User Satisfaction Research Articles

Harnessing AI and Predictive Analytics: Transforming the Electric Vehicle Market in India

The electric vehicle (EV) market in India is undergoing a transformative phase, driven by growing environmental concerns, government incentives, and the rapid integration of advanced technologies such as Artificial Intelligence (AI) and Predictive Analytics. These technologies are proving essential in optimizing various aspects of EV development, including battery management, predictive maintenance, and customer experience personalization. This research article investigates the influence of AI and Predictive Analytics on the Indian EV market, highlighting their roles in improving operational efficiency, reducing costs, and enhancing safety and user satisfaction. Statistical data and case studies of leading Indian EV companies, such as Tata Motors, Ola Electric, and Ather Energy, illustrate the impact of these technologies on market growth and performance. Despite facing challenges like high initial costs and regulatory barriers, AI and Predictive Analytics are poised to play a pivotal role in the future expansion of the EV market in India. The findings suggest that with increased adoption and ongoing technological advancements, AI and Predictive Analytics will significantly contribute to the growth and sustainability of the EV sector in India. Keywords: Electric Vehicles (EVs), Artificial Intelligence (AI), Predictive Analytics, Battery Management Systems (BMS), Predictive Maintenance

User experiences of selfcare interventions for sexual and reproductive health services in community pharmacies in Rwanda: A qualitative study

ABSTRACT Self-care interventions for sexual and reproductive health (SRH) empower individuals to autonomously access services, reducing their reliance on traditional healthcare settings. However, there is limited knowledge about community pharmacy-based SRH services in Rwanda. Therefore, this study aims to explore the experiences of community pharmacy-based SRH users and inform strategies to enhance service delivery effectiveness. This study employed a qualitative research approach employing a phenomenological design. Semi-structured in-depth interviews were conducted with 22 participants from 1st June 2023 to 20th March 2024, in 5 Districts of Rwanda. Participants were recruited using purposive sampling methods. The interviews were recorded, transcribed, and imported into NVivo 11 software for further analysis. Users of pharmacy-based SRH products intended for self-care interventions reported positive experiences. Three main themes emerged from the interviews: community pharmacies ensured the utmost confidentiality, provided fast service delivery with minimal procedures, and expressed user satisfaction with friendly communication. Users of community pharmacy-based SRH services intended for self-care use reported positive experiences with community pharmacies. They received the needed confidentiality, faster service delivery, and friendly communication with community pharmacists. There is a pressing need for close collaboration among the public, private, and stakeholders to empower citizens over their SRH.

An Expectation-Maximization framework for Personalized Itinerary Recommendation with POI Categories and Must-see POIs

In this paper, we introduce a novel deterministic method based on Expectation Maximization (EM) to solve the rather complex problem of designing a tourist trip or Personalized Itinerary Recommendation (PIR). PIR objective is to recommend a personalized tour consisting of successive Points of Interest (POIs), which maximizes user satisfaction and respects user time-frame constraints. On top of that, the POIs are divided into categories, in order for travelers to be able to set limits on the maximum (and minimum) number of POIs that belong to one category and are included in the itinerary. In the proposed framework, emphasis is given on the POIs sequence selection, which exploits the customized POI recommendations offered by a recommender system. Additionally, the proposed methodology with POIs categories is able to solve the TourMustSee problem, so that the tour includes a set of POIs that must be visited. The proposed system has been successfully incorporated into a mobile app, offering a complete tourist trip design. The high performance, resilience, and computational efficiency of the proposed framework are demonstrated by experimental findings and comparisons to existing approaches on numerous synthetic and real datasets.

User experience of neighbourhood greenspace and perceived impact on health and wellbeing: the case of an urban extension to a ‘Healthy New Town’ in England

ABSTRACT Recognising the provision of public greenspace in urban areas as a key tool to supporting health and wellbeing, the UK government has committed to ensuring that, by 2043, all citizens can reach greenspace within 15 minutes from their homes. However, it is increasingly acknowledged that simply providing access to greenspace may not be enough to ensure that positive benefits are realised. Characteristics including their accessibility, size, aesthetics, features, condition and safety can all distinguish between greenspaces that encourage visitation and those that are uninviting. This paper reports a neighbourhood-level study of the residential development of Kingsmere, which forms a significant urban extension to Bicester, England, and was developed as part of the government-funded ‘Healthy New Town’ programme. A cross-sectional survey, including follow-up interviews, was conducted to investigate residents’ use of different types of greenspace, satisfaction, and the perceived impact on personal health and wellbeing. Findings reveal that the provision of a variety of greenspaces within a 15-20-minute walk of homes has been effective in terms of use and satisfaction but that attention to access within sites, perceived safety, cleanliness and maintenance are important factors that could encourage greater use and enhance user satisfaction.

