantitative data. These are needed to better address the interplay of qualitative and quantitative phenomena that are both found in everyday discourse, a phenomenon that has been overlooked.
Qualitative research uses linguistic symbols and stories to describe and understand actual behavior in real settings (Denzin & Lincoln, 1994). Understanding requires describing “specific instances of social phenomena” (Van Maanen, 1998, p. xi) to determine what this behavior means to lay participants and to scientific researchers. This process produces “narratives-non-fiction division that link events to events in storied or dramatic fashion” to uncover broad social science principles at work in specific cases (p. xii).
A research design and/or proposal is often created at the outset of research to act as a guide. But qualitative research is not a rule-governed process and “no one knows” the rules to write memorable and publishable qualitative research (Van Maanen, 1998, p. xxv). Thus qualitative research “is anything but standardized, or, more tellingly, impersonal” (p. xi). Design is emergent and is often created as it is being done.
Qualitative research is also complex. This complexity is addressed by providing a framework with three distinct layers of knowledge creation resources that are assembled during qualitative research: the methods layer, the logic layer, and the paradigmatic layer. Research methods are addressed first because “there is no necessary connection between research strategies and methods of data collection and analysis” (Blaikie, 2010, p. 227). Research methods (e.g., interviews) must be adapted for use with the specific logical strategies and paradigmatic assumptions in mind.
The first, or methods, layer uses qualitative methods to “collect data.” That is, to observe phenomena and record written descriptions of observations, often through field notes. Established methods for description include participant and non-participant observation, ethnography, focus groups, individual interviews, and collection of documentary data. The article explains how established methods have been adapted and used to answer a range of qualitative research questions.
The second, or logic, layer involves selecting a research strategy—a “logic, or set of procedures, for answering research questions” (Blaikie, 2010, p. 18). Research strategies link research objectives, data collection methods, and logics of analysis. The three logical strategies used in qualitative organizational research are inductive logic, deductive logic and abductive logic (Blaikie, 2010, p. 79). 1 Each logical strategy makes distinct assumptions about the nature of knowledge (epistemology), the nature of being (ontology), and how logical strategies and assumptions are used in data collection and analysis. The task is to describe important methods suitable for each logical strategy, factors to consider when selecting methods (Blaikie, 2010), and illustrates how data collection and analysis methods are adapted to ensure for consistency with specific logics and paradigms.
The third, or paradigms, layer of research design addresses broad frameworks and scholarly traditions for understanding research findings. Commitment to a paradigm or research tradition entails commitments to theories, research strategies, and methods. Three paradigms that do empirical research and seek scientific knowledge are addressed first: positivism, interpretive induction, and interpretive abduction. Then, three scholarly and humanist approaches that critique conventional research and practice to encourage organizational change are discussed: critical theory and research, postmodern perspectives, and organization development (OD). Paradigms or traditions provide broad scholarly contexts that make specific studies comprehensible and meaningful. Lack of grounding in an intellectual tradition limits the ability of research to contribute: contributions always relate to advancing the state of knowledge in specific unfolding research traditions that also set norms for assessing research quality. The six research designs are explained to show how consistency in design levels can be achieved for each of the different paradigms. Further, qualitative research designs must balance the need for a clear plan to achieve goals with the need for adaptability and flexibility to incorporate insights and overcome obstacles that emerge during research.
Our general goal has been to provide a practical guide to inspire and assist readers to better understand, design, implement, and publish qualitative research. We conclude by addressing future challenges and trends in qualitative research.
A research design is a written text that can be prepared prior to the start of a research project (Blaikie, 2010, p. 4) and shared or used as “a private working document.” Figure 1 depicts the elements of a qualitative research design and research process. Interest in a topic or problem leads researchers to pose questions and select relevant research methods to fulfill research purposes. Implementation of the methods requires use of logical strategies in conjunction with paradigms of research to specify concepts, theories, and models. The outcomes, depending on decisions made during research, are scientific knowledge, scholarly (non-scientific) knowledge, or applied knowledge useful for practice.
Figure 1. Elements of qualitative research design.
Adapted from Blaikie (2010), p. 33, fig. 1.1.Research designs describe a problem or research question and explain how to use specific qualitative methods to collect and analyze qualitative data that answer a research question. The purposes of design are to describe and justify the decisions made during the research process and to explain how the research outcomes can be produced. Designs are thus future-oriented plans that specify research activities, connect activities to research goals and objectives, and explain how to interpret the research outcomes using paradigms and theories.
In contrast, a research proposal is “a public document that is used to obtain necessary approvals for a research proposal to proceed” (Blaikie, 2010, p. 4). Research designs are often prepared prior to creating a research proposal, and research proposals often require the inclusion of research designs. Proposals also require greater formality when they are the basis for a legal contract between a researcher and a funding agency. Thus, designs and proposals are mutually relevant and have considerable overlap but are addressed to different audiences. Table 1 provides the specific features of designs and proposals. This discussion focuses on designs.
Title or topic of project
Research problem and rationale for exploring problem
Research questions to address problem: purpose of study
Choice of logic of inquiry to investigate each research question
Statement of ontological and epistemological assumptions made
Statement or description of research paradigms used
Explanation of relevant concepts and role in research process
Statement of hypotheses to be tested (positivist), orienting proposition to be examined (interpretive) or mechanisms investigated (critical realism)
Description of data sources
Discussion of methods used to select data from sources
Description of methods of data collection, summarization, and analysis
Discussion of problems and limitations
RESEARCH PROPOSALS: add the items below to items above
Statement of aims and research significance
Background on need for research
Budget and justification for each item
Timetable or stages of research process
Specification of expected outcomes and benefits
Statement of ethical issues and how they can be managed
Explanation of how new knowledge will be disseminated
Source: Based on Blaikie (2010), pp. 12–34.
The “real starting point” for a research design (or proposal) is “the formulation of the research question” (Blaikie, 2010, p. 17). There are three types of research questions: “what” questions seek descriptions; “why” questions seek answers and understanding; and “how” questions address conditions where certain events occur, underlying mechanisms, and conditions necessary for change interventions (p. 17). It is useful to start with research questions rather than goals, and to explain what the research is intended to achieve (p. 17) in a technical way.
The process of finding a topic and formulating a useful research question requires several considerations (Silverman, 2014, pp. 31–33, 34–40). Researchers must avoid settings where data collection will be difficult (pp. 31–32); specify an appropriate scope for the topic—neither too wide or too narrow—that can be addressed (pp. 35–36); fit research questions into a relevant theory (p. 39); find the appropriate level of theory to address (p. 42); select appropriate designs and research methods (pp. 42–44); ensure the volume of data can be handled (p. 48); and do an effective literature review (p. 48).
A literature review is an important way to link the proposed research to current knowledge in the field, and to explain what was previously known or what theory suggests to be the case (Blaikie, 2010, p. 17). Research questions can used to bound and frame the literature review while the literature review often inspires research questions. The review may also provide bases for creating new hypotheses and for answering some of the initial research questions (Blaikie, 2010, p. 18).
There are three layers of research design. The first layer focuses on research methods for collecting data. The second layer focuses on the logical frameworks used for analyzing data. The third layer focuses on the paradigm used to create a coherent worldview from research methods and logical frameworks.
Qualitative research addresses the meanings people have for phenomena. It collects narratives of organizational activity, uses analytical induction to create coherent representations of the truths and meanings in organizational contexts, and then creates explanations of this conduct and its prevalence (Van Maanan, 1998, pp. xi–xii). Thus qualitative research involves “doing research with words” (Gephart, 2013, title) in order to describe the linguistic symbols and stories that members use in specific settings.
There are four general methods for collecting qualitative data and creating qualitative descriptions (see Table 2). The in-depth case study approach provides a history of an event or phenomenon over time using multiple data sources. Observational strategies use the researcher to observe and describe behavior in actual settings. Interview strategies use a format where a researcher asks questions of an informant. And documentary research collects texts, documents, official records, photographs, and videos as data—formally written or visually recorded evidence that can be replayed and reviewed (Creswell, 2014, p. 190). These methods are adapted to fit the needs of specific projects.
