An integrated human capital and responsible AI framework for sustainable organizational performance using PLS-SEM analysis
4 affiliations
- 1K L University, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India
- 2BBA Department, K L University, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, AP, India
- 3Department of Information Management, Chaoyang University of Technology, No 168, Jifeng E. Rd., Wufeng District, Taichung City, 413310, Taiwan; Corresponding author.
- 4School of Business, Aditya University, Surampalem, Kakinada, Andhra Pradesh, India
Abstract
The rapid digital transformation of higher education has increased the urge for institutions to balance human-centered management practices with the responsible adoption of emerging technologies. This study investigates how human capital management factors and Artificial Intelligence (AI) related organizational capabilities together influence Sustainable Organizational Performance (SOP) in business schools. The study, anchored in Human Capital Theory (HCT) and ethical AI and responsible management theory, develops an integrated conceptual model consisting of Leadership Practices (LP), Knowledge Accessibility (KA), Learning Capability (LC), Ethical AI Culture (EAIC), AI Readiness (AIR) and Socially Responsible Human Resource Management (SRHRM) as predictors of SOP. Data from staff members of business schools in India were collected through a structured survey, and the proposed model was tested using SmartPLS 3.0. The measurement model exhibited strong reliability and validity, and the structural analysis supported all the hypothesized relationships. LP found to be the most influential predictor, followed by LC, AIR, KA, SRHRM, and EAIC. It follows that sustainable performance in academic institutions needs both robust human capabilities and ethically governed technological readiness. The study makes a theoretical contribution by integrating perspectives from human capital and ethical AI and provides practical guidance to institutions aiming to improve performance through balanced human-technology strategies. Suggestions for improving institutional practice and directions for future research are also presented.
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BibTeX
@article{monda2026integrated,
title = {{An integrated human capital and responsible AI framework for sustainable organizational performance using PLS-SEM analysis}},
author = {Geetha Monda and Anuradha Averineni and Venkateswarlu Nalluri and Anil Kumar Akkala},
journal = {Sustainable Futures},
year = {2026},
month = oct,
doi = {10.1016/j.sftr.2026.102239},
url = {https://doi.org/10.1016/j.sftr.2026.102239},
note = {CC BY},
}
RIS
TY - JOUR
TI - An integrated human capital and responsible AI framework for sustainable organizational performance using PLS-SEM analysis
AU - Geetha Monda
AU - Anuradha Averineni
AU - Venkateswarlu Nalluri
AU - Anil Kumar Akkala
PY - 2026
DA - 2026/10/30
JO - Sustainable Futures
DO - 10.1016/j.sftr.2026.102239
UR - https://doi.org/10.1016/j.sftr.2026.102239
AB - The rapid digital transformation of higher education has increased the urge for institutions to balance human-centered management practices with the responsible adoption of emerging technologies. This study investigates how human capital management factors and Artificial Intelligence (AI) related organizational capabilities together influence Sustainable Organizational Performance (SOP) in business schools. The study, anchored in Human Capital Theory (HCT) and ethical AI and responsible management theory, develops an integrated conceptual model consisting of Leadership Practices (LP), Knowledge Accessibility (KA), Learning Capability (LC), Ethical AI Culture (EAIC), AI Readiness (AIR) and Socially Responsible Human Resource Management (SRHRM) as predictors of SOP. Data from staff members of business schools in India were collected through a structured survey, and the proposed model was tested using SmartPLS 3.0. The measurement model exhibited strong reliability and validity, and the structural analysis supported all the hypothesized relationships. LP found to be the most influential predictor, followed by LC, AIR, KA, SRHRM, and EAIC. It follows that sustainable performance in academic institutions needs both robust human capabilities and ethically governed technological readiness. The study makes a theoretical contribution by integrating perspectives from human capital and ethical AI and provides practical guidance to institutions aiming to improve performance through balanced human-technology strategies. Suggestions for improving institutional practice and directions for future research are also presented.
SN - 2666-1888
ER -