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This paper examines the drivers of brain gain by investigating the motivations of migrants who plan to return and contribute to their home country. It focuses on highly skilled Sudanese migrants in Japan, including a group of “plan-to-return” migrants (P-group), who intend to gain knowledge abroad that they will use to contribute to their homeland upon their return.

The participants are 24 highly skilled Sudanese migrants in Japan, 10 of whom are part of the P-group. To understand their motivation to contribute to their home country, the study applies the qualitative life course approach, using Elder's four life course themes: lives in time and space, the timing of lives, linked lives and human agency.

The P-group is characterised by a high level of motivation for self-development, which motivates them to study abroad. The analysis finds that the P-group's drive to contribute had been nurtured by a spirit of mutual aid in Sudanese society, which emphasises Islamic values and social ties. Religious norms, personal interactions and emotional ties to Sudan are especially influential on the P-group's motivation to contribute to their home society.

This study identifies drivers that lead to brain gain. Whereas previous studies have noted the relationship between return intentions and willingness to contribute to the home countries; they have not investigated influences on motivations to contribute. The results suggest that Sudan might already possess a system for local human resource development to encourage brain gain.

The paper aims to examine the role played by property tax in influencing strategic decisions regarding marital separation and divorce in Italian municipalities.

The empirical analysis is conducted on a sample of 6,458 Italian municipalities by applying the ordinary least squares (OLS) and instrumental variables (IVs) approaches.

The estimation results show a small increase in marital separations and divorces as the difference between the municipal secondary and primary home tax rate increases. Specifically, an increase of 1‰ in the property tax rate differentials is accompanied by an increase of six marital separations and four divorces per 1,000 inhabitants.

The main limitation of the analysis is that the strategic behavior of the married couple is inferred from econometric analysis with data aggregated at the municipal level. To investigate this phenomenon more precisely, it would be useful to have individual data collected by surveys on strategic divorce decisions due to property tax incentives.

This study contributes to the scant existing literature on the tax incentives for strategic divorce. It is the first study to empirically investigate the effects of property tax on separation and divorce decisions by investigating the Italian context. In Italy, a property tax was introduced in 1993, encouraging “false” divorces by spouses with a second home since the tax on the secondary home was set at a rate higher than that on the primary residence. Moreover, there were no tax deductions and no additional tax breaks on the secondary home, while they were established on the primary one. Higher property taxes and the absence of tax breaks on the secondary home may have encouraged a strategic behavior whereby many married couples filed for false separation and divorce in order to recover part of property tax rebates.

This paper aims to investigate the increasing trend of multigenerational co-living in the USA and to research the socioeconomic and cultural determinants of such decision.

This study uses the 2017 American Housing Survey data to run descriptive and regression analysis.

The authors find household income appears consistently to be the most significant factor determining multigenerational co-residence decision across all household compositions. Latino households are most likely to co-reside with multiple generations, followed by Asian and African American households. Immigrants tend to live in multigenerational co-residential housing units with smaller sizes and more impoverished neighborhoods, but show greater flexibility in making residential arrangements once they gain better education. In addition, older householders or female householders are significantly more likely to co-reside with multiple generations. Living in metropolitan areas has no impact on co-residence choice, although some evidence suggests that multigenerational co-residential families tend to live in inferior neighborhoods.

This study provides updated evidence on multigenerational co-residence choice in the contemporary United States. The findings provide evidence on how households make residential choices in response to financial hardships and contribute to the theoretical understanding of the variations of such decisions among immigrants and different ethnic and aging groups.

This study on multigenerational co-residence choice imposes important practical implications. The unprecedented COVID-19 pandemic creates ideal research setting to study how households cope with the tremendous uncertainties in the job markets and financial markets. Although multigenerational co-living may work well for some households with lower or moderate-income for financial reasons, it is not an attractive option for every family.

Sharing a home with multiple generations can be challenging. Policymakers should design policies and programs to provide households with guidance on how to live peacefully in multigenerational settings and make multigenerational co-living an appealing and cost-effective housing option for American families of all means.

This study contributes to the existing literature by providing new evidence on the determinants of multigenerational co-residence decision. This study’s findings are fundamental to guide policymakers in carrying out policies and programs aimed at providing a more appealing and cost-effective housing arrangement for American families. The evidence on the senior and minority subsamples are especially meaningful as the vast majority of the baby boom generation in the USA is aging and substantial growth is expected in multigenerational households over the next several decades. Understanding the increasing burden of old-age depression in aging societies will help policymakers prioritize public resources in city planning to address the needs of this rapidly growing population.

As the world's largest emerging market, the evidence of momentum effect in China is also mixed. Meanwhile, prior studies mainly examined individual stock momentum in China, with little concern for industry momentum and its relationship with trading volume. The motivation of this study is to investigate industry momentum in China and examine whether trading volume can enhance its profitability.

Firstly, the authors test the existence of industry momentum in China; secondly, the authors test the correlation between trading volume and momentum returns using the double ranking method; finally, the authors test whether trading volume enhances the momentum returns using Fama–French five-factor model.