Key Example(s) and Reference Source(s)
Provides thick description of a single event or phenomenon unfolding over time
Perlow (1997); Mills, Duerpos, and Wiebe (2010); Stake (2005); Piekkari and Welch (2012)
Observational Strategies
Observe, participate in, and describe actual settings and behaviors
McCall and Simmons (1969)
Insider description of micro-culture developed through active participation in the culture
Van Maanen (1988); Ybema, Yanow, Wels, and Kamsteeg (2009); Cunliffe (2010); Van Maanen (2010)
Strategy for training lay informants to observe and immediately record selected experiences
Rodrguez, Ryave, and Tracewell (1998); Rodriguez and Ryave (2002)
Interview Strategies
Traditional structured interview
Pose preset and fixed questions and record answers to produce (factual) information on phenomena, explore concepts and test theory
Easterby-Smith, Thorpe, and Jackson et al. (2012)
Use interview guide with themes to develop and pose in situ questions that fit unfolding interview
Easterby-Smith et al. (2012)
Unstructured interview with questions and answers co-constructed with informant that reveals the co-construction of meaning
Holstein and Gubrium (1995)
Meeting where researcher meets informant to pose systematic questions that teach the researcher about the informant’s questions
McCurdy, Spradley, and Shandy (2005)
Extended use of structured interview method that includes demographic and open-ended questions. Designed to efficiently uncover the worldview of informants without prolonged field involvement
Gephart and Richardson (2008)
Group Interview
A group interview used to collect data on a predetermined topic (focus) and mediated by the researcher
Records and Texts
Photographic and visual methods
Produce accurate visual images of physical phenomena in field settings that can be analyzed or used to elicit informant reports
Greenwood, Jack, and Haylock (2019)
Produce “different views’ of activity and permanent record that can be repeatedly examined and used to verify accuracy and validity of research claims
LeBaron, Jarzabkowski, Pratt, and Fetzer (2018)
Textual data and documentary data collection
Maniha and Perrow (1965) provide an example of a case study concerned with organizational goal displacement, an important issue in early organizational theorizing that proposed organizations emerge from rational goals. Organizational rationality was becoming questioned at the time that the authors studied a Youth Commission with nine members in a city of 70,000 persons (Maniha & Perrow, 1965). The organization’s activities were reconstructed from interviews with principals and stakeholders of the organization, minutes from Youth Commission meetings, documents, letters, and newspaper accounts (Maniha & Perrow, 1965).
The account that emerged from the data analysis is a history of how a “reluctant organization” with “no goals to guide it” was used by other aggressive organizations for their own ends. It ultimately created its own mission (Maniha & Perrow, 1965). Thus, an organization that initially lacked rational goals developed a mission through the irrational process of goal slippage or displacement. This finding challenged prevailing thinking at the time.
Observational strategies involve a researcher present in a situation who observes and records, the activities and conversations that occur in the setting, usually in written field notes. The three observational strategies in Table 2—participant observation, ethnography, and systematic self-observation—differ in terms of the role of the researcher and in the data collection approach.
Participant observation. This is one of the earliest qualitative methods (McCall & Simmons, 1969). One gains access to a setting and an informant holding an appropriate social role, for example, client, customer, volunteer, or researcher. One then observes and records what occurs in the setting using field notes. Many features or topics in a setting can become a focus for participant observers. And observations can be conducted using continuum of different roles from the complete participant, observer as participant, and participant observer, to the complete observer who observes without participation (Creswell, 2014, Table 9.2, p. 191).
Ethnography. An ethnography is “a written representation of culture” (Van Maanen, 1988) produced after extended participation in a culture. Ethnography is a form of participant observation that focuses on the cultural aspects of the group or organization under study (Van Maanen, 1988, 2010). It involves prolonged and close contact with group members in a role where the observer becomes an apprentice to an informant to learn about a culture (Agar, 1980; McCurdy, Spradley, & Shandy, 2005; Spradley, 1979).
Ethnography produces fine-grained descriptions of a micro-culture, based on in-depth cultural participation (McCurdy et al., 2005; Spradley, 1979, 2016). Ethnographic observations seek to capture cultural members’ worldviews (see Perlow, 1997; Van Maanen, 1988; Watson, 1994). Ethnographic techniques for interviewing informants have been refined into an integrated developmental research strategy—“the ethno-semantic method”—for undertaking qualitative research (Spradley, 1979, 2016; Van Maanen, 1981). The ethnosemantic method uses a structured approach to uncover and confirm key cultural features, themes, and cultural reasoning processes (McCurdy et al., 2005, Table 3; Spradley, 1979).
Systematic Self-Observation. Systematic self-observation (SSO) involves “training informants to observe and record a selected feature of their own everyday experience” (Rodrigues & Ryave, 2002, p. 2; Rodriguez, Ryave, & Tracewell, 1998). Once aware that they are experiencing the target phenomenon, informants “immediately write a field report on their observation” (Rodrigues & Ryave, 2002, p. 2) describing what was said and done, and providing background information on the context, thoughts, emotions, and relationships of people involved. SSO generates high-quality field notes that provide accurate descriptions of informants’ experiences (pp. 4–5). SSO allows informants to directly provide descriptions of their personal experiences including difficult to capture emotions.
Interviews are conversations between researchers and research participants—termed “subjects” in positivist research and informants in “interpretive research.” Interviews can be conducted as individual face-to-face interactions (Creswell, 2014, p. 190) or by telephone, email, or through computer-based media. Two broad types of interview strategies are (a) the individual interview and (b) the group interview or focus group (Morgan, 1997). Interviews elicit informants’ insights into their culture and background information, and obtain answers and opinions. Interviews typically address topics and issues that occur outside the interview setting and at previous times. Interview data are thus reconstructions or undocumented descriptions of action in past settings (Creswell, 2014, p. 191) that provide descriptions that are less accurate and valid descriptions than direct, real-time observations of settings.
The informant interview (Spradley, 1979) poses questions to informants to elicit and clarify background information about their culture, and to validate ethnographic observations. In interviews, informants teach the researcher their culture (Spradley, 1979, pp. 24–39). The informant interview is part of a developmental research sequence (McCurdy et al., 2005; Spradley, 1979) that begins with broad “grand tour” questions that ask an informant to describe an important domain in their culture. The questions later narrow to focus on details of cultural domains and members’ folk concepts. This process uncovers semantic relationships among concepts of members and deeper cultural themes (McCurdy et al., 2005; Spradley, 1979).
The long interview (McCracken, 1988) involves a lengthy, quasi-structured interview sessions with informants to acquire rapid and efficient access to cultural themes and issues in a group. Long interviews differ ethnographic interviews by using a “more efficient and less obtrusive format” (p. 7). This creates a “sharply focused, rapid and highly intense interview process” that avoids indeterminate and redundant questions and pre-empts the need for observation or involvement in a culture. There are four stages in the long interview: (a) review literature to uncover analytical categories and design the interview; (b) review cultural categories to prepare the interview guide; (c) construct the questionnaire; and (d) analyze data to discover analytical categories (p. 30, fig. 1).
The active interview is a dynamic process where the researcher and informant co-construct and negotiate interview responses (Holstein & Gubrium, 1995). The goal is to uncover the subjective meanings that informants hold for phenomenon, and to understand how meaning is produced through communication. The active approach is common in interpretive, critical, and postmodern research that assumes a negotiated order. For example, Richardson and McKenna (2000) explored how ex-patriate British faculty members themselves interpreted and explained their expatriate experience. The researchers viewed the interview setting as one where the researchers and informants negotiated meanings between themselves, rather than a setting where prepared questions and answers were shared.
Documents, records, artifacts, photographs, and video recordings are physically enduring forms of data that are separable from their producers and provide mute evidence with no inherent meaning until they are read, written about, and discussed (Hodder, 1994, p. 393). Records (e.g., marriage certificate) attest to a formal transaction, are associated with formal governmental institutions, and may have legally restricted access. In contrast, documents are texts prepared for personal reasons with fewer legal restrictions but greater need for contextual interpretation. Several approaches to documentary and textual data analysis have been developed (see Table 3). Documents that researchers have found useful to collect include public documents and minutes of meetings; detailed transcripts of public hearings; corporate and government press releases; annual reports and financial documents; private documents such as diaries of informants; and news media reports.
Photographs and videos are useful for capturing “accurate” visual images of physical phenomena (Ray & Smith, 2012) that can be repeatedly reexamined and used as evidence to substantiate research claims (LeBaron, Jarzabkowski, Pratt, & Fetzer, 2018). Photos taken from different positions in space may also reveal different features of phenomena. Videos show movement and reveal activities as processes unfolding over time and space. Both photos and videos integrate and display the spatiotemporal contexts of action.
The second research design layer links data collection and analysis methods (Tables 2 and 3) to three logics of enquiry that answer specific questions: inductive, deductive, and abductive logical strategies (see Table 4). Each logical strategy focuses on producing different types of knowledge using distinctive research principles, processes, and types of research questions they can address.
