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This study aims to present an integrated green transport sustainability model (GTSM) to comprehensively understand and explain the multifaceted dynamics of green transport initiatives. The purpose of the study is to evaluate gaps in understanding the interactions between socio-economic and environmental goals in green transport systems using structural equation modelling (SEM) to help in drafting sustainable transportation policy for larger acceptance and true implementation by the stakeholders. The study examines different constructs that collectively influence green transport policy effectiveness (GTPE). Ultimately, the study aims to provide a robust framework for improving the effectiveness of green transport policies and regulations.

Grounded in empirical evidence, the study utilizes SEM to demonstrate the interplay between policymaking, socio-economic factors, technological consideration and environmental outcomes in green transport. The research framework is developed based on the comprehensive review of the literatures to embrace sustainability in transportation considering stakeholders perceptions. The study navigated a GTSM under socio-economic and environmental goals for road-mapping sustainability and larger acceptance of green transportation.

It is found that technological advancements in transportation are the most significant determinants of GTPE. This implies the need to develop advancements in technologies to embrace the larger acceptance of green transport. Promotion of environmentally sustainable transportation practices, socio-economic factors and use of eco-friendly transportation modes are also found as significant predictors of GTPE, which suggested that the policies aimed at up-gradation of socio-economic standards and the use of environment friendly modes of transport can help in promoting the active involvement of stakeholders to use green transportation.

The study originally investigated critical constructs to assist in preparing sustainable transportation policy for larger acceptance and true implementation by the stakeholders. The study reciprocated its originality by presenting an integrated model related with green transport sustainability dimensions based on theoretical constructs to examine the interplay between policy effectiveness, technological advancements, socio-economic factors and environmental outcomes. The study addressed the key pillars of green transportation and originally highlighted the importance of socio-economic factors and technological advancements in advancing green transport sustainability. It is recommended that the policymaker should make investments in green transport infrastructure and should design a policy for integration of green transportation with a focus on the engagement of all stakeholders for practical implementations.

The study attempts to explore the effectiveness of green supply chain strategies (GSCS) and sustainable practices (SP) in achieving a circular supply chain (CSC) within a business-to-business (B2B) context. The study further investigates the moderating role of green innovation (GIN) on the relationship between GSCS and SP.

The conceptual model was developed by adopting constructs from the existing studies. A self-administered tool was created, and data were gathered from supply chain (SC) specialists in the food, energy, tire, textile and paper industries. The structural equation model was employed to test the hypothesis, analyzing 243 responses obtained.

The findings indicate an affirmative association between GSCS, SP and the achievement of CSC, with SP acting as a partial mediator between GSCS and CSC. Results show that GSCS and SP are crucial for transitioning toward a circular model in the SC, emphasizing resource regeneration and sustainability. The data from our sample suggest that GIN significantly moderates the relationship between GSCS and CSC. These insights underline the importance of green strategies and sustainable practices (SP) in fostering CSCs in a B2B setting. The study’s implications are significant for SC management, suggesting that firms must integrate green and SP to achieve circularity and long-term viability.

This article brings forward a distinctive perspective on sustainability within the field of SC management emphasizing the crucial need for implementing CSC and GSCS in a B2B context.

The global construction industry has a history of poor project success, with evident and frequent overruns in cost and schedule. This industry is a highly interconnected and complex system in which the components, i.e. suppliers, contractors, end-users, and stakeholders, are delicately linked to each other, the community, and the environment. Therefore, defining and measuring project success can be challenging for sponsors, contractors, and the public. To address this issue, this study develops and analyzes a more comprehensive set of success criteria for medium and large construction projects.

After reviewing the existing literature, this study identified 19 success criteria for medium and large construction projects, which were categorized into five groups. The fuzzy decision-making trial and evaluation laboratory (fuzzy DEMATEL) method was used to gain further insight into the interrelationships between these categories and explain the cause-and-effect relationships among them. Next, this study applied the modified logarithmic least squares method to determine the importance weight of these criteria using the fuzzy analytic hierarchy process.

28 project managers working in the construction industries in Australia and New Zealand participated in this study. Results suggest that “project efficiency” and “impacts on the project team” are cause criteria that affect “business success,” “impacts on stakeholders,” and “impacts on end-users.” Effective risk management emerged as the most crucial criterion in project efficiency, while customer satisfaction and return on investment are top criteria in “impacts on end-users” and “business success.”

