| Due to the complexity of users’ cognitive characteristics in web navigating, as well as problems caused by web navigation design, a valid Web navigation mechanism for users has become the bottleneck of Web information service quality and utilization efficiency. The followings had been carried out based on the cognitive processes and behaviors of user’s navigating on websites:(1) Summarizing the related theory of web navigation systematically, promoting to theory study of Web navigation in academics. Analyzing the characteristics of the web information space structure as well as the existing Web link relations and content relationship; Introducing the concept of Web navigation and the theory basis of designing the Web navigation; Analyzing the typical Web navigation model to explore the user’s Web navigation process; Comparing various Web navigation mechanisms to enrich the study of Web navigation mechanism.(2) Structuring the user Web navigation process model according to the problem solving theory. Directed by the problem solving theory, the model simulates the user navigation process in Web as a process of solving information problems, including problem establishment, strategy-making, strategy implementation and results evaluation. And through a literature review of the impact of individual characteristics, task characteristics, structure characteristics and cognitive characteristics on the navigation behaviors, the paper has put forward the research model and approach.(3) With the method of experiment, studying the cognitive characteristics of users’web navigation behavior as well as the influencing factors. This process is divided into two stages. Firstly, by the thinking aloud method, studying the user’s cognitive process and exploring the cognitive factors and variables during navigating. By the encode software, analyzing the cognitive characteristics in different phases of users’navigation process and then incorporating the users’ cognitive characteristics into the navigation process model based on the problem solving theory to make it more perfect.Secondly, by social statistical methods, exploring whether there are significant relations between objective task complexity, subjective task complexity, navigation behavior characteristics and cognitive characteristics. And then taking gender and target hierarchy structures as control variables to seek for whether the control variables have impacts on the latent variables.During the experiment process,25volunteer are invited to carry out12navigating tasks which are provided by the author on the Chinese website (http://www.paper.edu.cn). After experiment, the datum of cognitive behaviors and navigation behaviors of the navigating process are collected through video recording, coding, qualitative analysis, video collection, etc methods and tools. And the relationships between potential variables and control variables are explored by the nonparametric test and Spearman correlation coefficient method of SPSS social statistical software. Some conclusions are made in with the experiment and exploring:(1) Various cognitive activities in users’Web navigation process can be divided into five main classes:①task cognition, including information requirement cognition and task difficulty cognition.②navigability cognition, including the directory structure cognition of the main navigation bar, the home page navigability cognition, other navigation mechanisms cognition, and the clues cognition on the page.③user experience cognition, including the interface usability cognition, and the semantic understanding cognition.④results failure and uncertainty cognition.⑤memory cognition, including background knowledge cognition and working memory cognition.(2) Exploring the users’main cognitive activities during the four-stage navigation process model based on the problem solving theory. It demonstrates that:①the users’ cognitive activities in the first stage problem establishment is mainly information requirement cognition, semantic understanding cognition, working memory cognition.②the strategy-making phase is the most complicated phase of user thinking process, with various cognitive characteristics occurred with a distinctive frequency.③The cognitive activities of the stage strategy implementation also are diverse.④in the results evaluation stage, the cognition for results failure and uncertainty is the most frequently happened activities, together with a few other cognitive activities.(3) The task complexity is divided into subjective task complexity and objective task complexity. But through statistics analysis, the author finds that the subjective task complexity is mainly impacted only by the object physical structure and the task implementation results, not by gender, objective task complexity, target logical structure, and cognitive characteristics.(4) The cognitive characteristics influencing factors.①the experiment results show that there is significant correlations between some cognitive characteristics, which means that a cognitive behavior can lead to the occurrence of another cognitive behavior, such as significant correlations between background knowledge cognition, working memory cognition, results failure and uncertainty cognition, clues cognition, and the directory structure cognition of the main navigation bar. The working memory cognition is significantly correlated with background knowledge cognition, in addition, with results failure and uncertainty cognition, as well as the directory structure cognition of the main navigation bar. Results failure and uncertainty cognition has significant negative correlation with information requirement cognition, and is significantly correlated with the directory structure cognition of the main navigation bar. Interface usability cognition only is significantly correlated with task difficulty cognition. There is a significant correlation between the semantic understanding cognition and the home page navigability cognition.②It is also demonstrated that the frequencies of various cognitive behaviors of user web navigation have not impacted by gender.(5) Navigation behavior influencing factors.①The significant correlation between the navigation behavior indicators is a reflection of the user navigation behavior. It is showed that the total number of pages we have visited each task has significant positive correlation with other navigation behavior indicators. The unique number of pages visited is not significantly correlated with the number of the homepage visit number, only has significant positive correlation with other navigation behavior indicators. The homepage visit number also is not significantly correlated with the visit number of the navigation bar, only has a significant positive correlation with the total number of pages visited each task, the number of clicking on the back button, and the revisit rate. The times of clicking on the back button and the revisit rate have a significant positive correlation with all navigation behavior indicators.②there are no significant differences for male and female users on the6navigation behavior indicators.③the objective task complexity has significant negative relationship with two navigation behavior indicators, which are the total number of pages visited and the unique number of pages visited. The objective task complexity and the number of the navigation bar visited have a significant positive correlation between.④Two of the navigation behavior indicators, the times of clicking on the back button and the revisit rate, have a significant influence on the user’s post-subjective judgment of the task complexity.⑤There is no significant difference on the navigation behavior characteristics whether the result of task is successful or not.⑥The target logic structure has a significant positive correlation with the total number of pages visited, the unique number of pages visited, and the number of the navigation bar visited, and is has a significant negative correlation with the number of the homepage visited and the times of clicking on the back button.⑦the user navigation behavior is to some extent related with cognitive characteristics. The total number of pages visited has a significant positive correlation with the background knowledge cognition, the working memory cognition, results failure and uncertainty cognition, and the directory structure cognition of the main navigation bar. The unique number of pages visited has a significant positive correlation with the background knowledge cognition, the working memory cognition, results failure and uncertainty cognition, and the directory structure cognition of the main navigation bar. The times of clicking on the back button has a significant positive correlation with the psychological behavior of clues cognition, the directory structure cognition of the main navigation bar. The revisit rate has a significant positive correlation with the psychological behavior of clues cognition and the directory structure cognition of the main navigation bar. The homepage visiting numbers is not significantly correlated with the occurrence frequency of various cognitive characteristics.In addition, the dissertation summarized four typical kinds of navigation strategy by clustering methods:easy navigation strategy cluster, persistent navigation strategy cluster, arduous navigation strategy cluster, and "testing" navigation strategy cluster. |
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