Online Performance Research Articles

E-Business Online Experience, Satisfaction and Trust: Basis for Customer Loyalty among Selected SMEs in Bulacan

This research paper investigates the relationship between e-business online experience, satisfaction, and trust as predictors of customer loyalty among small and medium enterprises (SMEs) in a selected area of Bulacan. Considering the increasing reliance on digital platforms for e-commerce, issues surrounding online security and user privacy have arisen, raising concerns about consumer trust in e-business environments. Employing a quantitative research methodology, the study utilizes a quantitative descriptive and correlational design to measure and identify the mean and standard deviation of online experience, satisfaction, and trust as independent variables, with customer loyalty as the dependent variable. The findings reveal that SMEs in Bulacan have a strongly positive perception of their e-business online experience, particularly highlighting the simplicity, visual appeal, and effectiveness of the websites they use. They appreciate the quality of information provided by these e-business platforms, which facilitates e-purchases through up-to-date content and straightforward transactions. The study also indicates a strong agreement among SMEs regarding the effectiveness of e-business websites in meeting consumer expectations for repeat purchases, while also identifying areas for improvement in online performance features. Moreover, SMEs generally perceive a high level of security regarding e-business platforms, particularly regarding information transmission and electronic payment safety. However, there was a slightly lower perception of user privacy concerns. The regression analysis indicates that both e-business online experience and customer satisfaction are significant predictors of customer loyalty, underscoring the importance of enhancing these aspects to cultivate lasting customer relationships.

Comicasts as a form of interaction: some preliminary observations

Nigerian online performance humour in different forms of comedy skits deploys a range of linguistic resources. However, existing conceptualisations of Nigerian online comedy skits ignore the significance of the language forms to the genre. In this paper, I argue that given the Nigerian multilingual context with the domineering presence of the English language, the pervasive use of ungrammatical English utterances in online comedy skits is an intentional strategy meant to contextualise the scripted humorous narratives as interactions with the viewers. Ungrammatical utterances are forms of deliberate language play which, together with digital play, facilitate the recipients’ participation in the performance of online humour in the Nigerian ESL multilingual context.

A - 7 An Examination of the Predictive Roles of Healthy Literacy and Age in Online and Print Health Information Search Performance among Older Adults

Abstract Objective As older adults increasingly seek health information online, evaluating complex technology-based everyday behaviors is important. This study examined the predictive roles of health literacy and age in health information search accuracy across online and print sources. Methods Participants were 96 community-dwelling older adults (age: M = 70.09, SD = 8.69; education: M = 16.57, SD = 2.14; 75% female; 96.88% White). Participants completed the Newest Vital Sign, a health literacy measure that required participants to interpret verbal and numerical information on a nutrition label, and a health search task (HST), which examined the ability to navigate web and print sources to search for health information. Results A multiple regression was conducted in which HST accuracy was regressed onto health literacy and age for web and print subtasks separately. Cognitive status (MoCA total score) was a covariate. Although health literacy was not a significant predictor of HST accuracy, age was a significant predictor for the web-based subtask, β = −0.29, b = −0.05, SEb = 0.02, 95% CI [−0.08, −0.02], t(92) = −2.94, p = 0.004, and cognitive status for the print-based subtask, β = 0.36, b = 0.18, SEb = 0.05, 95% CI [0.08, −0.28], t(92) = 3.50, p = 0.001. Conclusions Compared to health literacy, age and cognitive status were stronger predictors of health information search performance across online and print sources in community-dwelling older adults. Future studies should consider the impact of age and comfort with technology in examining older adults’ ability to successfully navigate online health sources.

Consumer Perception of the Performance of Online Catering Food Safety Regulations: The Case of Shanghai, China.

