Structural Equation Modeling Approach Research Articles

Effect of blockchain technology adoption on construction supply chain: a structural equation modelling (SEM) approach

Purpose The construction industry is frequently scrutinised by the public for a variety of issues, including waste, inefficiency, narrow profit margins, scheduling setbacks, budget overruns, quality concerns, trust deficits, transparency issues, coordination challenges, communication issues and fraud. The purpose of this paper is to assess the effect of blockchain technology adoption on the construction supply chain. Design/methodology/approach This study used a quantitative research approach through a questionnaire survey that was conducted among professionals in the Nigerian construction industry using the snowball sampling method, which resulted in a selection of 155 respondents. The collected data were analysed using partial least squares structural equation modelling, enabling a thorough assessment of the proposed relationships and offering valuable insights specific to the construction industry. Findings The study’s findings validated the conceptual framework established. The results indicated that implementing blockchain across all stages of the construction supply chain has the potential to improve the construction process. The study also revealed that blockchain technology will significantly affect the construction supply chain in a positive manner. Research limitations/implications This research was carried out in the South-western region which is one of the six geo-political zones in Nigeria using a cross-sectional survey method. The study holds implications not only for local construction practices but will also contribute to the broader discourse on national construction sector challenges and possible solutions. Practical implications The findings of this study will be immensely beneficial to both professionals, practitioners and stakeholders in the Nigerian construction industry in learning about the potential of blockchain technology application in improving the construction supply chain. Originality/value The study in this paper constructed and evaluated a conceptual framework by exploring the connections between the variables. The results have significant implications for the construction sector, as they provide avenues for enhancing the construction process and the overall supply chain. These findings are valuable for researchers examining the potential effects of blockchain technology on the construction supply chain.

Knowledge attitude and convenience on self-medication practices among university students in Bangladesh exploration using structural equation modeling approach

Self-medication is a prevalent practice among university students globally and is a significant public health concern. However, previous research has been limited in scope, focusing primarily on adolescents or the general public, leaving a gap in understanding the causal relationships associated with self-medication; thus, this study aimed to investigate the factors influencing self-medication practices among university students in Bangladesh by developing a comprehensive causal model. Data from 417 students across five public universities were collected using the simple random walk technique by a team of 10 members. The study utilized constructs of knowledge, attitude, and convenience related to self-medication as independent variables, while self-medication practice as the dependent variable. One-way ANOVA and structural equation modeling (SEM) were employed to develop a causal model of self-medication practice among university students in Bangladesh. The findings revealed that students with better medication knowledge and adverse drug reactions (ADRs) were more likely to practice self-medication. A positive attitude towards self-medication and ADRs was also significantly associated with higher self-medication practice scores. Additionally, those who perceived self-medication as convenient and prescribed medication as inconvenient had higher self-medication practice scores. The attitude towards self-medication had the most substantial negative effect on self-medication practice, followed by the inconvenience of prescribed medication and the convenience of self-medication. The model explained 87% of the variance in self-medication practice, indicating a good fit for the data. University students in Bangladesh possess intermediate knowledge of medication and primary knowledge of ADRs. They exhibit a positive attitude towards self-medication and ADRs. Physical convenience favors self-medication, while the inconvenience of prescribed medication contributes to its lower preference. Policymakers should focus on evidence-based guidelines to reduce the extent of unnecessary self-medication practice and to enhance the quantity and accessibility of prescribed medications to address the issue effectively.

Nutrient contents and microbial communities as mediators of the effects of land-use in ecosystem functioning in alpine ecosystems from Central Mexico

