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Quantitative analysis based on multiple linear regression was applied in this study to test the hypothesis regarding the influence of Employee Competence and Organizational Culture variables on the Quality of Public Services in the Regional Secretariat of Gorontalo City. Through the involvement of sample data from 47 respondents, a partial analysis portrait shows that the Employee Competence dimension contributes positively and significantly to escalating service quality with a share of 31.40%. On the other hand, the Organizational Culture component also recorded a significant positive impact with an influence of 47.30%. In aggregate (simultaneous), the combination of apparatus competence and strengthening institutional culture triggers a significant acceleration of public service quality with a determination value of 78.70%. The proportion of the remaining variance in the goodness of fit of the model is influenced by variables outside the scope of this research. Based on this basis, efforts to increase employee expertise capacity and improve organizational values ​​​​are crucial instruments for optimizing public service standardization.

Women who simultaneously assume dual roles as employees and household managers face complex social and structural pressures, particularly in urban areas such as Dago Subdistrict, Bandung City. Spousal social support serves as a crucial factor in sustaining the continuity of these dual roles. This study aims to identify the forms and intensity of social support provided by husbands to their wives within the context of dual-role management. A descriptive survey was conducted involving 50 married working women, selected through a two-stage cluster random sampling technique across five neighborhood units (RW), complemented by in-depth interviews with five husband–wife pairs using purposive sampling. The findings indicate that most husbands provide a high level of social support, encompassing emotional, instrumental, informational, and appraisal dimensions. Such support plays a significant role in reducing role conflict, strengthening psychological resilience, and enhancing women’s adaptive capacity in balancing domestic and public responsibilities. In conclusion, the active involvement of husbands reflects a shift toward a more egalitarian household partnership model, contributing to well-being and fostering family resilience.

This study examines the effect of digital banking transformation on financial inclusion in emerging economies, using Indonesia as a case study. Digital banking transformation refers to the technological development of banking services, including accessibility, transaction speed, system security, service integration, and the availability of user-relevant financial features. Financial inclusion is defined as individuals’ ability to access, use, and benefit from formal financial services in an easy, secure, affordable, and sustainable way. This research employs a quantitative explanatory design. Data were collected through structured questionnaires distributed to users of mobile banking, internet banking, digital bank applications, and digital payment services in Indonesia. Respondents were selected using purposive sampling with criteria of being at least 17 years old, residing in Indonesia, and having used digital banking services within the last six months. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that digital banking transformation has a positive and significant effect on financial inclusion, with a path coefficient of 0.684, a t-value of 14.276, and a p-value of 0.000. The R-square value of 0.468 shows that 46.8% of the variance in financial inclusion is explained by digital banking transformation. These findings suggest that digital banking expands access to financial services, increases usage of formal financial products, and reduces barriers related to distance and cost. However, its effectiveness still depends on digital literacy, security, trust, infrastructure, and consumer protection. The study implies that banks and regulators should strengthen inclusive, secure, and affordable digital financial services for underserved communities.

This study aims to analyze the role of online sales in maintaining the financial stability of Micro, Small, and Medium Enterprises (MSMEs) in West Sumatra, Indonesia. The increasing adoption of digital channels, such as marketplaces, social media, messaging applications, live selling, and digital payment systems, has transformed online sales into not only marketing tools but also mechanisms that may affect cash flow continuity and business sustainability. Despite their growing importance, empirical evidence regarding the contribution of online sales to MSME financial stability remains limited. This study employs a quantitative explanatory research design. The population consists of MSME owners in West Sumatra who utilize online sales, while purposive sampling was used to select respondents who had engaged in online selling for at least one year and maintained cash flow records. Data were collected from 102 respondents through a structured questionnaire using a five-point Likert scale and analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM). The findings reveal that online sales have a positive and significant effect on financial stability, with a path coefficient of 0.632, a t-value of 9.214, and a p-value below 0.001. These results indicate that effective use of online sales enhances cash flow continuity, income regularity, working capital adequacy, and financial resilience. However, the benefits depend on disciplined management of digital costs, discounts, platform fees, shipping expenses, product returns, and cash flow records. This study contributes to MSME digitalization literature by highlighting online sales as a strategic instrument for strengthening financial stability rather than merely a marketing channel.

