User Satisfaction Research Articles

A Usable Encryption Solution for File-Based Geospatial Data within a Database File System

Developing a security solution for spatial files within today’s enterprise Geographical Information System (GIS) that is also usable presents a multifaceted challenge. These files exist in “data silos” of different file server types, resulting in limited collaboration and increased vulnerability. While cloud-based data storage offers many benefits, the associated security concerns have limited its uptake in GIS, making it crucial to explore comparable alternative security solutions that can be deployed on-premise and are also usable. This paper introduces a reasonably usable security solution for spatial files within collaborative enterprise GIS. We explore a Database File System (DBFS) as a potential repository to consolidate and manage spatial files based on its enterprise document management capabilities and security features inherited from the underlying legacy DBMS. These files are protected using the Advanced Encryption Standard (AES) algorithm with practical encryption times of 8 MB per second. The final part focuses on an automated encryption solution with schemes for single- and multi-user files that is compatible with various GIS programs and protocol services. Usability testing is carried out to assess the solution’s usability and focuses on effectiveness, efficiency, and user satisfaction, with the results demonstrating its usability based on the minimal changes it makes to how users work in a collaborative enterprise GIS environment. The solution furnishes a viable means for consolidating and protecting spatial files with various formats at the storage layer within enterprise GIS.

User Satisfaction on Library Resources and Services in BMS College of Law, Bangalore-A Study

The main motive of this study was to examine and analyze the users’ satisfaction with library resources and services among the faculty members and students of BMS College of Law, Bangalore. The present study demonstrates the satisfaction levels of users towards various library resources and services provided by the college library. The result of the study found that a large number of respondents were satisfied with library resources and services. It also finds that the books had become a most widely used resources and circulation services was emerged most preferred service. Some suggestions have been given by the respondents to make about the library resources and services more effective and efficient manner.

Smart allocation and sizing of fast charging stations: a metaheuristic solution

ABSTRACT The widespread adoption of electric vehicles (EVs) hinges significantly on effective network management, particularly the allocation and sizing of fast-charging stations (FCS) for EV users. Applying queuing theory based on M/M/c, the model seeks to identify the most effective number of chargers to minimise waiting time for electric vehicle users and ensure optimal utilisation of the FCS. The problem is formulated as a multi-optimisation problem where finding the optimal solution is done by implementing the non-dominated sorting genetic algorithm focusing on EV user satisfaction, voltage stability, and carbon dioxide emissions. Simulation results indicate that the model successfully enhances EV user satisfaction, environmental impact, and overall FCS utilisation. In a comparative analysis with state-of-the-art models, the proposed approach demonstrates a notable 40% improvement in EV user satisfaction and a significant 45% enhancement in FCS utilisation. This proves the effectiveness of the proposed model in optimising the performance of the network.

Assessment of mothers’ satisfaction towards child vaccination service in South Omo zone, South Ethiopia region: a survey on clients’ perspective

