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Studies on developed economies showed that imposing taxes on animal-based foods could effectively reduce agricultural greenhouse gas emissions (AGHGEs), while this taxation may not be appropriate in developing countries due to the complex nutritional status across income classes. Hence, this study aims to explore optimal tax rate levels considering both emission reduction and nutrient intake, and examine the heterogenous effects of taxation across various income classes in urban and rural China.

The authors estimated the Quadratic Almost Ideal Demand System model to calculate the price elasticities for eight food groups, and performed three simulations to explore the relative optimal tax regions via the relationships between effective animal protein intake loss and AGHGE reduction by taxes.

The results showed that the optimal tax rate bands can be found, depending on the reference levels of animal protein intake. Designing taxes on beef, mutton and pork could be a preliminary option for reducing AGHGEs in China, but subsidy policy should be designed for low-income populations at the same time. Generally, urban residents have more potential to reduce AGHGEs than rural residents, and higher income classes reduce more AGHGEs than lower income classes.

This study fills the gap in the literature by developing the methods to design taxes on animal-based foods from the perspectives of both nutrient intake and emission reduction. This methodology can also be applied to analyze food taxes and GHGE issues in other developing countries.

To investigate the potential of raising the retirement age and reforming pension insurance in mitigating intra- and inter-generational income inequality, thereby offering empirical support for governmental policy formulation.

A dynamic general equilibrium model with intertemporal iteration is developed to comprehensively assess the impact of policies raising the retirement age on income inequality, taking into account delayed retirement, survival probability, and pension insurance. The theoretical hypotheses are validated through simulation using MATLAB.

Through theoretical analysis, it is determined that, given certain assumptions are satisfied, raising the retirement age can effectively mitigate intra-generational income inequality, inter-generational income inequality under both the pay-as-you-go and fund accumulation systems. Simulation results indicate that, under current parameter settings, raising the retirement age can reduce the Gini coefficient. Furthermore, this study reveals that regardless of the pay-as-you-go or fund accumulation system, pension insurance serves as a mechanism for income redistribution and alleviating income inequality.

It offers a theoretical foundation for the government's policy on delayed retirement and endowment insurance.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

This study analyzes the performance implications of adopting blockchain to support supply chain business processes. The technology holds as many promises as implementation challenges, so interest in its impact on operational performance has grown steadily over the last few years.

Drawing on transaction cost economics and the contingency theory, we built a set of hypotheses. These were tested through a long-term event study and an ordinary least squares regression involving 130 adopters listed in North America.

Compared with the control sample, adopters displayed significant abnormal performance in terms of labor productivity, operating cycle and profitability, whereas sales appeared unaffected. Firms in regulated settings and closer to the end customer showed more positive effects. Neither industry-level competition nor the early involvement of a project partner emerged as relevant contextual factors.

This research presents the first extensive analysis of operational performance based on objective measures. In contrast to previous studies and theoretical predictions, the results indicate that blockchain adoption is not associated with sales improvement. This can be explained considering that secure data storage and sharing do not guarantee the factual credibility of recorded data, which needs to be proved to customers in alternative ways. Conversely, improvements in other operational performance dimensions confirm that blockchain can support inter-organizational transactions more efficiently. The results are relevant in times when, following hype, there are signs of disengagement with the technology.

Manual monitoring is a conventional method for monitoring and managing construction safety risks. However, construction sites involve risk coupling - a phenomenon in which multiple safety risk factors occur at the same time and amplify the probability of construction accidents. It is challenging to manually monitor safety risks that occur simultaneously at different times and locations, especially considering the limitations of risk manager’s expertise and human capacity.

To address this challenge, an automatic approach that integrates point cloud, computer vision technologies, and Bayesian networks for simultaneous monitoring and evaluation of multiple on-site construction risks is proposed. This approach supports the identification of risk couplings and decision-making process through a system that combines real-time monitoring of multiple safety risks with expert knowledge. The proposed approach was applied to a foundation project, from laboratory experiments to a real-world case application.

In the laboratory experiment, the proposed approach effectively monitored and assessed the interdependent risks coupling in foundation pit construction. In the real-world case, the proposed approach shows good adaptability to the actual construction application.

The core contribution of this study lies in the combination of an automatic monitoring method with an expert knowledge system to quantitatively assess the impact of risk coupling. This approach offers a valuable tool for risk managers in foundation pit construction, promoting a proactive and informed risk coupling management strategy.

This research examined the effect of information literacy (IL) on academic engagement (AE), cognitive engagement (CE) and academic performance among business students in Pakistan.