Improving the accuracy of the information retrieval evaluation process by considering unjudged document lists from the relevant judgment sets

Introduction. To improve user satisfaction and loyalty to the search engines, the performance of the retrieval systems has to be better in terms of the number of relevant documents retrieved. This can be evaluated through the information retrieval evaluation process. This study identifies two methodologies that help to recover and better rank relevant information resources based on a query, while at the same time suppressing the irrelevant one. Method. A combination of techniques was used. Documents that were relevant and not retrieved by the systems were found from the document corpus and assigned new scores based on the Manifold fusion techniques then moved into the relevant judgment sets. Documents based on judgment sets and good contributing systems have been considered in the proposed methodologies. Analysis. Kendall Tau Correlation Coefficient, Mean Average Precision (MAP), Normalized Discounted Cumulative Gain (NDCG), and Rank Biased Precision (Rbp) have been used to evaluate the performance of the methodologies. Results. The proposed methodologies outperformed the baseline works and enhanced the quality of the judgment sets, achieving a better result even with lesser pool depth. Conclusion. This research proposes two methodologies that increase the quality of the relevant documents in the judgment sets based on document similarity techniques and, thus, raise the evaluation process accuracy and reliability of the systems.

Audit Tools and Linked Archive System (ATLAS) in the Process of Preparing Audit Working Papers: Approach with the End User Computing Satisfaction (EUCS) Model"

This study aims to investigate the factors that influence user satisfaction with the ATLAS application for preparing Audit Working Papers, utilizing the Computing End User Satisfaction (EUCS) model. It adopts a quantitative approach with a sample of 83 auditors from KAPs in East Java Province who use the ATLAS application, selected through convenience sampling. Data analysis was performed using Partial Least Squares with SmartPLS 2.0. The findings suggest that content, accuracy, format, and timeliness positively affect user satisfaction with the ATLAS application for preparing Audit Working Papers. However, ease of use does not significantly impact auditor satisfaction as an application user.

Assessing the Impact of Cultural Differences on Educational Technology Acceptance

Cultural characteristics in relation to educational technology acceptance (ETA) aims at analyzing the effect of cultural factors that include norms of the society, values, and practices which shape the perception and usage of technology by the individual as incorporated in his/her education. This concept illustrates that cultural factor increases that perception of helpfulness, ease of implementation, and total satisfaction with technology by a significant notch. Understanding these cultural factors is critical to the development and implementation of pedagogical technologies that are aligned with specific target audiences. A number of people in the study was 2100, out of which 1115 were from Mumbai, India and 985 others were from other areas. The sample included people with various education levels, occupations and ages, which gives a comprehensive idea of the variety of users’ perceptions of educational technology. Employing the data based on the collected set of factors, descriptive analysis, factor analysis, and correlation matrixes were used in a process of distinguishing key patterns and relations between particular variables. This cross-sectional research has relied on multiple regression analysis to assess the significant predictor factors that include perceived ease of integration and expected performance of the technology by the users on the two outcomes: user engagement and technology satisfaction. As earlier stated, SPSS was used in this research study to conduct statistical tests. The comparative analysis revealed that there were significant regional differences concerning the social impact expectations, EE and PC but there was no impact of regions as per PEI and TS. The research thus brings out the importance of cultural differences in the explanation of ETA. This just underline the need for a strategic and specific approach that takes into account regional and cultural factors to improve the reception of technologies as well as to improve the user satisfaction even if it is a diverse population. The paper provides important view on rather engaging culturally competent educational technology systems.

