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This study aims to explore the antecedent factors that directly and indirectly influence electronic word of mouth (eWoM) for social commerce (s-commerce) in two developing countries (e.g. Kuwait and the United Arab Emirates [UAE]) by extending social cognitive theory.

This study uses a previous robust model (Rouibah et al., 2021) as theoretical background to investigate and compares the antecedents (trust in Instagram, perceived risks) on eWoM for s-commerce through the mediation of three mediators (perceived enjoyment, perceived value and customer satisfaction) among two Arab countries. Data was collected from Kuwait (n = 1,132) and the UAE (n = 190). Different statistical analyses and structured equation modeling-based analysis of moment structure are used to test the robustness of the research model.

This study found customer satisfaction to be most important factor that mediates the effect independent factors on eWoM for s-commerce in both countries. Surprisingly, perceived enjoyment has no effect, and trust in Instagram and perceived risks are the most important factors that are considered imperative for customer satisfaction and positive feedback.

One limitation of this study is that the author does not focus on the difference between the effects of textual and graphical information on customers’ decisions and trust in buying merchandise. Another limitation is that this study focuses on Kuwait and the UAE. Other Gulf Cooperation Council countries are also growing exponentially, and mobile and internet penetration rates are booming; they could be a trigger for more studies on whether differences occur among all of them.

The first implication is that it is the first in its field to extend the effects of eWoM. To the best of the author’s knowledge, compared to the online research this study is unique because the authors examine six factors for eWoM in s-commerce using the Instagram platform as opposed to other platforms.

The third implication of this study is that the previous ones have applied eWoM to different subjects of e-commerce such as tourism and marketing but have concentrated less on s-commerce, where in-depth research is needed much more to explore factors and theories that explain human behavior.

Furthermore, most of these studies have focused on the intention to use (Dincer and Dincer, 2023; X. Hu, Chen, Davison, and Liu, 2022; Zhou et al., 2023). However, the attention in this research is on the actual use.

This study aims to address the critical challenge of implementing lean manufacturing (LM) in emerging economies, where sustainability complexities on the production floor hinder production efficiency and the transition towards a circular economy (CE). Addressing a gap in existing research, the paper introduces a path analysis model to systematically identify, prioritize and overcome LM implementation barriers, aiming to enhance performance through strategic removal.

The authors used a mixed-method approach, combining empirical survey data with literature reviews to pinpoint key LM barriers. Using the grey-based Decision-Making Trial and Evaluation Laboratory (DEMATEL) along with the Network Knowledge (NK) method, they mapped causal relationships and barrier intensities. This formed the basis for developing a path simulation algorithm, integrating heuristic considerations for practical decision-making.

This analysis reveals that the primary barriers to LM adoption is the negative perception and inadequate understanding of lean tools and CE principles. The study provides a strategic framework for managers, offering new insights into barrier prioritization and overcoming strategies to facilitate successful LM adoption.

This research provides a strategic pathway for overcoming LM implementation barriers, empowering managers in emerging economies to enhance sustainability and competitive advantage through LM and CE integration. It emphasizes the significance of structured barrier management in the manufacturing sector.

This research pioneers a systematic exploration of LM implementation barriers in the CE context, making a significant contribution to the literature. It identifies, evaluates barriers and proposes a practical model for overcoming them, enriching sustainable manufacturing practices in emerging markets.

This study aims to computational numerical simulations to clarify and explore the influences of periodic cellular lattice (PCL) morphological parameters – such as lattice structure topology (simple cubic, body-centered cubic, z-reinforced body-centered cubic [BCCZ], face-centered cubic and z-reinforced face-centered cubic [FCCZ] lattice structures) and porosity value ( ) – on the thermal-hydraulic characteristics of the novel trussed fin-and-elliptical tube heat exchanger (FETHX), which has led to a deeper understanding of the superior heat transfer enhancement ability of the PCL structure.

A three-dimensional computational fluid dynamics (CFD) model is proposed in this paper to provide better understanding of the fluid flow and heat transfer behavior of the PCL structures in the trussed FETHXs associated with different structure topologies and high-porosities. The flow governing equations of the trussed FETHX are solved by the CFD software ANSYS CFX® and use the Menter SST turbulence model to accurately predict flow characteristics in the fluid flow region.

The thermal-hydraulic performance benchmarks analysis – such as field synergy performance and performance evaluation criteria – conducted during this research successfully identified demonstrates that if the high porosity of all PCL structures decrease to 92%, the best thermal-hydraulic performance is provided. Overall, according to the obtained outcomes, the trussed FETHX with the advantages of using BCCZ lattice structure at 92% porosity presents good thermal-hydraulic performance enhancement among all the investigated PCL structures.

To the best of the authors’ knowledge, this paper is one of the first in the literature that provides thorough thermal-hydraulic characteristics of a novel trussed FETHX with high-porosity PCL structures.