Abstract Purpose The interplay between microbial taxonomy, functional diversity, and land-use changes across diverse ecosystems must be better understood. This study explores, through a statistical modeling approach, the intricate relationships between microbial communities, soil properties (nutrient contents and physicochemical parameters), and land-use changes, using stable isotopes ratio (δ13C/δ15N) as ecosystem function indicator. Methods Using Structural Equation Modeling (SEM) this study aims to investigate how soil microbial communities, soil properties and ecosystem function are interconnected in three ecosystems undergoing land-use change in Nevado de Toluca, Mexico. Results Multivariate analyses of microbial communities and soil parameters for the different land-uses reveal significant variation in both microbial composition and soil properties (i.e. nutrient contents) mainly explained by the land-use history of the studied ecosystems. Through the SEM approach, it was possible to disentangle the relative contribution of land-use change, microbial community composition and nutrient contents to the ecosystem function defined as the δ13C/δ15N ratio. Conclusions Microbial communities are the main drivers of soil ecosystem functioning. However, through SEM approach it is possible to formally test direct and indirect relationships impacting microbial communities and the resulting functional consequences, contributing to a mechanistic understanding of the ecological implications of land-use change, aiding in sustainable land-use decision-making.

The dynamics of employability capitals for the transition to work: career identity, cultural capital, job interview self-efficacy and self-perceived employability

Purpose This study aims to understand how employability capitals’ dynamics foster self-perceived employability (SPE) among students and graduates, which is still being empirically explored. Building upon the Employability Capital Growth Model and the Social Cognitive Career Theory’s career self-management model, we aimed to understand how different capitals associate by testing a serial mediation model connecting career identity (reflecting career identity capital) and SPE through the serial mediation of cultural capital and job interview self-efficacy (ISE) (an element of psychological capital).Design/methodology/approach We adopted a two-wave design involving 227 Italian University students and graduates. We recruited participants through multi-channel communication. The hypothesised relationships were analysed employing the structural equation modelling approach with the SPSS AMOS statistical package.Findings The results indicated that career identity, cultural capital, ISE and SPE are meaningfully related. In particular, in line with our expectations, we observed that career identity predicts cultural capital, which is positively associated with ISE which, ultimately, impacts SPE.Originality/value Our work adds to existing research by advancing the understanding of employability capitals, explaining how they interact and influence SPE, which is crucial for a sustainable transition into the workforce. At a practical level, our findings call upon, and guide, efforts from various stakeholders in the graduate career ecosystem (i.e. universities and their partners) to offer students and graduates meaningful experiences to form and use their employability capitals.

Unveiling the relationships between visibility, information technologies and innovation management for sustainability performance: an empirical study

PurposeThe paper investigates how visibility, information technology and innovation management impact sustainability performance. It proposes a framework explaining the role of visibility in driving firms' sustainable performance and the relevance of innovation management and information technologies in enhancing organisational visibility. This study intends to add to the discussion within the management literature about the potential of innovation management to drive sustainability. It seeks to provide insight into the practices that small and medium-sized enterprises (SMEs) can adopt to improve their sustainable performance.Design/methodology/approachUsing empirical methods, the study investigates SMEs in central Mexico. The demographic information in the dataset includes 15 years as an average length of service from firms. Of the surveyed firms, 70% were from the manufacturing sector and 30% were from the service sector, as these are the most representative sectors of the productive region. A variance-based structural equation model approach was used to test the hypotheses, processed with the partial least squares (PLS) regression method.FindingsThe research results show that visibility significantly impacts sustainability performance. Innovation management has a higher influence on visibility than information technologies, emphasising the need to improve the quality of information in firms, not just the tools. The findings support managers in comprehending the crucial importance of visibility in aiding firms to achieve higher sustainability performance.Research limitations/implicationsThe study only examined a sample of Mexican SMEs; therefore, the findings' generalizability must be considered within this context. Secondly, the survey only focused on services and manufacturing firms and a more detailed analysis of the sector could provide further clarity on the relationships between variables. As a result, future research should consider these limitations and explore additional contexts to improve the overall understanding of the topic. Moreover, the scale used to measure the variables was adapted from other researchers with similar context research and reflective variables.Practical implicationsThe results provide helpful information for SME managers about the importance of focusing on innovation management processes and employing information technologies as crucial managerial strategies. This will aid in increasing visibility and supporting the development of sustainability performance in firms.Social implicationsThe world red-code, among others, with climate change and social gaps, has generated the need to contribute to sustainable development, and it has mobilised people on all levels all over the world for the simple purpose of preserving life. Therefore, society, as a crucial group that affects and is affected by this red-code situation, should act in favour of visibility, the use of high-quality information (e.g. transparent, accessible and relevant) and information technologies to promote sustainable practices. This could mean that society should be prepared to incorporate new capabilities and spaces to interchange knowledge as a participatory community that can contribute to better sustainable dynamics that could expand its participation in public decisions. Also, the government should encourage digital democracy (e.g. develop social participation platforms), opening and harmonising rules and mechanisms combining high-quality information with IT to provide flexible and adequate services that support sustainable development, such as efforts towards constructing sustainable and smart cities.Originality/valueThis study explores how innovation management can drive firms' sustainability performance, which is crucial for improving competitiveness. The question of how to enhance sustainability performance through managerial drivers is a critical one. This study empirically investigates the nexus of visibility and sustainability performance, innovation management and information technology with visibility.

