Adoption of Research Utility Tools Among Research Scholars in Central University Libraries of North India
DOI:
https://doi.org/10.56042/alis.v73i2.28601Keywords:
Adoption of Research Utility Tools (ARUTs), Research Planning Management (RPM), Technology Adoption, UTAUT/UTAUT1Abstract
In today’s world, adopting advanced research utility tools (ARUTs) for information access are essential for every research scholar to maximize digital innovation in their research work and academic field or other research domains. Now a day’s researchers are recognized the power of these research utility tools in maximizing their capabilities and effectively addressing complex issues. This study explores various research utility tools, focusing on their practical applications and research scholars’ perceptions of these tools, ultimately leading to the development of even more effective research tools for their benefit. This study investigated among research scholars from seven well-known Central Universities of North India who have vast experience in research utility tools. The researchers shared a structured questionnaire administrated via an online survey link on WhatsApp and email to collect data from 210 research scholars from January 15, 2026 to January 30, 2026. The descriptive statistical analysis showed a strong correlation between performance expectancy and behavioral intention (r = 0.71, p < .01), as similar facilitating conditions (r = 0.67, p < .01), effort expectancy (r = 0.64, p < .01), and social influence (r = 0.52, p < .01). These results clearly demonstrate that perceived usefulness, ease of use, social influence, and institutional support play a significant role in adoption of research utility tools in universities. The results of multiple regression analysis confirmed that performance expectancy (β = 0.45, p < .001) is the strongest predictor of behavioral intention to use research utility tools. Accordingly, effort expectancy (β = 0.29, p < .001), facilitating conditions (β = 0.26, p < .001), social influence (β = 0.21, p < .001) and this model explains (68%) of the variance in behavioral intentions (R² = 0.68), indicating the model's high explanatory power (F = 108.72, p < .001).