January 31, 2025
Beyond numbers: qualitative research in organizational dynamics
Approach is useful as a tool to explore the complexity of human and social interactions
Regression, graphs, boxplots, tables, software… Data science dominates the organizational research landscape. Quantitative approaches have been gaining ground due to their precision, objectivity of numbers, and ease of translating results into impactful graphs and data, which are the main tools used for decision-making by managers and leaders in organizations.
On the other hand, as Albert Einstein already said, “not everything that can be counted counts and not everything that counts can be counted.” This reflection raises a question: to what extent do numbers, however precise they may be, manage to capture the complexity and nuances that compose human relationships in organizational contexts? It is not a matter of considering one approach superior to the other, but rather of understanding that the choice depends on what one wishes to discover and on the specific objective of the researcheror manager. It is essential to pay attention to these aspects to determine the most appropriate path (Creswell, 2014).
Imagine an organization that assesses the satisfaction level of its employees through a quantitative survey. The data indicate an average of 7.5 on a scale of 0 to 10, suggesting a seemingly comfortable satisfaction scenario, often labeled as “above average”. However, a complementary analysis with in-depth interviews or even direct observation of daily routines and processes can reveal a reality much more complex: silent conflicts — common to living organisms that interact — which can reveal gaps and resentments that the “7.5” index would not be able to translate.
On the other hand, qualitative research faces a series of challenges that permeate theoretical critiques, encompassing practical, methodological and acceptance aspects in the academic and organizational environment. One of the main points raised is its interpretive subjectivity, as the data analysis heavily relies on the researcher’s perspective and experiences. Furthermore, many critics argue that qualitative results lack replicability, since they are generally based on small samples and specific contexts, making it difficult to apply the findings in other scenarios (Günther, 2006; Gomes et al., 2014).
In contrast, in González Rey’s theory on qualitative epistemology, the researcher plays an active role in interpretation, with this interaction being fundamental to revealing dimensions of phenomena that quantitative methods cannot capture (Pinto and Paula, 2018). Furthermore, he emphasizes that the pursuit of contextualized and in-depth understanding should prevail, valuing the specificities of each context. Creswell (2014) emphasizes that, to minimize possible biases, the researcher must clarify their position from the beginning of the study. In this approach, qualitative research positions itself as a tool to explore the complexity of human and social interactions, highlighting subjectivity in the knowledge construction process.
From theory to practice
To answer questions like those addressed, different qualitative methods have been developed and adapted over time, each with its particularities and specific applications. These approaches offer methodologies with a theoretical foundation, in order to apply practices focused on the exploration of complex phenomena. These phenomena are often related to specific groups, actions, projects, or individuals that are exclusive to a given context and time. The table below presents the main qualitative methods and how they can be applied both to understand the operation of organizations and to identify strategic solutions based on their realities.
Note: ¹full references are available in a file at the end of the text
These methods make it clear why qualitative research is not limited to answering “what” and “how much,” but also “how” and “why.” As Eco (2010) highlights, this type of study requires the researcher to adopt an analytical and interpretive stance, guided by a broad understanding of the context. Imagine a company seeking to identify why certain employees have difficulty adapting to remote work. Through qualitative interviews, it is possible to identify comprehensive issues, such as lack of social interaction, difficulty balancing personal and professional life, or even technological challenges, whose subjectivity quantitative research cannot always capture, due to the complexity of human experiences, which require methods that allow for a richer analysis.
In the organizational context, Denzin and Lincoln (2011) emphasize that qualitative research is fundamental for a deeper understanding of the internal dynamics of teams and leadership. For example, when analyzing organizational culture, this method can be relevant in cases of high employee turnover, as, through focus groups and case studies, it is possible to identify underlying causes, such as communication failures or lack of recognition, which are often not captured by numerical metrics and persist due to the absence of interventions based on a more contextualized and detailed understanding.
Mixed methods
Mixed methods research, as emphasized by Creswell (2014), offer an interesting approach by integrating the rigor of quantitative research with the depth of qualitative research. This combination allows to validate and enrich the collected data, broadening the understanding of the context and people, and promoting more complete analyses. In methodologies that are carried out sequentially, the approaches can be applied complementarily, for example, using quantitative surveys to map general patterns and then qualitative methods to explore the underlying reasons. In the convergent model, both methodologies are applied simultaneously, integrating the results to generate a more robust view. But there are challenges and criticisms in this combination.
One of the main challenges of mixed methods is the difficulty of aligning distinct epistemological paradigms (Kuhn, 1998); while quantitative methods seek objectivity and generalization, qualitative methods emphasize the understanding of specific contexts and subjective interpretations. In these circumstances, methodological flexibility becomes essential, especially in areas such as health, education, and business, where the analysis of complex phenomena demands approaches that are both detailed and adaptable to the reality of each research.
Creswell (2014) points out that this combination may not be feasible in studies with funding and time limitations. Furthermore, in the case of programs such as master’s or postgraduate degrees, applying two such distinct methods can become extremely challenging for a researcher who generally has a limited timeframe of up to two years to complete the course.
The innovation and the future of qualitative research
Qualitative research is adapting with the aid of new technologies; with the help of artificial intelligence (AI), it is expanding its possibilities for data analysis and collection. AI enables, for example, automatic transcription of interviews, sentiment analysis in texts, and identification of patterns in large volumes of qualitative data, as pointed out by Marcolin et al. (2023). This not only reduces the time for operational steps but also aids in the depth of interpretations, allowing the researcher to focus on the central issues of the analysis and no longer on how much time will be needed to transcribe the countless pages of interviews conducted with the help of their trusty pocket recorder.
A good example is Read.ai, a tool designed to optimize virtual meetings that can also be used as a complementary resource in qualitative research. The application offers automatic transcription of meetings and interactions on platforms such as Zoom, Microsoft Teams, and Google Meet, in addition to sentiment and engagement analysis functionalities. These applications allow researchers to identify emotional patterns, participation metrics, and conversation highlights, contributing to the analysis of group dynamics and interviews in online contexts.
Even gamification is assisting qualitative researchers. Some adapted games are helping in how data is collected, making the process more dynamic and engaging for participants. Instead of traditional interviews, it is possible to create interactive scenarios that simulate organizational situations, extracting authentic insights and attitudes in simulated contexts (Mozzato and Bert, 2024). In addition to these examples, the image below presents other tools that can assist in qualitative research.
Source: Prepared by the author based on Martins (2022)
These tools, when integrated with qualitative approaches, not only help overcome their limitations but also offer new ways to understand organizational dynamics (Marcolin et al., 2023), challenging the excessive dependence on numbers. Once associated with technological innovations, this approach becomes even more powerful, providing a richer and more contextualized analysis of the phenomena studied.
It is in this context that qualitative research, enhanced by innovations such as artificial intelligence, reaffirms its relevance, offering a more complete, detailed analysis centered on human complexity. After all, the true richness lies in understanding what numbers alone and isolated words cannot reveal, as already well cited here by Einstein!
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This content was produced by:
Luiz Eduardo Giovanelli
Assistant Editor of the Strategies and Solutions Journal. Master’s student in Administration from the University of São Paulo. Specialist in Public Management from the State University of Ponta Grossa and graduate in Administration from the State University of Northern Paraná.
Who wrote this column
Editora Pecege