Practically implementing an LLM-supported collaborative vulnerability remediation process: A team-based approach

Incorporating LLM into cybersecurity operations, a typical real-world high-stakes task, is critical but non-trivial in practice. Using cybersecurity as the study context, we conduct a three-step mix-method study to incorporate LLM into the vulnerability remediation process effectively. Specifically, we deconstruct the deficiencies in user satisfaction within the existing process (Study 1). This inspires us to design, implement, and empirically validate an LLM-supported collaborative vulnerability remediation process through a field study (Study 2). Given LLM’s diverse contributions, we further investigate LLM’s double-edge roles through the analysis of remediation reports and follow-up interviews (Study 3). In essence, our contribution lies in promoting an efficient LLM-supported collaborative vulnerability remediation process. These first-hand, real-world pieces of evidence suggest that when incorporating LLMs into practical processes, facilitating the collaborations among all associated stakeholders, reshaping LLMs’ roles according to task complexity, as well as approaching the short-term side effects of improved user engagement facilitated by LLMs with a rational mindset.

Integrating GRA with intuitionistic fuzzy VIKOR model to explore attractive design solution of wickerwork cultural and creative products

ABSTRACT With the popularisation of the concept of sustainable traditional wickerwork, wickerwork cultural and creative products have become the most popular craft production. Kansei consensus on the design of wickerwork lamps has become a key factor influencing the communication between manufacturers and customers. Therefore, the purpose of this paper is to develop a product evaluation model for wickerwork cultural and creative products that meet the emotional satisfaction of users. Firstly, this paper could build a three-level evaluation grid diagram driven by user attractiveness through EGM, and extracts key dimensions in the perceptual vocabulary using FA. Secondly, evaluation indicators are constructed and grey relation analysis (GRA) is used to synthetically assess the priority order of evaluation indicators based on the obtained weight values. Finally, in order to effectively deal with uncertain product evaluation information, the intuitionistic fuzzy VIKOR method was used to evaluate representative product design solutions, and the most perceptually attractive product design solution was preferentially selected. This study selected wickerwork lamps as the design case of cultural and creative products, the results of the study enable designers to accurately grasp customers’ emotional perceptions of wickerwork products and obtain the best wickerwork production design solution.

Customer adoption of Chat GPT for web development and programming assistance in the Zimbabwe tech industry

ChatGPT, an advanced conversational AI model by OpenAI, signifies a significant leap in human-computer interaction. Optimized for dialogue, ChatGPT has the potential to transform web development and programming support. In Zimbabwe, 60% of IT programmers express concerns about UI/UX design, while 70% face challenges in service quality due to software flaws and inadequate testing. This study investigates ChatGPT's application in Zimbabwe's tech industry for web development and programming support, and its impact on organizational performance. Employing an integrated Delone and McLean IS Success Model, the research examines adoption trends, barriers, and benefits of integrating ChatGPT into development workflows.A quantitative approach was utilized, employing survey data and statistical methods. A 5-point Likert Scale survey was distributed to 30 Zimbabwean ICT workers. Data analysis with SmartPLS 3.0 included descriptive and confirmatory factor analyses to assess reliability (Cronbach's alpha, composite reliability) and validity (factor loadings, AVE, HTMT ratio). Results indicate that ChatGPT significantly influences system, information, and service quality, enhancing user satisfaction and organizational benefits, with system quality exerting the strongest impact. Moreover, system quality positively affects organizational performance, though the moderating role of flexible organizational culture was insignificant. Recommendations include enhancing training data tailored to local technological needs, integrating real-time data sources, offering a paid version for updated data, ensuring robust error handling for service quality, providing educational resources, implementing feedback mechanisms, conducting developer training programs, and promoting cross-functional collaboration.

Optimal management of shared energy storage in remote microgrid: A user-satisfaction approach

This study investigates the utilization of renewable energy technologies, such as solar photovoltaics (PV) and energy storage, to reduce reliance on fossil-fuel microgrid generators in remote areas. However, renewable energy alone is not enough to address the existing challenges. Therefore, to optimize microgrid performance, it is crucial to incorporate shared energy storage and demand-response (DR) strategies from the demand side. Additionally, prosumers engaging in DR often encounter user-satisfaction issues. In this study, we propose a shared energy storage model that considers user satisfaction in remote areas. Additionally, we compared three energy storage models: individual, neighbor, and communal, to assess their effectiveness. To validate our findings, we juxtaposed two pricing scenarios: fixed and time-of-use (TOU) rates. The results of our study indicate that using shared energy storage leads to operational cost savings and increases user satisfaction compared to other scenarios. Finally, we employed the social index rate and performed a sensitivity analysis to determine the optimal operation that balances economic and environmental considerations.

