Evaluation of perceived value and behavioral intentions of citizens in Chinese commercially open communities:A case study of Liuyun Community

As urban development shifts from the incremental era to the stock era,urban design is becoming increasingly focused on high-quality living and human needs.However,the opening of traditional communities has been insufficiently examined.This study took Liuyun Community,a typical open community in Guangzhou(China)that has undergone a transformation from residential to commercial use,as its research object.First,more than 300 residents,tourists,and merchants were surveyed via questionnaires administered in field investigations and interviews.Next,the data from the completed questionnaires were used to construct a structural equation model consisting of five dimensions,namely commercial service value,cost value,risk value,perceptual value,and social value.The data were then analyzed to determine the correlations between perceived value and behavioral intention,satisfaction,and other psychological attributes.The following results were obtained:(1)As commercial activities in the community increased,the influence of social value,cost value,and commercial service value on behavioral intention decreased.(2)The open community inevitably had negative impacts on its citizens.However,these negative impacts were mitigated to some extent by high levels of social value.(3)Perceptions of commercial service value varied between groups.

Exploration of Community Image Construction Based on CIS——Take Taizhou Open University as an Example

CIS strategy has been widely used in enterprise management,which brings economic and social benefits to enterprises.Based on the introduction of CIS strategic connotation,implementation effect and shaping cases,the purpose of this paper is to improve the image of adult education community,strengthen the construction and management level of community,enhance the attraction of community,and promote the high quality development of adult open education.

Rev Rec: A two-layer reviewer recommendation algorithm in pull-based development model

Code review is an important process to reduce code defects and improve software quality. In social coding communities like GitHub, as everyone can submit Pull-Requests, code review plays a more important role than ever before, and the process is quite time-consuming. Therefore, finding and recommending proper reviewers for the emerging Pull-Requests becomes a vital task. However, most of the current studies mainly focus on recommending reviewers by checking whether they will participate or not without differentiating the participation types. In this paper, we develop a two-layer reviewer recommendation model to recommend reviewers for Pull-Requests （PRs） in GitHub projects from the technical and managerial perspectives. For the first layer, we recommend suitable developers to review the target PRs based on a hybrid recommendation method. For the second layer, after getting the recommendation results from the first layer, we specify whether the target developer will technically or managerially participate in the reviewing process. We conducted experiments on two popular projects in GitHub, and tested the approach using PRs created between February 2016 and February 2017. The results show that the first layer of our recommendation model performs better than the previous work, and the second layer can effectively differentiate the types of participation.