User Experience Method for Deeper Understanding People’s Values to Design and Change Their Behavior

2.1 Understanding human behaviors for achieving a state of well-being

To lead a person to a state of well-being, it is important to understand the behaviors (e.g. moving from A to B, staying at A, eating, etc.) and states (e.g. physical and mental conditions, etc.) of that person and the surrounding environment. As mentioned above, data collection has been rapidly progressing due to the wide spread of IoT. Thus, we are pursuing research and development based on the framework shown in Fig. 3, aiming at a world that uses data to bring people into a state of well-being. The most important point is not only to understand the behavior recorded in observation data but also construct a behavior model based on those data. Since this understanding and modeling make it possible to understand the behavior of people in situations in which there are no observational data and predict future behaviors, we believe it will be easier to decide on strategies to change the future for the better.

Fig. 3. A framework that uses data for people to achieve a state of well-being.

To achieve this, people flow estimation (PFE) [2] makes it possible to understand the people flow not explicitly included in data by using the characteristics of human behavior obtained from data analysis as knowledge.

As shown in Fig. 4, from population information of each area at each time step, PFE estimates the people flow between cells over time. The point with this method is that it estimates the people flow between areas from only the population information of each area at each time step, which does not explicitly represent crowd movement. When the number of areas where people are moving is N, this method solves the problem of estimating people flow between areas (number of unknown values (transition in population between each area pair) is N²) from population information for two time steps (number of known values (population of each area) is N × 2). If this problem is considered, there will be too many solutions that are consistent with the observed population data. Since there will be multiple movement patterns that can explain the change in population for each observed area of the input data, it will be difficult to accurately estimate the correct people flow.

Fig. 4. Estimating people flow from spatio-temporal population data.

In response to this problem, we first clarified the characteristics of human behavior in cities by analyzing real data. We then introduced the characteristics of human behavior obtained from data analysis as a constraint to the problem of estimating people flow between areas to narrow down the solutions.

Specifically, we used the following findings, which were obtained based on people-moving data in cities.

• Human movement is affected by the distance between areas (Saitama → Tokyo > Saitama → Yokohama)
• People’s departure probabilities vary from area to area (weekday mornings: residential district > business district)
• The ease with which people gather depends on the area (weekday mornings: business district > residential district).

Based on the above findings, instead of directly estimating the population between areas (number of unknown values is about N²), we estimate scores that reflect the characteristics of each area (e.g. score of how easily people gather in each area, score of how likely people depart from each area; number of unknown values is about N × 2) that affect the movement of people (Fig. 5). PFE uses the following formula to understand the flow of people.

(Ease of moving from area i to area j) ≈ (probability of departure from i) × (ease of people gathering at j) × (ease of movement based on the distance between i and j).

Fig. 5. Observed population changes and examples of possible people-flow patterns.

This model makes it possible to select a solution that seems to move people. It is also possible to drastically reduce the unknown values that need to be calculated, resulting in much lighter computation cost.

The above is an example of incorporating the knowledge obtained from data analysis into a model to more accurately estimate human behavior without explicit observed data of the behavior. In addition to the knowledge gained from data analysis, we will also try to model human behavior using social science knowledge. We believe that this modeling will enable us to understand various human behaviors to effectively change them for the better.

2.2 Research of understanding human values and interaction for achieving a state of well-being

Clarifying the mechanism of well-being is obviously not something that can be done overnight. A state of well-being is very different for each person and changes depending on the surrounding environment and circumstances. Moreover, it is well known that people’s thoughts and judgments are often irrational. To achieve our goal of clarifying the mechanism of well-being, we are conducting research to both deeply understand the diverse values of people and develop support methods (interaction technology) by using knowledge from a wide range of fields such as psychology, engineering, and design (research to understand values).

To understand people’s values related to well-being, our approach is to clearly define targets and study people’s values both quantitatively and qualitatively. For example, when we previously focused on parents who are raising small children, we conducted both web surveys and interviews to understand their values and child-rearing behaviors [3]. The results indicated that parents have both short-term and long-term goals. There are six long-term goals regarding their children’s futures. Figure 6 illustrates the general results. Some parents wanted to entertain or calm their children (“To make the kids feel calm and happy” in the diagram). These factors can be explained as short-term goals. The same parents may also whish their children “To be a person who can do what s/he wants (goal 1)” or “to be a person who is independent-minded in his/her life or choices (goal 2)”, which can be explained as long-term goals. Having both long and short-term goals, items that are given priority differ depending on the state of the parent’s mind, environments, and the state of the child, which may lead to disparate child-rearing behavior.

Fig. 6. Values of parents that affect their child-rearing behavior.

As shown in Fig. 6, parents’ long-term goals for their children’s future can be categorized into six types with the degree of emphasis depending on the parent. There are also multiple types of “good life goals” positioned above the long-term goals. The various factors make it difficult to understand the reason of behavior. However, our approach of using both qualitative methods, such as interviews, and quantitative methods, such as large-scale questionnaires, enable us to deeply understand human values. In the case of understanding parents, after modeling values, we developed scales to enable easy measurement of value types, which would lead to developing support services that match parents’ values.

The reason we focus on interaction research is because people are social creatures, and interactions with others have a significant impact on the state of well-being. Therefore, we are focusing on the effects of interactions among diverse people and developing methods for achieving a state of well-being. Since it is important to understand the interactions among people and what changes the motives and thoughts of people, we are conducting research in the real world on limited targets and situations to observe them as naturally as possible. We are mostly designing and developing tools/programs to support dialogue in living labs. Living labs is an approach that involve users as co-creators in the service-design process. It is attracting attention not only for service design but also as a methodology useful for community development including local revitalization. It also contributes to a state of well-being. Incorporating living lab activities in the community enables people to live with better awareness. Hence, we are implementing living labs in multiple locations, such as in the Japanese cities of Omuta, Sendai, and Yokohama, while engaging in dialogue with partners with differing values such as residents, companies, local governments, and universities. For example, at Tama-Plaza in Yokohama, we are studying how to support and accelerate the efforts of the local residents, companies, and government by developing frameworks and tools to communicate with one another to solve various problems the town has. We are also conducting research in Omuta, a city that places importance on a person-centered view of people that integrates the idea of “living true to oneself” while remaining connected with surrounding people, in collaboration with the local government, organizations, and companies to understand what is required to improve the town so that people can live in it with this view. Through this field research, we are painstakingly clarifying important viewpoints and developing frameworks and tools to support people in interacting with one another to achieve states of well-being.