Compliance Model Research Articles

Development and Validation of a Predictive Model of Prostate Screening Compliance: A Nationwide Population-Based Study.

Prostate cancer (PCa) is the second most common cancer in men worldwide, with significant incidence and mortality, particularly in Mexico, where diagnosis at advanced stages is common. Early detection through screening methods such as digital rectal examination and prostate-specific antigen testing is essential to improve outcomes. Despite current efforts, compliance with prostate screening (PS) remains low due to several barriers. This study aims to develop and validate a predictive model for PCa screening compliance in Mexican men. Retrospective observational design with data from the Mexican Health and Aging Study (MHAS). Participants were men aged 50-69 from three cohorts: development/internal validation, temporal validation, and external validation. Key predictors were identified using relaxed Least Absolute Shrinkage and Selection Operator (LASSO) regression, and model performance was assessed using the area under the curve (AUC) from receiver operating characteristic (ROC) analyses, along with calibration and decision curve analysis (DCA). A web nomogram was also developed. The final model included seven key predictors. AUC values indicated good predictive performance: 0.783 for the training subgroup, 0.722 for the test subgroup, 0.748 for the time cohort, and 0.756 for the external cohort, with sensitivities of 73.5%. The DCA demonstrated the superior clinical utility of the model compared to the reference strategies. The predictive model developed for performance to PCa screening is robust across different cohorts and highlights critical factors influencing performance. The accompanying web-based nomogram enhances clinical applicability and supports interventions aimed at improving PCa screening rates among Mexican men.

O MODELO DE COMPLIANCE NA PREVENÇÃO DA SAÚDE MENTAL NAS UNIVERSIDADES BRASILEIRAS

The pandemic that began in early 2020 in Brazil brought change to workers. There was an overlap of personal and professional tasks and caused physical and mental illness, as reported by several studies. Telework has the possibility of continuing to be the work model for some professions, such as teaching. A compliance model is proposed for Higher Education Institutions – HEIs and the objective is to collect the opinion of higher education professors in Brazil, in the public and private networks, to verify the importance of this preventive model of illness. The follow-up would be offered free of charge by the employing HEI and the hypothesis is that the teacher understands that it would improve his quality of life, his performance as a professional and the HEI should reap fruits with the mitigation of dissatisfaction on the part of students and teachers, with a view to the well-being of professionals and the sustainability of the image and financial order.

Clinical Predictors of Medication Compliance in Patients With Acute Herpetic Neuralgia

PurposePain is one of the most common and harmful symptoms experienced by individuals with acute herpetic neuralgia (AHN). In this population, studies to determine the causes that affect patients taking medications compliance are rare. This study aimed to construct a predictive model for medication compliance of patients with AHN and to verify its performance. Design and MethodsIn this prospective study of 398 patients with AHN who were discharged from a tertiary hospital with medications from July 2020 to October 2022, we used logistic regression analysis to explore the predictive factors of medication compliance of patients with AHN and to construct a nomogram. The area under the curve was used to evaluate the predictive effect of the model. ResultsA predictive model of drug compliance of patients with AHN was constructed based on the following four factors: disease duration, pain severity before treatment, medication beliefs, and comorbidity of chronic diseases. The area under the curve of the model was 0.766 (95% confidence interval [0.713, 0.819]), with a maximum Youden's index of 0.431, sensitivity of 0.776, and specificity of 0.655. A linear calibration curve was found with a slope close to 1. ConclusionsThe prediction model constructed in this study had good predictive performance and provided a reference for early clinical screening of independent factors that affected the medication compliance of patients with AHN.

Conceptual framework for AI-driven tax compliance in fintech ecosystems

This paper comprehensively reviews the integration of Artificial Intelligence (AI) into tax compliance processes within fintech ecosystems. It explores the theoretical foundations of AI technologies, such as machine learning and predictive analytics, and how they can automate tax reporting, auditing, and compliance monitoring. Conceptual models for AI-driven tax compliance are proposed, highlighting the potential for increased efficiency, accuracy, and cost reduction. The paper also examines challenges associated with AI adoption, including algorithmic biases, ethical concerns, data privacy issues, and regulatory hurdles. Strategies for overcoming these challenges and fostering broader adoption are discussed. Finally, the paper offers recommendations for fintech companies and policymakers, emphasizing the need for transparent AI models, bias mitigation, data privacy, and updated regulatory frameworks to ensure fair and effective AI-enabled tax systems.

