• Journal of Korean Home Economics Education Association
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• v.6 no.2
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• pp.161-174
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• 1994

In this study, among the cognitive learning models for information process, Taba’s inductive thinking model and Joyce&Weil’s concept attainment model, which help to obtain and study the concepts, can be applied to the ranges of consumer-education. Considering this, a new teaching-paln can be made. Applying the plan to the present teaching environments. I will do the research possibilities of applying the concept-learning teaching-plan to the consumer education. In the method of this research, many books, related to home economics & consumer-education, characters of concept-learning, and concept teaching-learning models, were studied. Also, on the basis of theoretical evidence, the teaching-plan, that apply the concept teaching model, were made. In addition, experimental research was done to find out the possibilities that the plan focusing on concept learning was applied or not. As a result of this study, two points are briefly summarized : 1. The teaching plan using Taba’s and Joyce & Weil’s concept-attainment model was made. 2. Concept-learning in consumer-education didn’t have a great a great influence to the changes of consumer-roles and attitudes, but had a great influence to the effects of consumer concept-knowledge(p<0.01) The effects of consumer-knowledge had much relation to consumer-roles and attitudes. The learners whose grade is higher in attainments of consumer-knowledge, also have a high grade in consumer-roles and attitudes.

• Journal of the Korean Society of Clothing and Textiles
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• v.16 no.1 s.41
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• pp.137-146
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• 1992

As a preliminary work for the interrelations between individuals' learning style and their consumer decision-making styles in purchasing apparel, its theoretical backgrounds were reviewed. Several major approaches to measuring and characterizing learning styles were theories of Hunt, Schroder, Kolb, and sproles. - Relevant literature suggests several consumer decision-making styles including Morchis' and Sproles'. Researches on the practical' implication of theoretical learning styles model in the area of consumer decision-making styles were also explored.

• Asia Marketing Journal
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• v.15 no.3
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• pp.159-172
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• 2013

In the present study, we apply the multiple cue probability learning (MCPL) paradigm to examine consumer learning from feedback in repeated trials. This paradigm is useful in investigating consumer learning, especially learning the relationships between the overall quality and attributes. With this paradigm, we can analyze what people learn from repeated trials by using the lens model, i.e., whether it is knowledge or consistency. In addition to introducing this paradigm, we aim to demonstrate that knowledge people gain from repeated trials with feedback is robust enough to weaken one of the most often examined contextual effects, the asymmetric dominance effect. The experiment consists of learning session and a choice task and stimuli are sport rafting boats with motor engines. During the learning session, the participants are shown an option with three attributes and are asked to evaluate its overall quality and type in a number between 0 and 100. Then an expert's evaluation, a number between 0 and 100, is provided as feedback. This trial is repeated fifteen times with different sets of attributes, which comprises one learning session. Depending on the conditions, the participants do one (low) or three (high) learning sessions or do not go through any learning session (no learning). After learning session, the participants then are provided with either a core or an extended choice set to make a choice to examine if learning from feedback would weaken the asymmetric dominance effect. The experiment uses a between-subjects experimental design (2 × 3; core set vs. extended set; no vs. low vs. high learning). The results show that the participants evaluate the overall qualities more accurately with learning. They learn the true trade-off rule between attributes (increase in knowledge) and become more consistent in their evaluations. Regarding the choice task, there is a significant decrease in the percentage of choosing the target option in the extended sets with learning, which clearly demonstrates that learning decreases the magnitude of the asymmetric dominance effect. However, these results are significant only when no learning condition is compared either to low or high learning condition. There is no significant result between low and high learning conditions, which may be due to fatigue or reflect the characteristics of learning curve. The present study introduces the MCPL paradigm in examining consumer learning and demonstrates that learning from feedback increases both knowledge and consistency and weakens the asymmetric dominance effect. The latter result may suggest that the previous demonstrations of the asymmetric dominance effect are somewhat exaggerated. In a single choice setting, people do not have enough information or experience about the stimuli, which may lead them to depend mostly on the contextual structure among options. In the future, more realistic stimuli and real experts' judgments can be used to increase the external validity of study results. In addition, consumers often learn through repeated choices in real consumer settings. Therefore, what consumers learn from feedback in repeated choices would be an interesting topic to investigate.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.214-222
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• 2021