To protect the safety of food bought from the online catering sector, the former State Food and Drug Administration of China issued two separate regulations in 2016 and 2017. Independently, the Shanghai government formulated local regulations, including the Shanghai Online Catering Service Supervision and Management Measures, to strengthen food safety supervision in this megacity with the largest catering sector in China. This study explored factors that influence consumer perceptions of the performance of online catering food safety regulations using survey data from 1050 respondents collected in 2019. The results indicate that consumers believe that Shanghai's online catering industry has improved by varying degrees in terms of food freshness, ingredient traceability, foreign matter control, food temperature control, internal packaging hygiene and environmental protection, and satisfaction with food safety. The factors that influenced the listed features include the number and effectiveness of government-issued regulations regarding the online catering sector, effectiveness of ordering platform food safety regulations, employee training frequency, employee food safety awareness, delivery box cleanliness and courier personal hygiene, consumer trust in ordering platform services, and consumer confidence in government supervision. These factors significantly and positively affected the consumers' perceptions of the performance of food safety regulations in the online catering sector.

PARENTAL INVOLVEMENT IN ONLINE LEARNING AND STUDENT LEARNING PERFORMANCE

Changes in the world of technology impact the world of education, and its development is very rapid daily. The problem that arises from online learning is miscommunication between schools, parents, and students. This research aims to determine parental involvement in online learning and the learning performance of class XI students at SMAK Syuradikara, Ende. The type of research used is quantitative descriptive, with the instrument being a questionnaire. Research data was obtained and analyzed using the SSPS software application, while data analysis was used using multiple regression. The results of this research indicate that there is a direct relationship between parental involvement in online learning and student learning performance. This can be shown by the calculated t value of 2.734, which is greater than the t table of 1.69092, with a degree of confidence or alpha of 1% and a df of 34. Another result is a moderate relationship between parental involvement in online learning and performance. Student learning is shown by an R-value of 0.425. Parental participation in online learning plays an essential role in student learning performance.

Actor as expert: Developing an improvisational actor training approach for social work simulations

The increased reliance on online platforms such as Zoom and Teams for skills training for allied health specialists has presented new challenges for the actor and the actor trainer. The research reported in this article grew out of a project set up to investigate these challenges. Since 2021, members of the Deakin University Theatre Department have been collaborating with the charity Anglicare Victoria to deliver online training for Rapid Response social workers who work with families in danger of having their children removed. This has involved recruiting and training actors to act as family members in relevant scenarios in ‘real play’ online simulations so the Anglicare Rapid Response trainee can develop essential skills. Within a practice-led research approach, we ask what might be required when this live embodied skillset is moved online. The article utilizes observations of the video recordings of the training sessions, recordings of debrief sessions and reflections from the author-actors to report on an emerging approach for preparing actors for this type of engagement. We reflect on the ways improvisation is impacted using the ‘online performance’ space, especially how status, eye gaze, body positioning and mise en scène can be utilized in the online environment. We also consider the importance of actor self-care. The article offers a basis of training for a field of endeavour that is becoming more prevalent in the post-COVID world: online simulation scenarios for care workers.

Regression Forest Approaches to Gravity Wave Parameterization for Climate Projection

AbstractWe train random and boosted forests, two machine learning architectures based on regression trees, to emulate a physics‐based parameterization of atmospheric gravity wave momentum transport. We compare the forests to a neural network benchmark, evaluating both offline errors and online performance when coupled to an atmospheric model under the present day climate and in 800 and 1,200 ppm CO2 global warming scenarios. Offline, the boosted forest exhibits similar skill to the neural network, while the random forest scores significantly lower. Both forest models couple stably to the atmospheric model, and control climate integrations with the boosted forest exhibit lower biases than those with the neural network. Integrations with all three data‐driven emulators successfully capture the Quasi‐Biennial Oscillation (QBO) and sudden stratospheric warmings, key modes of stratospheric variability, with the boosted forest more accurate than the random forest in replicating their statistics across our range of carbon dioxide perturbations. The boosted forest and neural network capture the sign of the QBO period response to increased CO2, though both struggle with the magnitude of this response under the more extreme 1,200 ppm scenario. To investigate the connection between performance in the control climate and the ability to generalize, we use techniques from interpretable machine learning to understand how the data‐driven methods use physical information. We leverage this understanding to develop a retraining procedure that improves the coupled performance of the boosted forest in the control climate and under the 800 ppm CO2 scenario.

