1388 American Journal of Economic and Management Business e-ISSN: 2835-5199 Vol. 4 No. 8 August 2025 The Influence Of Emotional Intelligence And Self-Leadership On The Utilization Of Artificial Intelligence By Accounting Students Across Indonesia, With Procrastination As A Moderating Variable Muhammad Hidayatullah1, Madani Hatta2 Faculty of Economics and Business, University of Bengkulu Email: muhammaddayattt004@gmail.com and madani.hatta@unib.ac.id Abstract This study aims to determine the influence of emotional intelligence and self-leadership on the use of Artificial Intelligence (AI), with procrastination as a moderation variable. This study adopts a quantitative approach with the active population of Accounting students throughout Indonesia. In this study, samples were taken using the lemeshow technique. The analysis method used in this study is Moderated Regression Analysis (MRA) using the SmartPLS application. The data analysis stages of this study include descriptive statistical analysis, PLS-Algorythm analysis (data quality test (including validity test and reliability test) and multicollinearity test) and bootstrapping analysis (hypothesis test (such as t, f and r2 tests) to identify the influence of interaction between emotional intelligence, self-leadership, and procrastination on the use of Artificial Intelligence (AI). The research was conducted by involving 384 accounting student respondents throughout Indonesia. The results of the study show that emotional intelligence and self-leadership have a positive effect on the use of AI. Procrastination directly affects the use of AI. Procrastination cannot moderate the relationship between emotional intelligence and self-leadership to the use of AI. Based on these results, the researcher suggested adding other variables outside of this study. Keywords: Artificial Intelligence; Emotional Intelligence; Self-Leadership; Accountancy; Procrastination INTRODUCTION The presence of technology and the digital era has brought a major wave of transformation that has changed various aspects of life, including in the field of education (McHaney, 2023; Ramasamy et al., 2024; Voronkova et al., 2023). In the field of education, digital technology has led to an important change in the approach to learning by changing methods that were previously more conventional. Conventional learning methods that used to rely on printed books are now slowly shifting to a technology-based digital approach, one of the technological developments is the existence of artificial intelligence or commonly called Artificial Intelligence (AI) (Panjaitan et al. 2024). Artificial Intelligence (AI) refers to the ability of computer systems and algorithms to think and act in a way that mimics human intelligence, both from a logical and rational point of view that focuses on the development of computer systems that are capable of performing tasks that usually require human intelligence. The use of Artificial Intelligence (AI) has become increasingly common among students for various academic purposes, such as helping to find information, complete assignments, and understand Muhammad Hidayatullah, Madani Hatta 1389 subject matter (Saputra and Serdianus 2023). Artificial Intelligence (AI) utilizes advanced natural language modeling technology, so it is able to understand and answer various questions and conversations with intelligence that is close to human capabilities. The use of Artificial Intelligence (AI) by students in the context of education has become common at this time. The use of Artificial Intelligence (AI) among students and students is also illustrated by a survey conducted (Hartanto and Rohmah 2024) regarding the use of Artificial Intelligence (AI) for schoolwork and college assignments with respondents spanning from 34 provinces in Indonesia. The survey results showed that of 1,501 student respondents aged 15-21 years, at the high school and college levels, as many as 86.21 percent admitted to using Artificial Intelligence (AI) assistance, at least once a month, to complete the assignments given. There are only around 13.79 percent who admit that they have never used Artificial Intelligence (AI) at all to do school or college assignments. Of the total respondents, 44.04 percent were high school students and almost 56 percent were college students. However, the majority still come from Java Island (68.09 percent). For context, Indonesian people are indeed one of the most widely used Artificial Intelligence (AI) technology in the world. This is illustrated by the results of a study from WriterBuddy, an Artificial Intelligence (AI)-based content service provider, in its report, which shows