An Assistant Decision-making Method of User Side Resource Interactive Transaction Based on Scipy Solver and Genetic Algorithm

Aim/Purpose: This study aimed to delve into the determinants of user satisfaction and the intention to continue using streaming services, employing structural equation modeling as the analytical framework. Understanding these determinants is crucial for service providers who seek to enhance user retention and ensure long-term success in a competitive market. By identifying the key factors that influence user satisfaction and continued usage, service providers can tailor their strategies to meet user needs more effectively. Introduction/Background: The rapid proliferation of streaming services has transformed how content is consumed, making it imperative to understand what drives user satisfaction and retention. This paper addresses the critical problem of identifying the factors that influence user satisfaction and the intention to continue using streaming services. The study aimed to illuminate how service providers can improve user satisfaction and foster continued usage by focusing on essential aspects such as the content library and user experience. By doing so, the research provides valuable insights into the elements that contribute to a positive user experience and sustained engagement with streaming platforms. Methodology: To achieve the study's objectives, data were collected from a sample of 487 respondents who are active users of streaming services. The data collection process involved a comprehensive survey designed to capture various aspects of user satisfaction and usage intentions. The collected data were then analyzed using Structural Equation Modeling (SEM) to test the proposed hypotheses. SEM is a robust statistical technique that allows the examination of complex relationships between multiple variables, providing a comprehensive understanding of the determinants of user satisfaction. Various statistical techniques, including confirmatory factor analysis and path analysis, were employed to validate the model and ensure the reliability and validity of the findings. Findings: The analysis revealed that both the content library and user experience were significant determinants of user satisfaction. A diverse and extensive content library was found to be a critical factor in enhancing user satisfaction, as it provides users with a wide range of options to choose from, catering to diverse preferences and interests. Similarly, a high-quality user experience, characterized by ease of use, seamless navigation, and reliable performance, was shown to significantly influence user satisfaction. Furthermore, the study found that user satisfaction had a direct and significant impact on the intention to continue using the streaming service. Contribution/Impact on Society: The findings of this study contribute to the existing body of knowledge by providing empirical evidence on the importance of the content library and user experience in driving user satisfaction and retention in the context of streaming services. The larger implications of these findings suggest that service providers should prioritize these aspects to maintain and grow their user base. By focusing on enhancing the content library and user experience, service providers can create a more engaging and satisfying experience for users, leading to higher retention rates and sustained growth. This, in turn, can positively impact society by ensuring that users have access to high-quality, diverse content and a seamless viewing experience. Recommendations: Based on the findings, it is recommended that streaming service providers focus on expanding their content library to include a wide variety of genres, languages, and formats to cater to diverse user preferences. Additionally, continuous improvements to the overall user experience should be prioritized, including user interface enhancements, performance optimizations, and personalized recommendations. Regular assessments of user preferences and experiences should be conducted to adapt to changing user needs effectively. By doing so, service providers can ensure that they remain competitive and meet the evolving demands of their user base. Research Limitation: The study was limited by its sample size and the specific context of streaming services, which may not be generalizable to other types of services or industries. The sample of 487 respondents, while substantial, may not have fully captured the diversity of streaming service users. Additionally, the study's focus on streaming services meant that the findings may not apply to other digital services or industries. Further research is needed to explore additional factors influencing user satisfaction and retention in different contexts and to validate the findings across larger and more diverse samples. Future Research: Future research should explore the determinants of user satisfaction and retention in different industries and contexts to build a comprehensive understanding of user behavior. This could include studies on other digital services, such as e-commerce platforms, social media, and online gaming, to identify common and unique factors influencing user satisfaction and retention. Additionally, longitudinal studies could provide insights into how these determinants evolve over time, aiding service providers in adapting their strategies accordingly. By examining the long-term trends and changes in user behavior, future research can offer valuable guidance for service providers looking to sustain user engagement and satisfaction over the long term.