The authors find that there is a significant industry momentum effect in China, and the momentum returns jointly come from winner and loser portfolios. The intervals between the formation and holding periods have an impact on the performance of momentum portfolios. In terms of trading volume, the authors find that high-volume industries have industry momentum effects while low-volume industries do not. The industry momentum strategies achieve higher excess returns in high-volume industries.

Prior literature found higher momentum returns in low-volume stocks in China, but the research in this study suggests that implementing an industry momentum strategy in low-volume industries will miss out on higher returns or even bring losses, and instead the investors should invest in high-volume industries to get the best performance.

This study extends existing research by focusing on industry momentum and its relationship with trading volume in the Chinese stock market and finds an interesting relationship between industry momentum returns and trading volume, which is different from related studies.

Knowledge-intensive public organizations (KIPOs henceforth) rely heavily on knowledge as the primary resource to provide public services. This study deals with a specific kind of KIPO in the judiciary system: the courts. The paper aims to explore the court’s managerial and organisational change resulting from the national recovery and resilience plan (NRRP) reform in response to Covid-19, focussing on how this neglected KIPO responds to change, either by showing acts of resistance or undergoing a hybridisation process.

The paper adopts a qualitative research design, developing an explorative case study to investigate the process of a court’s managerial and organisational change caused by NRRP reform and to shed light on how this neglected KIPO reacts to change, showing resistance acts and developing the hybridisation process. Thirty-one interviews in six months have been conducted with the three main actors in Courts: judges, clerks and trial clerks.

The paper shows that in this understudied KIPO, judges fiercely resist the managerial logic that decades of reforms have been trying to impose. The recent introduction of an office for speeding up trials (Ufficio Per il Processo (UPP)) was initially opposed. Then, the resistance strategy changed, and judges started to benefit from UPP delegating repetitive and low-value tasks while retaining their core activities. Clerks approached the reform with a more positive attitude, seeing in UPP the mechanism to bridge the distance between them and the judges.

Considering their relevance to society, courts must be more addressed in KIPOs' studies. This paper allows the reader to enter such KIPO and understand its peculiar features. Secondly, the article helps to understand micropractices of resistance that may hinder the effectiveness of managerial reforms.

This paper aims to address a distributed assembly permutation flow-shop scheduling problem (DAPFSP) considering budget constraints and factory eligibility. The first stage of the considered production system is composed of several non-identical factories with different technology levels and so the factories' performance is different in terms of processing time and cost. The second stage is an assembly stage wherein there are some parallel work stations to assemble the ready parts into the products. The objective function is to minimize the maximum completion time of products (makespan).

First, the problem is formulated as mixed-integer linear programing (MIP) model. In view of the nondeterministic polynomial (NP)-hard nature, three approximate algorithms are adopted based on variable neighborhood search (VNS) and the Johnsons' rule to solve the problem on the practical scales. The proposed algorithms are applied to solve some test instances in different sizes.

Comparison result to mathematical model validates the performance accuracy and efficiency of three proposed methods. In addition, the result demonstrated that the proposed two-level self-adaptive variable neighborhood search (TLSAVNS) algorithm outperforms the other two proposed methods. Moreover, the proposed model highlighted the effects of budget constraints and factory eligibility on the makespan. Supplementary analysis was presented by adjusting different amounts of the budget for controlling the makespan and total expected costs. The proposed solution approach can provide proper alternatives for managers to make a trade-off in different various situations.

The problem of distributed assembly permutation flow-shop scheduling is traditionally studied considering identical factories. However, processing factories as an important element in the supply chain use different technology levels in the real world. The current paper is the first study that investigates that problem under non-identical factories condition. In addition, the impact of different technology levels is investigated in terms of operational costs, quality levels and processing times.

The construction collapse is one of the most serious accidents since it has several attributes (e.g. accident type and consequence) and its occurrence involves various kinds of causal factors (e.g. human factors). The impact of causal factors on construction collapse accidents and the interrelationships among causal factors remain poorly explored. Thus, the purpose of this paper is to use association rule mining (ARM) for cause analysis of construction collapse accidents.

An accident analytic framework is developed to determine the accident attributes and causal factors, and then ARM is introduced as the method for data mining. The data are from 620 historical accident records on government websites of China from 2010 to 2020. Through the generated association rules, the impact of causal factors and the interrelationships among causal factors are explored.

Collapse accident is easily caused by human factors, material and machine condition and management factors. Furthermore, the results show a close interrelationship between many causal factors and construction scheme and organization. The earthwork collapse is greatly related to environmental condition and the scaffolding collapse is greatly related to material and machine condition.

This study found relevant knowledge about the key causes for different types of construction collapses. Besides, several suggestions are further provided for construction units to prevent construction collapse accidents.

This study uses data mining methods to extract knowledge about the causes of collapse accidents. The impact of causal factors on various types of construction collapse accidents and the interrelationships among causal factors are explained from historical accident data.