Information security policy compliance: a replication study in Ethiopia

Purpose This study aims to replicate and assess the applicability and generalizability of Bulgurcu et al.'s (2010) information security policy compliance model to participants from Ethiopia, focusing on understanding compliance behavior with information security policies (ISPs). Design/methodology/approach The study replicates Bulgurcu et al.’s (2010) research methodology. They empirically tested their model using data collected through a survey of 464 employees from a diverse set of organizations in the United States. In this study, the authors used a self-administered survey approach to collect data from 318 valid responses from university students in Ethiopia. The structural equation modeling technique with SmartPLS 4.0 is used to test hypotheses derived from the original model. Findings The replication results confirm most of the hypotheses from the original study, indicating that their model is generalizable to the Ethiopian population. However, the study also identifies differences between the original and replicated results, suggesting potential cultural variations requiring further examination. Originality/value This study contributes to the literature by replicating and extending the original study’s findings in a different cultural context, namely Ethiopia. It highlights the importance of understanding compliance behavior with ISPs across diverse populations and emphasizes the need for future replication studies to explore the impact of culture on intention toward information security behavior.

Ontological perception of a smart unified organizational compliance model to mitigate financial risk and promote sustainability in the manufacturing sector

This research intends to find out the compliance acts based on the manufacturing industry of Bangladesh and lead to the development of the integrated theory of compliance model. There are several compliance regulations, that are separately dealt with in any manufacturing organization. These compliance regulations are handled at various ends of the organization making the process quite scattered, time-consuming, and tedious. To fix this problem, the integration of organizational compliance regulations is brought under one platform. Researchers have applied the qualitative approach with multiple case studies methodology scrutinizing the in-depth interviews and transcripts. Furthermore, the NVIVO tool has been used to analyze, where the necessary themes of the Organizational Compliance Regulations are found. Therefore, we have proposed a conceptual framework to inaugurate a standalone combined framework, which is an innovative and novel measure.

Norms as obligations

Economists model legal compliance as the process of maximizing utility while weighing the consequences from norm violation against other (monetary and non-monetary) considerations. Legal philosophers, on the other hand, believe that the normative side of law is central. Citizens comply because they have an obligation to do so. Legal norms provide exclusionary reasons that prevent weighing up on other issues. We test and compare both models in a controlled online experiment. We conduct a modified dictator game with partially unknown yet ascertainable payoffs, and vary between treatments the presence and content of authoritative norms. Our experimental results show that – in the presence of a norm – participants follow norms without searching for information that they deem important in the absence of a norm. This pattern is independent of the specific content of the norm. Our results are consistent with the legal model of norm compliance.

Nomogram model for predicting medication adherence in patients with various mental disorders based on the Dryad database

ObjectiveTreatment compliance among psychiatric patients is related to disease outcomes. How to assess patient compliance remains a concern. Here, we established a predictive model for medication compliance in patients with...

Implementing sensor-based digital health technologies in clinical trials: Key considerations from the eCOA Consortium.

The increased use of sensor-based digital health technologies (DHTs) in clinical trials brought to light concerns about implementation practices that might introduce burden on trial participants, resulting in suboptimal compliance and become an additional complicating factor in clinical trial conduct. These concerns may contribute to the lower-than-anticipated uptake of DHT deployment and data use for regulatory decision-making, despite well-articulated benefits. The Electronic Clinical Outcome Assessment (eCOA) Consortium gathered collective experience on deploying sensor-based DHTs and supplemented this with relevant literature focusing on mechanisms that may enhance participant compliance. The process for DHT implementation starts with identifying a clinical concept of interest followed by a digital measure selection, defining active or passive data capture and their sources, the number of sensors with respective body location, plus the duration and frequency of use in the context of perceived participant burden. Roundtable discussions among patient groups, physicians, and technology providers prior to protocol development can be very impactful for optimizing trial design. While diversity and inclusion are essential for any clinical trial, patient populations should be considered carefully in the context of trial-specific aims, requirements, and anticipated patient burden. Minimizing site burden includes assessment of training, research engagement, and logistical burden which needs to be triaged differently for early and late-stage clinical trials. Additional considerations include sharing trial results with study participants and leveraging publicly available data for compliance modeling. To the best of our knowledge, this report provides holistic considerations for sensor-based DHT implementation that may optimize participant compliance.

