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Investigating the Relationship Between Service Quality and Student Intention Behavior in Higher Education Service Experience
Full text linkThis study aims to investigate the relationship between service quality and student intention behaviour in Higher Education Institutions. This research used a correlational method with a quantitative approach. The online questionnaire was distributed to 110 State University of Surabaya students following the cluster random sampling. In this research, data gathered was analyzed using the Pearson Product Moment correlation analysis. The results showed a significant positive correlation between higher education service quality and student intention behavior. The better service quality provided linier with linearnt intention behavior fobehaviourending and their intention to loyal in
in the institutton. This study's findings remind us to improve the quality dimension as a key factor in attracting and retaining students to student education Institutions
Psychological Constructions in Influencing Female Intentions to Pursue Science, Technology, Engineering and Mathematics (STEM) Fields
Full text linkAlthough there is a surge in female students attaining bachelor’s degrees, their involvement in Science, Technology, Engineering, and Mathematics (STEM) in China remains disproportionately low, posing a continuing concern. This study explores the reasons behind this gender gap and identifies factors influencing females’ intentions to pursue STEM Education in China. The study aims at investigating the
level of psychological constructs related to identity, interest, the role of self-concept, and self-efficacy. It
seeks to identify the differences in these psychological constructs based on interests and to examine the relationships between variables within psychological construction. The independent variables include identity, interests, the role of self-concept and self-efficacy. Data were gathered from 64 females with educational qualifications from different levels in Cheng Gong districts in Yunnan province, China. The data were analysed using descriptive statistics and inferential analysis including one-way MANOVA and Pearson correlation analysis. The findings reveal that overall identity, interest, self-concept, and self efficacy of female students towards STEM is at a moderately high level. The results of the study suggest that female students with an interest in arts exhibit higher scores in identity, interests, and the role of self concept compared to respondents with an interest in science. The relationships between identity and interests, the role of self-concept, and self-efficacy are very strong. Hence policymakers are encouraged to propose key initiatives to empower more female students to pursue STEM
Exploring the Impact of Acceptance and Commitment Therapy on Perceived Stress and Psychological Flexibility among Psychiatric Nurses: A Randomized Controlled Trial
Full text linkNursing in psychiatric wards is recognized as an exceptionally stressful profession due to the challenging nature of the patients and difficulties in communication. Addressing this stress effectively can enhance nurses' overall well-being and the quality of their work. This study aimed to examine the effects of Acceptance and Commitment Therapy (ACT) on the perceived stress (PS) and psychological flexibility (PF) of nurses in psychiatric settings. Methods: Seventy nurses from the Razi Psychiatric Center in Rawalpindi were randomly assigned to either an experimental group or a control group, each comprising 35 participants. While the control group continued with their usual interventions, the experimental group participated in eight 2-hour sessions of ACT training in addition to their routine care. Assessments, including demographic information, the Perceived Stress Scale, and the Acceptance and Action Questionnaire (2nd Edition), were conducted before the intervention and one month after the final session. Results: The findings revealed a significant difference in PS (P = 0.002) and PF (P = 0.001) between the control and experimental groups, with the experimental group experiencing lower PS and greater PF. Conclusions: ACT appears to be an effective approach to reducing perceived stress and enhancing psychological flexibility, offering a potential strategy to empower nurses working in psychiatric wards
Critical Success Model for the Implementation of the Online Tax System in Indonesia
Full text linkThe Indonesian government has introduced a tax reporting system using electronic tax returns (e-SPT) to improve compliance. Despite this, taxpayer awareness remains low, hindered by various issues like technical challenges, lack of tech-savvy skills, concerns over data security, and frequent changes in rules and policies. To address these issues and enhance compliance, the government developed a self-assessment application for tax reporting. The e-SPT system enables taxpayers, whether individuals, companies, or other entities, to complete and submit their tax forms electronically. However,
evaluating the success of this system requires a robust model. One such model is the DeLone and McLean 2003 model, which includes six variables: information quality, system quality, service quality, user satisfaction, intention to use, and net benefit. While useful, this model has been criticized for its universal applicability, limited organizational impact, and instability of variables. To address these limitations,
researchers developed an enhanced model by incorporating four variables from the DeLone and McLean model: information quality, system quality, service quality, and user satisfaction. Then adding new variables: culture, tax content, trust, and tax system success. These additional variables are derived from the works of Alattas and Kang, Aparicio, Reis and Freitas, Gupta. This refined model serves as a reference for
evaluating the effectiveness of the tax reporting system. To test this model, researchers conducted a study focusing on micro, small, and medium enterprises (MSMEs) in West Java. They used a quantitative approach, involving three main steps: a pretest, a pilot study, and a survey. The survey collected data from 394 respondents, and the analysis was performed in two stages: demographic analysis and inferential analysis.
