Introduction
The digital transformation of Chinese companies has placed them at the center of a global shift in how work is done, managed, and studied (Li, Nucciarelli, Roden, & Graham, Reference Li, Nucciarelli, Roden and Graham2016). In recent years, Chinese firms have adopted new technologies at a remarkable speed and scale, changing the fundamentals of organizational life. The widespread use of integrated workplace platforms like DingTalk and Enterprise WeChat, alongside advanced AI in human resources, has turned these organizations into living laboratories for research on technology and organizational behavior (Parker, Van Alstyne, & Choudary, Reference Parker, Van Alstyne and Choudary2016). As organizations increasingly deploy AI and robotic systems, understanding how employees interact with and respond to these technologies has become crucial for effective implementation (Yam et al., Reference Yam, Bigman, Tang, Ilies, De Cremer, Soh and Gray2021). This rapid change has produced an important byproduct: large archives of unobtrusive data that capture work life in granular detail.
The rise of unobtrusive data presents both an opportunity and a challenge for researchers. Unlike traditional survey methods that have long defined management research, these new data sources offer continuous and objective insights into workplace behavior (Hill, White, & Wallace, Reference Hill, White and Wallace2014). Digital traces of employee interactions, collaboration, and performance provide direct access to the dynamic processes of organizational life. At the same time, this data abundance has revealed the limits of traditional research methods that rely on static, self-reported measures (Salganik, Reference Salganik2017).
For decades, management research has relied heavily on surveys, which capture employee perceptions at a single point in time. While these methods are useful for understanding attitudes and intentions, they often miss the temporal dynamics, behavioral patterns, and contextual details of day-to-day organizational processes. These limitations become especially clear when studying fast-moving phenomena like digital collaboration, real-time decision making, or the formation of informal networks.
Unobtrusive data can complement traditional research by allowing scholars to ask new questions, develop new constructs based on actual behavior, find hidden relationships in organizational systems, and even build new theories from observed patterns (Knight, Reference Knight, Bryman and Buchanan2018). Combining computational and traditional methods offers a way to bridge the gap between what people do in organizations and what traditional research can measure (Creswell & Plano Clark, Reference Creswell and Plano Clark2017).
The story of this special issue reflects both the promise and the difficulties of this new research area. Our initial call for papers on unobtrusive data in Chinese organizations drew more than twenty submissions, showing strong interest in the topic. The editorial process, however, was longer than expected. In the end, we selected three high-quality papers that show how to effectively use data and technology to generate real insights into organizational life in China. Their success was not a simple feat; it highlights a central challenge for organizational behavior scholars today – how to turn a wealth of data into valuable theoretical and practical knowledge. While Chinese organizations offer unique opportunities with their rich data and advanced analytics, researchers face a real tension between data availability and the ability to contribute to organizational theory.
The three papers in this issue represent a small fraction of what is possible with big data and unobtrusive methods. Still, they offer important methodological and theoretical contributions and point the way forward for future work. With their diverse methods and ideas, these studies provide a foundation for future research that uses the unique advantages of organizational big data while maintaining the rigor and relevance of top-tier research.
The Rise of Organizational Big Data in China
Digital workplace technologies have created a massive data ecosystem within Chinese organizations, changing how researchers can observe and understand work (Lazer et al., Reference Lazer, Pentland, Adamic, Aral, Barabási, Brewer, Christakis, Contractor, Fowler, Gutmann, Jebara and Van Alstyne2009). Platforms like DingTalk, Enterprise WeChat, and Lark are no longer just communication tools; they are integrated digital environments that capture nearly every facet of work (Parker, Van Alstyne, & Choudary, Reference Parker, Van Alstyne and Choudary2016). Each interaction and task generates digital traces, creating a rich record of behavior that shows organizational dynamics in real time.
The Chinese context offers distinct advantages for research using big data. The sheer scale of many Chinese companies produces datasets large enough for powerful statistical analysis. A general cultural acceptance of workplace data collection, alongside government policies promoting the digital economy, creates a favorable environment for this type of research. This push for innovation has led companies to adopt advanced analytics and monitoring systems that produce data streams well-suited for academic study.
The main challenge and opportunity lies in turning this abundance of data into meaningful insights (Boyd & Crawford, Reference Boyd and Crawford2012). Chinese organizations produce enormous amounts of behavioral data, but making it theoretically and practically useful requires sophisticated analysis and strong theoretical framing. These granular data allow researchers to see temporal dynamics, network relationships, and behavioral patterns that were invisible to older methods.
