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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.

The study explores manufacturers' supply chain social capital (SCSC) (structural social capital and relational social capital) and supply chain performance, respectively, as drivers and outcome of green supply chain management practices (GSCMPs). Additionally, the study explores the direct relationship between SCSC and supply chain performance of manufacturers.

The author develops and tests a research model grounded in the resource-based view and the natural resource-based view theory using survey data from 100 manufacturing firms operating in Ghana. The measurement model and hypothesized paths were examined using partial least squares structural equation modeling.

The findings revealed that relational social capital of manufacturers has a positive and significant relationship with supply chain performance, but structural social capital does not. Additionally, manufacturers' structural social capital and relational social capital were found to have a positive and significant relationship with GSCMPs. Lastly, GSCMPs were found to have a positive and significant relationship with supply chain performance.

The study contributes to the limited literature demonstrating the contribution of intangible relational assets, specifically SCSC, toward GSCMPs implementation.

The effect of dynamic capabilities on operational excellence and the moderating effect of environmental dynamism on the relationship between operational excellence and dynamic capabilities in the apparel industry in Sri Lanka were investigated while developing new psychometric scales to assess operational excellence and dynamic capacities constructs.

We followed the exploratory sequential research design with a mixed-method research approach, aligning with the pragmatic research philosophy. Thus, both qualitative and quantitative research methods were followed.

Dynamic capabilities positively affect operational excellence, and environmental dynamism moderates the relationship between operational excellence and dynamic capabilities in the apparel industry in Sri Lanka such that when a higher environmental dynamism exists, a weaker positive relationship exists between dynamic capabilities and operational excellence. The two main dimensions of the operational excellence construct are continuous improvement of sustainable operational performance and sustainable competitive advantages. It empirically confirmed that sensing, seizing and reconfiguring capabilities are the three main dimensions of the dynamic capabilities construct.

This study was limited to the apparel industry in Sri Lanka. This research phenomenon should be explored in other industrial sectors worldwide to generalize the findings. The practitioners in the apparel sector may improve the organizational dynamic capabilities to achieve operational excellence and keep a strong positive relationship between dynamic capabilities and operational excellence in a highly dynamic environment if they address out-of-family situations with out-of-the-box thinking.

We generated two new empirical findings: (1) dynamic capabilities positively affect operational excellence, and (2) environmental dynamism moderates the relationship between dynamic capabilities and operational excellence. Also, we introduced validated new scales for assessing operational excellence and dynamic capabilities.

Supply chain vulnerability (SCV) analysis is vital for manufacturers globally because it creates a pathway for building resilient supply chains in uncertain environments. This study aims to identify drivers of SCV in the Indian manufacturing sector.

Sixteen drivers were identified from the literature review and followed by expert interviews. Interpretive structural modeling was used to determine the hierarchical structural relationship among identified SCV factors.

It was found that risk is not a board room agenda. Misaligned performance measures with incentives and lack of risk dashboard are the causal factors of SCV. Supply chain security, centralized production and distribution and lack of trust in the supply chain were driven factors.

This provides new insights to assess and prioritize initiatives for supply chain sustainability in terms of continuing business operations. The structural model provides a systemic view of SCV and helps reduce vulnerability.

Idea of circular economy defies the classical “make-use-dispose” approach of linear economic model. In the context of health-care industry, it relies heavily on the supply chain practices implemented by industry stakeholders. The purpose of this study is to explore such relationships, study their structure and put it across for attaining sustainability at large.

This study is an empirical research conducted on 145 health-care firms. The collected data is analysed to develop structural and measurement model. The five constructed hypotheses are examined and tested through structural equation modelling.

The study illustrates the latent relationships that exist among the stakeholders involvement, sustainable supply chain practices, sustainable performance and circular economy for health-care industry. It is found that the adoption of sustainable supply chain practices improves health-care performance, which, in turn, have positive influence on circular economy.

The structural and measurement model is developed in the context of circular health-care economy. It can be validated or improvised by conducting similar research in other industry using different methods. This research work fulfils the long existing gap in research by offering a linkage between various constructs to achieve health-care circular economy. Based on the research results, future researchers can build theories of circular economy and sustainability for health-care industry.

