Contextual bias by Forensic Document Examination trainees: An empirical study from China.

The impact of contextual bias has been repeatedly demonstrated across forensic domains; however, research on this topic in China is scarce. To examine the prevalence of contextual bias in pattern feature-comparison disciplines, we conducted an experiment involving 24 forensic document examination students. The aim was to determine whether knowledge of different contextual information influenced their forensic decision-making. Participants were divided into different context groups and tasked with examining whether questioned signatures with ambiguous features matched reference signatures. The results of independent-samples t-tests for their decision score data in the two context groups exhibited a statistically significant difference (p < 0.05, Cohen's d > 0.8). Moreover, the submitted forensic reports by participants disclosed a biased evaluation of handwriting features. These findings show how contextual information can bias forensic decision-making in handwriting examination. Context management with complementary strategies such as case triage, cognitive training and decision-making transparency must be implemented to minimize bias in handwriting examination.

OMGMed: Advanced System for Ocular Myasthenia Gravis Diagnosis via Eye Image Segmentation.

This paper presents an eye image segmentation-based computer-aided system for automatic diagnosis of ocular myasthenia gravis (OMG), called OMGMed. It provides great potential to effectively liberate the diagnostic efficiency of expert doctors (the scarce resources) and reduces the cost of healthcare treatment for diagnosed patients, making it possible to disseminate high-quality myasthenia gravis healthcare to under-developed areas. The system is composed of data pre-processing, indicator calculation, and automatic OMG scoring. Building upon this framework, an empirical study on the eye segmentation algorithm is conducted. It further optimizes the algorithm from the perspectives of "network structure" and "loss function", and experimentally verifies the effectiveness of the hybrid loss function. The results show that the combination of "nnUNet" network structure and "Cross-Entropy + Iou + Boundary" hybrid loss function can achieve the best segmentation performance, and its MIOU on the public and private myasthenia gravis datasets reaches 82.1% and 83.7%, respectively. The research has been used in expert centers. The pilot study demonstrates that our research on eye image segmentation for OMG diagnosis is very helpful in improving the healthcare quality of expert doctors. We believe that this work can serve as an important reference for the development of a similar auxiliary diagnosis system and contribute to the healthy development of proactive healthcare services.

Contextual bias in forensic toxicology decisions: A follow-up empirical study from China.

The impact of contextual bias has been demonstrated repeatedly across forensic domains; however, research on this topic in forensic toxicology is very limited. In our previous study, experimental data from only one context version were compared with the actual forensic biasing casework. As a follow-up, this controlled experiment with 159 forensic toxicology practitioners was conducted, to test whether knowledge of different contextual information influenced their forensic decision-making. Participants in different context groups were tasked to identify testing strategies for carbon monoxide and opiate drugs. The results of chi-squared tests for their selections and two context groups exhibited statistically significant differences (p < 0.05 or p < 0.01). These findings show contextual information can bias forensic toxicology decisions about testing strategies, despite it is a relatively objective domain in forensic science.

What are developers talking about information security? A large-scale study using semantic analysis of Q&A posts.

Background: Digitalization and rapid technological improvement in the present day bring numerous benefits, but they also raise the complexity and diversity of cyber security risks, putting critical information security issues on the agenda. Growing issues and worries about information security endanger not only the security of individuals and organizations but also global social and economic stability. Methods: This study investigates the issues and challenges regarding information security by analyzing all the postings on ISSE (Information Security Stack Exchange), a Q&A website focused on information security. In order to identify the primary topics addressed in postings shared on the ISSE platform, we employed a probabilistic topic modeling method called latent Dirichlet allocation (LDA), which is generative in nature and relies on unsupervised machine learning processes. Results: Through this investigation, a total of 38 topics were identified, demonstrating the present state of information security issues and challenges. Considering these topics, a comprehensive taxonomy of seven categories was devised to address information security issues, taking into account their backgrounds and perspectives. Subsequently, we conducted an examination of the prevalence and complexity of the matters at hand. In addition, we have defined the prevailing technologies utilized in the realm of information security, including tasks, certifications, standards, methods, tools, threats, and defenses. We have provided a number of implications for different stakeholders, including academics, developers, educators, and practitioners, who are working towards advancing the field of information security.

Predictivism and avoidance of ad hoc-ness: An empirical study.

