Variable Usage Research Articles

OVERVIEW OF FINANCIAL TECHNOLOGY IN BANKING SECTOR: A BIBLIOMETRIC STUDY

This study aims to provide empirical evidence regarding the growth and trend of fintech-related publications in the banking sector and examine what variables are often associated with fintech. Utilizing bibliometric analysis via the VOSviewer application, this study analyzes 816 articles published on Scopus from 2013 to 2023. The results of this study indicate that research publications on fintech in the banking sector have been widely carried out in various countries. Several variables related to fintech and banking topics include technology, industry, covid, future, and blockchain. Some of the authors that are widely found are M.K. Hassan, H. Banna, and M.R. Rabbani which can be considered as references. The study's limitation lies in its inability to provide an overview of variable usage trends in 2023. This research is expected to provide implications for the development of future research, especially related to fintech and banking.

Enhancing Static Analysis for Practical Bug Detection: An LLM-Integrated Approach

While static analysis is instrumental in uncovering software bugs, its precision in analyzing large and intricate codebases remains challenging. The emerging prowess of Large Language Models (LLMs) offers a promising avenue to address these complexities. In this paper, we present LLift, a pioneering framework that synergizes static analysis and LLMs, with a spotlight on identifying use-before-initialization (UBI) bugs within the Linux kernel. Drawing from our insights into variable usage conventions in Linux, we enhance path analysis using post-constraint guidance. This approach, combined with our methodically crafted procedures, empowers LLift to adeptly handle the challenges of bug-specific modeling, extensive codebases, and the unpredictable nature of LLMs. Our real-world evaluations identified four previously undiscovered UBI bugs in the mainstream Linux kernel, which the Linux community has acknowledged. This study reaffirms the potential of marrying static analysis with LLMs, setting a compelling direction for future research in this area.

The Influence Of Go Food Usage, Business Duration, And Education Level On The Income Of Culinary Businesses In Denpasar City

Economic growth, poverty, and unemployment are long-term issues faced by every country. How to reduce these rates is a primary concern for governments striving to achieve success in economic development. One approach to decreasing unemployment and poverty rates is by promoting home businesses, commonly known as MSMEs (Micro, Small, and Medium Enterprises). This research aims to determine the impact of Go-Food usage, the duration of the business, and the education level on the income levels of culinary businesses in Denpasar City. The rapid development of MSMEs has a positive impact on the economy. In this study, the author focuses on MSMEs in the culinary sector in Denpasar City. The culinary business is the fastest-growing sector in Denpasar compared to other sectors. This research is located in Denpasar City, focusing on culinary businesses in the area. The sample is determined using accidental sampling techniques. Data analysis is conducted using multiple linear regression, with simultaneous and partial testing. The results of data processing using SPSS show that the variables of Go-Food usage, business duration, and education level have a positive impact on the income of culinary businesses in Denpasar City.

Unorthodox Information Sources of Coping With the COVID-19 Crisis in the Ultra-Orthodox Society

This study examines the role of information sources in the ultra-Orthodox (Haredi) Jewish community’s coping with the coronavirus (COVID-19) pandemic in Israel by comparing their use of digital versus traditional information platforms. The study examined coping with COVID-19, considering explanatory variables such as Community Sense of Coherence (C-SOC), Internet usage, and other demographic variables. Using an online survey, 212 participants responded who identified as ultra-Orthodox and had access to the Internet, of which 47.2% were women and 52.8% were men, with a mean age of 37.66 ( SD = 12.60). Findings showed that the emotional and cognitive coping levels of members of ultra-Orthodox society with COVID-19 utilizing digital information sources were significantly better than those among community members using traditional information sources. Furthermore, the more the Internet was used for information or social needs, the more digital information sources helped community members cope with the crisis from an emotional and cognitive viewpoint. Conversely, the more participants felt that ultra-Orthodox society is a significant factor that helps them face life’s challenges (C-SOC), the better they coped with the pandemic utilizing traditional information sources. This study presents a novel, previously unstudied approach to ultra-Orthodox society’s coping methods with a worldwide crisis, whether through digital or traditional information sources. The study’s findings emphasize the need to make reliable and timely digital information accessible to this community, especially during a crisis, while respecting the culture and values of ultra-Orthodox society.