Recently, the growth of e-commerce in Saudi Arabia has been exponential, bringing new remarkable challenges. A naive approach for product matching and categorization is needed to help consumers choose the right store to purchase a product. This paper presents a machine learning approach for product matching that combines deep learning techniques with standard artificial neural networks (ANNs). Existing methods focused on product matching, whereas our model compares products based on unstructured descriptions. We evaluated our electronics dataset model from three business-to-consumer (B2C) online stores by putting the match products collectively in one dataset. The performance evaluation based on k-mean classifier prediction from three real-world online stores demonstrates that the proposed algorithm outperforms the benchmarked approach by 80% on average F1-measure.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.31-49
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• 2023

With the growth of the food-catering industry, consumer preferences and the number of dine-in restaurants are gradually increasing. Thus, personalized recommendation services are required to select a restaurant suitable for consumer preferences. Previous studies have used questionnaires and star-rating approaches, which do not effectively depict consumer preferences. Online reviews are the most essential sources of information in this regard. However, previous studies have aggregated online reviews into long documents, and traditional machine-learning methods have been applied to these to extract semantic representations; however, such approaches fail to consider the surrounding word or context. Therefore, this study proposes a novel review textual-based restaurant recommendation model (RT-RRM) that uses deep learning to effectively extract consumer preferences from online reviews. The proposed model concatenates consumer-restaurant interactions with the extracted high-level semantic representations and predicts consumer preferences accurately and effectively. Experiments on real-world datasets show that the proposed model exhibits excellent recommendation performance compared with several baseline models.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.437-442
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• 2021

In this study, a recommendation algorithm was implemented by learning a deep learning-based classification model for consumer data. For this purpose, a meaningful result is presented as a result of learning using ResNet50, which is commonly used in classification tasks by converting user data into images.

• Korean Journal of Human Ecology
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• v.19 no.2
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• pp.295-310
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• 2010

This study measured college students' levels of satisfaction with their cyber learning through an online survey of students who had taken one or more cyber learning before. 500 returned and usable questionnaires were analyzed and SPSS WIN 12.0 was used for the descriptive statistics, t-test, factor analysis and analysis of covariance structures. The results are as follows; First, college students were very interested in their cyber learning. Their decision to take the cyber learning was initiated more by emotional motives (m=3.13) than by rational motives (m=3.35). Second, the consumers' expectations for the cyber learning were divided into the 'Expectation for service quality' and the 'Expectation for economy,' and their 'Expectation for economy'(m=4.02) was higher than their 'Expectations for service quality'(m=3.60). Third, the consumers' expectations for the cyber learning and the results of the cyber learning were analyzed, and a discrepancy between these two were also analyzed. The analysis of discrepancy between the two showed that the average of the results was lower than that of the expectations, which means that the cyber learning did not meet the consumers' expectations in every aspect, However, the average satisfaction level was 3.20, which means consumers were satisfied with the cyber learning overall. Fourth, causes of dissatisfaction with the cyber learning were divided into internal factors due to personal matters and external factors due to classes and other factors. It was found that dissatisfaction due to internal factors was greater than that due to external factors. Lastly, the factors affecting satisfaction/dissatisfaction with the cyber learning and willingness to use it again were analyzed. The results showed that the motive for its use affected the formation of expectation but it did not affect the results. Satisfaction with the cyber learning affected the willingness to use it again positively. However, the effect of dissatisfaction on the willingness to use it again was not statistically significant.

• Asia pacific journal of information systems
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• v.18 no.4
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• pp.105-130
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• 2008