Predictivism is the thesis that evidence successfully predicted by a scientific theory counts more (or ought to count more) in the confirmation of that theory than already known evidence would. One rationale that has been proposed for predictivism is that predictive success guards against ad hoc hypotheses. Despite the intuitive attraction of predictivism, there is historical evidence that speaks against it. As valuable as the historical evidence may be, however, it is largely indirect evidence for the epistemic attitudes of individual - albeit prominent - scientists. This paper presents the results of an empirical study of scientists' attitudes toward predictivism and ad hoc-ness (n = 492), which will put the debate on a more robust empirical footing. The paper also draws attention to a tension between the ad hoc-ness avoidance rationale of predictivism and the ways philosophers have spelled out the notion of ad hoc-ness.

Low awareness of the transitivity assumption in complex networks of interventions: a systematic survey from 721 network meta-analyses.

BACKGROUND: The transitivity assumption is the cornerstone of network meta-analysis (NMA). Violating transitivity compromises the credibility of the indirect estimates and, by extent, the estimated treatment effects of the comparisons in the network. The present study offers comprehensive empirical evidence on the completeness of reporting and evaluating transitivity in systematic reviews with multiple interventions. METHODS: We screened the datasets of two previous empirical studies, resulting in 361 systematic reviews with NMA published between January 2011 and April 2015. We updated our evidence base with an additional 360 systematic reviews with NMA published between 2016 and 2021, employing a pragmatic approach. We devised assessment criteria for reporting and evaluating transitivity using relevant methodological literature and compared their reporting frequency before and after the PRISMA-NMA statement. RESULTS: Systematic reviews published after PRISMA-NMA were more likely to provide a protocol (odds ratio (OR): 3.94, 95% CI: 2.79-5.64), pre-plan the transitivity evaluation (OR: 3.01, 95% CI: 1.54-6.23), and report the evaluation and results (OR: 2.10, 95% CI: 1.55-2.86) than those before PRISMA-NMA. However, systematic reviews after PRISMA-NMA were less likely to define transitivity (OR: 0.57, 95% CI: 0.42-0.79) and discuss the implications of transitivity (OR: 0.48, 95% CI: 0.27-0.85) than those published before PRISMA-NMA. Most systematic reviews evaluated transitivity statistically than conceptually (40% versus 12% before PRISMA-NMA, and 54% versus 11% after PRISMA-NMA), with consistency evaluation being the most preferred (34% before versus 47% after PRISMA-NMA). One in five reviews inferred the plausibility of the transitivity (22% before versus 18% after PRISMA-NMA), followed by 11% of reviews that found it difficult to judge transitivity due to insufficient data. In justifying their conclusions, reviews considered mostly the comparability of the trials (24% before versus 30% after PRISMA-NMA), followed by the consistency evaluation (23% before versus 16% after PRISMA-NMA). CONCLUSIONS: Overall, there has been a slight improvement in reporting and evaluating transitivity since releasing PRISMA-NMA, particularly in items related to the systematic review report. Nevertheless, there has been limited attention to pre-planning the transitivity evaluation and low awareness of the conceptual evaluation methods that align with the nature of the assumption.

Comparison of statistical methods used to meta-analyse results from interrupted time series studies: an empirical study.

BACKGROUND: The Interrupted Time Series (ITS) is a robust design for evaluating public health and policy interventions or exposures when randomisation may be infeasible. Several statistical methods are available for the analysis and meta-analysis of ITS studies. We sought to empirically compare available methods when applied to real-world ITS data. METHODS: We sourced ITS data from published meta-analyses to create an online data repository. Each dataset was re-analysed using two ITS estimation methods. The level- and slope-change effect estimates (and standard errors) were calculated and combined using fixed-effect and four random-effects meta-analysis methods. We examined differences in meta-analytic level- and slope-change estimates, their 95% confidence intervals, p-values, and estimates of heterogeneity across the statistical methods. RESULTS: Of 40 eligible meta-analyses, data from 17 meta-analyses including 282 ITS studies were obtained (predominantly investigating the effects of public health interruptions (88%)) and analysed. We found that on average, the meta-analytic effect estimates, their standard errors and between-study variances were not sensitive to meta-analysis method choice, irrespective of the ITS analysis method. However, across ITS analysis methods, for any given meta-analysis, there could be small to moderate differences in meta-analytic effect estimates, and important differences in the meta-analytic standard errors. Furthermore, the confidence interval widths and p-values for the meta-analytic effect estimates varied depending on the choice of confidence interval method and ITS analysis method. CONCLUSIONS: Our empirical study showed that meta-analysis effect estimates, their standard errors, confidence interval widths and p-values can be affected by statistical method choice. These differences may importantly impact interpretations and conclusions of a meta-analysis and suggest that the statistical methods are not interchangeable in practice.