China’s library and intelligence organizations have a long history, rich resources and excellent talents and have played an important role in various consulting services. They are currently facing fierce market competition, making it challenging to meet users’ needs. This paper first establishes the principles of digital information resource services, based on the principles, reference to the ACSI model and considers a number of indicators to establish a scientific user satisfaction measurement model of digital resources in line with the actual development of library intelligence. The relationship between intelligent library intelligence and user satisfaction is investigated by combining a measurement model and structural model. Relevant hypotheses are proposed, research variables are identified, and empirical analysis is utilized to evaluate the model’s practicability. The results of the empirical research show that the hypotheses that library intelligence services can improve user satisfaction are verified through the path significance test, and after analysis, we know that the standardized path coefficients of paths 1, 3-7 are 0.526, 0.395, 0.426, 0.536, 0.163, and 0.168, respectively, which are greater than 0 and the P-value is less than 0.05 so that the hypotheses 1, 3-7 are valid. Intelligent library intelligence-related variables have a positive effect on user satisfaction improvement.

With the rapid development of digital technology, online fitness live streaming platforms have become effective tools for helping users enhance their physical activity and health. However, the attitudes of Generation Z users, who form a significant portion of the platform’s user base, remain unclear. Identifying the factors influencing Generation Z’s use of these platforms and improving user satisfaction can assist in refining platform design and services, promoting sustainable development. A mixed-methods approach was employed to collect 1,788 user reviews, which were consolidated into 40 items. A questionnaire was then distributed to 314 respondents. In the first round, 165 open-ended questionnaires were distributed to identify specific influencing factors through factor analysis, leading to the development of a user experience evaluation scale. In the second round, 314 scale-based questionnaires were distributed, and structural equation modeling (SEM) was employed to explore the relationships between various factors and user satisfaction, resulting in the construction of a user satisfaction model. The factors influencing Generation Z users’ engagement with the platforms include social interaction, usefulness, convenience, functional quality, and technical quality. Among these, social interaction is the most critical factor affecting user satisfaction. Generation Z has unique needs regarding digital platforms. Therefore, platforms should provide services tailored to their psychological and behavioral characteristics, optimize business operation models, and further enhance the user experience.

The purpose of this study is to identify the characteristics of Agile development processes that impact user satisfaction. We used user reviews of OSS smartphone apps and various data from version control systems to examine the relationships, especially time-series correlations, between user satisfaction and development metrics that are expected to be related to user satisfaction. Although no metrics conclusively indicate an improved user satisfaction, motivation of the development team, the ability to set appropriate work units, the appropriateness of work rules, and the improvement of code maintainability should be considered as they are correlated with improved user satisfaction. In contrast, changes in the release frequency and workload are not correlated.

Determinants of user satisfaction with solar home systems in rural Bangladesh

PurposeAlthough the use of e-book readers has become increasingly widespread, there are few studies to evaluate e-book user behavior and satisfaction with commercial e-book stores, and even fewer approaches from the perspective of task-technology fit (TTF). In order to fill this gap, the purpose of this paper is to adopt the TTF theory to explore the factors that affect the behavior satisfaction of users of commercial e-book stores.Design/methodology/approachA survey was conducted to collect data from 183 e-book users. Data were collected from an online survey. The results were analyzed via the structural equation model.FindingsThe results show that functional service, mobility, convenience, and searching task are the important factors that influence users’ TTF behavior. Moreover, TTF may improve user satisfaction, flow, and scanpath. Finally, satisfaction was affected by TTF, scanpath, and flow factors. An analysis of the research explained 46 percent of the variance for the users’ TTF, and 59 percent of the variance for satisfaction of using the e-book store.Originality/valueThe research model modifies utility and performance from TTF theory in order to focus on individual flow, scanpath, and user satisfaction measurement because general e-book store users are not typically concerned about work performance issues for their leisure activities. These results provide a new perspective to e-book researchers and can help e-book store managers and designers in making policies and designing platforms.

