Satisfactory User Experience Research Articles

Enhancing User Interface and Experience of the Bukalapak Application: A Sentiment Analysis Approach for Improved Usability and User Satisfaction in Indonesia's E-Commerce Sector

In this research, we use sentiment analysis to refine the user interface (UI) and user experience (UX) of the Bukalapak application, a leading online trading platform in Indonesia. We focus our scrutiny on 4,462 reviews related to the UI within a larger dataset of 246,947. Almost a third of these critiques express dissatisfaction, predominantly pointing out issues related to the UI design and its functionality. The critiques underscore the potential of sentiment analysis as a tool to uncover areas of user-centric design that need improvement. To address these issues, it is necessary to incorporate user feedback and sentiment analysis into the design workflow, allowing a more in-depth understanding of user needs and facilitating more effective service enhancements. Embracing a user-centered methodology allows for UI fine-tuning, leading to better functionality and increased user contentment. Our investigation reveals a positive link between design refinements and usability ratings, indicating improved user experience satisfaction. To summarize, this research highlights the essential contribution of user feedback and sentiment analysis to detect and correct UI shortfalls, thus augmenting UX and contributing to the triumph of platforms like Bukalapak within Indonesia's dynamically changing e-commerce environment.
 
 
 

A Shorter Form of the Game User Experience Satisfaction Scale in Turkish: GUESS-20-TR

A Shorter Form of the Game User Experience Satisfaction Scale in Turkish: GUESS-20-TR

Study on User Satisfaction Prediction and Influencing Factors based on GBDT Modeling

A deep understanding of the factors affecting the characteristics of user experience helps to promote the benign development of China's mobile social network services, while how to improve the satisfaction of China's user experience has become a major key to the development of China's network commercialization applications. This paper draws on the application of machine learning algorithms in other areas of classification and identification, replicated to the classification and identification of user experience satisfaction to promote the high-quality sustainable development of mobile networks, and then through the analysis of the various factors affecting user satisfaction, to provide a basis for decision-making, so as to achieve an earlier and more comprehensive enhancement of user satisfaction. This paper adopts SMOTE oversampling for the unbalanced data of mobile user scoring dataset, and then adopts factor analysis for the main factors affecting customer satisfaction, and carries out Bartlett's spherical test. After that, GBDT model is established for user satisfaction prediction, and robustness test is used to optimize the model parameters, and the results show that the AUC value of GBDT model prediction is about 0.99. The time complexity of the algorithm can be greatly reduced and is more acceptable to industry.

MADDPG-empowered slice reconfiguration approach for 5G multi-tier system

With the advancement of fifth-generation (5G) communication technology, plenty of vertical applications have emerged. These applications usually center on highly different quality of service (QoS) requirements, posing challenges for the supporting communication network. To address these challenges, network slicing has been proposed to partition the underlying network into multiple tailored logical networks, called as network slices. Each slice corresponds to a specific application. However, fluctuations in user activities for applications may lead to workload variation across corresponding slices, which compromising the consistent QoS provisioning. In this context, slice reconfiguration becomes imperative to ensure satisfactory user experiences. In this work, we introduce the multi-agent deep reinforcement learning method to tackle the slice reconfiguration problem and propose the multi-agent deep deterministic policy gradient-empowered slice reconfiguration (MADDPG-SR) algorithm. In MADDPG-SR, each network function is abstracted as an agent that learns to optimize its own migration in the multi-layer network to minimize the preference-weighted processing resource cost while adhering QoS and resource constraints. The proposed method is compared with four other benchmarks, and the comparison results demonstrate that MADDPG-SR can significantly reduce both the preference-weighted resource cost, and the running time for acquiring slice reconfiguration decisions while ensuring the QoS performance of slices.

Analysis of Factors Influencing The Loyalty of Online Transportation Users in Indonesia

The main objective of the research is to analyze the effect of e-service quality, price and consumer trust on the loyalty of online transportation users. The research method uses quantitative research. Based on the results of data analysis, e-service quality, price and consumer trust have a positive and significant effect on the loyalty of online transportation users. The implications of this research confirms that efforts to provide satisfactory user experience, fast and efficient service, good communication, qualified drivers, effective problem solving, and create a good reputation, online transportation platforms can strengthen user loyalty.

