MOR News

CALL FOR PROPOSALS | New Technology and OB/HRM in China: Captializing on Big Data to Expand Organizational Research and Practice

Workshop Organizers and Guest Editors

Ning Li,1 Wei He,2 Kai Chi Yam,3 and Helen Hailin Zhao4
1Tsinghua University, 2Nanjing University, 3National University of Singapore, 4University of Hong Kong

The rise of technology has fundamentally changed our everyday lives and especially how we work. The COVID-19 pandemic has further accelerated the impact of technology on the workplace. Artificial Intelligence (AI), big data, blockchain, cloud computing, and other new technologies have a profound influence on not only the external environment of enterprises but also the structure, jobs, and work procedures and processes inside organizations (Kellogg, Valentine, & Christin, 2020; Lindebaum, Vesa, & Hond, 2020; Liu, Brynjolfsson, & Dowlatabadi, 2021; Parent-Rocheleau & Parker, 2021; Parker & Grote, 2020; Raisch & Krakowski, 2021). The application of these new technologies can modify and change Human Resource Management (HRM) systems and transform the way employees work in contemporary organizations (Glikson & Woolley, 2020; Vrontis et al., 2021). In line with this trend, organizations have accumulated a large amount of employee-generated data such as the digital trace of team collaborations, project management progress, and intranet discussions, which offer exciting possibilities to answer new research questions faced by organizations and management scholars (Connelly, Fieseler, Černe, Giessner, & Wong, 2021; Lanzolla et al., 2020; Larson & DeChurch, 2020; Leicht-Deobald et al., 2019; Ranganathan & Benson, 2020). Traditionally, OB/HR scholars have predominantly relied on survey methods to study organizational phenomena. There are rarely any attempts to integrate data science in the organizational research domain, and extant discussions tend to be theoretical in nature (Leavitt et al., 2020; Simsek et al., 2019). As a result, it has been unclear how to leverage new technologies and the richness of employee-oriented big data to study important management phenomena. This trend has been particularly salient in China and among Chinese enterprises due to fast economic and technology development. Thus, Management and Organization Review (MOR) is calling for submissions that can capitalize on the emergence of organizational big data (specifically defined below) to develop new theories and constructs, explore new phenomena, and answer new research questions, with the hope of contributing to the theoretical developments and empirical extensions concerning the application of new technologies and big data in OB/HR in China.

Scope of the Special Research Forum

The key feature of this special issue is to encourage scholars to leverage an alternative, unconventional data source, such as unobstrusive, employee autogenerated data, prompted by new technologies (e.g., Artificual intelligence (AI), Big Data, mobile internet, cloud computing), to answer new research questions in organizational research that are otherwise difficult to study when only relying on traditional OB/HR methods. Given this focus, we specifically define the scope of the special issue below.

  1. The proposal should be empirically oriented, preferably including at least one unobstrusive data source (i.e., the data collection process does not interfere with the subjects under study, such as digital trace data, public or private archival data, contrived observation [Knight, 2018]). Although we encourage authors to leverage unobstrusive, big data to answer new questions, we also allow researchers to use traditional methods to study how technology can impact the workplace more generally (e.g., Borau, Otterbring, Laporte, & Fosso Wamba, 2021; Newman, Fast, & Harmon, 2020; Jackson, Castelo, & Gray, 2020; Yam et al., 2020). We welcome both quantitative and inductive/qualitative- oriented research.
  2. The proposal should address research questions that appeal to the interests of OB/HR practitioners and scholars. Common topics include but are not limited to recruitment and selection, staffing, newcomer socialization, training and development, turnover, compensation, employee well-being, teamwork, leadership, organizational network analysis, organizational culture, innovation, and creativity.
  3. In terms of methods, researchers can use traditional management analytic frameworks (e.g., regression-based models), unconventional methods borrowed from data science (machine learning-based, content analysis, predictive modeling), or both. The proposed method should be determined by the match between the research question and the data structure.

Typical Research Questions

We see many opportunities to expand the current management literature by leveraging big data in organizations to answer new questions or using traditional approaches to examine the impact of technology in the workplace. Below is a brief description of some typical research questions that are suitable for this special issue. The proposed examples are illustrative.

