A Qualitative Investigation of Product Smartness utilizing User Experience Data

Abstract

We are surrounded by products that have minds of their own. Smart products share the ability to collect, process, and produce information and can be described as thinking for themselves. The smart design has been applied to so many products so frequently, and in so many different contexts, that it is becoming more of a marketing claim than a well-defined technical description. Which consumer devices should be categorized as smart products? While the rapid growth in embedded computing power and the absolute number of microcontroller chips are well-documented facts, the definition of a smart product is still evolving. Definitions and characteristics differ considerably depending on the point of view of researchers. The purpose of this study is to identify product smartness and users implicit needs more effectively and efficiently for developing new ideas/concepts for smart products. The overall procedure of developing idea/concept for new smart products/services is as follow. In phase I, product smartness is identified. A conceptual model of product smartness was identified based on the literature review and the expert interview. As a result, five main dimensions of product smartness are selected (Autonomy, Adaptability, Multi-functionality, Connectivity, and Personalization). Also, to explore the relationship between product smartness and user experience, user experiences of smartphones were classified according to the reason of emotions (positive/negative) utilizing social media data (Twitter). According to the results, there were many positive experiences for all of dimensions, but there were negative experiences only for multi-functionality and connectivity. In phase II, to identify users needs, a Day Reconstruction Method (DRM) and a Self-Organizing Map (SOM) were conducted. To collect more natural and longitudinal user experience, the DRM was conducted with a case study on smart TVs. Also, to analyze the collected user experience data more effectively and efficiently, a clustering analysis was conducted using the SOM. As a result, similarity between episodes could be identified by analyzing a two-dimensional map, and 15 groups were classified from 330 episodes of user experience on smart TVs. The results from the two phases are integrated in phase III, where new ideas/concepts for smart products/services are developed with relationship analysis. From the case study of developing new idea/concept for smart TV, a total of seven detail concepts were developed with five extracted user experiences of smart TV. Also, to explore the relationship between idea quality, product smartness and satisfaction of new ideas/concepts, the evaluation experiments were conducted by practitioners and researchers. Idea quality metrics were collected from previous studies, and then they were re-organized into three dimensions: Workability, Relevance, and Attractiveness. To validate the conceptual model, an evaluation experiment was conducted. According to the results of the experiment, the relationship between idea quality, product smartness, and satisfaction on new idea/concepts were identified and explored. In the idea quality, Relevance is relatively more important than the others. It is found that Workability has no significant influence on satisfaction. Also, in the product smartness, Autonomy is the most important factor for satisfaction of new idea/concept. However, Multi-functionality shows no significant influence on satisfaction of new idea/concept. This study will motivate researchers and practitioners to develop and improve smart products and its applications. Even though developed new idea/concept is difficult to be implemented or acceptable to users, users and developers will be satisfied if the idea/concept is relevant and attractive. Also, developers may want to implement their ideas for multi-functional products in a stepwise manner and to provide consumers with the opportunity to get used to certain levels of product smartness.