Donggun Park

An Analysis of User Experience of Smartphone based on Product Smartness utilizing Social Media Data

With rapid technology advancement and an expanding product domain, the definition of smart products has slightly varied (Rijsdijk & Hultink, 2009; Zaeh, Reinhart, Ostgathe, Geiger, & Lau, 2010). Also, from previous studies (Freudenthal & Mook, 2003; Rijsdijk & Hultink, 2003, 2009; Park & Lee, 2014), the relationship between product smartness and consumer appreciations or values can be identified. However, it is unclear to understand implicit needs of the consumers through conducting questionnaire based survey method. This method does not often provide sufficient information on the underlying meaning of the data, and strong evidences of causation to an answer (Gable, 1994). Hence, it could be more effective to collect unrefined and numerous user experiences, which are freely expressed in their own words, for better observation of natural user behaviors. Therefore, we tried to observe user experiences utilizing social media data, which can infer people’s opinions, both at an individual level as well as in aggregate, regarding potentially any subject or event (Schonfeld, 2009), to identify perceived product smartness. Since a smartphone is one of the most successful smart products, it could be represent the characteristics of smart products better than other products. Thus, ‘smart phone’ and ‘mobile phone’ are selected as search keywords. Through literature reviews, the dimensions and attributes related to product smartness from various previous studies were collected. Then, the collected dimensions of product smartness were re-categorized into five main dimensions as follow: ‘Autonomy’, ‘Adaptability’, ‘Multi-functionality’, ‘Connectivity’, and ‘Personalization’. The overall procedure of analyzing the relationship between perceived product smartness and collected user experiences of smart products from external data source (Twitter) is as follow. First, user experience of smart products was collected through mining Twitter data using software tool (SOCIAL metrics). SOCIAL metrics (http://socialmetrics.co.kr), which is developed by DaumSoft, can help for analyzing big data. It enables to collect Twitter data and show the frequency of keywords related to user’s search keyword. Second, data pre-processing was conducted. In the search results, the tweets which are not related to user experiences of smart products are eliminated. Third, collected user experiences were categorized according to the conceptual model of product smartness. Then, identifying the relationship between each dimension of product smartness and users’ positive/negative experiences was performed by manually. Finally, the reason of users’ positive or negative emotions on experiences of smart products was identified. A total of 19,288 tweets including ‘smartphone’ were collected from 2014.06.01 ~ 2014.08.31. Among them, a total of 699 tweets are actually related to user experiences of smartphones. The collected tweets were categorized according to the dimension of product smartness and the reason of user’s emotion. According to the results, there were many positive experiences for all of dimensions, but there were negative experiences only for multi-functionality and connectivity. Some results were supported by existing studies. The reason for positive experience on autonomy corresponded with the result of other study that productive daily life is a critical means for users to develop sense of confidence (Jung, 2014). Negative experience of autonomous was not shown in the results, but actually autonomous product does not always increase satisfaction of product. According to Rijsdijk and Hultink (2003), high complexity in using products would decrease satisfaction of products. Providing an autonomous product with indicators that inform the user about what the product is doing may reduce risk perceptions (Rijsdijk & Hultink, 2009). The study suggested that a mining technique can be used to gather and analyze user experience effectively and quantitatively without bias. It is expected that the proposed method could be helpful for understanding user’s implicit needs on the products.

Personal Mobility Device and User Experience: A State-of-the-art Literature Review

Background Personal Mobility Device (PMD) refers to ‘Smart Mobility’ or ‘Micro Mobility’ for one or two persons, and it is appreciated as an environmental-friendly means operated by electric resources (Burns, 2013). According to Transparency Market Research in 2016, the world’s personal transport market is expected to grow two times bigger from 2014 to 2023. In fact, PMD has various product types, competence levels and safety problems, so it cannot help covering many user experiences (UXs). Thus, this study aimed to analyze previous researches about PMD in terms of UX and usability. Method In this study, 160 papers were collected through five search engines with keyword combinations of UX (e.g., user interaction and interface) and PMD (e.g., Segway and electric vehicle). Through two filtering processes, the 19 papers were finally selected, and each was analyzed by the next six criteria; device types, research environments, participants, user experience factors, UX evaluation methodologies, and UX measurement. Results The results showed that Segway was the most common device type rather than electric vehicles, powered wheelchair, proposed product, E-scooter, E-bicycle, and E-bike. In addition, the outdoor environment accounted for the biggest percentage of researches beyond indoor, semi-outdoor, and online survey. People who participated in researches were usually expert and novice, who got used to handle the assigned PMD for each paper. A total of 26 UX factors (e.g., effectiveness, safety, usability, and acceptability) was collected and classified considering each experimental context; safety and usability turned out to be the most two important factors. From the UX methodological view, the PMD were usually evaluated by a questionnaire rather than by objective methods, which enable the participant to give more instinctive reactions and the researchers to gather quantitative data easily. In this way, UX objective measurements on previous researches were categorized into body observation (e.g., body angle on device), usage behavior (e.g., running distance) and compatibility on field (e.g., riding against traffic). Discussion & Conclusion The collected 28 UX factors were classified by a methodology whether it was objective or subjective measure, and it was called the UX factors framework for PMD in this study. First of all, the four main UX factors were determined: usability, satisfaction, acceptability and safety. The rest of 24 UX factors belonged to the corresponding main UX factors. For example, usability included compatibility (objectively measured factors; OMF), effectiveness, device performance, efficiency, and physical load (subjectively measured factors; SMF). Satisfaction included intuitive, customer-services-quality, charging, operability, comfort, predictability, mobility, and maintainability (SMF). Acceptability included compatibility (OMF), aesthetics, device learnability, cost, and training (SMF). Safety included stability, risk behavior (OMF), independency, guard, controllability, security, and testability (SMF). These results implied that most UX factors for PMD were usually measured subjectively rather than objectively. In particular, satisfaction had not been measured objectively even though it plays a significant role in UX factors with usability, acceptability, and safety. In other areas, some researches used objective measurements such as heart rate, EEG, or action log for sensing satisfaction (Gao, 2012; Taylor, 2015), so it is also possible to measure PMD satisfaction using objective methods. In this study, the previous researches about UX and PMD were analyzed to identify the trend of the UX research of PMD. A total of 19 papers were collected and classified by device type, research environment, participants, UX factors, evaluation method, and objective measurements. As a result, it was found that various UX factors were introduced, and the systematized UX factors framework was proposed. Through this UX framework, we expect to apply more objective measurements on UX factors of PMD in future researches.

Creating Personas with Identified Accessibility Issues for People with Disabilities: Refrigerator Usage Case

Inclusive design means a design that is designed to enable everyone to use products, architecture, and environmental services more easily and safely, regardless of the presence or absence of disability and age.