An Empirical Survey on the Adaption of Neoadjuvant Chemotherapy in Bladder Cancer.

INTRODUCTION: The aim of the study was to determine the adaption of neoadjuvant chemotherapy (NAC) in patients with muscle-invasive bladder cancer (MIBC) in Germany, Austria, and Switzerland and especially underlying reasons for potential low adherence to guidelines. METHODS: We conducted a non-validated survey among 336 urologic departments in Germany, Austria, and Switzerland. RedCap questionnaires were electronically distributed and included 23 items concerning the general NAC administration standards and guideline compliance in patient counseling regarding the actual treatment. RESULTS: The return rate of the questionnaire was 19.1% (63/336). Although 45 departments (71.4%) claim to perform NAC as the standard of care, only 49% of eligible patients actually receive NAC. An advanced disease stage (≥cT3) and a high tumor volume were mentioned to support the application of NAC, whereas 35% of responders worry about deterioration of patients' preoperative status due to NAC. Furthermore, 26.7% of respondents are concerned about the low extent of survival benefit. CONCLUSION: Application of NAC in eligible MIBC patients in Germany, Austria, and Switzerland remains low. Although the majority of urologic departments discuss NAC and acknowledge the need for intensified treatment in advanced disease stages, not all eligible patients will actually receive NAC before radical cystectomy.

Privacy Concerns About Sharing General and Specific Health Information on Twitter: Quantitative Study.

BACKGROUND: Twitter is a common platform for people to share opinions, discuss health-related topics, and engage in conversations with a wide audience. Twitter users frequently share health information related to chronic diseases, mental health, and general wellness topics. However, sharing health information on Twitter raises privacy concerns as it involves sharing personal and sensitive data on a web-based platform. OBJECTIVE: This study aims to adopt an interactive approach and develop a model consisting of privacy concerns related to web-based vendors and web-based peers. The research model integrates the 4 dimensions of concern for information privacy that express concerns related to the practices of companies and the 4 dimensions of peer privacy concern that reflect concerns related to web-based interactions with peers. This study examined how this interaction may affect individuals' information-sharing behavior on Twitter. METHODS: Data were collected from 329 Twitter users in the United States using a web-based survey. RESULTS: Results suggest that privacy concerns related to company practices might not significantly influence the sharing of general health information, such as details about hospitals and medications. However, privacy concerns related to companies and third parties can negatively shape the disclosure of specific health information, such as personal medical issues (ß=-.43; P<.001). Findings show that peer-related privacy concerns significantly predict sharing patterns associated with general (ß=-.38; P<.001) and specific health information (ß=-.72; P<.001). In addition, results suggest that people may disclose more general health information than specific health information owing to peer-related privacy concerns (t165=4.72; P<.001). The model explains 41% of the variance in general health information disclosure and 67% in specific health information sharing on Twitter. CONCLUSIONS: The results can contribute to privacy research and propose some practical implications. The findings provide insights for developers, policy makers, and health communication professionals about mitigating privacy concerns in web-based health information sharing. It particularly underlines the importance of addressing peer-related privacy concerns. The study underscores the need to build a secure and trustworthy web-based environment, emphasizing the significance of peer interactions and highlighting the need for improved regulations, clear data handling policies, and users' control over their own data.

Smartphone-derived Virtual Keyboard Dynamics Coupled with Accelerometer Data as a Window into Understanding Brain Health: Smartphone Keyboard and Accelerometer as Window into Brain Health.

We examine the feasibility of using accelerometer data exclusively collected during typing on a custom smartphone keyboard to study whether typing dynamics are associated with daily variations in mood and cognition. As part of an ongoing digital mental health study involving mood disorders, we collected data from a well-characterized clinical sample (N = 85) and classified accelerometer data per typing session into orientation (upright vs. not) and motion (active vs. not). The mood disorder group showed lower cognitive performance despite mild symptoms (depression/mania). There were also diurnal pattern differences with respect to cognitive performance: individuals with higher cognitive performance typed faster and were less sensitive to time of day. They also exhibited more well-defined diurnal patterns in smartphone keyboard usage: they engaged with the keyboard more during the day and tapered their usage more at night compared to those with lower cognitive performance, suggesting a healthier usage of their phone.