that there were 1.4 billion visits to Artificial Intelligence (AI) sites originating from Indonesia, between September 2022-August 2023. Based on the study, Indonesia is the third largest contributor to the number of visits to Artificial Intelligence (AI) devices available in the world today. Data Analytics and Artificial Intelligence (AI) offer an innovative approach to managing emotional intelligence in the digital age. Artificial Intelligence (AI) enables in-depth analysis of the emotional data generated by users in interactions with digital technologies. According to Lee et al. (2018), "Artificial Intelligence (AI) can help recognize emotional patterns from user data to provide relevant feedback in the development of emotional intelligence". This allows for personalization in learning approaches that take into account individual differences in emotional responses (Suryaningsih et al. 2024). The application of Artificial Intelligence (AI) in education not only facilitates a better understanding of students' emotional intelligence but also helps in managing and improving mental well-being. According to (El Kaliouby 2018), "Artificial Intelligence (AI) technology can support teaching that is responsive to students' emotions by analyzing facial expressions and emotional intelligence levels." It is changing the way education faces new challenges in managing the balance between academic development and the emotional aspects of students. Accounting students are required to not only understand accounting theory, but also develop the ability to manage themselves and technology simultaneously. Student self-leadership skills are the most important dimension that greatly determines the self-regulated learning of students because each student is required to have independent/autonomous behavior and full responsibility in the process and results of their own academic studies. Self-leadership is the ability to lead oneself, which can help students overcome these challenges and adapt to rapid changes in the learning environment. Self-leadership provides accounting students with a American Journal of Economic and Management Business Vol. 4 No. 8 August 2025 1390 framework to build a strong leadership foundation while improving adaptability to the ever- changing academic environment. By mastering self-leadership, students are able to understand and manage themselves effectively, while integrating elements of adaptive leadership to deal with external challenges. This includes the ability to utilize Artificial Intelligence (AI), so that it is used as a supporting tool, not as a substitute for the role of humans as a whole. The combination of self-leadership with the responsible use of technology provides a great opportunity for accounting students to develop self-awareness. This awareness is important to encourage a responsible attitude in the use of Artificial Intelligence (AI), both in academic and professional contexts (Primasatya et al. 2024). Artificial Intelligence (AI) offers a great opportunity to improve work efficiency and effectiveness, but the use of this technology is often hampered by psychological factors, one of which is procrastination. According to Wicaksana (2014), psychological and emotional conditions can affect individual behavior such as procrastination. Students who often procrastinate tend to postpone their assignments until they are close to the deadline. In these desperate and hurried conditions, they can feel stressed, lose focus, and produce less than optimal work. Procrastination is closely related to emotional management and self-motivation, which is also the focus of the concepts of emotional intelligence and self-leadership. Individuals with high emotional intelligence are able to manage stress and inhibiting emotions, while those with good self-leadership can direct themselves to achieve goals more effectively. However, procrastination can be a moderation variable that changes the relationship between emotional intelligence, self- leadership, and the use of Artificial Intelligence (AI). Even if a person has good emotional intelligence and self-leadership, procrastination can reduce the effectiveness of both factors in adopting and utilizing Artificial Intelligence (AI) to the fullest. On the other hand, individuals who have a high procrastination tendency may have difficulty utilizing Artificial Intelligence (AI) even though they have a good level of emotional intelligence and self-leadership. Thus, it is important to understand how procrastination can moderate the relationship between emotional intelligence, self-leadership, and the use of technologies such as Artificial Intelligence (AI) in