Volume 2 • Issue 2 • 2026
Abstract
The process of talent acquisition has undergone rapid transformation because of the impact and influence exercised by Artificial Intelligence (AI). Although organizations have started to accept, adopt and deploy AI tools, there are concerns regarding trustworthiness and its ability to treat people fairly. The existing research shows that AI systems deliver operational effectiveness, but for higher level of adoption, trust in AI systems when fairness becomes a priority. The development of trust through technological features and value appreciation through technology needs additional introspection because trust serves as an essential bridging factor. The research investigates these missing elements through empirical analysis of the perception of HR professionals pertaining to trust development which stems from their perception of usefulness and their organization's readiness and their belief in algorithmic fairness. The research combines Technology-Organization-Environment and Task-Technology-Fit frameworks to conduct quantitative analysis through Partial Least Squares-Structural Equation Modelling (PLS-SEM) using data from 357 purposefully chosen HR professionals. The research findings support all four proposed hypotheses which demonstrate that algorithmic fairness produces the most substantial effect on trust development through a path coefficient of 0.327 and organizational readiness produces an effect of 0.291 and perceived usefulness produces an effect of 0.254. The research shows that trust leads people to perceive higher value through a path coefficient of 0.557 which explains 51% of the differences between people. The model explains 54.1% of trust formation variance which proves its strong ability to describe the process. The research study las limitations because of cross-sectional design and selection of participants from specific industry sectors. Future research scholars need to apply longitudinal studies across multiple settings to identify new factors which affect how organizations accept AI systems for their recruitment operations.
Keywords: Artificial Intelligence, Talent Acquisition, Trust, Algorithmic Fairness, Perceived Value
Abstract
The concept of renewable energy is now one of the cornerstones of the India economic development strategy as the country tries to reconcile between the rapid rates of economic growth and environmental sustainability and climate commitments. This paper analyses the contribution of expansion of renewable energies in economic development of India, in terms of implications in terms of creation of employment opportunities, income growth, living standards, and macroeconomic stability. The paper compares this by using secondary sources of data on the growth of solar energy capacity and how it relates with the GDP dynamics over the year 2014-2025 by utilizing the data provided by the Ministry of New and Renewable Energy (MNRE), the Central Electricity Authority (CEA), and the international communities like IEA and IRENA. The results show that the installed renewable energy capacity was growing in India, with a level of about 76 GW in 2014 to more or less 254 GW in 2025, covering over 51 percent of the total installed capacity of non-fossil fuel-based capacity. Even though growth levels in the GDP were quite volatile, renewable energy growth continued to be high, which points to its structural and policy-based character. The industry has also become a major source of employment with well more than a million jobs created and the availability of energy, less dependence on fossil fuels and living standards have also improved. The paper concludes that renewable energy is not only a need to the environment but also strategic in terms of sustainable and inclusive economic growth in India.
Abstract
The study investigates the relationship between technology adoption factors and information quality with the symbolic adoption of a Human Resource Information System (HRIS). Data is collected from 415 HRIS end-users from Small and Medium Enterprises in India. The data were analyzed using Structural Equation Modeling (SEM). Findings revealed that performance expectancy, effort expectancy, social influence, and information quality have significantly related to HRIS symbolic adoption.
Further, the study examines the moderating effects of HRIS training between the technology adoption factors and HRIS Symbolic adoption. This study contributes to the HRIS literature by integrating the UTAUT model with information quality from De Lone and McLean& Information System success research.
The study has taken the small and medium organization’s employees and their symbolic adoption factors that help and trigger them to adopt such technology. The study reveals that HRIS training moderates the relationship between effort expectancy, social influence, and information quality with HRIS symbolic adoption.
Keywords: Human Resource Information System, Symbolic Adoption, UTAUT, Information Quality, Small and Medium Enterprises, Moderation, Training