Developments in HR Research: Novel Data and Methodological Advances


The aim of this Special Issue is to develop creative scholarship and promote scientific progress in HR research by encouraging the use of research methods that are aligned with HR issues in the real world. Advanced research methods such as longitudinal designs, multi-level modelling and other advanced, methodological settings (adopted/derived from other social science disciplines) have become well established in fields such organizational behavior, sociology and economics, but they have not yet diffused widely into HR research.

 For example, institutional embeddedness speaks to the concept that workplace interactions are also influenced by the formal and informal rules that govern power relations between employers and employees and interactions among employees. Governance structures constitute the settings in which employees weigh alternatives and make decisions concerning the duration and timing of efforts expended for the organization. The content of an organization’s governance structures is evident in HRM policies. Elements of formal governance structures are HR practices and policies such as remuneration policies and performance management policies. It is thus easy to discern that institutional embeddedness requires multi-level research that takes into account the context of the research. There is indeed a growing recognition of the multi-level nature of social phenomena among social scientists of various disciplinary backgrounds. The basic problem of ignoring the multi-level structure of organizations in the HR research domain is the misspecification of the measured level in comparison to the theoretical level. It follows that it important to consider the level of measurement, as attributing individual data to the organization or attributing organizational data to the individual has an impact on the construct validity of the research. For recent examples utilizing and pointing at the importance of multi-level modeling, please see e.g.: Meyer, Li and Schotter (2020); Nam and Lee (2018); Shipton, Sparrow, Budhwar, and Brown (2017).

 In a similar vein, much of the existing empirical work on identifying the antecedents of employees’ intangible attributes (e.g. motivation; enthusiasm; gratification; satisfaction) is based on the assumption that such antecedents are inputs that influence the attribute as an output variable. In such cases, attributes are predicted to reach a certain level, which is determined by the availability of inputs. However, availability alone is arguably not necessarily a good predictor, yet examining the efficient utilization of inputs remains greatly ignored. This point has attracted very limited attention in the extant literature and the gap could conceivably be narrowed by employing methods derived from the scholarship in operational research which is known to examine resource efficiency parameters (see, for example, the work by Poggi (2010), utilizing stochastic frontier methods in a job satisfaction context).

Furthermore, innovative methods are encouraged to advance the understanding of systemic inequality and diversity in the workplace. Diverse groups of employees may have various experience of policies, programs and initiatives at individual, interpersonal, and social structural levels. Identity factors such as gender, race, ethnicity, religion, age, and mental or physical disability may interact with each other, rendering joint effects on people’s experience and work outcomes. As a result, it is important for HR researchers to acknowledge the importance of intersecting identities, develop research with such an intersectionality perspective, and adopt innovative research approaches to deal with these methodological challenges.

Finally, we note that research methods developed in different social science fields (behavioral experiments, survey, archived data, machine learning and Big Data, etc.) each contribute to the understanding of important HR questions, and yet may have their own limitations regarding method biases. Therefore, it is important to recognize the interconnection among methods in different fields and seek the synergy of adopting various methods to understand the important questions in the HR fields. Recent contributions in this context include e.g.: Garg, Sinha, Kar, and Mani (2021); Malik, Budhwar, and Srikanth (2020).

Against the above background, this Special Issue aims at enhancing creative scholarship and promoting scientific progress in HR research by encouraging the adoption of research methods that are aligned with HR issues in the real world. We hope that it helps with the advancement of the HR research field by exploring the relevance of the improvement and expansion of data collection and analytic techniques to the development of new theories and the real problems of organizations.

List of topic areas:

  • 1. Research studies in HR that align research methods with the nature of organizational settings in the HR research. For example, the multi-level structure of organizations makes it necessary to consider the alignment between the theoretical level and the measurement levels in the HR research domain.
  • 2. Research studies that use novel data in the examination of HR research questions, such as new methods of collecting data and an integration of different theoretical frames, data sources and data types. 
  • 3. Research studies that address HR issues with an integration of research methodologies across different social science fields such as management, economics, psychology, and sociology.
  • 4. Research studies that address the intersectionality of employee identities with innovative research methodologies.

Submissions Information:
Submissions are made using ScholarOne Manuscripts. Registration and access are available at:

Author guidelines must be strictly followed. Please see:

Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to “Please select the issue you are submitting to”.

Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.

Key deadlines: 
Opening date for manuscripts submissions: 25/01/2022

Closing date for manuscripts submission: 31/07/2022                                                          

Email for submissions:
Dr. Lin Xiu, [email protected];
Dr. Thomas Lange, [email protected]    

Garg, S., Sinha, S., Kar, A.K. and Mani, M. (2021), "A review of machine learning applications in human resource management", International Journal of Productivity and Performance Management, Vol. ahead-of-print No. ahead-of-print.

Malik, A., Budhwar, P. and Srikanth, N.R. (2020), "Gig Economy, 4IR and Artificial Intelligence: Rethinking Strategic HRM", Kumar, P., Agrawal, A. and Budhwar, P. (Ed.) Human & Technological Resource Management (HTRM): New Insights into Revolution 4.0, Emerald Publishing Limited, Bingley, pp. 75-88.

Meyer, K.E., Li, C. and Schotter, A.P.J. (2020), “Managing the MNE subsidiary: Advancing a multi-level and dynamic research agenda”, Journal of International Business Studies, Vol. 51, pp. 538–576.

Nam, J. and Lee, H. (2018), “High commitment human resource practices and employee behavior: a multi-level analysis”, International Journal of Manpower, Vol. 39 No. 5, pp. 674-686.

Poggi, A. (2010), “Job satisfaction, working conditions and aspirations”, Journal of Economic Psychology, Vol. 31, pp. 935–949.

Shipton, H., Sparrow, P., Budhwar, P., and Brown, A. (2017), “HRM and innovation: looking across levels”, Human Resource Management Journal, Vol. 27, pp. 246– 263.