Empirical Validation Research Articles

Employment Shift in Response to a Technology Shock: An Analysis of Two Rigidities and Two Agents

This paper examines the relationship between a technology shock and employment, considering price, wage rigidities, and heterogeneous agents. To explore this relationship, we utilized a Dynamic Stochastic General Equilibrium (DSGE) model, incorporating households with varying savings rates. For empirical validation, we conducted a Structural Vector Autoregression (SVAR) analysis using data from two economies with distinct savings patterns—the United States and China. This approach allowed us to assess the impact of technology shocks on employment dynamics across different savings environments. Under these conditions, we observe that the effect of technology on aggregate employment is initially positive. Still, it gradually decreases in the mid-term, eventually switching to a negative impact before slowly recovering to equilibrium. The reason for this phenomenon depends on (i) the magnitude of fluctuations in price and wage, precisely, which variable’s fluctuations have a greater magnitude, and (ii) which effect, between income effect and substitute effect, is preferred by restricted and unrestricted households. Due to (i), real wages change, and because of (ii), households make different labor supply decisions, leading to fluctuations in employment in response to technology shocks.

An exploratory analysis of repeat victimisation of domestic violence disclosure scheme applicants

ABSTRACT This study explores the impact of the Domestic Violence Disclosure Scheme (DVDS) on repeat victimisation among applicants. By analysing all ‘Right to Ask’ applications to Avon & Somerset Constabulary in 2021, the study investigates the extent of prior domestic abuse victimisation and the influence of disclosure decisions on subsequent reports. The findings reveal that 80% of applicants had prior victimisation records, and disclosures were made in 53% of the cases. The probability of subsequent abuse reports increased significantly for those with prior victimisation when disclosures were made, highlighting the need for robust support mechanisms post-disclosure. The study complements debate around the theoretical tension between empowerment and responsibilisation within DVDS and calls for enhanced support systems and rigorous outcome evaluations. These results suggest that while DVDS aims to protect victims, its effectiveness in reducing victimisation requires further empirical validation and comprehensive support strategies.

Elucidating the role of CYFIP2 in conferring cisplatin resistance in esophageal squamous cell carcinoma

Cisplatin (CDDP) serves as a vital component in the chemotherapeutic approach to treat esophageal squamous cell carcinoma (ESCC). However, prolonged CDDP application frequently culminates in resistance, compromising therapeutic outcomes. Through genome-wide CRISPR library screening, our study elucidates the mechanisms underlying this resistance, pinpointing CYFIP2 as a pivotal mediator. Notably, the involvement CYFIP2 is characterized by pronounced autophagic activity and the modulation of multiple cellular pathways. Empirical validation was achieved by treating ESCC cell lines with CDDP, which resulted in an upsurge of CYFIP2 expression. The functional impact of CYFIP2 was further delineated through knockdown experiments, where a marked suppression in cell proliferation was observed, alongside a discernible decline in reactive oxygen species levels. This was complemented by a suite of assays and microscopic techniques, including GFP-LC3, mRFP-GFP-LC3, electron microscopy and western blot, which collectively affirmed the inhibitory effect of CYFIP2 knockdown on autophagic processes, particularly impeding autophagosome formation and their subsequent fusion with lysosomes. In vivo studies have also confirmed that CYFIP2 knockdown limits tumor progression and increases CDDP efficacy. Conclusively, our findings introduce CYFIP2 as a novel contributor to CDDP resistance in ESCC, underscoring its potential as a therapeutic target. This revelation not only deepens our understanding of resistance mechanisms but also paves the way for novel oncotherapeutic strategies, promising enhanced treatment efficacy against ESCC.

Curvilinear lineament extraction: Bayesian optimization of Principal Component Wavelet Analysis and Hysteresis Thresholding