With the rapid development of mobile applications, user experience (UX) has become a critical aspect in ensuring the success and satisfaction of users. This research paper presents an evaluation of the user experience of the Hiip Indonesia application, focusing on the assessment of its usability and user satisfaction. The study was conducted as a case study at PT. Hiip Inovasi Indonesia. The research methodology employed two primary methods: USE Questionnaire and Usability Testing. The USE Questionnaire is a validated and widely used tool for assessing user satisfaction, while usability testing involves observing users' interactions with the application and collecting qualitative and quantitative data. The findings of this research shed light on various aspects of the Hiip Indonesia application's user experience, including ease of use, efficiency, satisfaction, and learnability. The USE Questionnaire results provided insights into users' perceptions and satisfaction levels, highlighting strengths and areas for improvement. Usability testing sessions facilitated direct observation of users' interactions with the application, identifying usability issues and generating actionable recommendations. Based on the analysis of the collected data, several key recommendations were proposed to enhance the user experience of the Hiip Indonesia application. These recommendations encompassed improvements in navigation, visual design, information architecture, and responsiveness. Additionally, suggestions were made to refine the application's onboarding process, error handling, and overall performance. This research contributes to the field of UX evaluation by applying a combination of the USE Questionnaire and usability testing methods in a real-world case study setting. The findings provide valuable insights for PT. Hiip Inovasi Indonesia, enabling them to make informed decisions for optimizing the Hiip Indonesia application to better meet user expectations and improve user satisfaction. Keywords: User experience, UX evaluation, USE Questionnaire, usability testing, mobile application, PT. Hiip Inovasi Indonesia.

With continually increased Electric Vehicles (EVs), the EVs Charging Scheduling is of great importance to managing multiple charging demands for maximizing user satisfactions and minimizing adverse influences on the grid. However, it is challenging to effectively manage EVs charging schedules when a large number of (on-the-move) EVs are planning to charge at the same time. With this concern, we focus on Charging Station (CS)-selection decision making by the global aggregator that is taken as controller to implement charging management for EVs and CSs. An Estimation of Distribution Algorithm (EDA)-based genetic algorithm is proposed to find constrained charging scheduling plans to maximize the charging efficiency, which may improve user satisfaction and alleviate impacts on the grid. Experimental results under a city scenario with realistic EVs and CSs show the advantage of our proposal, in terms of minimized queuing time and maximized charging performance at both the EV and CS sides. The code and data are available at https://github.com/EV-charging-scheduling-algorithm.

Improving user satisfaction is at the forefront of industrial recommender systems. While significant progress has been made by utilizing logged implicit data of user-item interactions (i.e., clicks, dwell/watch time, and other user engagement signals), there has been a recent surge of interest in measuring and modeling user satisfaction, as provided by orthogonal data sources. Such data sources typically originate from responses to user satisfaction surveys, which explicitly ask users to rate their experience with the system and/or specific items they have consumed in the recent past. This data can be valuable for measuring and modeling the degree to which a user has had a satisfactory experience on the recommendation platform, since what users do (engagement) does not always align with what users say they want (satisfaction as measured by surveys). We focus on a large-scale industrial system trained on user survey responses to predict user satisfaction. The predictions of the satisfaction model for each user-item pair, combined with the predictions of the other models (e.g., engagement-focused ones), are fed into the ranking component of a real-world recommender system in deciding items to present to the user. It is therefore imperative that the satisfaction model does an equally good job on imputing user satisfaction across slices of users and items, as it would directly impact which items a user is exposed to. However, the data used for training satisfaction models is biased in that users are more likely to respond to a survey when they will respond that they are more satisfied. When the satisfaction survey responses in slices of data with high response rate follow a different distribution than those with low response rate, response rate becomes a confounding factor for user satisfaction estimation. We find positive correlation between response rate and ratings in a large-scale survey dataset collected in our case study. To address this inherent response rate bias in the satisfaction data, we propose an inverse propensity weighting approach within a multi-task learning framework. We extend a simple feed-forward neural network architecture predicting user satisfaction to a shared-bottom multi-task learning architecture with two tasks: the user satisfaction estimation task, and the response rate estimation task. We concurrently train these two tasks, and use the inverse of the predictions of the response rate task as loss weights for the satisfaction task to address the response rate bias. We showcase that by doing this, (i) we can accurately model whether a user will respond to a survey, (ii) we improve the user satisfaction estimation error for the data slices with lower response rate while not hurting slices with higher response rate, and (iii) we demonstrate in live A/B experiments that applying the resulting satisfaction predictions to rank recommendations translates to higher user satisfaction.