How to Talk to AI: The Role of Preset Prompt Language Styles in Shaping Conversational Experience

With the proliferation of AI conversational agents, the design of preset prompts—text suggestions that guide user interactions—has become crucial for enhancing user experience. This study investigates the impact of different preset prompt language styles (social-oriented vs. task-oriented) on user satisfaction. Utilizing two empirical studies, we examined how these language styles influence user perceptions of an AI agent’s warmth and competence, and how these perceptions mediate overall satisfaction. In the first study, participants interacted with an AI agent using either social-oriented or task-oriented prompts under conditions of service success or failure. The results indicated that social-oriented prompts significantly enhance user satisfaction by increasing perceptions of warmth, but not competence. However, this positive effect diminishes in the event of service failure. In the second study, we explored the moderating effect of task urgency. Findings revealed that the positive impact of social-oriented prompts on satisfaction is significant in low-urgency tasks but not in high-urgency scenarios. These insights underscore the importance of prompt language style in AI interactions and provide practical implications for designing more effective AI communication strategies, especially in customer service contexts.

Effect of Accounting Information System Quality, Service Quality and System Security on User Satisfaction of M- Banking Based Applications at Bank Sumsel Babel

This study aims to analyze the effect of accounting information system quality, service quality and system security on user satisfaction of m-banking-based applications at Bank Sumsel Babel. This research uses descriptive methods with a quantitative approach. The population in this study were customers who used mobile banking at Bank Sumsel Babel, with as many as 198,017 customers. The number of samples taken in this study was 400 respondents. The sampling technique was carried out using the Slovin technique, namely the random sampling method (random sampling technique). The type of data used in this study is primary data collected directly through the data collection process using the distribution of questionnaires using Google Forms. The results showed that the quality of accounting information systems, service quality, and system security had a positive and significant effect on user satisfaction of m-banking-based applications at Bank Sumsel Babel and simultaneously that the quality of accounting information systems, service quality and system security had a positive and significant effect on user satisfaction of m-banking-based applications at Bank Sumsel Babel

Perception of the quality of nursing care and user satisfaction in hospitals in Chile

Introduction: the quality of health depends on whether the service offers the desired results. This is related to health services since users base their preferences and experiences to express satisfaction. Nursing is a central being in the perception of quality. Objective: Describe the perception of the quality of nursing care and user satisfaction in hospitals in Chile. Methodology: a study was carried out with a descriptive-cross-sectional, quantitative design. The sample was made up of 111 users over 18 years of age who had been hospitalized for at least one night in a public hospital in Chile. The instrument used was SERVQHOS-E, which measures the perception of users in relation to the quality of nursing care from corporate aspects and a survey that provides opinion data on perceived quality. Results: an average of 3.28±0.81 could be observed in the users' expectations regarding the quality of nursing care and 75.67 % of the users stated that they were very satisfied or satisfied with the nursing care received. Conclusions: the research revealed positive perception of users about the quality of nursing care in Chilean hospitals. Areas for improvement were identified such as communication and waiting times. The data obtained can collaborate in decision-making and improvements in user care in health facilities, guiding possible strategies to guarantee quality and satisfaction

Analysis of the Influence of User Experience and Product Quality on Repurchase Intention with User Satisfaction as an Intervening Variable Uniqlo App

Analysis of the Influence of User Experience and Product Quality on Repurchase Intention with User Satisfaction as an Intervening Variable Uniqlo App

