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This study aims to explore the innovative governance structures enabled by blockchain-based decentralized autonomous organizations (DAOs) in the context of international business (IB). As nonhierarchical entities managed through collective voting and peer-to-peer interactions, DAOs challenge traditional assumptions of multinational corporation (MNC) governance.

The authors perform a literature review, which combines qualitative content analysis of both academic papers on DAO and whitepapers with quantitative analysis of bibliometrics using VosViewer.

The findings highlight DAOs’ ability to generate new research questions and offer a foundation for future studies on decentralized governance in IB. Through a bibliometric analysis of 73 papers from Web of Science and qualitative content analysis, the authors identify four primary research streams in the DAO literature. A deep dive into financial DAOs, or DeFi DAOs, reveals insights into their governance mechanisms and implications for the studies in the field of IB.

Research on DAOs is in its early stages, and the practice in IB is nascent. As the regulatory, economic and technological landscape for DAOs changes, more research will be needed on the governance mechanisms and their sustainability.

This paper advances the field by providing a multilevel analysis of DAOs’ potential impact on IB, considering macro (country), meso (MNC/DPE) and micro (multicultural team and individual) levels.

This study aims to explore how the three dimensions of karma – karmic duty orientation, indifference to rewards and equanimity – influence empathy and moral obligation, with implications for social economics.

The research was conducted on a valid sample of 401 university students in Vietnam, using hierarchical regression to test formulated hypotheses.

The analysis reveals that karmic duty orientation and indifference to rewards significantly enhance empathy and moral obligation, indicating that individuals with a strong sense of moral duty and intrinsic motivation are more likely to engage in ethical and pro-social behaviors. Equanimity was not found to impact empathy or moral obligation significantly.

Organizations and policymakers should focus on cultivating moral duties, intrinsic motivations and resilience in ethical behavior to promote social responsibility and sustainability and ensure long-term social and economic stability.

This study contributes to social economics by integrating ethical dimensions of karma into the analysis of moral behavior, offering a fresh perspective that challenges traditional economic models centered on self-interest. The research provides a comprehensive framework for understanding how moral principles influence economic decisions and social outcomes.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-07-2023-0571

This study aims to examine the relationship between blockchain technology (BCT) adoption and firm performance (FIP) mediated by cyber-security risk management (CSRM) in the context of Vietnam, a developing country. Besides, the mediating effect of risk-taking tendency (RTT) has been considered in the BCT–CSRM nexus.

Data is collected using a survey questionnaire of Vietnamese financial firms through strict screening steps to ensure the representativeness of the population. The ending pattern of 449 responses has been used for analysis.

The findings of partial least squares structural equation modeling demonstrated that CSRM has a positive effect on FIP and acts as a mediator in the BCT–FIP nexus. Furthermore, RTT moderates the relationship between BCT and CSRM significantly.

This study introduces the attractive attributes of applying BCT to CSRM. Accordingly, managers should rely on BCT and take advantage of it to improve investment resources, business activities and functional areas to enhance their firm's CSRM. Especially, managers should pay attention to enhancing their RTT, which improves FIP.

This study supplements the previous literature in the context of CSRM by indicating favorable effects of BCT and RTT. Additionally, this study identifies the effectiveness of RTT as well as its moderating role. Ultimately, this paper has been managed as a pioneering empirical study that integrates BCT, RTT and CSRM in the same model in a developing country, specifically Vietnam.

This paper aims to investigate the efficiency and performance of the Islamic banking industry amid the COVID-19 pandemic.

The authors used a two-stage data envelopment analysis to first estimate the efficiency of 78 Islamic banks (IBs) across 15 countries for the 2005–2020 period (a total of 782 bank-year observations) and then to examine their determinants, including the COVID-19 pandemic.

The authors found that the Islamic banking industry performed at a moderate level during the 2005–2020 period, providing evidence that IBs are resilient to the financial shocks created by COVID-19. The authors also found that bank-level characteristics (such as bank size) and country-level characteristics (such as inflation) can contribute to the bank’s operational efficiency.

The results of this study suggested that banking management and government macroeconomic policy, especially in terms of precautions and continuous support, are important for IBs to improve their performance.

To the best of the authors’ knowledge, this is the first study to examine the efficiency and performance of IBs amid COVID-19.

The purpose of this study is to forecast the mechanical properties of ternary blended concrete (TBC) modified with oyster shell powder (OSP) and shea nutshell ash (SNA) using deep neural network (DNN) models.

DNN models with three hidden layers, each layer containing 5–30 nodes, were used to predict the target variables (compressive strength [CS], flexural strength [FS] and split tensile strength [STS]) for the eight input variables of concrete classes 25 and 30 MPa. The concrete samples were cured for 3–120 days. Levenberg−Marquardt's backpropagation learning technique trained the networks, and the model's precision was confirmed using the experimental data set.

