THE INFLUENCE OF AI-DRIVEN SUSTAINABLE HUMAN RESOURCE MANAGEMENT ON EMPLOYEE CREATIVE PERFORMANCE: ANALYZING IDIOSYNCRATIC DEALS IN THE INDIAN INFORMATION TECHNOLOGY SECTOR

Autori

  • Karthikeyan Thangaraju Research scholar full time, Faculty of Management, SRM Institute of Science and Technology, Kattankulathur, SRM Nagar, Kattankulathur, Chengalpattu, India
  • Poonguzhali Palani Assistant professor, Faculty of Management, SRM Institute of Science and Technology, Kattankulathur, SRM Nagar, Kattankulathur, Chengalpattu, India

DOI:

https://doi.org/10.2478/eoik-2025-0081

Ključne reči:

AI-enabled HR practices, Employee creative performance,, Idiosyncratic deals, SEM Analysis, Indian IT industry, SPSS AMOS

Apstrakt

The research explores how AI-powered sustainable HR practices

influence employee creative performance within India’s IT sector

through the mediating role of individualized agreements. The research

applies structural equation modeling to examine survey data from 360

IT professionals based on the frameworks of the Job Demands-Re-

sources model and Social Exchange Theory. AI-based training and

performance management systems raise creative performance levels

and show that ideals partially mediate these relationships. The re-

search results reveal contextual differences because ideals mediate

recruitment effects and performance management outcomes but show

no significant mediation for training interventions, likely because of

the sector’s inclination toward standardized learning approaches. The

research delivers significant theoretical advancements by analysing

AI-HRM systems in emerging economies and exploring personal

work arrangements’ limits in tech-heavy settings. These insights serve

as essential guidance for practitioners deploying HR technologies that

successfully combine standardization with personalization to promote

workplace innovation. The research reveals surprising results about

the minimal direct influence of sustainability orientation. The research

advocates for integrated strategies to synchronize sustainability initia-

tives with innovation objectives within India’s IT sector.

 

Reference

Agrawal, A., Gans, J., & Goldfarb, A. (2017). What to expect from artificial intelligence. MIT Sloan

Management Review, 58(3), 23-26. http://mitsmr.com/2jZdf1Y

Aguinis, H. (2022). Performance management (5th ed.). Chicago Business Press.https://tuannguhanhson.

com/uploads/download/2019_10/herman_aguinis_performance_managementz-lib.org.pdf

Aguinis, H., & Glavas, A. (2012). What We Know and Don’t Know About Corporate Social Respon-

sibility: A Review and Research Agenda. Journal of Management, 38(4), 932-968. https://doi.

org/10.1177/0149206311436079

Amabile, T. M. (2018). Creativity in context: Update to the social psychology of creativity. Routledge.

Amabile, T.M. (1996). Creativity In Context: Update To the Social Psychology Of Creativity

(1st ed.). Routledge. https://doi.org/10.4324/9780429501234

Anand, S., Vidyarthi, P. R., Liden, R. C., & Rousseau, D. M. (2010). Good citizens in poor-quality rela-

tionships: Idiosyncratic deals as a substitute for relationship quality. Academy of Management

Journal, 53(5), 970–988. https://doi.org/10.5465/AMJ.2010.54533176

Bakker, A. B., & Demerouti, E. (2017). Job demands-resources theory: Taking stock and looking for-

ward. Journal of Occupational Health Psychology, 22(3), 273–285. https://doi.org/10.1037/

ocp0000056

Balasubramanian, N., Ye, Y., & Xu, M. (2020). Substituting Human Decision-Making with Machine

Learning: Implications for Organizational Learning. Academy of Management Review, 47(3),

–465. https://doi.org/10.5465/amr.2019.0470

Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1),

–120. https://doi.org/10.1177/014920639101700108

Barrett, P. (2007). Structural equation modelling: Adjudging model fit. Personality and Individual Dif-

ferences, 42(5), 815–824. https://doi.org/10.1016/j.paid.2006.09.018

BCG. (2023). The future of work in India’s IT sector. Boston Consulting Group. https://web-assets.bcg.

com/8c/b7/35b5c1ca4948990d8866c707cda1/bcg-nasscom-future-of-work-report.pdf

Bekiros, S. (2024). A Hypergame Model of Conflict Cusp Helix for Noncooperative Boundedly Rational

Multi-Polar Actors Using Deep Adversarial Reinforcement Learning and Misperception Infor-

mation Strategies. ECONOMICS - Innovative and Economics Research Journal, 13(3). https://

doi.org/10.2478/eoik-2025-0010

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2),

–246. https://doi.org/10.1037/0033-2909.107.2.238

Black, J. S., & van Esch, P. (2020). AI-enabled recruiting: What is it, and how should a manager use it?

