influence of service quality and customer satisfaction of the BRImo application on customer loyalty

This study was conducted to determine service quality and customer satisfaction on the BRImo application on customer loyalty at PT Bank Rakyat Indonesia (Persero), Tbk Sekayu branch office. The population in this study were all customers at PT. Bank Rakyat Indonesia (Persero), Tbk Sekayu Branch Office, as many as 850 customers in the K1 customer category, which focuses on BRImo users in 2020-2021. The sample used in this study was 123 respondents with purposive sampling techniques. The multiple linear regression analysis results show that service quality has a positive and significant effect on customer loyalty. Customer satisfaction has a positive and significant effect on customer loyalty. The results of this study are expected. PT Bank Rakyat Indonesia (Persero), Tbk can improve the application network regularly and provide additional employees in the IT department so that customers can always use the BRImo application at any time. In addition, the increase in the appearance and needs of customers in the BRImo application service can be further improved so that customers can be more efficient in conducting transactions using only smartphones without the need to come directly to the BRI bank.

A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields

This paper reviews the research trends that link the advanced technical aspects of recommendation systems that are used in various service areas and the business aspects of these services. First, for a reliable analysis of recommendation models for recommendation systems, data mining technology, and related research by application service, more than 135 top-ranking articles and top-tier conferences published in Google Scholar between 2010 and 2021 were collected and reviewed. Based on this, studies on recommendation system models and the technology used in recommendation systems were systematized, and research trends by year were analyzed. In addition, the application service fields where recommendation systems were used were classified, and research on the recommendation system model and recommendation technique used in each field was analyzed. Furthermore, vast amounts of application service-related data used by recommendation systems were collected from 2010 to 2021 without taking the journal ranking into consideration and reviewed along with various recommendation system studies, as well as applied service field industry data. As a result of this study, it was found that the flow and quantitative growth of various detailed studies of recommendation systems interact with the business growth of the actual applied service field. While providing a comprehensive summary of recommendation systems, this study provides insight to many researchers interested in recommendation systems through the analysis of its various technologies and trends in the service field to which recommendation systems are applied.

Experimental Study of SaaS, Its Categories and Contribution in Cloud Computing

SaaS is software as a service. In previous days when the concept of providing the facility of distributed system from where information can be shared was introduced then the concept of Application service provider came into the picture. The idea was to provide application-based services to the companies using client server techniques. It became popular. SaaS is introduced to provide software also as a service which is upgraded version of the ASP. In this paper SaaS will be discussed in detail. There are two main types of SaaS. The challenges in implementation of SaaS will also be discussed.

Model-Based Cloud Service Deployment Optimisation Method For Minimisation of Application Service Operational Cost

Many of the currently existing solutions for cloud cost optimisation are aimed at cloud infrastructure providers, and they often deal only with specific types of application services, leaving the providers of cloud applications without the suitable cost optimization solution, especially concerning the wide range of different application types. In this paper, we present an approach that aims to provide an optimisation solution for the providers of applications hosted in the cloud environments, applicable at the early phase of a cloud application lifecycle and for a wide range of application services. The focus of this research is development of the method for identifying optimised service deployment option in available cloud environments based on the model of the service and its context, with the aim of minimising the operational cost of the cloud service, while fulfilling the requirements defined by the service level agreement. A cloud application context metamodel is proposed that includes parameters related to both the application service and the cloud infrastructure relevant for the cost and quality of service. By using the proposed optimisation method, the knowledge is gained about the effects that the cloud application context parameters have on the service cost and quality of service, which is then used to determine the optimised service deployment option. The service models are validated using cloud application services deployed in laboratory conditions, and the optimisation method is validated using the simulations based on proposed cloud application context metamodel. The experimental results based on two cloud application services demonstrate the ability of the proposed approach to provide relevant information about the impact of cloud application context parameters on service cost and quality of service, and use this information in the optimisation aimed at reducing service operational cost while preserving the acceptable service quality level. The results indicate the applicability and relevance of the proposed approach for cloud applications in the early service lifecycle phase since application providers can gain useful insights regarding service deployment decision without acquiring extensive datasets for the analysis.