Three Approaches to Technology and Organizations
The three papers in this special issue take distinct but complementary paths to studying technology’s impact on organizational life, showing the range of methods and theories in this emerging field. Each study examines a different level of organizational life and uses a unique strategy to combine research rigor with technology-enhanced approaches.
Liu and colleagues’ study of emoji use in leader-follower communication is a careful examination of how technology affects interpersonal dynamics, using experimental methods to understand digital communication features. By focusing on emojis, the authors show how technology-mediated interactions can reveal subtle patterns of influence that traditional measures cannot easily capture. Their approach connects expectancy violation theory (Burgoon, Reference Burgoon1993) to digital contexts and demonstrates how small technological features can have significant impacts on work relationships.
Pan and colleagues’ paper on employee productivity during a crisis shows the power of using internal digital traces to measure organizational phenomena with high granularity over time. Their research uses real-time productivity data from company platforms instead of relying on employee memory, allowing them to track performance changes throughout a crisis. This approach offers a more precise test of Conservation of Resources Theory (Hobfoll, Reference Hobfoll1989; Hobfoll, Halbesleben, Neveu, & Westman, Reference Hobfoll, Halbesleben, Neveu and Westman2018) and avoids the biases common in crisis management research. Their real-time measurements allow for a detailed look at temporal dynamics that was previously out of reach for management scholars.
Gao and colleagues’ research is the most boundary-breaking of the three, showing how machine learning can be combined with inductive theory building to find new theoretical insights in complex data (Leavitt, Schabram, Hariharan, & Barnes, Reference Leavitt, Schabram, Hariharan and Barnes2021). Their study moves beyond traditional hypothesis testing by using machine learning as a tool for theory discovery, allowing patterns to emerge from the data itself. This combination of machine learning and theory development is a paradigm shift, revealing complex relationships that traditional analysis might miss and creating a template for how AI can support human theoretical thinking (Choudhury, Allen, & Endres, Reference Choudhury, Allen and Endres2021).
Together, these three papers show the methodological diversity possible when technology-enhanced approaches are applied thoughtfully to important organizational questions. They build a foundation for future research that leverages both experimental and unobtrusive methods while maintaining theoretical depth and practical relevance, illustrating how technology can advance our understanding of organizations across many research traditions.
Moving Forward: New Data, Methods, and Research Skills
The papers in this special issue have only scratched the surface of what is possible with big data. They represent a small sample of the work that can be done, as organizations continue to produce richer and more varied data that open new doors for research.
New Data and Research Questions
Beyond the structured data common in organizational research, the vast amount of unstructured data from company platforms offers rich ground for future work. Internal reviews, innovation platforms, and performance evaluations all create large archives of natural language that capture employee experiences in their own words. The integration of multimodal data – including text from internal messages, voice from virtual meetings, and video from digital interactions – creates powerful new ways to understand behavior.
This new data allows researchers to ask questions that were once unanswerable. For example, analyzing internal communications over time can show how organizational culture shifts during major changes, while real-time collaboration data can reveal the micro-foundations of team effectiveness (Goh & Pentland, Reference Goh and Pentland2019). Video analysis can provide objective measures of engagement, and text analysis of employee feedback can spot emerging problems or opportunities for innovation. Recent advances have demonstrated how computational topic modeling of employee reviews can create new constructs such as organizational cultural heterogeneity, revealing nuanced relationships between individual and collective cultural beliefs (Corritore, Goldberg, & Srivastava, Reference Corritore, Goldberg and Srivastava2020).
New Methodological Tools
Since our call for papers, the rapid development of large language models (LLMs) has given researchers powerful new tools that make complex analysis more accessible (Grimmer, Roberts, & Stewart, Reference Grimmer, Roberts and Stewart2022). These AI technologies can process unstructured text and multimodal data directly, going far beyond the limits of traditional natural language processing. LLMs can identify sentiment, find themes, code qualitative data, and even help generate theoretical insights from large datasets, changing how researchers can work with unstructured organizational data. Recent comprehensive reviews have established natural language processing as a scalable, unobtrusive method for capturing attitudes, affect, and social dynamics in behavioral science, while highlighting the importance of validation and ethical considerations in workplace text analysis (Feuerriegel et al., Reference Feuerriegel, Maarouf, Bär, Geissler, Schweisthal, Pröllochs, Robertson, Rathje, Hartmann, Mohammad, Netzer, Siegel, Plank and Van Bavel2025).