order to design strategies that can reduce the negative impact of procrastination and increase the acceptance and utilization of Artificial Intelligence (AI) in various contexts. Technological advancements, especially Artificial Intelligence (AI), open up great opportunities in the world of education, but they also leave ethical challenges. (V. A. Putri, Sotyawardani, and Rafael 2023) highlighted that Artificial Intelligence (AI) has helped students of the State University of Surabaya improve learning efficiency. However, ironically, students often only utilize the results of Artificial Intelligence (AI) without any effort to re-understand or modify them, which instead leads to serious problems such as plagiarism and academic cheating. Meanwhile, (Harianti, Artaningrum, and Suryantari 2022) found that emotional intelligence plays an important role in the academic success of accounting students. Students who are able to manage their emotions well have a deeper understanding of the material, while those who have difficulty controlling emotions tend to get caught up in learning mistakes. (Primasatya et al. 2024) brings a fresh perspective by highlighting the importance of self-leadership in responding Muhammad Hidayatullah, Madani Hatta 1391 to the development of Artificial Intelligence (AI) technology, such as chatbots. In their view, students who have self-leadership skills are able to use this technology wisely, making it an effective learning support tool without sacrificing ethical values. On the other hand, (Salsabila and Indrawati 2020) reveal the dark side of academic procrastination. Students who often procrastinate on assignments, especially those with low emotional intelligence, are more prone to academic cheating such as plagiarism. This shows that these bad habits not only have an impact on productivity, but also on academic integrity. This study aims to analyze the influence of emotional intelligence and self-leadership on the use of Artificial Intelligence (AI) technology among Accounting students throughout Indonesia. In addition, this study seeks to explore the role of procrastination as a moderation variable that can affect the relationship between emotional intelligence, self-leadership, and the use of Artificial Intelligence (AI) technology. This study offers a more comprehensive approach by combining three main variables, namely emotional intelligence, self-leadership, and procrastination, to explore their influence on the simultaneous use of Artificial Intelligence (AI) technology. Unlike previous studies that generally focused on one variable, this study tried to understand the interaction between these variables and how procrastination plays a role as a moderation factor. Thus, this study not only provides a broader picture of the factors that affect the use of Artificial Intelligence (AI) technology by students, but also provides a new contribution in the literature related to self-management strategies in the digital era. RESEARCH METHODS This study uses a quantitative research method. By using this approach, it is hoped that it will be possible to determine the influence between independent variables on dependent variables that are moderated with moderation variables in depth. The population in this study is active accounting students throughout Indonesia. In this study, samples were taken using random sampling techniques. The total population in this study is unknown because many universities have not yet do Update regarding the data on the number of students on the official website of PDDIKTI. Therefore, the researcher used the lameshow formula to find the required number of samples (Wright & Bonett, 2007). Here is the calculation using the lameshow formula: 𝑛 = 𝑧2 x 𝑝 x π‘ž 𝑑2 Where: 𝑛 = number of samples sought z = z score at a given confidence level (in this study 1.96 for 95 % confidence level) p = assumed proportion of population (in this study it was 0.5) q = 1- p (1-0.5 = 0.5) d = Error rate of 0.5 % Thus, the sample in this study is: 𝑛 = 𝑧2 x 𝑝 Γ— π‘ž 𝑑2 American Journal of Economic and Management Business Vol. 4 No. 8 August 2025 1392 𝑛 = (1,962 x 0,5 Γ— 0,5) 0,052 𝑛 = (3,84162 Γ— 0,25) 0,052 𝑛 = 0,9604 0,0025 𝑛 = 384,16 π‘‘π‘–π‘π‘’π‘™π‘Žπ‘‘π‘˜π‘Žπ‘› π‘šπ‘’π‘›π‘—π‘Žπ‘‘π‘– 384 π‘Ÿπ‘’π‘ π‘π‘œπ‘›π‘‘π‘’π‘› Variables and Operational Definitions of Artificial Intelligence Utilization The AI Utilization Variable acts as a dependent variable (Y), the extent to which students use AI features and technology effectively to complete academic assignments. As a dependent variable, it includes how students use various AI-based features, tools, or applications to complete tasks, such as