Sociolinguistic auto-coding has fairness problems too: measuring and mitigating bias

Abstract Sociolinguistics researchers can use sociolinguistic auto-coding (SLAC) to predict humans’ hand-codes of sociolinguistic data. While auto-coding promises opportunities for greater efficiency, like other computational methods there are inherent concerns about this method’s fairness – whether it generates equally valid predictions for different speaker groups. Unfairness would be problematic for sociolinguistic work given the central importance of correlating speaker groups to differences in variable usage. The current study examines SLAC fairness through the lens of gender fairness in auto-coding Southland New Zealand English non-prevocalic /r/. First, given that there are multiple, mutually incompatible definitions of machine learning fairness, I argue that fairness for SLAC is best captured by two definitions (overall accuracy equality and class accuracy equality) corresponding to three fairness metrics. Second, I empirically assess the extent to which SLAC is prone to unfairness; I find that a specific auto-coder described in previous literature performed poorly on all three fairness metrics. Third, to remedy these imbalances, I tested unfairness mitigation strategies on the same data; I find several strategies that reduced unfairness to virtually zero. I close by discussing what SLAC fairness means not just for auto-coding, but more broadly for how we conceptualize variation as an object of study.

A branch-and-price algorithm for unrelated parallel machine scheduling with machine usage costs

This paper considers unrelated parallel machine scheduling involving machine usage costs, in addition to classic job completion time-related costs. The usage cost of each machine is made up of a fixed usage cost and a variable usage cost proportional to the total processing time of the jobs assigned to it. These features model many practical situations where machine usage costs include, for example, rental fees when the machines are not owned but rented. To tackle this problem, four mathematical models based on the Shortest Weighted Processing Time (SWPT) rule are introduced. Additionally, the problem is formulated into a set-partitioning model, for which a branch-and-price algorithm is proposed with an appropriate branching strategy. This facilitates the development of an efficient pseudo-polynomial dynamic programming algorithm and a polynomial-time heuristic to solve the pricing problem. Extensive numerical experiments demonstrate the superior performance of the proposed branch-and-price algorithm over the four SWPT-based mathematical formulations and an existing branch-and-price algorithm designed for a special case. Notably, it can optimally solve instances involving up to 225 jobs and 15 machines within one hour. Moreover, statistical analyses reveal that the proposed polynomial-time heuristic significantly reduces the computation time, and the mathematical model based on the contribution of every job to the total weighted completion time exhibits the best overall performance.

Technology Usage and Learning Outcomes of Students in the New Normal: Basis of Learning Continuity Plan

Technology in education plays a vital role, especially during the COVID-19 pandemic. Teachers can harness online learning as a powerful educational tool by integrating technology into existing curricula. Thus, this study determined the extent of technology usage and learning outcomes of the students in the different subjects in terms of the most essential learning competencies (MELCs). Moreover, significant differences and relationships were explored for further analysis. The results revealed that most of the students used technology to search and study their lessons, watched DEPED TV episodes to learn, participated in online courses, and answered modules. Additionally, usually installed apps on their phones, but they were unlikely to listen to the radio. As such, the student’s learning outcomes are significantly high across subject areas despite of difficulties experienced. Relatively, highly significant differences and relationships were seen in some areas in terms of variables for both usage and learning outcomes. Therefore, technology is a factor in education. It is then recommended to utilize the continuity plan for improvement.

Inferring in-air gestures in complex indoor environment with less supervision

People have high demands for comfort and technology in indoor environments. Gestures, as a natural and friendly human computer interaction (HCI) method, have received widespread attention and have been the subject of many research studies. Traditional approaches are based on wearable devices and cameras, which can be cumbersome to operate and infringe upon users’ privacy. Millimeter-wave (mmWave) radar avoids these problems by detecting gestures in a noninvasive manner. However, it encounters practical challenges in complex indoor environments, such as dynamic disturbance from surroundings, variable usage conditions and diverse gesture patterns, which conventionally require considerable manual effort to address. In this paper, we attempt to minimize human supervision and propose a noninvasive gesture recognition method named RaGe that involves a commercial mmWave indoor radar. First, a parameter optimization framework considering gesture prior constraints is proposed for radar configuration, which functions to weaken the disturbance from surroundings. Second, we alleviate data shortages in variable usage conditions and achieve low-cost data augmentation by applying affine transformations. Third, we combine deformable convolution operations with an unsupervised attention mechanism, thus exploring the intrinsic features involved in diverse gesture patterns. Experimental results show that RaGe is able to recognize 7 gestures with 99.3% accuracy and less human supervision, surpassing the state-of-the-art methods in comparative experiments.