Internationalization of medical education in China: An investigation of medical graduates' global competence and its influencing factors.

AIM: Global competence has become an important competence for medical graduates in the globalized world. However, research on it is scarce. This study was built upon the scholarship published in the field to measure medical graduates' global competence. MATERIALS AND METHODS: A questionnaire was administered to China's medical graduates sampled from four institutions of medical education. Descriptive statistics were made to analyze the level of medical graduates GC. Influencing factors were investigated using multiple linear regression. Multiple levels of regression analysis were used to identify the influence of different independent variables on the dependent variable. RESULTS: The sample had a relatively good level of global competence in general, but lacked skills in cross-cultural communication and international academic communication. "Internationalization Concept and System" and "International Development of Teachers" in the school dimension and "Taking International Courses", "International Publication" and "Participation in International Conference" in the dimension of individual international involvement had a significant positive impact on the cultivation of global competence. CONCLUSIONS: The universities should aim for the construction of an effective institutional mechanism for internationalization to help improve students' global competence.

Sociodemographic Factors Predict Incident Mild Cognitive Impairment: A Brief Review and Empirical Study.

OBJECTIVES: Mild cognitive impairment (MCI) is a transitional stage between normal cognitive aging and dementia that increases the risk of progressive cognitive decline. Early prediction of MCI could be beneficial for identifying vulnerable individuals in the community and planning primary and secondary prevention to reduce the incidence of MCI. DESIGN: A narrative review and cohort study. SETTING AND PARTICIPANTS: We review the MCI prediction based on the assessment of sociodemographic factors. We included participants from 3 surveys: 8915 from wave 2011/2012 of the China Health and Retirement Longitudinal Study (CHARLS), 9765 from the 2011 Chinese Longitudinal Healthy Longevity Survey (CLHLS), and 1823 from the 2014 Rugao Longevity and Ageing Study (RuLAS). METHODS: We searched in PubMed, Embase, and Web of Science Core Collection between January 1, 2019, and December 30, 2022. To construct the composite risk score, a multivariate Cox proportional hazards regression model was used. The performance of the score was assessed using receiver operating characteristic (ROC) curves. Furthermore, the composite risk score was validated in 2 longitudinal cohorts, CLHLS and RuLAS. RESULTS: We concluded on 20 articles from 892 available. The results suggested that the previous models suffered from several defects, including overreliance on cross-sectional data, low predictive utility, inconvenient measurement, and inapplicability to developing countries. Our empirical work suggested that the area under the curve for a 5-year MCI prediction was 0.861 in CHARLS, 0.797 in CLHLS, and 0.823 in RuLAS. We designed a publicly available online tool for this composite risk score. CONCLUSIONS AND IMPLICATIONS: Attention to these sociodemographic factors related to the incidence of MCI can be beneficially incorporated into the current work, which will set the stage for better early prediction of MCI before its incidence and for reducing the burden of the disease.

Relationship between Racial Diversity in Medical Staff and Hospital Operational Efficiency: An Empirical Study of 3870 U.S. Hospitals.

Demand for foreign nurses and medical staff is rapidly increasing due to the severe labor shortage in U.S. hospitals triggered by the COVID-19 pandemic. However, empirical studies on the effect of the racial diversity of medical staff on hospital operations are still lacking. This research gap is thus investigated based on the foreign medical staff working in 3870 U.S. hospitals. Results show that workforce racial diversity has a significantly positive relationship with hospital operational efficiency regarding occupancy rate, manpower productivity, capacity productivity, and case mix index. Notably, this study empirically supports that increasing the ratio of foreign nurses positively affects the overall operational efficiency of hospitals. In addition, the study results also indicate that the hospital location, size, ownership, and teaching status act as significant control variables for the relationship between racial diversity and hospital efficiency. These results imply that hospitals with these specific operating conditions need to pay more attention to racial diversity in the workplace, as they are structurally more sensitive to the relationship between racial diversity and operational efficiency. In short, the findings of this study suggest that hospital efficiency can be operationally improved by implementing workforce ethnic diversity. For this reason, hospital stakeholders and healthcare policymakers are expected to benefit from this study's findings. Above all, the results of this study imply that if an organization adapts to extreme external environmental changes (e.g., the COVID-19 pandemic) through appropriate organizational restructuring (i.e., expanding the workforce racial diversity by hiring foreign medical staff), the organization can gain a competitive advantage, a claim that is supported by contingency theory. Further, investors are increasingly interested in ESG, especially companies that embody ethical and socially conscious workplaces, including a diverse and inclusive workforce. Thereby, seeking racial diversity in the workforce is now seen as a fundamental benchmark for organizational behavior that predicts successful ESG business practices, a claim that is supported by stakeholder theory. Therefore, in conclusion, the findings of this study suggest that workforce racial diversity is no longer an optional consideration but should be considered as one of the essential determinants of competitive advantage in organizations, particularly in the healthcare sector.