Enhancing sustainability in sewage treatment: A least squares support vector regression-based modeling approach for optimizing regeneration conditions of iFeCu

Nanoparticles in wastewater treatment offer sustainable, efficient removal of persistent pollutants, with minimal resource usage and reusability. This study examines the reusability of immobilized iron‑copper nanoparticles (iFeCu) for sewage treatment. The effect of the regeneration condition of iFeCu including the regeneration time and temperature on the performance of iFeCu for sewage treatment was investigated. Moreover, a soft sensor model, namely principal component analysis discrimination analysis ensemble least squares support vector regression (PCA-DA-E-LSSVR) model was developed to predict the performance of iFeCu, and an online performance of the model was studied to evaluate its accuracy. Investigating regeneration conditions like time and temperature, it was found that iFeCu regenerated in 1 mol (M) sodium carbonate (Na2CO3) at 40 °C for 5 h performed comparably to that regenerated at 60 °C for 15 h. Higher regeneration temperatures (>40 °C) decreased carbon dioxide and monoxide emissions but reduced ammonia (NH3) removal by ∼22 %. Shorter regeneration times (<5 h) decreased Na2CO3-iFeCu interaction, leading to an ∼11 % NH3 removal reduction. The correlation coefficient, R2 value of 0.8514 was obtained for the proposed PCA-DA-E-LSSVR model. Overall, the developed model well-fits the nonlinear data in sewage treatment and provides acceptable ranges of online performance results.

Embedding-based retrieval: measures of threshold recall and precision to evaluate product search

Modern product retrieval systems are becoming increasingly complex due to the use of extra product representations, such as user behavior, language semantics and product images. However, adding new information and complicating machine learning models does not necessarily lead to an improvement in online and business search performance, since after retrieval the product list is ranked, which introduces its own bias. Nevertheless, the business performance of a product search will be worse from ranking an incomplete list of products than a complete one, and the relevance of search results will not improve from perfect sorting of products that do not match the search query. Therefore, the main quality indicators for the products retrieval phase remain Recall and Precision at the k threshold. This paper compares several architectures of product retrieval systems in product search for e-commerce. To do this, the concepts of threshold Recall and Precision for information retrieval are investigated and the dependence of these measures on the order of issuance is revealed. An automatic procedure has been developed for calculating R@k and P@k, which allows us to compare the effectiveness of information retrieval systems. The proposed automatic procedure has been tested on the WANDS public dataset for several key architectures. The obtained values R@1000 = 84% ± 9% and P@10 = 67% ± 17% are at the level of SOTA models.

The faster or richer the response, the better performance? An empirical analysis of online healthcare platforms from a competitive perspective

The emergence of online healthcare platforms has changed the competitive environment among physicians. However, little is known about how physicians can improve their performance in this new environment. Platforms also face challenges in comprehending the competitive mechanisms among physicians, which might hinder them from formulating strategic managerial decisions that foster sustained growth. In this light, we extract medical service-related information from physicians' response behavioral data on a prominent healthcare platform, and empirically investigate the factors affecting physicians' online performance from a competitive perspective as well as the gender differences in these effects. The results indicate that physicians' responses significantly impact their online performance, revealing a competitive relationship between physicians and their colleagues in the same department. Specifically, a fast response time and informative responses are positively correlated with the focal physician's performance, whereas colleagues' informative responses negatively impact the focal physician's performance, and this relationship is mediated by the focal physician's response informativeness. Nevertheless, there is no significant correlation between colleagues' response time and the focal physician's performance. The results also unveil that gender moderates the effect of response informativeness on the focal physician's performance. Specifically, colleagues' response informativeness has a more significant impact on male physicians' performance than on female physicians' performance, suggesting a greater propensity for competition among male physicians. Our findings could offer decision support for enhancing physician performance and healthcare platform management.

