Online Services Research Articles

Public Library Services in Rural Australia: Challenges and Prospects

ABSTRACT This article reports the findings of a survey of more than 100 Australian outer regional and remote public library managers. Public libraries in these areas serve more than 2.5 million Australians, representing an important yet under-researched topic. Using an online questionnaire, with a response rate of more than 40%, we asked public library managers in rural Australia about the services provided by both fixed-site and mobile libraries (where applicable), as well as online and other outreach services. Questions also covered the value, issues and challenges of respondents’ library services. Findings suggest that while rural libraries perform some similar functions to their more urban counterparts, and share some challenges, they are especially valued by their communities in the absence of alternative services and social outlets, making the library even more of a key facilitator, potentially, of social inclusion and community interaction. Respondents also noted an increasing demand for IT services and digital literacy support among their users, a need sometimes at odds with the challenge these libraries face maintaining up-to-date technical infrastructure.

Online Employment Services for Immigrant Professionals: An Environmental Scan

This environmental scan assesses the current landscape of online employment services for immigrant professionals in Canada. The data collection method involved web scanning of twelve urban centers and two rural census areas with high per capita immigrant populations. The study analyzed 80 online services for immigrants in Canada based on geographical variation, the type of digital modality, clientele, and needs addressed. The findings reveal disparities in access to online employment programs tailored for skilled immigrants across different geographic locations. In addition, e-learning and self-paced online courses were identified as the most prevalent digital modalities. In terms of targeted clientele, findings show that the services primarily targeted immigrants as a homogenous group, with a notable emphasis on supporting skilled immigrants in high-demand fields such as IT and healthcare. Finally, access to job search resources emerged as the highest priority among the needs addressed, while areas such as financial assistance have the potential for further growth.

Exploring the impact of online payment methods on consumer behavior: A study of Generation X in Thailand using structural equation modeling

This research investigates how different online payment methods impact the behavior of consumers, the efficiency of banking services, and the overall economic growth of the e-commerce industry. The study focuses on how Generation X online shoppers in Thailand perceive and use online financial services. A survey conducted between January and April 2023 gathered data from 400 participants. By using statistical methods like mean rating, standard deviation, and structural equation modeling (SEM), the study revealed important insights. While the ease of using online services did not directly affect transaction behavior, the study found that the perceived usefulness indirectly influenced behavior through ease of use, highlighting the importance of usefulness as a mediator. By enhancing the perceived usefulness of online financial services, businesses can strengthen this mediating role between ease of use and service usage. This strategic approach can enhance user engagement, foster loyalty, and promote early adoption of online financial services.

Influence of Online to Offline on a Chain Store: Two Moderated Mediation Models Investigation

In the online to offline (O2O) model, online marketing and online purchases drive offline (non-Internet) operations and offline consumption. E-commerce must use more effective methods in combination with offline physical stores for consumers to obtain better online services. However, if there are positive effects from offline from customers on the re-purchase, it can generate a positive power return to online. In addition, could online to offline moves on a path that have either stronger or weaker influence of re-purchase on chain stores with customers’ electronic word-of-mouth and online involvement? To understand the influence of O2O on chain stores, this study investigates the relationships between chain store brand images, perceived value, customer satisfaction and re-purchase intention on offline considerations in a Taiwan telecom chain store ( N = 1,602). The research results find that customer satisfaction plays a fully mediating role. In addition, electronic word-of-mouth and online involvement play two moderating roles on the relationships between chain store brand image, perceived value, customer satisfaction, and re-purchase intention respectively. Finally, theoretical and practical implications, conclusion, limitations and future research are presented.