This study aims to analyze the effect of personalized marketing on customer satisfaction and customer retention, as well as to explain the role of customer satisfaction as a mechanism linking marketing personalization to customers’ decision to remain with a brand. The background of this study is based on the increasing use of data-driven marketing strategies, product recommendations, personalized promotions, and digital communication tailored to customer preferences. In an increasingly competitive business environment, companies are required not only to attract new customers but also to retain existing customers through relevant, convenient, and valuable experiences. This study employed a quantitative explanatory approach, with the population consisting of customers who had received personalized marketing from a particular company or brand. The sampling technique used was purposive sampling, with the criteria that respondents had received personalized promotions and had made repeat purchases. The total sample consisted of 110 respondents. Data were collected using a structured questionnaire with a five-point Likert scale and analyzed using Partial Least Squares Structural Equation Modeling. The findings show that personalized marketing has a positive and significant effect on customer satisfaction. This indicates that promotions, recommendations, and marketing messages that are relevant to customer needs can create more positive customer experiences. Customer satisfaction is also found to have a positive and significant effect on customer retention, meaning that satisfied customers are more likely to make repeat purchases, continue using products or services, and avoid switching to competitors. In addition, personalized marketing has a direct effect on customer retention, although its effect becomes stronger when mediated by customer satisfaction. The implications of this study emphasize that companies need to develop personalization strategies that are not only data-driven but also relevant, ethical, non-intrusive, and oriented toward customer value. Therefore, personalized marketing can serve as an important strategy for improving customer satisfaction and maintaining long-term customer retention.

This research aims to evaluate the Naval Academy's Technical Corps Education Program in enhancing cadets' readiness to face assignments on the Indonesian Navy Warships (KRI). The research uses a quantitative approach with an evaluative method and the CIPP model (Context, Input, Process, Product). The research sample consisted of 154 respondents, including cadets from the Technical Corps, educators, and officers who are users of the graduates. Data were collected thru questionnaires, interviews, observations, and documentation, and then analyzed using descriptive statistics and the CIPP model evaluation. The research results show that the Technical Corps Education Program falls into the good category with an average score of 4.15. The context component received a score of 4.18, input 4.05, process 4.12, and product 4.23. The results indicate that the education program has been effective in enhancing the cadets' readiness to face assignments on KRI. The main supporting factors include curriculum alignment, educator competence, and field practice experience. However, the improvement of practical facilities, the updating of modern warship technology materials, and the strengthening of simulation-based training are still needed to continuously enhance the quality of graduates.

The transition toward Society 5.0 has transformed the educational landscape by requiring teachers to integrate digital technology into learning while maintaining a human-centered approach. However, preliminary observations at SMK Paramitha Bukittinggi revealed that many teachers still experienced difficulties in using digital technologies effectively, indicating challenges related to leadership support, digital competence, and self-efficacy. This study aims to analyze the influence of transformational leadership, digital competence, and self-efficacy on teacher performance in the Society 5.0 era. The research employed a quantitative approach with a causal associative design. The population consisted of all 35 teachers at SMK Paramitha Bukittinggi, and total sampling was applied so that the entire population served as research respondents. Data were collected through questionnaires using a five-point Likert scale and analyzed using multiple linear regression with the Ordinary Least Squares (OLS) method through SPSS version 25. The findings revealed that transformational leadership had a positive and significant effect on teacher performance (t = 2.407; p = 0.016), digital competence also had a positive and significant effect (t = 2.277; p = 0.027), and self-efficacy showed the strongest positive and significant effect (t = 15.658; p = 0.000). Simultaneously, the three independent variables significantly influenced teacher performance (F = 378.268; p = 0.000), with an adjusted R² value of 0.859, indicating that 85.9% of the variation in teacher performance could be explained by the model. The study concludes that transformational leadership, digital competence, and self-efficacy are critical determinants of teacher performance in the Society 5.0 era. Strengthening these factors can significantly enhance teachers' ability to adapt to technological advances and improve the quality of learning processes.

Fierce competition in the tourist bus industry, especially in the price segment, requires companies to understand the key factors that influence customer satisfaction. This study aims to analyze the influence of service quality, price, and brand image on customer satisfaction of PO Jurus Elang in Semarang City through a quantitative approach with an explanatory survey involving 136 respondents selected by purposive sampling. Data were obtained from a Likert scale questionnaire and analyzed using SEM-PLS via SmartPLS. The results of the analysis indicate that service quality and price have a positive but insignificant influence, while brand image has a positive and significant influence on customer satisfaction, with an R-square value of 0.721, reflecting good model strength. These findings emphasize that brand image is a major factor in increasing customer satisfaction, while service quality and price are more likely to be viewed as minimum standards by customers.

