• Journal of Distribution Science
• /
• v.21 no.8
• /
• pp.47-55
• /
• 2023

Purpose: Online meeting is chosen instead of face-to-face conferences as a solution that ensures both effectiveness and legality during times of strong epidemic outbreaks. In the current period, managers can have different types of meeting options for information distribution. This study has examined the effect of past behavior on the managers' intention of organizing online meetings. Research design, data and methodology: Data were collected from a survey with 475 managers and put into SmartPLS 4.0 for analysis. Partial least squares structural equation modeling (PLS-SEM) was employed to test relationships in the research model. Results: The findings indicated that past behavior plays the most critical role in explaining the organizing online meeting intention of managers, followed by attitude and subjective norms. Meanwhile, the perceived behavioral control factor has absolutely no effect on intention in the context of this study. Notably, attitude and subjective norms also remarkably mediated the impact of past behavior on managers' intention. Conclusions: This study has added to the understanding of the meeting organization behavior of managers. Even if the epidemic is under control, the administrators should still organize some meetings in the form of online because it will affect the social perceptions of future behavior and behavioral intention.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2002.11a
• /
• pp.445-453
• /
• 2002

Past and current customer behavior is the best predicator of future customer behavior. This paper introduces a procedure on personalized defection detection and prevention for an online game site. The basic idea for our defection detection and prevention is adopted from the observation that potential defectors have a tendency to take a couple of months or weeks to gradually change their behavior (i.e. trim-out their usage volume) before their eventual withdrawal. For this purpose, we suggest a SOM (Self-Organizing Map) based procedure to determine the possible states of customer behavior from past behavior data. Based on this representation of the state of behavior, potential defectors are detected by comparing their monitored trajectories of behavior states with frequent and confident trajectories of past defectors. The key feature of this study includes a defection prevention procedure which recommends the desirable behavior state for the ext period so as to lower the likelihood of defection. The defection prevention procedure can be used to design a marketing campaign on an individual basis because it provides desirable behavior patterns for the next period. The experiments demonstrate that our approach is effective for defection prevention and efficient for defection detection because it predicts potential defectors without deterioration of prediction accuracy compared to that of the MLP (Multi-Layer Perceptron) neural network.

• Journal of Information Science Theory and Practice
• /
• v.7 no.4
• /
• pp.45-55
• /
• 2019

Currently there are many groups of Indonesian faculty members on social media. This research aims to find out the information behavior of Indonesian faculty members on social media, especially on Facebook, Telegram, and WhatsApp. The focus of this research is in-depth understanding of the needs, search, organization, and use of information by Indonesian faculty members on social media. This research is qualitative research using a virtual ethnographic approach. The research data was obtained through participatory observation, in-depth interviews, and a literature review. The selection of informants was done by purposive sampling, while triangulation was done by data sources and theories triangulation. The results showed that the information behavior of Indonesian faculty members on social media began with the need for information, choosing social media, choosing and entering into one or several groups, sharing information, and discussing in a group. Some faculty members keep the information, and some choose to ask when they need the information, even though the information has been discussed. The information obtained is used when they need it, and they usually share their experiences with other group members.

• Environmental Analysis Health and Toxicology
• /
• v.23 no.4
• /
• pp.231-245
• /
• 2008

Various methods for detecting changes in response behaviors of indicator specimens are presented for monitoring effects of toxic treatments. The movement patterns of individuals are quantitatively characterized by statistical (i.e., ANOVA, multivariate analysis) and computational (i.e., fractal dimension, Fourier transform) methods. Extraction of information in complex behavioral data is further illustrated by techniques in ecological informatics. Multi-Layer Perceptron and Self-Organizing Map are applied for detection and patterning of response behaviors of indicator specimens. The recent techniques of Wavelet analysis and line detection by Recurrent Self-Organizing Map are additionally discussed as an efficient tool for checking time-series movement data. Behavioral monitoring could be established as new methodology in integrative ecological assessment, tilling the gap between large-scale (e.g., community structure) and small-scale (e.g., molecular response) measurements.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.33 no.1
• /
• pp.321-343
• /
• 2022

This study presents the direction of services and policies for digital file management in universities by identifying undergraduate students' digital file management behavior. The research defined the Life Cycle of Digital Objects. In addition, This research collected data from 154 undergraduate students using an online survey on their file Creation, Storing, Naming, Organizing, and Backup based on the Digital File Management Workflow. Also, an in-depth interview was conducted for 8 students, two for each major in engineering, arts, social science, and humanities. The result showed that students mostly used personal computers as storage media and USB drive as backup media and had their own file Naming and Organizing methods. Furthermore, students' satisfaction with digital file management was high when universities supported software and cloud storage. Therefore, this study suggests that universities need to provide services reflecting the students' digital file management behavior.