From awareness to behaviour: understanding cybersecurity compliance in Vietnam

From awareness to behaviour: understanding cybersecurity compliance in Vietnam

Toward Sustainable Global Product Development Performance: Exploring the Criticality of Organizational Factors and the Moderating Influence of Global Innovation Culture

In today’s dynamic and global landscape, innovation and globalization are intricately linked drivers that propel modern businesses forward, serving as indispensable pillars upon which organizations heavily rely to maintain their competitive edge. Leveraging innovation within the context of global product development (GPD) practices is imperative for organizations seeking to survive and effectively compete in the rapidly evolving marketplace. While preceding studies have primarily focused on the importance of individual and organizational capabilities, policies, and cultural factors in driving product development (PD) performance, they often overlook their interconnectedness within a global context. To address this gap, this study delves into the relationship between organizational factors and global product development performance (GPDP) while examining the moderating influence of global innovation culture (GIC). These organizational factors encompass technological innovation capabilities (TICs), team creativity (TC), dynamic capabilities (DCA), and competitive advantage (CA). Our hypothesis suggests a positive impact of these factors on GPD performance. Employing a structural equation modeling (SEM) approach through SMART-PLS 3, we analyzed data collected from 480 questionnaire forms distributed among registered global product development (GPD) organizations operating in China. The empirical findings underscore the significant influence of organizational factors on GPD performance, highlighting the critical role of global innovation culture in shaping these relationships. These insights shed light on the importance of establishing a harmonious balance between organizational culture and various factors to enhance interaction among organizational elements and optimize global product development performance. Ultimately, these efforts can lead to improvements in the overall effectiveness and sustained global product development.

Adding External Artificial Intelligence (AI) into Internal Firm-Wide Smart Dynamic Warehousing Solutions

This study advances knowledge in the AI field. It provides deep insight into current industry generative AI inclusion systems. It shows both literature and practical leading industry operations can link, overlap, and complement each other when it comes to AI and understanding its complexities. It shows how to structurally model and link AI inclusions towards delivering a suitable sustainability positioning. It shows approaches to integrate external AI contributions from one firm into another firm’s intelligences developments. It shows how to track, and maybe benchmark, the progress of such AI inclusions from either an external or an integrated internal software developer perspective. It shows how to understand and create a more sustainable, AI-integrated business positioning. This study considers firm artificial intelligence (AI) and the inclusion of additional external software developer engineering as another AI related pathway to future firm or industry advancement. Several substantive industrial warehousing throughput areas are discussed. Amazon’s ‘smart dynamic warehousing’ necessitates both digital and generative ongoing AI system prowess. Amazon and other substantive, digitally focused industry warehousing operations also likely benefit from astute ongoing external software developer firm inclusions. This study causally, and stagewise, models significant global software development firms involved in generative AI systems developments—specifically ones designed to beneficially enhance both warehouse operational productivity and its ongoing sustainability. A structural equation model (SEM) approach offers unique perspectives through which substantive firms already using AI can now model and track/benchmark the relevance of their prospective or existing external software developer firms, and so create rapid internal ‘net-AI’ competencies incorporations and AI capabilities developments through to sustainable operational and performance outcomes solutions.