User Satisfaction Analysis of Pln Mobile Application at Jabodetabek

User Satisfaction Analysis of Pln Mobile Application at Jabodetabek

User requirements analysis of new energy vehicles based on improved Kano model

The ever-changing market demand accelerates the iterative upgrading of new energy vehicles, making understanding user requirements crucial for optimizing product strategies. However, the imbalance in reviews reduces the accuracy of user requirements analysis, which may mislead the purchasing intentions of potential consumers and the product development strategies of manufacturers. To address this, this study develops a BERT-TCBAD-Kano-based requirements analysis method. First, a sentiment analysis method is used to identify user preferences in online reviews. Second, the Text Classification Based on Attribute Dictionary (TCBAD) is proposed to categorize online complaints of users. Finally, user satisfaction and concern are calculated based on preference identification and complaint classification results. User requirements are prioritized based on the idea of the Kano model. Based on the online data of a new energy vehicle, 342 attribute words and 10 user requirements are extracted. The results show that the proposed method improves prediction accuracy by 30 % compared to the traditional Kano model. The method provides significant decision-making support for user-centered product development.

Research on enhancing human-machine interaction in medical exoskeleton devices through the integration of artificial intelligence

This paper explores the integration of Artificial Intelligence (AI) to enhance human-machine interaction in medical exoskeleton devices. AI technologies such as machine learning, natural language processing (NLP), and predictive analytics can significantly improve the efficiency and comfort of these devices. Machine learning algorithms analyze sensor data in real-time, optimizing control strategies to adapt to user movements. NLP enables intuitive control through voice commands, reducing the cognitive and physical burden on users. Predictive analytics anticipates user needs, enhancing responsiveness and reducing the risk of errors. Case studies of AI-integrated exoskeletons, like the EksoGT and ReWalk, demonstrate improved rehabilitation outcomes and user satisfaction. Despite challenges such as data privacy and the need for significant investment, the benefits of AI integration are substantial. This paper provides insights into the potential of AI to transform medical exoskeletons, offering new levels of independence and quality of life for individuals with mobility impairments. Future research should focus on developing advanced AI algorithms and exploring new applications to further enhance user experience and device performance.

Assessment of Mobile Telephony User Satisfaction in the Federal District Through Structural Equation Modeling

Objective: To maintain competitiveness and profitability in the telecommunications sector, it is essential to assess customer satisfaction, as mobile telephony provides convenience and is widely available to the population. Theoretical Framework: The growing demand for low-cost products, especially automotive fuels, reflects a constant in the Brazilian market, significantly impacting the economy and consumers' daily lives. Method: The research was conducted using a questionnaire developed by Anatel to collect data on user satisfaction. The questionnaire items were grouped into four constructs: Perceived Quality, Expectations, Perceived Value, and Overall Satisfaction. Structural Equation Modeling was used to evaluate the relationships between these constructs and consumer satisfaction. Results and Discussion: The results indicate that Perceived Quality, Expectations, and Perceived Value have a significant impact on Overall Satisfaction. The rejected hypotheses suggest areas where consumer perceptions may not align with expectations or where certain factors may not have the anticipated influence on satisfaction. Research Implications: The results provide valuable insights for mobile telephony companies, highlighting the importance of improving perceived quality and aligning customer expectations with the perceived value of the service. Originality/Value: This study contributes to the understanding of factors affecting consumer satisfaction in the mobile telephony sector, with a detailed analysis applied to the Federal District context.

Deciphering digital popularity from question quality: the preeminence of social attributes over textual dimensions in driving CQA platform traffic

ABSTRACT Community Question Answering (CQA) sites, now navigating through millions of daily user visits, hinge significantly on the quality of user-generated questions to bolster user satisfaction and, consequently, website traffic – crucial for platform sustainability. While the quality of questions ostensibly sets the stage for insightful answers, thereby attracting substantial traffic, existing research has predominantly sidelined the significance of questions, favoring a focus on traditional factors, such as answer quality, to elucidate traffic determinants. In this study we quantified the interrelation between question quality and traffic by extracting 15 relevant question-quality features and corresponding traffic (i.e. pageview) data from a leading Chinese CQA platform. Utilizing Grey Relational Analysis, we assessed these attributes’ relative influence on pageviews and created a ranking list of features of questions. Based on the rankings, our findings spotlight the paramount influence of a question's digital popularity and attributes tied to the questioner – central pillars of social features – surpassing the impact of textual elements. This study further delineates the underlying reasons for such patterns based on traffic sourced from diverse channels. Our revelations offer pivotal insights for optimizing CQA platform engagement and furnish avenues for prospective research.