Compassionate Research Methods
Immersive and experimental approach to using ethnographic understanding to enhancing care for others
Dutton, Workman, and Hardin (2014)
Hansen and Trank (2016)
Computer-Aided Interpretive Textual Analysis
Strategy for computer supported interpretive textual analysis of documents and discourse that capture members’ first-order meanings
Establishing categories for a document or text then counting the occurrences of categories and showing concern with issues of reliability and validity
Sonpar and Golden-Biddle (2008)
Duriau, Reger, and Pfarrer (2007)
Greckhamer, Misngyi, Elms, and Lacey (2008)
Document, Record and Artifact Analysis
Uses many procedures for contemporary, non-document data analysis
Technique for detecting countertransference of emotions from researcher to informant to uncover how researchers are tacitly and unconsciously embedded in their own observations and interpretations
de Rond and Tuncalp (2017)
A sociological approach to analysis of sensemaking practices used in face to face communication
Systematic approach to uncover first-order concepts and terms of members, verify their meaning, and construct folk taxonomies for meaningful cultural domains
McCurdy, Spradley, and Shandy (2005)
Form of discourse analysis that produces a detailed, line by line, data-driven interpretation of a text or transcript
Gephart, Topal, and Zhang (2010)
Inductive development of theory from systematically obtained and analyzed observations
Glaser and Strauss (1967)
A methodology for doing scientific research using abduction that provides discovery oriented replicable scientific knowledge that is interpretive and not positivist
Unspecified process of matching/finding patterns in qualitative data, often confirmed by subjects’ verbal reports and quantitative analysis
Lee and Mitchell (1994)
Lee, Mitchell, Wise, and Fireman (1996)
Methodology/ies for examining individuals’ experiences
Six distinct approaches to storytelling useful for eliciting fine-grained and detailed stories from informants
Rosile, Boje, Carlon, Downs, and Saylors (2013)
Boje and Saylors (2014)
Narrative and Textual Analysis
Analysis of written and spoken verbal behavior and documents using techniques from literary criticism, rhetoric, and sociolinguistic analysis to understand discourse
Ganzin, Gephart, and Suddaby (2014)
Calas and Smircich (1991)
Organization Development/Action Research
Approaches to improving organizational structure and functioning through practice-based interventions
Cummings and Worley (2015)
Buono and Savall (2007)
Worley, Zardet, Bonnet, and Savall (2015)
Objective world that is perceived subjectively; hence perceptions of reality can differ
Single objective reality independent of people’s perceptions
Objective world that is interpreted and given subjective meaning
Intersubjectivity connects objective and subjective worlds through creation of a sense of shared reality and meaning
What—describe and explain phenomena
Why—explain associations between/among phenomena
What, why, and how—describe and explain conditions for occurrence of phenomena from lay and scientific perspectives
Uncover the features and characteristics of phenomena to understand presence and absence of phenomena in specific settings
Establish limited generalizations about distribution and patterns of features of individuals and social phenomena
Explain associations between/among phenomena by proposing a theory, describing variables and association then testing associations
Test theories, eliminate false alternatives, establish “facts” and universal relationships
Discover why people do what they do by uncovering sensemaking practices and subjective meanings of social actors and processes that allow for or breach intersubjectivity
Describe and understand social life actors’ meaning and motives from the point of view of members
Build second-order scientific theories from first- order members’ meanings
Linear: Begin with singular statements and conclude via induction with generalizations
Linear: Establish associations via induction or abduction then test them using deductive reasoning
Spiral processes: Analytical process moves from lay actors’ accounts to technical descriptions using scientific accounts
Scientist makes an hypothesis that appears to explain observations then proposes what gave rise to it (Blaikie, 1993, p. 164)
Objective features of settings described through subjective, personal perspectives
Objective features of broad realities described from objective, unbiased perspectives
Intersubjective meanings and interpretations used in everyday life to construct objective features and reveal subjective meanings
Facts gained by unbiased observations
Hypotheses are not used to compare facts
Borrow or invent a theory, express it as a deductive argument, deduce a conclusion, test the conclusion. If it passes, treat the conclusion as the explanation.
Construct second-order scientific theories by generalization/induction and inference from observations of actors’ activities, terms, meanings, and theories.
Incorporate members’ meanings—phenomena left out of inductive and deductive research.
Describes features of domain of social action and infers from one set of facts to another: hence can confirm existence of phenomena in initial domain but cannot discover phenomena outside of previously known domain
Scientist has great freedom to propose theory but nature decides on the validity of conclusions: knowledge limited to prior hypotheses, no discovery possible (Blaikie, 1993, p. 144)
Describes first-order meanings and practices of members and iteratively develops second-order theory to subsume first-order concepts and meanings. Second-order theory makes first-order meanings recoverable.
Discovers new insights via retroduction, i.e., occurs by creating new hypothesis to explain phenomenon and testing this with deduction and induction (Blaikie, 1993, p. 165)
Based in part on Blaikie (1993), ch. 5 & 6; Blaikie (2010), p. 84, table 4.1
Induction is the scientific method for many scholars (Blaikie, 1993, p. 134), and an essential logic for qualitative management research (Pratt, 2009, p. 856). Inductive strategies ask “what” questions to explore a domain to discover unknown features of a phenomenon (Blaikie, 2010, p. 83). There are four stages to the inductive strategy: (a) observe and record all facts without selection or anticipating their importance; (b) analyze, compare, and classify facts without employing hypotheses; (c) develop generalizations inductively based on the analyses; and (d) subject generalizations to further testing (Blaikie, 1993, p. 137).
Inductive research assumes a real world outside human thought that can be directly sensed and described (Blaikie, 2010). Principles of inductive research reflect a realist and objectivist ontology. The selection, definition, and measurement of characteristics to be studied are developed from an objective, scientific point of view. Facts about organizational features need to be obtained using unbiased measurement. Further, the elimination method is used to find “the characteristics present in all the positive cases, which are absent in all the negative cases, and which vary in appropriate degrees” (Blaikie, 1993, p. 135). This requires data collection methods that provide unbiased evidence of the objective facts without pre-supposing their importance.
Induction can establish limited generalizations about phenomena based solely on the observations collected. Generalizations need to be based on the entire sample of data, not on selected observations from large data sets, to establish their validity. The scope of generalization is limited to the sample of data itself. Induction creates evidence to increase our confidence in a conclusion, but the conclusions do not logically follow from premises (Blaikie, 1993, p. 164). Indeed, inferences from induction cannot be extended beyond the original set of observations and no logical or formal process exists to establish the universality of inferences.
Key data collection methods for inductive designs include observational strategies that allow the researcher to view behavior without making a priori hypotheses, to describe behavior that occurs “naturally” in settings, and to record non-impressionistic descriptions of behavior. Interviews can also elicit descriptions of settings and behavior for inductive qualitative research. Data analysis methods need to describe actual interactions in real settings including discourse among members. These methods include ethnosemantic analysis to uncover key terms and validate actual meanings used by members; analyses of conversational practices that show how meaning is negotiated through sequential turn taking in discourse; and grounded theory-based concept coding and theory development that use the constant comparative method.
Facts or descriptions of events can be compared to one another and generalizations can be made about the world using induction (Blaikie, 2010). Outcomes from inductive analysis include descriptions of features in a limited domain of social action that are inferred to exist in other similar settings. Propositions and broader insights can be developed inductively from these descriptions.
Deductive logic (Blaikie, 1993, 2010) addresses “why” questions to explain associations between concepts that represent phenomena of interest. Researchers can use induction, abduction, or any means, to develop then test the hypotheses to see if they are valid. Hypotheses that are not rejected are temporarily corroborated. The outcomes from deduction are tested hypotheses. Researchers can thus be very creative in hypothesis construction but they cannot discover new phenomena with deduction that is based only on phenomena known in advance (Blaikie, 2010). And there is also no purely logical or mechanical process to establish “the validity of [inductively constructed] universal statements from a set of singular statements” from which deductive hypotheses were formed (Hempel, 1966, p. 15 cited in Blaikie, 1993, p. 140).
The deductive strategy uses a realist and objectivist ontology and imitates natural science methods. Useful data collection methods include observation, interviewing, and collection of documents that contain facts. Deduction addresses the assumedly objective features of settings and interactions. Appropriate data analysis methods include content coding to identify different types, features, and frequencies of observed phenomena; grounded theory coding and analytical induction to create categories in data, determine how categories are interrelated, and induce theory from observations; and pattern recognition to compare current data to prior models and samples. Content analysis and non-parametric statistics can be used to quantify qualitative data and make it more amenable to analysis, although quantitative analysis of qualitative data is not, strictly speaking, qualitative research (Gephart, 2004).
