• Science of Emotion and Sensibility
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• v.24 no.3
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• pp.41-60
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• 2021

The purpose of this research was to determine whether multimodal cues in an online shopping environment could enhance tactile consumer mental imagery, purchase intentions, and attitudes towards an apparel product. One limitation of online retail is that consumers are unable to physically touch the items. However, as tactile information plays an important role in consumer decisions especially for apparel products, this study investigated the effects of multimodal cues on overcoming the lack of tactile stimuli. In experiment 1, to explore the product, the participants were randomly assigned to four conditions; picture only, video without sound, video with corresponding sound, and video with discordant sound; after which tactile mental imagery vividness, ease of imagination, attitude, and purchase intentions were measured. It was found that the video with discordant sound had the lowest average scores of all dependent variables. A within-participants design was used in experiment 2, in which all participants explored the same product in the four conditions in a random order. They were told that they were visiting four different brands on a price comparison web site. After the same variables as in experiment 1, including the need for touch, were measured, the repeated measures ANCOVA results revealed that compared to the other conditions, the video with the corresponding sound significantly enhanced tactile mental imagery vividness, attitude, and purchase intentions. However, the discordant condition had significantly lower attitudes and purchase intentions. The dual mediation analysis also revealed that the multimodal cue conditions significantly predicted attitudes and purchase intentions by sequentially mediating the imagery vividness and ease of imagination. In sum, vivid tactile mental imagery triggered using audio-visual stimuli could have a positive effect on consumer decision making by making it easier to imagine a situation where consumers could touch and use the product.

• Journal of Global Scholars of Marketing Science
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• v.20 no.2
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• pp.123-134
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• 2010

Considering the benefits for both consumers and suppliers, firms are taking advantage of the Internet as a medium to communicate with and sell products to their consumers. This trend makes the online shopping environment a growing field for both researchers and practitioners. This paper contributes by testing a model of online consumer behavior with websites varying in levels of hedonism. Unlike past studies, we included all three types of emotions (arousal, pleasure, and dominance) and flow into the model. In this study, we assumed that website interfaces, such as background colors, music, and fonts impact the three types of emotions at the initial exposure to the site (Mazaheri, Richard, and Laroche, 2011). In turn, these emotions influence flow and consumers' perceptions of the site atmospherics-perception of site informativeness, effectiveness, and entertainment. This assumption is consistent with Zajonc (1980) who argued that affective reactions are independent of perceptual and cognitive operations and can influence responses. We, then, propose that the perceptions of site atmospherics along with flow, influence customers' attitudes toward the website and toward the product, site involvement, and purchase intentions. In addition, we studied the moderating impact of the level of hedonism of websites on all the relationship in the model. Thus, the path coefficients were compared between "high" and "low" hedonic websites. We used 39 real websites from 12 product categories (8 services and 4 physical goods) to test the model. Among them, 20 were perceived as high hedonic and 19 as low hedonic by the respondents. The result of EQS 6.1 support the overall model: $\chi^2$=1787 (df=504), CFI=.994; RMSEA=.031. All the hypotheses were significant. In addition, the results of multi-groups analyses reveal several non-invariant structural paths between high and low hedonic website groups. The findings supported the model regarding the influence of the three types of emotions on customers' perceptions of site atmospherics, flow, and other customer behavior variables. It was found that pleasure strongly influenced site attitudes and perceptions of site entertainment. Arousal positively impacted the other two types of emotions, perceptions of site informativeness, and site involvement. Additionally, the influence of arousal on flow was found to be highly significant. The results suggested a strong association between dominance and customers' perceptions of site effectiveness. Dominance was also found to be associated with site attitudes and flow. Moreover, the findings suggested that site involvement and attitudes toward the product are the most important antecedents of purchase intentions. Site informativeness and flow also significantly influenced purchase intentions. The results of multi-group analysis supported the moderating impacts of hedonism of the websites. Compared to low (high) hedonic sites, the impacts of utilitarian (hedonic) attributes on other variables were stronger in high (low) hedonic websites. Among the three types of emotions, dominance (controlling feelings) effects were stronger in high hedonic sites and pleasure effects were stronger in low hedonic sites. Moreover, the impact of site informativeness was stronger for high hedonic websites compared to their low-hedonic counterparts. On the other hand, the influence of effectiveness of information on perceptions of site informativeness and the impact of site involvement on product attitudes were stronger for low hedonic websites than for high hedonic ones.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.

