• Title/Summary/Keyword: Consumer Learning Model

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The Marketing Model Applying the Concepts of Educational Psychology in the Private Educational Service Sector

  • KIM, Seong-Gon
    • Journal of Distribution Science
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    • v.18 no.11
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    • pp.15-22
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    • 2020
  • Purpose: A marketing strategy for private institutions requires marketers to leverage consumer behaviors and educational psychologies when advertising and promoting product portfolios. Indeed, understanding consumers can make marketers more effective, and the purpose of this research is to tackle private institutions' education marketing by combining marketing theories and learning theories. Research design, data and methodology: The content analysis used in this study will be suitable because there exists numerous prior studies regarding marketing strategy and educational theories. Therefore, the current author could obtain and collect adequate textual facts from much of the literature review. Results: Marketing strategies that are mixed with educational theories increase consumer enrolment due to perceived usefulness, and this implies that an adequate marketing model could help improve sustainability and income as a result of enrollment in private educational institutions. The research also identified that marketing is connected to psychology and that marketers can exploit educational and psychological theories to increase successful enrolment in private educational institutions. Conclusions: Most importantly, the target market for private educational institutions is diverse, and institutions can use direct marketing to appeal to specific audiences. Also, the research implies that diversification strategies can increase enrolment if marketers exploit behavioral learning theories in the marketing process.

Developing a Deep Learning-based Restaurant Recommender System Using Restaurant Categories and Online Consumer Review (레스토랑 카테고리와 온라인 소비자 리뷰를 이용한 딥러닝 기반 레스토랑 추천 시스템 개발)

  • Haeun Koo;Qinglong Li;Jaekyeong Kim
    • Information Systems Review
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    • v.25 no.1
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    • pp.27-46
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    • 2023
  • Research on restaurant recommender systems has been proposed due to the development of the food service industry and the increasing demand for restaurants. Existing restaurant recommendation studies extracted consumer preference information through quantitative information or online review sensitivity analysis, but there is a limitation that it cannot reflect consumer semantic preference information. In addition, there is a lack of recommendation research that reflects the detailed attributes of restaurants. To solve this problem, this study proposed a model that can learn the interaction between consumer preferences and restaurant attributes by applying deep learning techniques. First, the convolutional neural network was applied to online reviews to extract semantic preference information from consumers, and embedded techniques were applied to restaurant information to extract detailed attributes of restaurants. Finally, the interaction between consumer preference and restaurant attributes was learned through the element-wise products to predict the consumer preference rating. Experiments using an online review of Yelp.com to evaluate the performance of the proposed model in this study confirmed that the proposed model in this study showed excellent recommendation performance. By proposing a customized restaurant recommendation system using big data from the restaurant industry, this study expects to provide various academic and practical implications.

Telepresence in Video Game Streaming: Understanding Viewers' Perception of Personal Internet Broadcasting

  • Kyubin Cho;Choong C. Lee;Haejung Yun
    • Asia pacific journal of information systems
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    • v.32 no.3
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    • pp.684-705
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    • 2022
  • A new trend has been emerging in recent years, with video game live streaming becoming a meeting ground for gamers, as well as a marketing strategy for game developers. In line with this trend, the emergence of the "Let's Play" culture has significantly changed the manner in which people enjoyed video games. In order to academically explore this new experience, this study seeks to answer the following research questions: (1) Does engaging in video game streaming offer the same feeling as playing the game? (2) If so, what are the factors that affect the feeling of telepresence from viewers' perspective? and (3) How does the feeling of telepresence affect viewers' learning experience of the streamed game? We generated and empirically tested a comprehensive research model based on the telepresence and consumer learning theories. The research findings revealed that the authenticity and pleasantness of the streamer and the interaction of viewers positively affect telepresence, which in turn is positively associated with the gained knowledge and a positive attitude toward the streamed game. Based on the research findings, various practical implications are discussed for game developers as well as platform providers.

Analysis on learning curves of end-use appliances for the establishment of price-sensitivity load model in competitive electricity market (전력산업 경쟁 환경에서의 요금부하모델 수립을 위한 부하기기의 학습곡선 분석)

  • Hwang, Sung-Wook;Kim, Jung-Hoon;Song, Kyung-Bin;Choi, Joon-Young
    • Proceedings of the KIEE Conference
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    • 2001.07a
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    • pp.386-388
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    • 2001
  • The change of the electricity charge from cost base to price base due to the introduction of the electricity market competition causes consumer to choose a variety of charge schemes and a portion of loads to be affected by this change. Besides, it is required the index that consolidate the price volatility experienced on the power exchange with gaming and strategic bidding by suppliers to increase profits. Therefore, in order to find a mathematical model of the sensitively-responding-to-price loads, the price-sensitive load model is needed. And the development of state-of-the-art technologies affects the electricity price, so the diffusion of high-efficient end-uses and these price affect load patterns. This paper shows the analysis on learning curves algorithms which is used to investigate the correlation of the end-uses' price and load patterns.