The legal arrangement of cannabis in Indonesia is based on Article 28H paragraph (1) of the 1945 Constitution of the Republic of Indonesia which guarantees the right to health services, elaborated through Law Number 35 of 2009 concerning Narcotics and Law Number 17 of 2023 concerning Health. Cannabis is classified as a Group I narcotic prohibited for health services under Article 8 paragraph (1) of Law Number 35 of 2009, while Article 139 of Law Number 17 of 2023 requires that the use of medicines containing narcotics may only be carried out based on a prescription from medical personnel. At the same time, Canada through the Cannabis Act (S.C. 2018, c. 16) and Uruguay through Ley No. 19.172 (2013) apply fundamentally different legal arrangements for cannabis. This research uses normative legal research methods with a comparative law approach, applying the criminal policy framework of Marc Ancel and the law enforcement theory of Joseph Goldstein. The results show: (1) cannabis arrangement in Indonesia is prohibitive through Article 8 paragraph (1) of Law Number 35 of 2009, while Article 6 paragraph (3) opens a mechanism for reclassification through Ministerial Regulation; (2) Canada through the Cannabis Act applies a regulated market model with a CAD 11.4 billion legal industry and a 70% reduction in arrests, while Uruguay through Ley No. 19.172 applies a state monopoly with an 85% reduction in arrests without an increase in problematic use; (3) fundamental differences in legal systems, political systems, socio-cultural backgrounds, religion, and narcotics policy philosophy mean that the Canadian and Uruguayan models are not relevant to be directly applied in the Indonesian criminal law system.

Recent Internet of Things (IoT) intrusion detection studies have reported near-perfect benchmark performance for Distributed Denial of Service (DDoS) detection, yet limited attention has been given to understanding how different traffic representations contribute to the detection process under highly imbalanced traffic conditions. This study presents an ablation-driven analysis to investigate the contribution of statistical and temporal representations for large-scale IoT DDoS detection using the CICIoT2023 dataset. Three experimental scenarios are evaluated, including statistical representation, temporal sequence representation, and hybrid statistical–temporal representation. Temporal representations are learned using a one-dimensional Convolutional Neural Network (1D-CNN) with lag-based traffic sequences, while ensemble tree-based classifiers are employed for final classification and representation analysis. In addition, multiple ablation configurations are designed to evaluate the impact of temporal dependency modeling and feature engineering strategies on detection performance. Experimental results show that statistical traffic representations remain highly effective for DDoS detection on CICIoT2023, achieving 99.36% accuracy and 99.31% weighted F1-score in the statistical representation scenario. Feature importance analysis further indicates that engineered statistical features contribute substantially more to the classification process than CNN-based temporal representations. Although temporal modeling captures sequential traffic behavior, its contribution is relatively limited and mainly acts as a complementary representation. Furthermore, the hybrid configuration produces only marginal improvements over the statistical representation alone. These findings highlight the importance of representation-level analysis for understanding the actual contribution of statistical and temporal modeling in modern IoT intrusion detection systems beyond relying solely on benchmark accuracy.

The completeness of nursing care documentation serves as a primary indicator of hospital service quality and remains a critical component of patient safety, clinical communication, and legal accountability. Despite its importance, empirical evidence from various healthcare facilities indicates that nursing records are frequently suboptimal, incomplete, or inconsistent. This study aims to analyze the complex interrelationships between burnout and coaching on the completeness of nursing care documentation, specifically examining the role of nursing competence as a mediating (intervening) variable at the Dr. Chasbullah Abdulmadjid General Hospital in Bekasi City. Utilizing a quantitative research framework with a cross-sectional design, this study sampled practicing nurses stationed across inpatient and intensive care units. Primary data were gathered through a validated, structured questionnaire designed to measure psychological burnout, the frequency of clinical coaching, perceived professional competence, and the objective completeness of documentation. The data were subjected to rigorous analysis using Structural Equation Modeling–Partial Least Squares (SEM-PLS) to test the hypothesized pathways within the conceptual model. The results demonstrated that burnout exerts a significant negative influence on both nursing competence and the quality of documentation, suggesting that emotional exhaustion hinders professional performance. Conversely, systematic coaching was found to have a significant positive impact, directly improving both competence levels and documentation adherence. Critically, the analysis confirmed that nursing competence acts as a vital intervening variable; it effectively mediates and strengthens the influence of both burnout reduction and coaching interventions on the overall completeness of nursing care records. This study concludes that proactive burnout management and the institutionalization of structured coaching programs are essential strategic priorities. By addressing these factors, hospital management can enhance individual nurse competence, thereby ensuring high-quality, comprehensive nursing documentation that supports patient safety and institutional integrity.