academic writing, self-learning. Emotional Intelligence The emotional intelligence variable becomes an independent variable (X1), as an individual's ability to recognize, understand, and manage one's own emotions. As an independent variable, emotional intelligence has an important role in how students interact with AI technology. Self-leadership The self-leadership variable (X2) is defined as the ability of an individual to lead himself or herself through the management of thoughts, behaviors, and motivations in making optimal use of AI. As an independent variable, self-leadership reflects the ability of students to take initiative, set targets, and organize personal strategies in the effective use of AI. Procrastination Variabel moderation at research Ini be procrastinating. Variabel This is as an individual's tendency to procrastinate on tasks, especially those related to exploration or the use of AI technology. Based on the Task-Technology Fit theory, procrastination can hinder the efficiency of technology users in matching relevant tasks and technological features. Table 1. Operational Research Variables Variable Instruments Reference Source Emotional Intelligence (X1) 1. Self-Awareness 2. Self-Regulation 3. Self-Motivation 4. Empathy 5. Social Skills According to Daniel Goleman in (Setiawan, Baihaqi, and Bebena 2022) Self-Leadership (X2) 1. Behavior-focused strategies (Aristayudha and Richadinata Muhammad Hidayatullah, Madani Hatta 1393 Variable Instruments Reference Source 2. Natural Reward Strategy 3. Constructive Mindset Strategy 2020) Utilization of Artificial Intelligence (Y) 1. Adopsi Artificial Intelligence 2. Readiness to Use Artificial Intelligence (Damerji dan Salimi 2021) Procrastination (M) 1. Delay in the implementation of academic tasks 2. Inaction and delay in doing academic assignments 3. Incompatibility between plans and actual performance 4. Do other activities that are more fun According to Ferarri in (Nurjan 2020) Data Collection Techniques In this study, the researcher used a primary data type. This research uses data obtained directly by using digital platforms to reach regions throughout Indonesia, such as Google Forms, WhatsApp groups, Instagram for accounting students, or other social media. Data Analysis Techniques Moderated Regression Analysis (MRA) is the analysis technique used in this study. The data collected for further research is calculated using Microsoft Excel. Then, the data calculated using the formula is processed and tested with the Smart Partial Least Square program, also known as SmartPLS. The stages of data analysis of this study include descriptive statistical analysis, data quality tests (validity tests and reliability tests), classical assumption tests (such as normality, multicollinearity, and heteroscedasticity tests), and hypothesis tests (such as t, f and r2 tests). The following is the Moderated Regression Analysis (MRA) in this study: Y= a + Ξ²1 X1 + Ξ²2 X2 + Ξ²3 Z + Ξ²4 (X1β‹…Z) + Ξ²5 (X2β‹…Z) + e Information: Y = Utilization Artificial Intelligence A = Konstanta X1 = Emotional Intelligence X2 = Self-Ledership Z = Procrastination Ξ² 1-Ξ² 5 = Regression coefficients Ξ΅ = Error Measurement Scale American Journal of Economic and Management Business Vol. 4 No. 8 August 2025 1394 The questions in this questionnaire or research questionnaire were measured using a likert scale with 5 (five) points. Assessment from points 1 to point 5. The sequence of points 1 is for the option of Strongly Disagree (STS), point 2 Disagree (TS), point 3 Neutral (N), point 4 Agree (S), and point 5 Very Se tu ju (SS). RESULTS AND DISCUSSION Based on the results of the PLS-Algoryhtm test, the following results were obtained: Figure 1. PLS-Algorythm Model Table 2. Outer Loading Procrastination Intelligence Emotional Self-Leadership Utilization AI Technology M x X1. M x X2. M1 0,838 M2 0,876 M3 0,894 M4 0,900 M5 0,889 M6 0,880 M7 0,728 M8 0,755 X1.1 0,826 X1.2 0,801 X1.3 0,811 X1.4 0,828 X1.5 0,812 X1.6 0,829 X1.7 0,825 X1.8 0,829 X1.9 0,832 X2.1 0,856 X2.2 0,842 Muhammad Hidayatullah, Madani Hatta 1395 Procrastination Intelligence Emotional Self-Leadership Utilization AI Technology M x X1. M x X2. X2.3 0,890 X2.4 0,859 X2.5 0,874 Y1 0,862 Y2 0,907 Y3 0,910 Y4 0,865 M x X2. 1,000 M x X1. 1,000 Source : SmartPLS Data Processing, Research Results 2025 Based on the results of the outer loading test carried out, it is known that the outer loading value of all question items in this study is above 0.70. Thus, it can be known that all of these research items are declared valid. Table 3. Reliability Value Cronbach's alpha Composite reliability (rho_a) Composite reliability (rho_c) (AVE) M 0,943 0,946 0,953 0,718 X1. 