Missing data matter: an empirical evaluation of the impacts of missing EHR data in comparative effectiveness research.

OBJECTIVES: The impacts of missing data in comparative effectiveness research (CER) using electronic health records (EHRs) may vary depending on the type and pattern of missing data. In this study, we aimed to quantify these impacts and compare the performance of different imputation methods. MATERIALS AND METHODS: We conducted an empirical (simulation) study to quantify the bias and power loss in estimating treatment effects in CER using EHR data. We considered various missing scenarios and used the propensity scores to control for confounding. We compared the performance of the multiple imputation and spline smoothing methods to handle missing data. RESULTS: When missing data depended on the stochastic progression of disease and medical practice patterns, the spline smoothing method produced results that were close to those obtained when there were no missing data. Compared to multiple imputation, the spline smoothing generally performed similarly or better, with smaller estimation bias and less power loss. The multiple imputation can still reduce study bias and power loss in some restrictive scenarios, eg, when missing data did not depend on the stochastic process of disease progression. DISCUSSION AND CONCLUSION: Missing data in EHRs could lead to biased estimates of treatment effects and false negative findings in CER even after missing data were imputed. It is important to leverage the temporal information of disease trajectory to impute missing values when using EHRs as a data resource for CER and to consider the missing rate and the effect size when choosing an imputation method.

Empirical Study of Surface Deterioration Analysis Based on Random Fields for Reinforced Concrete Structures in Marine Environment.

Corrosion-induced deterioration of the in-service marine reinforced concrete (RC) structures may result in unsatisfactory serviceability or insufficient safety. Surface deterioration analysis based on random fields can provide information regarding the future development of the surface damage of the in-service RC members, but its accuracy needs to be verified in order to broaden its applications in durability assessment. This paper performs an empirical study to verify the accuracy of the surface deterioration analysis based on random fields. The batch-casting effect is considered to establish the "step-shaped" random fields for stochastic parameters in order to better coordinate their actual spatial distributions. Inspection data from a 23-year-old high-pile wharf is obtained and analyzed in this study. The simulation results of the RC panel members' surface deterioration are compared with the in-situ inspection results with respect to the steel cross-section loss, cracking proportion, maximum crack width, and surface damage grades. It shows that the simulation results coordinate well with the inspection results. On this basis, four maintenance options are established and compared in terms of the total amounts of RC panel members needing restoration and the total economic costs. It provides a comparative tool to aid the owners in selecting the optimal maintenance action given the inspection results, to minimize the lifecycle cost and guarantee the sufficient serviceability and safety of the structures.

How did COVID-19 affect logistics and supply chain processes? Immediate, short and medium-term evidence from some industrial fields of Italy.

This paper provides empirical evidence on the impact of the Covid-19 pandemic on logistics and supply chain processes of five industrial sectors of Italy, namely food & beverage, machine manufacturing, metal mechanical industry, logistics & transport, and textile & fashion. A questionnaire survey, with 82 useful responses, was conducted to investigate various effects of Covid-19 on these businesses, such as the volumes handled and the service performance in the immediate-, short- and medium-term, the countermeasures implemented by companies and the future decision-making strategies. The period of analysis spans from January 2020 to June 2021. Results show that the impact of Covid-19 on volumes and service performance varied across the sectors: the food & beverage and logistics & transport were poorly affected by the pandemic and experienced a general increase in the demand and volumes, while mechanical or textile & fashion industries were mostly affected by a decrease in demand. The positive/negative impacts were particularly evident at the beginning of the pandemics, but, depending on the sector, the effects could cease quite quickly or last in the short-term. The countermeasures adopted against the Covid-19 emergency differ again across sectors; in general, industry fields that were particularly impacted by the pandemic emergency have applied more countermeasures. Typical strategies for risk management (e.g., the diversification in transport modes or the stock increase) turned out to be applied as immediate countermeasures or in plan for the future in few industries only. Differences across sectors were also observed about the sourcing strategies already in use, implemented to counteract the pandemics or expected to be maintained in time. Empirical outcomes offered are expected to help researchers gain a deep understanding of Covid-19 related phenomena, thus inspiring further research activities.

