• Journal of Distribution Science
• /
• v.18 no.7
• /
• pp.49-62
• /
• 2020

Purpose: The purpose of this study is to examine under what conditions suggestion programs conducted by organizations actually increase individual perception of their work effectiveness. Specifically, this study looks into the effects of cooperation perception and relational demography of employee on work effectiveness of suggestion programs. It does this by focusing on the interaction effect of organizational commitment. Research design, data and methodology: Data was collected from 1,872 participants who took part in the suggestion program of HCCP 6th DATA. This data was subjected to multiple regression analysis. Results: a) higher employee cooperation perception enhances work effectiveness of suggestion program, but relational difference of knowledge diversity between team members has no effect on work efficiency; b) Positive effect of cooperation perception, and difference in education level on work effectiveness become greater as commitment increases. However, organizational commitment decreases the positive effect of difference in organizational tenure on work effectiveness by suggestion program. Conclusion: The results point to the importance of broadening the current conceptual models of employee work effectiveness of suggestion program to include relational demography, as well as the utility of conducting additional cross-level research on suggestion programs.

• International Journal of Internet, Broadcasting and Communication
• /
• v.13 no.2
• /
• pp.224-230
• /
• 2021

YouTube is an OTT service that leads the home economy, which has emerged from the 2020 Corona Pandemic. With the growth of OTT-based individual media, creators are required to establish attractive storytelling strategies that can be preferred by viewers and elected for YouTube recommendation algorithms. In this study, we conducted a study on modeling that proposes a content storyline for creators. As the ability for Creators to create content that viewers prefer, we have presented the data literacy ability to find patterns in complex and massive data. We also studied the importance of compelling storytelling configurations that viewers prefer and can be selected for YouTube recommendation algorithms. This study is of great significance in that it deviated from the viewer-oriented recommendation system method and proposed a story suggestion model for individual creaters. As a result of incorporating this story proposal model into the production of the YouTube channel Tiger Love video, it showed a certain effectiveness. This story suggestion model is a machine learning text-based story suggestion system, excluding the application of photography or video.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.7
• /
• pp.2840-2861
• /
• 2020

The purpose of this survey-based study is to make an analysis of search engine users' behaviors on the Search Engine Results Pages (SERPs) based on the three demographic characteristics gender, age, and program studying. In this study, a questionnaire was designed with 12 closed-ended questions. Remaining questions other than the demographic characteristic related ones were about "tab", "advertisement", "spelling suggestion", "related query suggestion", "instant search suggestion", "video result", "image result", "pagination" and the amount of clicking results. The questionnaire was used and the data collected were analyzed with the descriptive statistics as well as the inferential statistics. 84.2% of the study population was reached. Some of the major results are as follows: Most of each demographic characteristic category (i.e. female, male, under-20, 20-24, above-24, English computer engineering, Turkish computer engineering, software engineering) have rarely or more click for tab, spelling suggestion, related query suggestion, instant search suggestion, video result, image result, and pagination. More than 50.0% of female category click advertisement rarely; however, for the others, 50.0% or more never click advertisement. For every demographic characteristic category, between 78.0% and 85.4% click 10 or fewer results. This study would be the first attempt with its complete content and design. Search engine providers and researchers would gain knowledge to user behaviors about the usage of the SERPs based on the demographic characteristics.

• The Journal of Information Systems
• /
• v.10 no.2
• /
• pp.5-32
• /
• 2001

The main goal of this research is at the development of a bundle product suggestion sub-system of an internet shopping mall for fishery products, which can reduce the search cost of user. To achieve the goal, we first study tie key factors of successful direct commerce for fishery products, and second, we design a bundle product suggestion module and its sub-module. For this, we identify the objectives of system, and write out the necessary functions of the system and models(process model, data model, dynamic model) through the analysis of user requirements. Based on the functions and models, we design user interfaces, database, processes, and knowledge base. In designing knowledge base and inferencing strategy, we consider two intelligent agent approach(optimal algorithms, heuristic rules) and suggest one more approach(case-based reasoning). The intelligent agent can be used in enhancing the suggestion of multiple fishery product simultaneously. The system analysis and design documents presented as the research results can be used to provide good guidelines to the companies who consider developing an production suggestion agents.