Holistic Antecedent Analysis of Behavioral Intention among Green Consumers in the Philippines: A Sustainability Theory of the Planned Behavior Approach

Concerns over sustainability have recently drawn more attention in a post-COVID-19 environment, particularly from developing countries. This heightened global awareness of sustainability highlights the importance of comprehending consumer behavior in purchasing green products. This study utilized an extended theory of planned behavior (TPB) called the sustainability theory of planned behavior (STPB) to holistically assess the behavioral intention among green consumers in a particular developing country: the Philippines. Convenience sampling was used with 500 participants, and 54 modified questions were distributed online. Different factors, such as perceived environmental concern, perceived economic concern, perceived authority support, subjective norm, attitude, perceived behavioral control, customer perceived value, and purchasing intention, were assessed concurrently through the structural equation modeling (SEM) approach. Through this, it was discovered that all the STPB predictors were significant drivers affecting the consumers’ purchasing intention, with customer perceived value having the highest direct effect. Moreover, it was found that the relationship between perceived economic concern on subjective norms and perceived behavioral control was insignificant. Further implications and comparisons were made based on the results of the study. The study’s findings can be utilized to help policymakers and marketers devise strategies that will effectively encourage sustainability through targeted interventions and increased product awareness. The study has validated the integration of new constructs into the TPB, enhancing the predictive power of the proposed model for assessing the behavioral intention to purchase green products. Thus, the model construct can be applied and utilized to investigate other topics regarding sustainability.

Green consumption behaviour among higher education students as an approach to achieving sustainable source reduction of marine plastic pollution

Purpose Marine plastic pollution (MPP) is increasing in recent times because of the high usage of plastic products. Green consumption behaviour (GCB) gaining attention as effective approach to achieving sustainable source reduction of plastic pollution, which negatively affects both human pollution and marine biodiversity and ecosystem. Although, Higher Education (HE) students are key stakeholders in addressing environmental issues, including MPP, there is limited empirical research in Ghana on factors influencing HE students’ GCB. This study, in an endeavour to bridge the gap, used the revised theory of planned behaviour (TPB) framework to investigate the factors influencing higher-education students’ green consumption behaviour in the Ghanaian context. Specifically, the purpose of the study is to examine the interplay of consumer novelty seeking (CNS), environmental concern (EC), perceived behavioural control and social influence on green consumption behaviour among higher-education students in Ghana. The study also explored the moderating role of gender in the relationship between CNS and green consumption behaviour. Design/methodology/approach This study used quantitative approach to obtain data from a sample of 233 students at the University of Cape Coast and used the partial least squares structural equation modelling approach for the data analysis. Findings The findings provide valuable insights, highlighting the important role of CNS and ECs in driving higher education students’ green consumption behaviour in Ghana. This study also found a revealing role for gender as a moderator in the relationship between CNS and green consumption behaviour, with females exhibiting a more pronounced response to CNS in influencing green consumption behaviour. On the contrary, the authors found a non-significant impact of perceived behavioural control and social influence. Research limitations/implications Although this study presents results that provide valuable insights for policy and practical implications, it has some limitations worth mentioning for future research directions. Firstly, the participants sampled for this study comprised only higher education students from the University of Cape Coast in Ghana, which may limit the applicability of the findings to other student populations at various universities in Ghana and beyond. Moreover, the exclusion of non-students who are considered as “Generation Z” (i.e. born within 1995–2010) may narrow the scope of generalisability in the context of young consumers’ green consumption behaviour in Ghana. To enhance the generalisability of future studies, it is recommended that the scope of this study be extended. Furthermore, it should be noted that this study primarily measured higher education students’ green consumption behaviour based on self-reported data. Therefore, future research could adopt alternative approaches, such as non-self-reported measures or experimental data so to reduce the complexities and the gap that may exist between attitudes and behaviour. Practical implications These results provide valuable insights for policymakers, educators and environmental advocates to develop targeted initiatives that resonate with Ghanaian higher education students to foster green consumption practices and contribute to global efforts against marine plastic pollution. Originality/value The novelty of this study lies in the decision to propose a TPB model by including variables like CNS and EC that are believed to positively shape attitudes towards green consumption behaviour. The rationale for examining these variables is grounded in the belief that they are appropriate factors that may predict students’ green consumer behaviour, which may serve as a potential solution to marine plastic pollution.