Exploring the fit between mobile application service and application privacy

Purpose With the popularity of mobile applications and increasing consumer awareness of application privacy, this paper aims to introduce a new construct of service-privacy fit (i.e. the perceived degree of match between the service of a mobile application and a privacy permission request) to predict consumers’ mobile application adoption. Design/methodology/approach Four experiments were carried out to test the hypothesized relationships. The first study investigated the direct impact of service-privacy fit on application adoption and the mediators of benefit expectancy and privacy concerns. The second, third and fourth studies further applied the moderated mediation model to analyze the moderating roles of benefit message type, regulatory focus type and privacy assurance. Findings The results show that service-privacy fit influences application adoption not only directly but also indirectly via the mediators of benefit expectancy and privacy concerns. Furthermore, the findings confirm the moderators of benefit message type, regulatory focus type and privacy assurance. Originality/value Drawn from the perspectives of task-technology fit and information boundary theory, this paper introduces a new construct of service-privacy fit as a determinant of application adoption. Grounded in privacy calculus theory, it further explains this relationship through mediating effects of benefit expectancy and privacy concerns. Furthermore, this paper proposes that benefit messages and privacy assurance are effective coping strategies to increase the benefit expectancy and reduce the privacy concerns of applications. Based on the perspective of regulatory fit theory, this study further shows that the effects of coping strategies rely on personal traits. The findings enrich the existing knowledge of mobile application adoption and application privacy, suggesting that practitioners should consider mobile consumers’ perception of service-privacy fit when developing applications.

What drives continuance intention to use a food-ordering chatbot? An examination of trust and satisfaction

PurposeArtificial intelligence (AI) customer service chatbots are a new application service, and little is known about this type of service. This study applies service quality, trust and satisfaction to predict users' continuance intention to use a food-ordering chatbot.Design/methodology/approachThe proposed model and hypotheses are tested using online questionnaire responses to collect users' perceptions of such services. One hundred and eleven responses of actual users were received.FindingsEmpirical results show that anthropomorphism and service quality, such as problem-solving, are the antecedents of trust and satisfaction, while satisfaction has the most significant direct effect on the users' intention.Originality/valueThe results provide further useful insights for service providers and chatbot developers to improve services.

The Relationship Between Applicant Gender and Internal Medicine Residency Interview Scores

ABSTRACT Background Gender inequity is widespread in academic medicine, including in the promotion, academic recognition, and compensation of female faculty. Objective To assess whether these inequities extend to the GME intern selection process, this study examines differences in the interview scores assigned to male and female applicants at one large internal medicine residency program. Methods Subjects include 1399 applicants who completed 3099 interviews for internship positions for the Brigham and Women's Hospital internal medicine residency in Electronic Residency Application Service (ERAS) cycles 2015–2016, 2017–2018, 2018–2019, and 2019–2020. Unadjusted and multivariable linear regressions were used to assess the simultaneous effect of applicant gender, interviewer gender, and applicant academic characteristics on pre-interview, post-interview, and change in interview scores. Results Our analysis included 3027 interviews (97.7%) of 1359 applicants (97.1%). There were no statistically significant differences in the interview scores assigned to female versus male applicants. This was true across pre-interview scores (difference = 0.03, P = .61), post-interview scores (difference = 0.00, P = .98), and change in interview scores (difference = 0.01, P = .24) as well as when adjusting for the baseline academic characteristics of both male and female applicants. This was also true when analyzing individual application years, individual residency tracks, and accounting for the gender of the faculty interviewers. Conclusions The findings do not support the presence of gender inequity in the interview scores assigned to male and female applicants included in this study.

