With the rapid development of artificial intelligence, companies have established exclusive chatbots to conduct conversational commerce and strengthen the emotional connection between customers and their brands. Therefore, shaping the chatbot personality to match the brand image is often the focus of chatbot design. This study investigates how users perceive chatbot personality via nonverbal communication (avatar, emoji, sticker) and explores the effect of chatbot personality on user satisfaction in a crowdfunding website. First, six chatbot conditions were selected using the Taguchi method. Then, an online survey based on the brand personality theory was conducted to investigate the impact of nonverbal communication on a chatbot's perceived personality. Based on the results, two chatbots with distinct personality traits, sincere and insincere, were selected as the conditions for the second experiment. Finally, participants interacted with one of the crowdfunding chatbots and received recommendations within FB messenger. One hundred fifty valid questionnaires and the click rate of participants during the experiment were collected to measure participants' satisfaction. The results showed that participants felt more satisfied with the "sincere" chatbot than the "insincere" chatbot. Furthermore, the personality of chatbots also affected participants' judgment on the message quality and the continuous intention to use chatbot services.

This research aims to develop an information-sharing system between customers and employees and try to put it into practical use. We named it "Caffe System (Customer Affection System)", a system for sharing customer service notes and customer feedback to improve customer satisfaction and loyalty and facilitate the sharing of customer information among employees.

As a non-mainstream service, "Message to customers" has been used by some stores. For example, Starbucks coffee is provided with a particular message service to impress customers: providing the drink with a message or illustration. But we have no way to know its specific impact on customer satisfaction and loyalty.

In this study, we launched the "cup message card", considering that we have been conducting demonstration experiments in a dine-in coffee shop. One of the focuses of this study was to investigate the satisfaction difference between "cup message card" and "conversation with clerks", which had already become one of the most mainstream service means.

Purpose of our research is to classify tea image at each shooting time.The flavor and quality of tea leaves change greatly depending on the time of picking.The more Umami ingredient “theanine”, the higher the quality.The more Astringent ingredient “catechin”, the lower the quality.Therefore, it is important to pick the tea at the timing when the theanine content is maximized.

In an actual farm, the timing of picking is decided at the leaf opening stage, not at the component analysis.The leaf opening period consists of 8 stages, from the germination period until the 7th leafopening period.As a rule of thumb, the highest quality is achieved at the 5th leaf opening stage.

Skilled technique is required to determine the leaf stage.We shoot a tea plantation with a large drone and analyze the shot image by deep learning.Analyzing each leaf opening stage leads to quality prediction.And I've been thinking about how to predict quality.

In order to determine the leaf opening stage, we tried to classify the images using a well-known method, Luminance histogram,Spectrum analysis by FFT and AKAZE.

However, none of the conventional methods were able to classify well.The cause is that it is difficult to extract the features due to the fuzzy of the tea image.

Therefore, we devised a classification method using deep learning.

We use DCGAN, SAE, LSTM and CWD.The originality of this research lies in the fact that these analyzes are not performed individually but continuously.

Today’s markets often offer different product solutions to the same customer need. In particular, new products offer new features requiring new customer behavior. This is typically driven by technology push, environmental concerns, or legal requirements. Due to inconvenience users do not easily change their behavior and thereby reject novel products unless they offer an obvious delight in the eyes of the user. Product developers need to take this in account when designing products. Tools such as UX, Kansei Engineering and Kano model are amongst those supporting product practitioners. This is exemplified by the affective evaluation of computer mice over three generations. It can be seen that in particular women have different affective needs in comparison to men related to auditive properties of mice. Hence it is important to adjust the user panel composition in accordance with the expected effect in order to avoid biases due to lacking diversity.

The study proposes a method for the assessment of e-shop product pages (PPs) through individual assessments of the touchpoints (TPs) customers interact with while using a PP (e.g., product title, main image, image gallery, Buy Box, rating/reviews number, product description, specifications, cross sales/recommended products, video, and product warnings) rather than an assessment of the product page as a whole. A specific Kansei list is created, customer preferences are collected, and touchpoints can be classified as positive, neutral, or negative (pain points). The assessment method was applied to the assessment of PPs of craft beer e-retailers. Once pain points were identified, two distinct types of TPs among them were individually analyzed (one visual, the Image gallery, and one textual, the Product description.) In both cases, a set of PPs were employed as stimuli and design elements that were deemed preferable by the customers were identified through Partial Least Squares analysis. The results provided valuable recommendations to web designers, content creators, and retailers for improving their PP content by taking into consideration the customers' emotional states and preferences.