Leveraging business analytics to build cyber resilience in fintech: Integrating AI and governance, risk and compliance (GRC) models

The rapid evolution of fintech has brought significant advancements in financial services but also increased vulnerability to cyber threats. This review explores how business analytics, artificial intelligence (AI), and Governance, Risk, and Compliance (GRC) models can be leveraged to build cyber resilience in the fintech sector. It begins by discussing the growing cyber threat landscape and how AI-driven solutions and predictive analytics enhance fintech cybersecurity. The paper further examines the integration of GRC frameworks to ensure effective governance, continuous risk monitoring, and compliance with regulatory standards. By synergizing AI, business analytics, and GRC models, fintech companies can strengthen their defenses against evolving cyber risks. The review concludes with recommendations for fintech firms to adopt a proactive, data-driven approach to cybersecurity, promoting long-term sustainability and protection in an increasingly digital financial environment.

Application of classification tree to construct a predictive model for screening compliance among populations at high risk for early cancer in the upper digestive tract

Application of classification tree to construct a predictive model for screening compliance among populations at high risk for early cancer in the upper digestive tract

A compliance and audit model for tackling tax evasion in Nigeria

Tax evasion remains a significant challenge for Nigeria, undermining economic stability and reducing public revenue. This paper proposes a comprehensive compliance and audit model designed to address tax evasion in Nigeria. The model integrates enhanced compliance strategies, advanced audit procedures, and the strategic use of technology to create a robust framework for tackling tax evasion. The proposed model emphasizes proactive measures to improve taxpayer compliance through targeted education and outreach programs, streamlined reporting processes, and incentivization. On the audit front, it advocates for the adoption of sophisticated audit techniques and technologies, including data analytics and automated systems, to detect and address evasion more effectively. The implementation framework outlines a phased approach involving key stakeholders such as the Federal Inland Revenue Service (FIRS), local tax authorities, and the private sector. By examining domestic and international case studies, the paper highlights successful practices and potential challenges, providing actionable insights for adapting these strategies to the Nigerian context. Evaluation metrics are proposed to assess the model’s effectiveness, with expected outcomes including improved tax revenue, enhanced compliance rates, and strengthened public trust in the tax system. The study concludes with recommendations for further research and policy adjustments to refine and expand the model's application. This approach aims to not only mitigate tax evasion but also contribute to a more transparent and equitable tax system in Nigeria.

Graph-logical models for (n, f, k) – and consecutive - k-out-of-n – systems

The article is devoted to methods of constructing graph-logical models of fault-tolerant multiprocessor systems. In particular, systems of the type (n, f, k), linear consecutive-k-out-of-n and circular consecutive-k-out-of-n are considered, which are characterized by the failure of the system when a certain number of consecutive processors fail. Graph-logical models can be used to estimate the reliability parameters of fault-tolerant multiprocessor systems by conducting statistical experiments with models of their behavior in the failure flow. The graph-logical models under construction are based on the basic models with a minimum of lost edges. It is determined that to build a graph-logical model of systems of this type, it is sufficient to calculate the maximum possible number of failed processors at which the system remains in operation. A graph-logical model of a basic system that can handle this number of failures is built, without taking into account the sequence of these failures. The next step is to identify all possible consecutive failures that cause the system to fail. Then, the base model is modified in such a way as to reflect the failure of the system when consecutive failures occur. This means weakening the base model on the previously determined vectors. The proposed methods of model construction can be used both for linear and circular consecutive-k-out-of-n systems and for (n, f, k) systems. A minor difference will be in the calculation of some parameters. The paper describes the calculation of such parameters as the maximum allowable number of failures at which the system remains in an operational state, as well as the calculation of the number of all combinations of consecutive failures at which the system fails. Experiments have been conducted to confirm the model's compliance with the system's behavior in the failure flow. Examples are given to demonstrate the process of building graph-logical models for linear consecutive-k-out-of-n, circular consecutive-k-out-of-n and (n, f, k) systems using the proposed methods.

