• Asia Marketing Journal
• /
• 제22권3호
• /
• pp.87-105
• /
• 2020

With the development of Information Technology, customers require promptly higher quality products and services. Companies try to make newly digital marketing strategies, but there are no empirical researches on them. This article empirically presents a new perspective that companies can shape the customer decision journey ahead by coordinating customer experience. In this article, based on Elaborated Likelihood Model (ELM) theory, customer experience consists of the emotional or cognitive experience. We surveyed about 200 subjects (N = 217) in their 20s and 30s based on the International Music Industry Association's Music Listening 2019 report, then analyzed four different models (before personalization-cognitive experience, before personalization-emotional experience, after personalization- cognitive experience, after personalization-emotional experience) by JASP and R Studio. We conducted Structural Equation Model (SEM) and paired t-test. Personalization factors are about recommendation systems in Spotify. The results of survey represent that companies can shape the Customer Decision Journey (CDJ) ahead especially through enhance cognitive experience. It empirically proves Elaborated Likelihood Model (ELM). The conclusion can be drawn that 'pulling' customer experience can be a new marketing strategies in the digital era.

• 한국정보전자통신기술학회논문지
• /
• 제1권3호
• /
• pp.53-60
• /
• 2008

최근 인터넷 서비스의 활용은 다양한 정보와 동시에 개인에 적합한 서비스를 제공하기위한 노력들로 이루어지고 있다. 이에 본 논문에서는 인터넷 멀티미디어 서브시스템을 기반으로 서비스 통합 과 개인화를 제공하는 웹서비스 활용 모델을 제시하고자 한다. 이러한 서비스 통합과 개인화를 위해 서비스 사용자 정보 분석 및 개인화 서비스 구성요소 추출과정이 요구되며 이를 위해 개인 서비스 제공 정책 수립이 필요하다. 이러한 과정을 통해 기존 서비스와 외부 서비스들 간의 효율적인 통합을 제공할 수 있으며 신규서비스 모델을 구축하여 서비스 이용자에 대한 보다 세밀한 서비스 제공이 가능할 것이다.

• 복식
• /
• 제58권4호
• /
• pp.58-71
• /
• 2008

The demand for personalized products and service of apparel product has increased dramatically. In order to acquire a personalized apparel product, consumers may have to sacrifice more expense or time. The purpose of this study was to investigate various personalization strategies in apparel business and to identify antecedents that influence the process. Clothing involvement and two price related variables (clothing expense and willingness to pay more) were included in the study as antecedents. Four personalization strategies were included in the study: design selection, size customization, in-store service and promotion personalization. For an empirical study, a conceptual model was designed and research questionnaire was developed. A measure of personalization of apparel shopping was developed based on existing scale items of prior research and a pilot study. Data from 766 men and women in their twenties to forties were used for statistical analysis. Structural Equation Modeling was used for the data analysis. Results indicated that the conceptual model was a good fit to data. Structural paths indicated that there was significant influence of clothing involvement on design selection and sales promotion personalization strategies. Involved consumers spent more on chothing products and were likely to pay more on personalized products and services. Monthly clothing expense influenced size customization significantly. It also had negative influence on service related personalization strategies. Consumers were willing to pay more when it comes to product related personalization strategies such as design and size but not necessarily to service related strategies. This study was an attempt to provide an in-depth and synthesized approach on consumer attitudes toward personalization of apparel products.

• Asian Journal of Innovation and Policy
• /
• 제10권3호
• /
• pp.274-293
• /
• 2021

Utilizing outside knowledge for innovation is an important task for companies in the competitive economy. Due to the rapid advance in the internet communication technology, the number and quality of innovation sourcing methods are increasing. We select co-creation, personalization and in-house R&D as the representative forms of innovation sourcing and suggest a game theory model that enables the comparative analysis between them. The decision and surplus outcome of the innovation mechanisms are compared under various settings of the input parameters of the model. The stakeholders voluntarily participate into all mechanisms when the product price is moderately high and the participation cost is low, while co-creation is the only feasible one when the product quality is niche. When the participation cost is relatively high, personalization outperforms co-creation.

• Journal of Information Technology Applications and Management
• /
• 제17권4호
• /
• pp.57-82
• /
• 2010

Many companies today use information systems to maintain competitive advantage and to give immediate responses to customer requests. Over time, however, many of these companies failed to make the most of such information systems because the users stopped utilizing the system. While many reasons offer explanations to the phenomenon, this study analyzes how usage intention of information system can be enhanced by changing the environment and usefulness of the system from the user's perspective. Active and wide-ranging researches using the Technology Acceptance Model (TAM) have been carried out on an individual's tendency to using new technology. But, many of the studies remain focused on improving user intention by enhancing the ease of use and usefulness of the system under PC applications or Enterprise applications. The personalized intranet system is not only bringing about sweeping changes to a company's information systems environment but also providing users with freedom to design their own working environment, personalization, to Corporate Information Systems (CIS). Results from empirical tests on intranet systems verify that through personalization, a more voluntary information system environment can be created and that by increasing the ease of use and usefulness of the system, users can become more favorable to accepting new technologies and ultimately result in improved usage intention. This study suggests personalization variables and model for implementing a voluntary CIS for information system developers.