Understanding deformation networks, visible as curvilinear lineaments in images, is crucial for geoscientific explorations. However, traditional manual extraction of lineaments is expertise-dependent, time-consuming, and labor-intensive. This study introduces an automated method to extract and identify geological faults from aeromagnetic images, integrating Bayesian Hyperparameter Optimization (BHO), Principal Component Wavelet Analysis (PCWA), and Hysteresis Thresholding Algorithm (HTA). The continuous wavelet transform (CWT), employed across various scales and orientations, enhances feature extraction quality, while Principal Component Analysis (PCA) within the CWT eliminates redundant information, focusing on relevant features. Using a Gaussian Process surrogate model, BHO autonomously fine-tunes hyperparameters for optimal curvilinear pattern recognition, resulting in a highly accurate and computationally efficient solution for curvilinear lineament mapping. Empirical validation using aeromagnetic images from a prominent fault zone in the James Bay region of Quebec, Canada, demonstrates significant accuracy improvements, with 23% improvement in Fβ Score over the unoptimized PCWA-HTA and a marked 300% improvement over traditional HTA methods, underscoring the added value of fusing BHO with PCWA in the curvilinear lineament extraction process. The iterative nature of BHO progressively refines hyperparameters, enhancing geological feature detection. Early BHO iterations broadly explore the hyperparameter space, identifying low-frequency curvilinear features representing deep lineaments. As BHO advances, hyperparameter fine-tuning increases sensitivity to high-frequency features indicative of shallow lineaments. This progressive refinement ensures that later iterations better detect detailed structures, demonstrating BHO's robustness in distinguishing various curvilinear features and improving the accuracy of curvilinear lineament extraction. For future work, we aim to expand the method's applicability by incorporating multiple geophysical image types, enhancing adaptability across diverse geological contexts.

Cross Retaliation Under TRIPS: A Creative Remedy When Power Disparity Exists

The WTO system has largely achieved procedural fairness, yet substantial challenges persist in addressing the equity of legal remedies and enforcement, particularly amid significant disparities in economic power among member states. Such power imbalances necessitate a nuanced consideration in the design of the system to ensure that fairness transcends procedural aspects and reflects in substantive outcomes. In that context, Intellectual Property (IP) retaliation emerges as an innovative and temporary method to address these imbalances, offering a mechanism to level the playing field between developed and developing countries. While IP retaliation has gained traction as a strategic tool to mitigate power disparities, its mechanisms remain inadequately defined and require further empirical validation and refinement in practice. This highlights the need for ongoing reform within the WTO system to enhance both the clarity and effectiveness of IP retaliation, ensuring it serves as a viable and fair recourse in international trade disputes.

Evaluating the Diagnosis of Malnutrition Based on Global Leadership Initiative on Malnutrition (GLIM) Criteria in Community-Dwelling Older Adults (Singapore Longitudinal Aging Study).

A minority of studies using the GLIM criteria for malnutrition diagnosis have performed formal empirical validation. To evaluate the concurrent and predictive validity of GLIM criteria with and without prior screening among community-dwelling older adults in Singapore. In the Singapore Longitudinal Aging Study (SLAS-2, n = 2477), malnutrition was diagnosed using single-step and two-step GLIM procedures using the Mini Nutritional Assessment Short Form (MNA-SF) and Elderly Nutritional Index for Geriatric Malnutrition Assessment (ENIGMA) for initial screening. Criterion validity was evaluated using MNA-Full Form (MNA-FF) as reference malnutrition diagnosis. Prognostic validity was evaluated using logistic and Cox regression analyses with respect to impaired quality of life (QoL) and 10-year mortality. GLIM malnutrition with and without MNA-SF or ENIGMA screening showed significant associations with known clinical correlates; single-step GLIM malnutrition: sensitivity = 80%, specificity = 83%; two-step MNA-SF-GLIM malnutrition: sensitivity = 80%, specificity = 85%; two-step ENIGMA-GLIM malnutrition: sensitivity = 74%, specificity = 88%; positive predictive values of around 20% and negative predictive values above 98%. Cohen's kappa values of agreement were uniformly low (0.26 to 0.32). All showed significant associations with about 50% increased odds of impaired QoL and 10-year mortality, adjusted for age, sex, ethnicity, education levels, and housing type, with the ENIGMA-GLIM malnutrition showing the highest risk estimates. Compared to MNA-FF malnutrition prevalence of 4.1%, GLIM-based malnutrition increased prevalence (14.6% to 19.7%) estimates. The GLIM criteria showed good construct and criterion validity. It increased the number of individuals diagnosed with malnutrition. The agreement between diagnoses of malnutrition was low. Diagnostic and prognostic accuracy vary with the screening instrument used. Early identification of malnutrition using appropriate tools can provide opportunities to delay or prevent the risk of important adverse outcomes such as impaired QoL and mortality.