This study aims to examine how schools foster togetherness amid religious diversity through a dialogical approach. This research employed a qualitative method and was conducted at SMA Negeri 1 Sumbul. Data were collected through observation, interviews, and documentation, while the analysis used the interactive model of Miles and Huberman, which includes data reduction, data display, and conclusion drawing. The findings reveal that three main religions Islam, Catholicism, and Protestantism coexist harmoniously within the school due to strong practices of tolerance. This is reflected in the implementation of religious worship conducted simultaneously at the same time but in different places according to each belief, as well as mutual respect during religious celebrations such as Ramadan and Eid al-Fitr, Easter, and Christmas. In addition, social interactions among students occur in an open, communicative, and non-discriminatory manner. These findings indicate that schools play a significant role in promoting unity through multicultural education, religious moderation, and dialogical approaches. Therefore, schools can serve as effective spaces for shaping students’ tolerant character and their ability to live harmoniously in a diverse society.

This study aims to develop a sentiment classification method for YouTube user comments related to the game Love and Deepspace using the Naïve Bayes algorithm, focusing on improving the text data processing and understanding user perceptions. Comment data were collected through scraping from YouTube videos, followed by preprocessing including text cleaning, normalization, stopword removal, stemming, and translation into English. Initial labeling was conducted using TextBlob, then the data were randomly sampled for training the Naïve Bayes model. Evaluation involved comparing sentiment distributions and visualization using Word Cloud and bar charts. The Naïve Bayes model achieved an accuracy of 77.36% in sentiment classification. The sentiment distribution shows differences between TextBlob (positive: 1,011, neutral: 1,312, negative: 575) and Naïve Bayes (positive: 901, neutral: 1,627, negative: 370), with Naïve Bayes being more conservative. The Word Cloud visualization identifies dominant words such as "bang," "game," and "main," while the bar chart shows the largest proportion of neutral sentiment. Naïve Bayes is effective for sentiment classification on informal comment data, with significant differences from rule-based methods like TextBlob. This research contributes to the development of text data processing techniques and user perception analysis, as well as opening up optimization opportunities with other algorithms like SVM for better accuracy.

The subject of corpse care is one of the important competencies in Islamic Religious Education (PAI) at the senior high school level, which is obligatory (fardhu kifayah). However, the complexity of the material and the presence of psychological barriers often make students less interested or feel afraid. This study aims to analyze in-depth the perceptions of SMA YATPI Godong students regarding the importance of corpse care, the factors that influence these perceptions, and the urgency of this competency as a provision for social life. This study uses a qualitative descriptive approach supported by simple quantitative data percentages. The subjects were 35 grade XI students, selected using a purposive sampling technique. Data collection was carried out through participant observation, questionnaire distribution, in-depth interviews, and documentation. Data analysis followed an interactive model that includes data condensation, data presentation, and drawing conclusions. The results showed that the majority of students (82.8%) had a very positive perception of the urgency of this material and were aware of its socio-cultural responsibility. The main factors influencing students' perceptions included the demonstration method using mannequins implemented by teachers, internal religiosity values, and the encouragement of societal expectations. These skills are considered essential not only for fulfilling academic obligations but also as a form of student readiness to engage and contribute significantly to their social environment in the event of grief.

