In today’s era, all firms face new challenges consequent to rising digitization. The urge of digital transformation has led to significant benefits including economic growth and sustainable development that ultimately help in addressing poverty and inequality issues. However, there is limited resources on the relation between digital transformation and sustainability. To address the issue, this paper presents a systematic literature review on the relationship between digital transformation and sustainable business performance, aiming to highlight the potential contributions of digital transformation to the environment, economy, and social sustainability. This study has analysed 130 academic articles from the Scopus database using content and descriptive analysis. The findings of this review process uncover the existing gap in knowledge, and it also provides the foundation for future research to be conducted in digital transformation and sustainable business performance. Additionally, this study provides guidelines for the policymakers in drafting policies and blueprints, as the way forward for the business owners.

This study aimed to predict the likelihood of small and medium-sized (SMEs) defaulting in loan repayment by developing a model using artificial intelligence technology, namely Machine Learning algorithms. The research employed the Louvain clustering algorithm to effectively group the loan recipients based on their cumulative repayment amounts over time, and two distinct machine learning techniques, namely logistic regression (LR) and k-nearest neighbour (k-NN) were harnessed to evaluate their efficacy in classifying recipients' risk levels, namely low-risk or high-risk. The LR model achieved mean accuracy score of 100% which indicates a high level of precision that can effectively predict the risk of SME loan repayment. This is further supported by the Area Under the Curve (AUC) value of 1.0 obtained by the LR model which suggests that the model has achieved optimal separation of the two classes, and therefore it is highly reliable for risk prediction. This technique is believed to enhance the efficiency and accuracy of credit risk assessment that could enable financial institutions (FIs) to optimize their decision-making processes and mitigate potential losses caused by the defaulting loans. Hence, this study is significant as it proves the effectiveness of machine learning technique in predicting loan repayment risk in FIs in Malaysia.

This study aimed to predict the performance of homestay entrepreneurs in digital competencies by developing a model using machine learning algorithms. The research adopted the k-Means clustering algorithm to effectively group the performances based on their competency levels, and three distinct machine learning techniques, namely random forest (RF), logistic regression (LR) and k-nearest neighbour (k-NN) were harnessed to evaluate their efficacy in classifying homestay entrepreneurs’ performance levels, namely high, good and average. The RF model achieved a mean accuracy score of 100% which indicates a high level of precision that can effectively predict the performance level of the homestay entrepreneurs based on digital competencies. This is further supported by the Area Under the Curve (AUC) value of 1.0 obtained by the RF model which suggests that the model has achieved optimal separation of the three classes, and therefore it is highly reliable for performance prediction. The application of these technique is believed to enhance the efficiency and accuracy of performance level assessment that could enable entrepreneurs to optimize their decision-making processes and mitigate potential losses caused by the poor performances. Thus, this study demonstrates the effectiveness of machine learning techniques in predicting the business performance levels of homestay entrepreneurs in Malaysia.