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Design Evaluation Model Based on Consumer Values: Three-step Approach from Product Attributes, Perceived Attributes, to Consumer Values (소비자 가치기반 디자인 평가 모형: 제품 속성, 인지 속성, 소비자 가치의 3단계 접근)

  • Kim, Keon-Woo;Park, Do-Hyung
    • Journal of Intelligence and Information Systems
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    • v.23 no.4
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    • pp.57-76
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    • 2017
  • Recently, consumer needs are diversifying as information technologies are evolving rapidly. A lot of IT devices such as smart phones and tablet PCs are launching following the trend of information technology. While IT devices focused on the technical advance and improvement a few years ago, the situation is changed now. There is no difference in functional aspects, so companies are trying to differentiate IT devices in terms of appearance design. Consumers also consider design as being a more important factor in the decision-making of smart phones. Smart phones have become a fashion items, revealing consumers' own characteristics and personality. As the design and appearance of the smartphone become important things, it is necessary to examine consumer values from the design and appearance of IT devices. Furthermore, it is crucial to clarify the mechanisms of consumers' design evaluation and develop the design evaluation model based on the mechanism. Since the influence of design gets continuously strong, various and many studies related to design were carried out. These studies can classify three main streams. The first stream focuses on the role of design from the perspective of marketing and communication. The second one is the studies to find out an effective and appealing design from the perspective of industrial design. The last one is to examine the consumer values created by a product design, which means consumers' perception or feeling when they look and feel it. These numerous studies somewhat have dealt with consumer values, but they do not include product attributes, or do not cover the whole process and mechanism from product attributes to consumer values. In this study, we try to develop the holistic design evaluation model based on consumer values based on three-step approach from product attributes, perceived attributes, to consumer values. Product attributes means the real and physical characteristics each smart phone has. They consist of bezel, length, width, thickness, weight and curvature. Perceived attributes are derived from consumers' perception on product attributes. We consider perceived size of device, perceived size of display, perceived thickness, perceived weight, perceived bezel (top - bottom / left - right side), perceived curvature of edge, perceived curvature of back side, gap of each part, perceived gloss and perceived screen ratio. They are factorized into six clusters named as 'Size,' 'Slimness,' 'No-Frame,' 'Roundness,' 'Screen Ratio,' and 'Looseness.' We conducted qualitative research to find out consumer values, which are categorized into two: look and feel values. We identified the values named as 'Silhouette,' 'Neatness,' 'Attractiveness,' 'Polishing,' 'Innovativeness,' 'Professionalism,' 'Intellectualness,' 'Individuality,' and 'Distinctiveness' in terms of look values. Also, we identifies 'Stability,' 'Comfortableness,' 'Grip,' 'Solidity,' 'Non-fragility,' and 'Smoothness' in terms of feel values. They are factorized into five key values: 'Sleek Value,' 'Professional Value,' 'Unique Value,' 'Comfortable Value,' and 'Solid Value.' Finally, we developed the holistic design evaluation model by analyzing each relationship from product attributes, perceived attributes, to consumer values. This study has several theoretical and practical contributions. First, we found consumer values in terms of design evaluation and implicit chain relationship from the objective and physical characteristics to the subjective and mental evaluation. That is, the model explains the mechanism of design evaluation in consumer minds. Second, we suggest a general design evaluation process from product attributes, perceived attributes to consumer values. It is an adaptable methodology not only smart phone but also other IT products. Practically, this model can support the decision-making when companies initiative new product development. It can help product designers focus on their capacities with limited resources. Moreover, if its model combined with machine learning collecting consumers' purchasing data, most preferred values, sales data, etc., it will be able to evolve intelligent design decision support system.

A study on the establishment of a differential standardization system for the franchisor for a successful math franchise business (성공적인 수학 프랜차이즈 사업을 위한 가맹본부의 차별화된 표준화 시스템 구축방안에 관한 연구)

  • Hong, Hee-dong
    • Journal of the Korea Convergence Society
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    • v.13 no.4
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    • pp.63-70
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    • 2022
  • Due to the recent changes in the education market and Corona, Consumers have moved to the tutoring and online learning markets, and the large-scale education service franchise business is reorganized into a small-scale franchise, a business model that maximizes the profit structure from a position where sales are important. Recently, a new learning balance model that can provide individualized services from teacher-centered to student-centered, motivate students is required. In this paper, we propose a new mathematical franchise model (K-MODEL) that can improve a company's profit structure while satisfying the recent education trends and consumer needs from the point of view of the franchise. K-MODEL expects franchisor and franchisees to have a stable profit structure by developing differentiated content and services, learning and operating processes, and various programs to improve learning achievement.

