<![CDATA[Female workers in the manufacturing sector in Batam City often face challenges in financial planning, particularly regarding pension funds. Lack of knowledge and information about the importance of pension funds, coupled with limited awareness campaigns, result in many workers not participating in pension programs. Additionally, a work culture focused on short-term targets often leads female workers in this sector to view long-term financial planning, such as retirement, as less important. This study aims to investigate and analyze the partial and simultaneous effects of framing effect, mental accounting, and financial decisions on the effectiveness of pension funds among female workers in manufacturing companies in Batam City. This study employs causal associative research, and the research method used is quantitative. The population in this study consists of all female workers in manufacturing companies in Batam City, whose exact number is unknown. Four manufacturing companies were randomly selected, and 50 respondents were selected from each company. The sampling method used non-probability sampling with purposive sampling techniques, with several criteria including women aged 40-50 years with a minimum of 1 year of work experience and companies employing more than 250 workers. The sample size for this study was 200 respondents. The data were analyzed using SPSS version 25. The results of the study indicate that the framing effect partially influences the effectiveness of pension funds, mental accounting partially influences the effectiveness of pension funds, financial decisions partially influence the effectiveness of pension funds, and the framing effect, mental accounting, and financial decisions simultaneously influence the effectiveness of pension funds. The adjusted R-squared value of 0.728 can be referred to as the coefficient of determination, This means that 0.741 (74.1%) of the effectiveness of pension funds can be obtained and explained by the framing effect, mental accounting, and financial decisions, while the remaining 25.3% (100% - 74.1% = 25.3%) is explained by variables outside the model that were not studied.]]>