To Investigate Unofficial Public Accounts on Weibo as the Third Space for University Students

This research particularly sheds light on the online space of social media and focuses on the unofficial public accounts of Guangdong Peizheng College initiated and run by students. By looking at the account performance like the students' engagement, media posts, and social networks, this research examines the commonalities and differences between these accounts. It aims at interpreting the online performance by thematic analysis, to understand the how students construct the third space by self-owned public accounts and engagement and expressions on social media, implying the prominent themes from the everyday online experience of college students. It can examine the virtual engagement of students in the form of third places and spaces in addition to the students physical campus life, and common and university-led virtual community. 60 posts of three related student-run accounts would be chosen to be the targets in thematic analysis. The outcomes of the research are expected to be beneficial to understanding college students, educational to the universities and social media research.

Measurement insights and error analysis of electronic parameters for ultrasonic transducers

Abstract Piezoelectric ultrasonic transducers with the function of energy conversation, as well as their numerous advantages in high-power density, quick response, flexible design, and service reliability, are involved in wide applications of industrial processing, precision driving, smart sensing, and medical services. The electromechanical equivalent circuit and Kirchhoff’s law indicate that mechatronics parameters are essential for performance evaluation, reliability analysis, and fault diagnosis of ultrasonic transducers. Importantly, the ultrasonic transducer is a time-variant system, data of one single parameter collected from a certain test cannot match with the data of another single parameter acquired from a different test. So, a synchronous and precise online measurement of electronic parameters is encouraged for performance evaluation and optimization design of ultrasonic transducers. With the combination of virtual instrument technology, an asynchronous measurement system of electrical excitation parameters for the ultrasonic transducers of linear driving motors was established in this study. Furthermore, the systematic measurement methods and error theory were illustrated, including the calculation methods and measuring circuits of electric signals. Experimental results proved that the proposed system and methods for measuring the input electronic power of ultrasonic transducer (e.g. effective value method for voltage and current, energy moment method for frequency, and spectrum analysis method for phase difference) are highly precise, quickly responsive, robust, and reliable for ultrasonic transducers. The findings of this study provide valuable references and suggestions for efficient, accurate, and online performance evaluation of ultrasonic transducers, particularly for piezoelectric transducers utilizing ultrasonic high-voltage exciting signals.

RaFSIP: Parameterizing Ice Multiplication in Models Using a Machine Learning Approach

AbstractAccurately representing mixed‐phase clouds (MPCs) in global climate models (GCMs) is critical for capturing climate sensitivity and Arctic amplification. Secondary ice production (SIP), can significantly increase ice crystal number concentration (ICNC) in MPCs, affecting cloud properties and processes. Here, we introduce a machine‐learning (ML) approach, called Random Forest SIP (RaFSIP), to parameterize SIP in stratiform MPCs. RaFSIP is trained on 16 grid points with 10‐km horizontal spacing derived from a 2‐year simulation with the Weather Research and Forecasting (WRF) model, including explicit SIP microphysics. Designed for a temperature range of 0 to −25°C, RaFSIP simplifies the description of rime splintering, ice‐ice collisional break‐up, and droplet‐shattering using only a limited set of inputs. RaFSIP was evaluated offline before being integrated into WRF, demonstrating its stable online performance in a 1‐year simulation keeping the same model setup as during training. Even when coupled with the 50‐km grid spacing domain of WRF, RaFSIP reproduces ICNC predictions within a factor of 3 when compared to simulations with explicit SIP microphysics. The coupled WRF‐RaFSIP scheme replicates regions of enhanced SIP and accurately maps ICNCs and liquid water content, particularly at temperatures above −10°C. Uncertainties in RaFSIP minimally impact surface cloud radiative forcing in the Arctic, resulting in radiative biases under 3 Wm−2 compared to simulations with detailed microphysics. Although the performance of RaFSIP in convective clouds remains untested, its adaptable nature allows for data set augmentation to address this aspect. This framework opens possibilities for GCM simplification and process description through physics‐guided ML algorithms.