The study attempts to study the supply chain ways to achieve circular economy for Indian health-care sector. It considered latent relationships among the set of constructs, which are needed for theory building at later stage.

To improve the loan repayment performance (LRP) of microfinance banks (MFBs) in Pakistan, this study aims to look at the direct impact of multiple borrowing (MB) on LRP and client-business performance (CBP), as well as the direct impact of CBP on LRP. The moderating function of pandemic factors in the relationship between MB and CBP, as well as the mediating effect of CBP in the association between MB and LRP, was also investigated in this study.

A questionnaire was used to obtain data from 531 lower-level workers of microfinance institutions (MFIs) for the study. The respondents were chosen using stratified sampling, which divided the target population into four influential groups: lending officers in agriculture, lending officers in businesses, lending officers in gold loans and lending officers in salary loans. In this study, a two-stage structural equation modeling approach was used, including a measurement model (outer model) and a structural model (inner model). The validity and reliability of the questionnaire were investigated using the measurement model (outer model), whereas PLS-SEM bootstrapping was performed to test the hypothesis and find the relationship among different underpinning constructs by using the structural model (inner model).

The outcomes of this study demonstrate that MB has a direct impact on CBP, and that CBP has a direct impact on LRP. MB, on the contrary, had no direct and significant impact on LRP in this study. The idea that CBP mediates the relationship between MB and LRP, as well as the moderating effect of pandemic factors on the relationship between MB and CBP, is supported by this research.

Until now, the influence of MB on LRP via the mediating role of CBP and the moderating role of a pandemic factor in the setting of Pakistani MFBs has received little attention. During the COVID-19 pandemic, this research also aids MFBs in better understanding MB and its impact on LRP. Furthermore, based on the findings of this study, Pakistani MFIs can enhance their LRP by implementing new lending regulations, particularly with reference to MB and the COVID-19 pandemic.

Construction enterprises are achieving the goal of production safety by increasingly focusing on the critical factor of “human” and the impact of individual characteristics on safety performance. Emotional intelligence is categorized into three models: skill-based, trait-based and emotional learning systems. However, the mechanism of action and the internal relationship between emotional intelligence and safety performance must be further studied. This study intends to examine the internal mechanism of emotional intelligence on safety performance in construction projects, which would contribute to the safety management of construction enterprises.

A structural equation model exploring the relationship between emotional intelligence and safety performance is developed, with political skill introduced as an independent dimension, situational awareness presented as a mediator, and management safety commitment introduced as a moderator. Data were collected by a random questionnaire and analyzed by SPSS 24.0 and AMOS 26.0. The structural equation model tested the mediation hypothesis, and the PROCESS macro program tested the moderated mediation hypothesis.

The results showed that construction workers' emotional intelligence directly correlates with safety performance, and situational awareness plays a mediating role in the relationship between emotional intelligence and the safety performance of construction workers. Management safety commitment weakens the positive predictive relationships between emotional intelligence and situational awareness and between emotional intelligence and safety performance.

This research reveals a possible impact of emotional intelligence on safety performance. Adding political skills to the skill-based model of emotional intelligence received a test pass. Political skill measures the sincere and cooperative skills of construction workers. Using people as a critical element plays a role in the benign mechanism of “Emotional Intelligence – Situational Awareness – Safety Performance.” Improving emotional intelligence skills through training, enhancing situational awareness, understanding, anticipation and coordination and activating management environment factors can improve safety performance. Construction enterprises should evaluate and train workers' emotional intelligence to improve workers' situational awareness and safety performance.

Building information modelling (BIM) has transformed the traditional practices of the Architecture, Engineering and Construction (AEC) industry. BIM creates a collaborative digital representation of built environment data. Competitive advantage can be achieved with collaborative project delivery and rich information modelling. Despite the abundant benefits, BIM’s adoption in the AEC is susceptible to confrontation. A substantial impediment to BIM adoption often cited is data interoperability. Other facets of interoperability got limited attention. Other academic areas, including information systems, discuss the interoperability construct ahead of data interoperability. These interoperability factors have yet to be surveyed in the AEC industry. This study aims to investigate the effect of interoperability factors on BIM adoption and develop a comprehensive BIM adoption model.

