The combination of qualitative and quantitative approaches enhances theoretical innovativeness and relevance while also ensuring methodological rigor and generalizability. Image generated by AI

Empirical research in the social sciences can generally follow two basic paths: qualitative research and quantitative research. In management studies, these approaches have long been cast as a binary opposition: Quantitative research is treated as “scientific” and “rigorous,” while qualitative research is labeled as “soft science” or “subjective.” Over the past several decades, this bias has deeply shaped academic evaluation systems, prompting many scholars to pursue methodological “standardization” even where it is ill suited to the problem at hand. The result is often a forced fit: Complex statistical models are imposed on relatively simple management questions, while the phenomena themselves receive too little close examination.

However, as a growing number of Chinese enterprises expand their global influence, their distinctive innovation pathways, management models, and governance mechanisms have generated a range of local phenomena rich in research value. These phenomena provide an unprecedented empirical foundation for scholarship and have drawn increasing attention from the global academic community. At the same time, quantitative methods alone are not sufficient to explain China’s distinctive management practices and innovative breakthroughs. These are complex, dynamic, and systemic processes; uncovering their underlying mechanisms and logic requires in-depth case tracking and process-based narrative analysis—in other words, qualitative methods.

Does this mean quantitative research should be abandoned altogether in favor of qualitative research? Clearly not. Every research method has its own boundaries and conditions of application. What we oppose is not quantitative research itself, but “quantitative supremacy.” Likewise, what we advocate is not the dominance of qualitative research, but methodological pluralism and complementarity. The key question, then, is when qualitative methods are more appropriate and when quantitative methods should be employed.

Qualitative research is especially well suited to the stages of “exploration” and “theory construction.” When a management phenomenon is entirely new and cannot be explained by existing theories, or when the research question concerns “why” and “how,” qualitative research is generally the most appropriate approach. Through small-sample, thickly descriptive methods, it helps researchers abstract theoretical frameworks from practice and identify new variables and mechanisms. For example, when the digital economy was first emerging and new organizational forms such as online platforms began to appear, it was necessary to first understand their operating logic through qualitative research rather than rush into quantitative analysis.

Quantitative research, by contrast, is better suited to the stages of “verification” and “generalization.” Once a theoretical framework has been preliminarily established, researchers need to test whether it has general applicability and can be validated in large samples. This is where quantitative research demonstrates its comparative advantage. Through large-sample statistical testing, it helps eliminate random noise and confirm causal relationships among variables, making theories more convincing to a broader audience and more transferable across contexts.

In fact, an increasing number of scholars in management studies are now adopting “mixed methods.” They employ qualitative research first to explore problems and build theory, then quantitative research to test hypotheses and validate models. This combination of qualitative and quantitative approaches enhances theoretical innovativeness and relevance while also ensuring methodological rigor and generalizability, making it an important direction for the future development of management research methods.

It is worth noting that advances in AI are opening new possibilities for integrating these two approaches. Large language models can help process vast numbers of interview transcripts, substantially improving coding efficiency in qualitative research. At the same time, AI can assist with more complex causal inference, strengthening the identification capabilities of quantitative research. In the future, as technology continues to advance, the boundary between qualitative and quantitative research may become increasingly blurred, giving way to a more intelligent and integrated research paradigm.

In summary, the shift in empirical research methods in management studies is not a transition from quantitative to qualitative research, but from methodological singularity to pluralism. We should set aside biases toward particular methods and return to the fundamental purpose of research: answering the most genuine management questions with the methods best suited to them.

Li Xiaohua is an associate professor from the School of Economics and Management at Northwest University. Zhang Wei is a professor from the School of Economics and Management at Tsinghua University.