The Influence of Introducing New Technologies and DSM Strategies on End-Use Learning Curves (신기술 보급 및 DSM 정책이 부하기기 학습곡선에 미치는 영향)

  • Hwang, Sung-Wook;Kim, Jung-Hoon
    • Proceedings of the KIEE Conference
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    • 2001.11b
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    • pp.435-437
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    • 2001
  • The change of the electricity charge from cost base to price base due to the introduction of the electricity market competition causes consumer to choose a variety of charge schemes and a portion of loads to be affected by this change. Besides, it is required the index that consolidate the price volatility experienced on the power exchange with gaming and strategic bidding by suppliers to increase profits. Therefore, in order to find a mathematical model of the sensitively-responding-to-price loads, the price-sensitive load model is needed. And the development of state-of-the-art technologies affects the electricity price, so the diffusion of high-efficient end-uses and these price affect load patterns. This paper shows the analysis on learning curves algorithms which is used to investigate the correlation of the end-uses' price and load patterns.

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Design and implementation of trend analysis system through deep learning transfer learning (딥러닝 전이학습을 이용한 경량 트렌드 분석 시스템 설계 및 구현)

  • Shin, Jongho;An, Suvin;Park, Taeyoung;Bang, Seungcheol;Noh, Giseop
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.10a
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    • pp.87-89
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    • 2022
  • Recently, as more consumers spend more time at home due to COVID-19, the time spent on digital consumption such as SNS and OTT, which can be easily used non-face-to-face, naturally increased. Since 2019, when COVID-19 occurred, digital consumption has doubled from 44% to 82%, and it is important to quickly and accurately grasp and apply trends by analyzing consumers' emotions due to the rapidly changing digital characteristics. However, there are limitations in actually implementing services using emotional analysis in small systems rather than large-scale systems, and there are not many cases where they are actually serviced. However, if even a small system can easily analyze consumer trends, it will help the rapidly changing modern society. In this paper, we propose a lightweight trend analysis system that builds a learning network through Transfer Learning (Fine Tuning) of the BERT Model and interlocks Crawler for real-time data collection.

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A Study on the Effect of Macroeconomic Variables on Apartment Rental Housing Prices by Region and the Establishment of Prediction Model (거시경제변수가 지역 별 아파트 전세가격에 미치는 영향 및 예측모델 구축에 관한 연구)

  • Kim, Eun-Mi
    • Journal of Cadastre & Land InformatiX
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    • v.52 no.2
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    • pp.211-231
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    • 2022
  • This study attempted to identify the effects of macroeconomic variables such as the All Industry Production Index, Consumer Price Index, CD Interest Rate, and KOSPI on apartment lease prices divided into nationwide, Seoul, metropolitan, and region, and to present a methodological prediction model of apartment lease prices by region using Long Short Term Memory (LSTM). According to VAR analysis results, the nationwide apartment lease price index and consumer price index in Lag1 and 2 had a significant effect on the nationwide apartment lease price, and likewise, the Seoul apartment lease price index, the consumer price index, and the CD interest rate in Lag1 and 2 affect the apartment lease price in Seoul. In addition, it was confirmed that the wide-area apartment jeonse price index and the consumer price index had a significant effect on Lag1, and the local apartment jeonse price index and the consumer price index had a significant effect on Lag1. As a result of the establishment of the LSTM prediction model, the predictive power was the highest with RMSE 0.008, MAE 0.006, and R-Suared values of 0.999 for the local apartment lease price prediction model. In the future, it is expected that more meaningful results can be obtained by applying an advanced model based on deep learning, including major policy variables

A Study on the Media Recommendation System with Time Period Considering the Consumer Contextual Information Using Public Data (공공 데이터 기반 소비자 상황을 고려한 시간대별 미디어 추천 시스템 연구)

  • Kim, Eunbi;Li, Qinglong;Chang, Pilsik;Kim, Jaekyeong
    • Journal of Intelligence and Information Systems
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    • v.28 no.4
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    • pp.95-117
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    • 2022
  • With the emergence of various media types due to the development of Internet technology, advertisers have difficulty choosing media suitable for corporate advertising strategies. There are challenging to effectively reflect consumer contextual information when advertising media is selected based on traditional marketing strategies. Thus, a recommender system is needed to analyze consumers' past data and provide advertisers with personalized media based on the information consumers needs. Since the traditional recommender system provides recommendation services based on quantitative preference information, there is difficult to reflect various contextual information. This study proposes a methodology that uses deep learning to recommend personalized media to advertisers using consumer contextual information such as consumers' media viewing time, residence area, age, and gender. This study builds a recommender system using media & consumer research data provided by the Korea Broadcasting Advertising Promotion Corporation. Additionally, we evaluate the recommendation performance compared with several benchmark models. As a result of the experiment, we confirmed that the recommendation model reflecting the consumer's contextual information showed higher accuracy than the benchmark model. We expect to contribute to helping advertisers make effective decisions when selecting customized media based on various contextual information of consumers.