Pengaruh Penggunaan Instagram Terhadap Keberhasilan Promosi di Objek Wisata Kali Bening Jepara Jawa Tengah

The purpose of this research is to determine the effect of using Instagram on the success of promotions at the Kali Bening Jepara tourist attraction. This research was conducted on tourists and managers. This research uses descriptive quantitative methods. The number of samples in this study was 97 respondents. Data collection was obtained from observation, documentation and the results of distributing questionnaires to tourists and managers of the Kali Bening Jepara tourist attraction. Data analysis in this study used simple linear regression analysis. The results of simple linear regression analysis show that the Instagram usage variable has a positive and significant effect on promotional success.

A type system for formal verification of cyber-physical systems C/C++ software

The subject: This study focuses on improving the quality of Cyber-Physical System (CPS) software by eliminating incorrect usage of units of measurement and orientation in C/C++ programs. Incorrect usage often leads to critical errors that conventional systems cannot effectively prevent. Manual examination of code using dimensional and orientation analysis can detect these errors in physical equations, but these methods become impractical when dealing with complex physical computations. Objectives: As suggested by Siano, the proposed approach uses physical quantities and prefixes defined by the International System of Units and orientation operations on physical objects. The elaborated system incorporates dimensional and orientation analysis and metaprogramming techniques. The methods used are dimensional & orientational analysis and metaprogramming. The following results were obtained: ensuring consistency of the units, incorporating orientation operations into the programming model for accurately handling physical object rotations and alignments, and using Siano’s work to precisely manipulate object orientation, thereby reducing the likelihood of orientation-related errors. Checking physical dimensions and orientations during the compilation stage identifies potential software defects before code execution, thereby reducing debugging time and lowering the cost of addressing issues later in development. The elaborated system represents a crucial step towards safer and more dependable Cyber-Physical System applications. This approach allows us to identify approximately 90% of incorrect usage of program variables; additionally, it detects over 50% of erroneous operations during compilation and execution of large-scale programs in real-world conditions. Conclusions. Scientific novelty: it proposed and developed a specialized C++-type library for formal compile-time software verification of Cyber-Physical Systems software. The proposed C++-type library leverages dimensional and orientational analysis to enhance software quality, reliability, and real-time formal verification. Although the proposed method for formal verification is not tailor-made for cyber-physical objects and systems, given its primary focus on software-level concerns, it does exhibit adaptability for verifying general-purpose software that incorporates various physical parameters. This versatility extends to diverse domains such as educational, gaming, and simulation software.

EXAMINING THE EFFECTS OF INFORMATION AND COMMUNICATION TECHNOLOGY ADVANCEMENTS AS WELL AS EDUCATIONAL INITIATIVES ON ECONOMIC GROWTH

In order to develop and stabilize a particular economy across various life domains, economic construction is a fundamental endeavor. The development and growth of the economy are two aspects that are always related and always impacting each other. Undoubtedly, several factors contribute to economic growth, including the development of technology, modalities, and Human Resources (HR). This study uses a quantitative analysis with an object of study in the Sumatera Utara Province for the 2019–2022 period. The research methodology employs a Multiple Linear Regression analysis with the aim of determining the relationship between dependent and independent variables. The research findings indicate that, while the variables investing in telemedicine do not negatively affect economic growth, the use of the internet and education do have a positive impact. On the other hand, simultaneously, the variables of internet usage, telecom investment, and education have an impact on the growth of the Sumatera Utara economy.

Factors affecting medical students’ intention to use Rain Classroom: a cross-sectional survey

BackgroundRain Classroom was one of the most popular online learning platforms in Chinese higher education during the pandemic. However, there is little research on user intention under the guidance of technology acceptance and unified theory (UTAUT).ObjectiveThis research aims to determine factors influencing students' behavioural intention to use Rain Classroom.MethodsIn this cross-sectional and correlational investigation, 1138 medical students from five medical universities in Guangxi Province, China, made up the sample. This study added self-efficacy (SE), motivation (MO), stress (ST), and anxiety (AN) to the UTAUT framework. This study modified the framework by excluding actual usage variables and focusing only on intention determinants. SPSS-26 and AMOS-26 were used to analyze the data. The structural equation modelling technique was chosen to confirm the hypotheses.ResultsExcept for facilitating conditions (FC), all proposed factors, including performance expectancy (PE), effort expectancy (EE), social influence (SI), self-efficacy (SE), motivation (MO), anxiety (AN), and stress (ST), had a significant effect on students' behavioural intentions to use Rain Classroom.ConclusionsThe research revealed that the proposed model, which was based on the UTAUT, is excellent at identifying the variables that influence students' behavioural intentions in the Rain Classroom. Higher education institutions can plan and implement productive classrooms.

