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This paper aims to analyze the dependence structure between the Green Sukuk Spread in Malaysia and uncertainty factors from January 1, 2017, to May 23, 2023, covering two main periods: the pre-COVID-19 and the COVID-19 periods.

This study contributes to the current literature by explicitly modeling nonlinear dependencies using the Regular vine copula approach to capture asymmetric characteristics of the tail dependence distribution. This study used the Archimedean copula models: Student’s-t, Gumbel, Gaussian, Clayton, Frank and Joe, which exhibit different tail dependence structures.

The empirical results suggest that Green Sukuk and various uncertainty variables have the strongest co-dependency before and during the COVID-19 crisis. Due to external uncertainties (COVID-19), the results reveal that global factors, such as the Infect-EMV-index and the higher financial stress index, significantly affect the spread of Green Sukuk. Interestingly, in times of COVID-19, its dependence on Green Sukuk and the news sentiment seems to be a symmetric tail dependence with a Student’s-t copula. This result is relevant for hedging strategies, as investors can enhance the performance of their portfolio during the COVID-19 crash period.

This study contributes to a better understanding of the dependency structure between Green Sukuk and uncertainty factors. It is relevant for market participants seeking to improve their risk management for Green Sukuk.

This study aims to empirically explore and compare the dynamic dependency between health-care sector and Islamic industries before, during and after the COVID-19 pandemic.

Time-varying student-t copula is used for before, during and after COVID-19 periods. The data used are the daily frequency price series of the selected markets from February 2017 to October 2023.

Empirical results found strong evidence of significant impact of the COVID-19 pandemic on the dependence structure of the studied indexes: Co-movements between various sectors are certain. The authors assist also in the birth of new dependence structure with the health-care industry in response to the COVID-19 crisis. This reflects the contagion occurrence from the health-care sector to other sectors.

By specifically examining the Islamic industry, this study sheds light on the resilience, challenges and opportunities within this sector, contributing novel perspectives to the broader discourse on pandemic-related impacts on economies and industries. Also, this paper conducts a comprehensive temporal analysis, examining the dynamics before, during and after the COVID-19 lockdown. Such approach enables an understanding of how the relationship between the health-care sector and the Islamic industry evolves over time, accounting for both short-term disruptions and long-term effects. By considering the pre-pandemic context, the paper adopts a longitudinal perspective, enabling a deeper understanding of how historical trends, structural factors and institutional frameworks shape the interplay between the health-care sector and the Islamic industry.

This study aims to explore the risk spillover effects among different sectors of the Chinese stock market after the outbreak of COVID-19 from both Internet sentiment and price fluctuations.

The authors develop four indicators used for risk contagion analysis, including Internet investors and news sentiments constructed by the FinBERT model, together with realized and jump volatilities yielded by high-frequency data. The authors also apply the time-varying parameter vector autoregressive (TVP-VAR) model-based and the tail-based connectedness framework to investigate the interdependence of tail risk during catastrophic events.

The empirical analysis provides meaningful results related to the COVID-19 pandemic, stock market conditions and tail behavior. The results show that after the outbreak of COVID-19, the connectivity between risk spillovers in China's stock market has grown, indicating the increased instability of the connected system and enhanced connectivity in the tail. The changes in network structure during COVID-19 pandemic are not only reflected by the increased spillover connectivity but also by the closer relationships between some industries. The authors also found that major public events could significantly impact total connectedness. In addition, spillovers and network structures vary with market conditions and tend to exhibit a highly connected network structure during extreme market status.

The results confirm the connectivity between sentiments and volatilities spillovers in China's stock market, especially in the tails. The conclusion further expands the practical application and theoretical framework of behavioral finance and also lays a theoretical basis for investors to focus on the practical application of volatility prediction and risk management across stock sectors.

This study is based on the heuristic-systematic model (HSM) to dynamically examine the effect of review variance on sales and the boundary conditions that mitigate this effect.

Based on the theoretical domain of HSM, a conceptual model is proposed that analyzes the nonlinear relationship between review variance and sales and the interaction and motivation factors that moderate these relationships. Review data from websites targeting the film industry in the USA and South Korea (Korea) were collected to empirically analyze the authors' hypothesis, and panel regression analysis was used for confirmation.

Moderated by interactive and motivational factors, review variance exhibits an inverse-U-shaped relationship with review variance. Specifically, as an interaction factor, review valence and owned social media (OSM) resulted in positive interaction effects, and as a motivation factor, the number of alternatives exhibited a positive interaction effect with review variance. The effect of review variance was less pronounced in the USA than in Korea.

The study outcomes reveal a nonlinear relationship between review variance and sales, thus supporting the contradictory findings of previous studies. This study contributes to the literature by using the HSM as a theoretical framework to verify various HSM mechanisms using online review data. This exploratory study also contributes to the international marketing literature by showing that the effects of review variance vary across cultures.

This study aims to introduce and evaluate the COPULA framework, a construction project monitoring solution based on blockchain designed to address the inherent challenges of construction project monitoring and management. This research aims to enhance efficiency, transparency and trust within the dynamic and collaborative environment of the construction industry by leveraging the decentralized, secure and immutable nature of blockchain technology.

This paper employs a comprehensive approach encompassing the formulation of the COPULA model, the development of a digital solution using the ethereum blockchain and extensive testing to assess performance in terms of execution cost, time, integrity, immutability and security. A case analysis is conducted to demonstrate the practical application and benefits of blockchain technology in real-world construction project monitoring scenarios.