Exploring experiences of pharmacists and pharmacy students using mHealth apps: A qualitative study of user experience

ABSTRACT Purpose Pharmacists can enhance mHealth delivery by promoting the awareness and use of health apps, while pharmacy students and early career pharmacists allow broader perspective for further development. This study aimed to explore the experiences and attitudes of pharmacy students and pharmacists, on the use, advantages and possible improvement of available mHealth platforms in Serbia. Methods One-on-one interviews were conducted online in two phases, during March and April 2020 (n = 40) and during March 2021 (n = 20), using a published, validated interview guide which was adapted for this study. Interviews were transcribed ad verbatim, coded and thematically analysed. Results Although participants recognized room for optimization, most apps had satisfactory user experience. Participants used health apps as a source of updated information and tools in education and work, but also for self-development. During working hours, participants mostly used medication-related apps, however, it was shown that they use different health apps in order to provide the best possible care to patients. Conclusions Greater insight into usage, expectations, benefits and challenges of mHealth in pharmacy was obtained and can lead into better informed design of health apps to encourage students and pharmacists, both as professionals and individuals, on the path of their continuous improvement.

Inbound marketing and e-commerce on user experience in Algeria

In today's digital age, businesses have undergone a profound transformation with the advent of digital technologies and marketing strategies. Digital transformation encompasses the integration of digital tools and technologies across all areas of a business, leading to fundamental changes in its operations, value delivery, and customer engagement. Simultaneously, digital marketing involves utilizing online platforms, including websites, social media, and online advertising, to promote products or services and expand the reach to a broader audience. The synergy between digital transformation and digital marketing enables businesses to adapt to the ever-evolving digital landscape, enhance customer experiences, streamline processes, and gain a competitive edge in a digitally interconnected world. This article aims to explore the significance of inbound marketing and e-commerce in shaping the user experience in the digital age. Through quantitative research involving a questionnaire, the study identifies key elements of inbound marketing, such as personalized content, targeted advertising, and user segmentation. These elements have a substantial impact on user experience satisfaction, engagement, and loyalty within the e-commerce context, particularly among Algerian consumers.

Pengukuran Antarmuka Pengguna 3D Menggunakan Metode System Usability Scale pada Aplikasi Simulasi Videography Virtual Reality

The development of Virtual Reality (VR) technology has revolutionized user interaction with mobile applications. However, the success of a VR application depends on how well the user interface (UI) is implemented. Therefore, measuring the usability of the UI is necessary to ensure a satisfactory user experience. In measuring the quality of the user interface, we utilized the SUS (System Usability Scale) method, which is a standardized tool for assessing the user interface of a system. Our respondents consisted of 84 participants who had experience using VR applications. They were asked to use the application and complete a 10-statement SUS questionnaire with a Likert scale. Data analysis indicated that the average SUS score for the user interface of the VR videography simulation mobile application was 78.51. This score signifies that the VR Videography application's user interface received ratings of Passive, Acceptable, Good, and Grade B in their respective categories. These scores indicate that the user interface received positive evaluations from users. The questionnaire also revealed that the majority of respondents found the application easy to use and believed that others would also find it easy to comprehend

Online Optimal Service Selection, Resource Allocation and Task Offloading for Multi-Access Edge Computing: A Utility-Based Approach

Multi-access edge computing promises satisfactory user experience by offloading tasks to the MEC server deployed at the network edge. However, since the MEC server is often resource-limited as compared to the cloud infrastructure, how to efficiently utilize its resources for system performance optimization becomes a challenge. In this paper, we study this problem with the aim at maximizing user's QoE through jointly optimizing service selection, computation resource allocation and task offloading decision, which is less studied in existing literature. We formulate a mixed-integer nonlinear programming problem (MINLP) for the task and propose a utility-based approach together with a low-complexity resource-efficiency based heuristic to address the problem. We consider realistic settings, where centralized solutions may not apply and an optimal mechanism needs to adapt as system operates. A distributed algorithm based on the Lagrangian-dual based decomposition theory is proposed, and we prove all sub-problems derived can be efficiently solved. In line with the current VM technology, we develop a cost-aware online algorithm that explicitly incorporates the cost of service switches into service selection and resource allocation. We evaluate our mechanism through both synthetic and trace-driven simulations, and results indicate they are effective as compared to representative baseline algorithms.