The theoretical foundations of the proposed model are based on the European interoperability framework (EIF) and technology, organization, environment framework (TOE). Quantitative data collection from construction firms is gathered. The model has been thoroughly examined and validated using partial least squares structural equation modelling in SmartPLS software.

The study’s findings indicate that relative advantage, top management support, government support, organizational readiness and regulation support are determinants of BIM adoption. Financial constraints, complexity, lack of technical interoperability, semantic interoperability, organizational interoperability and uncertainty are barriers to BIM adoption. However, compatibility, competitive pressure and legal interoperability do not affect BIM adoption.

Finally, this study provides recommendations containing the essential technological, organizational, environmental and interoperability factors that AEC stakeholders can address to enhance BIM adoption.

To the best of the authors’ knowledge, this paper is one of the first studies to combine TOE and EIF in a single research model. This research provides empirical evidence for using the proposed model as a guide to promoting BIM adoption. As a result, the highlighted determinants can assist organizations in developing and executing successful policies that support BIM adoption in the AEC industry.

This study aims to explore the holistic context of organisational staff retention in small, medium and large organisations. It also aims to identify the factors affecting the retention of organisations of different sizes.

The study implements an empirical test of a model created during previous research with the participation of 511 employees. The responses to the online questionnaire and the modelling were analysed using the partial least squares structural equation modelling method. The models were tested for internal consistency reliability, convergent and discriminant validity, multicollinearity and model fit.

Two models were tested by organisation size, which revealed a total of 62 significant correlations between the latent variables tested. Identical correlations were present in both models in 22 cases. After testing the hypotheses, critical variables (nature of work, normative commitment, benefits, co-workers and organisational commitment) were identified that determine employees’ organisational commitment and intention to leave, regardless of the size of the organisation.

As a result of this research, the models developed are suitable for identifying differences in organisational staffing levels, but there is as yet no empirical evidence on the use of the scales for homogeneous groups of employees.

The results show that employees’ normative commitment and organisational commitment are critical factors for retention. Of the satisfaction factors examined, the nature of work, benefits and co-workers have a significant impact on retention in organisations, so organisational retention measures should focus on improving satisfaction regarding these factors.

The readers of the journal would appreciate the work, which highlights the significance of employee psychology and retention for organisational success.

The study is based on primary data and, to the best of the authors’ knowledge, is one of the few studies that take a holistic approach to organisational staff retention in the context of the moderating effect of organisational size. This study contributes to a comprehensive understanding of the phenomenon of employee retention and in contrast to previous research, examines the combined effect of several factors.

Rebar is the prime component of reinforced concrete structures, and rebar monitoring is a time-consuming and technical job. With the emergence of the fourth industrial revolution, the construction industry practices have evolved toward digitalization. Still, hesitation remains among stakeholders toward the adoption of advanced technologies and one of the significant reasons is the unavailability of knowledge frameworks and implementation guidelines. This study aims to investigate technical factors impacting automated monitoring of rebar for the understanding, confidence gain and effective implementation by construction industry stakeholders.

A structured study pipeline has been adopted, which includes a systematic literature collection, semistructured interviews, pilot survey, questionnaire survey and statistical analyses via merging two techniques, i.e. structural equation modeling and relative importance index.

The achieved model highlights “digital images” and “scanning” as two main categories being adopted for automated rebar monitoring. Moreover, “external influence”, “data-capturing”, “image quality”, and “environment” have been identified as the main factors under “digital images”. On the other hand, “object distance”, “rebar shape”, “occlusion” and “rebar spacing” have been highlighted as the main contributing factors under “scanning”.

The study provides a base guideline for the construction industry stakeholders to gain confidence in automated monitoring of rebar via vision-based technologies and effective implementation of the progress-monitoring processes. This study, via structured data collection, performed qualitative and quantitative analyses to investigate technical factors for effective rebar monitoring via vision-based technologies in the form of a mathematical model.