• Korean Journal of Human Ecology
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• v.20 no.4
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• pp.849-860
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• 2011

This study explores a structural model to examine the relationship between web browsing motivation, retail strategy and purchase conversion for apparel on shopping websites. A self-administered questionnaire based on existing scales includes web browsing motivation, retail strategy, and purchase conversion intention of apparel on the shopping websites. A total of 499 usable questionnaires were obtained from consumers aging 20 to 49 who reside in metropolitan cities in Korea. For data analysis, descriptive statistics, exploratory factor analysis, confirmatory factor analysis, and structural equation models were used via SPSS 12.0 and LISREL 8.8. Findings concluded that web browsing motivations consisted of three factors: hedonic, informational, and recreational browsing for apparel. Hedonic browsing had a negative effect on purchase conversion intention, whereas informational browsing had a positive effect on the purchase conversion intention for apparel on shopping sites. Retail strategies on the website were classified into service, merchandise assortment, and price & promotion; the three elements of retail strategies mediated the relationship between web browsing motivations and purchase conversion intention for apparel. Specially, merchandise assortment had significantly direct effect on the purchase conversion intention of apparel on shopping websites. Managerial implications were discussed for fashion marketers to develop retail strategies and web content in order to convert web browsers or visitors into purchasers.

• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.11
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• pp.1707-1718
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• 2009

This study examines the effects of perceived risks on purchase decision behavior among Internet fashion consumers. The study survey used a self-administered questionnaire and a total data of 244 responses were used for analysis. The results of this study are as follows: First, the perceived risks consist of 6 factors, quality risk, counterfeit product risk, credit dealing risk, social/psychological risk, size and appearance risk, and delivery risk. The purchase decision behavior consist of 3 factors, delay of purchase decision, website switching, and offline conversion behavior. Second, purchase time positively affected the quality risk and credit dealing risk. Purchase frequency negatively affected the quality risk and credit dealing risk. Third, the quality risk, size and appearance risk, counterfeit product risk, and credit dealing risk positively affected the delay of purchase decisions. Quality risk and counterfeit product risk positively affected website switching. In addition, quality risk, social/psychological risk, and credit dealing risk positively affected the offline conversion behavior. Fourth, credit dealing risk negatively affected a short term purchase intention and the delivery risk negatively affected a long term purchase intention. The social/psychological risk and credit dealing risk negatively affected the repurchase intention.

• Journal of Korean Society for Quality Management
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• v.37 no.3
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• pp.74-82
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• 2009

This article presents a six sigma project for improving purchase conversion rate in mobile game site. The project was carried out based on DMAIC process. First, a defect rate is defined as low purchase conversion rate. In addition, 60-days purchase data was analyzed and it is shown that the defects level was 2.48 sigma level. In this study, in order to raise the sigma level, six personalization services were used in the mobile game site. Six factors were determined based on FDPM(Functional Deployment Process Map), fishbone chart, linear regression analysis, effort-performance matrix, and so on. The sigma level of defects has improved from 2.48 to 2.93.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.1
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• pp.123-138
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• 2013

Keyword advertising has been used as a promotion tool rather than the advertising itself to online retailers. This is because the online retailer expects the direct sales increase when they deploy the keyword sponsorship. In practice, many online sellers rely on keyword advertising to promote their sales in short term with limited budget. Most of the previous researches use direct revenue factors as dependent variables such as CTR (click through rate) and CVI (conversion per impression) in their researches on the keyword advertising[14, 16, 22, 25, 31, 32]. Previous studies were, however, conducted in the context of aggregate-level due to the limitations on the data availability. These researches cannot evaluate the performance of keyword advertising in the individual level. To overcome these limitations, our research focuses on conversion of keyword advertising in individual-level. Also, we consider manageable factors as independent variables in terms of online retailers (the costs of keyword by implementation methods and meanings of keyword). In our study we developed the keyword advertising conversion model in the individual-level. With our model, we can make some theoretical findings and managerial implications. Practically, in the case of a fixed cost plan, an increase of the number of clicks is revealed as an effective way. However, higher average CPC is not significantly effective in increasing probability of purchase conversion. When this type (fixed cost plan) of implementation could not generate a lot of clicks, it cannot significantly increase the probability of purchase choice. Theoretically, we consider the promotional attributes which influence consumer purchase behavior and conduct individuals-level research based on the actual data. Limitations and future direction of the study are discussed.

• Korea Trade Review
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• v.47 no.6
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• pp.71-96
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• 2022

This research will study the conversion intention of the users in China from fuel vehicle to new energy vehicles through the empirical methods. To this end, a questionnaire survey was conducted with car users as the object, combined with the theory of user migration and the PPM model to analyze the impact of fuel vehicle users' conversion intention to new-energy vehicles factor. The results showed that purchase experience contains the moderating effect, in which perceived risk and switching costs had a greater impact on the groups without purchase experience, whereas social identity, perceived value, personal attitude, and willingness to switch had a greater impact on groups with the purchase experience. Among all five factors, perceived risk had no discernible impact on the switching intention, but social identity, perceived value, attitude toward switching, and switching costs all had discernible impact on the switching intention. This study expects to come out with sustainable advises for the future growth of new energy vehicles from the study of car users' switching intention and the collective difference test of purchasing experience.

