Call for Papers: Special Issue on The Evolution of Organizational & HRM Practices: Big Data, Data Analytics, and New Forms of Work

[EURAM 2020 – Track on Big Data, Data Analytics and new forms of Work: Implications for individual and organizational level behaviour, attitudes and value creation]

Guest Editors:

Fabian Homberg, Associate Professor, Department of Business & Management, LUISS University, Rome, Italy
Yumei Yang, Lecturer HR & Organisational Behaviour, Bournemouth University, UK
Saqib Shamim, Lecturer Innovation and Entrepreneurship, University of Kent, UK
Dinuka B. Herath, Senior Lecturer in Organization Studies, Department of Management, University of Huddersfield Business School, UK
Davide Secchi, Director, Research Centre for Computational & Organisational Cognition, Associate Professor of Organisational Cognition, University of Southern Denmark

Submission deadline: 31st March 2021

Springer’s “Review of Managerial Science” (RMS) [Impact Factor: 3.0; VHB JQ3 “B”; ABS: 2*] will publish a special issue on “The evolution of organizational & HRM practices: Big data, data analytics, and new forms of work”.

The special issue is associated with the EURAM 2020 track on the same topic, but open for submissions from all scholars. Papers accepted by and presented at EURAM 2020, Track T09_05 – Big Data, Data Analytics and New Forms of Work will have preferred/accelerated access to the second review round, although the special issue will also be open for papers not presented at the conference. For papers submitted to and accepted by EURAM 2020, T09_05 – Big Data, Data Analytics and New Forms of Work, the two reviews for the conference track will be considered as the first round of reviews.

In today’s digital economy, big data and new forms of work (e.g., Industry 4.0) has provided many opportunities for organizations to make informed decisions based on the volume, variety and velocity of data collected from customers, competitors and market (Levit, 2018). It is believed that, in a modern environment, big data is the source of organizations’ innovation, productivity and competitiveness (Ghasemaghaei and Calic, 2020). However, organizations often denounce a lack of talent, manifested in the difficulty to find people with the right skills in both areas of data analytic and management. As a result, value creation through (big) data requires management capabilities such as leadership, training, talent management, employee upskilling and the creation of an evidence-based, data-driven culture (Mcafee et al., 2012; Shamim et al., 2019a).

Furthermore, fast paced changes in technology, computing and communication facilitate changes in the work environment (Mcafee et al., 2012), often referred to as Industry 4.0 (Shamim et al., 2019a, b). Many firms have introduced analytics based on big data in order to increase efficiency, the hope being that the quality of their decisions enables change and enhances agility (Angrave et al., 2016; Shamim et al., 2018). Others use leaps in IT development for the implementation of artificial intelligence and automated cognitive systems such as intelligent and self-learning autonomous agents thereby trying to leverage efficiencies arising from human-machine interactions. Many of these technologies are said to be performance-enhancing at the firm level (Zeng and Glaister, 2018), with beneficial effects on knowledge creation (Pauleen, & Wang, 2017; Khan et al., 2017), innovation (Chae, 2019) and decision making (Acharya et al. 2019). However, the impact of big data applications on employees is not well explored yet, neither do we know if the workforce has received sufficient levels of training to use new work technologies in a responsible way.

As a result, many of these technology-enabled work practices have unknown consequences on health and wellbeing of employees, their willingness to embrace change, and other work relevant outcomes at the employee and organizational level (Shah et al., 2017). Furthermore, the continuous collaborative exchange between human beings and automated-cognitive systems increasingly substitutes human face-to-face experience. For example, sole reliance on data for decision-making might hurt the self-esteem of highly skilled and experienced employees. As a result, human-machine interactions shape the daily experiences of employees and thus become an essential determinant in the employment relationship. This fact poses completely new challenges and problems that HRM professionals need to master. All these developments boost the changes to work 4.0, have a long-lasting impact on the work of HR professionals and traditional HR practices which need to be understood.

Thus, the main objective of the proposed Special Issue is to provide a space for the dissemination of empirical and theoretical insights on how the challenges, issues, concerns, and opportunities generated by Work 4.0 can be handled. The Special Issue aims at developing an understanding of where current theories fall short of capturing the new scenario and need to be extended, changed, or dropped. A particular focus should be given to individual level attitudes, behaviors, associated HRM practices and organizational level outcomes that shape day-to-day working lives.

The results of the research featured in the special issue will provide evidence-based guidelines for HRM practitioners with regard to enhancing the employment relationship and the well-being of employees in their workplaces.