Space, Time, and Reactivity: Designing Software for Online Theatre

Participatory digital theatre is a relatively new medium, at least in the form that emerged largely out of the COVID-19 pandemic. The increased uptake of ultra-low-latency video-conferencing platforms like Zoom offer new opportunities for designers to create interactive shows that incorporate real-time audience feedback. While far from perfect, these platforms offer a starting place for examining what works and what could be improved for online performance platforms. In this article, some reflections and recommendations are made regarding online participatory theatre design. For example, creating a sense of shared space in an online show can be aided through how audiences and performers are represented onscreen. Obscuring the representation of an audience can be used as a technique to create a more isolated, solitary experience, while the use of chats and avatars can create a more communal experience. Altering the length of streaming delays also affects the experience of a show, with shorter delays favouring more interaction and feedback. It is posited that audience avatars may have a meaningful effect on audience engagement, while the display of self-facing cameras for performers and audience members alike may have a negative impact on the experience of audience members, and the engagement of performers with an audience. The future of digital theatre is perhaps best explored by embracing what the medium does uniquely well—create experiences that are not tied directly to a physical space or time, which embrace interactivity, and which can be long-living online.

역할자 중심의 서비스 가치 네트워크 분석을 통한 대중음악 온라인 공연의 개선 방안 연구

This study aims to analyze BTS’s online performances from the perspective of the service value network model and, based on this, to suggest the role of actors in the online performance market of popular music and future improvement measures. For this purpose, BTS’s online performances were analyzed, and the research method was in-depth interviews with experts. The findings of the study are as follows First, consumers tend to avoid online performances that require a relatively long time to watch due to the difficulty in choosing due to the flood of online performances and the habitualization of watching short-form videos. Therefore, consumers should be encouraged to discover the fun of online performances through live chat participation in online performances. Second, Service Providers, such as online-only venues and broadcasting platforms, should pursue economic efficiency by actively utilizing external platforms, and should actively consider utilizing DRM-enabled platforms for copyright protection. Third, Tier 1 Enablers, such as singers and performers, should create a realistic environment like offline venues to compensate for the expressive limitations of online performances. Fourth, Tier 2 Enablers, such as planning and production companies, should secure support for their services through step-by-step integration with Service Enablers (PR and marketing agencies, MD production companies, etc.) from a long-term perspective. Fifth, Auxiliary Enablers such as investors and sponsors tend to be reluctant to invest and sponsor due to unexpected variables such as internet disconnection that may occur during live broadcasting, so it is necessary to add contractual clauses to hedge the risk. Finally, Service Enablers, which are MD producers, should diversify their revenue by producing content-type MDs such as emoticons, as online performances and MD sales are not proportional.

Simulation of performance skills teaching system based on machine vision and sensor audio signal processing

In the traditional network media education system, there is little content about skill performance, and the malleability is poor. With the combination of Internet technology, online teaching videos of performance skills began to be promoted on a large scale, and with the help of the Internet, the learning of online performance skills has been updated. The aim of this study is to develop a performance skills teaching system based on machine vision and sensor audio signal processing technology to provide a simulated performance environment and personalized instruction. In this study, multiple sensors were used to capture various movement data during the performance of students. Through the use of sensor technology, the system is able to capture information about students' posture, finger position and force in real time, and analyze posture and movement in combination with machine vision technology. The system can also process and analyze the audio signals in the students' performance to evaluate the accuracy and fluency of the playing technique. The application of sensor technology enables the system to accurately capture students' movements and provide timely feedback. The use of machine vision technology allows the system to accurately analyze the student's posture and movements to provide more targeted guidance. The application of audio signal processing algorithm enables the system to objectively evaluate the students' playing skills and help them improve the accuracy and fluency in the playing process.