Gratitude, academic self-efficacy, and well-being outcomes among Filipino undergraduate students

Existing literature has shown that gratitude matters for students’ well-being outcomes. However, there is limited research underscoring the learning processes that account for the mental health benefits of gratitude. This research explored the association of gratitude with life satisfaction and psychological well-being and whether gratitude would have indirect effects on such well-being outcomes via its link to academic self-efficacy through a structural equation modeling approach. Five-hundred eighty-one Filipino undergraduate students ( M age = 20.00; SD age = 2.95) from two universities in the Philippines participated in this investigation. Results showed that gratitude was linked to greater perceived life satisfaction and psychological well-being through its association with higher levels of academic self-efficacy. This study reinforces extant evidence on the psychological and mental health payoffs associated with feeling grateful.

62 Implications of factor analyses and Bayesian network learning to develop composite traits of size attributes in developing beef heifers

Abstract Advancements in phenotypic data collection methods provide opportunities to capture complex biological traits more efficiently. Even so, interpreting those phenotypes and using them in selection decisions is challenging in beef cattle production. Objectives of this study were to 1) develop composite phenotype(s) of economic importance in beef heifers given attributes of their body conformation and ultrasound-based measures of reproductive and carcass traits, and 2) identify associated genomic regions under selection using the structure equation modeling (SEM) approach. In this study, we hypothesized that the interplay of factor analysis and Bayesian Network Learning (BNL) approaches would better define the complex composite traits for size and carcass-related traits in beef heifers. Phenotypic data of admixed beef heifers (n = 336) for different reproductive, body conformational, and carcass-associated phenotypic traits (n = 16) were collected. Using exploratory and confirmatory factor analyses, two latent variables explaining 14 phenotypic traits were identified, herein called Body Size (BS; n = 7 traits represented) and Body Composition (BC; n = 7 traits represented). To efficiently model them in genome-wide association studies (GWAS), fixed effects of data collection year, dam age, and primary breed group as well as causal network structure identified through BNL were used within SEM model of GWAS. Body Size was directly or indirectly contributing to BC based on BNL structure. Given the data-driven approach and novelty of the data, BayesCπ model was applied with the sliding window of 1 Mb to avoid noise and linkage disequilibrium implications. Regions capturing large amounts of genetic variance (Posterior Probability of Association, PPA ≥ 0.8) were mapped to their closest gene for enrichment analysis. Twenty-four different genes were identified associated with both composite phenotypes. Functional enrichment analysis of significantly involved pathways (FDR < 0.05) indicated that genes from the HERC family (HERC3, HERC6, and HERC5) were found involved in cellular growth, and those from desmosomes (DC2, and DC3) were involved in morphogenesis like BS. Genes related to energy metabolism like IMPAD1, and PIGY, along with genes associated with traits like muscular development, and other carcass traits (i.e., FAM184B, NCAPG, and LCORL), were also found associated with BS and BC. Heritability ranged from 0.551 ± 0.048 to 0.897 ± 0.145 for BS and BC, respectively. The combined heritability of reproductive and carcass traits (BC) was much greater than the heritability of any related individual trait reported to date and was comparable for individual BS-related traits. Given this, we conclude that the implications of factor analyses with BNL can provide better biological insights for improved genomic selection in beef cattle, even for traits of low heritability.

Stage-wise green supply chain management and environmental performance: Impact of blockchain technology

In the era of globalisation, the use of technology and concerns for sustainability is eminent in the supply chain management practices. The current study focuses on sustainable and green supply practices in different stages of supply chain management and how they can be facilitated by blockchain technology (BT). The study has addressed the existing gaps in the area namely, the lack of research assessing stage-wise green supply chain for environmental performance focusing on BT. The current study aims to assess the impact of BT on different stages of the green supply chain and a firm's environmental performance. The study also focuses on analyzing the impact of green supply chain stages on environmental performance. The study uses PLS-based structural equation modelling approach to investigate the hypothesised relationships between BT adoption and stage-wise green supply chain practices. The data was collected from individuals from medium-sized enterprises from the manufacturing industry in India. The findings reveal a positive association between blockchain adoption and green supply chain management practices leading to enhancement in environmental performance. Furthermore, the study indicates a positive relationship between blockchain integration and different stages of the green supply chain, underscoring its multi-faceted impact on environmental performance. The findings imply that the BT adoption can facilitate the realization of sustainable supply chain practices and performance improvement.