Drivers of Iran’s Africa Engagement: An Analysis of High-Level Leadership Visits

The nuanced dynamics of Iran’s engagement with Africa have captivated the attention of a broad spectrum of analysts and academicians. Endeavoring to elucidate the underpinnings of Tehran’s involvement on the continent, these scholars have posited a variety of determinants, yet the empirical validation of these factors remains largely unexplored. Leveraging a comprehensive dataset of Iran’s high-level diplomatic visits to African nations, this study seeks to critically assess the relevance and explanatory capacity of the determinants in the literature. Our findings indicate that ideological imperatives, market needs and access to strategic resources predominantly fuel Iran’s interests in Africa.

Evolucollateral dynamics in stroke: Evolutionary pathophysiology, remodelling and emerging therapeutic strategies.

Leptomeningeal collaterals (LMCs) are crucial in mitigating the impact of acute ischemic stroke (AIS) by providing alternate blood flow routes when primary arteries are obstructed. This article explores the evolutionary pathophysiology of LMCs, highlighting their critical function in stroke and the genetic and molecular mechanisms governing their development and remodelling. We address the translational challenges of applying animal model findings to human clinical scenarios, emphasizing the need for further research to validate emerging therapies-such as pharmacological agents, gene therapy and mechanical interventions-in clinical settings, aimed at enhancing collateral perfusion. Computational modelling emerges as a promising method for integrating experimental data, which requires precise parameterization and empirical validation. We introduce the 'Evolucollateral Dynamics' hypothesis, proposing a novel framework that incorporates evolutionary biology principles into therapeutic strategies, offering new perspectives on enhancing collateral circulation. This hypothesis emphasizes the role of genetic predispositions and environmental influences on collateral circulation, which may impact therapeutic strategies and optimize treatment outcomes. Future research must incorporate human clinical data to create robust treatment protocols, thereby maximizing the therapeutic potential of LMCs and improving outcomes for stroke patients.

Addressing pandemic-wide systematic errors in the SARS-CoV-2 phylogeny.

The SARS-CoV-2 genome occupies a unique place in infection biology - it is the most highly sequenced genome on earth (making up over 20% of public sequencing datasets) with fine scale information on sampling date and geography, and has been subject to unprecedented intense analysis. As a result, these phylogenetic data are an incredibly valuable resource for science and public health. However, the vast majority of the data was sequenced by tiling amplicons across the full genome, with amplicon schemes that changed over the pandemic as mutations in the viral genome interacted with primer binding sites. In combination with the disparate set of genome assembly workflows and lack of consistent quality control (QC) processes, the current genomes have many systematic errors that have evolved with the virus and amplicon schemes. These errors have significant impacts on the phylogeny, and therefore over the last few years, many thousands of hours of researchers time has been spent in "eyeballing" trees, looking for artefacts, and then patching the tree. Given the huge value of this dataset, we therefore set out to reprocess the complete set of public raw sequence data in a rigorous amplicon-aware manner, and build a cleaner phylogeny. Here we provide a global tree of 4,471,579 samples, built from a consistently assembled set of high quality consensus sequences from all available public data as of June 2024, viewable at https://viridian.taxonium.org . Each genome was constructed using a novel assembly tool called Viridian ( https://github.com/iqbal-lab-org/viridian ), developed specifically to process amplicon sequence data, eliminating artefactual errors and mask the genome at low quality positions. We provide simulation and empirical validation of the methodology, and quantify the improvement in the phylogeny. We hope the tree, consensus sequences and Viridian will be a valuable resource for researchers.

The CAPM: Exploring its Empirical Evidence on NSE Nifty-From the Period 2008 to 2023