Adaptive fast terminal sliding mode control of robotic manipulators based on joint torque estimation and friction compensation

AbstractIn this work, an adaptive fast terminal sliding mode control (AFSMC) approach based on joint torque estimation and friction compensation is proposed to enhance the trajectory tracking accuracy of robotic manipulators under variable load conditions. The joint torque estimation utilizes an improved harmonic drive compliance model and adaptive low‐pass filtering, and friction compensation employs a hybrid model accounting for velocity and load torque effects. These compensations reduce the upper bound of the uncertainty, while AFSMC further reduces dependency on upper uncertainty bounds and minimizes the chattering. The stability analysis using the Lyapunov method confirms the effectiveness of this approach. Experimental results demonstrate that the proposed controller achieves smaller root mean square and maximum error of trajectory tracking, thus significantly improving trajectory tracking accuracy under variable load conditions.

Validating and extending the unified model of information security policy compliance

PurposeThe purpose of this study is to further validate and extend the unified model of information security policy compliance (UMISPC) developed by Moody et al. (2018).Design/methodology/approachTo be able to compare the results of this study and those reported by Moody et al. (2018) (and followers), the same quantitative data collection method (questionnaire) and variable measurement instruments were used. Specifically, questionnaire data were collected from a department within a Swedish governmental organization comprising 150 employees. Of these, 90 answered the questionnaire which rendered a response rate of 60%. Following Moody et al. (2018), the collected data were analyzed by means of structural equation modeling.FindingsThis study generally provides empirical support for the original UMISPC as a large majority of the findings are in line with those reported by Moody et al. (2018). However, it also suggests important differences and boundary conditions.Originality/valueThis study extends the original study of Moody et al. (2018) and subsequent replication studies by testing it in a new national/organizational context. Based on their call for future research, it also develops and empirically tests the effects of a new, socially visible information system security violation scenario. Related to this, this study also revisits the role of the variable subjective norms for better understanding employee non-/compliance to information security policies by suggesting that their effects may be indirect (i.e. running through other variables in the UMISPC) rather than direct.

Modeling and scaling of cavitation compliance in Venturi

Understanding cavitation compliance in Venturi is important for research on system oscillations in fluid machinery, which describes the responsiveness of the cavity size to alternating inflow parameters. To consider both Venturi geometries and inflow conditions in evaluating the cavitation compliance, the cavitation region is modeled as a large vapor bubble, based on the experimental characteristics of the attached cavity characterizing the cavity size. Steady-state numerical solutions of cavity sizes show the influencing trends by geometrical and inflow parameters consistent with experiments. According to the analytical solution approximating the numerical solution, effective cavitation number σe and effective length ratio L̃* are defined to unify these influencing parameters. Based on the causes of cavitation pressure drops at the throat indicated by σe, cavitation compliance can be categorized into two regimes. Regime I features a smaller σe, and the dynamic pressure dominates in the pressure drops, theoretically resulting in L̃*∝σe−9. Regime II features a larger σe, and the bend-flow inertia and dynamic pressure are comparable, resulting in L̃*∝σe−2 in contrast. The power-law predictions of cavitation compliance are validated by experiments and literature.

Consumer data protection laws and their impact on business models in the tech industry

This study investigates the impact of consumer data protection laws on the business models of technology companies through a mixed methods research (MMR) approach. In an era where data privacy concerns are paramount and regulatory landscapes are rapidly evolving, understanding how businesses adapt their models for compliance while fostering innovation is crucial. This paper offers a detailed examination of the legislative requirements imposed by prominent global data protection laws, including the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), as well as frameworks from Asia and Africa, such as Singapore's Personal Data Protection Act (PDPA) and South Africa's Protection of Personal Information Act (POPIA). This inclusive perspective enhances our understanding of how technology businesses strategically adapt across various regulatory environments and explores the operational, financial, and strategic impacts of these laws on tech companies. Utilizing a dual-phase mixed methods research (MMR) design, the study initially analyzes quantitative data from surveys conducted with a wide range of technology firms, assessing the direct effects of data protection legislation on business operations and financial performance. This is complemented by qualitative insights drawn from in-depth interviews with business leaders and legal experts, shedding light on the strategic adaptations, challenges, and opportunities these laws present. The findings reveal a multifaceted impact, highlighting not only the compliance challenges but also the strategic opportunities for innovation and competitive differentiation that data protection laws offer. Integrated analysis of the quantitative and qualitative data provides a nuanced understanding of how tech companies navigate the complex interplay between legal compliance and business model innovation. This study contributes to the existing literature by offering empirical evidence and practical insights into the adaptation processes of tech companies in response to consumer data protection laws. It also provides valuable recommendations for both policymakers and business leaders, aiming to foster an environment where legal compliance coexists with technological innovation and business growth.