Internet Users' Willingness to Disclose Biometric Data for Continuous Online Account Protection: An Empirical Investigation

Continuous authentication has emerged as a promising approach to increase user account security for online services. Unlike traditional authentication methods, continuous authentication provides ongoing security throughout the session, protecting against session takeover attacks due to illegitimate access. The effectiveness of continuous authentication systems relies on the continuous processing of users' sensitive biometric data. To balance security and privacy trade-offs, it's crucial to understand when users are willing to disclose biometric data for enhanced account security, addressing inevitable privacy concerns and user acceptance. To address this knowledge gap, we conducted an online study with 830 participants from the U.S., aiming to investigate user perceptions towards continuous authentication across different classes of online services. Our analysis identified four groups of biometric traits that directly reflect users' willingness to disclose them. Our findings demonstrate that willingness to disclose is influenced by both the specific biometric traits and the type of online service involved. User perceptions are strongly shaped by factors such as response efficacy, perceived privacy risks associated with the biometric traits, and concerns about the service providers' handling of such data. Our results emphasize the inadequacy of one-size-fits-all solutions and provide valuable insights for the design and implementation of continuous authentication systems.

Multi-Behavior Graph Neural Networks for Recommender System.

Recommender systems have been demonstrated to be effective to meet user's personalized interests for many online services (e.g., E-commerce and online advertising platforms). Recent years have witnessed the emerging success of many deep-learning-based recommendation models for augmenting collaborative filtering (CF) architectures with various neural network architectures, such as multilayer perceptron and autoencoder. However, the majority of them model the user-item relationship with single type of interaction, while overlooking the diversity of user behaviors on interacting with items, which can be click, add-to-cart, tag-as-favorite, and purchase. Such various types of interaction behaviors have great potential in providing rich information for understanding the user preferences. In this article, we pay special attention on user-item relationships with the exploration of multityped user behaviors. Technically, we contribute a new multi-behavior graph neural network (MBRec), which specially accounts for diverse interaction patterns and the underlying cross-type behavior interdependencies. In the MBRec framework, we develop a graph-structured learning framework to perform expressive modeling of high-order connectivity in behavior-aware user-item interaction graph. After that, a mutual relationship encoder is proposed to adaptively uncover complex relational structures and make aggregations across layer-specific behavior representations. Through comprehensive evaluation on real-world datasets, the advantages of our MBRec method have been validated under different experimental settings. Further analysis verifies the positive effects of incorporating the multi-behavioral context into the recommendation paradigm. In addition, the conducted case studies offer insights into the interpretability of user multi-behavior representations. We release our model implementation at https://github.com/akaxlh/MBRec.

Pengaruh E-Service Quality dan E-Trust terhadap E-Satisfaction dan Dampaknya pada E-Repurchase Intention Pengguna Layanan Konsultasi Media Online

Apps for telemedicine are becoming increasingly popular as a result of the COVID-19 outbreak. Telemedicine thus competes with other providers to meet patient demand. Many tactics are used to overcome this, such as providing confidence and quality patients. The purpose of this study is to analyze the effect of E-Service Quality and E-Trust on E-Satisfaction and its impact on E-Repurchase Intention on users of Online Medical Consultation Services. This type of research is quantitative. The sample of this study amounted to 100 respondents who had an Online Medical Consultation Service application on a mobile phone and had at least consulted through the application or website at least once. The analysis method used is path analysis. The results of the study show that there is a direct influence between the variables of E-service Quality on E-Satisfaction in users of online medical consultation services. There is a direct influence between E-Trust variables on E-Satisfaction in users of online medical consultation services. There is a direct influence between the variables E-service Quality and E-Trust on E-Satisfaction in users of online consulting services. There is a direct influence between the variables E-service Quality, E-Trust, and E-Satisfaction, on E-Repurchase Intention in users of online medical consultation services. There is an Effect of E-service Quality and E-Trust on E-Satisfaction of 9.9% while the remaining 90.1% (100% - 9.9%) is contributed by other factors. There is an effect of E-service Quality, E-Trust, and E-Satisfaction on E-Repurchase Intention of 34% while the remaining 66% (100% - 34%) is contributed by other factors.