This study aims to examine the influence of Content Marketing and Product Quality on Purchase Decisions among consumers of Aldyan Store. The research employed a quantitative method with a survey approach. The population consisted of Aldyan Store consumers, with a sample of 100 respondents selected using a sampling technique appropriate to the characteristics of the study. Data were collected through questionnaires and analyzed using validity tests, reliability tests, classical assumption tests, multiple linear regression analysis, the coefficient of determination (R²), t-test, and F-test with the assistance of SPSS version 23. The results showed that, partially, Content Marketing had a positive and significant effect on Purchase Decisions, with a t-value of 2.544 and a significance level of 0.013 < 0.05. Product Quality also had a positive and significant effect on Purchase Decisions, with a t-value of 2.288 and a significance level of 0.024 < 0.05. Simultaneously, Content Marketing and Product Quality had a positive and significant effect on Purchase Decisions, with an F-value of 9.358 and a significance level of 0.000 < 0.05. The coefficient of determination (R²) was 0.162, indicating that Content Marketing and Product Quality explained 16.2% of the variance in Purchase Decisions, while the remaining 83.8% was influenced by other factors outside the research model. Based on the findings, it can be concluded that Content Marketing and Product Quality play an important role in improving consumers' Purchase Decisions at Aldyan Store. Therefore, Aldyan Store should continue to enhance the quality of its marketing content and maintain product quality to increase consumer trust and purchase intention.

Tax administrations are undergoing a fundamental transition from conventional audit practices based on manual inspection and limited sampling toward data-driven supervision supported by big data analytics, artificial intelligence, and digital transaction infrastructures. However, developing economies, particularly in Southeast Asia, continue to face structural constraints such as fragmented legacy systems, informal economic activities, uneven digital literacy, corruption risks, weak data interoperability, and evolving privacy regulations. This study aims to develop a contextual framework for detecting potential tax-reporting fraud by integrating big data tax analytics, localized machine learning, explainable artificial intelligence, blockchain-enabled value-added tax data integrity, and socio-organizational governance. The study adopts a mixed-method sequential explanatory approach combined with Design Science Research. The methodological design integrates policy and institutional analysis, machine learning model design, and socio-organizational validation using secondary literature, Southeast Asian case studies, regulatory review, and simulated data architecture. The main contribution of this study is the Contextual Tax Analytics with AI and Blockchain Framework, or C-TAX-AIB Framework, consisting of three interrelated layers: Data Layer, Analytics Layer, and Governance and Human Layer. The Data Layer proposes a hybrid blockchain architecture for e-Faktur and value-added tax reporting integrity; the Analytics Layer introduces localized machine learning and explainable AI to support transparent risk scoring and anomaly detection; and the Governance and Human Layer embeds privacy protection, taxpayer digital literacy, auditor readiness, and trust-building mechanisms. The framework advances prior studies by moving beyond algorithmic fraud detection toward an integrated governance model suitable for developing economies. The study provides theoretical implications for public finance analytics and practical guidance for ASEAN tax administrations in designing accountable, explainable, and context-sensitive digital tax systems.

Artificial intelligence has the potential to support radiology workflows by assisting in the identification of cases that may require additional clinical attention. However, alert-oriented medical AI systems should provide not only classification outputs but also interpretable evidence that can be reviewed and audited by clinicians. This study develops and evaluates an explainable multimodal framework for binary chest X-ray alert classification using paired radiology reports and chest X-ray images. The text branch employs TF-IDF n-gram features with a class-balanced Logistic Regression classifier, while the image branch fine-tunes a pretrained ResNet18 model. The two branches are integrated through probability-level late fusion using a validation-selected fusion weight. Explainability is implemented in a modality-specific manner: global coefficient analysis is used to identify influential textual cues, while Grad-CAM heatmaps are used to visualize salient image regions. Experiments were conducted on paired samples from the Open-i/IU X-Ray dataset using text-only, image-only, and fusion-based evaluation settings. Additional analyses include case-level complementarity analysis, bootstrap confidence intervals for ROC-AUC, shortcut-feature inspection, and qualitative Grad-CAM auditing. The results indicate that the text modality provides the dominant predictive signal under the current proxy-label setting. Late fusion produced a small descriptive improvement on the test set, increasing accuracy from 0.8533 to 0.8667, F1-score from 0.8817 to 0.8936, and ROC-AUC from 0.8936 to 0.9025 compared with the text-only baseline. However, the observed ROC-AUC improvement was not statistically conclusive based on bootstrap analysis. These findings suggest that the proposed framework is useful as a reproducible and auditable multimodal prototype, while also highlighting important limitations, including proxy-label ambiguity, potential label leakage from radiology reports, limited image-branch contribution, lack of external validation, and the need for stronger explanation and calibration assessment.

