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Nowadays, residential buildings have become increasingly important due to the growing communities. The purpose of this study is to investigate the behavior of a steel structural framing system that incorporates lightweight load-bearing walls and slabs, and to compare the weight of materials used in cold-formed and hot-finished steel structural systems for affordable housing.

Four types of models consisting of 243 members were simulated. Model 1 is a cold-formed steel structural framing system, while Model 2 is a hot-finished steel structural framing system. Both Models 1 and 2 use lightweight wall panels and lightweight composite slabs. Models 3 and 4 are made with brick walls and precast reinforced concrete systems, respectively. These structures use different wall and slab materials, namely, brick walls and precast reinforced concrete. The analysis includes bending behavior, buckling resistance, shear resistance and torsional rotation analysis.

This study found that using thinner steel sections can increase the deflection value. Meanwhile, increasing member length and the ratio of slenderness will decrease buckling resistance. As the applied load increases, buckling deformation also increases. Furthermore, decreasing shear area causes a reduction in shear resistance. Thicker sections and the use of lightweight materials can decrease the torsional rotation value.

The weight comparison of the steel structures shows that Model 1, which is a cold-formed steel structure with lightweight wall panels and lightweight composite slabs, is the most suitable model due to its lightweight and affordability for housing. This model can also be used as a reference for the optimal design of modular structural framing using cold-formed steel materials in the field of civil engineering and as a promotional tool.

This article introduces SigBERT, a novel approach that fine-tunes bidirectional encoder representations from transformers (BERT) for the purpose of distinguishing between intact and impaired structures by analyzing vibration signals. Structural health monitoring (SHM) systems are crucial for identifying and locating damage in civil engineering structures. The proposed method aims to improve upon existing methods in terms of cost-effectiveness, accuracy and operational reliability.

SigBERT employs a fine-tuning process on the BERT model, leveraging its capabilities to effectively analyze time-series data from vibration signals to detect structural damage. This study compares SigBERT's performance with baseline models to demonstrate its superior accuracy and efficiency.

The experimental results, obtained through the Qatar University grandstand simulator, show that SigBERT outperforms existing models in terms of damage detection accuracy. The method is capable of handling environmental fluctuations and offers high reliability for non-destructive monitoring of structural health. The study mentions the quantifiable results of the study, such as achieving a 99% accuracy rate and an F-1 score of 0.99, to underline the effectiveness of the proposed model.

SigBERT presents a significant advancement in SHM by integrating deep learning with a robust transformer model. The method offers improved performance in both computational efficiency and diagnostic accuracy, making it suitable for real-world operational environments.

Firms embedded in networks of relations are theorized through Gnyawali and Madhavan’s (2001) (G&M) structural embeddedness model to gain competitive advantage from topological characteristics. Empirical studies to support their theory have never been executed in full. Our study provided a full empirical test of their model in a digital trading network to achieve a higher degree of certainty that those network structural characteristics can have a major impact on the degree to which certain firms lead to competitiveness in a digital trading network environment.

To examine how firms respond in competitive situations, we chose the hyper-active digital trading network, eBay as our empirical context. We used eBay auction data to analyze how the network characteristics of eBay resellers impact their competitive behaviors.

Our study found strong support for the G&M model of competitiveness. We offer explanations for where support was not as strong as the Gynawali and Madavan theory proposes.

Our research is limited by our chosen context and findings in support of part of G&M model. Future studies in other digital contexts are needed to enhance the modeling of network topologies and further study the impacts of network density and structural autonomy on competitive action.

Our study suggests that managers proceed cautiously in forming partnerships, weighing circumstances where the firm can find itself with increased information power and avoiding, to the greatest extent possible, situations where the playing field is roughly equal.

Theory-making in this domain has begun as well as initial empirical testing. Much more needs to be accomplished, though, before embeddedness modeling can be thought of as being well established.

The G& M Model of competitiveness is an SNA explanation of why some competitive units succeed and others do not. Our study is the first, full blown empirical analysis of the theory.

The study aimed to analyze the influence of network embeddedness on the innovation performance of high-tech enterprises in Guangdong Province.