The issue of forecasting or predicting asset returns is fundamental to financial economics and most often the Capital Asset Pricing Model (CAPM) is adopted for estimating the expected rate of return based on systematic risk. This paper focuses on the empirical testing of the CAPM in the Indian market based on the Nifty 50 index from April 2008 to March 2023, which covers the period of the COVID-19 pandemic, the Goods and Services Tax (GST) reforms and demonetisation. In the context of this study, Lintner, Miller and Scholes, Black, Jensen and Scholes tests are used with a new dataset to examine the CAPM. This study examines the empirical validity of the CAPM in the context of the Indian market, more specifically the Nifty 50 index, during a period of significant economic change. Thus, the analysis is centred around the interaction of systematic risk, residual variance and average returns. The study identifies a positive but non-significant relationship between average returns with systematic risk and a slightly higher risk-free return compared to that of Lintner. The variance of residuals also helps to explain average returns, while at the same time, actual returns are higher than market returns. The findings of the current study partially support Lintner, Miller and Scholes’ earlier work while not supporting Black, Jensen and Scholes’(BJS) conclusions. In the context of the BJS methodology, 38 per cent of the securities have significant non-zero alphas, which means CAPM does not operate perfectly in the Indian market. The findings that alphas are non-zero indicate the inefficiency of the National Stock Exchange (Nifty 50), therefore, the need for regulation by agencies such as the Securities and Exchange Board of India (SEBI) arises. Equity analysts and portfolio managers must not only use CAPM for asset valuation blindly without appreciating its empirical flaws. This paper revisits the CAPM with recent data thus enriching the limited literature on the CAPM in the Indian scenario where CAPM has however a very restricted application in explaining asset returns in the context of the Nifty 50.

Development and Empirical Validation of an Information Technology Acceptance Framework

This paper explores the constructs affecting IT acceptance in modern organisations using theoretical frameworks such as the TAM and the UTAUT models. Realising the drawbacks of such dichotomous models for capturing the dynamics of contemporary IT enablers and use processes, the present study plans to offer and test new antecedent and moderating constructs that may provide a more accurate picture of today’s IT deployment and utilisation. The type of research in the study is the quantitative research method, which is adopted in the study where the researcher adopted a structured questionnaire to be administered to 200 participants across the different sectors of the economy, such as health, education, and business. Hypotheses of primary concern are those related to the construct of IT acceptance, including perceived usefulness, ease of use, trust, system transparency, perceived risk, and ethical concerns. The analysis of the data gathered is done using Structural Equation Modeling (SEM) because of the possibility of analysing the associations between several variables. Behavioural construct analysis revealed a significant positive correlation with IT acceptance behaviours, with the value of R for the new construct being 0.935 and the R square value being 0.874, which means that newly derived constructs explain a fair percentage of IT acceptance behaviours. The findings, therefore, point out the lack of conventional frameworks in addressing the complex nature of IT use in various organisational and cultural environments. Full-spectrum system requirements in newly developing domains, such as AI, machine learning, and blockchain, require new fundamental concepts regarding user trust, system openness and ethical considerations. This work extends the IT acceptance literature by providing a better view of the determinants of IT uptake in modern organisations. In totality, the validated constructs have practical implications for organisations desirous of adopting new IT systems, particularly highlighting the role of trust and perceived risk and the need for transparency or the adoption of transparency to guarantee user satisfaction rates and consequent adoption of new IT systems. In conclusion, the study points to the need to develop new models that address today’s diverse IT contexts to inform the next generation of IT acceptance and adoption research.

Multidimensional Directionality-Enhanced Segmentation via large vision model

Optical Coherence Tomography (OCT) facilitates a comprehensive examination of macular edema and associated lesions. Manual delineation of retinal fluid is labor-intensive and error-prone, necessitating an automated diagnostic and therapeutic planning mechanism. Conventional supervised learning models are hindered by dataset limitations, while Transformer-based large vision models exhibit challenges in medical image segmentation, particularly in detecting small, subtle lesions in OCT images. This paper introduces the Multidimensional Directionality-Enhanced Retinal Fluid Segmentation framework (MD-DERFS), which reduces the limitations inherent in conventional supervised models by adapting a transformer-based large vision model for macular edema segmentation. The proposed MD-DERFS introduces a Multi-Dimensional Feature Re-Encoder Unit (MFU) to augment the model’s proficiency in recognizing specific textures and pathological features through directional prior extraction and an Edema Texture Mapping Unit (ETMU), a Cross-scale Directional Insight Network (CDIN) furnishes a holistic perspective spanning local to global details, mitigating the large vision model’s deficiencies in capturing localized feature information. Additionally, the framework is augmented by a Harmonic Minutiae Segmentation Equilibrium loss (LHMSE) that can address the challenges of data imbalance and annotation scarcity in macular edema datasets. Empirical validation on the MacuScan-8k dataset shows that MD-DERFS surpasses existing segmentation methodologies, demonstrating its efficacy in adapting large vision models for boundary-sensitive medical imaging tasks. The code is publicly available at https://github.com/IMOP-lab/MD-DERFS-Pytorch.git.

Exploring Unique Patterns of Self-Injury Recovery: A Latent Profile Analysis.