Consumers of e-learning market differ from those of other markets in that they are replaced in a specific time scale. For example, e-learning contents aimed at highschool senior students cannot be consumed by a specific consumer over the designated period of time. Hence e-learning service providers need to attract new groups of students every year. Due to lack of information on products designed for continuously emerging consumers, the consumers face difficulties in making rational decisions in a short time period. Increased uncertainty of product purchase leads customers to herding behaviors to obtain information of the product from others and imitate them. Taking into consideration of these features of e-learning market, this study will focus on the online herding behavior in purchasing e-learning contents. There is no definite concept for e-learning. However, it is being discussed in a wide range of perspectives from educational engineering to management to e-business etc. Based upon the existing studies, we identify two main view-points regarding e-learning. The first defines e-learning as a concept that includes existing terminologies, such as CBT (Computer Based Training), WBT (Web Based Training), and IBT (Internet Based Training). In this view, e-learning utilizes IT in order to support professors and a part of or entire education systems. In the second perspective, e-learning is defined as the usage of Internet technology to deliver diverse intelligence and achievement enhancing solutions. In other words, only the educations that are done through the Internet and network can be classified as e-learning. We take the second definition of e-learning for our working definition. The main goal of this study is to investigate what factors affect consumer intention to purchase e-learning contents and to identify the differential impact of the factors between consumers with purchase experience and those without the experience. To accomplish the goal of this study, it focuses on herding behavior and perceived usefulness as antecedents to behavioral intention. The proposed research model in the study extends the Technology Acceptance Model by adding herding behavior and usability to take into account the unique characteristics of e-learning content market and e-learning systems use, respectively. The current study also includes consumer experience with e-learning content purchase because the previous experience is believed to affect purchasing intention when consumers buy experience goods or services. Previous studies on e-learning did not consider the characteristics of e-learning contents market and the differential impact of consumer experience on the relationship between the antecedents and behavioral intention, which is the target of this study. This study employs a survey method to empirically test the proposed research model. A survey questionnaire was developed and distributed to 629 informants. 528 responses were collected, which consist of potential customer group (n = 133) and experienced customer group (n = 395). The data were analyzed using PLS method, a structural equation modeling method. Overall, both herding behavior and perceived usefulness influence consumer intention to purchase e-learning contents. In detail, in the case of potential customer group, herding behavior has stronger effect on purchase intention than does perceived usefulness. However, in the case of shopping-experienced customer group, perceived usefulness has stronger effect than does herding behavior. In sum, the results of the analysis show that with regard to purchasing experience, perceived usefulness and herding behavior had differential effects upon the purchase of e-learning contents. As a follow-up analysis, the interaction effects of the number of purchase transaction and herding behavior/perceived usefulness on purchase intention were investigated. The results show that there are no interaction effects. This study contributes to the literature in a couple of ways. From a theoretical perspective, this study examined and showed evidence that the characteristics of e-learning market such as continuous renewal of consumers and thus high uncertainty and individual experiences are important factors to be considered when the purchase intention of e-learning content is studied. This study can be used as a basis for future studies on e-learning success. From a practical perspective, this study provides several important implications on what types of marketing strategies e-learning companies need to build. The bottom lines of these strategies include target group attraction, word-of-mouth management, enhancement of web site usability quality, etc. The limitations of this study are also discussed for future studies.

• Human Ecology Research
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• v.61 no.3
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• pp.297-318
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• 2023

The purpose of this study was to develop a high school home economics financial consumer education program based on backward design and validation by experts. The program was designed and developed by first selecting learning content elements through a review of existing research and an analysis of relevant literature. The next step was to categorize these elements into seven themes and apply the backward design instructional design model 2.0. The program was prepared in the form of a 21st teaching-learning course plan and workbook and was verified by nine home economics teachers with working experience in high school. The evaluation revealed that the average value for all questions was 3.81 (out of 4 points) and the CVR was .99, indicating that the program was valid. In addition, positive evaluations were received in terms of learning goals, content level, and learner participation by class. This study has significance in that a systematic financial consumer education program was developed by Education of Home Economics to improve the financial literacy of high school students. It can therefore be used as an elective course (mini-course) in Home Economics in the high school credit system. A follow-up study will be required to assess the improvement in financial literacy after implementing this program.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1499-1508
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• 2018

Although fast-growing e-commerce markets gave a lot of companies opportunities to expand their customer bases, it is also the case that there are growing number of cases in which the so-called 'black consumers' cause much damage on many companies. In this study, we will implement and optimize a machine learning model that detects black consumers using customer data from e-commerce store. Using filter method for feature selection and 4 different algorithms for classification, we could get the best-performing machine learning model that detects black consumer with F-measure 0.667 and could also yield improvements in performance which are 11.44% in F-measure, 10.51% in AURC, and 22.87% in TPR.