A conceptual model of the influence of network embeddedness on the innovation performance of high-tech enterprises in Guangdong province is established, which takes the business model as the mediating variable and political association as the moderating variable. Multivariate statistical analysis and the MacKinnon confidence interval method were used to analyze 418 questionnaires.

The results show that both relational embeddedness and structural embeddedness have significant positive effects on the innovation performance of high-tech enterprises in Guangdong Province. The business model has a partial mediating effect between relationship embeddedness, structure embeddedness, and innovation performance of high-tech enterprises in Guangdong Province, respectively. Political relevance has a significant negative moderating effect on the relationship between the relationship embeddedness and innovation performance of high-tech enterprises in Guangdong Province, but the moderating effect on structural embeddedness and innovation performance of high-tech enterprises in Guangdong province has not been verified.

The study of this paper also has some shortcomings: very few data research samples exist; the external factors affecting the performance of high-tech enterprises in Guangdong Province need to be further refined. The research scale needs further improvement.

In this paper, embedding theory, transaction cost theory, resource dependence theory, rent-seeking theory, new institution theory and uncertainty management theory were integrated by system attempt to reveal the mediating and moderating roles of business model and political relevance, respectively, between network embeddedness behavior and entrepreneurial innovation performance of high-tech enterprises. The research conclusions expand the relevant research in the field of entrepreneurial innovation. At the same time, the research results provide theoretical support and reference for the innovative growth of high-tech enterprises and government behavior decision-making in Guangdong province.

Network embeddedness will have a profound impact on the entrepreneurial innovation performance of high-tech enterprises. Existing research has overlooked discussing this issue from the perspective of internal and external influencing factors within the enterprise. Therefore, this study addresses this issue by (1) introducing the business model as the mediating variable from an internal perspective of the enterprise, (2) introducing political association as the moderating variable from an external perspective of the enterprise and (3) 418 original questionnaires of high-tech enterprises in Guangdong Province were used to test the effect of the study variables.

This paper introduces a novel method, Variance Rule-based Window Size Tracking (VR-WT), for deriving a sequence of estimation window sizes. This approach not only identifies structural change points but also ascertains the optimal size of the estimation window. VR-WT is designed to achieve accurate model estimation and is versatile enough to be applied across a range of models in various disciplines.

This paper proposes a new method named Variance Rule-based Window size Tracking (VR-WT), which derives a sequence of estimation window sizes. The concept of VR-WT is inspired by the Potential Scale Reduction Factor (PSRF), a tool used to evaluate the convergence and stationarity of MCMC.

Monte Carlo simulation study demonstrates that VR-WT accurately detects structural change points and select appropriate window sizes. The VR-WT is essential in applications where accurate estimation of model parameters and inference about their value, sign, and significance are critical. The VR-WT has also helped us understand shifts in parameter-based inference, ensuring stability across periods and highlighting how the timing and impact of market shocks vary across fields and datasets.

The first distinction of the VR-WT lies in its purpose and methodological differences. The VR-WT focuses on precise parameter estimation. By dynamically tracking window sizes, VR-WT selects flexible window sizes and enables the visualization of structural changes. The second distinction of VR-WT lies in its broad applicability and versatility. We conducted empirical applications across three fields of study: CAPM; interdependence analysis between global stock markets; and the study of time-dependent energy prices.

This research paper aims to investigate reinforced concrete (RC) walls' behaviour under fire and identify the thermal and mechanical factors that affect their performance.

A three-dimensional (3D) finite element (FE) model is developed to predict the response of RC walls under fire and is validated through experimental tests on RC wall specimens subjected to fire conditions. The numerical model incorporates temperature-dependent properties of the constituent materials. Moreover, the validated model was used in a parametric study to inspect the effect of the fire scenario, reinforcement concrete cover, reinforcement ratio and configuration, and wall thickness on the thermal and structural behaviour of the walls subjected to fire.

The developed 3D FE model successfully predicted the response of experimentally tested RC walls under fire conditions. Results showed that the fire resistance of the walls was highly compromised under hydrocarbon fire. In addition, the minimum wall thickness specified by EC2 may not be sufficient to achieve the desired fire resistance under considered fire scenarios.