The Effects of Non-Cash Transactions on Consumer Satisfaction (A Case Study on Consumers of the Oro-Oro Dowo Market, Malang)

The Internet of think is considered incredibly adaptable in the present digital world, one of which is non-cash payments, making it easier, faster, and decreasing human errors. It is not surprising that the government is now promoting Gerakan Nasional Non-Tunai (GNNT) program by ratifying a new application owned by one of the BUMN subsidiaries to enable payment transactions. The GNNT is now being encouraged in many sectors of Indonesian needs, including traditional markets. The objectives of this study were: 1) identifying the consumer characteristics who uses non-cash payment applications in Oro-Oro Dowo market Malang, and 2) investigating the impact of service quality, product image, and promotion on consumer satisfaction as non-cash payment application users. The research site was in Oro-Oro Dowo market, Malang. It is the first traditional market to employ non-cash payments as a payment option, with the research period beginning in April 2020. This study had 100 participants selected through the distribution of questionnaires. The data analysis employed Partial Least Squares-Structural Equation Modeling. The findings showed that the majority of respondents who use non-cash applications were female consumers (76%), had an average age of 20-25 years (47%), had undergraduate education (46%), worked as private employee (37%), and had an average income of < Rp2.000.000 (37%). The path coefficient test revealed that product image and promotion variables had a significant effect on consumer satisfaction as they had a sig value of > 0.05 while the application service variable had a sig value greater than 0.05, it had a negligible effect on consumer satisfaction.

Mission-Based Filters in the Electronic Residency Application Service: Saving Time and Promoting Diversity

ABSTRACT Background Holistic review promotes diversity, but widespread implementation remains limited. Objective We aimed to develop a practical approach to incorporate holistic review principles in screening applicants in the Electronic Residency Application Service (ERAS) and to assess the impact on diversity. Methods Three residency programs (internal medicine [IM], pediatrics, and surgery) at McGovern Medical School developed filters to identify applicants with experiences/attributes aligned with the institutional mission. These filters were retroactively applied to each program's 2019–2020 applicant pool using built-in ERAS capabilities to group applicants by user-defined features. We compared the demographics of applicants reviewed during the cycle with those identified retrospectively through experiences/attributes filters. Results The IM, pediatrics, and surgery programs received 3527, 1341, and 1313 applications, respectively, in 2019–2020. Retrospective use of experiences/attributes filters, without scores, narrowed the IM applicant pool for review to 1301 compared to 1323 applicants reviewed during actual recruitment, while the pediatrics filters identified 514 applicants compared to 384 at baseline. The surgery filters resulted in 582 applicants, but data were missing for baseline comparison. Compared to the baseline screening approach utilizing scores, mission-based filters increased the proportions of underrepresented in medicine applicants selected for review in IM (54.8% [95% CI 52.1–57.5] vs 22.7% [20.4–24.9], P &lt; .0001) and pediatrics (63.2% [95% CI 59.1–67.4] vs 25.3% [20.9–29.6], P &lt; .0001). Conclusions Program directors can leverage existing ERAS features to conduct application screening in alignment with holistic review principles. Widespread implementation could have important repercussions for enhancing physician workforce diversity.

IMPLEMENTASI ALGORITMA NAÏVE BAYES CLASSIFIER DAN SUPPORT VECTOR MACHINE PADA KLASIFIKASI SENTIMEN REVIEW LAYANAN TELEMEDICINE HALODOC

Halodoc is a telemedicine-based healthcare application that connects patients with health practitioners such as doctors, pharmacies, and laboratories. There are some comments from halodoc users, both positive and negative comments. This indicates the public's concern for the Halodoc application so it is necessary to analyze the sentiment or comments that appear on the Halodoc application service, especially during the COVID-19 pandemic in order for Halodoc application services to be better. The Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithms are used to analyze the public sentiment of Halodoc's telemedicine service application users. The negative category sentiment classification result was 12.33%, while the positive category sentiment was 87.67% from 5,687 reviews which means that the positive review sentiment is more than the negative review sentiment. The accuracy performance of the Naive Bayes Classifier Algorithm resulted in an accuracy rate of 87.77% with an AUC value of 57.11% and a G-Mean of 40.08%, while svm algorithm with KERNEL RBF had an accuracy value of 86.1% with an AUC value of 60.149% and a G-Mean value of 49.311%. Based on the accuracy value of the model can be known SVM Kernel RBF model better than NBC on classifying the review of user sentiment of halodoc telemedicine service