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Amid rising environmental concerns, Industry 4.0 and blockchain technology (BCT) are transforming circular economy practices and prevailing business models. Recognizing the same, the current study examines the role of advanced technology in circular practices and their impact on eco-environmental performance, which influences organizational performance. The study collects data from 185 food processing enterprises that are located in Malaysian territories. By employing CB-SEM modeling, the study provides three key findings. First, Industry 4.0 significantly improves the circular economy practices. Second, circular economy practices help to improve firms' environmental performance but did not stimulate operational performance. Third, higher eco-environmental performance significantly boosts organizational performance. This study set out the foundations for participating countries/firms that help to achieve sustainable goals through the integration of blockchain technology in circular economy practices.

Past studies on housework and marital studies seldom considered the possible endogeneity between these two factors. This chapter analyses data of the Women’s Status Survey 2010 to investigate the association between satisfaction with family status and housework participation in dual-earner married couples in China. The authors examine the association by ordinary least squares (OLS) regression models and structural equation models (SEM), taking into account of time constraints, economic resources and other demographic characteristics. Results suggest that both men and women are less satisfied with their family status if they share more housework than their partners, after controlling for household income, relative economic contribution, educational qualifications and other factors. Moreover, relative housework contribution is associated more consistently and significantly with satisfaction with family status than absolute housework time. In SEM, the authors include a correlated error term between housework time and satisfaction in the models to take endogeneity between these factors into account. For both urban men and women, relative contribution of housework, but not absolute time of housework, is still negatively associated with family status satisfaction.

We use design thinking in the context of accounting pedagogy to exploit recent advances in cybernetics in the form of generative artificial intelligence technology. Relying on the intuition that supplementing or augmenting human argumentation (natural intelligence or NI) with parallel AI output can produce better student written assignments, we posit the “augmentation premise,” that is, ((NI + AI) > AI > NI). To test the augmentation premise, we compare student written submissions in an Accounting Information Systems (AIS) course with and without the benefit of parallel generative AI output. We then evaluate how the generative AI output enhances student-crafted revisions to their initial submissions. Using a summative quality improvement index (QII) consisting of quantitative and qualitative assessments, we present preliminary evidence supporting the augmentation premise. The augmentation premise likely extends to other accounting subdisciplines and merits generalization for enriching accounting pedagogy.

Three types of industrial buyer-seller relational process models are available: joining theory, stage theory, and state theory. However, historically, these models have developed based on the knowledge and cultural context of the Western world. Several researchers note that national culture may have an impact on international industrial buyer-seller relationships. Including culture in the models is highly important, especially as the business environment is increasingly more global and different countries have different business cultures. The goal of this paper is to define the most suitable industrial buyer-seller relational process models for describing relationships in various contexts. The paper includes a through literature review and a single case study in order to reach this objective. A new state theory model evolved during the research. It consists of two beginning states: searching and starting; four purely middles states: constant/static, decline, growth, and troubled; and a purely end state: termination. The state of dormant/inert is both a middle state and an end state, that is, when the relational actors are not in contact does not mean that the relationship has ended, but instead, for example, new legislation may have been implemented, which requires the actors to evaluate their relationship and its future. A relationship goes through the two beginning states in the order mentioned above, but after that, any state may occur.

The industrial buyer-seller relational process models from the Eastern and Western worlds have not been combined. The Western world has dominated the development of the models, while there exist only a very limited amount of guanxi development models from the East. This paper is exploratory in nature, focusing on combining the development of these two worlds into one intercultural model. Four case relationships verify the proposed model.

This paper focuses on only one cultural context outside of the West, that is to say, China. In order to justify the model to be completely an intercultural one, research in other cultural contexts is necessary.

Three types of industrial buyer-seller relational process models are available: joining theory, stage theory, and state theory. However, historically, these models have developed based on the knowledge and cultural context of the Western world. Several researchers note that national culture may have an impact on international industrial buyer-seller relationships. Including culture in the models is highly important, especially as the business environment is increasingly more global and different countries have different business cultures. The goal of this paper is to define the most suitable industrial buyer-seller relational process models for describing relationships in various contexts. The paper includes a through literature review and a single case study in order to reach this objective. A new state theory model evolved during the research. It consists of two beginning states: searching and starting; four purely middles states: constant/static, decline, growth, and troubled; and a purely end state: termination. The state of dormant/inert is both a middle state and an end state, that is, when the relational actors are not in contact does not mean that the relationship has ended, but instead, for example, new legislation may have been implemented, which requires the actors to evaluate their relationship and its future. A relationship goes through the two beginning states in the order mentioned above, but after that, any state may occur.

This chapter investigates the effect of the geographical distance between institutional investors and firms on managers' financial misconduct. The evidence shows that the likelihood of committing financial misconduct by management is positively associated with distance. The distance effect is more prominent for firms with higher information asymmetry and more dedicated institutional investors. In line with the balance between risk-taking and benefit extraction from misconduct, the severity of financial misconduct is higher for firms closer to their institutional investors. Results show that geographical proximity can significantly reduce the cost of information production and facilitate monitoring through access to soft information.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.