There is limited research on the performance of RC walls exposed to fire scenarios. The study contributed to the current state-of-the-art research on the behaviour of RC walls of different concrete types exposed to fire loading, and it also identified the factors affecting the fire resistance of RC walls. This guides the consideration and optimisation of design parameters to improve RC walls performance in the event of a fire.

Rigorous applications of analytical tools in information systems (IS) research are important for developing new knowledge and innovations in the field. Emerging tools provide building blocks for future inquiry, practice and innovation. This article summarizes the findings of an analysis of the adoption and reporting of partial least squares structural equation modeling (PLS-SEM) analytical tools by Industrial Management & Data Systems authors in the most recent five-year period.

Selected emerging advanced PLS-SEM analytical tools that have experienced limited adoption are highlighted to broaden awareness of their value to IS researchers.

PLS-SEM analytical tools that facilitate understanding increasingly complex theoretical models and deliver improved prediction assessment are now available. IS researchers should explore the opportunities to apply these new tools to more fully describe the contributions of their research.

Findings demonstrate the increasing acceptance of PLS-SEM as a useful alternative research methodology within IS. PLS-SEM is a preferred structural equation modeling (SEM) method in many research settings and will become even more widely applied when IS researchers are aware of and apply the new analytical tools.

Emerging PLS-SEM methodological developments will help IS researchers examine new theoretical concepts and relationships and publish their work. Researchers are encouraged to engage in more complete analyses by applying the applicable emerging tools.

Applications of PLS-SEM for prediction, theory testing and confirmation have increased in recent years. Information system scholars should continue to exercise sound practice by applying these new analytical tools where applicable. Recommended guidelines following Hair et al. (2019; 2022) are included.

This paper examines the nexus between supply chain social capital (relational social capital and structural social capital), supply chain responsiveness (operations system responsiveness and supplier network responsiveness) and firm performance. Additionally, the study examines the mediating role of supply chain responsiveness on the relationship between supply chain social capital and firm performance.

The authors test their hypotheses on a sample of 120 firms operating in Ghana. The measurement model and hypothesized paths were assessed using partial least squares structural equation modelling.

The findings revealed that structural social capital had a significant direct effect on firm performance, but relational social capital did not. It was also revealed that both relational and structural social capital have significant effects on operations system responsiveness and supplier network responsiveness. Additionally, operations system responsiveness fully mediated the effect of relational social capital on firm performance and partially mediated the effect of structural social capital on firm performance. Supplier network responsiveness, on the other hand, partially mediated the effect of both relational and structural social capital on firm performance.

This study contributes to the limited literature on supply chain social capital by unearthing the mechanisms through which supply chain social capital enhances firm performance. Specifically, the study demonstrates the intervening role of operations system responsiveness and supplier network responsiveness in the supply chain social capital–firm performance link.

This paper aims to examine project management segments based on customer satisfaction drivers and loyalty rather than traditional demographic or behavioural variables.

Data were gathered over 18 consecutive months, and 3,129 surveys were completed using a questionnaire. The statistical methods included partial least squares (PLS) structural equation modelling, finite mixture segmentation, prediction-oriented segmentation (PLS-POS) and multi-group analysis (PLS-MGA).

The findings indicate the existence of three segments among system delivery project customers based on the differences in the strengths of the path coefficients in the customer-centric structural model. In Segment 1, satisfaction based on the proposal was crucial for loyalty, with the value-for-money construct negatively impacting the repurchase intent construct. Segment 2 had a solid value-for-money orientation. In Segment 3, the critical path indicated that satisfaction drove repurchase intention, with satisfaction based mainly on the installation.

The research contributes to the segmentation theory by introducing a new way to segment the systems delivery projects customers based on the perceived strength of the relationships in a customer-centric structural model, which aligns with traditional segmentation theory in a way that most segmentation analyses do not. A new segmentation approach to the domain of project management theory is presented. Based on the results, treating the system delivery project customer base as a single homogenous group can lead to managerially misleading conclusions.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.