Business Horizons, 63(2), 215–226. https://doi.org/10.1016/j.bushor.2019.12.001

Blau, P. M. (2017). Exchange and power in social life. In Routledge eBooks. https://doi.

org/10.4324/9780203792643

Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. Sociological Methods &

Research, 21(2), 230–258. https://doi.org/10.1177/0049124192021002005

Budhwar, P., Malik, A., De Silva, M. T. T., & Thevisuthan, P. (2022). Artificial intelligence – challenges

and opportunities for international HRM: a review and research agenda. The International Journal

of Human Resource Management, 33(6), 1065–1097. https://doi.org/10.1080/09585192.2022.203

Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and pro-

gramming (3rd ed.). Routledge. https://doi.org/10.4324/9781315757421

Cappelli, P., & Tavis, A. (2022). HR goes agile. Harvard Business Review, 96(2), 46–52. https://hbr.

org/2018/03/hr-goes-agile

Cropanzano, R., & Mitchell, M. S. (2005). Social exchange theory: An interdisciplinary review. Journal

of Management, 31(6), 874–900. https://doi.org/10.1177/0149206305279602

The Influence of Ai-Driven Sustainable Human Resource Management on Employee Creative Performance:

Analyzing Idiosyncratic Deals in The Indian Information Technology Sector

De Prins, Peggy & Van Beirendonck, Lou & Vos, Ans & Segers, Jesse. (2014). Sustainable HRM: Bridg-

ing theory and practice through the ‘Respect Openness Continuity (ROC)’-model. Management

revue. 25. 263-284. https://doi.org/10.1688/mrev-2014-04-Prins

Deloitte. (2023). Global human capital trends 2023: The rise of the social enterprise. Deloitte Insights.

https://www.deloitte.com/us/en/insights/topics/talent/human-capital-trends/2023.html

Diamantopoulos, A., & Siguaw, J. A. (2000). Introducing LISREL. London: Sage Publications. https://

doi.org/10.4135/9781849209359

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, Phone, Mail, and Mixed-Mode Surveys:

The Tailored Design Method, 4th Edition. (n.d.). Wiley.com. https://www.wiley.com/en-fr/In-

ternet%2C+Phone%2C+Mail%2C+and+Mixed-Mode+Surveys%3A+The+Tailored+Design+-

Method%2C+4th+Edition-p-9781118456149

Dutta, D., Mishra, S. K., & Tyagi, D. (2022). Augmented employee voice and employee engagement

using artificial intelligence-enabled chatbots: a field study. The International Journal of Human

Resource Management, 34(12), 2451–2480. https://doi.org/10.1080/09585192.2022.2085525

Grover P., Kar A. K., Dwivedi Y. K. (2022). Understanding artificial intelligence adoption in operations

management: Insights from the review of academic literature and social media discussions. An-

nals of Operations Research, 308(1–2), 177–213. https://doi.org/10.1007/s10479-020-03683-9

Ehnert, I., Parsa, S., Roper, I., Wagner, M., & Muller-Camen, M. (2015). Reporting on sustainability and

HRM: a comparative study of sustainability reporting practices by the world’s largest compa-

nies. The International Journal of Human Resource Management, 27(1), 88–108. https://doi.or

g/10.1080/09585192.2015.1024157

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable vari-

ables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.

org/10.1177/002224378101800104

Garg, S., & Fulmer, I. (2017). Ideal or an ordeal for organizations? The spectrum of co-worker re-

actions to idiosyncratic deals. Organizational Psychology Review, 7(4), 281-305. https://doi.

org/10.1177/2041386617733136 (Original work published 2017)

Gefen, D., Straub, D., & Boudreau, M.-C. (2000). Structural equation modeling and regression: Guidelines for

research practice. Communications of the AIS, 4(7), 1–77. https://doi.org/10.17705/1CAIS.00407

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cen-

gage. https://eli.johogo.com/Class/CCU/SEM/_Multivariate%20Data%20Analysis_Hair.pdf

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares

structural equation modeling (PLS-SEM) (3rd ed.). SAGE. https://uk.sagepub.com/en-gb/eur/a-

primer-on-partial-least-squares-structural-equation-modeling-pls-sem/book270548

Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis (2nd ed.).

