Purpose: This study aimed to determine the effect of customer default on the Cash Conversion Cycle and Net Working Capital in construction companies. Method: This study uses secondary data from companies’ financial reports to calculate the Cash Conversion Cycle, Net Working Capital, and Spearman's rho Correlation Test to determine the relationship between the two variables. Results: The results show that SOE customer default affects the condition of the Cash Conversion Cycle, especially in 2023, where the Cash Conversion Cycle value in Q123 (85 days) and Q223 (64 days), but the worst Cash Conversion Cycle results during the observation period are in Q122 (134 days). In Net Working Capital, there are only three periods with negative results: Q219 (-3.1B), Q319 (-461M), and Q421 (-4B), but not in 2023, because in 2023, the result is positive (or liquid). Spearman's rho Correlation Test shows that the relationship between the two variables is negatively correlated by -0.319, and the significance is 0.184, or the two variables are not significant. Limitations: This study was limited to construction companies in Bandung. The data taken from 2019 to Q3-2023 only focus on the influence of SOE customer defaults on the company's Cash Conversion Cycle and Net Working Capital.   Contribution: This study provides the best solution to the problem of customer default in the Cash Conversion Cycle and Net Working Capital in a construction company. If the solution is applied to the company, an implementation plan is created to fix the problem.

Purpose: Investors continuously achieve abnormal returns (ARs) by adopting advanced strategies. Therefore, this study aimed to compare the performance of the IDX Value30 and IDX Growth30 indices, which represent value and growth investment strategies in Indonesia. Method: The comparison in this study was conducted using return- and risk-adjusted variables represented by Information Ratios and Jensen’s alpha. Based on this approach, Mann-Whitney and independent sample t-tests were performed using the SPSS program. Results: Both the IDX Value30 and IDX Growth30 indices show positive abnormal returns. However, a comparison of returns, Information Ratios, and Jensen's alpha showed no significant differences between the IDX Value30 and IDX Growth30. Limitations: The secondary data of IDX Value30 and IDX Growth30 indices were limited to the period from January 30, 2014, to September 30, 2022.   Contribution: IDX Value30 and IDX Growth30 could serve as references for investors and Investment Managers in executing value- and growth-investing strategies to outperform IHSG. Furthermore, Investment Managers could use these indices as benchmarks for issuing index funds or ETFs. Novelty: This study uniquely compares the performance of value and growth investing using the IDX Value30 and IDX Growth30 indices, a comparison that has not been previously conducted.

Purpose: This study examined the implementation of Artificial Intelligence-driven Automation as a game changer in accounting research. Specifically, this study assessed the advantages and disadvantages of AI-driven automation in enhancing the quality of accounting research. Methods: A descriptive survey design was used in the study. The study sample comprised of 137 accounting academics. Primary data for this study were collected using a structured questionnaire. The collected data were assigned quantitative measurements using a Likert scale system of ranks. Descriptive analytical tools (frequency and mean-point analyses) were used to analyze the data with the aid of the SPSS version 25 software. Results: The findings  show a general consensus that AI-driven automation enhances the accuracy, efficiency, and comprehensiveness of accounting research, with high acceptance of its benefits. However, there are notable concerns about potential drawbacks such as reduced originality, difficulties in validation, and the risk of introducing biases or compromising ethical standards. Limitations: This study’s limitations include a narrow sample of academics, potential response biases, and the inability to assess long-term AI impacts across diverse accounting professionals.   Contribution: The implementation of AI-driven automation represents a game-changer in accounting research because it offers new opportunities to enhance the quality, efficiency, and scope of academic inquiry, as well as challenges and risks that must be carefully managed to ensure that the benefits of AI are fully realized while maintaining the integrity and rigor of the research process. Therefore, this study recommends that academic institutions and research ethics committees develop workable training programs that emphasize the importance of maintaining human oversight, creativity, and ethical standards when utilizing AI-driven automation in accounting research.