• Journal of Biomedical Engineering Research
• /
• v.42 no.6
• /
• pp.277-286
• /
• 2021

The global market size of AI based SaMD for medical image in 2023 will be anticipated to reach around 620 billion won (518 million dollars). In order for Korean manufacturers to efficiently obtain CE marking for marketing in the EU countries, the paper is to introduce the recommendation and suggestion of how to reclassify SaMD based on classification rules of MDR because, after introducing the Regulation EU 2017/745, classification rules are quite modified and newly added compared to the Directive 93/42/EEC. In addition, the paper is to provide several rules of MDR that may be applicable to decide the classification of SaMD. Lastly, the paper is to examine and demonstrate various secondary data supported by qualitative data because the paper focuses on the suggestion and recommendation with a public trust on the basis of various secondary data conducted by the analysis of field data. In conclusion, the paper found that the previous classification of SaMD followed by the rule of MDD should be reclassified based on the Regulation EU 2017/745. Therefore, the suggestion and recommendation are useful for Korean manufacturers to comprehend the classification of SaMD for marketing in the EU countries.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.8 no.1
• /
• pp.183-190
• /
• 2012

Mobile web applications are different from desktop web applications because of their small screen size and small user input devices. Therefore user interface designers have spent their effort and time to re-design the user interface of mobile web applications to meet these differences. In this paper, we introduce five user interface patterns for mobile web applications to reduce their effort and time. Two of them are for utilizing small screen size efficiently, and they are space overloading pattern and data filtering pattern. These patterns enable designers to reduce screen usage. The other three patterns - data suggestion pattern, input reuse pattern, and incremental data input pattern - are for helping users' data input on mobile devices. These three patterns enable users to reduce direct data input. Our work will help user interface designers develop mobile web interface to utilize screen space efficiently and get data with less errors and less efforts from users.

• Fashion & Textile Research Journal
• /
• v.13 no.4
• /
• pp.511-522
• /
• 2011

This study analyzed the effects of internet fashion consumer's impulse buying tendency on positive and negative purchasing behaviors. A survey was conducted from October 1 to December 15 in 2010, and 407 responses from internet fashion consumers who made impulse purchases on the internet at least once for the last 6 months were used in the data analysis. As a result, the impulse buying tendency of internet fashion consumers was classified into pure impulse buying, reminder impulse buying, suggestion impulse buying, and stimulus impulse buying. The positive purchasing behaviors such as repurchase intention and purchase satisfaction were influenced by the impulse buying tendency. The all factors of impulse buying tendency had an effect on repurchase intention, while purchase satisfaction was influenced by the reminder impulse buying, suggestion impulse buying, and stimulus impulse buying. The negative purchasing behaviors were classified into delay in decision making and switching intention of purchase. The delay in decision making was influenced by the stimulus impulse buying, suggestion impulse buying, and reminder impulse buying. Also, the reminder impulse buying, suggestion impulse buying and pure impulse buying had an effect on switching intention of purchase. In addition, there were significant differences in the impulse buying tendency and delay in decision making between male and female internet fashion consumers.

• KOMUNHWA
• /
• no.35
• /
• pp.95-101
• /
• 1989

• Review of Korea Contents Association
• /
• v.19 no.2
• /
• pp.13-18
• /
• 2021

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.6
• /
• pp.261-266
• /
• 2016

Chart suggestion method provided by various existing data visualization tools makes chart recommendations without considering the user intention. Data visualization is not properly carried out and thus, unclear in some tools because they do not follow the segmented quantitative data classification policy. This paper provides a guideline that clearly classifies the quantitative input data and that effectively suggests charts based on user intention. The guideline is two-fold; the analysis guideline examines the quantitative data and the suggestion guideline recommends charts based on the input data type and the user intention. Following this guideline, we excluded charts in disagreement with the user intention and confirmed that the time user spends in the chart selection process has decreased.