BackgroundEven though childhood vaccination is a common and cost-effective public health intervention in preventing and reducing childhood disease and death, significant numbers of children do not complete vaccination within the first year of life. Studies indicated that user satisfaction influences service utilization and used as a key indicator of quality care. However, evidence on the level of mothers’ satisfaction with immunization service are limited in urban and accessible places and not well investigated among remote and pastoral communities. As such, this study aimed to address this gap and investigated mothers’ satisfaction towards child vaccination in a pastoralist and agrarian community of the South Omo zone in Southern region of Ethiopia.MethodsAn institution-based cross-sectional study was conducted among 1221 randomly selected mothers with children eligible for childhood vaccination using a structured, pretested, and interviewer-administered questionnaire. Maternal positive evaluations of the overall vaccination service were measured using 5-point Likert scale questions. Data were entered into Epi data version 3.5.1 and analyzed using IBM SPSS statistical package version 25. Exploratory factor analysis was used for Likert scale questions to extract factor scores which facilitate treatment of variables as continuous for further analysis. Bivariate and multivariable logistic regression analysis was employed to identify factors associated with the outcome variable. A P-value < 0.05 and adjusted odds ratio with 95% CI respectively were used to declare statistical significance and degree of association.ResultA total of 849 (69.53%) study participants were satisfied with the vaccination care provided for their children. Factors associated with mother’s satisfaction with child vaccination care include maternal age less than 30 years (AOR = 2.12; 95% CI = 1.61–2.79), infants age between 8 and 12 months (AOR = 1.83; 95% CI = 1.28, 2.62), not having history of adverse events following immunization (AOR = 1.57; 95% CI = 1.01–2.45), having 1 child under the age of 5 years (AOR = 1.34; 95% CI = 1.02–1.76), waiting 30 min or less to get the service (AOR = 1.39; 95% CI = 1.05–1.85), traveling 30 min or less to the vaccination center (AOR = 1.46; 95% CI = 1.08–1.98), having poor knowledge about the importance of vaccination (AOR = 1.51; 95% CI = 1.06–2.16), and having moderate knowledge about the importance of vaccination (AOR = 1.52; 95% CI = 1.06–2.18).ConclusionInterestingly, mothers’ satisfaction with their children’s vaccination service was relatively higher in a predominantly pastoral community compared with most of previous studies conducted in Ethiopia. Maternal and child age, number of children under the age of 5 years, history of adverse events following immunization, distance to the vaccination center, waiting time to get service and maternal knowledge were factors significantly associated with mothers’ satisfaction. Proactive measures with focus on increasing access to vaccination service, improving waiting time and raising awareness among mothers were recommended.

Democratizing Data Visualization and Insights Extraction with Pandas, Generative AI, and CSV Data

Data visualization and insights extraction are crucial components of modern data-driven decision-making processes. However, traditional methods often require extensive coding knowledge, creating barriers for non-technical users. This whitepaper presents a comprehensive solution that integrates the powerful data manipulation capabilities of the Pandas library with cutting-edge Generative AI and natural language processing techniques. By leveraging a fine-tuned GPT-3 model trained on a diverse corpus of data analysis and visualization resources, our approach enables users to upload CSV data files and receive automated insights, default visualizations, and the ability to generate custom visualizations through intuitive natural language prompts. The solution streamlines the workflow, eliminating the need for coding expertise while ensuring data privacy and integrity within a secure execution environment. User studies and benchmarking demonstrate increased productivity, time savings, and high user satisfaction. This solution has the potential to democratize data analysis and visualization, empowering decision-makers across various industries with data-driven insights and informed decision-making processes.

Appraisal or Critique? Analyzing Sentiments in Google Play Store Reviews with Python

In today's world of mobile applications, understanding how users feel about your app is key for developers and businesses looking to enhance their offerings. Our project takes a people-centric approach to analyzing sentiments expressed in Google Play Store reviews. We have crafted a comprehensive method using Python to delve into user feedback. We rely on the VADER sentiment analysis tool to gauge the emotional tone of reviews, sorting them into positive, negative, or neutral categories. Our code covers everything from gathering data to preprocessing, sentiment analysis, and visualization. This allows users to extract valuable insights from the feedback loop. By deciphering the sentiments within these reviews, our project becomes a potent resource for developers and businesses, empowering them to refine user experiences and make informed decisions. Our Python-based solution is not just powerful but also flexible and efficient, capable of handling large volumes of data. This approach contributes to more informed app development strategies, fostering better user satisfaction and business success.