The DNN model with a 25-node structure yielded a strong relation for training, validating and testing the input and output variables with the lowest mean squared error (MSE) and the highest correlation coefficient (R) values of 0.0099 and 99.91% for CS and 0.010 and 98.42% for FS compared to other architectures. However, the DNN model with a 20-node architecture yielded a strong correlation for STS, with the lowest MSE and the highest R values of 0.013 and 97.26%. Strong relationships were found between the developed models and raw experimental data sets, with R² values of 99.58%, 97.85% and 97.58% for CS, FS and STS, respectively.

To the best of the authors’ knowledge, this novel research establishes the prospects of replacing SNA and OSP with Portland limestone cement (PLC) to produce TBC. In addition, predicting the CS, FS and STS of TBC modified with OSP and SNA using DNN models is original, optimizing the time, cost and quality of concrete.

This study aims to unravel the complex coopetitive interactions in supply chains. It delves into the paradoxical relationship between cooperative and competitive interactions among supply chain entities, offering a comprehensive exploration of coopetition’s manifestations and management across various supply chain types.

The study uses a three-phase methodology, beginning with a scoping review to establish a theoretical framework, followed by a systematic literature review yielding 130 papers and concluding with correspondence analysis using similarity indexes. This approach facilitates a deep dive into the diverse aspects of coopetition, including its drivers, practices, outcomes and associated risks.

The research identifies three distinct types of coopetition in supply chains: technology-based, socially based and channel-based. These models are underscored by specific drivers and outcomes, with technology-based coopetition focusing on market competitiveness and operational capacity, socially based on trust and power dynamics and channel based on product characteristics. The study introduces five propositions for further investigation and provides a comprehensive typology of coopetition within supply chains.

The study’s findings are limited by the scope of the existing literature and the chosen academic databases. Future research should empirically validate the proposed coopetition configurations and propositions, exploring their applicability in other, less studied supply chains.

The study offers practitioners a valuable typology and framework to understand and manage coopetition in their respective supply chains. This typology serves as a decision-making tool for identifying suitable coopetition strategies and maximizing their benefits while mitigating associated risks.

This study stands out for its unique approach to categorizing coopetition in supply chains, offering a novel typology that goes beyond the manufacturer’s perspective. It fills a significant gap in the literature by providing a broad view of coopetition, considering various supply chain types and their respective coopetitive dynamics.

This research examines how bank competition and corporate social responsibility (CSR) affect the stability of Vietnamese commercial banks.

Utilizing data collected from 24 commercial banks spanning the period from 2015 to 2022, the paper employs the two-step system generalized method of moments (SGMM) regression method to find the impact of competition and CSR on commercial banks’ stability in Vietnam.

The key findings are (1) increased competition boosts commercial bank stability; (2) economic and environmental CSR initiatives adversely affect bank stability, while social CSR has a positive impact; (3) seven other factors are also identified to enhance bank stability, including bank size, cost management efficiency, independent management, inflation, gross domestic product (GDP) growth, monetary policy and volatility time.

Prior studies have not concurrently incorporated both CSR and bank competition in their investigations of bank financial stability. Specifically, the comprehensive components of CSR remain underexplored, with a predominant focus on its environmental dimension. This research stands out as one of the few endeavors scrutinizing the influence of competition and CSR on commercial bank financial stability in Vietnam, with a detailed investigation of all three components of CSR.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2024-0316

In today’s fast-paced and interconnected market, companies must adapt to the evolving demands of their customers. Therefore, it is essential to examine the impact of online reviews on potential customers' intent to purchase. This study seeks to identify the characteristics of electronic word-of-mouth (eWOM) that influence a buyer’s intention to purchase goods and services.

We used the snowball sampling method to collect data using a pre-tested survey instrument with a five-point Likert scale. We received 696 usable responses. We conducted assumption tests to ensure that we could use covariance-based structural equation modelling (CB-SEM) for data analysis. The collected data were analysed using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to compute the latent variables. We then tested our research hypotheses using CB-SEM.

Eight latent constructs – perceived persuasion, perceived information, image aesthetics, ease_of_experience, eWOM_credibility, eWOM_usefulness and eWOM_adoption – have been identified, which determine the influence of eWOM on purchase intent (PI) for both tangible and experiential products. Though the structural model emerged relatively similar, the constructs had differential impacts on PI for commodities and services. The perceived information quality and source trustworthiness had a greater impact on eWOM credibility in the case of services than in the case of commodities, while image aesthetics played a more crucial role in determining the eWOM credibility for commodities than services. In both cases, credible eWOM was found useful, but a persuasive eWOM influenced its perceived usefulness more in the case of commodities. The likelihood of adopting a useful eWOM and converting it to positive PI is present in the case of both services and commodities, but the impact is much higher in the case of services.

