China has the largest aging population of all countries and faces severe aging issues. As an important indicator to measure the quality of life, the life satisfaction of elderly Chinese people has received increasing attention. Based on the cross-sectional survey data collected from 2002 to 2014, which were provided by the CLHLS (Chinese Longitudinal Healthy Longevity Survey) project as open datasets, this study investigated how the influence and importance of factors associated with life satisfaction in the elderly have changed during these years. In view of previous research and questionnaire data, demographic, physiological, psychological, economic and social characteristics were selected as potential influencing factors of life satisfaction. With the R programming language, we used IV (information value) as the indicator to measure the influence of associated factors and determined the importance of each factor by establishing a random forest model for each year. Data visualization was used to demonstrate change in each factor with the Microsofot PowerPoint 2016. The results show that, for most factors, their influence has fluctuated. Since 2002, the most significant factors have always been self-rated health, self-evaluation of economic level, economic self-sufficiency and bright personality.

Purpose: Online knowledge communities make great contributions to global knowledge sharing and innovation. Resource tagging approaches have been widely adopted in such communities to describe, annotate and organize knowledge resources mainly through users’ participation. However, it is unclear what causes the adoption of a particular resource tagging approach. The purpose of this paper is to identify factors that drive users to use a hybrid social tagging approach. Design/methodology/approach: Technology acceptance model and social cognitive theory are adopted to support an integrated model proposed in this paper. Zhihu, one of the most popular online knowledge communities in China, is taken as the survey context. A survey was conducted with a questionnaire and collected data were analyzed through structural equation model. Findings: A new hybrid social resource tagging approach was refined and described. The empirical results revealed that self-efficacy, perceived usefulness (PU) and perceived ease of use exert positive effect on users’ attitude. Moreover, social influence, PU and attitude impact significantly on users’ intention to use a hybrid social resource tagging approach. Originality/value: Theoretically, this study enriches the type of resource tagging approaches and recognizes factors influencing user adoption to use it. Regarding the practical parts, the results provide online information system providers and designers with referential strategies to improve the performance of the current tagging approaches and promote them.

This paper presents a case study of Google Language Tools, especially its cross-language search service. Cross-language search integrates machine translation (MT) and cross-language information retrieval (CLIR) technologies and allows Web users to search and read pages written in languages different from their search terms. In addition to cross-language search, Google Language Tools provides various language support services to multilingual information access. Our study examines the functions of Google Language Tools and the performance of its cross-language search. The results and analysis show that Google Language Tools are useful for Web users. Its cross-language search service provides quality query translation while the automatic translation of result pages needs further improvement. The paper suggests that cross-language search could be used by different types of Web users. The authors also discuss the strategies and important issues with regard to implementing multilingual information access services for information systems.

Purpose: The purpose of this study is to evaluate freely available machine translation (MT) services' performance in translating metadata records.Design/methodology/approach: Randomly selected metadata records were translated from English into Chinese using Google, Bing, and SYSTRAN MT systems. These translations were then evaluated using a five point scale for both fluency and adequacy. Missing count (words not translated) and incorrect count (words incorrectly translated) were also recorded.Findings: Concerning both fluency and adequacy, Google and Bing's translations of more than 70 percent of test data received scores equal to or greater than three, representative of “non‐native Chinese” and “much coverage,” respectively. SYSTRAN scored lowest in both measures. However, these differences were not statistically significant. A Pearson correlation analysis demonstrated a strong relationship (r=0.86) between fluency and adequacy. Missing count and incorrect count strongly correlated with fluency and adequacy.Originality/value: Most existing digital collections can be accessed in English alone. Few digital collections in the USA support multilingual information access (MLIA) that enables users of differing languages to search, browse, recognize and use information in the collections. Human translation is one solution, but it is neither time nor cost effective for most libraries. This study serves as a first step to understand the performance of current MT systems and to design effective and efficient MLIA services for digital collections.