Self-Disclosure Counsel in Revealing Psychological Problems (Study On Online Counseling Service Counselives)

People with psychological problems generally find it difficult to express themselves and have poor communication skills. This research focuses on the counselor's self-disclosure in revealing his psychological problems in online counseling services. The research used a qualitative approach with descriptive research type. Data collection techniques using interviews, observation, documentation, and literature study. This research uses the concept of self-disclosure as well as the theory of the Johari Window and Communication Privacy Management. The results showed that the main factors that influence the counselee's self-disclosure are listeners and channels. The counselee's trust in the listener and the response given by the listener is the counselee's consideration. The channel factor in the form of a disinhibition effect affects the counselor's self-disclosure in online counseling services. The supporting factors for self-disclosure were found, namely the topic. The reasons for the counselor's self-disclosure include the tendency of dangerous psychological problems, physical pain, complaints, emotional support, asking for help, and psychological control. The counselor tends to get positive effects from self-disclosure such as catharsis, self-knowledge, and self-acceptance. The effects felt different for each individual, a negative effect was found in the form of vulnerability.

Improving Access to Care Through Youth-Focused Virtual Sexual Health Navigation

PurposeGender and sexually diverse adolescents and young adults in Baltimore City, Maryland, are disproportionately impacted by HIV. The Virtual and Online Integrated Sexual Health Services for Youth program is a health navigation program which combines virtual sexual health service delivery and health navigation to link youth at risk for HIV acquisition to HIV testing/prevention and sexual healthcare services. MethodsYouth between 13 and 26 years old and residing in the Baltimore area were eligible to participate in the program. Demographic and engagement data from 238 youth (average age 21.4, SD = 2.5) who requested navigation were collected and recorded in a Health Insurance Portability and Accountability Act (HIPAA)-secure medical database and examined for associations between demographics, referral source, and the number of navigational services to which they were linked. Focused populations were defined as residents of high HIV prevalence zip codes who identify as sexual and gender diverse youth. ResultsReceipt of navigational services was significantly associated with self-identifying as sexually diverse. A multivariate regression revealed a significant association between the count of navigational services a youth was linked to and recording one's sexual orientation, identifying as a cisgender male, and residing in a high HIV-prevalence zip code. DiscussionVirtual health navigation has the potential to engage priority populations, including sexual and gender diverse youth. By refining linkage and identification approaches to health navigation, future outreach attempts can be tailored to support vulnerable communities, with the potential to improve sexual healthcare access.

Machine learning approach to detect android malware using feature-selection based on feature importance score

The hazards posed by malware are proliferating along with technology’s rapid advancement and the use of online services. Specifically, attacks on Android devices are growing enormously because of the boost in the popularity of Smartphones. Existing research confirms that identifying benign or malware applications on the Android platform is possible by analysing the permissions through the machine-learning classifier. There are machine-learning techniques that create models to detect Android malware using permission-based attributes. However, further research is still needed to develop effective feature selection strategies for malware detection mechanisms in Android. In this study, a machine-learning-based Android malware detection mechanism is proposed, and standard machine-learning algorithms are used on multiple permission-based datasets to classify malware. This study suggests a feature selection method that uses the feature importance score computed using Gradient boosting to identify the essential permissions. The proposed methodology decreases the feature vector’s dimension, reducing the model’s training time. We also compare the classifier’s performance with the complete feature set and with the reduced feature set. Examining the results, we notice that the algorithm’s execution time improved significantly for all datasets with negligible loss in accuracy.

Unlocking personal data from online services: user studies on data export experiences and data transfer scenarios

ABSTRACT In recent years, online services have started to make personal data of users more accessible by offering dedicated ways of exporting data. The introduction of download portals is associated with increasing demands by privacy regulations regarding the rights of users, such as the Right of Access (Art. 15) and the Right to Data Portability (Art. 20) of the European Union’s General Data Protection Regulation (GDPR). These rights aim to empower users by increasing their control over the personal data that online services hold about them. They allow users to export their personal data and thereby gain insights on the scope of personal data held by the services and to transfer this data to other services. However, until now, little is known about how users experience and evaluate the process of accessing and exporting their data and how it impacts individual-level factors such as privacy-related attitudes (i.e. attitudes regarding sharing personal data, perceived control over data, and using privacy-protective strategies). In this paper, we report the results of an online survey with an experimental condition (N = 728) and a second online survey (N = 817) where participants from two university courses were asked to request real data exports from online services and inspect the exported data afterward. We find that inspecting exported personal data has a statistically significant positive effect on users’ privacy-related attitudes. However, users perceive limited usefulness in switching scenarios where personal data is transferred to a new substitutional service and rather prefer to use the data at multiple complementary services.