The findings reveal that the COPULA framework effectively addresses critical issues such as centralization, privacy and security vulnerabilities in construction project management. It facilitates seamless data exchange among stakeholders, ensuring real-time transparency and the creation of a tamper-proof communication channel. The framework demonstrates the potential to significantly enhance project efficiency and foster trust among all parties involved.

While the study provides promising insights into the application of blockchain technology in construction project monitoring, future research could explore the integration of COPULA with existing project management methodologies to broaden its applicability and impact. Further investigations into the solution’s scalability and adaptation to various construction project types and sizes are also suggested.

This research offers a comprehensive blockchain solution specifically tailored for the construction industry. Unlike prior studies focusing on theoretical aspects, this paper presents a practical, end-to-end solution encompassing model formulation, digital implementation, proof-of-concept testing and validation analysis. The COPULA framework marks a significant advancement in the digital transformation of construction project monitoring, providing a novel approach to overcoming longstanding industry challenges.

Organizational justice and its impact on employee commitment have received a lot of attention these days. The objective of this study is to see the effect of job satisfaction as a mediator in the relationship between organizational justice and employee commitment. Also, the role of academic rank as a moderator in the model is probed.

A moderating mediation structural equation model was used for randomly collected cross-section data on 285 employees from public universities in Ethiopia. Necessary condition analysis (NCA) was employed to check the importance of the variables. The Gaussian copula approach was used to check endogeneity in the structural model.

NCA confirms the importance of organizational justice and employee satisfaction as the independent variables. The Gaussian copula approach reveals no endogeneity problems in the structural model. The results supported the partial mediating role of job satisfaction in organizational justice and academic staff’s commitment. Moreover, though staff rank is not a necessary condition, it plays the role of moderator in the relationship between academic staff’s job satisfaction and commitment.

This paper affirms that public institutions must implement fair initiatives and procedures to promote academic staff satisfaction and commitment.

This is the first study to check the job rank as a moderator in the model comprising organization justice, employee commitment and satisfaction. Moreover, application of NCA and Gaussian copula adds to methodological innovation.

The rise of artificial intelligence (AI) applications has driven enterprises to provide many intelligent services to consumers. For instance, customers can use chatbots to make relevant inquiries and seek solutions to their problems. Despite the development of customer service chatbots years ago, they require significant improvements for market recognition. Many customers have reported negative experiences with customer service chatbots, contributing to resistance toward their use. Therefore, this study adopts the innovation resistance theory (IRT) perspective to understand customers’ resistance to using chatbots. It aims to integrate customers’ negative emotions into a predictive behavior model and examine users’ functional and psychological barriers.

In this study, we collected data from 419 valid individuals and used structural equation modeling to analyze the relationships between resistance factors and negative emotions.

The results confirmed that barrier factors affect negative emotions and amplify chatbot resistance influence. We discovered that value and risk barriers directly influence consumer use. Moreover, both functional and psychological barriers positively impact negative emotions.

This study adopts the innovation resistance theory perspective to understand customer resistance to using chatbots, integrates customer negative emotions to construct a predictive behavior model and explores users’ functional and psychological barriers. It can help in developing online customer service chatbots for e-commerce.

This study aims to investigate the drivers of political embeddedness and the possible outcome in terms of autonomy and subsidiary unique competences.

This study draws on resource dependence theory and applies structural equation modeling on a sample of 193 subsidiaries.

Political embeddedness is confirmed as a source of potential autonomy and the development of competences and is usually boosted by previous existing networks at the internal and external levels.

The authors investigate and discuss how multinational corporations can leverage political resources in host-country political arenas, extending their understanding of the interplay between political activities and market strategies.

This research examines how universities enhance the virality of their social media messages among students. Specifically, we explore whether and how positive affective content in universities’ social media posts can influence sharing behavior. We also investigate the mediating roles of perceived effort and positive emotional reaction, as well as the moderating effect of visual content (i.e. photos).

Drawing upon the emotions as social information model, we conducted (1) an online experiment (N = 222) and (2) text analysis of 1,269,798 Twitter posts extracted from the accounts of 94 UK universities over 11 years (2010–2020) to test our hypotheses.

The findings show that social media posts containing positive affective content encourage sharing behavior and the relationship is mediated by both perceived effort and positive emotional reaction. An additional finding suggests that the use of visual content (photos) strengthens the relationship between positive affective content and sharing behaviors through an interaction effect.

This study contributes to the scant research focusing on positive affective content in the higher education context. The findings shed light on how universities could create social media communications that engage current and prospective students.

This study aims to understand what primary relationship problem mechanisms can exist in the franchise channel and how exchange partners respond to them. This study demonstrates how the franchisor’s relationship problem mechanisms (threat, contract enforcement) affect the franchisee’s negative active responses (venting, threatened withdrawal).

This study tested hypotheses through multiple regression analysis using data from 200 franchisees in Korea-based food franchise systems.

The results indicated that threat increases venting and threatened withdrawal, while contract enforcement only increases venting. Venting increases threatened withdrawal. In addition, the results indicated that the franchisor’s behavior monitoring positively moderates the relationship between relationship problem mechanisms and negative active responses.

This study helps strategically manage responses to relationship problems by categorizing ‘relationship problem mechanisms’ into intentional relationship problem mechanisms based on communication (threat) and unintentional relationship problem mechanisms based on action (contract enforcement). This study finds that both relationship problem mechanisms, intentional or unintentional, eventually cause threatened withdrawal directly or indirectly. Even if the threat is merely communication, not action, it is more likely to cause relationship dissolution than contract enforcement by directly triggering any negative active response. This study also finds that behavior monitoring can affect exchange partners through interaction with other management mechanisms rather than directly affecting them.