Research on online anomaly detection methods for bearing degradation

In industrial applications, rolling bearings operate under conditions of high precision and high speed, and their physical and mechanical characteristics change with the increase in operating time. Traditional diagnostic methods struggle to adapt well to the changing characteristics of bearings for online anomaly detection. Therefore, this research proposes an online anomaly detection method for rolling bearings based on time-density-weighted incremental support vector data description (TISVDD). A classification strategy is proposed to prevent samples misclassification in the updating process. The Detection Boundary is established based on SVDD decision boundary to enhance the recognition of abnormal samples in the process of model updating. A dual-screening mechanism update strategy for support vectors is proposed. It involves establishing a preliminary screening mechanism based on the Elimination Boundary. On this basis, an in-depth screening mechanism based on time density weight is introduced by considering spatiotemporal characteristics of samples, enhancing the real-time performance of online anomaly detection for bearings. Building upon the fused dual-boundary SVDD, a TISVDD framework for online anomaly detection is proposed, enabling the detection model to dynamically update in response to data changes over time. To validate the effectiveness of the proposed method, experiments were conducted using the XJTU-SY bearing dataset and real-time datasets collected on an online hardware platform. The results demonstrate the effectiveness and superiority of the method in practical applications.

Navigating Emergency Online Learning: A Comparative Study of University Students in Bangladesh and Indonesia During the COVID-19 Pandemic

The present study compares the university student’s experiences of learning in virtual mode during unprecedented impacts of the COVID-19 in Bangladesh and Indonesia with a focus on the enjoyment, performance, satisfaction, and cost of online learning.This was quantitative cross-sectional research conducted by using an online survey with the participation of 438 and 529 university students in Bangladesh and Indonesia. The findings reveal that university students in Bangladesh have little more comfortable with online learning compared to those in Indonesia. The students in both countries have no problem with adaptation to avant-garde online learning platforms. University students in Bangladesh appear little more satisfied with online teaching while those in Indonesia tend to be less satisfied. University students either in Bangladesh or in Indonesia consider online learning as an expensive learning mode. The present study depicts an early online remote learning experience of university students during the COVID-19 pandemic. Due to the nature of the descriptive comparative study focusing on Bangladesh and Indonesia, the results of this study may not be generalised. Specific factors underlying their experiences are in need of further investigation. In future endeavours, lectures are urged to actively harness proper technologies, mix between conventional and virtual learning or blend synchronous and asynchronous learning to provide more enjoyable and interesting online learning thus the students can be more satisfied.

Experiments in Zoomumentary – telepresence and absence beyond the grid*

ABSTRACT This report describes and reflects on the practical workshop activity that was part of the Telepresence to Teletrust online Symposium in July 2021 hosted by Bournemouth University Experimental Media Research Centre led by Professor David Garcia. The Zoom workshops co-convened by Dr Trevor Hearing and Dr. Brad Gyori were a forum for experimentation and discussion by attendees to performatively investigate the notion of ‘the third space’, in the context of an emerging hybrid environment of shared online presence and performance. The theme was set out by Garcia, acknowledging the foundational conceptualisation and use of the term in other contexts previously in the work of Bhabha, Soja, Tuan and others. The workshops explored the experience of ‘risk’, ‘liveness’ and ‘presence and absence’ in the telepresence context and were reflected on by participants as they discovered the value of practice as a method of enquiry in this developing field.

A system-level interface sampling and reduction method for component mode synthesis with varying parameters

This paper presents a new interface sampling and reduction method for varying parameters within the framework of the component mode synthesis (CMS) method. In the conventional interpolation-based parametric reduced-order model (IB-PROM) based on CMS, reduced subsystems are synthesized, and interface degrees-of-freedom are reduced via the secondary eigenvalue analysis in the online stage, which is essential to obtain highly reduced system matrices. However, it inhibits rapid parameter updating and requires extra computational resources in the online stage. In the present study, the pre-reduced interface sampling method is proposed by synthesizing substructures and reducing the interface before beginning the online stage. The interface solution is obtained through offline and online adaptation, likewise the procedure of computing the interior solution of previous studies. Additionally, congruence transformations are applied to each reduced interface, which is mandatory for interpolating symmetric positive definite matrices with respect to a new input parameter. As a result, the online performance of IB-PROM is greatly enhanced, showing rapid and near real-time adaptations to the variations in system parameters.