A cross-sectional survey was conducted to collect data from business students, recruited through a proportionate stratified convenient sampling technique, of the top 13 business institutions in Pakistan. The questionnaire was personally administered by visiting each institution with permission for data collection. A total of 554 responses were received and analyzed using the partial least squire-structural equation modeling approach.

The results exhibited that these business students perceived themselves as information literate. Furthermore, IL of business students appeared to predict positively their AE, CE and academic performance.

These results provided empirical and pragmatic insights for business educators, business librarians and accreditation bodies about IL effectiveness in academia. These findings may also inform policy and practice for IL instruction programs being carried out in business-related educational institutions not only in Pakistan but also in other countries of South Asia as they share similar characteristics.

This research would be a great contribution to the existing literature on IL, especially in the academic context as the interrelationship between IL, AE, CE and academic performance has not been investigated so far.

This paper aims to describe the use of optimization approaches to increase the range of near-future howitzer ammunition.

The performance of a gliding projectile concept is assessed using an aeroballistic workflow, comprising aerodynamic characterization and flight trajectory computation. First, a single-objective optimization is run with genetic algorithms to find the maximal attainable range for this type of projectile. Then, a multi-objective formulation of the problem is proposed to consider the compromise between range and time of flight. Finally, the aerodynamic model used for the gliding ammunition is evaluated, in comparison with direct computational fluid dynamics (CFD) computations.

Applying single-objective range maximization results in a great improvement of the reachable distance of the projectile, at the expense of the flight duration. Therefore, a multi-objective optimization is implemented in a second time, to search sets of parameters resulting in an optimal compromise between fire range and flight time. The resulting Pareto front can be directly interpreted and has the advantage of being useful for tactical decisions.

The main limitation of the work concerns the aerodynamic model of the gliding ammunition, which was initially proposed as an alternative to reduce significantly the computational cost of aerodynamic characterization and enable optimizations. When compared with direct CFD computations, this method appears to induce an overestimation of the range. This suggests future evolution to improve the accuracy of this approach.

To the best of the authors’ knowledge, this paper presents an original ammunition concept for howitzers, aiming at extending the range of fire by using lifting surfaces and guidance. In addition, optimization techniques are used to improve the range of such projectile configuration.

The digital transformation of family-owned small businesses (F-OSBs) has become a critical area of research to maintain their economic contribution in today’s rapidly evolving digital landscape. This study examines the effect of internet entrepreneurial self-efficacy on the digital transformation of F-OSBs by mediating strategic agility and moderating artificial intelligence usage.

This study employed a cross-sectional survey design to collect primary data from 378 descendent entrepreneurs of F-OSBs in Pakistan’s five major cities.

The study revealed that leadership ability, internet marketing, technology utilization, and artificial intelligence used by the F-OSBs can contribute to their digital transformation, but e-commerce ability does not. The strategic agility of the descendant entrepreneur enhances the abilities of e-commerce, leadership, and technology utilization, leading to the digital transformation of F-OSB. However, strategic agility reduces the role of Internet marketing in digital transformation. Artificial intelligence usage moderates leadership’s ability to improve strategic agility but increases technology utilization for strategic agility and digital transformation of F-OSB.

The digital transformation through a combination of strategic agility and artificial intelligence can increase the F-OSBs' proactive approach to respond to changing market conditions even during economic recessions like COVID-19.

This study broadens the existing literature by examining the effect of descendent entrepreneur’s internet entrepreneurial self-efficacy, strategic agility, artificial intelligence usage, and their interplay on the digital transformation of F-OSB through the unified theory of acceptance and the use of technology.

Teams across a wide range of contexts must look beyond task performance to consider the affective, cognitive and behavioral health of their members. Despite much interest in team health in practice, consideration of team health has remained scant from a research perspective. The purpose of this paper is to address these issues by advancing a definition and model of team health.

The authors review relevant literature on team stress, processes and emergent states to propose a definition and model of team health.

The authors advance a definition of team health, or the holistic, dynamic compilation of states that emerge and interact as a team resource to buffer stress. Further, the authors argue that team health improves outcomes at both the individual and team level by improving team members’ well-being and enhancing team effectiveness, respectively. In addition, the authors propose a framework integrating the job demands-resources model with the input-mediator-output-input model of teamwork to illustrate the behavioral drivers that promote team health, which buffers teams stress to maintain members’ well-being and team effectiveness.

This work answers calls from multidisciplinary industries for work that considers team health, providing implications for future research in this area.