Linghe Huang, Qinghua Zhu, Jia Tina Du and Baozhen Lee
Wiki is a new form of information production and organization, which has become one of the most important knowledge resources. In recent years, with the increase of users in…
Abstract
Purpose
Wiki is a new form of information production and organization, which has become one of the most important knowledge resources. In recent years, with the increase of users in wikis, “free rider problem” has been serious. In order to motivate editors to contribute more to a wiki system, it is important to fully understand their contribution behavior. The purpose of this paper is to explore the law of dynamic contribution behavior of editors in wikis.
Design/methodology/approach
After developing a dynamic model of contribution behavior, the authors employed both the metrological and clustering methods to process the time series data. The experimental data were collected from Baidu Baike, a renowned Chinese wiki system similar to Wikipedia.
Findings
There are four categories of editors: “testers,” “dropouts,” “delayers” and “stickers.” Testers, who contribute the least content and stop contributing rapidly after editing a few articles. After editing a large amount of content, dropouts stop contributing completely. Delayers are the editors who do not stop contributing during the observation time, but they may stop contributing in the near future. Stickers, who keep contributing and edit the most content, are the core editors. In addition, there are significant time-of-day and holiday effects on the number of editors’ contributions.
Originality/value
By using the method of time series analysis, some new characteristics of editors and editor types were found. Compared with the former studies, this research also had a larger sample. Therefore, the results are more scientific and representative and can help managers to better optimize the wiki systems and formulate incentive strategies for editors.
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Liangbin Chen, Lihong Zhao, Keren Ding, Kaibo Xu and Xianzhe Tang
This study aims to optimize the preparation conditions and modify the nanofiltration (NF) membranes to prepare high-performance polysulfone/sulfonated polysulfone composite…
Abstract
Purpose
This study aims to optimize the preparation conditions and modify the nanofiltration (NF) membranes to prepare high-performance polysulfone/sulfonated polysulfone composite nanofiltration (PSF/SPSF-NF) membranes through interfacial polymerization.
Design/methodology/approach
Investigating the impacts of anhydrous piperazine (PIP) concentration, trimesoyl chloride (TMC) concentration and basement membrane type on NF membrane performance, the optimal membrane was prepared. In addition, nano-SiO2 was added to the active separation layer to modify the NF membranes.
Findings
The comprehensive performance of PSF/SPSF-NF membranes was optimized when the concentration of PIP was 0.75 Wt.% and the concentration of TMC was 0.15 Wt.%, at which time the water flux was 66.1 L·m−2·h−1 and the retention rate of Na2SO4 was 98.1%. The comprehensive performance of polysulfone/sulfonated polysulfone-SiO2 nanofiltration (PSF/SPSF-SiO2-NF) membranes was optimized when the blending ratio of nano-SiO2 to PIP was 2:3, with a pure water flux of 81.9 L·m−2·h−1 and a Na2SO4 retention rate of 95.9%. Compared to polysulfone nanofiltration (PSF-NF) membranes and PSF/SPSF-NF membranes, NF membranes with nano-SiO2 increased the flux recovery rate by 22.9% and 8.7%.
Practical implications
PSF/SPSF-SiO2-NF membrane exhibits excellent antifouling properties.
Originality/value
There is currently no literature available on the preparation of NF membranes using polysulfone/sulfonated polysulfone (PSF/SPFS) as a substrate. This provides a method for modifying NF membranes, starting with the modification of the basement membrane and then modifying the active separation layer.
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Ling T. He, Chenyi Hu and K. Michael Casey
The purpose of this paper is to forecast variability in mortgage rates by using interval measured data and interval computing method.
Abstract
Purpose
The purpose of this paper is to forecast variability in mortgage rates by using interval measured data and interval computing method.
Design/methodology/approach
Variability (interval) forecasts generated by the interval computing are compared with lower‐ and upper‐bound forecasts based on the ordinary least squares (OLS) rolling regressions.
Findings
On average, 56 per cent of annual changes in mortgage rates may be predicted by OLS lower‐ and upper‐bound forecasts while the interval method improves forecasting accuracy to 72 per cent.
Research limitations/implications
This paper uses the interval computing method to forecast variability in mortgage rates. Future studies may expand variability forecasting into more risk‐managing areas.
Practical implications
Results of this study may be interesting to executive officers of banks, mortgage companies, and insurance companies, builders, investors, and other financial decision makers with an interest in mortgage rates.
Originality/value
Although it is well‐known that changes in mortgage rates can significantly affect the housing market and economy, there is not much serious research that attempts to forecast variability in mortgage rates in the literature. This study is the first endeavor in variability forecasting for mortgage rates.