Assessing Users’ Demand for Library Space: Insights from an Architecture School

The purpose of this paper is to conduct a post-occupancy evaluation to assess users’ feedback on the use of the architecture school library at Thammasat University, Thailand. The proposed study also has a specific objective: to explore users’ demand for library space in terms of academic library attributes. A case study of the library in the Faculty of Architecture and Planning was conducted at Thammasat University. Multiple data collection methods were used, including document analysis, a questionnaire survey, a workshop, and focus group interviews, to gather information on users’ demand for the library space. The findings confirm the relevance of the variables in the conceptual model from previous studies, which encompass three key attributes regarding users’ demand for library space: physical, social, and digital spaces. Functional obsolescence and changing learning approaches are revealed as dominant influential factors affecting users’ demand for library space. The connection between post-occupancy evaluation (POE) and pre-design evaluation (PDE) in the redevelopment of a real estate project is considered as a shift from performance measurement to performance management, assisting in establishing agreed-upon performance goals for the operation. This study’s findings, derived from a single case study, do affect affect generalisability. However, the study provides insights into academic library design and renovation by emphasising the alignment of library spaces with user needs for improved service effectiveness and user satisfaction. The research introduces an integration of POE and PDE, proposing a methodological framework for future library space redevelopment that could contribute to the broader discussion on library design and user experience.

Baby Vaccination Web Application

The Baby Vaccination System project aims to develop a comprehensive platform for managing child vaccination schedules efficiently. It incorporates frontend and backend development to provide users with tools for vaccine registration, tracking, and locating nearby vaccine centres. By targeting efficient management of vaccination schedules, the system contributes to public health initiatives and ensures timely immunisation for children. The objectives of the study include assessing the effectiveness of the system, evaluating user satisfaction, and determining the impact on vaccination completion rates. The study employs a mixed-methods approach, combining quantitative data analysis with qualitative feedback collection. Participants include parents or guardians of children eligible for vaccination, healthcare providers, and system administrators. Data collection methods include system usage metrics, surveys, and interviews. Ethical considerations include obtaining informed consent, ensuring participant confidentiality, and adhering to relevant ethical guidelines. The study findings will provide insights into the usability and effectiveness of the Baby Vaccination System in improving vaccination management.In today’s life style due to the heavy burden and pressure of work it is difficult to remember vaccine period on new born baby as well as small once. Keywords: Blockchain Vaccination System, Healthcare, Immunization, Public Health, Technology Integration

Bug Tracking System

For many years, bug-tracking mechanisms have been employed only in some of the large software development houses. Most of the others never bothered with bug tracking at all, and instead simply relied on shared lists and email to monitor the status of defects. This procedure is error-prone and tends to cause those bugs judged least significant by developers to be dropped or ignored. Bug Tracking System is an ideal solution to track the bugs of a product, solution or an application. Bug Tracking System allows individuals or groups of developers to keep track of outstanding bugs in their product effectively. This can also be called the Defect Tracking System. The Bug Tracking System can dramatically increase the productivity and accountability of individual employees by providing a documented workflow and positive feedback for good performance. Moreover, the project explores the use of machine learning for automated root cause analysis, assisting developers in identifying underlying issues contributing to bug occurrences. Recommendation systems are also developed to suggest potential fixes or solutions for reported bugs based on past incidents and code repositories, aiding developers in efficient bug resolution. Through these ML-driven functionalities, the proposed bug tracking software aims to revolutionize the bug management process, enabling faster bug detection, prioritization, and resolution, thereby enhancing software quality, user satisfaction, and overall development productivity. Key Words: Bug Tacking system, apps, users.

Factors influencing user experience in AI chat systems – a satisfaction study based on factor analysis and linear regression

PurposeSince the introduction of the outstanding web AI chat system, ChatGPT, it has caused a significant impact in both academia and the business world. Many studies have started to explore its potential applications in various fields. However, there is a lack of research from the perspective of user experience. To fill this theoretical gap and provide a theoretical basis for the operation and design of related services, this study plans to develop a set of evaluation scales for AI chat system user experience and explore the relationship between various factors and user satisfaction.Design/methodology/approachThis study obtained 41 evaluation indicators through literature review and user research. Subsequently, these indicators were used as questionnaire items, combined with satisfaction metrics. A total of 515 questionnaires were distributed, and factor analysis and linear regression were employed to determine the specific elements influencing user experience and the user satisfaction model.FindingsThis study found that the factors influencing user experience are usefulness, accuracy, logical inference, interactivity, growth, anthropomorphism, convenience, credibility, ease of use, creativity, and security. Among these factors, only accuracy, anthropomorphism, creativity, and security indirectly influence satisfaction through usefulness, while the rest of the factors have a direct positive impact on user satisfaction.Originality/valueThis study provides constructive suggestions for the design and operation of related services and serves as a reference for future theoretical research in this area.