ANALISIS SIMULASI PROTOKOL ROUTING DINAMIS OSPF UNTUK PENINGKATAN EFISIENSI JARINGAN HYBRID KAMPUS UNUD DENPASAR

UNUD GDLN building located in UNUD Sudirman campus acts as an information and communication technology service center as an internet access service provider, radio broadcast and online learning. The UNUD Campus network currently uses a static hybrid routing protocol, where the static routing protocol has the disadvantage that the network administrator must always reconfigure the network if there is a topology change. The Open Shortest Path First (OSPF) dynamic routing protocol can be used as a solution to overcome the weaknesses of static routing protocols because it is more suitable for larger networks and has the advantage of automatically determining the best route. This research aims to analyze the application of OSPF routing protocol in UNUD Denpasar Campus with GNS3 simulation. This research uses Quality of Service (QoS) parameters to analyze network performance, and test scenarios by PING with large data transmission that imitates YouTube video streaming. The results showed that OSPF dynamic routing protocol has better performance and has met the TIPHON standard with very good results for packet loss, throughput, and delay parameters and good results for jitter parameters. Tests on static routing protocol simulations and OSPF routing protocols are carried out by comparing the transmission process based on the resolution on YouTube video streaming imitation, starting with 360p, 480p, 720p and 1080p resolutions. Based on the results of the study, it can be concluded that OSPF routing is more suitable and produces better parameter values when transmitting streaming video with a resolution of 480p, 720p and 1080p, while the transmission of streaming video with a resolution of 360p is more suitable for static routing which is more suitable for transmission with lower resolutions.

ServeItNow - Home Services Made Easy

Abstract: ServeItNow, an innovative online household service platform, is revolutionizing the way people access and engage with home services. This abstract provides an overview of how ServeItNow simplifies the lives of both homeowners and service providers. ServeItNow acts as a bridge connecting users with a diverse array of local service providers, including plumbers, electricians, cleaners, and movers. The platform's core mission is to provide a seamless, convenient, and trustworthy way for individuals to find and book essential household services. One of the standout features of ServeItNow is its user-friendly interface. With just a few clicks or taps, users can effortlessly navigate through a directory of local service providers, read authentic reviews from fellow customers, and select the services that precisely match their needs. This intuitive approach has made ServeItNow a preferred choice for users seeking reliable and efficient household services.In the world of online services, trust is paramount, and ServeItNow recognizes this importance. The platform employs stringent verification processes to ensure that all registered service providers are qualified, competent, and dependable. Moreover, users have the ability to contribute to transparency and accountability within the community by leaving reviews and ratings for the services they receive. For service providers, ServeItNow offers a valuable platform to showcase their skills and expand their client base. By establishing a digital presence and accumulating positive reviews, service providers can enhance their credibility and grow their businesses within the ServeItNow community. Market trends indicate that ServeItNow is part of a larger trend in the online household service sector, which is experiencing substantial growth. The presence of competition is driving innovation and raising the bar for service quality. To stay ahead, ServeItNow continually enhances its features and offerings to meet evolving user needs. Technology plays a pivotal role in ServeItNow's success. The platform's mobile app ensures users can access services conveniently, even while on the move. Furthermore, ServeItNow explores the potential integration of smart devices and IoT technology to further streamline service delivery and elevate user experiences. In conclusion, ServeItNow stands as a transformative force in the realm of household services. It simplifies the process of discovering, booking, and receiving dependable home services. Its commitment to trust, transparency, and user-friendliness benefits both homeowners and service providers. As technology continues to evolve, and competition drives innovation, platforms like ServeItNow are poised to shape the future of home services, offering homeowners a seamless and stress-free experience and creating new opportunities for service professionals.