• Journal of Information Technology Applications and Management
• /
• v.12 no.4
• /
• pp.71-92
• /
• 2005

As the Internet establishes and reinforces connections between people, virtual community is becoming one of considerable business model. We believe that benefits of the virtual community go to both customer and vendor organizing virtual community. Despite the explosive growth of virtual communities on the Internet, empirical research has been focused to study the issues related to characteristics of virtual community The objective of this study is to enhance the understanding about virtual communities as an e-business model by Customer Relation Management and by empirically validating their effect on the performance of website. Through path analysis, we find support for relations behavior influence the online commitment. we also find that the online commitment enhance the organization citizenship behavior. Finally, we discuss several theoretical and practical Implications, and suggest limitations for research and future research issue.

• Proceedings of the KAIS Fall Conference
• /
• 2003.11a
• /
• pp.141-148
• /
• 2003

Customer retention is a common concern for many industries and a critical issue for the survival in today's greatly compressed marketplace. Current customer retention models only focus on detection of potential defectors based on the likelihood of defection by using demographic and customer profile information. In this paper, we propose a dynamic procedure for defection detection and prevention using past and current customer behavior by utilizing SOM and Markov chain. The basic idea originates from the observation that a customer has a tendency to change his behavior (i.e. trim-out his usage volumes) before his eventual withdrawal. This gradual pulling out process offers the company the opportunity to detect the defection signals. With this approach, we have two significant benefits compared with existing defection detection studies. First, our procedure can predict when the potential defectors could withdraw and this feature helps to give marketing managers ample lead-time for preparing defection prevention plans. The second benefit is that our approach can provide a procedure for not only defection detection but also defection prevention, which could suggest the desirable behavior state for the next period so as to lower the likelihood of defection. We applied our dynamic procedure for defection detection and prevention to the online gaming industry. Our suggested procedure could predict potential defectors without deterioration of prediction accuracy compared to that of the MLP neural network and DT.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.4
• /
• pp.1160-1168
• /
• 2000

Due to the increase in importance of factory-automation (FA) in the field of production, the importance of he autonomous guided vehicle's (AGV) role has also increased. This paper is about an active and effective controller which can flexibly prepare for changeable circumstances. For this study, research about an behavior-based system evolving by itself is also being considered. In this paper, constructed an active and effective AGV fuzzy controller to be able to carry out self-organization. To construct it, we tuned suboptimally membership function using a genetic algorithm (GA) and improved the control efficiency by self-correction and the generation of control rules.

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.1883-1884
• /
• 2008

This paper presents a framework for decentralized control of self-organizing swarm agents based on the artificial potential functions (APFs). The framework explores the benefits by associating agents based on position information to realize complex swarming behaviors. A key development is the introduction of a set of association rules by APFs that effectively deal with a host of swarming issues such as flexible and agile formation. In particular, this paper presents an association rule for swarming that requires less movements for each agent and compact formation among agents. Extensive simulations are presented to illustrate the viability of the proposed framework.

• The Korean Journal of Applied Statistics
• /
• v.32 no.3
• /
• pp.391-404
• /
• 2019

Customers' fixed characteristics have often been used to predict customer behavior. It has recently become possible to track customer web logs as customer activities move from offline to online. It has become possible to collect large amounts of web log data; however, the researchers only focused on organizing the log data or describing the technical characteristics. In this study, we predict the decision-making time until each customer makes the first reservation, using Airbnb customer data provided by the Kaggle website. This data set includes basic customer information such as gender, age, and web logs. We use various methodologies to find the optimal model and compare prediction errors for cases with web log data and without it. We consider six models such as Lasso, SVM, Random Forest, and XGBoost to explore the effectiveness of the web log data. As a result, we choose Random Forest as our optimal model with a misclassification rate of about 20%. In addition, we confirm that using web log data in our study doubles the prediction accuracy in predicting customer behavior compared to not using it.