Abduction is “the process used to produce social scientific accounts of social life by drawing on the concepts and meanings used by social actors, and the activities in which they engage” (Blaikie, 1993, p. 176). Abductive reasoning assumes that the socially meaningful world is the world experienced by members. The first abductive task is to discover the insider view that is basic to the actions of social actors (p. 176) by uncovering the subjective meanings held by social actors. Subjective meaning (Schutz, 1973a, 1973b) refers to the meaning that actions hold for the actors themselves and that they can express verbally. Subjective meaning is not inexpressible ideas locked in one’s mind. Abduction starts with lay descriptions of social life, then moves to technical, scientific descriptions of social life (Blaikie, 1993, p. 177) (see Table 4). Abduction answers “what” questions with induction, why questions with deduction, and “how” questions with hypothesized processes that explain how, and under what conditions, phenomena occur. Abduction involves making a logical leap that infers an explanatory process to explain an outcome in an oscillating logic. Deductive, inductive, and inferential processes move recursively from actors’ accounts to social science accounts and back again in abduction (Gephart, 2018). This process enables all theory and second-order scientific concepts to be grounded in actors’ first-order meanings.
The abductive strategy contains four layers: (a) everyday concepts and meanings of actors, used for (b) social interaction, from which (c) actors provide accounts, from which (d) social scientific descriptions are made, or theories are generated and applied, to interpret phenomena (Blaikie, 1993, p. 177). The multifaceted research process, described in Table 4, requires locating and comprehending members’ important everyday concepts and theories before observing or creating disruptions that force members to explain the unstated knowledge behind their action. The researcher then integrates members’ first-order concepts into a general, second-order scientific theory that makes first-order understandings recoverable.
Abduction emerged from Weber’s interpretive sociology ( 1978 ) and Peirce’s (1936) philosophy. But Alfred Schutz (1973a, 1973b) is the contemporary scholar who did the most to extend our understanding of abduction, although he never used the term “abduction” (Blaikie, 1993, 2010; Gephart, 2018). Schutz conceived abduction as an approach to verifiable interpretive knowledge that is scientific and rigorous (Blaikie, 1993; Gephart, 2018). Abduction is appropriate for research that seeks to go beyond description to explanation and prediction (Blaikie, 1993, p. 163) and discovery (Gephart, 2018). It employs an interpretive ontology (Schutz, 1973a, 1973b) and social constructionist epistemology (Berger & Luckmann, 1966), using qualitative methods to discover “why people do what they do” (Blaikie, 1993).
Dynamic data collection methods are needed for abductive research to capture descriptions of interactions in actual settings and their meanings to members. Observational and interview approaches that elicit members’ concepts and theories are particularly relevant to abductive understanding (see Table 2). Data analysis methods must analyze situated, first-order (common sense) discourse as it unfolds in real settings and then systematically develop second-order concepts or theories from data. Relevant approaches to produce and validate findings include ethnography, ethnomethodology, and grounded theorizing (see Table 3). The combination of what, why, and how questions used in abduction produces a broader understanding of phenomena than do what and why deductive and inductive questions.
Scholarly paradigms integrate methods, logics, and intellectual worldviews into coherent theoretical perspectives and form the most abstract level of research design. Six paradigms are widely used in management research (Burrell & Morgan, 1979; Cunliffe, 2011; Gephart, 2004, 2013; Gephart & Richardson, 2008; Hassard, 1993). The first three perspectives—positivism, interpretive induction, and interpretive abduction—build on logics of design and seek to produce rigorous empirical research that constitutes evidence (see Table 5). Three additional perspectives pursue philosophical, critical, and practical knowledge: critical theory, postmodernism, and organization development (see Table 6). Tables 5 and 6 describe important features of each research design to show similarities and differences in the processes through which theoretical meaning is bestowed on research results in management and organization studies.
Nature of Reality
Realism: Single objective, durable, knowable reality independent of people
Socially constructed reality with subjective and objective features
Material reality socially constructed through inter-subjective practices that link objective to subjective meanings
Discover facts and causal interrelationships among facts (variables)
Provide descriptive accounts, theories and data-based understandings of members’ practices
Develop second-order scientific theories from lay members’ first-order concepts and everyday understandings
What, why, and how questions
Variables, hypotheses, associations, and correlations
Meanings: Describe language use in real life contexts, communication, meaning during organizational action
Meaning: Describe how members construct and maintain a sense of shared meaning and social structure (intersubjectivity)
Mirror reality of facts with reliable and valid measures or findings that show associations among variables
Emphasis on prediction and explanation
Capture meanings and behaviors with thick descriptions of who said what to whom, why, when, and how
Provide prescriptions for action
Emphasis on description
Understand, predict, and explain meanings by building second-order testable theory from first-order lay concepts and theories
Emphasis on description, understanding, prediction, and verification
Induction to construct hypotheses addressing limited class of objects
Deduction to test/falsify hypotheses
Data Collection Methods
Audio and video records
Ethnography Participant observation
Audio or video tape recording
Field notes Document collection
Audio or video with detailed transcriptions of conversation and recording
Data Analysis Methods
Grounded theory coding
Schutz’s abductive method
Interpretive textual analysis
Propose hypothesis ho
Use prior supported hypotheses to deduce conclusion that is an advance in understanding
Test the conclusion
Reject theory as false if data are not consistent with conclusion; temporarily corroborate theory if conclusion not rejected
Observe and record facts without bias
Analyze and compare facts without using hypotheses
Draw generalizations from analysis
Subject generalizations to further testing
Observe, describe and record members’ activities, meanings and terms in use as means to access their social world (Blaikie, 2010, p. 90)
Breach, question, or disrupt unreflective action to trigger active searches to restore meaning that reveal tacit practices
Piece together fragments of meaning into categories and concepts that provide an understanding of the phenomenon or problem at hand
Research Design Stages
Propose hypothesis that can form theory
Deduce conclusion from hypothesis
Examine conclusions and logic of argument to existing theories to ascertain if this is an advance
Test conclusion with data
If theory is not consistent with conclusion (test fails) theory is false
If data are consistent with theory, theory is temporarily corroborated
Record all facts without bias or prejudgment of importance
Analyze, compare, and classify facts before presenting hypotheses
Inductively draw generalizations regarding relationships among phenomena
Subject generalizations to additional testing.
Collect descriptive data on everyday conversations and interactions to understand members’ concepts
Analyze first-order lay concepts to induce second-order technical concepts
Construct ideal type models of social roles and interactions that depict trajectories of behavior
Refine ideal type models to include course of action models and typical actor motives to preserve subjective meanings, insure logical consistency, and present lay actors’ point of view
Vary features of models to understand variations in interaction outcomes
Construct scientific descriptions OR generate theories OR extend theories to broaden understanding
Formal deductive theory
Second-order scientific concepts and generalizations that relate to but are not built from members’ concepts and meanings
Knowledge and insights are intended to be representative, not exhaustive, of data themes
Inductive knowledge: cannot be verified or replicated
Second-order scientific theory or insights built from richly detailed, fine-grained descriptions of phenomena
Theory based on extensive research capturing members’ first-order meanings
Reliability, validity, generalizability of results and findings
Sample specified and fixed in advance
Replicable design and outcomes may be possible
Accuracy of recorded truths in relation to the “true,” unbiased reality
Prespecified, fixed and/or emergent design
Sample may be modified during course of research
Without exhaustive analysis of all cases the research outcomes are not reliable
Persuasive account provided, replicability and accuracy of findings may be limited
Validity: Insure second-order scientific theory build from first-order lay concepts in logical manner valid for all sciences, construct ideal types as devices to link common sense and scientific knowledge
Reliability: Use of members’ in vivo concepts as first-order concept labels, ensure consistency in meaning
Insure clear methods description, replicable and testable second-order models
Prespecified design, adapted during course of study
Sample likely to evolve and be enlarged during research
Exhaustive analysis of data allows verification that findings based on complete sample and enhances validity, reliability
Replicable design made possible by describing and documenting a priori research design and emergent adjustments
Replicable results and findings produced
Types of Knowledge Sought
Scholarly knowledge that is interpretive and has scientific features
Scientific knowledge that is replicable, reliable and valid
Practice-oriented knowledge of members’ gained based on first-order understandings
Sources: Based on and adapted and extended from Blaikie (1993, pp. 137, 145, & 152); Blaikie (2010, Table 4.1, p. 84); Gephart (2013, Table 9.1, p. 291) and Gephart (2018, Table 3.1, pp. 38–39).
