Study of matching model between tariff package and user behavior

PurposeThis study attempts to gain insight into what factors influence individual behavior towards the adoption of telemedicine application during coronavirus disease 2019 (COVID-19) pandemic. The research model incorporates two well-known theories namely the extended unified theory of acceptance and use of technology (UTAUT2) and DeLone and McLean information success model to examine individual behavior towards the adoption of telemedicine application.Design/methodology/approachThe research design of this study is based on quantitative research approach. During research survey, 350 valid responses were received from Pakistani citizens and examined to understand citizen's behavior towards the adoption of telemedicine applications. The research model was empirically tested with the latest statistical approach namely variance-based structural equation modeling (VB-SEM).FindingsThe results of the structural equation modeling have revealed that altogether performance expectancy, social influence, effort expectancy, facilitating condition, habit, hedonic motivation, price values, information quality, system quality and service quality explained 77.9% variance in determining user behavior towards adoption of telemedicine application. The predictive relevance of the research model was found substantial in measuring user behavior to adopt telemedicine applications. The research framework is further extended with moderating role of perceived severity between the relationship of user intention and actual usage behavior. Results confirmed that the positive relationship between intention to adopt telemedicine health application and usage behavior will be stronger when perceived severity is higher.Practical implicationsTheoretically, this study integrates extended UTAUT2 and DeLone and McLean information success model and contributes to e-health literature. Practically, this research suggests that by improving user performance expectancy and effort expectancy, managers and healthcare professionals can boost user confidence towards the adoption of telemedicine applications.Originality/valueThis study is unique as it integrates the extended UTAUT2 with DeLone and McLean information success model and perceived severity to investigate user behavior towards adoption of telemedicine application during COVID-19 pandemic. Additionally, the integration of theories contributes to information system literature in the context of the adoption of telemedicine applications.

Employing a data mining approach for identification of mobile opinion leaders and their content usage patterns in large telecommunications datasets

Preventing execution of unauthorized software on a given computer plays a pivotal role in system security. The key problem is that although a program at the beginning of its execution can be verified as authentic, its execution flow can be redirected to externally injected malicious code us-ing, for example, a buffer overflow exploit. We introduce a novel, simplified, hardware-assisted in-trusion prevention platform. Our platform introduces over-lapping of program execution and MAC verification. It par-titions a program binary into blocks of instructions. Each block is signed using a keyed MAC that is attached as a footer to the block. When the control flow reaches a partic-ular block, its instructions are speculatively executed, while dedicated hardware verifies the attached MAC at run-time. The computation state is preserved during speculative exe-cution using a mediating buffer placed between the proces-sor and L1 data cache. Upon MAC verification, the results from this buffer are propagated externally. Central to this paper is the proposal of a novel optimization technique that initially identifies instructions that are likely to stall exe-cution, and reorders basic blocks within a given instruc-tion block to minimize the execution overhead. While the presented optimization technique is problem specific, it is flexible such that it can be adjusted for different optimiza-tion goals. Preliminary results showed that our optimiza-tion methods produced an average overhead reduction of 60 % on the SPEC2000 benchmark suite and Microsoft Vi-sual FoxPro. 1.

Data processing of industrial and field work practices in SMK Pasundan Rancaekek is still semi manual using a spreadsheet application. This can lead to various problems, namely checking students who have not made payment and registration of PRAKERIN, making evidence of transactions and reports effectively, placing students in accordance with the capacity of the place of PRAKERIN and scheduling supervisors in conducting visits. The method used is the System Development Life Cycle (SDLC), which includes system analysis, system design, system implementation, and system operation. Whereas to analyze user system requirements using Flowmap analysis, Context Diagrams, Data Flow Diagrams (DFD), Entity Relationship Diagrams (ERD), Data Dictionaries, and Structure Charts. The design of the PRAKERIN data processing information system uses Microsoft Visual FoxPro 9.0 as a system implementation, report generation, and for database processing. Thus the system designed by the author is expected to facilitate the PRAKERIN data processing process so that it is more effective and efficient.