A virtual reality-based dual-mode robot teleoperation architecture

Abstract This paper proposes a virtual reality-based dual-mode teleoperation architecture to assist human operators in remotely operating robotic manipulation systems in a safe and flexible way. The architecture, implemented via a finite state machine, enables the operator to switch between two operational modes: the Approach mode, where the operator indirectly controls the robotic system by specifying its target configuration via the immersive virtual reality (VR) interface, and the Telemanip mode, where the operator directly controls the robot end-effector motion via input devices. The two independent control modes have been tested along the task of reaching a glass on a table by a sample population of 18 participants. Two working groups have been considered to distinguish users with previous experience with VR technologies from the novices. The results of the user study presented in this work show the potential of the proposed architecture in terms of usability, both physical and mental workload, and user satisfaction. Finally, a statistical analysis showed no significant differences along these three metrics between the two considered groups demonstrating ease of use of the proposed architecture by both people with and with no previous experience in VR.

An artificial intelligence-based dental semantic search engine as a reliable tool for dental students and educators.

This study proposes the utilization of a Natural Language Processing tool to create a semantic search engine for dental education while addressing the increasing concerns of accuracy, bias, and hallucination in outputs generated by AI tools. The paper focuses on developing and evaluating DentQA, a specialized question-answering tool that makes it easy for students to seek information to access information located in handouts or study material distributed by an institution. DentQA is structured upon the GPT3.5 language model, utilizing prompt engineering to extract information from external dental documents that experts have verified. Evaluation involves non-human metrics (BLEU scores) and human metrics for the tool's performance, relevance, accuracy, and functionality. Non-human metrics confirm DentQA's linguistic proficiency, achieving a Unigram BLEU score of 0.85. Human metrics reveal DentQA's superiority over GPT3.5 in terms of accuracy (p=0.00004) and absence of hallucination (p=0.026). Additional metrics confirmed consistent performance across different question types (X2(4,N=200)=13.0378,p=0.012). User satisfaction and performance metrics support DentQA's usability and effectiveness, with a response time of 3.5 s and over 70% satisfaction across all evaluated parameters. The study advocates using a semantic search engine in dental education, mitigating concerns of misinformation and hallucination. By outlining the workflow and the utilization of open-source tools and methods, the study encourages the utilization of similar tools for dental education while underscoring the importance of customizing AI models for dentistry. Further optimizations, testing, and utilization of recent advances can contribute to dental education significantly.

Mindfulness and Emotional Intelligence: A Study on Millennials on Hospitality

In software development, dependability and quality are critical. A Bug Tracking System (BTS) designed specifically for large-scale software projects is introduced in this significant project with the goal of streamlining bug tracking, reporting, detection, and resolution. The system provides secure authentication, a backend database that is reliable, and an easy-to-use web interface. Using cutting-edge web technologies, it offers critical features including automated notifications, progress tracking, severity assignment, and thorough bug reporting together with user-friendly interfaces for a range of user roles. Tools for data visualization help identify defect patterns and monitor projects. The project follows best practices recommended by the industry for requirements analysis, design, implementation, testing, and deployment, and it solicits ongoing input. The result is a thorough BTS that addresses defect management difficulties. Thorough testing and assessment in comparison to standards confirms its efficacy. This approach could have a big impact on software development, improving user satisfaction and quality. Furthermore, issues with bug tracking are covered, including distributed team communication, scalability, and automation balancing. Proposals for strategies to address these issues are grounded in scholarly research and professional opinions. This summary highlights the critical role that bug tracking systems play in contemporary software development, making it an invaluable tool for researchers, practitioners, and teams looking to improve bug tracking procedures and raise the caliber and effectiveness of the software development lifecycle. Keywords— Bug Tracking System, Software Development, Defect Management, Quality Assurance, Automation, Collaboration, Large-scale Projects.

Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation.

