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The behavioral decision-making of digital-self is one of the important research contents of the network of crowd intelligence. The factors and mechanisms that affect decision-making have attracted the attention of many researchers. Among the factors that influence decision-making, the mind of digital-self plays an important role. Exploring the influence mechanism of digital-selfs’ mind on decision-making is helpful to understand the behaviors of the crowd intelligence network and improve the transaction efficiency in the network of CrowdIntell.

In this paper, the authors use behavioral pattern perception layer, multi-aspect perception layer and memory network enhancement layer to adaptively explore the mind of a digital-self and generate the mental representation of a digital-self from three aspects including external behavior, multi-aspect factors of the mind and memory units. The authors use the mental representations to assist behavioral decision-making.

The evaluation in real-world open data sets shows that the proposed method can model the mind and verify the influence of the mind on the behavioral decisions, and its performance is better than the universal baseline methods for modeling user interest.

In general, the authors use the behaviors of the digital-self to mine and explore its mind, which is used to assist the digital-self to make decisions and promote the transaction in the network of CrowdIntell. This work is one of the early attempts, which uses neural networks to model the mental representation of digital-self.

This study aims to identify the counterproductive knowledge behavior (CKB) of volunteers in nonprofit organizations and its influencing factors, based on the theories of planned behavior and well-being.

An online survey was used to collect 496 valid responses. A structural equation model was constructed, and the relationships among the constructs were estimated via the maximum likelihood method. To analyze the direct and indirect effects, 2,000 bootstrapping runs were conducted. A Kruskal-Wallis test was also conducted to analyze the relationship between the variables.

A combination of organizational factors and individual attitudes and perceptions can be used to explain CKB. Insecurity about knowledge sharing had the greatest impact on CKB. A competitive organizational norm induced CKB while a knowledge-sharing organizational norm did not have a significant impact. Further, the more self-determined the volunteer activity was, the more the CKB was suppressed. However, well-being did not have a significant direct effect. Volunteers with high levels of well-being and self-determination had significantly lower levels of insecurity about knowledge sharing compared to those who did not.

Well-being arising from volunteering did not directly suppress CKB. To improve organizational efficiency by reducing CKB, nonprofit organization managers should provide intrinsically motivating tasks and interact with the volunteers.

There is a lack of empirical research on CKB in volunteer organizations; therefore, the authors propose a new approach to knowledge management in volunteer activities.

Faced with the challenges of rural population decline, combined with the widespread expansion of homesteads in rural areas, local Chinese governments hope to strictly control and minimize rural housing land. Accurately decomposing the process of rural housing expansion and revealing its driving factors will be helpful for land-use regulation by the government.

In this study, an unusually rich dataset of rural housing registration from Pudong New Area in Shanghai is employed. The study aimed to decompose the fragmented accumulation process and its expansion determinants on rural housing assets. The dataset covers all samples of rural households and housing plots at 72 surveyed villages in six towns.

Housing offers profitable capital and earning assets to villagers at the urban fringe, so they have a powerful incentive to build and expand more. The results of this analysis showed that the expansion of rural housing is largely due to the haphazard construction of auxiliary rooms by villagers, especially on plots of arable land that are adjacent to their houses that have been stealthily converted into auxiliary rooms and sheds. Low costs and weak penalties have led to an increase in rent-seeking expansions to rural houses. Houses with the smaller initial areas, families with more laborers and household heads, and the proximity of villages to downtown with convenient living services were the main driving factors for expanding houses. A concerted effort is needed to control the disorganized and unlicensed expansion of housing. This effort should include formulating areas for free use by villagers, high taxes on overused areas, serious penalties for unlicensed housing expansion and effective land-use planning.

An understanding of the expansion status and control measures related to rural houses in Shanghai provides an important reference that can help to guide the formulation of rural housing policies, and the sustainable development of cities worldwide. Of course, this study cannot generalize about housing distribution and expansion status worldwide based on the study area in China, because China's land tenure policies are unique. But land registry data exists that makes research like this feasible. There is a need to carefully examine the detailed housing distribution in each country before it can be decided on how best to address the disorderly increase in rural housing stock, and promote the reduction of rural residential expansion.

First, the process of rural housing expansion by using an unique dataset which covers ten thousands of samples is revealed. Second, the results have policy implications for reducing the amount of idle and inefficiently rural homestead. The focus is on rural housing growth and its driving factors in Shanghai, and the villagers' motivations for housing expansion are explored.