Guilford Press. https://www.guilford.com/books/Introduction-to-Mediation-Moderation-and-Con-

ditional-Process-Analysis/Andrew-Hayes/9781462549030?srsltid=AfmBOor4tp3aZ7vCk-

ld-5ClI9oUEGOfHqTAsiq9JnQ2vob7XWNI-6QQO

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity

in variance-based structural equation modeling. Journal of the Academy of Marketing Science,

(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Hornung, S., Rousseau, D. M., Weigl, M., Muller, A., & Glaser, J. (2014). Redesigning Work through

Idiosyncratic Deals. European Journal of Work and Organizational Psychology, 23, 608-626.

https://doi.org/10.1080/1359432X.2012.740171

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conven-

tional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org

/10.1080/10705519909540118

Jackson, Susan & Renwick, Douglas & Jabbour, Charbel & Muller-Camen, Michael. (2011). State-of-

the-Art and Future Directions for Green Human Resource Management: Introduction to the

Special Issue. Zeitschrift fuer Personalforschung. German Journal of Research in Human Re-

source Management. 25. 99-116. https://doi.org/10.1177/239700221102500203

Thangaraju K. & Palani P. / Economics - Innovative and Economics Research Journal, doi: 10.2478/eoik-2025-0081

Jia, L., & Hou, Y. (2024). AI-powered training and employee creativity: The mediating role of skill mas-

tery. Human Resource Management, 63(1), 89–105. https://doi.org/10.1002/hrm.22145

Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2015). The performance of RMSEA in models with

small degrees of freedom. Sociological Methods & Research, 44(3), 486–507. https://doi.

org/10.1177/0049124114543236

Kline, R. B. (2023). Structural equation modeling in neuropsychology research. In G. G. Brown, B. Crosson,

K. Y. Haaland, & T. Z. King (Eds.), APA handbook of neuropsychology: Neuroscience and neuro-

methods, 681–698. American Psychological Association. https://doi.org/10.1037/0000308-034

Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. Interna-

tional Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101

Kramar, R. (2022). Sustainable human resource management: six defining characteristics. Asia Pacific

Journal of Human Resources, 60(1), 146–170. https://doi.org/10.1111/1744-7941.12321

MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of

sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149. https://

doi.org/10.1037/1082-989X.1.2.130

Malik, A., De Silva, M. T. T., Budhwar, P., & Srikanth, N. R. (2021). Elevating talents’ experience

through innovative artificial intelligence-mediated knowledge sharing: Evidence from an

IT-multinational enterprise. Journal of International Management, 27(4), 100871. https://doi.

org/10.1016/j.intman.2021.100871

Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR analytics. International Journal

of Human Resource Management, 28(1), 3–26. https://doi.org/10.1080/09585192.2016.1244699

Marmad, T., & Ritahi, O. (2025). Spatial Modeling of the Impact of Human and Public Capital on Employ-

ment Convergence Between Regions in Morocco: Time Period From 2010 To 2023. ECONOMICS

- Innovative and Economics Research Journal, 13(2), 5–24. https://doi.org/10.2478/eoik-2025-0028

Marsh, C. J. (2004). Key Concepts for Understanding Curriculum. London: Falmer Press.

https://doi.org/10.4324/9780203326893

McKinsey. (2022). The future of work in India: Seizing the AI opportunity. McKinsey Global Institute.

https://www.mckinsey.com/featured-insights/future-of-work

Nader AlOqaily, A., Farid Qawasmeh, E., & Tawalbeh, J. (2025). The Effect of Implementing AI on Job Burn-

out Through the Mediating Role of Work-Life Balance in the Context of HRM. ECONOMICS - In-

novative and Economics Research Journal, 13(2), 465–484. https://doi.org/10.2478/eoik-2025-0049

NASSCOM. (2023). Strategic review of India’s IT industry: Navigating digital transformation. NASSCOM.

https://nasscom.in/knowledge-center/publications/technology-sector-india-2023-strategic-review

Peterson, R. A., & Kim, Y. (2013). On the relationship between coefficient alpha and composite reliabil-

ity. Journal of Applied Psychology, 98(1), 194–198. https://doi.org/10.1037/a0030767

Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science

research and recommendations on controlling it. Annual Review of Psychology, 63, 539–569.