The effect of node features on GCN-based brain network classification: an empirical study.

Brain functional network (BFN) analysis has become a popular technique for identifying neurological/mental diseases. Due to the fact that BFN is a graph, a graph convolutional network (GCN) can be naturally used in the classification of BFN. Different from traditional methods that directly use the adjacency matrices of BFNs to train a classifier, GCN requires an additional input-node features. To our best knowledge, however, there is no systematic study to analyze their influence on the performance of GCN-based brain disorder classification. Therefore, in this study, we conduct an empirical study on various node feature measures, including (1) original fMRI signals, (2) one-hot encoding, (3) node statistics, (4) node correlation, and (5) their combination. Experimental results on two benchmark databases show that different node feature inputs to GCN significantly affect the brain disease classification performance, and node correlation usually contributes higher accuracy compared to original signals and manually extracted statistical features.

Are Health Information Systems Ready for the Digital Transformation in Portugal? Challenges and Future Perspectives.

PURPOSE: This study aimed to reflect on the challenges of Health Information Systems in Portugal at a time when technologies enable the creation of new approaches and models for care provision, as well as to identify scenarios that may characterize this practice in the future. DESIGN/METHODOLOGY/APPROACH: A guiding research model was created based on an empirical study that was conducted using a qualitative method that integrated content analysis of strategic documents and semi-structured interviews with a sample of fourteen key actors in the health sector. FINDINGS: Results pointed to the existence of emerging technologies that may promote the development of Health Information Systems oriented to "health and well-being" in a preventive model logic and reinforce the social and management implications. ORIGINALITY/VALUE: The originality of this work resided in the empirical study carried out, which allowed us to analyze how the various actors look at the present and the future of Health Information Systems. There is also a lack of studies addressing this subject. RESEARCH LIMITATIONS/IMPLICATIONS: The main limitations resulted from a low, although representative, number of interviews and the fact that the interviews took place before the pandemic, so the digital transformation that was promoted was not reflected. Managerial implications and social implications: The study highlighted the need for greater commitment from decision makers, managers, healthcare providers, and citizens toward achieving improved digital literacy and health. Decision makers and managers must also agree on strategies to accelerate existing strategic plans and avoid their implementation at different paces.

An Empirical Investigation of Critical Factors Affecting Acceptance of E-Learning Platforms: A Learner's Perspective.

E-learning is evolving as the paradigm of modern-day education. Globally, e-learning has seen a rise; however, failures happen. There is a dearth of studies that discuss why a lot of learners quit e-learning after a preliminary experience. Preceding research studies carried out under diverse task settings have proposed an assortment of factors impacting learners' satisfaction with e-Learning. This study developed an integrated conceptual model with the instructor, course, and learners' dimensions and then empirically validates it. The technology acceptance model (TAM) has been employed for testing the acceptance of various technologies and software within an e-learning context. This study intends to examine the salient factors of effective e-learning acceptance by learners. A survey investigates the critical factors using a self-administered questionnaire influencing the satisfaction of learners in the e-Learning system/platform. The study uses quantitative methodology and data were collected from 348 learners. On performing the structured equation modeling for testing the hypothesized model, outcomes reveal the significant factors influencing learners' perceived satisfaction studied in three dimensions of the instructor, course, and learner. It will facilitate educational institutes and provide directions on improving learners' satisfaction and additionally improve e-Learning implementation.

Assessing Factors That Influence Womenpreneurs' Intention to Use Technology: A Structural Equation Modeling Approach.

The growth of financial literacy is significant nowadays. Because of this, more people are becoming increasingly responsible in their financial planning, investments, and living expenses. In developing new technology, it is necessary to know the technological acceptance of the prospective users of the technology itself. This study aims to identify the primary factors influencing the technology acceptance levels of lower-middle socio-economic users for a digital financial literacy application. The proposed model in this research was developed based on UTAUT, TAM, and Usability model, and it consists of six primary constructs: (1) Performance Expectancy; (2) Effort Expectancy; (3) Social Influence; (4) Resources and Cost; (5) Satisfaction; (6) Behavior Intention. All the hypotheses used in this study were statistically measured using SmartPLS tools. This study found that because many lower-middle socio-economic users lack sufficient understanding of technology to properly utilize it, that a digital platform is not the right tool to teach them financial literacy.