The Sustainability Journey of Chinese Ready-Meal Companies Going Global: Configurational Analysis Using the Technology-Organization-Environment Framework.

In the new wave of globalization, China's pre-cooked meal industry has rapidly emerged as a significant player in the food supply chain domain, owing to its convenience and diverse choices. Amidst intensifying market competition, crafting a sustainable supply chain for overseas expansion has become an indispensable core element for driving corporate internationalization. This study focuses on the sustainability generation mechanism of the overseas supply chain of Chinese pre-cooked meal A-share listed companies, employing the technology-organization-environment (TOE) framework and fuzzy-set qualitative comparative analysis (fsQCA) method. Herein, an in-depth analysis of 388 data points from 17 listed companies has been conducted. The study systematically reveals the key factors affecting the sustainability of overseas supply chains and identifies three critical sustainability generation paths: the technology-driven and norm compliance model (path 1), the market sensitivity and product innovation model (path 2), and the robust operation and risk control model (path 3). The present study not only introduces a novel perspective on the application of a TOE framework within the realms of sustainable supply chain management and environmental, social, and governance (ESG) criteria but also offers clear directions for companies to enhance sustainability in their overseas expansion process. This includes strengthening technological research and development, compliance construction, market adaptability, product diversity, infrastructure construction, and risk management. Moreover, the research findings provide valuable insights for policymakers in promoting the internationalization of the pre-cooked meal industry and enhancing industry competitiveness.

Handling Partially Observed Trial Data After Treatment Withdrawal: Introducing Retrieved Dropout Reference-Base Centred Multiple Imputation.

The ICH E9(R1) Addendum (International Council for Harmonization 2019) suggests treatment-policy as one of several strategies for addressing intercurrent events such as treatment withdrawal when defining an estimand. This strategy requires the monitoring of patients and collection of primary outcome data following termination of randomised treatment. However, when patients withdraw from a study early before completion this creates true missing data complicating the analysis. One possible way forward uses multiple imputation to replace the missing data based on a model for outcome on- and off-treatment prior to study withdrawal, often referred to as retrieved dropout multiple imputation. This article introduces a novel approach to parameterising this imputation model so that those parameters which may be difficult to estimate have mildly informative Bayesian priors applied during the imputation stage. A core reference-based model is combined with a retrieved dropout compliance model, using both on- and off-treatment data, to form an extended model for the purposes of imputation. This alleviates the problem of specifying a complex set of analysis rules to accommodate situations where parameters which influence the estimated value are not estimable, or are poorly estimated leading to unrealistically large standard errors in the resulting analysis. We refer to this new approach as retrieved dropout reference-base centred multiple imputation.

A modified linear-elastic model for calibration of resonant column devices accommodating drive system compliance

Measurements of small-strain shear modulus of soils and rocks using the resonant column apparatus are highly dependent on accurate physical characterisation of the apparatus's drive components. This is typically achieved by way of a calibration exercise, yet it is widely reported that existing interpretation methods yield physical properties that vary with frequency, complicating subsequent interpretation of test data. Previous research has indicated that much of this apparent frequency dependency occurs due to compliance of the drive system. In this study the linear elastic model of the resonant column apparatus is adapted to include a new drive stiffness parameter, supported by finite-element analysis of the drive system. A novel calibration method is presented based on this drive compliance model. Thirteen calibration bars and three added masses are used to conduct an extensive calibration exercise for both a Hardin- and Stokoe-type resonant column apparatus. The novel calibration method is compared with three existing methods in the literature. Limitations of the four methods are identified and discussed. The drive compliance model and its associated calibration method is shown to be a robust means of characterising the apparatus behaviour while successfully eliminating frequency dependency of the calibration parameters. The model is valid where total system damping is small and is particularly valuable when testing stiff specimens where common apparatus assumptions may not apply.