The study has examined the interplay of three theoretical consumer behaviour models: elaboration likelihood model (ELM), stimulus-organism-response model (SOR), and information adoption model (IAM). Thus, it adds to the existing literature on the characteristics of eWOM that influence the PI of online buyers.

This study’s findings demonstrate how eWOM influences consumers' perceptions of the utility of goods and services, impacting their intention to purchase. It also provides valuable insights into marketing and consumer behaviour in the Indian market. Thus, this study assists marketers in adjusting their digital marketing strategies to ensure the effective use of eWOM characteristics to positively influence the PI of the target audience in the Indian market.

This research study examines the relationship between eWOM characteristics and PI for both goods and services sectors. Most existing literature is skewed towards specific service sectors, such as hospitality and health care. A generalised comparative study is what makes this research work unique.

Social commerce has brought about a significant transformation in consumer experience due to diverse factors. As a result, users often find themselves prone to impulsive buying behaviour when exposed to such an environment. Prior research was limited to demonstrating the expanding influence of celebrities on social media and the linkage between social engagement and impulse buying context. Furthermore, the impulse buying tendency of consumers on social media in the context of celebrity posts has yet to be validated. This paper aims to assess the influence of consumer awareness, consumer trust and observational learning on the latent state-trait (LST) theory regarding celebrity posts on impulse buying tendencies.

The empirical research builds on a sample survey involving 750 students from the “Big Four” economics universities in Hanoi. The proposed model was analysed using a partial least squares structural equation modelling technique.

The authors find that consumer trust and observational learning from celebrity’ posts positively affect impulse buying tendency. Yet celebrity influence awareness directly impacts trust in celebrity’ posts rather than directly impacting impulse buying tendency. Perceiving the importance of interactive and authentic posts by a celebrity in influencing consumers’ purchase behaviour on social media, this research offers valuable insights for stakeholders in the digital celebrity sphere of communication and marketing.

Perceiving the importance of interactive and authentic posts by a celebrity in influencing consumers’ purchase behaviour on social media, this research offers valuable insights for stakeholders in the digital celebrity sphere of communication and marketing.

From a theoretical perspective, this expands the applicability of the LST theory in social commerce to promote impulse buying tendencies. Second, this contributes to the literature on the emerging phenomenon of social media celebrities, as existing literature does not clarify their influence on impulse buying behaviour. Third, this research applies the concept of observational learning in online shopping through key features of social media platforms, namely, likes, shares and comments, to investigate their influence on the impulse buying tendency of consumers. Concerning managerial implications, the authors propose practical recommendations for practitioners, particularly those involved or interested in the commercial services industry and social media marketing (namely, celebrities and partner companies).

Prismatic specimens were fabricated, consisting of two ceramic tiles bonded by an intermediate adhesive mortar layer. To simulate pre-existing defects, an acetate film was strategically inserted into one of the mortar-ceramic interfaces, creating controlled cracks of varying sizes. The specimens were subjected to low-amplitude cyclic loading, inducing simultaneous tensile and shear stresses within the system. The vibrational responses of the samples were recorded, and their frequency-domain amplitudes were analyzed.

Ceramic tile detachment remains a significant issue in contemporary construction, despite advancements in technology and updates to regulatory standards. The majority of these failures occur at the adhesive mortar-ceramic tile interface due to the combined effects of tensile and shear stresses. While this phenomenon is well-documented, experimental studies assessing the adhesion integrity of façade cladding systems are limited. This study aims to evaluate the adhesion performance of adhesive mortars using a non-destructive vibrational analysis technique.

The results enabled the characterization of the dynamic behavior of the specimens, identification of frequency regions corresponding to individual components, and formulation of diagnostic criteria for damage detection. Analysis of the resonance spectra also made it possible to identify a peak that can be related to the behavior of the mortar-substrate interface. Additionally, the elastic and dissipated energy metrics were calculated to quantify the system’s mechanical behavior. The study further investigated the impact of crack size, curing time and adhesive mortar type on the overall structural integrity.

This work aims to evaluate the adhesion performance of adhesive mortars using a non-destructive vibrational analysis technique. Resonance frequency measurements have also been used in research to analyze the propagation paths of cracks in metallic samples from aircraft engines. However, despite the widespread use of non-destructive techniques based on resonance frequencies for damage assessment, their application in the field of coating systems seems to be virtually unexplored. Therefore, this research attempts to address this gap by investigating the bond integrity of adhesive mortar joints, focusing on the analysis of the material’s resonance frequencies and their associated peak parameters.