Research Design and Methodological Choices

Among the 154 articles included in our analysis, 70.1% adopted a quantitative research design, 28.6% employed qualitative methods, and 1.3% utilized mixed methods. This distribution reflects a strong preference for quantitative approaches while maintaining a healthy representation of qualitative inquiry that enriches theoretical development and contextual understanding. These methodological orientations shape the field’s theoretical contributions in complementary ways. Quantitative studies support the testing of established frameworks and the identification of structural relationships, while qualitative research offers insights into dynamic processes and facilitates theory building in less explored domains (Creswell & Creswell, Reference Creswell and Creswell2017). The limited presence of mixed-method designs presents an opportunity for integrating these strengths – particularly when studying complex, multilevel organizational phenomena in the Chinese context.

Types and Sources of Data

Our analysis identified three primary categories of data used in Chinese management research published in MOR. Archival and secondary data formed the largest category, appearing in 53.2% of the articles. These sources include datasets provided by government agencies, corporations, and institutional repositories (see Table 1 for a list of commonly used datasets and sources). The China Stock Market and Accounting Research (CSMAR) database was especially prominent, featuring in 37% of all quantitative studies. Studies leveraging CSMAR often focused on firm-level financial data, executive characteristics, and corporate governance indicators. Another widely used dataset was WIND (万得信息), which researchers employed for macroeconomic indicators and industry-level benchmarks. Many studies combined multiple archival sources – for example, pairing CSMAR with WIND to link firm-specific characteristics to broader market trends, or incorporating data from China’s National Bureau of Statistics (CNBS) to capture regional economic conditions, sectoral growth, and firm census information. Additionally, some studies integrated specialized databases such as Hexun for corporate social responsibility metrics, the China Research Data Service Platform (CNRDS) for ownership and board composition data, and the General Catalog of Chinese Genealogies for kinship and clan information. These resources have enabled scholars to identify large-scale patterns across firms and industries, particularly regarding financial performance, corporate governance, and strategic decision-making.

Table 1.

List of commonly used datasets and sources in Chinese management research

Survey-based research represented the second most common data category, used in 22.7% of the articles. These studies employed structured questionnaires to investigate topics such as leadership styles and their effects on organizational outcomes, employee behaviors, including voice and creativity, and firm-level capabilities related to innovation and performance. Most surveys utilized researcher-designed instruments tailored to specific research questions, while a smaller number relied on internal company surveys or questionnaires administered by professional data collection agencies. Several studies adopted multi-source designs, collecting data from complementary sources such as employee-supervisor pairs. Taken together, these survey approaches have provided insights into the perceptual and behavioral dimensions of organizational life, shedding light on individual and team-level dynamics that shape broader organizational outcomes.

Interview-based research constituted the third major category, featuring in 15.6% of the articles. These studies relied on semi-structured interviews with organizational actors – including executives, middle managers, frontline employees, and in some cases, industry or policy stakeholders. Common research themes included how organizations navigate institutional complexity, engage in ecosystem building, and develop context-specific capabilities, such as guanxi-based networks. Many studies strengthened their findings by supplementing interview data with archival materials or field observations, enhancing contextual interpretation. These qualitative approaches provided valuable insights into organizational processes and culturally embedded practices, contributing to theory development by capturing the nuanced perspectives of actors operating within China’s distinctive institutional environment.

Beyond these three dominant categories, a smaller yet notable subset of studies employed alternative methodological approaches. Some used laboratory experiments to test causal mechanisms related to leadership cues, incentive structures, and ethical dilemmas. Others drew on ethnographic observation and textual or discourse analysis to explore organizational narratives, strategic communications, and situated institutional practices. Computational techniques such as natural language processing, though limited in number, have begun to appear in analyses of corporate communications. While collectively representing less than 10% of the articles reviewed, these diverse approaches signal emerging methodological pluralism – a development that enhances the field’s capacity to investigate complex research questions across multiple levels of analysis and domains.

Figure 1 illustrates the distribution of MOR publications by data type and research design.

Figure 1.

Distribution of research designs by data type*

*Note: Studies using multiple data types are counted once for each data type used. As a result, totals may exceed the number of unique articles.

Based on our review of MOR articles, we now turn to two key areas of discussion. First, we examine the data infrastructure underlying management research in the Chinese context and highlight new directions for enhancing methodological diversity, with particular attention to emerging data sources. Second, we address recent developments in data access and regulatory frameworks, offering practical guidance to help scholars navigate this increasingly complex research environment.

Data Infrastructure in the Chinese Context and New Directions

The widespread use of institutionalized databases such as CSMAR and WIND reflects both the strengths and constraints of China-based data infrastructures. While these established datasets provide consistency, comparability, and ease of access, over-reliance on them can lead to analytical redundancy and narrow the scope of inquiry. In particular, they focus disproportionately on publicly listed companies, while offering limited coverage of other sectors of China’s diverse organizational landscape – including private enterprises, nonprofit organizations, informal networks, and hybrid entities that operate across institutional boundaries.

We encourage scholars to explore less commonly used datasets, including the China Family Panel Studies (CFPS), the Simuton database on venture capital, and the General Catalog of Chinese Genealogies. These data sources offer valuable opportunities to broaden the empirical foundations of Chinese management research, providing access to organizational settings and practices that often lie beyond the scope of conventional disclosure-based data. Beyond identifying alternative data sources, researchers may also create value through integrative approaches. Integrating widely-used datasets with new field data and surveys to analyze new questions within well-studied populations can yield insights that neither approach alone could capture (see, for example, Jiang, Cannella, & Jiao, Reference Jiang, Cannella and Jiao2018). Both strategies – exploring less common datasets and developing integrative approaches – represent complementary paths toward more comprehensive theories that better reflect the diversity and dynamism of organizational life in China.