0,940 0,940 0,949 0,675 X2. 0,916 0,917 0,937 0,748 And 0,909 0,910 0,936 0,786 Source : SmartPLS Data Processing, Research Results 2025 Based on the table, it can be seen that the composite reliability of all variables in this study has a β‰₯ value of 0.7. So, overall the items that measure each variables in this study are consistent or reliable in measuring variables such as emotional intelligence, self-leadership, procrastination, and artificial intelligence. Table 4. R2 Values R Square R Square Adjusted Utilization of AI Technology 0,546 0,532 Source : SmartPLS Data Processing, Research Results 2025 According to (Prof. Dr. Imam Ghozali, M.Com, Aktβ€―; Hengky Latan, 2021) The R-Squre value of 0.67 indicates a strong model, 0.33 indicates a moderate model and 0.19 indicates a weak model. Based on table 4, it shows that the influence of independent variable relationships on dependents has a strong influence The estimated value for the path relationships in the structural model must be significant. This significant value can be obtained by bootstrapping procedure. This test is usually also used to test hypotheses that have been proposed and to test the influence of moderation variables in moderating independent variables against dependent variables. American Journal of Economic and Management Business Vol. 4 No. 8 August 2025 1396 Figure 2. SmartPLS Data Source : SmartPLS Data Processing, Research Results 2025 In hypothesis testing with moderation regression analysis, namely using the SmartPLS program. This hypothesis was tested at a significant level of 0.05 (95% confidence level). The following is a table illustrating the results of the path coefficient test. Table 5. Bootstrapping test results Original sample (O) Sample mean (M) Standard deviation (STDEV) T statistics (|O/STDEV|) P values M -> Y 0,136 0,136 0,039 3,499 0,000 X1. -> Y 0,264 0,271 0,079 3,326 0,000 X2. -> Y 0,491 0,487 0,070 7,012 0,000 M x X1. -> Y -0,004 0,000 0,073 0,052 0,479 M x X2. -> Y -0,071 -0,073 0,067 1,063 0,144 Source : SmartPLS Data Processing, Research Results 2025 Based on the table, it can be concluded that: 1. Emotional intelligence had a positive effect on the use of AI technology where the origin sample value was (0.264) t-statistic (3.326<1.96) with a p value (0.000>0.05). Emotional intelligence has a positive influence on the use of AI technology. The higher the emotional intelligence possessed by students, the more the potential for the use of AI technology, in other words, the first hypothesis is accepted. These results are in line with research conducted by (Khilmiyah & Wiyono, 2023) which states that students who are able to manage their emotions well show higher creativity in using AI-based applications for data analysis or report creation. Moreover (Latifah & Supriyadi, 2024) mentioning that emotional intelligence contributes to the success of digital learning involving AI. This happens because individuals with good emotion management skills are more likely to be open to learning new technologies. 2. Self-Leadership has a positive effect on the use of AI technology where the origin Muhammad Hidayatullah, Madani Hatta 1397 sample value is (0.491) t-statistic (7.012<1.96) with a p value (0.000>0.05). Self- leadership has a positive influence on the use of AI technology. The higher it is self- leadership owned by students will further increase the potential for the use of AI technology, in other words the second hypothesis is accepted. These results are in line with research by (Effendi & Pribadi, 2021) shows that students with a level of self- leadership those who are high tend to be more likely to master and use AI-based technology to complete academic tasks, such as data analysis or information processing automatically. Individuals with abilities self-leadership be able to independently identify their needs, select appropriate AI technology features, and use those tools to improve work efficiency. In addition, self-leadership contributes significantly to the confidence of technology users, which allows them to be more proactive and innovative in adopting AI technology in various fields. 