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As the revolution of information takes place, industrialized societies are going to become information societies. Developing countries such as Pakistan are going to change due to technology and, in turn, transform the whole structure of libraries. The concept of digital libraries (DL) has emerged due to technological advancements. This study aims to highlight the factors that encourage and discourage the use of the Higher Education Commission’s (HEC) National Digital Library (NDL).

A quantitative research approach was selected for the investigation, while the data collection instrument was a questionnaire. Postgraduate research students were the population of the study. A convenient sampling technique was used for sample selection.

The results of the study indicated that the use of HEC (NDL) was frequent monthly. The display of search results, 24/7 access, the authenticity of items, availability of navigational assistance and up-to-date information encourage researchers to use DL. However, their preference for print material over electronic material, slow downloading speed of the internet and non-availability of older and archival publications were the common reasons for the low use of DL. Furthermore, there are significant differences in terms of using the NDL based on gender, the program of study and the stage of the study.

This study will contribute significantly to the literature from the developing countries and would also helpful for HEC (NDL) authorities and university libraries to design information services.

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.