The data processing that took place at the Harapan Baru Savings and Loans Cooperative was not computerized or still manual such as processing customer data, savings data, loan data and installment data. Officers are still using handwriting and are backed up on paper. With the increase in customers, the Harapan Baru Cooperative, especially cooperative officers, will experience difficulties so that the service is less than optimal. This results in data processing taking a long time and if something happens it is possible that there will be no data backup because it was written manually. The security of managed data is also vulnerable to the risk of being lost, so it must be maintained. The purpose of this research is to build an information system in the Harapan Baru Savings and Loans Cooperative. The research method used for data collection by observation and direct interviews with the leadership of the Harapan Baru Cooperative to obtain the necessary information. Materials and research materials in the form of information about the Harapan Baru Cooperative. The tools used in this research were a laptop unit and Microsoft Visual Foxpro 9.0 software. The results of the study obtained a desktop-based system in the form of a savings and loan information system application at the Harapan Baru Cooperative. Applications can be used as an alternative for cooperatives to facilitate customer data management by presenting information quickly, accurately and able to save time so that the required reports will be easily obtained and on time.

Online interventions, such as the iFightDepression (iFD) tool, are increasingly recognized as effective alternatives to traditional face-to-face psychotherapy or pharmacotherapy for treating depression. However, particularly when used outside of study settings, low adherence rates and the resulting diminished benefits of the intervention can limit their effectiveness. Understanding the factors that predict adherence would allow for early, tailored interventions for individuals at risk of nonadherence, thereby enhancing user engagement and optimizing therapeutic outcomes. This study aims to develop and evaluate a random forest model that predicts adherence to the iFD tool to identify users at risk of noncompletion. The model was based on characteristics collected during baseline and the first week of the intervention in patients with depression. Log data from 4187 adult patients who registered for the iFD tool between October 1, 2016, and May 5, 2022, and provided informed consent were statistically analyzed. The resulting data set was divided into training (2932/4187, 70%) and test (1255/4187, 30%) sets using a randomly stratified split. The training data set was utilized to train a random forest model aimed at predicting each user's adherence at baseline, based on the hypothesized predictors: age, self-reported gender, expectations of the intervention, current or previous depression treatments, confirmed diagnosis of depression, baseline 9-item Patient Health Questionnaire (PHQ-9) score, accompanying guide profession, and usage behavior within the first week. After training, the random forest model was evaluated on the test data set to assess its predictive performance. The importance of each variable in predicting adherence was analyzed using mean decrease accuracy, mean decrease Gini, and Shapley Additive Explanations values. Of the 4187 patients evaluated, 1019 (24.34%) were classified as adherent based on our predefined definition. An initial random forest model that relied solely on sociodemographic and clinical predictors collected at baseline did not yield a statistically significant adherence prediction. However, after incorporating each patient's usage behavior during the first week, we achieved a significant prediction of adherence (P<.001). Within this prediction, the model achieved an accuracy of 0.82 (95% CI 0.79-0.84), an F1-score of 0.53, an area under the curve of 0.83, and a specificity of 0.94 for predicting nonadherent users. The key predictors of adherence included logs, word count on the first workshop's worksheet, and time spent on the tool, all measured during the first week. Our results highlight that early engagement, particularly usage behavior during the first week of the online intervention, is a far greater predictor of adherence than any sociodemographic or clinical factors. Therefore, analyzing usage behavior within the first week and identifying nonadherers through the algorithm could be beneficial for tailoring interventions aimed at improving user adherence. This could include follow-up calls or face-to-face discussions, optimizing resource utilization in the process.