A comparison of univariate and meta-analytic structural equation modeling approaches to reliability generalization applied to the Maslach Burnout Inventory

IntroductionReliability is a property of tests scores that varies from sample to sample. One way of generalizing reliability of a test is to perform a meta-analysis on some reliability estimator. In 2011, a reliability generalization meta-analysis on the Maslach Burnout Inventory (MBI) was conducted, concluding that average alpha values for the MBI dimensions ranged from 0.71 to 0.88. In the present study, we aimed to update the average reliability values of the MBI by conducting a literature search from 2010 until now and comparing to statistical procedures of meta-analysis: the Univariate approach, that were used in the previous study, and a novel meta-analytic approach based on structural equation modeling.MethodAn estimation of average reliability was done based on 69 independent primary reliability coefficients for the Univariate approach. The average reliability was based on 9 independent studies in the case of the Meta-analytic Structural Equation Modeling (MASEM) approach. Given that MASEM has the additional capability of testing the internal structure of a test, we also fitted several models.ResultsThe data was well-suited to the bifactor model, revealing the dominance of the general factor over the domain-specific ones. Acceptable overall alpha and omega coefficients were achieved for the two of the MBI dimensions, having depersonalization reliability estimates below recommendations.DiscussionIn general, the MBI can be viewed as a highly interconnected three-factor scale, being its appropriate for research purposes.

Exploring the effects of AI literacy in teacher learning: an empirical study

As most practitioners (including teachers) do not know how AI functions and cannot make full use of AI in education, there is an urgent need to investigate teachers’ intentions to learn AI and related determinants so as to promote their AI learning. This study collected survey data from a total of 318 K-12 teachers from sixteen provinces or municipalities in China. A two-step structural equation modeling approach was performed to analyze the data. Our findings show that K-12 teachers’ perceptions of the use of AI for social good and self-efficacy in learning AI are two direct determinants of behavioral intentions to learn AI, while awareness of AI ethics and AI literacy are two indirect ones. AI literacy has a direct impact on perceptions of the use of AI for social good, self-efficacy in learning AI and awareness of AI ethics and has an indirect impact on behavioral intentions to learn AI. This study represents one of the earliest attempts to empirically examine the power of AI literacy and explore the determinants of behavioral intentions to learn AI among K-12 teachers. Our findings can theoretically and practically contribute to the virgin field of K-12 teachers’ AI learning.

Assessing Green Practices on Eco-Friendly Hotel Customer Loyalty: A Partial Least Squares Structural Equation Modeling and Fuzzy-Set Qualitative Comparative Analysis Hybrid Approach

With a global focus on environmental sustainability, hotels worldwide are actively transitioning their services from conventional to eco-friendly practices. This study aims to comprehensively understand the factors that contribute to visitors’ satisfaction in eco-friendly hotels and how this satisfaction influences customers’ future reactions towards such environmentally conscious establishments. Employing the Stimulus-Organism-Response theory, this study collected data from 277 respondents using a robust quantitative research strategy. A combined approach of Partial Least Squares Structural Equation Modeling (PLS-SEM) and Fuzzy-Set Qualitative Comparative Analysis (fsQCA) was employed, to uncover deep insights into visitors’ satisfaction and their reactions towards eco-friendly hotels. The PLS-SEM results reveal significant associations between customers’ satisfaction towards eco-friendly hotel services and service quality, green practices, perceived value, and environmental sensitivity. Moreover, this study highlights a positive correlation between satisfaction and crucial outcomes like revisit intention (RVI), willingness to pay a premium (WTPP), and word-of-mouth intention (WOMI). Complementing these findings, the fsQCA analysis uncovers intricate causal relationships among antecedents that influence customer satisfaction in eco-friendly hotels. By offering critical marketing insights, this study provides guidance for hotels, the tourism industry, and policymakers on attracting customers to eco-friendly hotels, to meet the increasing demands for environmental sustainability.