<p>This research aims to examinethe influences of information quality, system quality, and service quality of<br />the user satisfaction of the Agency Accrual-Based Accounting Systems (SAIBA). In addition, it will also<br />examine the influence of user satisfaction toward the user effectiveness particularly at Election<br />Committee of West Nusa Tenggara Province (KPU NTB). This research uses purposive<br />samplingtechnique with a sample of 66 respondents. A path analysis model which has been conducted by<br />modifying DeLone and McLean model is used to test the hypothesis.<br />The results show that system quality and service quality have a positive influence on user satisfaction,<br />whereas information quality has no influence on user satisfaction. It is also found that user satisfaction<br />has a significant influence on effectiveness of the user.This research provides important implications for<br />a successful implementation of SAIBA which is influenced by user satisfaction of information system. The<br />system quality which is reliable, flexible, user-friendly, accessible, and secure plays an important rule in<br />improving user satisfaction. Moreover, the service that provided by KPPNsuch as willingness to help,<br />understanding, and respondsiveness to the needs of user has an impact on increasing user satisfaction.</p>

With the rapid development of intelligent systems, the optimization of human-machine interface (HMI) layout has become a key issue for enhancing user experience and operational efficiency. Traditional layout optimization methods rely on manual design or static rules, which makes them difficult to adapt to diverse user needs and dynamic task scenarios. Therefore, this study proposes an adaptive human-machine interface layout optimization model that combines reinforcement learning (RL) and genetic algorithm (GA) techniques, aiming to dynamically adjust the layout of interface elements through intelligent algorithms to meet individual needs. Firstly, the model uses a genetic algorithm to generate the initial layout population and evaluates the rationality of the layout using a fitness function. Then, the Q-learning algorithm in reinforcement learning is introduced to dynamically optimize the layout strategy based on user interaction data, thereby achieving a balance between exploration and exploitation. In the experiment, 50 users were selected to participate in the test, representing different age groups and operating habits, and the performance of traditional static layouts and optimization models was compared. The results show that the optimization model reduces task completion time by 23.7%, improves user satisfaction by 18.4%, and decreases the layout adjustment response time to an average of 1.2 s. The layout stability of the model in complex task scenarios reaches 92.3%, which is significantly better than the single algorithm-driven solution. The research demonstrates that the hybrid strategy of reinforcement learning and genetic algorithms can effectively address the dynamic adaptation problem of human-machine interface layout, providing a new approach for intelligent interaction design.

To meet the functional and emotional needs of users for outdoor leisure chairs, the three-level theory of harmonious design was applied. A product design process was put together based on the Kano model and analytic hierarchy process (AHP). The Kano model obtained demand attributes and influence coefficients, while the AHP obtained total weights. The target products were designed and evaluated based on the three-level theory of harmonious design to improve the user experience and satisfaction of outdoor leisure chairs. The attribute categories of harmonious demand were obtained based on the Kano model, and the harmonious demands were ranked by importance. The design analysis and design practice were conducted with the goal of harmonious design. The AHP was used to analyze the comprehensive weights of the index factors, evaluate the user satisfaction of the three design schemes, and conduct consistency test and feasibility verification of the design schemes. The optimal design scheme was selected based on the total weight mean of three design schemes. The design and analysis method based on the Kano-AHP model can focus on user demand. It can objectively and efficiently analyze design pain spots, and effectively guide the harmonious design practice, which improves user satisfaction and market transformation efficiency of creative products.