Advanced AI can support sentiment analysis of thousands of employee messages, automated coding of performance reviews, and real-time topic modeling of company discussions. Computer vision can provide objective measures of collaboration quality from video, while deep learning can uncover complex, non-linear relationships in data that challenge existing theories (Molina & Garip, Reference Molina and Garip2019). Recent research has demonstrated how such approaches can reveal curvilinear patterns in organizational phenomena, such as the inverted U-shaped relationship between multi-project work and performance, providing evidence-based insights for managing complex work arrangements common in Chinese high-tech firms (Colicev, Hakkarainen, & Pedersen, Reference Colicev, Hakkarainen and Pedersen2023).
New Research Skills and Collaboration
These new technologies are also changing the skills and collaboration models needed for high-quality research. Traditionally, many organizational behavior scholars have lacked training in computational methods, and interdisciplinary work has been difficult due to different goals and standards. The rise of user-friendly AI tools is changing this by allowing researchers to use AI for data analysis tasks that once required deep technical expertise.
For example, researchers can use AI for ‘vibe coding’ – analyzing the emotional tone in thousands of organizational messages. Similarly, LLMs can help with thematic analysis of employee feedback or find emerging trends in digital data. Cloud platforms and easy-to-use machine learning interfaces are lowering the technical barriers for researchers.
However, even as AI tools become more accessible, researchers are still responsible for the accuracy and theoretical meaning of their findings (Tufekci, Reference Tufekci2014). Using AI effectively requires careful validation, strong theoretical grounding, and methodological rigor to ensure that computational findings lead to real organizational knowledge. This balance between technology and theory is both an opportunity and a responsibility for the field.
A Future Research Agenda
Advancing big data research in organizations requires a coordinated focus on methods, theory, and practical application. Research priorities should include developing strong validation frameworks to ensure that data sources are reliable and meaningful. Ethical guidelines for privacy and consent are also urgently needed, especially as AI makes large-scale analysis easier (Boyd & Crawford, Reference Boyd and Crawford2012).
Theoretically, there are opportunities to build process-based theories that explain change over time, cultural adaptation models that show how technology is used in specific contexts, and network theories that account for real-time collaboration (Eisenhardt & Graebner, Reference Eisenhardt and Graebner2007). Practically, this research can inform human resource analytics, organizational design, performance management, and culture.
Building a global research community will require investment in cross-cultural studies, knowledge sharing, and training. International collaboration can speed up theoretical development and ensure that findings from China contribute to a global understanding of management.
Conclusion
This special issue marks an important step in the evolution of organizational research, showing how technology-enhanced methods can join traditional rigor with computational innovation to create new insights about organizational behavior in China. The three papers here lay a methodological foundation that spans from digital-context experiments to real-time behavioral measurement and machine learning-assisted theory building.
The unique context of Chinese organizations, with their rapid adoption of digital technology, offers valuable lessons that extend beyond China, informing a global understanding of the intersection of technology and organizations (Teece, Reference Teece2007). The challenges and opportunities in these studies reflect a broader transformation happening in companies worldwide as digital technology reshapes work.
The future of organizational big data research depends on a sustained commitment from scholars to develop computational skills, encourage interdisciplinary collaboration, and maintain the balance between technical sophistication and theoretical depth. As organizations produce ever-richer data and AI tools make analysis more accessible, the research community must meet the challenge of turning data abundance into theoretical and practical knowledge. The papers in this special issue offer both inspiration and guidance for researchers, pointing toward a future where our understanding of organizations can become more precise, comprehensive, and relevant.
Ning Li (lining@sem.tsinghua.edu.cn) is the Flextronics Chair Professor and head of the Leadership and Organization Management Department at Tsinghua University. He earned his PhD from Texas A&M University. His current research focuses on generative AI, big data in management, organizational network analysis, team collaboration and leadership, and proactive behavior in organizations.
Wei He (whe@nju.edu.cn) is a professor of management at the School of Business, Nanjing University, China. He received his PhD in business administration from Huazhong University of Science and Technology, China. His research interests include compensation, work motivation, and employee performance management.
Kai Chi Yam (bizykc@nus.edu.sg) is the Jardine Cycle & Carriage Professor at the National University of Singapore. His research focuses primarily on the future on work, examining how employees and consumers react to new technologies such as robots, AI, algorithms, and autonomous vehicles. He received his PhD in Organizational Behavior from the University of Washington.
Helen H. Zhao (hhzhao@hku.hk) is an Associate Professor in the Management and Strategy Department at the University of Hong Kong Business School. Her research focuses primarily on organizational behavior, examining how team social networks, leader succession, and work and time dynamics shape workplace outcomes. She received her PhD in Management and Organization from the University of Iowa in 2015.