User behavior visualization as a powerful tool can help analysts better understand user behavior data. Previous research such as Information Architecture believes that a user journey map is the regular way to visualize user behavior patterns. The technique is a diagram that shows the steps, touch-points, and emotions that users go through when using their products or services. Current studies mainly focus on the procedure and framework of applying user behavior data into visualization tools and its benefit to business development. Studies also found that visualization in user behavior analysis has difficulty in dealing with complex data. However, few studies have discussed the possible solution for the current limitation of visualization in user behavior analysis. With the popularization of the Internet and mobile devices, the scale and scale of user behavior data and the complexity will continue to increase, and user behavior visualization will be an important tool for analyzing this data, helping to tap potential business opportunities and improve the efficiency of data analysis. Therefore, we would make the review on visualization in user behavior analysis. We will first provide the background of the user behavior and its visualization and then introduce the definition of user behavior. Afterward, we will discuss the current process of visualization in user behavior analysis and its current limitations. Next, we will propose the possible solution of introducing machine learning, deep learning, predicting the data with a sequence model, and using cluster analysis with usability analysis tools to contribute to the future development of visualization in user behavior analysis. We believe that with data amount and scale keep increasing, the visualization tools need to develop themselves in diversity and data processing speed.

Aim/Purpose: This study examined how service fairness and SERVQUAL dimensions influenced customer satisfaction and loyalty in mobile financial applications (MFAs) in Phnom Penh, Cambodia. As digital transformation reshapes financial services, understanding these factors is crucial for improving user experiences and fostering long-term engagement. Introduction/Background: Mobile financial applications (MFAs) have become an essential part of Cambodia’s urban financial ecosystem by providing accessible and cost-effective financial solutions. Despite their increasing usage, limited research exists on the factors influencing customer satisfaction and loyalty in this sector. This study aimed to bridge this gap by examining how service fairness dimensions (distributive, procedural, interpersonal, and informational) and SERVQUAL dimensions (reliability, responsiveness, assurance, and empathy) impacted customer satisfaction and loyalty. Methodology: A quantitative research methodology was employed to explore the relationships among service fairness, SERVQUAL factors, customer satisfaction, and loyalty. The study targeted individuals aged 18 and above who actively use MFAs in Phnom Penh. Due to the absence of precise population data, stratified purposive sampling was used to ensure diverse representation across Phnom Penh’s districts. Using Cochran’s formula with a 95% confidence level and a 5% margin of error, the study aimed for a sample size of 400 respondents, but 560 surveys were distributed to enhance reliability, yielding 470 valid responses. Data collection took place between May and July 2024 through online surveys and self-administered questionnaires distributed across various digital platforms. The survey instrument was structured into four key sections: demographic information, MFA usage experience, behavioral factors, and service fairness/SERVQUAL dimensions, which were measured on a five-point Likert scale. Survey items were adapted from prior studies to ensure measurement reliability and validity. The questionnaire was pretested with 30 respondents, and refinements were made to improve clarity and relevance. Advanced statistical techniques were used to analyze the data. Descriptive statistics provided an overview of respondent demographics and usage behavior. Cronbach’s Alpha was employed to assess reliability, while Confirmatory Factor Analysis (CFA) validated the construct dimensions. Structural Equation Modeling (SEM) was utilized to test the relationships among service fairness, service quality, customer satisfaction, and loyalty, with results presented through detailed statistical models and tables. Findings: The study revealed that both service fairness and SERVQUAL dimensions significantly influenced customer satisfaction, which, in turn, drives customer loyalty. Among service fairness dimensions, distributive and procedural fairness had the strongest effects on satisfaction, highlighting the importance of transparent processes and fair service outcomes. Customers responded positively to equitable resource allocation and well-structured procedural guidelines. Regarding SERVQUAL dimensions, responsiveness and assurance emerged as the most influential factors shaping customer satisfaction. Customers highly valued prompt responses to inquiries and the expertise and reliability of service providers. While empathy contributed to satisfaction, its impact was comparatively less pronounced. Customer satisfaction was found to mediate the relationships between service fairness, service quality, and loyalty. This underscored the necessity of fostering positive user experiences to enhance long-term customer retention and loyalty. The findings suggest that MFA providers should prioritize procedural fairness and responsiveness to optimize customer satisfaction and long-term engagement. Contribution/Impact on Society: This study provides empirical evidence of the role of service fairness and SERVQUAL factors in shaping customer satisfaction and loyalty in the Cambodian MFA market. While previous research has examined these factors in other industries, this study uniquely focused on MFAs in a developing Southeast Asian context. The findings emphasize the importance of fair service design and high-quality service delivery in building customer trust and loyalty. By implementing transparent service processes and prioritizing customer support quality, MFA providers can foster financial inclusion and promote sustainable business growth in emerging markets. Recommendations: For MFA providers, the study recommends emphasizing transparency, equitable service outcomes, and efficient customer support to enhance satisfaction and trust. Specifically, providers should: Improve distributive and procedural fairness by implementing clear, transparent, and fair resource allocation policies. Enhance responsiveness by ensuring customer inquiries are addressed promptly and effectively. Strengthen assurance through professional training programs that enhance staff expertise and reliability. Invest in customer service training to improve staff empathy and engagement with users. For researchers, further exploration into additional variables such as trust, security, and digital literacy could offer a more comprehensive understanding of MFA user behavior. Research Limitation: This study had several limitations. Its cross-sectional research design captured data at a single point in time, limiting the ability to infer causal relationships. Additionally, the study focused exclusively on Phnom Penh, restricting the generalizability of findings to other regions or countries. The reliance on self-reported data may have introduced potential bias, including social desirability bias. Future studies should adopt longitudinal designs and include a broader geographic scope to enhance research validity Future Research: Future studies should expand on this research by incorporating additional factors such as perceived security, trust, and digital literacy to provide deeper insights into customer satisfaction and loyalty in MFAs. Comparative studies across different regions or countries could reveal cultural differences in how service fairness and quality affect customer perceptions. Additionally, longitudinal studies tracking changes in satisfaction and loyalty over time would provide a dynamic perspective on user behavior and evolving service expectations.