3. Procrastination had a positive effect on the use of AI of (0.136) with t-statistics (3.499>1.96) with a p value (0.000<0.05). These results show that the procrastination possessed by accounting students in Indonesia is quite high in the use of AI technology. In other words, the third hypothesis is rejected. Individuals tend to procrastinate work, often failing to make effective use of time to explore the features of technology Artificial Intelligence (AI), which ultimately lowers the level of suitability and effectiveness of its use. These results support the theory used in this study, namely the contingency theory where This theory states that There is no one universally applicable managerial or behavioral approach; The effectiveness of an action depends on the situation and contextual factors that surrounds it. In this case, procrastination As a behavior procrastinating tasks can have different impacts depending on the learning environment, time pressure, and technology availability. Students who procrastinate tend to be less likely to explore AI features optimally due to time constraints and psychological pressure. But in certain contexts, such as when students face urgent deadlines, the use of AI can actually increase as a Quick Solution to complete the task. These results are not in line with studies by (Suhadianto & Pratitis, 2020) It found that students with high levels of procrastination had a low tendency to adopt technology in the completion of academic assignments, even though such technology was available and easily accessible. 4. Procrastination is not able to moderate the influence of emotional intelligence on the use of AI technology. This is shown by the value of the path coefficient of -0.004 and the statistical t of 0.052<1.96. The P-value in this test is 0.479>0.05. Because the coefficient of procrastination interaction is negative, procrastination significantly does not moderate the influence of emotional intelligence on the use of AI technology. In other words, the fourth hypothesis is rejected. These results show that individuals tend to have high procrastination so that with high or low emotional intelligence they are unable to integrate Artificial Intelligence (AI) into their tasks. It also rejects the Task-Technology Fit theory, which states that the successful application of American Journal of Economic and Management Business Vol. 4 No. 8 August 2025 1398 technology depends on the match between the tasks performed, individual abilities, and available technological features. 5. Procrastination is not able to moderate the influence of self-leadership on the use of AI technology. This is shown by the value of the path coefficient of -0.071 and the statistical t of 1.063<1.96. The P-value in this test is 0.144>0.05. Because the coefficient of procrastination interaction is negative, procrastination significantly does not moderate the influence of self-leadership on the use of AI technology. In other words, the fifth hypothesis is rejected. The results of the study show that procrastination tends not to be able to weaken or strengthen the influence of self- leadership on the use of Artificial Intelligence (AI), even though they have good self- leadership skills . Conversely, in individuals with low levels of procrastination, this self-leadership ability can increase their effectiveness in utilizing Artificial Intelligence (AI). CONCLUSION Based on the results of this study, it can be concluded that emotional intelligence and self- leadership both have a positive and significant effect on the utilization of Artificial Intelligence (AI) technology by accounting students in Indonesia, where students with higher emotional intelligence are better able to use AI creatively, and those with strong self-leadership are more proactive and innovative in applying AI to improve academic efficiency. Interestingly, procrastination also shows a positive effect on AI utilization, indicating that students who tend to delay tasks often rely on AI as a shortcut to complete academic work; however, procrastination was not found to moderate the relationship between emotional intelligence, self-leadership, and AI usage, meaning it neither strengthens nor weakens their influence. For future research, it is suggested to explore additional variables or other influential factors that may explain AI utilization more comprehensively, considering that only 54.6% of the variance was explained in this study. Moreover, narrowing the scope of research objects is recommended to reduce the difficulties faced in obtaining responses across a wide research population. REFERENCES Aristayudha, A. A. N. B., & Richadinata, K. R. S. (2020). Self efficacy as a mediation between self leadership and entrepreneur performance in young entrepreneurs in Denpasar. E-Journal of Management of Udayana University, 9(11), 3580. https://doi.org/10.24843/ejmunud.2020.v09.i11.p08 Damerji, H., & Salimi, A. (2021). Mediating effect of use perceptions on technology readiness and adoption of artificial intelligence in accounting. Accounting Education, 30(2), 107–130. https://doi.org/10.1080/09639284.2021.1872035 Effendi, G. N., & Pribadi, U. (2021). The Effect