Optimizing the allowance allocations of water consumption, industrial solid waste and carbon emissions in China

Based on relevant data from 2014 to 2018, this research first evaluates the efficiencies of the industry production stage and environmental treatment stage via the dynamic two-stage SBM recycle model. Second, it employs the two-stage ZSG-SBM recycle model to optimize the allocation of resource usage and pollutant recovery variables of different provinces of China in 2025 according to the national plan, so as to find a path to realize the policy document goals of Made in China 2025. The empirical results show that the efficiencies of the east, central, and west regions nearly show a downward trend, and that two-level differentiation in the central region is large. In order to meet the planning requirements, the eastern provinces need to adjust and emit the least amount of carbon dioxide emissions per unit of industrial added value.

Effect of formulations over a Periodic Capacitated Vehicle Routing Problem with multiple depots, heterogeneous fleet, and hard time-windows.

This article presents a novel contribution to the Periodic Vehicle Routing Problem (PVRP) by introducing two new problem formulations which differ in the usage of the crucial flow variable. The formulations are tailored to meet the specific demands of the vending machine industry in Medellín, Colombia, and require considering a PVRP with time windows, a heterogeneous fleet, and multiple depots. This scenario, tailored to address real-world complexity and computational challenges, brings to light an exponential surge in integer variables as customer numbers increase. The research presents an analysis of PVRPs that include the four mentioned attributes, compares their similarities, and delves into their nuances. From the analysis it is derived that the variant of the PVRP presented has not been considered previously, taking into account not only these attributes, but also the restrictions involved. Empirical experiments are conducted to examine the intricate interplay between the two proposed formulations, highlighting their impact on the performance of the GUROBI solver. The study provides valuable insights into problem-specific adaptations and algorithmic approaches, emphasizing the significance of the proposed formulations in addressing multifaceted PVRPs. In essence, this research positions the introduction of these two formulations as a pioneering step, offering a new paradigm for approaching the PVRP.

Examining technological performance-related variables for effective usage of ChatGPT in academic learning of tertiary learners

Examining technological performance-related variables for effective usage of ChatGPT in academic learning of tertiary learners

서빙로봇 디자인속성 및 사용변수가 지속이용 의도에 미치는 영향 연구

This study was conducted to identify positive and negative perspectives that should be considered in the design process of serving robots and to derive directions for improving the customer experience process. The S-O-R framework was applied to reclassify attributes into functional and emotional design elements of serving robots and derive usage variables based on innovation resistance theory. For O, we applied use satisfaction, which is psychologically evaluated in the process of using a serving robot, and for R, we applied continuous use intention. The results showed that the key factors of serving robot design are usability and playfulness, factors that constitute extrinsic value are related to use satisfaction and influence, and convenience factors are related to perceived value and influence. Recently, serving robots are trying to expand additional functions and emotional approaches, so it is expected that the presentation of design attributes can help strengthen product competitiveness.

Electronic government system measurement model: a systematic testing of e-government implementation

<span>The demands to improve electronic government systems are increasing due to the large gap in the unsuccessful implementation in government. This study aimed to develop a measurement model consisting of policy, governance, and service, resources (usage) variables with their indicators to reduce the gap. <br /> A quantitative method was used with structural equation modelling (SEM) analysis based on partial least square (PLS) variance using SmartPLS version 3.0 software. Data collection for this study involved the direct distribution of questionnaires to 320 respondents, resulting in a successful collection rate of 95.3%. Subsequently, the collected data underwent analysis using the stages of SEM techniques. The results of the study show that the developed measurement models and indicators can be used as measuring tools for the execution of e-government. The developed model exhibits a satisfactory level of predictive relevance concerning the relationships among the variables, namely policy, governance, service, and usage. Furthermore, hypothesis testing was conducted to assess the validity of the proposed hypotheses within the model.</span>