The results indicated that out of the fifteen hypothesized paths, nine were positively supported, while six were rejected. The study's contributions are threefold: theoretically, it validates the enhanced model; methodologically, it employs
quantitative methods to substantiate the model's validity; and practically, it identifies key factors affecting the performance of the tax reporting system, offering insights into
the relationships between dependent and independent variables in this context
E-Voting Application Interface Design Based on User Emotional Preferences Using Kansei
Full text linkThe increasing use of technology has made people accustomed to using it, especially in the digital age. In the field of politics, information technology is used to support the holding of elections remotely through the internet. The appearance and design of the user interface of an e-voting application is a crucial factor in determining its success, as the display design can affect user psychology when using it. A well-designed interface can improve the user experience and increase the likelihood of successful adoption and usage of the e-voting system. On the other hand, a poorly designed interface can create confusion and frustration for users, leading to a decrease in trust and adoption of the system. This research has allowed for a deeper understanding of the emotional and psychological factors that influence people's perceptions and interactions with the e-voting application. By gathering and analyzing data about users' emotional feelings through the use of Kansei words and statistical multivariate analysis, the researchers were able to identify the key emotional and psychological factors that should be considered when designing the user interface, these findings can be used to inform the design of the e-voting application in order to create a more appealing and satisfying user experienc
Online Consumer Purchase Decision of Alcohol Products in Sri Lanka
Full text linkThis study investigates the changing aspects of online purchasing decisions within Sri Lanka's alcohol industry, focusing on the Colombo district. Directed by specific objectives, including the impacts of advertising, pricing, social factors, and perceived risks, a deductive approach with a Positivism philosophy was employed to gather, analyze, and draw conclusions from data collected from over 6000 online consumers. Utilizing simple random sampling, 361 respondents were surveyed via an online questionnaire, with data analyzed through descriptive, factor, reliability, and correlation analyses using SPSS. Results indicate a male dominated demographic aged 30 to 34 engaging in online purchases, with advertising significantly influencing brand preference, albeit messaging playing a minor role. Price emerged as a minimal factor, though discounted offers garnered interest, and social recommendations strongly influenced purchasing decisions. The study concludes that advertising, pricing, perceived risk, and social factors are directly correlated with consumer purchasing decisions, with recommendations emphasizing tailored messaging across diverse media channels, competitive pricing strategies, and a focus on social dynamics and cultural adaptation within the industry
Intelligent Social Media Text Mining for Political Influence Analysis: A Case Study on Makassar Mayor Election
Full text linkSocial media has become a common means of communication in society and plays a big part in many community activities in areas such as politics, economy, education, and information sharing. Important platforms such as Instagram, Twitter, Facebook and others can drive increased participation in elections. In this context, this research uses the Twitter application to influence political conversations through social media platforms. In the context of political communication during the Makassar mayor election, this study examines the application of a hybrid text mining approach that combines Support Vector Machine (SVM), Naïve Bayes, and K-means clustering for sentiment analysis. Traditional methods of sentiment analysis often fail to capture the nuanced sentiments of the electorate because of the complex nature of political discourse. This study aims to address these limitations by leveraging the use of SVM, Naïve Bayes, and K-Means. This research conducts data preparation involves cleansing and organizing textual data, segmenting it using K-means clustering, classifying it into sentiment classes using Naïve Bayes classifier, and enhancing classifications with the SVM. The results demonstrate that the hybrid model has superior performance compared to traditional methods, attaining an accuracy rate of 85.43% in contrast to the 64.96% accuracy rate achieved by traditional approaches. The hybrid approach demonstrates superior performance in sentiment accuracy and thematic analysis compared to traditional methods, highlighting its potential to extract meaningful insights from complex textual data. The findings reveal significant sentiment trends and discourse themes that influenced public opinion during the election. Furthermore, the research showcases the adaptability of the hybrid approach to diverse data sources and its applicability to other domains requiring detailed sentiment and thematic analysis. These findings constitute a valuable contribution to the fields of political science and computational linguistics by presenting a novel framework for sentiment analysis. This framework improves the analytical abilities of political analysts, campaign strategists, and policymakers
Information Technology Readiness and Acceptance Model for Social Media Adoption in Blended Learning Among Higher Educational Institutions in West Java, Indonesia