DOES TRUSTWORTHINESS INFLUENCE TRAVEL SERVICE USE INTENTIONS AT AN ONLINE TRAVEL AGENCY? A STUDY ON THE DIGITALIZATION OF THE TOURISM SECTOR IN BANGLADESH

The use of online travel agency (OTA) in the sphere of tourism and hospitality industry is steadily rising along with digitalization. The aim of the investigation is to explore the relationship between the elements of trustworthiness (perceived ability, perceived benevolence, and perceived integrity) and the use of online travel agencies. In this study, a purposive sampling technique was used to collect data from the 292 clients of online travel agencies in Bangladesh. With the application of SPSS version 24 and AMOS version 26, structural equation modeling (SEM) was used to examine the research model. The findings indicate that all three elements of trustworthiness - perceived ability, perceived benevolence, and perceived integrity positively influence tourists to use online travel agencies. It scaled a new development in social exchange theory (SET) by introducing the elements of trustworthiness in the context of online travel agencies (OTAs). The present study's findings have important and useful ramifications for online travel service providers, online service agents, and managers of various tourism platforms. They should develop and put into practice strategies to manage trustworthiness across various levels of online travel services to achieve improved responses from travelers.

An intelligent sequential fraud detection model based on deep learning

Fraud detection and prevention has received a lot of attention from the research community due to its high impact on financial institutions’ revenues and reputation. The increased use of the web and the provision of online services open up the pathway for exposing these systems to numerous threats and jeopardizing their effective functioning. Naturally, financial frauds are increased in number and form imposing various requirements for their efficient and immediate detection. These requirements are related to the performance of the adopted models as well as the timely response of the decision-making mechanism. Machine learning and data mining are two research domains that can provide a number of techniques/algorithms for fraud detection and setup the road for mitigation actions. However, these methods still need to be improved with respect to the detection of unknown fraud patterns and the incorporation of big data processing mechanisms. This paper presents our attempt to build a hybrid system, i.e., a sequential scheme for combining two deep learning models and efficiently detecting potential financial frauds. We elaborate on the combination of an autoencoder and a Long Short-Term Memory Recurrent Neural Network trained upon datasets which are processed through the use of an oversampling technique. Oversampling is adopted to handle heavily imbalanced datasets which is the ‘natural’ scenario due to the limited number of frauds compared to the humongous volumes of transactions. The proposed approach tends to capture much more fraud events in comparison with other conventional ML techniques. Our experimental evaluation exposes that our model exhibits a good performance in terms of recall and precision.

A hybrid machine learning with process analytics for predicting customer experience in online insurance services industry

It is essential to innovate and improve service levels by predicting possible process outcomes due to the growth of online service providers. This study estimates the customer satisfaction level based on customer experience analysis. In doing so, we answer recent calls for research about a more thorough exploration of customer behavior using predictive process monitoring techniques. In particular, a hybrid framework of supervised/unsupervised machine learning methods is proposed to predict the outcomes of customers’ experiences while dealing with the problem of high intra-class variance. This problem occurs due to the large dispersion of traces identified in the customer journeys. In this regard, customer journeys are first matched with the event log format aiming to implement a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering technique based on the similarity between the customer journeys. After summarizing the journeys by removing low-value activities, the multi-class decision tree classification method is applied, and the level of customer satisfaction is predicted. Due to the imbalanced nature of the data, the oversampling for imbalanced classification is applied to achieve good results in accuracy indicators such as recall, precision, and F1-score. Finally, the proposed approach has been evaluated on a real-life event log, BPI Challenge 2016, to investigate unsatisfied customers. The results of the machine learning models on the test data show a high degree of accuracy in predicting customer dissatisfaction.

AN COMPARATIVE ANALYSIS OF WORKING CAPITAL AND PROFITABILITY IN ONLINE SERVICES SECTOR WITH REFERENCE TO IRCTC AND NYKAA

The Online Services Sector is one of the most important industry in India. In India there are many large and developing industries (Crude oil, Coal, Refinery, Cement, Textile, Steel, FMCG, Agriculture) and has significant contribution to GDP of India. The main Objective of the study is to analyse the Working Capital and Profitability of company during the study period is 2019-20 to 2022-23. Data Collection is based on secondary data collection method. Sample Selection is judgemental non parametric sampling method is used. For Data Analysis T-test is applied for Analysis of Working Capital and Profitability, Findings is that there is a significant difference between Current Ratio and Quick Ratio of company, and there is no significant difference between Inventory Turnover Ratio of both company. Due to their Working Capital Ratio Profitability of company also effected so NYKAA should maintain their Working Capital Ratio for increase their Profitability.