Heart rate variability as an indicator of fatigue: A structural equation model approach

Driver fatigue significantly impairs performance and is a major risk factor for road crashes. However, fatigue is difficult to objectively measure and quantify. This study aimed to elucidate associations between heart rate variability (HRV) metrics and multidimensional fatigue construct—encompassing objective and subjective measures—in young drivers with self-reported short sleep. Eighty-four young adults underwent assessments during simulated driving. HRV indices RMSSD and LF/HF ratio were derived from an electrocardiogram, while relative theta power, oculography drowsiness, lane position variability, and Karolinska Sleepiness Score measured fatigue. RMSSD negatively correlated with theta power, and LF/HF ratio positively correlated with position variability. Exploratory factor analysis extracted three factors, with RMSSD and LF/HF loading onto one. Structural equation modelling tested the prediction of a latent fatigue construct from HRV. Two models demonstrated an acceptable fit. RMSSD negatively predicted fatigue, explaining 6.4% of the variance, while the LF/HF ratio positively predicted fatigue, accounting for 3.7% of the variance. In summary, HRV, particularly RMSSD, showed significant relationships with multidimensional fatigue. Lower RMSSD and higher LF/HF ratio are associated with greater fatigue levels. This study demonstrates that HRV indices exhibit significant relationships within a multidimensional model of fatigue incorporating objective performance, ocular, and subjective measures. The findings provide initial evidence towards using HRV for monitoring complex manifestations of fatigue, rather than single dimensions. Further applied research should investigate translating these findings to naturalistic settings and validating HRV thresholds predictive of on-road impairment.

Self-reported cognitive function mediates the relationship between employment status and cognitive functioning in persons with multiple sclerosis

BackgroundCognitive impairment (CI) is common in people with MS (PwMS). Evidence is lacking for the self-reported CI's mediation effect on employment status and objective cognitive performance. Self-reported CI was found to be unreliable and seemed to be more associated with depression rather than formal cognitive performance. We hypothesized that the link between subjective and objective assessments of cognitive functions, mood, and employment status may be more complex in PwMS than previously reported. ObjectiveThe aims of this study are the following: (Romero-Pinel et al., 2022) to determine whether employment status could affect performance in cognitive function testing and (Rao et al., 1991) whether their relationship may be mediated by self-reported CI; and (Deluca et al., 2013) to determine whether self-reported depression interacts with self-reported CI in influencing performance in various cognitive domains in PwMS. MethodologyA retrospective study was performed involving PwMS who completed the self-report Multiple Sclerosis Neuropsychological Questionnaire (MSNQ), Hospital Anxiety and Depression Scale-depression scale (HADS-D), Minimal Assessment of Cognitive Function in MS (MACFIMS) and had data regarding employment status. Included PwMS were classified as employed or unemployed. A structural equation modeling (SEM) approach was taken due to the advantage of examining multiple cognitive outcomes simultaneously while accounting for shared associations. First, a latent factor of memory and executive functioning modeled the error-free associations between both factors and a processing speed task (SDMT). Next, the model tested for the indirect effect of self-reported cognition (MSNQ) on employment status differences in each outcome (memory, speed, and executive functioning). Finally, we tested interactions between MSNQ and HADS-D on each of the outcomes. ResultsWe included 590 PwMS: 72.5% female, mean age 44.2 years (SD = 10.5), mean disease duration 8.6 years (SD 9.0). The majority (n = 455, 77.1%) had relapsing MS; 357 (60.5%) were employed. About half (n = 301, 51%) did not report CI on the MSNQ; of those, 213 (70.8%) were employed. The mean MSNQ for employed PwMS was 24.5 (SD = 10.7) and 29.8 (SD = 11.2) for unemployed PwMS. Employed PwMS had significantly better memory (β = .16, p < .05), executive functioning (β = .25, p < .05), and processing speed (β = .22, p < .05). MSNQ partially indirectly mediated the effect of employment status on memory (Δβ = .03, p < .05) and executive functioning (Δβ = .03, p < .05) and processing speed (Δβ = .04, p < .05), indicating that self-report CI partially explains the influence of employment status on these cognitive domains. The association between MSNQ with both memory and executive functioning was moderated by depression, meaning that in PwMS with high HADS-D scores, MSNQ was more strongly related to worse memory and executive functioning. The final model was an acceptable fit to the data (χ2(87) = 465.07, p < .05; CFI = .90, RMSEA = .08, 90% CI [.06, .09], SRMR = .05) explaining 41.20%, 38.50% and 33.40% of the variability in memory, executive functioning, and processing speed, respectively. ConclusionSelf-reported CI partially explains the associations between employment status and objective cognitive assessment in PwMS. Depression may moderate the relationship between self-reported cognitive assessment and objective cognitive performance. Thus, employment status and mood may guide the interpretation of self-reported CI.