Translation, adaptation and validation of the German version of the Game User Experience Satisfaction Scale (GUESS-GA-18) for adolescents

ABSTRACT The short version of the Game User Experience Satisfaction Scale (GUESS-18) uses nine factors to assess video game satisfaction. As video games play an important role for children and adolescents, the aim of this study was to translate and adapt the adult English version to an adolescent German version (GUESS-GA-18) and validate the new scale. Translation and adaptation were done in a participatory process with Austrian adolescents in two consecutive focus group sessions. In the validation study with 401 adolescents, fit indices demonstrated good model fit and a confirmatory factor analysis confirmed the nine-factor structure of the new scale. Internal consistency is acceptable (Cronbach’s alpha = .791). Concurrent validity shows that the GUESS-GA-18 measures whether children like the game. The German GUESS-GA-18 for adolescents is a short and comprehensive tool for iterative development of games and studies testing player experience with German-speaking adolescents.

Evaluation of the smoking cessation effects of QuitAction, a smartphone WeChat platform.

Many smokers in China desire to quit, though the success rate among adults is low. This study evaluated the effects of QuitAction, a WeChat smoking cessation platform, summarized the intervention experience of the smoking cessation platform, identified aspects of the platform that necessitated improvement, and provided references for further optimization of the smoking cessation platform. This single-arm study was conducted in Hunan, China, from September 2020 to October 2021. Regular smokers, who were aged ≥15 years and willing to quit smoking using QuitAction, were recruited. An in-application questionnaire evaluated participants' baseline smoking status and intention to quit smoking. The QuitAction program included questionnaires regarding the participants' ongoing smoking cessation status at 24 hours, one week, one month and three months after quitting. The smoking cessation procedure was discontinued if the participant had no intention of continuing. The smoking cessation rate, influencing success factors, frequency of use satisfaction, and helpfulness of QuitAction were recorded. A total of 303 participants registered and logged into the QuitAction program, including 59 with incomplete information and 64 with no intention of quitting. The study finally included 180 participants. The smoking cessation rate was 33.9% at 24 hours, 27.2% at one week, 26.1% at one month, and 25.0% at three months. QuitAction was reported as helpful by 94.9% of participants and 95.7% were satisfied with the program. Participants with a quitting difficulty score of 80-100 were less likely to quit smoking than participants with a difficulty score of 0-60 (OR=0.28; 95% CI: 0.10-0.78; p=0.015). Participants using the platform ≥5 times were more likely to quit smoking than those who used the platform <5 times (OR=3.59; 95% CI: 1.51-8.52; p=0.004). The QuitAction platform provides smoking cessation services that can improve smokers' success rate and improve user experience satisfaction.

PedaleoVR: Usability study of a virtual reality application for cycling exercise in patients with lower limb disorders and elderly people.

Achieving adherence to physical exercise training is essential in elders and adults with neurological disorders. Immersive technologies are seeing wide adoption among new neurorehabilitation therapies, as they provide a highly effective motivational and stimulating component. The aim of this study is to verify whether the developed virtual reality system for pedaling exercise is accepted and could be safety, useful and motivating for these populations. A feasibility study was conducted with patients with neuromotor disorders and elderly people from Lescer Clinic and the residential group Albertia, respectively. All the participants performed a pedaling exercise session with virtual reality platform. Then, the Intrinsic Motivation Inventory, the System Usability Scale (SUS), Credibility and Expectancy Questionnaire, were assessed in the group of 20 adults (mean age = 61.1; standard deviation = 12.617, 15 males and 5 females) with lower limb disorders. While the Simulator Sickness Questionnaire, Presence Questionnaire, Game user Experience Satisfaction Scale and SUS were assessed in the group of 18 elders (mean age = 85.16; standard deviation = 5.93, 5 males and 13 females). In light of the outcomes, PedaleoVR is considered to be a credible, usable and motivational tool towards adults with neuromotor disorders to perform cycling exercise, and therefore its usage could contribute to adherence to lower limb training activities. Moreover, PedaleoVR does not generate negative effects related to cybersickness while the sensation of presence and the degree of satisfaction generated have been positively evaluated by the geriatric population. This trial has been registered at ClinicalTrials.gov under the identifier: NCT05162040, Dec 2021.

Understanding Use Intention of mHealth Applications Based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2) Model in China