A plethora of weight management apps are available, but many individuals, especially those living with overweight and obesity, still struggle to achieve adequate weight loss. An emerging area in weight management is the support for one's self-regulation over momentary eating impulses. This study aims to examine the feasibility and effectiveness of a novel artificial intelligence-assisted weight management app in improving eating behaviors in a Southeast Asian cohort. A single-group pretest-posttest study was conducted. Participants completed the 1-week run-in period of a 12-week app-based weight management program called the Eating Trigger-Response Inhibition Program (eTRIP). This self-monitoring system was built upon 3 main components, namely, (1) chatbot-based check-ins on eating lapse triggers, (2) food-based computer vision image recognition (system built based on local food items), and (3) automated time-based nudges and meal stopwatch. At every mealtime, participants were prompted to take a picture of their food items, which were identified by a computer vision image recognition technology, thereby triggering a set of chatbot-initiated questions on eating triggers such as who the users were eating with. Paired 2-sided t tests were used to compare the differences in the psychobehavioral constructs before and after the 7-day program, including overeating habits, snacking habits, consideration of future consequences, self-regulation of eating behaviors, anxiety, depression, and physical activity. Qualitative feedback were analyzed by content analysis according to 4 steps, namely, decontextualization, recontextualization, categorization, and compilation. The mean age, self-reported BMI, and waist circumference of the participants were 31.25 (SD 9.98) years, 28.86 (SD 7.02) kg/m2, and 92.60 (SD 18.24) cm, respectively. There were significant improvements in all the 7 psychobehavioral constructs, except for anxiety. After adjusting for multiple comparisons, statistically significant improvements were found for overeating habits (mean -0.32, SD 1.16; P<.001), snacking habits (mean -0.22, SD 1.12; P<.002), self-regulation of eating behavior (mean 0.08, SD 0.49; P=.007), depression (mean -0.12, SD 0.74; P=.007), and physical activity (mean 1288.60, SD 3055.20 metabolic equivalent task-min/day; P<.001). Forty-one participants reported skipping at least 1 meal (ie, breakfast, lunch, or dinner), summing to 578 (67.1%) of the 862 meals skipped. Of the 230 participants, 80 (34.8%) provided textual feedback that indicated satisfactory user experience with eTRIP. Four themes emerged, namely, (1) becoming more mindful of self-monitoring, (2) personalized reminders with prompts and chatbot, (3) food logging with image recognition, and (4) engaging with a simple, easy, and appealing user interface. The attrition rate was 8.4% (21/251). eTRIP is a feasible and effective weight management program to be tested in a larger population for its effectiveness and sustainability as a personalized weight management program for people with overweight and obesity. ClinicalTrials.gov NCT04833803; https://classic.clinicaltrials.gov/ct2/show/NCT04833803.

Barriers to and Facilitators of Key Stakeholders Influencing Successful Digital Implementation of Remote Monitoring Solutions: Mixed Methods Analysis.

Implementation of remote monitoring solutions and digital alerting tools in health care has historically been challenging, despite the impetus provided by the COVID-19 pandemic. To date, a health systems-based approach to systematically describe barriers and facilitators across multiple domains has not been undertaken. We aimed to undertake a comprehensive mixed methods analysis of barriers and facilitators for successful implementation of remote monitoring and digital alerting tools in complex health organizations. A mixed methods approach using a modified Technology Acceptance Model questionnaire and semistructured interviews mapped to the validated fit among humans, organizations, and technology (HOT-fit) framework was undertaken. Likert frequency responses and deductive thematic analyses were performed. A total of 11 participants responded to the questionnaire and 18 participants to the interviews. Key barriers and facilitators could be mapped onto 6 dimensions, which incorporated aspects of digitization: system use (human), user satisfaction (human), environment (organization), structure (organization), information and service quality (technology), and system quality (technology). The recommendations proposed can enhance the potential for future remote sensing solutions to be more successfully integrated in health care practice, resulting in more successful use of "virtual wards." ClinicalTrials.gov NCT05321004; https://www.clinicaltrials.gov/study/NCT05321004.