This study aims to explore issues faced by students and the contribution of guidance and counseling (BK) services in addressing various challenges at MTSN Cirebon. The issues studied include low levels of discipline, school withdrawal behavior, lack of learning motivation, and smoking habits among students. This study employed a qualitative, descriptive approach. Participants in the study consisted of BK teachers, students, and student organizations (OSIM and MPK) selected using a purposive sampling technique. Data were collected through in-depth interviews, observation, and documentation. Data analysis was conducted using an interactive model involving data reduction, data presentation, and conclusion drawing. Its validity was tested through triangulation of sources and techniques. The findings of this study indicate that the issues faced by students are influenced by internal and external factors, such as lack of self-awareness, low learning motivation, environmental impacts, and excessive use of social media. BK services play a vital role in addressing these issues through preventive and curative approaches, as well as collaboration between teachers, students, parents, and school organizations. In addition, consistent discipline, engaging learning methods, and monitoring of student behavior are also crucial elements in problem-solving efforts. This research confirms that resolving student issues requires a comprehensive and collaborative approach across all parties within the school environment. Guidance and counseling services play a strategic role in supporting students in overcoming challenges and developing their full potential.

This study aims to determine and analyze the effect of entrepreneurial knowledge and entrepreneurial skills on business success in Micro, Small, and Medium Enterprises (MSMEs) in Aesesa District, Nagekeo Regency. The background of this research is driven by fluctuations in MSME performance, unstable turnover, suboptimal management, low customer satisfaction, and limited practical skills and knowledge. This study utilizes a quantitative approach with a causal associative method. The population consists of all 3,705 active MSMEs in Aesesa District. The sampling technique used was purposive sampling with specific criteria, and the sample size was calculated using the Slovin formula with a 10% margin of error, resulting in exactly 100 respondents. Data collection was conducted via structured questionnaires with a 1–5 Likert scale. Data analysis techniques include descriptive statistics, validity and reliability tests, classical assumption tests (multicollinearity, heteroscedasticity, and normality), multiple linear regression analysis, t-test, F-test, and coefficient of determination administered through SPSS. The multiple linear regression analysis yielded the equation Y = 12.450 + 0.412X1 + 0.385X2. The partial test (t-test) demonstrates that entrepreneurial knowledge has a positive and significant effect on business success (t count 4.120 > t table 1.984, sig 0.000). Entrepreneurial skills also exert a positive and significant impact on business success (t count 3.850 > t table 1.984, sig 0.000). Simultaneously (F-test), entrepreneurial knowledge and entrepreneurial skills have a significant combined effect on business success with F count 38.450 > F table 3.09. The coefficient of determination (Adjusted R Square) value of 0.442 indicates that the variation in business success is influenced by both independent variables by 44.2%, while the remaining 55.8% is explained by external factors outside this research model.

This study aims to analyze the effect of Digital visual marketing and Service Innovation, both partially and simultaneously, on the decision to rent Sikka traditional clothing at Meliana Collection. The background of this research is based on the unstable fluctuations in the number of rental transactions at the object of research, so a strategy is needed that can increase consumer appeal, especially through strengthening visual-based digital marketing and improving service quality. This study uses a quantitative approach with causal associative methods. The population in this study consists of all consumers who have rented traditional clothing at Meliana Collection. Determination of the number of samples was carried out using a probability sampling technique with the Slovin formula at a 10% error level, resulting in 88 respondents as the research sample. Data were collected through the distribution of questionnaires and further analyzed using descriptive analysis, classical assumption tests, multiple linear regression analysis, and hypothesis testing (t-test and F-test) using SPSS version 23 software. The results of the research partially (t-test) show that the Digital visual marketing variable has a positive and significant effect on rental decisions (t-count 2.955> 1.988; Sig 0.004). The Service Innovation variable also has a positive and significant effect on rental decisions and is proven to be the most dominant variable influencing consumer decisions (t-count 11.386 > 1.988; Sig 0.000). Simultaneously (F-test), the two independent variables have a significant combined effect on consumer rental decisions (F-count 77.606>2.71; Sig 0.000). The combination of the Digital visual marketing and Service Innovation variables contributes 64.6% (R Square 0.646) to rental decisions, while the remaining 35.4% is influenced by other factors or variables outside of this research model.