The COVID-19 pandemic has significantly impacted the healthcare industry, especially public health resources and resource allocation. With the change in people's lifestyles and increased demand for medical and health care in the post-pandemic era, the Internet and home healthcare have rapidly developed. As an essential part of Internet healthcare, mobile health (mHealth) applications help to fundamentally address the lack of medical resources and meet people's healthcare needs. In this mixed-method study, we conducted in-depth interviews with 20 users in China (mean age = 26.13, SD = 2.80, all born in China) during the pandemic, based on the unified theory of acceptance and use of technology 2 (UTAUT-2) mode, and identified four dimensions of user needs in mHealth scenarios: convenience, control, trust, and emotionality. Based on the interview results, we adjusted the independent variables, deleted the hedonic motivation and the habit, and added the perceived trust and perceived risk as the variables. Using a structural equation model (SEM), we designed the questionnaire according to the qualitative results and collected data from 371 participants (above 18 years old, 43.9% male) online to examine the interrelationships these variables. The results show that performance expectancy (β = 0.40, p < 0.001), effort expectancy (β = 0.40, p < 0.001), social influence (β = 0.14, p < 0.05), facilitating condition (β = 0.15, p < 0.001), and perceived trust (β = 0.31, p < 0.001) had positive effects on use intention. Perceived risk (β = -0.31, p < 0.001) harmed use intention, and price value (β = 0.10, p > 0.5) had no significant effects on use intention. Finally, we discussed design and development guidelines that can enhance user experience of mHealth applications. This research combines the actual needs and the main factors affecting the use intention of users, solves the problems of low satisfaction of user experience, and provides better strategic suggestions for developing mHealth applications in the future.

“Give me happy pop songs in C major and with a fast tempo”: A vocal assistant for content-based queries to online music repositories

This paper presents an Internet of Musical Things system devised to support recreational music-making, improvisation, composition, and music learning via vocal queries to an online music repository. The system involves a commercial voice-based interface and the Jamendo cloud-based repository of Creative Commons music content. Thanks to the system the user can query the Jamendo music repository by six content-based features and each combination thereof: mood, genre, tempo, chords, key and tuning. Such queries differ from the conventional methods for music retrieval, which are based on the piece’s title and the artist’s name. These features were identified following a survey with 112 musicians, which preliminary validated the concept underlying the proposed system. A user study with 20 musicians showed that the system was deemed usable, able to provide a satisfactory user experience, and useful in a variety of musical activities. Differences in the participants’ needs were identified, which highlighted the need for personalization mechanisms based on the expertise level of the user. Importantly, the system was seen as a concrete solution to physical encumbrances that arise from the concurrent use of the instrument and devices providing interactive media resources. Finally, the system offers benefits to visually-impaired musicians.

Pedestrian Augmented Reality Navigator.

Navigation is often regarded as one of the most-exciting use cases for Augmented Reality (AR). Current AR Head-Mounted Displays (HMDs) are rather bulky and cumbersome to use and, therefore, do not offer a satisfactory user experience for the mass market yet. However, the latest-generation smartphones offer AR capabilities out of the box, with sometimes even pre-installed apps. Apple's framework ARKit is available on iOS devices, free to use for developers. Android similarly features a counterpart, ARCore. Both systems work well for small spatially confined applications, but lack global positional awareness. This is a direct result of one limitation in current mobile technology. Global Navigation Satellite Systems (GNSSs) are relatively inaccurate and often cannot work indoors due to the restriction of the signal to penetrate through solid objects, such as walls. In this paper, we present the Pedestrian Augmented Reality Navigator (PAReNt) iOS app as a solution to this problem. The app implements a data fusion technique to increase accuracy in global positioning and showcases AR navigation as one use case for the improved data. ARKit provides data about the smartphone's motion, which is fused with GNSS data and a Bluetooth indoor positioning system via a Kalman Filter (KF). Four different KFs with different underlying models have been implemented and independently evaluated to find the best filter. The evaluation measures the app's accuracy against a ground truth under controlled circumstances. Two main testing methods were introduced and applied to determine which KF works best. Depending on the evaluation method, this novel approach improved the accuracy by 57% (when GPS and AR were used) or 32% (when Bluetooth and AR were used) over the raw sensor data.

Fog Computing Quality of Experience

Since its inception in 2012, fog computing has played a dominant role in addressing the quality of service (QoS). However, with the emergence of the internet of things and artificial intelligence technologies, which create a “smart world” where everything is automated, offering quality of service alone is no longer sufficient as it does not offer a satisfactory user experience. Quality of experience (QoE), which satisfies user experience and improves user performance, becomes vital and fog computing remains a key technology. To understand QoE, there was a need to distinguish it from QoS based on stance, scope, perspective, focus, and methods. A systematic literature review was done looking at works that use fog computing to maintain or improve QoE with the focus being on problems being addressed in a paper and how the results contributed to improving QoE. Critical analysis of the review showed that even though strides have been made to improve QoE, open research challenges still exist that require intervention to improve or maintain acceptable QoE in fog computing to satisfy user needs.

Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture

In urban air mobility (UAM), video streaming platforms have gained significant attention from media companies due to their growing necessity for on-demand video streaming services-as-you-go in flights. Video streaming services can provide constant data transactions in a huge amount, especially in its operational digital twin (ODT). As a result, the ability to provide a satisfactory user experience through video streaming platforms is critical and complex. This requires continuously operating services while handling numerous user requests for near real-time video streaming. At the same time, high-quality video with high resolution and minimal interruptions is often expected. Therefore, the availability and performance (i.e., performability) of the Back-End video streaming infrastructure are crucial parameters for these platforms. However, evaluating novel video-on-demand architectures in real-world scenarios can be costly due to the numerous parameters involved. Analytical models, such as stochastic Petri nets (SPNs), can serve as an alternative in this complex scenario as they can be used to analyze systems during the design process. In this study, we developed a set of SPN models to assess the performance of a video-on-demand system. These models were designed to illustrate and to evaluate a video-on-demand architecture while considering performance. We had a base SPN model as well as three enhanced variants available. The extended models were generated using the Design of Experiments (DoE) technique and sensitivity analysis results. The DoE identified the factors with the greatest impacts on performance, and the most significant factor interactions. Redundancy strategies were applied to the extended models to increase the availability of the most important components. This redundancy increased the availability of “9 s” from three to five. It is worth noting that this study can help the designers of video streaming systems, to plan and to optimize their ideas based on the provided models.

Sampling, Communication, and Prediction Co-Design for Synchronizing the Real-World Device and Digital Model in Metaverse

The metaverse has the potential to revolutionize the next generation of the Internet by supporting highly interactive services with satisfactory user experience. The synchronization between devices in the physical world and their digital models in the metaverse is crucial. This work proposes a sampling, communication and prediction co-design framework to minimize the communication load subject to a constraint on the tracking error. To optimize the sampling rate and the prediction horizon, we exploit expert knowledge and develop a constrained deep reinforcement learning algorithm. We validate our framework on a prototype composed of a real-world robotic arm and its digital model. The results show that our framework achieves a better trade-off between the average tracking error and the average communication load compared with a communication system without sampling and prediction. For example, the average communication load can be reduced up to 87% when the average track error constraint is 0.007°. In addition, our policy outperforms the benchmark with the static sampling rate and prediction horizon optimized by exhaustive search, in terms of the tail probability of the tracking error. Furthermore, with the assistance of expert knowledge, the proposed algorithm achieves better convergence time, stability, communication load, and average tacking error.

Automated Detection of Voice in News Text – Evaluating Tools for Reported Speech and Speaker Recognition

The automated content analysis of text has become integral to contemporary communication and journalism research. However, automated approaches are seldom utilized to analyze reported voice in text, while doing so would offer valuable insights into media and communication practices. Bridging the fields of communication science and computational linguistics, this study reviews and evaluates off-the-shelf tools for automated voice detection (of direct/indirect speech and of speakers) with respect to user experience and validity. Manually annotated English news articles and Twitter data served as baseline for evaluating the automated detection of voice. Findings indicate that the tools being assessed offer a satisfactory user experience and provide promising solutions for detecting direct speech automatically, encouraging fellow researchers to utilize automated detection for direct quotations. However, the recognition of indirect speech and speakers needs considerable improvement.

Analysis Analysis Analysis of Virtual Reality-Based Warehouse Simulation Applications Using The Game User Experience Satisfaction Scale Method

In the world of simulation, there are many issues related to how to make a simulation interesting and make users want to use it again. One of them is user satisfaction. User satisfaction is the extent to which players feel satisfied when using an application. The warehouse simulation application is an application that is based on three dimensions and can only be run using virtual reality technology. This application aims to simulate how the activities of a picker in the warehouse. This research was conducted to determine the level of user satisfaction when using the warehouse simulation application. The research was conducted using the Game User Experience Satisfaction Scale (GUESS) method which has nine subscales, namely usability, narratives, play engrossment, enjoyment, creative freedom, audio aesthetics, personal gratification, social connectivity, and visual aesthetics. Data collection was carried out by distributing questionnaires through online media. Respondents in this study were 30 students of the Batam State Polytechnic International Trade Logistics study program. The analysis method uses a descriptive quantitative method. The results of the analysis show that of the nine subscales of user satisfaction with the Warehouse simulation application, four subscales are below average, namely enjoyment, audio aesthetics, personal gratification, and social connectivity. From these results, what needs to be improved is the addition of audio and UI that gives appreciation to users to increase personal gratification, changing the color concept in each scene so that users do not feel bored with the application.