As nonsuicidal self-injury (NSSI) has become an increasing public health concern, the last few years have seen the emergence of efforts to address NSSI recovery. Although many recovery efforts adopt a medical view of self-injury and focus on cessation of the behaviour, recovery can mean many different things to different people. In this study, we provide initial empirical validation of the self-injury recovery framework, by assessing whether different recovery profiles exist. Our sample comprised 733 participants with lived experience of NSSI (M age = 24.54, sd = 6.39). Participants completed self-report measures of constructs related to NSSI recovery and NSSI characteristics. Using latent profile analysis, we identified six unique profiles reflecting differences in thoughts/urges to self-injure, self-efficacy, social support, optimism, coping, underlying adversities, perceptions of scarring, disclosure, resilience and self-compassion. Multivariate analyses of variance confirmed these profiles differed according to NSSI characteristics such as frequency of NSSI, a self-assessment of recovery, the desire to self-injure or avoid self-injury and the number of people disclosed to. A homogenous sample and cross-sectional design limit generalisability of our findings across populations and across time. Our findings reinforce that recovery can take many different forms, with different factors being relevant to different individuals. Adopting a person-centred approach that centres an individual's lived experience and emphasises what is important to them in the recovery process offers opportunities for more empathic responses to self-injury and better outcomes for individuals who self-injure.

Boronize Coatings Studied with a New Mass Transfer Model.

This study examined the development of Fe2B (diiron boronize) coatings on the surface of 35NiCrMo4 steel through the thermochemical surface hardening process called boronizing. The morphology and thickness of the boronize coatings were assessed using Scanning Electron Microscopy (SEM) and optical microscopy (OM). A novel mathematical mass transfer model was developed to estimate the diffusion coefficients of boron in hard coating. The presence of uniformly distributed boronize coatings with a typical sawtooth pattern on the surface of the substrate was confirmed. The boronize coating's chemical composition and phase constituents were analyzed utilizing X-ray energy dispersive spectroscopy (EDS) and X-ray diffraction analysis (XRD). The study confirmed the presence of a single-phase boronize coating (Fe2B). Furthermore, microhardness tests indicated that the boronized specimen's surface demonstrated an average hardness of approximately 1953 HV. The wear study were conducted using the pin-on-disk method under dry debonding conditions at room temperature to estimate the coefficient of friction (COF) of the boronized (average ≈ 0.35) and untreated (0.725) specimens. The results revealed approximately 200% improvement in wear resistance due to the boronized coating. The empirical validation of the mathematical model was carried out for two additional boronizing conditions at 1223 K for 3 h and 1273 K for 1.5 h, resulting in an estimated percentage error of around 2.5% for both conditions. Additionally, an ANOVA analysis was performed, taking into account the temperature and time factors. The findings indicate that both factors exert a substantial influence on the dependent variable (u), with temperature (T) contributing 64.68%, time (t) contributing 27.37%, and the interaction of both factors (T × t) contributing 5.13%.

The quest for social justice amid war: exploring the role of civil society resilience in Ukraine’s social justice and cohesion

Purpose Russia’s large-scale invasion of Ukraine has inflicted catastrophic consequences, profoundly impacting Ukrainian sustainability across the environment, society and economic dimensions. Despite these challenges, Ukraine’s civilian population has displayed remarkable resilience, emerging as a persistent stronghold amid the turbulence of conflict. In this study, the author has explored the key role of civil society in strengthening resilience and social justice during wartime. Design/methodology/approach This study uses a qualitative multi-method research design, combining a systematic literature review (SLR) and a single case study to understand and describe the phenomenon. The SLR provided a replicable, scientific and transparent conceptual framework, which the case study empirically validated and enriched. Additionally, the Delphi method gathered relevant insights from academic experts and Ukrainian nationals residing in the EU, enhancing this research. Findings This research revealed complex social dynamics and community resilience, highlighting various resilience-building efforts undertaken by voluntary organizations and emerging Ukrainian movements. The findings highlight civil society’s pivotal role in promoting resilience and addressing adversity to enhance social justice and unity in Ukraine. Specific contributions include countering disinformation, providing psychosocial support and mobilizing community efforts. Furthermore, the paper identified six key steps that epitomize societal resilience and formulated a conceptual model based on these steps. Originality/value This research offers a comprehensive and practical analysis of grassroots organizations’ efforts to sustain social resilience and unity amid war. It builds upon previous studies, extending their scope by providing empirical validation and offering new insights into civil society’s contributions to Ukraine’s defense and social cohesion. The study also explores the obstacles faced by civil society and the implications for Ukraine’s future sustainability and social cohesion.

