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Qualitative Designs and Methodologies for Business, Management, and Organizational Research

2020· reference-entry· en· W3091478429 on OpenAlexaff
Robert P. Gephart, Rohny Saylors

Bibliographic record

VenueOxford Research Encyclopedia of Business and Management · 2020
Typereference-entry
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsQualitative researchData collectionManagement scienceResearch designProcess (computing)Qualitative propertyKnowledge managementComputer scienceData scienceSociologySocial scienceEngineering

Abstract

fetched live from OpenAlex

Abstract 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.897
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.005
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.255
GPT teacher head0.414
Teacher spread0.159 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations12
Published2020
Admission routes1
Has abstractyes

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