Mengukur Pengaruh Kualitas Pelayanan Terhadap Kepuasan dan Loyalitas Pengguna Perpustakaan UMS

This research aims to evaluate the impact or effects of effect of Service, information control, and the Library as a Place on the satisfaction and loyalty of library users. The samples in this test were active students and users who utilized library services at the Muhammadiyah University of Surakarta. Sampling was done using a purposive sampling technique to obtain a total of 150 respondents. The data management is carried out using the SmartPLS 3.0 statistical calculation tool and it can be stated that the effect of Service, Information Control, and Library as Place has a significant effect and has a positive impact on user satisfaction and loyalty so the hypothesis can be accepted.

Pengaruh Dukungan Manajemen Puncak, Partisipasi Pemakai, Pelatihan dan Pendidikan terhadap Kepuasan Pemakai Sistem Informasi Akuntansi (Studi Kasus pada LPK Monarch Bali)

Pengaruh Dukungan Manajemen Puncak, Partisipasi Pemakai, Pelatihan dan Pendidikan terhadap Kepuasan Pemakai Sistem Informasi Akuntansi (Studi Kasus pada LPK Monarch Bali)

Analysis of Image Interaction and Social Motivation in Virtual Communities

The purpose of this paper is to explore the relationship between image interaction and social motivation in virtual communities, and take Instagram and TikTok as case studies. First, the conceptual characteristics of virtual communities and the defined forms of image interaction are analysed, and then the classification of social motivation and its impact on user behaviour are explored, as well as the role of image interaction in shaping the social ecosystem, the social atmosphere and the user's social network. In Instagram, image interaction behaviours were found to be positively correlated with user engagement, increasing users' exposure and social influence. In TikTok, on the other hand, social identity based on image interaction was closely related to user satisfaction, promoting user loyalty and stickiness to the platform. In summary, image interaction, as an important form of social behaviour in virtual communities, has a positive impact on the development of the community and the social experience of users, but it also needs to pay attention to issues such as information security and content regulation.

Effectiveness of a web-enabled psychoeducational resource for postpartum depression and anxiety among women in British Columbia.

Postpartum depression (PPD) and anxiety (PPA) affect nearly one-quarter (23%) of women in Canada. eHealth is a promising solution for increasing access to postpartum mental healthcare. However, a user-centered approach is not routinely taken in the development of web-enabled resources, leaving postpartum women out of critical decision-making processes. This study aimed to evaluate the effectiveness, usability, and user satisfaction of PostpartumCare.ca, a web-enabled psychoeducational resource for PPD and PPA, created in partnership with postpartum women in British Columbia. Participants were randomized to either an intervention group (n = 52) receiving access to PostpartumCare.ca for four weeks, or to a waitlist control group (n = 51). Measures evaluating PPD (Edinburgh Postnatal Depression Scale) and PPA symptoms (Perinatal Anxiety Screening Scale) were completed at baseline, after four weeks, and after a two-week follow-up. User ratings of website usability and satisfaction and website metrics were also collected. PPD and PPA symptoms were significantly reduced for the intervention group only after four weeks, with improvements maintained after a two-week follow-up, corresponding with small-to-medium effect sizes (PPD: partial η2 = 0.03; PPA: partial η2 = 0.04). Intervention participants were also more likely than waitlist controls to recover from clinical levels of PPD symptoms (χ 2 (1, n = 63) = 4.58, p = .032) and PostpartumCare.ca's usability and satisfaction were rated favourably overall. Findings suggest that a web-enabled psychoeducational resource, created in collaboration with patient partners, can effectively reduce PPD and PPA symptoms, supporting its potential use as a low-barrier option for postpartum women. Protocol for this trial was preregistered on NIH U.S. National Library of Medicine, ClinicalTrials.gov as of May 2022 (ID No. NCT05382884).

Gen Transcribe

The Advanced Speech Interaction System integrates STT, NLP, and TTS components, achieving high accuracy, multilingual support, and user satisfaction, with a focus on future development for enhanced multimodal capabilities and user-centric features.