Organization Development Research
Nature of Reality
Dialectical reality with objective contradictions and reified structures that produce power-based inequities
Realities are value laden
No single or superior reality or truth exists Multiple, fragmented realities exist that are contested, negotiated, and in flux
Real material world with organizational problems and dysfunctions
Uncover, dereify, and challenge taken-for-granted meanings and practices to reduce power inequities, enable emancipation, and motivate social change
Understand how social categories are produced in rigid discourses and impose identity-related constraints on individuals
Examine intertextual relations—how texts become embedded in other texts
Improve organizational structures and functioning
Reduce hidden costs
Enhance value added for humans
Research Questions
Who benefits, what are the hidden interests here, how are these operative?
How can the status quo be changed?
What categories are people cast into? How?
How can people resist?
What are the effective organizational features and practices?
What are the problematic organizational features and practices?
How can organizations change to be more effective
Methods Foci
Actions and ideologies that create reified, objective social structures that are oppressive—OR—disrupt reified structures
Analysis of texts and discourse that shape and bestow power to show their value-laden nature
Methods Orientation
Describe and uncover sources of oppression and discord
Produce accounts that enable or encourage social action and change
Emphasis on description, unveiling of reified structure, change
Describe and uncover sources of oppression and discord
Produce accounts that enable or encourage social action and change
Emphasis on description, unveiling of reified structure, change
Detect strengths and problems
Logical Strategies
Data Collection
All methods possibly useful
Collect documents and texts
Data Analysis
All qualitative methods are possibly useful
Critical discourse analysis
Narrative and rhetorical analysis
Hidden cost analysis
Research Outcomes
Unmasking of oppression
Development of political strategies for action
Trigger actions that produce change
Trace the conflictual role of power in organizational life
Create texts that disrupt the readers’ conceptions and viewpoints
Challenge status quo knowledge
Expose hidden knowledge and hidden interests
Motivate action to resist categorizations
Qualitative and quantitative improvements in organizational functioning and performance
Reduction of hidden costs
Assessing Knowledge
Quality of theory developed
Positive impacts on management policies and practices to reduce oppression, inequities
Novel research to
Types of Knowledge Sought
Political knowledge, historical knowledge, change orientation
Disruptive knowledge, change orientation, philosophical, literary, and rhetorical texts
Based in part on Gephart (2004, 2013, 2018).
The qualitative positivist approach makes assumptions equivalent to those of quantitative research (Gephart, 2004, 2018). It assumes the world is objectively describable and comprehensible using inductive and deductive logics. And rigor is important and achieved by reliability, validity, and generalizability of findings (Kirk & Miller, 1986; Malterud, 2001). Qualitative positivism mimics natural science logics and methods using data recorded as words and talk rather than numerals.
Positivist research (Bitektine, 2008; Su, 2018) starts with a hypothesis. This can, but need not, be based in data or inductive theory. The research process, aimed at publication in peer-reviewed journals, requires researchers to (a) identify variables to measure, (b) develop operational definitions of the variables, (c) measure (describe) the variables and their inter-relationships, (d) pose hypotheses to test relationships among variables, then (e) compare observations to hypotheses for testing (Blaikie, 2010). When data are consistent with theory, theory passes the test. Otherwise the theory fails. This theory is also assessed for its logical correctness and value for knowledge. The positivist approach can assess deductive and inductive generalizations and provide evidence concerning why something occurs—if proposed hypotheses are not rejected.
Positivists view qualitative research as highly subject to biases that must be prevented to ensure rigor, and 23 methodological steps are recommended to enhance rigor and prevent bias (Gibbert & Ruigrok, 2010, p. 720). Replicability is another concern because methodology descriptions in qualitative publications “insufficiently describe” how methods are used (Lee, Mitchell, & Sablynski, 1999, p. 182) and thereby prevent replication. To ensure replicability, a qualitative “article’s description of the method must be sufficiently detailed to allow a reader . . . to replicate that reported study either in a hypothetical or actual manner.”
Qualitative research allows positivists to observe naturally unfolding behavior in real settings and allow “the real world” of work to inform research and theory (Locke & Golden-Biddle, 2004). Encounters with the actual world provide insights into meaning construction by members that cannot be captured with outsider (etic) approaches. For example, past quantitative research provided inconsistent findings on the importance of pre- and post-recruitment screening interviews for job choices of recruits. A deeper investigation was thus designed to examine how recruitment impacts job selection (Rynes, Bretz, & Gerhart, 1991). To do so, students undergoing recruitment were asked to “tell us in their own words” how their recruiting and decision processes unfolded (Rynes et al., 1991, p. 399). Using qualitative evidence, the researchers found that, in contrast to quantitative findings, “people do make choices based on how they are treated” (p. 509), and the choices impact recruitment outcomes. Rich descriptions of actual behavior can disconfirm quantitative findings and produce new findings that move the field forward.
An important limitation of positivism is its common emphasis on outsiders’ or scientific observers’ objective conceptions of the world. This limits the attention positivist research gives to members’ knowledge and allows positivist research to impose outsiders’ meanings on members’ everyday behavior, leading to a lack of understanding of what the behavior means to members. Another limitation is that no formal, logical, or proven techniques exist to assess the strength of “relationships” among qualitative variables, although such assessments can be formally done using well-formed quantitative data and techniques. Thus, qualitative positivists often provide ambiguous or inexplicit quantitative depictions of variable relations (e.g., “strong relationship”). Alternatively, the analysts quantify qualitative data by assigning numeric codes to categories (Greckhamer, Misngyi, Elms, & Lacey, 2008), using non-parametric statistics, or quantitative content analysis (Sonpar & Golden-Biddle, 2008) to create numerals that depict associations among variables.
An illustrative example of positivist research. Cole (1985) studied why and how organizations change their working structures from bureaucratic forms to small, self-supervised work teams that allow for worker participation in shop floor activities. Cole found that existing research on workplace change focused on the micropolitical level of organizations. He hypothesized that knowledge could be advanced differently, by examining the macropolitical change in industries or nations. Next, a testable conclusion was deduced: a macro analysis of the politics of change can better predict the success of work team implementation, measured as the spread of small group work structures, than an examination of the micropolitics of small groups ( 1985 ). Three settings were selected for the research: Japan, Sweden, and the United States. Japanese data were collected from company visits and interviews with employment officials and union leaders. Swedish documentary data on semiautonomous work groups were used and supplemented by interviews at Volvo and Saab, and prior field research in Sweden. U.S. data were collected through direct observations and a survey of early quality circle adopters.
Extensive change was observed in Sweden and Japan but changes to small work groups were limited in the United States (Cole, 1985). This conclusion was verified using records of the experiences of the three nations in work reform, compared across four dimensions: timing and scope of changes, managerial incentives to innovate, characteristics of mobilization, and political dimensions of change. Data revealed the United States had piecemeal experimentation and resistance to reform through the 1970s; diffusion emerged in Japan in the early 1960s and became extensive; and Swedish workplace reform started in the 1960s and was widely and rapidly diffused.
Cole then answered the questions of “why” and “how” the change occurred in some countries but not others. Regarding why Japanese and Swedish managers were motivated to introduce workplace change due to perceived managerial problems and the changing national labor market. Differences in the political processes also influenced change. Management, labor, and government interest in workplace change was evident in Japan and Sweden but not in the United States where widespread resistance occurred. As to how, the change occurred through macropolitical processes (Cole, 1985, p. 120), specifically, the commitment of the national business leadership to the change and whether or not the change was contested or uncontested by labor impacted the adoption of change. Organizational change usually occurs through broad macropolitical processes, hence “the importance of macro-political variables in explaining these outcomes” (p. 122).
Two streams of qualitative research claim the label of “interpretive research” in management and organization studies. The first stream, interpretive induction, emphasizes induction as its primary logical strategy (e.g., Locke, 2001, 2002; Pratt, 2009). It assumes a “real world” that is inherently objective but interpreted through subjective lenses, hence different people can perceive or report different things. This research is interpretive because it addresses the meanings and interpretations people give to organizational phenomena, and how this meaning is provided and used. Interpretive induction contributes to scientific knowledge by providing empirical descriptions, generalizations, and low-level theories about specific contexts based on thick descriptions of members’ settings and interactions (first-order understandings) as data.
The interpretive induction paradigm addresses “what” questions that describe and explain the existence and features of phenomena. It seeks to uncover the subjective, personal knowledge that subjects have of the objective world and does so by creating descriptive accounts of the activities of organizational members. Interpretive induction creates inductive theories based on limited samples that provide low-scope, abstract theory. Limitations (Table 5) include the fact that inductive generalizations are limited to the sample used for induction and need to be subjected to additional tests and comparisons for substantiation. Second, research reports often fail to provide details to allow replication of the research. Third, formal methods for assessing the accuracy and validity of results and findings are limited. Fourth, while many features of scientific research are evident in interpretive induction research, the research moves closer to humanistic knowledge than to science when the basic assumptions of inductive analysis are relaxed—a common occurrence.