Although numerous studies have been conducted on project success criteria, multicriteria analyses of success criteria are rare. This paper presents a comprehensive set of success criteria tailored to medium and large construction projects. The aim is to analyze their interrelationships and prioritize them thoroughly, which will aid practitioners in focusing on the most important criteria for achieving higher success rates.

Knowledge sharing in online health communities (OHCs) disrupts consumers' health information-seeking behavior patterns such as seeking health information and consulting. Based on social exchange theory, this study explores how the two dimensions of experts' free knowledge sharing (general and specific) affect customer transactional and nontransactional engagement behavior and how the quality of experts' free knowledge sharing moderates the above relationships.

We adopted negative binomial regression models using homepage data of 2,982 experts crawled from Haodf.com using Python.

The results show that experts' free general knowledge sharing and free specific knowledge sharing positively facilitate both transactional and nontransactional engagement of consumers. The results also demonstrate that experts' efforts in knowledge-sharing quality weaken the positive effect of their knowledge-sharing quantity on customer engagement.

This study provides new insights into the importance of experts' free knowledge sharing in OHCs. This study also revealed a “trade-off” between experts' knowledge-sharing quality and quantity. These findings could help OHCs managers optimize knowledge-sharing recommendation mechanisms to encourage experts to share more health knowledge voluntarily and improve the efficiency of healthcare information dissemination to promote customer engagement.

The paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.

First, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.

The paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.

The paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.

This study aims to examine the impact of online travel reviews (OTR) on tourists’ intention to travel based on the stimulus–organism–response (SOR) model. Further, it explored the mediating effects of tourist trust in OTR.

In this direction, this study proposes and empirically validates a conceptual model after collecting data from 299 Indian consumers. Proposed hypotheses were tested by applying the structural equation modelling technique. Bootstrapping method was used for mediation testing.

The findings revealed that various attributes of OTR exert differential impacts on travel intention. The study also confirmed the mediating role of tourist trust in OTR.

This study offers significant practical implications for travel marketers. To capitalize on OTR, travel marketers are recommended to develop an effective and efficient online reviews management system. This will improve the quality, valence, quantity and consistency of OTR, which in turn will enhance tourist trust in OTR, leading to improved travel intention.

No empirical evidence has been traced on how OTR enhances tourist trust in OTR and their travel intention. In support of this, the present study proposes and empirically validates an extensive model to comprehend the role of various drivers of OTR in improving tourist trust in OTR, leading to enhanced travel intention based on the SOR model.

This paper aims to examine the extent to which board composition and ownership structure (OS) affect a firm’s dividend payout (DP) policy.

The sample comprises a total of 1,432 firm-year observations for Gulf Cooperation Council (GCC) nonfinancial firms. Total 179 firms were analyzed from 2009 to 2016. To test the research hypotheses, the paper used panel data analysis (i.e. fixed effects model) and instrumental variable method to ensure the robustness of results against endogeneity effects.

Corporate governance (CG) variables were found to significantly impact DP. Specifically, independent directors on board, institutional ownership and royal ownership were positively associated with DP. In contrast, board size, management, government and family ownership had a negative association with DP. The empirical evidence presented in this study supports that CG elements can be both an outcome and substitute of DP as a disciplinary mechanism.

This study excluded financial firms from the sample list. Future studies should carry out on financial firms to observe if the findings are different. Future research is suggested to incorporate more CG mechanisms. Future studies are suggested to use a dynamic panel regression due to its advantages.

Practitioners, such as investors and lenders operating in GCC markets, can derive valuable insights from this study regarding the influence of board components on DP policy. The study also provides information about the investment and business environment in GCC.

The contribution of this study lies in providing empirical evidence regarding the impact of CG mechanisms and OS on DP in GCC countries.

This study examines the moderating effects of cultural distance and the nationality of foreign subsidiaries’ CEOs on the relationship between internal/external network tie strength and technological innovation. This study focuses on foreign subsidiaries of Korean firms operating in emerging markets, based on the knowledge-based view.

To test the hypotheses, a survey was administered to 3,840 foreign subsidiaries of Korean firms in emerging markets. Questionnaires were distributed via phone and email, and 282 (7.34%) completed questionnaires were returned. Accordingly, an empirical analysis was conducted, and the hypotheses were tested.