• Korean Journal of Health Education and Promotion
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• v.18 no.3
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• pp.37-58
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• 2001

This study placed its objectives in suggesting the basic data for setting up an approach to protect the educational environment, by analyzing the relevance between the misconducts of adolescence and the harmful environment around the school, as an object of study, middle school students and high school students all over the country. Thus, this study carried out the questionnaire survey, by the multi-stage of stratified sampling in 2,114 middle school and high school students from June 29, 2000 through July 29, 2000. And the results of analysis were as follows: 1. In case of the ratio of students using harmful environment, the electronic game room had the highest ratio (78.3％); next, the PC room (75.6％), the singing room (71.6％), and the cartoon room (34.3％). 2. In terms of the experiences of using the harmful environment according to the personal characteristics, high school students used it in a higher ratio, compared with middle school students (p〈0.001); the students, whose father graduated from a high school, comparatively used it much more(p〈0.05). Also, when a school is located near to amusement quarters or shopping centers, students used the harmful environment most highly (p〈0.001). And the differences were found to be statistically significant. 3. In case of the perceived susceptibility factors, the harmful environment was found to be used in lower ratio, by the students who answered “very so” to the question item, The more harmful environment facilities are positioned around school, the more student have the opportunities to use them. (p〈0.001). That is, the findings showed that the higher students' degree of perceived susceptibility factors was the less students used harmful environment facilities. The differences were statistically significant. In terms of the ratio of using harmful environment according to perceived seriousness factors, it was founded out that the students, who answered, “If I use any harmful environment facilities, it will be very harmful to myself.”. had the less opportunities of having used them, compared with the students who did not answer so (p〈0.001). This indicated that the higher the degrees perceived seriousness of students, the less they used harmful environment facilities. And the differences were statistically significant. In the side of the ratio of using harmful environment according to the perceived barriers, it was found out that there were any special large differences. That is, perceived barriers had nothing to do with students' using harmful environment. 4. As the result of having analyzed the factors influencing the behaviors of using harmful environment, the factor to explain the behaviors of using harmful environment was found to be the degree of perceived seriousness, among individual perceiving factors; next, the location of a school - one of personal characteristics, the degree of perceived susceptibility and ages, m sequence. 5. Among students' misconduct experiences, drinking was highest (21.6％), next, smoking (11.9％), drug abuse (4.3％), and sexual relations (1.6％), In sequence. Among other problematic behaviors, excessive waste was highest (14.6％); next, disobedience and lie (10.7％), night wandering (7.8％), and bad dressing and making-up (5.5％), in sequence. 6. In terms of the misconducts according to the behaviors of using harmful environment, compared with the students who did not commit any misconducts, harmful environment facilities were used more highly, by each group of students who experienced drinking (p〈0.00l), smoking (p〈0.001), sexual relations (p〈0.05), excessive waste (p〈0.001), disobedience ＆ lie (p〈0.001), and bad dressing ＆ making-up (p〈0.05). And the differences were statistically significant.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.1-31
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• 2017

Modern businesses are adopting new technologies to serve their markets better as well as to improve efficiency and productivity. The advertising industry has continuously experienced disruptions from the traditional channels (radio, television and print media) to new complex ones including internet, social media and mobile-based advertising. This case study focuses on proposing intelligent advertising business model in Seoul's metro network. Seoul has one of the world's busiest metro network and transports a huge number of travelers on a daily basis. The high number of travelers coupled with a well-planned metro network creates a platform where marketers can initiate engagement and interact with both customers and potential customers. In the current advertising model, advertising is on illuminated and framed posters in the stations and in-car, non-illuminated posters, and digital screens that show scheduled arrivals and departures of metros. Some stations have digital screens that show adverts but they do not have location capability. Most of the current advertising media have one key limitation: space. For posters whether illuminated or not, one space can host only one advert at a time. Empirical literatures show that there is room for improving this advertising model and eliminate the space limitation by replacing the poster adverts with digital advertising platform. This new model will not only be digital, but will also provide intelligent advertising platform that is driven by data. The digital platform will incorporate location sensing, e-commerce, and mobile platform to create new value to all stakeholders. Travel cards used in the metro will be registered and the card scanners will have a capability to capture traveler's data when travelers tap their cards. This data once analyzed will make it possible to identify different customer groups. Advertisers and marketers will then be able to target specific customer groups, customize adverts based on the targeted consumer group, and offer a wide variety of advertising formats. Format includes video, cinemagraphs, moving pictures, and animation. Different advert formats create different emotions in the customer's mind and the goal should be to use format or combination of formats that arouse the expected emotion and lead to an engagement. Combination of different formats will be more effective and this can only work in a digital platform. Adverts will be location based, ensuring that adverts will show more frequently when the metro is near the premises of an advertiser. The advertising platform will automatically detect the next station and screens inside the metro will prioritize adverts in the station where the metro will be stopping. In the mobile platform, customers who opt to receive notifications will receive them when they approach the business premises of advertiser. The mobile platform will have indoor navigation for the underground shopping malls that will allow customers to search for facilities within the mall, products they may want to buy as well as deals going on in the underground mall. To create an end-to-end solution, the mobile solution will have a capability to allow customers purchase products through their phones, get coupons for deals, and review products and shops where they have bought a product. The indoor navigation will host intelligent mobile-based advertisement and a recommendation system. The indoor navigation will have adverts such that when a customer is searching for information, the recommendation system shows adverts that are near the place traveler is searching or in the direction that the traveler is moving. These adverts will be linked to the e-commerce platform such that if a customer clicks on an advert, it leads them to the product description page. The whole system will have multi-language as well as text-to-speech capability such that both locals and tourists have no language barrier. The implications of implementing this model are varied including support for small and medium businesses operating in the underground malls, improved customer experience, new job opportunities, additional revenue to business model operator, and flexibility in advertising. The new value created will benefit all the stakeholders.