The thermal perception of outdoor urban spaces in a hot arid climate: A structural equation modelling (SEM) approach

Human thermal comfort is linked to multi-sensory attributes and psychological cognition besides the influence of prominent meteorological parameters. To date, even the most advanced thermal comfort models like UTCI consider the interaction between human and external surroundings to be a heat transfer problem applying thermodynamic equations in the derivation behind thermal comfort indices. Machine learning (ML) models offer the advantage of improving the accuracy of predictor variable, but they fail to determine the relationship between the independent variables and data-driven prediction models. Structural Equation Modelling (SEM), an advanced modelling technique, can expose all hidden relationships among variables using latent variables while not being data-driven like ML. This study aimed to develop a structural framework which systematically explains these relationships and quantify the effects of 24 variables on outdoor thermal comfort using SEM due to its ability to represents the response of the dependent variable to a unit change in an explanatory variable. The method employed 635 structured interviews, observations, and wide-ranging micrometeorological measurements conducted concurrently in three Pedestrianized urban alleys in Cairo. The model statistically confirmed the hypothesis that overall thermal comfort in urban spaces is significantly influenced by multiple factors. Of these, background microclimatic conditions had the greatest relevance and certain urban features the least. 1 unit increase microclimate leads to a change in thermal comfort by 0.423 and 0.349 in summer and winter, respectively. In terms of thermal perception, the model shows that individuals are willing to tolerate a wider range if certain urban features are present such as shaded seating opportunities. These outcomes may aid to provide important guidance for urban designers to enhance thermal perception in urban streets.

Quantifying reciprocal relationships between poverty and health: combining a causal loop diagram with longitudinal structural equation modelling.

This study takes on the challenge of quantifying a complex causal loop diagram describing how poverty and health affect each other, and does so using longitudinal data from The Netherlands. Furthermore, this paper elaborates on its methodological approach in order to facilitate replication and methodological advancement. After adapting a causal loop diagram that was built by stakeholders, a longitudinal structural equation modelling approach was used. A cross-lagged panel model with nine endogenous variables, of which two latent variables, and three time-invariant exogenous variables was constructed. With this model, directional effects are estimated in a Granger-causal manner, using data from 2015 to 2019. Both the direct effects (with a one-year lag) and total effects over multiple (up to eight) years were calculated. Five sensitivity analyses were conducted. Two of these focus on lower-income and lower-wealth individuals. The other three each added one exogenous variable: work status, level of education, and home ownership. The effects of income and financial wealth on health are present, but are relatively weak for the overall population. Sensitivity analyses show that these effects are stronger for those with lower incomes or wealth. Physical capability does seem to have strong positive effects on both income and financial wealth. There are a number of other results as well, as the estimated models are extensive. Many of the estimated effects only become substantial after several years. Income and financial wealth appear to have limited effects on the health of the overall population of The Netherlands. However, there are indications that these effects may be stronger for individuals who are closer to the poverty threshold. Since the estimated effects of physical capability on income and financial wealth are more substantial, a broad recommendation would be that including physical capability in efforts that are aimed at improving income and financial wealth could be useful and effective. The methodological approach described in this paper could also be applied to other research settings or topics.