Research engaging underexplored organizations illustrates the value of these approaches. For example, Zhang, Sun, and Qiao (Reference Zhang, Sun and Qiao2020) examine how private ventures navigate political dependence by cultivating ties to local officials, demonstrating how firm size, geographic location, and resource constraints shape context-specific engagement strategies – dynamics that differ from those observed in research focused on large, publicly listed firms. Similarly, several studies in our review show that while family firms may be represented in institutionalized databases, their distinctive attributes – such as intergenerational succession (Zhu & Kang, Reference Zhu and Kang2022; Zhu & Zhou, Reference Zhu and Zhou2022) and culturally embedded governance approaches (He & Liu, Reference He and Liu2022; Lu, Huang, Xu, Chung, & He, Reference Lu, Huang, Xu, Chung and He2022) – often benefit from supplementary data collection methods to capture the complex interplay between family dynamics and organizational decision-making.

The rise of computational methodologies, textual analytics, and digital trace data is expanding research possibilities and enriching the repertoire of data sources available to organizational scholars. Advancements in machine learning and large language models (LLMs) not only complement and refine traditional empirical approaches but also open new avenues for theorizing and analysis. Yue, Zheng, and Mao’s (Reference Yue, Zheng and Mao2024) study on corporate nationalism, which received the 2025 MOR-Responsible Research in Business and Management (RRBM) Best Paper Award, provides a compelling example. Traditionally, nationalism has been conceptualized at the national or individual level and examined through surveys or event studies. Within this paradigm, organizations were typically portrayed as passive entities, constrained by societal sentiment, state coercion, or the liability of foreignness. Yue and colleagues, however, reconceptualize organizations as active agents capable of expressing and leveraging nationalism as a strategic or value-driven posture. Using natural language processing (NLP) techniques on corporate annual reports, they develop and empirically validate a four-dimensional theoretical framework for how organizations manifest nationalism. This framework, along with the open-sourced database they created, paves the way for a broader research agenda exploring the antecedents, mechanisms, and outcomes of organizational nationalism.

As digital technologies become increasingly embedded in organizational processes, they are reshaping both the nature and granularity of empirical data. Enterprise software, communication platforms, and algorithmic decision tools enable the observation of organizational phenomena in situ with unprecedented detail and scale (Salganik, Reference Salganik2019). Beyond structured disclosures such as annual reports, researchers can now access and analyze a wide range of digital traces – from public communications and social media posts to investor commentary and consumer reviews – yielding new insights into shareholder engagement, reputational dynamics, and market sentiment.

The proliferation of AI and digital technologies has also given rise to new forms of data on internal organizational activity, including A/B testing, workflow tracking, and automated decision-making processes. Emerging employment models such as platform-mediated work further illustrate how these technological infrastructures produce detailed traces of evolving relationships among firms, workers, and regulatory institutions. Beyond analyzing existing digital traces, emerging AI technologies are creating new research possibilities. LLMs now enable simulation-based experiments using virtual agents that can mimic human behavior, allowing researchers to explore counterfactual scenarios or test theoretical mechanisms in controlled, interactive environments – particularly valuable when real-world experimentation is impractical or constrained.

While these novel data sources offer exciting opportunities, their use also invites careful consideration of validity and replicability. For algorithm-generated data, researchers might consider following established validation practices, such as splitting ground-truth data into training and testing samples in analyses involving LLMs. Where possible, cross-validation with traditional measures from archival or survey sources can further strengthen confidence in findings. Providing detailed documentation of data collection methods, validation checks, and known limitations enhances transparency. Some journals have begun developing guidelines for evaluating these emerging data types, including recommendations for data sharing and methodological transparency.

Data Access and Regulatory Developments

The methodological patterns and data sourcing practices we identified are taking shape within an evolving regulatory environment that is transforming the research landscape in China. Recent legislation – including the Personal Information Protection Law (PIPL, 2021), Data Security Law (DSL, 2021), and Network Data Security Management Regulations (NDSMR, 2025) – has established formalized frameworks for data classification, privacy protection, and cross-border data flows. While these policies primarily target commercial enterprises and data-intensive digital platforms, they also have implications for academic researchers, particularly those engaged in primary data collection.

For studies relying on established databases like CSMAR and WIND, regulatory changes may have minimal direct impact, as these platforms already operate within formal data governance frameworks. However, for researchers conducting fieldwork or collecting primary data, new requirements around consent, data security, and cross-border transfer create procedural considerations that may influence research design and collaboration strategies. For example, researchers may benefit from engaging earlier with local institutional partners to navigate approval processes, clarify compliance obligations, or structure modular research designs that can adapt to potential access constraints. These considerations become particularly relevant when studying organizational forms not systematically covered by institutionalized databases, such as private enterprises and nonprofit organizations. As these entities navigate evolving compliance obligations, they may approach data sharing with increased caution. Similarly, firms operating in sectors closely tied to China’s digital infrastructure – such as fintech, telecommunications, AI, cloud services, and logistics – may be more selective about research participation due to heightened oversight of data access and sharing. In such cases, field access may depend not only on building trust with organizational leadership but also on aligning with a firm’s internal data governance policies. Research proposals may require more precise scoping, clearly defined data handling protocols, and formal structures that minimize real or perceived regulatory risks.

In this evolving regulatory environment, collaboration between international and Chinese researchers takes on renewed importance. These partnerships are increasingly essential for mediating data access, navigating compliance requirements, and enhancing the credibility that is needed to engage with organizations. Beyond practical benefits, they also offer conceptual advantages, combining international theoretical perspectives with deep contextual knowledge to create insights that emerge through collaboration.