1 Background

In the Mobile HCI, touching is one of the most significant interactions, which directly affects a user experience. Recent studies demonstrate that a good user experience requires that the feedback generated by gestures should be consistent with a user’s existing cognitive habits and his learnt Mental Model. If a user’s inherent Mental Model (UIMM) and his cognitive habits are consistent with a product’s operating steps, information feedback, operation results, and the interaction logic, which are all mapped out of a product, the user can gain a good experience; on the contrary, he may get a bad one. However, it remains unclear that to what extent and in what ways the consistency between UIMM and a product’s embedded Mental Model (PEMM) would affect a user’s operating experience.

This paper, by performing experiments, has studied the extent and the way in which the consistency between PEMM and UIMM influences the user experience. The first section was to extract the UIMM and PEMM, and the second one was to explore the affecting mechanism between the two Mental Models’ matching degree and the user experience.

2 Literature Review

Since the 1950 s, academia has started to study Mental Model. The concept of Mental Model was first put forward by Kenneth Craik, and he thought that Mental Model was an explanation of someone’s thought process about how something works in the real world [1]. Then, many researchers from different angles perfected and complemented it. Some scholars even came up with several new opinions. For example, Johnson Laird proposed that Mental Model described a human thinking pattern by using the existing knowledge to solve problems [2], and Indi Young thought Mental Model was the people’s behavioral purpose, thinking processes, and the changes from emotions and thoughts in the process of implementing actions [3].

In short, Mental Models is the thinking mode and thoughts hidden in the human brain. It is an internal representation mapped by the external reality in the brain, and conversely affects a person’s external behaviors. When he meets new things, Mental Models will be the first guidelines for his behaviors [4]. In HCI, Mental Model can help designers better understand the user, and also can help users better understand the product [5].

By researchers’ constantly studying, several types of Mental Model were found. For example, Norman decomposed the interaction process into three models related to Mental Model, which were “design model”, “user model”, and “system model” [6]. Design model was a bridge between the system model and the user model, which determined the usability of the product. If the overlap ratio between the design model and the user model was high, the product’s user experience would be improved. In the book of About Face 3, Alan Cooper also summarized the interaction system into three models: the implementation model, the user’s mental model and the represented model. The practical operation models of the machine and the procedure were called the implementation model, which could be seen as a model of the engineer. The user’s understanding of the system operation principles was named the user’s mental model. And the way of displaying the system’s functions by designers was called the represented model [7]. Mental Model was further refined by Martina Angela Sasse into the user model, the designer’s user model, and the researchers’ user model [8]. Although scholars have proposed many different theories about Mental Model, they all pay attention to two key concepts, the design model and the user model, which determine the product’s visual presentation created by designers and the one expected by the real users. Based on these two models, we put forward two concepts of the “Product’s Embedded Mental Model” (PEMM) and the “User’s Inherent Mental Model” (UIMM).

This paper focused on the extent and the way of consistency between PEMM and UIMM influencing user experience from the two correlative experiments. The first one was the extraction of UIMM and PEMM, and the second one was a study of the affecting mechanism between the two Mental Models’ matching degree and user experience.

3 Preparations for the Experiment

First, three typical Chinese App samples were confirmed, which were The Paper, ZAKER, and Netease News respectively. The reasons for selecting these news App are: (1) it is a representative interaction system from the real behaviors to the internet behaviors, and then to the mobile internet behaviors; (2) its contents and Information Architecture are relative simpler than other Apps; and (3) these three Apps have a large user base.

Second, the tasks suitable for extracting Mental Models were confirmed in this section. We analyzed three Apps’ Information Architecture and all the task flows, and then classified each natural task flow into several function modules and the corresponding behaviors, and draw them into a mesh structure afterward.

Take Netease News for example. If a user accesses to the application, firstly, he needs to slide the App’s homepage, then browses the default news list on the homepage, selects the target news item, and clicks the button of “confirm”. When the page of detailed news appearing, he swipes up and down to view the news content and comments, or do other operations. From such a brief natural task flow, we can note that there are several different functions and behaviors involved in this natural task.

By analyzing the type and the amount of the functions and behaviors involved in all the typical News App samples, 5 groups of the frequent functions and behaviors were abstracted, as follows:

  • Group 1: Looking for a piece of news and then reading it.

  • Group 2: Reading the news’ comments and then tapping “like”.

  • Group 3: Adding comments under the news.

  • Group 4: Switching to another piece of news.

  • Group 5: Sharing the news to Weibo, Wechat Moments, or any other social Apps.