of Leadership Style on the Implementation of Artificial Intelligence in Government Services. IOP Conference Series: Earth and Environmental Science, 717(1). https://doi.org/10.1088/1755-1315/717/1/012018 El Kaliouby, R. (2018). Girl decoded: A scientist’s quest to reclaim our humanity by bringing Muhammad Hidayatullah, Madani Hatta 1399 emotional intelligence to technology. https://ranaelkaliouby.com/girldecoded/ Harianti, R. G. A., & Suryantari, E. P. (2022). The influence of learning behavior and emotional intelligence of accounting students at Dhyana Pura University on the level of accounting understanding. Journal of Economics, Business, and Humanities (JAKADARA), 1(2), 273– 282. https://doi.org/10.30871/jama.v6i1.3989 Hartanto, A. Y., & Rohmah, F. N. (2024, December 8). More and more students are using AI to do assignments. Tirto.id. https://tirto.id/penggunaan-ai-di-dunia-pendidikan-makin-widespread- and-even-distributed-gZax Khilmiyah, A., & Wiyono, G. (2023). Assessment of Emotional and Social Intelligence Using Artificial Intellegent BT - HCI International 2023 Posters (C. Stephanidis, M. Antona, S. Ntoa, & G. Salvendy (ed.); hal. 447–453). Springer Nature Switzerland. Latifah, L., & Supriyadi, T. (2024). The Influence of Emotional Intelligence on Student Learning Motivation. Journal of Social Science, 5(4), 1218–1233. https://doi.org/10.46799/jss.v5i4.901 McHaney, R. (2023). The new digital shoreline: How Web 2.0 and millennials are revolutionizing higher education. Taylor & Francis. Nurjan, S. (2020). Theoretical analysis of student academic procrastination. Muaddib: Education and Islamic Studies, 1(1), 61. https://doi.org/10.24269/muaddib.v1i1.2586 Panjaitan, K. L., Sinurat, J. M., Tarigan, Y., & University of North Sumatra. (2024). The effect of ChatGPT on student homework in the society era 5.0. [Journal name missing], 6(1), 1–19. Primasatya, R. D., et al. (2024). Self-leadership in responding to the development of AI chatbot technology in the world of accounting education: An overview of adaptive leadership perspectives. Owner, 8(2), 1944–1955. https://doi.org/10.33395/owner.v8i2.2313 Prof. Dr. Imam Ghozali, M.Com, Aktβ€―; Hengky Latan, S. (2021). Partial Least Square, Konsep Teknik Dan Aplikasi Menggunakan Program Smartpls. BP Universitas Diponegoro. Putri, R. (2016). The influence of emotional intelligence, spiritual intelligence and learning behavior on students' accounting comprehension levels. Scientific Journal of Business Oration, 15, 11–32. Putri, V. A., Sotyawardani, K. C. A., & Rafael, R. A. (2023). The role of artificial intelligence in the student learning process at Surabaya State University. In Proceedings of the National Seminar of the State University of Surabaya (Vol. 2, pp. 615–630). Ramasamy, I., Saravanan, S. A., Rangasamy, G., & Subramanian, D. (2024). Exploring the impact of digitalisation on rural women’s socio-economic status: A bibliometric and scoping study. Multidisciplinary Reviews, 8(2), 2025063. https://doi.org/10.31893/multirev.2025063 Salsabila, W. K., & Indrawati, E. S. (2020). The relationship between emotional intelligence and academic procrastination in students of the Department of History, Faculty of Cultural Sciences, Diponegoro University. EMPATI Journal, 8(4), 773–780. https://doi.org/10.14710/empati.2019.26522 Saputra, T., & Serdianus, S. (2023). The role of ChatGPT artificial intelligence in learning planning. Journal of Social Sciences and Education, 3(1), 1–18. Setiawan, H., Baihaqi, M. I., & Bebena, I. (2022). The impact of intellectual intelligence and emotional intelligence on employee performance. JURISMA: Journal of Business & Management Research, 12(1), 156–172. https://doi.org/10.34010/jurisma.v12i1.5253 Suhadianto, & Pratitis, N. (2020). Exploration of causal factors, impacts and strategies for handling academic procrastination in students. Journal of RAP (Actual Research in Psychology, Padang State University), 10(2), 193. https://doi.org/10.24036/rapun.v10i2.106266 https://ranaelkaliouby.com/girldecoded/ American Journal of Economic and Management Business Vol. 4 No. 8 August 2025 1400 Suryaningsih, C., Saripudin, Widjiyati, N., & Sumiyanto, A. (2024). Emotional intelligence in the digital era (A. P. Hawari, Ed.; 1st ed.). PT Media Penerbit Indonesia. Voronkova, V., Vasyl’chuk, G., Nikitenko, V., Kaganov, Y., & Metelenko, N. (2023). Transformation of digital education in the era of the fourth industrial revolution and globalization. Wright, T. A., & Bonett, D. G. (2007). Job satisfaction and psychological well-being as nonadditive predictors of workplace turnove. Journal of Management, 33(2), 141–160. https://doi.org/10.1177/0149206306297582