An illustrative example of interpretive induction research. Adler and Adler (1988, 1998) undertook a five-year participant-observation study of a college basketball program (Adler, 1998, p. 32). They sought to “examine the development of intense loyalty in one organization.” Intense loyalty evokes “devotional commitment of . . . (organizational) members through a subordination that sometime borders on subservience” (p. 32). The goal was to “describe and analyze the structural factors that emerged as most related” to intense loyalty (p. 32).
The researchers divided their roles. Peter Adler was the active observer and “expert” who undertook direct observations while providing counsel to players (p. 33). Patricia Adler took the peripheral role of “wife” and debriefed the observer. Two research questions were posed: (a) “what” kinds of organizational characteristics foster intense loyalty? (b) “how” do organizations with intense loyalty differ structurally from those that lack intense loyalty?
The first design stage (Table 5) recorded unbiased observations in extensive field notes. Detailed “life history” accounts were obtained from 38 team members interviewed (Adler & Adler, 1998, p. 33). Then analytical induction and the constant comparative method (Glaser & Strauss, 1967) were used to classify and compare observations (p. 33). Once patterns emerged, informants were questioned about variations in patterns (p. 34) to develop “total patterns” (p. 34) reflecting the collective belief system of the group. This process required a “careful and rigorous means of data collection and analysis” that was “designed to maximize both the reliability and validity of our findings” (p. 34). The study found five conceptual elements were essential to the development of intense loyalty: domination, identification, commitment, integration, and goal alignment (p. 35).
The “what” question was answered by inducing a generalization (stage 3): paternalistic organizations with charismatic leadership seek people who “fit” the organization’s style and these people require extensive socialization to foster intense loyalty. This description contrasts with rational bureaucratic organizations that seek people who fit specific, generally known job descriptions and require limited socialization (p. 46). The “how” question is answered by inductive creation of another generalization: organizations that control the extra-organizational activities of members are more likely to evoke intense loyalty by forcing members to subordinate all other interests to those of the organization (p. 46).
The second stream of interpretive research—interpretive abduction—produces scientific knowledge using qualitative methods (Gephart, 2018). The approach assumes that commonsense knowledge is foundational to how actors know the world. Abductive theory is scientifically built from, and refers to, everyday life meanings, in contrast to positivist and interpretive induction research that omits concern with the worldview of members. Further, interpretive abduction produces second-order or scientific theory and concepts from members’ first-order commonsense concepts and meanings (Gephart, 2018, p. 34; Schutz, 1973a, 1973b).
The research process, detailed in Table 5 (process and stages), focuses on collecting thick descriptive data on organizations, identifying and interpreting first-order lay concepts, and creating abstract second-order technical constructs of science. The second-order concepts describe the first-order principles and terms social actors use to organize their experience. They compose scientific concepts that form a theoretical system to objectively describe, predict, and explain social organization (Gephart, 2018, p. 35). This requires researchers to understand the subjective view of the social actors they study, and to develop second-order theory based on actors’ subjective meanings. Subjective meaning can be shared with others through language use and communication and is not private knowledge.
A central analytical task for interpretive abduction is creating second-order, ideal-type models of social roles, motives, and interactions that describe the behavioral trajectories of typical actors. Ideal-type models can be objectively compared to one another and are the special devices that social science requires to address differences between social phenomena and natural phenomena (Schutz, 1973a, 1973b). The models, once built, are refined to preserve actors’ subjective meanings, to be logically consistent, and to present human action from the actor’s point of view. Researchers can then vary and compare the models to observe the different outcomes that emerge. Scientific descriptions can then be produced, and theories can be created. Interpretive abduction (Gephart, 2018, p. 35) allows one to addresses what, why, and how questions in a holistic manner, to describe relationships among scientific constructs, and to produce “empirically ascertainable” and verifiable relations among concepts (Schutz, 1973b, p. 65) that are logical, hold practical meaning to lay actors, and provide abstract, objective meaning to interpretive scientists (Gephart, 2018, p. 35). Abduction produces knowledge about socially shared realities by observing interactions, uncovering members’ first-order meanings, and then developing technical second-order or scientific accounts from lay accounts.
Interpretive abduction (Gephart, 2018) uses well-developed methods to create, refine, test, and verify second-order models, and it provides well-developed tools to support technical, second-level analyses. Research using the interpretive abduction approach includes a study of how technology change impacts sales automobile practices (Barley, 2015) and an investigation study of how abduction was used to develop new prescription drugs (Dunne & Dougherty, 2016).
An illustrative example of the interpretive abduction approach. Perlow (1997) studied time management among software engineers facing a product launch deadline. Past research verified the widespread belief that long working hours for staff are necessary for organizational success. This belief has adversely impacted work life and led to the concept of a “time bind” faced by professionals (Hochschild, 1997). One research question that subsequently emerged was, “what underlies ‘the time bind’ experienced by engineers who face constant deadlines and work interruptions?” (Perlow, 1997, p. xvii). This is an inductive question about the causes and consequences of long working hours not answered in prior research that is hard to address using induction or deduction. Perlow then explored assumption underlying the hypothesis, supported by lay knowledge and management literature, that even if long working hours cause professionals to destroy their life style, long work hours “further the goals of our organizations” and “maximize the corporation’s bottom line” (Perlow, 1997, p. 2).
The research commenced (Table 5, step 1) when Perlow gained access to “Ditto,” a leader in implementing flexible work policies (Perlow, 1997, p. 141) and spent nine months doing participant observation four days a week. Perlow collected descriptive data by walking around to observe and converse with people, attended meetings and social events, interviewed engineers at work and home and spouses at home, asked participants to record activities they undertook on selected working days (Perlow, 1997, p. 143), and made “thousands of pages of field notes” (p. 146) to uncover trade-offs between work and home life.
Perlow (1997, pp. 146–147) analyzed first-order concepts uncovered through his observations and interviews from 17 stories he wrote for each individual he had studied. The stories described workstyles, family lives, and traits of individuals; provided objective accounts of subjective meanings each held for work and home; offered background information; and highlighted first-order concepts. Similarities and differences in informant accounts were explored with an empirically grounded scheme for coding observations into categories using grounded theory processes (Gioia, Corley, & Hamilton, 2012). The process allowed Perlow to find key themes in stories that show work patterns and perceptions of the requirements of work success, and to create ideal-type models of workers (step 3). Five stories were selected for detailed analysis because they reveal important themes Perlow (1997, p. 147). For example, second-order, ideal-type models of different “roles” were constructed in step 3 including the “organizational superstar” (pp. 15–21) and “ideal female employee” (pp. 22–32) based on first-order accounts of members. The second-order ideal-type scientific models were refined to include typical motives. The models were compared to one another (step 4) to describe and understand how the actions of these employee types differed from other employee types and how these variations produced different outcomes for each trajectory of action (steps 4 and 5).
Perlow (1997) found that constant help-seeking led engineers to interrupt other engineers to get solutions to problems. This observation led to the abductively developed hypothesis that interruptions create a time crisis atmosphere for engineers. Perlow (1997) then created a testable, second-order ideal-type (scientific) model of “the vicious working cycle” (p. 96), developed from first-order data, that explains the productivity problems that the firm (and other research and development firms)—commonly face. Specifically, time pressure → crisis mentality → individual heroics → constant interruptions of others’ work to get help → negative consequences for individual → negative consequences for the organization.
Perlow (1997) then tested the abductive hypothesis that the vicious work cycle caused productivity problems (stage 5). To do so, the vicious work cycle was transformed into a virtuous cycle using scheduling quiet times to prevent work interruptions: relaxed work atmosphere → individuals focus on own work completion → few interruptions → positive consequences for individual and organization. To test the hypothesis, an experiment was conducted (research process 2 in Table 5) with engineers given scheduled quiet times each morning with no interruptions. The experiment was successful: the project deadline was met. The hypothesis about work interruptions and the false belief that long hours are needed for success were supported (design stage 6). Unfortunately, the change was not sustained and engineers reverted to work interruptions when the experiment ended.
There are three additional qualitative approaches used in management research that pursue objectives other than producing empirical findings and developing or testing theories. These include critical theory and research, postmodernism, and change intervention research (see Table 6).
The term “critical” has many meanings including (a) critiques oriented to uncovering ideological manifestations in social relations (Gephart, 2013, p. 284); (b) critiques of underlying assumptions of theories; and (c) critique as self-reflection that reflexively encapsulates the investigator (Morrow, 1994, p. 9). Critical theory and critical management studies bring these conceptions of critical to bear on organizations and employees.
