Artificial Intelligence (AI) has emerged at a fast pace as a technology reshaping multifacted systems of the environment, namely economies, organizations and industries (Brynjolfsson & McAfee, 2017; Jarrahi, 2018). Beyond automation, AI has filled in areas such as decision-making, innovation processes, designing workforce and customer engagement (Davenport & Ronanki, 2018). While adoption of AI is often related to technological capability, there prevails scholarly argument pertaining to successful adoption which is human: adaptability of the workforce (Wilson & Daugherty, 2018; Vodanovich et al., 2018).
Future work readiness has gained attention in the recent past, as many organizations navigate AI driven change. As per (Frey & Osborne, 2017; Clarke, 2017), future work readiness refers to the readiness of employees and organizations to adapt to evolving technological enhancements, skill development, and sustain performance even under incertain situations. It was well said by (Spreitzer et al., 2017; Luthans et al., 2021) that being ready is not just competence development but most importantly, on psychological adaptability, resilient, and overall organizational culture.
Despite its importance and relevance, current scholarship remains disintegrated. There seems to a visible gap where AI adoption studies quite often omit the human adaptability dimension to it, while research on adaptability and readiness do not integrate with technological game changers. This gap is identified as a conceptual one to observe how AI adoption and human adaptability of it can interact to shape future work readiness.
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Baxter, G., & Sommerville, I. (2011). Socio-technical systems: From design methods to systems engineering. Interacting with computers, 23(1), 4-17.
Bessen, J. (2018). Artificial intelligence and jobs: The role of demand. In The economics of artificial intelligence: an agenda (pp. 291-307). University of Chicago Press.
Bogg, T. (2017). Social media membership, browsing, and profile updating in a representative US sample: independent and interdependent effects of big five traits and aging and social factors. Frontiers in psychology, 8, 1122.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77-101.
Bughin, J., Seong, J., Manyika, J., Chui, M., & Joshi, R. (2018). Notes from the AI frontier: Modeling the impact of AI on the world economy. McKinsey Global Institute, 4(1), 2-61.
Chan, D. (Ed.). (2014). Individual adaptability to changes at work: New directions in research.
Clarke, M. (2018). Rethinking graduate employability: The role of capital, individual attributes and context. Studies in higher education, 43(11), 1923-1937.
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard business review, 96(1), 108-116.
Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation?. Technological forecasting and social change, 114, 254-280.
Fugate, M., Kinicki, A. J., & Ashforth, B. E. (2004). Employability: A psycho-social construct, its dimensions, and applications. Journal of Vocational behavior, 65(1), 14-38.
Griffin, B., & Hesketh, B. (2003). Adaptable behaviours for successful work and career adjustment. Australian Journal of psychology, 55(2), 65-73.
Heijde, C. M. V. D., & Van Der Heijden, B. I. (2006). A competence‐based and multidimensional operationalization and measurement of employability. Human Resource Management: Published in Cooperation with the School of Business Administration, The University of Michigan and in alliance with the Society of Human Resources Management, 45(3), 449-476.
Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the academy of marketing science, 49(1), 30-50.
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business horizons, 61(4), 577-586.
Liao, C., Wayne, S. J., & Rousseau, D. M. (2016). Idiosyncratic deals in contemporary organizations: A qualitative and meta‐analytical review. Journal of organizational behavior, 37, S9-S29.
Luthans, F., Youssef-Morgan, C. M., & Avolio, B. J. (2015). Psychological capital and beyond. Oxford university press.
Martin, A. J., Nejad, H., Colmar, S., & Liem, G. A. D. (2012). Adaptability: Conceptual and empirical perspectives on responses to change, novelty and uncertainty. Australian Journal of Guidance and Counselling, 22(1), 58-81.
McAfee, A., & Brynjolfsson, E. (2017). Machine, platform, crowd: Harnessing our digital future. WW Norton & Company.
Munyon, T. P., Summers, J. K., & Ferris, G. R. (2011). Team staffing modes in organizations: Strategic considerations on individual and cluster hiring approaches. Human Resource Management Review, 21(3), 228-242.
Parkes, L. P., & Langford, P. H. (2008). Work–life bal ance or work–life alignment? A test of the importance of work-life balance for employee engagement and intention to stay in organisations. Journal of management & organization, 14(3), 267-284.
Pulakos, E. D., Arad, S., Donovan, M. A., & Plamondon, K. E. (2000). Adaptability in the workplace: development of a taxonomy of adaptive performance. Journal of applied psychology, 85(4), 612.
Sarker, S., Ahuja, M., & Sarker, S. (2018). Work–life conflict of globally distributed software development personnel: An empirical investigation using border theory. Information Systems Research, 29(1), 103-126.
Savickas, M. L., & Porfeli, E. J. (2012). Career Adapt-Abilities Scale: Construction, reliability, and measurement equivalence across 13 countries. Journal of vocational behavior, 80(3), 661-673.
Shrestha, Y. R., Ben-Menahem, S. M., & Von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California management review, 61(4), 66-83.
Spreitzer, G. M., Cameron, L., & Garrett, L. (2017). Alternative work arrangements: Two images of the new world of work. Annual Review of Organizational Psychology and Organizational Behavior, 4, 473-499.
Tarafdar, M., Beath, C. M., & Ross, J. W. (2017). Enterprise cognitive computing applications: Opportunities and challenges. It Professional, 19(4), 21-27.
Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the longwall method of coal-getting: An examination of the psychological situation and defences of a work group in relation to the social structure and technological content of the work system. Human relations, 4(1), 3-38.
Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial intelligence and the public sector—applications and challenges. International journal of public administration, 42(7), 596-615.
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