1. Theory-building research based on discovering unique patterns and empirical observations. The development of new technologies and data science has provided new opportunities to develop and test new management theories. For example, exploring longitudinal trace data of employee behaviors can allow researchers to develop temporal management theories. There are different ways to achieve this objective. For example, researchers could observe some unique patterns based on various data mining techniques

and make sense of the findings using qualitative methods to develop a new theory. Researchers could also use private organizational archival data as a source of case studies. For example, Goh and Pentland (2019) developed a new theory of the dynamics of organizational routines by deeply analyzing data generated by the project management software. Possible extensions of this theme include but are not limited to the following topics:

  1. Big data and decision (e.g., how could enterprises use big data to make strategic HRM decisions; the ethics of using big data in HRM)
  2. New technologies, HRM, and Chinese culture (e.g., the role of Chinese culture in the integration of new technologies into HRM)
  3. Human resource management based on a digital platform (e.g., gig workers, flexible employment)
  4. Temporal theory of teamwork
  1. Instead of developing a new theory, which could be a challenging task, researchers canrely on the new data source and methods to innovate new constructs and extend classic constructs. For example, Corritore, Goldberg, and Srivastava (2020) used topic modeling to analyzes massive online review data on specific companies to create a new concept – organizational cultural heterogeneity and demonstrated its impact on organizational innovation. Similarly, Jiang, Yin, and Liu (2019) used an automated facial expression analysis technology to capture emotions displayed in recorded videos and linked the emotion to an entrepreneur’s funding performance. Essentially, researchers can develop new constructs that are difficult capture by traditional survey methods (e.g., specific creative ideas generated by employees [Fuchs, Sting, Schlickel, & Alexy, 2019]) or add new dimensions to classic constructs (e.g., dynamic collaboration network). A potential limitation of this approach is the lack of evidence to validate the construct developed based on unobstrusive data. We encourage scholars to validate the new measures with different methods (e.g., survey, interview, field observation, etc).
  2. Testing new management phenomena prompted by new technologies. We encourage empirical research that can challenge, change, or advance previous management theories and practices in these emerging contexts that incorporate various new technologies. Possible extensions include but are not limited to the following topics:
    1. Big Data and precise recruitment (e.g., the effectiveness of big data in recruitment)
    2. Telecommuting and employees’ performance and well-being (e.g., how does telecommuting influences’ employees’ performance and well-being; the effectiveness of flexible employment)
    3. Agile performance appraisal and management (e.g., the dynamic effects of agile performance system on employees’ affect, attitudes, and behaviors)
    4. Digital platform and employment (e.g., how does the psychological ownership evolve under different employments in the digital platform)
  3. Exploratory research. Some leading technology firms in China (e.g., Baidu, Alibaba,

Tencent) have pioneered utilizing new technologies in improving the efficiency and effectiveness of HRM systems and accumulated rich internal data that can be used to analyze HRM efficacy. We encourage the empirical exploration of an organization’s internal non-obtrusive data to unravel the efficacy of and/or problems associated with the utilization of new technology-based HRM practices in Chinese firms. These exploratory studies are encouraged to use some advanced data analytic methods and platforms (e.g., machine learning, digital voice/video systems, and the internal email system). Possible extensions of this stream of exploratory research include but are not limited to the following topics:

  1. Using content analyses to study performance evaluation, selection, promotion, etc.
  2. Emotion and video coding
  3. Network and connections, teamwork, collaboration
  4. Novel-app based, video recording, digital trace, audience, recorded voice,creativity, and innovation

We only highlight some possible research questions above, but submissions do not need to be constrained by these topics or suggestions. We encourage submissions of insightful and novel empirical work with quantitative, qualitative, and mixed methods research involving multidisciplinary lenses, different levels of analyses, and creative methodologies.

Unobstrusive Data Source and Examples

Unobstrusive data is a broad term and includes many different types of data. In organizations, it typically includes both static and dynamic data generated by employees. We list some common examples below. Again, there are illustrative examples.

  1. Internal archival HR data such as hiring-related data (demographic, interview ratings, comments), job history (internal job change, promotion, job rotation, turnover, internal transfer), performance evaluation history.
  2. Digital trace of human interactions (could be content-free) such as email exchange, digital communications (instant message), interaction generated by project management apps (working on the same task), meeting calendar, data from collaboration apps (Lark, DingDing, Enterprise Wechat, etc.)
  3. Text-based data such as internal reviews, promotion reviews, enterprise intranet posts and discussion boards, contents of communications, and idea discussions.
  4. Business operation and workflow-related data such as idea submission system, voice system, project management process.
  5. Employee generated data such as weekly reports, objective and key results (OKR, a performance management system commonly used in high tech companies), login data, facial recognition.
  6. Video and audio recordings.