Critical theory and research extend the theories Karl Marx, and the Frankfurt School in Germany (Gephart & Kulicki, 2008; Gephart & Pitter, 1995; Habermas, 1973, 1979; Morrow, 1994; Offe, 1984, 1985). Critical theory and research assume that social science research differs from natural science research because social facts are human creations and social phenomena cannot be controlled as readily as natural phenomena (Gephart, 2013, p. 284; Morrow, 1994, p. 9). As a result, critical theory often uses a historical approach to explore issues that arise from the fundamental contradictions of capitalism. Critical research explores ongoing changes within capitalist societies and organizations, and analyzes the objective structures that constrain human imagination and action (Morrow, 1994). It seeks to uncover the contradictions of advanced capitalism that emerge from the fundamental contradiction of capitalism: owners of capital have the right to appropriate the surplus value created by workers. This basic contradiction produces further contradictions that become sources of workplace oppression and resistance that create labor issues. Thus contradictions reveal how power creates consciousness (Poutanen & Kovalainen, 2010). Critical reflection is used to de-reify taken-for-granted structures that create power inequities and to motivate resistance and critique and escape from dominant structures (see Table 6).
Critical management studies build on critical theory in sociology. It seeks to transform management and provide alternatives to mainstream theory (Adler, Forbes, & Willmott, 2007). The focus is “the social injustice and environmental destruction of the broader social and economic systems” served by conventional, capitalist managers (Adler et al., 2007, p. 118). Critical management research examines “the systemic corrosion of moral responsibility when any concern for people or for the environment . . . requires justification in terms of its contribution to profitable growth” (p. 4). Critical management studies goes beyond scientific skepticism to undertake a radical critique of socially divisive and environmentally destructive patterns and structures (Adler et al., 2007, p. 119). These studies use critical reflexivity to uncover reified capitalist structures that allow certain groups to dominate others. Critical reflection is used to de-reify and challenge the facts of social life that are seen as immutable and inevitable (Gephart & Richardson, 2008, p. 34). The combination of dialogical inquiry, critical reflection, and a combination of qualitative and quantitative methods and data are common in this research (Gephart, 2013, p. 285). Some researchers use deductive logics to build falsifiable theories while other researchers do grounded theory building (Blaikie, 2010). Validity of critical research is assessed as the capability the research has to produce critical reflexivity that comprehends dominant ideologies and transforms repressive structures into democratic processes and institutions (Gephart & Richardson, 2008).
An illustrative example of critical research. Barker (1998, p. 130) studied “concertive control” in self-managed work teams in a small manufacturing firm. Concertive control refers to how workers collaborate to engage in self-control. Barker sought to understand how control practices in the self-managed team setting, established to allow workers greater control over their work, differed from previous bureaucratic processes. Interviews, observations, and documents were used as data sources. The resultant description of work activities and control shows that rather than allowing workers greater control, the control process enacted by workers themselves became stronger: “The iron cage becomes stronger” and almost invisible “to the workers it incarcerates” (Barker, 1998, p. 155). This study shows how traditional participant observation methods can be used to uncover and contest reified structures and taken-for-granted truths, and to reveal the hidden managerial interests served.
The postmodern perspective (Boje, Gephart, & Thatchenkery, 1996) is based in philosophy, the humanities, and literary criticism. Postmodernism, as an era, refers to the historical stage following modernity that evidences a new cultural worldview and style of intellectual production (Boje et al., 1996; Jameson, 1991; Rosenau, 1992). Postmodernism offers a humanistic approach to reconceptualize our experience of the social world in an era where it is impossible to establish any foundational underpinnings for knowledge. The postmodern perspective assumes that realities are contradictory in nature and value-laden (Gephart & Richardson, 2008; Rosenau, 1992, p. 6). It addresses the values and contradictions of contemporary settings, how hidden power operates, and how people are categorized (Gephart, 2013). Postmodernism also challenges the idea that scientific research is value free, and asks “whose values are served by research?”
Postmodern essays depart from concerns with systematic, replicable research methods and designs (Calas, 1987). They seek instead to explore the values and contradictions of contemporary organizational life (Gephart, 2013, p. 289). Research reports have the character of essays that seek to reconceptualize how people experience the world (Martin, 1990; Rosenau, 1992) and to disrupt this experience by producing “reading effects” that unsettle a community (Calas & Smircich, 1991).
Postmodernism examines intertextual relations—how texts become embedded in other texts—rather than causal relations. It assumes there are no singular realities or truths, only multiple realities and multiple truths, none of which are superior to other truths (Gephart, 2013). Truth is conceived as the outcome of language use in a context where power relations and multiple realities exist.
From a methodological view, postmodern research tends to focus on discourse: texts and talk. Data collection (in so far as it occurs) focuses on records of discourse—texts of spoken and written verbal communication (Fairclough, 1992). Use of formal or official records including recordings, texts and transcripts is common. Analytically, scholars tend to use critical discourse analysis (Fairclough, 1992), narrative analysis (Czarniawska, 1998; Ganzin, Gephart, & Suddaby, 2014), rhetorical analysis (Culler, 1982; Gephart, 1988; McCloskey, 1984) and deconstruction (Calais & Smircich, 1991; Gephart, 1988; Kilduff, 1993; Martin, 1990) to understand how categories are shaped through language use and come to privilege or subordinate individuals.
Postmodernism challenges models of knowledge production by showing how political discourses produce totalizing categories, showing how categorization is a tool for social control, and attempting to create opportunities for alternative representations of the world. It thus provides a means to uncover and expose discursive features of domination, subordination, and resistance in society (Locke & Golden-Biddle, 2004).
An illustrative example of postmodern research. Martin (1990) deconstructed a conference speech by a company president. The president was so “deeply concerned” about employee well-being and involvement at work that he encouraged a woman manager “to have her Caesarian yesterday” so she could participate in an upcoming product launch. Martin deconstructs the story to reveal the suppression of gender conflict in the dialogue and how this allows gender conflict and subjugation to continue. This research established the existence of important domains of organizational life, such as tacit gender conflict, that have not been adequately addressed and explored the power dynamics therein.
OD involves a planned and systematic diagnosis and intervention into an organizational system, supported by top management, with the intent of improving the organization’s effectiveness (Beckhard, 1969; Palmer, Dunford, & Buchanan, 2017, p. 282). OD research (termed “clinical research” by Schein, 1987) is concerned with changing attitudes and behaviors to instantiate fundamental values in organizations. OD research often follows the general process of action research (Lalonde, 2019) that involves working with actors in an organization to help improve the organization. OD research involves a set of stages the OD practitioner (the leader of the intervention) uses: (a) problem identification; (b) consultation between OD practitioner and client; (c) data collection and problem diagnosis; (d) feedback; (e) joint problem diagnosis; (f) joint action planning; (g) change actions; and (h) further data gathering to move recursively to a refined step 1.
An illustrative example of the organization development approach. Numerous OD techniques exist to help organizations change (Palmer et al., 2017). The OD approach is illustrated here by the socioeconomic approach to management (SEAM) (Buono & Savall, 2007; Savall, 2007). SEAM provides a scientific approach to organizational intervention consulting that integrates qualitative information on work practices and employee and customer needs (socio) with quantitative and financial performance measures (economics). The socioeconomic intervention process commences by uncovering dysfunctions that require attention in an organization. SEAM assumes that organizations produce both (a) explicit benefits and costs and (b) hidden benefits and costs. Hidden costs refer to economic implications of organizational dysfunctions (Worley, Zardet, Bonnet, & Savall, 2015, pp. 28–29). These include problems in working conditions; work organization; communication, co-ordination, and co-operation; time management; integrated training; and strategy implementation (Savall, Zardet, & Bonnet, 2008, p. 33). Explicit costs are emphasized in management decision-making but hidden costs are ignored. Yet hidden costs from dysfunctions often greatly outstrip explicit costs.
For example, a fishing company sought to protect its market share by reducing the price and quality of products, leading to the purchase of poor-quality fish (Savall et al., 2008, pp. 31–32). This reduced visible costs by €500,000. However, some customers stopped purchasing because of the lower-quality product, producing a loss of sales of €4,000,000 in revenue or an overall drop in economic performance of €3,500,000. The managers then changed their strategy to focus on health and quality. They implemented the SEAM approach, assessed the negative impact of the hidden costs on value added and revenue received, and purchased higher-quality fish. Visible costs (expenses) increased by €1,000,000 due to the higher cost for a better-quality product, but the improved quality (performance) cut the hidden costs by increasing loyalty and increased sales by €5,000,000 leaving an increased profit of €4,000,000.