Feature-Interaction-Enhanced Sequential Transformer for Click-Through Rate Prediction

Click-through rate (CTR) prediction plays a crucial role in online services and applications, such as online shopping and advertising. The performance of CTR prediction can have a direct impact on user experience and the revenue of the online platforms. For CTR prediction models, self-attention-based methods have been widely applied to this field. Recent works generally adopted the Transformer architecture, where the self-attention mechanism can capture the global dependencies of the user’s historical interactions and predict the next item. Despite the effectiveness of self-attention methods in modeling sequential user behaviors, most sequential recommenders hardly exploit feature interaction techniques to extract high-order feature combinations. In this paper, we propose a Feature-Interaction-Enhanced Sequence Model (FESeq), which integrates feature interaction and the sequential recommendation model in a cascading structure. Specifically, the interacting layer in FESeq is an automatic feature engineering step for the Transformer model. Then, we add a linear time interval embedding layer and a positional embedding layer to the Transformer in the sequence-refiner layer to learn both the time intervals and the position information in the user’s sequence behaviors. We also design an attention-based sequence pooling layer that can model the relevance of the user’s historical behaviors and the target item representation through scaled bilinear attention. Our experiments show that the proposed method beats all the baselines on both public and industrial datasets.

The dark side of smartphone application’s smart push function: Exploring its impact on fear of missing out and smartphone addiction

This article aims to investigate the negative effects of smart push technology, which is becoming increasingly popular in digital devices and online services, particularly in smartphone-based applications (apps). Specifically, empirical relationships among the features of the app content delivered by smart push technology, fear of missing out, and smartphone addiction are explored by constructing an integrated model. The proposed relationships were tested by analyzing survey-based data collected from 227 valid samples through partial least squares-structural equation modeling. The analysis confirmed the hypothesized positive relationships among the features of app content delivered by smart push technology (entertainment and timeliness) in smartphone-based apps, fear of missing out, and smartphone addiction. Moreover, fear of missing out served as a mediator between the features of smart push technology and smartphone addiction. This study makes a significant theoretical contribution to the digital communication technology and smartphone addiction literature by revealing the influence mechanism of smart push technology on smartphone addiction. Furthermore, this study has a number of practical implications for policymakers as well as app developers.

Assessing the impact of socio-demographics and farming activity on ward-level mortality patterns using farm and population decennial censuses.

Farmers experience a specific set of unique dangers, which increases their risk of mortality compared with any other occupation. This study hypothesised that Northern Ireland's (NIs) agriculturally saturated Wards have a higher risk of mortality compared against non-agriculturally based Wards. The Population Census and Farm Census information were downloaded from the Northern Ireland Neighbourhood Service (NINIS) online depository to compile three mortality-based data sets (2001, 2011, pooled data sets). Assessing the impact of socio-demographics and farming activity on Ward-level mortality patterns using farm and population decennial censuses. This study analysed all 582 Ward areas of NI, which enclosed the entire populace of the country in 2001 and 2011. Path analysis was utilised to examine direct and indirect paths linked with mortality within two census years (2001; 2011), alongside testing pathways for invariance between census years (pooled data set). Ward-level results provided evidence for exogenous variables to mortality operating through three/four endogenous variables via: (i) direct effects (age), (ii) summed indirect effects (age; males; living alone; farming profit; and deprivation) and (iii) total effects (age; males; living alone; and deprivation). Multi-group results cross-validated these cause-and-effect relationships relating to mortality. This study demonstrated that farming intensity scores, farming profits and socio-demographics' influence on mortality risk in a Ward were dependent on the specific social-environmental characteristics within that area. In line with earlier area level research, results support the aggregated interpretation that higher levels of farming activity within a Ward increase the risk of mortality within those Wards of NI. This was an essential study to enable future tailoring of new strategies and upgrading of current policies to bring about significant mortality risk change at local level.