78 Remotely monitored in-home IADLs can discriminate between normal cognition and mild cognitive impairment

Objective:Approximately 6.5 million Americans ages 65 and older have Alzheimer’s disease and related dementias, a prevalence projected to triple by 2060. While subtle impairment in cognition and instrumental activities of daily living (IADLs) arises in the mild cognitive impairment (MCI) phase, early detection of these insidious changes is difficult to capture given limitations. Traditional IADL assessments administered infrequently are less sensitive to early MCI and not conducive to tracking subtle changes that precede significant declines. Continuous passive monitoring of IADLs using sensors and software in home environments is a promising alternative. The purpose of this study was to determine which remotely monitored IADLs best distinguish between MCI and normal cognition.Participants and Methods:Participants were 65 years or older, independently community-dwelling, and had at least one daily medication and home internet access. Clinical assessments were performed at baseline. Electronic pillboxes (MedTracker) and computer software (Worktime) measured daily medication and computer habits using the Oregon Center for Aging and Technology (ORCATECH) platform. The Survey for Memory, Attention, and Reaction Time (SMART; Trail A, Trail B, and Stroop Tests) is a self-administered digital cognitive assessment that was deployed monthly. IADL data was aggregated for each participant at baseline (first 90 days) in each domain and various features developed for each. The receiver operating characteristic area under the curve (ROC-AUC) was calculated for each feature.Results:Traditional IADL Questionnaires.At baseline, 103 participants (normal n = 59, Mage = 73.6±5.5; MCI n = 44, Mage = 76.0±6.1) completed three functional questionnaires (Functional Activities Questionnaire; Measurement of Everyday Cognition (ECog), both self-report and informant). The Informant ECog demonstrated the highest AUC (72% AUC, p = <.001).Remotely monitored in-home IADLs and self-administered brief online cognitive test performance.Eighty-four had medication data (normal n = 48, Mage = 73.2±5.4; MCI n = 36, Mage = 75.6±6.9). Four features related to pillbox-use frequency (73% AUC) and four features related to pillbox-use time (62% AUC) were developed. The discrepancy between self-reported frequency of use versus actual use was the most discriminating (67% AUC, p = .03).Sixty-six had computer data (normal n = 38, Mage = 73.6±6.1; MCI n = 28, Mage = 76.6±6.8). Average usage time showed 64% AUC (p = .048) and usage variability showed 60% AUC (p = .18).One hundred and two completed the SMART (normal n = 59, Mage = 73.6±5.5; MCI n = 43, Mage = 75.9±6.2). Eleven features related to survey completion time demonstrated 80% AUC in discriminating cognition. Eleven features related to the number of clicks during the survey demonstrated 70% AUC. Lastly, seven mouse movement features demonstrated 71% AUC.Conclusions:Pillbox use frequency combined features and self-administered brief online cognitive test combined features (e.g., completion times, mouse cursor movements) have acceptable to excellent ability to discriminate between normal cognition and MCI and are relatively comparable to informant rated IADL questionnaires. General computer usage habits demonstrated lower discriminatory ability. Our approach has applied implications for detecting and tracking older adults’ declining cognition and function in real world contexts.

Unveiling the Dynamics of Expectation, Flow, Enjoyment, and Satisfaction: Drivers of Continued Subscription Intentions Among Netflix Users

"Research Aims: This study investigates the determinants impacting the intention of Netflix users to continue using the platform. By leveraging the extended Expectation-Confirmation Model of continued IT usage and hedonic variables, including Confirmation, Flow, Perceived Usefulness, Perceived Enjoyment, and Satisfaction, the research aims to discern the most influential factors. Design/Methodology/Approach: The research recruited 271 participants through non-probabilistic purposive sampling, targeting Indonesian Netflix subscribers active within the preceding six months who were at least 17 years old at the time of the study. Covariance-based Structural Equation Modelling was employed to analyse the collected data. Research Findings: User enjoyment and Satisfaction mediate the relationships between Flow and Confirmation with Netflix's continuance intention. Flow emerges as the preeminent determinant of continuance intention among the exogenous variables. While Flow's influence on continuance intention is mediated by Satisfaction and Enjoyment, there is no direct effect between Flow and Satisfaction. Perceived Usefulness exhibits no substantial correlation with continuance intention. Theoretical Contribution/Originality: This research underscores the pivotal role of Flow in driving enjoyment and continuance intention within video subscription services like Netflix. Managerial Implications in the South East Asian Context: The study offers insights into the preferences and behaviours of Netflix users in the Southeast Asian context, employing Indonesia as a focal point. This valuable information equips Netflix and other regional SVOD providers to strategically enhance their offerings and services. Research Limitations & Implications: To enhance the robustness of the findings, future research should encompass broader and more diverse populations for comparative purposes. Including new variables, such as digital piracy and perceived ease of use, would contribute to a comprehensive understanding of the dynamics at play. Keywords: Technology Acceptance Model, Expectation Confirmation Model, Flow, Continuance Intention, Netflix"

A state-of-the-art appraisal of bankruptcy prediction models focussing on the field’s core authors: 2010–2022

PurposeThe primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.Design/methodology/approachThe authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.FindingsThis paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.Research limitations/implicationsThe authors identify several gaps in the literature which this research does not address but could be the focus of future research.Practical implicationsThe authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.Social implicationsBankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.Originality/valueTo the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.