The findings will enhance our understanding on how the division of roles between machines and humans can be designed such that it enables productive collaborative processes and preserves well-being and self-determination while enabling higher organizational performance. Thus, in offering a platform where researchers present latest insights on Big Data, Data Analytics and New Forms of Work, this special issue acts as a crossfertilizer and catalyst of the management of new technologies for the enhancement of HRM.

Against this background, we are especially looking for papers that explore relevant areas of interest. Possible themes include (non-exhaustive list):

1) analyses of the effects of increased usage of big data analytics, and technology driven work practices on different types of work attitudes

2) analyses of the link between technology-driven employee related decision making with respect to both individual and organizational performance

3) the set of institutional, contextual and organizational factors that may influence the development or the inhibition of new forms of work, or new data driven work practices

5) studies on potentially negative consequences of new forms of work, data driven decision making on employees, such as e.g. increased stress, impacts on general well-being, taking charge behaviors organizational commitment, OCB, leadership, and trust

7) effects and best practice approaches to human-machine interactions for enabling performance and maintaining employee well-being

8) effects of new forms of work on organizational cognition, organizational learning and knowledge management.

9) role of HR practices in big data management and capabilities development

10) factors affecting digital readiness of organizations and employees to harness big data for value creation

We welcome all methodological approaches, i.e. quantitative (incl. experiments and simulations), qualitative, and conceptual works. Submitted manuscripts need to be original submissions, not under review anywhere else and must not have been published previously. All submitted manuscripts must strictly adhere to RMSC’s general submission guidelines.

Participation in the conference itself or acceptance of a paper for presentation does not guarantee that the authors will be selected for the RMSC special issue.

Submission Deadlines & Instructions:

The submission deadline is 31st of March 2021.

Full papers should be submitted using the online submission system which can be accessed here: https://www.editorialmanager.com/rmsc/default.aspx

Please choose the “Special Issue” option during the submission process and select this special issue.

RMS manuscripts should be not longer than 8000 words including references, tables, figures and other material. Detailed author instruction can be accessed here:

https://www.springer.com/journal/11846/submission-guidelines?IFA

Planned publishing date of the print issue in 2022, although accepted papers will of course already appear “Online First” with a DOI after acceptance.


References

Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1-11.

Acharya, A., Singh, S. K., Pereira, V., & Singh, P. (2018). Big data, knowledge co-creation and decision making in fashion industry. International Journal of Information Management, 42, 90-101.

Chae, B. 2019, A General framework for studying the evolution of the digital innovation ecosystem: The case of big data, International Journal of Information Management, 45(2019), 83-94.

Ghasemaghaei, M. and Calic, G. (2020) ‘Assessing the impact of big data on firm innovation performance: Big data is not always better data’, Journal of Business Research. Elsevier, 108(November 2019), pp. 147–162. doi: 10.1016/j.jbusres.2019.09.062.

Khan, Z., & Vorley, T. (2017). Big data text analytics: an enabler of knowledge management. Journal of Knowledge Management, 21(1), 18-34.

Levit, A. (2018). Humanity Works: Merging Technologies and People for the Workforce of the Future. Kogan Page Publishers.

McAfee, A., Brynjolfsson, E., & Davenport, T. H. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.

Pauleen, D. J., & Wang, W. Y. (2017). Does big data mean big knowledge? KM perspectives on big data and analytics. Journal of Knowledge Management, 21(1), 1-6.

Shah, N., Irani, Z., & Sharif, A. M. (2017). Big data in an HR context: Exploring organizational change readiness, employee attitudes and behaviors. Journal of Business Research, 70, 366-378.

Shamim, S., Zeng, J., Choksy, U. and Shariq, M. (2019a). Connecting Big Data Management Capabilities with Employee Ambidexterity in Chinese Multinational Enterprises Through the Mediation of Big Data Value Creation at the Employee Level. International Business Review[Online]. Available at: https://doi.org/10.1016/j.ibusrev.2019.101604

Shamim, S., Cang, S., & Yu, H. (2019b). Impact of knowledge oriented leadership on knowledge management behaviour through employee work attitudes. The International Journal of Human Resource Management, 30(16), 2387-2417.

Shamim, S. et al. (2018). Role of big data management in enhancing big data decision-making capability and quality among Chinese firms: A dynamic capabilities view. Information & Management [Online]. Available at: https://doi.org/10.1016/j.im.2018.12.003.

Zeng, J., & Glaister, K. W. (2018). Value creation from big data: Looking inside the black box. Strategic Organization, 16(2), 105-140.