Full text linkTechnological developments, including the internet, and learning opportunities are increasing. This also causes learning strategies and models to develop. The blended learning model is applied in almost all universities in Indonesia and throughout the world. With so many universities in Indonesia, implementing blended learning is a challenge because it requires a lot of technological preparation and human resources; this often needs to be solved by policymakers in higher education. Blended learning requires a lot of strategies and technology to be implemented well. One of them is social media because social media has become a lifestyle for urban and remote communities to communicate. Based on the above, this research investigates the readiness and acceptance of social media information technology in blended learning to determine the factors that influence it among students in higher education. This research uses quantitative methods to develop a model by adopting concepts, theories and models such as information processing theory, technology readiness, technology acceptance, perceived validity and perceived trust, and information literacy theory. The number of respondents used was 384, and the purposive sampling technique was used in the student population in West Java. The novelty of the model developed is the addition of three new variables, namely information literacy, perceived validity, and perceived trust in the social media technology acceptance model in the application of blended learning. In the data processing results based on statistical tests, results were obtained that explained the 31 hypotheses constructed, 19 hypotheses were accepted, and 12 hypotheses were rejected. Information Literacy, Optimism, Innovativeness, Perceived Validity, Perceived Trust, Perceived Usefulness, Perceived Ease of Use, Intention to Use, and Usage Behavior are the accepted factors. The findings in this study contribute to academic knowledge and provide actionable insights for policymakers, institutions, and educational practitioners seeking to increase the acceptance and adoption of social media technologies in blended learning. The identified factors provide a strong foundation for strategic planning, policy development, and learning initiatives to encourage the successful adoption of social media technology in blended learning among private university students in West Java, Indonesia
Evaluating Readiness and Acceptance of Artificial Intelligence Adoption Among Elementary School Teachers
Full text linkArtificial Intelligence (AI) is a computer system that mimics the human brain's ability to process information and make decisions. AI technology is used to learn patterns in data and make predictions or decisions based on that learning. Despite the potential benefits of AI in education, elementary school teachers face significant challenges in adopting AI technology due to limited training, lack of resources, and resistance to change. This research aims to identify the factors influencing the adoption of AI technology among primary school teachers in West Java, Indonesia. The study involved 384 participants and employed a quantitative approach. Specific factors influencing AI adoption were identified by developing a model for AI-based teaching and learning and assessing readiness factors. The results identified optimism, innovativeness, insecurity, discomfort, perceived validity, trust, usefulness, and ease of use as critical factors for successful AI adoption among primary school teachers in West Java. The customized adoption model provides a practical roadmap for integrating AI into teaching and learning processes, addressing regional specificities while remaining relevant to similar educational challenges worldwide. The assessment of readiness factors offers actionable insights for fostering a supportive environment for technology integration. The study concludes with recommendations for future research and implications for educators, administrators, and policymakers
Weather Prediction for Strawberry Cultivation Using Double Exponential Smoothing and Golden Section Optimization Methods
Full text linkStrawberry is one of the fruit commodities that has a high demand so that it is widely cultivated by most people in Bantaeng Regency to meet with the market needs. The high intensity of weather changes is the main challenge in the strawberry production, which is influenced by climate dynamics and the start season time changes. Climate change does not only affect the amount of rainfall, but also
causes a shift in the rainy season and dry season start. As a result, in the cultivation of plants such as strawberries, there are often difficulties in adjusting or slow anticipation in the extreme changes of rainfall. This research began with the data collection stage through field observations, interviews, and literature studies. The design tool used a systematically organized UML, which included a use case diagram, then an activity diagram, as well as an elaboration into sequence diagrams, and class diagrams. The system was developed by implementing the PHP programming language on the interface design as well as MySQL as a database processing. The algorithm used to predict the air temperature feature, wind speed feature, and rainfall feature was Double Exponential Smoothing, followed by the optimization of the Golden Section method to select the right smoothing value. Referring to the results of this study, the system can provide planting time recommendations based on prediction of rainfall, air temperature, and wind speed parameters through a web-based platform. Based on the calculation of the accuracy value of the prediction results using the Mean Absolute Percentage Error (MAPE), the obtained forecast error value was of 5.89% for wind speed, 0.63% for air temperature, and 0.69% for rainfall. The Golden Section Optimization in Double Exponential Smoothing provided the best smoothing for prediction