First, internal network tie strength had a significant negative effect on foreign subsidiaries’ technological innovation. Second, external network tie strength had a significant positive effect on foreign subsidiaries’ technological innovation. Third, as for the moderating effect, cultural distance negatively moderates the significant negative effect of internal network tie strength on the technological innovation of foreign subsidiaries. Fourth, cultural distance positively moderates the positive effect of external network tie strength on foreign subsidiaries’ technological innovation. Fifth, foreign subsidiaries’ CEO nationality (parent country nationals) positively moderates the negative effect of internal network tie strength on foreign subsidiaries’ technological innovations. Sixth, foreign subsidiaries’ CEO nationality (parent country nationals) negatively moderates the positive effect of external network tie strength on foreign subsidiaries’ technological innovation.

Previous studies found a positive relationship between dual network tie strength and technological innovation in foreign subsidiaries from a knowledge-based view. This study’s significance lies in its finding that the varying strengths of foreign subsidiaries’ internal and external network connections can have different impacts on their technological innovation in the context of subsidiaries operating in emerging markets. Additionally, the moderating effects of cultural distance and the nationality of the foreign subsidiary’s CEO were examined. Therefore, this study is significant because it expands the literature on technological innovation in foreign subsidiaries.

Accurate predictions of the steady-state corrosion phase and service life to achieve specific safety limits are crucial for assessing the service of reinforced concrete (RC) structures. Forecasting the service life (SL) of structures is imperative for devising maintenance and repair strategy plans. The optimization of maintenance strategies serves to prolong asset life, mitigate asset failures, minimize repair costs and enhance health and safety standards for society.

The well-known empirical conventional (traditional) approaches and machine learning (ML)-based SL prediction models were presented and compared. A comprehensive parametric study was conducted on existing models, considering real-world conditions as reported in the literature. The analysis of traditional and ML models underscored their respective limitations.

Empirical models have been developed by considering simplified assumptions and relying on factors such as corrosion rate, steel reinforcement diameter and concrete cover depth, utilizing fundamental mathematical formulas. The growth of ML in the structural domain has been identified and highlighted. The ML can capture complex relationships between input and output variables. The performance of ML in corrosion and service life evaluation has been satisfactory. The limitations of ML techniques are discussed, and its open challenges are identified, along with insights into the future direction to develop more accurate and reliable models.

To enhance the traditional modeling of service life, key areas for future research have been highlighted. These include addressing the heterogeneous properties of concrete, the permeability of concrete and incorporating the interaction between temperature and bond-slip effect, which has been overlooked in existing models. Though the performance of the ML model in service life assessment is satisfactory, models overlooked some parameters, such as the material characterization and chemical composition of individual parameters, which play a significant role. As a recommendation, further research should take these factors into account as input parameters and strive to develop models with superior predictive capabilities.

Recent deployment has revealed that ML algorithms can grasp complex relationships among key factors impacting deterioration and offer precise evaluations of remaining SL without relying on traditional models. Incorporation of more comprehensive and diverse data sources toward potential future directions in the RC structural domain can provide valuable insights to decision-makers, guiding their efforts toward the creation of even more resilient, reliable, cost-efficient and eco-friendly RC structures.

This study aims to develop a pH-functional thin-film sensor for non-invasive measurement of spoilage of packed fish.

At first, polymers of natural origin such as hydroxy(propyl)methyl cellulose, potato dextrose agar and starch alongside a pH sensitive-mixed indicator formulation were used to produce thin film sensor. The developed thin film sensor was tested for monitoring the spoilage of seafood stored at 4°C. Using ultraviolet-visible and Fourier-transform infrared spectroscopy, the halochromic sensor was characterised. In addition, the halochromic response of the thin film was directly correlated to the total volatile base nitrogen emitted by the packaged fish, pH, microbial activity and sensory evaluation.

The results suggested the developed biopolymer-based thin film sensor showed different colours in line with the spoilage of the packed fish, which could be well correlated with the total volatile base nitrogen, microbial activity and sensory evaluation. In addition, the thin film sensors exhibited a high degree of biodegradability. The biopolymers-based thin film halochromic sensor has exhibited excellent biodegradability along with sensitiveness towards the spoilage of the packed fish.

In the future, consumers and retailers may prefer seafood containers equipped with such halochromic sensors to determine the degree of food deterioration as a direct indicator of food quality.