• Asia pacific journal of information systems
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• v.33 no.3
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• pp.737-767
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• 2023

With the availability of big customer data and advances in machine learning techniques, the prediction of customer behavior at the session-level has attracted considerable attention from marketing practitioners and scholars. This study aims to predict customer purchase conversion at the session-level by employing customer profile, transaction, and clickstream data. For this purpose, we develop a multimodal deep learning fusion model with dynamic and static features (i.e., DS-fusion). Specifically, we base page views within focal visist and recency, frequency, monetary value, and clumpiness (RFMC) for dynamic and static features, respectively, to comprehensively capture customer characteristics for buying behaviors. Our model with deep learning architectures combines these features for conversion prediction. We validate the proposed model using real-world e-commerce data. The experimental results reveal that our model outperforms unimodal classifiers with each feature and the classical machine learning models with dynamic and static features, including random forest and logistic regression. In this regard, this study sheds light on the promise of the machine learning approach with the complementary method for different modalities in predicting customer behaviors.

• Journal of the Korean Society of Clothing and Textiles
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• v.27 no.9_10
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• pp.1036-1047
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• 2003

This study was carried out in the purpose of proposing internet marketing strategy which can make conversion rate higher through analysis of internet shopping behavior types. This study was executed in two stages; qualitative study, quantitative study. In the qualitative study, internet shopping behavior types were investigated through the In-depth interview and direct observation, and then in the quantitative study, differences of internet shopping behavior types according to consumer characteristics, product properties and shopping-mall types were examined. For qualitative study, 30 samples by focus sampling were inquired and for quantitative study, 334 data were collected through web survey. The results of this study are as follows: First, 7 Internet shopping behavior types of apparel were found through the qualitative study: cautious purchase by price comparison, searching purchase, special low price purchase, impulse purchase, prepurchase deliberation, information accumulation, recreation-oriented. Second, in relation to consumer characteristics, consumers that have many internet purchase experiences showed goal-directed behavior more and female did more special low price purchase behavior and impulse purchase behavior than male. Third, according to product properties, high price product led more cautious purchase by price comparison & prepurchase deliberation behavior and fashionable product led more information-searching behavior. In the case of low price and fashionable products, impulse purchase behaviors were showed more. Forth, according to Internet shopping mall types, category killer shopping mall visitors showed information search behavior, recreation-oriented behavior more.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.143-146
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• 2004

With the rapid growth of e-commerce, marketers are able to observe not only purchasing behavior on what and when customers purchased, but also the individual Web usage behavior that affect purchasing. The richness of this information has the potential to provide marketers with an in-depth understanding of customer. Using commonly available Web log data, this paper examines Web usage behaviors at the individual level. By decomposing the buying process into a pattern of visits and purchase conversion at each visit, we can better understand the relationship between Web usage behavior and purchase decision. This allows us to more accurately forecast a shopper's future purchase decision at the site and hence determine the value of individual customers to the siteAccording to our research, not only information seeking behavior but also visiting duration of a customer and participative behavior such as participation in event should be considered as important predicators of purchase decision of customer in a cosmetic internet shopping mall.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.289-305
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• 2023

The rapid increase in the use of mobile devices is changing consumers' online shopping behavior. However, the difference in the effect on the conversion rate according to the time when consumers switch from a small screen to a large screen has not been sufficiently studied. In addition, the differences in the effect of device conversion on purchase performance according to the characteristics of each country's infrastructure have not been sufficiently studied. Against this background, this study aims to analyze whether the timing of switching from mobile devices to PC devices and the country's mobile Internet penetration rate are moderating the positive effect of device switching on purchase performance. For empirical analysis, Google Merchandise Store data was collected and 101,466 data from 130 countries were analyzed with a multilevel model. As a result of the analysis, consumers' device switching (i.e., mobile to PC) had a positive effect when it occurred in the middle of the consumer journey. However, it was analyzed that when device switching occurred at the later stage of the consumer journey, it had a negative effect on purchase performance. In addition, it was analyzed that the higher the mobile Internet penetration rate, the weaker the positive effect of consumer device conversion on purchase performance.

• The Research Journal of the Costume Culture
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• v.30 no.5
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• pp.656-674
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• 2022

The purpose of this study is to reveal and compare the differences in the types and characteristics of purchase channel journeys of MZ generation consumers. In this study a survey was conducted on the purchase channel journey of 20 women in the MZ generation using the ethnographic method of in-depth interviews and observations. As a result, three purchase channel journeys were identified: mobile, multi-channel, and offline. These were variously subdivided according to the characteristics of the MZ generations. Gen Z's journey was categorized into types: fashion platform app, Youtube, multi-channel supplement, multi-channel non-planned store visit, offline loyalty store, and impulsive offline store. Gen M's journey was categorized as: an online community bond, portal site, online loyalty store, multi-channel brand involvement, multi-channel efficiency, a multi-channel conversion, offline efficiency and offline task. The difference in mobile journey between generations was found in the time and length of the purchase. Gen M recognized both online and offline search processes to be tiring, while Gen Z enjoyed the search process using the online path. In the offline journey Gen Z began with their own intention to purchase, while Gen M sometimes recognized that purchasing fashion products necessary for work was a cumbersome task.