• 대한안전경영과학회지
• /
• 제5권3호
• /
• pp.107-119
• /
• 2003

Expectation and interest about e-CRM are rising for more efficient customer management in on-line including electronic commerce. The decision-making tree can be used usefully as the data mining technology for e-CRM. In this paper, the representative decision making techniques, CART, C4.5, CHAID analyzed the differences in personalization point of view with actuality customer data through an experiment. With these analysis data, it is proposed a new decision-making tree system that has big advantage in personalization techniques. Through new system, it can get following advantage. First, it can form superior model more qualitatively in personalization by adding individual's weight value. Second it can supply information personalized more to customer. Third, it can have high position about customer's loyalty than other site of similar types of business. Fourth, it can reduce expense that cost marketing and decision-making. Fifth, it becomes possible that know that customer through smooth communication with customer who use personalized service wants and make from goods or service's quality to more worth thing.

• 한국IT서비스학회:학술대회논문집
• /
• 한국IT서비스학회 2008년도 춘계학술대회
• /
• pp.364-367
• /
• 2008

Cyber office is a personalized enterprise portal model for knowledge workers. The key services of cyber office are both personalization and emergent collaboration. Personalization service is provided by choosing service from service repository and then dropping it in the cyber office. Service repository contains various kinds of services supporting business activities. Cyber office provides blog service and profile service for knowledge workers to participate in knowledge based collaboration voluntarily.

• 한국의류산업학회지
• /
• 제18권1호
• /
• pp.38-47
• /
• 2016

This research analyzes the influence of mobile commerce characteristics on consumer's purchase intention using a theoretical Technology Acceptance Model (TAM) constructed on previous studies and a review of the literature to explain the effect of mobile fashion shopping characteristics on consumer's purchase intention. In constructing structural equation model, Mobile commerce characteristics variables such as 'security', 'enjoyment', and 'personalization' were selected as external variables affecting TAM. A questionnaire was distributed to consumers in their 20's-30's who had purchased fashion products using a mobile shopping channel. Statistical methods of confirmatory factor analysis, correlation, and covariance structural analysis using Amos 19.0 package were employed for the analysis of 453 effective data responses. The results were as follows. First, extended TAM was shown be the appropriate model to explain the influence of mobile commerce characteristics on consumer's purchase intention in mobile fashion shopping. Second, 'security' had a significant positive influence on perceived usefulness (PU), however it affected perceived ease of use (PEOU) negatively. Third, 'enjoyment' had a significant influence only on PEOU, while 'personalization' was found to affect both PEOU and PU significantly. Fourth, PEOU affected PU significantly. Finally, both PEOU and PU had a significant influence on consumer's purchase intention.

• 한국산업정보학회논문지
• /
• 제28권2호
• /
• pp.31-45
• /
• 2023

본 연구는 유튜브 이용자의 사용 시간이 양적으로 증대하면서 나타나는 질적 단계와 경로에 주목하였다. 그리고 심리학과 신경과학의 이론을 적용하여 추천시스템의 개인화 알고리즘과 적극이용의 구간을 세분화하였고, 이론연구와 실증연구를 병행하였다. 이론연구에서 심리학과 신경과학의 관점으로 포그의 행동모델(FBM), 가변적 보상, 도파민 중독을 적용하였다. 포그의 행동모델(FBM)은 연관 콘텐츠 제시 기능인 개인화 추천 알고리즘이 트리거(계기)로서 쉬운 클릭을 유발하고, 가변적 보상은 검색하는 콘텐츠에 대한 예측불가능성으로 동기부여의 효과성을 높이며, 도파민 중독은 도파민 신경을 자극하면 지속적 적극적으로 콘텐츠를 소비하게 하는 것으로 요약된다. 본 연구는 개인화 추천 알고리즘과 적극이용 구간에서 콘텐츠의 이용 목적을 심리적 측면에서 처음이용, 재이용, 지속이용, 적극이용의 4단계로 구분하고, 경로를 분석하였다는 점에서 학문적 실무적 기여를 할 것으로 기대한다.

• 디지털산업정보학회논문지
• /
• 제6권3호
• /
• pp.19-29
• /
• 2010

A marketing model is changed from a customer acquisition to customer retention and it is being moved to a way that enhances the quality of customer interaction to add value to our customers. Such personalization is emerging from this background. The Web site is accelerate the adoption of a personalization, and in contrast to the rapid growth of data, quantitative analytical experience is required. For the automated analysis of large amounts of data and the results must be passed in real time of personalization has been interested in technical problems. A recommendation algorithm is an algorithm for the implementation of personalization, which predict whether the customer preferences and purchasing using the database with new customers interested or likely to purchase. As recommended number of users increases, the algorithm increases recommendation time is the problem. In this paper, to solve this problem, a recommendation system based on clustering and dimensionality reduction is proposed. First, clusters customers with such an orientation, then shrink the dimensions of the relationship between customers to low dimensional space. Because finding neighbors for recommendations is performed at low dimensional space, the computation time is greatly reduced.