4 Extraction of the PEMM

The purpose of this section is to extract the sample’s PEMM. We designed 5 sets of continuous tasks, each of which included some groups achieved in the above preparation section. Then we calculated the number of the different gestures provided by the App, the corresponding interface elements’ types, and analyzed their forms and the feedback forms in the process of completing each continuous task. After that, we mapped the PEMMs of The Paper, ZAKER, and Netease News respectively. The following figures are the ZAKER’s PEMM.

By analyzing the PEMMs of The Paper, ZAKER, and Netease News, we can see that a task usually needs more pages to display its content and feedbacks. Therefore, to avoid the sense of separation produced by different pages, we should pay high attention to the interactive effects between two pages’ switching. Although the interactive effects are diverse, the feedback form of the same operations follows the same design logic (Figs. 1, 2 and 3).

Fig. 1.
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The ZAKER’s PEMM (gestures and functional modules)

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Fig. 2.
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The ZAKER’s PEMM (interface elements and functional modules)

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Fig. 3.
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The ZAKER’s PEMM (feedback forms and functional modules)

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5 Extraction of the UIMM

Methods of User Interview and Situation Investigation were used to get the raw information about UIMM. First of all, 20 subjects were asked to recall the daily situation about using news Apps. The recalling points included “browsing news”, “viewing news”, “viewing comments”, “adding comments” and “sharing news”. Then they were asked to draw out the path of interaction, the corresponding interface elements, the corresponding information feedback, and write down the types of used gestures in completing each task.

Indi Young’s method was used to construct the UIMM. The steps were the following:

  1. (1)

    analyzing the mental information gained from User Interview and Situation Investigation;

  2. (2)

    picking out the mental information about tasks;

  3. (3)

    putting tasks with the same attributes together and naming these different task stacks;

  4. (4)

    putting task stacks with the same attributes together and naming them mental space.

The following figures are the UIMM about news reading (Figs. 4, 5 and 6).

Fig. 4.
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The UIMM about news reading (gestures and functional modules)

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Fig. 5.
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The UIMM about news reading (interface elements and functional modules)

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Fig. 6.
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The UIMM about news reading (feedback forms and functional modules)

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From the UIMM about news reading, we can see that: (1) the most common gestures are “click on” and “swipe”; (2) the contact area for “click on” is usually interface controls such as the button while the one for “swipe” is often the central content area; (3) subjects do not pay more attention to interaction effects but some basic ones although sometimes they may feel confused and bored by the disordered and chaotic interaction effects.

We compared the PEMM with the UIMM by computing matching degrees of the gesture type, the corresponding interface element’s type and form, and the feedback form respectively. The result is seen in Fig. 7, which is the foundation of studying the influencing mechanism of matching degree on Mobile User Experience. In it, the vertical axis is the matching rate between each sample’s PEMM and the UIMM, and the horizontal axis is the task type.

Fig. 7.
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Matching degree between PEMM and UIMM

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6 Experiment

The main purpose of this section is to explore the influencing mechanism of matching degree on Mobile User Experience. The objective performance (Usability Testing) and the subjective perception (USE questionnaire) are both considered in this experiment.

The independent variable is the type of task. It has five levels, which are “browsing news”, “viewing news”, “viewing comments”, “adding comments”, and “sharing news” respectively. The control variable is the device platform. Here the Android system is the only operating system in this experiment. The dependent variables are the task’s completion rate, the completion time, the efficiency, the error rate, the effectiveness, the ease of use, the learnability, and the satisfaction. The former four are related to Usability Testing, and the latter are related to USE questionnaire.

25 university students aged 18–25 took part in this experiment. 9 of them came from Industrial Design, and the rest of them majored in Mechanical Engineering and Materials Science. All the subjects had the experience of using news Apps everyday.

The data from the experiment were analyzed by SPSS. Some key results are shown in Table 1 and Fig. 8.

Table 1. The standardized results of Usability Testing
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Fig. 8.
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The result of subjective perception (USE questionnaire)

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7 Conclusion

By putting the matching degree, the standardized results of the Usability Testing and the USE questionnaire together, seen in Fig. 9, we can see that:

Fig. 9.
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Comparison of the matching degree with Usability Testing and USE questionnaire

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  1. (1)

    there is a high correlation between the two mental models’ matching degree and the user experience. When the matching degree, the consistency, between the two kinds of mental models is high, the user’s feeling about the product usability is also high; on the contrary, the user will feel a low product usability and a low user experience;

  2. (2)

    there is a significant correlation between the two mental models’ matching degree and the task type. It is the tasks of “browsing news” and “adding comments”, especially the former, that have a higher matching degree between UIMM and PEMM, and there is a lower one in the tasks of “viewing news”, “viewing comments” and “sharing news”. It shows that there is a bigger difference between users and designers in these three tasks.