Schedule and Timeline

  1. Preliminary Proposal (deadline Nov 30, 2021): The preliminary proposal should specify the targeted data source, format, develop intended research questions, and justify the motivation. The initial proposal should be within five pages (single-spaced). Please submit proposals to Ning Li ( with the subject line: ‘MOR New Technology of OB/HRM in China Proposal’.
  2. Proposal Development Workshop (location and time TBD): Accepted proposals to be invited to a developmental workshop to further refine the focused research questions based on the secured data source. At the end of the workshop, we will extend invitations to some promising proposals to submit a revised version of the proposal.
  3. Revised Proposal (deadline Jan 31, 2022): The revised proposal should detail the data, structure, describe intended methods, and develop the focused research questions (a quick decision will be made to invite for the full submission).
  4. Paper Submission Deadline (Aug 31, 2022). Please submit full papers via the MOR submission website:
  5. Paper Development Workshop for R&R papers (location TBD): Further revise the paper with known results and findings
  6. Submit Final Paper
  7. Publication of the special issue (TBD)


Borau, S., Otterbring, T., Laporte, S., & Fosso Wamba, S. 2021. The most human bot: Female gendering increases humanness perceptions of bots and acceptance of AI. Psychology & Marketing. Advanced online publication.

Connelly, C. E., Fieseler, C., Černe, M., Giessner, S. R., & Wong, S. I. 2021. Working in the digitized economy: HRM theory & practice. Human Resource Management Review31(1). doi:10.1016/j.hrmr.2020.100762

Corritore, M., Goldberg, A., & Srivastava, S. B. 2020. Duality in diversity: How intrapersonal and interpersonal cultural heterogeneity relate to firm performance. Administrative Science Quarterly65(2): 359–394.

Fuchs, C., Sting, F. J., Schlickel, M., & Alexy, O. 2019. The ideator’s bias: How identity-induced self-efficacy drives overestimation in employee-driven process innovation. Academy of Management Journal62(5): 1498–1522.

Glikson, E. & Woolley, A. W. 2020. Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals14(2): 627–660.

Goh, K. T. & Pentland, B. T. 2019. From actions to paths to patterning: Toward a dynamic theory of patterning in routines. Academy of Management Journal62(6): 1901–1929.page5image18607168page5image18597568

Jackson, J. C., Castelo, N., & Gray, K. 2020. Could a rising robot workforce make humans less prejudiced? American Psychologist, 75(7): 969–982.

Jiang, L., Yin, D., & Liu, D. 2019. Can joy buy you money? The impact of the strength, duration, and phases of an entrepreneur’s peak displayed joy on funding performance. Academy of Management Journal62(6): 1848–1871.

Kellogg, K. C., Valentine, M. A., & Christin, A. 2020. Algorithms at work: The new contested terrain of control. Academy of Management Annals14(1): 366–410.

Knight, A. P. 2018. Innovations in unobtrusive methods. In Bryman, A. & Buchanan, D. A. (Eds.) Unconventional methodology in organization and management research: 64–82. United Kingdom: Oxford University Press.

Lanzolla, G., Lorenz, A., Miron-Spektor, E., Schilling, M., Solinas, G., & Tucci, C. L. 2020. Digital transformation: What is new if anything? Emerging patterns and management research. Academy of Management Discoveries6(3): 341–350.

Larson, L. & Dechurch, L. A. 2020. Leading teams in the digital age: Four perspectives on technology and what they mean for leading teams. The Leadership Quarterly31(1).

Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. 2019. The challenges of algorithm-based HR decision-making for personal integrity. Journal of Business Ethics160(2): 377–392.

Lindebaum, D., Vesa, M., & Hond, F. D. 2020. Insights from ‘the machine stops’ to better understand rational assumptions in algorithmic decision making and its implications for organizations. Academy of Management Review45(1): 247–263.

Liu, M., Brynjolfsson, E. & Dowlatabadi, J. 2021. Do digital platforms reduce moral hazard? The case of Uber and Taxis. Management Science. Advanced online publication.

Newman, D. T., Fast, N. J., & Harmon, D. J. 2020. When eliminating bias isn’t fair: Algorithmic reductionism and procedural justice in human resource decisions. Organizational Behavior and Human Decision Processes160: 149–167.

Parent-Rocheleau, X. & Parker, S. K. 2021. Algorithms as work designers: How algorithmic management influences the design of jobs. Human Resource Management Review. Advanced online publication.

Parker, S. K. & Grote, G. 2020. Automation, algorithms, and beyond: Why work design matters more than ever in a digital world. Applied Psychology. Advanced online publication.

Raisch, S. & Krakowski, S. 2021. Artificial intelligence and management: The automation– augmentation paradox. Academy of Management Review46(1): 192–210.

Ranganathan, A. & Benson, A. 2020. A numbers game: Quantification of work, auto- gamification, and worker productivity. American Sociological Review85(4): 573–609.

Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. 2021. Artificial intelligence, robotics, advanced technologies and human resource management: A systematic review. The International Journal of Human Resource Management.

Advanced online publication. Yam, K. C., Bigman, Y. E., Tang, P. M., Ilies, R., De Cremer, D., Soh, H., & Gray, K. 2020.

Robots at work: People prefer—and forgive—service robots with perceived feelings. Journal of Applied Psychology. Advanced online publication.