Theoretical and Methodological Foundations of Uncertainty Modeling in Real Estate Markets

Motivation: The need to improve the accuracy and reliability of market valuation and risk assessment in real estate markets, especially under conditions of uncertainty.Aim: To integrate theoretical foundations and methodological approaches for modeling aleatoric and epistemic uncertainties in real estate markets using credal networks and confidence boxes (c-boxes).Approach: This paper presents a comprehensive theoretical and methodological framework for uncertainty modeling in real estate markets, focusing on the application of credal networks and confidence boxes. It does not include empirical validation or practical case studies, instead providing a detailed conceptual and methodological discussion.Results: The proposed method demonstrates significant improvements in uncertainty quantification and market analysis accuracy in theoretical terms, offering valuable insights for investors, urban planners, and policymakers. However, empirical validation is suggested for future research to confirm practical applicability.

Multiphysics Simulation and Concept of an Electromagnetically Control Volumetric Pixel as a Step Towards a Shape Morphing Composite

Abstract Constant development of robotics forces scientists and engineers to work on robots that are more visually and rigidly compatible with the environment around us. To make this possible, new flexible structures are necessary that enable programmatic shape change. To meet this need, in this work we present the concept and modelling methodology of a new structure enabling shape change using electromagnetic forces produced in liquid metal conductor and its stiffening using a granular jamming mechanism. This work presents the structure concept, the description of modelling methodology and empirical validation including the magnetitic field, scanned by magnetic field camera, and displacement distribution.

Post-disaster housing recovery estimation: Data and lessons learned from the 2017 Tubbs and 2018 Camp Fires

Post-wildfire housing recovery is a complex process for which systematically collected data remains scarce. Consequently, our ability to anticipate obstacles and plan for housing recovery from future events is limited. This study leverages housing permit datasets collected in Santa Rosa and Unincorporated Sonoma County, impacted by the 2017 Tubbs Fire, and Paradise, impacted by the 2018 Camp Fire. Permit and tax assessor data are combined to gain insights into the recovery processes for these communities. Although the percentage of rebuilt destroyed homes varies significantly between regions, the peak construction demand occurs around 1.5 years after each wildfire, with a substantial decline in the reconstruction rate after 2.5 years. Moreover, the pace of transition from permit application to reconstruction completion is similar across all three regions. Using this finding, we propose a methodology to forecast the number of parcels rebuilt per unit of time based on observations from prior events. A proof-of-concept application of the proposed methodology indicates that it estimates long-term housing recovery patterns based on permit application data collected within one year of the event. These findings indicate that a longitudinal housing recovery data database would help forecast housing recovery from future disasters by providing a source for early empirical validation of predictive models.

Information Technology Acceptance: Construct Development and Empirical Validation

Information Technology Acceptance: Construct Development and Empirical Validation

Analysis of Enterprise Internet of Things Maturity Models: A Review

Maturity models are valuable tools when assessing the readiness and progress of technology incorporation in organizations, providing information for decision-making, resource allocation, and competitive advantage. The Internet of Things is a technology paradigm of global importance, especially for organizations, as it supports productivity improvements, real-time analysis, and customer satisfaction. Therefore, adopting and implementing this technology in enterprises brings several challenges, such as technological, organizational, security, and maturity issues. However, secondary studies that systematically compile the existing literature on these specific mechanisms for the enterprise domain are still being determined. This article aims to address this knowledge gap by conducting a review to deepen and synthesize the existing knowledge. This research followed established methodologies and protocols to synthesize and analyze the state of the art in the area; 489 documents were retrieved from seven bibliographic databases, and, applying inclusion and exclusion criteria, 36 primary studies were selected. The results indicate that the typical structures of maturity models incorporate technological, organizational, human, performance, and security dimensions through graded levels that denote the sophistication of the Internet of Things. Measurement techniques and metrics vary from model to model. There are few empirical validations or standardized improvement frameworks. The main conclusion is that there is a diversity of models, dimensions, indicators, and methods and a need for more comprehensive, adaptable, and user-friendly tools to help companies assess their Internet of Things maturity and inform future development strategies.