PurposeThis study aims to investigate the factors that influence behavioral intention (BI) and usage of e-wallets by extending the unified theory of acceptance and use of technology (UTAUT) with constructs, namely, mobile self-efficacy, perceived enjoyment and satisfaction.Design/methodology/approachThis quantitative study used partial least squares structural equation modeling on a sample of 576 mobile e-wallet users surveyed online.FindingsThe key findings indicate that the model can explain 58.8% of the variance in behavioral intention and 53.8% in usage. Moreover, mobile self-efficacy has a significant influence on perceived enjoyment. Perceived enjoyment significantly affects satisfaction, effort expectancy and performance expectancy. Furthermore, effort expectancy significantly influences customer satisfaction in contrast to performance expectancy. In addition, although performance expectancy, social influence and satisfaction significantly impact consumers’ behavioral intention, effort expectancy and facilitating conditions condition have an insignificant influence on consumers’ behavioral intention. E-wallet stakeholders can use the findings of this study to make strategic decisions regarding the e-wallet ecosystem.Originality/valueAlthough previous studies have independently addressed the impact of mobile self-efficacy, perceived enjoyment and satisfaction on consumers’ behavioral intention and usage behavior, the expanded framework with the possible relationships proposed in this study has never been adequately studied in previous research in the context of e-wallets in developing countries based on an empirical analysis. This study represents one of the first attempts to improve the UTAUT by empirically analyzing these relationships.

As global energy resources get more limited and environmental problems worsen, it is crucial to enhance energy efficiency and reduce energy consumption in end-use products. This research focuses on electric water heaters, a significant household energy consumer, and collects a large amount of data through questionnaires and analyzes the current usage patterns of water heater use, as well as the impact of the users’ personal characteristics and energy-saving consciousness on usage behaviors. It also evaluates the energy-saving potential under different scenarios, considering both consumer behaviors and product efficiency levels. Results indicate that a substantial number of users still purchase high-energy-consuming water heaters and fail to adjust temperatures according to their specific needs, resulting in considerable energy waste. Electric water heaters exhibit significant potential for energy savings, with the efficiency of the product and user behaviors identified as key factors influencing overall energy consumption. The study provides important insights into the usage behavior of electric water heaters and offers actionable recommendations for manufacturers and government agencies: advocating the use of certified energy-efficient water heaters, raising public awareness of energy efficiency in appliance use, etc., which is in line with the country’s goals of energy conservation and environmental sustainability.

