Factors Affecting the Liquid Workforce in Different Organizations and its Effectiveness
DOI:
https://doi.org/10.17010/pijom/2019/v12i4/143348Keywords:
Adaptability
, Digital Technology, Disruption, Flexi-Time, Integration, Liquid Workforce, Multi-Skilled, People First, Workforce Agility.JEL Classification
, M100, M140, 0150Paper Submission Date
, May 21, 2018, Paper sent back for Revision, December 5, Paper Acceptance Date, February 20, 2019Abstract
Background: Ushering in the edge of digital economy has instituted changes in different spheres of people management. Understanding the importance of talent management and the adaptability of employees to various functional domains of the business, corporate houses slowly brought about infusing inescapable changes to keep abreast with the rhythm of future changes.
Purpose: The purpose of this study was to determine the relative importance of certain factors that enhance the efficiency of liquid workforce in improving the overall performance of the organization, and it elaborated a practical approach of deploying liquid workforce in improving organizational economy.
Design/Methodology: Researchers used descriptive research with cross-sectional design. The study used multiple regression analysis to get the results which were collected from different organizations.
Findings: We found from the survey that four factors were mainly influencing liquid workforce and these are : organizations seeking creativity, virtual organizations, flexi-time, and multi-skilled workers, with the last one being the most significant.
Research Limitations: This study was conducted in Kolkata with a sample size of 200 respondents taken only from IT, ITES, and telecom organizations. The term 'liquid workforce' is quite new, and respondents had some hesitation during answering questions regarding the same.
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References
Accenture. (2016). People first : The primacy of people in a digital age. Retrieved from https://www.accenture.com/in-en/insight-trends-insurance-technology-vision-2016-infographic
Awasthi, S. (2018). Study on the role of HRM in creativity and innovation with special reference to Indian organizations: A case study. Journal of Business Management and Social Science Research, 7 (2), 18-25.
Azuara, V., & Alejandro, M. S. (2015). A human resource perspective on the development of workforce agility. California: Pepperdine University. ProQuest LLC.
Bergson, H. (1911). The evolution of life-mechanism and teleology. In Creative evolution (pp. 1-97) (A. Mitchell, Trans.). New York : Henry Holt and Company.
Betchoo, N. K. (2016). An insight into the practice of e-government a road map for contemporary public administration. International Journal of Trend in Research and Development, 3 (2), 526-530.
Brynjolfsson, E., Hitt, L. M., & Kim, H. H. (2011). Strength in numbers: How does data driven decision making affect firm performance ? Global Social Service Workforce Alliance. Retrieved from http://www.socialserviceworkforce.org/resources/strength-numbers-how-does-data-driven-decision-making-affect-firm-performance
Daly, K. A. (1997). Managing the contingent work force : Lessons for success. Industrial Relations Centre. Kingston, Ontario, Canada : Queen’s University. Retrieved from http://cxcglobal.asia/wp-content/uploads/2015/06/Queen%E2%80%99s-University-Managing-the-Contingent-Work-Force-Lessons-for-Success.pdf
Dyne, L. V., & Soon A. (1998). Organizational citizenship behavior of contingent workers in Singapore. The Academy of Management Journal, 41 (6), 692-703. http://dx.doi.org/10.2307/256965
Gupta, M. (2017). Engaging employees at work: Insights from India. Advances in Developing Human Resources, 20(1), 3-10. https://doi.org/10.1177/1523422317741690
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis (7th edition). New Jersey : Pearson.
Hogan, T. P., Benjamin, A., & Brezinski, K. L. (2000). Reliability methods: A note on the frequency of use of various types. Educational and Psychological Measurement, 60, 523-531. https://doi.org/10.1177/00131640021970691
Iacobucci, D. (Ed.). (2001). Methodological and statistical concerns of the experimental behavioral researcher [Special issue]. Journal of Consumer Psychology, 10(1-2), 1-121. http://dx.doi.org/10.1207/S15327663JCP1001&2_01
Itegi, F. M. (2015). Improving organization performance: Project management approach sustainable in the field of globalization. Journal of Entrepreneurship and Organization Management, 4(3), 1-6. http://dx.doi.org/10.4172/2169-026X.1000155
James, F., & Sudha, S. (2017). Moderating effect of work place support on the social life of night shift employees. Prabandhan: Indian Journal of Management, 10 (6), 49-56. https://doi.org/10.17010/pijom/2017/v10i6/115375
Kam, C., Morin, A. J. S., Meyer, J. P., & Topolnytsky, L. (2013). Are commitment profiles stable and predictable? A latent transition analysis. Journal of Management, 42 (6), 1462-1490. doi: https://doi.org/10.1177/0149206313503010
Kothari, C. R. (2003). Research methodology: Methods and techniques (2nd ed.). Delhi : New Age International.
Kumar, H., & Raghavendran, S. (2015). Gamification, the finer art: Fostering creativity and employee engagement. Journal of Business Strategy, 36 (6), 3-12. doi: https://doi.org/10.1108/JBS-10-2014-0119
Lengnick-Hall, C. A. (1986). Technology advances in batch production and improved competitive position. Journal of Management, 12 (1), 75-90. https://doi.org/10.1177/014920638601200107
MacKenzie, R. (2008). Why would contingent workers join a trade union? Union responses to restructuring and the organization of contingent workers in the Irish telecommunications sector (Working Paper No. 2). Centre for Employment Relations Innovation and Change. UK: Leeds University Business School. Retrieved from https://business.leeds.ac.uk/fileadmin/public/Research/Research_Centres/CERIC/Publications/WP2_MacKenzie.pdf
Mittal, K., Singh, K., & Sharma, G. (2017). Work-life balance and employee health: A cross-sectional analysis of manufacturing and service sectors. Prabandhan: Indian Journal of Management, 10 (7), 34-49. https://doi.org/10.17010/pijom/2017/v10i7/116493
Nunnally, J. C. (1978). Psychometric theory. NY : McGraw – Hill.
Osborne Clarke. (2017). The future of work-Contingent workers and new employment models. Retrieved from https://www.osborneclarke.com/wp-content/uploads/2017/11/OC-The-Future-of-Work-Contingent-workers-8pp-A4-36099747-SOFT.pdf
Pandita, D., & Bedarkar, M. (2015). Factors affecting employee performance: A conceptual study on the drivers of employee engagement. Prabandhan: Indian Journal of Management, 8 (7), 29-40. https://doi.org/10.17010/pijom/2015/v8i7/72347
Pedulla, D. S. (2011). The hidden costs of contingency: Employers’ use of contingent workers and standard employees’ outcomes (Working Paper 6). Retrieved from https://csso.princeton.edu/file/236/download?token=vSmBdKQw
Peterson, R. A. (1994). A meta-analysis of Cronbach’s coefficient alpha. Journal of Consumer Research, 21 (2), 381-391. https://doi.org/10.1086/209405
Sidibe, M., & Campbell, J. (2015). Reversing a global health workforce crisis. Bulletin of the World Health Organization, 93(1), 3. http://dx.doi.org/10.2471/BLT.14.151209
Zimmerman, T., Gavrilova-Aguilar, M., & Cullum, P. (2013). Rethinking human resource strategies: A shift in the treatment of contingent workers. International Journal of Business and Management, 8 (7), 28-34. http://dx.doi.org/10.5539/ijbm.v8n7p28