https://doi.org/10.1146/annurev-psych-120710-100452

Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and compar-

ing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891.

https://doi.org/10.3758/BRM.40.3.879

PwC India. (2022). India’s IT talent landscape: Challenges and opportunities. PwC India. https://www.

pwc.in/assets/pdfs/hopes-and-fears/india-workforcehopes-and-fearssurvey-2022.pdf

Ren, Zhengyu & Hussain, Rana. (2022). A mediated–moderated model for green human resource

management: An employee perspective. Frontiers in Environmental Science. 10. https://doi.

org/10.3389/fenvs.2022.973692

Renwick, Douglas & Redman, Tom & Maguire, Stuart. (2012). Green Human Resource Management:

A Review and Research Agenda. International Journal of Management Reviews. 15. https://doi.

org/10.1111/j.1468-2370.2011.00328.x

The Influence of Ai-Driven Sustainable Human Resource Management on Employee Creative Performance:

Analyzing Idiosyncratic Deals in The Indian Information Technology Sector

Ringle, C. M., Wende, S., & Becker, J.-M. (2020). SmartPLS 3. SmartPLS GmbH. Ringle, C.M., Wende, S.

and Becker, J.M. (2015) SmartPLS 3. SmartPLS GmbH, Boenningstedt. http://www.smartpls.com

Rousseau, D. M. (2005). I-deals: Idiosyncratic deals that employees bargain for themselves. M.E.

Sharpe. https://www.routledge.com/I-deals-Idiosyncratic-Deals-Employees-Bargain-for-Them-

selves/Rousseau/p/book/9780765610430?srsltid=AfmBOoqcRKuaZenjYKOQz6cOtDPOVI-

yrRSjx2FUx2ASjKuWk74ansV_n

Sarstedt, M., Hair, J. F., & Ringle, C. M. (2022). Partial least squares structural equation modeling.

Handbook of Market Research, 1(1), 1–47. https://doi.org/10.1007/978-3-319-05542-8_15-2

Schaufeli, W. B., & Taris, T. W. (2014). A critical review of the job demands-resources model: Impli-

cations for improving work and health. In G. F. Bauer & O. Hämmig (Eds.), Bridging occupa-

tional, organizational and public health: A transdisciplinary approach (pp. 43–68). Springer

Science + Business Media. https://doi.org/10.1007/978-94-007-5640-3_4

Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the Fit of Structural Equation

Models: Tests of Significance and Descriptive Goodness-of-Fit Measures. Methods of Psycho-

logical Research, 8(2), 23–74. https://www.stats.ox.ac.uk/~snijders/mpr_Schermelleh.pdf

Spreitzer, G. M. (1995). Psychological empowerment in the workplace: Dimensions, measurement, and

validation. Academy of Management Journal, 38(5), 1442–1465. https://doi.org/10.2307/256865

Strohmeier, Stefan. (2020). Digital human resource management: A conceptual clarification.

German Journal of Human Resource Management: Zeitschrift für Personalforschung. 34.

https://doi.org/10.1177/2397002220921131

Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial Intelligence in Human Resources Man-

agement: Challenges and a Path Forward. California Management Review, 61, 15-42.

https://doi.org/10.1177/0008125619867910

Ulrich, D., & Dulebohn, J. H. (2015). The future of HR: Rethinking capabilities and roles. Human Re-

source Management Review, 25(2), 95–101. https://doi.org/10.1016/j.hrmr.2015.01.002

Villajos, E., Tordera, N., & Peiró, J. M. (2019). HRM systems and employee affective commitment: The

role of I-deals. Personnel Review, 48(3), 839–856. https://doi.org/10.1108/PR-03-2018-0094

Wernerfelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5(2), 171–180.

https://doi.org/10.1002/smj.4250050207

Zhang, X., & Bartol, K. M. (2010). Linking empowering leadership and employee creativity: The in-

fluence of psychological empowerment, intrinsic motivation, and creative process engagement.

Academy of Management Journal, 53(1), 107–128. https://doi.org/10.5465/amj.2010.48037118

Zhou, J., & Hoever, I. J. (2023). Understanding the dynamic interplay between actor and context for cre-

ativity: Progress and desirable directions. Annual Review of Organizational Psychology and Or-

ganizational Behavior, 10, 109–135. https://doi.org/10.1146/annurev-orgpsych-120920-055457

##submission.downloads##

Objavljeno

2025-09-01