The personal health record (PHR) is a system that enables borderless medical care services by combining technological innovation and human consideration. This study explored factors affecting the adoption of PHR from technical, medical, and social perspectives according to the Protection Motivation Theory (PMT) and Unified Theory of Acceptance and Use of Technology (UTAUT) model. A survey using a structured questionnaire was subsequently conducted, which produced the following results: (1) The PMT and UTAUT were effective at predicting PHR usage behaviors; (2) Perceived ease-of-use was the most decisive factor influencing the use of PHR, followed by self-efficacy and perceived usefulness; and (3) Behavioral intention for PHR was significantly and positively correlated with usage behavior. From the obtained results, this study recommends that health authorities and medical institutions promote self-efficacy in the use of PHR to improve the levels of behavioral intention and usage behavior among the people. Additionally, medical care institutions are recommended to promote health management and preventive healthcare concepts to help improve public acceptance of the PHR system as a means to self-manage their health. Finally, community centers, medical institutions, and health authorities are urged to work together to enhance public medical knowledge and pool resources for the PHR system, both of which are essential for improving the popularity of the PHR, public quality of life, and the effectiveness of health management.

An Information Security Engineering Environment (ISEE) based on ISO/IEC security standards has been proposed. ISEE integrates various tools such that its users can use these tools to ensure the whole security of their target information system at anytime consistently and continuously according to ISO/IEC security standards. In order to defend attacks and prevent damage beforehand, ISEE should provide users with some way to control user behavior by giving appropriate suggestions anticipatorily and actively. Such facility to control user behavior should be provided by a user management mechanism of ISEE, i.e., the user management mechanism of ISEE should deal with not only authentication, authorization, accounting or auditing, but also generating effective suggestions from records of user behavior anticipatorily, and informing the suggestions actively. Any traditional user management system is a storehouse of user data and works passively according to queries or transactions explicitly issued by its users and/or application programs, but has no active behavior to do something by itself. This paper presents an anticipatory user management mechanism of ISEE, as a new type of user management mechanism for SaaS-based cloud services with facility to control user behavior.

Understanding how users participate is of key importance to social computing systems since their value is created from user contributions. In many social computing systems, users decide sequentially whether to participate or not and, if participate, whether to create a piece of content directly, i.e., answering, or to rate existing content, i.e., voting. Moreover, there exists an answering-voting externality as a user’s utility for answering depends on votes received in the future. We present in this paper a game-theoretic model that formulates the sequential decision making of strategic users under the presence of such an answering-voting externality. We prove theoretically the existence and uniqueness of a pure strategy equilibrium. To further understand the equilibrium participation of users, we show that there exist advantages for users with higher abilities and for answering earlier. Therefore, the equilibrium has a threshold structure and the threshold for answering gradually increases as answers accumulate. We further extend our results to a more general setting where users can choose endogenously their efforts for answering. To show the validness of our model, we analyze user behavior data collected from a popular Q&A site Stack Overflow and show that the main qualitative predictions of our model match up with observations made from the data. Finally, we formulate the system designer’s problem and abstract from numerical simulations several design principles that could potentially guide the design of incentive mechanisms for social computing systems in practice.

In the context of the rapid growth of financing transactions and the significant impact on the stock market, this paper analyzes the generating mechanism of financing transactions. On the basis of theoretical analysis, this paper puts forward the hypothesis that the real economy, monetary conditions, market operation and investor behavior can play a role in financing transactions through the two variables of securities firms' ability and financing demand. The monthly data of the A-share market in China from April 2012 to April 2016 is used as the sample data, and the empirical research is carried out through VEC model test. Based on the results of empirical research, this paper proposes relevant policy recommendations.

A retentive consumer behavior assessment model of the online purchase decision-making process