In the era of data-driven digital commerce, Artificial Intelligence (AI)-based product personalization has become a key strategy to enhance user experience and foster customer loyalty. However, in the Indonesian e-commerce landscape, there remains a lack of empirical understanding of how personalization systems influence long-term user engagement. This study investigates the impact of AI-driven product personalization on customer loyalty among Indonesian e-commerce users. Employing a mixed-methods approach, quantitative data were collected through an online survey of 150 active users, and qualitative insights were obtained from in-depth interviews with six informants. Statistical analysis using simple linear regression revealed that personalization significantly influences customer loyalty, with a beta coefficient of 0.653 (t = 8.241, p < 0.001) and an R² value of 0.567. Qualitative findings highlight user concerns over recommendation accuracy, interface overload, and repetitive suggestions, which affect emotional satisfaction and platform attachment. This research contributes to the growing body of knowledge on AI adoption in e-commerce by integrating behavioral and technological dimensions of loyalty formation. It also offers practical implications for designing more context-sensitive personalization systems that prioritize not only algorithmic precision but also user control and experience quality.

In the digital economy era, financial transparency has become a critical aspect for the sustainability and credibility of Micro, Small, and Medium Enterprises (MSMEs). Despite their substantial contribution to employment and economic growth, many MSMEs in Indonesia still rely on manual accounting systems, which limit the accuracy and traceability of financial reporting. This study aims to empirically examine the effect of cloud-based accounting automation on financial transparency among Indonesian MSMEs. Using a mixed-methods approach, the research collected quantitative data from 120 MSME respondents in Jakarta and West Java through structured questionnaires and qualitative data from six key informants through semi-structured interviews. The quantitative data were analyzed using simple linear regression, resulting in a coefficient of determination (R²) of 0.513 and a beta coefficient of 0.598 (p = 0.000), indicating a strong and significant influence of accounting automation on financial transparency. Qualitative findings also revealed improvements in real-time access, reporting accuracy, and transaction traceability. The study confirms that cloud-based digital accounting systems enhance financial transparency by enabling automated integration, audit trails, and timely reporting. These findings contribute to the literature on digital transformation in MSMEs and offer practical implications for policymakers, financial institutions, and software providers to accelerate technology adoption and improve financial governance in the MSME sector.

Savings and loan cooperatives play an important role in strengthening the community's economy, especially in the micro and small sectors. However, many cooperatives face various challenges related to proper financial management and recording. Limited human resources and lack of understanding of relevant accounting standards are the main obstacles. This community service aims to increase understanding and application of SAK ETAP (Financial Accounting Standards for Entities Without Public Accountability) in the Koperasi Simpan Pinjam (KSP) Inti Dana Semarang.  The methods used include training, mentoring, and evaluation of the implementation of  SAK ETAP. The results of this program show an improvement in the quality of financial reports and transparency in cooperative management. This initiative has a positive impact on member trust and the sustainability of cooperative businesses

The purpose of the study is to determine and test whether partially the size of the company, the age of the company, is significant to the audit tenure and audit delay and also to test whether the audit tenure is able to mediate the effect of reducing the effect of the relationship between company size, company age and audit delay. Secondary quantitative financial statements of 48 Indonesia Stock Exchange companies in the property and real estate sector. Samples were obtained by 48 companies by purposive sampling. Analysis with path analysis software SPSS v24. The company's age results are the only significant variable to audit delay. While audit tenure, partial company size is not significant to audit delay, and audit tenure cannot increase influence as an intervening variable.

This research examines the use of Artificial Intelligence (AI) for demand prediction and supply chain management in Indonesian e-commerce firms. E-commerce businesses face significant challenges in predicting consumer demand fluctuations and maintaining supply chain efficiency. Using a mixed-method approach, data were collected from eight e-commerce companies to evaluate the impact of AI on demand forecasting accuracy, supply chain efficiency, and operational cost reduction. The findings show that AI improves demand forecasting accuracy by 30%, accelerates delivery times by 40%, and reduces operational costs by 20%. However, data quality remains a key challenge in maximizing AI performance. This research contributes to the literature on supply chain management and AI, offering practical insights for companies aiming to enhance their operational efficiency through AI adoption. Recommendations include improving data quality, employee training, and gradual AI implementation to achieve optimal outcomes.