SEAM allows organizations to uncover hidden costs in their operations and to convert these costs into value-added human potential through a process termed “qualimetrics.” Qualimetrics assesses the nature of hidden costs and organizational dysfunctions, develops estimates of the frequencies and amounts of hidden costs in specific organizational domains, and develops actions to reduce the hidden costs and thereby release additional value added for the organization (Savall & Zardet, 2011). The qualimetric process is participative and involves researchers who use observations, interviews and focus groups of employees to (a) describe, qualitatively, the dysfunctions experienced at work (qualitative data); (b) estimate the frequencies with which dysfunctions occur (quantitative data); and (c) estimate the costs of each dysfunction (financial data). Then, strategic change actions are developed to (a) identify ways to reduce or overcome the dysfunction, (b) estimate how frequently the dysfunction can be remedied, and (c) estimate the overall net costs of removing the hidden costs to enhance value added. The economic balance is then assessed for changes to transform the hidden costs into value added.
OD research creates actionable knowledge from practice (Lalonde, 2019). OD intervention consultants use multistep processes to change organizations that are flexible practices not fixed research designs. OD plays an important role in developing evidence-based practices to improve organizational functioning and performance. Worley et al. (2015) provide a detailed example of the large-scale implementation of the SEAM OD approach in a large, international firm.
Here we discuss implication of qualitative research designs for covert research, reporting qualitative work and novel integrations of qualitative and quantitative work.
University ethics boards require researchers who undertake research with human participants to obtain informed consent from the participants. Consent requires that all participants must be informed of details of the research procedure in which they will be involved and any risks of participation. Researchers must protect subjects’ identities, offer safeguards to limit risks, and insure informant anonymity. This consent must be obtained in the form of a signed agreement from the participant, obtained prior to the commencement of research observations (McCurdy et al., 2005, pp. 29–32).
Covert research that fails to fully disclose research purposes or practices to participants, or that is otherwise deceptive by design or tacit practice, has long been considered “suspect” in the field (Graham, 1995; Roulet, Gill, Stenger, & Gill, 2017). This is changing. Research methodologists have shown that the over/covert dimension is a continuum, not a dichotomy, and that unintended covert elements occur in many situations (Roulet et al., 2017). Thus all qualitative observation involves some degree of deception due practical constraints on doing observations since it is difficult to do fully overt research, particularly in observational contexts with many people, and to gain advance consent from everyone in the organization one might encounter.
There are compelling benefits to covert research. It can provide insights not possible if subjects are fully informed of the nature or existence of the research. For example, the year-long, covert observational study of an asylum as a “total institution” (Goffman, 1961) showed how ineffective the treatment of mental illness was at the time. This opened the field of mental health to social science research (Roulet et al., 2017, p. 493). Covert research can also provide access to institutions that researchers would otherwise be excluded from, including secretive and secret organizations (p. 492). This could allow researchers to collect data as an insider and to better see and experience the world from members’ perspective. It could also reduce “researcher demand effects” that occur when informants obscure their normal behavior to conform to research expectations. Thus, the inclusion of covert research data collection in research designs and proposals is an emerging trend and realistic possibility. Ethics applications can be developed that allow for aspects of covert research, and observations in many public settings do not require informed consent.
The appropriate style for reporting qualitative research has become an issue of concern. For example, editors of the influential Academy of Management Journal have noted the emergence of an “AMJ style” for qualitative work (Bansal & Corley, 2011, p. 234). They suggest that all qualitative work should use this style so that qualitative research can “benefit” from: “decades of refinement in the style of quantitative work.” The argument is that most scholars can assess the empirical and theoretical contributions of quantitative work but find it difficult to do so for qualitative research. It is easier for quantitatively trained editors and scholars “to spot the contribution of qualitative work that mimics the style of quantitative research.” Further, “the majority of papers submitted to . . . AMJ tend to subscribe to the paradigm of normal science that aims to find relationships among valid constructs that can be replicated by anyone” (Bansal, Smith, & Vaara, 2018, p. 1193). These recommendations appear to explicitly encourage the reporting of qualitative results as if they were quantitatively produced and interpreted and highlights the advantage of conformity to the prevailing positivist perspective to gain publication in AMJ.
Yet AMJ editors have also called for researchers to “ensure that the research questions, data, and analysis are internally consistent” (Bansal et al., 2018, p. 1193) and to “Be authentic, detailed and clear in argumentation” (emphasis added) (Bansal et al., 2018, p. 1193). These calls for consistency appear to be inconsistent with suggestions to present all qualitative research using a style that mimics quantitative, positivist research. Adopting the quantitative or positivist style for all qualitative reports may also confuse scholars, limit research quality, and hamper efforts to produce innovative, non-positivist research. This article provides six qualitative research designs to ensure a range of qualitative research publications are internally consistent in methods, logics, paradigmatic commitments, and writing styles. These designs provide alternatives to positivist mimicry in non-positivist scholarly texts.
Qualitative research often omits consideration of the naturally occurring uses of numbers and statistics in everyday discourse. And quantitative researchers tend to ignore qualitative evidence such as stories and discourse. Yet knowledge production processes in society “rely on experts and laypeople and, in so doing, make use of both statistics and stories in their attempt to represent and understand social reality” (Ainsworth & Hardy, 2012, p. 1649). Numbers and statistics are often used in stories to create legitimacy, and stories provide meaning to numbers (Gephart, 1988). Hence stories and statistics cannot be separated in processes of knowledge production (Ainsworth & Hardy, 2012, p. 1697). The lack of attention to the role of quantification in everyday life means a huge domain of organizational discourse—all talk that uses numbers, quantities, and statistics—is largely unexplored in organizational research.
Qualitative research has, however, begun to study how words and numbers are mutually used for organizational storytelling (Ainsworth & Hardy, 2012; Gephart, 2016). This focus offers the opportunity to develop research designs to explore qualitative features and processes involved in quantitative phenomena such as financial crises (Gephart, 2016), to address how stories and numbers need to work together to create legitimate knowledge (Ainsworth & Hardy, 2012), and to show how statistics are used rhetorically to convince others of truths in organizational research (Gephart, 1988).
Ethnostatistics (Gephart, 1988; Gephart & Saylors, 2019) provides one example of how to integrate qualitative and quantitative research. Ethnostatistics examines how statistics are constructed and used by professionals. It explores how statistics are constructed in real settings, how violations of technical assumptions impact statistical outcomes, and how statistics are used rhetorically to convince others of the truth of research outcomes. Ethnostatistics has been used to reinterpret data from four celebrated network studies that themselves were reanalyzed (Kilduff & Oh, 2006). The ethnostatistical reanalyses revealed how ad hoc practices, including judgment calls and the imputation of new data into old data set for reanalysis, transformed the focus of network research from diffusion models to structural equivalence models.
Another innovative study uses a Bayesian ethnostatistical approach to understand how the pressure to produce sophisticated and increasingly complex theoretical narratives for causal models has impacted the quantitative knowledge generated in top journals (Saylors & Trafimow, 2020). The use of complex causal models has increased substantially over time due to a qualitative and untested belief that complex models are true. Yet statistically speaking, as the number of variables in a model increase, the likelihood the model is true rapidly decreases (Saylors & Trafimow, 2020, p. 3).
The authors test the previously untested (qualitative) belief that complex causal models can be true. They found that “the joint probability of a six variable model is about 3.5%” (Saylors & Trafimow, 2020, p. 1). They conclude that “much of the knowledge generated in top journals is likely false” hence “not reporting a (prior) belief in a complex model” should be relegated to the set of questionable research practices. This study shows how qualitative research that explores the lay theories and beliefs of statisticians and quantitative researchers can challenge and disrupt conventions in quantitative research, improve quantitative practices, and contribute qualitative foundations to quantitative research. Ethnostatistics thus opens the qualitative foundations of quantitative research to critical qualitative analyses.
The six qualitative research design processes discussed in this article are evident in scholarly research on organizations and management and provide distinct qualitative research designs and approaches to use. Qualitative research can provide research insights from several theoretical perspectives, using well-developed methods to produce scientific and scholarly insights into management and organizations. These approaches and designs can also inform management practice by creating actionable knowledge. The intended contribution of this article is to describe these well-developed methods, articulate key practices, and display core research designs. The hope is both to better equip researchers to do qualitative research, and to inspire them to do so.
The authors wish to acknowledge the assistance of Karen Lund at The University of Alberta for carefully preparing Figure 1. Thanks also to Beverly Zubot for close reading of the manuscript and helpful suggestions.
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