A constant concern of management information systems (MIS) researchers and practitioners has been improving user satisfaction with the information services function (USISF). Due to the growth of end‐user computing, decentralization, and alternative sources of supply, an organization's information services function (ISF) is now faced with serving customers that possess substantial discretion in their use and purchase of information systems (IS) services. This increasingly market‐oriented environment demands sensitivity to IS customers' expectations and perceived value of ISF services. One important source of guidance in such an IS management environment is to look at marketing literature for frameworks that may permit the ISF to more effectively determine and convey the value of their services. Recognizing the need to improve existing MIS measures of user satisfaction with the ISF, this study adapts the SERVQUAL measure from marketing to provide more specific information about user satisfaction with the information service function. It was found that, while the three original dimensions of the traditional user information satisfaction (UIS) measure remain strong predictors of overall ISF user satisfaction, two aspects of IS service quality, “reliability” and “empathy,” are also significant predictors. The results suggest that the original dimensions of UIS may not be comprehensive enough to capture the more detailed dimensions of ISF service quality in SERVQUAL, and that the reliability and empathy dimensions of service quality may be needed to supplement the traditional UIS measure in determining user satisfaction with the information services function. Implications for research and practice resulting from these findings are discussed.

Adaptive User Interface (AUI) can change its layout, appearance, and/or elements based on the needs of its user requirements and current usage context. The AUIs are used in state-of-the-art software products, applications for mobile devices, and websites. Moreover, AUI is an emerging research field in a mobile context, as it can enhance usability, performance, and user satisfaction. This study aims to propose a conceptual framework for developing a real-time self-adaptive user interface based on the Android Operating System (OS). Furthermore, the focus is on developing the core algorithms for the modules of the proposed framework. To evaluate the performance of the proposed framework, three case studies have been designed based on the daily and weekly activities of the user. Moreover, an expert-based validation approach is employed to obtain the expert’s feedback regarding the proposed framework. The result indicates that the proposed framework helps improve user satisfaction and experience by making an intelligent mobile device interface. The results of the framework’s evaluation and validation show the proposed framework’s feasibility and effectiveness. We conclude that the current work is beneficial in filling the identified research gap. Moreover, this research shows the significance of an adaptive interface in an Android OS-based context. In addition, it not only helps in improving the user interest and satisfaction but also enhances the overall performance of the mobile device.

User satisfaction is a crucial quality indicator in health service provision. Few studies have measured user satisfaction among homeless and formerly homeless individuals, despite the high prevalence of mental health disorders (MHD) in this population. The purpose of this study was to assess overall satisfaction among 455 homeless and formerly homeless individuals who were receiving health and community services, and to identify factors associated with user satisfaction. Data collection occurred between January and September 2017. Study participants were 18years old or over, with experience of homelessness in the current or recent past. They completed a questionnaire eliciting socio-demographic information, and data on residential history, service use and satisfaction and health profiles. Multivariate linear analysis was performed on overall satisfaction with health and community services in the previous 12months. Independent variables were organised as predisposing, enabling and needs factors based on the Gelberg-Andersen Behavioral Model. The mean satisfaction score was 4.11 (minimum: 1; maximum: 5). Variables associated with greater user satisfaction included: older age, residence in permanent housing, common MHD (e.g., depression, anxiety), having a family physician, having a case manager, strong social network, good quality of life and, marginally, male sex and having substance use disorders (SUD). By contrast, frequent users of public ambulatory health services were the most dissatisfied. User satisfaction was more strongly associated with enabling factors. Strategies for improving satisfaction include: promoting more tailored primary care programmes (including family physician) adapted to the needs of this population, better integrating primary care with specialised services including SUD integrated treatment and enhancing continuity of care through the reinforcement of case management services. Further efforts aimed at increasing access to permanent housing with supports, and eliciting more active involvement by relatives and friends may also improve user satisfaction with services, and reduce unnecessary service use.